The Shepherd University Journal of Undergraduate … · E 10 µL 10 µL D 0.3125 mg/mL Table 3:...

Volume I 2014-2016

The Shepherd University Journal of

Undergraduate Research

Contents

Assessment of Steroidogenic Pathway Components Following Exposure of L. palustris to the Herbicide Roundup or Its ComponentsPriya Arumuganathan, Colleen J. Nolan, and Carol Z. Plautz Pages 1-9

The Effects of Roundup and its Constituents on Steroid Hormone Levels in the Snail Lymnaea palustris Preetha Phillips, Colleen J. Nolan, and Carol Z. Plautz Pages 10-20

The Effects of ldb1 Dosage on the Expression of dll1 and Histology in the Developing Vertebrate Eye Hannah C. Williams and Carol Z. Plautz Pages 21-30

Design and Implementation of Indoor Positioning System Using Radio Frequency and Infrared Communication Vasyl Shtanko Pages 31-40

Stimulants and Stimuli: The Effects of Neuroactive Pharmaceuticals on the Process of Learning and Memory Formation Richard M. Goodman and Carol Z. Plautz Pages 41-48

Pythagorean Propositions Project Emilie Piatek and Caitlyn Shane Pages 49-53

Implementing an Automatic Proctoring and Audit System for Ib based Tests and Examinations through Facial Biometrics Kirsten Logsdon Pages 54-61

Molecular Investigations into the Genome of Lymnaea palustris Dustin Revell and Carol Z. Plautz Pages 62-70

The Effect of Games and Rewards on Math Performance Ethan Hotz Pages 71-75

The development of a low-cost Arduino and Raspberry Pi-based system for environmental monitoring Jared Tomlin and Jeffrey R. Groff Pages 76-93

Assessment of Steroidogenic Pathway Components Following Exposure of L. palustris to the Herbicide Roundup or Its Components

Priya Arumuganathan, Colleen J. Nolan, and Carol Z. Plautz

Department of Biology Shepherd University

P.O. Box 5000 Shepherdstown, WV 25443

[email protected]; [email protected]

ABSTRACT We are analyzing the effects of Roundup and its components on the pond snail, Lymnaea palustris. Roundup is a commonly used herbicide composed of the chemicals glyphosate, POEA (a surfactant), and Diquat dibromide (DD). By testing the effects of Roundup and its components on a hermaphroditic organism such as L. palustris, one can observe significant alterations in reproductive output as well as shifts in hormone levels or steroidogenic pathway components following exposure to these chemicals. Preliminary results from our laboratory showed chronic Roundup treatment yielded significantly decreased testosterone levels as well as altered estradiol and progesterone levels. Additionally, chronic treatments reduced expression of Steroidogenic Acute Regulatory protein (StAR), especially within the ovotestis. In the current study, snails (N=60) were chronically treated in solutions of pond water supplemented with Roundup, DD, POEA, or glyphosate. During the treatment period, snail fecundity and mortality were noted on a biweekly basis for each treatment group. After the treatment period, the kidney, gonadodigestive complex, and brain were harvested from each snail to analyze the expression of StAR, p450SCC, and p450AROM enzymes via Western Blotting. Standardized amounts of protein from control and treated animals were compared during the analysis for each organ. In a concurrent study, testosterone, cortisol, and estradiol levels were tested at the three and six week marks by enzyme immunoassay. A significant decrease in fecundity was observed in snails in some treatment groups. Changes in abundance were observed in steroidogenic protein levels when comparing the results of the treatment groups to the control group. KEYWORDS: Lymnaea palustris, steroidogenesis, snail, Roundup, reproduction

1. INTRODUCTION Roundup is a widely used agricultural herbicide that is primarily composed of the chemicals glyphosate, Polyethoxylated tallowamine (POEA), and Diquat dibromide (DD). Glyphosate is the main ingredient due to its ability to block the action of a key growth enzyme within plants, 5-enol pyruvylshikimate 3-phosphate (EPSP) synthase [1]. POEA is a surfactant that facilitates the entry of glyphosate into plants while DD nonselectively damages any part of the plant to which it is applied [2,3]. With the advent of genetically modified cash crops such as soy and corn, Roundup sales have considerably increased as farmers are now able to spray entire fields to destroy weeds while keeping their crop unharmed. These crops are known to be “Roundup Ready” as they have been genetically modified to resist the herbicide [4]. Between 1987 and 2012, the amount of Roundup used within the US increased by 289 million pounds [5]. As a result, Roundup has become widespread within the environment. According to a study done by the USGS Toxic Substances Hydrology Program in 2014, 59% of surface water samples collected from throughout the United States had Roundup present [6]. By using a hermaphroditic organism, such as the pond snail Lymnaea palustris, one can observe shifts in sex hormone levels in all

SUJUR | 2014-2016 | Volume I

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mailto:[email protected]


individuals following exposure to chemical agents such as Roundup and its components. In addition, L. palustris resides in freshwater and reproduces year-round, making it an excellent model organism to use in studies analyzing the effects of Roundup on surface and ground water. In previous experiments in our laboratory, researchers endeavored to determine the effects of Roundup and its components on the mortality and fecundity of this pond snail. Through these experiments, it has been established that glyphosate has a relatively low toxicity in comparison to POEA, complete Roundup, or DD. While POEA exerted the highest mortality, complete Roundup caused the greatest suppression of fecundity and most developmental abnormalities – even more so than POEA and glyphosate in combination. After testing the effects of DD alone on L. palustris, it was observed that DD is a potent developmental teratogen [7]; it is possible that DD works synergistically with the other components of Roundup and contributes to the strong effects of complete Roundup on the fecundity and development of L. palustris [8]. Furthermore, researchers hypothesized that Roundup alters the reproductive output of L. palustris by shifting its sex hormone levels. After exposing snails to chronic Roundup treatments (over a six week period), testosterone levels were significantly decreased while estradiol and progesterone levels were somewhat increased [9]. Other experimentation showed that these chronic treatments reduced the abundance of Steroidogenic Acute Regulatory protein, StAR [9]. StAR is responsible for ushering cholesterol to be used in the steroidogenic pathway to the inner mitochondrial membrane. This protein was most significantly decreased in the sex organs – the ovotestis of L. palustris.

Figure 1: The steroidogenic pathway leading to the production of sex hormones in molluscs such as Lymnaea palustris. In the present study, we endeavored to explore these findings on a molecular level by studying key proteins within the steroidogenic pathway of L. palustris: StAR, p450SCC, and p450AROM. p450SCC is a cholesterol side chain cleavage enzyme that is responsible for the conversion of cholesterol into pregnenolone. p450AROM (also known as Aromatase or CYP19) is an enzyme that catalyzes the conversion of testosterone into estradiol [10]. After treating snails for six weeks in complete Roundup, DD, POEA, glyphosate, or normal pond water, total proteins were isolated and the abundance of key proteins was analyzed. In a concurrent study, the hormone levels in the hemolymph of the chronically treated snails were analyzed. The purpose of these studies is to establish the targets of these environmental chemicals given the reproductive alterations noted in prior experiments. 2. MATERIALS AND METHODS 2.1. Fecundity and Mortality Study 60 snails of 17 mm minimum were selected from the adult L. palustris tanks. They were then housed in solutions of either normal pond water (control), Roundup, DD, POEA, or glyphosate for six weeks. The chemical working solutions were created by mixing 12 mL stock solution with 1.2L artificial pond water (APW). Stock solutions were


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based on five times the recommended Maximum Contaminant Level (MCL) of glyphosate for each chemical in Roundup; MCL is suggested by the Environmental Protection Agency. See Table 1 for these amounts. Each group type (control, Roundup, DD, POEA, or glyphosate) consisted of 12 snails. Within each group, the snails were separated into three 400 mL mesocosms of four snails each. Each mesocosm was fitted with an air stone and tube leading from an air pump and clearly designated a letter (C, R, D, P, or G) and number (1,2, or 3) to identify their treatment type and to differentiate between the tanks within a group. Romaine lettuce was fed ad lib. Each tank was cleaned, had its contents replaced with fresh solution, and was replenished with clean lettuce twice a week throughout the six week period. Embryo-containing jelly masses were removed and counted (along with the embryos) for each tank at these intervals. Results were recorded biweekly in Microsoft Excel. Any mortality within the tanks was recorded and removed. Stock solutions were covered in Parafilm and foil and stored at 4°C to minimize degradation of the chemicals; they were replaced with freshly made solutions at the three week mark. Chemical Type

Glyphosate POEA DD RU

Stock Solution (100X)

350 mg/L 3.25 mg/L

14 mg/L

1950 mg/L

Working Solution

3.5 mg/L 0.0325 mg/L

0.14 mg/L

19.5 mg/L

Table 1: The stock chemical solutions, in APW, used for 6-week chronic treatment. The working solutions for each group were diluted as indicated with APW. 2.2 Microsurgery This procedure occurred following the six week treatment and after hemolymph extractions by other researchers. Working with three people, under a dissecting

microscope, the gonadodigestive complex, kidney, and brain were isolated. For consistency, each researcher retrieved one particular organ. Snails were dissected one tank at a time, after transfer from the mesocosm into ice water until their activity slowed. Snails were patted dry, carefully removed from the shell, and uncoiled to expose and remove the gonadodigestive complex, kidney, and brain. These organs were placed into prelabeled eppendorf tubes then snap-frozen in liquid nitrogen After snap-freezing, all specimens were placed at -80°C for long-term storage. 2.3 Protein Extraction and Quantification Total protein from organs was solubilized in RIPA buffer (Sigma R0278). See Table 2 for the amounts added to each organ. In eppendorf tubes, the tissue samples were disrupted using a pestle and subsequent vortexing. They were then centrifuged at 10,000g for five minutes. Afterwards, the supernatant was transferred to a new, prelabeled tube. To determine how much protein was in each organ/tissue sample, the samples underwent EZQ Protein Quantitation (Thermo Fisher Scientific R33200). The only organs that did not undergo this procedure were the brain samples. EZQ protein standards were prepared according to Table 3. Samples to undergo quantification were diluted 1:50 with RIPA buffer so they were within the range of the standard curve. Organ Brain Gonadodigestive

Complex Kidney

Amount of RIPA buffer

50 µL 1000 µL 300 µL

Table 2: For protein extraction, indicated amounts of RIPA buffer were added to each organ sample across treatment groups. The samples were then crushed and vortexed for subsequent protein analysis.


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Vial Volume of RIPA

Volume and

Source of BSA

Final BSA Concentration

A 10 µL 10 µL Stock

5 mg/mL

B 10 µL 10 µL A 2.5 mg/mL

C 10 µL 10 µL B 1.25 mg/mL

D 10 µL 10 µL C 0.625 mg/mL

E 10 µL 10 µL D 0.3125 mg/mL

Table 3: Dilution scheme for preparation of protein standards for EZQ Protein Analysis. A standard curve was made from the above samples to determine the amount of protein present in experimental samples.

2.4 Western Blotting Using the quantitation results, the volume that contained 1 mg of protein was calculated for each kidney or gonadodigestive sample. For the brain samples, 25 µL (the equivalent of half of a brain) was used. The samples were mixed with Complete Sample Buffer (950 µL Laemmli sample buffer with 50 µL β-Mercaptoethanol) in a 1:1 ratio. Samples whose final volume did not exceed 50 µL were then used for PAGE for Western Blotting. For Western Blotting Protocol, please refer to Appendix. Western Blotting was performed with primary Aromatase (1:100), p450SCC (1:100), and StAR (1:1000) antibodies, and ECL HRP donkey anti-rabbit secondary antibody at 1:15,000. Immun-Star (Bio-Rad) was used to generate chemiluminescence and images were detected in a Bio-Rad ChemiDoc and analyzed with Image Lab software. 3. RESULTS 3.1: Fecundity and Mortality Study Using the snail fecundity data recorded biweekly, the number of embryos

throughout the six week period was tallied for each tank (1, 2, 3) of each group (control, RU, DD, POEA, glyphosate). The mean and standard deviation was calculated for each group. A T-test was run for each treatment group against the control group; if p<0.05, the group was considered significantly different. As shown in Figure 2, DD embryo production was less than control.

Figure 2: Results of the fecundity study. The average number of embryos over the entire study per treatment group shown; T-test compared each group to control. p value for DD (yellow) = 0.037. Additionally, snail mortality for each week of the treatment period was tallied for each group. The results (absolute values) are shown in Figure 3.

Figure 3: Results of the snail mortality study. For each week, the number of snail mortalities for each treatment group as well as the control were tallied. Roundup exhibited the highest mortality with four deaths.


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3.2: Western Blotting Each kidney or gonadodigestive sample that was loaded for PAGE was 1 mg of total protein. Each sample was assayed with StAR, Aromatase, or p450SCC antibody. See Figures 4, 5, and 7 for the results of the StAR blotting, Figure 6 for the Aromatase blotting, and Figure 8 for the p450SCC blotting. Refer to Table 4 for a key of the abbreviations used for blots. Abbreviation Meaning C Control RU Roundup DD Diquat Dibromide G Glyphosate P Polyethoxylated Tallowamine K Kidney B Brain GD Gonadodigestive complex Table 4: Abbreviations for Western blot results.

Figure 4: Kidney, brain, and gonadodigestive complex samples for each treatment group run in two separate Western Blots with StAR antibody. StAR bands (orange arrow) were present for DD-K, C-K, P-K, and RU-K.

Figure 5: A kidney-specific Western Blot was performed for all treatment groups with StAR antibody. Bands were present in all lanes. Glyphosate exhibited a 71% StAR reduction in comparison to the control group. Roundup showed a 1% decrease, DD showed a 18% increase, and POEA showed a 24% decrease.

Figure 6: A gonadodigestive complex-specific western blot was performed for all treatment groups. Two additional lanes were added with 2 mg of RU and C respectively. This blot was assayed with Aromatase antibody but no bands were detected.

Figure 7: The GD-specific blot from Figure 6 was stripped of the Aromatase antibody and reprobed with StAR antibody. Bands (orange arrow) were present in the RU-2 mg, RU, and G lanes.

Figure 8: Kidney, brain, and gonadodigestive complex samples for each treatment group run in two separate Western Blots with p450SCC antibody. p450SCC bands (orange arrow) were present for R-B, G-B, and C-B within the top gel and DD-B, P-B, and C-B within the bottom gel. 4. DISCUSSION This study was conducted to determine how Roundup and its components affect StAR, p450SCC, and Aromatase protein expression within L. palustris when its reproduction is reproducibly altered as in previous experiments. In previous studies, it was found that Roundup-treated groups exhibited the highest suppression of


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fecundity [8]. The findings of the current study show that DD produced significant (p<0.05) suppression of fecundity as the DD group only produced approximately 1/6th of the offspring (652 embryos) that control animals produced (3114 embryos). Complete Roundup produced approximately 1/3rd of the offspring (1052 embryos) that control animals produced and had the second most notable fecundity suppression. The Roundup group exhibited the highest mortality (30%). Yet, even though Roundup and DD produced the most notable fecundity and mortality findings, it was not confirmed in the present study that these treatments alter StAR expression. Kidney from Roundup-treated animals showed a negligible decrease in StAR expression in comparison to the control group; kidney from DD-treated animals showed a slight increase (Figure 5). Thus, it is possible that DD (and complete Roundup) sickens the animals overall and does not cause specific steroidogenic effects. Western Blot results showed a drastic reduction of StAR expression within the glyphosate-treated kidneys with a 71% decrease (Figure 5). In a concurrent study [11], Phillips et al. found elevated estradiol (46.2%) and significantly (p<0.05) increased cortisol levels (62%) as well as significantly reduced testosterone levels (5.9%) in glyphosate-treated animals when compared to control animals. This resulted in an elevated E:T ratio (33%) in the glyphosate group. Based on these data, we hypothesize that glyphosate produces the observed sex and stress hormone alteration by decreasing StAR expression. Decreased StAR expression would result in less cholesterol entering the mitochondria and ultimately decreased production of testosterone. Additionally, the observed increased estradiol levels could have been the result of higher rates of testosterone-to-estradiol

conversions, thus contributing to the decreased testosterone in these animals. The observed elevated cortisol levels may have also contributed to the decreased testosterone levels as available progesterone could have been used in this pathway at the expense of being converted into androgens. Figure 9 illustrates this hypothesis.

Figure 9: Hypothesis for glyphosate’s effects on the steroidogenic pathway of L. palustris. Glyphosate decreases StAR expression which lowers overall steroidogenesis. The observed increased cortisol levels could indicate available progesterone utilized for cortisol production at the expense of androgens. The observed trend toward elevated estradiol levels and increased E:T may be linked to increased testosterone-to-estradiol conversions (aromatization) and thus contribute to the significantly decreased testosterone levels. Asterisks indicate significantly (p<0.05) altered hormone levels in hemolymph assayed at 3 weeks (red arrow) and 6 weeks (blue arrow). See [11]. Regarding the p450SCC Western Blot results, similar bands were detected for all of the brain samples of the different treatment groups. None of the other organs yielded detectable p450SCC in this study (Figure 8). In mammals, p450SCC in the brain may not be regulated by the same transcription factors as within the kidneys and gonads. Additionally, cholesterol – the lipid that p450SCC cleaves to yield pregnenolone – can be synthesized locally within the brain and does not have to be imported from the systemic circulation [12]. It is possible that similar differences contribute to the observed higher p450SCC expression within


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the brain than in the other organs in our study. In Figure 8 (top), the Roundup and glyphosate-treated snail brains experienced negligible (<12%) decreases in p450SCC expression. However, DD and POEA-treated snail brains demonstrated substantial increases in expression (>100%; Figure 8 bottom). However, this result has not been repeated and it is possible these increases and decreases are largely artefactual. Future analysis should begin with a brain-specific p450SCC Western Blot to assess whether expression is altered in the various treatment groups. Western Blot results for StAR expression were uneven for the various tissues sampled; none of the brain samples produced detectable StAR. This is likely due to insufficient brain protein to produce a signal. In addition, there were difficulties detecting StAR and Aromatase expression in the gonadodigestive complex. In previous work in our lab, it was established that only the brain, kidney and ovotestis express StAR protein [13]. It is possible that excess protein from the digestive and albumin glands within the gonadodigestive complex obscured protein detection from the ovotestis since they were isolated together in an attempt to obtain the entire ovotestis. Additionally, the reagents and amounts of reagents (RIPA Buffer, see Table 2 and Materials and Methods) used to disrupt each tissue sample will need to be optimized for retention and detection of the proteins of interest. Concentration of ovotestis and brain protein extracts prior to Western Blot analysis by altering dissection techniques and sample dilution will be key to unambiguous detection of the abundance of these steroidogenic proteins. ACKNOWLEDGEMENTS This work was supported by grants from the NASA WV Space Grant Consortium and

Shepherd University, and the SURE Program of WV-EPSCoR. REFERENCES [1] Schönbrunn, E., Eschenburg, S.,

Shuttleworth, W. A., Schloss, J. V., Amrhein, N., Evans, J. N. S., & Kabsch, W. (2001). Interaction of the herbicide glyphosate with its target enzyme 5-enolpyruvylshikimate 3-phosphate synthase in atomic detail. Proceedings of the National Academy of Sciences of the United States of America, 98(4), 1376–1380.

[2] Cuhra, M., Traavik, T., & Bøhn, T. (2013). Clone- and age-dependent toxicity of a glyphosate commercial formulation and its active ingredient in Daphnia magna. Ecotoxicology (London, England), 22(2), 251–262.

[3] Diquat Dibromide Chemical Fact Sheet (2012).Wisconsin Department of Natural Resources.

[4] Delano, M. (2009). About Roundup Ready Crops. Massachusetts Institute of Technology.

[5] Grossman, E. (2015). What Do We Really Know About Roundup Weed Killer? National Geographic.

[6] Battaglin, W.A., Meyer, M.T., Kuivila, K.M., & Dietze, J.E. (2014). Glyphosate and its degradation product AMPA occur frequently and widely in U.S. soils, surface water, groundwater, and precipitation: Journal of the American Water Resources Association, v. 50, no. 2, p. 275-290, doi:10.1111/jawr.12159.

[7] Mines, S. and Plautz, C.Z., Investigating reproductive and developmental abnormalities in aquatic invertebrates exposed to components of the herbicide Roundup. Meeting Abstract, 88th Annual Meeting of the WV Academy of Science, 2013.


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[8] Cain J, Nolan C, Plautz CZ (2012). Effects of RoundUp and its constituents on the freshwater snail Lymnaea palustris, with respect to mortality, fecundity, growth, and developmental abnormalities. SUJUR 2012; 1:8-22.

[9] Plautz, C.Z., Brooks, A., and Nolan, C.J., Effects of Roundup on reproduction, steroid hormone levels, and the steroidogenic pathway in Lymnaea palustris. Meeting Abstract, 90th Annual Meeting of the WV Academy of Science, 2015.

[10] Payne, A. H, Hardy, A.P., (2007). Contemporary Endocrinology: The Leydig Cell in Health and Disease (p. 157). Totowa, NJ: Humana Press Inc.

[11] Phillips P, Nolan CJ, Plautz CZ (2016). The Effects of Roundup and its Constituents on Steroid Hormone Levels in the Snail Lymnaea palustris. In this issue.

[12] Bradley, R. J., Harris, A. R., Jenner, P., Biggio, G., & Purdy, R. H. (2001). Neurosteroids and Brain Function (Vol. 46, International Review of Neurobiology). Academic Press.

[13] Chaney, J., and Plautz, C.Z. The link between disturbances in reproduction and expression of enzymes in the steroidogenic pathway in aquatic invertebrates exposed to components of the herbicide Round-Up. Meeting Abstract, 88th Annual Meeting of the WV Academy of Science, 2013.

APPENDIX GEL ELECTROPHORESIS Sample Preparation:

1. Bio-Rad Laemmli Sample Buffer is used as a 2X buffer (950 µl Laemmli SB + 50 µl βME)

2. Add the proper amount of protein sample (5-25 µl) and the equal

amount complete Laemmli SB (5-25 µl)

3. Heat samples 4 min @ 95˚C 4. Spin down sample briefly and load. 5. Load 10 µl pre-stained molecular

weight markers (Bio-Rad 161-0374). Gel / Electrophoresis Module Preparation:

1. Prepare 400 ml 1X Bolt MES SDS Running Buffer

2. Prepare 12% Bolt Mini Gel and Gel Tank; run at 200 V for 22 minutes or until dye front reaches bottom.

After the Run: 1. Equilibrate the gel in 20% ethanol

for 5–10 minutes prior to Western transfer to increase overall protein transfer efficiency.

WESTERN TRANSFER with iBlot 2 Dry

Blotting System iBlot 2 NC Regular Stack for two mini gels:

1. Turn ON the iBlot 2 Gel Transfer Device. Use Method P0 for 7 minutes.

2. Place the Bottom Stack on the blotting surface. Align electrical contacts on the tray with the corresponding electrical contacts on the blotting surface of the iBlot device.

3. Place gels on the transfer membrane of the Bottom Stack.

4. Place a pre-soaked (in RO water) iBlot Filter Paper on the gel and remove air bubbles using the Blotting Roller.

5. Place the Top Stack over the pre-soaked filter paper; remove air bubbles using the Blotting Roller.

6. Place the Absorbent Pad on top of the Top Stack such that the electrical contacts are aligned with the corresponding electrical contacts on the blotting surface of the iBlot Device.


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7. Close the lid of the device, check Method, touch “Start Run”.

After the transfer: 1. Remove the transfer membrane from

the stack and confirm that pre-stained molecular weight standards transferred to the membrane.

2. Rinse the membrane briefly in water, air dry, then store at room temperature.

ANTIBODIES Blocking

1. PBST Preparation a. 1X PBS + 0.25% TWEEN-20

(2.5 ml TWEEN-20 per 1L PBS)

2. Blocking Buffer Preparation a. 4% milk in PBST (4 g instant

powdered milk + 100 ml PBST)

3. Blocking 1hr at RT or overnight at 4˚C.

Antibody Incubation 1. Primary Antibody Preparation

a. In PBST + 2% milk (1 g instant powdered milk + 50 ml PBST, or dilute 4% with PBST)

b. Add primary antibody (rabbit) at correct dilution.

2. Remove the membrane from blocking buffer and place it into Primary Antibody solution. Incubate for 1 hour at RT, rotating.

3. Secondary Antibody Preparation a. In PBST + 2% milk (1 g

instant powdered milk + 50 ml PBST, or dilute 4% with PBST)

b. ECL HRP donkey anti-rabbit @ 1:15,000 dilution (0.7 μl antibody + 10 ml of 2% milk solution)

4. Remove the Primary Antibody solution; Wash the membrane in PBST 3X – 5 minutes each – at room temperature while shaking – in trays

5. After last wash place into Secondary Antibody solution. Incubate 30 minutes at RT, rotating.

6. Remove the Secondary Antibody solution; wash membrane in PBST 3X – 15 minutes each – at room temperature while shaking – in trays

Chemiluminescence 1. Reagent preparation

a. 1:1 ratio of Immun-Star HRP Peroxide buffer and Immun-Star Luminol Enhancer (Bio-Rad 170-5041)

2. Pour onto membrane and incubate for 5 minutes, making sure the surface is covered by the fluid the whole time (rock gently)

3. Drain the blot and dab the edge on a paper towel. Seal in a baggie so the blot cannot dry out – smooth out all bubbles.

4. Image in Bio-Rad Chemi-Doc under chemiluminescence settings.


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The Effects of Roundup and its Constituents on Steroid Hormone Levels in the Snail Lymnaea palustris

Preetha Phillips, Colleen J. Nolan, and Carol Z. Plautz




ABSTRACT Roundup, a commonly used herbicide, has been shown to have adverse effects on different non-target organisms, particularly on reproduction. Roundup has been demonstrated to affect the steroidogenic pathway, specifically the rate-limiting step in steroidogenesis, steroid acute regulatory protein (StAR). Previous research in snails demonstrated significant reduction in fecundity and increase in developmental abnormalities as a result of exposure to Roundup; preliminary results also suggested that Roundup impacted the production of steroid hormones derived from cholesterol, including the sex hormones. The present study aimed to determine a possible target in this pathway in order to understand the mechanism of disruption by the chemicals. This was done by treating Lymnaea palustris, an aquatic snail, with Roundup or its constituents (POEA, glyphosate, and diquat dibromide) for six weeks. Cortisol, testosterone and estradiol levels in the hemolymph were analyzed using enzyme linked immunosorbent assay (ELISA). Throughout the six week treatment period there was a decrease in reproduction among the snails in all treatment groups. The snails treated with complete Roundup and diquat dibromide showed significant decreases in both testosterone and estradiol levels. These could be related to the decrease in StAR (less overall material for steroid hormone production), or possibly to a disruption in aromatase activity. Snails treated with glyphosate exhibited a significant decrease in testosterone, significant increase in cortisol, and a notable increase in estradiol; these results suggest a shift in the steroidogenic pathway to produce more stress hormone at the expense of steroid sex hormones. KEYWORDS: Lymnaea palustris, steroidogenesis, snail, Roundup, reproduction

1. INTRODUCTION Roundup is a widely-used herbicide demonstrated to have adverse effects on non-target organisms, particularly on fecundity and development. The active ingredients are glyphosate and diquat dibromide (DD). Glyphosate is one of the best-selling herbicides across the world, and is used in over 90 countries [1]. The degradation of glyphosate has been questioned, and it has been shown that its toxicity may be enhanced by the other ingredients that comprise Roundup [2]. DD is a desiccant that can be used as a broad spectrum herbicide. Previous studies in our lab have shown DD to cause increased mortality and decreased fecundity, as well as developmental abnormalities and mortality in snail embryos [3]. While polyethoxylated tallow amine (POEA) is considered among the “inactive ingredients” of Roundup – it acts as a surfactant, which allows the herbicide to stick to the plant so it can gain access to the plants’ cells – it may play a role in the effects of Roundup on aquatic animals as well. The pond snail Lymnaea palustris is an ideal model organism for this study because it is found in freshwater which can be exposed to Roundup. Also L. palustris has quick developmental and reproductive ability. This organism is hermaphroditic,


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which allows it to undergo fertilization internally via self- or cross-fertilization. The hermaphroditic nature of the snail makes it an ideal model to study fecundity and sex hormone production levels. Multiple studies have been conducted in our lab to investigate the effects of Roundup on L. palustris. One study showed that both glyphosate and POEA led to higher mortality and decreased fecundity [4]. However, that study revealed that POEA or glyphosate alone or in combination did not cause as great of an effect on the snails as complete Roundup. Due to the lower overall effects on the snails when treated individually with POEA or glyphosate, it was hypothesized another active ingredient was causing the effects of Roundup. Another project was conducted in order to isolate the effects of DD, and DD was found to cause increased mortality and decreased fecundity, as well as developmental abnormalities and arrest in embryos [3]. We have since sought to identify the individual effects of POEA, glyphosate, and DD in Roundup on the snails. In a preliminary study on snail hormone levels conducted in the lab, it was found that Roundup impacted the production of different steroid hormones derived from cholesterol, including the sex hormones [5]. The hormone study was undertaken because Roundup has been demonstrated to affect the pathways of steroidogenesis in snails, specifically the rate-limiting step in steroidogenesis, StAR or steroid acute regulatory protein [6]. It was suggested that Roundup caused a decrease in testosterone and increases in progesterone and estradiol in snails, which may have been linked to the decreased fecundity of the chronically-treated snails. In the present study the hemolymph estradiol, testosterone, and cortisol levels were analyzed using enzyme

immunoassay (EIA/ELISA). The goal was to determine the disruption to the steroidogenic pathway in order to elucidate the target of the chemicals that compose Roundup. This study focused on the effects these chemicals have on the hormone levels of the snails and how they correlate with altered fecundity and increased mortality of the organisms. 2. MATERIALS AND METHODS 2.1. Fecundity Study: Sixty snails were selected for the 6 week treatment period. All selected snails were assessed to be healthy and 17 mm +/-1 mm. A 6 week treatment in one of four experimental solutions or one control group was set up. Each treatment group consisted of 12 snails treated with glyphosate, DD, POEA or complete Roundup. The 12 snails of each group were placed in three tanks of 4 snails in each tank. The concentrations of the chemicals used in the study were based on the Maximum Contaminant Level (MCL) of glyphosate, 0.7 mg/L or 700ppb, which represents the maximum concentration of a chemical allowed in public drinking water systems as set by the U.S Environmental Protection Agency. Controversy exists on the degradation of the ingredients of Roundup, especially glyphosate. Glyphosate is considered to readily break down in sunlight; however, it has been shown that glyphosate has a median half-life between 2 and 197 days in soil, with the typical half-life being approximately 47 days [7]. The half-life of glyphosate is observed to vary from a few days to 91 days [7]. But some studies have shown that if glyphosate reaches surface water, it is not likely for it to be broken down readily by water or sunlight [8]. POEA and DD are known to be more stable than glyphosate, which would make them more difficult to break down. Also during peak seasons agriculture runoff can


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lead to herbicides flowing into different bodies of water. In many cases this water becomes contaminated, and unsafe for consumption [9]. So it is possible the accumulation of Roundup caused by runoff and stability can lead to contamination and negative environmental impacts. Based on the available information on environmental levels and degradability of these compounds we used the concentrations as seen in Table 1 to treat the animals. The concentrations used were based on 5 times the MCL of Roundup to account for accumulation of these chemicals, and to investigate its possible consequences. The MCL value of total Roundup is calculated at 19.5 ppm based on the MCL of glyphosate. The specific concentrations of each chemical used in the study were determined by calculating the percentage of the ingredient in Roundup times 5 times the calculated MCL value of Roundup. The concentration of the chemicals used in the experimental groups can be seen in Table 1. Solutions were changed every 3 days in order to provide a consistent chronic treatment. Glyphosate POEA Diquat

Dibromide Roundup

Working Solution

3.5 mg/L 0.0325 mg/L

0.14 mg/L 19.5 mg/L

% of Active ingredients in Roundup

18% Inactive Ingredient

0.73% ______

Table 1: Concentrations of Roundup constituents used in each experimental tank. The snails were removed every 3 days from the tanks in order to replenish the tanks with clean water, collect jelly masses, and add fresh food. Both the number of jelly masses and embryos within them were counted using a stereomicroscope.

Observations on the effects on the snails and all mortality in each tank were also recorded.

Figure 1.1: Snails in mesocosms. Figure 1.2: Jelly masses containing embryos. 2.2 Hemolymph Extraction: Hemolymph was extracted from the snails in order to analyze circulating hormone levels using the enzyme-linked immunosorbent assay (ELISA) kits. The hemolymph was extracted twice during the six-week treatment period; the first time after three weeks and the second time at the end of the sixth week. The hemolymph was extracted quickly in order to prevent excess stress on the snails. The snails were wiped dry with a Kimwipe, and poked gently once or twice with a micropipette tip. After the snails were poked they secreted hemolymph, and the hemolymph was extracted using the pipette, transferred into an eppendorf tube and then flash frozen with liquid nitrogen [10]. Through each extraction the goal was to acquire at least 50 µl from each snail, in order to assay duplicates for each sample in the ELISA procedure. Most of the tanks contained four snails except for the tanks in which mortality occurred during the study. The hemolymph of all the snails in each tank was pooled. All the hemolymph samples were stored at -80˚C until they were used for the ELISA.


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2.3 Enzyme-linked Immunosorbent Assay Three ELISA assay kits (Cayman Chemical) were used in order to test the concentration of estradiol, testosterone and cortisol of the hemolymph at halfway and at the end of the six-week treatment period. Each kit used color absorption to determine the concentration of the specific hormone. The assay uses a tracer that is specific to each hormone type that competes with the hormone to bind with the antibody. Because of this the amount the tracer binds the antibody will be inversely proportional to the concentration of the hormone in the well. The procedure in preparing the plate was according to the manufacturer. The plate was washed to remove any unbound reagents and then Ellman’s Reagent used to quantify the amount of tracer concentration measured. The absorption of the (yellow) Ellman’s reagent is optimal at 405 to 420 nm. This absorption was used along with a standard curve in order to calculate the concentration of each hormone in the samples. This assay determined the average concentrations of estradiol, cortisol and testosterone in each tank at each timepoint. 3. RESULTS 3.1: Six-Week Treatment Period

Figure 2: Average number of embryos of each group counted each week during the treatment period. * denotes p<0.05. Figure 2 displays the average number of embryos counted from the jelly masses from

each group throughout each week of the treatment period. The snails treated with Roundup and DD had the lowest reproduction rate, with DD significantly different from controls (T-test).

Figure 3: Average number of embryos throughout the six-week treatment period of each group. * denotes p<0.05. Figure 3 displays the average total number of embryos that were counted throughout the whole study for each group. As seen in the figure, the snails treated with DD had a significant decrease in reproduction with a p-value less than 0.05 (T-test).

Figure 4: Snail mortality in each group during every week of the study. Figure 4 displays all the snail mortalities that occurred throughout the study. The most snail deaths occurred with the snails treated with complete Roundup.


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3.2: ELISA

Figure 5: The average concentrations of estradiol in each group found in the hemolymph extractions after 3 weeks into the treatment period and at the end of the study. * denotes p <0.05.

Figure 6: The average concentrations of testosterone in each group found in the hemolymph extractions after 3 weeks into the treatment period and at the end of the study. * denotes p <0.05. ** denotes a p-value less than 0.001. As shown in Figure 5, there were decreased levels of estradiol in the snails treated with DD or Roundup. There was a significant decrease in estradiol levels in the snails treated with DD. Elevated estradiol levels were seen in the snails treated with glyphosate. As seen in Figure 6, the testosterone ELISA showed there were decreased levels of testosterone seen in the snails treated with DD, Roundup and glyphosate. There was a significant decrease in testosterone in the samples from the first hemolymph draw in both the DD and Roundup treatment groups. The samples from both draws from the

snails treated with glyphosate showed a significant decrease in testosterone.

Figure 7: The average concentrations of cortisol in each group found in the hemolymph extractions after 3 weeks into the treatment period and at the end of the study. * denotes p <0.05. Figure 7 displays the average cortisol concentrations of each group from the samples of both hemolymph draws. The snails from all the treatment groups experienced an increase in cortisol levels. The snails treated with POEA had a significant increase in cortisol as seen from the samples from the second hemolymph draw. The snails treated with glyphosate had a significant increase in cortisol as seen in the samples from the first hemolymph draw.

Figure 8: The average estradiol to testosterone ratios of each treatment group calculated from the average concentrations of estradiol and testosterone found after both hemolymph draws.


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Figure 8 displays the average estradiol to average testosterone ratio of each group. There was a decreased estradiol to testosterone ratio in the snails treated with Roundup and DD. However, an increased estradiol to testosterone ratio was seen in the snails treated with glyphosate. The snails treated with POEA displayed a similar estradiol to testosterone ratio as that of the control. 4. DISCUSSION This study was conducted in order to evaluate the specific effects Roundup and its substituents have on the production of hormones and fecundity in Lymnaea palustris. Previous studies found that Roundup affected the pathways of steroidogenesis, specifically the rate-limiting step in steroidogenesis, steroidogenic acute regulatory protein (StAR) [11]. This research study focused on determining the true impact Roundup has on steroid production in L. palustris. The rate-limiting step in steroidogenesis is StAR translocating cholesterol from the outer membrane of the mitochondria to the inner membrane of the mitochondria, which provides the key raw material for steroid hormone formation [12]. It is possible that Roundup and its constituents directly affect StAR, which decreases the raw material available to produce steroid hormones, and leads to the decrease in sex hormones and fecundity. It was also hypothesized that Roundup caused a decrease in testosterone production and an alteration in estradiol production by affecting the enzyme aromatase. Aromatase is the enzyme that facilities the conversion of testosterone to estradiol. A change in estradiol production could lead to the observed decreased fecundity in the snails. It was also hypothesized that there was a decrease in sex hormone production and overall fecundity due to a possible shift in

the steroidogenic pathway to produce more cortisol. It is possible that Roundup inflicted stress on the snails which led to increased production of the stress hormone cortisol. In the steroidogenic pathway, cholesterol is converted into pregnenolone. Pregnenolone can then either serve as the precursor to the androgens and estrogens, or it can instead be used to produce cortisol. It is possible that increased stress led to increased cortisol production. The increase in cortisol production would then leave fewer materials to convert into the sex hormones, and eventual decreased fecundity in the snails as the possible outcome. Assays were conducted in order to analyze the concentrations of each hormone compared to the controls, and to detect possible differences in concentration levels at halfway through the study and at the end of the study. The estradiol to testosterone ratio was calculated to detect subtle changes in estradiol and testosterone production, which could explain a change in fecundity. The data from the treatment period indicate that the snails treated with Roundup and DD experienced the most adverse effects. The snails treated in these groups produced the least number of embryos. The snails treated with DD produced significantly less embryos compared to the control snails. As seen in Figure 2 the snails treated with DD and the snails treated with Roundup both had a decreased number of average embryos produced throughout the entire study. These effects were especially seen in DD, which produced a relatively low constant average number of embryos throughout the six weeks. This provides more evidence of the high toxic effect DD has in these animals. Another interesting trend that can be seen in Figure 2 is the relatively constant embryo production seen in the control, POEA and glyphosate groups. This trend continues until week 3 to week 4,


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where the snails in these groups reach a peak and then start to decrease in embryo production. However, the snails treated with glyphosate experience a sudden increase in embryo production after their drastic decline in reproduction that lasted until week 5. In fact, the snails treated with glyphosate are able to reach around the same average production of offspring as they had before the snails experienced a decline in reproduction. This aligns with the results indicating the elevated estradiol to testosterone ratio seen in the snails treated with glyphosate, and may account for the increased reproduction rate of these animals. It is possible that these snails had a decrease in reproduction due to stress from the hemolymph draws that occurred in the third week. It is possible that in response to stress, these animals increased their reproductive rate due to evolutionary mechanisms, as can be seen in the sudden peak then decrease. This suggests a change in the methods for hemolymph extraction in order to ensure the animals do not become too stressed. However, it is interesting to see the animals able to return to a higher reproductive level following glyphosate treatment, which indicates the chemicals impact the snails in different ways. The results from the ELISA assays were extremely revealing on the effects of Roundup and its constituents on the steroidogenic pathway. The snails treated with Roundup and DD had very similar effects on all hormone levels. It was seen that both DD and Roundup exerted a decrease in estradiol levels. The snails treated with DD had a significant decrease in estradiol levels as seen from the results of the samples from the second hemolymph draw. A similar trend was seen in cortisol levels in groups treated with DD and Roundup, in which there were increased levels of cortisol seen in samples from both

hemolymph draws. The group treated with DD had a significant increase in cortisol levels comparing the first hemolymph draw to the second hemolymph draw. This indicates that the snails in the DD treatment group had an increased production of cortisol throughout the study. The results from the assay of testosterone showed that both DD and Roundup treatment groups showed a decrease in testosterone levels. There was a significant decrease in both these groups in testosterone levels seen in the samples after the first hemolymph draws. Both groups also indicated a decreased average estradiol to testosterone ratio as seen in Figure 8. A decreased estradiol to testosterone ratio can cause a decrease in reproduction, which is seen in both groups throughout the study. Based on the results of the assay, a possible mechanism for the effects of both Roundup and DD was formulated, and are shown in the figures below. Figure 9: Predicted mechanism of the effect of DD on the steroidogenic pathway. As seen in Figures 9 and 10, it is predicted that both Roundup and DD have very similar effects on the steroidogenic pathway. Both these chemicals lead to decreased production of testosterone and estradiol, and an increased production of cortisol. It is possible that this occurs


16

because aromatase is a direct target of DD and Roundup. Figure 10: Predicted mechanism of the effect of Roundup on the steroidogenic pathway. Aromatase is the enzyme that converts testosterone to estradiol, and a decrease in its activity can explain the low estradiol to testosterone ratio. This could explain the decreased reproduction rate seen in both groups throughout the study. The results from the assays for the snails treated with glyphosate indicated that estradiol levels tended to be increased, as seen in Figure 5. Testosterone levels in snails treated with glyphosate were significantly decreased as seen in the samples from both the first and second hemolymph draws. Due to this, glyphosate treatment yielded an increased estradiol to testosterone ratio. As seen in the other treatment groups, the snails treated with glyphosate showed an increase in cortisol levels. The snails had a significant increase in cortisol as seen in the samples from the first hemolymph draws. Through these results a mechanism for the effect of glyphosate on the steroidogenic pathway was predicted and can be seen in Figure 11. It is possible that the decreased production of both sex hormones could be due to the shift in the steroidogenic pathway to produce more cortisol, which leads to less

Figure 11: Predicted mechanism of the effect of glyphosate on the steroidogenic pathway. raw material to produce the sex hormones. This is further evident as Figure 11 shows glyphosate treated animals had significantly lower levels of testosterone as seen from the samples from both hemolymph draws. Unlike the snails treated with either Roundup or DD, the snails treated with glyphosate exhibited levels of estradiol similar or slightly increased compared to that of the control snails. This could indicate that aromatase activity may be either increased or not affected by glyphosate. It is possible that a decrease in StAR activity or abundance led to decreased raw material to make the sex hormones, which led to low levels of testosterone. However, if aromatase continued to catalyze the reaction to convert the testosterone to estradiol, this possibly led to a further decrease in testosterone and relatively no change in estradiol levels in the snails. A study that investigated the potential effects of glyphosate on human breast cancer found interesting results with similarities to the results found in the current study. The breast cancer study found that glyphosate caused additive estrogenic effects which increased estrogen levels that may cause an increased risk of breast cancer [13]. This is comparable to the results found in this study, which showed a relative increase in estradiol levels at the expense of a significant


17

decrease in testosterone levels in the glyphosate treatment group. A concurrent study is underway to analyze the expression of StAR in the kidney, brain and gonadodigestive complex in the same animals from this study using Western Blot. Preliminary results indicate that organs of glyphosate-treated animals expressed the lowest amount of StAR relative to controls, compared to other treatment groups. That result aligns with the results from this study, and further suggests that glyphosate causes decreased StAR abundance or activity. Compared to the results of the other treatment groups, POEA led to less adverse effects on the snails. Like the snails from the other treatment groups, the snails treated with POEA demonstrated an increase in cortisol levels. There was a significant increase in cortisol levels compared to the samples from the first hemolymph draws and the second hemolymph draws, which indicated that as the study progressed the snails produced significantly more cortisol. Also based on the results from the assays, the snails exhibited a relative decrease in testosterone throughout the study, and in the beginning of the study a slight increase in estradiol then at the end a relative decrease in estradiol. The changes in the hormone levels can be seen in Figure 12. As seen by the minimal change in estradiol and testosterone levels, it can be understood that aromatase was not greatly affected by POEA. Also the estradiol to testosterone ratio was similar to the estradiol to testosterone ratio of the control group. It is possible as seen from the second draw, that the increase in cortisol levels led to a decreased amount of raw material to produce the steroid sex hormones, as can be seen by the decreased levels of estradiol and testosterone. Based on these results it can be assumed that POEA does not produce as

Figure 12: Predicted mechanism of the effect of POEA on the steroidogenic pathway. adverse effects as the other chemicals do on the animals. It is also probable that the effects of POEA take longer to manifest. POEA acts as a surfactant, which allows the chemicals to enter the plant. It does this by lowering the water’s surface tension, which prevents water from forming droplets on most surfaces. This allows the chemicals to disperse and penetrate onto the waxy surface of the plant [14]. It is possible that even though POEA causes adverse effects, it creates greater damage when paired with different chemicals, as it is in Roundup. 5. CONCLUSIONS This research project was able to shed light on the impact of Roundup and its constituents on the steroidogenic pathway of the aquatic snail. These findings suggest mechanisms for how these chemicals affect steroid hormone production and fecundity. For future research projects it is pivotal to repeat the results. It would also be informative to analyze progesterone or pregnenolone levels. Analyzing the progesterone concentration can shed more light on the possible changes Roundup and its constituents have on the whole pathway.


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It is also necessary to develop a more efficient means to extract sufficient hemolymph from the snails or increase assay sensitivity. In this research project there was a shortage of hemolymph samples, which led to a lack of duplicates for some samples in the assays; this hindered subsequent statistical analysis for some groups. It will also be important to further evaluate the expression of StAR, aromatase, and other steroidogenic enzymes in order to develop a clear picture of the effects of these chemicals. The findings from this research project will provide a basis for future research on the action of these chemicals on the specific enzymes and products of the steroidogenic pathway. Given that the steroidogenic pathway of snails is similar to that of humans, it is pivotal to elucidate the effects of these chemicals to shed light on human health risks. ACKNOWLEDGEMENTS This work was supported by grants from the NASA WV Space Grant Consortium and Shepherd University, and the SURE Program of WV-EPSCoR. REFERENCES [1] Sesana, L. EPA raises levels of

glyphosate residue allowed in food. The Washington Times, Communities. Web. (2013)

[2] Benachour N and Séralini G-E. Glyphosate Formulations Induce Apoptosis and Necrosis in Human Umbilical, Embryonic, and Placental Cells. Chem. Res. Toxicol. (2009) 22(1): 97–105 DOI: 10.1021/tx800218n

[3] Mines, S. and Plautz, C.Z., Investigating reproductive and developmental abnormalities in aquatic invertebrates exposed to components of the herbicide Roundup. Meeting Abstract, 88th Annual

Meeting of the WV Academy of Science, 2013.

[4] Cain J, Nolan C, Plautz CZ (2012). Effects of Roundup and its constituents on the freshwater snail Lymnaea palustris, with respect to mortality, fecundity, growth, and developmental abnormalities. SUJUR 2012; 1:8-22.

[5] Plautz C.Z., Brooks A, and Nolan, C.J. Effects of Roundup on reproduction, steroid hormone levels, and the steroidogenic pathway in Lymnaea palustris. Meeting Abstract, 90th Annual Meeting of the WV Academy of Science, 2015.

[6] Plautz C.Z., et al. Disturbances in reproduction and expression of steroidogenic enzymes in aquatic invertebrates exposed to components of the herbicide Roundup. In prep. 2016

[7] NPIC. Glyphosate Technical Fact Sheet. National Pesticide Information Center. NPIC, (2015)

[8] EPA (2015). Glyphosate. EPA. Environmental Protection Agency, 1 July 2015.

[9] Swanson, A. What Is Farm Runoff Doing To The Water? Scientists Wade In. NPR. Web. (2013)

[10] Dikkeboom R., Van der Knaap, W. P.W., Meuleman E.A., and Sminia T. Differences between blood cells of juvenile and adult specimens of the pond snail Lymnaea stagnalis. Cell Tissue Research (1984) 238:43-47

[11] Chaney, J., and Plautz, C.Z. The link between disturbances in reproduction and expression of enzymes in the steroidogenic pathway in aquatic invertebrates exposed to components of the herbicide Round-Up. Meeting Abstract, 88th Annual Meeting of the WV Academy of Science, 2013.

[12] Stocco, D. The Role of the StAR Protein in Steroidogenesis: Challenges


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for the Future. Journal of Endocrinology 164.3 (2000): 247-53.

[13] Thongprakaisang S, Thiantanawa A., Rangkadilok N., Suriyo T., Satayavivad J. Glyphosate induces human breast cancer cells growth via estrogen receptors. Food and Chemical Toxicology 29:129-136 (2013).

[14] Gammon C. Weed-Whacking Herbicide Proves Deadly to Human Cells. Environmental Health News. Web. (2009)


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The Effects of ldb1 Dosage on the Expression of dll1 and Histology in the Developing Vertebrate Eye

Hannah C. Williams and Carol Z. Plautz




ABSTRACT ldb1 is a relatively new gene in the realm of eye development. Its identity as a transcriptional cofactor is well-known, but its targets and direct effects on the developing optic cup, retina, and lens are still to be understood. This project uses Xenopus laevis as a model system of the vertebrate eye in studying the effects of knocking down or overexpressing the active truncation of the ldb1 gene on the eye. ldb1 RNA or morpholino oligonucleotide, together with fluorescein dextran (FLDX), were microinjected into one dorsal cell at the 4-cell or 8-cell embryonic stages. In situ hybridization and histological sectioning were used to visualize the effects of the ldb1 manipulation on dll1, a candidate target of ldb1, and the structures of the eye. In preliminary results, dll1 expression in both the optic cup and neural tube of Stage 28-30 embryos appears to show a shift in expression in the injected areas. Altering the levels of ldb1 in the dorsal, animal quadrant of the embryo appears to affect normal eye development; increase in ldb1 dosage appears to result in advanced eye development and an increase in neural tube and retinal dll1 expression. Further results on dll1 expression in ectoderm explanted from ldb1 RNA-injected zygotes will inform the suggestion that dll1 is a possible direct target of ldb1 in the developing embryo.

KEYWORDS: Xenopus, lens, ldb1

1. INTRODUCTION Many components, events, signals, and tissues come together to form a vertebrate eye. A network of transcription factors and induction signals ensure that a properly developing embryo will create an iris and ciliary body from the anterior optic vesicle, a retina from the posterior invagination of the optic cup, and a lens and cornea from the overlying ectoderm [1]. This study looks at the development of the vertebrate eye and genes that can be manipulated to alter normal or faulty development in components of the eye such as the lens or retina. Spemann found that lens induction was dependent on the underlying optic cup when he analyzed eye development, but an unknown number and pattern of inductive events occur throughout early embryonic development of the eye [1].

LIM-domain binding (ldb) proteins are involved in regulating developmental pathways and cell differentiation [2], and although different species express multiple forms of ldb, ldb1 is highly conserved across a number of animal models [2, 3]. ldb1 is considered a transcriptional cofactor and likely one of the earliest and most essential regulators of embryonic development [2]. Originally ldb1 was reported as ubiquitously expressed throughout development of embryos of mouse [4], Xenopus [4], and zebrafish [3]. However, Plautz et al. [5] found highly enriched regions of ldb1


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expression in the neural tissue of Xenopus in early development and in the anterior neural plate of developing embryos; further, knockout of ldb1 in mouse led to severe anterior neural, including eye, defects [6].

Previous work with ldb1 in Xenopus focused on its role in early lens induction [5]. When ldb1 was knocked down, the lens and retina underwent poor development [5]. The novel techniques used to identify ldb1 in 2014 [5] were used in the present study, and are detailed in our methods manuscript and video [7]. However, questions remain about ldb1’s targets in eye development, as well as its influence on inductive processes.

Plautz et al. [5] demonstrated that a truncated form of ldb1 (Δldb1), was not only a fully active form of the gene, but that it showed even higher activity than the full form. By using an injected morpholino oligonucleotide (MO) blocking ldb1 translation and analyzing expression of other genes by in situ hybridization on these embryos, reduced eye size was the found in tailbud (stage 21-26) embryos [5]. By stage 30, embryos that survived had reduced lens size, reduced γ-crystallin expression in the lens, and decreased retinal pigmented epithelium. Since ldb1 was identified in a functional assay for activation of early lens genes, the authors knew it was involved with lens induction, but still questioned whether the lens was directly targeted or if lens development was disrupted from poor inductive signaling from the altered retina [5].

Since many early events in eye development produce a cascade of effects that are later difficult to disentangle, the present study has attempted to adopt the same principles of previous studies on ldb1 effects while using increasingly well-controlled and concise methods to hone in on the early eye genes and morphological effects to see the effect of ldb1 manipulation on embryos. Previously, ldb1 was thought

to primarily disrupt the lens, but the eye develops via reciprocal inductive effects of the retina and lens, and the lens and cornea. This work includes the analysis of dll1, a gene expressed in the neural tube and retina but not the lens. Previously, when the ldb1 MO was injected into embryos, dll1 showed the greatest reduction in response to the injection [5], which could suggest that Notch-dll1 signaling may be a direct target in a ldb1-mediated pathway. In studies on rat embryos, Notch and Delta expression patterns were studied as key components in the differentiation of retinal neurons and development of the vertebrate retina [8]. Using dll1 as a candidate target allows us to study the likelihood that it is a target of ldb1, but also whether ldb1 is affecting retinal development, which could lead to the deterioration in lens development at later embryonic stages.

In the first experiment, MO, which block the translation of ldb1, were administered with a fluorescent marker into one cell of the two-cell, four-cell, or eight-cell cleavage stage embryos. In the more developed embryos, the dorsal blastomere was specifically targeted. This disrupted only a portion of the embryo that involves the head and eye, limiting confounding results in non-targeted cells, and allowing the embryos’ development to reveal visible effects from the injection contents. These embryos were analyzed both as whole mount embryos and in histological sections. The second experiment was similar to the first but administered Δldb1 RNA in place of the MO. The third experiment was undertaken to gain more insight into the possible target dll1 when ldb1 was upregulated in the embryo. Δldb1 RNA was injected into one-cell stage embryos. Following culture to gastrula stages, animal cap ectoderm was isolated, and these ectodermal isolates were analyzed by in situ hybridization for expression of dll1 in both


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animal caps and whole embryos with and without the Δldb1 injection. Through analysis of gene expression patterns or histological examination, the phenotypic changes resulting from the perturbations were observed in the X. laevis embryos.

This study draws upon the relationship of X. laevis eyes with all vertebrate eyes. By understanding early lens or retinal changes in the frog embryos, studies like this can help not only the understanding of ldb1’s activity and the other genes with which it interacts in Xenopus, but also the possible causes of blindness or eye deformities in other species including human. Gene therapy studies are already underway with a variety of congenital blindness diseases [9]. Recently, Nakayama et al. [10] have studied phenotypic similarities between the animal pax6 mutation and human aniridia patients, which feature defects in iris, cornea, lens, and retina. These studies suggest the future impact that gene expression studies will have on aiding and curing currently unpreventable types of human blindness disease. 2. MATERIALS AND METHODS 2.1 Microinjection Needle Calibration Glass capillary tubing and World Precision Instruments’ PUL-1 were used to pull appropriately sized needles, calibrated by diameter under a compound microscope by calculating the volume of a water droplet injected onto mineral oil [11]. World Precisions Instruments’ Nanoliter 2000 microinjection apparatus was used for injections. A needle with an injection volume of approximately 11 nL was used for each of the three experiments. 2.2 In Vitro Fertilization Male and female wild-type frogs were primed with human chorionic gonadotropin (HCG) according to the schedule of Ethridge

and Richter [12]. On the day of embryo retrieval, the male was prepped for surgery [11] in 0.05% MS222 (Sigma). Eggs were massaged from the female and treated with sperm solution in 1x MBS followed by 0.1x MBS to initiate fertilization. This method allowed for synchronous fertilization. Embryos were de-jellied with 2% cysteine in 0.1x MBS and reared at 22˚C to the two-cell, four-cell, and eight-cell stages for microinjection. 2.3 Natural Mating Fertilization A male and a female frog were each primed with HCG [12] and mated to naturally fertilize eggs in 0.1x MBS. Embryos were de-jellied as above and sorted to determine the stage and vitality of each embryo before microinjection. 2.4 Microinjection and Explants All working solutions were made fresh; see Appendix. Experiment 1 Injections: Knock-down (MO) working solution made at a concentration that a 10 nL injection volume would deliver 40 ng MO and 25-50 ng fluorescein dextran (FLDX). Upregulation (mRNA) working solution made at a concentration that a 10 nL injection volume would deliver 300 pg of the Δldb1 RNA and 25-50 ng FLDX. These injections were administered to one cell at the 2-cell stage or a dorsal blastomere at the 4-cell or 8-cell stage. Experiment 2 Injections: mRNA working solution modified so injection would deliver 400 pg of Δldb1 RNA and 50-100 ng FLDX. Experiment 3 Injections: mRNA working solution made at a concentration that a 10 nl injection would deliver 400 pg of Δldb1 RNA. This injection was administered at the one-cell stage and contained no FLDX. Embryos were transferred to 3% Ficoll-1x MBS solution in small dishes lined with clay wells to provide high pressure and high salt to aid healing of injected embryos. Embryos


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were set aside to heal for one to three hours at 22˚C, then transferred to 0.1x MBS until reaching the desired stage. For experiment 3, animal caps of gastrula (stage 11) embryos were explanted then cultured along with controls to stage 24-26. Embryos (and caps) were fixed in MEMFA and stored in EtOH at -20˚C until further analysis. 2.5 Whole Mount Analysis of Embryos RNase-free conditions were used to examine the embryos under a compound microscope with both epi-illumination and epi-fluorescence to compare morphology of each embryos’ injected versus uninjected sides. 2.6 In Situ Hybridization Following the established protocol [7], the embryos or animal caps were analyzed for expression of dll1. 2.7 Histological Sectioning Embryos were embedded in paraffin to obtain transverse sections through the head and anterior trunk. Standard procedures were followed for embedding and sectioning techniques [13]. Rotary microtome was calibrated to yield paraffin sections approximately 20µm thick. Meyer’s Albumin Fixative was used to prepare slides for adhesion of paraffin sections during dewaxing and coverslipping. 3. RESULTS Figures shown are representatives for each experimental group tested throughout the project. Figure 1 shows dll1 expression in a control embryo. dll1 appears in the central retina, as well as the anterior neural tube. In lateral view, dll1 in the retina appears as a “bull’s eye” dot directly in the center of the eye; the dll1 domain is C-shaped along the proximal eye cup without reaching the edges that border the head ectoderm and cornea. As can be seen in Figures 2-5, dll1

expression patterns appear to have shifted in response to the injected reagents, both MO and RNA.

Figure 1. Control embryo with dll1 in situ hybridization. dll1 is expressed in the anterior neural tube and the central retina (arrow).

Figure 2. MO directed against ldb1 injected into embryo followed by dll1 in situ hybridization. Embryo was injected on the right (lower) side of the body as seen in 2A (fluorescence). dll1 expression is reduced in the neural tube on the uninjected vs injected side as seen in 2B (arrow).

1

2B

2A


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Figure 3. MO directed against ldb1 injected into embryo followed by dll1 in situ hybridization. The left side of the embryo was injected. The left eye is more darkly pigmented and shows more dll1 (arrow) relative to the right eye. dll1 in neural tube appears shifted relative to the uninjected side (arrowhead).

Figure 4. Δldb1 RNA injected into embryo followed by dll1 in situ hybridization. Embryo was injected on the right side of the head as seen in 4B (fluorescence). The right side (arrow) shows dll1 expression in the central retina and neural tube.

Figure 5. Δldb1 RNA injected into embryo followed by dll1 in situ hybridization. Embryo was injected on the right side of the head as seen in 5B (fluorescence). dll1 expression appears shifted relative to the uninjected side (arrow). Histological paraffin sections were also analyzed for both the knock-down and overexpression of ldb1embryos, following whole mount in situ hybridization for dll1. Figure 6 shows the effects of increased ldb1 dosage in an embryo via sections.

3A

3B

4A

4B

5A

5B

6A


25

Figure 6. Section through the eyes and head of embryo injecied with Δldb1 RNA; dll1 in situ hybridization. Embryo appears to have been injected on the right side, as seen in 6B (fluorescence). dll1 expression (purple) is evident in the eye (6A, bracket). After isolation of the animal caps of control and ldb1-overexpressing gastrula-stage embryos, these were cultured to the early 20-stages before fixation and in situ hybridization. Results are shown in Figures 7 and 8.

Figure 7. dll1 expression in control animal caps (7A) and embryos (7B). Two animal caps showed staining (arrows) in the tissue (in addition to the background staining in the cavities formed by several explants) of the nine analyzed at the endpoint.

Figure 8. dll1 expression in ldb1-overexpressing animal caps (8A) and embryos (8B). Three explants showed staining in the tissue of the nine analyzed at the endpoint.. Many of the whole embryos (8B) showed deformities in head and axial development.

6B 7B

7A

8A


26

4. DISCUSSION This study had two primary areas of focus: what happened to the structures of the eye when ldb1 was altered in the embryo, and is dll1 a direct target of ldb1? ldb1 appears to exert dosage-dependent effects in the developing embryo. Previous studies noted severe phenotypes that arose from ldb1 null mutant embryos to demonstrate the necessity of the gene in development overall. Embryos were small, truncated in the head, disrupted in brain, heart and foregut, but most notably had anterior-posterior axial truncations [6, 2]. This study went a different route and altered ldb1 on only one side of the embryo (or one side of the head). A visibly distinguishable internal control within each embryo for the up- and downregulation experiments allowed for comparison between the two eyes when embryos were analyzed from the dorsal aspect in whole mount or when sectioned. Not all of the embryos survived the injections or retained all the injection

contents, but administering injections to only one cell of an already dividing embryo likely increased the chances of selecting for healthy embryos. However, a number of the first experiment’s embryos developed a bulge of cytoplasm following transfer from post-injection Ficoll solution back into 0.1x MBS to continue to develop. Of these embryos only 46.43% of the RNA group and 50.91% of the MO group showed a discernable difference in the level of fluorescein dextran between the two sides of the embryos’ heads. Some embryos discarded due to ambiguous fluorescent tracking may have received the full dosage of RNA or MO, but any differences within the embryos could not be definitively linked to effects from the ldb1 alteration. Other embryos were discarded if stunted in growth (possibly an effect from injection) or if fluorescence was detected in the ventral region of the body, indicating improper injection placement. Measurements of the neural tube dll1 staining on both the injected and uninjected sides of whole mount embryos were taken to compare how the expression may be shifted with knockdown or overexpression of ldb1. MO-injected embryos exhibited a 22-42% decrease in the amount of dll1 expressed on the injected side. ldb1 RNA-injected embryos varied more in dll1 expression, but averaged a 38% increase in dll1 expression on the injected side. Individual ldb1 RNA-injected embryos varied widely, however, and exhibited as little as an 8% increase and as much as a 63% increase in dll1 expression. No embryos analyzed by whole-mount measurements demonstrated the opposite effect (ie, a decrease in dll1 expression in ldb1-overexpressed embryos or vice versa). In the appearance of the eye, both injections yielded similar results in whole-mount analysis. As seen in Figures 2 and 3 for the MO injection and Figures 4, 5, and

8B


27

6 for the RNA injection, the injected side often gives the appearance of a more well-developed eye than the contralateral counterpart. The natural pigmentation and the roundness or symmetry of the optic cup’s shape are both indicators of the eye progressing in development. The ldb1-overexpressing embryos of Figures 4 and 5 also show a clearly discernable difference in the amount of dll1 present in the injected eye, more so than the uninjected eye. Although this cannot easily be quantified, it leaves open the question about the relationship between ldb1 and dll1. The knockdown embryos of Figures 2 and 3 gave questionable results in comparing the dll1 expression in just the eye. Figure 3 dll1 expression was more obvious in the left eye, but many of these embryos (including data not shown) did not exhibit notable staining discernable beyond the natural eye / retinal pigmentation. Both injection experiments thus yielded fairly similar morphological effects in the eyes, but the dramatic increase of dll1 in the central retina of the injected eye following the upregulation of ldb1 proposes the idea that the embryos – and dll1 regulation – are likely very sensitive to the amount of ldb1 present in the developing embryo. As for the aim to find whether or not dll1 is a direct target of ldb1, these results alone are inconclusive. The animal caps were isolated from gastrula stage embryos to ensure that the anterior ectodermal region of the embryos had not received inductive signals to activate early eye or neural genes. However, if the extra dosage of ldb1 in the injected embryos was at a concentration high enough to turn on such genes (namely dll1), in situ hybridization would reveal their upregulation. Whole embryos of both uninjected controls and RNA-injected embryos were also kept at the same conditions as the isolated animal caps to serve as staging controls. It was also a

question how embryos injected with RNA at the one-cell stage would progress through development, since most previous studies looked instead at a depletion of ldb1 in the embryo [6, 2, 5]. After culturing the control embryos to the mid-twenties stages, where elongation had taken place and spontaneous movements had begun, the embryos and caps were fixed to stop further development and dll1 expression assessed in each group. Some of the whole embryos that were injected appeared to be lagging in stage slightly or deformed. This may have been an artefact of the injection itself or the embryos could have been slightly asynchronous to control embryos. As seen in Figures 7 and 8, nine animal caps (in each, control and injected) survived the entire procedure. Of those nine, both groups only had two or three explants with dll1 staining over background in the tissue. Albino embryos were used in this experiment, so the (purple) expression was easy to recognize without natural pigmentation in the tissue and embryos. However, many of the explants had developed a bubble-like cavity, which picked up background stain. Nine animal caps in each group is not enough to compare, and the difference between the groups is not significant with only one experiment. dll1 was expected to have been expressed in the control embryos by the mid-twenty stages, but longer development time to increase the chances of seeing meaningful results (and more advanced eye and neural development in the whole embryos) would have been preferable. The variability of the color reaction in the in situ hybridization process is also a point that could have been extended to yield more results and potentially make a distinction between the injected and control groups. A different analysis technique may also need to be used to more accurately quantify a difference between RNA-injected and


28

control animal caps; while in situ hybridization is useful for whole embryo analysis because it provides visualization of where the gene of interest is expressed, an isolated piece of ectoderm does not yield much visual information. Real-time PCR, on the other hand, could provide a clearer method for distinguishing level of target gene expression in injected vs control groups. Our evidence appears to show that ldb1 acts in a dosage-dependent manner. MO injection causes a decrease in the amount of its protein to be translated, while RNA injection increases the amount of template for its protein to be produced. The results from our first and second experiments are consistent with predictions. dll1 should still be considered a candidate target of ldb1, but without further and modified testing, our third experiment cannot confirm or deny its relationship with ldb1.

DEDICATION/ ACKNOWLEDGEMENT Twelve years ago, my older sister was diagnosed with Type One Diabetes, and our optometrist was the first one to see this as a possible diagnosis. I have seen first-hand how humans’ eyes can change physically when other systems in the body change, how vision can change with age, and how eyes develop and adapt over time. This only fueled my desire to go into the science field and study optometry. -HCW This work was supported by grants from the NASA WV Space Grant Consortium and Shepherd University, and the SURE Program of WV-EPSCoR. REFERENCES [1] Graw, J. (1996). Genetic Aspects for

Embryonic Eye Development in Vertebrates. Developmental Genetics 18: 181-197.

[2] Mylona, M-A. (2009). The diverse role of ldb1 in cell differentiation and mouse embryonic development. Dissertation for Department of Cell Biology of the Erasmus University Rotterdam, The Netherlands.

[3] Toyama, R., Kobayashi, M., Tomita, T., Dawid, I.B. (1998). Expression of LIM-domain binding protein (ldb) genes during zebrafish embryogenesis. Mechanisms of Development, 71: 197-200.

[4] Agulnick, A.D., Taira, M., Breen, J.J., Tanaka, T., Dawid, I.B., and Westphal, H. (1996). Interactions of the LIM-domain-binding factor Ldb1 with LIM homeodomain proteins. Nature 384, 270-272.

[5] Plautz, C. Z., Zirkle, B. E., Deshotel, M. J., Grainger, R. M. (2014). Early stages of induction of anterior head ectodermal properties in Xenopus embryos are mediated by transcriptional cofactor ldb1. Dev Dyn. 243(12):1606-18. doi: 10.1002/dvdy.24193.

[6] Mukhopadhyay, M. et. al. (2003). Functional ablation of the mouse ldb1 gene results in severe patterning defects during gastrulation. Development, 130: 495-505. Doi: 10.1242/dev.00225.

[7] Plautz, C. Z., Williams, H. C., Grainger, R. M. (2016). Functional Cloning Using a Xenopus Oocyte Expression System. J. Vis. Exp. (107) e53518, doi: 10.3791/53518.

[8] Bao, Z-Z, Cepko, C. L. (1997). The Expression and Function of Notch Pathway Genes in the Developing Rat Eye. J. Neurosci., 17(4): 1425-1434.

[9] Pollack, A. (2015, October 7). Eye Treatment Closes In On Being First Gene Therapy Approved in U.S. The New York Times.

[10] Nakayama, T., et. al., Xenopus pax6 mutants affect eye development and other organ systems, and have


29

phenotypic similarities to human aniridia patients. Dev Biol (2015) http://dx.doi.org/10.1016.j.ydbio.2015.02.012

[11] Sive, H. L., Grainer, R. M., Harland, R. M. (2000). Early Development of Xenopus laevis: A Laboratory Manual. Cold Spring Harbor, New York: Cold Spring Harbor Laboratory Press.

[12] Etheridge, A. L., Richter, S. M. A. (2005). Xenopus laevis: Rearing and Breeding the African Clawed Frog. Fort Atkinson, Wisconsin: Nasco Production Facility.

[13] Humason, G. L. (1972). Animal Tissue Techniques: Third Edition. San Francisco, CA: W. H. Freeman and Company.

APPENDIX

0.05% Tricaine methanesulfonate (MS222) 0.5g MS222 in 10mL of ethanol Top to 1L of water

1x Modified Barth’s Saline (MBS) 100mL 10x MBS Salts [11] 7mL Calcium chloride Water to 1L pH to 7.8 0.1x MBS 1:10 dilution of 1x MBS 2% Cysteine-MBS 4g Cysteine 200mL 0.1x MBS pH to 7.8-7.9 using 11 NaOH pellets

3% Ficoll-MBS [7] Ficoll 1x MBS MEMFA fixative 10x MEM Salts [11] Water 37% Formaldehyde P-Tween, PTw 1x PBS 0.1% Tween Proteinase K, in PTw 10μg/mL Proteinase K Hybridization Buffer [11] 2x SSC + Tween 5mL 20x SSC [11] 50uL Tween 20® 45mL water 0.2x SSC + Tween 1:10 dilution of 2x SSC + Tween Maleic Acid Buffer (MAB) Maleic Acid Sodium chloride Alkaline Phosphate Buffer 100mM Tris, pH 9.5 50mM Magnesium chloride 100mM Sodium chloride 0.1% Tween Bouin’s fixative [11] Picric acid in Water 37% Formaldehyde Glacial Acetic Acid Meyer’s Albumin fixative [13] Egg white, beaten Glycerin 37%Formaldehyde


30

Design and Implementation of Indoor Positioning System

Using Radio Frequency and Infrared Communication

Vasyl Shtanko

Department of Computer Sciences, Mathematics, and Engineering

Shepherd University

P.O. Box 3210

Shepherdstown, WV 25443

[email protected]

ABSTRACT

Most people are familiar with the term Global

Positioning System(or GPS). Based on

Merriam-Webster definition, the term refers to

“a radio system that uses signals from

satellites to tell you where you are and to give

you directions to other places”(GPS). Most

often, however, the term is used to describe a

device capable of determining the absolute

position of a person or an object within the

surface of the Earth. This may pose a problem

of using GPS in certain scenarios. For

example, when it relates to indoor

environment, GPS location can be extremely

inaccurate or even impossible to determine.

The main reason is that in order to calculate

GPS location, satellites are being used to send

and receive a signal in order to predict the

location. When it comes to the indoor

environments, however, the signal becomes

scattered and distorted. In certain situations,

the signal gets so distorted, that the link with a

satellite is lost. The goal of this project is to

develop an alternative to GPS for previously

unknown indoor environments by placing RF

and Infrared tags within a certain range in

order to map specific locations and to be able

to find them within the environment. Once the

mapping process is complete, the goal is to use

existing tags to create a grid system with

absolute coordinates that could be used for the

future reference. The use of such system could

include, but not limited to: robotics and

automation, navigation within certain

buildings(Example: finding a specific office in

a large building or a warehouse), and indoor

robotic sensor networks(Temperature or

humidity control).

KEYWORDS: GPS, Global Positioning System,

robotics, Indoor, Radio Frequency, Automation.

1. INTRODUCTION

We have all used a GPS-based devices. Most

people have GPS navigators in their cars, some

use those for jogging and doing other types of

outdoor activities. Those systems are also used

is a variety of robotics and automation

applications. Although many are familiar with

the devices that use GPS signals, very few

people actually know how a GPS system

works and what it is based on. For the purpose

of this discussion, GPS is a grid of satellites

operating together to provide accurate

positioning information on the surface of

Earth[1].

The system is very flexible and can be

implemented inside any device. It is also

accurate enough to provide position-based

navigation, velocity, altitude, and time

information. While it almost seems to be

perfect, GPS has one major flaw. It does not

work indoors. Following sections will go in-

depth to describe different types of GPS

systems and how they could implemented or

modified to achieve good accuracy in indoor

environments.


31

2. EXISTING INDOOR GPS DESIGNS

Since existing GPS systems do not work

indoors, a new approach needs to be taken.

Several designs were proposed in order to be

able to either utilize existing satellite signals,

or create a smaller scale system for positioning

and navigation indoors. One of these

approaches is described in COIN-GPS

paper[6]. Instead of trying to design a new

system, the idea behind this research was to

use a directional antenna to utilize and amplify

the signal from GPS. The project also went on

to develop an algorithm to predict the location

indoors using existing GPS data. Such

approach has a one-time cost associated with

it, but it also requires more processing power,

since the GPS signal may be modified and

distorted in the process. While processing

power is not a great issue for modern devices,

the battery life is. Since more processing is

equivalent to using up more battery, this

certainly poses an issue.

Another approach to the system uses an

ultrasonic and LASER synchronized

pulses[10]. This design is quite a fascinating

piece of hardware, since the system utilizes

only one main station to be able to map

virtually any environment. The issue comes in

when designing a system for larger

environments. Ultrasonic signals are not very

accurate to begin with, so at significantly long

distances, their accuracy could be impacted

even more. Also, in terms of the speed of light

using LASER, a very sensitive sensors would

need to be used. While the system is quite

cheap in a short run, it is not very modular and

may create an issue when trying to modify or

extend the environment.

Similar approach with using LASER-based

technologies was proposed in research done by

Ziyue Zhao and his team. Instead of focusing

on engineering aspect of the system, they

explored the mathematical constructs of the

system by mapping environment using

spherical coordinates. This allowed them to be

able to map the environment as a three-

dimensional sphere. While this may not be the

most optimal coordinate system to use, this

idea gives a good start to trying to determine

not only x and y coordinates as done by most

GPS systems, but also to take in account the

height at which receiver is located relative to

the transmitter. This could definitely be used in

future research as a basis for three-dimensional

navigation.

Moving away from single-station designs, we

can now create a modular system that can be

used in virtually any environment. This is

demonstrated in Indoor GPS research done by

Domenico Maisano[5]. He and his team used a

four-transmitter system approach. This

allowed them to not only calculate the position

of the object, but also gave a way to double

check the obtained values. This definitely

made the system more modular, however the

system still used light as the means of signal

transmission and acquisition.

There has also been research going on for quite

a while that focused on using Wi-Fi and

Bluetooth signals as a basis for an indoor GPS

system[4]. This is a quite feasible solution, but

it has its own limitations. First of all, such

signals have a very limited range. Second issue

results from the signals being very common.

Nearly every device nowadays uses 2.4GHz

frequency also occupied by Wi-Fi and

Bluetooth. This may pose an viable

interference issue. One more problem with

such devices in the idea that the 2.4GHz is an

ultra high frequency, so the signals have

trouble traveling through obstacles. This

allows for multi-path effect, the issue where

signal’s path is altered, which results in a

distorted and unreliable signal.

Many of these flaws were fixed in a new

proposed design, however some of them still

remain since it may take more resources to

solve them.


32

3. NEW IMPLEMENTATION The first step in developing a GPS is picking

a coordinate system. In most cases, precision is

defined by standard units such as feet or

meters. In this case, however; the GPS is

designed for a previously unknown

environment. This fact presents a difficulty as

it relates to standard units. Since the system

does not know the dimensions of the

environment, it cannot find the distance in

terms of meters or feet. Instead, the coordinate

system is relative to the setup posed by the

environment. This being said, the user

interaction may make it more difficult to find

exact distance, but the system should still be

able to function based on the relative distance

between each station.

The term relative distance in this case, relates

not to difference in distance measurements

among each tag pair, but instead relates to the

difference in distance based on the

environment chosen for the system to be

applied to. To be more specific, there is a

difference in placing tags in the corners of a

warehouse that’s 17,400 square feet in size,

versus placing them in the corners of an

average room the size of which is 120 square

feet. Although the velocity of a radio wave

may not change drastically over small

distances, within a mile, the accuracy of its

time stamp will. Another problem is that if you

are installing such GPS inside of a small room,

there are a lot of factors that may influence the

distance readings. For example, interference

from other devices may impact the readings

and rule them invalid. The system described in

this research pertains to medium and large size

warehouses and other buildings with large

open areas. This could include office

buildings, auditoriums and convention centers.

Though the solution is mostly designed for

commercial grade, with enough testing and

error analysis it could be migrated to devices

usable inside houses and buildings with large

number of smaller rooms. In that case, the uses

could include but are not limited to home

automation, navigation within large multistory

buildings as well as search and rescue missions

performed by teams of firefighters and the

crews of other emergency relief personnel. In

either case, such device could simplify and

protect the lives of many people and could be

used practically in any area of engineering,

robotics as well as many more. 4. SYSTEM HARDWARE

In order to start the setup process a starting

point is an important requirement. Any

program requires a starting and an ending

point and though it may branch out into many

threads or processes after it started, there is

always only one starting point. First let’s talk

about the coordinate system. As mentioned in

the previous section, the coordinate system is

not going to be based on the standardized units

of measurement. This allows for the flexibility

of modification and interpretation of the values

gathered in the process. The question still

remains, “Where do we begin?”. To answer

this question let’s go back to the definition of

the system that we are building. The two parts

of the system are stationary reference tags and

the main controller module. We are going to

begin by explaining the tags. Each tag consists

of Arduino based microcontroller as well as a

wireless transmitter and a receiver pair. This

setup is connected to a power source which

allows it to freely communicate with the main

controller module as well as other tags around

it. A prototype of such tag is displayed in the

Figure 1.


33

Figure 1. Reference Tag

As it can be seen in the figure above, the tag

contains arduino mini pro microcontroller as

well as 433MHz transmitter and a receiver

pair. It also contains NE7505 voltage regulator

to be able to regulate power to the tag board.

This allows for any power source under 24

Volts to be used to power the tag carrier board.

This makes this product more modular and

easy to use. A step-up module could be

implemented into the tag in future in order to

prevent voltage drop from the battery over

time. This will minimize the errors in distances

due to low transmission power.

On the other side of the system, consider main

control module. The structure of it is very

similar to that of a reference tag. However,

some of the hardware was modified to in order

to create a portable station capable of

navigating the mapping environment. Sample

main control module is shown in the Figure 2.

Figure 2. Main Control Module

Main control module includes all same

hardware as a reference tag. Instead of using

Arduino Mini Pro, it uses Arduino Uno, since

the size constraint posed by the tags is no

longer there. Main Control Module also

contains a motor driver, a modular chassis and

a higher capacity Lithium-Polymer battery.

This setup will ensure that the main control

module is capable of navigating through the

environment.

As it has been previously mentioned, a

frequency of 433MHz was used in order to

establish a communication link. This wasn’t a

random choice. A frequency of 2.4GHz and

433MHz were tested during the prototyping

stage. Those are two most common

commercially available wireless transmitter-

receiver pairs. While 433MHz may not be the

best choice in most scenarios, it proved itself

quite effective in the design of the indoor

global positioning system. The problem was to

find a transmitter and a receiver pair that

would be fast enough to transmit several bytes

of data over a certain period of time, while

maintaining a consistent delay. From what was

seen in case with the 2.4GHz data transfer, is

that a very fast clock is required to keep track

of delay. Since the data is sent over the

wireless communication is received almost

instantly, the delay in transmission becomes

very small in terms of nanoseconds. In that

case, a clock with frequency of at least 1GHz


34

would be required. While this could be

achieved by switching to a more powerful

microcontroller, the price certainly becomes a

factor. Considering a warehouse could contain

anywhere from 4 to 200 tags, the price would

have to be put into an account. This does not

include any replacement tags that would need

to be manufactured or purchased. Since 433

MHz is not a very high frequency, due to error

filtering and distance required for a wave to

travel, 16MHz clock on the Arduino board is

sufficient to keep track of the time when a

packet is sent or received. This definitely

brings the cost down. Compared to other

microcontroller boards on the market, Arduino

mini pro could be purchased for as low as $3

for a complete microcontroller board. This

means that even more tags could be installed

in order to check for errors between any other

two tags without drastically affecting the cost

of the system. Another reason for using

433MHz band is the wavelength. Since the

wave does not oscillate quite as fast as it does

at 2.4GHz, it does not generate as much

interference and is capable of traveling longer

distances within the line of sight of a

transmitter. This allows for the system to be

used is large open areas, although it may cause

the need to optimization inside small closed

spaces. An algorithm used for communication

between tags and the main control station is

described in the next section.

5. INITIALIZATION STEP The first step in initialization process is

defining each tag. In order to communicate

with other people, each person needs a name.

Same goes for the computer world. In order for

any of the tags to communicate with each

other, they need to recognize who they are

talking to. Just as you wouldn’t want to talk to

a complete stranger, the tags discriminate

against previously unknown tags. This way

there is no confusion between where each tag

is located. While this could bring confusion to

how the system works, this is the only way to

distinguish between each tag. For the sake of

proper explanation we are going to start with

the most simple scenario and then expand to

make the setup more modular. In this case,

instead of giving each tag a proper name, we

will associate each one with a unique

identifier. The identifiers will consist of

numbers starting at two and going up to and

including the number of tags used. For

example, if the system uses n number of tags,

the tag identifiers will be 2 to n. While this

could be somewhat difficult concept to

understand for most computer applications,

where numbering always starts at zero, this

makes sense for this particular system. The

reason the numbering cannot start with zero is

because we need a reserved value that will

always be there regardless of number of the

tags used in the system. While this number

could be arbitrarily defined, it makes more

sense to use zero. When using any computer

structure, zero position is always defined thus

making it more stable and requiring less

resources to store the reserved value. In

addition to zero, we are also reserving the first

identifier. This will be explained later. For

now, let’s take care of the zero value. So what

is the reason to store this extra value required

by the system. The reason is unclear until there

is a need to reference all of the tags at once.

There are very few reasons for that to happen,

but perhaps the most important one is the

initialization process. In order to begin the

setup, each tag needs to be ready. One hundred

milliseconds should be enough for each tag to

initialize, the problem is to get each tag ready

for incoming information after that time has

passed. In order to do that, each tag receives a

packet with a ready command and the setup

transmission begins. In order to understand the

setup step, some definitions need to be made.

First of all, in order to define the simplest

system, three tags are used. Each tag is spaced

out from the others forming a triangle. A robot

containing the main control module is placed

within the limits of the triangle. Its task is to


35

map out the area and define an accurate grid to

be used with the environment. This is where

the extra position one comes into place. In

order to communicate back to the main control

module, each tag needs to know what identifier

to use. Since each identifier has to be unique

and each station has to have one, the identifier

of one is reserved by the main control module.

This allows it to be used no matter how many

tags are inside the environment. So what is the

reason for a tag to communicate back to the

central station. In the case of initialization, the

last tag in the chain would have to send a

command packet back stating that the

initialization is finished. Before going into the

specifics of the initialization algorithm, let us

step back and define the environment. Figure 2

displays a sample environment with three tags

and a main control module.

Figure 3. Sample Environment

As it could be seen from the figure above, each

tag is properly identified. This means that each

tag knows its own name as well as any other

tags around it. The way this system works is

the following. Each tag knows two of its

neighbors, the control module, general and its

own identifiers. This allows each tag to

communicate properly without overloading the

transmission line and still getting the message

across. If the proper response is not achieved,

the main control module takes over. Using the

Figure 2 as an example, let us properly

understand how each tag works. As was

mentioned previously, the main control

module is capable of sending packets to any

tag without prior acknowledgment. This could

present difficulty if used as the case by any

tag, but since the system is being controlled

primarily by the main control module, the

errors due to a bottleneck should not occur. In

the case above, tag 2 is the first tag in line.

What this means, is that it can only talk to the

control module as well as tag 3. All other tags

in the system are currently off limits. This

setup could be dynamically modified by the

control module if necessary. In this case, tag 3

can only communicate with 3 other tags. Its

previous in line tag is tag 2, next in line is tag

4, and as usual it is able to talk to the control

module. Whenever we get to the tag 4, this one

has only 2 other devices that it can

communicate with. Since it doesn’t have a next

in line, it can only communicate to its previous

in line, tag 3; as well as the main control

module. Tags 2 and 4 in this case have special

roles. When tag 2 receives a command, it waits

for 20 milliseconds and then begins the

initialization chain. The initialization chain is

shown in Figure 3.


36

Figure 4. The Initialization Chain.

The idea behind the initialization chain is to

find the distance between each pair of adjacent

tags. This could be expanded on by the main

control module in order to find rest of the

distances between tags. In order to start the

chain, tag 2 sends a packet to tag 3. The packet

contains four components. The first component

is the destination identifier, in this case 3. The

second component is the time stamp at which

the packet was send. The third component is

the request to send a return packet. The fourth

component is the source tag that sent the

packet in the first place. And the final

component is the check sum value of the

packet. This packet structure allows the tag

receiving the packet quickly scan through the

information and discard any packets that are

not addressed to it. For example, if tag 4 reads

a packet and sees 3 in the first position, it will

quickly discard the packet without having to

go through the whole packet in order to find

out its contents. Once the packet is received by

tag 3, it is relayed back to tag 2 to identify the

time delay. For the future reference, tag 3 also

includes its own time stamp into the packet

following the original time stamp sent by tag 2

in the first place. Tag 2 then returns the packet

to tag 3 but this time the packet does not

contain the original time stamp or the request

for a return packet.

6. POST-INITIALIZATION STEP

Once the main control module receives the

time stamp from the last reference tag in a

chain, it goes through post-initialization stage.

First, it queries each tag for a distance value.

Each tag will respond with a packet of a

variable size. This will ensure that all distance

values can be relayed back to the main control

module. As usual, each packet will start with

the id of the main control module. The number

will follow. This will let the main control

module know how many values the packet

contains. Once all value are read, the check

sum will follow. This part will ensure that the

packet was transferred correctly. In case of

check sum being wrong, the main control

module has the right to request another packet

or skip the reference tag by disconnecting it

from the chain. This will only result if the

packets received from the reference tag are

constantly being corrupted. Once distance

values are received by the main control

module, it determines the duplicates. In case

more than one distance value is obtained, it

will store the average of those. However, this

will not occur until all distance values are

known. This step will ensure the accuracy of

the distance measurements and reduce random

error as well as error that could result from

interference produced by other devices within

the environment.

7. TRANSITION TO CARTESIAN

COORDINATES

So far, all distances we have worked with were

based on the arbitrary points and directions.

While this can provide some useful

information about the system, the world we

live inside is not one dimensional. It cannot be

defined as a single line or a point. In order to

adjust our system to be able to use it in real-

life cases, we need to define the system in

terms of a grid system that we are familiar


37

with. One of the simplest transitions to make is

to translate it to a Cartesian coordinate system.

While it is possible to translate it to a 3-

dimensional environment, the idea adds a lot

of complexity to the existing system. For now

we will treat our environment as a rectangle.

Therefore, each point inside the environment

can be represented as an (x,y) coordinate.

First, let’s make some modifications to the

existing diagram of our system and define a

few more values. Figure 5 shows the current

diagram of the system.

Figure 5. Diagram of the system

In order to be able to perform necessary

calculations, first we subdivide side d2 into

(d2-y) and y. (d2-y) being the vertical distance

from tag 3 to the main control module. We

also do a similar modification to the side d3.

This side gets split into (d3-x) and x. (d3-x) in

this case represents the horizontal distance

from tag 3 to the main control module. Once

this is complete, we then draw two lines going

from d2 and d3 to the main control module.

Each line is perpendicular to d2 and d3

respectively. The final diagram is shown in the

Figure 6.

Figure 6. Modified diagram of the

In order to obtain the necessary values, we

need to solve several of the formed triangles.

Let us consider triangle with sides (d3-x), (d2-

y), and d5. Assuming the angle between d2

and d3 is a 90º angle, we can use Pythagorean

Theorem to solve this triangle. Figure 7 shows

the resulting equation.

Figure 7. Equation 1

Next step is to do the same calculation for

triangles y, d1, (d3-x) and x, d4, (d2-y). From

these two triangles we are able to obtain

equations shown in Figure 8 and Figure 9.

Figure 8. Equation 2.


By plugging the Equation 2 into the Equation 1

and doing some algebraic manipulation, we

can obtain an equation for y shown in Figure

10.


38


By doing same thing to the equation 3, we now

get an equation that defines x. This equation is

shown in the Figure 11.


While these values are referenced from the

point opposite from Tag 3, it is possible to

modify those to use Tag 3 as a (0,0) point. In

order to do that, we would need to subtract

both sides of the equation 4 from d2 and

subtract both side of the equation 5 from d3.

8. EXPANDING THE SYSTEM

So far, we assumed that our system contains

only 3 tags. This made sense for a small,

highly accurate system of tags. One of the

reasons three-tag system was chosen is

because this is the smallest size of such system

that would produce enough values to perform

necessary calculations. In order to modify our

system to be able to use it inside of a

warehouse, we need to expand to more tags.

Depending on the amount of power and

antenna used with each reference tag, we will

get a specific range. In order to create a fully

functional system, the ranges of tags would

have to overlap creating a chain. That way

each pair of neighboring tags can communicate

with each other. The ranges of all tags in the

chain would have to cover the whole ground of

the warehouse.

In order to add additional tags to our setup, we

will first need to split each tag into x and y

components. This will allow us to reference

each tag regardless of its position. Once that

process is over, we can then add as many tags

as necessary. More tags in the system adds to

complexity of calculations, because more tags

would result in more triangles having to be

solved. However, there are some benefits of

adding extra tags to the system. One of those is

being able to check values gathered for each

distance. More tags result in more ways to

check each distance.

There is yet another advantage of using more

tags. Once we are able to add tags we can now

calculate angles based on the distances

between the tags. Once that is known, we can

now solve arbitrary triangles by using the Law

of Cosines. This is very useful when the

environment is now known ahead of time or

when the environment is not a rectangular

area. For example, for referencing distances

inside enclosed oval stadium. This can also be

used for mapping warehouses with rooms

within them. For those cases, the environment

can be decomposed into rectangles, but the

complete environment is not a single rectangle.

9. RESOLVING COMMUNICATION

ISSUES As is a case with any radio communication,

there are going to be cases when issues with

receiving and transmitting signals occur. In

order to be able to use the system even in those

conditions, some troubleshooting will have to

be be done. Since our system has to be fully

autonomous, those issues have to be fixed

automatically within the system. Since the

main control module controls the actions of the

whole system, in order to fix communication

links, it will be used to resolve any problems.

If a tag is not able to communicate with its

neighbor, it will send a request to the main

control module. From this point on, the main

control module is fully in charge of the

situation.

First, it will query the non-responsive tag and

attempt to reconnect it to the system. If the

communication can be fixed, a solution will be


39

found. On the other hand, if the main control

module is unable to establish a link with the

tag, it will reroute the chain of tags to skip

over the broken tag.

10. CONCLUSION This paper explored the indoor GPS and

various designs involved with it. If designed

well, indoor positioning system can be used in

a variety of applications. It is very easy to use

and can provide a user with the exact location

within indoor environment. It can be used with

absolute as well as relative coordinate systems

which makes it very flexible.

The proposed design of the Indoor Positioning

System is very cost effective, easy to

implement and utilize. It is very modular

because of being based on the relative

coordinates. This however, does not prevent

the system from being able to provide absolute

coordinates within the environment. The

design is also able to find any communication

issues within the system and resolve these

without any assistance from the user.

REFERENCES [1] "GPS." Merriam-Webster. Merriam-

Webster, n.d. Web. 24 Dec. 2014.

[2] Dedes, George, and Andrew G. Dempster.

"Indoor gps positioning." Proceedings of

the IEEE Semiannual Vehicular

Technology Conference. 2005.

[3] Freeman, Philip L., Thomas E. Shepherd,

and Christopher K. Zuver. "Assembly task

verification system and method." U.S.

Patent No. 8,630,729. 14 Jan. 2014.

[4] "Indoor Positioning System." Wikipedia.

Wikimedia Foundation, n.d. Web. 11 Apr.

2015.

[5] Maisano, Domenico A., et al. "Indoor GPS:

system functionality and initial

performance evaluation." International

Journal of Manufacturing Research 3.3

(2008): 335-349.

[6] Nirjon, Shahriar, et al. "COIN-GPS: indoor

localization from direct GPS receiving."

Proceedings of the 12th annual

international conference on Mobile

systems, applications, and services. ACM,

2014.

[7] Van Diggelen, Frank, and Charles

Abraham. "Indoor GPS technology."

CTIA Wireless-Agenda, Dallas (2001).

[8] Xu, Jing, et al. "Cooperative distributed

optimization for the hyper-dense small cell

deployment." Communications Magazine,

IEEE 52.5 (2014): 61-67.

[9] Zeimpekis, Vasileios, George M. Giaglis,

and George Lekakos. "A taxonomy of

indoor and outdoor positioning techniques

for mobile location services." ACM

SIGecom Exchanges 3.4 (2002): 19-27.

[10] Wu, Jun, et al. "A novel ultrasonic

ranging method used for single station

indoor GPS." Transactions of the Institute

of Measurement and Control 37.1 (2015):

25-32.

[11] Zhao, Ziyue, et al. "Optimization for

calibration of large-scale optical

measurement positioning system by using

spherical constraint." JOSA A 31.7 (2014):

1427-1435.


40

Stimulants and Stimuli: The Effects of Neuroactive Pharmaceuticals on the Process of Learning and Memory Formation

Richard M. Goodman and Carol Z. Plautz



[email protected]

ABSTRACT The use of model organisms is very helpful in elucidating questions of interest involving humans. One such model organism is Lymnaea palustris, a pond snail which has been conditioned previously in our lab to study the effects of substances on learning and memory. Caffeine’s reputed ability to enhance learning and memory formation was tested in the current study by conducting learning and memory tests on snails that were administered caffeine compared to a control medium. A third group, where caffeine was administered but then withdrawn, was used to test the theory of state-dependent learning. All groups exhibited clear learning and memory trends, but the effect of caffeine on the process was not significant. However, the test of state-dependence on learning and memory yielded a significant difference between groups, suggesting that while caffeine did not affect learning or memory formation in the present study, it may alter an individual’s state, resulting in variation based on the effects of state-dependent learning. KEYWORDS: Lymnaea, learning and memory, caffeine, operant conditioning 1. INTRODUCTION Learning and memory are two closely related but separate concepts, both of which are essential for the development of conscious

thought and the mechanisms of survival. Learning is defined as the act of acquiring knowledge or a skill, while memory is the use of that learned knowledge or skill, either consciously recollected, or resulting in a certain behavior.1 Memory is comprised of many subcategories that describe how various processes function. Primarily, memory is broken down into declarative memory and procedural memory. Declarative memory consists of knowledge that can be consciously recalled (such as items on a test), while procedural memory refers to unconscious aspects of memory (such as tying your shoes).1 Both of these types of memory are considered to be long-term memory. Long-term memory (LTM) is a subcategory of memory which coexists with two other subcategories known as intermediate-term memory (ITM) and short-term memory (STM). Each of these has discernible components concerned with the duration of memory retention. Short-term memory contains items that last only seconds to minutes. Intermediate-term memory contains concepts for several hours. Long-term memory can contain concepts from as little as six hours to those that last throughout an entire lifetime.2, 7

Some substances are rumored to acutely enhance the mechanisms involved in memory, one of which is caffeine. Caffeine is a


41

neuroactive stimulant which acutely blocks the action of endogenous adenosine receptors, resulting in a variety of physical effects including reduced drowsiness and increased alertness; however, its effect on memory is inconclusive. 3 It has also been suggested that an individual’s psychological, emotional, and physical state may have an effect on memory. This theory is known as state-dependent learning.4 The idea of state-dependent learning suggests that it may be easier to recall stored information if in the same state as when this information was initially learned. Testing such theories requires an adequate assessment of the mechanisms of memory. Memory has been studied previously through use of conditioning, which can be broken down into two categories. In one form of conditioning, an involuntary response is preceded by a conditioned stimulus, which is associated with a separate neutral stimulus. Eventually, this neutral stimulus becomes associated with the involuntary response, and results in promotion of this response without use of the original conditioned stimulus. This is known as classical conditioning (or Pavlovian conditioning, named after its most famous contributor, Ivan Pavlov). The other form of conditioning occurs when a voluntary response is followed by a consequence to either promote or discourage that response. Ideally, this process eventually will result in the desired frequency or quality of this response, otherwise known as operant conditioning (or instrumental conditioning). Conditioning and memory are often studied in model organisms and may be preferable to humans for studying biological mechanisms due to simplicity, expense, or ethical issues. Particularly, studying aspects of nervous system function in model organisms is useful, given the complexity of the human nervous system and relative simplicity of many other organisms. Many invertebrates have been

used to study memory, including pond snails, sea slugs, nematodes, and honey bees.2 Lymnaea is a genus of pond snail used for tasks involving learning and procedural memory using operant conditioning.5 These snails are an optimal model organism for studying neurologic functions, given their simple central nervous system consisting of a ring of nine ganglia which have been studied extensively, leading to an understanding of the individual functions of each ganglion. 2, 6 One of the mechanisms most often studied in Lymnaea is respiratory function. Lymnaea are bimodal breathers; they can either stay submerged and absorb oxygen cutaneously, or if the organism is in a hypoxic environment, they can undergo aerial respiration by rising to the surface to open their pneumostome, a small sphincterate orifice.2 In eumoxic conditions, these organisms primarily undergo cutaneous respiration, and thus previous studies involving respiratory conditioning required oxygen content of the organism’s surrounding environment to be lowered. This encourages the snails to use aerial respiration, and provides the opportunity to administer a weak tactile stimulus to the pneumostome upon opening. This stimulus has been demonstrated to result in conditioning of the animal to avoid aerial respiration, despite the hypoxic environment.2 The organism most often studied utilizing such conditioning is Lymnaea stagnalis, a larger species of Lymnaea. This organism has served extensively as a model for research regarding neurologic systems and learning and procedural memory, but studies using other species of Lymnaea are rare. This study utilizes Lymnaea palustris, a smaller relative of L. stagnalis. Lymnaea palustris has been used in our laboratory in previous studies concerned with effects of various substances on reproduction, embryonic development, as well as learning and procedural memory.


42

In this study, Lymnaea palustris is exposed to caffeine and introduced to a method to determine the prevalence of the theory of state dependence. The first hypothesis of this study is that these organisms will more effectively be conditioned if exposed to caffeine during training sessions. Secondly, it is hypothesized that the results of conditioning will be optimal if the organisms are tested under consistent conditions throughout trials, which will serve as an analysis of the prevalence of the theory of state-dependent learning. 2. MATERIALS AND METHODS 2.1. Animals The organisms used in this study were raised in the laboratory, in aquaria filled with artificial pond water (APW), consisting of 0.45 g/L Instant Ocean® aquarium salts in reverse osmosis water, with a supplemental amount of (0.02 g/L) CaSO4 and (0.04 g/L) NaHCO3. Each aquarium was covered by a large sheet of glass to reduce external influences. Each aquarium was filtered and provided oxygen using a pump-based filtering system. The laboratory was kept at room temperature (21-23oC) and the pH was monitored and adjusted as needed. Snails were fed with washed romaine lettuce. Only snails appearing in good health and those of mature size were used in this study. Any snails with excessive calcification of their shell, or snails that were less than 1 cm in length from the anterior to the posterior aspect of their shell were not used in this study. Chosen snails were isolated from their original aquarium and placed into weigh boats, where their shells were allowed to dry. After shells had dried, a drop of cyanoacrylate was placed onto the dorsal aspect of each shell immediately before placing one of four colored rhinestones onto this area. This

rendered individuals distinguishable (see Figure 1). The adhesive was then allowed to dry prior to placing four snails (one of each rhinestone color) into one of several separate 500ml plastic bowls with similar conditions as the aquaria.

Figure 1. Lymnaea palustris labeled with rhinestones.

2.2. Training This study used hypoxic conditions to promote aerial respiration. This was achieved by bubbling nitrogen gas (N2) into a 1 L beaker containing 700 mL of APW. Once the oxygen saturation of this water was below 4% oxygen (oxygen saturation was determined using a YSI 55 handheld dissolved oxygen meter), snails were introduced to this hypoxic environment. Once submerged in the hypoxic environment, snails were given a 10 minute acclimation phase (only during the initial exposure) to adjust to this new environment.7 After this 10 minute acclimation phase, the initial 45 minute training session (“learning session”) commenced. During each session, a set of four snails were observed closely and their number of pneumostome openings (aerial respirations) were counted per snail. Each time a snail opened its pneumostome, it would receive a weak tactile stimulus at the pnuemostome. Stimuli were only provided if the pneumostome was fully opened. Stimuli were provided using a thin dowel rod, sharpened to a blunt point. Stimuli were administered briefly, only to allow the animal


43

to withdraw its pneumostome, but not to completely withdraw its entire body into its shell. After the learning session, animals were placed back into their eumoxic APW. One hour later these same snails would undergo their second 45 minute training session (“reinforcement session”); exactly like the first, except for the exclusion of the 10 minute acclimation phase. After the reinforcement session, snails were placed back into their eumoxic APW. After a 24 hour break, these same snails would then undergo their third training session (“memory test”); exactly like the reinforcement session. A total of forty snails underwent the testing as controls. These controls confirmed these organisms could be conditioned, as previously suggested. 2, 5, 7

2.3. HPLC In order to administer a biologically relevant amount of caffeine, it was required to determine a comparable concentration of caffeine to that of humans who have consumed an average amount of coffee [3.02 milligrams per kilogram per day, resulting in 1-10 mg/L (0.0001%-0.001%) plasma caffeine levels].8,14 Some specific concentrations (5-9 mg/L) have been identified which may provide benefit to mental processes.15 High Performance Liquid Chromatography (HPLC) was used in this study.9 12 snails were separated into three groups (4 snails per group) which were either presoaked in a 0.01% caffeinated APW solution, a 0.002% caffeinated APW solution, or a control solution (APW). Hemolymph samples were drawn from these snails using a noninvasive method11 whereby the snail is poked on the foot and excretes hemolymph from its body cavity.10, 11

Animals were allowed to dry to discourage dilution of excreted hemolymph, which was collected in 5-10 µL increments. Each

collection was immediately injected into 8% formic acid to avoid coagulation of the hemolymph. A total of ≥100 µL of hemolymph was collected for each group. 300 µL of 10% theobromine (a stimulant in the same family of alkaloids as caffeine, and a metabolite of caffeine12) was added to each tube as an internal standard. These tubes were centrifuged using an Amicron® Ultra .5 mL Ultracel 10K molecular weight filter at 7,000 RPM/g for five minutes. After centrifugation, tubes were weighed before and after an adequate amount of sample was placed into pre-weighed HPLC tubes. The samples were run in the HPLC machine, then reweighed one final time to provide data for the determination of the amount of sample injected into the machine for later quantifying the components in the sample. A chromatogram was generated for each sample (Figures 2, 3, 4, and 5).

Figure 2. HPLC chromatogram for calibration standard.

Figure 3. HPLC chromatogram for control.


44

Figure 4. HPLC results for low concentration (.002% caffeine).

Figure 5. HPLC chromatogram for high concentration (.01% caffeine) .

The peaks beginning at approximately 7.4 minutes in the calibration standard, the low concentration, and the high concentration chromatograms were consistent with the retention time for caffeine.13 This peak did not appear in the chromatogram for the control substance, in which snails did not receive caffeine. Using the amount of caffeine and theobromine added to the calibration standard, and the amount of theobromine added to samples, an approximate quantification of caffeine present in the hemolymph of snails from each sample was obtained; 0.00002% caffeine in the control, 0.00092% caffeine in the low concentration, and 0.00499% caffeine in the high concentration. Trace caffeine found in the control was likely due to residual caffeine left in the needle when the HPLC

machine switched from the calibration standard to the control sample. As previously stated that average caffeine consumption in humans produces plasma caffeine levels between 1 mg/L and 10 mg/L, these HPLC results suggest that the low caffeinated APW concentration (0.002% caffeine) would best serve as a biologically relevant concentration for Lymnaea and provide comparable conditions (0.00092% is equivalent to approximately 9 mg/L). This concentration was thus used for caffeine administration. 2.4 Caffeine Administration In addition to continuing the control trials to reduce any chance of errors due to weekly change in overall respiration, two groups of four snails were initially pre-soaked for three hours in a .02g of caffeine (Sigma) per liter prior to conditioning. Two of the three groups initially received caffeine, while one continued to be exposed to caffeine before all three training sessions, as indicated in Table 1.

Table 1. Caffeine administration procedures.

The second group was pre-soaked in the .02g/L caffeinated solution prior to the learning and reinforcement sessions, but not the memory session, while the first and third groups had consistent application of either no caffeine or caffeine for three hours immediately before each session, to test our hypotheses on the effects of caffeine, and the effects of state-dependent learning.


45

3. RESULTS With an α level of .01, a series of two-tailed T tests were performed to analyze the results of the testing (Figures 6 and 7).

Figure 6. Results of conditioning trials, analyzing the significance of difference between reinforcement session/memory test with learning sessions. ** signifies an alpha level less than .01.

As displayed in Figure 6, in all three groups, both the reinforcement session and the memory session were significantly different than the learning session.

Figure 7. Results of conditioning trials, significance of difference between memory test and reinforcement session. ** signifies an alpha level less than .01.

Figure 7 shows a significant difference between the memory session and learning session of the control group and the group exposed to caffeine before each session, but no significant difference in the group given inconsistent treatment before trials. The effect of caffeine on conditioning was not significant (p = 0.433).

4. DISCUSSION The fact that in all three groups, both later sessions were significantly different than the initial learning session suggests that these organisms did learn and remember, regardless of their group. Though these figures did show a slightly greater difference between the initial learning session and the memory test of the consistently caffeinated group than that of the control group, this effect was not significant. Therefore, we reject the first hypothesis that caffeine has an effect on conditioning and on the process of learning and memory. However, these results revealed a significant difference between the memory test and the reinforcement session only of groups that were provided consistent conditions before trials. Although the group that was initially exposed to a caffeinated solution and was later withdrawn (and placed into the control solution) did learn and remember, the difference between the memory test and reinforcement session was not significant, suggesting that these snails did not remember as efficiently as the other groups. This supports the second hypothesis, suggesting that state-dependent learning does have a positive effect on conditioning, and therefore, the process of learning and memory. It may be interesting in future studies to incorporate similar conditioning using an increased dosage of caffeine to reflect a human who has indulged in an excessive amount of caffeine, to test whether this may provide some further advantage. It may also prove to be interesting to use this model of conditioning with other substances such as nicotine, and particularly stimulants such as theobromine and prescription drugs used to treat attention deficits, such as Adderall and Ritalin. Finally it may be useful to study the possibility of rescuing the organism from the detrimental effects of state-dependent learning


46

by providing them with their initial condition (in this study, the original caffeinated solution). ACKNOWLEDGEMENT This work was supported by a grant from the NASA WV Space Grant Consortium and Shepherd University for STEM undergraduate research. The authors thank Dr. Dan DiLella for assistance and instruction on the HPLC, Dr. Peter Vila for assistance in monitoring dissolved oxygen, and Dr. Colleen Nolan for providing key discussions and insight. REFERENCES

[1] Kazdin, A. E. (2000). Memory. In Encyclopedia of Psychology. (Vol. 5, pp. 160-177). Washington, D.C.: American Psychological Association.

[2] Parvez, K.., Rosenegger, D., Orr, M., Martens, K., Lukowiak, K. (2006). Learning at a snail’s pace. Canadian Journal of Neuroscience, 33, 347-356.

[3] Rogers, P. J. (2007). Caffeine, mood and mental performance in everyday life. Nutrition Bulletin, 3284-89.

[4] Schramke, C. J., & Bauer, R. M. (1997). State-dependent learning in older and younger adults. Psychology and Aging, 12(2), 255-262. doi: 10.1037/0882-974.12.2.255

[5] Lukowiak, K., Sangha, S., McComb, C., Varshney, N., Rosenegger, D., Sadamoto, H., & Scheibenstock, A. (2003). Associative learning and memory in Lymnaea stagnalis: How well do they remember? The Journal of Experimental Biology, 206, 2097-2103, doi: 10.1242/jeb.00374

[6] Benjamin, P. R. (2012). Distributed network organization underlying feeding behavior in the mollusk Lymnaea. Neural Systems & Circuits, 2(4), doi:10.1186/2042-1001-2-4.

[7] Lukowiak, K., Adatia, N., Krygier, D., Syed, N. (2000). Operant conditioning in Lymnaea: Evidence for intermediate- and long-term memory. Learning & Memory, 7(3), 140-150.

[8] de Leon, J., Diaz, F. J., Rogers, T., Browne, D., Dinsmore, L., Ghosheh, O. H., Dwoskin, L. P., Crooks, P. A. (2003). A pilot study of plasma caffeine concentration in a US sample of smoker and nonsmoker volunteers. Progress in Neuro-Psychopharmacology and Biological Psychiatry, 27(1), 165-171. doi: 10.1016/S0278-5846(02)00348-2.

[9] Prado, S., Villamarín, A., & Ibarguren, I. (2013). Simultaneous determination of adenosine and related purines in tissues and hemolymph of mussle by HPLC. Journal Of Liquid Chromatography & Related Technologies, 36(4), 470-485. doi:10.1080/10826076. 2012.660723

[10] Dikkeboom, R., van der Knapp, W. P. W., Meuleman, A., Sminia, T. (1984). Differences between blood cells of juvenile and adult speciments of the pond snail Lymnaea stagnalis. Cell and Tissue Research, 238, 43-47.

[11] van der Knapp, W. P. W., Adema, C. M., & Sminia, T. (1993). Invertebrate Blood Cells: Morphological and functional aspects of the haemocytes in the pond snail Lymnaea stagnalis. Comparative haemotology international, 3, 20-26.

[12] Zandvliet, A. S., Huitema, A. D. R., de Jonge, M. E., den Hoed, R., Sparidans, R. W., Hendriks, V. M., van den Brink, W., van Ree, J. M., Beijnen, J. H. (2005). Population pharmacokinetics of caffeine and its metabolites theobromine, paraxanthine, and theophylline after


47

inhalation in combination with diacetylmorphine. Basic & Clinical Pharmacology & Toxicoogy, 96, 71-79.

[13] D. DiLella, personal communication, February 28, 2014.

[14] Owen, D. (1998). Frequently Asked Questions about Caffeine. Retrieved from:https://cs.uwaterloo.ca/~alopez-o/Coffee/caffaq.html

[15] Seng, K. Y., Fun, C. Y., Law, Y. L., Lim, W. M., Fan, W., Lim, C. L. (2009). Population pharmacokinetics of caffeine in healthy male adults using mixed-effects models. Journal of Clinical Pharmacy and Therapeutics, 34, 103-114.


48

Pythagorean Propositions Project Emilie Piatek and Caitlyn Shane

Department of Computer Sciences, Mathematics, and Engineering

Shepherd University

P.O. Box 3210

Shepherdstown, WV 25443

[email protected]

ABSTRACT

Every mathematical project includes proof,

but reproof is often overlooked as a vital

method of mathematical research. Case in

point, in 1799 the University of Helmstedt

granted Gauss a Ph.D. in mathematics for a

dissertation that gave a new proof of the

Fundamental Theorem of Algebra. Another

similarly fine example is the Pythagorean

Theorem. The Pythagorean Propositions

compiled by E. S. Loomis includes over 300

such proofs. The text is written to follow the

manner of the original proof by authors such

as Copernicus, Descartes, Euclid, Galileo,

Gauss, Leibniz, Lobachevsky, Napier,

Newton, Pythagoras, and Sylvester; thus, it is

difficult to read in terms of the notation, not

trivial in terms of the mathematical concepts,

and the few illustrations that are included are

primitive.

The Pythagorean Propositions Project

digitally visualizes selected proofs of Loomis’

Pythagorean Propositions. The project

creates a web friendly environment upon

which students construct and disseminate

visually enhanced proofs. Each selected proof

from Loomis is presented in a dual

representation format -- an enhanced

mathematical argument on the left side

reinforced by the corresponding geometric

figure on the right. As the viewer “mouses”

over the logical argument on the left, the

corresponding image on the right will provide

an immediate visualization of the concept.

The outcome is an enhanced mathematical

presentation of classic proofs that leads to

improved understanding of the Pythagorean

Theorem as well as mathematics as a whole.

KEYWORDS: Euclid, Loomis, Pythagorean

proposition

1. INTRODUCTION

One goal of the Pythagorean Proposition

Project is to enrich the understanding of

mathematics through visualization. By using

technology to enhance the proofs of the

Pythagorean Theorem, students will be

learning visually as well as kinesthetically,

which has been shown to improve the

comprehension and retention of information.

The intent is that the dual display will strongly

demonstrate the connection between the

notation presentation of the author’s argument

and the corresponding figure.

A second project goal is to encourage

students, particularly females, to enter and

persist STEM fields. The U.S. Department of

Labor predicts that 9 out of the 10 fastest

growing careers that require a bachelor’s

degree will require significant training in

mathematics and the sciences. In order to

develop a strong workforce in the United

States, more students must enter the STEM

fields. Interest in science, technology,

engineering, and mathematics starts in the K-

12 school system. It is vitally important to

engage students and enhance their


49

understanding of mathematics and science.

Research has shown that the greatest

difference in academic performance between

boys and girls is in mathematical reasoning

and geometry [4]. The Pythagorean

Proposition Project aims to increase the

comprehension of geometric representations

of proofs among all students, but specifically

girls, who historically underperform in tasks

involving spatial abilities as compared to

boys.

A third goal is to heighten awareness of

important mathematic proofs. Selected proofs

will support the requirement within many

state teaching programs to present historical

references of mathematics. The connection to

historical content may help to engage students

who initially have little interest in

mathematics.

2. PRODUCTION

2.1. Original Concept

We began by examining and dissecting

proofs from Loomis’ text. The first proof

selected was Euclid’s proof of the

Pythagorean Theorem as shown from the

original text in Figure 1.

Figure 1. Euclid’s Proof

As one can see, the proof is non-trivial and

difficult to interpret. The text is very

condensed and a minimal explanation is

provided. For a student who may not

necessarily be mathematically focused, this

proof may appear to be intimidating and

unapproachable. Our goal was to analyze

this proof and break it down into several

easier to understand steps. We wanted to be

able to present the proof is a way that a

student could see both the logical argument as

well as the corresponding image. We intended

to publish on a website domain so that it

could be accessed by anyone who was

interested.

Since many people naturally read from left-

to-right, it only made sense to present the

theorem statements on the left with the

corresponding image on the right as shown in

Figure 2.

Figure 2. Original Concept

With this is mind, we began searching the

Internet for a suitable template, something

that had a bold midsection for the proof and

subtle portions to contain links to the index

(also referred to as the home page). After

much searching and debating, we settled on

the template shown in Figure 3. As one can

see, the template does not meet the exact

requirements for the desired website;

however, it is a promising start.


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Figure 3. Original Template

2.2. Transformation Process

There were numerous alterations needing to

be made to this template. Upper and lower

hyperlinks needed to be updated, font faces

and colors needed to be corrected and the

midsection that was to contain the main

attraction had to be rearranged; while this may

not seem like much, there were many trials

that had to be overcome, and the best place to

start was comparison of the cascading style

sheets and HTML files.

Within the CSS files, there were numerous

lines of code that did not seem to be

implemented within the HTML files.

Example:

#breadcrumb a{

color:#FFFFFF;

background-color:#02ACEE;

}

#breadcrumb ul{

margin:0;

padding:0;

list-style:none;

}

#breadcrumb ul li{display:inline;}

#breadcrumb ul li.current a{text-

decoration:underline;}

However, after removal of this seemingly

unnecessary code, it became obvious that this

syntax was very necessary; if this code is

removed, the webpage is no longer formatted.

Therefore, until further inspection is

completed, these lines will remain within the

files.

Another issue faced with the HTML and

CSS files was syntax undermining. Examples

of this are font face, color and decoration.

Many times we attempted to alter the fonts

and, while at times we were successful, other

times we were not. For instance, we wanted

to add over-lines to pieces of text to indicate

line segments; however, when the code was

added, the text resembling segments

disappeared. We have yet to find the cause of

this.

Yet the major issue we faced dealt with the

rearrangement of the main content. Often, we

would attempt a method to no avail. We had

to print sheet after sheet of paper, highlight

corresponding segments and studied each line

of syntax. Finally, the key to our issues was

discovered:

.featured_box img{

display:block;

top: 25%;

position: relative;

float:right;

width:500px;

height:500px;

}

Originally, the “position” was not expressed

and “float” was set to left. It was easy to see

the “float” setting required alteration, but it

was not so easy to realize our issue could be

resolved with the simple addition of position:

relative;.


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Statement hovering and image correlation

were also difficult, yet after much foolery,

solutions were discovered.

As the website was coming together, the

proofs were being sliced into smaller, bite size

pieces. Euclid’s original proof was eventually

broken into 13 individual steps. The first

seven steps guide the user through the

construction of the figure displayed in

Loomis’ text (Figure 1). Suddenly, this image

is much easier to visualize—it is now easy to

see that it is simply a right triangle with a

square upon each edge. After the structure is

created, it is much more obvious that Euclid’s

argument is a proof through areas.

The same process was applied to a second

proof from Loomis’ text—Bhaskara’s proof.

See Figure 4. As one can see, the proof is

similar to Euclid’s in that it is hard to

understand and very condensed. Both proofs

were taken apart, analyzed, and rewritten in a

form that is easy to understand and interpret,

even for those that are not necessarily

“mathematically minded.”

Figure 4. Bhaskara’s Proof

2.3. Completion

The completed site can be found at the

following address as of Spring 2014:

webpages.shepherd.edu/cshane01/Cover_Page

_Pythagorean_Theorem.html

Figure 5. Cover Page, Euclid and Bhaskara

3. APPLICATIONS & FUTURE WORK

This site is intended to be used by high

schools and undergraduate students to further

explain the fundamentals of the Pythagorean

Theorem; however, this website can also be

used to help further the knowledge of web

developers.


52

We would also like to expand the website to

encompass the Great Theorems, including

Cauchy-Euler, Erdos-Mordell Theorem and

the Fundamental Theorem of Calculus.

ACKNOWLEDGEMENT

This project was funded by the NASA WV

Consortium Fellowship. The authors would

also like to thank Dr. Karen Adam, their

advisor, Shepherd University’s department of

Computer Science, Mathematics and

Engineering, along with family and friends for

their much needed support throughout this

project.

REFERENCES

[1] J. Suzuki, But How do I Do Mathematical

Research?, Focus 24 (2004) 10.

[2] E. S. Loomis, The Pythagorean

Proposition, NCTM, 1968.

[3] The American Association of University

Women report Why So Few? Women in

Science, Technology, Engineering and

Mathematics (2010),

[4] Fennema, E., Sowder, J., & Carpenter, T.

(1999). Creating classrooms that promote

understanding. In E. Fennema, & T.

Romberg (Eds.), Mathematics classrooms

that promote understanding (pp. 185-199).

Mahwah, NJ: Lawrence Erlbaum

Associates


53

Implementing an Automatic Proctoring and Audit System for Ib based Tests and Examinations through Facial Biometrics

Kirsten Logsdon

Department of Computer Sciences, Mathematics, and Engineering Shepherd University


ABSTRACT

There are many benefits to Web based education including time and space flexibility, document organization, automatic grading, and decreased paper wastes. However, online education still has a long way to go as the standard username and password login is easily shared leading to academic dishonesty, and is no longer secure from hackers. This report details the development of a Web application intended to create two factor authentication using facial biometrics.

The concept of facial recognition requires a known image and an unknown image. The known image is the first created and is referenced any time the face should be recognized, and the unknown images should always be compared to the same known image. This application currently functions as an environment to create a known image. Development of this application required a logical user interface capable of determining whether a user should be added to the database or compared to the database. It also required a database capable of storing many users’ information simultaneously. To store a user’s photo in the database, the application is required to access the computer’s camera, display the image captured, detect a face in the frame, extract the face from the frame, parse the extracted image to a byte array, and create a query to insert the image and username in the database.

Over the course of the semester I successfully implemented face training functionality. In the future I would like continue to work on the main goals of the project and implement facial recognition. The application’s security can also be greatly enhanced with the addition of bitmap encryption and exam proctoring. Ultimately, I would like to publish my application, which would require thorough testing in a full scale academic testing environment.

KEYWORDS: Biometrics, Education, OpenCV, Face Detection, Web Application, MySQL

1. INTRODUCTION In a technology and Web-driven world, traditional tasks are executed digitally every day. One of the common institutions taking advantage of this trend is the education system. Schools benefit tremendously, and in many ways, by going digital. Students

and educators are given time and space flexibility to assign and complete assignments outside of normal business hours. Online education allows for efficient data organization, databases organize assignments, attendance, and grades more efficiently, and automatic grading decreases


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assignment turnaround time. Paper waste is also diminished, saving money and preserving the environment’s natural resources. However, there are also many disadvantages to the growing use of online education. One of these disadvantages is a product of the worldwide access presented by the Internet, location is no longer a deterrent for academic dishonesty. Usernames and passwords can be shared with others around the world, making cheating and information sharing easier than ever before. With technology advancing daily, the simple username and password method of login authentication is also no longer secure from hackers. Any person or machine can find a way to access an account guarded by only a pair of unique identifying phrases, leaving sensitive data vulnerable to theft and alteration. The single factor authentication prevalent in online education is flawed. A possible solution to this security problem is the implementation of two factor authentication. In this scenario, information would not only be guarded by user knowledge, or username and password, it would also be guarded by user inherence, or what the user is or looks like. Users should be required to verify their identity with facial biometrics, adding another layer of security to pre-existing online education suites. I proposed the development of a facial recognition Web application for this purpose.

2. SOFTWARE SELECTION AND ACQUISITION The first step in developing an application using pre-existing software is to choose which software to use. Biometric facial recognition is still a very new, and potentially very lucrative subject. Therefore, there are countless companies and private programmers working on developing and marketing the perfect facial recognition software [5, 6]. After researching many

different facial recognition softwares, I decided to use an open-sourced option, transparency allows for complete manipulation of an application. I chose to use the open-sourced softwares OpenCV (Open-sourced Computer Vision) and EmguCV (a .NET wrapper for OpenCV) for my facial recognition Web application. There are many reasons I found this combination to be the most beneficial for my needs. These software development packages are easily downloaded for free from SourceForge.com [2], have many tutorials and sample projects [10], feature thousands of optimized algorithms [7], and include a huge library of functions [4]. EmguCV is written in C# and is intended to work with the Microsoft Visual Studio Web development environment [4] which is perfect for my needs. 3. USER INTERFACE DEVELOPMENT After downloading the EmguCV library to my local disk, I was able to create a new project with Visual Studio and add the appropriate dynamic link library files to the project references. Emgu.CV.dll, Emgu.CV.GPU.dll, Emgu.CV.UI.dll and Emgu.Util.dll were the references necessary for my project [Figure 1].

Figure 1: References


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The next step in creating my Web application is to develop the user interface, back end functionality will be possible once this important step is complete. I first had to consider the business rules for my software, users should not be able to manipulate their own data for example. My system should be designed so that the user enters (or the Website passes) a username, if the username is in the database the user simply needs to be recognized, if the username is not in the database the application needs to be trained and the user should be added to the database. This functionality was accomplished by creating three forms with Microsoft Visual Studio, a login form [Figure 2], a face trainer form, and a recognizer form. For the most part, all three of these forms were created with Visual Studio’s library of form development tools.

Figure 2: Login Form

4. CAMERA INTERACTION IMPLEMENTATION Accessing a computer’s webcam through an application can be a difficult task. However, EmguCV distributes a component that can be added to Visual Studio’s form toolbox called ImageBox [3][Figure 3]. Once the Emgu toolkit is added to the Visual Studio toolbox, it can be added to the form similar to the method one would add a Visual Studio PictureBox.

Figure 3: ImageBox

This component was added to both the face trainer and the recognizer forms as both will need to access the camera, I called my ImageBox “CamImageBox”. Once this step was complete, I could implement the backend logic to access the camera and print the capture to the screen. An EmguCV method called Capture is included in the Emgu.CV package, I needed only to declare an instance of it, which I named “capture”. To fetch the frame captured by the camera I needed to create an Emgu.CV.Image called “ImageFrame” to store the capture. This was then able to be displayed in the CamImageBox as a live feed. I also wanted to implement camera start and stop logic, so I added a button to my form to control this [Figure 4]. When the button is clicked, the capture starts and the button’s text changes to “Stop Camera” and the reverse happens when the button is clicked again. After thorough testing of the camera capture application, I duplicated the logic for the recognizer form and was ready to move onto the face detection implementation.


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Figure 4: Start / Stop Camera Logic

5. FACE DETECTION IMPLEMENTATION It is important to understand the basics of face detection algorithms in order to understand the process of face detection. EmguCV uses the popular Viola Jones algorithm for its face detection which requires a greyscale image. The entire image is scanned piece by piece repeatedly reading areas of high depth as black rectangles and areas of low depth as white rectangles [Figure 5]. This process allows the computer to quickly and easily read and understand images. [1]

Figure 5: Viola Jones Algorithm [1]

In order to implement face detection, the application needs a haar cascade XML file [Figure 6] to reference. This file contains one thousand different generic face coordinates for the face detector algorithm

to compare the camera capture to. This file was found in the downloaded EmguCV package and was copied to the debug\ folder of my Visual Studio project directory to eliminate absolute path referencing. This file was then loaded into the haar object. To do this I first declared an object called “haar” of the Emgu.CV.HaarCascade class, then I declared a new instance of it with the XML file.

Figure 6: HaarCascade XML File

With the haar cascade XML file properly connected the haar object, the face detection logic can be implemented. A method called “ProcessFrame” [Figure 7] is invoked when the “Start Camera” button is clicked, this is also where the capture logic is found. A conditional statement was added so the face detection process would only occur when there is an image to read. When there is an image, the image is converted to an Emgu.CV.Image in greyscale to be easily readable by the computer. This greyscale image is then run through the HaarCascade algorithm creating an array of all of the detected faces, drawing a green rectangle around each one and displaying the new image to the form in real time.

Figure 7: Face Detection Logic


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6. IMAGE EXTRACTION IMPLEMENTATION

Figure 8: Image Extraction

Facial recognition requires two images, one that is known and one that is unknown. This is true both in life and with computers, a person must first introduce oneself becoming a known image in memory for later facial recognition. To save this known image in my application, I first needed to extract the detected face from the live camera capture [Figure 8].

Figure 9: Extraction Logic

This process first requires the entire captured image to be parsed to a bitmap image which I call “BmpInput”. The detected face’s coordinates are saved, and a Graphics canvas is used to draw the detected greyscale face which is then parsed to a new bitmap image which I call “ExtFace” [Figure 9]. This image is then ready to be painted onto the form, which also occurs in real time every time the user’s face is detected. This extracted bitmap image “ExtFace” will be useful when storing to a database when the face training is implemented.

7. DATABASE DEVELOPMENT This application’s database was built using MySQL for Visual Studio [8] for usability and scalability purposes. MySQL for Visual Studio is unique in that it can be created and implemented in the same environment making connectivity natural. This option was also chosen for its scalability, MySQL is a Web based database service from Oracle with virtually unlimited resources. Therefore, if my application were to grow in the future, my database would be able to support the expansion.

Figure 10: Table Structure

My database consists of one table with three attributes [Figure 10]. The username attribute is defined as the primary key containing a 10 character string and the image attribute is a blob, or a binary large object, type containing a byte array. The third attribute is an integer labeled “faceCount” which is used solely for login purposes.

Figure 11: Database Connection

When any form is loaded, a connection to the database is attempted. The MySql.Data.MySqlClient package contains the necessary classes to establish a connection. A connection string is stored in the MySqlConnection object “con” which is then opened in a try statement [Figure 11]. For testing purposes, MessageBoxes display connection events.


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Figure 12: Login SQL Query

The first query in the application occurs when a user attempts a login. The application is intended to load different forms based on whether the user exists in the system or not. Therefore, this decision lies in the hands of a database query. This is where the “faceCount” attribute of the table is used. When the user clicks the login button, the application reads the username entered and creates a SQL query to update the user with the given username [Figure 12]. This query is executed using MySql.Data.MySqlClient.MySqlCommand.ExecuteNonQuery() which returns the number of rows affected. If a row is affected, the username exists in the database and the recognizer should run, but if no rows are affected the user does not yet exist in the database and the face trainer should run. 8. FACE TRAINING IMPLEMENTATION The face trainer form is responsible for successfully adding new users and their photos to the database for later recognition. This is accomplished by accessing the computer’s camera, detecting the user’s face, extracting a bitmap image of the user’s face, parsing the bitmap image to a byte array, and finally storing the byte array

along with the user’s entered username into the created database using an insert query. To add a user to the database, a query similar to the update query used during login must be created. The database stores images as byte arrays and the extracted face image is stored locally as a bitmap image, so in order to save the image to the database I must first parse the bitmap image to a byte array. System.Drawing.ImageConverter contains a method that can do this task easily while being added as a parameter to the SQL command. A query in MySQL Workbench [9] confirms the successful addition of an image. 9. DATABASE IMAGE RETRIEVAL The final step in facial recognition preparation is to retrieve a previously stored image from the database for comparison. The user images are stored in the MySQL database as byte arrays but they need to be parsed into bitmap images for viewing. To do this, a SQL command must be executed to select the requested image from the database according to the username. This executed command is then run through a MySqlDataAdapter and stored in a data table which is used to fill a local byte array. A memory stream is then used to draw the byte array as a bitmap image. In this application, this bitmap image is displayed in a form directly after the user is added to the database to confirm the update was successful [Figure 14].

Figure 13: Adding a User to the Database

Figure 14: Retrieving a Photo from the Database


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10. FUTURE WORK The goal of this project is to create a Web based application to add facial biometrics to the pre-existing simple username and password authentication present in online education. In order to publish this application, I would first and foremost need to implement facial recognition. After this is implemented, exam proctoring should be implemented to continuously monitor the user’s identity during exams. To further the security of my application, the images stored in the database should be encrypted. Once all of these implementations are added and functional, the application should be thoroughly tested on a full-scale academic testing site. Finally, when there are no security flaws present, the application should be ready for publication. 11. CONCLUSION There are many advantages to online education. Students and educators are given time and space flexibility to submit and complete assignments, databases organize information efficiently, automatic grading decreases assignment turnaround time, and diminished paper wastes save money and preserve natural resources. There are also large disadvantages to online education. Academic dishonesty is no longer restricted by location, and simple user authentication is no longer secure from hackers leaving sensitive data vulnerable to attacks. I proposed a solution to this problem by adding a second form of authentication to the standard username and password login method. Facial biometrics is a growing field, webcams are included with almost every computer sold and facial recognition algorithms are improving. The online education environment would benefit greatly from a Web based proctoring system based on facial biometrics. There were many third party softwares used to develop this Web based application. EmguCV is a .NET wrapper that accesses

OpenCV’s face detection and recognition algorithm sets through function calls from Microsoft Visual Studio in C#. Oracle’s MySQL for Visual Studio was used to develop a database and access stored information easily and MySQL Workbench allowed for easy SQL querying during development. All of these softwares worked together perfectly to create this functioning application. During the course of a semester, I was able to successfully use third party software along with my own programming skills to create an application that can be used in the development of a facial recognition application. The application has the functionality to pass information between forms depending on user input, access a computer’s camera, display live camera feed in a form, detect all faces present in a frame in real time, extract a bitmap image of a detected face, store the extracted bitmap image as a byte array in a database and retrieve the stored byte array and store it locally or display it as a bitmap image. In the future, I would like to add facial recognition functionality, bitmap image encryption and proctoring before testing the application on a full-scale educational website and eventual publication


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REFERENCES [1] Borenstein, Greg, “Adam Harvey Explains Viola Jones Face Detection,” Makematics.com, 2012, http://makematics.com/research/viola-jones/ [2] Canming, “Emgu CV,” SmyceForge.net, April 2014, http://smyceforge.net/projects/emgucv/ [3] EMGU, “Add ImageBox Control,” Emgu.com, November 2010,

http://www.emgu.com/wiki/index.php/Add_ImageBox_Control [4] EMGU, “EmguCV Main Page,” Emgu.com, http://www.emgu.com/wiki/index.php/Main_Page [5] Frischholz, Robert W., “Software,” FaceDetection.com, March 2013,

http://www.facedetection.com/facedetection/software.htm [6] MaShape.com, “List of 50+ Face Detection / Recognition APIs, Libraries, and Software,”

June 2013 blog.mashape.com/post/53379410412/list-of-50-face-detection-recognition-apis [7] OpenCV Dev Team, “FaceRecognizer - Face Recognition with OpenCV,” OpenCV.org,

2014, http://docs.opencv.org/trunk/modules/contrib/doc/facerec/ [8] Oracle, “MySQL for Visual Studio,” MySQL.com, 2014,

http://www.mysql.com/why-mysql/windows/visualstudio/ [9] Oracle, “MySQL Workbench,” MySQL.com, 2014,

http://dev.mysql.com/doc/workbench/en/index.html [10] ShubhamSaxena, “Starting with Emgu CV,” CodeProject.com, January 2013,

http://www.codeproject.com/Articles/528275/StartingpluswithplusEmguplusCV


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http://makematics.com/research/viola-jones/

http://sourceforge.net/projects/emgucv/

http://www.emgu.com/wiki/index.php/Add_ImageBox_Control

http://www.emgu.com/wiki/index.php/Main_Page

http://www.facedetection.com/facedetection/software.htm

http://blog.mashape.com/post/53379410412/list-of-50-face-detection-recognition-apis

http://docs.opencv.org/trunk/modules/contrib/doc/facerec/

http://www.mysql.com/why-mysql/windows/visualstudio/

http://dev.mysql.com/doc/workbench/en/index.html

http://www.codeproject.com/Articles/528275/StartingpluswithplusEmguplusCV

Molecular Investigations into the Genome of Lymnaea palustris

Dustin Revell and Carol Z. Plautz



[email protected]

ABSTRACT The pond snail Lymnaea palustris has been a useful model for studying aquatic toxicology and the effects of contaminants on aquatic invertebrates. Previous studies have demonstrated perturbation of embryological development, reproduction, and learning and memory formation in L. palustris, likely through mechanisms disrupting Steroidogenic Acute Regulatory Protein (StAR) and Protein Kinase C (PKC), among other molecules. In order to determine if these disruptions are direct effects, and whether they are caused by changes in gene transcription or post-transcriptional regulatory mechanisms, the genetic sequences of L. palustris StAR and PKC mRNAs are required. However, as none of the genome has yet been published, it is required to clone these genes from the pond snail. We designed degenerate primers via homology at the amino acid level with genes in a variety of phyla, and performed RT-PCR with RNA isolated from embryos as well as from adult cerebral ganglia. Amplicons of appropriate size were then purified, cloned into an appropriate vector, sequenced, and compared to homologues in other species in GenBank. Analysis of the sequences generated and future directions with these new reagents will be presented. KEYWORDS: Lymnaea, degenerate primers, cloning, RT-PCR, PKC

1. INTRODUCTION The pond snail Lymnaea palustris has been utilized in our lab for studying reproductive and developmental biology, learning and memory formation, and aquatic toxicology. L. palustris is easily maintained in a laboratory setting, and in recent years, it has been demonstrated [1] that certain environmental contaminants such as RoundUp and diquat dibromide negatively affect reproduction and memory formation in L. palustris, likely through the disruption of Steroidogenic Acute Regulatory Protein (StAR) and Protein Kinase C (PKC). In order to study whether this involves changes in gene expression or mRNA regulatory mechanisms, the genetic sequences of L. palustris StAR and PKC mRNAs are required. However, at the start of this project, no sequence data had been published for L. palustris. If the mRNA sequences for L. palustris StAR and PKC are known, further work can be done to study their expression quantitatively throughout development or in different organs using qPCR, or qualitatively using in situ hybridizations. In addition to studying normal expression patterns, we wish to study expression changes in response to contaminants, to alter expression, and attempt to elucidate the mechanisms involved. Snails are often not used in molecular biological research, however, due to the large polysaccharide content of their shells and shell gland, which interferes with downstream


62

applications such as PCR and cloning. We sought to avoid this problem by using snail embryos and, later, adult snail ganglia. Since no sequence information was known, we attempted to clone a variety of cDNAs for specific mRNAs, including those for PKC and StAR, using degenerate primers designed by protein homology. This entailed extracting RNA from snail embryos or adult snail ganglia, in some cases enriching for mRNA, RT-PCR, agarose gel electrophoresis, band excision and purification, ligation, transformation and selection, and sequencing. We also attempted to determine the size of the L. palustris 18S and 28S rRNAs. 2. MATERIALS AND METHODS 2.1. Primer Design Degenerate primers were designed by aligning the amino acid sequences for StAR, Pax6, and several isoforms of PKC using NCBI COBALT or Aligning through UniProt, then looking for highly conserved areas across several unrelated species. Once areas were found, we used the universal genetic code to translate amino acids to the corresponding mRNAs which encoded them; however, in many cases multiple codons were observed because of the degeneracy of the universal code. Forward primers were designed to be equivalent to this mRNA sequence so they would bind to the cDNA generated when the Reverse Transcriptase (RT) transcribed it. Reverse primers were designed to be complementary to the mRNA so they would bind to the complement of the primary copy the polymerase generates during PCR. Only degenerate primers with a degeneracy of 128-fold or less were used in order to prevent spurious binding to non-specific cDNAs. Non-degenerate primers were designed from the mRNA sequence of an atypical Protein Kinase C (aPKC) and the sequence of the Serotonin Receptor (SerRec) of another Lymnaean, the giant pond snail, Lymnaea

stagnalis. These primers were made to generate amplicons of different sizes, ranging from 300bp to 1.5kbp. Other primers used were C51/C31, which was shown to prime the cyclic-AMP response element binding protein (CREB) mRNA in L. stagnalis [2], and primer pairs designed for Cu/Zn-Superoxide Dismutase (Sod) and Retinoid-X Receptor (Rxr), which was also shown to work in L. stagnalis [3]. All primers and their origins are available as supplemental materials. 2.2. Tissue Isolation Jelly masses were obtained from the snail tanks and trochophores and veligers were chosen, for they have many cells but still lack the polysaccharide producing shell gland, to be mechanically dissected and embryos isolated. From this, 50 embryos were chosen to be decapsulated and 50 to remain capsulated before undergoing DNA isolation according to the procedure of [4]. DNA quality was inferred from the OD260/280, where ≈1.8 was taken to be pure DNA [5], using the Take3 application of the plate reader. Duplicates were run and this was performed twice. Ganglia were extracted by placing snails in 4oC sterile snail saline (SSS) for approximately 10 minutes, or until their activity decreases substantially. Snails would then be removed from the SSS and placed under a dissecting microscope. The shell was removed and the mantle cut to reveal the buccal mass, underneath which lies the ring of ganglia (Fig. 1). The ganglia were then removed and immediately snap frozen or used for RNA extraction. 2.3. RNA Extraction Total RNA was extracted using PureZol (BioRad #732-6890). 50 dejellied, capsulated embryos are added to a 1.5mL tube and 1mL Purezol is added. They are ground using a microfuge tube pestle and incubated at room


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temperature for 5 minutes. After incubation, they are centrifuged 10 minutes at 13,000 RPM at 4oC. The supernatant is then extracted and added to a 2.0mL tube and 200uL of chloroform is added. The tubes are shaken vigorously for 15 seconds and incubated at room temperature for 5 minutes. They are centrifuged for 15 minutes at above conditions. The aqueous phase is then extracted, placed into a 1.5mL tube and has 500uL isopropanol added. This is incubated for 5 minutes at room temperature before being centrifuged 10 minutes at above conditions. The supernatant is discarded and the RNA pellet washed with 75% EtOH in DEPC-treated H2O. This is then centrifuged 5 minutes at 7,500g at 4oC. The supernatant is discarded and the pellet air dried and resuspended in DEPC-treated H2O. The purity of the RNA was determined using the Take3 application, where an OD260/280 ≈ 2.0 was taken to be pure RNA [5]. RNA quality was also checked by the presence of the 18S and 28S rRNA bands on an agarose gel. If genomic DNA carryover is detected on the gel, a DNase treatment is performed. Total RNA samples with contaminating genomic DNA were further purified by treatment with Optizyme RNase-free DNase (Fisher #BP8107-1). 10uL of total RNA obtained from 50 embryos contains approximately 4ug total RNA and was added to 3uL DEPC-treated H2O, 1uL RNase Inhibitor, 2uL 10x Optizyme DNase Buffer, and 4uL of Optizyme DNase at 1unit/uL. This reaction is then incubated at 37oC for 30 minutes. DNase can then be inactivated by incubation at 65oC in the presence of EDTA (1uL 50mM EDTA per 10uL reaction volume), however, due to the labile nature of RNA, we purified the RNA using phenol:chloroform extraction. mRNA can be selected from total RNA, or directly from tissue. For our purposes, we selected for poly(A)+ RNA by adding and

homogenizing 200+ embryos or 10 adult snail ganglia in 500uL of Lysis/Binding buffer. This was then centrifuged and the supernatant added to 200uL of poly-dT magnetic beads (NEB #S1550S) and isolated following the manufacturer’s protocol for isolating mRNA from tissue using a magnetic rack (NEB #S1506S). Purity and quantity of the mRNA was determined using the Take3 application of the plate reader. 2.4 RT-PCR Reverse transcription was originally performed with iScript (BioRad) using the iScript One-Step RT-PCR Kit with SYBR Green (BioRad #170-8892) according to manufacturer’s protocol. We later switched to M-MuLV Reverse Transcriptase (NEB #M0253S) and used the following protocol: 10uL mRNA, 2uL dNTPs at 5mM, and 4uL H2O are added to a 1.5mL tube. This is heated 5 minutes at 70oC and moved immediately to ice. 2uL of 10x RT Reaction buffer, 1uL RNase inhibitor, and 1uL MMLV-RT are added to the tube and incubated at 42oC for 1 hour. The reaction is heated at 90oC for 10 minutes and can be used immediately for PCR or quantified using the Take3 application of the plate reader. PCR was originally performed with iScript One-Step RT-PCR Kit with SYBR Green (BioRad #170-8892) with 25uL of 2x Supermix, Forward and Reverse Primers at 300nM, 1uL iScript RT, 100ng total RNA, and nuclease free H2O to 50uL. We then incubated in a CFX96 Thermal Cycler (BioRad) for 10 minutes at 50oC, 5 minutes at 95oC, and 40-45 cycles of 10 seconds at 95oC and 30 seconds at 60oC. Products of these reactions were then run on an agarose or low-melt agarose gel, as described below. Switching to MMLV-RT, the overall reaction volume became 25uL supermix, forward and reverse primers at 300nM, 10uL of the 20uL RT Reaction, and H2O to 50uL. The new


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protocol for the CFX96 left out the initial 10 minutes at 50oC, but otherwise remained the same as before. We later switched to SsoAdvanced Universal SYBR Green Supermix (BioRad #172-5270) with the following protocol: 10uL of supermix, forward and reverse primers at 300nM, 100ng of cDNA, and nuclease-free H2O to 20uL. This was then incubated in the CFX96 for 30 seconds at 98oC and 40 cycles of 10 seconds at 98oC and 40 seconds at 60oC. Products of these reactions were then run on an agarose gel, as described below. In some instances where traditional PCR methods failed, we attempted to use Touchdown PCR. In our Touchdown PCR, reactions began with the elongation temperatures at 70oC and decreased by 0.5oC per cycle until 60oC was reached, then an additional 15 cycles were performed. Several variants of touchdown PCR were performed according to published protocols [6,7,8]. 2.5 Electrophoresis Agarose gels were made as either 30 or 40mL gels with 9/10 RO H2O and 1/10 10x TBE (BioRad #161-0733) and with 1.0 to 1.5% agarose. DNA or cDNA samples were buffered with a 5x Nucleic Acid Sample Buffer and loaded (BioRad #161-0767), while RNA samples were buffered with a TBE/Urea Sample Buffer (BioRad #161-0768) and heated at 70oC for 4 minutes prior to loading. Ladders used were a 1kb DNA Ladder (NEB #N3232S) and a 100bp Low Ladder (Sigma #P1473). Gels were run on the Mini-Sub Cell GT Agarose Gel Electrophoresis System (BioRad #170-4401) in TBE Buffer at 80V for ≈1.5 hours. Gels were stained for 30 minutes in a 0.5ug/uL Ethidium Bromide solution (3uL of 10mg/mL solution of EtBr [BioRad #161-0433] in 60mL RO H2O). Gels were destained in 60mL RO H2O for 30 minutes before imaging on a Bio-Rad ChemiDoc. Any bands of interest are then

excised and the DNA purified using Freeze ‘N Squeeze DNA Gel Extraction Spin Columns (BioRad #732-6165), described below. Low-Melt Agarose Gels are made with TBE as 30mL, 1.0 or 1.2% gels with Low-Melt Agarose (BioRad #161-3113). These are also run at 80V for 1.5 hours and stained, destained, and imaged as described above. Bands of interest are then excised and purified by melting and using a phenol:chloroform extraction, described below. The denaturing 1.2% agarose gel was made with 29.4mL H2O, 4mL 10x MOPS buffer, and 0.48g agarose. This was melted before addition of 6.6mL formaldehyde. Each total RNA sample was prepared with 2uL 10x MOPS, 3.5uL formaldehyde, 10uL formamide, and 5uL of total RNA. The samples were heated 10 minutes at 65oC, then moved immediately to ice and spun down, before adding 3uL of Nucleic Acid Buffer (BioRad). The gel was run in 1x MOPS at 120V until the bromophenol blue was 1/3 way down the gel. The gel was stained in 1ug/mL EtBr for 20 minutes and destained overnight in ddH2O. The following day, the H2O was discarded, replaced, and the gel was allowed to destain for an additional 30 minutes before being imaged on the ChemiDoc. 2.6 Purification To minimize loss, overall volume of samples was raised to between 300 and 500uL before beginning. One volume of phenol:chloroform (Sigma #P3803) is added to the sample and mixed well. This is then centrifuged at 10oC at 16,000RPM for 10 minutes. The aqueous phase is then extracted and 1 volume of chloroform is added, mixed well, and centrifuged at above conditions. The aqueous phase is extracted and 1/10 volume of 3M Sodium Acetate, pH 5, and 2 volumes of 95% EtOH are added. This was placed at -20oC for at least 1 hour or at -80oC for at least 30 minutes. This was centrifuged at 4oC at


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13,000RPM for 25 minutes. The nucleic acid will form a pellet or smear, and the supernatant is then discarded. The pellet was washed with either 70% EtOH, for DNA, or 75% EtOH in DEPC-treated H2O, for RNA. This was centrifuged at 4oC at 13,000RPM for 5 minutes. The supernatant was discarded and the pellet air dried for approximately 6-8 minutes, before resuspending in appropriate volume of TE Buffer, for DNA, or DEPC-treated H2O, for RNA. Quantity and quality of nucleic acids were checked using the Take3 application of a plate reader. Bands of interest from agarose gels were excised on a UV box and excess agarose trimmed away. Nucleic acid was purified using Freeze ‘N Squeeze Spin Columns (Bio-Rad #732-6165). Agarose with band of interest was cut into small pieces and placed in the spin column, then placed at -20oC for 5 minutes. This was centrifuged at room temperature at 13,000g for 3 minutes. To further purify the sample, an EtOH precipitation and resuspension in TE or nuclease-free H2O was performed. 2.7 Ligation, Transformation, Selection Ligation, transformation, and selection were performed using the pGEM-T Easy Vector System (Promega #A1380). Ligations were performed by adding 5uL 2x Ligation Buffer (#C671A), 1uL pGEM-T Easy Vector (#A137A), 1uL T4 DNA Ligase (#M180A), Insert at a 1:1 ratio to vector, and DEPC-treated H2O to 10uL. These were then incubated overnight at 4oC. A positive control with control insert DNA (#A363A) and a no insert control were also performed. Transformation was performed the following day by adding 2uL of the ligation reaction to a sterile microfuge tube and then adding 50uL of JM109 High Efficiency Competent Cells (#L200A), thawed on ice, to the 2uL ligation reaction. This was mixed by flicking the tubes, then incubated on ice for 20

minutes, before heat shocking at 42oC for 45 seconds. The tubes were immediately returned to ice for 2 minutes before adding 950uL room temperature SOC medium and incubating at 37oC with shaking (≈120rpm) for 1.5 hours. After incubation, the microfuge tubes were centrifuged at room temperature at 1000g for 10 minutes to pellet the cells. Cells were then resuspended in 200uL SOC medium before plating 100uL onto two LB/Ampicillin/IPTG/X-Gal plates. Plates were incubated overnight at 37oC and observed the following day for growth. Colonies which grew on the plates must contain vector because of their ampicillin resistance. Blue colonies, however, did not contain insert because of their expression of lacZ and digestion of the X-Gal. White colonies were selected using the following technique: With a sterile loop, half of the white colony of interest was picked up and placed into 5mL of LB/Amp media to be placed in a 37oC incubator with shaking. The other half of the colony was placed into a PCR reaction tube with SP6 and T7 primers at 300nM in order to amplify the area our insert should occupy, in order to ensure insert was actually taken in before isolating plasmid and sending it for sequencing. However, in at least one instance we isolated the plasmid (as described below), quantified it, and used 100ng in the PCR reaction to ensure fidelity. 2.8 Plasmid Isolation Of the 5mL of bacteria grown up in LB/Amp from selection, 4mL were used in plasmid isolation using Nucleospin Plasmid DNA Purification Spin Columns (Macherey-Nagel #740588.10). For each sample, 4mL was placed into two 2mL tubes and centrifuged at room temperature at 11,000g for 30 seconds. The supernatant was discarded and 125uL of buffer A1 added to each tube and pooled together. 250uL of buffer A2 was added, mixed by 8x inversion, and incubated for 5


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minutes. 300uL of buffer A3 was added, mixed, and centrifuged for 5 minutes at 11,000g. The supernatant was added to the Nucleospin Plasmid Column, placed in a collection tube, and centrifuged for 1 minute at 11,000g. 500uL of washing buffer AW, preheated to 50oC, was added and centrifuged 1 minute at 11,000g. 600uL of buffer A4 was added and centrifuged 3 minutes at 11,000g. The silica membrane was dried by spinning an additional 3 minutes at 11,000g. Elution of the DNA is performed by adding 50uL of buffer AE, heating at 70oC for 2 minutes, and spinning 1 minute at 11,000g. Plasmid isolated this way is then EtOH precipitated and resuspended in an appropriate volume of nuclease-free H2O to send for sequencing. 2.9 Sequencing and Analysis Samples were prepared in nuclease-free H2O at concentrations close to 200ng/uL and were sequenced using SP6 and T7 primers. All sequencing reactions were performed by the WVU Genomics Core Facility, Morgantown, WV. Sequence data were analyzed using NCBI BLAST and checking for homology to sequence from known organisms. 3. RESULTS The capsulated and decapsulated embryos were found to yield comparable RNA (average OD260/280 of capsulated embryos was 1.80±0.27, average for decapsulated embryos was 1.72±0.24; average OD260/230 of capsulated embryos was 0.92±0.14, average for decapsulated embryos was 0.92±0.30). However, to ensure that genes not yet expressed in embryos would be present in the cDNA, we primarily used the ganglia (Figure 1) as starting material.

Figure 1: L. palustris ganglia removed from adult specimen. Running total RNA under denaturing conditions yielded two prominent bands; one at 1.9kb and one at 2.2kb (Figure 2). A total of 7 clones were generated. The combination of StAR Fwd/Rev generated 2 clones, while Pax6 Fwd2/Rev2 generated 1, PKC3 Fwd/Rev generated 1, PKC3 Fwd2/Rev2 generated 1, PKC3 Fwd2/Rev3 generated 1, and Serotonin Receptor Fwd/Rev generated 1. Figures 3 and 4 display two gels on which amplicons were analyzed and selected for cloning. Of the 7 clones, PKC3 F2/R2 and Serotonin Receptor F/R both generated inserts which, respectively, were homologous to PKC and Serotonin Receptor of different species.

Figure 2: Image of total RNA on formaldehyde gel after staining with EtBr. The first lane contains a 1kb ladder; numbers listed beside the lane refer to the kbp size. Lanes 3 and 4 both contain 5uL of ≈150ug/uL total RNA. The two bands in each RNA lane separated are of ≈1.9kb and ≈2.2kb in size. These correspond to the 18S and 28S rRNA.


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Figure 3: Image of a gel containing, in lane number 5, the PCR reaction of PKC3 F2/R3. Ladder is a 100bp ladder; numbers indicate how many 100bp. PKC3 F2/R3 generated an 800bp fragment which could then be excised, purified, and used for ligation.

Figure 4: Image of a gel which contains, from left to right; 100bp ladder, PKC3 F2/R3, PKC3 F2/R2, PKC3 F/R3, Pax6 F1/R1, Pax6 F2/R2, PKC2 F/R, and Ser. Rec. F/R. Bands of interest were excised, purified, and used for ligation.

4. DISCUSSION Our initial thoughts in this research on using embryos to avoid the polysaccharide content in the adults led us to question whether capsulated or decapsulated embryos should be used. After isolating DNA from capsulated and decapsulated embryos, we saw no significant difference between their quality as determined by their OD260/280 and OD260/230, so we continued using capsulated embryos. However, after several failed PCRs, we questioned whether mRNAs for the genes of interest were being expressed during embryonic development. We later switched to dissecting adult snail ganglia and isolating RNA from multiple snails’ ganglia to ensure the mRNA expression. After running total RNA on a denaturing gel, two distinct bands were present; one at 1.9kbp and one at 2.2kbp. These bands likely correspond to the L. palustris 18S and 28S rRNA bands. Supporting this, L. stagnalis 18S rRNA is 1.85kbp (GenBank #Z73984.1). However, 2.2kbp is quite small for the 28S rRNA, as another invertebrate, D. melanogaster, has a 3.9kbp 28S rRNA [9]. The 28S rRNA may appear as only 2.2kbp because of a “hidden break”, as in A. mellifera [10]. In the honeybee and many other insects, the 28S rRNA contains an endogenous hydrogen bonding area which, when exposed to denaturing conditions, breaks apart and causes the 28S rRNA to migrate together as two smaller fragments close to the 18S band, often appearing as a smear. Figure 2 suggests that the hidden break may not only be present in insects, but could be a commonality in many invertebrates. Further research will need to be conducted to determine the true size of L. palustris 28S rRNA. Throughout this research, several obstacles were encountered with obtaining quality RNA, optimizing PCR reactions, generating fragments of interest, and ligating in certain fragments. As seen in Figure 3, PKC3 F2/R3


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seemed to generate a ≈800bp fragment. However, upon ligating, cloning, and sequencing, only a 246bp sequence, corresponding to nothing of interest, was obtained. Additionally, PKC3 F/R and StAR2 inserts (available as Supplemental Material) have the same initial 20 and final 20 bases, which are equivalent to the PKC3 F/R primers, yet the sequence within those regions are different from one another. This leads us to believe there was some contamination in those two reactions, and they should be repeated in a future experiment. Two fragments were generated from PKC3 F2/R2 and Ser. Rec. F/R, however, corresponding to aPKC and Serotonin Receptor. The first clone, PKC, is 330bp and shares 92% homology with L. stagnalis aPKC mRNA, 74% homology to A. californica aPKC mRNA, as well as greater than 70% to aPKCs of several other species ranging from A. aegypti to B. taurus. This leads us to believe this is a fragment of an atypical PKC mRNA from L. palustris. Moreover, when using BLASTX to compare the amino acid sequence obtained from our clone, we see that this fragment corresponds at 74% identity to amino acids 195-274 of L. stagnalis aPKC (GenBank #BAK09601.1). This region includes a segment of the enzyme’s catalytic domain as well as a segment of the ATP-binding domain. The second fragment was the Serotonin Receptor. The Serotonin Receptor fragment shares 89% homology to L. stagnalis Serotonin Receptor mRNA, 75% homology to A. californica G-protein coupled 5-HT Receptor 2 mRNA, as well as greater than 70% to several other species. This leads us to believe this is a fragment of the Serotonin Receptor mRNA from L. palustris. As with our other fragment, we used BLASTX to compare the amino acid sequence. The Serotonin Receptor fragment corresponds at 93% identity to amino acids 288-423 of L.

stagnalis Serotonin Receptor (GenBank #Q25414.1). This region is a topological cytoplasmic region of the Serotonin Receptor. The goal of this project was to obtain sequence data from a novel organism, L. palustris, which could then be used for future studies. In particular, the cDNAs for PKC and StAR were sought. We did not succeed in cloning StAR, but we did obtain fragments of aPKC as well as the Serotonin Receptor. We also determined that the approximate size of the pond snail’s 18S rRNA is 1.9kbp, and that its 28S rRNA could contain a hidden break. Certain PCR reactions, such as PKC3 F/R and StAR F/R, should be repeated in order to determine if they generate a different fragment. The ligation reaction with the product of PKC3 F2/R3 should also be repeated with the insert at a higher ratio in order to ensure proper incorporation into the plasmid before sequencing again. Now that fragments of L. palustris Serotonin Receptor and aPKC have been obtained, several new studies can be performed. Larger fragments of these cDNAs, including complete sequences, can be obtained through methods such as 3’ or 5’ rapid amplification of cDNA ends (RACE walking). Probes specific for these fragments can also be designed for quantitative studies, using qRT-PCR, of expression levels in L. palustris throughout development and senescence, in different organs, or in response to contaminants. In situ hybridizations can also be performed in order to look qualitatively at expression patterns and levels. Expression can also be knocked down using RNAi or morpholino oligonucleotides in order to see effects on the snail in vivo. Much work remains to be done in L. palustris, but by developing protocols for this species and generating the first sequences, some of that work can now begin.


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ACKNOWLEDGEMENT This work was supported by a grant from the NASA WV Space Grant Consortium and Shepherd University for STEM undergraduate research. Thanks are due to Dr. Colleen Nolan of the Shepherd University School of Natural Science and Mathematics for support and discussion, and to Dr. Carol Hurney of James Madison University for invaluable advice. REFERENCES [1] Plautz, CZ, Cain J, Chaney J, Mines S, Seal S, and Nolan CJ. (2014) Disturbances in reproduction and expression of steroidogenic enzymes in aquatic invertebrates exposed to components of the herbicide RoundUp. In preparation. [2] Sadamoto, Hisayo et al. (2003) CREB in the pond snail Lymnaea stagnalis: Cloning, gene expression, and function in identifiable neurons of the central nervous system. Journal of Neurobiology. 58(4):455-466. [3] Bouétard, Anthony et al. (2013) Impact of the Redox-cycling Herbicide Diquat Dibromide on Transcript Expression and Antioxidant Enzymatic Activities of the Freshwater Snail Lymnaea stagnalis. Aquatic Toxicology. 126:256-265. [4] Gharbiah, Maey, et al. (2009) Isolation of Genomic DNA from Ilyanassa Snail Larvae. Cold Spring Harbor Protocols. 4(4):1-3. [5] Thermo Scientific. (2011) Assessment of Nucleic Acid Purity. Technical Bulletin T042. [6] Don, R.H. et al. (1991) ‘Touchdown’ PCR to Circumvent Spurious Priming during Gene Amplification. Nucleic Acids Research. 19(14):4008. [7] Korbie, Darren J. and Mattick, John S. (2008) Touchdown PCR for Increased

Specificity and Sensitivity in PCR Amplification. Nature Protocols. 3(9):1452-1456. [8] Roux, Kenneth H. (2009) Optimization and Troubleshooting in PCR. Cold Spring Harbor Protocols. 4(4):1-6. [9] Agilent. Nucleic Acids Sizes and Molecular Weights. Web. http://www.genomics.agilent.com/files/Mobio/Nucleic%20Acids_Sizes_and_Molecular_Weights_2pgs.pdf [10] Winnebeck, Eva C., Millar, Craig D., and Warman, Guy R. (2010) Why Does Insect RNA Look Degraded? Journal of Insect Science. 10:159, insectscience.org/10.159.


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The Effect of Games and Rewards on Math Performance

By: Ethan Hotz

Advisor: Dr. Zhijun Wang

Department of Computer Sciences, Mathematics, and Engineering Shepherd University


[email protected], [email protected]

ABSTRACT

This study examined the effects of games and motivators on learning abilities. Games have been found to be effective teaching tools across a variety of subjects, while motivators have also been found to have complex effects on learning abilities. Subjects, 3rd grade elementary school students, were placed into groups and tested using a custom made multiplication game. Subjects were divided by class into a control group, a testing group without reward, and a testing group with reward. Ultimately, there were no significant differences between pre-test and post-test scores for any group. Further research with more participants and slight methodology alterations is likely necessary to see significant results.

Keywords: Educational games, motivation, learning

1. INTRODUCTION

Considering how widespread and diverse computing devices have become, it is natural that educational software would find its way into the classroom. According to a U.S. Department of Education survey[7], about half of the surveyed teachers regularly used educational games or other interactive materials in their classrooms. There have been many studies on the effectiveness of educational games; in general, they have been found to be beneficial teaching tools. These games have been shown to be effective for learning elementary concepts like basic mathematics as well as advanced

concepts like city planning[6]. Studies examining these games generally find that they are effective because they present material in novel ways that help engage the user more than a traditional textbook. For example, one study[3] found positive results with Humunology, a tower defense game that aims to teach concepts of immunology. Users who enjoy these games will find them intrinsically rewarding; considering intrinsic motivation has been shown to be positively correlated with a greater ability to learn and perform in school[4], it stands to reason that any teaching method that inspires intrinsic motivation should be used as much as possible.

Rewards and motivators have also been found to have impacts on learning. Some behavioral programs use token economies to incentivize participants, and have found success using reinforcement to promote good academic performance[1]. Other studies have found that extrinsic rewards undermine memory retention[5] and intrinsic motivation[2]. Considering these conflicting results, further study on the effect of rewards on learning is warranted. This study attempts to test the results of offering an external reward to the intrinsically rewarding activity of playing a computer game.


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2. METHODS

2.1 Subjects

Participants were three classes of third grade students from Shepherdstown Elementary school. Each student that participated received parental permission. One class (n = 10) served as a control group, and did not play the game. Another group (n = 9) played the game with no reward offered. The third class (n = 14) played the game, and were offered a reward(candy) for the student with the highest score on the game.

2.2 Materials

All groups were given pre-tests and post-tests to measure baseline abilities and learning progress. Tests were paper worksheets containing 100 multiplication problems; students were given 3 minutes to complete as many problems as possible.

Game playing groups played MultiMatch, a click-and-drag multiplication game custom designed for this study. In game, players are presented with a random sequence of numbers which must be rearranged to the corrected multiplication sequence. For example, the sequence “8657” would be rearranged to “7 x 8 = 56”. The game can be set to play for 1-3 rounds, at 3 minutes per round. Each correctly solved problem adds one point, while an incorrect guess adds one error. When the game ends, players are presented with their average scores and errors, and scores are e-mailed to a dedicated e-mail address. Screenshots of the game are presented in Figure 1 and Figure 2.

Figure 1. Players are presented with a sequence of numbers to be rearranged.

Figure 2. When a slot is filled, a green border appears to indicate a number has been placed. When the “Next” button is

pressed, the program evaluates the current sequence. If correct, a point is added and a new sequence appears. If incorrect, the numbers return to their

original positions and an error is added.

2.3 Procedure

The experiment was completed over one week. On Monday, all groups took the pre-test. On Tuesday, Wednesday, and Thursday, the game-playing groups played the game for one session per day, two rounds per session. On Friday, all groups took the post-test.

Pre-tests and post-tests were graded and scored based on number of correct answers and number of errors made. The game sent data about total correct answers, total errors made, average correct answers per round, average errors made per round, and total number of rounds played per game session.


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3. RESULTS

Due to technical errors, a complete set of data from the game sessions was not received and therefore not analyzed. Results from the paper tests are shown below, in Figure 3:

Figure 3. Average correct answers across groups. C marks the control group, TNR marks the “test with no reward” group, and TWR marks the “test with reward”

group.

All groups showed an increase in test averages between the pre-test and post-test. Averages were analyzed using a one way, repeated measures ANOVA, shown in

Figure 4:

Figure 4. One way, repeated measures ANOVA of average correct answers

across groups.

Errors made were small or nonexistent across tests and were not analyzed. Tests found no significant differences between groups for pre-test or post-test results.

4. DISCUSSION

While group averages slightly increased across all groups, the lack of statistically significant differences indicates that the game cannot be attributed to any of these changes. There are a few possible causes for these results. Primarily, a sample size of only 33 total students is rather small, and obtaining meaningful results is less likely. A small sample size will be more greatly affected by outliers and fluctuations among individuals compared to a larger sample size. Variance of skill levels between students was likely also a significant factor influencing these results; when comparing post-test results, the lowest score was 16 correct while the highest score was 92 correct. These were not large outliers; scores were distributed throughout these ranges.

Thus, it is possible that the game was too challenging for students who still struggled with multiplication, while giving little


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benefit to students who have already mastered their times tables.

For the future, I would test more students to see if significant results emerge from a larger amount of students playing the game. Testing other grade levels with more experienced students might also yield significant results; students more familiar with multiplication may find the game more helpful. I would also try offering different rewards; it is possible that candy is not an effective motivator for all students. I would also make some revisions to the game to more closely mimic the pre-test and post-test procedure. The paper tests allow a student to skip around and answer questions they know, while the game forces its players to complete whatever sequence the game gives, with no option to skip a difficult problem. I would add in the function to skip problems, or to give hints to prevent players getting stuck. I would also want to implement testing for other operations such as addition and subtraction into the game, to see if students respond better to other mathematics. Lastly, I would want to analyze and solve whatever bugs caused the connection issues that prevented me from receiving gameplay results.

Ultimately, this study shows that successful educational games and learning in general have many nuances to be considered. In order to deploy an educational game, one must implement a great deal of testing and revision to ensure the game is an effective use of students’ time.

ACKNOWLEDGEMENT

This work was supported by a grant from the NASA WV Space Grant Consortium and

Shepherd University. I would like to thank Dr. Zhijun Wang for advising me on this project.

REFERENCES

[1] Alter, P. J., Wyrick, A., Brown, E. T., & Lingo, A. (2008). Improving Mathematics Problem Solving Skills for Students With Challenging Behavior. (Cover story). Beyond Behavior, 17(3), 2-7.

[2] Baranek, Lori Kay. (1996). The Effect of Motivation on Student Achievement. Masters Theses, Paper 285.

[3] Cheng, M., Su, T., Huang, W., & Chen, J. (2014). An Educational Game for Learning Human Immunology: What Do Students Learn and How Do They Perceive?. British Journal Of Educational Technology, 45(5), 820-833.

[4] Froiland, J. M., & Worrell, F. C. (2016). INTRINSIC MOTIVATION, LEARNING GOALS, ENGAGEMENT, AND ACHIEVEMENT IN A DIVERSE HIGH SCHOOL. Psychology In The Schools, 53(3), 321-336.

[5] Kuhbandner, C., Aslan, A., Emmerdinger, K., & Murayama, K. (2016). Providing Extrinsic Reward for Test Performance Undermines Long-Term Memory Acquisition. Frontiers In Psychology, 1-6.

[6] Noemí, P., & Máximo, S. H. (2014). Educational Games for Learning. Universal Journal Of Educational Research, 2(3), 230-238.

[7] U.S. Department of Education, National Center for Education Statistics. (2010). Teachers' Use of Educational


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https://nces.ed.gov/pubsearch/pubsinfo.asp?pubid=2010040

Technology in U.S. Public Schools: 2009 (NCES 2010-040).


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The development of a low-cost Arduino and Raspberry Pi-based system for environmental monitoring

1Jared Tomlin and 2Jeffrey R. Groff

Institute of Environmental and Physical Sciences Shepherd University


[email protected], [email protected]

ABSTRACT The proliferation of open-source hardware and software has made development of low-cost environmental monitoring systems possible. Here we detail our progress toward developing such a system based on the Arduino and Raspberry Pi platforms. The system includes a wireless sensor module, a wireless receiver, and the software stack for hardware control and data processing. The wireless sensor module, which we call “Scout”, is Arduino-compatible and can be programmed to accommodate sensors for environmental parameters like temperature, light, and humidity. One such sensor module, which we call “Rucksack TL”, was also developed. The wireless receiver, which we call “Basecamp”, is built upon a Raspberry Pi running Linux. Wireless communication from Scout to Basecamp is accomplished using XBee RF modules that are integrated into the Scout sensor and connected to the Basecamp Raspberry Pi via its general purpose input output pins. Prototype printed circuit boards for Scout, Rucksack, and Basecamp have been designed, manufactured, and tested. Software has been written to control the power state of the Scout, to take temperature and light readings, and to relay this data to the Basecamp. In addition, software has been written for the Basecamp to access data

being relayed to its XBee radio via serial communication. The Basecamp also includes a web server and a database so data can be processed, stored, and accessed via Internet connections. This type of system could have many applications where inexpensive wireless sensor networks are necessary, such as greenhouse monitoring, or creating geospatial overlays of environmental data. KEYWORDS: Arduino, Raspberry Pi, microcontroller, environmental monitoring, electronics, wireless sensors, XBee radio, ATmega 1. INTRODUCTION Open source is a community-based approach to hardware and software development that results in products that are free to use, modify, and distribute. This approach makes hardware, such as the Arduino, affordable to develop and allows for community support and collaboration. The purpose of the project described here was to develop a low-cost system for wireless environmental monitoring using open source tools including a Raspberry Pi and Arduino-compatible components. Arduino is a popular electronic prototyping platform that can be hand built by or purchased pre-made. Arduino is affordable and can be configured to interact with analog and digital components such as light and temperature sensors, LED lights,


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and servos. The Arduino software language is based on C++, a very popular general purpose programming language. Off-the-shelf Arduinos were used for much of the prototyping and testing of different scenarios in the development of the system described here. The Scout has the same microcontroller as the Arduino Uno and uses the same bootloader as the Uno Pro running at 8 MHz, making it programmable using the Arduino integrated development environment (IDE). For more background on the Arduino platform including hardware and software documentation see the Arduino project website [1]. The Raspberry Pi Model B was originally developed in order to provide a low-cost all-in-one computer that could be used to help children learn computer programming. The entire computer is about the size of a deck of playing cards and has USB, HDMI, Ethernet, SD Card, and audio ports as well as general-purpose input/output (GPIO) pins. The Python programming language is a native language for the Raspberry Pi. The 256MB of ram and built in Ethernet port provides ample memory for data processing and allows this data to be transferred over IP networks. For the system described here, Raspian, a Raspberry-Pi-specific Debian Linux distribution, was installed on the device giving it all of the capabilities of a Linux computer. The operating system was loaded onto a SD card, which was plugged into the SD card slot of the Raspberry Pi. For more background about the Raspberry Pi platform see the online documentation [2]. 2. OVERVIEW OF SYSTEM DESIGN In addition to off-the-shelf routers, PCs, and mobile devices, the wireless monitoring system described here is comprised of three custom components, namely the wireless sensor (Scout), the temperature and light

sensor module (Rucksack TL), and the receiver (Basecamp). Figure 1 shows a schematic overview of the system. The custom components use hardware and software that builds about existing open-source technologies. The Scout is a programmable environmental sensor based on the Atmel ATmega 328 microcontroller [3] and the boot loader utilized by the Arduino Uno Pro 8 MHz. Compared to the Arduino, the Scout is smaller, uses less power, is extendable, and accomplishes serial communicates via an integrated XBee radio. The Basecamp is an extension of the Raspberry Pi computer. The operating system, Raspian, is a distribution of the very popular Debian distribution of Linux. A custom XBee radio shield attachment for the Raspberry Pi was designed to connect to the Raspberry Pi’s GPIO pins in order to accomplish serial communication with the Scout. The Basecamp was programmed to store data in a MySQL database and allow for network communication via an Ethernet connection.

Figure 1. Starting at the top left: The Scout picks up temperature and light data (1) using the sensors on the Basecamp TL (2). The data is sent via an XBee radio to the Basecamp (3) for processing, storage, and serving. The Basecamp is connected to a Wi-Fi router (4) and it serves the data via a website on the local area network (5). The data can


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be viewed in a web browser on any device connected to the Wi-Fi router. Alternatively, the data can be sent to an external webhost for access anywhere. 2.1 Scout The Scout, which is shown in Figure 2, functions as the system’s sensory appendage. It's core functions included Arduino capability, low-power battery operation (longer than a month of operation on a cell-phone-sized battery), adaptable physical and wireless I/O for flexible sensor networks and minimal footprint and cost.

Figure 2. Fully operational Scout prototype board with Series 1 XBee radio. All the processing in the Scout is handled by an ATmega328 microcontroller [3]. The microcontroller was programmed using the Arduino programming language allowing for the use of readily available Arduino libraries. The microcontroller is running an Arduino Uno Pro bootloader to accommodate 3.3 V operation at 8 MHz. Data was collected from and commands issued to the sensors using the analog, digital, and RX/TX pins routed to headers. Data was sent to a receiver (Basecamp) using the integrated Series 1 XBee RF radio [4]. The circuit schematic and printed circuit board (PCB) layout for the Scout can be found in Appendix A. Appendix B contains example Arduino code and C++ libraries for the Scout sensor module utilizing the Rucksack TL (details below) to relay

temperature and light data to the Basecamp (also detailed below). Power for the Scout is provided by any 3.7-9 V DC source plugged into a 5.5-mm barrel jack. The voltage is regulated to 3.3 V with a Microchip Corporation low-dropout MCP1702 voltage regulator [5]. Using this voltage regulator, a standard 2.4 GHz XBee radio, and the low-power states enabled by the Scout C++ libraries shown in Appendix B, the Scout can operate for more than 90 days reporting data every 5 minutes when powered by a 3.7-V 2000-mAh lithium-polymer cell-phone battery. The Scout libraries allow the system to enter a low-power sleep state waking at arbitrarily defined intervals to take environmental data and relay this data back to the Basecamp. To further save power, the indicator LEDs on the Scout can be switched off. The more frequently data is reported, the less time the battery will last. By far the most power intensive task performed by the Scout is radio transmission. The XBee Series 1 radio alone pulls up to 60 mW of power when transmitting data. 2.2 Rucksack TL (Temperature / Light) The Scout makes several analog and digital input and output pins, a serial port (TX/ RX), and power and ground pins available to peripherals via headers. The Rucksack is a PCB shield for the Scout that plugs into these headers. It incorporates a temperature sensor and a light-to-frequency sensor. Switches are also integrated to turn these sensors on and off. The temperature sensor is a DS1820 digital sensor with 0.5o C accuracy [6]. This sensor communicates with the microcontroller using the one-wire protocol. The light sensor is the TAOS light-to-frequency converter, which produces an output square-wave function having a


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frequency that is proportional to the amount of light hitting its sensor [7]. An Arduino C++ library composed for the Rucksack includes methods that use the microcontroller's on-board comparator and 16-bit timer to measure the frequency of this output. Figures 3 and 4 show the Rucksack attached to the Scout. See Appendix A and B for the Rucksack TL’s circuit schematic, PCB layout, and example code for interacting with the light and temperature sensors.

Figure 3. A picture of the Scout with the Rucksack TL shield attached.

Figure 4. The Scout with the Rucksack attached for collection of light and temperature data. Keys are shown in the background for scale.

2.3 Basecamp The Basecamp unit shown in Figure 5 consisted of a Raspberry Pi Model B running the Raspian OS and a breakout board for the Raspberry Pi’s GPIO pins to accommodate an XBee radio for wireless serial communication with the Scout. A circuit schematic and PCB layout of this breakout board is shown in Appendix A. The purpose of Basecamp is to receive data from the Scout via an XBee radio, store the data in a database and serve the data over a local or wide area network. Preliminary work aimed to develop the Basecamp utilizing an Arduino Uno with a shield to accomplish Ethernet communication and a second shield to accommodate an XBee radio (not shown). However, the Arduino Uno has just 32 KB of flash memory for program storage. This small amount of memory made it impossible to accomplish all of the desired functionality including data storage, time-stamping of data, and the serving of stored data to clients via an Ethernet connection. In comparison to the Arduino Uno, the Raspberry Pi Model B device utilized for Basecamp is ideally suited to serve as a gateway between the Scout hardware and the software stack necessary to accomplish data logging and Ethernet communication. The Raspberry Pi Model B has an integrated Ethernet port in addition to 2 USB ports, 512 MB of RAM, and GPIO pins to interface with external hardware. A 4 GB SD card provided ample storage for data, and the 400 MHz SOC on which the system is based provided significantly more computing power than the Arduino Uno. Essentially, the Raspberry Pi is an extremely low cost and extremely modest PC running a Linux operating system, Raspian, which is a striped down version of the Debian Linux distribution that includes most of the software packages needed to operate the


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Basecamp. Some additional packages where installed to enable SSH and FTP communication with the Basecamp. Additionally, the PHP interpreter, Apache web server, and MySQL database server where installed. Package installation in Raspian is easily accomplished using the apt-get command, which accesses the Debian package repositories.

Figure 5. The Raspberry Pi with the major components of the Basecamp labeled. The Basecamp shield (purple) is shown attached to the Raspberry Pi’s GPIO pins to accommodate an XBee radio for communication with the Scout. The HDMI and USB ports are unnecessary once the Basecamp is configured and powered but utilized to accommodate a monitor, keyboard, and mouse for programming. Ethernet communication with the Basecamp is accomplished via the onboard Ethernet port. A Brown Marmorated Stink Bug (Halyomorpha halys) is shown for scale.

The Raspberry Pi’s GPIO pins provide access to a UART for serial communication. However, this UART is used by default as a debugging console, which must be disabled to make the UART available for communication with an XBee radio. A pair of Python libraries (PySerial and MySQLdb) were installed in order to give Python code access to the UART serial

buffer and the ability to send queries to a MySQL database.

The example Phython code shown in Appendix B shows how incoming data from the Scout was read out of the Raspberry Pi’s serial buffer using Python and the pySerial library and placed into a MySQL database using the MySQLdb library. A PHP webpage (also in Appendix B) was created so HTTP requests to the Basecamp from any web browser could retrieve data from the database and display a rudimentary list of the last ten data points (temperature and light) reported by the Scout. 3. CONCLUSIONS AND NEXT STEPS The purpose of the project described here was to develop a low-power, low-cost system for wireless environmental monitoring using open source hardware and software. The Scout wireless sensor modules needed to be small, have the flexibility to accommodate various types of environmental sensors, and operate using small batteries for weeks. The Basecamp data receiver needed to be able to accommodate an XBee radio to receive data from the Scout, parse this data, store the data in a database, and serve the data to a web browser when requested. The system that was developed met all of these requirements. Importantly, the entire system composed of the Scout, the Rucksack TL, and the Basecamp can be constructed at a total cost of about $100.

While the system described here solves many of the challenges of developing low-cost and low-power wireless sensor networks for environmental monitoring, several improvements are still needed to adapt the system for larger scale deployments. For example, software must be developed to allow multiple Scout sensors to simultaneously link to the Basecamp. In addition, future work could aim to


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generalize the Basecamp tools to accommodate other low-cost PC-like systems like the Intel Atom-based MinnowBoard Max. Future revisions to the Scout hardware should add ICSP headers to make programming and debugging easier. Finally, weatherproof enclosures for both the Basecamp and the Scout should be designed and fabricated. More specifically, enclosures that could be manufactured on-demand and at low cost using a consumer-grade 3D printer would be desirable. ACKNOWLEDGEMENT This work was supported by a NASA WV Space Grant Consortium Undergraduate Research Fellowship awarded to Jared Tomlin REFERENCES [1] “Arduino language reference”, Arduino SA 2014, arduino.cc/en/Reference/HomePage [2] “Raspberry Pi documentation”,

Raspberry Pi Foundation 2014, www.raspberrypi.org/documentation [3] “Atmel 8-bit AVR microcontroller with 4/8/16/32K bytes in-system programmable flash”, Atmel Corporation Data Sheet rev. 8161D-AVR-10/09, www.atmel.com/images/doc8161.pdf [4] XBee Series 1 RF Module, http://ftp1.digi.com/support/documentation/90000982_P.pdf [5] Microchip MCP1702 low-dropout voltage regulator, http://ww1.microchip.com/downloads/en/DeviceDoc/22008E.pdf [6] DS18S20 high-precision 1-wire digital thermometer, http://datasheets.maximintegrated.com/en/ds/DS18S20.pdf [7] TAOS light-to-frequency sensor, https://www.sparkfun.com/datasheets/Sensors/Imaging/TSL235R-LF.pdf


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APPENDIX A: SCHEMATICS AND PCB LAYOUTS Scout v1 – Schematic (top) and PCB Layout (bottom)


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Rucksack TL v1 – Schematic (top) and PCB Layout (bottom)


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Basecamp v1 – Schematic (top) and PCB Layout (bottom)


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APPENDIX B: EXAMPLE CODE Scout Arduino Code // Scout with Rucksack TL // Developed By: Jeffrey R. Groff // // Last Modified: 03/22/2014 // // License: This work is license under the Creative Commons // Attribution-NonCommercial-ShareAlike 4.0 International License // This work may be adapted and shared freely as long as derivative // work is shared alike. This work cannot be used for commercial // purposes. // // Note to Self: This code must be uploaded to a ATmega328 running // the Arduino Pro 3.3 V 8 MHz bootloader via a programmer #include <OneWire.h> #include <SoftwareSerial.h> #include "Scout.h" #include "Rucksack.h" #define XBeeRX 4 // XBee RX Pin #define XBeeTX 3 // XBee TX Pin #define MCUStatusPin 13 #define sleepIntervals 4 // about 8 seconds of sleep per interval #define XBeeSleepPin 6 // MCU pin connected to XBee Sleep Pin uint8_t DS_address[1][8]; // only one temp sensor on the bus float lightLevel = 0; float temp = 0; SoftwareSerial XBeeSerial(XBeeRX,XBeeTX); Scout myScout; Rucksack myRucksack(8); // (tempSensorPin) void setup(void) { myScout.StartWDTimer(); // start watchdog timer myScout.PWRSave(); // disable peripherals to save power pinMode(XBeeRX,INPUT); // set up XBee serial pins pinMode(XBeeTX,OUTPUT); pinMode(7,INPUT); // analog comparator inverting input


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} void loop(void) { myScout.MCUSleep(sleepIntervals); pinMode(MCUStatusPin,OUTPUT); digitalWrite(MCUStatusPin,HIGH); // turn on MCU status LED myRucksack.PWROn(); // turn on sensors myRucksack.search(DS_address[0]); // find temp sensor address myScout.XBeeWake(XBeeSleepPin); // wake up XBee radio XBeeSerial.begin(9600); // begin serial communication with XBee delay(200); // a short wait temp = myRucksack.MeasureTemp(DS_address[0]); // maybe with a longer // wait this first call won't be necessary delay(600); temp = myRucksack.MeasureTemp(DS_address[0]); // get temperature lightLevel = myRucksack.MeasureLight(); // get light level XBeeSerial.print(temp); // send data to basecamp via XBee XBeeSerial.print('\t'); XBeeSerial.println(lightLevel); delay(300); // wait for data to finish sending myScout.XBeeSleep(XBeeSleepPin); // put XBee to sleep myRucksack.PWROff(); // turn off sensors digitalWrite(MCUStatusPin,LOW); pinMode(MCUStatusPin,INPUT); // turn off MCU status LED } Scout Class Header File #ifndef SCOUT_H #define SCOUT_H // Scout with Rucksack TL // Developed By: Jeffrey R. Groff // // Last Modified: 03/22/2014 // // License: This work is license undr the Creative Commons // Attribution-NonCommercial-ShareAlike 4.0 International License // This work may be adapted and shared freely as long as derivative // work is shared alike. This work cannot be used for commercial // purposes. class Scout


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{ private: void MCUSleepNow(); public: Scout(); // constructor void StartWDTimer(); void MCUSleep(int nIntervals); void XBeeWake(int XBeeSleepPin); void XBeeSleep(int XBeeSleepPin); void PWRSave(); }; #endif Scout Class C++ Code #include "Scout.h" #include <Arduino.h> #include <avr/sleep.h> volatile int watchdog_counter; Scout::Scout() { watchdog_counter = 0; } void Scout::StartWDTimer() { MCUSR = MCUSR & B11110111; WDTCSR = WDTCSR | B00011000; WDTCSR = B00100001; WDTCSR = WDTCSR | B01000000; MCUSR = MCUSR & B11110111; } void Scout::MCUSleepNow() { set_sleep_mode(SLEEP_MODE_PWR_DOWN); sleep_enable(); sleep_mode(); sleep_disable(); } void Scout::XBeeWake(int XBeeSleepPin = 6) {


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pinMode(XBeeSleepPin, OUTPUT); digitalWrite(XBeeSleepPin, LOW); // wake up XBee delay(500); // wait for XBee to wake up } void Scout::XBeeSleep(int XBeeSleepPin = 6) { pinMode(XBeeSleepPin, INPUT); //high-impedence state digitalWrite(XBeeSleepPin, HIGH); // put XBee to sleep delay(500); // wait for XBee to go to sleep } ISR(WDT_vect) { watchdog_counter++; // increment wd_count } void Scout::MCUSleep(int nIntervals) { while (watchdog_counter < nIntervals) { MCUSleepNow(); } watchdog_counter = 0; } void Scout::PWRSave() { ADCSRA = ADCSRA & B01111111; // diable ADC (bit 7 = zero) DIDR0 = DIDR0 | B00111111; // disable digital input buffers on analog pins // (bits 0-5 = one) } Rucksack Class Header File #ifndef RUCKSACK_H #define RUCKSACK_H // Scout with Rucksack TL // Developed By: Jeffrey R. Groff // // Last Modified: 03/22/2014 // // License: This work is license undr the Creative Commons // Attribution-NonCommercial-ShareAlike 4.0 International License // This work may be adapted and shared freely as long as derivative


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// work is shared alike. This work cannot be used for commercial // purposes. #include <OneWire.h> class Rucksack: public OneWire { private: public: Rucksack(int OWPin); // constructor void PWROn(); void PWROff(); float MeasureLight(); float MeasureTemp(uint8_t *address); }; #endif Rucksack Class C++ Code #include "Rucksack.h" #include <Arduino.h> #include <OneWire.h> volatile unsigned int overflows = 0; volatile unsigned long edges = 0; volatile unsigned long tstart = 0; volatile unsigned long tstop = 0; volatile unsigned long tnow = 0; int cycles = 100; Rucksack::Rucksack(int OWPin):OneWire(OWPin) { } void Rucksack::PWROn() { pinMode(9,OUTPUT); // turn on power to sensors digitalWrite(9,HIGH); delay(300); // wait a short time for power to stabilize } void Rucksack::PWROff() {


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digitalWrite(9,LOW); pinMode(9,INPUT); delay(300); } float Rucksack::MeasureLight() { edges = 0; ACSR = ACSR | B01000010; // enable analog comparator interrupt (bit 3) // on failing edge (bit 1) using internal bandgap voltage reference (bit 6) delay(5); // wait for bandgap voltage to stabalize overflows = 0; TCCR1A = B00000000; // set timer/counter 1 in normal mode (counts to 0xFFFF then repeats) TCCR1B = B10000000; // enable input capture on falling edge (bit 6 = 0) and // noise canceler (bit 7 = 1) TIMSK1 = TIMSK1 | B00000001; // enables timer1 input capture interrupt (bit 5) // and/or overflow interrupt (bit 0) TCCR1B = TCCR1B | B00000001; // start counter with no prescaler ACSR = ACSR | B00001000; // enable analog comparator interrupt (bit 3) while (edges < (cycles+1)) { // do nothing } float frequency = (float)8000000*(float)cycles/(float)(tstop - tstart); return frequency; } ISR(TIMER1_OVF_vect) { overflows += 1; } ISR(ANALOG_COMP_vect) { tnow = TCNT1; edges += 1; if (edges == 1) { tstart = overflows*65536 + tnow; } else if (edges == cycles + 1) { tstop = overflows*65536 + tnow;


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// turn off the clock and comparator ACSR = 0; TCCR1B = 0; } } float Rucksack::MeasureTemp(uint8_t *address) { //returns the temperature from one DS18S20 in DEG Celsius // code from bildr.org/2011/07/ds18b20-arduino/ to handle // one-wire protocol communication with DS18S20 temperature sensors byte data[12]; int x; this->reset(); this->select(address); this->write(0x44,1); // start conversation with DS18S20 at address selected this->reset(); this->select(address); this->write(0xBE,1); // read scratchpad for(x=0;x<9;x++) { // we need 9 bytes data[x] = this->read(); } byte MSB = data[1]; // most significant bit byte LSB = data[0]; // least significant bit float tempRead = ((MSB << 8) | LSB); //using two's compliment float TemperatureSum = tempRead / 16; return TemperatureSum; } Basecamp Python Code # Developed By: Jeffrey R. Groff & Jared Tomlin # Last Modified: 03/22/2014 # License: This work is license under the Creative Commons # Attribution-NonCommercial-ShareAlike 4.0 International License # This work may be adapted and shared freely as long as derivative # work is shared alike. This work cannot be used for commercial # purposes.


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#Must have MySQLdb, pyserial installed import MySQLdb import serial import sys import re import time try: ser = serial.Serial("/dev/ttyAMA0", 9600, timeout=3) # open first serial port print ser.portstr # check which port was really if (ser.isOpen() == False): ser.open() while 1: #always check if ser.inWaiting() > 0: iWait = ser.read(ser.inWaiting()) #The longer you let it rest between checking will #allow for more information to hit the buffer, #incase its getting cut off time.sleep(1) iWait += ser.read(ser.inWaiting()) break #when the bufer is read, print print iWait d1,d2 = iWait.split('\t') print d1 print d2 #close the serial port ser.close() #connect to database conn = MySQLdb.connect (host = "localhost", user = "pi", passwd = "raspberry", db = "basecamp") cursor = conn.cursor () #insert data ti table cursor.execute("INSERT INTO sData VALUES(NULL,"+d1+","+d2+")" ) conn.commit() cursor.close () conn.close () #Look for SQL error for debugging except MySQLdb.Error, e: print "Error %d: %s" % (e.args[0], e.args[1])


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Basecamp PHP Code <?php # Developed By: Jeffrey R. Groff & Jared Tomlin # Last Modified: 03/22/2014 # License: This work is license under the Creative Commons # Attribution-NonCommercial-ShareAlike 4.0 International License # This work may be adapted and shared freely as long as derivative # work is shared alike. This work cannot be used for commercial # purposes. #connect to db include 'dbConnect.php'; echo "<h1>Basecamp v1.0 </h1>"; #get data $res = $mysqli->query("SELECT * FROM `sData`"); echo "<h3>sData</h3>"; #Display data for ($row_no = $res->num_rows - 1; $row_no >= 0; $row_no--) { $res->data_seek($row_no); $row = $res->fetch_assoc(); echo "<p>".$row['data1']." | ".$row['data2']."</p>"; } ?>


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Transcript of The Shepherd University Journal of Undergraduate … · E 10 µL 10 µL D 0.3125 mg/mL Table 3:...