pursue undergraduate research journalpursue issn 2473-6201 |(2019) volume 2 (1) p. 5 economic...

PURSUE ISSN 2473-6201 |(2019) Volume 2 (1) p. 1

PURSUE

Undergraduate Research JournalVolume 2 (Issue 1) 2019

Aim and Scope

Pursue (Print) ISSN 2473-6201

The scholarly journal, “PURSUE”, provides undergraduates an avenue to publish their original research abstracts and articles in the following areas: (but not limited to) psychology, sociology, biology, chemistry, physics, engineering, computer science, mathematics, humanities, agriculture, architecture, business, and education.

The original research articles included in this journal are peer-reviewed and selected by the journal’s Editorial Board. The review process allows undergraduate researchers to receive feedback from notable scientists in their field of study and teach them about the publication process. Publication of their work will not only inform the scientific community; it will also impact the greater society.

The journal is housed at Prairie View A&M University, a Historically Black University, and is available to all young scientists conducting research. This journal also serves as a means for faculty to extend knowledge beyond the classroom and encourage their students to perform and publish quality research. All undergraduate research is produced in conjunction with a faculty mentor and is peer-reviewed. The journal is open to undergraduates from all colleges and universities.


PURSUE

Undergraduate Research JournalVolume 2 (Issue 1) 2019

Table of Contents

A Note from the Executive Editor, Yolander R. Youngblood, Ph.D.5 Economic Potential of Okra Cultivation for Limited Resource Farmers Phillip Harris, Eric Obeng, Aruna Weerasooriya, and Peter A.Y. Ampim

14 Spatial Variability of Manganese Oxide (MnOx) in Two Soilscapes: Upland-Lowland, and Riparian Buffer-Wetland Boundary-Wetland

Benjamin A. Onweni, Richard W. Griffin, PhD, Robert J.F. Thomas, Edward K. Timms, Javon D. Polk, and Annette A. James

28 The Importance of Properly Modeling the Hydrogen Bond in Histidine

Falonne Moumbogno Tchodimo, Guoquan Zhou, Hua- Jun Fan

39 About the Authors

42 PURSUE Editorial Board

Inside Back cover

Acknowledgements

Additional Review Editors


A NOTE FROM THE EXECUTIVE EDITOR

It is with great pleasure that I present to you Volume 2 Issue 1 of PURSUE. It has truly been a labor of love since we started this venture in 2016. This venture gives undergraduate students a platform to present their research. Each article in this issue has been carefully reviewed and edited by scientists and academic faculty in the field. This issue includes insightful articles in the areas of agriculture and chemistry. These undergraduates are to be commended for their attention to detail and their persistence. These students have great futures ahead of them because they worked with great Research Mentors who took the time to guide them along the way.

This issue was produced with the help of several people. I want to thank each of them. First, let me thank Drs. Gehring, Moore and Thompson. They managed and helped ensure the integrity of the process at each step. Next, I have to thank our outside Review Editors and the Editorial Board. They volunteered countless hours to make this issue happen. Drs. Foster, Frizell and Garner were especially important to the proofing process. Lastly, I want to thank Prairie View A & M University’s Office for Academic Affairs and Dr. James Palmer, for the generous use of office resources.

Happy Scientific Reading!

Yolander R. Youngblood, Ph.D.

Executive Editor

PURSUE


Economic Potential of Okra Cultivation for Limited Resource Farmers

Phillip Harris1, Eric Obeng2, Aruna Weerasooriya2, and Peter A.Y. Ampim1, 2

1Department of Agriculture Nutrition and Human Ecology, College of Agriculture and Human Sciences, Prairie View A&M

University

2Cooperative Agricultural Research Center, College of Agriculture and Human Sciences, Prairie View A&M University

Corresponding Author: Peter A.Y. Ampim; Department of Agriculture, Nutrition and Human Ecology, Prairie View A&M University, P.O. Box 519; MS 2008, Prairie View, TX 77446;

[email protected]

Abstract

Background: Okra (Abelmoschus esculentus L. Moench), is an economically important vegetable crop with a potential to increase farm incomes of small producers. This is because okra is popular, easy to grow, and valuable with average retail prices of up to $7.07/kg. In Texas, research has shown that diversification of farm operations boosts income and farm sustainability. Hence, exposing farmers to economically important crops that are not typically grown is necessary. Production success is linked to crop variety choices. As result, the objective of this study was to evaluate the performance of multiple varieties of okra (Red Burgundy, Jambalaya, Zarah and Hybrid Green Sparkler) to determine the variety with the highest yield and profitability. We hypothesized that yield and revenue will differ among the okra varieties. Methods: In this study, each okra variety was grown in replicates on three plots. The plants were established at a density of 16,600 plants ha-1 using plasticulture and drip irrigation. N and K were


supplied at 33.60 kgha-1 and 11.2 kgha-1 respectively according to soil test recommendations. The okra was picked every other day to prevent development of undesirable pods. Results and Conclusion: When comparing the number of pods per plant, Red Burgundy had a greater yield as compared to Jambalaya (p < 0.05), but the yield was similar to the Zarah and Hybrid Green Sparkler varieties. Similarly, Zarah had a greater yield as compared to Jambalaya but similar to Hybrid Green Sparkler. In terms of pod weight per plant, Red Burgundy’s weight was statistically greater than Jambalaya but similar to the other varieties. Estimated revenue per hectare for Red Burgundy, Zarah, Jambalaya, Hybrid Green Sparkler were $9,565.00, $7,018.20, $6,290.60 and $6,020.00, respectively. These represent 58.9%, 16.6% and 4.5% revenue increase over the green hybrid sparkler variety. Frozen okra revenue estimates followed the same trend. These findings suggests that Red Burgundy provides the highest revenue potential in terms of production and economics and would be the best variety for farmers in East Texas to grow.

Introduction

Okra (Abelmoschus esculentus L. Moench), is a nutritious and economically important vegetable that is typically produced in tropical and subtropical climates (Kochhar, 1986, Raemaekers, 2001; Iqbal et al., 2008; Philip et al., 2010). Other names of okra, which is an annual plant, include “lady’s finger” or “gumbo” (Tiwari et al., 1998; Sabitha et al., 2011). It belongs to the mallow family, which also includes the hibiscus and cotton plants.

Okra contains several minerals and vitamins in addition to carbohydrates, fiber, sugar and fat. These include calcium, magnesium, phosphorus, potassium, iron, sodium, zinc, vitamins A, B (B1, B2, B3, B6, B9), C and K (USDA Nutrient Database, 2016). In retail markets, okra is expensive, yet popular, particularly in the southern United States, Africa, Asia and the Caribbean (Calisir et al., 2005; Adelakun et al., 2009; Sengkhamparn et al., 2009).


Thus, okra has a high value with average retail market prices ranging between $3.45/kg in the frozen form to $7.07/kg when sold fresh (USDA ERS, 2016). It can also be pickled or canned. It is relatively easy to grow and has high yields (Franklin et al., 2015). These attributes make okra a good crop for small producers to consider for cultivation, especially if they are considering diversifying their operations. This is especially important in Texas where research has shown that farm diversification is good for sustaining farms (Barbieri and Mahoney, 2009).

Choosing the right variety of a crop is important for a famer’s production success. Though there is information on popular okra varieties, there is scant material on the specialty varieties. Production and economic information on specialty and non-specialty varieties will help farmers to select varieties based on their production and economic goals. Hence, the objective of this study was to evaluate the performance of multiple varieties of okra to determine the variety with the highest yield and profitability. We hypothesized that yield and revenue will differ among the okra varieties.

Materials and Methods

This study was carried out on raised beds at the Agricultural Research Farm of Prairie View A&M University in the first week of June 2017. The four varieties of okra used in this study were Red Burgundy, Zarah, Hybrid Green Sparkler, and Jambalaya. Seeds of these okra varieties were purchased from various commercial seed companies. Red Burgundy was bought from Seed Savers Exchange (Decorah, IA); Zarah seeds were attained from Stoke Seeds (St. Catharines, ON); and Hybrid Sparkler and Jambalaya seeds were purchased from Evergreen Seeds (Bloomington, IL) and Johnny’s Selected Seeds (Winslow, ME), respectively. The beds were covered with plastic mulch to control weeds and conserve water. Watering was done using drip irrigation, which was laid in the beds.


The okra seeds were planted 0.6 m apart in the row and about 1.3 cm deep in a completely randomized design with three replications. The okra varieties were randomly assigned to plots in rows. Nutrient needs were supplied based on pre-plant soil test results using calcium (15.5-0-0) and potassium (13-0-46) nitrate fertilizers. Irrigation was supplied when needed to supplement natural rainfall. Harvesting commenced for Red Burgundy, Jambalaya, Zarah, and Hybrid Green Sparkler at 55, 55, 42, and 50 days after planting, respectively. Fruits were picked every other day (Figure 1). The number and weight (g) of okra produced per replication was recorded at each harvest. Analysis of variance (ANOVA) was done using JMP software version 11 (SAS Institute Inc., Cary, NC). Means were compared using the all pairs Tukey-Kramer HSD method, p values of < 0.05 were considered significant.

Figure 1. Photo of each variety of harvested okra.


Results and Discussion

Fresh pods per plant and pod fresh weight

When comparing the number of fresh pods produced per plant, Red Burgundy produced the most. However, Red Burgundy, Zarah and Hybrid Green Sparkler all had similar production rates (3.1-4.2) and were 50% greater than Jambalaya (p=0.003) (Table 1). Similarly, differences in fresh pod weight per plant was recorded, and Jambalaya pods were 65% less than Red Burgundy. Jambalaya was similar in weight to Zarah and Hybrid Green Sparkler (p<0.0001) (Table 1).

Table 1. Fresh pods per plant and pod weight per plant for four okra varieties

Okra Variety Fresh Pods/Plant†

Fresh Pod Weight/Plant (g)

Red Burgundy 4.2a 90.5a

Zarah 4.0a 62.3a,b

Hybrid Green Sparkler

3.1a,b 56.4a,b

Jambalaya 2.0b 31.9b

p-value 0.0033 <0.0001†Means within column followed by same letter(s) are not significantly

different.


Figure 2. Harvested weight per plant and estimated weight per hectare of okra varieties.

Harvest weight per plant was greatest for Red Burgundy (0.0905 kg), followed by Zarah and Hybrid Green Sparkler. Jambalaya had the least harvest weight per plant compared to the other varieties (Figure 2). Estimated weight per hectare for Red Burgundy was 3-fold greater than Jambalaya. On the other hand, estimated weight per hectare for Zarah and Hybrid Green Sparkler were 2-fold greater than Jambalaya (Fig. 2).

Fig. 3. Estimated potential revenue from fresh and frozen okra. Revenue was estimated based on four weeks of harvest and a planting density of 16, 600 plants/ha. Prices used per kg of fresh and frozen okra were $7.07 and $3.45 respectively.


From an economic perspective, revenue in dollars per hectare for fresh okra was greatest for Red Burgundy, followed by Zarah and Hybrid Green Sparkler (Figure 3). On the other hand, Jambalaya had the least revenue potential (Figure 3). Potential revenue from frozen okra followed a similar trend. Red Burgundy had the greatest revenue from frozen okra, followed by Zarah and Hybrid Green Sparkler (Figure 3). Jambalaya had the least revenue when frozen compared to the other okra varieties (Figure 3).

Conclusions

Red Burgundy was the highest yielding and most profitable variety in this study. Zarah and Hybrid Green Sparkler, the specialty varieties, produced yields comparable to Red Burgundy which is a popular variety. Although potential earnings from growing the varieties vary by variety, growing okra can increase the revenue of farmers steadily over a long period of time. This is due to the fact that okra can be harvested daily or every other day over a period of 2-3 months. Based on our findings, we accept the hypothesis that yield and revenue were different for the okra varieties evaluated.

Future studies will focus on understanding the nutrient requirements of okra under East Texas growing conditions and harvesting over the entire season since this current study examined yield data collected only over a four week period.

Acknowledgements

This study was supported with USDA-NIFA 1890 Evans-Allen Formula funds.

References

Adelakun, O.E., O.J. Oyelade, B.I.O. Ade-Omowaye, I.A. Adeyemi, and M. Van de Venter. 2009. Chemical composition and the antioxidative properties of Nigerian Okra Seed (Abelmoschus esculentus Moench) Flour. Food


Chem. Toxicol. 47:1123-1126.

Barbieri, C. and E. Mahoney. 2009. Why is diversification an attractive adjustment strategy? J. of Rural Studies 25:58-66.

Çalışır, S., M. Özcan, H. Hacıseferoğulları, and M.U. Yıldız. 2005. A study on some physico-chemical properties of Turkey okra (Hibiscus esculenta L.) seeds. J. Food Eng. 68:73-78.

Franklin M.A., A. Suzuki and H. Hongu. 2015. Okra. AZ1649. The University of Arizona Cooperative Extension Service. pp.73-78.

Iqbal, J., H. Mansoor, A. Muhammad, T. Shahbaz, and A. Amjad. 2008. Screening of okra genotypes against jassid, Amrasca biguttula biguttula (Ishida) (Homoptera: Cicadellidae). Pak. J. of Agri. Sci. 45:448-451.

Kochar, S.L. 1986. Tropical Crops. A text book of economic botany. pp. 263-264. Macmillan Indian Ltd.

Philip, C.B., A.A. Sajo, and K.N. Futuless. 2010. Effect of spacing and NPK fertilizer on the yield and yield components of okra (Abelmoschus esculentus L.) in Mubi, Adamawa State, Nigeria. J. Agron. 9:131-134.

Raemaekers, R.H. 2001. Crop Production in Tropical Africa. 1st Edn., DGIC, Brussels, Belgium. pp.1-1540.

Sabitha, V., S. Ramachandran, K.R. Naveen, and K. Panneerselvam. 2011. Antidiabetic and antihyperlipidemic potential of Abelmoschus esculentus (L.) Moench. in streptozotocin-induced diabetic rats. J. Pharm. Bioallied Sci. 3 pp.397.


Sengkhamparn, N., R. Verhoef, H.A. Schols, T. Sajjaanantakul, and A.G. Voragen. 2009. Characterisation of cell wall polysaccharides from okra (Abelmoschus esculentus (L.) Moench). Carbohydr. Res. 344:1824-1832.

Tiwari, K.N., P.K. Mal, R.M. Singh, and A. Chattopadhyay. 1998. Response of okra (Abelmoschus esculentus (L.) Moench.) to drip irrigation under mulch and non-mulch conditions. Agric. Water Manag. 38:91-102.

USDA (2016). Okra- Average retail price per pound and per cup equivalent. USDA Economic Research Service.

USDA (2016). USDA National Nutrient database for standard reference.


Spatial Variability of Manganese Oxide in Two Soilscapes: Upland-Lowland, and Riparian Buffer-Wetland Boundary-

Wetland

Benjamin A. Onweni,1 Richard W. Griffin, PhD,1 Robert J.F. Thomas,1 Edward K. Timms,1 Javon D. Polk,1 and Annette A.

James, PhD1

1Department of Agriculture, Nutrition and Human Ecology, Cooperative Agricultural Research Center, Prairie View A&M

University

Corresponding Author: Richard W. Griffin, Ph.D., Department of Agriculture, Nutrition and Human Ecology, Cooperative

Agricultural Research Center, Prairie View A&M University, PO BOX 519: MS 2008, Prairie View, TX 77446; Phone: (936)

261-5039; Email: [email protected]

Abstract

Background: This research project describes the development of a quantitative measurement methodology to determine the concentration of manganese oxide (MnOx) in t w o soilscape positions (Upland- Lowland and Riparian Buffer-Wetland Boundary-Wetland). Methods: A reaction between the MnOx

in the soil sample and hydrogen peroxide (H2O2) was initiated to determine the level of MnOx reactivity in the soil sample. Data was collected from four sites on Soilscape 1 (Upland, Lowland, and two sites between the Uplands and Lowlands); within each site, five soil profile depths and three sample replicates were measured which comprised a total of 60 samples. Additionally, data was collected from three sites on Soilscape 2 (Riparian Buffer-Wetland Boundary-Wetland), within each site, three soil profile depths and three sample replicates were measured which comprised a total of 27 samples. Measurements were collected and revalidated to assess the accuracy of the measurement


protocol. Results: Analysis of data collected from the surface layers in Soilscape 1 indicated that the Lowland (Site 4) had the highest level of MnOx followed by Site 3 with the lowest value occurring at Site 2. A follow up, revalidation study of three of the four sites from the surface layers on Soilscape 1 indicated that the Lowland (Site 4) had the highest level followed by the Midslope (Site 3) with the lowest value occurring at the Upland (Site 1). Therefore, the revalidation study results matched two of the three sites from the initial study. Correspondingly, the data collected from the three sites from the surface layers on Soilscape 2 indicated that the Wetland Boundary had the highest level followed by Riparian Buffer with the lowest value occurring at Wetland. The revalidation study results matched the initial study for each of the three sites from the surface layers on Soilscape 2, which indicated that the Wetland Boundary had the highest level followed by the Riparian Buffer with the lowest value occurring at the Wetland. Conclusion: The results of this study can be used to easily determine the spatial variability o f MnOx levels in soilscapes that range from Upland-Lowland and Riparian Buffer-Wetland Boundary-Wetland, and the movement of soluble MnOx ions within soilscapes by mass flow and/or diffusion processes. In soils with adequate levels of MnOx ions, the use of this methodology can assist in the delineation of the wetland boundary, which has both an economic and land-use importance to society, because of the importance of the ecological functions of wetland ecosystems.

Keywords: Wetlands, Wetland Boundary, Soilscapes, Manganese Oxide (MnOx), Hydrogen Peroxide (H2O2)

Introduction

A wetland is an area that has hydrophytic vegetation, hydric soils, and wetland hydrology, as noted in the National Food Security Act Manual and the 1987 Corps of Engineers


wetlands delineation manual (Environmental Laboratory, 1987). Wetlands can be either seasonal or permanently saturated and they represent one of the world’s most crucial ecosystems that are threatened by human development activities (Mitsch and Gosselink, 2015). On a national basis, it is estimated that around 53% of wetlands present at the time of European settlement in the early 1600s have since been lost from the conterminous United States (WARPT, 2010). Wetlands provide significant economic, social, and cultural benefits, since they are important for primary products, such as timber and fish, and they support recreational and tourist activities. Also, wetlands help to reduce the impacts from storm damage and flooding, maintain good water quality in rivers, recharge groundwater, store carbon, help to stabilize climatic conditions, and control pests (NSW, 2013). The process of defining the boundary of a wetland ecosystem is defined as wetland delineation that aims to provide a legally defensible line that officially outlines the wetland from the non-wetland area on an examined landscape (Environmental Laboratory, 1987; Tiner, 2017). Hydric soils and wetland hydrology are included in the delineation of a wetland with the criteria for their determination being included in the Field Indicators of Hydric Soils in the United States (United States Department of Agriculture, Natural Resources Conservation Service, 2017).

In general, the most common soil features used to identify hydric soils are based on iron (Fe) which imparts red, yellow, and brown colors to well and moderately well drained soils (Soil Science Division Staff, 2017). When soils are waterlogged for an extended time period, microbial respiration can lead to anaerobic conditions in which iron and manganese (Mn) oxides are reduced to the soluble forms that diffuse from the soil matrix to zones of oxidation. The ions are then oxidized as well as precipitated in the form of redoximorphic features (United States Department of Agriculture, Natural Resources Conservation Service, 2017). The redox (Eh) ladder indicates that Mn oxides are used as an


alternate electron acceptor by microbes before using Fe oxides, therefore Mn becomes reduced and subsequently mobile in the soil solution before reduced Fe (ferrous) ions (Sylvia et al., 1999; Coyne, 1999; Gambrell, 1996). However, in the dry down phase when the waterlogged soil has experienced an extended period of reduction, upon return of aerobic conditions, the ferrous Fe ions will oxidize before the manganous Mn ions which migrate along an extended diffusion gradient away from the source pool (wetland or waterlogged zone).

Iron-based soil features have been more readily accepted as part of the methods for delineation of wetlands, but the presence of Mn should be considered as important based on its role in biogeochemical cycling within seasonally and permanently saturated ecosystems that possess hydric soils. Soils that do not reflect typical redoximorphic features that are associated with the typical hydric soil morphological characteristics are called “Problem Soils” (United States Department of Agriculture, Natural Resources Conservation Service, 2017). Soil conditions such as red parent material, low organic carbon levels, and high pH levels are associated with calcareous material or saline conditions; as a result, these soils are difficult to define as hydric soils while also creating an issue when attempting to categorize the landscape as a wetland. Presently, the presence of ferrous iron in the soil is determined by using the photochemical dye, alpha alpha dipyridyl, but the majority of the tests are qualitative based on the positive reaction producing a faint to bright red color with increasing concentrations of reduced iron in the soil sample (United States Department of Agriculture, Natural Resources Conservation Service, 2017). This test can provide a quantitative measure of ferrous iron in solution when measured against a ferrous iron standard with selected concentration levels and a corresponding gradient in color development. A quantitative test of ferrous iron is beneficial when comparing soils from different geologic materials or the soils’ potential to supply a concentrated zone of iron oxides


that tends to occur in the wetland boundary.

The presence of Mn oxide in a soil sample can be measured qualitatively using the reaction with hydrogen peroxide which produces an exothermic reaction accompanied by fizzing, bubbles, and potentially a smoke plume as hydrogen gas is evolved from the reaction. A quantitative measure of Mn oxide is beneficial as a tool in examining the movement of soluble ions and the precipitation in concentrated zones on seasonally wet and wetland soilscapes that exhibit different hydrodynamic conditions. Herein, a novel method (GOTTP) is described; the authors hypothesize that this method will accurately and consistently measure manganese oxide (MnOx) by measuring the reaction between MnOx and hydrogen peroxide (H2O2). This project sought to identify patterns of movement and concentration of MnOx in two soil ecosystems.

In theory, Site 4 should have the highest MnOx reactivity level, because the greatest amount of manganese should be precipitated in the landscape position that is farthest from the source point (Site 1) in saturated periods and the flow through zone (Sites 2 and 3) during seasonally wet periods. The Wetland Boundary (WB) should have the highest MnOx reactivity level, because the greatest amount of manganese should be precipitated in the landscape position that is between the source pool (Wetland, WL) in saturated periods and the flow through zone (Riparian Buffer, RB) during seasonally wet periods. The investigators hypothesized that the level of MnOx reactivity is the greatest at the Lowland sites as compared with the Upland sites; further, the level of MnOx reactivity is higher at the WB sites as compared with the WL sites. The results of this study can be used to increase the pool of scientific knowledge gathered from this new quantitative method that may contribute to determination of hydric soils as part of the wetland delineation process.


Materials and Methods

Site Selection

On Soilscape One, four sites were selected which represented Uplands (Sites 1 and 2) and Lowlands (Sites 3 and 4) landscape positions. Soil samples were collected from five depths within each soil profile from the sites. Sample 1 was collected from a depth of 0-2 inches, Sample 2 was collected from a depth of 2-4 inches, Sample 3 was collected from a depth of 4-6 inches, Sample 4 was collected from a depth of 6-8 inches, while Sample 5 was collected from a depth of 8-10 inches. Soil samples were packaged in plastic bags and transported to the laboratory for testing. The second trial was conducted a separate scientist for revalidation, three sites were selected which represented Upland (Site 1), MidSlope (Site 3), and Lowland (Site 4) landscape positions. Soil samples were tested from the same depths as Samples 1-3 of the first trial.

On Soilscape Two, three sites were selected which represented the Wetland (Site 1), Wetland Boundary (Site 2) and Riparian Buffer (Site 3) landscape positions. Soil samples were collected and packaged from 3 depths: 0-2 inches, 5-7 inches, and 10-12 inches. As before, soil samples were packaged and transported to the laboratory for testing. The second trial was conducted by a separate scientist but following the same protocol.

Method

The method used to measure MnOx in the study is novel, so the authors have taken the liberty to use the initials of their last names to label the method GOTTP, in reference to Griffin, Onweni, Thomas, Timms, and Polk.

GOTTP Protocol for Determining Soil MnOx Levels


1) Assemble a standard metal lab stand with a vertical post for attachment of a vertical clamp used to hold the digital timer.

2) Weigh approximately 2 grams of soil sample in an aluminum weighing boat using an electronic balance.

3) Place the weighed soil in a 15 mL graduated conical centrifuge tube with 0.5mL serial measurement indices. The soil should occupy approximately 2 mL volume within the tube.

4) Record initial level of the solution in the Centrifuge Tube (Time 0:00), then immediately start the timer

5) Using a syringe, add 2 mL of 3% hydrogen peroxide to the soil sample in the centrifuge tube.

6) Record level of the solution after each 15-second interval for the complete duration of the monitoring cycle (total of 7 minutes).

7) Repeat method for each additional sample

Experimental Design and Data Analysis

On Soilscape One on the first trial, 3 replicate soil samples were tested from each of the 4 sites and 5 depths that represented a grand total of 60 samples. Tests were conducted on samples and the results from the data that was gathered were used to compare the sites using descriptive and inferential statistical analyses. The statistical analyses were used to determine the level of MnOx reactivity that should theoretically be greatest at the Lowland sites as compared with the Upland sites. For the second trial, 3 replicate soil samples were tested from 3 sites and 3 depths that represented a grand total of 27 samples. A smaller number of samples were measured due to elimination of one of the intermediate sites.



Results gathered from the Soilscape One, indicated that Site 4 had a grand average MnOx reactivity level of 3.90cc that was the highest compared to the other sites (Table 1). These results corresponded with the hypothesis that Site 4 should have the highest MnOx reactivity level. Using a geographic site location analysis, the data indicated that the highest MnOx level, 5.94cc, occurred at the surface of Site 4 (4.1), which illustrated that the diffusion of manganese proceeded from the source point (Site 1) to the Site 4 at which point manganese ions precipitated within the soil matrix. Site 3 indicated an elevated MnOx level (5.38cc) at the surface (3.1), which corresponded with this site location serving as a flow through and discharge zone for soluble Mn ions during alternating wet-dry conditions. Site 1 had elevated levels (4.55cc and 4.03cc) in the surface (1.1 and 1.2) due to the naturally occurring MnOx nodules that migrate to the surface of the sandy soil during the natural weathering and erosion processes on this landscape. Site 2 is clearly the flow through zone based on the lower MnOx levels (3.13-3.43cc) that were recorded in the surface (Depth 1) and lower surface (Depth 2) sampling zones. This site provided a clear indication of the dynamic nature of the MnOx in these soils that receive an average of 44 inches of rainfall per year which promotes ample opportunity for redox processes to drive the dissolution (depletion) and precipitation (concentration) of mineral oxides.


Table 1 Comparison of average manganese oxide reactivity levels (cc) on Soilscape One at Sites 1, 2, 3, and 4 are presented by depth and averaged by site. The color of the cell indicates the level of MnOx.

Results gathered from the second iteration of experiments on Soilscape One indicated that Lowland (Site 4) had a grand average MnOx reactivity level of 5.01cc that was the highest compared to the other sites (Table 2). These results matched, with a variance of 0.61, the initial project work which indicated that the testing procedure can produce repeatable results that may not produce the same numerical data, but the trends as related to the null and research hypotheses remained intact.

Table 1 Comparison of average manganese oxide reactivity levels (cc) on Soilscape One at Sites 1, 2, 3, and 4 are presented by depth and averaged by site. The color of the cell indicates the level of MnOx.

Depth Site 1 Site 2 Site 3 Site 4 Scale:

1 4.55 3.37 5.38 5.94 MnOx Level 0

2 4.03 3.13 4.26 3.64 MnOx Level 1

3 3.86 3.39 3.15 3.56 MnOx Level 2

4 3.40 3.36 3.20 3.13 MnOx Level 3

5 3.17 3.43 3.28 3.29 MnOx Level 4

Average 3.80 3.34 3.85 3.90 MnOx Level 5


Table 2 Comparison of average manganese oxide reactivity levels on Soilscape One at Upland (Site 1), MidSlope (Site 3), and Lowland (Site 4) are presented by depth and averaged by site. The color of the cell indicates the level of MnOx.

The geographic site location analysis data indicated that the highest MnOx level occurred at the surface of the Lowland (Site 4), MidSlope (Site 3) data indicated an elevated MnOx level at the surface, and the lowest values occurred at (Upland) Site 1. The Lowland (Site 4) surface had a level of 6.25cc, which was the highest level recorded during this monitoring cycle, also the value was higher than the initial testing value of 5.94cc.

Results gathered from the Soilscape Two indicated that the WB had a grand average MnOx reactivity level of 3.87cc which

Table 2 Comparison of average manganese oxide reactivity levels on Soilscape One at Upland (Site 1), MidSlope (Site 3), and Lowland (Site 4) are presented by depth and averaged by site. The color of the cell indicates the level of MnOx.

Depth Upland (Site 1)

MidSlope (Site 3)

Lowland (Site 4)

Scale:

1 4.43 4.81 6.25

MnOx Level 3

2 4.04 3.90 4.09

MnOx Level 4

3 5.01 3.00 4.68

MnOx Level 5

Average 4.49 3.90 5.01

MnOx Level 6


was higher than the RB level of 3.64cc and the WL level of 3.07cc (Table 3). These results corresponded with the hypothesis that WB should have the highest MnOx reactivity level. Using a geographic site location analysis, the data indicated that the highest MnOx level occurred at the surface of WB with a value of 5.26cc, which illustrated that the diffusion of manganese proceeded from the source pool to the WB at which point manganese ions precipitated within the soil matrix. The WL levels were the lowest during the monitoring cycle which corresponded with lower redox conditions that lead to diffusion of the soluble Mn ions, therefore the WL served as a zone of loss of Mn ions (source pool) with subsequent gain or concentration of Mn ions in the WB and even in the RB during the wettest climatic periods.

Table 3 Comparison of average manganese oxide reactivity levels on Soilscape Two at Riparian Buffer (RB), Wetland Boundary (WB), and Wetland (WL) by depth averaged by site. Two trials are compared: Initial measurements are listed with 1 and follow-up is listed as 2.

Results gathered from the Soilscape Two follow-up research project work indicated that the WB had a grand average MnOx reactivity level of 3.95cc which was higher than the RB

Table 3 Comparison of average manganese oxide reactivity levels on Soilscape Two at Riparian Buffer (RB), Wetland Boundary (WB), and Wetland (WL) by depth averaged by site. Two trials are compared: Initial measurements are listed with 1 and follow-up is listed as 2.

Depth RB 1

RB 2

WB 1

WB 2

WL 1

WL 2

Range 1

Range 2 Scale:

1 3.45 3.59 5.26 5.06 3.00 3.07 2.26 2.00 MnO x Level 0

2 3.95 4.04 3.29 3.39 3.11 3.34 0.84 0.70 MnO x Level 1

3 3.53 3.35 3.05 3.41 3.10 3.38 0.47 0.05 MnO X Level 2

Avgerage 3.64 3.66 3.87 3.95 3.07 3.26 0.80 0.69 MnO X Level 3

MnO X Level 4

MnO X Level 5


level of 3.66cc and the WL level of 3.26cc (Table 3). The results matched the initial project work which provided another indication that the testing procedure can produce repeatable results. The geographic site location analysis data indicated that the highest MnOx level occurred at the surface of WB with a value of 5.06cc, which was similar to 5.26cc from the initial testing period. Also, the WL levels were the lowest during the monitoring cycle which matched the initial results. The RB Depth 2 value was 4.04cc, which was the second highest reactivity level recorded across the three sites that corresponded with the diffusion of Mn oxides from the wetland boundary into the subsurface of the RB during the wettest period when the Mn ions were mobile in the soil water solution with subsequent precipitation upon intercepting oxidized conditions.

Conclusions

In conclusion, the most important points gathered from this research include the following. 1) The project results from Soilscape One corresponded with the research hypothesis. The Lowland (Site 4) should have the highest MnOx reactivity level, due to concentration of ions in this zone that either moved by mass flow or diffusion from the Upland (Site 1) or by through flow in the Upland (Site 2) and MidSlope (Site 3) soils. 2) The project results from Soilscape Two corresponded with the research hypothesis that the WB site should have the highest MnOx reactivity level, due to concentration of ions in this zone that diffused from the WL site. 3) The GOTTP method used in this research project, based upon the reaction between MnOx and H2O2, serves as a new, quantitative method of wetland delineation and determination of Mn ions present in various soilscape positions. 4) the GOTTP method, when compared to soils with no manganese and elevated manganese levels, can be used to produce repeatable results that can support scientific investigations of soluble Mn ions and precipitated Mn


Oxides produced during fluctuating hydrodynamic conditions in wetlands, hydric, and seasonally wet soils.

Acknowledgements

The Department of Agriculture, Nutrition and Human Ecology laboratory space was used to conduct part of the research work. The Cooperative Agricultural Research Center internal funding supported the field and laboratory research work, and the project is a part of the Natural Resources and Environmental Systems Research Program Area. The Summer Research Experience Program (SREP) supported one student (B.A. Onweni) as part of the 2017 Research Experience for Undergraduates (REU) and one student (R.J.F. Thomas) as part of the 2017 Research Experience for High School Students (REH) programs.

References

Coyne, M.S. ((1999). Soil microbiology: an exploratory approach. (Delmar Publishers, Albany, N.Y.

Environmental Laboratory. (1987). Corps of Engineers wetlands delineation manual. United States Army Corps of Engineers. Waterways Experiment Station Technical Report Y-87-1.

Gambrell, R.P. 1996. Manganese, Chapter 24. In Methods of Soil Analysis, Part 3.

Chemical Methods – SSSA Book Series no. 5. (Soil Science Society of America and American Society of Agronomy, Madison, WI.

Mitsch, W.J. and Gosselink, J.G. (2015). Wetlands. Fifth Edition. John Wiley & Sons.

NSW. (2013). New South Wales, Australia. Office


of Environment & Heritage. Retrieved from http://www.environment.nsw.gov.au/wetlands/WhyAreWetlandsImportant.htm

Soil Science Division Staff. (2017). Soil survey manual. Ditzler, C., Scheffe, K., and Monger,

H.C. (eds.). USDA Handbook 18. (Government Printing Office, Washington, D.C.

Sylvia, D.M., Fuhrmann, J.F., Hartel, P.G., and Zuberer, D.A. (1999). Principles and applications of soil microbiology. 2nd Edition. (Pearson Education, Inc., Upper Saddle River, N.J., 2005).

Tiner, R.W. (2017). Wetland Indicators: A Guide to Wetland Formation, Identification, Delineation, Classification, and Mapping, Second Edition. CRC Press. Taylor & Francis Group.

United States Department of Agriculture, Natural Resources Conservation Service. (2017).

Field Indicators of Hydric Soils in the United States, Version 8.1. Vasilas, L.M., Hurt, G.W. and Berkowitz, J.F. (eds.). USDA, NRCS, in cooperation with the National Technical Committee for Hydric Soils. Retrieved from http://www.nrcs.usda.gov/wps/portal/nrcs/main/soils/use/hydric.

WARPT. (2010). Wetland-At-Risk Protection Tool. Retrieved from http://www.wetlandprotection.org/estimate-wetland-loss.html (2010).


The Importance of Properly Modeling the Hydrogen Bond in Histidine

Falonne Moumbogno Tchodimo,1 Guoquan Zhou,1 Hua-Jun Fan1

1Department of Chemistry, Prairie View A&M University

Corresponding Author: Hua-Jun Fan, Department of Chemistry, Prairie View A&M University, PO BOX 519: MS 2215, Prairie View, TX 77446; Phone: (936) 261-3111; [email protected]

The hydrogen bond plays a vital role in many reactions. This study is to investigate the effect of different hydrogen bonding modes, such as single and double hydrogen bonds, via the quantum mechanic (QM) methods. Four histidine models (A, B, C, and D) with different numbers of intra-molecular hydrogen bonds were studied. The QM model results indicate that the double hydrogen bond is plausible and has a more stable geometry. However, this study suggests that the data from QM methods require further evaluation with solvation models to produce an even better energy profile.

Keywords: hydrogen bond, density functional theory, basis set, electronic structure

Introduction

Hydrogen bonds determine the molecule structure of many proteins and are required for proper function. Tuck introduced the hydrogen bond in 1968 (Emsley, 1980), and since then, it has been investigated by scientists around the world. Its importance is very significant in the fields of physics, chemistry, and biology. The hydrogen bond plays a vital role in protein folding, water molecule interaction, solution structure, nucleic acid base pairing,

Abstract


etc. (Parthasarathi, Subramanian, & Sathyamurthy, 2006). Also, one of its primary roles is in molecular recognition and chemical and enzymatic reactions (Tamura, Tamura, Takeda, Nakagawa, & Tomishige, 2014).

Intra- or inter-molecular hydrogen bonding is a noncovalent interaction between two different atoms (X-H…Y) within the same molecule or between two different molecules. One of the atoms is a proton donor (X-H), and the other one is a proton acceptor (Y). As for the proton donor, hydrogen has to be bonded to an electronegative atom (nitrogen [N], oxygen [O] or fluorine [F]). The proton acceptor can be any electronegative atom or a region of the electron. Moreover, within a molecule, it is possible to form a single or double hydrogen bond, which could produce different chiral (Jiang & Fang, 2016) and optical properties (Breuer et al., 2004).

With the ever-increasing power of computer hardware and the complexity of the software, more scientific problems can be modeled and solved. Gaussian 09 (G09) is a quantum mechanics (QM) modeling software (Revision C.01 2010, Gaussian Inc.). We investigated the ability of the different density functional theory (DFT) to use the functional and basis sets implemented in the G09 software package to reasonably reproduce the final geometric and electronic structure of a hydrogen bond. This investigation compared the electronic and geometric structures with the known experimental results. With the emerging DFT functionals, the modeling can provide better guidance and more accurately predict the electronic properties of novel molecules. This work will test the new DFT functionals and compare with the widely used DFT functionals; the results can determine the effectiveness of the new DFT functionals in hydrogen bond modeling. The amino acid’s skeletal structure enables the formation of peptide bonds through the C-terminus (–COOH group) and N-terminus (–NH2 group). Complex and rich protein structures arise from the different side chains of twenty amino acids in the human body. Because of the


proximate of C-terminus and N-terminus, neutral amino acids can take on a unique form called a zwitterion, also known as inner salt or dipolar ion. This is an ion with a positive and negative electrical charge at different locations within a molecule. The various protonation states of amino acids can provide different inter- and intra-hydrogen bonding. Therefore, the histidine amino acid is used as a model for this study.

General Modeling Procedures

The G09 modeling software arranges valence electron in the molecule. However, because of their speed, electrons are invisible to the human eye; that is why electronic structures are difficult to understand and too abstract for many students. In QM, the fundamental Schrödinger equation, coupled with the Born–Oppenheimer approximation provides a reasonable mathematic solution to understand the electronic movement and electronic structure of interactions in various materials. Based on a QM solution to the Schrödinger equation, there is the Pauli Exclusion Principle, the Aufbau Principle, Hund’s Rule, and the Heisenberg’s uncertainty principle or Heisenberg’s indeterminacy principle. All of these principles govern the movement of electrons in matter and can be used in molecular models. Molecular modeling or computational chemistry does have an advantage over the bench work of mixing chemicals, because modeling does not incur physical safety hazards. In this paper, we will use computational chemistry and visualization modeling tools to design optimized molecule forms under various DFT functional and basis sets to illustrate the impact of different DFT models on the structure and property relationship of the hydrogen bond. We will study and compare the difference between the electronic structure and geometry representation.


Methods

To better explore different bindings, we employed the DFT to compare the different functional sets (B3LYP, M062x, PBE1PBE, B3PW91, TPSSTPSS, X3LYP, and ωB97XD). We used solvation modeling to identify the most effective modeling approach. The different functional sets were set up through a Linux server. Before any actual data could be collected, investigators learned how to model and create the different forms of histidine. This was done via a chemistry modeling software, GaussView (Gaussian, Inc.), which was developed for Linux workstations to create different chemical and biological molecules from scratch. With GaussView, an individual can quickly and efficiently construct different molecular systems and molecules from the input file; the output is compatible with the G09 software. This simple graphic user interface program incorporates a variety of user-friendly features, which enables a user to adapt and begin constructing models.


Zwitterion Form of Histidine

Because zwitterions form from compounds that contain both acid and base groups (namely ampholytes), the amino acid will have a negatively charged –COO- and a positively charged –NH3

+. Studies have shown that, depending on the pH value of the histidine solution, amino acids can have four forms of different protonation states: A, HA, H2A, and H3A where “A” here represents the basic framework of histidine and “H” represents the protons from solution that can be added/attached to the base framework (Titration Curve 2019). This variation of protonation states of histidine earned it the name of polyprotic acid. At pH=8, histidine will be at its neutral state and can form a zwitterion form as shown in Figure 1.


Figure 1. The zwitterion form of neutral histidine

We began our study by modeling the neutral histidine form (HA). We developed models of different hydrogen bond modes including the following: single hydrogen bond mode (models A and B) and double hydrogen bond mode (models C and D).

Single Hydrogen Bond Mode (A & B)

Single hydrogen bonding is when only one intra-hydrogen bond is used. In our case, the obvious hydrogen bond was between the oxygen of the C-terminus and the nitrogen of the N-terminus as shown in Figure 2. Model A creates a single hydrogen bond between N1-H and O2. A hydrogen bond was constructed between N1-H and O2. This hydrogen bond length was measured at 1.17 Å.


Figure 2. Model A showing the single hydrogen bonding between N1-H and O2. All carbon molecules are dark grey, oxygen molecules are red, hydrogen molecules are white, and nitrogen molecules are blue. The nomenclature is as follows: oxygen 1 (O1, #19); oxygen 2 (O2, #18); carbon 1 (C1, #6); carbon 2 (C2, #3); nitrogen 1 (N1, #1); carbon 3 (C3, #5); carbon 4 (C4, #9); nitrogen 2 (N2, #11); and carbon 5 (C5, #12) is connected to C4 and nitrogen 3 (N3, #16) of the ring. Carbon 6 (C6, #14) is connected to both N2 and N3.

Model B is displayed in Figure 3. A single a hydrogen bond was constructed between N1 and O2-H. This hydrogen bond length was measured at 1.31Å.

Figure 3. Model B shows the single hydrogen bonding between N1 and O2-H. All carbon molecules are dark grey, oxygen molecules are red, hydrogen molecules are white, and nitrogen molecules are blue. The nomenclature is as follows: oxygen 1 (O1, #19); oxygen 2 (O2, #18); carbon 1 (C1, #6); carbon 2 (C2, #3); nitrogen 1 (N1,


#1); carbon 3 (C3, #5); carbon 4 (C4, #9); nitrogen 2 (N2, #10); carbon 5 (C5, #11); nitrogen 3 (N3, #14); carbon 6 (C6, #13).

It is important to point out that both models A and B were constructed to only have one possible hydrogen bond, because the results of these two models would provide insight as to why models C and D are necessary. In model A, the donor hydrogen atom (#2) was on N-terminus while the donor hydrogen atom (#20) for model B is from C-terminus.

After G09 software was used to optimize model A and model B to their lowest energy forms, model A has only one hydrogen bond, but model B has two hydrogen bonds. Further, the bonds were not constructed in the same places for each model. The Newman Projection was utilized to understand the orientation of the hydrogen bonds better. Model B created two Hydrogen bonds in its lowest energy form. The O2-H bond is slightly longer in model B (0.9850 Å) than that of model A (0.9678 Å). Since the geometric environments around these hydrogen bonds are different, could it be that the double bond affects the bonding of model B? Or could it be that the O2 has to retain a hydrogen bonding no matter what? To answer those questions, the third and fourth models (C and D) with two hydrogen bonds were constructed.

Double Hydrogen bonding Model

To create two hydrogen bonds in histidine, one must use the nitrogen atom on the imidazole ring as a hydrogen receptor. As Figure 4 shows model C and has the double hydrogen bond formed between O1 and N2-H, which is 0.2300 Å and another bond between O2 and N1-H, which is 0.1800 Å. Figure 5 shows model D with a double hydrogen bond as well. One of the Hydrogen bonds is between O1-H and N1, which is 0.2497 Å and the Hydrogen bond is between O2 and N2-H, which is 0.3600 Å. Even though the bonds were initially constructed at different places for the two different models, the bond lengths seem to


be close. It is very important to determinate what happens after geometry optimization to see if there is any differences occur.

Figure 4. Model C has a double hydrogen bond between O2 and N1-H and O1 and N2-H.

Figure 5. Model D has a double hydrogen bond between O1-H and N1 and O2 and N2-H.

After optimization produced models with the lowest energy forms, the differences can be seen (Figure 6). Even though model C started with a double hydrogen bond, it ends up with only a single hydrogen bond. However, in the model D the double hydrogen bond increased in length, meaning that the molecules


did not get close together but instead retracted from each other while maintaining the hydrogen bond. The model D geometry resemblances much of model B. This suggests that regardless of the construction of the initial geometry/conformation, the double hydrogen bond structure formed by models B and D might still be the more stable version.

Figure 6. Optimized geometries of double hydrogen bonding in models C (6a) and D (6b)

Conclusions

In this study, we carefully designed four histidine models (A, B, C, and D) that contained different constructions of intra-molecular hydrogen bonds. The QM model results suggest that the double hydrogen bond is plausible and has a more stable geometry. Since the current study did not employ any solvation models such as Polarizable Continuum Model (Tomasi, Mennucci, & Cammi,

6A

6B

6A

6B


2005) or the Solvation Model based on Density (Marenich, Cramer, & Truhlar, 2009), these modeling results do not match published experimental observations where the zwitterion form is the most stable form of amino acid. Furthermore, comparing the bond length differences between each model enable scientist to further understand bonding mechanics and to find the optimized geometry structure.

Acknowledgments

H.-J.F. gratefully acknowledges the Department of Chemistry at Prairie View A&M University for release time and funding support of this work and partially financial support from the U.S. Department of Energy, National Nuclear Security Administration grant (DE-NA 0001861 & DE-NA 0002630) and the Welch Foundation Grant (#L0002). GQ Zhou would like to acknowledge the Ningbo University of Technology for the support of visiting scholar and research exchange program.

References

Breuer, M., Ditrich, K., Habicher, T., Hauer, B., Kesseler, M., Sturmer, R., & Zelinski, T. (2004). Industrial methods for the production of optically active intermediates. Angew Chem Int Ed Engl, 43(7), 788-824. doi:10.1002/anie.200300599

Emsley, J. (1980). Very Strong Hydrogen Bonding. Chem Soc Rev, 9, 91-124.

Jiang, W., & Fang, B. (2016). Construction of a tunable multi-enzyme-coordinate expression system for biosynthesis of chiral drug intermediates. Sci Rep, 6, 30462. doi:10.1038/srep30462


Marenich, A. V., Cramer, C. J., & Truhlar, D. G. (2009). Universal solvation model based on solute electron density and on a continuum model of the solvent defined by the bulk dielectric constant and atomic surface tensions. J Phys Chem B, 113(18), 6378-6396. doi:10.1021/jp810292n

Parthasarathi, R., Subramanian, V., & Sathyamurthy, N. (2006). Hydrogen Bonding without Boarders: An Atoms-in-Molecules Perspective. J Phys Chem A, 110(10), 3349-3351.

Tamura, M., Tamura, R., Takeda, Y., Nakagawa, Y., & Tomishige, K. (2014). Catalytic hydrogenation of amino acids to amino alcohols with complete retention of configuration. Chem Commun (Camb), 50(50), 6656-6659. doi:10.1039/c4cc02675f

Titration Curve (2019), Titration Curve, retrieved from

http://chemistry.tutorvista.com/analytical-chemistry/ titration-curve.html

Tomasi, J., Mennucci, B., & Cammi, R. (2005). Quantum mechanical continuum solvation models. Chem Rev, 105(8), 2999-3093. doi:10.1021/cr9904009


About the Authors

Dr. Peter Ampim is an Assistant Professor in the Department of Agriculture, Nutrition and Human Ecology of the College of Agriculture and Human Sciences at Prairie View A&M University (PVAMU). His current research activities span sustainable agriculture and specialty crops. He is the Supervisor of Dr. Eric Obeng.

Dr. Hua-Jun Fan is a Professor in the Department of Chemistry at PVAMU. His research is computational modeling of the reaction mechanism of various organic, organometallic compounds. He has mentored ~300 students and has published more than 30 peer-reviewed journals.

Dr. Richard W. Griffin is a tenured Professor in the Agriculture program of the College of Agriculture and Human Sciences at PVAMU. His research interests include: soils, soil health, hydric soils, wetlands, water quality, and environmental science.

Phillip Harris majored in Agriculture with a concentration in Agricultural Economics. He graduated on May 13, 2017 and is working with the Texas Department of Agriculture in the Organics Division. Phillip’s goal is to become a Regional Director in the Agricultural Consumer Protection Division of the Texas Department of Agriculture.

Dr. Annette A. James is a former Assistant Professor in the Agriculture program of the College of Agriculture and Human Sciences at PVAMU. Her research interests include: Agronomy, Crops, Horticulture, Soils, and Agro-meteorology. She has taught courses that ranged from Crop Science to Environmental Soil Science.


Dr. Eric Obeng is a Postdoctoral Researcher at the Cooperative Agricultural Research Center of the College of Agriculture and Human Sciences. He works with Dr. Peter Ampim. He is an Agronomist and currently works on specialty crops.

Benjamin A. Onweni is a senior Mechanical Engineering major at PVAMU. He participated in the 2017 Research Experience for Undergraduate Students Program at PVAMU. His expected graduation date is May, 2020.

Javon D. Polk is a senior in Agriculture with a concentration in Plant and Soil Sciences and a minor in Health at PVAMU. He has worked as a Student Hourly Research Assistant in the College of Agriculture and Human Sciences since his sophomore year. During the summer of 2018, he worked as a Summer Research Assistant in the Natural Resources and Environmental Systems Research Group of the CARC. His expected graduation date is May, 2019.

Falonne Moumbogno Tchodimo was a undergraduate student when she participated with this project. She pursued a masters in Chemistry at PVAMU. She will pursue her doctorate degree at The University of Texas-San Antonio in the fall 2019.

Robert J.F. Thomas is a junior in high school from Huffman, Texas. He participated in the 2017 Research Experience for High School Students Program at PVAMU. His expected graduation date is May, 2020.

Edward K. Timms III is a sophomore in Agriculture with a concentration in Plant and Soil Sciences at PVAMU. He has worked as a Student Research Assistant in the College of Agriculture and Human Sciences since his freshman year, and during the past two summers, the USDA – ARS in Riesel, Texas, has employed him. His expected graduation date is May 2021.


Dr. Aruna Weerasooriya is Research Scientist. He is the leader of the Plant Systems Research Group at the Cooperative Agricultural Research Center of the College of Agriculture and Human Sciences. Dr. Weerasooriya is a Plant Scientist and focuses on medicinal plants.

Dr. Guoquan Zhou is an Associate Professor of Ningbo University of Technology. He was a visiting Research Associate in the Department of Chemistry at PVAMU. He finished the project and has since moved back to China.


EDITORIAL BOARD

Executive Editor and Co- Founder Yolander Youngblood, Ph.D.Asst. ProfessorBiology DepartmentPrairie View A & MUniversity, TX

Managing Editors Audie Thompson, Ph.D.PURSUE Co-FounderAsst. ProfessorChemical EngineeringUniversity of Arkansas

Quincy Moore, Ph.D.Assoc. Professor of Biology andDirector of the Honors ProgramPrairie View A & M University

Copy Editor R. Michelle Sauer Gehring,PhD, ELS, CRASenior Research ScientistCenter for Advanced Heart FailureUniversity of Texas HealthScience Center at Houston


Associate Editors Samesha Barnes, Ph.D., University of Florida

Olga Bolden-Tiller, Ph.D., Tuskegee University

Laurette Foster, Ed. D., Prairie View A & MUniversity

Sherri Frizell, Ph.D., Prairie View A & M UniversityBianca Garner, Ph.D., Tougaloo College

Kelley Mack, Ph.D., STEM Education, AAC&U

Lisa Mims-Devezin, Ph.D.,Southern Universityat New Orleans

Orlando Taylor, Ph.D., Fielding Graduate University

And Environmental SciencesTuskegee UniversityLaurette Foster, Ed. D.Professor of Mathematics andDirector of Center for Teaching ExcellencePrairie View A & M University

Sherri Frizell, Ph.D.Professor of Computer SciencePrairie View A & M University

Bianca Garner, Ph.D.ProvostTougaloo College

Kelley Mack, Ph.D.Vice President for UndergraduateSTEM Education and ExecutiveDirector of Project KaleidoscopeOffice of Undergraduate STEM Education, AAC&U

Lisa Mims-Devezin, Ph.D.ChancellorSouthern University at New OrleansOrlando Taylor, Ph.D.Vice President for Strategic Initiatives andResearch and Executive Director of CenterFor Advancement in STEM Leadership

Fielding Graduate University


ACKNOWLEDGEMENTS

We greatly appreciate our Additional Review Editors. Their time and effort were necessary to complete this issue. They

respectfully represented their discipline and their school with the knowledge and expertise necessary for each article in this issue.

ADDITIONAL REVIEW EDITORS

Bashir Mahmoud Rezk Atteia, Ph.D.Southern University at New Orleans

Subani Bundara, Ph.D.Prairie View A & M University

Lashunda Hodges, Ph.D.Alcorn State University

Linda R. Johnson, Ph.D.University of Maryland Eastern Shore

Melissa Mason, Ph.D.Alcorn State University

Kevin Rutherford, Ed.D.Panola College

Illya Tietzel, Ph.D.Southern University of New Orleans

Undergraduate EditorChonique Long

Prairie View A & M University

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