Texas A&M University-Kingsville: AKM Digital Repository
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An improved scoring function for biological network alignment
No full textProtein-protein interactions (PPI) are important in biological processes. These interactions are mostly involved in biologic cell processes. Understanding PPIs is important in drug manufacturing as drugs can affect protein interactions. Protein-protein interactions occur when proteins bond together to perform their biological functions. System biology is heavily focused on the structure and nature of protein-protein interaction networks, the most well-known biological networks. There are so many diverse datasets of protein interactions that can be studied. In this thesis, I study algorithms for comparing PPI networks using network alignment. Network alignment (NA) is the process of aligning (or mapping) the nodes of one network with those of another network such that the edges and topological structures of the networks are also aligned. An algorithm typically maximizes the number of aligned edges by maximizing some scoring function. In addition to its application in comparing PPI networks, NA can also be used in aligning network regions (subnetworks) for the purpose of transferring biological information between species. There are some existing methods such as IsoRank, MI-GRAAL, GHOST, MAGNA, and MAGNA++ for network alignment.
In general, MAGNA is a better algorithm and provides better alignments in comparison with the other existing methods. MAGNA is a genetic algorithm that starts with a set of random alignments (initial population) and repeatedly executes many generations. In each generation, it tries to improve a given scoring function that evaluates an alignment. The original MAGNA algorithm used three different scoring functions: induced conserved structure (ICS), edge correctness (EC), and symmetric substructure score (S3). In this thesis, I design a new scoring function, named asymmetric substructure score (AS2), and performed rigorous experimentation with multiple PPI networks. In AS2, alignment quality is improved by increasing the penalty of the non-matching edges. This new scoring function is compared with the previous scoring functions. The experimental results show that AS2 provide better alignment for many PPI networks comparing to the other scoring functions. I also performed some other experiments on the effect of the number of generations and the size of the initial population on the quality of alignment. The results of these experiments can be useful in deciding the appropriate number of generations and size of initial population for a genetic algorithm
Isolation, identification, and monitoring of fungal plant pathogens in irrigation water from the Rio Grande River
No full textThe Lower Rio Grande Valley of South Texas is an important region for agriculture which depends on the Rio Grande River as a source of water for irrigation. The suitability of the river water for irrigation is dependent on the levels of total dissolved solids and other characteristics. However, water can be a main source of plant pathogen distribution, causing economic and yield loss in agricultural crops. The aim of this study was to isolate, identify, and monitor fungal plant pathogens present in irrigation water from the Rio Grande river. Monthly water sampling from the river was plated onto potato dextrose agar and individual fungal colonies were isolated in pure culture for identification by morphological characteristics and sequencing analysis. Results showed that total colony counts fluctuate during the year, with summer months having the largest amount of colony forming units. Fungal isolates were identified by DNA homology analysis of the ITS, TEF, and TUB genes using Blast n algorithm. A total of 31 genera and 42 species were identified and 19 of them are reported plant pathogens to a wide range of hosts. Eight fungal pathogens identified in this study were chosen for further characterization which belong to the following genera: Aspergillus, Cladosporium, Fusarium, Penicillium, Exserohilum, and Epicoccum. Monitoring of the selected fungal pathogens revealed a temporal fluctuation within species as well as abundance variation between species. Cladosporium spp. were the most abundant and were present in every sampling period except April. Tomato stem-inoculations with fungal plugs revealed that all of the isolates were able to colonize and were recovered from inoculated stems except for Cladosporium perangustum. This work shows that plant pathogenic fungi are present in irrigation water from the river which presents a potential risk for pathogen distribution and plant disease
Optional Flexible School Day Program effectiveness in Texas public schools
No full textThe Optional Flexible School Day Program (OFSDP) allows districts to provide flexible hours and days of attendance for students who meet at least one of the requirements of the Texas Education Code §29.0822. The goal of OFSDP is to improve graduation rates for students who are at-risk of dropping out of school, have dropped out, or are behind in core subject courses such as English Language Arts, math, science, and social studies. The purpose of this study was to evaluate whether the OFSDP program impacted graduation and dropout rates within 1, 3, 5 and 10 years of implementation in Texas schools. Academic Mediation Theory, the framework of this study, suggests poor academic achievement as a factor mediating the effects of other variables when predicting school dropouts. A comparative non-experimental ex post facto time-series research design was conducted. There are 184 school districts included in the data set. Texas Education Agency’s (TEA) Texas Academic Performance Reports (TAPR) was used to compile data. Quantitative data were analyzed utilizing a paired t-test to determine whether to reject or retain null hypotheses of no statistical significance between multiple time periods. Understanding the effectiveness of OFSDP will provide educational administrators needed data to make an informed decision when contemplating the implementation of OFSDP. The comparative quasi-experimental ex post facto time-series research design found statistically significant decreases in dropout rates in all datasets focused on dropout rates. There were significant increases in federal graduation rates (FGR) by over 10% in years 3, 5, and 10. Organizational change within each school district has allowed reaching at-risk students within their community by implementing a program designed to recover students who have dropped out of school and are at risk of dropping out of school. OFSDP was evaluated as highly effective in changing the dropout and 4-year FGRs among schools implementing the program even for just one year
Exploration and analysis of the dynamic behaviors of wave energy and Its correlations with wind conditions
No full textWave energy has drawn researchers’ attention for decades with the concentration of the research study focusing on wave energy large-scale generation. Comparing with wind and solar energy utilization in power generation, wave energy harvesting technologies have lagged significantly, which haven’t been considered as commercial operation stage. It leads to enormous potential embedding with non-mature harvesting capability. Understanding wave energy behavior is critical for expanding wave energy commercial applications, site selection, layout optimization, harvesting, and forecasting. Recent research has been focused on dynamic significant wave height, dominant wave period, and wave directions. Extreme high fluctuation on wave energy behavior causes various challenges on its harvesting and forecasting. Wave power density (wave energy flux) is one of the direct ways to address the wave power potential.
To explore the underlying principles , research was conducted to achieve three objectives: 1) exploring wave energy behavior through wave power density hotspots identification and classification using deep learning-based methodologies; 2) analyzing the wave power density hotspot distribution and correlations patterns with corresponding factors in the Gulf of Mexico; 3) revealing the correlations between wave energy hotspots and wind conditions for wave energy forecasting.
Large amount of Meteorological data (1979-2019) was obtained from WaveWatch III including significant wave height, dominate period in three-hour, wind speed in vertical and horizontal components at 10-meter above sea level. Buoy measurement, salinity, temperature, bathymetry, oil and gas platform data, and hurricane historical data were obtained from the National Oceanic and Atmospheric Administration and different databases. Offshore wind energy and its conditions were considered as another energy resource to lower the variability and uncertainty of wave energy. It is possible to eliminate or reduce the temporal variation of both energy sources through the understanding of the correlation between the two energy sources, which could create more stable clean energy supply for different types of demands, including offshore oil platforms currently heavily relying on gas turbines.
Furthermore, this research explored wave energy behavior exploration based on the concept of wave energy hotspots, and investigated its distribution, identification, and classification using a deep learning-based method. Convolutional neural network (CNN), RCNNs (regions with convolutional neural network features), Recurrent neural network (RNN), and Long-short term memory (LSTM) were implemented to identify, classify and tracking the
wave power density hotspot in Gulf of Mexico ranged at 18–30° N and 80–98° W under 3-hour time increments and 1/6 degree and 1/2 degree in longitude and latitude spatial increments. Forty years of data with different units were pre-processed to be aligned together in the same magnitude, and temporal and spatial resolutions. The variability, correlation patterns, and forecasting models were developed through many trials and attempts to train, test, and validate the model’s performance on different size of training data. Different locations and long-term prediction scenarios were conducted to compare accuracy and the impact of wind conditions to wave power density. In addition, the research also analyzed the correlations between wave energy hotspots and ocean surface salinity, surface temperature, and wind speeds, which creates a distinct method to combine and consider direct or indirect factors to explore the wave energy behavior. The results from the research help provide, improve, and support the wave energy harvesting and forecasting through different point of views
Texas Gulf Coast Shrimp project- consumer preferences update
No full textThis study investigates how Texas consumer preferences for Texas Gulf Shrimp have changed from previous years while also adding recent impacts on demand due to COVID-19. A survey instrument was developed and disseminated via the Survey Monkey™ platform for a three-month period. Participants from a previous study completed for the Texas Department of Agriculture as well as new participants were asked to respond to questions about their demographic information, purchasing habits, the importance of price and attributes, and their views of purchasing online during COVID-19. New respondents were asked to complete the survey through social media posts and email outreach efforts. The survey hosted a total of 17 questions with the formatting of multiple choice, fill-in-the-blank, and ranking questions. A total of 1.534 individuals completed the survey, ensuring that we met the needed number of respondents to wholistically represent the population of Texas. Data indicates that most respondents rarely travel to the Texas Gulf Coast, purchase Texas Gulf Shrimp once every couple of months, and mostly purchase from grocery stores and gather information there. In reference to online buying, a very low percentage of respondents indicated using this method and hold concerns about product timely shipments, cost of shipping, unsure of product safety, product selection, and packing options. Additional data analysis and correlational values show that price is a concern for both high and low-frequency buyers. The labeling of products as sourced from the U.S. shows to be beneficial for high-frequency buyers. There is also a strong association between online buyers and the purchasing of more pre-cooked meals. Recommendations for future research include looking deeper into consumers' purchasing and information sources based on the marketing efforts of various Texas grocery store chains as well as other parts of the supply chain to identify the difference
Identification of amino acid residues responsible for the pro-apoptotic activity of BMRP by functional analysis of alanine substitution mutants and chimeras
No full textApoptosis is a physiological and evolutionarily conserved cell death program that maintains tissue homeostasis, and is critical for the development and normal function of metazoans. Deregulation of this strictly controlled process leads to several abnormalities and diseases, including cancer and neurodegenerative disorders. The Bcl-2 family of proteins plays significant regulatory roles in apoptosis. Bcl-2 is the founder member of this family and has antiapoptotic activity.
The mitochondrial ribosomal protein BMRP was identified in our laboratory as a novel Bcl-2 interacting protein with pro-apoptotic activity. Deletion mutants of BMRP were generated to delimit the region(s) responsible for its binding to Bcl-2, as well as those required for its proapoptotic activity. Functional studies performed with these deletion mutants delimited the region responsible for its pro-apoptotic activity to its amino (N)-terminal two thirds.
Six alanine substitution mutants of BMRP targeting conserved amino acid residues within the N-terminal two thirds of the protein were generated to further narrow down the region(s) responsible for its pro-apoptotic activity. Functional studies performed with these mutants indicate that they exhibit similar death inducing activity to that of wild-type (WT) BMRP. Western blot analysis shows that these various mutants are expressed in mammalian cells at levels also similar to those exhibited by WT BMRP.
To further characterize the binding of BMRP with Bcl-2, two chimeric proteins of human BMRP (hBMRP) and Drosophila melanogaster BMRP (dBMRP), designated hp25dBMRP-CH1 and hp25dBMRP-CH2, were previously generated in our laboratory, and their interaction with human Bcl-2 (hBcl-2) tested by yeast Two-Hybrid assays. The ability of these chimeric proteins to induce apoptosis was also investigated by performing cell viability reduction assays in mammalian cells. The expression levels of these proteins in mammalian cells cannot be assessed by using the anti-BMRP antibody that has been generated in our laboratory due to the chimeric nature of these proteins. To circumvent this problem, the open reading frames (ORFs) encoding these chimeric proteins, as well as dBMRP were fused upstream of the ORF encoding Aequorea coerulescens Green Fluorescent Protein 1 (AcGFP1) in the eukaryotic expression plasmid pAcGFP1-N2. The resulting constructs (pAcGFP1N2-hp25dbmrp-ch1, pAcGFP1N2-hp25dbmrp-ch2, and pAcGFP1N2-dbmrp) were used in functional studies by carrying out cell reduction viability assays in NIH/3T3 cells. Preliminary data obtained in these studies suggests that the chimeric hBMRP-dBMRP proteins fused to AcGFP1 (hp25dBMRP-Ch1-AcGFP1, and hp25dBMRP-Ch2-AcGFP1) exhibit higher cell death inducing activity than dBMRP fused to AcGFP1 (dBMRP-AcGFP1), as has been observed in our laboratory with the proteins not fused to AcGFP1. The expression levels in mammalian cells of these chimeric proteins fused to AcGFP1 needs to be tested in future studies that will be conducted in our laboratory
Linkage synthesis for solar tracking
No full textA solar panel faces the sun or has the solar ray normal to its face to enhance power reaping. A fixed solar panel can only meet this condition at one moment. A solar panel’s orientation has to be adjusted in order to meet this condition during the daytime. A solar tracker is integrated with a single or a set of solar panels to make them better meet the condition and improve their solar power reaping. In addition to extra cost and maintenance, a solar tracker consumes power when it adjusts the orientation of its related solar panels. Only when the net power gaining from a set of solar panels that have their integrated solar tracker is considerably above that from the same set of solar panels without their solar tracker, it is then worthwhile for the solar tracker to be integrated with the solar panels. A solar tracker needs to have at least two degrees of freedom to make its solar panels face the sun during the daytime since there are two independent relative motions between earth and sun: rotation and revolution. If earth is considered as being stationary for developing solar trackers, sun then has its daily east-west rotation and yearly north-south tilting. It is evident that the sun’s east-west motion on any day is well above its north-south one. If the small north-south daily tilting is ignored for solar tracking, single-axis solar tracks have their advantages over two-axis or multi-axis counterparts because of relatively simple structure and low power consumption. A compromise among the current fourbar single-axis solar trackers is between solar tracking oscillation and self-locking function. Planar 4R solar trackers are capable of producing large solar tracking oscillation, but they lack self-locking function. On the other hand, planar 3R1P solar trackers have self-locking function, but they are incapable of producing large solar tracking oscillation. This compromise is surmounted by the 6R1P six-bar solar trackers that are studied in this research. They can not only generate reasonably large solar tracking oscillation, but also have self-locking function. The disadvantage of six-bar solar trackers is their structural complexity when compared with four-bar solar tracking counterparts. Six-bar solar tracking linkages are investigated in this research. Their solar tracking motion performance is analyzed. This research aims at improving single-axis solar tracking linkages
Radar-assisted drivable path estimation for autonomous vehicles-a survey
No full textIn the realm of autonomous driving, one of the critical challenges is establishing a comprehensive understanding of the surrounding environment using sensors. Autonomous vehicles rely on sensors like Lidar, cameras, and radar for drivable paths and tracking. Lidar provides dense and precise range measurements but is costly and has limited range. Deep learning has improved vision-based detection, but training for all scenarios is resource-intensive. Challenges still need to be solved in detecting distant obstacles with Lidar and cameras due to range limitations and weather conditions. Radar excels in long-range detection, is cost-effective, and is comparatively reliable and robust in adverse weather conditions. In this Thesis, by integrating radar data with advanced perception and tracking in drivable region algorithms, our method enables precise localization of drivable paths in complex environments. We demonstrate the effectiveness of our approach through simulations and real-world experiments, showcasing improved navigation capabilities and robustness in challenging conditions. Our findings highlight the potential of radar-based solutions in enhancing autonomous driving systems' performance and safety
Effects of an exceptional drought on tree mortality and stand development in the national forests of Texas
No full textIn 2011, Texas experienced a record-breaking drought resulting in the driest year in recorded state history. This study determined both short- and long-term effects of an exceptional drought on tree mortality and long-term impacts on stand development in the four national forests of Texas. I evaluated major factors influencing mortality patterns and forest stand development when subjected to drought stress further exacerbated by climate change. Long-term forest tree records for this study are comprised of 229 Forest Inventory and Analysis (FIA) plots that have been measured four times over 19 years and divided into four study periods based on drought severity. Forest tree mortality calculations were divided into 3 groups, the ten most dominant species, all existing sixty-one species in the study area combined by individual forest, and all species and forests combined. Results indicate mortality rates were highly variable in the national forests (-28% to 550%) during the drought and post-drought periods (2010 to 2019). I found that stand age, stand density, and summer temperature significantly contributed to tree mortality. I analyzed Landsat multispectral imagery of each national forest with a Normalized Difference Vegetation Index (NDVI) and a differenced Normalized Difference Vegetation Index (dNDVI). The multispectral analysis showed variation corresponding to my tree mortality calculations. A spatially explicit forest landscape model, LANDIS-II, simulated potential long-term effects of drought and climate change on forest stand development in the national forests for 80 years (2019-2099), under low and high carbon emission scenarios. Model results indicate 13-49% lower mean aboveground biomass (AGB) under drought relative to no disturbance scenarios and ecosystems demonstrate 34-97% lower mean AGB under exceptional drought. This research provides a comprehensive understanding of drought impacts on forest mortality patterns and stand development in Texas’ national forests, providing public and private stakeholders with useful information for forestland management planning
Mutagenesis of the gRICH70 gene promoter region to identify regulatory elements, and development of a GFP-based assay to detect effects of RICH proteins on neuronal plasticity
No full textNerve regeneration in the adult mammalian Central Nervous System (CNS) is severely compromised due to its intrinsic inability to regenerate after damage to axons. It is an unsolved puzzle for the modern biomedical scientist to find reliable therapies for patients with damage to nerve fibers of the CNS, though significant progress has been achieved in understanding the axon regeneration capabilities of neurons. Studies on the remodeling of neuronal cells at cellular & molecular levels can give insights about the molecules and genes that can trigger or enhance their plasticity.
One such attempt from our lab to study the genes that help in neuronal regeneration is targeting the gene gRICH70 from goldfish and its homologous counterpart zRICH from zebrafish, which encode proteins that enhance neuronal plasticity. From previous work performed in our laboratory, a 0.42 Kbp region of the gRICH70 gene was identified that demonstrated strong promoter activity in PC12 pro-neuronal cells. The fragment contains a TATA box and several phylogenetically conserved sequence motifs that may harbor elements that regulate promoter activity. This study was geared to understand the role of these conserved sequences in gRICH70 gene regulation by performing Polymerase Chain Reaction (PCR) mutagenesis to delete specific conserved sequences and sub-clone the mutated fragments into a reporter plasmid (pGL3-Basic). The experiments performed demonstrated that the 260 bp region downstream of the TATA box harbor the required elements for expression in pro-neuronal cells. Two new reporter plasmids were also generated that target two highly conserved sequences in this region. The reporter plasmids should be useful to assess the presence of regulatory elements in these conserved sequences (by testing for promoter activity by luciferase assay). A second project was also initiated to develop an enhanced method to detect the effect of RICH proteins on structural neuronal plasticity. PC12 stable transfectant cells constitutively expressing EGFP-zRICH-WT fusion protein were generated that were easily detected by fluorescence microscopy for several weeks. These cells should be a very valuable tool to develop sensitive and simplified assay for detecting effects on neuritogenesis