College of Science and Engineering Projects and Theses

Permanent URI for this collectionhttps://hdl.handle.net/10657.1/830

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A Framework for Improving Performance Testing in Agile Software Development
(2020-11-20) Whiting, Erik; Datta, Soma; Helm, James; Dabney, James
The subdiscipline of software quality assurance concerned with non-functional requirements (NFRs) and hardware metrics is known as performance testing. Conducting effective performance testing is complicated, time consuming, and expensive. These attributes put performance testing at odds with agile software development methodologies, which incrementally build software systems in quick cycles while being supported by exhaustive unit and integration test coverage. Due to a variety of challenges, performance testing often cannot keep up with an agile release cadence, and there is a growing body of research that catalogues and describes these challenges and proposes solutions to some of them. This study presents a software testing framework which implements several of the proposed solutions. The framework, called Lulu Performance Test (LPT), aims to confront many of the challenges noted in recent research, with the goal of making effective performance testing more palatable to agile software development methodologies.
A Framework for Planetary Resource Classifications to Further Sustainability in Space Exploration Missions
(2020-12-21) Alvi, Arjumand; Dabney, James P; Bozkurt, Ipek; Sun, Dongmin; Helm, James
The long-term impacts and overall sustainability of space exploration missions in the space environment were often unknown in past space missions. Historically, the space exploration vision of various space-faring agents refers to "planetary sustainability" as a synonym for mission assurance, rather than as an evaluation of long-term viability or as a means to ensure the sanctity of the space environment. Moreover, past missions have treated the space environment as an infinite frontier and not as a finite resource. NASA's Artemis program aims to return to the moon and achieve sustainable presence in lunarspace by 2028. Many planned future endeavors require resource extraction or in-situ resource utilization efforts. Resource prospecting is considered the first step in accessing resources in the lunarscape. Prospecting is a term utilized most in the mining and extractive industries and, by definition, is a means of experimental drilling and excavation. Prospecting, however, is not the same as classifying. Resource prospecting is more invasive than resource classification, although resource prospecting can advance resource classification efforts. When terrestrial (Earth-based) resources are evaluated on various measures – including availability, recoverability, accessibility – quantifying resource reserve estimates are a part of the evaluation; however, there is no framework established to characterize planetary resources on the basis of mission resource metrics. This investigation develops a framework to classify resources on the lunar surface, in response to the current, heightened interest in resource recovery and utilization in planetary resource-focused missions. Resource and risk classification methods established by the Society of Petroleum Engineers and General Electric will guide framework development. In the process, the investigation considers existing research proposals to establish resource limits and discusses how resource restrictions and risk thresholds are implemented in the final proposed framework. A novel resource classification framework is the final deliverable and is applied to geologic data from lunar fly-by and surface missions, thereby increasing the yield of existing mission data. Additionally, the framework integrates availability, recoverability, and accessibility metrics, while also addressing a composite sustainability metric. These four metrics are established as essential resource classification benchmarks to ensure that sustainable mission design is implemented early in the space systems engineering lifecycle by space systems engineers and mission designers in multidisciplinary teams.
A Mixed-Method Study of the Impact of Covid-19 Pandemic on U.S. Students’ Educational Attainment
(2021-07-27) Beheshti, Mohammadali 1988-; Shan, Xiaojun "Gene"; Jeong, Ki-Young; Bozkurt, Ipek
In this case study, we implemented a quantitative and sentiment analysis of the effects of the COVID-19 pandemic on the education, physical, and mental health of college students in the United States. As students are the future workforce of society and play a significant role in the future prosperity of a country, appropriate measures should be taken to minimize the impacts of adverse events on their education and health. As compared to natural disasters, health disasters could have more psychological impacts leading to stress and anxiety, as reflected in their educational performance. This study is based on survey data from students at the University of Houston at Clear Lake. The survey consists of twenty-three questions that ask the students about their experiences during the pandemic of COVID-19. Based on the analyzed data, UHCL students’ attainment is impacted by the COVID-19 pandemic. The average GPAs of participants show a decreasing trend from Fall 2019 to Fall 2020 and mental health issues might lead to such problems as lacking behind the study. Increased levels of anxiety and nervousness are the most significant influencers on student achievement during the pandemic of COVID-19.
A scalable image/video FPGA processing platform with approximate design
(2018-12-04) Zhang, Yunxiang; Yang, Xiaokun; Wu, Lei; Lu, Jiang
This dissertation presents a scalable image/video platform with approximate computing design on Field-Programmable Gate Array (FPGA). The platform is able to capture images in real time with a low-cost OV7670 camera and display the original, in-process and final results of images on a VGA-interfaced monitor. To make the platform reusable and expandable, the design with Verilog Hardware Description Language (HDL) and the verification environment including six Open Verification Components (OVCs) are provided. Compared to prior works, our proposed work achieves the least FPGA resource cost (753 Look Up Tables (LUTs) and 277 Registers) on the design of a Camera-FPGA-VGA platform. Furthermore, we present a novel approximate design library with FPGA and provide several slice-energy cost solutions corresponding to different application constrains. Specifically three approximations of multipliers and two approximations of adders, along with the exact designs, are presented and integrated as twelve benchmarks to implement RGB to grayscale conversion as a case study. Experimental results show that the minimum slice-energy cost, integrated with approximate\#2 adder and approximate\#3 multiplier, achieves 25.17% slice-energy saving compared with the exact design by sacrificing the quality of results as 5.69% error for multiplier and 2.85% for adder.
A Sliding Window Based Voting Classifier for Activity Sensor Based User Identification
(2021-04-30) Vallam Sudhakar, Sai Ram; Sha, Kewei; Wei, Wei; Yue, Kwok-Bun
Identification is the core of any authentication protocol design as the purpose of the authentication is to verify the user’s identity. The efficient establishment and verification of identity remain a big challenge. Recently, biometrics-based identification algorithms gained popularity as a means of identifying individuals using their unique biological characteristics. In this thesis, we propose a novel and efficient identification framework, ActID, which can identify a user based on his/her hand motion while walking. ActID not only selects a set of high-quality features based on Optimal Feature Evaluation and Selection and Correlation-based Feature Selection algorithms but also includes a novel sliding window based voting classifier. Therefore, it achieves several important design goals for gait authentication based on resource-constrained devices, including lightweight and real-time classification, high identification accuracy, a minimum number of sensors, and a minimum amount of data collected. Performance evaluation shows that ActID is cost-effective and easily deployable, selects only a minimum number of 10 high-quality features, uses only accelerometer sensor and increases the cost efficiency of user identification, collects only a small amount of 10 seconds of activity data, satisfies real-time requirements, and achieves a high identification accuracy of 100% when applied to a 30 user dataset.
A Systems Engineering Approach to Staff Scheduling Problems, as Applied to Flight Controller Scheduling at NASA’S Mission Control Center
(2019-05-17) Honey, Benjamin; Dabney, James B; Harman, Thomas; Helm, James C; Bozkurt, Ipek
The flight controllers of NASA’s Flight Operations Directorate (FOD) have become experts at human spaceflight operations over the past six decades. Despite this expertise, flight controllers and their managers are still struggling with the challenges of building effective console schedules in a 24/7 operations environment. By studying the lessons learned from the operational research field, NASA FOD can improve their staff scheduling practices, which have been largely home-grown. Likewise, the field of staff scheduling research itself suffers from an insular focus on finding more and more optimal mathematical techniques for solving schedules, ignoring important factors such as operator preferences and the health effectives of shift work. By adopting a systems engineering framework, these optimized solutions can become even more useful. These concepts are developed and then applied in a case study of the Attitude Determination and Control Officer (ADCO) flight controller group within FOD, demonstrating the effectiveness of systems engineering thinking when applied to new fields. As a result, the ADCO flight controller group has adopted the new framework into their standard scheduling practices, which has improved measured outcomes.
Application of MALDI-TOF MS for multiple source tracking from sewage and septic tanks along with identification of antibiotic-resistant pathogenic Escherichia coli
(2022-05-26) Mirani, Akshita; LaMontagne, Michael G; Santiago-Vàzquez, Lory Z; Labonté, Jessica; Allen, Michael
The intensity of extreme flooding events, driven by tropical cyclones and sea-level rise, may increase dramatically this century. These extreme weather events can spread untreated sewage from wastewater treatment plants and onsite wastewater treatment systems (OWTS, septic tanks), which creates a possibility of outbreaks of water-borne diseases. Human waste represents a particular threat because it is laden with antibiotic-resistant bacteria. In particular, bacteria in waters that appear to be contaminated with human waste show a high level of resistance to the antibiotic polymyxin. To assess the risk of disease spread, managers need tools to track the source of this contamination. Escherichia coli (E. coli) is widely used as a fecal indicator bacteria (FIB). Elevated levels of this FIB suggest microbial contamination but not the source. Commonly used microbial source tracking tools, are time-consuming and expensive. Matrix-assisted laser desorption/ionization-time-of-flight mass spectrometry (MALDI-TOF MS) is time and cost-effective way to identify bacteria. This proteomics method can distinguish strains of bacterial species but has not yet been widely used for microbial source tracking. In this study, E. coli strains were isolated from wastewater treatment plants and a sample composited from OWTSs. For comparison, a library of isolates was also generated from seal scat and dog feces. Isolates were then identified by MALDI-TOF MS, and cluster analysis was performed of mass spectra to determine if this technique could differentiate the sources of these FIB. To get knowledge on E. coli resistance towards antibiotics, a disc diffusion assay was implemented to screen representative isolates for sensitivity to broad spectrum antibiotics, and polymyxin B. Colistin resistance was checked by PCR using primers mcr-1, mcr-2, and mcr-3. MALDI-TOF MS distinguished E. coli strains isolated from sewage and OWTSs from E. coli isolated from animal sources. Antibiotic resistance assays indicated that E. coli strains isolated from all sources were resistant to ampicillin, streptomycin, and gentamicin. Bacteria isolated from sewage and OWTS showed resistance to colistin and polymyxin B. This suggests that MALDI-TOF MS could be applied to track the sources of fecal contamination of waterways. This could improve risk assessment and point to mitigation strategies.
Application of Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (MALDI-TOF MS) for Differentiating Sources of Fecal Pollution
(2022-05-27) Kmiecik, Caroline; LaMontagne, Michael G; Santiago-Vazquez, Lori; Howard, Cindy
Since the passage of the Marine Mammal Protection Act in 1973, pinniped populations in coastal waters of the United States have increased exponentially. These high populations of large mammals could contribute to fecal contamination of recreational waters. Enterococci species counts are used to assess the degree of fecal contamination and elevated counts of this fecal indicator bacteria (FIB) force managers to close beaches; however, contribution of pinnipeds to high Enterococci counts is not known. This may reflect the high cost of methods of tracking the source of microbial contamination. Matrix Assisted Laser Desorption Ionization – Time of Flight Mass Spectrometry (MALDI-TOF MS) is a time and cost-effective way to identify bacteria through protein mass spectra analysis. MALDI-TOF can distinguish strains of bacteria of the same species but has not been evaluated as a tool for tracking Enterococci isolated from pinnipeds. In this study, Enterococci isolates were cultured from fresh seal scat samples taken from a pen housing Harbor seals. Enterococci were isolated by plating serial dilutions onto Rapid Enterococci ChromoSelect agar to generate a library of isolates from captive seals. Libraries of Enterococci were also generated from dog scat and a composite sample of human waste from a wastewater treatment plant, respectively. These isolates were identified with a MALDI-TOF system and cluster analysis was performed of all Enterococci isolates showed overall clusters between Enterococci isolated from a single source. Cluster analysis of isolates from harbor seal and dog scat reliably identified as E. faecalis with Bruker MALDI Biotyper system (> 2.3) was performed for source discrimination comparison. 22 E. faecalis isolates clustered into four separate MTUs, with two MTUs being harbor seal-specific. The distinct source-dependent MALDI-TOF MS clusters suggest that MALDI-TOF MS may be a valuable tool in microbial source tracking.
Application of Whole Genome Sequencing and Maldi-Tof to Identification of Bacillus Species Isolated from Cleanrooms at Nasa Johnson Space Center
(2021-12-15) Mazhari, Farnaz; LaMontagne, Michael G.; Regberg, Aaron B.; Davis, Richard E.; Santiago-Vázquez, Lory Z.
Astromaterial cleanrooms at NASA Johnson Space Center are built environments that hold samples, such as lunar rocks, from different space exploration missions. Bacillus sp. are frequently detected in routine microbial monitoring of these facilities. Since this, and related genera, can form endospores that can withstand harsh conditions, they could contaminate astromaterials. This could confound searches for extraterrestrial life. Whole genome sequencing (WGS) is widely used for identifying bacterial strains and tracking their source; however, WGS is expensive and time consuming. Matrix-assisted laser desorption ionization– time of flight mass spectrometry (MALDI-TOF) shows promise as a low-cost, rapid method of identifying strains of bacteria, but few studies have compared this proteomics method to WGS. To evaluate a high throughput method of tracking the source of contamination of this built environment, WGS and MALDI-TOF was conducted on 18 bacterial strains isolated from surfaces in astromaterials cleanrooms. WGS identified 14 Bacillus, 2 Paenibacillus, 1 Solibacillus and 1 Alcaligenes strains. These isolates showed similarity to strains commonly observed in spacecraft assembly cleanrooms at other facilities. Cluster analysis of mass spectra generated by MALDI-TOF grouped strains together that were greater than 94% similar to each other in terms of amino acid sequences of single copy core genes, as assessed by WGS. This suggests that MALDI-TOF and WGS results are consistent with each other and MALDI-TOF can rapidly identify strains of Bacillus sp. isolated from cleanroom environments with a resolution comparable to WGS. Based on phylogenomic analysis, these results also suggest the presence of a cosmopolitan class of Bacillus sp. that are more likely to be found in cleanrooms and similar built environments than in natural systems.
Applying a Distributed Elasticsearch Cluster to Increased Security in IOT Devices
(2021-04-28) Riner, Spencer Clay; Yang, Andrew; Sha, Kewei; Wei, Wei
Internet of Things devices have permeated the daily lives of many by offering useful insights into their daily lives through the use of sensors and cameras to collect and display real-world data. Increasingly, these devices are the targets of cyber attacks that exploit various protocols essential to their functionality such as Telnet and UPnP. Elasticsearch is a distributed indexing system that allows for the horizontal scaling of coordinated node devices to increase their performance. Elasticsearch, along with its companion software elements Logstash and Kibana, are studied to identify their role in increasing the security of Internet of Things by testing the CallStranger UPnP exploit.
Assessment of shorebirds and wading birds in Galveston Bay using conventional and UAV techniques
(2018-05-02) Vallery, Anna Claire; Guillen, George; Mokrech, Marc; Howard, Cynthia
Nearly 75% of all U.S. bird species utilize Galveston Bay as either a permanent or seasonal habitat (Galveston Bay Foundation, 1996). Critical coastal bird habitat, including Galveston Bay, is at risk from continued loss due to various factors, including anthropogenic influences (Atkinson, 2003). One of the first steps in conserving and protecting this habitat is to understand the relationship among coastal bird population sizes, density and various intertidal habitats by establishing effective monitoring programs. Collecting data on intertidal and non-tidal habitat use by waterbirds using traditional survey methods can be difficult, though. New emerging technology in the form of Unmanned Aerial Vehicles (UAV) may, however, facilitate large-scale aerial surveys of these areas with less risk, expense, effort, and disturbance (McEvoy et al., 2016). Waterbodies such as Bastrop Bayou and Bastrop Bay provide the ideal setting to test UAV technology for population surveys and habitat selection by wading birds. Conventional boat surveys were conducted in Bastrop Bay bi-monthly from August 2016 to July 2017. These surveys collected base-line information on species abundance and composition for the Bastrop Bay system. Water level was observed to affect which species were observed. Substrate was found to direct patterns of species diversity and abundance more than seasonality for shorebird and wading birds in Bastrop Bay. Two UAVs were used to survey areas around Bastrop Bay as well. The fixed-wing UAV was found to cause more disturbance than the quadcopter UAV. The footage collected with the quadcopter was provided images of more birds than were observed during the concurrent field surveys. Of these birds, 11 of 15 species were able to be identified using the footage. The fixed-wing footage, however, only provided enough detail to identify three species. Though the results collected using the UAVs during this study are promising, further research needs to be conducted to continue to outline standard operating procedures for using UAV technology for surveying shorebirds and wading birds in intertidal habitats.
Biology of the perforate dome snail, Ventridens Demissus (Gastropoda: Zonitidae) from Seabrook, Texas
(2018-05-07) Medellin, Adrian; Minton, Russell; Howard, Cynthia; Harris, Phillip
The perforate dome snail (Ventridens demissus) is a terrestrial gastropod in the Zonitidae family. In this study I observed morphological variance, shell strength, reproductive behavior, egg size, hatchling growth, microbial gut content and species distribution of V. demissus. Shell height, width, and distance from aperture to callus were measured using a digital caliper. Whorl count was measured visually under a dissecting microscope. Shell strength was observed by placing shells between two metal plates and applying force in 0.1N increments until shell failure. Reproductive behavior was observed visually. Egg width was measured using digital calipers. Hatchling growth was measured by shell width using digital calipers every week. DNA extraction and NGS were used to examine microbial gut content. GEOLocate and DIVA-GIS software were used to create a point distribution map from museum records. Shell measurements were 5.66 mm ± 0.69 mm height, 8.19 mm ±0.71 mm width, 5.0 ± 0.5 whorls, and 1.94 mm ± 1.27 mm aperture to callus. Shell width was significantly positively correlated with shell height (R2 = 0.73, p<0.05) and whorl count (R2 = 0.18, p<0.05). Shell width was not significantly correlated with lamellar callus distance (p=0.53). Mean shell crushing strength was 4.6 N ± 2.5 N. Shell strength was not significantly correlated with shell width (p=0.32). I observed face-to-face simultaneous mating behavior in one couple. Average egg width (n=321) was 1.53 mm ± 0.12 mm. Hatchling growth rate was y =0.0632x + 1.4183. Mycoplasma, Peanibacillus, Simkaniaceae, and Enterobacteria were the four most abundant microbes in gut content. Species distribution is assumed to be from the southeastern to northeastern U.S. with a concentration in the Appalachian region. Information gathered from this study will fill data gaps in land snail literature and can be used to better understand evolution and systematics of land snail communities.
Biometrics-based user identification with optimal feature evaluation and selection
(2019-12-16) Kayastha, Namrata; Sha, Kewei; Yue, Kwok-Bun; Wei, Wei
Recently, biometrics-based identification algorithms have gained popularity as a means of identifying a person using their unique behavioral characteristics such as gait or hand movement pattern. Classifications based on biometric features are broadly used in modern healthcare applications, including user identification, authentication, and tracking. The complexity and accuracy of classification algorithms largely depend on the size and the quality of the feature set used to build classifiers. In this thesis, we mostly focus on feature evaluation and selection as these are the essential steps to decide a small set of high-quality features to build accurate and efficient classifiers in user identification. We propose a novel and efficient approach to evaluate and select biometric features for user identification based on activity sensor data collected from the users’ wrists while they are walking. For each feature, we first generate an NRMSD matrix, each entry of which represents the similarity level of any two users. Based on the observations from NRMSD matrices, we define two heuristics, Farness Value and Farness Ratio to evaluate the quality of the feature. We evaluated a total of 72 features and selected 18 high-quality features based on our evaluation results. Finally, we train our data with different classifiers and select KNN as the best classification model. Compared to other feature evaluation and selection techniques, this approach is more efficient and yields a higher accuracy of 98.3%.
Build a Game-Theoretic Framework for Community’s Role in Counter-Terrorism
(2021-03-24) Gekpe, Ashley Nchewi; Shan, Xiaojun "Gene"; Bozkurt, Ipek; Jeong , Ki-Young
Terrorism poses a major challenge to the government whenever it surfaces in any nation. It degrades the economic, infrastructural and trading systems. Furthermore, it reduces international revenues from tourism and foreign investments. Most importantly, it causes socio-ethnic tension and safety concerns to the nationals. Game-theoretic models have been widely applied to study the strategic interactions between the defender and attacker. In this thesis, we build three two-player subgames, and a three-player game-theoretic model, where the government, community and terrorist are involved. The community could support either the government or the terrorists. Both sequential and simultaneous games are studied with different orders of moves. We identify conditions where the community equilibrium strategy is to support the government. This paper finds first-mover advantage confirming previous research, identifies conditions where the community would support the government at equilibrium, and studies how robust their optimal behavior is to various external and internal factors.
Classification of Cocaine Addicted Patients Using 3D to 1D Hilbert Space-Filling Curve Ordering of fMRI Activation Maps
De Leon, Jessica; Sakoglu, Unal; Koc, Hakduran; Shihn, Liwen
In analysis of functional magnetic resonance imaging (fMRI), transformation of the 3D brain imaging data to 1D is required for further analyses, which often includes the classification of different groups of participants. The conventional transformation method is linear ordering, which results in a 1D vector that has a high amount of discontinuity which does not preserve the structure of the brain. A Hilbert space-filling curve can better preserve the structure of the brain after the transformation. Features obtained after a transformation based on Hilbert space-filling curve should lead to better classification performance. In this work, we applied Hilbert curve transformation to completely de-identified brain fMRI activation maps from 59 cocaine-addicted and 25 age-matched control participants and classify them as controls vs. patients using machine learning algorithms. Classification based on features from Hilbert space-filling curve ordering resulted in higher classification accuracy of cocaine-addicted patients vs. controls than those of conventional linear ordering.
Classification of fMRI Brain Activation Maps by Using Space Filling Curves
(2019-12-04) Bhupati, Lohit Ravi Teja; Unal, 'Zak' Sakoglu; Ahmed, Ahed Abukmail; Khondker, Shajadul Hasan
Functional magnetic resonance imaging or functional MRI (fMRI) is a brain imaging technique which is used to measure brain activity by detecting changes associated with the blood flow and oxygenation, which are indirect measures of neural activity. When participants perform a task and/or have some stimuli during their fMRI scans, fMRI data helps us to obtain brain activation maps, which have three spatial dimensions (3D). 3D activation maps need to be converted (ordered, or vectorized) to 1D vectors for further analyses such as localization and classification of activations and/or participants. Traditionally, the 3D to 1D conversion has been done using linear ordering, which loses most of the information about the spatial structure of the brain. Instead, one can use space-filling curves (SFC) for vectorization, such as a 3D Hilbert curve, which can better preserve the structure of the brain; however, it is still far from being optimal. Finding an SFC which is adaptive to human brain can better preserve the structure of the human brain in 3D-to-1D ordering. The problem of finding an adaptive optimal SFC is inherently a modified traveling salesman problem (TSP). In this work, we obtained an approximation of the SFC practically using a heuristic solution to the modified TSP. We used completely de-identified fMRI brain activation maps from schizophrenia fMRI experiment participants. We first applied a Hilbert SFC to obtain features and apply deep learning and other machine learning algorithms to classify participants from their brain activation maps and to fine-tune algorithm parameters. We also used an approximation of the optimal SFC using a TSP heuristic, converted the brain maps to 1D and obtained features for classification. The classification based on the heuristic approximations of adaptive SFC’s orderings yielded comparable or better classification accuracies than those of linear ordering and Hilbert ordering.
Classification of Positive and Negative Stimuli Using EEG Data Functional Connectivity and Machine Learning
(2019-12-06) Bhamidipati, Sai Jaya Sasanka; Sakoglu, Unal; Lu, Jiang; Shih, Liwen
Electroencephalography (EEG) provides electrical measures of brain activity by monitoring voltage fluctuations of the collective neural activity in different parts on the cortex of the brain. Recently, there have been numerous applications of machine learning techniques to classify events or participants based on EEG data in the biomedical field. EEG data are rich in the sense that one can extract many features from the data. This makes feature selection and reduction an important step in EEG based classification. Feature selection and correlations between features for classification of EEG data typically depend on time-frequency characteristics of the EEG channels, which represent data from different parts of the brain cortex. In this proposed work, we calculated functional connectivity (FC) between different EEG channels as our features for classification and applied it for classification of positive and negative visual stimuli. Previously, EEG data were collected from 12 participants (6 females and 6 males) while they were observing positive and negative images in a random order and the data were completely de-identified. After filtering of the noise in the data, we extracted FC features. From these FC features for each of the stimuli, we reduced the number of features using techniques which included correlation-based and principal components based methods. Once the features were selected, we implemented classification of positive vs. negative stimuli using classification techniques support vector machines, decision trees, random forests, k nearest neighbors, Gaussian process, Adaboost, quadratic discriminant analysis and logistic regression. We compared the classification accuracy results Support vector machine and Logistic regression provided the highest classification accuracy of whether a participant was seeing a positive or negative image, with accuracies of up to 71.9% and 71.4% for each of the participant, respectively.
Comparative study of decelluralized extracellular matrix from porcine derived tissues as a substrate for in-vitro mouse mesenchymal stem cell culture
(2018-12-14) Dave, Shivangi; Puzdrowski, Richard; Rhode, Larry; Giblin, Leonard
The main goal of this research is to create a 3-D natural scaffold for the Mouse Mesenchymal stem cells (MSCs) to carry out in-vitro growth without losing its physiological and morphological characteristics. MSCs are multipotent adult stem cells that can develop into several cell types belonging to bone, skeletal and fat tissues. These cells require extracellular matrix (ECM) for their growth. ECM is a critical environmental factor for cells to maintain normal function. It provides structural and biochemical support to them. Without ECM substrates, in-vivo and in-vitro stem cell research is of limited use. Furthermore, therapeutic applications require large numbers of MSCs. Because of MSCs’s biological importance and diverse role, the ECM has been the focal point of increasing interest in the field of regenerative medicine and stem cell research. Therefore, tissue culture plates have been developed providing ECM components to maintain an artificial environment for the cells to grow. However, this approach has limited success since it provides a flat 2-D growth surface and hence the standard culturing techniques have proven insufficient for this purpose. In addition, the tissue culture plates are expensive. This research is to prepare a complete ECM comprised of all the components required to provide a natural environment for the growth of healthy cells with intact tissues. The context discussed indicates that minimally altered decellularized porcine tissues-derived ECM can provide effective substrates for MSCs. In the present study it was found that bone marrow (BM), adipose and dermis smeared cultivation plates support in-vitro cultivation of MSCs while maintaining homogenous, physiological and stable cell population. Characterization and analyses of BM, adipose and dermis was carried out by performing immunohistochemistry and other staining techniques which demonstrate the presence of major ECM proteins and glycosaminoglycans (GAGs) in the substrates. The results of this research will establish a porcine derived ECM substrate with application potential in high fidelity cultivation techniques for stem cells. On the basis of our results, we suggest that decellularized ECM has a significant impact on tissue reconstruction and regenerative medicine.
Computational Analysis of Various Reactions Between Hydroxyl Radicals and Organic Mercury Species
(2021-07-29) Perry, Cody James; Su, Yi; Dubrovskiy, Anton; Wang, Daniel
The interactions between hydroxy radicals and both dimethylmercury and methylmercury hydroxide have been studied using ab-initio methods. The density functional theory (DFT) method M06-2X was used to optimize the structures of the stationary points in each reaction channel. The single point energies were determined using coupled-cluster theory (CCSD(T)). For the reaction with dimethylmercury there are three possible channels, with the favored product as methylmercury hydroxide with the smallest energy barrier of 7.3 kcal mol-1 (k(TST)=1.23x10-12 cm3 molecule-1 s-1). For the reaction between hydroxyl radical and methylmercury hydroxide there are six possible channels with the most probable channel being a hydrogen abstraction to form MeHgO radical. This channel has the lowest energy barrier of 2.5 kcal mol-1 with a rate constant of 3.87x10-9 cm3 molecule-1 s 1. Based on these results, dimethylmercury will undergo demethylation due to hydroxyl radicals whereas methylmercury hydroxide will not.
Data Visualization in Augmented Reality
(2019-05-12) Samford, Travis C; Findler, Michael J; Henry, Anne; Datta, Soma
During critical procedures with limited resources, astronauts are expected to operate at a high level of efficiency and effectiveness. Recently, NASA has begun development of an advanced space suit design with many technological upgrades that will make astronauts more efficient and effective during extravehicular activities (EVAs). These upgrades include the introduction of an informatics subsystem projected on the inside of the helmet in an astronaut’s spacesuit. The current informatics system relies heavily on voice communication to a team member, “mission control,” and an EVA partner. During an EVA, the astronaut needs access to two streams of data: the instructions on how to complete the EVA task and sensor readings on the health of the suit. The task data stream is a spiral notebook attached to the forearm, the system health display, and controls are embedded in the spacesuit’s chest and must be monitored using a mirror on the forearm. The aim of this thesis is to reduce the workload of the astronaut user by leveraging an augmented reality display as part of the new informatics subsystem to increase efficiency and effectiveness. This includes two displays: the task display and the suit consumables display. Two types of consumable displays were tested. One resembles the current astronaut display on their arm and the other has a sprocket design. The subjects were asked to complete an activity using one of these two consumable displays. The subject’s performance was then compared between the two displays.

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