136879 research outputs found
Sort by
Deciphering Cell Systems: Machine Learning Perspectives and Approaches for the Analysis of Single-Cell Data
No full textThis doctoral dissertation delves into the application of machine learning techniques in molecular biology, exploring gene expression regulation at the single-cell level and navigating the intricacies of cellular biology. The study specifically focuses on the utilization of modern neural networks to address cell-cell communications, gene function inference, and decipher protein expression. These applications aim to elucidate the complex interactions governing cellular behavior, as evidenced by the analysis of single-cell RNA sequencing (scRNA-seq) data.
In pursuit of these goals, I have developed and implemented advanced computational methodologies that combine systems biology and modern neural networks techniques. These methods are specifically crafted to manage the high-dimensionality and complexity of single-cell data, facilitating a more nuanced comprehension of genotype-phenotype relationships.
This research makes a significant contribution to the field of computational biology by proposing the use of neural networks to tackle the longstanding optimization problem in manifold learning. Furthermore, the study investigates generative models for learning gene regulatory networks and simulates gene knockout at the single-cell resolution. Lastly, the research delves into enhancing the interpretability of black box neural network models, applying them to multimodality data.
This research also contributes to the cell biology field by first providing an in-depth analysis of cell-cell interactions, highlighting how these interactions shape cellular behavior and influence disease progression. In addition, this research investigates gene function prediction, focusing on how gene knockouts can affect cellular phenotypes and their potential therapeutic implications. Lastly, this research looks into how gene expression patterns translate into protein expression and how accurately and interpretably this translation process can be predicted. This aspect of this research yields important insights into the functional implications of gene expression, which may be applied to the understanding of disease mechanisms and drug responses.
This research serves as a valuable resource because, in addition to the three introduced tools, it provides a comprehensive overview of state-of-the-art methodologies and their respective applications in the analysis of single-cell data within the recent years.
In conclusion, this doctoral dissertation represents a significant contribution to the field of computational biology and cellular biology by providing novel methods and insights into the genotype-phenotype relationships at the single cell level. These methods and discoveries not only improve our understanding of cellular behavior, but also pave the way for the creation of novel therapeutic strategies, thereby potentially enhancing our ability to combat a wide range of diseases
Impacts of Hurricanes and Algae-Based Jet Fuel on Energy Production Economics and Markets
No full textThis thesis addresses energy economics aspects involving industry disruptions, low carbon technology, and market responses. The first essay explores the impacts of hurricanes on US refineries and fuel markets. Namely we examine hurricane effects on refinery input and output, crude oil imports, gasoline, diesel, and crude oil stocks, as well as pricing dynamics for gasoline and diesel. In terms of findings this study sheds light on disruptions caused by hurricanes and the resultant welfare implications. Findings reveal that hurricane landfall in the Gulf Coast is associated with decreased refinery production for up to six weeks thereafter, with varied effects beyond the Gulf Coast region across the US. Also, hurricanes lead to regional price increases. Overall, we find hurricane strikes decrease total US social welfare, but with differing effects on producers and consumers.
The second essay reports on life cycle assessment and techno-economic analysis of direct air capture supported algae cultivation systems. The analysis focuses on prospects for generating algae-based limonene that is transformed into aviation fuel and biomass for animal feed. The analysis examines the greenhouse gas and economic consequences of a potential technology that integrates CO2 capture to algae growth to limonene and animal feed productions. Challenges of CO2 source, integrating sorbent production and manufacturing, and nutrient recovery processes associated with algae cultivation are explored. The analysis reveals that achieving environmental and economic feasibility requires more than a hundredfold reuse of the CO2 sorbent and hydrogel, currently deemed unreachable. Additionally, the study underscores that utilization of renewable electricity could further diminish the carbon footprint.
In the third essay, we extend the second essay into the strategic domains of product mix and market penetration. We consider multiple limonene applications and algae biomass markets. We find that prioritizing limonene for cleaning and fragrance is superior to using it to produce jet fuel. We also find substantial penetration of the remaining algae-based biomass as animal feed and as a feedstock for electricity generation. Scenarios involving carbon credits and technology subsidies reinforce these findings. Furthermore, we investigate the influence of enhanced limonene yield which is needed for entry into the jet fuel market
Teleoperation for Time-sensitive and Challenging Construction Environments: Dive into Cognitive Challenges
No full textDespite the dream of fully autonomous construction robots, the inherently unpredictable and dynamic scenarios of construction tasks make complete automation challenging. In many scenarios, situations that are outside the planned tasks or the pre-programmed robots��� capabilities underscore the necessity for human adaptability and decision-making. Teleoperation, therefore, has emerged as an effective approach in the construction domain and has been extensively studied. In the past two decades, prior studies primarily focused on technological advancements such as sensing for distant workplaces, computation algorithms, and automated control systems. However, this focus often neglected one crucial element of the teleoperation system: the human operator. As humans are decisionmakers and vital in addressing construction challenges effectively, a more in-depth understanding of human operators is necessary. Particularly, we must dive into the limited human cognition capacity and the challenges it poses during construction teleoperation, as cognitive workload directly influences their decision-making quality and control performance. This study thus employs a human-centric approach to navigate and manage the cognitive challenges faced by operators during the teleoperation of construction robots, especially those focused on the excavators. There are three research objectives: (1) the first objective aims to explore human factors that significantly affect teleoperation performance in construction from a systematic perspective, this objective involves identifying and prioritizing factors focused on cognitive aspects to understand their impact on operational efficiency and safety; (2) the second objective examines how various demanding and challenging construction scenarios impact teleoperators' cognitive and task performance along with safety behavior, this objective seeks to deepen understanding of the impact of the cognitive challenges identified in the first objective; and (3) the third objective is to assess the effectiveness of a novel assistive technology that has been specifically designed to reduce cognitive load and promote safer and more efficient performance, even under highly demanding conditions towards a cognitive-resilient teleoperation system. The findings reveal that excavator performance is negatively affected not only by tangible obstacles but also by intangible obstacles that impact the operator's cognitive state, such as time pressure and risk perception in remote environments. During our investigation, we found that teleoperators conducting demanding tasks performed the best under reasonable time pressure, even better than without pressure, whereas under excessive time pressure, they experienced cognitive overload and impaired risk perception, resulting in significant control errors as a result. However, teleoperators using electro-tactile feedback, a novel assistive technology, achieve high levels of safety and productivity regardless of the level of time pressure. This assistive technology is shown to help prevent cognitive overload by distributing cognitive tasks to the robotic system, enhancing awareness of remote hazards. This dissertation significantly contributes to the body of knowledge of construction teleoperation by bridging the gap in understanding human aspects within teleoperation systems. The research also offers valuable insights for designing user-centered systems for human-robot interaction or collaboration in construction teleoperation, applicable across various scenarios ranging from normal to highly demanding and challenging workplaces
Development and Verification of a Nuclear Forensics Methodology for the Attribution of Plutonium Using Data Science Methods
No full textAn advantage the global community has in preventing nuclear terrorism is the difficulty for a nonstate actor to procure special nuclear material (SNM). The regulation of SNM is essential in stymying adversaries. A nuclear forensics methodology, able to determine the provenance of SNM, like plutonium (Pu), will aid the international community in deterring nuclear smuggling. If Pu is recovered outside of regulatory control, an attribution capability would help inform conventional investigators. In both cases of theft and state hand-off of Pu, a guarantee that an offending party would be discovered and punished could force preemptive abandonment of any planned misdeeds.
The goal of this research was to develop and verify a nuclear forensics methodology for attributing unknown separated Pu samples using machine learning techniques. The methodology needed to be capable of identifying the following three attributes: the reactor-type that produced the Pu sample, the burnup of the irradiated uranium fuel that produced the Pu sample, and the time since irradiation (TSI). The methodology also needed to be robust enough to attribute samples that contain a mixture of Pu from multiple different reactor sources. A set of isotope ratios was used as the forensics signature and the training of the machine learning models utilized data from a library of Monte Carlo reactor neutronic and fuel burnup simulations. Lastly, the methodology needed to be validated by demonstrating that it could successfully attribute physical Pu samples.
Research proceeded in three main parts. First, machine learning models suitable for this application were identified, and were then trained and tested for attributing single reactor type Pu samples to assess feasibility. Second, the methodology was validated with a Pu sample separated from low enriched uranium dioxide (LEUO2) irradiated in a thermal neutron flux spectrum. Third, the machine learning methodology was adapted to attribute samples that were sourced from multiple reactor types. Additionally, a method for estimating the machine learning models��� prediction uncertainty that considered the Pu sample���s measurement uncertainty was investigated. Ultimately, all main objectives were successfully achieved. This is the first example in open literature of a methodology for attributing mixed reactor type Pu samples
Characterization of Soil Physical and Hydraulic Properties of TexMesonet Monitoring Sites
No full textOperated by the Texas Water Development Board, the TexMesonet comprises 100 monitoring stations across Texas. These stations record 11 environmental variables, which include soil moisture measurements at depths of 5, 10, 20, and 50 cm. While soil moisture data from other networks have been utilized to enhance crop yield estimation, develop drought indices, estimate potential groundwater recharge, and validate remote sensing soil moisture data, TexMesonet soil moisture data remain underutilized for such applications. This underutilization is due to the absence of comprehensive, site- and depth-specific data on soil physical and hydraulic properties. The objectives of this project are twofold: firstly, to characterize the soil physical and hydraulic properties at TexMesonet sites, and secondly, to estimate site- and depth-specific soil hydraulic parameters using the Rosetta3 pedotransfer function. This was achieved through extensive field sampling and laboratory measurements at 30 stations, including determining sand, silt, and clay percentages, bulk density, and volumetric water content at field capacity (-33 kPa) and permanent wilting point (-1500 kPa). These measurements were then applied in the Rosetta3 pedotransfer function to derive site-and depth-specific soil hydraulic parameters. The resulting database includes ten soil physical and hydraulic properties for each site and depth, leading to 218 data points across the 30 study sites. Within the TexMesonet, ten of the twelve USDA soil textural classes were identified, with silt and sand not represented. The mean absolute error (MAE) between the measured and predicted water retention curves ranged from 0.025 cm�� cm- �� to 0.13 cm�� cm- ��. Additionally, the MAE for volumetric water content estimates from TexMesonet sensors versus direct sampling was 0.081 cm�� cm- ��. With further sampling and expansion to all TexMesonet sites, this database has strong potential to provide critical information for improving water resource management applications in Texas, including irrigation scheduling and predicting floods and droughts
Essays in Behavioral and Experimental Economics
No full textMy dissertation consists of three independent essays on the topics of behavioral economics, experimental economics, and information economics.
In Chapter 1, we investigate how rule enforcers leverage the options to hide or reveal their privately-informed detection ability and how agents respond utilizing a disclosure game of verifiable information with either transparent or opaque enforcement objectives. When governing entities levy financial penalties for rule violation, they may aim to maximize compliance or revenues. Agents may be uncertain of these objectives; further they may also not know enforcers��� detection ability for rule violation. Our model derives multiple equilibria. To examine the selection among those equilibria, we conduct laboratory experiments where the enforcer���s objective is known to the agent in transparent treatments, but unknown to the agent in the opaque treatment. In transparent treatments, unraveling occurs. However, under the opaque treatment, only compliance-maximizing enforcers with strong detection ability reveal their detection ability, and agents violate the rule when enforcers hide. Our results outline that when the enforcement objective is opaque to agents, strategic withholding information related to the detection ability benefits revenue-maximizing enforcers.
In Chapter 2, we provide the means to have the largest comprehensive standardized test of all such elicitation mechanisms that are strategically-equivalent but cognitively-simpler than the Becker-Degroot-Marschak (BDM) mechanism. The dominant-strategy BDM mechanism is the prevailing mechanism for eliciting individuals��� valuations within economic research. However, recent research has highlighted systematic bidding mistakes under the BDM mechanism. We examine a BDM design for induced-value sellers in a controlled, laboratory environment. Treatments vary across three additional formats of elicitation mechanisms: (1) a descending price clock mechanism that satisfies refinements of dominant strategy, namely obviousness; (2) a BDM mechanism with additional contingent protocols that improves subjects��� understanding of the payoff function; and (3) a dynamic multiple price list with descending prices that simplifies the structure of the game. Our experimental results show that (3) appears to improve the game form misconceptions of the BDM mechanism but cannot improve overall accuracy of bids. Meanwhile, contrary to previous theoretical findings and online experiments, neither (1) nor (2) provides more accurate elicited values than the BDM mechanism in the laboratory.
In Chapter 3, we experimentally examine of two distinct channels influencing market behavior in a post-price mechanism of experienced goods: (1) whether there is a mandatory return option that the buyer can utilize or not, and (2) the degree to which buyers share different perceptions for the range of the random distribution that draws the experienced good���s true value. Previous literature has documented that a mandatory return option increases seller���s uniform price and buyer���s purchasing tendency in online sales platforms. However, limited attention has been given to its effect on experience goods���where the good���s true value is drawn from a random distribution, and only the buyer can learn it after purchase. Our results show that a mandatory return option decreases the seller���s uniform price for the experienced good. Conversely, for experienced goods with more heterogeneous perceptions, such heterogeneity benefits the buyer but hurts the seller. Our findings provide insights regarding the potential negative impacts of a mandatory return option on market behavior, particularly when buyer���s perception about the experienced good���s value varies in terms of its possible range
University Faculty Perceptions About Disability, Accommodations, and Access
No full textOpportunities and expectations for students with disabilities to earn a postsecondary degree have grown over recent decades. Despite positive improvements, disabled students lag behind non-disabled peers in degree attainment. Simultaneously, growing numbers of disabled students are enrolling with increasingly complex experiences, including a significant growth in mental health conditions among college students, and institutions of higher education are struggling to respond.
Faculty and staff in higher education have noticed these trends, but have done little more than keep up with increasing demands. Though prior researchers have long focused on the experiences of students, considerably less effort has been put into understanding the experience of faculty or staff involved in creating disability access. Faculty experiences are essential to understand because of their critical role in disability access.
Prior research on faculty experiences teaching disabled college students has largely featured survey-based, quantitative designs that have provided valuable aggregated insights, but offer little context of their faculty participants or the factors behind their responses. The purpose of this study was to qualitatively assess faculty experiences and perceptions of teaching students with disabilities. Faculty participants with at least five years of experience were recruited from a single, culturally bound university.
This study yielded data that add new insights to the literature, and implications for practice. The findings identified faculty hold strong, paradoxical views about bureaucratic systems that shape how disability access occurs in a university setting. Specifically, they have a dynamic and interactive relationship with the systems at hand, which are modified through relationships and trust of those involved. Faculty were generally supportive and positive of disabled students and disability resource staff, while simultaneously questioning and fearing the bureaucratic system deployed to ensure disability access. This study situated participant perspectives relative to their experiences navigating institutional systems, and therefore adds factors related to bureaucracy and trust to the literature, while also confirming a variety of prior researcher���s findings. While this study contributes new insights to the literature and best practices, further research is needed to ensure faculty are adequately prepared and supported when teaching disabled students
Testing the Benefits of Using Silicon Photomultipliers on Organic Scintillator Portal Monitors
No full textRapid and accurate detection of radiation at checkpoints is of vital importance to national security. One proposed method for improving radiation detection capabilities is using Silicon Photomultipliers (SiPMs) on the organic scintillation panels used at checkpoints to detect radiation. Research on SiPMs has been relegated to small detectors with volumes in the cubic millimeter range, but large portal monitors are often used at ports of entry and traffic control points to monitor radiation. This research examined the efficacy of SiPMs placed on larger plastic scintillators with volumes orders of magnitude larger than previous tests have analyzed. For this research two arrays of SiPMs were determined to be the most advantageous due to superior intrinsic efficiency and were tested on plastic scintillators using three different gamma emitting isotopes. Experiments testing a single SiPM to a PMT when placed in geometrically similar configurations gave an average SiPM to PMT total count ratio of 0.0963 �� 0.0006 for the highest-energy gammas, which was within 5% of the expected value based on the ratio of the active areas of the single SiPM and PMT. However, when two arrays of SiPMs were selected based on numerical simulations and tested, the best ratio of SiPM to PMT total count ratio for a single array observing a source was 0.394 �� 0.001, which was within 5% of the active area ratio. SiPM arrays and PMTs were also compared by the ratio of source counts to background counts observed for each isotope, with the best SiPM array observing a ratio of 1.391 �� 0.003 while the PMT���s ratio was 1.584 �� 0.003 for the same isotope. Ultimately, the results of the study do not suggest that SiPMs are an optimal substitute for PMTs on large-volume plastic scintillators, but this result is not concluded decisively by the experiment���s results
John Bickham field notebook: AK20001-AK20500.pdf
No full textBound book, each page corresponds to a karyotype slide data.Data pages for AK20501-AK21000 corresponding to unique identifiers of specimens/samples examined for biological research. Specimens are primarily housed at Texas A&M University; Biodiverstiy Research and Teaching Collection
The Coordination Between Peptidoglycan Hydrolases and Synthases in Myxococcus xanthus
No full textPeptidoglycan (PG) defines cell shape and protects bacteria against osmotic stress. The growth and integrity of PG require coordinated actions between synthases that insert new PG strands and hydrolases that generate openings to allow the insertion. However, the mechanisms of their coordination remain elusive. Here, I show that moenomycin that inhibits a family of PG synthases known as Class-A penicillin-binding proteins (aPBPs), triggers cell lysis despite aPBPs being non- essential for cell growth. We demonstrate that inhibited PBP1a2, an aPBP, accelerates the degradation of cell poles by DacB, a hydrolytic PG peptidase, in the bacterium Myxococcus xanthus. Moenomycin reduces the mobility of DacB molecules through PBP1a2, potentially promoting the binding between DacB and PG. Conversely, DacB also regulates the distribution and dynamics of aPBPs. These findings reveal the lethal action of moenomycin and suggest that disrupting the coordination between PG synthases and hydrolases could be more lethal than eliminating individual enzymes. The process of sporulation provides a unique scenario in M. xanthus to study PG regulation. Sporulation is the process by which spherical spores are formed in M. xanthus, from its regular rod shape in vegetative cells. Surprisingly, in our model organism, there is complete degradation of PG during this rod-to-sphere transition during chemically induced sporulation as well as during the formation of fruiting bodies. During this process, there is an upregulation of PG hydrolases, enzymes which are usually redundant in bacteria. Lytic transglycosylases, which cleave the glycan backbone, are important for the process of sporulation, and our results identified MXAN_3363 (LtgA) and MXAN_4034 (LtgB) as important for the process of sporulation, the former for glycerol induced sporulation and the latter for both sporulation pathways