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High-quality assembly and annotation of the genome of a species are critical in understanding the genetic basis of almost all aspects of the biology of the species. Although many genome assembly pipelines have been developed, they are either difficult to use or their assemblies are too fragmental. Moreover, although gene annotation pipelines have been developed at large genome centers, they are either too complicated for individual labs to use or not available to public. In this dissertation project, we have proposed a user-friendly pipeline that can assemble genome at chromosome-level with high-quality using PacBio/Nanopore long reads, Illumina paired-end short reads and Hi-C paired-end short reads. We have also developed an accompanying gene annotation pipeline using a combination of homology-based and RNA-based approached. The pipeline achieves high accuracy in protein-coding gene and pseudogene annotations.
Moreover, although multiple chicken genomes have been assembled, high-quality indigenous chicken genomes are still lacking, hampering the understanding of chicken domestication and evolution. Using the pipelines, we assembled and annotated the genomes of four indigenous chickens with distinct morphological traits at the chromosome-level. Our results challenge two earlier conclusions regarding chicken domestication and evolution. First, we found a total of 1,420 new protein-coding genes in the four chickens and recovered 51 of the 274 “missing” genes in birds in general and 36 of the 174 “missing” genes in chickens in particular. Most of these new genes are also found in previously assembled GRCg6a and GRCg7b/w chicken genomes, and might play house-keeping roles. Counting these new genes, chicken genomes encode more genes than originally thought. Second, we identified a total of 2,015 non-processed pseudogenes in the seven genomes. Most pseudogenization mutations are fixed in their respective populations and preferentially occur at the two ends of genes. Purifying selection is relaxed on the pseudogenes, suggesting that they might lose their gene functions. Pseudogenization mutations segregate in the chickens as their phylogenetic tree does, which is based on more than 6,000 essential protein-coding genes. Thus, in contrast to the previous conclusion, loss-of-function mutations play a critical role in chicken domestication and evolution. Moreover, these assembled genomes are valuable resources for studying chicken domestication and evolution.
Furthermore, although many studies related to artificial selection signatures of commercial and indigenous chickens have been carried out, quite a small number of genes have been found to be under selection. To fill these gaps, we re-sequenced 85 individuals of five indigenous chicken breeds with distinct traits from Yunnan, a southwest province of China. By analyzing these indigenous chickens together with 116 individuals of commercial chickens (broilers and layers) and 35 individuals of red jungle fowl (RJF), we find a substantially large number of selective sweeps and affected genes for each chicken breed using a rigorous statistic model than previously reported. We confirm most of previously identified selective sweeps and affected genes. Meanwhile, the vast majority (~98.3%) of our identified selective sweeps overlap known chicken quantitative trait loci. Thus, our predictions are highly reliable. For each breed, we also identify candidate genes and selective sweeps that might be related to the unique traits of the chickens. Most of these genes do not contain nonsense mutations, we therefore quantified the expression levels of eight genes in relevant tissues using RT-qPCR and found that most of them showed differential expression compared to their counterparts.
Finally, eared pheasant species are closely related but inhabit at highly varying altitudes from northeast to southwest China. To understand genetic bases of closely related species to adapt to different altitudes, we sequenced a population of 10 white eared pheasants (WT) (Crossoptilon crossoptilon) inhibiting in 3,000~4,300 m altitude niches in Yunnan, China, and assembled the genome of an individual at chromosome-level with a contig N50 of 19.63 Mb, a scaffold N50 of 29.59 Mb, and a total length of 1.02 Gb. This assembly with only few gaps is of higher quality than a previous one of a brown eared pheasant (BR) (C. mantchuricum) individual living at 20~1000 m altitude in northeast China. Interestingly, the WT genome encodes more protein genes than the BR genome (16,315 VS. 15,003), while the later contains more pseudogenes than the former (1,519 VS. 1,976). The two genomes shared 14,178 genes and 1,040 pseudogenes with 2,137 and 825 unique genes, and 479 and 936 unique pseudogenes, respectively. The unique genes and unique pseudogenes of both species are mainly involved in biological pathways of cardiovascular, energy metabolic, neuronal and immune functions, which are known to be related to adaptation to high altitude. Moreover, we compared the selective sweeps in the genomes of WT, BR and an additional species blue eared pheasant (BL) (C. auritum) inhibiting at 1,500~3,000 m altitude in central west China, using re-sequencing data of 10 WT, 12 BL and 41 BR individuals, respectively. Interestingly, genes under selection in each species converge on the same pathways of the aforementioned four functional categories. These results suggest that these species adapted to highly varying altitudes by loss-of-function mutation and fine-tuning of genes in these common pathways. Our assembled WT genome and re-sequencing data can be valuable resources for studying the biology, evolution and developing conservation strategies of these endangered species.
While hypertension is largely preventable, its rates have been increasing in young adults. Hypertension is associated with substantial costs to the US health care system and therefore a public health burden. In recent times, there has been a shift in focus toward the role of upstream factors and how they influence the risk of hypertension. The primary objective of this dissertation was to evaluate whether upstream social factors, namely, county intergenerational deprivation, state income inequality and state minimum wage are associated with hypertension in young adults. The secondary objective was to assess whether race-ethnicity and geographical region were effect modifiers of these associations. To address these two objectives, three separate studies were done. Paper 1 examined the association between county intergenerational deprivation and hypertension among young adults, 18-39 years, using the 2009 and 2011 Behavioral Risk Factor Surveillance System (BRFSS), 1996-2012 Opportunity Insights database, 2008-2012 American Community Survey (ACS), and 2010 County Health Rankings and Roadmaps (CHR&R) data. Paper 2 examined the associations between state income inequality and hypertension in young adults, 18-39 years, using the 2019 BRFSS and 2015-2019 ACS data. Last, paper 3 assessed the association between state minimum wage and hypertension among young adults 18-39 years with a high school education or less. Taken together, findings indicated that within the young adult hypertension literature, county intergenerational deprivation may be a more salient upstream factor than state income inequality and state minimum wage. Also, findings suggested that race-ethnicity and geographical region were effect modifiers of the exposure-disease associations. Additional population-based studies are necessary to confirm findings.
Social media platforms such as Facebook, TikTok and Instagram have witnessed increasing use of offensive language by online users, which can be harmful to other users. Recently the continuance of the pandemic has propelled the propagation of offensive content associated with Covid-19 on social media. Some researchers begin to develop effective methods for detecting online offensive language from social media content automatically, yet automatic intervention of offensive language after it is detected remains largely understudied. To address the gaps, this dissertation develops an effective text style transfer-based approach, TST-IOC, for automatic offensive intervention tasks. The promising outcome suggests that our proposed method shows significant potential and could be a preferred choice among users for offensive intervention tasks. This dissertation provides some contributions. First, it contributes significantly to the field of offensive language research by introducing a novel text style transfer-based approach, which has been rarely explored in existing intervention studies. This approach shows a step forward in the development of an automatic offensive intervention system, addressing the limitations of current filtering systems deployed by social media platforms. Second, existing research has mainly focused on using performance metrics for evaluating offensive intervention methods quantitatively. However, this study goes beyond by proposing a comprehensive automatic evaluation paradigm. By exploring both quantitative and qualitative aspects of automatic intervention assessment, it fills a crucial gap in the current offensive language research landscape. Finally, it recognizes the scarcity of studies comparing human evaluation with automatic evaluation in automatic intervention systems. To bridge this gap, we conduct a user study, which allows for an investigation of user acceptance of the proposed automatic intervention approach in real-world scenarios. The insights gained from this user study not only guide the design of more comprehensive automatic intervention systems but also hold the potential to shape the development of human-centric automatic intervention systems in the future.
According to crash statistics, the United States witnessed 6,205 pedestrian fatalities and 76,000 injuries on its roads. These numbers are still unacceptably high and urge the need for proactive measures to mitigate pedestrian-vehicle conflicts and strive toward achieving a crash-free society. This research focuses on object detection and tracking algorithms, specifically YOLOv4 and Deep SORT, to examine pedestrian safety at a signalized intersection with a fixed cycle time and an intersection controlled by rectangular rapid flashing beacons (RRFBs). Long short-term memory (LSTM) neural network and adjacent-category models were developed for both intersections to predict the severity of pedestrian-vehicle conflicts and examine the effects of pedestrian, vehicle, and signal timing-related factors. The system can warn drivers 2s ahead about a potential conflict with a pedestrian, fostering a proactive approach to mitigating conflicts and enhancing overall road safety. The findings also provided evidence that increasing the yellow time and the RRBF flashing time significantly lowered the severity of pedestrian-vehicle conflicts at both intersections, emphasizing the importance of these two signal timing factors as integral measures for enhancing pedestrian safety and minimizing potential conflicts with vehicles.
This dissertation seeks to improve the budget allocation process for economic mobility policy portfolios by leveraging multi-objective optimization as a decision support tool, accounting for population, political, social, and budgetary constraints. Economic mobility is measured as the difference in income between Black and White populations, known as the racial wealth gap. First, I run regression, mediation, and moderated mediation analyses to understand the impact of local authority, consolidation, local partisanship, unified government, and racial demographics on aid, budget expenditures and economic mobility. I then propose a novel application of multi-objective optimization1 to identify optimal mixes aimed at increasing economic mobility in urban cities. In doing so, I seek to improve decision support tools available to local urban governments. My work intends to enable local urban governments to leverage multi-objective optimization to guide their decisions regarding policy selection and budget allocation. Better informed policy processes lead to a better mix of policies, which allows for more holistic solutions with greater societal returns. This not only improves outcomes for residents; it also recovers waste in the governmental process, increasing effectiveness and efficiency.
Global warming and climate change keep causing a catastrophic impact on the natural, social, economic, and political environment in many parts of the world. The urgency for the transition to a low-carbon economy through CO2 emissions reduction calls for innovative methods to harvest renewable energy sources to displace unsustainable fossil fuel power in North America. This work presents proposed methods for marine hydrokinetic and solar renewable power generation. On another front, since addressing the causes of global warming and climate change is not timely enough, this author proposes technologies to minimize their effects, which manifest through extreme weather events. Since renewables harvesting generates variable power profiles during extreme weather events, this work investigates high voltage interconnectors to smooth the total power variability of wind power farms far distant between themselves under hurricane events. Another effect of climate change is the increasing frequency of failures on overhead transmission lines due to extreme weather events. The author thus proposes a wide-area controller with phasor measurement and battery actuator to minimize the post-fault transients.
Beta-lactamase proteins are major contributors to antibiotic resistance, rendering beta-lactam antibiotics ineffective against bacterial infections. The emergence of novel beta-lactamases with expanded substrate specificity poses a global health threat. This study utilizes computational techniques to investigate the mechanisms by which beta-lactamases expand their substrate specificity, enabling bacteria to resist new antibiotics. By exploring the relationship between protein dynamics and function, the impact of enzyme motion on substrate specificity is elucidated.
Molecular dynamics simulations are conducted and analyzed to identify the functional dynamics involved in substrate recognition in beta-lactamase. Dynamic signatures are identified using a novel approach called Supervised Projective Learning with Orthogonal Completeness (SPLOC). Increased flexibility in loops neighboring the enzyme's active site facilitates optimal interactions with different antibiotics through local conformational flexibility. Notably, dynamic signatures differ between protein-antibiotic systems, highlighting the complexity of antibiotic binding mechanisms. These dynamic signatures are demonstrated as viable predictors of antibiotic resistance in beta-lactamase enzymes.
A proof-of-concept is presented for designing de-novo peptides that target these regions, offering a potential new class of beta-lactamase inhibitors capable of hindering the motions necessary for substrate recognition. This approach presents a promising strategy for controlling beta-lactamase-mediated antibiotic resistance.
We describe using detailed numerical simulations, the properties of detonation waves occurring in single-phase rotating detonation engines and the evolution of a shock-driven liquid fuel droplet. The studies span vastly different scales from the microscale at which the behavior of an isolated liquid fuel droplet has been investigated to device-scale simulations of a gas-phase rotating detonation engine.
Rotating Detonation Engines (RDEs) represent a relatively new concept in pressure gain combustion, where a detonation wave (DW) formed from injected mixture, travels circumferentially within an annular channel. The DW compresses the fuel to much higher pressures, resulting in the extraction of additional work and efficiencies not accessible through the conventional Brayton cycle. Mode transition in RDEs refers to an abrupt change in the number of detonation waves due to a change in inlet conditions such as the injected fuel reactivity and total pressure, and can affect engine performance. Through detailed numerical simulations in a 2D unrolled RDE geometry, an alternate mechanism for mode transition is proposed, along with a corresponding quantitative criterion that is validated using simulation data. A simple model to predict the number of DWs following mode transition is proposed and verified using simulation data.
In the second part of this thesis, we describe detailed numerical simulations of a liquid fuel droplet impacted by a Mach 5 shock wave, considering the effects of chemical reactions and phase change due to evaporation. The fuel droplet undergoes significant deformation and morphological changes following shock impingement, as the droplet surface becomes unstable to the Kelvin-Helmholtz instability. The production of fuel vapors by the droplet impairs the growth of such surface instabilities, leading to reduced growth of the droplet surface area when compared with a non-evaporating droplet. As the fuel vapors react, a diffusion flame is formed on the droplet-windward side, leading to intense droplet heating and enhanced vapor production in this region. Our results show significant spatial inhomogeneities are present in the droplet flowfield in all the cases investigated, which must be considered in the development of reduced order point-particle models for system-level simulations of detonation engines.
The increased reliance on Big Data Analytics (BDA) in society, politics, policy, and industry has catalyzed conversations related to the need for promoting ethical reasoning and decision-making in the mathematical sciences. While the majority of professional data scientists today come from privileged positions in society, those processed by the decisions made using data science are more often members of one or more marginalized social groups, translating into disproportionately negative outcomes for these individuals in society. Thus, it is argued that future citizens must develop an ethical mathematics consciousness (EMC) that human beings do mathematics; thus, there are potential ethical dilemmas and implications of mathematical work which may affect entities at the individual, group, societal, and/or environmental level. Drawing from this conjecture, the purpose of this Design-based research study was to develop a local instruction theory and materials that promote students’ ethical mathematics consciousness in a high school Ethical Data Science (EDS) course grounded in a feminist, relational ethic of caring and social response-ability. Outputs include the identification of design heuristics, including the task structures, participation structures, and discursive moves that supported students' development of EMC and equitable participation in classroom activities, an initial curriculum for the EDS course, and a student-use protocol and corresponding analytic framework for making critically conscious ethical decisions in data science.
The increased reliance on Big Data Analytics (BDA) in society, politics, policy, and industry has catalyzed conversations related to the need for promoting ethical reasoning and decision-making in the mathematical sciences. While the majority of professional data scientists today come from privileged positions in society, those processed by the decisions made using data science are more often members of one or more marginalized social groups, translating into disproportionately negative outcomes for these individuals in society. Thus, it is argued that future citizens must develop an ethical mathematics consciousness (EMC) that human beings do mathematics; thus, there are potential ethical dilemmas and implications of mathematical work which may affect entities at the individual, group, societal, and/or environmental level. Drawing from this conjecture, the purpose of this Design-based research study was to develop a local instruction theory and materials that promote students’ ethical mathematics consciousness in a high school Ethical Data Science (EDS) course grounded in a feminist, relational ethic of caring and social response-ability. Outputs include the identification of design heuristics, including the task structures, participation structures, and discursive moves that supported students' development of EMC and equitable participation in classroom activities, an initial curriculum for the EDS course, and a student-use protocol and corresponding analytic framework for making critically conscious ethical decisions in data science.
