Prashansa
Goel
Sponsor: Leigh ann Simmons, Ph.D. Human Ecology Adverse childhood experiences (ACEs) have been linked to preterm birth, gestational diabetes, lack of fetal movement, and more biological and psychosocial risks resulting in poor pregnancy outcomes. Current literature suggests that screening for ACEs during prenatal care allows for improved patient- provider relationships and prevention of these known pregnancy outcomes. However, due to the sensitive nature of the ACE screener, a patient's trust - or lack thereof - in their provider may impede screening. I am conducting a concept analysis of trust in prenatal ACE screening using the Rodgers and Knafl (2000) concept analysis method to define the antecedents, attributes, and consequences of this construct. This concept analysis seeks to elucidate the role of trust in prenatal ACE screening to improve patient-provider relationships, increase patient uptake of ACE screening, and ensure that ACE screening is implemented in a strengths-based, trauma-informed way. Findings may be used to enhance patient trust and improve outcomes in prenatal care ACE screening by breaking barriers and misconceptions associated with talking about trauma. Clarifying the attributes of trust will provide physicians, nurses, and other healthcare providers guidance on what patients need for trust to be achieved in this context. HapSolo2: New Methods and Code Refactoring of a Leading Optimization Approach for Removing Secondary Haplotigs During Diploid Genome Assembly
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Prashansa Goel
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Reconstructing diploid genomes, which contain information from both parents, is challenging due to their inherent heterozygous nature, (alternative contigs) which lead to ambiguities in the final reconstruction. Programs like PurgeDups and HapSolo exist to help solve this problem. HapSolo is a recently published method that identifies secondary contigs and defines a primary assembly based on multiple pairwise contig alignment metrics.HapSolo evaluates candidate primary assemblies using highly conserved single-copy genes and classifies alternative contigs based on this information using an AI-based optimization approach. The original implementation uses a random forward walking hill climber to minimize traverse the search space, minimizing the cost function by identifying and removing duplicate single-copy genes, and has been effective at classifying and removing alternative contigs across multiple species. In this project, we refactor the code base, implement a forward-looking hill-climbing optimization search algorithm with random restart, and take advantage of GPU compute for highly parallelizing optimization. This can help accelerate genetic research, by reducing genomic noise and assist in producing more complete and continuous genomes, which can lead to important discoveries in fields such as medicine, agriculture, and biotechnology. Evaluating the Relation Between Obesity, Breast Cancer, and Outcomes after Bariatric Surgery Neha Gondra
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UC Davis / Engr Computer Science / 2023
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Prashansa Goel