How to Dump Dewey: Developing Your Own System of Classification

A poster about the development of new library classification schemes highlighting the case study of The University of New Mexico Center for Development & Disability (CDD). The poster includes a sample process for developing an autism spectrum disorder classification tailored to the needs of the case study library

Toward Simulating 2D Cell Surfaces in a Disk

Certain evolution models of cell surfaces (treated in two-dimensions) involve the solution of the Helmholtz equation with jump conditions enforced on an immersed closed curve. This thesis presents a sparse, modal spectral method for solving such Helmholtz problems. The solution is required to be continuous across the curve, but with a jump discontinuity in the normal derivative proportional to the planar curvature. The method relies on classical Fourier-Chebyshev basis functions, with the application of modal Chebyshev integration matrices to achieve sparse, banded approximations of the Helmholtz equation. The method achieves spectral convergence, despite the inherent low regularity of the relevant solutions. While the target application involves a disk domain, to focus on complexity issues and the complication of the jump conditions, this thesis adopts an annular domain. Three separate scenarios are considered: a single annulus, a multi-annulus domain decomposition with disjoint subdomain interiors, and a multi-annulus domain decomposition for which precisely two annuli (subdomains) overlap. The third scenario allows for treatment of the jump conditions with the overlap region containing an immersed curve. For each scenario, this thesis considers the structure of the linear system arising in the corresponding approximation and a strategy for its fast inversion. Numerical tests of the described spectral method examine accuracy and convergence

Leveraging Attention Mechanism to Unlock Gene and Protein Attributes

Advancing personalized medicine depends on effectively integrating and interpreting the vast, heterogeneous landscape of biological data, from genomic sequences and transcriptomics to the insights embedded in scientific literature. Current machine learning models often focus on single data modalities, limiting their capacity to capture the multifaceted nature of biological systems. We address this gap by developing three attention-based machine-learning models integrating diverse data modalities. Firstly, DeepVul is a multi-task model that leverages cancer transcriptome data to predict genes critical for cancer survival and their corresponding drugs. Subsequently, LitGene refines gene representations by integrating textual information from the scientific literature. Finally, Protein2Text is a large language model that translates protein sequences into natural language descriptions, making complex biochemical data accessible and interpretable. These models echo a comprehensive approach to integrating various data modalities to provide an alternative view of biological systems, paving the way for truly personalized medicine for everyone

Effect of tRNA synthetase inhibitors on aging in Caenorhabditis elegans

Aging is a multifaceted biological process characterized by the progressive decline in physiological integrity, ultimately leading to impaired function and increased vulnerability to disease. One emerging aging intervention involves the modulation of the Integrated Stress Response (ISR) via the transcription factor ATF-4, a conserved regulator of longevity across species. This dissertation investigates the effects of tRNA synthetase inhibitors- compounds that activate ATF-4 signaling- on lifespan and healthspan in Caenorhabditis elegans. I hypothesize that tRNA synthetase inhibition will lead to increased lifespan, healthspan, and autophagy in C. elegans in an atf-4-dependent manner. This work supports a conserved mechanism of longevity via translational control of ATF-4 and highlights tRNA synthetase inhibitors as a promising new class of geroprotective compounds

Powering Up Clinical Scholarship in Oncology

This presentation provides practical strategies to empower clinical scholarship among oncology fellows. Content includes research question development (PICOT, FAC rubric), oncology-focused resource navigation (databases, BrowZine, health statistics, open data), advanced literature search methods (Boolean, proximity, subject headings), critical appraisal tools, review and publishing processes, TriNetX, and an overview of HSLIC educational and research support services. Aimed at inspiring and equipping oncology fellows to engage in evidence-based scholarship and professional growth.https://digitalrepository.unm.edu/hslic-posters-presentations/1202/thumbnail.jp

Assessing viral susceptibility in tilapia species using plithogenic fuzzy soft set: a multi-expert approach for TiLV

This research assessed tilapia species susceptibility to Tilapia Lake Virus (TiLV) via a new plithogenic fuzzy soft set (CFS). This mathematical conception offers a holistic assessment via biological, environmental, and epidemiological attributes, merging unknown and variable data across multiple researching experts. Thus, the findings emerged from controlled and field assessed mortality, viral titers, and microenvironments of up to five species processed through the CFS model to determine vulnerability rankings. CFS revealed Oreochromis niloticus was the most susceptible to TiLV with red hybrids second and O. mossambicus most resistant. In addition, the CFS model revealed specific parameters, including temperature and population density, which can increase transmission. These results suggest that multi-expert assessments and fuzzy modeling are invaluable for future prediction of viral epidemics in aquaculture. Ultimately, species vulnerability will allow for species-specific management and increased awareness during critical temperature or population density situations to decrease the economic impact of TiLV in the tilapia industry. This assessment is an ideal decision support system for aquaculture health under imperfect conditions