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Discovery and characterization of arsenic-containing ribosomally synthesized and post-translationally modified peptides (AsRiPP): a potential novel antimicrobial peptide from Roseimarinus sediminis
No full textArsenic, a widely recognized environmental toxin, surprisingly shows promise in medicine. Antimicrobial resistance poses a global health crisis, highlighting the urgent need for new potent antimicrobials. Notably, bacteria leverage environmental arsenic to synthesize unique antibiotics, as represented by arsinothricin (AST), a recently identified arsenic-containing antibiotic. AST is effective against multiple pathogens while exhibiting low toxicity on human cell lines, demonstrating the potential of arsenic-based natural products as antimicrobials. This project aims to identify additional novel arsenic-containing antibiotics. AST biosynthesis involves two arsenic-biotransforming enzymes, ArsL and ArsM. arsL/arsM-guided genome mining identified several prospective biosynthetic gene clusters (BGCs) for new arsenic-containing antibiotics. Among them, this work focuses on the arsM-containing BGC from Roseimarinus sediminis. The BGC contains genes for a SPASM-domain radical S-adenosylmethionine (SAM) enzyme, which is often involved in biosynthesis of ribosomally synthesized and post-translationally modified peptides (RiPPs), along with a short gene that is predicted to be for a RiPP precursor peptide. Its most distinctive feature is the presence of arsM, encoding an arsenic SAM methyltransferase. It is therefore hypothesized that this cluster produces an arsenic-containing RiPP, named AsRiPP, where methylated arsenic species produced by ArsM is incorporated into the peptide during maturation. When R. sediminis was cultured with arsenite, an unknown arsenic species was produced. This compound, which was crudely purified through column chromatography, exhibited antibiotic activity, suggesting that R. sediminis produces a novel arsenic-containing antibiotic, presumably the AsRiPP. Further purification and analyses will be performed to identify the compound. In parallel, Escherichia coli strains expressing the precursor peptide gene solely or co-expressing it with the other AsRiPP gene(s) were constructed, and some of the gene expression was successfully confirmed. These constructs are being analyzed to elucidate the biosynthetic pathway and verify the association between the AsRiPP BGC and the bioactive arsenic compound produced by R. sediminis
P3 and Implicit Bias
No full textImplicit bias is an unconscious, automatic preference or stereotype that impacts our understanding and actions towards others. While involuntary, this bias can influence how an individual makes decisions, particularly how the human brain compartmentalizes information for processing and storage. Utilizing electroencephalogram equipment (EEG), we aimed to measure participants’ implicit bias while completing a picture categorization task (PCT). The PCT seeks to classify a picture as Black or White by presenting either a positive or negative word for 100ms, followed by a photo either a Black or White male face. Words ranged from positive or negative traits, such as cheerful or criminal. We then calculated the P3 component of the evoked response potential, a component related to novelty, arousal, and attention. Correlation analyses for P3 latency and amplitude, and measures of personality (Big Five Traits, social dominance orientation, state and trait anxiety) were conducted through SPSS. Results showed a significant correlation between the Big Five trait of openness and P3 amplitude. Greater P3 amplitude was associated with lower openness to experience
Comparison of Gas Chromatography – Mass Spectrometry and Ultra-High Performance Liquid Chromatography in the Quantification of Amygdalin from Apple Seeds
No full textAmygdalin had been used in anti-cancer treatments in the past but was discontinued due to the cyanide group present in the molecule. This dissociates in vivo from beta-glucosidases, cleaving off the two glucose molecules, which creates mandelonitrile, quickly decomposing into benzaldehyde and hydrogen cyanide. Amygdalin is present in many popular fruit seeds such as apples, grapes, and apricots, but the quantity differs between species. Therefore, analyzing the amount of amygdalin presents itself as a safety standard, and selecting the correct analytical technique becomes significant for quantification of cyanide consumption. The purpose of this experiment is to compare the analytical quantification of amygdalin between gas chromatography mass spectrometry (GC-MS) and ultra-high performance liquid chromatography (UHPLC). Apple seeds were dried, then extracted via Soxhlet distillation. The diluted ethanolic solution was quantified using both instruments. The unknown was compared to an amygdalin standard to discover which instruments’ results yielded higher precision, selectivity, sensitivity, and robustness
EEG Microstate Dynamics Predict Cognitive Functioning in Older Adults
No full textEEG Microstate Dynamics Predict Cognitive Functioning in Older Adults
Resting-state EEG microstates provide insight into the brain’s dynamic organization and have been linked to cognitive efficiency. In this study, we examined whether transition probabilities between specific microstates predicted overall cognitive performance. EEG data were collected from approximately 120 older adults, and Markov transition probabilities were calculated for transitions between four microstates at both pre- and post-assessment periods. Cognitive performance was measured using the NIH Toolbox Total Fully Corrected T-score. Correlational analyses revealed that post-assessment transitions from B→A were negatively associated with cognition (r = –.24, p = .008), whereas transitions from B→C were positively associated (r = .21, p = .022). A multiple regression including both predictors was also significant, F(2,117) = 4.26, p = .016, Adjusted R² = .05, and a standard error of 14.98. These results suggest that specific EEG microstate transitions reflect neural patterns linked to lower or higher cognitive performance, with the B→A transition potentially indicating less efficient brain-state dynamics
Discovery of a new arsenic-containing antibiotic: a derivative of arsinothricin (AST)
No full textThe rapid emergence and spread of antimicrobial resistance (AMR) present a critical public health challenge, highlighting the urgent need for novel antimicrobial agents. Arsenic, despite its toxicity, has a long history in medicine, from traditional Chinese remedies and Paul Ehrlich’s Salvarsan for syphilis to modern arsenic trioxide therapy for leukemia. A notable example is arsinothricin (AST), an arsenic-containing non-proteinogenic glutamate analog produced by Burkholderia gladioli. AST inhibits the growth of various pathogens while sparing human cells, further demonstrating the therapeutic potential of arsenic. Building on AST as a model, we aimed to discover additional novel arsenic-containing antibiotics. AST is biosynthesized via two steps catalyzed by two S-adenosylmethionine (SAM)-dependent enzymes ArsL and ArsM. arsL-guided genome mining revealed that various bacterial species possess arsLM-containing biosynthetic gene clusters (BGCs) with gene compositions distinct from the AST BGC. Among them, this study focuses on BGCs from Alicyclobacillus acidocaldarius and Deinococcus misasensis, both of which commonly contain two additional genes, ars1 and ars2, annotated to encode 4-carboxymuconolactone decarboxylase and biotin carboxylase, respectively. We hypothesize that Ars1 decarboxylates AST while Ars2 carboxylates its amino group, producing an arsenic mimetic of the phosphonate antibiotic fosmidomycin (FMS), provisionally termed methylarsmidomycin (MeASM). Liquid chromatography–inductively coupled plasma mass spectrometry (LC–ICP-MS) analysis showed that, when cultured with As (III), A. acidocaldarius and D. misasensis produce unknown arsenic species in addition to AST, supporting the hypothesis that MeASM is generated via AST derivatization. The unknown species will be purified for chemical characterization and antimicrobial assessment. In parallel, Escherichia coli cells expressing the MeASM structural genes, individually or in combinations, are being constructed to elucidate the MeASM biosynthetic pathway. This study connects a pressing clinical problem to genome-guided discovery, advancing our understanding of arsenic-based natural products and providing a foundation for developing novel therapeutics against AMR
Temporal Effects of Climate Change on Georgia Freshwater Parasite Diversity
No full textThe long-term effects of climate change on biodiversity are becoming increasingly relevant. There have been observations of declines in species richness and abundance across many taxa, from insects to fish to birds. However, there seems to be a lack of representation in the literature about how these temporal environmental dynamics affect parasite diversity, despite them being vital parts of ecosystems and indicative signs of ecological stress. With this study we examined how long-term climate change influences the diversity of freshwater parasites in Georgia. To analyze this, we paired parasite diversity data collected by our lab with long term temperature records, for which the preliminary data will be presented. By inspecting temporal patterns between parasite biodiversity and climate trends, this study aimed to identify potential correlations between warming freshwater ecosystems and changes in parasite community diversity. In total we hope to seek to showcase how parasite diversity responds to climate changes and gauge the potential of parasites as signs of freshwater ecosystem health
Exploring Large Language Models for Curriculum Advising
No full textUniversities face increasing challenges in optimizing academic advising and course enrollment. Many advisors report burnout, over 40% experiencing it at least weekly especially during peak advising periods, while first-year students often feel overwhelmed by complex graduation requirements and lengthy course catalogs. These challenges highlight the need for an intelligent, data-driven approach to support both advisors and students. This research explores the use of Large Language Models (LLMs) for curriculum advising, aiming to develop a personalized recommender system that leverages institutional catalog data and student profiles to generate accurate degree planning recommendations. The study compares three approaches: Retrieval-Augmented Generation (RAG), fine-tuning using Low-Rank Adaptation (LoRA), and a hybrid RAG + LoRA method across different open-source models with different parameter size (e.g., LLaMA 3B, Gemma 7B). PDF catalogs from multiple programs, colleges, and universities are embedded into a vector database to support grounded retrieval. The system is evaluated using metrics of accuracy, grounding (hallucination rate), and speed (latency) using a test set of advising-related questions derived from the university catalog. Preliminary findings suggest that RAG ensures strong factual grounding by retrieving precise information from external sources, while fine-tuning, especially through tools like LoRA, is designed for specific applications where data is used to train the model to understand the industry. The combination of both methods yields the most balanced results, demonstrating improved advising accuracy while keeping the source of each answer easy to verify. Overall, this research contributes to the development of AI-assisted academic advising systems capable of reducing advisor workload, supporting personalized student decision-making, and improving the overall advising experience in higher education
Natural Language Classifcation of Job Acceptance Emails
No full textAs individuals attempt to enter the job market, the importance of proper electronic mail (e-mail) management rises. The ability to respond to and sort through hundreds or even thousands of messages becomes a necessary task that can take otherwise valuable time. These conditions call for an all encompassing system that can help automate the process of job email acceptance or denial while also handling other use cases for normal email usage.This study compares a developed DeBERTa language model implementation against a developed Support Vector Machine (SVM) implementation using the Naive Bayes algorithm for text classification in terms of efficiency and accuracy on a dataset of various types of emails. The research involves determining accuracy of classification and the time taken to find the proper results of email classifications. Two separate text classification implementations will be directly compared to each other based on accuracy and number of positive classifications of email types. These implementations include the DeBERTa language model and SVM using Naive Bayes method for classifying emails. Emails are separated into specific types including job acceptance, job rejection, interview emails, pending application emails, and normal non-job application emails. The data will be trained using multiple datasets that include job application emails and non-job application emails. The results of this experiment have not been concluded, but we hypothesize that the SVM implementation will outperform the DeBERTa language model implementation. The DeBERTa language model is meant for very large datasets while also needing various adjustments in order to work properly. Further results will be posted by the time of the symposium
Investigating environmental effects upon alternative reproductive tactic frequencies in an urban salamander
No full textA major challenge in biology is understanding the mechanisms maintaining genetic and phenotypic variation. Genetically determined alternative reproductive tactics—discrete, intrasexual variation within populations—provide a unique opportunity to investigate this question. In Southern Two-lined Salamanders (Eurycea cirrigera), two male tactics are determined by a Y-linked genetic polymorphism and coexist in metro Atlanta: “searching” males have mental glands and cirri used in terrestrial courtship, while “guarding” males have enlarged jaws for mate-guarding in aquatic environments. As part of a Team Research course, we evaluated how spatial heterogeneity in environmental variables influences the frequencies of searching and guarding males. We collected environmental data from streams and tissue samples from larvae, extracted DNA, and used three qPCR assays to genotype samples from 26 streams across metro Atlanta. We then modeled how environmental variables (e.g., substrate type, stream size, microhabitat, and land cover) influence tactic frequency. We hope that our results provide insight about how urbanization influences the frequency of searching and guarding male Eurycea and, more broadly, how spatial heterogeneity supports the maintenance of genetic and phenotypic variation
Theta/Beta Ratio is Associated with Blood Pressure
No full textTheta/Beta ratio is an electroencephalograph (EEG) reading that has been shown to be a marker of cognitive capacity. A higher theta/beta ratio (TBR) is an indicator of increased central nervous system dysfunction in cortical areas. Blood pressure (BP) on the other hand has been significantly correlated with cognitive decline, specifically in the areas of memory and attention. However, there is less of a relationship between BP, speed of processing, and executive functioning. We intend to bridge this gap in knowledge by identifying the relationship between Theta/Beta ratio and blood pressure and what that may mean for cognitive decline in older individuals. We measured BP and TBR in a sample of 120 older community-dwelling adults, around half of whom were diagnosed with mild cognitive impairment (88 with complete data). Theta/Beta ratio was negatively correlated with blood pressure. This means that higher blood pressure may be an indicator of lower TBR.
Keywords: Theta/Beta ratio, blood pressure, electroencephalography, mild cognitive impairmen