UAB Digital Commons (Univ. of Alabama at Birmingham)
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Building Social Butterflies and Learning Circles: The Key to Organizational Safety Potential
Get PDF2025 ASEM 620 Capstone Projecthttps://digitalcommons.library.uab.edu/asem/1009/thumbnail.jp
Disrupted Diencephalon Development and Neuropeptidergic Pathways in Zebrafish with Autism Risk Mutations
No full textImaging, RNA-seq, and behavioral data to support the manuscript: Diencephalic and Neuropeptidergic Dysfunction in Zebrafish with Autism Risk Mutation
PharmaKG Explorer: Knowledge Graph Embedding-Driven Conversational Querying and Real Time Link Prediction in Drug-Gene-Disease Networks
No full textIn this project, we develop an end-to-end platform for interactive knowledge graph embedding tailored to accelerate drug discovery. Starting from curated biomedical datasets-PharmAlchemy, we extract and filter 311496 triplets linking drugs, genes, diseases, and side effects, then deploy the complete graph including nodes, relations, and properties on Neo4j Aura. A natural‑language interface built with Gradio and LangChain leverages LLM prompting (Gemma2‑9b‑It) to translate user inputs into Cypher queries, enabling seamless, conversational exploration of the graph. We also implemented and compared three state‑of‑the‑art embedding methods - TransE, RotatE, and ComplEx- on link prediction benchmarks, evaluating performance via metrics such as Hits@k and mean reciprocal rank. To make link prediction findings actionable, we also provide an interactive GUI that allows researchers to visualize and test potential new relationships in real time. This integrated framework combines powerful embedding techniques with user‑friendly interfaces to support rapid hypothesis generation and discovery in biomedical research
Monoicy, dioicy, and genetic structure in three species of Sheathia (Batrachospermales, Rhodophyta)
No full textAll multilocus haploid genotypes are provided in GenAlEx format in the following .xlsx files:Sheathia americana: Sam_Genotypes.xlsxSheathia grandis: Sgr_Genotypes.xlsxSheathia involuta: Sinv_Genotypes.xlsx
Site acronyms:AL_KIS: King Spring, AlabamaCT_MAL: Maltby Park, ConnecticutNY_PEC: Peconic River, New YorkPA_DEC: Decker Creek, PennsylvaniaRI_PHI: Phillips Brook, Rhode IslandMI_ASR: Au Sable River Park, MichiganMI_BLA: Black River, MichiganMI_CLC: Crumbly Creek, MichiganMI_MNR: Manistee River, MichiganMI_RAP: Rapid River, MichiganMI_WSR: West Branch Sturgeon River, MichiganNY_HAM: Stream in Hamilton, New YorkNY_ROG: Rogers Environmental Center in Sherburne, New YorkWI_TOM: Strait between Lake Orlando and Beasley Lake, near Waupaca, WIWI_BLU: Bluff Creek, WisconsinMI_TRR: Trap Rock River, MichiganMI_BOT: Matthaei Botanical Gardens (Fleming Creek), MichiganMI_HEL: Hell Creek, MichiganMI_HIA: Rock River, MichiganMI_HLK: Knappen Creek, MichiganWI_HRT: Hermann Creek, WisconsinWI_TIC: Tichigan Creek, Wisconsin
The RMarkDown file includes the R code used for data visualizations and for calculating pairwise linkage disequilibrium in Genepop
Development of a Lightweight Dataset Catalog with Digital Commons
Get PDFMany academic libraries maintain dataset catalogs to make the research data produced by their institutions more findable, accessible, interoperable, and reusable (FAIR). These catalogs use a variety of approaches to gather metadata for dataset records and may be hosted on one of several platforms. Our approach is to develop Python code and leverage APIs to locate relevant datasets, then harvest and clean the associated metadata. The cleaned metadata is manually curated and enhanced before being added to the Dataset Catalog. We employ the hosted institutional repository Digital Commons to display the dataset records. Combining API harvesting and automated data cleaning with the batch upload feature of Digital Commons yields efficient ingestion of many dataset records, allowing us to prioritize manual curation and enhancement of the dataset metadata to make it FAIRer. This methodology is ideal for institutions unable to dedicate a large personnel team or significant technical resources to launching a Dataset Catalog. The resulting dataset catalog currently contains over 100 records from multiple repositories. This project was supported through the Data Services Continuing Professional Education (DSCPE) program
Data related to Human TDP-43 expression worsens FTD-related phenotypes in progranulin-insufficient mice
No full textLoss-of-function progranulin (GRN) mutations cause frontotemporal dementia with TDP-43 pathology (FTD-TDP). Nearly all pathogenic GRN mutations cause progranulin haploinsufficiency, but it is unclear how progranulin insufficiency causes FTD-TDP. To address this question, we crossed progranulin-insufficient mice with a human TDP-43 transgenic mouse line (RRID:IMSR_JAX:012836) in which homozygous mice (hTDP++) develop TDP-43 aggregates at an early age, but hemizygous mice (hTDP+) do not. Despite observing no change in TDP-43 aggregation with Grn genotype in hTDP+ or hTDP++ mice, we found that human TDP-43 expression worsened FTD-related phenotypes in progranulin-insufficient mice. Human TDP-43 expression worsened social dominance behavior in Grn+/− mice, which was associated with dendritic spine abnormalities in medial prefrontal cortex (mPFC). Human TDP-43 also had distinct effects on RNA splicing in frontal cortex of Grn+/− versus wild-type mice. All hTDP++ mice developed severe motor deficits and neuroinflammation. However, human TDP-43 expression induced worse motor deficits in Grn−/−:hTDP++ mice, as well as an abnormal inflammatory response characterized by increased markers of disease-associated microglia and signs of an impaired adaptive immune response. These results highlight dysfunction of mPFC neurons as a potential mechanism of behavioral changes in FTD-GRN, and implicate dysregulated inflammation as a potential driver of disease progression in FTD-GRN