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Abstract Supernova (SN) 2014C is a rare transitional event that exploded as a hydrogen-poor, helium-rich Type Ib SN and subsequently interacted with a hydrogen-rich circumstellar medium (CSM) a few months postexplosion. This unique interacting object provides an opportunity to probe the mass-loss history of a stripped-envelope SN progenitor. Using the James Webb Space Telescope (JWST), we observed SN 2014C with the Mid-Infrared Instrument Medium Resolution Spectrometer at 3477 days postexplosion (rest frame), and the Near-Infrared Spectrograph Integral Field Unit at 3568 days postexplosion, covering 1.7–25μm. The bolometric luminosity indicates that the SN is still interacting with the same CSM that was observed with the Spitzer Space Telescope 40–1920 days postexplosion. JWST spectra and near-contemporaneous optical and near-infrared spectra show strong [Neii] 12.831μm, He 1.083μm, Hα, and forbidden oxygen ([Oi]λλ6300, 6364, [Oii]λλ7319, 7330, and [Oiii]λλ4959, 5007) emission lines with asymmetric profiles, suggesting a highly asymmetric CSM. The mid-IR continuum can be explained by ∼0.036M_⊙of carbonaceous dust at ∼300 K and ∼0.043M_⊙of silicate dust at ∼200 K. The observed dust mass has increased tenfold since the last Spitzer observation 4 yr ago, with evidence suggesting that new grains have condensed in the cold dense shell between the forward and reverse shocks. This dust mass places SN 2014C among the dustiest SNe in the mid-IR and supports the emerging observational trend that SN explosions produce enough dust to explain the observed dust mass at high redshifts. 

We created GNQA, a generative pre-trained transformer (GPT) knowledge base driven by a performant retrieval augmented generation (RAG) with a focus on aging, dementia, Alzheimer’s and diabetes. We uploaded a corpus of three thousand peer reviewed publications on these topics into the RAG. To address concerns about inaccurate responses and GPT ‘hallucinations’, we implemented a context provenance tracking mechanism that enables researchers to validate responses against the original material and to get references to the original papers. To assess the effectiveness of contextual information we collected evaluations and feedback from both domain expert users and ‘citizen scientists’ on the relevance of GPT responses. A key innovation of our study is automated evaluation by way of a RAG assessment system (RAGAS). RAGAS combines human expert assessment with AI-driven evaluation to measure the effectiveness of RAG systems. When evaluating the responses to their questions, human respondents give a “thumbs-up” 76% of the time. Meanwhile, RAGAS scores 90% on answer relevance on questions posed by experts. And when GPT-generates questions, RAGAS scores 74% on answer relevance. With RAGAS we created a benchmark that can be used to continuously assess the performance of our knowledge base. Full GNQA functionality is embedded in the freeGeneNetwork.orgweb service, an open-source system containing over 25 years of experimental data on model organisms and human. The code developed for this study is published under a free and open-source software license athttps://git.genenetwork.org/gn-ai/tree/README.md. 

Pangenome graphs can represent all variation between multiple reference genomes, but current approaches to build them exclude complex sequences or are based upon a single reference. In response, we developed the PanGenome Graph Builder, a pipeline for constructing pangenome graphs without bias or exclusion. The PanGenome Graph Builder uses all-to-all alignments to build a variation graph in which we can identify variation, measure conservation, detect recombination events and infer phylogenetic relationships. 

Abstract We construct Bayesian and frequentist finite-sample goodness-of-fit tests for three different variants of the stochastic blockmodel for network data. Since all of the stochastic blockmodel variants are log-linear in form when block assignments are known, the tests for the latent block model versions combine a block membership estimator with the algebraic statistics machinery for testing goodness-of-fit in log-linear models. We describe Markov bases and marginal polytopes of the variants of the stochastic blockmodel and discuss how both facilitate the development of goodness-of-fit tests and understanding of model behaviour. The general testing methodology developed here extends to any finite mixture of log-linear models on discrete data, and as such is the first application of the algebraic statistics machinery for latent-variable models. 

Abstract The Biorepository and Integrative Genomics (BIG) Initiative in Tennessee has developed a pioneering resource to address gaps in genomic research by linking genomic, phenotypic, and environmental data from a diverse Mid-South population, including underrepresented groups. We analyzed 13,152 exomes from BIG and found significant genetic diversity, with 50% of participants inferred to have non-European or several types of admixed ancestry. Ancestry within the BIG cohort is stratified, with distinct geographic and demographic patterns, as African ancestry is more common in urban areas, while European ancestry is more common in suburban regions. We observe ancestry-specific rates of novel genetic variants, which are enriched for functional or clinical relevance. Disease prevalence analysis linked ancestry and environmental factors, showing higher odds ratios for asthma and obesity in minority groups, particularly in the urban area. Finally, we observe discrepancies between self-reported race and genetic ancestry, with related individuals self-identifying in differing racial categories. These findings underscore the limitations of race as a biomedical variable. BIG has proven to be an effective model for community-centered precision medicine. We integrated genomics education, and fostered great trust among the contributing communities. Future goals include cohort expansion, and enhanced genomic analysis, to ensure equitable healthcare outcomes. 

Abstract Pangenome graphs can represent all variation between multiple genomes, but existing methods for constructing them are biased due to reference-guided approaches. In response, we have developed PanGenome Graph Builder (PGGB), a reference-free pipeline for constructing unbi-ased pangenome graphs. PGGB uses all-to-all whole-genome alignments and learned graph embeddings to build and iteratively refine a model in which we can identify variation, measure conservation, detect recombination events, and infer phylogenetic relationships.