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Six novel phages belonging to the family were isolated using as a host. Phages MuffinTheCat, Badulia, DesireeRose, Bee17, SCoupsA, and LuzDeMundo were purified from environmental samples by students participating in the Science Education Alliance Phage Hunters Advancing Genomics and Evolutionary Science (SEA-PHAGES) program at Alliance University, New York. The phages have linear dsDNA genomes 15,438-15,636 bp with 112-120 bp inverted terminal repeats.

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The Antibody Mediated Prevention (AMP) trials showed that passively infused VRC01, a broadly neutralizing antibody (bNAb) targeting the CD4 binding site (CD4bs) on the HIV-1 envelope protein (Env), protected against neutralization-sensitive viruses. We identified six individuals from the VRC01 treatment arm with multi-lineage breakthrough HIV-1 infections from HVTN703, where one variant was sensitive to VRC01 (IC < 25 ug/mL) but another was resistant. By comparing Env sequences of resistant and sensitive clones from each participant, we identified sites predicted to affect VRC01 neutralization and assessed the effect of their reversion in the VRC01-resistant clone on neutralization sensitivity.

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Massively parallel characterization of transcriptional regulatory elements.

Nature

January 2025

Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA.

The human genome contains millions of candidate cis-regulatory elements (cCREs) with cell-type-specific activities that shape both health and many disease states. However, we lack a functional understanding of the sequence features that control the activity and cell-type-specific features of these cCREs. Here we used lentivirus-based massively parallel reporter assays (lentiMPRAs) to test the regulatory activity of more than 680,000 sequences, representing an extensive set of annotated cCREs among three cell types (HepG2, K562 and WTC11), and found that 41.

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Artificial intelligence (AI) and machine learning (ML) are driving innovation in biosciences and are already affecting key elements of medical scholarship and clinical care. Many schools of medicine are capitalizing on the promise of these new technologies by establishing academic units to catalyze and grow research and innovation in AI/ML. At Stanford University, we have developed a successful model for an AI/ML research center with support from academic leaders, clinical departments, extramural grants, and industry partners.

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