Life Sciences

185 items

bioRxiv Sep 26, 2026
The authors present SHERLOCK, an interpretable deep generative framework that represents genetic and pharmacological perturbations as structured interventions on a latent baseline cellular state, learns correlated and sparse perturbation representations, enables counterfactual estimation of downstream transcriptional effects under explicit identifiability assumptions, quantifies condition-dependent responses, and compositionally models combinatorial perturbations; across genome-scale CRISPR, chemical, and spatial perturbation datasets, it recovers perturbation relationships concordant with known biological pathways and pharmacological properties, identifies condition-dependent responses, and predicts combinatorial perturbation effects.
The authors present SHERLOCK, an interpretable deep generative framework that represents genetic and pharmacological perturbations as structured interventions on a latent baseline cellular state, learns correlated and sparse perturbation representations, enables counterfactual estimation of downstream transcriptional effects under explicit identifiability assumptions, quantifies condition-dependent responses, and compositionally models combinatorial perturbations; across genome-scale CRISPR, chemical, and spatial perturbation datasets, it recovers perturbation relationships concordant with known biological pathways and pharmacological properties, identifies condition-dependent responses, and predicts combinatorial perturbation effects.
SHERLOCK models perturbation effects as structured interventions on a latent state, recovering pathway relationships and predicting combinatorial effects across CRISPR, chemical, and spatial perturbation data
The authors present SHERLOCK, an interpretable deep generative framework that represents genetic and pharmacological perturbations as structured interventions on a latent baseline cellular state, learns correlated and sparse perturbation representations, enables counterfactual estimation of downstream transcriptional effects under explicit identifiability assumptions, quantifies condition-dependent responses, and compositionally models combinatorial perturbations; across genome-scale CRISPR, chemical, and spatial perturbation datasets, it recovers perturbation relationships concordant with known biological pathways and pharmacological properties, identifies condition-dependent responses, and predicts combinatorial perturbation effects.
The authors present SHERLOCK, an interpretable deep generative framework that represents genetic and pharmacological perturbations as structured interventions on a latent baseline cellular state, learns correlated and sparse perturbation representations, enables counterfactual estimation of downstream transcriptional effects under explicit identifiability assumptions, quantifies condition-dependent responses, and compositionally models combinatorial perturbations; across genome-scale CRISPR, chemical, and spatial perturbation datasets, it recovers perturbation relationships concordant with known biological pathways and pharmacological properties, identifies condition-dependent responses, and predicts combinatorial perturbation effects.
Mendeley Data Sep 26, 2026
Using nine-camera volumetric imaging, Social-Seq behavioral syllable parsing, and GRAB-DA3m fiber photometry in PND 35-85 wild-type and Shank3+/- rats during same-sex dyadic interaction, this study characterized nucleus accumbens dopamine dynamics, finding that wild-type males show dopamine surges during proactive play such as pouncing and pinning while forced submission suppresses dopamine, wild-type females show increases during evasion and rearing but not contact-heavy play, Shank3 mutants show blunted responses during sniffing and chasing and a sign-inverted dopamine response during pouncing, a multi-agent reinforcement learning model parameterized with empirical dopamine amplitudes reproduced the mutant phenotype, and closed-loop optogenetic stimulation of VTA-NAc established causal s
Using nine-camera volumetric imaging, Social-Seq behavioral syllable parsing, and GRAB-DA3m fiber photometry in PND 35-85 wild-type and Shank3+/- rats during same-sex dyadic interaction, this study characterized nucleus accumbens dopamine dynamics, finding that wild-type males show dopamine surges during proactive play such as pouncing and pinning while forced submission suppresses dopamine, wild-type females show increases during evasion and rearing but not contact-heavy play, Shank3 mutants show blunted responses during sniffing and chasing and a sign-inverted dopamine response during pouncing, a multi-agent reinforcement learning model parameterized with empirical dopamine amplitudes reproduced the mutant phenotype, and closed-loop optogenetic stimulation of VTA-NAc established causal s
Shank3-deficient rats show a sign-inverted accumbal dopamine response during pouncing, and closed-loop optogenetic VTA-NAc stimulation persistently lengthens social play
Using nine-camera volumetric imaging, Social-Seq behavioral syllable parsing, and GRAB-DA3m fiber photometry in PND 35-85 wild-type and Shank3+/- rats during same-sex dyadic interaction, this study characterized nucleus accumbens dopamine dynamics, finding that wild-type males show dopamine surges during proactive play such as pouncing and pinning while forced submission suppresses dopamine, wild-type females show increases during evasion and rearing but not contact-heavy play, Shank3 mutants show blunted responses during sniffing and chasing and a sign-inverted dopamine response during pouncing, a multi-agent reinforcement learning model parameterized with empirical dopamine amplitudes reproduced the mutant phenotype, and closed-loop optogenetic stimulation of VTA-NAc established causal s
Using nine-camera volumetric imaging, Social-Seq behavioral syllable parsing, and GRAB-DA3m fiber photometry in PND 35-85 wild-type and Shank3+/- rats during same-sex dyadic interaction, this study characterized nucleus accumbens dopamine dynamics, finding that wild-type males show dopamine surges during proactive play such as pouncing and pinning while forced submission suppresses dopamine, wild-type females show increases during evasion and rearing but not contact-heavy play, Shank3 mutants show blunted responses during sniffing and chasing and a sign-inverted dopamine response during pouncing, a multi-agent reinforcement learning model parameterized with empirical dopamine amplitudes reproduced the mutant phenotype, and closed-loop optogenetic stimulation of VTA-NAc established causal s
Mendeley Data Sep 26, 2026
This work collected approximately 127 audio-visual dog recordings from various localities of Mangalore, organized them into four emotion classes—Happy, Sad, Angry, and Relaxed—and identified and assigned emotion labels through clustering algorithms, aiming to provide a data basis for multimodal, audio-based, video-based, and emotion classification systems and to investigate the feasibility of recognizing dog emotions from audio-visual cues.
This work collected approximately 127 audio-visual dog recordings from various localities of Mangalore, organized them into four emotion classes—Happy, Sad, Angry, and Relaxed—and identified and assigned emotion labels through clustering algorithms, aiming to provide a data basis for multimodal, audio-based, video-based, and emotion classification systems and to investigate the feasibility of recognizing dog emotions from audio-visual cues.
Researchers collected about 127 audio-visual dog recordings in Mangalore to build a multimodal canine emotion dataset covering Happy, Sad, Angry, and Relaxed
This work collected approximately 127 audio-visual dog recordings from various localities of Mangalore, organized them into four emotion classes—Happy, Sad, Angry, and Relaxed—and identified and assigned emotion labels through clustering algorithms, aiming to provide a data basis for multimodal, audio-based, video-based, and emotion classification systems and to investigate the feasibility of recognizing dog emotions from audio-visual cues.
This work collected approximately 127 audio-visual dog recordings from various localities of Mangalore, organized them into four emotion classes—Happy, Sad, Angry, and Relaxed—and identified and assigned emotion labels through clustering algorithms, aiming to provide a data basis for multimodal, audio-based, video-based, and emotion classification systems and to investigate the feasibility of recognizing dog emotions from audio-visual cues.
arXiv Sep 25, 2026
The study presents a percept-aware surgical planning framework for cortical visual prostheses that treats 3D electrode coordinates as learnable parameters and optimizes them end-to-end through a differentiable forward model of prosthetic vision on FreeSurfer fsaverage folded cortical geometry, minimizing task-level perceptual error subject to vascular avoidance and gray matter feasibility constraints; on simulated MNIST reading and CIFAR-10 natural image tasks it consistently improved reconstruction fidelity over visual field tiling and visual field coverage baselines (median MSE reductions of up to 67.7% and 33.4% on MNIST, with downstream classification accuracy gains of 62.6% and 22.4%), eliminated all 300 µm safety-margin violations with only 1.7% (MNIST) and 4.
The study presents a percept-aware surgical planning framework for cortical visual prostheses that treats 3D electrode coordinates as learnable parameters and optimizes them end-to-end through a differentiable forward model of prosthetic vision on FreeSurfer fsaverage folded cortical geometry, minimizing task-level perceptual error subject to vascular avoidance and gray matter feasibility constraints; on simulated MNIST reading and CIFAR-10 natural image tasks it consistently improved reconstruction fidelity over visual field tiling and visual field coverage baselines (median MSE reductions of up to 67.7% and 33.4% on MNIST, with downstream classification accuracy gains of 62.6% and 22.4%), eliminated all 300 µm safety-margin violations with only 1.7% (MNIST) and 4.
Treating electrode coordinates as learnable parameters: percept-aware optimization on folded cortex improves reconstruction fidelity while eliminating vascular safety-margin violations
The study presents a percept-aware surgical planning framework for cortical visual prostheses that treats 3D electrode coordinates as learnable parameters and optimizes them end-to-end through a differentiable forward model of prosthetic vision on FreeSurfer fsaverage folded cortical geometry, minimizing task-level perceptual error subject to vascular avoidance and gray matter feasibility constraints; on simulated MNIST reading and CIFAR-10 natural image tasks it consistently improved reconstruction fidelity over visual field tiling and visual field coverage baselines (median MSE reductions of up to 67.7% and 33.4% on MNIST, with downstream classification accuracy gains of 62.6% and 22.4%), eliminated all 300 µm safety-margin violations with only 1.7% (MNIST) and 4.
The study presents a percept-aware surgical planning framework for cortical visual prostheses that treats 3D electrode coordinates as learnable parameters and optimizes them end-to-end through a differentiable forward model of prosthetic vision on FreeSurfer fsaverage folded cortical geometry, minimizing task-level perceptual error subject to vascular avoidance and gray matter feasibility constraints; on simulated MNIST reading and CIFAR-10 natural image tasks it consistently improved reconstruction fidelity over visual field tiling and visual field coverage baselines (median MSE reductions of up to 67.7% and 33.4% on MNIST, with downstream classification accuracy gains of 62.6% and 22.4%), eliminated all 300 µm safety-margin violations with only 1.7% (MNIST) and 4.
Nature Communications Sep 25, 2026
The work presents StriMap, a unified framework that predicts TCR–peptide–HLA interactions by integrating physicochemical, sequence-context, and structural features at recognition interfaces, reporting state-of-the-art performance with improved generalizability; as a case study, the authors screened 13 million peptides from 43,241 bacterial proteins and identified candidate molecular mimics that were experimentally validated to activate T cells expressing an ankylosing spondylitis (AS)-associated TCR, with a top validated peptide enriched in patients with inflammatory bowel disease (IBD), suggesting potential shared microbial triggers.
The work presents StriMap, a unified framework that predicts TCR–peptide–HLA interactions by integrating physicochemical, sequence-context, and structural features at recognition interfaces, reporting state-of-the-art performance with improved generalizability; as a case study, the authors screened 13 million peptides from 43,241 bacterial proteins and identified candidate molecular mimics that were experimentally validated to activate T cells expressing an ankylosing spondylitis (AS)-associated TCR, with a top validated peptide enriched in patients with inflammatory bowel disease (IBD), suggesting potential shared microbial triggers.
StriMap integrates physicochemical, sequence-context, and interface-structural features to predict TCR–peptide–HLA recognition, and screening 13 million peptides from 43,241 bacterial proteins yielded candidate molecular mimics that activated T cells expressing an ankylosing spondylitis-associated TCR
The work presents StriMap, a unified framework that predicts TCR–peptide–HLA interactions by integrating physicochemical, sequence-context, and structural features at recognition interfaces, reporting state-of-the-art performance with improved generalizability; as a case study, the authors screened 13 million peptides from 43,241 bacterial proteins and identified candidate molecular mimics that were experimentally validated to activate T cells expressing an ankylosing spondylitis (AS)-associated TCR, with a top validated peptide enriched in patients with inflammatory bowel disease (IBD), suggesting potential shared microbial triggers.
The work presents StriMap, a unified framework that predicts TCR–peptide–HLA interactions by integrating physicochemical, sequence-context, and structural features at recognition interfaces, reporting state-of-the-art performance with improved generalizability; as a case study, the authors screened 13 million peptides from 43,241 bacterial proteins and identified candidate molecular mimics that were experimentally validated to activate T cells expressing an ankylosing spondylitis (AS)-associated TCR, with a top validated peptide enriched in patients with inflammatory bowel disease (IBD), suggesting potential shared microbial triggers.
Nature Communications Sep 25, 2026
The authors developed multiplexed Deep Visual Proteomics (mxDVP), an end-to-end workflow combining high-plex imaging, automated computational analysis, and spatially guided ultra-sensitive mass spectrometry, powered by the open-source image analysis framework PIPΣX for whole-slide membrane-aware segmentation, annotation, and laser microdissection export, achieving more than 6,000 protein identifications from as few as 100 small islet cells; applied to human pancreatic islets it segmented over 860,000 cells and resolved twelve endocrine subtypes, including rare polyhormonal and intermediate-state populations showing spatial organization patterns, co-expression of INSM1 and SCG3, and hybrid α/β/δ signatures.
The authors developed multiplexed Deep Visual Proteomics (mxDVP), an end-to-end workflow combining high-plex imaging, automated computational analysis, and spatially guided ultra-sensitive mass spectrometry, powered by the open-source image analysis framework PIPΣX for whole-slide membrane-aware segmentation, annotation, and laser microdissection export, achieving more than 6,000 protein identifications from as few as 100 small islet cells; applied to human pancreatic islets it segmented over 860,000 cells and resolved twelve endocrine subtypes, including rare polyhormonal and intermediate-state populations showing spatial organization patterns, co-expression of INSM1 and SCG3, and hybrid α/β/δ signatures.
mxDVP identifies over 6,000 proteins from as few as 100 small islet cells and resolves twelve endocrine subtypes in human pancreatic islets
The authors developed multiplexed Deep Visual Proteomics (mxDVP), an end-to-end workflow combining high-plex imaging, automated computational analysis, and spatially guided ultra-sensitive mass spectrometry, powered by the open-source image analysis framework PIPΣX for whole-slide membrane-aware segmentation, annotation, and laser microdissection export, achieving more than 6,000 protein identifications from as few as 100 small islet cells; applied to human pancreatic islets it segmented over 860,000 cells and resolved twelve endocrine subtypes, including rare polyhormonal and intermediate-state populations showing spatial organization patterns, co-expression of INSM1 and SCG3, and hybrid α/β/δ signatures.
The authors developed multiplexed Deep Visual Proteomics (mxDVP), an end-to-end workflow combining high-plex imaging, automated computational analysis, and spatially guided ultra-sensitive mass spectrometry, powered by the open-source image analysis framework PIPΣX for whole-slide membrane-aware segmentation, annotation, and laser microdissection export, achieving more than 6,000 protein identifications from as few as 100 small islet cells; applied to human pancreatic islets it segmented over 860,000 cells and resolved twelve endocrine subtypes, including rare polyhormonal and intermediate-state populations showing spatial organization patterns, co-expression of INSM1 and SCG3, and hybrid α/β/δ signatures.
bioRxiv (Cold Spring Harbor Laboratory) Sep 25, 2026
The study presents HARP (Human Cell Atlas Reference for PBMCs), an integrated atlas of ~9 million peripheral blood mononuclear cells (PBMCs) from >2,600 donors across 15 studies spanning four continents, neonates to 97 years, and health and diverse immune-related diseases; it develops optimized integration workflows with novel label-free metrics for integration quality, generates community-driven consensus annotations for 192 immune cell subsets including rare populations as low as 0.
The study presents HARP (Human Cell Atlas Reference for PBMCs), an integrated atlas of ~9 million peripheral blood mononuclear cells (PBMCs) from >2,600 donors across 15 studies spanning four continents, neonates to 97 years, and health and diverse immune-related diseases; it develops optimized integration workflows with novel label-free metrics for integration quality, generates community-driven consensus annotations for 192 immune cell subsets including rare populations as low as 0.
HARP integrates ~9 million PBMCs with 192 immune cell annotations and uncovers a sex dimorphism in prostaglandin signaling
The study presents HARP (Human Cell Atlas Reference for PBMCs), an integrated atlas of ~9 million peripheral blood mononuclear cells (PBMCs) from >2,600 donors across 15 studies spanning four continents, neonates to 97 years, and health and diverse immune-related diseases; it develops optimized integration workflows with novel label-free metrics for integration quality, generates community-driven consensus annotations for 192 immune cell subsets including rare populations as low as 0.
The study presents HARP (Human Cell Atlas Reference for PBMCs), an integrated atlas of ~9 million peripheral blood mononuclear cells (PBMCs) from >2,600 donors across 15 studies spanning four continents, neonates to 97 years, and health and diverse immune-related diseases; it develops optimized integration workflows with novel label-free metrics for integration quality, generates community-driven consensus annotations for 192 immune cell subsets including rare populations as low as 0.
Frontiers in Pharmacology Sep 25, 2026
This review integrates evidence on microbial drug metabolism, excipient–microbiome interactions, nanocarrier–microbiome interfaces, microbiota-responsive release mechanisms, and experimental models to propose an authors' evidence-informed framework for oral formulation design, comprising an excipient–microbiome risk classification, nanocarrier design rules, microbiota-responsive delivery decision logic, and a tiered testing roadmap, illustrated by clinically relevant examples such as digoxin inactivation by Eggerthella lenta, bacterial levodopa metabolism, and microbial beta-glucuronidase-mediated irinotecan toxicity.
This review integrates evidence on microbial drug metabolism, excipient–microbiome interactions, nanocarrier–microbiome interfaces, microbiota-responsive release mechanisms, and experimental models to propose an authors' evidence-informed framework for oral formulation design, comprising an excipient–microbiome risk classification, nanocarrier design rules, microbiota-responsive delivery decision logic, and a tiered testing roadmap, illustrated by clinically relevant examples such as digoxin inactivation by Eggerthella lenta, bacterial levodopa metabolism, and microbial beta-glucuronidase-mediated irinotecan toxicity.
Review proposes a microbiome-aware oral formulation framework: excipient risk classification, nanocarrier design rules, and a tiered testing roadmap
This review integrates evidence on microbial drug metabolism, excipient–microbiome interactions, nanocarrier–microbiome interfaces, microbiota-responsive release mechanisms, and experimental models to propose an authors' evidence-informed framework for oral formulation design, comprising an excipient–microbiome risk classification, nanocarrier design rules, microbiota-responsive delivery decision logic, and a tiered testing roadmap, illustrated by clinically relevant examples such as digoxin inactivation by Eggerthella lenta, bacterial levodopa metabolism, and microbial beta-glucuronidase-mediated irinotecan toxicity.
This review integrates evidence on microbial drug metabolism, excipient–microbiome interactions, nanocarrier–microbiome interfaces, microbiota-responsive release mechanisms, and experimental models to propose an authors' evidence-informed framework for oral formulation design, comprising an excipient–microbiome risk classification, nanocarrier design rules, microbiota-responsive delivery decision logic, and a tiered testing roadmap, illustrated by clinically relevant examples such as digoxin inactivation by Eggerthella lenta, bacterial levodopa metabolism, and microbial beta-glucuronidase-mediated irinotecan toxicity.
Nature News Sep 25, 2026
Anthropic announced a life-sciences research group and wet lab on 23 September and released a non-peer-reviewed preprint: roughly 950 AI agents ran autonomously for more than 21 hours, searching a DNA database encoding billions of proteins for proteins that might work with reverse transcriptases, and while examining the DNA around a reverse transcriptase gene in a giant virus they repeatedly saw the same short DNA sequence, then found similar patterns in the genomes of other viruses; the repeat arrays resemble those in some microbial CRISPR immune systems, but their function is undetermined and no DNA-slicing enzyme is known to partner with them.
Anthropic announced a life-sciences research group and wet lab on 23 September and released a non-peer-reviewed preprint: roughly 950 AI agents ran autonomously for more than 21 hours, searching a DNA database encoding billions of proteins for proteins that might work with reverse transcriptases, and while examining the DNA around a reverse transcriptase gene in a giant virus they repeatedly saw the same short DNA sequence, then found similar patterns in the genomes of other viruses; the repeat arrays resemble those in some microbial CRISPR immune systems, but their function is undetermined and no DNA-slicing enzyme is known to partner with them.
Anthropic's AI biolab ran roughly 950 agents over billions of proteins and found CRISPR-like repeat DNA arrays in giant virus genomes
Anthropic announced a life-sciences research group and wet lab on 23 September and released a non-peer-reviewed preprint: roughly 950 AI agents ran autonomously for more than 21 hours, searching a DNA database encoding billions of proteins for proteins that might work with reverse transcriptases, and while examining the DNA around a reverse transcriptase gene in a giant virus they repeatedly saw the same short DNA sequence, then found similar patterns in the genomes of other viruses; the repeat arrays resemble those in some microbial CRISPR immune systems, but their function is undetermined and no DNA-slicing enzyme is known to partner with them.
Anthropic announced a life-sciences research group and wet lab on 23 September and released a non-peer-reviewed preprint: roughly 950 AI agents ran autonomously for more than 21 hours, searching a DNA database encoding billions of proteins for proteins that might work with reverse transcriptases, and while examining the DNA around a reverse transcriptase gene in a giant virus they repeatedly saw the same short DNA sequence, then found similar patterns in the genomes of other viruses; the repeat arrays resemble those in some microbial CRISPR immune systems, but their function is undetermined and no DNA-slicing enzyme is known to partner with them.
NVIDIA Technical Blog Sep 24, 2026
Sudhakar Singh, Varun Thumbe, Santosh Santosh, Timur Rvachov, and Chris Hoge at NVIDIA published a tutorial on training MoE-based biological foundation models with the NVIDIA BioNeMo MoE recipe and Transformer Engine: GroupedLinear replaces the per-expert Python loop with a single grouped GEMM, MXFP8 (one scaling factor per block of 32 consecutive values) cuts weights and activations from 16 bits to 8 bits, and the Sequential API fuses GroupedLinear to ScaledSwiGLU to GroupedLinear into the ForwardGroupedMLP_CuTeGEMMSwiGLU_MXFP8 forward op plus a matching backward op; in the training benchmark on eight NVIDIA B200 Tensor Core GPUs the recipe delivered up to 2.21x the throughput of the Hugging Face baseline.
Sudhakar Singh, Varun Thumbe, Santosh Santosh, Timur Rvachov, and Chris Hoge at NVIDIA published a tutorial on training MoE-based biological foundation models with the NVIDIA BioNeMo MoE recipe and Transformer Engine: GroupedLinear replaces the per-expert Python loop with a single grouped GEMM, MXFP8 (one scaling factor per block of 32 consecutive values) cuts weights and activations from 16 bits to 8 bits, and the Sequential API fuses GroupedLinear to ScaledSwiGLU to GroupedLinear into the ForwardGroupedMLP_CuTeGEMMSwiGLU_MXFP8 forward op plus a matching backward op; in the training benchmark on eight NVIDIA B200 Tensor Core GPUs the recipe delivered up to 2.21x the throughput of the Hugging Face baseline.
NVIDIA team's BioNeMo MoE recipe with Transformer Engine lifts biological foundation model training throughput to 2.21x the Hugging Face baseline on eight B200 GPUs
Sudhakar Singh, Varun Thumbe, Santosh Santosh, Timur Rvachov, and Chris Hoge at NVIDIA published a tutorial on training MoE-based biological foundation models with the NVIDIA BioNeMo MoE recipe and Transformer Engine: GroupedLinear replaces the per-expert Python loop with a single grouped GEMM, MXFP8 (one scaling factor per block of 32 consecutive values) cuts weights and activations from 16 bits to 8 bits, and the Sequential API fuses GroupedLinear to ScaledSwiGLU to GroupedLinear into the ForwardGroupedMLP_CuTeGEMMSwiGLU_MXFP8 forward op plus a matching backward op; in the training benchmark on eight NVIDIA B200 Tensor Core GPUs the recipe delivered up to 2.21x the throughput of the Hugging Face baseline.
Sudhakar Singh, Varun Thumbe, Santosh Santosh, Timur Rvachov, and Chris Hoge at NVIDIA published a tutorial on training MoE-based biological foundation models with the NVIDIA BioNeMo MoE recipe and Transformer Engine: GroupedLinear replaces the per-expert Python loop with a single grouped GEMM, MXFP8 (one scaling factor per block of 32 consecutive values) cuts weights and activations from 16 bits to 8 bits, and the Sequential API fuses GroupedLinear to ScaledSwiGLU to GroupedLinear into the ForwardGroupedMLP_CuTeGEMMSwiGLU_MXFP8 forward op plus a matching backward op; in the training benchmark on eight NVIDIA B200 Tensor Core GPUs the recipe delivered up to 2.21x the throughput of the Hugging Face baseline.

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