Life Sciences

185 items

bioRxiv Sep 29, 2026
The authors present DECIPHER, an end-to-end representation-learning framework for cell-type deconvolution that learns a domain-constant representation (Zc) for deconvolution and a domain-specific representation (Zs) to model domain-associated variation, estimates cell-type proportions from Zc via differentiable non-negative least-squares optimization, shows robust and competitive deconvolution performance across simulated datasets, experimentally generated bulk-cell mixtures, real-world datasets and multiple molecular modalities, and further shows that the learned Zc supports chronological age prediction across independent cohorts and prognostic stratification in lung adenocarcinoma.
The authors present DECIPHER, an end-to-end representation-learning framework for cell-type deconvolution that learns a domain-constant representation (Zc) for deconvolution and a domain-specific representation (Zs) to model domain-associated variation, estimates cell-type proportions from Zc via differentiable non-negative least-squares optimization, shows robust and competitive deconvolution performance across simulated datasets, experimentally generated bulk-cell mixtures, real-world datasets and multiple molecular modalities, and further shows that the learned Zc supports chronological age prediction across independent cohorts and prognostic stratification in lung adenocarcinoma.
DECIPHER estimates cell-type proportions from bulk omics via disentangled representation learning and supports prognostic stratification in lung adenocarcinoma
The authors present DECIPHER, an end-to-end representation-learning framework for cell-type deconvolution that learns a domain-constant representation (Zc) for deconvolution and a domain-specific representation (Zs) to model domain-associated variation, estimates cell-type proportions from Zc via differentiable non-negative least-squares optimization, shows robust and competitive deconvolution performance across simulated datasets, experimentally generated bulk-cell mixtures, real-world datasets and multiple molecular modalities, and further shows that the learned Zc supports chronological age prediction across independent cohorts and prognostic stratification in lung adenocarcinoma.
The authors present DECIPHER, an end-to-end representation-learning framework for cell-type deconvolution that learns a domain-constant representation (Zc) for deconvolution and a domain-specific representation (Zs) to model domain-associated variation, estimates cell-type proportions from Zc via differentiable non-negative least-squares optimization, shows robust and competitive deconvolution performance across simulated datasets, experimentally generated bulk-cell mixtures, real-world datasets and multiple molecular modalities, and further shows that the learned Zc supports chronological age prediction across independent cohorts and prognostic stratification in lung adenocarcinoma.
bioRxiv Sep 29, 2026
The authors present TRIDENT-2, a multimodal artificial intelligence model trained on 560,780 toxicity assays spanning 82,775 chemicals, 6,793 species, and multiple exposure scenarios to predict chemical toxicity across evolutionarily diverse eukaryotic species, reporting an average median absolute error of 1.76 to 3.80 and remaining accurate across broad chemical and taxonomic distances, which allows toxicity assessment for species and chemicals beyond current experimental evidence.
The authors present TRIDENT-2, a multimodal artificial intelligence model trained on 560,780 toxicity assays spanning 82,775 chemicals, 6,793 species, and multiple exposure scenarios to predict chemical toxicity across evolutionarily diverse eukaryotic species, reporting an average median absolute error of 1.76 to 3.80 and remaining accurate across broad chemical and taxonomic distances, which allows toxicity assessment for species and chemicals beyond current experimental evidence.
TRIDENT-2 predicts chemical toxicity across Eukaryota from 560,780 assays with average median absolute error of 1.76 to 3.80
The authors present TRIDENT-2, a multimodal artificial intelligence model trained on 560,780 toxicity assays spanning 82,775 chemicals, 6,793 species, and multiple exposure scenarios to predict chemical toxicity across evolutionarily diverse eukaryotic species, reporting an average median absolute error of 1.76 to 3.80 and remaining accurate across broad chemical and taxonomic distances, which allows toxicity assessment for species and chemicals beyond current experimental evidence.
The authors present TRIDENT-2, a multimodal artificial intelligence model trained on 560,780 toxicity assays spanning 82,775 chemicals, 6,793 species, and multiple exposure scenarios to predict chemical toxicity across evolutionarily diverse eukaryotic species, reporting an average median absolute error of 1.76 to 3.80 and remaining accurate across broad chemical and taxonomic distances, which allows toxicity assessment for species and chemicals beyond current experimental evidence.
bioRxiv Sep 29, 2026
This work presents the first investigation, to the authors' knowledge, of the detectability of computationally modified sequences: the authors generate modified 16S rRNA sequences via random substitutions that pass the SILVA database quality-control inclusion criteria, and build classifiers that distinguish them from natural 16S rRNA using conserved motifs, with the best classifier achieving over 90% sensitivity and specificity on the testing set at a 5% artificial mutation rate, and one feature, gapped k-mers built from universally conserved nucleotides, conserved across all three domains of life despite relying on exact matches to patterns found in E. coli.
This work presents the first investigation, to the authors' knowledge, of the detectability of computationally modified sequences: the authors generate modified 16S rRNA sequences via random substitutions that pass the SILVA database quality-control inclusion criteria, and build classifiers that distinguish them from natural 16S rRNA using conserved motifs, with the best classifier achieving over 90% sensitivity and specificity on the testing set at a 5% artificial mutation rate, and one feature, gapped k-mers built from universally conserved nucleotides, conserved across all three domains of life despite relying on exact matches to patterns found in E. coli.
Researchers use conserved-motif classifiers to separate randomly substituted 16S rRNA from natural sequences, exceeding 90% sensitivity and specificity at a 5% mutation rate
This work presents the first investigation, to the authors' knowledge, of the detectability of computationally modified sequences: the authors generate modified 16S rRNA sequences via random substitutions that pass the SILVA database quality-control inclusion criteria, and build classifiers that distinguish them from natural 16S rRNA using conserved motifs, with the best classifier achieving over 90% sensitivity and specificity on the testing set at a 5% artificial mutation rate, and one feature, gapped k-mers built from universally conserved nucleotides, conserved across all three domains of life despite relying on exact matches to patterns found in E. coli.
This work presents the first investigation, to the authors' knowledge, of the detectability of computationally modified sequences: the authors generate modified 16S rRNA sequences via random substitutions that pass the SILVA database quality-control inclusion criteria, and build classifiers that distinguish them from natural 16S rRNA using conserved motifs, with the best classifier achieving over 90% sensitivity and specificity on the testing set at a 5% artificial mutation rate, and one feature, gapped k-mers built from universally conserved nucleotides, conserved across all three domains of life despite relying on exact matches to patterns found in E. coli.
bioRxiv Sep 29, 2026
The authors developed SpatialTRACE, comprising graph- and image-based models: SpatialTRACE-Graph combines gene-expression profiles with spatial neighborhoods to predict crypt-villus and epithelial-distance axis coordinates and to identify Peyer's patches in mouse small-intestine sections using as few as 10 annotated training villi, while SpatialTRACE-Image, a multiscale vision transformer that learns from the coordinate and region predictions generated by SpatialTRACE-Graph, predicts the same anatomical axis coordinates and regions across entire tissue images from DAPI alone and was applied to immunofluorescence images to map antigen-specific P14 CD8 T cells responding to acute systemic LCMV Armstrong infection in the small intestine, showing that a retinoic acid receptor inhibitor-treated
The authors developed SpatialTRACE, comprising graph- and image-based models: SpatialTRACE-Graph combines gene-expression profiles with spatial neighborhoods to predict crypt-villus and epithelial-distance axis coordinates and to identify Peyer's patches in mouse small-intestine sections using as few as 10 annotated training villi, while SpatialTRACE-Image, a multiscale vision transformer that learns from the coordinate and region predictions generated by SpatialTRACE-Graph, predicts the same anatomical axis coordinates and regions across entire tissue images from DAPI alone and was applied to immunofluorescence images to map antigen-specific P14 CD8 T cells responding to acute systemic LCMV Armstrong infection in the small intestine, showing that a retinoic acid receptor inhibitor-treated
SpatialTRACE extends sparse annotations into tissue-wide anatomical axis and region maps and predicts the same coordinates from DAPI images alone
The authors developed SpatialTRACE, comprising graph- and image-based models: SpatialTRACE-Graph combines gene-expression profiles with spatial neighborhoods to predict crypt-villus and epithelial-distance axis coordinates and to identify Peyer's patches in mouse small-intestine sections using as few as 10 annotated training villi, while SpatialTRACE-Image, a multiscale vision transformer that learns from the coordinate and region predictions generated by SpatialTRACE-Graph, predicts the same anatomical axis coordinates and regions across entire tissue images from DAPI alone and was applied to immunofluorescence images to map antigen-specific P14 CD8 T cells responding to acute systemic LCMV Armstrong infection in the small intestine, showing that a retinoic acid receptor inhibitor-treated
The authors developed SpatialTRACE, comprising graph- and image-based models: SpatialTRACE-Graph combines gene-expression profiles with spatial neighborhoods to predict crypt-villus and epithelial-distance axis coordinates and to identify Peyer's patches in mouse small-intestine sections using as few as 10 annotated training villi, while SpatialTRACE-Image, a multiscale vision transformer that learns from the coordinate and region predictions generated by SpatialTRACE-Graph, predicts the same anatomical axis coordinates and regions across entire tissue images from DAPI alone and was applied to immunofluorescence images to map antigen-specific P14 CD8 T cells responding to acute systemic LCMV Armstrong infection in the small intestine, showing that a retinoic acid receptor inhibitor-treated
bioRxiv Sep 29, 2026
The study benchmarked a doxycycline-inducible, hTERT-immortalized iPSC-derived mesenchymal stromal cell line engineered to overexpress TGFB1 (TGFB1-iMSCs) against multiple adipose tissue-derived MSC (MSC(AT)) donors across predefined immunomodulatory and angiogenic potency attributes, finding that TGFB1-iMSCs were smaller and more circular with comparable or higher proliferative rates, had a distinct angiogenic signature (EDIL3, EDN1, PDGFA) and nine differentially expressed microRNAs, secreted less VEGF with intermediate HUVEC tube formation, yet matched or exceeded all MSC(AT) donors in a monocyte-macrophage transwell immunomodulatory assay; in a murine DMM post-traumatic osteoarthritis model, a single intra-articular injection of TGFB1-iMSCs, but not MSC(AT), reduced total synovial macr
The study benchmarked a doxycycline-inducible, hTERT-immortalized iPSC-derived mesenchymal stromal cell line engineered to overexpress TGFB1 (TGFB1-iMSCs) against multiple adipose tissue-derived MSC (MSC(AT)) donors across predefined immunomodulatory and angiogenic potency attributes, finding that TGFB1-iMSCs were smaller and more circular with comparable or higher proliferative rates, had a distinct angiogenic signature (EDIL3, EDN1, PDGFA) and nine differentially expressed microRNAs, secreted less VEGF with intermediate HUVEC tube formation, yet matched or exceeded all MSC(AT) donors in a monocyte-macrophage transwell immunomodulatory assay; in a murine DMM post-traumatic osteoarthritis model, a single intra-articular injection of TGFB1-iMSCs, but not MSC(AT), reduced total synovial macr
A single intra-articular injection of TGFB1-engineered iMSCs lowered synovial macrophage numbers and the MHCII/CD206 ratio in a mouse osteoarthritis model but did not reduce cartilage degeneration
The study benchmarked a doxycycline-inducible, hTERT-immortalized iPSC-derived mesenchymal stromal cell line engineered to overexpress TGFB1 (TGFB1-iMSCs) against multiple adipose tissue-derived MSC (MSC(AT)) donors across predefined immunomodulatory and angiogenic potency attributes, finding that TGFB1-iMSCs were smaller and more circular with comparable or higher proliferative rates, had a distinct angiogenic signature (EDIL3, EDN1, PDGFA) and nine differentially expressed microRNAs, secreted less VEGF with intermediate HUVEC tube formation, yet matched or exceeded all MSC(AT) donors in a monocyte-macrophage transwell immunomodulatory assay; in a murine DMM post-traumatic osteoarthritis model, a single intra-articular injection of TGFB1-iMSCs, but not MSC(AT), reduced total synovial macr
The study benchmarked a doxycycline-inducible, hTERT-immortalized iPSC-derived mesenchymal stromal cell line engineered to overexpress TGFB1 (TGFB1-iMSCs) against multiple adipose tissue-derived MSC (MSC(AT)) donors across predefined immunomodulatory and angiogenic potency attributes, finding that TGFB1-iMSCs were smaller and more circular with comparable or higher proliferative rates, had a distinct angiogenic signature (EDIL3, EDN1, PDGFA) and nine differentially expressed microRNAs, secreted less VEGF with intermediate HUVEC tube formation, yet matched or exceeded all MSC(AT) donors in a monocyte-macrophage transwell immunomodulatory assay; in a murine DMM post-traumatic osteoarthritis model, a single intra-articular injection of TGFB1-iMSCs, but not MSC(AT), reduced total synovial macr
bioRxiv Sep 29, 2026
The study developed a tool that detects and compares insertion sequence insertions from short reads without a reference genome, applied it to 10,000 strains of the Mycobacterium tuberculosis complex (MTBC), and combined it with ancestral state reconstruction on presence-absence patterns to describe the distribution of IS6110 copy numbers (from 1 in some clades to more than 30 in strains of La3 (M.
The study developed a tool that detects and compares insertion sequence insertions from short reads without a reference genome, applied it to 10,000 strains of the Mycobacterium tuberculosis complex (MTBC), and combined it with ancestral state reconstruction on presence-absence patterns to describe the distribution of IS6110 copy numbers (from 1 in some clades to more than 30 in strains of La3 (M.
Across 10,000 Mycobacterium tuberculosis complex strains, researchers reconstruct IS6110 dynamics, finding copy numbers from 1 to over 30 and 5% hotspot regions carrying half of independent insertions
The study developed a tool that detects and compares insertion sequence insertions from short reads without a reference genome, applied it to 10,000 strains of the Mycobacterium tuberculosis complex (MTBC), and combined it with ancestral state reconstruction on presence-absence patterns to describe the distribution of IS6110 copy numbers (from 1 in some clades to more than 30 in strains of La3 (M.
The study developed a tool that detects and compares insertion sequence insertions from short reads without a reference genome, applied it to 10,000 strains of the Mycobacterium tuberculosis complex (MTBC), and combined it with ancestral state reconstruction on presence-absence patterns to describe the distribution of IS6110 copy numbers (from 1 in some clades to more than 30 in strains of La3 (M.
bioRxiv Sep 29, 2026
The work introduces DeepFisFis, a waveform-based neural network that classifies consecutive 5 ms audio segments to detect mouse ultrasonic vocalizations (USVs) while they are being produced, processing each segment in approximately 2.5 ms and thus faster than the incoming audio stream; in a deployed closed-loop system, detections triggered an external stimulus, demonstrating online control of ongoing vocal behaviour, and event-triggered acquisition preserved more than 99% of vocalization time while retaining only approximately 22% of the continuous recording.
The work introduces DeepFisFis, a waveform-based neural network that classifies consecutive 5 ms audio segments to detect mouse ultrasonic vocalizations (USVs) while they are being produced, processing each segment in approximately 2.5 ms and thus faster than the incoming audio stream; in a deployed closed-loop system, detections triggered an external stimulus, demonstrating online control of ongoing vocal behaviour, and event-triggered acquisition preserved more than 99% of vocalization time while retaining only approximately 22% of the continuous recording.
DeepFisFis processes 5 ms audio segments in about 2.5 ms, detecting mouse ultrasonic vocalizations in real time and triggering closed-loop stimulation
The work introduces DeepFisFis, a waveform-based neural network that classifies consecutive 5 ms audio segments to detect mouse ultrasonic vocalizations (USVs) while they are being produced, processing each segment in approximately 2.5 ms and thus faster than the incoming audio stream; in a deployed closed-loop system, detections triggered an external stimulus, demonstrating online control of ongoing vocal behaviour, and event-triggered acquisition preserved more than 99% of vocalization time while retaining only approximately 22% of the continuous recording.
The work introduces DeepFisFis, a waveform-based neural network that classifies consecutive 5 ms audio segments to detect mouse ultrasonic vocalizations (USVs) while they are being produced, processing each segment in approximately 2.5 ms and thus faster than the incoming audio stream; in a deployed closed-loop system, detections triggered an external stimulus, demonstrating online control of ongoing vocal behaviour, and event-triggered acquisition preserved more than 99% of vocalization time while retaining only approximately 22% of the continuous recording.
bioRxiv Sep 29, 2026
The authors present and open-source Murmurent, shared software that sits beneath agentic AI for biomedical labs, offering multi-member project and "choreography" infrastructure, specialized agents for typical biomedical data science tasks, tiered memory, traceability records, SOP and data-governance enforcement, and multi-user collaboration, and they use the system to identify putative inhibitors of Peptidyl-prolyl cis-trans Isomerase NIMA-interacting 1 (Pin1), describing several approaches and the results they yield.
The authors present and open-source Murmurent, shared software that sits beneath agentic AI for biomedical labs, offering multi-member project and "choreography" infrastructure, specialized agents for typical biomedical data science tasks, tiered memory, traceability records, SOP and data-governance enforcement, and multi-user collaboration, and they use the system to identify putative inhibitors of Peptidyl-prolyl cis-trans Isomerase NIMA-interacting 1 (Pin1), describing several approaches and the results they yield.
Murmurent layers agentic AI beneath lab collaboration and is used to seek putative Pin1 inhibitors
The authors present and open-source Murmurent, shared software that sits beneath agentic AI for biomedical labs, offering multi-member project and "choreography" infrastructure, specialized agents for typical biomedical data science tasks, tiered memory, traceability records, SOP and data-governance enforcement, and multi-user collaboration, and they use the system to identify putative inhibitors of Peptidyl-prolyl cis-trans Isomerase NIMA-interacting 1 (Pin1), describing several approaches and the results they yield.
The authors present and open-source Murmurent, shared software that sits beneath agentic AI for biomedical labs, offering multi-member project and "choreography" infrastructure, specialized agents for typical biomedical data science tasks, tiered memory, traceability records, SOP and data-governance enforcement, and multi-user collaboration, and they use the system to identify putative inhibitors of Peptidyl-prolyl cis-trans Isomerase NIMA-interacting 1 (Pin1), describing several approaches and the results they yield.
arXiv Sep 29, 2026
The study unifies EEG self-supervised masking strategies into a three-parameter framework of spatial radius, temporal length and mask ratio, trains 58 models under a fixed REVE-Small backbone and corpus across MAE and JEPA, evaluates them with a linear probe on the 12 OpenEEGBench datasets, and finds that both frameworks agree on a moderate spatial radius with short temporal blocks as the optimum while identifying a JEPA-specific bias-inflation collapse at full-channel masking.
The study unifies EEG self-supervised masking strategies into a three-parameter framework of spatial radius, temporal length and mask ratio, trains 58 models under a fixed REVE-Small backbone and corpus across MAE and JEPA, evaluates them with a linear probe on the 12 OpenEEGBench datasets, and finds that both frameworks agree on a moderate spatial radius with short temporal blocks as the optimum while identifying a JEPA-specific bias-inflation collapse at full-channel masking.
Treating EEG masking geometry as the only variable: 58 pre-trained models point to a moderate spatial radius with short temporal blocks, and expose a JEPA-specific bias-inflation collapse
The study unifies EEG self-supervised masking strategies into a three-parameter framework of spatial radius, temporal length and mask ratio, trains 58 models under a fixed REVE-Small backbone and corpus across MAE and JEPA, evaluates them with a linear probe on the 12 OpenEEGBench datasets, and finds that both frameworks agree on a moderate spatial radius with short temporal blocks as the optimum while identifying a JEPA-specific bias-inflation collapse at full-channel masking.
The study unifies EEG self-supervised masking strategies into a three-parameter framework of spatial radius, temporal length and mask ratio, trains 58 models under a fixed REVE-Small backbone and corpus across MAE and JEPA, evaluates them with a linear probe on the 12 OpenEEGBench datasets, and finds that both frameworks agree on a moderate spatial radius with short temporal blocks as the optimum while identifying a JEPA-specific bias-inflation collapse at full-channel masking.
Scientific Reports Sep 28, 2026
The study presents a data-driven ecosystem risk assessment framework that treats risk as an emergent property of interacting environmental, anthropogenic, and biological stressors, combining a clustering-based Multi K-means technique with a Variational Autoencoder deep learning model and applying it to 2020 data from the central and western Baltic Sea with abundance information on 145 marine species, commercially relevant species, and cod; it identifies spatially concentrated risk hotspots in species-abundant areas where bottom-oxygen depletion, depth-related constraints, and fishing pressure co-occur, and cross-model concordance analysis shows the two models are both consistent and complementary.
The study presents a data-driven ecosystem risk assessment framework that treats risk as an emergent property of interacting environmental, anthropogenic, and biological stressors, combining a clustering-based Multi K-means technique with a Variational Autoencoder deep learning model and applying it to 2020 data from the central and western Baltic Sea with abundance information on 145 marine species, commercially relevant species, and cod; it identifies spatially concentrated risk hotspots in species-abundant areas where bottom-oxygen depletion, depth-related constraints, and fishing pressure co-occur, and cross-model concordance analysis shows the two models are both consistent and complementary.
Unsupervised models identify Baltic Sea species-rich hotspots threatened jointly by bottom-oxygen depletion and fishing pressure
The study presents a data-driven ecosystem risk assessment framework that treats risk as an emergent property of interacting environmental, anthropogenic, and biological stressors, combining a clustering-based Multi K-means technique with a Variational Autoencoder deep learning model and applying it to 2020 data from the central and western Baltic Sea with abundance information on 145 marine species, commercially relevant species, and cod; it identifies spatially concentrated risk hotspots in species-abundant areas where bottom-oxygen depletion, depth-related constraints, and fishing pressure co-occur, and cross-model concordance analysis shows the two models are both consistent and complementary.
The study presents a data-driven ecosystem risk assessment framework that treats risk as an emergent property of interacting environmental, anthropogenic, and biological stressors, combining a clustering-based Multi K-means technique with a Variational Autoencoder deep learning model and applying it to 2020 data from the central and western Baltic Sea with abundance information on 145 marine species, commercially relevant species, and cod; it identifies spatially concentrated risk hotspots in species-abundant areas where bottom-oxygen depletion, depth-related constraints, and fishing pressure co-occur, and cross-model concordance analysis shows the two models are both consistent and complementary.

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