Life Sciences

185 items

发表出处待核验 Sep 22, 2026
This chapter argues that the literary historiography of science (LH) offers its own fresh perspectives on the history of science, using the history of evolutionary theory as its case: it shows how LH scholarship and methods have been integral to the re-evaluation of Charles Darwin's significance by both historians of science and literary scholars, and it models a history of evolutionary theory extending well beyond the conventional "Modern Synthesis" of Darwinian natural selection and Mendelian genetics by showing how Olaf Stapledon's evolutionary science fiction has shaped disciplines from astrobiology and Earth system science to the theorisation of Artificial Intelligence.
This chapter argues that the literary historiography of science (LH) offers its own fresh perspectives on the history of science, using the history of evolutionary theory as its case: it shows how LH scholarship and methods have been integral to the re-evaluation of Charles Darwin's significance by both historians of science and literary scholars, and it models a history of evolutionary theory extending well beyond the conventional "Modern Synthesis" of Darwinian natural selection and Mendelian genetics by showing how Olaf Stapledon's evolutionary science fiction has shaped disciplines from astrobiology and Earth system science to the theorisation of Artificial Intelligence.
Literary History of Science: From Reassessing Darwin to Stapledon's Evolutionary Science Fiction
This chapter argues that the literary historiography of science (LH) offers its own fresh perspectives on the history of science, using the history of evolutionary theory as its case: it shows how LH scholarship and methods have been integral to the re-evaluation of Charles Darwin's significance by both historians of science and literary scholars, and it models a history of evolutionary theory extending well beyond the conventional "Modern Synthesis" of Darwinian natural selection and Mendelian genetics by showing how Olaf Stapledon's evolutionary science fiction has shaped disciplines from astrobiology and Earth system science to the theorisation of Artificial Intelligence.
This chapter argues that the literary historiography of science (LH) offers its own fresh perspectives on the history of science, using the history of evolutionary theory as its case: it shows how LH scholarship and methods have been integral to the re-evaluation of Charles Darwin's significance by both historians of science and literary scholars, and it models a history of evolutionary theory extending well beyond the conventional "Modern Synthesis" of Darwinian natural selection and Mendelian genetics by showing how Olaf Stapledon's evolutionary science fiction has shaped disciplines from astrobiology and Earth system science to the theorisation of Artificial Intelligence.
Scientific Reports Sep 22, 2026
The study developed an AI-powered image analysis pipeline, “CozyScores,” built on the ResNet18 architecture, to score progressive nest organization on a 5-point system in young and old wild-type and 3xTg-AD mice from 0.5 to 24 h, and it found a significant age- and genotype-dependent difference at the 4 h benchmark (Young WT > Young AD > Old WT > Old AD, P < 0.05), lower maximum 24 h performance in Old versus Young groups, and generally better performance in males than females.
The study developed an AI-powered image analysis pipeline, “CozyScores,” built on the ResNet18 architecture, to score progressive nest organization on a 5-point system in young and old wild-type and 3xTg-AD mice from 0.5 to 24 h, and it found a significant age- and genotype-dependent difference at the 4 h benchmark (Young WT > Young AD > Old WT > Old AD, P < 0.05), lower maximum 24 h performance in Old versus Young groups, and generally better performance in males than females.
Deep learning quantification of mouse nesting behavior for tracking cognitive decline in models of aging & Alzheimer’s disease
The study developed an AI-powered image analysis pipeline, “CozyScores,” built on the ResNet18 architecture, to score progressive nest organization on a 5-point system in young and old wild-type and 3xTg-AD mice from 0.5 to 24 h, and it found a significant age- and genotype-dependent difference at the 4 h benchmark (Young WT > Young AD > Old WT > Old AD, P < 0.05), lower maximum 24 h performance in Old versus Young groups, and generally better performance in males than females.
The study developed an AI-powered image analysis pipeline, “CozyScores,” built on the ResNet18 architecture, to score progressive nest organization on a 5-point system in young and old wild-type and 3xTg-AD mice from 0.5 to 24 h, and it found a significant age- and genotype-dependent difference at the 4 h benchmark (Young WT > Young AD > Old WT > Old AD, P < 0.05), lower maximum 24 h performance in Old versus Young groups, and generally better performance in males than females.
bioRxiv Sep 22, 2026
This work initialized and biased the HIDDEN GEM structure-guided generative virtual screening workflow with screening data from a focused DNA-encoded library against the 53BP1 tandem Tudor domain (UNCDEL003, 58,080 compounds), nominated 57 purchasable compounds from the roughly 37-billion-compound Enamine REAL Space, and validated 14 as active hits by TR-FRET displacement (3 with IC50 ≤50 µM and 11 with IC50 ≤100 µM), with the AI-nominated hits showing greater chemical diversity, improved drug-likeness, and off-the-shelf purchasability relative to the initial DEL hits.
This work initialized and biased the HIDDEN GEM structure-guided generative virtual screening workflow with screening data from a focused DNA-encoded library against the 53BP1 tandem Tudor domain (UNCDEL003, 58,080 compounds), nominated 57 purchasable compounds from the roughly 37-billion-compound Enamine REAL Space, and validated 14 as active hits by TR-FRET displacement (3 with IC50 ≤50 µM and 11 with IC50 ≤100 µM), with the AI-nominated hits showing greater chemical diversity, improved drug-likeness, and off-the-shelf purchasability relative to the initial DEL hits.
Expansion of DNA-Encoded Library Hits Using Generative Chemistry and Ultra-Large Compound Catalogs
This work initialized and biased the HIDDEN GEM structure-guided generative virtual screening workflow with screening data from a focused DNA-encoded library against the 53BP1 tandem Tudor domain (UNCDEL003, 58,080 compounds), nominated 57 purchasable compounds from the roughly 37-billion-compound Enamine REAL Space, and validated 14 as active hits by TR-FRET displacement (3 with IC50 ≤50 µM and 11 with IC50 ≤100 µM), with the AI-nominated hits showing greater chemical diversity, improved drug-likeness, and off-the-shelf purchasability relative to the initial DEL hits.
This work initialized and biased the HIDDEN GEM structure-guided generative virtual screening workflow with screening data from a focused DNA-encoded library against the 53BP1 tandem Tudor domain (UNCDEL003, 58,080 compounds), nominated 57 purchasable compounds from the roughly 37-billion-compound Enamine REAL Space, and validated 14 as active hits by TR-FRET displacement (3 with IC50 ≤50 µM and 11 with IC50 ≤100 µM), with the AI-nominated hits showing greater chemical diversity, improved drug-likeness, and off-the-shelf purchasability relative to the initial DEL hits.
bioRxiv Sep 22, 2026
This study developed a graph-based QSAR modeling pipeline integrating assay data preprocessing, fingerprint and molecular graph feature representations, and benchmarking of classical machine learning, graph neural networks, graph transformers, and their consensus ensembles, applied to predict Caspase-3/7 activation, mitochondrial membrane potential disruption, and FDA drug-induced liver injury, where Graphormer achieved the highest F1 of 0.79 and the full consensus model achieved the highest AUC of 0.69 on DILI prediction, surpassing the previous best model with AUC 0.63 and F1 0.65, and identified structural motifs associated with dual activation and cell-line-specific responses through fragment enrichment analysis.
This study developed a graph-based QSAR modeling pipeline integrating assay data preprocessing, fingerprint and molecular graph feature representations, and benchmarking of classical machine learning, graph neural networks, graph transformers, and their consensus ensembles, applied to predict Caspase-3/7 activation, mitochondrial membrane potential disruption, and FDA drug-induced liver injury, where Graphormer achieved the highest F1 of 0.79 and the full consensus model achieved the highest AUC of 0.69 on DILI prediction, surpassing the previous best model with AUC 0.63 and F1 0.65, and identified structural motifs associated with dual activation and cell-line-specific responses through fragment enrichment analysis.
A Graph-based QSAR Modeling Pipeline for Predicting In vitro PubChem Assays and In vivo Human Hepatotoxicity: Mechanistic Analysis of Caspase-3/7 Activation
This study developed a graph-based QSAR modeling pipeline integrating assay data preprocessing, fingerprint and molecular graph feature representations, and benchmarking of classical machine learning, graph neural networks, graph transformers, and their consensus ensembles, applied to predict Caspase-3/7 activation, mitochondrial membrane potential disruption, and FDA drug-induced liver injury, where Graphormer achieved the highest F1 of 0.79 and the full consensus model achieved the highest AUC of 0.69 on DILI prediction, surpassing the previous best model with AUC 0.63 and F1 0.65, and identified structural motifs associated with dual activation and cell-line-specific responses through fragment enrichment analysis.
This study developed a graph-based QSAR modeling pipeline integrating assay data preprocessing, fingerprint and molecular graph feature representations, and benchmarking of classical machine learning, graph neural networks, graph transformers, and their consensus ensembles, applied to predict Caspase-3/7 activation, mitochondrial membrane potential disruption, and FDA drug-induced liver injury, where Graphormer achieved the highest F1 of 0.79 and the full consensus model achieved the highest AUC of 0.69 on DILI prediction, surpassing the previous best model with AUC 0.63 and F1 0.65, and identified structural motifs associated with dual activation and cell-line-specific responses through fragment enrichment analysis.
bioRxiv Sep 22, 2026
Using DEFT, an LLM-guided interpretable decision-tree framework, the authors found that pause sites are discriminated by a G at the pause position, C or T at +1 and a minimum G content in the 49 upstream bases (held-out AUROC 0.92, accuracy 0.87); inserting a 377 bp G-less cassette at the 5' ends of NDRG1 and HSP90AA1 abolished the promoter-proximal pause in an orientation-dependent way without impairing hypoxic or heat-shock activation (NDRG1 was even super-activated), while ChIP 5' RNAPII, Ser5P and NELF peaks persisted without NET-seq-detectable pausing and CTD deletion weakened the pause and shifted its peak from ~+80 to ~+40, supporting a model in which the pause is initiated by G-dependent arrest and stabilized by factors including CTD-mediated tethering.
Using DEFT, an LLM-guided interpretable decision-tree framework, the authors found that pause sites are discriminated by a G at the pause position, C or T at +1 and a minimum G content in the 49 upstream bases (held-out AUROC 0.92, accuracy 0.87); inserting a 377 bp G-less cassette at the 5' ends of NDRG1 and HSP90AA1 abolished the promoter-proximal pause in an orientation-dependent way without impairing hypoxic or heat-shock activation (NDRG1 was even super-activated), while ChIP 5' RNAPII, Ser5P and NELF peaks persisted without NET-seq-detectable pausing and CTD deletion weakened the pause and shifted its peak from ~+80 to ~+40, supporting a model in which the pause is initiated by G-dependent arrest and stabilized by factors including CTD-mediated tethering.
AI discovery of sequence rules for RNA polymerase II pausing revises the pause-release model of gene activation
Using DEFT, an LLM-guided interpretable decision-tree framework, the authors found that pause sites are discriminated by a G at the pause position, C or T at +1 and a minimum G content in the 49 upstream bases (held-out AUROC 0.92, accuracy 0.87); inserting a 377 bp G-less cassette at the 5' ends of NDRG1 and HSP90AA1 abolished the promoter-proximal pause in an orientation-dependent way without impairing hypoxic or heat-shock activation (NDRG1 was even super-activated), while ChIP 5' RNAPII, Ser5P and NELF peaks persisted without NET-seq-detectable pausing and CTD deletion weakened the pause and shifted its peak from ~+80 to ~+40, supporting a model in which the pause is initiated by G-dependent arrest and stabilized by factors including CTD-mediated tethering.
Using DEFT, an LLM-guided interpretable decision-tree framework, the authors found that pause sites are discriminated by a G at the pause position, C or T at +1 and a minimum G content in the 49 upstream bases (held-out AUROC 0.92, accuracy 0.87); inserting a 377 bp G-less cassette at the 5' ends of NDRG1 and HSP90AA1 abolished the promoter-proximal pause in an orientation-dependent way without impairing hypoxic or heat-shock activation (NDRG1 was even super-activated), while ChIP 5' RNAPII, Ser5P and NELF peaks persisted without NET-seq-detectable pausing and CTD deletion weakened the pause and shifted its peak from ~+80 to ~+40, supporting a model in which the pause is initiated by G-dependent arrest and stabilized by factors including CTD-mediated tethering.
bioRxiv Sep 22, 2026
Using GISTIC copy number from the TCGA PanCancer Atlas to classify tumors as PTEN intact, hemizygous, or homozygous deletion, this study scored a five-gene glutaminolysis signature (GLS, SLC1A5, GOT1, GLUD1, GPT2) against loss severity across fourteen tumor types and found that the signature decreased with PTEN loss in all fourteen (significantly in twelve) but was not MYC-mediated; instead the decline tracked chromosomal position, since GLUD1 and GOT1 flank PTEN on 10q and thirty-seven neighboring genes carrying no glutaminolysis annotation tracked PTEN copy number just as closely (mean rho 0.843 versus 0.842), with co-deletion fidelity falling monotonically with distance from PTEN (rho = -0.
Using GISTIC copy number from the TCGA PanCancer Atlas to classify tumors as PTEN intact, hemizygous, or homozygous deletion, this study scored a five-gene glutaminolysis signature (GLS, SLC1A5, GOT1, GLUD1, GPT2) against loss severity across fourteen tumor types and found that the signature decreased with PTEN loss in all fourteen (significantly in twelve) but was not MYC-mediated; instead the decline tracked chromosomal position, since GLUD1 and GOT1 flank PTEN on 10q and thirty-seven neighboring genes carrying no glutaminolysis annotation tracked PTEN copy number just as closely (mean rho 0.843 versus 0.842), with co-deletion fidelity falling monotonically with distance from PTEN (rho = -0.
Passenger co-deletion confounds glutaminolysis signatures anchored on PTEN loss: a cautionary case for location-aware signature design
Using GISTIC copy number from the TCGA PanCancer Atlas to classify tumors as PTEN intact, hemizygous, or homozygous deletion, this study scored a five-gene glutaminolysis signature (GLS, SLC1A5, GOT1, GLUD1, GPT2) against loss severity across fourteen tumor types and found that the signature decreased with PTEN loss in all fourteen (significantly in twelve) but was not MYC-mediated; instead the decline tracked chromosomal position, since GLUD1 and GOT1 flank PTEN on 10q and thirty-seven neighboring genes carrying no glutaminolysis annotation tracked PTEN copy number just as closely (mean rho 0.843 versus 0.842), with co-deletion fidelity falling monotonically with distance from PTEN (rho = -0.
Using GISTIC copy number from the TCGA PanCancer Atlas to classify tumors as PTEN intact, hemizygous, or homozygous deletion, this study scored a five-gene glutaminolysis signature (GLS, SLC1A5, GOT1, GLUD1, GPT2) against loss severity across fourteen tumor types and found that the signature decreased with PTEN loss in all fourteen (significantly in twelve) but was not MYC-mediated; instead the decline tracked chromosomal position, since GLUD1 and GOT1 flank PTEN on 10q and thirty-seven neighboring genes carrying no glutaminolysis annotation tracked PTEN copy number just as closely (mean rho 0.843 versus 0.842), with co-deletion fidelity falling monotonically with distance from PTEN (rho = -0.
Amazon Science Sep 22, 2026
This article presents three efforts from Amazon Bio Discovery: MochiBind, a sequence-only predictor that reframes binding affinity as pairwise comparison aggregated by TrueSkill into a global ranking, achieving higher pairwise accuracy than every structure-based baseline on four held-out antigens and scoring 200,000 antibody pairs in roughly 13 seconds on a CPU; CA-MAP, a context-aware multi-property predictor that uses example antibodies in the prompt to absorb batch offsets, holding a 0.99 correlation under a simulated batch effect where standard fine-tuning falls to 0.
This article presents three efforts from Amazon Bio Discovery: MochiBind, a sequence-only predictor that reframes binding affinity as pairwise comparison aggregated by TrueSkill into a global ranking, achieving higher pairwise accuracy than every structure-based baseline on four held-out antigens and scoring 200,000 antibody pairs in roughly 13 seconds on a CPU; CA-MAP, a context-aware multi-property predictor that uses example antibodies in the prompt to absorb batch offsets, holding a 0.99 correlation under a simulated batch effect where standard fine-tuning falls to 0.
Designing and Characterizing Antibodies with AI: Three Efforts Spanning Epitope Selection, Affinity Ranking, and Developability Prediction
This article presents three efforts from Amazon Bio Discovery: MochiBind, a sequence-only predictor that reframes binding affinity as pairwise comparison aggregated by TrueSkill into a global ranking, achieving higher pairwise accuracy than every structure-based baseline on four held-out antigens and scoring 200,000 antibody pairs in roughly 13 seconds on a CPU; CA-MAP, a context-aware multi-property predictor that uses example antibodies in the prompt to absorb batch offsets, holding a 0.99 correlation under a simulated batch effect where standard fine-tuning falls to 0.
This article presents three efforts from Amazon Bio Discovery: MochiBind, a sequence-only predictor that reframes binding affinity as pairwise comparison aggregated by TrueSkill into a global ranking, achieving higher pairwise accuracy than every structure-based baseline on four held-out antigens and scoring 200,000 antibody pairs in roughly 13 seconds on a CPU; CA-MAP, a context-aware multi-property predictor that uses example antibodies in the prompt to absorb batch offsets, holding a 0.99 correlation under a simulated batch effect where standard fine-tuning falls to 0.
Nature News Sep 22, 2026
Drawing on a Nature news feature and a Nature Reviews Molecular Cell Biology review, this column-style summary surveys roughly 20 new cell-death modes described since 1999 — including alkaliptosis, pyroptosis, necroptosis, ferroptosis, cuproptosis, sodium-overload death and ruptosis — showing that how a cell dies shapes the signals it releases to neighbours, tissues and immune responses, and that death is not always irreversible, with 'bucket list' delays and anastasis revival.
Drawing on a Nature news feature and a Nature Reviews Molecular Cell Biology review, this column-style summary surveys roughly 20 new cell-death modes described since 1999 — including alkaliptosis, pyroptosis, necroptosis, ferroptosis, cuproptosis, sodium-overload death and ruptosis — showing that how a cell dies shapes the signals it releases to neighbours, tissues and immune responses, and that death is not always irreversible, with 'bucket list' delays and anastasis revival.
Many Ways for a Cell to Die: From Apoptosis and Necroptosis to Alkaliptosis and Reversible Death
Drawing on a Nature news feature and a Nature Reviews Molecular Cell Biology review, this column-style summary surveys roughly 20 new cell-death modes described since 1999 — including alkaliptosis, pyroptosis, necroptosis, ferroptosis, cuproptosis, sodium-overload death and ruptosis — showing that how a cell dies shapes the signals it releases to neighbours, tissues and immune responses, and that death is not always irreversible, with 'bucket list' delays and anastasis revival.
Drawing on a Nature news feature and a Nature Reviews Molecular Cell Biology review, this column-style summary surveys roughly 20 new cell-death modes described since 1999 — including alkaliptosis, pyroptosis, necroptosis, ferroptosis, cuproptosis, sodium-overload death and ruptosis — showing that how a cell dies shapes the signals it releases to neighbours, tissues and immune responses, and that death is not always irreversible, with 'bucket list' delays and anastasis revival.
PLoS biology Sep 21, 2026
The study presents AlloPool, a graph neural network framework that combines temporal attention with iterative edge pooling to learn minimal, time-evolving residue interaction networks from equilibrium and non-equilibrium molecular dynamics trajectories, reconstructing trajectories at sub-angstrom RMSD across Pin1, the GAIN mechanosensor domain, dopamine D2 and beta-1 adrenergic receptors, the SdrG adhesin, PDZ3, and the Engrailed homeodomain, and using those networks to map allosteric pathways, predict ligand pharmacology, mechanical loading states, and mutation effects.
The study presents AlloPool, a graph neural network framework that combines temporal attention with iterative edge pooling to learn minimal, time-evolving residue interaction networks from equilibrium and non-equilibrium molecular dynamics trajectories, reconstructing trajectories at sub-angstrom RMSD across Pin1, the GAIN mechanosensor domain, dopamine D2 and beta-1 adrenergic receptors, the SdrG adhesin, PDZ3, and the Engrailed homeodomain, and using those networks to map allosteric pathways, predict ligand pharmacology, mechanical loading states, and mutation effects.
AlloPool: a graph neural network framework that infers protein allostery from molecular dynamics simulations
The study presents AlloPool, a graph neural network framework that combines temporal attention with iterative edge pooling to learn minimal, time-evolving residue interaction networks from equilibrium and non-equilibrium molecular dynamics trajectories, reconstructing trajectories at sub-angstrom RMSD across Pin1, the GAIN mechanosensor domain, dopamine D2 and beta-1 adrenergic receptors, the SdrG adhesin, PDZ3, and the Engrailed homeodomain, and using those networks to map allosteric pathways, predict ligand pharmacology, mechanical loading states, and mutation effects.
The study presents AlloPool, a graph neural network framework that combines temporal attention with iterative edge pooling to learn minimal, time-evolving residue interaction networks from equilibrium and non-equilibrium molecular dynamics trajectories, reconstructing trajectories at sub-angstrom RMSD across Pin1, the GAIN mechanosensor domain, dopamine D2 and beta-1 adrenergic receptors, the SdrG adhesin, PDZ3, and the Engrailed homeodomain, and using those networks to map allosteric pathways, predict ligand pharmacology, mechanical loading states, and mutation effects.
medRxiv Sep 21, 2026
In 1,095 carriers of pathogenic or likely pathogenic sarcomere variants across three international centers, a previously validated AI-ECG model applied to 12-lead ECG images yielded an AUROC of 0.91 for the HCM phenotype at baseline and 0.92 for manifest HCM, a higher AI-ECG score among genotype-positive/phenotype-negative individuals predicted incident HCM during follow-up (unadjusted HR 1.55 per 1-SD; adjusted HR 1.38), and in 57,007 UK Biobank participants the AI-ECG score and a polygenic risk score were independent and additive, with adjusted odds of HCM of 60.2 when both were high.
In 1,095 carriers of pathogenic or likely pathogenic sarcomere variants across three international centers, a previously validated AI-ECG model applied to 12-lead ECG images yielded an AUROC of 0.91 for the HCM phenotype at baseline and 0.92 for manifest HCM, a higher AI-ECG score among genotype-positive/phenotype-negative individuals predicted incident HCM during follow-up (unadjusted HR 1.55 per 1-SD; adjusted HR 1.38), and in 57,007 UK Biobank participants the AI-ECG score and a polygenic risk score were independent and additive, with adjusted odds of HCM of 60.2 when both were high.
Artificial Intelligence-Enhanced Electrocardiography for Detection and Prediction of Hypertrophic Cardiomyopathy across Monogenic and Polygenic Susceptibility
In 1,095 carriers of pathogenic or likely pathogenic sarcomere variants across three international centers, a previously validated AI-ECG model applied to 12-lead ECG images yielded an AUROC of 0.91 for the HCM phenotype at baseline and 0.92 for manifest HCM, a higher AI-ECG score among genotype-positive/phenotype-negative individuals predicted incident HCM during follow-up (unadjusted HR 1.55 per 1-SD; adjusted HR 1.38), and in 57,007 UK Biobank participants the AI-ECG score and a polygenic risk score were independent and additive, with adjusted odds of HCM of 60.2 when both were high.
In 1,095 carriers of pathogenic or likely pathogenic sarcomere variants across three international centers, a previously validated AI-ECG model applied to 12-lead ECG images yielded an AUROC of 0.91 for the HCM phenotype at baseline and 0.92 for manifest HCM, a higher AI-ECG score among genotype-positive/phenotype-negative individuals predicted incident HCM during follow-up (unadjusted HR 1.55 per 1-SD; adjusted HR 1.38), and in 57,007 UK Biobank participants the AI-ECG score and a polygenic risk score were independent and additive, with adjusted odds of HCM of 60.2 when both were high.

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