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An inference paradigm for extracting neuronal correlations from two-photon imaging data, without requiring intermediate spike deconvolution, provides significant performance gains over existing methods as demonstrated by theoretical analysis, simulation studies, and real-data applications.

Calanoid copepods achieve efficient mate finding in turbulence through active swimming and turbulence advection, indicating that reproduction is not restricted to spatial and temporal windows of calm hydrodynamic conditions.

Combining quantitative biological experiment and physical description of actomyosin cortex reveals a contractile instability in the cortex of C. elegans embryo, and its biochemical control in order to robustly drive morphogenetic events.

A transcriptome dataset of nearly 200 genetically identified mouse neuronal cell types revealed that short low-noise homeobox transcription factors and long neuronal effector genes best distinguish neuronal cell types.

Integration of language model and geometric deep learning enables accurate and efficient genome-scale annotation of comprehensive protein-ligand binding sites.

A novel statistical framework and approach using sibling phenotype pairs allows inference of genetic architecture in the tails of complex traits to be made without genetic data.

A theoretical framework for the growth of microtubules quantifies the roles of geometry, mechanics, kinetics and randomness and provides a phase diagram for dynamic instability in these self-assembled polymers.

Simultaneous recording from many neurons in macaque lateral intraparietal area reveals the elusive drift-diffusion signal, long suspected to underlie individual perceptual decisions and response times.

It provides evidence for the feasibility of using combined graph convolutional neural network and transformers’ based systems with patient demographics, and CT-derived imaging features as inputs into the transformer model for survival prediction in early stage lung cancer patients.

Using self-organization, cells can collectively leverage defects in nematic and hexatic orders to localize mechanical stresses and remove an unwanted cell with their preferred path to achieve this explored here by independently tuning cell–cell and cell–substrate adhesion strengths.