We develop a Fokker-Planck theory of tissue growth with three types of cells (symmetrically dividing, asymmetrically dividing and non-dividing) as main agents to study the growth dynamics of human cerebral organoids. ...
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We introduce Cell2Sentence (C2S), a novel method to directly adapt large language models to a biological context, specifically single-cell transcriptomics. By transforming gene expression data into"cell sentences...
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We introduce Cell2Sentence (C2S), a novel method to directly adapt large language models to a biological context, specifically single-cell transcriptomics. By transforming gene expression data into"cell sentences," C2S bridges the gap between natural language processing and biology. We demonstrate cell sentences enable the finetuning of language models for diverse tasks in biology, including cell generation, complex cell-type annotation, and direct data-driven text generation. Our experiments reveal that GPT-2, when fine-tuned with C2S, can generate biologically valid cells based on cell type inputs, and accurately predict cell types from cell sentences. This illustrates that language models, through C2S finetuning, can acquire a significant understanding of single-cell biology while maintaining robust text generation capabilities. C2S offers a flexible, accessible framework to integrate natural language processing with transcriptomics, utilizing existing models and libraries for a wide range of biological applications. Copyright 2024 by the author(s)
The development of artificial intelligence(AI) and the mining of biomedical data complement each other. From the direct use of computer vision results to analyze medical images for disease screening, to now integratin...
The development of artificial intelligence(AI) and the mining of biomedical data complement each other. From the direct use of computer vision results to analyze medical images for disease screening, to now integrating biological knowledge into models and even accelerating the development of new AI based on biological discoveries, the boundaries of both are constantly expanding, and their connections are becoming closer.
Proteins fold to a specific functional conformation with a densely packed hydrophobic core that controls their stability. We develop a geometric, yet all-atom model for proteins that explains the universal core packin...
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Strain-controlled isotropic compression gives rise to jammed packings of repulsive, frictionless disks with either positive or negative global shear moduli. We carry out computational studies to understand the contrib...
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Strain-controlled isotropic compression gives rise to jammed packings of repulsive, frictionless disks with either positive or negative global shear moduli. We carry out computational studies to understand the contributions of the negative shear moduli to the mechanical response of jammed disk packings. We first decompose the ensemble-averaged, global shear modulus as 〈G〉=(1−F−)〈G+〉+F−〈G−〉, where F− is the fraction of jammed packings with negative shear moduli and 〈G+〉 and 〈G−〉 are the average values from packings with positive and negative moduli, respectively. We show that 〈G+〉 and 〈|G−|〉 obey different power-law scaling relations above and below pN2∼1. For pN2>1, both 〈G+〉N and 〈|G−|〉N∼(pN2)β, where β∼0.5 for repulsive linear spring interactions. Despite this, 〈G〉N∼(pN2)β′ with β′≳0.5 due to the contributions from packings with negative shear moduli. We show further that the probability distribution of global shear moduli P(G) collapses at fixed pN2 and different values of p and N. We calculate analytically that P(G) is a Γ distribution in the pN2≪1 limit. As pN2 increases, the skewness of P(G) decreases and P(G) becomes a skew-normal distribution with negative skewness in the pN2≫1 limit. We also partition jammed disk packings into subsystems using Delaunay triangulation of the disk centers to calculate local shear moduli. We show that the local shear moduli defined from groups of adjacent triangles can be negative even when G>0. The spatial correlation function of local shear moduli C(r⃗) displays weak correlations for pnsub2<10−2, where nsub is the number of particles within each subsystem. However, C(r⃗) begins to develop long-ranged spatial correlations with fourfold angular symmetry for pnsub2≳10−2.
Short Read Alignment Mapping Metrics (SRAMM): is an efficient and versatile command line tool providing additional short read mapping metrics, filtering, and graphs. Short read aligners report MAPing Quality (MAPQ), b...
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We consider the problem of embedding point cloud data sampled from an underlying manifold with an associated flow or velocity. Such data arises in many contexts where static snapshots of dynamic entities are measured,...
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A number of methods have been proposed for causal effect estimation, yet few have demonstrated efficacy in handling data with complex structures, such as images. To fill this gap, we propose Causal Multi-task Deep Ens...
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Artificial intelligence (AI)-driven methods can vastly improve the historically costly drug design process, with various generative models already in widespread use. Generative models for de novo drug design, in parti...
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Experimental data on compressive strength σmax versus strain rate ɛ̇eng for metallic glasses undergoing uniaxial compression show varying strain rate sensitivity. For some metallic glasses, σmax decreases with incre...
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Experimental data on compressive strength σmax versus strain rate ɛ̇eng for metallic glasses undergoing uniaxial compression show varying strain rate sensitivity. For some metallic glasses, σmax decreases with increasing ɛ̇eng, while for others, σmax increases with increasing ɛ̇eng, and for certain alloys σmax versus ɛ̇eng is nonmonotonic. To understand their strain rate sensitivity, we conduct molecular dynamics simulations of metallic glasses undergoing uniaxial compression at finite strain rates and coupled to heat baths with a range of temperatures T0 and damping parameters b. In the T0→0 and b→0 limits, we find that the compressive strength σmax versus temperature T obeys a “chevron-shaped” scaling relation. In the low-strain-rate regime, σmax decreases linearly with increasing T, whereas σmax grows as a power law with decreasing T in the high-strain-rate regime. For T0>0, σmax(T) deviates from the scaling curve at low strain rates, but σmax(T) rejoins the scaling curve as the strain rate increases. Enhanced dissipation reduces compression-induced heating, which causes σmax(T) to deviate from the b→0 scaling behavior for intermediate strain rates, but σmax(T) converges to the high-strain-rate power-law scaling behavior at sufficiently high strain rates. Determining σmax(T) as a function of b and T0 provides a general framework for explaining the strain rate sensitivity of metallic glasses under compression.
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