arXiv preprints from January 1, 2026 through September 21, 2026 — 07:58:54 EST
Recent approaches to 30-day hospital readmission prediction rely on pre-trained language models applied to discharge summaries. Although these methods achieve strong performance, they depend on the availability of clinical notes, incur substantial computational costs, and yield representations that lack interpretability. We propose a...
Non-tuberculous mycobacterial (NTM) infections are a clinical challenge in cystic fibrosis (CF), where impaired mucociliary clearance and altered mucus rheology promote bacterial colonization despite host immune responses. Understanding how bacterial growth, immune cell dynamics, and mucus transport regulate infection progression is...
Large brains are metabolically costly, and associations with changing environments do not imply they evolved there, as the Cognitive Buffer Hypothesis (CBH) would suggest. They may instead evolve in stable conditions and later facilitate colonization of changing environments. Using neuro-evolution in an artificial seasonal foraging...
Co-folding models have advanced rapidly, yet no single model consistently performs best across all biomolecular complexes. This raises the question of whether independently trained co-folding models encode complementary information that can be transferred across co-folding models. We introduce SoupFold, which improves co-folding...
Traumatic brain injury (TBI) often leads to subtle white matter damage that remains undetected on conventional MRI. Diffusion kurtosis imaging (DKI), an extension of diffusion tensor imaging (DTI), provides complementary information on non-Gaussian water diffusion and is sensitive to complex white-matter microstructure. With the...
Protein-protein interaction databases record evidence without weighing it. In BioGRID, an interaction asserted once by a single high-throughput screen and one confirmed by twenty laboratories across a dozen assays are the same kind of row in the same file. Tools built on such databases inherit that flattening: they draw every reported...
Animal activity often consists of intermittent bursts separated by prolonged periods of inactivity. Here, we describe this behavior using a two-state telegraph process with extrinsically fluctuating transition rates. We derived analytical expressions for the stationary behavior of the system and characterized how stationary...
Single-cell foundation models (scFMs) increasingly rely on large-scale transcriptomic pretraining, yet expanding pretraining data can yield diminishing gains while substantially increasing computational cost. Our data scaling analyses showed that incorporating biological knowledge, including cell-level text annotation and gene-level...
Immune therapies act across cell-intrinsic programs, tissue ecosystems, and patient-specific immune states, yet most predictors address these scales separately. We used a governed evolutionary AI Scientist to construct the Immune World Model, an action-conditioned model that learns how interventions move immune states across cellular,...
We study the evolution of the speed of life history in populations competing for a single growth-limiting resource subject to demographic stochasticity and mass mortality events. We focus on a quasi-neutral regime in which competing types have equal resource-use efficiency but differ in life-history speed. In the...
In many biological contexts selection is strong relative to genetic drift. Working under the diffusion approximation, we define $R = 2N_{e}|s|$, where $N_{e}$ is the effective population size and $s$ is the selection coefficient associated with a focal allele. Strong selection corresponds to $R \gg1$ and can occur for relatively...
Deep learning now underpins structure-based drug design, from complex and affinity prediction to ligand ranking and pose generation. Recent co-folding models reportedly approach free-energy-perturbation accuracy at far lower cost. Yet standard evaluation, a single held-out correlation or pooled pose-success rate, cannot separate...
Classical theories of random neural networks typically assume independent connectivity, overlooking the local motif structures prevalent in biological circuits. Here, we investigate how four second-order synaptic motifs (chain, reciprocal, convergent, and divergent) shape the dynamics of nonlinear firing-rate networks. While previous...
Single-cell foundation models (scFMs) are transforming computational biology by enabling generalizable, task-agnostic representations for versatile single-cell analysis. Despite their progress in facilitating rapid deployment for downstream tasks, off-the-shelf scFMs still have some overlooked concerns: (I) (Pretraining Cost.)...
Efferocytosis, the clearance of dying cells typically by macrophages, is essential for tissue homeostasis and the resolution of inflammation. Previous experiments by Ford et al. (Proc. R. Soc. B, 2019) showed that cannibalistic efferocytosis redistributes endogenous lipid from dying macrophages into the surviving population, but...
The Interconnectedness Coefficient (IC) is a bounded semi-local graph-theoretic node measure designed to identify connector vertices between cohesive network regions. Such connector vertices, also referred to as bridging nodes, may mediate between locally cohesive regions even when they are neither hubs nor themselves highly...
The BabyLM challenge measures how much language a model can learn from developmentally-plausible, child-scale data rather than internet-scale corpora, yet prior language models forgo the biological constraints of the neural circuitry that acquires human language: spiking neurons separated into excitatory and inhibitory populations...
How can a neural circuit rhythmically burst when none of its constituent neurons can endogenously do so? We address this question through a bottom-up reconstruction of a 4-cell neural circuit modeled after the swim central pattern generator (CPG) of the sea slug \textit{Dendronotus iris}. We first map the intrinsic regimes of a swim...
The basic reproduction number $R_0$ is the central dimensionless quantity in mathematical epidemiology, characterizing the threshold for disease outbreak and the early growth rate of an epidemic. The algebraic form of $R_0$ varies widely across models of distinct transmission mechanisms, and its interpretation can yield further...
Purpose: To develop and validate a deep learning-based method for the automated identification and volumetric segmentation of microaneurysms (MAs) in diabetic retinopathy (DR) using OCT. Participants: A total of 125 participants were enrolled, including 20 healthy eyes, 27 with mild NPDR, 30 with moderate NPDR, 30 with severe NPDR,...
Brain-computer interfaces (BCIs) decode neural activity to restore lost function. Typically, training a high-performance neural decoder requires a large labeled dataset to be collected from every new subject. One way to reduce the labeled data cost is self-supervised pretraining, which learns general neural representations from...
Scientific research is a continuous process that emphasizes inheritance. Methods developed by predecessors are often expanded upon by new researchers to explore more novel and in-depth scientific questions. However, the change of lab staff, such as student graduation, leads to a lack of personnel capable of replicating methods....
In this paper, we study a stochastic Susceptible-Infected-Removed (SIR) model where the infection and the recovery rates depend on individual covariates for susceptibility and infectiousness of the infector and the infectee. Such models allow explicit nonlinearity in the incidence term. They are also important from a practical...
Characterizing longitudinal changes in region-specific distribution of environmental exposures, such as total nitrate (TNO$_3$) concentrations, is critical for understanding localized ecological risks that are otherwise obscured by standard mean-level modeling. However, modeling longitudinal distributional outcomes across spatial...
We state general conditions under which the full-data law is identified in the presence of latent variables, leveraging key observed variables ("proxies") associated with unobserved variables. These assumptions extend those used in existing examples from the literature that recover the full-data law under relatively flexible model...