Geriatrics

Deep Learning Reveals the Modular Genetic Architecture of Cardiovascular Aging

Chronological age is a potent determinant of clinical events, but it is conventionally treated as a linear function of time rather than a dynamic process shaped by genetics and tissue-specific senescence. Deep learning models derived from cardiovascular imaging offer an opportunity to quantify biological age across multiple domains and to examine the extent to which these measures capture shared o...

Lightweight Retrieval-Augmented Generation and Large Language Model-Based Modeling for Scalable Patient-Trial Matching

Patient-trial matching requires reasoning over long, heterogeneous electronic health records (EHRs) and complex eligibility criteria, posing significant challenges for scalability, generalization, and computational efficiency. Existing approaches either rely on full-document processing with large language models (LLMs), which is computationally expensive, or use traditional machine learning method...

Assessing medication-related burden and medication adherence among older patients from Central Nepal: A machine learning approach

We are pleased to submit our Original article entitled "Assessing medication-related burden and medication adherence among older patients from Central...

Metaxa: A Transformer-Based Deep Learning Model for Taxonomic Classification of Long Nanopore Reads

A significant fraction of the microbial diversity remains unclassified, hindering our understanding of microbial roles in health and ecosystems. State...

Modular organization and selective motifs in the insula provide structural priors for efficient learning

The insular cortex (IC) integrates diverse sensory and interoceptive signals to support emotional and cognitive functions, exhibiting topologically fu...

UHR-DETR: Efficient End-to-End Small Object Detection for Ultra-High-Resolution Remote Sensing Imagery

Ultra-High-Resolution (UHR) imagery has become essential for modern remote sensing, offering unprecedented spatial coverage. However, detecting small ...

Opportunistic Bone-Loss Screening from Routine Knee Radiographs Using a Multi-Task Deep Learning Framework with Sensitivity-Constrained Threshold Optimization

Background: Osteoporosis and osteopenia are often undiagnosed until fragility fractures occur. Dual-energy X-ray absorptiometry (DXA) is the reference...

OneDrive: Unified Multi-Paradigm Driving with Vision-Language-Action Models

Vision-Language Models(VLMs) excel at autoregressive text generation, yet end-to-end autonomous driving requires multi-task learning with structured o...

E2E-GMNER: End-to-End Generative Grounded Multimodal Named Entity Recognition

Grounded Multimodal Named Entity Recognition (GMNER) aims to jointly identify named entity mentions in text, predict their semantic types, and ground ...

Improved Multiscale Structural Mapping with Supervertex Vision Transformer for the Detection of Alzheimer's Disease Neurodegeneration

Alzheimer's disease (AD) confirmation often relies on positron emission tomography (PET) or cerebrospinal fluid (CSF) analysis, which are costly and i...

LeapAlign: Post-Training Flow Matching Models at Any Generation Step by Building Two-Step Trajectories

This paper focuses on the alignment of flow matching models with human preferences. A promising way is fine-tuning by directly backpropagating reward ...

Multiscale transcriptomic organization of the human brain with DigitalBrain

The human brain varies across anatomical regions, cell types, development, ageing and disease states, yet existing single-cell transcriptomic resource...

ORION: An agentic reasoning construct for the analysis of complex human immune profiling

The capacity to generate high-dimensional biological datasets has outpaced the ability to interpret them. Technologies such as phage immunoprecipitati...

TRACE: End-to-end temporal inference and annotation of animal behaviors from video

Quantitative analysis of animal behavior is fundamental to neuroscience and ethology but remains constrained by the scalability, subjectivity, and lim...

Resting-state fMRI foundation models enable robust and generalizable latent neural target discovery in cognitive aging interventions

The benefits of interventions targeting cognitive aging vary substantially across individuals, largely owing to heterogeneity in aging-related comorbi...

ADP-DiT: Text-Guided Diffusion Transformer for Brain Image Generation in Alzheimer's Disease Progression

Alzheimer's disease (AD) progresses heterogeneously across individuals, motivating subject-specific synthesis of follow-up magnetic resonance imaging ...

From Pixels to Nucleotides: End-to-End Token-Based Video Compression for DNA Storage

DNA-based storage has emerged as a promising approach to the global data crisis, offering molecular-scale density and millennial-scale stability at lo...

Explainable machine learning identifies candidate shared neuroanatomical features in Alzheimer's and Parkinson's via importance inversion transfer

Despite significant neurobiological and pathological overlaps, Alzheimer's (AD) and Parkinson's (PD)-the primary threats to healthy aging-are still ma...

A Machine Learning Approach for Physiological Role Prediction in Protein Contact Networks: a large-scale analysis on the human proteome

Proteins are fundamental macromolecules involved in virtually all biological processes. Their physiological roles are tightly linked to their three-di...

Information-Theoretic Optimization for Task-Adapted Compressed Sensing Magnetic Resonance Imaging

Task-adapted compressed sensing magnetic resonance imaging (CS-MRI) is emerging to address the specific demands of downstream clinical tasks with sign...