clustering

Low-fidelity simulator selection for multi-fidelity inference of epithelial vertex models

The paper introduces PRISM to select low-fidelity simulators for multi-fidelity inference in vertex models, finding that early stopping is unreliable while coarser timesteps are effective.

BEHAV3D-Explorer enables natural language-assisted profiling of 3D cellular dynamics

BEHAV3D-Explorer provides a user-friendly platform for analyzing 3D live-cell imaging data using natural language guidance to select analysis strategies and configure workflows.

Functional Data Clustering of Filopodia Dynamics

The paper clusters filopodia dynamics using functional data analysis to identify distinct temporal patterns linked to Notch signaling in fruit fly development.

SNAzzy: an image processing pipeline for investigating global Synchronous Network Activity

SNAzzy processes and analyzes high-throughput microscopy data from Drosophila embryos to study synchronous network activity.

Clinical relevance of diffusion tensor imaging-based parameters estimated with deep learning: the effect of model retraining

This study evaluates how retraining deep learning models affects the statistical consistency of diffusion tensor imaging parameters in bipolar disorder, showing significant variability across models.

RadMultiBench: A Reproducible Dual-Task Benchmark Exposing the Limits of Pre-Trained Vision-Language Models Across Thoracic and Breast Imaging

RadMultiBench evaluates pre-trained vision-language models on medical imaging tasks, showing performance gaps in report generation and classification across thoracic and breast imaging.

Geometric Morphometrics of Pistillate Flowers in Astrocaryum sect. Huicungo (Arecaceae): Taxonomic Implications for the Peruvian Species

This study uses geometric morphometrics to analyze pistillate flower shapes in Peruvian Astrocaryum species, revealing taxonomic insights through quantitative shape analysis.

The C. elegans Connectome Toolbox: an open, extensible framework for multimodal connectomic data integration and analysis

The paper introduces the C. elegans Connectome Toolbox, a framework for integrating and analyzing multimodal connectomic data, enabling comparison across datasets and modalities.

From smartphone to gene: Automated image-based phenotyping captures flowering diversity of common buckwheat

The paper uses smartphone imaging and automated analysis to phenotype flowering traits in buckwheat, identifying heritable traits and genomic regions associated with flowering patterns.

Discovering compositional, interpretable structure in social behavior

This paper introduces a method to model social behavior using decomposed linear dynamical systems, showing it captures complex, compositional actions more effectively than existing methods.

2D U-NET Deep Learning Architecture for Atherosclerotic Lesion Segmentation and Quantification

This paper develops a 2D U-NET model to segment and quantify atherosclerotic lesions in mouse aortic sections, achieving high accuracy and significantly reducing measurement time.

Predicting seabird-vessel interactions from bird-borne data alone

This paper develops a method to predict seabird-vessel interactions using only seabird telemetry data, showing high accuracy but limitations in distinguishing fishing from nonfishing vessels.

Stable large-scale network architecture and structured individual variability in the awake mouse brain

The paper introduces the Awake Mouse Connectome Project (AMCP), a dataset of 212 awake mice, and presents functional network atlases and analysis tools for studying brain connectivity and variability.

Mixing, competence, and commitment set the stability of cell identity

The paper models cell identity stability using a Markov framework, showing how mixing, competence, and commitment influence transition rates and proposes experiments to test these mechanisms.

CardioShift: Resolving cell-to-cell functional heterogeneity in hiPSC-derived cardiomyocytes from high-throughput single-cell imaging

CardioShift resolves cell-to-cell functional heterogeneity in hiPSC-derived cardiomyocytes by accounting for nested variance, revealing hidden subpopulations and condition-dependent phenotypes.

Cerebrovascular Structure-Flow Gradients Revealed by Volumetric Ultrasound Localization Microscopy

This study uses volumetric ultrasound localization microscopy to identify two reproducible gradients of cerebrovascular structure and flow in mice, defining axes of vascular density/flow complexity and flow speed/acceleration.

PhenoMe: An interactive AI framework for interpretable, microscopy-driven phenotyping

PhenoMe provides an open-source framework for analyzing microscopy images using foundation model-derived embeddings and experimental metadata to enable phenotyping and data exploration.

MaxCAT-Net: Hybrid Transformer-Based Skin Lesion Classification and Lightweight Segmentation with Calibration and Cross-Dataset Validation

MaxCAT-Net improves skin lesion classification and segmentation with hybrid architecture and calibration, showing performance on benchmark datasets but with preliminary results.

Cross-species structure-mechanics prediction in the intervertebral disc

This study develops a cross-species framework linking collagen structure to mechanical vulnerability in intervertebral discs using multiscale imaging and modeling.

EigenBrain maps cortical molecular architecture from functional, geometric, and structural eigenmodes

The paper shows functional MRI eigenmodes can approximate specific receptor and transporter distributions in the cortex, while structural and geometric eigenmodes cannot, and applies this to detect molecular differences in autism and schizophrenia.

Quantitative imaging of native tissue architecture combined with deep multi-omics reveals the spatial biology of kidney aging

This paper introduces NORIGAMI, a multi-omics pipeline that combines imaging and deep learning to analyze kidney aging, revealing age-related structural and molecular changes.

Expertise modifies neural responses to causal inference during language comprehension

The paper investigates how medical expertise affects neural responses to causal inferences in language comprehension, finding that physicians show stronger activation in animacy-related brain networks during illness-related causal reasoning.

oidyPeaks: R package for automated flow cytometry cell cycle analysis and visualization in fungal microbes

The paper introduces an R package for automated flow cytometry analysis of fungal cell cycle and karyotypic subpopulations, demonstrating its utility on evolving Candida albicans populations.

CountAnywhere: frugal cell counting and viability from smartphone hemocytometer photos

CountAnywhere provides a smartphone-based tool for accurate and efficient cell counting and viability assessment using hemocytometer images.

OocystMeter, a machine-learning algorithm to count and measure Plasmodium oocysts, reveals clustering patterns in the Anopheles midgut

OocystMeter automates oocyst counting and sizing in mosquito midguts, revealing clustered spatial patterns in malaria infections.

Data-driven gait cycle decomposition based on whole-body coordination dynamics

This study decomposes gait cycles using a dynamic system framework to identify coordination patterns in human locomotion.

Movements for Grooming in Rhesus Macaques Co-opted from Feeding Movements

This study shows that grooming movements in rhesus macaques are adapted from feeding movements, with similar hand motions used for both tasks.

Rapid changes in wing size and shape during the transition to laboratory conditions in a Neotropical drosophilid

This study tracks rapid wing size and shape changes in Drosophila sturtevanti across generations in lab conditions, showing genetic and environmental influences on morphology.

Cornealia: automated collective order analysis reveals density-dependent reorganization of donor corneal endothelium

Cornealia analyzes collective order in donor corneal endothelium, revealing density-dependent reorganization and mechanical responses not captured by traditional metrics.

Live-cell imaging of fetal microglia reveals time-dependent sex differences in their responsiveness to prenatal maternal stress

This study uses live-cell imaging to show sex-specific differences in fetal microglial responses to prenatal maternal cold stress, revealing time-dependent changes in morphology, motility, and phagocytic behavior.