Bioterrorism

A new machine learning approach for predicting the response to anemia treatment in a large cohort of End Stage Renal Disease patients undergoing dialysis.

Chronic Kidney Disease (CKD) anemia is one of the main common comorbidities in patients undergoing End Stage Renal Disease (ESRD). Iron supplement and especially Erythropoiesis Stimulating Agents (ESA) have become the treatment of choice for that anemia. However, it is very complicated to find an adequate treatment for every patient in each particular situation since dosage guidelines are based on...

Predicting long-term outcome of Internet-delivered cognitive behavior therapy for social anxiety disorder using fMRI and support vector machine learning.

Cognitive behavior therapy (CBT) is an effective treatment for social anxiety disorder (SAD), but many patients do not respond sufficiently and a substantial proportion relapse after treatment has ended. Predicting an individual's long-term clinical response therefore remains an important challenge. This study aimed at assessing neural predictors of long-term treatment outcome in participants with...

Live predators, robots, and computer-animated images elicit differential avoidance responses in zebrafish.

Emotional disturbances constitute a major health issue affecting a considerable portion of the population in western countries. In this context, anima...

Novel method to predict body weight in children based on age and morphological facial features.

A new and novel approach of predicting the body weight of children based on age and morphological facial features using a three-layer feed-forward art...

Preparation of agar nanospheres: comparison of response surface and artificial neural network modeling by a genetic algorithm approach.

Multivariate nature of drug loaded nanospheres manufacturing in term of multiplicity of involved factors makes it a time consuming and expensive proce...

Forecasting viral evolution from phylogenetic trees

Viral mutation forecasting plays a key role in pandemic preparedness by enabling researchers to anticipate novel variants and design proactive interve...

Predicting Quantifiability from Primary Screens to Prioritize Dose-Response Profiling

High-throughput drug screening relies on low-cost primary assays to prioritize compounds for more expensive dose-response profiling, where potency is ...

A mechanism-annotated benchmark reveals limited fidelity to drug-response signatures in single-cell perturbation models

Single-cell drug perturbation models are increasingly used to predict how compounds remodel cellular states, but they are still largely assessed by ex...

Phthalate exposure and obesity in US adults: a small but robust association, and three leakage mechanisms that inflate it

Background. Phthalates are hypothesised to act as metabolic disruptors, and machine learning applied to the National Health and Nutrition Examination ...

SPARK-SAM: Self-Prompt Adaptation with Response Knowledge for SAM in Infrared Small Target Segmentation

Promptable segmentation models provide a reusable interface, but direct transfer to automatic infrared small-target segmentation (IRSTD) exposes a mis...

Single-Molecule Proteomics via a Dynamic Translocase and Physics-Informed Machine Learning

Single-molecule protein sequencing promises to democratize clinical proteomics, but platforms retrofitting static DNA-sequencing nanopores face a fund...

Beyond Correctness: Benchmarking and Aligning Response Behaviors in Hybrid-Thinking MLLMs

Hybrid-thinking multimodal large language models (MLLMs) allow a single model to alternate between deliberative thinking and latency-efficient non-thi...

CoMedBench: A Multi-Source Benchmark of Synthetic Medical Data Fidelity and Downstream Utility

Access to clinical data is essential for developing reliable healthcare machine learning systems, but direct use of electronic health records is const...

Structure-aware deep learning predicts influenza antigenicity and guides vaccine strain recommendation

The continuous accumulation of genetic mutations in influenza A viruses (IAVs) drives antigenic drift, necessitating precise antigenic prediction for ...

InteracVid: Building a Real Interactive Audio-Visual Response Dataset from Live-Chat Videos

Large language models have made text the default medium for human--AI interaction, buttext alone cannot express the full range of responses required b...

TREA-Net: A Transferable Residual Epidemiological Adaptation Network for Dengue Incidence Forecasting

Accurate multi-week dengue forecasting supports timely vector-control interventions, outbreak preparedness, and healthcare resource allocation. Howeve...

Simulation-Trained Deep Learning for Automated Cell-Based HLA Antibody Assay Interpretation in Pre-Transplant Diagnostics

Preformed and de novo antibodies against donor human leukocyte antigen (HLA) antigens remain a major cause of antibody-mediated rejection and graft lo...

Perturbation response decomposition enables biologically aligned generalization to unseen perturbations and cellular contexts

Predicting single-cell responses to genetic perturbations could reveal the vast combinatorial space of perturbations and cellular contexts that is inf...

Leveraging Uncertainty Estimates for Drug Response Prediction in Cancer Cell Lines

Machine learning models for drug response prediction in cancer cell lines carry the potential to advance precision oncology by tailoring treatments to...

Beyond the Static Caliper: Dynamical Translocases and the Mathematical Imperative for Single-Molecule Proteomics

The advent of single-molecule nanopore sequencing established a powerful platform for modern genomics by using static biological pores to report the t...