Environmental Health

Predicting Strain-Specific Metabolic Capabilities in the Genus Pseudomonas with a Flux-to-AI Approach Reveals Hidden Cell Envelope Properties.

Bacteria from the Pseudomonas genus are omnipresent in air, soil, and water. They have been widely studied for their broad metabolic versatility and their presence in epidemiological chains, bioproduction, bioremediation, and disease processes. Each year, more genomic sequences are reported in databases and repositories. However, the relationship between genomic variability in Pseudomonas strains ...

Interpretable short-term PM2.5 forecasting using meteorology-pollution coupling across multiple Beijing monitoring stations.

Accurate short-term PM2.5 forecasting is important for urban environmental monitoring because it supports early warning and short-term emission-management decisions. Recent research has advanced air-quality prediction through statistical, machine-learning, deep-learning, and spatiotemporal architectures. Complementing these developments, this study evaluates leakage-safe temporal validation, cross...

Application of stacked Transformer-XGBoost model and SHAP interpretability in air quality index prediction in Linfen City.

This study proposes a stacked Transformer-XGBoost framework with Shapley Additive Explanations (SHAP) interpretability to improve Air Quality Index pr...

Machine Learning-Driven Sensor Array Based on a DNA-Programmed Pt Nanozyme with Enhanced Dual-Enzyme-like Activities for Discrimination of Multiple Sulfur-Containing Species.

Nanozyme sensor arrays have garnered significant attention in the sensing field, whereas their practical application remains limited due to the issues...

Modeling and forecasting oyster norovirus outbreaks of long-range dependence on antecedent marine environmental conditions.

This study presents three artificial intelligence-based models - XGBoost, Random Forest (RF), and Deep Artificial Neural Network (DANN) - with 2-day l...

Advances and opportunities in point-of-care detection of toxic metals: a review of electrochemical and non-electrochemical approaches.

Heavy metal contamination remains a persistent global threat to human health and the environment, driving the urgent need for rapid, portable, and sen...

Enhancing cardiovascular disease diagnosis through knowledge distillation and hybrid Transformer-CNN-SENet architecture.

Worldwide, cardiovascular diseases (CVDs) are the leading cause of mortality, making early detection a critical public health objective. The analysis ...

From Environmental Lead Exposure to Colorectal Cancer: Integrated Transcriptomic and Genetic Analyses Identify HMOX1 as a Prioritized Candidate.

Colorectal cancer (CRC) is a leading cause of cancer mortality, and the mechanisms linking lead exposure to CRC remain unclear. In this study, we inte...

A team science blueprint for nurse-led artificial intelligence research: Lessons from building AI tools to detect stigmatizing language in obstetric records.

BACKGROUND: Nurse scientists are increasingly expected to lead research applying artificial intelligence (AI) to advance health equity. However, there...

Influence of organic matter on treatment and resource utilization of dredged river sediment.

River sediment dredging is an important way to solve internal pollution in rivers and improve their water environment. How to dispose of and utilize t...

SUITPy: A Python-based toolbox for the analysis of cerebellar functional and anatomical imaging data across the human lifespan.

The human cerebellum plays a central role in motor, emotional, and cognitive functions, and is implicated in many brain disorders. To improve the anal...

Determining groundwater quality and its associated human health risk using hydrochemical signatures and some machine learning techniques.

Groundwater is a major source of domestic water in coastal Ghana, but its quality is increasingly threatened by salinisation, nutrient enrichment, geo...

Machine-Learning-Guided Polarization-Lattice Decoupling Enables Ultrahigh Energy Storage in Lead-Free Dielectric Ceramics.

Achieving ultrahigh energy storage in lead-free dielectric ceramics is fundamentally constrained by the intrinsic trade-off between large polarization...

Slime Mold Algorithm: a novel approach for estimating total petroleum hydrocarbons in soil.

Estimating the pollution trend of biological resources such as soil is an important factor in environmental management. The present descriptive-applie...

Modeling the Coupled PM2.5-Ozone System in China: Chemical Mechanisms, Persistent Model Biases, and Future Perspectives.

China's air quality governance has transitioned from separate particulate matter and ozone controls into a compound pollution era where particulate ma...

Comprehensive assessment of Per- and polyfluoroalkyl substances (PFAS) pollution characteristics and environmental health risks in the Northeast Black Soil Region of China.

Per- and polyfluoroalkyl substances (PFAS) pose long-term risks to ecosystems and human health because of their persistence, bioaccumulation, long-ran...

Predicting antifouling paint particle contamination based on 16S rRNA gene sequencing data using random forest-based machine learning.

Antifouling paints often contain biocides designed to inhibit biological growth, and antifouling paint particles (APPs) have been previously shown to ...

Optimizing Cancer Drug Treatments Using Big Data Integration of Genomic and Clinical Data for Personalized Medicine.

Personalized cancer care depends on the seamless integration of genetic profiles, medical histories, and continuous patient monitoring to optimize the...

Large Language Model-Based Localization of Premature Ventricular Contraction Origins: A Retrospective Diagnostic Accuracy Study.

BACKGROUND: Accurate localization of premature ventricular contraction (PVC) origin from 12-lead electrocardiography (ECG) is important for procedural...

Evaluation of an AI-Based Constraint-Optimization Scheduler to Optimize On-Call Schedule Equity and Reduce Administrative Burden in a Pediatric Residency: Retrospective Comparative Study.

BACKGROUND: Resident scheduling is a high-dimensional optimization problem with implications for workload, fatigue risk, and equity. Real-world evalua...