Environmental Health

Leveraging the wearable 1-lead ECG signal: From cardiac rhythm to cardiac function assessment

The electrocardiogram (ECG) is a critical tool in the diagnosis and monitoring of cardiovascular disease. Although traditional 12-lead ECGs offer comprehensive insights into the electrical activity of the heart, they typically require clinical settings and expert interpretation, which limits their accessibility. In contrast, smartwatch 1-lead ECGs can be recorded at home, allowing more frequent an...

Microplastics drive both linear and threshold-type shifts in soil multifunctionality along concentration gradients

Microplastics are increasingly recognized as emerging contaminants in terrestrial ecosystems, yet their mechanistic impacts on soil multifunctionality remain poorly understood. Here, we evaluated the influence of two microplastic types, polyethylene terephthalate and polypropylene, on soil functioning by subjecting soils to a gradient of concentrations of both microplastics, and measuring six vari...

Accurate Network Traffic Matrix Prediction via LEAD: an LLM-Enhanced Adapter-Based Conditional Diffusion Model

Driven by the evolution toward 6G and AI-native edge intelligence, network operations increasingly require predictive and risk-aware adaptation under ...

Biophysically realistic network-level transport model of tau progression with exosome-mediated release and uptake processes

The spatiotemporal progression of tau aggregates in neurodegenerative diseases like Alzheimer's follows the brain's structural connectome, yet a profo...

Feature-Aware Test Generation for Deep Learning Models

As deep learning models are widely used in software systems, test generation plays a crucial role in assessing the quality of such models before deplo...

Deep learning enables diagnosis of atrial cardiomyopathy from routine 12-lead electrocardiogram

BackgroundAtrial cardiomyopathy (AtCM) is both a cause and a consequence of atrial fibrillation and flutter (AF) and can lead to ischemic stroke. Imag...

Automatic and precise identification of volatile organic compounds from gas chromatography in prolonged atmospheric monitoring.

Long-term continuous monitoring of volatile organic compounds (VOCs) is pivotal for climate change research, air quality assessment, pollution source ...

Deep learning predicts the effect of neoadjuvant chemotherapy for patients with triple negative breast cancer.

BACKGROUND: Triple negative breast cancer (TNBC) is an aggressive subcategory of breast cancer with poor prognosis and high risk of recurrence after t...

Soil type and content of macro-elements determine hotspots of Cu and Ni accumulation in soils of subarctic industrial barren: inference from a cascade machine learning.

Aerial technogenic pollution from the activity of ferrous and non-ferrous metallurgy resulting in degradation of vulnerable natural ecosystems is a pr...

Quantifying regional transport contributions to PM-bound trace elements in a southeast coastal island of China: Insights from a machine learning approach.

Identifying and quantifying pollution sources and their associated health risks are essential for formulating effective pollution control policies. Th...

Low-cost sensors for atmospheric NO measurement: A review.

Nitrogen dioxide (NO) is a major air pollutant in urban areas, prompting the development of numerous analytical methods for its monitoring. Among thes...

EEG-based prediction of reaction time during sleep deprivation.

Prolonged wakefulness is known to adversely affect basic cognitive abilities such as object recognition and decision-making. It affects the dynamics o...

AmpLyze: A Deep Learning Model for Predicting the Hemolytic Concentration

Red-blood-cell lysis (HC50) is the principal safety barrier for antimicrobial-peptide (AMP) therapeutics, yet existing models only say "toxic" or "n...

Self-Review Framework for Enhancing Instruction Following Capability of LLM

Various techniques have been proposed to improve large language models (LLMs) adherence to formatting and instruction constraints. One of the most e...

Robotic System with AI for Real Time Weed Detection, Canopy Aware Spraying, and Droplet Pattern Evaluation

Uniform and excessive herbicide application in modern agriculture contributes to increased input costs, environmental pollution, and the emergence o...

QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation

Existing text-to-image models often rely on parameter fine-tuning techniques such as Low-Rank Adaptation (LoRA) to customize visual attributes. Howe...

Integrating Traditional and Deep Learning Methods to Detect Tree Crowns in Satellite Images

Global warming, loss of biodiversity, and air pollution are among the most significant problems facing Earth. One of the primary challenges in addre...

A Closed-Loop Environmental Health Management Platform: A Multifunctional MgFeO Nanoparticle Enabling AI-Enhanced Detection, Bioimaging, and Degradation of Tetracycline Antibiotics.

Emerging pollutants such as tetracycline antibiotics (TCs) have garnered attention due to their ecological impacts and the evolution of drug resistanc...

Using machine learning to unravel chemical and meteorological effects on ground-level ozone: Insights for ozone-climate control strategies.

In the context of climate change, various countries/regions across East Asia have witnessed severe ground-level ozone (O) pollution, which poses poten...

Prenatal exposure to criteria air pollution and traffic-related air toxics and risk of autism spectrum disorder: A population-based cohort study of California births (1990-2018).

BACKGROUND: Autism spectrum disorder (ASD) prevalence has risen steadily in California (CA) over several decades, with environmental factors like air ...