Environmental Health

A Novel Approach for Multi-Lead ECG Classification Using DL-CCANet and TL-CCANet.

Cardiovascular disease (CVD) has become one of the most serious diseases that threaten human health. Over the past decades, over 150 million humans have died of CVDs. Hence, timely prediction of CVDs is especially important. Currently, deep learning algorithm-based CVD diagnosis methods are extensively employed, however, most such algorithms can only utilize one-lead ECGs. Hence, the potential inf...

Opportunities and Challenges in Phenotypic Screening for Neurodegenerative Disease Research.

Toxic misfolded proteins potentially underly many neurodegenerative diseases, but individual targets which regulate these proteins and their downstream detrimental effects are often unknown. Phenotypic screening is an unbiased method to screen for novel targets and therapeutic molecules and span the range from primitive model organisms such as , which allow for high-throughput screening to patient...

Multifunctional and biodegradable self-propelled protein motors.

A diversity of self-propelled chemical motors, based on Marangoni propulsive forces, has been developed in recent years. However, most motors are non-...

Solving visual pollution with deep learning: A new nexus in environmental management.

Visual pollution is a relatively new concern amidst the existing plethora of mainstream environmental pollution, recommending the necessity for resear...

DeepHarmony: A deep learning approach to contrast harmonization across scanner changes.

Magnetic resonance imaging (MRI) is a flexible medical imaging modality that often lacks reproducibility between protocols and scanners. It has been s...

Characterization and potential antitumor effect of a heteropolysaccharide produced by the red alga .

Taking into account the rising trend of the incidence of cancers of various organs, effective therapies are urgently needed to control human malignanc...

Predicting toxicity of chemicals: software beats animal testing.

We created earlier a large machine-readable database of 10,000 chemicals and 800,000 associated studies by natural language processing of the public p...

Machine learning models accurately predict ozone exposure during wildfire events.

Epidemiologists use prediction models to downscale (i.e., interpolate) air pollution exposure where monitoring data is insufficient. This study compar...

Comparison of serum biochemical markers in patients with mesothelioma and pleural plaques versus healthy individuals exposed to environmental asbestos.

BACKGROUND: In this study, we aimed to compare serum biochemical markers in patients with malignant pleural mesothelioma and pleural plaques versus he...

Using machine learning to understand the temporal morphology of the PM annual cycle in East Asia.

PM air pollution is a significant issue for human health all over the world, especially in East Asia. A large number of ground-based measurement sites...

Determination of the physical domain for air quality monitoring stations using the ANP-OWA method in GIS.

Air pollution is a major concern in some megacities of Iran. Specific cities in the country have reached an extremely harmful level of air pollution w...

A Deep Learning-Based Automatic Mosquito Sensing and Control System for Urban Mosquito Habitats.

Mosquito control is important as mosquitoes are extremely harmful pests that spread various infectious diseases. In this research, we present the prel...

A comparison of linear regression, regularization, and machine learning algorithms to develop Europe-wide spatial models of fine particles and nitrogen dioxide.

Empirical spatial air pollution models have been applied extensively to assess exposure in epidemiological studies with increasingly sophisticated and...

Effects of different moisture and temperature levels on survival in poultry fat.

Fat products have been historically thought to have too low water activity to harbor pathogens. However, it has been recently reported that high moist...

Machine Learning-Based Integration of High-Resolution Wildfire Smoke Simulations and Observations for Regional Health Impact Assessment.

Large wildfires are an increasing threat to the western U.S. In the 2017 fire season, extensive wildfires occurred across the Pacific Northwest (PNW)....

The application of machine learning methods for prediction of metal sorption onto biochars.

The adsorption of six heavy metals (lead, cadmium, nickel, arsenic, copper, and zinc) on 44 biochars were modeled using artificial neural network (ANN...

Computational determination of hERG-related cardiotoxicity of drug candidates.

BACKGROUND: Drug candidates often cause an unwanted blockage of the potassium ion channel of the human ether-a-go-go-related gene (hERG). The blockage...

Unveiling tropospheric ozone by the traditional atmospheric model and machine learning, and their comparison:A case study in hangzhou, China.

Tropospheric ozone in the surface air has become the primary atmospheric pollutant in Hangzhou, China, in recent years. Previous analysis is not enoug...

Analysis of noise pollution emitted by stationary MF285 tractor using different mixtures of biodiesel, bioethanol, and diesel through artificial intelligence.

In the present study, the noise pollution from different compositions of biodiesel, bioethanol, and diesel fuels in a four-cylinder and four-stroke en...

Enhancing Acute Oral Toxicity Predictions by using Consensus Modeling and Algebraic Form-Based 0D-to-2D Molecular Encodes.

Quantitative structure-activity relationships (QSAR) are introduced to predict acute oral toxicity (AOT), by using the QuBiLS-MAS (acronym for quadrat...