Environmental Health

Deep learning to design nuclear-targeting abiotic miniproteins.

There are more amino acid permutations within a 40-residue sequence than atoms on Earth. This vast chemical search space hinders the use of human learning to design functional polymers. Here we show how machine learning enables the de novo design of abiotic nuclear-targeting miniproteins to traffic antisense oligomers to the nucleus of cells. We combined high-throughput experimentation with a dire...

Swarming Aqua Sperm Micromotors for Active Bacterial Biofilms Removal in Confined Spaces.

Microscale self-propelled robots show great promise in the biomedical field and are the focus of many researchers. These tiny devices, which move and navigate by themselves, are typically based on inorganic microstructures that are not biodegradable and potentially toxic, often using toxic fuels or elaborate external energy sources, which limits their real-world applications. One potential solutio...

Machine learning-based identification and characterization of 15 novel pathogenic SUOX missense mutations.

Isolated sulfite oxidase deficiency (ISOD) is a rare hereditary metabolic disease caused by absence of functional sulfite oxidase (SO) due to mutation...

Feature extraction and machine learning techniques for identifying historic urban environmental hazards: New methods to locate lost fossil fuel infrastructure in US cities.

U.S. cities contain unknown numbers of undocumented "manufactured gas" sites, legacies of an industry that dominated energy production during the late...

Explainable deep learning predictions for illness risk of mental disorders in Nanjing, China.

Epidemiological studies have revealed the associations of air pollutants and meteorological factors with a range of mental health conditions. However,...

Towards a better understanding of deep convolutional neural network processes for recognizing organic chemicals of environmental concern.

Deep convolutional neural network (DCNN) has proved to be a promising tool for identifying organic chemicals of environmental concern. However, the un...

A model of modified -iodobenzylguanidine conjugated gold nanoparticles for neuroblastoma treatment.

Iodine-131 -iodobenzylguanidine (I-IBG) has been utilized as a standard treatment to minimize adverse side effects by targeting therapies to bind to t...

ECG quality assessment based on hand-crafted statistics and deep-learned S-transform spectrogram features.

Background and Objective Electrocardiogram (ECG) quality assessment is significant for automatic diagnosis of cardiovascular disease and reducing the ...

Discovery of quinolone derivatives as antimycobacterial agents.

Tuberculosis (TB), an infectious disease caused by (), is an important public health issue. Current first-line drugs administered to TB patients have...

Asbestos Detection with Fluorescence Microscopy Images and Deep Learning.

Fluorescent probes can be used to detect various types of asbestos (serpentine and amphibole groups); however, the fiber counting using our previously...

Leveraging high-throughput screening data, deep neural networks, and conditional generative adversarial networks to advance predictive toxicology.

There are currently 85,000 chemicals registered with the Environmental Protection Agency (EPA) under the Toxic Substances Control Act, but only a smal...

Thiol modified bimodal mesoporous silica nanoparticles for removal and determination toxic vanadium from air and human biological samples in petrochemical workers.

Investigation of exposure to toxic vanadium (V) in petrochemical workers is very important for human health, and it must be removed and determined in ...

CASTELO: clustered atom subtypes aided lead optimization-a combined machine learning and molecular modeling method.

BACKGROUND: Drug discovery is a multi-stage process that comprises two costly major steps: pre-clinical research and clinical trials. Among its stages...

Enzyme cascade-amplified immunoassay based on the nanobody-alkaline phosphatase fusion and MnO nanosheets for the detection of ochratoxin A in coffee.

Ochratoxin A (OTA) is a common food contaminant with multiple toxicities and thus rapid and accurate detection of OTA is indispensable to minimize the...

Augmenting Product Defect Surveillance Through Web Crawling and Machine Learning in Singapore.

INTRODUCTION: Substandard medicines are medicines that fail to meet their quality standards and/or specifications. Substandard medicines can lead to s...

Video-Sensing Characterization for Hydrodynamic Features: Particle Tracking-Based Algorithm Supported by a Machine Learning Approach.

The efficient and reliable monitoring of the flow of water in open channels provides useful information for preventing water slow-downs due to the dep...

A new deep learning algorithm of 12-lead electrocardiogram for identifying atrial fibrillation during sinus rhythm.

Atrial fibrillation (AF) is the most prevalent arrhythmia and is associated with increased morbidity and mortality. Its early detection is challenging...

Validated spectral manipulations for determination of an anti-neoplastic drug and its related impurities including its hazardous degradation product.

Innovative and specific double dual wavelength, dual ratio subtraction spectrophotometric methods were carried out along with a successive ratio subtr...

Machine learning analyses of antibody somatic mutations predict immunoglobulin light chain toxicity.

In systemic light chain amyloidosis (AL), pathogenic monoclonal immunoglobulin light chains (LC) form toxic aggregates and amyloid fibrils in target o...

Deep learning based regression for optically inactive inland water quality parameter estimation using airborne hyperspectral imagery.

Airborne hyperspectral remote sensing has the characteristics of high spatial and spectral resolutions, and provides an opportunity for accurate and e...