COVID-19

Decoding Missense Variants by Incorporating Phase Separation via Machine Learning.

Computational models have made significant progress in predicting the effect of protein variants. However, deciphering numerous variants of uncertain significance (VUS) located within intrinsically disordered regions (IDRs) remains challenging. To address this issue, we introduce phase separation, which is tightly linked to IDRs, into the investigation of missense variants. Phase separation is vit...

Restoring of Interhemispheric Symmetry in Patients With Stroke Following Bilateral or Unilateral Robot-Assisted Upper-Limb Rehabilitation: A Pilot Randomized Controlled Trial.

Bilateral robotic rehabilitation has proven helpful in the recovery of upper limb motor function in patients with stroke, but its effects on the cortical reorganization mechanisms underlying recovery are still unclear. This pilot Randomized Controlled Trial (RCT) aimed to evaluate the effects on the interhemispheric balance of unilateral or bilateral robotic treatments in patients with subacute st...

Development, study, and comparison of models of cross-immunity to the influenza virus using statistical methods and machine learning.

INTRODUCTION: The World Health Organization considers the values of antibody titers in the hemagglutination inhibition assay as one of the most import...

DMGNet: Depth mask guiding network for RGB-D salient object detection.

Though depth images can provide supplementary spatial structural cues for salient object detection (SOD) task, inappropriate utilization of depth feat...

Does COVID-19 lockdown matter for air pollution in the short and long run in China? A machine learning approach to policy evaluation.

This paper leverages a data-driven two-step approach to effectively evaluate the effects of COVID-19 lockdown on air pollution in both the short and l...

Investigating Older Adults' Use of a Socially Assistive Robot via Time Series Clustering and User Profiling: Descriptive Analysis Study.

BACKGROUND: The aging population and the shortage of geriatric care workers are major global concerns. Socially assistive robots (SARs) have the poten...

Prediction of aptamer affinity using an artificial intelligence approach.

Aptamers are oligonucleotide sequences that can connect to particular target molecules, similar to monoclonal antibodies. They can be chosen by system...

Deep humoral profiling coupled to interpretable machine learning unveils diagnostic markers and pathophysiology of schistosomiasis.

Schistosomiasis, a highly prevalent parasitic disease, affects more than 200 million people worldwide. Current diagnostics based on parasite egg detec...

Co-firing characteristic prediction of solid waste and coal for supercritical CO power cycle based on CFD simulation and machine learning algorithm.

The co-firing technology of combustible solid waste (CSW) and coal in the supercritical CO (S-CO) circulating fluidized bed (CFB) can effectively deal...

Tissue-aware interpretation of genetic variants advances the etiology of rare diseases.

Pathogenic variants underlying Mendelian diseases often disrupt the normal physiology of a few tissues and organs. However, variant effect prediction ...

Salivary detection of Chikungunya virus infection using a portable and sustainable biophotonic platform coupled with artificial intelligence algorithms.

The current detection method for Chikungunya Virus (CHIKV) involves an invasive and costly molecular biology procedure as the gold standard diagnostic...

Deep Learning-Based Barley Disease Quantification for Sustainable Crop Production.

Net blotch disease caused by is a major fungal disease that affects barley () plants and can result in significant crop losses. In this study, we dev...

A Machine Learning Method for RNA-Small Molecule Binding Preference Prediction.

The interaction between RNA and small molecules is crucial in various biological functions. Identifying molecules targeting RNA is essential for the i...

Developing a Machine Learning 'Smart' Polymerase Chain Reaction Thermocycler Part 2: Putting the Theoretical Framework into Practice.

The introduction of PCR into forensic science and the rapid increases in the sensitivity, specificity and discrimination power of DNA profiling that f...

AI-derived comparative assessment of the performance of pathogenicity prediction tools on missense variants of breast cancer genes.

Single nucleotide variants (SNVs) can exert substantial and extremely variable impacts on various cellular functions, making accurate predictions of t...

Developing a Machine-Learning 'Smart' PCR Thermocycler, Part 1: Construction of a Theoretical Framework.

The use of PCR is widespread in biological fields. Some fields, such as forensic biology, push PCR to its limits as DNA profiling may be required in s...

A Preliminary Study to Evaluate the Immune Response to a Booster Dose of the Adult Tetanus-Diphtheria Vaccine (Td) Available in Iran.

BACKGROUND: Despite the availability of tetanus-diphtheria (TD) vaccines in Iran, the seroconversion rate of these products as a booster dose is unkno...

Deep learning Radiomics Based on Two-Dimensional Ultrasound for Predicting the Efficacy of Neoadjuvant Chemotherapy in Breast Cancer.

We investigate the predictive value of a comprehensive model based on preoperative ultrasound radiomics, deep learning, and clinical features for path...

Antibiotic profile classification of Proteus mirabilis using machine learning: An investigation into multidimensional radiomics features.

Antimicrobial resistance (AMR) presents a significant threat to global healthcare. Proteus mirabilis causes catheter-associated urinary tract infectio...

Aging-related biomarkers for the diagnosis of Parkinson's disease based on bioinformatics analysis and machine learning.

Parkinson's disease (PD) is a multifactorial disease that lacks reliable biomarkers for its diagnosis. It is now clear that aging is the greatest risk...