Evidence-driven spatiotemporal COVID-19 hospitalization prediction with Ising dynamics.

In this work, we aim to accurately predict the number of hospitalizations during the COVID-19 pandemic by developing a spatiotemporal prediction model. We propose HOIST, an Ising dynamics-based deep learning model for spatiotemporal COVID-19 hospitalization prediction. By drawing the analogy between locations and lattice sites in statistical mechanics, we use the Ising dynamics to guide the model to extract and utilize spatial relationships across locations and model the complex influence of granular information from real-world clinical evidence. By leveraging rich linked databases, including insurance claims, census information, and hospital resource usage data across the U.S., we evaluate the HOIST model on the large-scale spatiotemporal COVID-19 hospitalization prediction task for 2299 counties in the U.S. In the 4-week hospitalization prediction task, HOIST achieves 368.7 mean absolute error, 0.6 [Formula: see text] and 0.89 concordance correlation coefficient score on average. Our detailed number needed to treat (NNT) and cost analysis suggest that future COVID-19 vaccination efforts may be most impactful in rural areas. This model may serve as a resource for future county and state-level vaccination efforts.

MedML: Fusing medical knowledge and machine learning models for early pediatric COVID-19 hospitalization and severity prediction.

The COVID-19 pandemic has caused devastating economic and social disruption. This has led to a nationwide call for models to predict hospitalization and severe illness in patients with COVID-19 to inform the distribution of limited healthcare resources. To address this challenge, we propose a machine learning model, MedML, to conduct the hospitalization and severity prediction for the pediatric population using electronic health records. MedML extracts the most predictive features based on medical knowledge and propensity scores from over 6 million medical concepts and incorporates the inter-feature relationships in medical knowledge graphs via graph neural networks. We evaluate MedML on the National Cohort Collaborative (N3C) dataset. MedML achieves up to a 7% higher AUROC and 14% higher AUPRC compared to the best baseline machine learning models. MedML is a new machine learnig framework to incorporate clinical domain knowledge and is more predictive and explainable than current data-driven methods.

SequencEnG: an interactive knowledge base of sequencing techniques.

SUMMARY: Next-generation sequencing (NGS) techniques are revolutionizing biomedical research by providing powerful methods for generating genomic and epigenomic profiles. The rapid progress is posing an acute challenge to students and researchers to stay acquainted with the numerous available methods. We have developed an interactive online educational resource called Sequencing Techniques Engine for Genomics (SequencEnG) to provide a tree-structured knowledge base of 66 different sequencing techniques and step-by-step NGS data analysis pipelines comparing popular tools. SequencEnG is designed to facilitate barrier-free learning of current NGS techniques and provides a user-friendly interface for searching through experimental and analysis methods. AVAILABILITY AND IMPLEMENTATION: SequencEnG is part of the project Knowledge Engine for Genomics (KnowEnG) and is freely available at http://education.knoweng.org/sequenceng/.

Stick-Slip Friction Reveals Hydrogel Lubrication Mechanisms.

The lubrication behavior of the hydrated biopolymers that constitute tissues in organisms differs from that outlined by the classical Stribeck curve, and studying hydrogel lubrication is a key pathway to understand the complexity of biolubrication. Here, we have investigated the frictional characteristics of polyacrylamide (PAAm) hydrogels with various acrylamide concentrations, exhibiting Young's moduli (E) that range from 1 to 40 kPa, as a function of applied normal load and sliding velocities by colloid probe lateral force microscopy. The speed-dependence of the friction force shows an initial decrease in friction with increasing velocity, while, above a transition velocity V*, friction increases with speed. This study reveals two different boundary lubrication mechanisms characterized by distinct scaling laws. An unprecedented and comprehensive study of the lateral force loops reveals intermittent friction or stick-slip above and below V*, with characteristics that depend on the hydrogel network, applied load, and sliding velocity. Our work thus provides insight into the closely tied parameters governing hydrogel lubrication mechanisms, and stick-slip friction.