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.

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.

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.

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.