Fluorescent double network ionogels with fast self-healability and high resilience for reliable human motion detection.

Fascinating properties are displayed by high-performance ionogel-based flexible strain sensors, thereby gaining increasing attention in various applications ranging from human motion monitoring to soft robotics. However, the integration of excellent properties such as optical and mechanical properties and satisfactory sensing performance for one ionogel sensor is still a challenge. In particular, fatigue-resistant and self-healing properties are essential to continuous sensing.

A Novel Probability Model for LncRNA⁻Disease Association Prediction Based on the Naïve Bayesian Classifier.

An increasing number of studies have indicated that long-non-coding RNAs (lncRNAs) play crucial roles in biological processes, complex disease diagnoses, prognoses, and treatments. However, experimentally validated associations between lncRNAs and diseases are still very limited. Recently, computational models have been developed to discover potential associations between lncRNAs and diseases by integrating multiple heterogeneous biological data; this has become a hot topic in biological research.

Prediction of microRNA-disease associations based on distance correlation set.

Background: Recently, numerous laboratory studies have indicated that many microRNAs (miRNAs) are involved in and associated with human diseases and can serve as potential biomarkers and drug targets. Therefore, developing effective computational models for the prediction of novel associations between diseases and miRNAs could be beneficial for achieving an understanding of disease mechanisms at the miRNA level and the interactions between diseases and miRNAs at the disease level. Thus far, only a few miRNA-disease association pairs are known, and models analyzing miRNA-disease associations based on lncRNA are limited.