Quantitative investigation reveals distinct phases in Drosophila sleep.

The fruit fly, Drosophila melanogaster, has been used as a model organism for the molecular and genetic dissection of sleeping behaviors. However, most previous studies were based on qualitative or semi-quantitative characterizations. Here we quantified sleep in flies.

Sparsity-Penalized Stacked Denoising Autoencoders for Imputing Single-Cell RNA-Seq Data.

Single-cell RNA-seq (scRNA-seq) is quite prevalent in studying transcriptomes, but it suffers from excessive zeros, some of which are true, but others are false. False zeros, which can be seen as missing data, obstruct the downstream analysis of single-cell RNA-seq data. How to distinguish true zeros from false ones is the key point of this problem.

Deepprune: Learning Efficient and Interpretable Convolutional Networks Through Weight Pruning for Predicting DNA-Protein Binding.

Convolutional neural network (CNN) based methods have outperformed conventional machine learning methods in predicting the binding preference of DNA-protein binding. Although studies in the past have shown that more convolutional kernels help to achieve better performance, visualization of the model can be obscured by the use of many kernels, resulting in overfitting and reduced interpretation because the number of motifs in true models is limited. Therefore, we aim to arrive at high performance, but with limited kernel numbers, in CNN-based models for motif inference.

A Novel Noninvasive Program for Staging Liver Fibrosis in Untreated Patients With Chronic Hepatitis B.

Objectives: Chronic hepatitis B (CHB) can progress into liver fibrosis and cirrhosis with poor outcomes. Early and accurate diagnosis of liver fibrosis/cirrhosis is important to guide the preventive strategy of their related complications.

Methods: A Chinese multicenter cross-sectional study was conducted to develop and validate a novel noninvasive program for staging liver fibrosis in untreated patients with CHB.

How Microbes Shape Their Communities? A Microbial Community Model Based on Functional Genes.

Exploring the mechanisms of maintaining microbial community structure is important to understand biofilm development or microbiota dysbiosis. In this paper, we propose a functional gene-based composition prediction (FCP) model to predict the population structure composition within a microbial community. The model predicts the community composition well in both a low-complexity community as acid mine drainage (AMD) microbiota, and a complex community as human gut microbiota.