Rapid development of high-throughput sequencing (HTS) techniques has led biology into the "big-data" era. Data analysis using various bioinformatics softwares or pipelines relying on programming and command-line environment is challenging and time-consuming for most wet-lab biologists. Bioinformatics tools with a user-friendly interface are preferred. Here, we present TBtools (a Toolkit for Biologists integrating various biological data handling tools), a stand-alone software with a user-friendly interface. It has powerful data handling engines for both bulk sequence processing and interactive data visualization. It includes a large collection of functions, which may facilitate much simple, routine but elaborate work on biological data, such as bulk sequence extraction, gene set enrichment analysis, Venn diagram preparation, heatmap illustration, comparative sequence visualization, etc. Availability and implementation:TBtools is a platform-independent software that can be run under all operating systems with Java Runtime Environment 1.

We propose a fully convolutional one-stage object detector (FCOS) to solve object detection in a per-pixel prediction fashion, analogue to semantic segmentation. Almost all state-of-the-art object detectors such as RetinaNet, SSD, YOLOv3, and Faster R-CNN rely on pre-defined anchor boxes. In contrast, our proposed detector FCOS is anchor box free, as well as proposal free. By eliminating the predefined set of anchor boxes, FCOS completely avoids the complicated computation related to anchor boxes such as calculating overlapping during training. More importantly, we also avoid all hyper-parameters related to anchor boxes, which are often very sensitive to the final detection performance. With the only post-processing non-maximum suppression (NMS), FCOS with ResNeXt-64x4d-101 achieves 44.7% in AP with single-model and single-scale testing, surpassing previous one-stage detectors with the advantage of being much simpler. For the first time, we demonstrate a much simpler and flexible detection framework achieving improved detection accuracy. We hope that the proposed FCOS framework can serve as a simple and strong alternative for many other instance-level tasks.

SUMMARY Microglia play a pivotal role in maintenance of brain homeostasis, but lose homeostatic function during neurodegenerative disorders. We identified a specific apolipoprotein E (APOE)-dependent molecular signature in microglia from models of amyotrophic lateral sclerosis (ALS), multiple sclerosis (MS) and Alzheimer’s disease (AD) and in microglia surrounding neuritic β-amyloid (Aβ) -plaques in human AD brains. The APOE pathway mediated a switch from a homeostatic to neurodegenerative microglia phenotype following phagocytosis of apoptotic neurons. Triggering receptor expressed on myeloid cells 2 (TREM2) induced APOE signaling, and targeting the TREM2-APOE pathway restored the homeostatic signature of microglia in ALS and AD mouse models and prevented neuronal loss in an acute model of neurodegeneration. APOE-mediated neurodegenerative microglia led to a loss in their tolerogenic function. Taken together, our work identifies the TREM2-APOE pathway as a major regulator of microglial functional phenotype in neurodegenerative diseases and serves as a novel target to restore homeostatic microglia.

Huge citizens expose to social media during a novel coronavirus disease (COVID-19) outbroke in Wuhan, China. We assess the prevalence of mental health problems and examine their association with social media exposure. A cross-sectional study among Chinese citizens aged�18 years old was conducted during Jan 31 to Feb 2, 2020. Online survey was used to do rapid assessment. Total of 4872 participants from 31 provinces and autonomous regions were involved in the current study. Besides demographics and social media exposure (SME), depression was assessed by The Chinese version of WHO-Five Well-Being Index (WHO-5) and anxiety was assessed by Chinese version of generalized anxiety disorder scale (GAD-7). multivariable logistic regressions were used to identify associations between social media exposure with mental health problems after controlling for covariates. The prevalence of depression, anxiety and combination of depression and anxiety (CDA) ) during COVID-19 outbroke in Wuhan, China. More than 80% (95%CI:80.9%-83.1%) of participants reported frequently exposed to social media. After controlling for covariates, frequently SME was positively associated with high odds of anxiety (OR = 1.72, 95%CI: 1.31-2.26) and CDA (OR = 1.91, 95%CI: 1.52-2.41) compared with less SME. Our findings show there are high prevalence of mental health problems, which positively associated with frequently SME during the COVID-19 outbreak. These findings implicated the government need pay more attention to mental health problems, especially depression and anxiety among general population and combating with "infodemic" while combating during public health emergency.

Background: Recently, dyslipidaemia was observed in patients with coronavirus disease 2019 (COVID-19), especially in severe cases. This study aimed to explore the predictive value of blood lipid levels for COVID-19 severity. Methods: All patients with COVID-19 admitted to HwaMei Hospital, University of Chinese Academy of Sciences, from January 23 to April 20, 2020, were included in this retrospective study. General clinical characteristics and laboratory data (including blood lipid parameters) were obtained, and their predictive values for the severity were analysed. Results: In total, 142 consecutive patients with COVID-19 were included. The non-severe group included 125 cases, and 17 cases were included in the severe group. Total cholesterol, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and apolipoprotein A1 (ApoA1) at baseline were signi cantly lower in the severe group. ApoA1 and interleukin-6 (IL-6) were recognized as independent risk factors for COVID-19 severity. ApoA1 had the highest area under the receiver operator characteristic curve (AUC) among all the single markers (AUC: 0.896, 95% CI: 0.834-0.941). Moreover, the risk model established using ApoA1 and IL-6 enhanced the predictive value (AUC: 0.977, 95% CI: 0.932-0.995). On the other hand, ApoA1 levels were elevated in the severe group during treatment, and there was no signi cant difference between the severe and non-severe groups during the recovery stage of the disease. Conclusion: The blood lipid pro le in severe COVID-19 patients is quite different from that in non-severe cases. Serum ApoA1 could severe as a good indictor to re ect the severity of COVID-19.