Deficient histone H3 propionylation by BRPF1-KAT6 complexes in neurodevelopmental disorders and cancer

Lysine acetyltransferase 6A (KAT6A) and its paralog KAT6B form stoichiometric complexes with bromodomain- and PHD finger-containing protein 1 (BRPF1) for acetylation of histone H3 at lysine 23 (H3K23). We report that these complexes also catalyze H3K23 propionylati

Cinnamaldehyde Altered Cellular Immune Responses of Tongue Sole (Cynoglossus semilaevis) In Vitro

Cinnamaldehyde, a liposoluble extract from cinnamon, is a natural compound with immunity enhancement efficacy on terrestrial animals. However, its immunoregulation effects on aquatic animals has rarely been investigated due to its poor water solubility and easy oxidability. Thus, cinnamaldehyde micro emulsion (CME) was prepared to overcome these limitations. Phagocytic, respiratory burst, bactericidal, and proliferative activity of Cynoglossus semilaevis leukocytes stimulated by CME were evaluated in vitro. Leukocytes were incubated with 0, 1, 10, 100 and 1000 μg/ml cinnamaldehyde or 100 μg/ml lipopolysaccharide. Results showed that cinnamaldehyde affected leukocytes phagocytic, respiratory burst, bactericidal and proliferative activity significantly. In conclusion, low doses of cinnamaldehyde (1, 10 μg/ml) exhibited significantly high bactericidal activity, while high doses (100, 1000 μg/ml) inhibited cellular immunity of C. semilaevis

Deep learning and its applications to machine health monitoring

Since 2006, deep learning (DL) has become a rapidly growing research direction, redefining state-of-the-art performances in a wide range of areas such as object recognition, image segmentation, speech recognition and machine translation. In modern manufacturing systems, data-driven machine health monitoring is gaining in popularity due to the widespread deployment of low-cost sensors and their connection to the Internet. Meanwhile, deep learning provides useful tools for processing and analyzing these big machinery data. The main purpose of this paper is to review and summarize the emerging research work of deep learning on machine health monitoring. After the brief introduction of deep learning techniques, the applications of deep learning in machine health monitoring systems are reviewed mainly from the following aspects: Auto-encoder (AE) and its variants, Restricted Boltzmann Machines and its variants including Deep Belief Network (DBN) and Deep Boltzmann Machines (DBM), Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). In addition, an experimental study on the performances of these approaches has been conducted, in which the data and code have been online. Finally, some new trends of DL-based machine health monitoring methods are discussed

Database related to: An open-source platform for analyzing and sharing worm-behavior data

Animal behavior is increasingly being recorded in systematic imaging studies that generate large datasets. To maximize the usefulness of these data, there is a need for improved resources for analyzing and sharing behavioral data that will encourage reanalysis and methodological developments. However, for behavioral data, unlike genomic or protein structural data, there are no widely used standards. It is therefore desirable to make data available in a relatively raw form to enable flexibility in data analysis. For computational ethology to approach the level of maturity of other areas of bioinformatics, at least three challenges must be addressed: storing and accessing video files; defining flexible data formats to facilitate data sharing; and developing software to read, write, browse, and analyze the data. We have generated an open resource to begin addressing these challenges for Caenorhabditis elegans behavioral data. The database currently (Spring 2018) consists of 14,874 single-worm tracking experiments representing 386 genotypes (building on 9,203 experiments and 305 genotypes in a previous publication2) and includes data from several larval stages as well as data from aging experiments consisting of more than 2,700 videos of animals tracked daily from the L4 stage to death (Nature Research Reporting Summary). Full-resolution videos are available in HDF5 containers that include gzip-compressed video frames, time stamps, worm outlines and midlines, feature data, and experimental metadata. HDF5 files are compatible with multiple languages including MATLAB, R, Python, and C. We have also developed an HDF5 video reader that allows video playback with adjustable speed and zoom (an important feature for reviewing high-resolution multiworm tracking data), as well as toggling of worm segmentation over the original video to verify segmentation accuracy during playback