Background Coronary artery disease (CAD) is associated with gut microbiota alterations in different populations. Gut microbe-derived metabolites have been proposed as markers of major adverse cardiac events. However, the relationship between the gut microbiome and the different stages of CAD pathophysiology remains to be established by a systematic study. Results Based on multi-omic analyses (sequencing of the V3-V4 regions of the 16S rRNA gene and metabolomics) of 161 CAD patients and 40 healthy controls, we found that the composition of both the gut microbiota and metabolites changed significantly with CAD severity. We identified 29 metabolite modules that were separately classified as being positively or negatively correlated with CAD phenotypes, and the bacterial co-abundance group (CAG) with characteristic changes at different stages of CAD was represented by Roseburia , Klebsiella , Clostridium IV and Ruminococcaceae . The result revealed that certain bacteria might affect atherosclerosis by modulating the metabolic pathways of the host, such as taurine, sphingolipid and ceramide, and benzene metabolism. Moreover, a disease classifier based on differential levels of microbes and metabolites was constructed to discriminate cases from controls and was even able to distinguish stable coronary artery disease from acute coronary syndrome accurately. Conclusion Overall, the composition and functions of the gut microbial community differed from healthy controls to diverse coronary artery disease subtypes. Our study identified the relationships between the features of the gut microbiota and circulating metabolites, providing a new direction for future studies aiming to understand the host–gut microbiota interplay in atherosclerotic pathogenesis. Electronic supplementary material The online version of this article (10.1186/s40168-019-0683-9) contains supplementary material, which is available to authorized users.

Intelligence is one of the most important aspects in the development of our future communities. Ranging from smart home, smart building, to smart city, all these smart infrastructures must be supported by intelligent power supply. Smart grid is proposed to solve all challenges of future electricity supply. In smart grid, in order to realize optimal scheduling, a Smart Meter (SM) is installed at each home to collect the near real-time electricity consumption data, which can be used by the utilities to offer better smart home services. However, the near real-time data may disclose user's privacy. An adversary may track the application usage patterns by analyzing the user's electricity consumption profile. In this paper, we propose a privacy-preserving and efficient data aggregation scheme. We divide users into different groups and each group has a private blockchain to record its members' data. To preserve the inner privacy within a group, we use pseudonym to hide user's identity, and each user may create multiple pseudonyms and associate his/her data with different pseudonyms. In addition, the bloom filter is adopted for fast authentication. The analysis shows that the proposed scheme can meet the security requirements, and achieve a better performance than other popular methods.

No abstract

Objectives Systemic lupus erythematosus (SLE) is a chronic autoimmune disease whose onset and progression are affected by genetic and environmental factors. The purpose of this study is to identify the influence of gut microbiota in the pathogenesis of SLE, and to investigate the mechanism involved. Methods Fecal microbiota from C57/BL6 mice and SLE prone mice were examined using next-generation sequencing (NGS). Germ free mice were given fecal microbiota transplantation (FMT), and their gut microbiome and gene expression in recipients’ colons were examined by NGS. The anti-double stranded DNA (anti-dsDNA) antibodies in recipients were determined using an enzyme-linked immunosorbent assay (ELISA). The immune cell profiles of mice were analyzed by flow cytometry at the 3rd week after FMT, and the expression of genes associated with SLE after FMT was determined using quantitative real-time PCR (qRT-PCR). Results The fecal microbiota of SLE mice had lower community richness and diversity than healthy mice. Fecal microbiota of recipient mice were similar to their donors. Fecal microbiome from SLE mice could lead to a significant increase of anti-dsDNA antibodies and promote the immune response in recipient mice. Our results also indicated that fecal microbiome from SLE mice resulted in significant changes in the distribution of immune cells and upregulated expression of certain lupus susceptibility genes. Conclusions SLE is associated with alterations of gut microbiota. Fecal microbiome from SLE mice can induce the production of anti-dsDNA antibodies in germ free mice and stimulate the inflammatory response, and alter the expression of SLE susceptibility genes in these mice. Electronic supplementary material The online version of this article (10.1186/s10020-019-0102-5) contains supplementary material, which is available to authorized users.

No abstract

In the field of object detection, recently, tremendous success is achieved, but still it is a very challenging task to detect and identify objects accurately with fast speed. Human beings can detect and recognize multiple objects in images or videos with ease regardless of the object’s appearance, but for computers it is challenging to identify and distinguish between things. In this paper, a modified YOLOv1 based neural network is proposed for object detection. The new neural network model has been improved in the following ways. Firstly, modification is made to the loss function of the YOLOv1 network. The improved model replaces the margin style with proportion style. Compared to the old loss function, the new is more flexible and more reasonable in optimizing the network error. Secondly, a spatial pyramid pooling layer is added; thirdly, an inception model with a convolution kernel of 1 ∗ 1 is added, which reduced the number of weight parameters of the layers. Extensive experiments on Pascal VOC datasets 2007/2012 showed that the proposed method achieved better performance.

Following a cerebral ischemic event, substantial alterations in both cellular and molecular activities occur due to ischemia-induced cerebral pathology. Mounting evidence indicates that the robust recruitment of immune cells plays a central role in the acute stage of stroke. Infiltrating peripheral immune cells and resident microglia mediate neuronal cell death and blood-brain barrier disruption by releasing inflammation-associated molecules. Nevertheless, profound immunological effects in the context of the subacute and chronic recovery phase of stroke have received little attention. Early attempts to curtail the infiltration of immune cells were effective in mitigating brain injury in experimental stroke studies but failed to exert beneficial effects in clinical trials. Neural tissue damage repair processes include angiogenesis, neurogenesis, and synaptic remodeling, etc. Post-stroke inflammatory cells can adopt divergent phenotypes that influence the aforementioned biological processes in both endothelial and neural stem cells by either alleviating acute inflammatory responses or secreting a variety of growth factors, which are substantially involved in the process of angiogenesis and neurogenesis. To better understand the multiple roles of immune cells in neural tissue repair processes post stroke, we review what is known and unknown regarding the role of immune cells in angiogenesis, neurogenesis, and neuronal remodeling. A comprehensive understanding of these inflammatory mechanisms may help identify potential targets for the development of novel immunoregulatory therapeutic strategies that ameliorate complications and improve functional rehabilitation after stroke.

Ischemic stroke is caused by insufficient cerebrovascular blood and oxygen supply. It is a major contributor to death or disability worldwide and has become a heavy societal and clinical burden. To date, effective treatments for ischemic stroke are limited, and innovative therapeutic methods are urgently needed. Hypoxia inducible factor-1α (HIF-1α) is a sensitive regulator of oxygen homeostasis, and its expression is rapidly induced after hypoxia/ischemia. It plays an extensive role in the pathophysiology of stroke, including neuronal survival, neuroinflammation, angiogenesis, glucose metabolism, and blood brain barrier regulation. In addition, the spatiotemporal expression profile of HIF-1α in the brain shifts with the progression of ischemic stroke; this has led to contradictory findings regarding its function in previous studies. Therefore, unveiling the Janus face of HIF-1α and its target genes in different type of cells and exploring the role of HIF-1α in inflammatory responses after ischemia is of great importance for revealing the pathogenesis and identifying new therapeutic targets for ischemic stroke. Herein, we provide a succinct overview of the current approaches targeting HIF-1α and summarize novel findings concerning HIF-1α regulation in different types of cells within neurovascular units, including neurons, endothelial cells, astrocytes, and microglia, during the different stages of ischemic stroke. The current representative translational approaches focused on neuroprotection by targeting HIF-1α are also discussed.