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Driver drowsiness is one of the main factors leading to road fatalities and hazards in the transportation industry. Electroencephalography (EEG) has been considered as one of the best physiological signals to detect drivers' drowsy states, since it directly measures neurophysiological activities in the brain. However, designing a calibration-free system for driver drowsiness detection with EEG is still a challenging task, as EEG suffers from serious mental and physical drifts across different subjects. In this paper, we propose a compact and interpretable Convolutional Neural Network (CNN) to discover shared EEG features across different subjects for driver drowsiness detection. We incorporate the Global Average Pooling (GAP) layer in the model structure, allowing the Class Activation Map (CAM) method to be used for localizing regions of the input signal that contribute most for classification. Results show that the proposed model can achieve an average accuracy of 73.22% on 11 subjects for 2-class cross-subject EEG signal classification, which is higher than conventional machine learning methods and other state-of-art deep learning methods. It is revealed by the visualization technique that the model has learned biologically explainable features, e.g., Alpha spindles and Theta burst, as evidence for the drowsy state. It is also interesting to see that the model uses artifacts that usually dominate the wakeful EEG, e.g., muscle artifacts and sensor drifts, to recognize the alert state. The proposed model illustrates a potential direction to use CNN models as a powerful tool to discover shared features related to different mental states across different subjects from EEG signals. National Research Foundation (NRF) Submitted/Accepted version This research is supported by the National Research Foundation, Singapore under its International Research Centres in Singapore Funding Initiative.

Situation awareness (SA) has received much attention in recent years because of its importance for operators of dynamic systems. Electroencephalography (EEG) can be used to measure mental states of operators related to SA. However, cross-subject EEG-based SA recognition is a critical challenge, as data distributions of different subjects vary significantly. Subject variability is considered as a domain shift problem. Several attempts have been made to find domain-invariant features among subjects, where subject-specific information is neglected. In this work, we propose a simple but efficient subject matching framework by finding a connection between a target (test) subject and source (training) subjects. Specifically, the framework includes two stages: (1) we train the model with multi-source domain alignment layers to collect source domain statistics. (2) During testing, a distance is computed to perform subject matching in the latent representation space. We use a reciprocal exponential function as a similarity measure to dynamically select similar source subjects. Experiment results show that our framework achieves a state-of-the-art accuracy 74.32% for the Taiwan driving dataset. National Research Foundation (NRF) This research is supported by the National Research Foundation, Singapore under its International Research Centres in Singapore Funding Initiative.

Beauveria bassiana is a harmful pathogen to the economically important insect silkworm, always causes serious disease to the silkworm, which results in great losses to the sericulture industry. In order to explore the silkworm (Bombyx mori) response to B. bassiana infection, differential proteomes of the silkworm responsive to B. bassiana infection were identified with isobaric tags for relative and absolute quantitation (iTRAQ) at the different stage of the 3rd instar silkworm larvae. Among the 5040 proteins identified with confidence level of ≥95 %, total 937 proteins were differentially expressed, of which 488 proteins were up-regulated and 449 proteins were down-regulated. 23, 15, 250, 649 differentially expressed proteins (DEPs) were reliably quantified by iTRAQ analysis in the B. bassiana infected larvae at 18, 24, 36, 48 h post infection (hpi) respectively. Based on GO annotations, 6, 4, 128, 316 DEPs were involved in biological processes, 12, 5, 143, 376 DEPs were involved in molecular functions, and 6, 3, 108, 256 DEPs were involved in cell components at 18, 24, 36, 48 hpi respectively. KEGG pathway analysis displayed that 18, 12, 210, 548 DEPs separately participated in 63, 35, 201, 264 signal transduction pathways at different time of infection, and moreover a higher proportion of DEPs involved in metabolic pathways. The cluster analysis on the DEPs of different infection stages distinguished a co-regulated DEP, lysozyme precursor, which was up-regulated at both the mRNA level and the protein level, indicating that the lysozyme protein kept playing an important role in defending the silkworm against B. bassiana infection. This was the first report using an iTRAQ approach to analyze proteomes of the whole silkworm against B. bassiana infection, which contributes to better understanding the defense mechanisms of silkworm to B. bassiana infection and provides important experimental data for the identification of key factors involved in the interaction between the pathogenic fungus and its host.

Background The rapid increase of drug resistance in conventional antibiotics promotes the discovery and development of antimicrobial peptides (AMPs), which are important components of natural immunity. Due to the specific action modes targeting for cell membrane, AMPs have become a promising substance for bacterium inhibitions barely with no drug resistance. Here we reviewed the structure characteristics related with bactericide actions, including peptides constituents, molecular length, molecular charges and so on. And we also summarized the common mode of actions of AMPs raised by researchers. The structure-mechanism relationship was discussed. The commonly used methods for mechanism study were also listed. Key findings and conclusions The structures of AMPs were strongly related with its efficacy, such as constituents, molecular length, charges, secondary structures. The main mechanisms of AMPs were divided into membrane targets and intracellular targets. Some advanced and new techniques were applied in the mechanism study. Most of the existed mechanisms are still suppositional, and techniques were mainly focused on membrane-associated mechanism, only few of them were related with intracellular factors. Therefore, more new methods are expected to explore the authentic mechanisms and better results as well as more intracellular targets. This review provides a reference for future AMPs chemical modification and mechanism exploration.

Motivation The prediction performance of Cox proportional hazard model suffers when there are only few uncensored events in the training data. Results We propose a Sparse-Group regularized Cox regression method to improve the prediction performance of large-scale and high-dimensional survival data with few observed events. Our approach is applicable when there is one or more other survival responses that 1. has a large number of observed events; 2. share a common set of associated predictors with the rare event response. This scenario is common in the UK Biobank dataset where records for a large number of common and less prevalent diseases of the same set of individuals are available. By analyzing these responses together, we hope to achieve higher prediction performance than when they are analyzed individually. To make this approach practical for large-scale data, we developed an accelerated proximal gradient optimization algorithm as well as a screening procedure inspired by Qian et al. Availabilityandimplementation https://github.com/rivas-lab/multisnpnet-Cox

Brain science accelerates the study of intelligence and behavior, contributes fundamental insights into human cognition, and offers prospective treatments for brain disease. Faced with the challenges posed by imaging technologies and deep learning computational models, big data and high-performance computing (HPC) play essential roles in studying brain function, brain diseases, and large-scale brain models or connectomes. We review the driving forces behind big data and HPC methods applied to brain science, including deep learning, powerful data analysis capabilities, and computational performance solutions, each of which can be used to improve diagnostic accuracy and research output. This work reinforces predictions that big data and HPC will continue to improve brain science by making ultrahigh-performance analysis possible, by improving data standardization and sharing, and by providing new neuromorphic insights. Keywords: Brain science, Big data, High-performance computing, Brain connectomes, Deep learning

Internal tandem duplication (ITD) of FMS-like tyrosine kinase 3 (FLT3-ITD) constitutes an independent indicator of poor prognosis in acute myeloid leukaemia (AML). AML with FLT3-ITD usually presents with poor treatment outcomes, high recurrence rate and short overall survival. Currently, polymerase chain reaction and capillary electrophoresis are widely adopted for the clinical detection of FLT3-ITD, whereas the length and mutation frequency of ITD are evaluated using fragment analysis. With the development of sequencing technology and the high incidence of FLT3-ITD mutations, a multitude of bioinformatics tools and pipelines have been developed to detect FLT3-ITD using next-generation sequencing data. However, systematic comparison and evaluation of the methods or software have not been performed. In this study, we provided a comprehensive review of the principles, functionality and limitations of the existing methods for detecting FLT3-ITD. We further compared the qualitative and quantitative detection capabilities of six representative tools using simulated and biological data. Our results will provide practical guidance for researchers and clinicians to select the appropriate FLT3-ITD detection tools and highlight the direction of future developments in this field. Availability: A Docker image with several programs pre-installed is available at https://github.com/niu-lab/docker-flt3-itd to facilitate the application of FLT3-ITD detection tools.

Motivation Large-scale and high-dimensional genome sequencing data poses computational challenges. General-purpose optimization tools are usually not optimal in terms of computational and memory performance for genetic data. Results We develop two efficient solvers for optimization problems arising from large-scale regularized regressions on millions of genetic variants sequenced from hundreds of thousands of individuals. These genetic variants are encoded by the values in the set {0,1,2,NA}. We take advantage of this fact and use two bits to represent each entry in a genetic matrix, which reduces memory requirement by a factor of 32 compared to a double precision floating point representation. Using this representation, we implemented an iteratively reweighted least square algorithm to solve Lasso regressions on genetic matrices, which we name snpnet-2.0. When the dataset contains many rare variants, the predictors can be encoded in a sparse matrix. We utilize the sparsity in the predictor matrix to further reduce memory requirement and computational speed. Our sparse genetic matrix implementation uses both the compact two-bit representation and a simplified version of compressed sparse block format so that matrix-vector multiplications can be effectively parallelized on multiple CPU cores. To demonstrate the effectiveness of this representation, we implement an accelerated proximal gradient method to solve group Lasso on these sparse genetic matrices. This solver is named sparse-snpnet, and will also be included as part of snpnet R package. Our implementation is able to solve Lasso and group Lasso, linear, logistic and Cox regression problems on sparse genetic matrices that contain 1 000 000 variants and almost 100 000 individuals within 10 min and using less than 32GB of memory. Availability and implementation https://github.com/rivas-lab/snpnet/tree/compact.

Motivation: Microsatellite instability (MSI) is a promising biomarker for cancer prognosis and chemosensitivity. Techniques are rapidly evolving for the detection of MSI from tumor-normal paired or tumor-only sequencing data. However, tumor tissues are often insufficient, unavailable, or otherwise difficult to procure. Increasing clinical evidence indicates the enormous potential of plasma circulating cell-free DNA (cfNDA) technology as a noninvasive MSI detection approach. Results: We developed MSIsensor-ct, a bioinformatics tool based on a machine learning protocol, dedicated to detecting MSI status using cfDNA sequencing data with a potential stable MSIscore threshold of 20%. Evaluation of MSIsensor-ct on independent testing datasets with various levels of circulating tumor DNA (ctDNA) and sequencing depth showed 100% accuracy within the limit of detection (LOD) of 0.05% ctDNA content. MSIsensor-ct requires only BAM files as input, rendering it user-friendly and readily integrated into next generation sequencing (NGS) analysis pipelines. Availability: MSIsensor-ct is freely available at https://github.com/niu-lab/MSIsensor-ct. Supplementary information: Supplementary data are available at Briefings in Bioinformatics online.

The aim of this study was to examine the anxiety status of the frontline clinical nurses in the designated hospitals for the treatment of coronavirus disease 2019 (COVID-19) in Wuhan and to analyze the influencing factors, to provide data for psychologic nursing. This study used a cross-sectional survey design and convenience sampling. The questionnaires were completed by 176 frontline clinical nurses. Anxiety was determined using the Hamilton anxiety scale. General data were collected using a survey. Correlation analyses were used. Among the 176 frontline nurses, 77.3% (136/176) had anxiety. The anxiety scores of the frontline clinical nurse fighting COVID-19 were 17.1 ± 8.1. Anxiety symptoms, mild to moderate anxiety symptoms, and severe anxiety symptoms were found in 27.3%, 25%, and 25% of the nurses, respectively. Sex, age, marital status, length of service, and clinical working time against COVID-19 were associated with anxiety (P

Neuroinflammation triggered by surgical trauma contributes to postoperative cognitive dysfunction (POCD). The receptor for advanced glycation end‑products (RAGE), a multiligand inflammatory receptor, is involved in the damaging effects of various cellular processes, contributing to neuroinflammation and neurodegeneration. However, the potential role of RAGE in the acute period of POCD has not been fully investigated. C57BL/6 male mice undergoing surgery of the tibia under isoflurane anesthesia were treated with the RAGE antagonist FPS‑ZM1 or vehicle control intraperitoneally for a period of 7 days. The cognitive function of the animals was tested using trace fear conditioning on the third postoperative day. To determine astrocytic activation, microgliosis, p65 expression, inflammatory factor levels and postsynaptic density protein‑95 (PSD‑95) expression in the hippocampus, the animals were euthanized on either the first, third or seventh postoperative day. Compared with the control group, the cognitive function of the surgical animals was impaired on the third postoperative day. Astrocytic activation, microgliosis and the expression levels of p65, interleukin (IL)‑1β, IL‑6, and PSD‑95 were significantly increased on the first, and third postoperative days. However, tumor necrosis factor‑α expression was significantly increased only on postoperative day 1. All of the surgical effects observed were partially inhibited by treatment with FPS‑ZM1. In summary, the results of the present study suggest that RAGE serves an important role in the acute inflammatory process of POCD, and blocking RAGE can inhibit neuroinflammation and attenuate POCD. Thus, the RAGE signaling pathway may be a novel target in the prevention, and treatment of POCD.

Next-generation sequencing (NGS) technology has revolutionised human cancer research, particularly via detection of genomic variants with its ultra-high-throughput sequencing and increasing affordability. However, the inundation of rich cancer genomics data has resulted in significant challenges in its exploration and translation into biological insights. One of the difficulties in cancer genome sequencing is software selection. Currently, multiple tools are widely used to process NGS data in four stages: raw sequence data pre-processing and quality control (QC), sequence alignment, variant calling and annotation and visualisation. However, the differences between these NGS tools, including their installation, merits, drawbacks and application, have not been fully appreciated. Therefore, a systematic review of the functionality and performance of NGS tools is required to provide cancer researchers with guidance on software and strategy selection. Another challenge is the multidimensional QC of sequencing data because QC can not only report varied sequence data characteristics but also reveal deviations in diverse features and is essential for a meaningful and successful study. However, monitoring of QC metrics in specific steps including alignment and variant calling is neglected in certain pipelines such as the ‘Best Practices Workflows’ in GATK. In this review, we investigated the most widely used software for the fundamental analysis and QC of cancer genome sequencing data and provided instructions for selecting the most appropriate software and pipelines to ensure precise and efficient conclusions. We further discussed the prospects and new research directions for cancer genomics.

Summary We develop a scalable and highly efficient algorithm to fit a Cox proportional hazard model by maximizing the $L^1$-regularized (Lasso) partial likelihood function, based on the Batch Screening Iterative Lasso (BASIL) method developed in Qian and others (2019). Our algorithm is particularly suitable for large-scale and high-dimensional data that do not fit in the memory. The output of our algorithm is the full Lasso path, the parameter estimates at all predefined regularization parameters, as well as their validation accuracy measured using the concordance index (C-index) or the validation deviance. To demonstrate the effectiveness of our algorithm, we analyze a large genotype-survival time dataset across 306 disease outcomes from the UK Biobank (Sudlow and others, 2015). We provide a publicly available implementation of the proposed approach for genetics data on top of the PLINK2 package and name it snpnet-Cox.

Motivation HotSpot3D is a widely used software for identifying mutation hotspots on the 3D structures of proteins. To further assist users, we developed a new HotSpot3D web server to make this software more versatile, convenient and interactive. Results The HotSpot3D web server performs data pre-processing, clustering, visualization and log-viewing on one stop. Users can interactively explore each cluster and easily re-visualize the mutational clusters within browsers. We also provide a database that allows users to search and visualize proximal mutations from 33 cancers in the Cancer Genome Atlas. Availability and implementation http://niulab.scgrid.cn/HotSpot3D/. Supplementary information Supplementary data are available at Bioinformatics online.

Clustering is a very fundamental while time-consuming compute operation in biological sequence analysis. New sequencing technologies such as NGS and 3GS have dramatically increased both the dataset size and the length of a single read sequence. However, existing tools lack scalability for handling large-scale datasets as well as long sequences. A feasible solution to this problem is to use parallel and distributed systems. The efficient deployment of such systems, however, requires high parallelism in both software implementations as well as algorithmic optimizations. In this paper, we propose DGCF, a Distributed Greedy Clustering Framework which is capable to handle large-scale datasets and long sequences. Our framework adopts a greedy clustering strategy which overlaps communication with computation among many distributed computing nodes. We also design and implement a sparse suffix array (SSA)-based alignment algorithm that can support long sequences. Experiments show that our framework achieves near-linear speedups on a distributed memory cluster.

Introduction: Traumatic brain injury is one of the leading causes of death and sources of heavy societal burden. Hypoxemia and hypercapnia are the 2 common complications of brain injury. Intubation seems to be an effective intervention for preventing the 2 complications in pre-hospital setting. But the results of the existing studies on the effect of pre-hospital intubation on prognosis of patients (aged less than 18) with severe traumatic brain injury are conflict. Thus, in this study, we aim to conduct a systematic review and meta-analysis to evaluate whether pre-hospital intubation is benefit for the prognosis in infants, children and adolescents with severe traumatic brain injury. Methods: We will develop a systematic search strategy which includes MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials, Chinese Biomedical Literature Database, WanFang Data and China National Knowledge Infrastructure. The WHO International Clinical Trials Registry Platform will be searched for the ongoing studies as well. The cohort studies which aim to evaluate the effect of pre-hospital intubation for infants, children and adolescents with severe traumatic brain injury will be selected. The Newcastle-Ottawa Scale will be used for assessing the risk of bias of the included studies. Results: The results of this study will be presented in the full-text of the systematic review. Conclusion: This is the first systematic review and meta-analysis about evaluation of the effect of pre-hospital intubation on prognosis in infants, children and adolescents with traumatic brain injury. PRESPERO registration number: CRD42019121214

Storage proteins in the 30 K family are ubiquitous in the hemolymph of insects and play important roles in adult metamorphosis, development, egg formation, carrier transport and even host immunity. Some studies have shown that the 30 K proteins can inhibit apoptosis and have certain antifungal effects. The silkworm protein Bm30K-19G1 is a low molecular weight apolipoprotein that is abundant in hemolymph of fifth instar larvae. Our previous transcriptome sequencing, real-time PCR analysis and proteomic studies showed that the expression level of the 30 K protein gene was significantly up-regulated in the silkworm infected with Beauveria bassiana. In this study, the ORF sequence of Bm30K-19G1 was amplified by PCR. The sequence is 1311 bp in length and encodes a 436 amino acid peptide. Bm30K-19G1 was expressed in all tested tissues of fifth instar larvae, with highest expression in fat body and the lowest expression in the midgut. Bm30K-19G1 protein was successfully expressed in the prokaryotic expression system using pET-28a(+) as vector and E. coli Arctic Express (DE3) as the expression bacterium strain. The expressed recombinant Bm30K-19G1 protein has an inhibitory effect on the conidial germination and hyphal growth of B. bassiana. Bm30K-19G1 also inhibited the growth and reproduction of B. bassiana in vivo; the median lethal time of infected silkworms was postponed by 6.4 h and the time for death of all infected larvae was postponed by 10 h. The results indicated that the silkworm storage protein 30K-19G1 is an antifungal protein against B. bassiana and help to elucidate the molecular mechanism of silkworm resistance against B. bassiana.

Background: The number of red blood cells (RBCs) increases significantly in response to high-altitude hypoxic environments, and the RBC microRNA (miRNA) expression pattern is similar to that in whole blood. Studies have shown that miRNA in plasma can act as a circulating hypoxia-associated marker, but the effect of a high-altitude hypoxic environment on RBC-derived miRNAs has not yet been reported. Methods: Blood samples were collected from 20 Han Chinese individuals residing at 500 m (Sichuan Han), 10 migrant Han Chinese citizens residing at 3,658 m (Tibet Han) and 12 native Tibetans, and RBC indices measurements and miRNA sequencing analyses were performed for the three sample groups. The levels of some markedly altered miRNAs at high altitude were subsequently measured from 5 randomly selected samples of each group by real-time PCR. Bioinformatic analyses was performed to determine the potential target genes of selected hypoxia-associated miRNAs. Results: Marked changes of several RBC indices were observed among the Tibet Han population, the Tibetan population and the Sichuan Han population. A total of 516 miRNAs derived from RBCs were initially identified by miRNA sequencing in the three sample groups. Compared with the Sichuan Han population, 49 miRNAs were differentially expressed in the Tibet Han population (17 upregulated and 32 downregulated). 12 upregulated and 21 downregulated miRNAs were observed in the Tibetan population compared with the Sichuan Han population. A total of 40 RBC miRNAs were differentially expressed in the Tibetan population (15 upregulated and 25 downregulated) compared with the Tibet Han population. Two significantly altered miRNAs with the highest expression levels (miRNA-144-5p and miR-30b-5p) were selected for real-time PCR analysis, and the results were consistent with those of miRNA sequencing. Furthermore, bioinformatic analyses showed that some potential target genes of miR-144-5p and miR-30b-5p are involved in the erythroid- hypoxia-, and nitric oxide (NO)-related signaling pathways in response to hypoxia. Conclusion: Our findings provide clear evidence, for the first time, that a high-altitude hypoxic environment significantly affects human RBC miRNA profiles.

SUMMARYTissue development results from lineage-specific transcription factors (TF) programming a dynamic chromatin landscape through progressive cell fate transitions. Here, we interrogate the epigenomic landscape during epidermal differentiation and create an inference network that ranks the coordinate effects of TF-accessible regulatory element-target gene expression triplets on lineage commitment. We discover two critical transition periods: surface ectoderm initiation and keratinocyte maturation, and identify TFAP2C and p63 as lineage initiation and maturation factors, respectively. Surprisingly, we find that TFAP2C, and not p63, is sufficient to initiate surface ectoderm differentiation, with TFAP2C-initiated progenitor cells capable of maturing into functional keratinocytes. Mechanistically, TFAP2C primes the surface ectoderm chromatin landscape and induces p63 expression and binding sites, thus allowing maturation factor p63 to positively auto-regulate its expression and close a subset of the TFAP2C-initiated early program. Our work provides a general framework to infer TF networks controlling chromatin transitions that will facilitate future regenerative medicine advances.

Rationales: Gene-targeting ribozymes represent promising nucleic acid-based gene interference agents for therapeutic application. We previously used an in vitro selection procedure to engineer novel RNase P-based ribozyme variants with enhanced targeting activity. However, it has not been reported whether these ribozyme variants also exhibit improved activity in blocking gene expression in animals. Methods and Results: In this report, R388-AS, a new engineered ribozyme variant, was designed to target the mRNA of assemblin (AS) of murine cytomegalovirus (MCMV), which is essential for viral progeny production. Variant R338-AS cleaved AS mRNA sequence in vitro at least 200 times more efficiently than ribozyme M1-AS, which originated from the wild type RNase P catalytic RNA sequence. In cultured MCMV-infected cells, R338-AS exhibited better antiviral activity than M1-AS and decreased viral AS expression by 98-99% and virus production by 15,000 fold. In MCMV-infected mice, R388-AS was more active in inhibiting AS expression, blocking viral replication, and improving animal survival than M1-AS. Conclusions: Our results provide the first direct evidence that novel engineered RNase P ribozyme variants with more active catalytic activity in vitro are also more effective in inhibiting viral gene expression in animals. Moreover, our studies imply the potential of engineering novel RNase P ribozyme variants with unique mutations to improve ribozyme activity for therapeutic application.

The accelerating growth of the public microbial genomic data imposes substantial burden on the research community that uses such resources. Building databases for non-redundant reference sequences from massive microbial genomic data based on clustering analysis is essential. However, existing clustering algorithms perform poorly on long genomic sequences. In this article, we present Gclust, a parallel program for clustering complete or draft genomic sequences, where clustering is accelerated with a novel parallelization strategy and a fast sequence comparison algorithm using sparse suffix arrays (SSAs). Moreover, genome identity measures between two sequences are calculated based on their maximal exact matches (MEMs). In this paper, we demonstrate the high speed and clustering quality of Gclust by examining four genome sequence datasets. Gclust is freely available for non-commercial use at https://github.com/niu-lab/gclust. We also introduce a web server for clustering user-uploaded genomes at http://niulab.scgrid.cn/gclust. Keywords: Microbial genome clustering, Parallelization, Sparse suffix array, Maximal exact match, Segment extension

There is a need for affordable, widely deployable maternal-fetal ECG monitors to improve maternal and fetal health during pregnancy and delivery. Based on the diffusion-based channel selection, here we present the mathematical formalism and clinical validation of an algorithm capable of accurate separation of maternal and fetal ECG from a two channel signal acquired over maternal abdomen. The proposed algorithm is the first algorithm, to the best of the authors' knowledge, focusing on the fetal ECG analysis based on two channel maternal abdominal ECG signal, and we apply it to two publicly available databases, the PhysioNet non-invasive fECG database (adfecgdb) and the 2013 PhysioNet/Computing in Cardiology Challenge (CinC2013), to validate the algorithm. The state-of-the-art results are achieved when compared with other available algorithms. Particularly, the F1 score for the R peak detection achieves 99.3% for the adfecgdb and 87.93% for the CinC2013, and the mean absolute error for the estimated R peak locations is 4.53 ms for the adfecgdb and 6.21 ms for the CinC2013. The method has the potential to be applied to other fetal cardiogenic signals, including cardiac doppler signals.

The selective suppression of inflammatory factors in activated microglia, rather than totally inhibiting their activation, might be an effective means of slowing the progression of certain neurodegenerative diseases. Diosgenin glucoside (Dios) is a saponin compound extracted from Tritulus terrestris L. We found that Dios suppressed the synthesis of molecules that promote inflammation (M1 markers, such as NO, IL-6, and TNF-α) in rat microglia and BV-2 cells induced with lipopolysaccharides (LPS). In contrast, Dios had no effects on the cellular production of anti-inflammatory factors (M2 markers, such as IL-10, IL-1Rα and CD206) in LPS and IL-4 treated microglia. Dios repressed IκB-α, ERK MAPK and p38 MAPK phosphorylation, but did not affect JNK in LPS-activated microglia. We also found that conditioned medium obtained from cultures of BV-2 cells incubated with Dios plus LPS was markedly less neurotoxic than conditioned medium obtained from cultures of BV-2 cells incubated with LPS alone. In conclusion, this study demonstrated that Dios can selectively suppress the production/expression of pro-inflammatory M1 markers by activated microglia, without affecting M2 markers, and might provide neuroprotection by regulating microglial M1 polarization. Our results suggest that Dios can be used in treatment of various neuroinflammatory diseases mediated by microglia.

Summary With the advent of next-generation sequencing, traditional bioinformatics tools are challenged by massive raw metagenomic datasets. One of the bottlenecks of metagenomic studies is lack of large-scale and cloud computing suitable data analysis tools. In this paper, we proposed a Spark-based tool, called MetaSpark, to recruit metagenomic reads to reference genomes. MetaSpark benefits from the distributed data set (RDD) of Spark, which makes it able to cache data set in memory across cluster nodes and scale well with the datasets. Compared with previous metagenomics recruitment tools, MetaSpark recruited significantly more reads than many programs such as SOAP2, BWA and LAST and increased recruited reads by ∼4% compared with FR-HIT when there were 1 million reads and 0.75 GB references. Different test cases demonstrate MetaSpark’s scalability and overall high performance. Availability https://github.com/zhouweiyg/metaspark Supplementary information Supplementary data are available at Bioinformatics online

Human epidermal growth factor receptor 2 (HER2) is one of the most studied tumor-associated antigens for cancer immunotherapy. An engineered anti-HER-2 chimeric A21 antibody (chA21) is a chimeric antibody targeted to subdomain I of the HER2 extracellular domain. Here, we report the anti-tumor activity of the novel engineered monoclonal antibody humanized chA21 (HuA21) that targets HER2 on the basis of chA21, and we describe the underlying mechanisms. Our results reveal that HuA21 markedly inhibits the proliferation and migration of HER2-overexpressing breast cancer cells and causes enhanced antibody-dependent cell-mediated cytotoxicity potency against HER2-overexpressing tumor cells. In particular, HuA21, but not trastuzumab (Tra), markedly suppresses growth and enhances the internalization of the antibody in Tra-resistant BT-474 breast cancer cells. These characteristics are highly associated with the intrinsic ability of HuA21 to down-regulate HER2 activation and inhibit the extracellular signal-regulated kinase 1/2 (ERK1/2) and protein kinase B (Akt) signaling pathways. Furthermore, the combination of HuA21 with Tra synergistically enhances the anti-tumor effects in vitro and in vivo and inhibits HER2 activation and the ERK1/2 and Akt signaling pathways. Altogether, our results suggest that HuA21 may represent a unique anti-HER2 antibody with potential as a therapeutic candidate alone or in combination with other anti-HER2 reagents in cancer therapy.

Leiomyosarcoma (LMS) is a soft tissue tumor with a significant degree of morphologic and molecular heterogeneity. We used integrative molecular profiling to discover and characterize molecular subtypes of LMS. Gene expression profiling was performed on 51 LMS samples. Unsupervised clustering showed three reproducible LMS clusters. Array comparative genomic hybridization (aCGH) was performed on 20 LMS samples and showed that the molecular subtypes defined by gene expression showed distinct genomic changes. Tumors from the 'muscle-enriched' cluster showed significantly increased copy number changes (P=0.04). A majority of the muscle-enriched cases showed loss at 16q24, which contains Fanconi anemia, complementation group A, known to have an important role in DNA repair, and loss at 1p36, which contains PRDM16, of which loss promotes muscle differentiation. Immunohistochemistry (IHC) was performed on LMS tissue microarrays (n=377) for five markers with high levels of messenger RNA in the muscle-enriched cluster (ACTG2, CASQ2, SLMAP, CFL2 and MYLK) and showed significantly correlated expression of the five proteins (all pairwise P