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Autoimmune disease is a condition in which the body responds to the autoantigens and causes damage to its own tissues. We conducted this research to investigate whether interleukin-23R (IL-23R) gene polymorphisms (rs11209026 A/G) are related to the risk of several common autoimmune diseases like rheumatoid arthritis (RA), ankylosing spondylitis (AS), and psoriasis. We screened articles about rs11209026 A/G for autoimmune disease by China Knowledge Infrastructure Project (CNKI), Web of Science as well as PubMed databases. The correlation strength was expressed by odds ratio (OR) and 95% confidence interval, and the trial sequential analysis (TSA) proved the reliability of the results. Generally, 33 studies were contained. There was a significant correlation between rs11209026 A/G polymorphism and the susceptibility of human autoimmune diseases (OR = 0.78, 95% CI = 0.65–0.94, p 0.05). In ethnic subgroup analysis, AA genotype could reduce the risk of AS in Caucasian people (OR = 0.46, 95% CI = 0.25–0.87, p

Background: Experimental verification of a drug discovery process is expensive and time-consuming. Therefore, efficiently and effectively identify drug–target interactions (DTIs) has been research focus. In present, many machine learning algorithms are used for predicting drug-target interactions. The key idea is to train the classifier using existing DTI so as to predict new or unknown DTI. However, there are various challenges such as class imbalance and parameter optimization of many classifiers that need to be solved before developing an optimal drug-target interaction model. Methods: In this paper, we propose a framework named SSELM-neg for drug-target interaction prediction, in which a screening approach was used to choose high quality negative samples and a spherical search approach is used to optimize the parameters of the extreme learning machine. Results: Results show that the performance of the proposed technique outperforms the other state-of-the-art methods in 10-fold cross-validation experiments in terms of AUC(0.986, 0.993, 0.988, 0.969 ) and AUPR(0.982, 0.991, 0.982, 0.946) for E dataset, GPCR dataset, IC dataset and NR dataset. Conclusion: Screening approach produce the high quality negative samples with the same numbers of the positive samples, which solve the class imbalance problem. Extreme learning machine was optimized by a spherical search approach in identifying DTIs. Therefore, our models achieve better performance than other state-of-the-art methods.

Background Existing studies have demonstrated that the integrative analysis of histopathological images and genomic data can be used to better understand the onset and progression of many diseases, as well as identify new diagnostic and prognostic biomarkers. However, since the development of pathological phenotypes are influenced by a variety of complex biological processes, complete understanding of the underlying gene regulatory mechanisms for the cell and tissue morphology is still a challenge. In this study, we explored the relationship between the chromatin accessibility changes and the epithelial tissue proportion in histopathological images of estrogen receptor (ER) positive breast cancer. Methods An established whole slide image processing pipeline based on deep learning was used to perform global segmentation of epithelial and stromal tissues. We then used canonical correlation analysis to detect the epithelial tissue proportion-associated regulatory regions. By integrating ATAC-seq data with matched RNA-seq data, we found the potential target genes that associated with these regulatory regions. Then we used these genes to perform the following pathway and survival analysis. Results Using canonical correlation analysis, we detected 436 potential regulatory regions that exhibited significant correlation between quantitative chromatin accessibility changes and the epithelial tissue proportion in tumors from 54 patients (FDR Conclusion This integrative analysis is a useful strategy for identifying potential regulatory regions in the human genome that are associated with tumor tissue quantification. This study will enable efficient prioritization of genomic regulatory regions identified by ATAC-seq data for further studies to validate their causal regulatory function. Ultimately, identifying epithelial tissue proportion-associated regulatory regions will further our understanding of the underlying molecular mechanisms of disease and inform the development of potential therapeutic targets.

Single-cell sequencing is widely used in biological and medical studies. However, its application with multiple samples is hindered by inefficient sample processing, high experimental costs, ambiguous identification of true single cells, and technical batch effects. Here, we introduce sample-multiplexing approaches for single-cell sequencing in transcriptomics, epigenomics, genomics, and multiomics. In single-cell transcriptomics, sample multiplexing uses variants of native or artificial features as sample markers, enabling sample pooling and decoding. Such features include: (1) natural genetic variation, (2) nucleotide-barcode anchoring on cellular or nuclear membranes, (3) nucleotide-barcode internalization to the cytoplasm or nucleus, (4) vector-based barcode expression in cells, and (5) nucleotide-barcode incorporation during library construction. Other single-cell omics methods are based on similar concepts, particularly single-cell combinatorial indexing. These methods overcome current challenges, while enabling super-loading of single cells. Finally, selection guidelines are presented that can accelerate technological application.

The distinguishable subregions that compose the hippocampus are differently involved in functions associated with Alzheimer’s disease (AD). Thus, the identification of hippocampal subregions and genes that classify AD and healthy control (HC) groups with high accuracy is meaningful. In this study, by jointly analyzing the multimodal data, we propose a novel method to construct fusion features and a classification method based on the random forest for identifying the important features. Specifically, we construct the fusion features using the gene sequence and subregions correlation to reduce the diversity in same group. Moreover, samples and features are selected randomly to construct a random forest, and genetic algorithm and clustering evolutionary are used to amplify the difference in initial decision trees and evolve the trees. The features in resulting decision trees that reach the peak classification are the important “subregion gene pairs”. The findings verify that our method outperforms well in classification performance and generalization. Particularly, we identified some significant subregions and genes, such as hippocampus amygdala transition area (HATA), fimbria, parasubiculum and genes included RYR3 and PRKCE. These discoveries provide some new candidate genes for AD and demonstrate the contribution of hippocampal subregions and genes to AD.

It has been demonstrated that miRNAs are involved in many biological processes including cell proliferation and differentiation, apoptosis, and stress responses. Although single-cell RNA sequencing technology is prevailing nowadays, it still remains challenging in quantifying miRNA at the single-cell level. Herein, we present the computational methods to infer the single-cell miRNA expression level using its target gene abundances. Firstly, we developed an enrichment-based approach in estimating miRNA expression considering miRNA-mRNA regulation information and miRNA-mRNA correlation signal captured from existing TCGA datasets. Further efforts were made to infer the miRNA expression with machine learning models. The methods were applied to compare the accuracy and robustness with the simulated single-cell data. Finally, we applied the method in single-cell RNA-seq triple negative breast cancer (TNBC) patients to further discover miRNA marker at the single-cell level for the malignant cells. Our tool is available online at: https://github.com/ChengkuiZhao/Single-cell-miRNA-prediction.

BackgroundCancer risks vary in different BRCA1/2 mutations. Previous studies based on Caucasian population have identified regions associated with elevated/reduced risks of breast/ovarian cancers. Since ethnic differences are known to affect BRCA1/2 mutation spectra, we are interested in defining Chinese-specific ovarian/breast cancer cluster regions (OCCR/BCCR) and comparing with previously reported Caucasian-based cluster regions. We also aim to characterize the distribution and estimate the cancer risks of different Chinese recurrent mutations.Methods7,919 (3,641 unselected cancer-free women + 4,278 female cancer patients) individuals were included in the study. Germline BRCA1/2 status were detected with amplicon-based next-generation sequencing. BRCA1/2 carriers were defined as bearing likely pathogenic or pathogenic mutations. We calculated odds ratio (OR) of breast cancer and OR of ovarian cancer, and their ratio of the two ORs (ROR) for each region. ROR > 1 indicated elevated odds of breast cancer and/or decreasing odds of ovarian cancer; ROR < 1 indicated increasing odds of ovarian cancer and/or decreasing breast cancer odds. The frequency, distribution and penetrance of six known Chinese founder mutations were characterize respectively. Haplotype analysis and age estimation were performed on the most prevalent and widely-spread founder mutation BRCA1:c.5470_5477del.ResultsA total of 729 subjects were detected with germline BRCA1/2 deleterious mutations, including 236 BRCA1 and 122 BRCA2 mutations. The putative Chinese OCCR/BCCR are partially overlapped with Caucasian-based OCCR/BCCR and shared structural-functional characteristics. The six known Chinese founder mutations vary greatly in both distribution and penetrance. The two most prevalent and widely-spread mutations are estimated to convey low penetrance, while the area-restricted founder mutations seemed to confer higher or nearly complete penetrance. The most prevalent founder mutation BRCA1:c.5470_5477del accounting for 9.5% - 18% of BRCA1 carriers is estimated to have emerged ∼2,090 years ago (70 B.C.) during the Han Dynasty, about 290 years (∼14.5 generations) prior to the Three Kingdoms Period when a major population migration occurred.ConclusionBRCA1/2 carriers with different genotypes have significantly different cancer risks. Hence ideally risk assessment should be mutation-specific, rather than concerning a single figure. The probably most ancient Chinese founder mutation may have originated more than 2,000 years ago.

Cleavage and polyadenylation are essential processes that can impact many aspects of mRNA fate. Most eukaryotic genes have alternative polyadenylation (APA) events. While the heterogeneity of mRNA polyadenylation isoform choice has been studied in specific tissues, less attention has been paid to the neuronal heterogeneity of APA selection at single-nucleus resolution. APA is highly controlled during development and neuronal activation, however, to what extent APA events vary in a specific neuronal cell population and the regulatory mechanisms are still unclear. In this paper, we investigated dynamic APA usage in different cell types using snRNA-seq data of 1424 human brain cells generated by single-cell 3′ RNA sequencing. We found that distal APA sites are not only favored by global neuronal cells, but that their usage also varies between the principal types of neuronal cell populations (excitatory neurons and inhibitory neurons). A motif analysis and a gene functional analysis indicated the enrichment of RNA-binding protein (RBP) binding sites and neuronal functions for the set of genes with neuron-enhanced distal PAS usage. Our results revealed the extensive involvement of APA regulation in neuronal populations at the single-nucleus level, providing new insights into roles for APA in specific neuronal cell populations, as well as utility in future functional studies.

The advancement of single cell sequencing technology and corresponding analytical methods allow for granular studies of cellular heterogeneity in tissues. However, there lacks a direct approach to infer clinical information in single-cell data to prioritize cells with respect to disease status or patient outcomes. We propose DEGAS (Diagnostic Evidence GAuge of Single cells), a novel deep learning and transfer learning framework, to transfer prognostic information from patients to cells. We call such transferrable information “impressions,” which allow individual cells to be associated with disease outcomes. Using simulated data as well as nine diverse single cell and patient bulk tissue transcriptomic datasets from Glioblastoma Multiforme (GBM), Alzheimer’s Disease (AD), and Multiple Myeloma (MM), we demonstrate the feasibility, flexibility, and broad applications of the DEGAS framework. The analysis on MM led to the identification of a PHF19high myeloma cell subtype associated with myeloma progression. DEGAS is freely available as an R package. Teaser By leveraging preexisting patient cohorts, DEGAS provides novel tools to identify high-risk cells in complex diseases at single cell resolution.

PURPOSE Tumor-infiltrating lymphocytes (TILs) and their spatial characterizations on whole-slide images (WSIs) of histopathology sections have become crucial in diagnosis, prognosis, and treatment response prediction for different cancers. However, fully automatic assessment of TILs on WSIs currently remains a great challenge because of the heterogeneity and large size of WSIs. We present an automatic pipeline based on a cascade-training U-net to generate high-resolution TIL maps on WSIs. METHODS We present global cell-level TIL maps and 43 quantitative TIL spatial image features for 1,000 WSIs of The Cancer Genome Atlas patients with breast cancer. For more specific analysis, all the patients were divided into three subtypes, namely, estrogen receptor (ER)–positive, ER-negative, and triple-negative groups. The associations between TIL scores and gene expression and somatic mutation were examined separately in three breast cancer subtypes. Both univariate and multivariate survival analyses were performed on 43 TIL image features to examine the prognostic value of TIL spatial patterns in different breast cancer subtypes. RESULTS The TIL score was in strong association with immune response pathway and genes (eg, programmed death-1 and CLTA4). Different breast cancer subtypes showed TIL score in association with mutations from different genes suggesting that different genetic alterations may lead to similar phenotypes. Spatial TIL features that represent density and distribution of TIL clusters were important indicators of the patient outcomes. CONCLUSION Our pipeline can facilitate computational pathology-based discovery in cancer immunology and research on immunotherapy. Our analysis results are available for the research community to generate new hypotheses and insights on breast cancer immunology and development.

The paper is mainly focused on the redundant fault-tolerant control of control system. It is to solve the problem in existing fault-tolerant models with low reliability and high fault-tolerant failure rate by using deep neural network. The existing fault-tolerant models are impossible to predict the faults that will occur. In this paper, the overall framework structure of the redundant fault-tolerant system is designed. In terms of fault-tolerant control, the idea of hardware redundancy is adopted to build the double redundant controllers and the deep neural network is used for prediction. Then the implementation of the specific scheme is carried out for the actual scene of the agent tracking control.

The paper is about redunant moving agent with intelligent unmanned control. The main task is to complete the construction of redundant fault-tolerant control system of deep neural network, including the infrastructure construction of unmanned agent simulation, the initialization of agent parameters, the construction of redundant controller, and the construction of reinforcement learning decision model. The main purpose is to generate simulated floating point data to train the model, including designing the expected rate and path, kinematics simulation, and training data generation. The kinematics simulation scene construction and decision-making model training use deep learning, whose effect of the system performance is significant.

This paper mainly focuses on the intelligent transmitter control system redundancy fault-tolerant control. We built decision model by an improved Deep Q-Learning of reinforcement learning and two redundant controllers for the system. The fault-tolerant performance of the model is tested, with a certain redundant control module made to fail by human intervention. When the redundant controller of the failed module is made to output abnormal control signals, the decision-making model can effectively switch over and keep the active controller without faults. The operation of the decision algorithm can ensure the intelligent transmitter control system normal and basically meet the real-time requirements.

In this paper, we investigate the robust secure transmit beamformming design for multiple-input single-output wiretap channels overheard by multiple non-collaborating single-antenna eavesdroppers. Assuming that at the transmitter, the instantaneous legitimate receiver’s channel state information (CSI) is imperfect and only the statistical eavesdroppers’ CSI is known, we seek to maximize the target secrecy rate $R$ with the total transmit power constraint and the secrecy outage probability constraint. The resulting outage-constrained secrecy rate maximization (O-SRM) is non-convex and difficult to solve. Thus, we develop an efficient algorithm to solve it and determine the robust secure beamforming design. Specifically speaking, we first prove that the original O-SRM problem can be solved by an equivalent outage-constrained power minimization problem (O-PMP), which is easy to perform semi-definite relaxation (SDR) and can be solved by semi-definite program (SDP) consequently. Second, we transform the non-convex secrecy rate outage constraints into deterministic ones by resorting to Bernstein-type inequality II. Finally, by solving the convex approximation of the O-PMP with different $R\text{s}$ and bisection search over $R$ , we convert the original O-SRM problem into a sequence of solvable SDPs. Simulation results are provided to verify the secrecy-rate performance improvement of the proposed robust beamforming compared with the existing worst-case design proposed by Li and Ma and the simple non-robust maximum-ratio transmission.

DNasel footprinting is an established approach for recognizing transcription factor binding sites. High-throughput DNase-seq arrays have been used to depict in vivo DNase footprints with single base pair resolution. Many different computational methods have been developed to predict binding sites through the DNase-seq footprinting. However, cleavage bias of DNaseI leads to a negative impact on the prediction accuracy. In this study, we present a new method to reduce the impact of this sequence-specific bias. In the experimental verification section, we use SVM to test our approach, the better classification result illustrates the effectiveness of our new method.

The patterns of wing rock motion at 52.5° angle of attack have already been investigated in detail (Rong, 2009; Wang, 2010). These patterns are completely different from those at other angles of attack. This phenomenon indicates that angle of attack affects wing rock motion. The present study aims to examine the different patterns of wing rock motion at different angles of attack. The flow mechanisms of the motion patterns are also revealed, especially the uncommanded lateral motions, including wing rock and lateral deflection, induced by regular asymmetric separated flow from wings at low angles of attack and forebody asymmetric vortices at angles of attack of 27.5°⩽ α ⩽ 70°. The test conditions, including the testing Reynolds number, wind tunnel, experimental techniques, and test model, are all the same as those used in a previous study at α = 52.5°. Finally, the experimental technique of rotating nose of the model to suppress the wing rock or lateral deflection, which is induced by forebody asymmetric vortex flow, is applied. The uncommanded lateral motions are successfully suppressed by this technique.

Large-bandgap perovskites offer a route to improve the efficiency of energy capture in photovoltaics when employed in the front cell of perovskite-silicon tandems. Implementing perovskites as the front cell requires an inverted (p-i-n) architecture; this architecture is particularly effective at harnessing high-energy photons and is compatible with ionic-dopant-free transport layers. Here, a power conversion efficiency of 21.6% is reported, the highest among inverted perovskite solar cells (PSCs). Only by introducing a secondary amine into the perovskite structure to form MA(1-)(x)DMA(x)PbI(3) (MA is methylamine and DMA is dimethylamine) are defect density and carrier recombination suppressed to enable record performance. It is also found that the controlled inclusion of DMA increases the hydrophobicity and stability of films in ambient operating conditions: encapsulated devices maintain over 80% of their efficiency following 800 h of operation at the maximum power point, 30 times longer than reported in the best prior inverted PSCs. The unencapsulated devices show record operational stability in ambient air among PSCs.

Based on the assumption of finite alphabet inputs, the precoding design for MIMO interference channels (MIMO-IC) is proposed by exploiting statistical channel state information (SCSI) at the transmitter. Most of the related precoding design relies on Gaussian input assumption instead of discrete constellation symbols. However, the input data consist of discrete constellation symbols in practice, which would inevitably lead to performance loss. Under the finite alphabet inputs, the upper bound of the sum average mutual information is firstly derived. Then, by maximizing this upper bound, the optimal precoding matrices are achieved. Simulation results demonstrate that the proposed precoding design could obtain higher mutual information than the existing schemes (such as the Max-SGINR scheme, D-SVD scheme and BM scheme).

Revealing the mechanism of gene regulation is one of central challenge in modern genomics research. Since transcription factors play vital roles in process of gene regulation, identification of their binding sites is quite important. In order to detect transcription factor binding sites more accurately and efficiently, DNase-Seq technology arises. In the study, we proposed a method to identify transcription factor binding sites based on DNase-Seq signals. Firstly, DNase-Seq data was extracted and processed to enhance the binding sites. Then, a statistical model was designed to depict pattern of particular transcription factor binding sites with sequence information and DNase-seq signals. With the model, the specific transcription factor binding sites can be predicted. In the experiment about C-jun transcription factor, the good prediction performance of its binding sites proves the validity of the proposed method.