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As the infrastructure of modern industry, the safe and stable operation of lightweight industrial control network is crucial for industrial production. Multi‐mode attack is a kind of highly customized and low resource consumption network attack method, which can target the specific vulnerabilities and configuration weaknesses of lightweight industrial control network. In order to improve the defense capability of industrial control network against multimodal attacks, a lightweight security defense algorithm based on momentum‐enhanced feature graph is proposed. The lightweight industrial control network operation mode is determined by abstract relationship, and the ontology structure of industrial control network multi‐mode attack is established; with reference to the ontology structure, the multi‐mode attack map of industrial control network is defined; the normal behavior and lightweight attack behavior are divided, and the convolutional network is used to enhance the characteristics of lightweight attack; and the momentum‐enhanced feature map technology is used to correspond to the enhancement process, and to block and defend against the multi‐mode attack of industrial control network. The results show that the studied algorithm can realize accurate prediction of attack behaviors with different attack behaviors as test objects, and ensure accurate blocking of all kinds of attack behaviors with better security defense effect. [ABSTRACT FROM AUTHOR]

Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) is a valuable experimental tool to study the immune state in health and following immune challenges such as infectious diseases, (auto)immune diseases, and cancer. Several tools have been developed to reconstruct B cell and T cell receptor sequences from AIRR-seq data and infer B and T cell clonal relationships. However, currently available tools offer limited parallelization across samples, scalability or portability to high-performance computing infrastructures. To address this need, we developed nf-core/airrflow, an end-to-end bulk and single-cell AIRR-seq processing workflow which integrates the Immcantation Framework following BCR and TCR sequencing data analysis best practices. The Immcantation Framework is a comprehensive toolset, which allows the processing of bulk and single-cell AIRR-seq data from raw read processing to clonal inference. nf-core/airrflow is written in Nextflow and is part of the nf-core project, which collects community contributed and curated Nextflow workflows for a wide variety of analysis tasks. We assessed the performance of nf-core/airrflow on simulated sequencing data with sequencing errors and show example results with real datasets. To demonstrate the applicability of nf-core/airrflow to the high-throughput processing of large AIRR-seq datasets, we validated and extended previously reported findings of convergent antibody responses to SARS-CoV-2 by analyzing 97 COVID-19 infected individuals and 99 healthy controls, including a mixture of bulk and single-cell sequencing datasets. Using this dataset, we extended the convergence findings to 20 additional subjects, highlighting the applicability of nf-core/airrflow to validate findings in small in-house cohorts with reanalysis of large publicly available AIRR datasets. Author summary: We have created nf-core/airrflow, a workflow to help researchers study the immune system in healthy and disease states, such as infections, autoimmunity, and cancer. The adaptive immune system is responsible for the third line of defense responses, specific to each particular threat, after physical barriers have been compromised and the nonspecific innate immune response has failed to clear the danger. Two types of white blood cells are central players in the adaptive response, namely B cells and T cells. These cells have surface receptors that recognize suspicious elements (antigens). Learning what receptors bind to which antigens is of utmost interest to understand immune responses. Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) is a technique that allows to determine the genetic sequence of these receptors. The amount of data generated in these experiments is large, and the analysis complex. nf-core/airrflow simplifies running a comprehensive analysis connecting tools from Immcantation, a specialized software project to analyze AIRR-seq data. The workflow can efficiently process large datasets on multiple computing platforms. We analyzed immune responses to COVID-19 in 97 infected individuals and 99 healthy people, and confirmed previous findings and provided new insights, demonstrating the workflow's applicability to reanalyzing large publicly available datasets. [ABSTRACT FROM AUTHOR]

To reduce the water pressure acting on the lining of the Shantou Bay cross-harbour tunnel and determine a reasonable design scheme for the controlled drainage of water inflow of the underwater tunnel under high-water pressure, ABAQUS software is used to numerically simulate the water inflow and water pressure acting on the lining of the tunnel with different relief values of three schemes for controlled drainage. Theses three schemes of controlled drainage are the "longitudinal blind tube only", "longitudinal blind tube+circular blind tube with an interval of 12 m", and "longitudinal blind tube+circular blind tube with an interval of 6 m". The comparison analysis shows that the water inflow of tunnel decreases linearly with the increase of valve pressure, and the water pressure of monitoring points acting on the tunnel cross-sectional lining increases linearly as the valve pressure increases. The denser the distribution of circular blind tubes, the more sensitive the water inflow of the tunnel to the change of the valve pressure, and the higher the degree of influence on the water pressure of monitoring points acting on the tunnel cross-sectional lining due to the change of the valve pressure. Besides, the farther the monitoring point in the tunnel cross-section from the longitudinal blind tube, the lower the degree of influence on the water pressure of the monitoring point acting on the tunnel cross-sectional lining due to the change of the valve pressure. When the "longitudinal blind tube+circular blind tube with an interval of 12 m" with the pressure relief value of 600 kPa is used for the controlled drainage, the water pressure acting on the lining at the arch foot of the most unfavorable tunnel cross-section can be minimized. The reasonable design scheme of the controlled drainage of water inflow for the Shantou Bay cross-harbour tunnel can be determined objectively. [ABSTRACT FROM AUTHOR]

Abstract Background High-throughput DNA methylation arrays are likely to accelerate the pace of methylation biomarker discovery for a wide variety of diseases. A potential problem with a standard set of probes measuring the methylation status of CpG sites across the whole genome is that many sites may not show inter-individual methylation variation among the biosamples for the disease outcome being studied. Inclusion of these so-called "non-variable sites" will increase the risk of false discoveries and reduce statistical power to detect biologically relevant methylation markers. Results We propose a method to estimate the proportion of non-variable CpG sites and eliminate those sites from further analyses. Our method is illustrated using data obtained by hybridizing DNA extracted from the peripheral blood mononuclear cells of 311 samples to an array assaying 1505 CpG sites. Results showed that a large proportion of the CpG sites did not show inter-individual variation in methylation. Conclusions Our method resulted in a substantial improvement in association signals between methylation sites and outcome variables while controlling the false discovery rate at the same level.

Abstract Background DNA methylation patterns have been shown to significantly correlate with different tissue types and disease states. High-throughput methylation arrays enable large-scale DNA methylation analysis to identify informative DNA methylation biomarkers. The identification of disease-specific methylation signatures is of fundamental and practical interest for risk assessment, diagnosis, and prognosis of diseases. Results Using published high-throughput DNA methylation data, a two-stage feature selection method was developed to select a small optimal subset of DNA methylation features to precisely classify two sample groups. With this approach, a small number of CpG sites were highly sensitive and specific in distinguishing lung cancer tissue samples from normal lung tissue samples. Conclusion This study shows that it is feasible to identify DNA methylation biomarkers from high-throughput DNA methylation profiles and that a small number of signature CpG sites can suffice to classify two groups of samples. The computational method we developed in the study is efficient to identify signature CpG sites from disease samples with complex methylation patterns.

Abstract Background Regulatory modules are segments of the DNA that control particular aspects of gene expression. Their identification is therefore of great importance to the field of molecular genetics. Each module is composed of a distinct set of binding sites for specific transcription factors. Since experimental identification of regulatory modules is an arduous process, accurate computational techniques that supplement this process can be very beneficial. Functional modules are under selective pressure to be evolutionarily conserved. Most current approaches therefore attempt to detect conserved regulatory modules through similarity comparisons at the DNA sequence level. However, some regulatory modules, despite the conservation of their responsible binding sites, are embedded in sequences that have little overall similarity. Results In this study, we present a novel approach that detects conserved regulatory modules via comparisons at the binding site level. The technique compares the binding site profiles of orthologs and identifies those segments that have similar (not necessarily identical) profiles. The similarity measure is based on the inner product of transformed profiles, which takes into consideration the p values of binding sites as well as the potential shift of binding site positions. We tested this approach on simulated sequence pairs as well as real world examples. In both cases our technique was able to identify regulatory modules which could not to be identified using sequence-similarity based approaches such as rVista 2.0 and Blast. Conclusion The results of our experiments demonstrate that, for sequences with little overall similarity at the DNA sequence level, it is still possible to identify conserved regulatory modules based solely on binding site profiles.