Ontological similarity network reasoning framework

To properly compute the ontological similarity, an ontological similarity network-based reasoning framework is proposed. It structurally integrates extension-based approach, intension-based approach, the similarity network-based reasoning to exploit the implicit similarity, and the feedback from the context to validate the similarity measures. A new similarity measure is also presented to construct concept similarity network, which scales the similarity using the relative depth of the least common super-concept between any two concepts. Subsequently, the graph theory, instead of predefined knowledge rules, is applied to perform the similarity network-based reasoning such that the knowledge acquisition can be avoided. The framework has been applied to text categorization and visualization of high dimensional data. Theory analysis and the experimental results validate the proposed framework.

Resiliency oriented control of a smart microgrid with photovoltaic modules

The resiliency of a standalone microgrid is of considerable issue because the available regulation measures and capabilities are limited.Given this background,this paper presented a new mathematical model for a detailed photovoltaic(PV)module and the application of new control techniques for efficient energy extraction.The PV module employs a single-stage conversion method to integrate it with the utility grid.For extraction the maximum power from PV and integrate it to power grid,a three-phase voltage source converter is used.For obtaining the maximum power at a particular irradiance a maximum power point tracking(MPPT)scheme is used.The fuzzy logic control and adaptive network-based fuzzy inference system are proposed for direct current(DC)link voltage control.The proposed model and control scheme are validated through a comparison with the standard power-voltage and current-voltage charts for a PV module.Simulation results demonstrate that the system stability can be maintained with the power grid and in the island mode,in contrast with the MPPT.

叉车装配流水线与网络看板集成系统设计

为了提高叉车装配流水线的生产效率,减少在制品储备率,提出了叉车装配流水线与网络看板集成系统,通过合理布局叉车装配流水线并设计相应的控制逻辑,充分利用看板管理理念,开发网络看板系统,实现与叉车装配流水线的无缝集成,并开发后台的网络看板及生产计划配置系统,实现零部件在规定的时间按规定的数量到达规定的地点,以尽可能减少在制品库存。

TIST: Transcriptome and Histopathological Image Integrative Analysis for Spatial Transcriptomics

Sequencing-based spatial transcriptomics(ST)is an emerging technology to study in situ gene expression patterns at the whole-genome scale.Currently,ST data analysis is still complicated by high technical noises and low resolution.In addition to the transcriptomic data,matched histopathological images are usually generated for the same tissue sample along the ST experiment.The matched high-resolution histopathological images provide complementary cellular phenotypical information,providing an opportunity to mitigate the noises in ST data.We present a novel ST data analysis method called transcriptome and histopathological image integrative analysis for ST(TIST),which enables the identification of spatial clusters(SCs)and the enhancement of spatial gene expression patterns by integrative analysis of matched transcriptomic data and images.TIST devises a histopathological feature extraction method based on Markov random field(MRF)to learn the cellular features from histopathological images,and integrates them with the transcriptomic data and location information as a network,termed TIST-net.Based on TIST-net,SCs are identified by a random walk-based strategy,and gene expression patterns are enhanced by neighborhood smoothing.We benchmark TIST on both simulated datasets and 32 real samples against several state-of-the-art methods.Results show that TIST is robust to technical noises on multiple analysis tasks for sequencing-based ST data and can find interesting microstructures in different biological scenarios.TIST is available at http://lifeome.net/software/tist/and https://ngdc.cncb.ac.cn/biocode/tools/BT007317.

Repurposable drugs for SARS-CoV-2 and influenza sepsis with scRNA-seq data targeting post-transcription modifications

Coronavirus disease 2019(COVID-19)has impacted almost every part of human lifeworldwide,posing amassive threat to human health.The lack of time for new drug discovery and the urgent need for rapid disease control to reduce mortality have led to a search for quick and effective alternatives to novel therapeutics,for example drug repurposing.To identify potentially repurposable drugs,we employed a systematic approach to mine candidates from U.S.FDA-approved drugs and preclinical small-molecule compounds by integrating gene expression perturbation data for chemicals from the Library of Integrated Network-Based Cellular Signatures project with a publicly available single-cell RNA sequencing dataset from patients withmild and severe COVID-19(GEO:GSE145926,public data available and accessed on 22 April 2020).We identified 281 FDA-approved drugs that have the potential to be effective against severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)infection,16 of which are currently undergoing clinical trials to evaluate their efficacy against COVID-19.We experimentally tested and demonstrated the inhibitory effects of tyrphostin-AG-1478 and brefeldin-a,two chemical inhibitors of glycosylation(a post-translational modification)on the replication of the single-stranded ribonucleic acid(ssRNA)virus influenza A virus as well as on the transcription and translation of host cell cytokines and their regulators(IFNs and ISGs).In conclusion,we have identified and experimentally validated repurposable anti-SARS-CoV-2 and IAV drugs using a systems biology approach,which may have the potential for treating these viral infections and their complications(sepsis).