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Listeria Monocytogenes Glutathione Synthase GshF Contributes to Resistance to the Oxidative Stress Through Regulating the PTS Gene Lmo2002

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SSRN

SSRN 2023年

作者： Chen, Mianmian Zhang, Jiaxue Xia, Jing Sun, Jing Zhang, Xian Xu, Jiali Deng, Simin Han, Yue Jiang, Lingli Song, Houhui Cheng, Changyong Key Laboratory of Applied Technology on Green-Eco-Healthy Animal Husbandry of Zhejiang Province Zhejiang Provincial Engineering Research Center for Animal Health Diagnostics & Advanced Technology Zhejiang International Science and Technology Cooperation Base for Veterinary Medicine and Health Management China-Australia Joint Laboratory for Animal Health Big Data Analytics College of Animal Science and Technology College of Veterinary Medicine of Zhejiang A&F University 666 Wusu Street Lin’an District Zhejiang Province Hangzhou311300 China Ningbo College of Health Sciences Ningbo315100 China

Glutathione (GSH) is a crucial component of the Grx system, and GSH is synthesized by glutathione synthase GshF in Listeria monocytogenes. GSH has been known to regulate Listeria virulence by modifying the virulence factors, LLO and PrfA. Here we demonstrated the roles of L. monocytogenes GshF in oxidative tolerance and intracellular infection. When exposed to diamide and copper stress, the deletion of gshF resulted in a significant decrease in bacterial growth in vitro and, more importantly, decreased the efficiency of intracellular invasion and proliferation in macrophages and mice organs. Moreover, GshF altered global transcriptional profiles under oxidative stress, especially for the phosphoenolpyruvate-carbohydrate phosphotransferase system (PTS) genes lmo1997-lmo2004. The lmo1997-lmo2004 in wild-type strain was transcriptionally significantly downregulated under the copper and diamide stress compared to those in the normal condition. However, in the absence of gshF, the transcripts of lmo1997-lmo2004 were upregulated in response to these stress conditions. Deletion of lmo2002 increased oxidative stress tolerance to copper, and overexpression of lmo2002 decreased this resistance. Collectively, for the first time, we clarified that L. monocytogenes GshF plays a vital role in bacterial anti-oxidation via the regulation of lmo2002. © 2023, The Authors. All rights reserved.

关键词： Genes

Reduce the rank calculation of a high-dimensional sparse matrix based on network controllability theory

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arXiv 2021年

作者： Zhao, Chen Liu, Yuqing Hu, Li Yuan, Zhengzhong College of Computer and Cyber Security Hebei Normal University Shijiazhuang050024 China Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics & Data Security Shijiazhuang050024 China Hebei Key Laboratory of Network and Information Security Shijiazhuang050024 China School of Mathematics and Statistics Minnan Normal University Zhangzhou363000 China Fujian Key Laboratory of Data Science and Statistics Minnan Normal University Zhangzhou363000 China Institute of Meteorological Big Data-Digital Fujian Minnan Normal University Zhangzhou363000 China

Numerical computing of the rank of a matrix is a fundamental problem in scientific computation. The datasets generated by the internet often correspond to the analysis of high-dimensional sparse matrices. Notwithstanding recent advances in the promotion of traditional singular value decomposition (SVD), an efficient estimation algorithm for the rank of a high-dimensional sparse matrix is still lacking. Inspired by the controllability theory of complex networks, we converted the rank of a matrix into maximum matching computing. Then, we established a fast rank estimation algorithm by using the cavity method, a powerful approximate technique for computing the maximum matching, to estimate the rank of a sparse matrix. In the merit of the natural low complexity of the cavity method, we showed that the rank of a high-dimensional sparse matrix can be estimated in a much faster way than SVD with high accuracy. Our method offers an efficient pathway to quickly estimate the rank of the high-dimensional sparse matrix when the time cost of computing the rank by SVD is unacceptable. Copyright © 2021, The Authors. All rights reserved.

关键词： Singular value decomposition

An automated workflow for integrating environmental sustainability assessment into parametric part design through standard reference models 27

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An automated workflow for integrating environmental sustaina...

27th CIRP Life Cycle engineering Conference, LCE 2020

作者： Bernstein, William Z. Tensa, Melissa Praniewicz, Maxwell Kwon, Soonjo Ramanujan, Devarajan Engineering Laboratory National Institute of Standards and Technology MD United States School of Mechanical Industrial and Manufacturing Engineering Oregon State University OR United States George W. Woodruff School of Mechanical Engineering Georgia Institute of Technology AtlantaGR United States Engineering Laboratory National Institute of Standards and Technology MD United States Korea Advanced Institute of Science and Technology Daejeon Korea Republic of Mechanical Engineering Section Center for Digitalization Big Data and Data Analytics Aarhus University Aarhus8000 Denmark

Integrating environmental sustainability assessment into parametric design presents an opportunity for enabling sustainable product design. However, this is a challenging task as quantitative methods for sustainability assessment are poorly integrated with parametric design and optimization tools. Furthermore, current streamlined approaches for computing environmental impact during the design stage often ignore manufacturing-related impacts resulting from the geometric complexity of parts. To address these gaps, we present a systematic workflow for computing environmental indicators from parametric design models. Our workflow utilizes the unit manufacturing process information model to evaluate manufacturing-phase resource consumption from process planning data, and consequently enables quantitative correlation of design parameters to the calculated environmental indicators. We demonstrate our workflow through a case study involving the design of a rigid flange coupling, wherein we evaluate the influence of geometric design parameters on the corresponding environmental impact of the design. © 2020 The Author(s). Published by Elsevier B.V.

关键词： Computer aided design

MoCA: Incorporating multi-stage domain pretraining and cross-guided multimodal attention for Textbook Question Answering

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arXiv 2021年

作者： Xu, Fangzhi Lin, Qika Liu, Jun Zhang, Lingling Zhao, Tianzhe Chai, Qi Pan, Yudai Department of Computer Science and Technology Xi'an Jiaotong University China National Engineering Lab for Big Data Analytics Xi'an Jiaotong University China Shaanxi Province Key Laboratory of Satellite and Terrestrial Network Tech. R&D Xi'an Jiaotong University China

Textbook Question Answering (TQA) is a complex multimodal task to infer answers given large context descriptions and abundant diagrams. Compared with Visual Question Answering(VQA), TQA contains a large number of uncommon terminologies and various diagram inputs. It brings new challenges to the representation capability of language model for domain-specific spans. And it also pushes the multimodal fusion to a more complex level. To tackle the above issues, we propose a novel model named MoCA, which incorporates multi-stage domain pretraining and multimodal cross attention for the TQA task. Firstly, we introduce a multi-stage domain pretraining module to conduct unsupervised post-pretraining with the span mask strategy and supervised pre-finetune. Especially for domain post-pretraining, we propose a heuristic generation algorithm to employ the terminology corpus. Secondly, to fully consider the rich inputs of context and diagrams, we propose cross-guided multimodal attention to update the features of text, question diagram and instructional diagram based on a progressive strategy. Further, a dual gating mechanism is adopted to improve the model ensemble. The experimental results show the superiority of our model, which outperforms the state-of-the-art methods by 2.21% and 2.43% for validation and test split respectively. Copyright © 2021, The Authors. All rights reserved.

关键词： Textbooks

FEDLAB: A FLEXIBLE FEDERATED LEARNING FRAMEWORK

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arXiv 2021年

作者： Zeng, Dun Liang, Siqi Hu, Xiangjing Wang, Hui Xu, Zenglin School of Computer Science and Engineering University of Electronic Science and Technology of China China Rich Media Big Data Analytics and Application Key Laboratory Shenzhen Research Institute The Chinese University of Hong Kong Hong Kong School of Computer Science and Technology Harbin Institute of Technology Shenzhen China Peng Cheng Laboratory Shenzhen China

Federated learning (FL) is a machine learning field in which researchers try to facilitate model learning process among multiparty without violating privacy protection regulations. Considerable effort has been invested in FL optimization and communication related researches. In this work, we introduce FedLab, a lightweight open-source framework for FL simulation. The design of FedLab focuses on FL algorithm effectiveness and communication efficiency. Also, FedLab is scalable in different deployment scenario. We hope FedLab could provide flexible API as well as reliable baseline implementations, and relieve the burden of implementing novel approaches for researchers in FL community. The source code is available at https://***/SMILELab-FL/FedLab. © 2021, CC BY.

关键词： Learning systems

Context-Aware Feature Attention Model for Coreference Resolution on Biomedical Texts

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Research Square

Research Square 2021年

作者： Li, Yufei Zhou, Xiangyu Ma, Jie Ma, Xiaoyong Li, Chen School of Computer Science and Technology Xi’an Jiaotong University Shaanxi Xi’an China National Engineering Lab for Big Data Analytics Xi’an Jiaotong University Shaanxi Xi’an China Shaanxi Province Key Laboratory of Satellite and Terrestrial Network Technology Research and Development Xi’an Jiaotong University Shaanxi Xi’an China

Background: Bio-entity Coreference resolution is an important task to gain a complete understanding of biomedical texts automatically. Previous neural network-based studies on this topic are domain system based methods which rely on some domain-specific information integration. However, for the identical mentions, this may lead to misleading information, as the model tends to get similar or even identical representations, which further leads to wrongful predictions. Results: we propose a new context-aware Feature Attention model to distinguish identical mentions effectively to better resolve coreference. The new model can represent identical mentions based on different contexts by adaptively exploiting features effectively. The proposed model substantially outperforms the state-of-the-art baselines on the BioNLP dataset with a 64.0% F1 score and further demonstrates superior performance on the differential representation and coreferential link of identical mentions. Conclusion: The context-aware Feature Attention model adaptively exploit features and represent identical mentions according to different contexts, which significantly makes the system obtain semantic information effectively and make more accurate predictions. Considering that this approach is still limited when context information is insufficient, we expect to utilize such information more fine-grained with the help of the external knowledge base in coreference resolution. © 2021, CC BY.

关键词： Semantics

Federated dynamic neural network for deep MIMO detection

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arXiv 2021年

作者： Yang, Yuwen Gao, Feifei Xue, Jiang Zhou, Ting Xu, Zongben Department of Automation Tsinghua University Beijing100084 China The National Engineering Laboratory for Big Data Analytics Xi'an International Academy for Mathematics and Mathematical Technology School of Mathematics and Statistics Xi'an Jiaotong University Xi'an710049 China The Shanghai Advanced Research Institute CAS Shanghai Frontier Innovation and Research Institute Shanghai201210 China

In this paper, we develop a dynamic detection network (DDNet) based detector for multiple-input multiple-output (MIMO) systems. By constructing an improved DetNet (IDetNet) detector and the OAMPNet detector as two independent network branches, the DDNet detector performs sample-wise dynamic routing to adaptively select a better one between the IDetNet and the OAMPNet detectors for every samples under different system conditions. To avoid the prohibitive transmission overhead of dataset collection in centralized learning (CL), we propose the federated averaging (FedAve)-DDNet detector, where all raw data are kept at local clients and only locally trained model parameters are transmitted to the central server for aggregation. To further reduce the transmission overhead, we develop the federated gradient sparsification (FedGS)-DDNet detector by randomly sampling gradients with elaborately calculated probability when uploading gradients to the central server. Based on simulation results, the proposed DDNet detector consistently outperforms other detectors under all system conditions thanks to the sample-wise dynamic routing. Moreover, the federated DDNet detectors, especially the FedGS-DDNet detector, can reduce the transmission overhead by at least 25.7% while maintaining satisfactory detection accuracy. Copyright © 2021, The Authors. All rights reserved.

关键词： MIMO systems

Effects of Supplementing Leucine in the Production of Enriched Branched-Chain Fatty Acids Milk Assessed by in Vitro and in Vivo Studies

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SSRN

SSRN 2022年

作者： Sun, Lingling Zhan, Tengfei Ma, Lu Guo, Zitai Brenna, J. Thomas Bu, Dengpan State Key Laboratory of Animal Nutrition Institute of Animal Sciences of Chinese Academy of Agricultural Sciences Beijing100193 China Key Laboratory of Applied Technology on Green-Eco-Healthy Animal Husbandry of Zhejiang Province Zhejiang Provincial Engineering Laboratory for Animal Health Inspection & Internet Technology Zhejiang International Science and Technology Cooperation Base for Veterinary Medicine and Health Management China-Australia Joint Laboratory for Animal Health Big Data Analytics College of Animal Science and Technology College of Veterinary Medicine of Zhejiang A&F University Hangzhou311300 China Departments of Pediatrics of Nutrition and of Chemistry Dell Pediatric Research Institute University of Texas AustinTX78723 United States Beijing100193 China

The feasibility of exogenously adding leucine to produce enriched branched-chain fatty acids (BCFAs) milk by in vitro fermentation and in vivo feeding study was investigated. The results showed that the addition of leucine improved the fermentation state of microorganisms, as indicated by an increase of dry matter degradation rate and volatile fatty acids content. Meanwhile, the concentrations of BCFAs in fermentation liquid were significantly increased by leucine addition. The results of 16S rRNA sequencing demonstrate that the norank_f__norank_o__clostridia_UCG-014 and Saccharofermentans may be the dominant species that utilizes leucine to synthesize BCFAs. The results of the dairy cow feeding trial showed that leucine significantly increased the concentrations of BCFAs in milk of cows. The current study provides a guide for production of enriched BCFA milk, which is useful for design and development of functional dairy products beneficial human health. © 2022, The Authors. All rights reserved.

关键词： Fermentation

C-Net: A reliable convolutional neural network for biomedical image classification

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arXiv 2020年

作者： Barzekar, Hosein Yu, Zeyun Big Data Analytics and Visualization Laboratory Department of Computer Science University of Wisconsin-Milwaukee MilwaukeeWI53211 United States Department of Biomedical Engineering University of Wisconsin-Milwaukee MilwaukeeWI53211 United States

Cancers are the leading cause of death in many countries. Early diagnosis plays a crucial role in having proper treatment for this debilitating disease. The automated classification of the type of cancer is a challenging task since pathologists must examine a huge number of histopathological images to detect infinitesimal abnormalities. In this study, we propose a novel convolutional neural network (CNN) architecture composed of a Concatenation of multiple Networks, called C-Net, to classify biomedical images. The model incorporates multiple CNNs including Outer, Middle, and Inner. The first two parts of the architecture contain six networks that serve as feature extractors to feed into the Inner network to classify the images in terms of malignancy and benignancy. The C-Net is applied for histopathological image classification on two public datasets, including BreakHis and Osteosarcoma. To evaluate the performance, the model is tested using several evaluation metrics for its reliability. The C-Net model outperforms all other models on the individual metrics for both datasets and achieves zero misclassification. This approach has the potential to be extended to additional classification tasks, as experimental results demonstrate utilizing extensive evaluation metrics. Copyright © 2020, The Authors. All rights reserved.

关键词： Computer aided diagnosis