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103. Screening of key genes in the pathogenesis of muscle atrophy in CKD-PEW children based on RNA sequencing

Author

Liang Ying, Jiang Yeping, Wang Hui, Zhou Nan, FuQian, and Shen Ying

Subjects
Chronic kidney disease, Protein-energy wasting, Muscle atrophy, Key genes, NF-κB, RNA sequencing, Internal medicine, RC31-1245, Genetics, QH426-470
Abstract

Abstract Background In children with CKD, Protein Energy Wasting (PEW) is common, which affects the outcome of children and is an important cause of poor prognosis. We are aiming to explore the pathogenesis of muscle wasting in CKD-PEW children. Methods Blood samples of 32 children diagnosed with chronic kidney disease (CKD) and protein energy wasting (PEW) in our hospital from January 2016 to June 2021 were collected. RNA sequencing and bioinformatics analysis were performed. Results Based on GO (Gene Ontology) functional enrichment analysis, KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analysis and differential gene expression analysis, a total of 25 CKD-PEW related genes were obtained including CRP, IL6, TNF, IL1B, CXCL8, IL12B, IL12A, IL18, IL1A, IL4, IL10, TGFB2, TGFB1, TGFB3, ADIPOQ, NAMPT, RETN, RETNLB, LEP, CD163, ICAM1, VCAM1, SELE, NF-κB1, NF-κB2. The most significantly differentially expressed gene was NF-κB2 (adjusted P = 2.81 × 10–16), and its expression was up-regulated by 3.92 times (corresponding log2FoldChange value was 1.979). Followed by RETN (adjusted P = 1.63 × 10–7), and its expression was up-regulated by 8.306 times (corresponding log2FoldChange value was 2.882). SELE gene were secondly significant (adjusted P = 5.81 × 10–7), and its expression was down-regulated by 22.05 times (corresponding log2FoldChange value was -4.696). Conclusions A variety of inflammatory factors are involved in the pathogenesis of CKD-PEW in children, and chronic inflammation may lead to the development of muscle atrophy in CKD-PEW. It is suggested for the first time that NF-κB is a key gene in the pathogenesis of muscle wasting in CKD-PEW children, and its increased expression may play an important role in the pathogenesis of muscle wasting in children with CKD-PEW.

Published
2023
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104. Research progress in early evaluation and feeding management of patients with dysphagia after stroke (脑卒中吞咽障碍患者早期评估及进食管理的研究进展)

Author

SHEN Weihong (沈卫红), LI li (李丽), LIU Dandan (刘丹丹), and SHEN Ying (沈英)

Subjects
stroke, dysphagia, screening, assessment, feeding management, 脑卒中, 吞咽障碍, 筛查, 评估, 进食管理, Nursing, RT1-120
Abstract

Dysphagia is a common complication after stroke, and early screening, correct assessment, and timely intervention can improve patient outcomes. This article reviews the research progress affecting the outcomes of patients with stroke dysphagia from the aspects of swallowing function screening and evaluation, and feeding management. (吞咽障碍是脑卒中患者常见并发症, 早期筛查、正确评估、及时干预可改善患者结局。本文从吞咽筛查和评估、进食管理等内容对影响脑卒中吞咽障碍患者结局的研究进展进行综述。)

Published
2023
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105. A Survey of Natural Language Generation

Author

Dong, Chenhe, Li, Yinghui, Gong, Haifan, Chen, Miaoxin, Li, Junxin, Shen, Ying, and Yang, Min

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

This paper offers a comprehensive review of the research on Natural Language Generation (NLG) over the past two decades, especially in relation to data-to-text generation and text-to-text generation deep learning methods, as well as new applications of NLG technology. This survey aims to (a) give the latest synthesis of deep learning research on the NLG core tasks, as well as the architectures adopted in the field; (b) detail meticulously and comprehensively various NLG tasks and datasets, and draw attention to the challenges in NLG evaluation, focusing on different evaluation methods and their relationships; (c) highlight some future emphasis and relatively recent research issues that arise due to the increasing synergy between NLG and other artificial intelligence areas, such as computer vision, text and computational creativity., Comment: Accepted by ACM Computing Survey (CSUR) 2022

Published
2021
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106. HRKD: Hierarchical Relational Knowledge Distillation for Cross-domain Language Model Compression

Author

Dong, Chenhe, Li, Yaliang, Shen, Ying, and Qiu, Minghui

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

On many natural language processing tasks, large pre-trained language models (PLMs) have shown overwhelming performances compared with traditional neural network methods. Nevertheless, their huge model size and low inference speed have hindered the deployment on resource-limited devices in practice. In this paper, we target to compress PLMs with knowledge distillation, and propose a hierarchical relational knowledge distillation (HRKD) method to capture both hierarchical and domain relational information. Specifically, to enhance the model capability and transferability, we leverage the idea of meta-learning and set up domain-relational graphs to capture the relational information across different domains. And to dynamically select the most representative prototypes for each domain, we propose a hierarchical compare-aggregate mechanism to capture hierarchical relationships. Extensive experiments on public multi-domain datasets demonstrate the superior performance of our HRKD method as well as its strong few-shot learning ability. For reproducibility, we release the code at https://github.com/cheneydon/hrkd., Comment: EMNLP 2021

Published
2021

110. Continual Learning for Task-oriented Dialogue System with Iterative Network Pruning, Expanding and Masking

Author

Geng, Binzong, Yuan, Fajie, Xu, Qiancheng, Shen, Ying, Xu, Ruifeng, and Yang, Min

Subjects
Computer Science - Computation and Language
Abstract

This ability to learn consecutive tasks without forgetting how to perform previously trained problems is essential for developing an online dialogue system. This paper proposes an effective continual learning for the task-oriented dialogue system with iterative network pruning, expanding and masking (TPEM), which preserves performance on previously encountered tasks while accelerating learning progress on subsequent tasks. Specifically, TPEM (i) leverages network pruning to keep the knowledge for old tasks, (ii) adopts network expanding to create free weights for new tasks, and (iii) introduces task-specific network masking to alleviate the negative impact of fixed weights of old tasks on new tasks. We conduct extensive experiments on seven different tasks from three benchmark datasets and show empirically that TPEM leads to significantly improved results over the strong competitors. For reproducibility, we submit the code and data at: https://github.com/siat-nlp/TPEM, Comment: Accepted by The Annual Meeting of the Association for Computational Linguistics (ACL), 2021

Published
2021

132. Rare predicted loss-of-function variants of type I IFN immunity genes are associated with life-threatening COVID-19

Author

Matuozzo, Daniela, Talouarn, Estelle, Marchal, Astrid, Zhang, Peng, Manry, Jeremy, Seeleuthner, Yoann, Zhang, Yu, Bolze, Alexandre, Chaldebas, Matthieu, Milisavljevic, Baptiste, Gervais, Adrian, Bastard, Paul, Asano, Takaki, Bizien, Lucy, Barzaghi, Federica, Abolhassani, Hassan, Abou Tayoun, Ahmad, Aiuti, Alessandro, Alavi Darazam, Ilad, Allende, Luis M., Alonso-Arias, Rebeca, Arias, Andrés Augusto, Aytekin, Gokhan, Bergman, Peter, Bondesan, Simone, Bryceson, Yenan T., Bustos, Ingrid G., Cabrera-Marante, Oscar, Carcel, Sheila, Carrera, Paola, Casari, Giorgio, Chaïbi, Khalil, Colobran, Roger, Condino-Neto, Antonio, Covill, Laura E., Delmonte, Ottavia M., El Zein, Loubna, Flores, Carlos, Gregersen, Peter K., Gut, Marta, Haerynck, Filomeen, Halwani, Rabih, Hancerli, Selda, Hammarström, Lennart, Hatipoğlu, Nevin, Karbuz, Adem, Keles, Sevgi, Kyheng, Christèle, Leon-Lopez, Rafael, Franco, Jose Luis, Mansouri, Davood, Martinez-Picado, Javier, Metin Akcan, Ozge, Migeotte, Isabelle, Morange, Pierre-Emmanuel, Morelle, Guillaume, Martin-Nalda, Andrea, Novelli, Giuseppe, Novelli, Antonio, Ozcelik, Tayfun, Palabiyik, Figen, Pan-Hammarström, Qiang, de Diego, Rebeca Pérez, Planas-Serra, Laura, Pleguezuelo, Daniel E., Prando, Carolina, Pujol, Aurora, Reyes, Luis Felipe, Rivière, Jacques G., Rodriguez-Gallego, Carlos, Rojas, Julian, Rovere-Querini, Patrizia, Schlüter, Agatha, Shahrooei, Mohammad, Sobh, Ali, Soler-Palacin, Pere, Tandjaoui-Lambiotte, Yacine, Tipu, Imran, Tresoldi, Cristina, Troya, Jesus, van de Beek, Diederik, Zatz, Mayana, Zawadzki, Pawel, Al-Muhsen, Saleh Zaid, Alosaimi, Mohammed Faraj, Alsohime, Fahad M., Baris-Feldman, Hagit, Butte, Manish J., Constantinescu, Stefan N., Cooper, Megan A., Dalgard, Clifton L., Fellay, Jacques, Heath, James R., Lau, Yu-Lung, Lifton, Richard P., Maniatis, Tom, Mogensen, Trine H., von Bernuth, Horst, Lermine, Alban, Vidaud, Michel, Boland, Anne, Deleuze, Jean-François, Nussbaum, Robert, Kahn-Kirby, Amanda, Mentre, France, Tubiana, Sarah, Gorochov, Guy, Tubach, Florence, Hausfater, Pierre, Meyts, Isabelle, Zhang, Shen-Ying, Puel, Anne, Notarangelo, Luigi D., Boisson-Dupuis, Stephanie, Su, Helen C., Boisson, Bertrand, Jouanguy, Emmanuelle, Casanova, Jean-Laurent, Zhang, Qian, Abel, Laurent, and Cobat, Aurélie

Published
2023
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134. Radiographic and α-fetoprotein response predict pathologic complete response to immunotherapy plus a TKI in hepatocellular carcinoma: a multicenter study

Author

Huang, Cheng, Zhu, Xiao-Dong, Shen, Ying-Hao, Xu, Bin, Wu, Dong, Ji, Yuan, Chen, Ling-Li, Song, Tian-Qiang, Zhang, Wei, Zeng, Zhi-Ming, Huang, Hua-Sheng, Wang, Kui, Huang, Lan-Qing, Chen, Yong-Jun, Yang, Yu-Chen, Zhou, Le-Du, Long, Guo, Zhao, Hai-Tao, Wang, Yun-Chao, Ge, Ning-Ling, Chen, Yi, Tan, Chang-Jun, Zhou, Jian, Fan, Jia, and Sun, Hui-Chuan

Published
2023
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139. Contextualized Knowledge-aware Attentive Neural Network: Enhancing Answer Selection with Knowledge

Author

Deng, Yang, Xie, Yuexiang, Li, Yaliang, Yang, Min, Lam, Wai, and Shen, Ying

Subjects
Computer Science - Computation and Language, Computer Science - Information Retrieval
Abstract

Answer selection, which is involved in many natural language processing applications such as dialog systems and question answering (QA), is an important yet challenging task in practice, since conventional methods typically suffer from the issues of ignoring diverse real-world background knowledge. In this paper, we extensively investigate approaches to enhancing the answer selection model with external knowledge from knowledge graph (KG). First, we present a context-knowledge interaction learning framework, Knowledge-aware Neural Network (KNN), which learns the QA sentence representations by considering a tight interaction with the external knowledge from KG and the textual information. Then, we develop two kinds of knowledge-aware attention mechanism to summarize both the context-based and knowledge-based interactions between questions and answers. To handle the diversity and complexity of KG information, we further propose a Contextualized Knowledge-aware Attentive Neural Network (CKANN), which improves the knowledge representation learning with structure information via a customized Graph Convolutional Network (GCN) and comprehensively learns context-based and knowledge-based sentence representation via the multi-view knowledge-aware attention mechanism. We evaluate our method on four widely-used benchmark QA datasets, including WikiQA, TREC QA, InsuranceQA and Yahoo QA. Results verify the benefits of incorporating external knowledge from KG, and show the robust superiority and extensive applicability of our method., Comment: Accepted by TOIS

Published
2021

140. Association between glomerular C4d deposition, proteinuria, and disease severity in children with IgA nephropathy

Author

Zhou, Weiran, Wang, Hui, Sun, Shuzhen, Shen, Ying, Liu, Xuemei, Zhen, Junhui, and Zhang, Hongxia

Subjects
Proteinuria -- Complications and side effects, IgA glomerulonephritis -- Risk factors -- Development and progression -- Demographic aspects, Health
Abstract

Background C4d may be used as a marker to evaluate the condition and prognosis of adults with IgA nephropathy, but there have been few studies of children with IgA nephropathy. Methods C4d immunohistochemical staining was performed on samples from children with IgA nephropathy with C1q-negative immunofluorescence. The clinical and pathological treatment and prognostic characteristics of children in the C4d-positive and -negative groups were compared. Results A total of sixty-five children with IgA nephropathy were included in the study and were followed up for an average of 37 months. C4d was mainly deposited along the capillary loops. The urinary protein-to-creatinine ratio (UPCR) in the C4d-positive group was significantly higher than that in the C4d-negative group (3.97 vs. 0.81, P < 0.001), and the average integrated optical density value of each child was positively correlated with the UPCR (r = 0.441, P < 0.001). There was a significant difference in the proportions of children with mesangial hypercellularity (M1) (68.97% vs. 44.44%, P = 0.048) and segmental glomerulosclerosis (S1) (65.52% vs. 33.33%, P = 0.010) between the C4d-positive group and the C4d-negative group. The proportion of children who received immunosuppressants in the C4d-positive group was higher than that in the C4d-negative group (86.21% vs. 36.11%, P < 0.001). There was no significant difference in the proportion of children developing kidney failure between the two groups. Conclusion C4d was found to be associated with proteinuria, segmental lesions, and immunosuppressant treatment. Activation of the lectin pathway may reflect the severity of clinical and pathological manifestations of IgA nephropathy in children. Graphical abstract, Author(s): Weiran Zhou [sup.1] , Hui Wang [sup.2] , Shuzhen Sun [sup.3] , Ying Shen [sup.2] , Xuemei Liu [sup.1] , Junhui Zhen [sup.4] , Hongxia Zhang [sup.1] , Fan [...]

Published
2023
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143. Study on Planning Strategy of TT&C Resource Scheduling Based on Data Mining

Author

Shen, Ying, Gao, Huili, Yang, Zhaoqiang, Zheng, Jianjun, Zhao, Wei, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Sun, Jiande, editor, Wang, Yue, editor, Huo, Mengyao, editor, and Xu, Lexi, editor

Published
2023
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