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3,407 results on '"Zhao, Liping"'

1. The Gut Microbiota and Diabetes: Research, Translation, and Clinical Applications-2023 Diabetes, Diabetes Care, and Diabetologia Expert Forum.

Author

Byndloss, Mariana, Devkota, Suzanne, Duca, Frank, Hendrik Niess, Jan, Nieuwdorp, Max, Orho-Melander, Marju, Sanz, Yolanda, Tremaroli, Valentina, and Zhao, Liping

Subjects
Humans, Gastrointestinal Microbiome, Diabetes Mellitus, Type 2, Diabetes Mellitus
Abstract

This article summarizes the state of the science on the role of the gut microbiota (GM) in diabetes from a recent international expert forum organized by Diabetes, Diabetes Care, and Diabetologia, which was held at the European Association for the Study of Diabetes 2023 Annual Meeting in Hamburg, Germany. Forum participants included clinicians and basic scientists who are leading investigators in the field of the intestinal microbiome and metabolism. Their conclusions were as follows: 1) the GM may be involved in the pathophysiology of type 2 diabetes, as microbially produced metabolites associate both positively and negatively with the disease, and mechanistic links of GM functions (e.g., genes for butyrate production) with glucose metabolism have recently emerged through the use of Mendelian randomization in humans; 2) the highly individualized nature of the GM poses a major research obstacle, and large cohorts and a deep-sequencing metagenomic approach are required for robust assessments of associations and causation; 3) because single-time point sampling misses intraindividual GM dynamics, future studies with repeated measures within individuals are needed; and 4) much future research will be required to determine the applicability of this expanding knowledge to diabetes diagnosis and treatment, and novel technologies and improved computational tools will be important to achieve this goal.

Published
2024

5. Time to rethink academic publishing: the peer reviewer crisis.

Author

Tropini, Carolina, Finlay, B, Nichter, Mark, Melby, Melissa, Metcalf, Jessica, Dominguez-Bello, Maria, Zhao, Liping, McFall-Ngai, Margaret, Geva-Zatorsky, Naama, Amato, Katherine, Undurraga, Eduardo, Poinar, Hendrik, and Gilbert, Jack

Subjects
artificial intelligence, early career researcher, peer review, peer review crisis, reviewer rewards
Abstract

There is concern that the time taken to publish academic papers in microbiological science has significantly increased in recent years. While the data do not specifically support this, evidence suggests that editors are having to invite more and more reviewers to identify those willing to perform peer review.

Published
2023

12. A Machine Learning Approach for Hierarchical Classification of Software Requirements

Author

Binkhonain, Manal and Zhao, Liping

Subjects
Computer Science - Software Engineering, Computer Science - Machine Learning, D.2.1, H.4, H.5
Abstract

Context: Classification of software requirements into different categories is a critically important task in requirements engineering (RE). Developing machine learning (ML) approaches for requirements classification has attracted great interest in the RE community since the 2000s. Objective: This paper aims to address two related problems that have been challenging real-world applications of ML approaches: the problems of class imbalance and high dimensionality with low sample size data (HDLSS). These problems can greatly degrade the classification performance of ML methods. Method: The paper proposes HC4RC, a novel ML approach for multiclass classification of requirements. HC4RC solves the aforementioned problems through semantic-role-based feature selection, dataset decomposition and hierarchical classification. We experimentally compare the effectiveness of HC4RC with three closely related approaches - two of which are based on a traditional statistical classification model whereas one uses an advanced deep learning model. Results: Our experiment shows: 1) The class imbalance and HDLSS problems present a challenge to both traditional and advanced ML approaches. 2) The HC4RC approach is simple to use and can effectively address the class imbalance and HDLSS problems compared to similar approaches. Conclusion: This paper makes an important practical contribution to addressing the class imbalance and HDLSS problems in multiclass classification of software requirements., Comment: 36 pages, 3 tables, 4 figures

Published
2023

13. Zero-Shot Learning for Requirements Classification: An Exploratory Study

Author

Alhoshan, Waad, Ferrari, Alessio, and Zhao, Liping

Subjects
Computer Science - Software Engineering, Computer Science - Artificial Intelligence, 68T50, D.2.1
Abstract

Context: Requirements engineering researchers have been experimenting with machine learning and deep learning approaches for a range of RE tasks, such as requirements classification, requirements tracing, ambiguity detection, and modelling. However, most of today's ML/DL approaches are based on supervised learning techniques, meaning that they need to be trained using a large amount of task-specific labelled training data. This constraint poses an enormous challenge to RE researchers, as the lack of labelled data makes it difficult for them to fully exploit the benefit of advanced ML/DL technologies. Objective: This paper addresses this problem by showing how a zero-shot learning approach can be used for requirements classification without using any labelled training data. We focus on the classification task because many RE tasks can be framed as classification problems. Method: The ZSL approach used in our study employs contextual word-embeddings and transformer-based language models. We demonstrate this approach through a series of experiments to perform three classification tasks: (1)FR/NFR: classification functional requirements vs non-functional requirements; (2)NFR: identification of NFR classes; (3)Security: classification of security vs non-security requirements. Results: The study shows that the ZSL approach achieves an F1 score of 0.66 for the FR/NFR task. For the NFR task, the approach yields F1~0.72-0.80, considering the most frequent classes. For the Security task, F1~0.66. All of the aforementioned F1 scores are achieved with zero-training efforts. Conclusion: This study demonstrates the potential of ZSL for requirements classification. An important implication is that it is possible to have very little or no training data to perform classification tasks. The proposed approach thus contributes to the solution of the long-standing problem of data shortage in RE., Comment: 60 pages, 22 tables, 1 figure

Published
2023

14. Guild-based approach for mitigating information loss and distortion issues in microbiome analysis

Author

Zhao, Liping, Wu, Guojun, and Zhao, Naisi

Subjects
Medical research -- Analysis, Medicine, Experimental -- Analysis, Type 2 diabetes -- Care and treatment, Information management -- Analysis, Information accessibility, Health care industry
Abstract

Introduction Microbiome research holds the promise of elucidating new mechanisms of disease development and developing innovative approaches for disease prevention and treatment. However, a substantial challenge that stymies this potential [...]

Published
2024
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18. Effectiveness of narrative exposure therapy for post-traumatic stress disorder in children and adolescents： a Meta-analysis

Author

Liu Junyu, Wang Jianjian, Luo Yuan, Zhao Liping, and Liu Zhijing

Subjects
narrative exposure therapy, post-traumatic stress disorder, children, adolescents, meta-analysis, Psychology, BF1-990, Psychiatry, RC435-571
Abstract

BackgroundNarrative exposure therapy （NET）， an integration of narrative therapy and exposure therapy， has been shown to be effective in relieving the symptoms of post-traumatic stress disorder （PTSD）， which can help patients gain a deeper understanding of their trauma and is also considered to be quite safe. PTSD is highly prevalent in children and adolescents， while the effectiveness of NET intervention varies among the subjects.ObjectiveTo systematically evaluate the effectiveness of NET for PTSD in children and adolescents， so as to provide references for the clinical application of NET.MethodsOn August 1， 2022， the Cochrane Library， PubMed， Web of Science， CINAHL， China National Knowledge Infrastructure （CNKI）， SinoMed， VIP and Wanfang database were searched from their inception to June 2022. Search was conducted with the use of a combination of medical subject heading and free text terms， and randomized controlled trials relevant to NET for PTSD in children and adolescents were collected. Then the quality of the controlled trials was evaluated according to the Cochrane Collaboration's tool for assessing risk of bias （2011）， and Meta-analysis was performed using RevMan 5.4 software.ResultsNine randomized controlled trials involving 394 children and adolescents with PTSD were included. Meta-analysis showed that NET and relaxation therapy reported comparable symptom relief in PTSD patients within 1 to 3 months after intervention （SMD=0.22， 95% CI： -0.84~1.28） and at 6 months after intervention （SMD=0.21， 95% CI： -0.75~1.17）， while NET provided greater PTSD symptom relief than routine therapy both within 1 to 3 months after intervention （SMD=-0.66， 95% CI： -1.04~-0.27） and at 6 months after intervention （SMD=-0.77， 95% CI： -1.36~-0.19）， with statistically significant differences. Regarding the alleviation of depressive symptoms， the effect was similar between NET and routine therapy within 1 to 3 months after intervention （SMD=-0.39， 95% CI： -0.98~0.21） and at 6 months after intervention （SMD=-0.74， 95% CI： -2.23~0.75）. No statistical difference was demonstrated between NET and routine therapy in relieving psychological distress （SMD=-0.54， 95% CI： -2.14~1.07） and suppressing hyperorexia （SMD=-0.17， 95% CI： -0.54~0.19） 1 to 3 months after intervention.ConclusionNET yields a better outcome and a medium- and long-term effectiveness in alleviating symptoms of PTSD in children and adolescents compared with routine therapy， while it does not offer any significant advantages in improving depression symptoms， psychological distress and hyperorexia.

Published
2024
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19. Classification of Natural Language Processing Techniques for Requirements Engineering

Author

Zhao, Liping, Alhoshan, Waad, Ferrari, Alessio, and Letsholo, Keletso J.

Subjects
Computer Science - Computation and Language, Computer Science - Software Engineering, A.1, D.m, I.7.m
Abstract

Research in applying natural language processing (NLP) techniques to requirements engineering (RE) tasks spans more than 40 years, from initial efforts carried out in the 1980s to more recent attempts with machine learning (ML) and deep learning (DL) techniques. However, in spite of the progress, our recent survey shows that there is still a lack of systematic understanding and organization of commonly used NLP techniques in RE. We believe one hurdle facing the industry is lack of shared knowledge of NLP techniques and their usage in RE tasks. In this paper, we present our effort to synthesize and organize 57 most frequently used NLP techniques in RE. We classify these NLP techniques in two ways: first, by their NLP tasks in typical pipelines and second, by their linguist analysis levels. We believe these two ways of classification are complementary, contributing to a better understanding of the NLP techniques in RE and such understanding is crucial to the development of better NLP tools for RE., Comment: 10 pages, 4 tables, 1 figure

Published
2022

29. Custom scoring based on ecological topology of gut microbiota associated with cancer immunotherapy outcome

Author

Derosa, Lisa, Iebba, Valerio, Silva, Carolina Alves Costa, Piccinno, Gianmarco, Wu, Guojun, Lordello, Leonardo, Routy, Bertrand, Zhao, Naisi, Thelemaque, Cassandra, Birebent, Roxanne, Marmorino, Federica, Fidelle, Marine, Messaoudene, Meriem, Thomas, Andrew Maltez, Zalcman, Gerard, Friard, Sylvie, Mazieres, Julien, Audigier-Valette, Clarisse, Sibilot, Denis Moro, Goldwasser, François, Scherpereel, Arnaud, Pegliasco, Hervé, Ghiringhelli, François, Bouchard, Nicole, Sow, Cissé, Darik, Ines, Zoppi, Silvia, Ly, Pierre, Reni, Anna, Daillère, Romain, Deutsch, Eric, Lee, Karla A., Bolte, Laura A., Björk, Johannes R., Weersma, Rinse K., Barlesi, Fabrice, Padilha, Lucas, Finzel, Ana, Isaksen, Morten L., Escudier, Bernard, Albiges, Laurence, Planchard, David, André, Fabrice, Cremolini, Chiara, Martinez, Stéphanie, Besse, Benjamin, Zhao, Liping, Segata, Nicola, Wojcik, Jérôme, Kroemer, Guido, and Zitvogel, Laurence

Published
2024
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47. Reporting guidelines for human microbiome research: the STORMS checklist

Author

Mirzayi, Chloe, Renson, Audrey, Zohra, Fatima, Elsafoury, Shaimaa, Geistlinger, Ludwig, Kasselman, Lora J, Eckenrode, Kelly, van de Wijgert, Janneke, Loughman, Amy, Marques, Francine Z, MacIntyre, David A, Arumugam, Manimozhiyan, Azhar, Rimsha, Beghini, Francesco, Bergstrom, Kirk, Bhatt, Ami, Bisanz, Jordan E, Braun, Jonathan, Bravo, Hector Corrada, Buck, Gregory A, Bushman, Frederic, Casero, David, Clarke, Gerard, Collado, Maria Carmen, Cotter, Paul D, Cryan, John F, Demmer, Ryan T, Devkota, Suzanne, Elinav, Eran, Escobar, Juan S, Fettweis, Jennifer, Finn, Robert D, Fodor, Anthony A, Forslund, Sofia, Franke, Andre, Furlanello, Cesare, Gilbert, Jack, Grice, Elizabeth, Haibe-Kains, Benjamin, Handley, Scott, Herd, Pamela, Holmes, Susan, Jacobs, Jonathan P, Karstens, Lisa, Knight, Rob, Knights, Dan, Koren, Omry, Kwon, Douglas S, Langille, Morgan, Lindsay, Brianna, McGovern, Dermot, McHardy, Alice C, McWeeney, Shannon, Mueller, Noel T, Nezi, Luigi, Olm, Matthew, Palm, Noah, Pasolli, Edoardo, Raes, Jeroen, Redinbo, Matthew R, Rühlemann, Malte, Balfour Sartor, R, Schloss, Patrick D, Schriml, Lynn, Segal, Eran, Shardell, Michelle, Sharpton, Thomas, Smirnova, Ekaterina, Sokol, Harry, Sonnenburg, Justin L, Srinivasan, Sujatha, Thingholm, Louise B, Turnbaugh, Peter J, Upadhyay, Vaibhav, Walls, Ramona L, Wilmes, Paul, Yamada, Takuji, Zeller, Georg, Zhang, Mingyu, Zhao, Ni, Zhao, Liping, Bao, Wenjun, Culhane, Aedin, Devanarayan, Viswanath, Dopazo, Joaquin, Fan, Xiaohui, Fischer, Matthias, Jones, Wendell, Kusko, Rebecca, Mason, Christopher E, Mercer, Tim R, Sansone, Susanna-Assunta, Scherer, Andreas, Shi, Leming, Thakkar, Shraddha, Tong, Weida, Wolfinger, Russ, Hunter, Christopher, Segata, Nicola, and Huttenhower, Curtis

Subjects
Human Genome, Genetics, Computational Biology, Dysbiosis, Humans, Microbiota, Observational Studies as Topic, Research Design, Translational Science, Biomedical, Genomic Standards Consortium, Massive Analysis and Quality Control Society, Medical and Health Sciences, Immunology
Abstract

The particularly interdisciplinary nature of human microbiome research makes the organization and reporting of results spanning epidemiology, biology, bioinformatics, translational medicine and statistics a challenge. Commonly used reporting guidelines for observational or genetic epidemiology studies lack key features specific to microbiome studies. Therefore, a multidisciplinary group of microbiome epidemiology researchers adapted guidelines for observational and genetic studies to culture-independent human microbiome studies, and also developed new reporting elements for laboratory, bioinformatics and statistical analyses tailored to microbiome studies. The resulting tool, called 'Strengthening The Organization and Reporting of Microbiome Studies' (STORMS), is composed of a 17-item checklist organized into six sections that correspond to the typical sections of a scientific publication, presented as an editable table for inclusion in supplementary materials. The STORMS checklist provides guidance for concise and complete reporting of microbiome studies that will facilitate manuscript preparation, peer review, and reader comprehension of publications and comparative analysis of published results.

Published
2021

48. Mechanism of Collision and Adhesion Between Adhesive Impact Ball and Spacecraft

Author

Xu, Bingyu, Wang, Shuquan, Zhao, Liping, Chaari, Fakher, Series Editor, Gherardini, Francesco, Series Editor, Ivanov, Vitalii, Series Editor, Cavas-Martínez, Francisco, Editorial Board Member, di Mare, Francesca, Editorial Board Member, Haddar, Mohamed, Editorial Board Member, Kwon, Young W., Editorial Board Member, Trojanowska, Justyna, Editorial Board Member, Xu, Jinyang, Editorial Board Member, Yadav, Sanjay, editor, Kumar, Harish, editor, Wan, Meher, editor, Arora, Pawan Kumar, editor, and Yusof, Yusri, editor

Published
2023
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