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3. Mining the Characteristics of COVID-19 Patients in China: Analysis of Social Media Posts

Author

Huang, Chunmei, Xu, Xinjie, Cai, Yuyang, Ge, Qinmin, Zeng, Guangwang, Li, Xiaopan, Zhang, Weide, Ji, Chen, and Yang, Ling

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Public aspects of medicine, RA1-1270
Abstract

BackgroundIn December 2019, pneumonia cases of unknown origin were reported in Wuhan City, Hubei Province, China. Identified as the coronavirus disease (COVID-19), the number of cases grew rapidly by human-to-human transmission in Wuhan. Social media, especially Sina Weibo (a major Chinese microblogging social media site), has become an important platform for the public to obtain information and seek help. ObjectiveThis study aims to analyze the characteristics of suspected or laboratory-confirmed COVID-19 patients who asked for help on Sina Weibo. MethodsWe conducted data mining on Sina Weibo and extracted the data of 485 patients who presented with clinical symptoms and imaging descriptions of suspected or laboratory-confirmed cases of COVID-19. In total, 9878 posts seeking help on Sina Weibo from February 3 to 20, 2020 were analyzed. We used a descriptive research methodology to describe the distribution and other epidemiological characteristics of patients with suspected or laboratory-confirmed SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) infection. The distance between patients’ home and the nearest designated hospital was calculated using the geographic information system ArcGIS. ResultsAll patients included in this study who sought help on Sina Weibo lived in Wuhan, with a median age of 63.0 years (IQR 55.0-71.0). Fever (408/485, 84.12%) was the most common symptom. Ground-glass opacity (237/314, 75.48%) was the most common pattern on chest computed tomography; 39.67% (167/421) of families had suspected and/or laboratory-confirmed family members; 36.58% (154/421) of families had 1 or 2 suspected and/or laboratory-confirmed members; and 70.52% (232/329) of patients needed to rely on their relatives for help. The median time from illness onset to real-time reverse transcription-polymerase chain reaction (RT-PCR) testing was 8 days (IQR 5.0-10.0), and the median time from illness onset to online help was 10 days (IQR 6.0-12.0). Of 481 patients, 32.22% (n=155) lived more than 3 kilometers away from the nearest designated hospital. ConclusionsOur findings show that patients seeking help on Sina Weibo lived in Wuhan and most were elderly. Most patients had fever symptoms, and ground-glass opacities were noted in chest computed tomography. The onset of the disease was characterized by family clustering and most families lived far from the designated hospital. Therefore, we recommend the following: (1) the most stringent centralized medical observation measures should be taken to avoid transmission in family clusters; and (2) social media can help these patients get early attention during Wuhan’s lockdown. These findings can help the government and the health department identify high-risk patients and accelerate emergency responses following public demands for help.

Published
2020
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4. XUAT-Copilot: Multi-Agent Collaborative System for Automated User Acceptance Testing with Large Language Model

Author

Wang, Zhitao, Wang, Wei, Li, Zirao, Wang, Long, Yi, Can, Xu, Xinjie, Cao, Luyang, Su, Hanjing, Chen, Shouzhi, and Zhou, Jun

Subjects
Computer Science - Artificial Intelligence
Abstract

In past years, we have been dedicated to automating user acceptance testing (UAT) process of WeChat Pay, one of the most influential mobile payment applications in China. A system titled XUAT has been developed for this purpose. However, there is still a human-labor-intensive stage, i.e, test scripts generation, in the current system. Therefore, in this paper, we concentrate on methods of boosting the automation level of the current system, particularly the stage of test scripts generation. With recent notable successes, large language models (LLMs) demonstrate significant potential in attaining human-like intelligence and there has been a growing research area that employs LLMs as autonomous agents to obtain human-like decision-making capabilities. Inspired by these works, we propose an LLM-powered multi-agent collaborative system, named XUAT-Copilot, for automated UAT. The proposed system mainly consists of three LLM-based agents responsible for action planning, state checking and parameter selecting, respectively, and two additional modules for state sensing and case rewriting. The agents interact with testing device, make human-like decision and generate action command in a collaborative way. The proposed multi-agent system achieves a close effectiveness to human testers in our experimental studies and gains a significant improvement of Pass@1 accuracy compared with single-agent architecture. More importantly, the proposed system has launched in the formal testing environment of WeChat Pay mobile app, which saves a considerable amount of manpower in the daily development work.

Published
2024

13. Mixing Signals: Data Augmentation Approach for Deep Learning Based Modulation Recognition

Author

Xu, Xinjie, Chen, Zhuangzhi, Xu, Dongwei, Zhou, Huaji, Yu, Shanqing, Zheng, Shilian, Xuan, Qi, and Yang, Xiaoniu

Subjects
Electrical Engineering and Systems Science - Signal Processing, Computer Science - Machine Learning
Abstract

With the rapid development of deep learning, automatic modulation recognition (AMR), as an important task in cognitive radio, has gradually transformed from traditional feature extraction and classification to automatic classification by deep learning technology. However, deep learning models are data-driven methods, which often require a large amount of data as the training support. Data augmentation, as the strategy of expanding dataset, can improve the generalization of the deep learning models and thus improve the accuracy of the models to a certain extent. In this paper, for AMR of radio signals, we propose a data augmentation strategy based on mixing signals and consider four specific methods (Random Mixing, Maximum-Similarity-Mixing, $\theta-$Similarity Mixing and n-times Random Mixing) to achieve data augmentation. Experiments show that our proposed method can improve the classification accuracy of deep learning based AMR models in the full public dataset RML2016.10a. In particular, for the case of a single signal-to-noise ratio signal set, the classification accuracy can be significantly improved, which verifies the effectiveness of the methods., Comment: The experiments in the paper are incomplete

Published
2022

17. Prospective evaluation of genome sequencing to compare conventional cytogenetics in acute myeloid leukemia

Author

Pitel, Beth A., Zepeda-Mendoza, Cinthya, Sachs, Zohar, Tang, Hongwei, Shivaram, Suganti, Sharma, Neeraj, Smadbeck, James B., Smoley, Stephanie A., Pearce, Kathryn E., Luoma, Ivy M., Cook, Joselle, Litzow, Mark R., Hoppman, Nicole L., Viswanatha, David, Xu, Xinjie, Ketterling, Rhett P., Greipp, Patricia T., Peterson, Jess F., and Baughn, Linda B.

Published
2023
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21. A systematic review and meta-analysis of the benefits of a gluten-free diet and/or casein-free diet for children with autism spectrum disorder

Author

Quan, Liuliu, Xu, Xinjie, Cui, Yonghong, Han, Heze, Hendren, Robert L, Zhao, Lidan, and You, Xin

Subjects
Biomedical and Clinical Sciences, Dentistry, Pediatric Research Initiative, Nutrition, Behavioral and Social Science, Brain Disorders, Pediatric, Prevention, Intellectual and Developmental Disabilities (IDD), Mental Health, Autism, Mental health, Autism Spectrum Disorder, Caseins, Child, Diet, Gluten-Free, Humans, Single-Blind Method, autism spectrum disorder, dietary intervention, gluten-free diet and casein-free diet, meta-analysis, Medical and Health Sciences, Psychology and Cognitive Sciences, Nutrition & Dietetics, Biomedical and clinical sciences, Health sciences
Abstract

ContextIt has been suggested that a gluten-free and casein-free (GFCF) diet may alleviate the symptoms of autism spectrum disorder (ASD) and facilitate neurodevelopment of children with ASD. Studies to date have been inconclusive.ObjectiveThis study aimed to evaluate (through quantitative meta-analysis) the efficacy and safety of a GFCF diet for children with ASD. To our knowledge, this is the first time such an analysis has been carried out.Data sourcesEight electronic databases were searched, from the establishment of each database up to March 27, 2020: PubMed, Web of Science, Embase (Ovid), PsycINFO (Ovid), Cochrane Library, CNKI, Wanfang, and VIP databases.Data extractionTwo authors independently performed the data extraction and risk-of-bias assessment.Data analysisA quantitative meta-analysis was performed with standard procedures by using Stata SE 15 software. Within the total of 8 studies, with 297 participants, 5 studies reported significant reductions in stereotypical behaviors [standard mean difference (SMD) = -0.41, 95% confidence interval (CI): -0.68 to -0.15], and 3 studies reported improvements in cognition (SMD = -0.46, 95% CI: -0.91 to -0.01) following GFCF dietary intervention . No statistically significant changes were observed in other symptomatic categories (all P > 0.05).ConclusionThe current meta-analysis showed that a GFCF diet can reduce stereotypical behaviors and improve the cognition of children with ASD. Though most of the included studies were single-blind, the benefits of a GFCF diet that have been indicated are promising. Additional studies on a larger scale are warranted.Systematic review registrationPROSPERO registration no. CRD42020177619.

Published
2022

23. Exploring the Challenges and Paths of Teachers’ Information Competence Development in the Context of Online Education

Author

Xu, Xinjie, Striełkowski, Wadim, Editor-in-Chief, Black, Jessica M., Series Editor, Butterfield, Stephen A., Series Editor, Chang, Chi-Cheng, Series Editor, Cheng, Jiuqing, Series Editor, Dumanig, Francisco Perlas, Series Editor, Al-Mabuk, Radhi, Series Editor, Scheper-Hughes, Nancy, Series Editor, Urban, Mathias, Series Editor, Webb, Stephen, Series Editor, Volodin, Alexander, editor, and Roumbal, Iana, editor

Published
2023
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28. Disparity in the detection of chromosome 15 centromere in patients of African ancestry with a plasma cell neoplasm

Author

Koleilat, Alaa, Tang, Hongwei, Sharma, Neeraj, Yan, Huihuang, Tian, Shulan, Smadbeck, James, Shivaram, Suganti, Meyer, Reid, Pearce, Kathryn, Baird, Michael, Zepeda-Mendoza, Cinthya J., Xu, Xinjie, Greipp, Patricia T., Peterson, Jess F., Ketterling, Rhett P., Bergsagel, P. Leif, Vachon, Celine, Rajkumar, S. Vincent, Kumar, Shaji, Asmann, Yan W., Elhaik, Eran, and Baughn, Linda B.

Published
2023
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31. A community approach to the cancer-variant-interpretation bottleneck

Author

Krysiak, Kilannin, Danos, Arpad M., Kiwala, Susanna, McMichael, Joshua F., Coffman, Adam C., Barnell, Erica K., Sheta, Lana, Saliba, Jason, Grisdale, Cameron J., Kujan, Lynzey, Pema, Shahil, Lever, Jake, Spies, Nicholas C., Chiorean, Andreea, Rieke, Damian T., Clark, Kaitlin A., Jani, Payal, Takahashi, Hideaki, Horak, Peter, Ritter, Deborah I., Zhou, Xin, Ainscough, Benjamin J., Delong, Sean, Lamping, Mario, Marr, Alex R., Li, Brian V., Lin, Wan-Hsin, Terraf, Panieh, Salama, Yasser, Campbell, Katie M., Farncombe, Kirsten M., Ji, Jianling, Zhao, Xiaonan, Xu, Xinjie, Kanagal-Shamanna, Rashmi, Cotto, Kelsy C., Skidmore, Zachary L., Walker, Jason R., Zhang, Jinghui, Milosavljevic, Aleksandar, Patel, Ronak Y., Giles, Rachel H., Kim, Raymond H., Schriml, Lynn M., Mardis, Elaine R., Jones, Steven J. M., Raca, Gordana, Rao, Shruti, Madhavan, Subha, Wagner, Alex H., Griffith, Obi L., and Griffith, Malachi

Published
2022
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33. Standards for the classification of pathogenicity of somatic variants in cancer (oncogenicity): Joint recommendations of Clinical Genome Resource (ClinGen), Cancer Genomics Consortium (CGC), and Variant Interpretation for Cancer Consortium (VICC)

Author

Horak, Peter, Griffith, Malachi, Danos, Arpad M., Pitel, Beth A., Madhavan, Subha, Liu, Xuelu, Chow, Cynthia, Williams, Heather, Carmody, Leigh, Barrow-Laing, Lisa, Rieke, Damian, Kreutzfeldt, Simon, Stenzinger, Albrecht, Tamborero, David, Benary, Manuela, Rajagopal, Padma Sheila, Ida, Cristiane M., Lesmana, Harry, Satgunaseelan, Laveniya, Merker, Jason D., Tolstorukov, Michael Y., Campregher, Paulo Vidal, Warner, Jeremy L., Rao, Shruti, Natesan, Maya, Shen, Haolin, Venstrom, Jeffrey, Roy, Somak, Tao, Kayoko, Kanagal-Shamanna, Rashmi, Xu, Xinjie, Ritter, Deborah I., Pagel, Kym, Krysiak, Kilannin, Dubuc, Adrian, Akkari, Yassmine M., Li, Xuan Shirley, Lee, Jennifer, King, Ian, Raca, Gordana, Wagner, Alex H., Li, Marylin M., Plon, Sharon E., Kulkarni, Shashikant, Griffith, Obi L., Chakravarty, Debyani, and Sonkin, Dmitriy

Published
2022
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35. Integrating single‐cell RNA sequencing data to decipher heterogeneity and function of macrophages in various organs and diseases.

Author

Xu, Xinjie, Wu, Zichen, Zeng, Zhiwei, Cao, Jiaying, and Chen, Liang

Subjects
*KUPFFER cells, *ALVEOLAR macrophages, *BONE marrow cells, *MYELOID cells, *INTERSTITIAL cells
Abstract

The article discusses the integration of single-cell RNA sequencing data to understand the heterogeneity and function of macrophages in different organs and diseases. It highlights the developmental origins of macrophages, their classification into M1 and M2 types, and the challenges of binary classification in explaining diverse pathophysiological processes. The use of single-cell RNA sequencing technology has revolutionized the study of macrophages, providing insights into their heterogeneity, functional diversity, lineage origin, differentiation trajectories, and cellular communication networks. The article also emphasizes the importance of validating findings from omics data and the advantages and challenges of integrating single-cell research across various organs and diseases. [Extracted from the article]

Published
2024
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39. Numerical Study of Data Center Composite Cooling System

Author

Zhou, Xuetao, Yuan, Xiaolei, Liu, Jinxiang, Kosonen, Risto, Xu, Xinjie, Förstner, Ulrich, Series Editor, Rulkens, Wim H., Series Editor, Salomons, Wim, Series Editor, Wang, Zhaojun, editor, Zhu, Yingxin, editor, Wang, Fang, editor, Wang, Peng, editor, Shen, Chao, editor, and Liu, Jing, editor

Published
2020
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48. Clinicopathologic Features and Outcomes of Acute Leukemia Harboring Picalm::Mllt10 Fusion

Author

Wang, Jeffrey, primary, Zhang, Weiwei, additional, Xu, Xinjie, additional, Buglioni, Alessia, additional, Li, Peng, additional, Chen, Xueyan, additional, Liu, Yajuan J., additional, Xu, Min, additional, Herrick, Jennifer L., additional, Horna, Pedro, additional, Zhang, Xiaohui, additional, Song, Jingming, additional, Jevremovic, Dragan, additional, He, Rong, additional, Shi, Min, additional, and Yuan, Jane, additional

Published
2024
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