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9. Correlating Deep Learning-Based Automated Reference Kidney Histomorphometry with Patient Demographics and Creatinine

Author

Lucarelli, Nicholas, Ginley, Brandon, Zee, Jarcy, Mimar, Sayat, Paul, Anindya S, Jain, Sanjay, Han, Seung Seok, Rodrigues, Luis, Ozrazgat-Baslanti, Tezcan, Wong, Michelle L, Nadkarni, Girish, Clapp, William L, Jen, Kuang-Yu, and Sarder, Pinaki

Subjects
Epidemiology, Biomedical and Clinical Sciences, Clinical Sciences, Health Sciences, Kidney Disease, Renal and urogenital, Humans, Creatinine, Deep Learning, Kidney, Glomerular Filtration Rate, Demography, cell and transport physiology, cell biology and structure, molecular biology, Clinical sciences
Abstract

KEY POINTS: The authors leverage the unique benefits of panoptic segmentation to perform the largest ever quantitation of reference kidney morphometry. Kidney features vary with age and sex; and glomeruli size may intricately link to creatinine, defying prior notions. BACKGROUND: Reference histomorphometric data of healthy human kidneys are largely lacking because of laborious quantitation requirements. Correlating histomorphometric features with clinical parameters through machine learning approaches can provide valuable information about natural population variance. To this end, we leveraged deep learning (DL), computational image analysis, and feature analysis to associate the relationship of histomorphometry with patient age, sex, serum creatinine (SCr), and eGFR in a multinational set of reference kidney tissue sections. METHODS: A panoptic segmentation neural network was developed and used to segment viable and sclerotic glomeruli, cortical and medullary interstitia, tubules, and arteries/arterioles in the digitized images of 79 periodic acid–Schiff-stained human nephrectomy sections showing minimal pathologic changes. Simple morphometrics (e.g., area, radius, density) were quantified from the segmented classes. Regression analysis aided in determining the association of histomorphometric parameters with age, sex, SCr, and eGFR. RESULTS: Our DL model achieved high segmentation performance for all test compartments. The size and density of glomeruli, tubules, and arteries/arterioles varied significantly among healthy humans, with potentially large differences between geographically diverse patients. Glomerular size was significantly correlated with SCr and eGFR. Slight, albeit significant, differences in renal vasculature were observed between sexes. Glomerulosclerosis percentage increased, and cortical density of arteries/arterioles decreased, as a function of increasing age. CONCLUSIONS: Using DL, we automated precise measurements of kidney histomorphometric features. In the reference kidney tissue, several histomorphometric features demonstrated significant correlation to patient demographics, SCr, and eGFR. DL tools can increase the efficiency and rigor of histomorphometric analysis.

Published
2023

10. SARS-CoV-2 variants divergently infect and damage cardiomyocytes in vitro and in vivo

Author

Bobo Wing-Yee Mok, Maxwell Kwok, Hung Sing Li, Lowell Ling, Angel Lai, Bin Yan, Cherie Tsz-Yiu Law, Chui Him Yeung, Anna Jinxia Zhang, Rachel Chun-Yee Tam, Anja Kukic, Conor J. Cremin, Yajie Zhang, Teng Long, Zhisen Kang, Ruibang Luo, Kam Tong Leung, Albert M. Li, Grace Lui, Stephen Kwok-Wing Tsui, Jasper Fuk-Woo Chan, Kelvin Kai-Wang To, Paul K. S. Chan, Bryan P. Yan, Honglin Chen, and Ellen Ngar-Yun Poon

Subjects
SARS-CoV-2, COVID-19, Cardiac infection, Omicron, Cardiomyocytes, Heart, Biotechnology, TP248.13-248.65, Biology (General), QH301-705.5, Biochemistry, QD415-436
Abstract

Abstract Background COVID-19 can cause cardiac complications and the latter are associated with poor prognosis and increased mortality. SARS-CoV-2 variants differ in their infectivity and pathogenicity, but how they affect cardiomyocytes (CMs) is unclear. Methods The effects of SARS-CoV-2 variants were investigated using human induced pluripotent stem cell-derived (hiPSC-) CMs in vitro and Golden Syrian hamsters in vivo. Results Different variants exhibited distinct tropism, mechanism of viral entry and pathology in the heart. Omicron BA.2 most efficiently infected and injured CMs in vitro and in vivo, and induced expression changes consistent with increased cardiac dysfunction, compared to other variants tested. Bioinformatics and upstream regulator analyses identified transcription factors and network predicted to control the unique transcriptome of Omicron BA.2 infected CMs. Increased infectivity of Omicron BA.2 is attributed to its ability to infect via endocytosis, independently of TMPRSS2, which is absent in CMs. Conclusions In this study, we reveal previously unknown differences in how different SARS-CoV-2 variants affect CMs. Omicron BA.2, which is generally thought to cause mild disease, can damage CMs in vitro and in vivo. Our study highlights the need for further investigations to define the pathogenesis of cardiac complications arising from different SARS-CoV-2 variants.

Published
2024
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11. SARS-CoV-2 variants divergently infect and damage cardiomyocytes in vitro and in vivo

Author

Mok, Bobo Wing-Yee, Kwok, Maxwell, Li, Hung Sing, Ling, Lowell, Lai, Angel, Yan, Bin, Law, Cherie Tsz-Yiu, Yeung, Chui Him, Zhang, Anna Jinxia, Tam, Rachel Chun-Yee, Kukic, Anja, Cremin, Conor J., Zhang, Yajie, Long, Teng, Kang, Zhisen, Luo, Ruibang, Leung, Kam Tong, Li, Albert M., Lui, Grace, Tsui, Stephen Kwok-Wing, Chan, Jasper Fuk-Woo, To, Kelvin Kai-Wang, Chan, Paul K. S., Yan, Bryan P., Chen, Honglin, and Poon, Ellen Ngar-Yun

Published
2024
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14. Cosmic kidney disease: an integrated pan-omic, physiological and morphological study into spaceflight-induced renal dysfunction

Author

Siew, Keith, Nestler, Kevin A., Nelson, Charlotte, D’Ambrosio, Viola, Zhong, Chutong, Li, Zhongwang, Grillo, Alessandra, Wan, Elizabeth R., Patel, Vaksha, Overbey, Eliah, Kim, JangKeun, Yun, Sanghee, Vaughan, Michael B., Cheshire, Chris, Cubitt, Laura, Broni-Tabi, Jessica, Al-Jaber, Maneera Yousef, Boyko, Valery, Meydan, Cem, Barker, Peter, Arif, Shehbeel, Afsari, Fatemeh, Allen, Noah, Al-Maadheed, Mohammed, Altinok, Selin, Bah, Nourdine, Border, Samuel, Brown, Amanda L., Burling, Keith, Cheng-Campbell, Margareth, Colón, Lorianna M., Degoricija, Lovorka, Figg, Nichola, Finch, Rebecca, Foox, Jonathan, Faridi, Pouya, French, Alison, Gebre, Samrawit, Gordon, Peter, Houerbi, Nadia, Valipour Kahrood, Hossein, Kiffer, Frederico C., Klosinska, Aleksandra S., Kubik, Angela, Lee, Han-Chung, Li, Yinghui, Lucarelli, Nicholas, Marullo, Anthony L., Matei, Irina, McCann, Colleen M., Mimar, Sayat, Naglah, Ahmed, Nicod, Jérôme, O’Shaughnessy, Kevin M., Oliveira, Lorraine Christine De, Oswalt, Leah, Patras, Laura Ioana, Lai Polo, San-huei, Rodríguez-Lopez, María, Roufosse, Candice, Sadeghi-Alavijeh, Omid, Sanchez-Hodge, Rebekah, Paul, Anindya S., Schittenhelm, Ralf Bernd, Schweickart, Annalise, Scott, Ryan T., Choy Lim Kam Sian, Terry Chin, da Silveira, Willian A., Slawinski, Hubert, Snell, Daniel, Sosa, Julio, Saravia-Butler, Amanda M., Tabetah, Marshall, Tanuwidjaya, Erwin, Walker-Samuel, Simon, Yang, Xiaoping, Yasmin, Zhang, Haijian, Godovac-Zimmermann, Jasminka, Sarder, Pinaki, Sanders, Lauren M., Costes, Sylvain V., Campbell, Robert A. A., Karouia, Fathi, Mohamed-Alis, Vidya, Rodriques, Samuel, Lynham, Steven, Steele, Joel Ricky, Baranzini, Sergio, Fazelinia, Hossein, Dai, Zhongquan, Uruno, Akira, Shiba, Dai, Yamamoto, Masayuki, A.C.Almeida, Eduardo, Blaber, Elizabeth, Schisler, Jonathan C., Eisch, Amelia J., Muratani, Masafumi, Zwart, Sara R., Smith, Scott M., Galazka, Jonathan M., Mason, Christopher E., Beheshti, Afshin, and Walsh, Stephen B.

Published
2024
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15. Integrative analysis reveals associations between oral microbiota dysbiosis and host genetic and epigenetic aberrations in oral cavity squamous cell carcinoma

Author

Cai, Liuyang, Zhu, Hengyan, Mou, Qianqian, Wong, Po Yee, Lan, Linlin, Ng, Cherrie W. K., Lei, Pu, Cheung, Man Kit, Wang, Daijuanru, Wong, Eddy W. Y., Lau, Eric H. L., Yeung, Zenon W. C., Lai, Ronald, Meehan, Katie, Fung, Sherwood, Chan, Kwan Chee A., Lui, Vivian W. Y., Cheng, Alfred S. L., Yu, Jun, Chan, Paul K. S., Chan, Jason Y. K., and Chen, Zigui

Published
2024
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16. Protein-truncating variants in BSN are associated with severe adult-onset obesity, type 2 diabetes and fatty liver disease

Author

Zhao, Yajie, Chukanova, Maria, Kentistou, Katherine A., Fairhurst-Hunter, Zammy, Siegert, Anna Maria, Jia, Raina Y., Dowsett, Georgina K. C., Gardner, Eugene J., Lawler, Katherine, Day, Felix R., Kaisinger, Lena R., Tung, Yi-Chun Loraine, Lam, Brian Yee Hong, Chen, Hsiao-Jou Cortina, Wang, Quanli, Berumen-Campos, Jaime, Kuri-Morales, Pablo, Tapia-Conyer, Roberto, Alegre-Diaz, Jesus, Barroso, Inês, Emberson, Jonathan, Torres, Jason M., Collins, Rory, Saleheen, Danish, Smith, Katherine R., Paul, Dirk S., Merkle, Florian, Farooqi, I. Sadaf, Wareham, Nick J., Petrovski, Slavé, O’Rahilly, Stephen, Ong, Ken K., Yeo, Giles S. H., and Perry, John R. B.

Published
2024
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20. Integrative analysis reveals associations between oral microbiota dysbiosis and host genetic and epigenetic aberrations in oral cavity squamous cell carcinoma

Author

Liuyang Cai, Hengyan Zhu, Qianqian Mou, Po Yee Wong, Linlin Lan, Cherrie W. K. Ng, Pu Lei, Man Kit Cheung, Daijuanru Wang, Eddy W. Y. Wong, Eric H. L. Lau, Zenon W. C. Yeung, Ronald Lai, Katie Meehan, Sherwood Fung, Kwan Chee A. Chan, Vivian W. Y. Lui, Alfred S. L. Cheng, Jun Yu, Paul K. S. Chan, Jason Y. K. Chan, and Zigui Chen

Subjects
Microbial ecology, QR100-130
Abstract

Abstract Dysbiosis of the human oral microbiota has been reported to be associated with oral cavity squamous cell carcinoma (OSCC) while the host-microbiota interactions with respect to the potential impact of pathogenic bacteria on host genomic and epigenomic abnormalities remain poorly studied. In this study, the mucosal bacterial community, host genome-wide transcriptome and DNA CpG methylation were simultaneously profiled in tumors and their adjacent normal tissues of OSCC patients. Significant enrichment in the relative abundance of seven bacteria species (Fusobacterium nucleatum, Treponema medium, Peptostreptococcus stomatis, Gemella morbillorum, Catonella morbi, Peptoanaerobacter yurli and Peptococcus simiae) were observed in OSCC tumor microenvironment. These tumor-enriched bacteria formed 254 positive correlations with 206 up-regulated host genes, mainly involving signaling pathways related to cell adhesion, migration and proliferation. Integrative analysis of bacteria-transcriptome and bacteria-methylation correlations identified at least 20 dysregulated host genes with inverted CpG methylation in their promoter regions associated with enrichment of bacterial pathogens, implying a potential of pathogenic bacteria to regulate gene expression, in part, through epigenetic alterations. An in vitro model further confirmed that Fusobacterium nucleatum might contribute to cellular invasion via crosstalk with E-cadherin/β-catenin signaling, TNFα/NF-κB pathway and extracellular matrix remodeling by up-regulating SNAI2 gene, a key transcription factor of epithelial-mesenchymal transition (EMT). Our work using multi-omics approaches explored complex host-microbiota interactions and provided important insights into genetic and functional basis in OSCC tumorigenesis, which may serve as a precursor for hypothesis-driven study to better understand the causational relationship of pathogenic bacteria in this deadly cancer.

Published
2024
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21. Statistics on Typhoon Intensity and Rice Damage in Vietnam and the Philippines

Author

Kai Wan Yuen, Adam D. Switzer, Paul P. S. Teng, and Janice Ser Huay Lee

Subjects
agricultural damage, rice, food security, natural hazard, typhoon, Environmental sciences, GE1-350
Abstract

Typhoons are destructive multi-hazard events. To assess the relationship between typhoon intensity and agricultural loss, there is a need for accurate and standardized information on loss and damage, which is currently lacking. To address this, a database for Vietnam and the Philippines was created to provide aggregated information on the magnitude of rice damage and to highlight the rice-growing areas which were prone to being damaged by typhoons. Our study period was from 1970 to 2018, and we focused on Vietnam and the Philippines as these two countries experience frequent and intense typhoons. As different crops respond differently to wind and rain, we limit our research to a single crop. In this study, we focused on rice as it is a major staple food in Southeast Asia, and rice fields were often damaged by typhoons in the two countries. Of the 829 typhoon events recorded, only 15% of the events resulted in rice damage. The average area of rice damaged per typhoon event ranged from 42,407 ha in Vietnam to 83,571 ha in the Philippines. Meanwhile, the average production loss per typhoon event ranged from 190,227 metric tonnes in the Philippines to 539,150 metric tonnes in Vietnam. The monetary value of rice crops lost was only reported in the Philippines, and this amounted to an average of US$ 42 million per typhoon event. There was a weak relationship between landfall wind speed and the three indicators of rice damage, which suggests that rice damage was not primarily due to strong winds. Our results showed that the rice fields in the coastal provinces of Vietnam and the northern parts of the Philippines were more vulnerable to being damaged by typhoons.

Published
2024
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22. Rare variant associations with plasma protein levels in the UK Biobank

Author

Dhindsa, Ryan S., Burren, Oliver S., Sun, Benjamin B., Prins, Bram P., Matelska, Dorota, Wheeler, Eleanor, Mitchell, Jonathan, Oerton, Erin, Hristova, Ventzislava A., Smith, Katherine R., Carss, Keren, Wasilewski, Sebastian, Harper, Andrew R., Paul, Dirk S., Fabre, Margarete A., Runz, Heiko, Viollet, Coralie, Challis, Benjamin, Platt, Adam, Vitsios, Dimitrios, Ashley, Euan A., Whelan, Christopher D., Pangalos, Menelas N., Wang, Quanli, and Petrovski, Slavé

Published
2023
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23. Genetics of circulating inflammatory proteins identifies drivers of immune-mediated disease risk and therapeutic targets

Author

Zhao, Jing Hua, Stacey, David, Eriksson, Niclas, Macdonald-Dunlop, Erin, Hedman, Åsa K., Kalnapenkis, Anette, Enroth, Stefan, Cozzetto, Domenico, Digby-Bell, Jonathan, Marten, Jonathan, Folkersen, Lasse, Herder, Christian, Jonsson, Lina, Bergen, Sarah E., Gieger, Christian, Needham, Elise J., Surendran, Praveen, Paul, Dirk S., Polasek, Ozren, Thorand, Barbara, Grallert, Harald, Roden, Michael, Võsa, Urmo, Esko, Tonu, Hayward, Caroline, Johansson, Åsa, Gyllensten, Ulf, Powell, Nick, Hansson, Oskar, Mattsson-Carlgren, Niklas, Joshi, Peter K., Danesh, John, Padyukov, Leonid, Klareskog, Lars, Landén, Mikael, Wilson, James F., Siegbahn, Agneta, Wallentin, Lars, Mälarstig, Anders, Butterworth, Adam S., and Peters, James E.

Published
2023
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25. Borrowing from Similar Code: A Deep Learning NLP-Based Approach for Log Statement Automation

Author

Gholamian, Sina and Ward, Paul A. S.

Subjects
Computer Science - Software Engineering, Computer Science - Machine Learning
Abstract

Software developers embed logging statements inside the source code as an imperative duty in modern software development as log files are necessary for tracking down runtime system issues and troubleshooting system management tasks. However, the current logging process is mostly manual, and thus, proper placement and content of logging statements remain as challenges. To overcome these challenges, methods that aim to automate log placement and predict its content, i.e., 'where and what to log', are of high interest. Thus, we focus on predicting the location (i.e., where) and description (i.e., what) for log statements by utilizing source code clones and natural language processing (NLP), as these approaches provide additional context and advantage for log prediction. Specifically, we guide our research with three research questions (RQs): (RQ1) how similar code snippets, i.e., code clones, can be leveraged for log statements prediction? (RQ2) how the approach can be extended to automate log statements' descriptions? and (RQ3) how effective the proposed methods are for log location and description prediction? To pursue our RQs, we perform an experimental study on seven open-source Java projects. We introduce an updated and improved log-aware code-clone detection method to predict the location of logging statements (RQ1). Then, we incorporate natural language processing (NLP) and deep learning methods to automate the log statements' description prediction (RQ2). Our analysis shows that our hybrid NLP and code-clone detection approach (NLP CC'd) outperforms conventional clone detectors in finding log statement locations on average by 15.60% and achieves 40.86% higher performance on BLEU and ROUGE scores for predicting the description of logging statements when compared to prior research (RQ3)., Comment: 27 pages

Published
2021

26. A Comprehensive Survey of Logging in Software: From Logging Statements Automation to Log Mining and Analysis

Author

Gholamian, Sina and Ward, Paul A. S.

Subjects
Computer Science - Software Engineering, Computer Science - Machine Learning
Abstract

Logs are widely used to record runtime information of software systems, such as the timestamp and the importance of an event, the unique ID of the source of the log, and a part of the state of a task's execution. The rich information of logs enables system developers (and operators) to monitor the runtime behaviors of their systems and further track down system problems and perform analysis on log data in production settings. However, the prior research on utilizing logs is scattered and that limits the ability of new researchers in this field to quickly get to the speed and hampers currently active researchers to advance this field further. Therefore, this paper surveys and provides a systematic literature review and mapping of the contemporary logging practices and log statements' mining and monitoring techniques and their applications such as in system failure detection and diagnosis. We study a large number of conference and journal papers that appeared on top-level peer-reviewed venues. Additionally, we draw high-level trends of ongoing research and categorize publications into subdivisions. In the end, and based on our holistic observations during this survey, we provide a set of challenges and opportunities that will lead the researchers in academia and industry in moving the field forward., Comment: Under second revision at Transactions on Software Engineering -- Second Revision

Published
2021

27. Transaction Placement in Sharded Blockchains

Author

Ren, Liuyang and Ward, Paul A. S.

Subjects
Computer Science - Distributed, Parallel, and Cluster Computing
Abstract

While many researchers adopt a sharding approach to design scaling blockchains, few works have studied the transaction placement problem incurred by sharding protocols. The widely-used hashing placement algorithm renders an overwhelming portion of transactions as cross-shard. In this paper, we analyze the high cost of cross-shard transactions and reveal that most Bitcoin transactions have simple dependencies and can become single-shard under a placement algorithm taking transaction dependencies into account. In addition, we perform a case study of OptChain, which is the state-of-the-art transaction placement algorithm for sharded blockchains, and find a shortcoming of it. A simple fix is proposed, and our evaluation results demonstrate that the proposed fix effectively helps OptChain overcome the shortcoming and significantly improve the system performance under a special workload. The authors of OptChain made some revisions to the algorithm description after noticing our work. Their updated algorithm does not exhibit the shortcoming under the workloads employed by this paper.

Published
2021

28. What Distributed Systems Say: A Study of Seven Spark Application Logs

Author

Gholamian, Sina and Ward, Paul A. S.

Subjects
Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Software Engineering
Abstract

Execution logs are a crucial medium as they record runtime information of software systems. Although extensive logs are helpful to provide valuable details to identify the root cause in postmortem analysis in case of a failure, this may also incur performance overhead and storage cost. Therefore, in this research, we present the result of our experimental study on seven Spark benchmarks to illustrate the impact of different logging verbosity levels on the execution time and storage cost of distributed software systems. We also evaluate the log effectiveness and the information gain values, and study the changes in performance and the generated logs for each benchmark with various types of distributed system failures. Our research draws insightful findings for developers and practitioners on how to set up and utilize their distributed systems to benefit from the execution logs., Comment: 2021 The 40th International Symposium on Reliable Distributed Systems (SRDS)

Published
2021

29. Strong protective effect of the APOL1 p.N264K variant against G2-associated focal segmental glomerulosclerosis and kidney disease

Author

Gupta, Yask, Friedman, David J., McNulty, Michelle T., Khan, Atlas, Lane, Brandon, Wang, Chen, Ke, Juntao, Jin, Gina, Wooden, Benjamin, Knob, Andrea L., Lim, Tze Y., Appel, Gerald B., Huggins, Kinsie, Liu, Lili, Mitrotti, Adele, Stangl, Megan C., Bomback, Andrew, Westland, Rik, Bodria, Monica, Marasa, Maddalena, Shang, Ning, Cohen, David J., Crew, Russell J., Morello, William, Canetta, Pietro, Radhakrishnan, Jai, Martino, Jeremiah, Liu, Qingxue, Chung, Wendy K., Espinoza, Angelica, Luo, Yuan, Wei, Wei-Qi, Feng, Qiping, Weng, Chunhua, Fang, Yilu, Kullo, Iftikhar J., Naderian, Mohammadreza, Limdi, Nita, Irvin, Marguerite R., Tiwari, Hemant, Mohan, Sumit, Rao, Maya, Dube, Geoffrey K., Chaudhary, Ninad S., Gutiérrez, Orlando M., Judd, Suzanne E., Cushman, Mary, Lange, Leslie A., Lange, Ethan M., Bivona, Daniel L., Verbitsky, Miguel, Winkler, Cheryl A., Kopp, Jeffrey B., Santoriello, Dominick, Batal, Ibrahim, Pinheiro, Sérgio Veloso Brant, Oliveira, Eduardo Araújo, Simoes e Silva, Ana Cristina, Pisani, Isabella, Fiaccadori, Enrico, Lin, Fangming, Gesualdo, Loreto, Amoroso, Antonio, Ghiggeri, Gian Marco, D’Agati, Vivette D., Magistroni, Riccardo, Kenny, Eimear E., Loos, Ruth J. F., Montini, Giovanni, Hildebrandt, Friedhelm, Paul, Dirk S., Petrovski, Slavé, Goldstein, David B., Kretzler, Matthias, Gbadegesin, Rasheed, Gharavi, Ali G., Kiryluk, Krzysztof, Sampson, Matthew G., Pollak, Martin R., and Sanna-Cherchi, Simone

Published
2023
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34. Microbial dysbiosis and the host airway epithelial response: insights into HIV-associated COPD using multi’omics profiling

Author

Jude, Marcia Smiti, Yang, Chen Xi, Filho, Fernando Studart Leitao, Hernandez Cordero, Ana I., Yang, Julia, Shaipanich, Tawimas, Li, Xuan, Lin, David, MacIsaac, Julie, Kobor, Michael S., Sinha, Sunita, Nislow, Corey, Singh, Amrit, Lam, Wan, Lam, Stephen, Guillemi, Silvia, Harris, Marianne, Montaner, Julio, Ng, Raymond T., Carlsten, Christopher, Paul Man, S. F., Sin, Don D., and Leung, Janice M.

Published
2023
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35. Elucidating mechanisms of genetic cross-disease associations at the PROCR vascular disease locus

Author

Stacey, David, Chen, Lingyan, Stanczyk, Paulina J, Howson, Joanna MM, Mason, Amy M, Burgess, Stephen, MacDonald, Stephen, Langdown, Jonathan, McKinney, Harriett, Downes, Kate, Farahi, Neda, Peters, James E, Basu, Saonli, Pankow, James S, Tang, Weihong, Pankratz, Nathan, Sabater-Lleal, Maria, de Vries, Paul S, Smith, Nicholas L, Gelinas, Amy D, Schneider, Daniel J, Janjic, Nebojsa, Samani, Nilesh J, Ye, Shu, Summers, Charlotte, Chilvers, Edwin R, Danesh, John, and Paul, Dirk S

Subjects
Biological Sciences, Genetics, Prevention, Cardiovascular, Atherosclerosis, Hematology, Heart Disease - Coronary Heart Disease, Heart Disease, Aetiology, 2.1 Biological and endogenous factors, Inflammatory and immune system, Antigens, CD, Crosses, Genetic, Endothelial Cells, Endothelial Protein C Receptor, Humans, Protein C, Receptors, Cell Surface, Thrombosis, Venous Thromboembolism, CHARGE Hemostasis Working Group
Abstract

Many individual genetic risk loci have been associated with multiple common human diseases. However, the molecular basis of this pleiotropy often remains unclear. We present an integrative approach to reveal the molecular mechanism underlying the PROCR locus, associated with lower coronary artery disease (CAD) risk but higher venous thromboembolism (VTE) risk. We identify PROCR-p.Ser219Gly as the likely causal variant at the locus and protein C as a causal factor. Using genetic analyses, human recall-by-genotype and in vitro experimentation, we demonstrate that PROCR-219Gly increases plasma levels of (activated) protein C through endothelial protein C receptor (EPCR) ectodomain shedding in endothelial cells, attenuating leukocyte-endothelial cell adhesion and vascular inflammation. We also associate PROCR-219Gly with an increased pro-thrombotic state via coagulation factor VII, a ligand of EPCR. Our study, which links PROCR-219Gly to CAD through anti-inflammatory mechanisms and to VTE through pro-thrombotic mechanisms, provides a framework to reveal the mechanisms underlying similar cross-phenotype associations.

Published
2022

40. Predicting student performance using data from an auto-grading system

Author

Chen, Huanyi and Ward, Paul A. S.

Subjects
Computer Science - Machine Learning, Computer Science - Computers and Society
Abstract

As online auto-grading systems appear, information obtained from those systems can potentially enable researchers to create predictive models to predict student behaviour and performances. In the University of Waterloo, the ECE 150 (Fundamentals of Programming) Instructional Team wants to get an insight into how to allocate the limited teaching resources better to achieve improved educational outcomes. Currently, the Instructional Team allocates tutoring time in a reactive basis. They help students "as-requested". This approach serves those students with the wherewithal to request help; however, many of the students who are struggling do not reach out for assistance. Therefore, we, as the Research Team, want to explore if we can determine students which need help by looking into the data from our auto-grading system, Marmoset. In this paper, we conducted experiments building decision-tree and linear-regression models with various features extracted from the Marmoset auto-grading system, including passing rate, testcase outcomes, number of submissions and submission time intervals (the time interval between the student's first reasonable submission and the deadline). For each feature, we interpreted the result at the confusion matrix level. Specifically for poor-performance students, we show that the linear-regression model using submission time intervals performs the best among all models in terms of Precision and F-Measure. We also show that for students who are misclassified into poor-performance students, they have the lowest actual grades in the linear-regression model among all models. In addition, we show that for the midterm, the submission time interval of the last assignment before the midterm predicts the midterm performance the most. However, for the final exam, the midterm performance contributes the most on the final exam performance.

Published
2021
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42. SkeletonVis: Interactive Visualization for Understanding Adversarial Attacks on Human Action Recognition Models

Author

Park, Haekyu, Wang, Zijie J., Das, Nilaksh, Paul, Anindya S., Perumalla, Pruthvi, Zhou, Zhiyan, and Chau, Duen Horng

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract

Skeleton-based human action recognition technologies are increasingly used in video based applications, such as home robotics, healthcare on aging population, and surveillance. However, such models are vulnerable to adversarial attacks, raising serious concerns for their use in safety-critical applications. To develop an effective defense against attacks, it is essential to understand how such attacks mislead the pose detection models into making incorrect predictions. We present SkeletonVis, the first interactive system that visualizes how the attacks work on the models to enhance human understanding of attacks., Comment: Accepted at AAAI'21 Demo

Published
2021

43. Early Metabolomic and Immunologic Biomarkers as Prognostic Indicators for COVID-19

Author

Zigui Chen, Erik Fung, Chun-Kwok Wong, Lowell Ling, Grace Lui, Christopher K. C. Lai, Rita W. Y. Ng, Ryan K. H. Sze, Wendy C. S. Ho, David S. C. Hui, and Paul K. S. Chan

Subjects
metabolome, chemokine, cytokine, COVID, coronavirus, SARS-CoV-2, Microbiology, QR1-502
Abstract

This prospective study in Hong Kong aimed at identifying prognostic metabolomic and immunologic biomarkers for Coronavirus Disease 2019 (COVID-19). We examined 327 patients, mean age 55 (19–89) years, in whom 33.6% were infected with Omicron and 66.4% were infected with earlier variants. The effect size of disease severity on metabolome outweighed others including age, gender, peak C-reactive protein (CRP), vitamin D and peak viral levels. Sixty-five metabolites demonstrated strong associations and the majority (54, 83.1%) were downregulated in severe disease (z score: −3.30 to −8.61). Ten cytokines/chemokines demonstrated strong associations (p < 0.001), and all were upregulated in severe disease. Multiple pairs of metabolomic/immunologic biomarkers showed significant correlations. Fourteen metabolites had the area under the receiver operating characteristic curve (AUC) > 0.8, suggesting a high predictive value. Three metabolites carried high sensitivity for severe disease: triglycerides in medium high-density lipoprotein (MHDL) (sensitivity: 0.94), free cholesterol-to-total lipids ratio in very small very-low-density lipoprotein (VLDL) (0.93), cholesteryl esters-to-total lipids ratio in chylomicrons and extremely large VLDL (0.92);whereas metabolites with the highest specificity were creatinine (specificity: 0.94), phospholipids in large VLDL (0.94) and triglycerides-to-total lipids ratio in large VLDL (0.93). Five cytokines/chemokines, namely, interleukin (IL)-6, IL-18, IL-10, macrophage inflammatory protein (MIP)-1b and tumour necrosis factor (TNF)-a, had AUC > 0.8. In conclusion, we demonstrated a tight interaction and prognostic potential of metabolomic and immunologic biomarkers enabling an outcome-based patient stratification.

Published
2024
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44. Assessment of Noise and Its Impact on Miner’s Health

Author

Sakinala, Vikram, Paul, P. S., Rajwade, Nitesh Kumar, Chan, Albert P. C., Series Editor, Hong, Wei-Chiang, Series Editor, Mellal, Mohamed Arezki, Series Editor, Narayanan, Ramadas, Series Editor, Nguyen, Quang Ngoc, Series Editor, Ong, Hwai Chyuan, Series Editor, Sachsenmeier, Peter, Series Editor, Sun, Zaicheng, Series Editor, Ullah, Sharif, Series Editor, Wu, Junwei, Series Editor, Zhang, Wei, Series Editor, Raj, Bhiksha, editor, Gill, Steve, editor, Calderon, Carlos A.Gonzalez, editor, Cihan, Onur, editor, Tukkaraja, Purushotham, editor, Venkatesh, Sriram, editor, M. S., Venkataramayya, editor, Mudigonda, Malini, editor, Gaddam, Mallesham, editor, and Dasari, Rama Krishna, editor

Published
2023
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45. Prediction of Heat Stress to Mitigate the Occupational Health Hazard in Mines

Author

Sakinala, Vikram, Paul, P. S., Anand, Sourabh, Chan, Albert P. C., Series Editor, Hong, Wei-Chiang, Series Editor, Mellal, Mohamed Arezki, Series Editor, Narayanan, Ramadas, Series Editor, Nguyen, Quang Ngoc, Series Editor, Ong, Hwai Chyuan, Series Editor, Sachsenmeier, Peter, Series Editor, Sun, Zaicheng, Series Editor, Ullah, Sharif, Series Editor, Wu, Junwei, Series Editor, Zhang, Wei, Series Editor, Raj, Bhiksha, editor, Gill, Steve, editor, Calderon, Carlos A.Gonzalez, editor, Cihan, Onur, editor, Tukkaraja, Purushotham, editor, Venkatesh, Sriram, editor, M. S., Venkataramayya, editor, Mudigonda, Malini, editor, Gaddam, Mallesham, editor, and Dasari, Rama Krishna, editor

Published
2023
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46. An atlas of genetic scores to predict multi-omic traits

Author

Xu, Yu, Ritchie, Scott C., Liang, Yujian, Timmers, Paul R. H. J., Pietzner, Maik, Lannelongue, Loïc, Lambert, Samuel A., Tahir, Usman A., May-Wilson, Sebastian, Foguet, Carles, Johansson, Åsa, Surendran, Praveen, Nath, Artika P., Persyn, Elodie, Peters, James E., Oliver-Williams, Clare, Deng, Shuliang, Prins, Bram, Luan, Jian’an, Bomba, Lorenzo, Soranzo, Nicole, Di Angelantonio, Emanuele, Pirastu, Nicola, Tai, E. Shyong, van Dam, Rob M., Parkinson, Helen, Davenport, Emma E., Paul, Dirk S., Yau, Christopher, Gerszten, Robert E., Mälarstig, Anders, Danesh, John, Sim, Xueling, Langenberg, Claudia, Wilson, James F., Butterworth, Adam S., and Inouye, Michael

Published
2023
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50. Dimensional structure of one-year post-COVID-19 neuropsychiatric and somatic sequelae and association with role impairment

Author

Owen N. W. Leung, Nicholas K. H. Chiu, Samuel Y. S. Wong, Pim Cuijpers, Jordi Alonso, Paul K. S. Chan, Grace Lui, Eliza Wong, Ronny Bruffaerts, Benjamin H. K. Yip, Philippe Mortier, Gemma Vilagut, Dora Kwok, Linda C. W. Lam, Ronald C. Kessler, and Arthur D. P. Mak

Subjects
Medicine, Science
Abstract

Abstract This study examined the latent structure of the broad range of complex neuropsychiatric morbidities occurring 1 year after COVID-19 infection. As part of the CU-COVID19 study, 248 (response rate=39.3%) of 631 adults hospitalized for COVID-19 infection in Hong Kong completed an online survey between March-2021 and January-2022. Disorder prevalence was compared against a random non-infected household sample (n=1834). 248 surveys were received on average 321 days post-infection (Mean age: 48.9, 54% female, moderate/severe/critical infection: 58.2%). 32.4% were screened to have at least one mental disorder, 78.7% of whom had concurrent fatigue/subjective cognitive impairment (SCI). Only PTSD (19.1%) was significantly more common than control (14%, p=0.047). Latent profile analysis classified individuals into P1 (12·4%)-no current neuropsychiatric morbidities, P2 (23.1%)-SCI/fatigue, P3 (45.2%)-anxiety/PTSD, P4 (19.3%)-depression. SCI and fatigue pervaded in all profiles (P2-4) with neuropsychiatric morbidities one-year post-infection. PTSD, anxiety and depressive symptoms were most important in differentiating P2-4. Past mental health and P4 independently predicted functional impairment. Neuropsychiatric morbidity was associated with past mental health, reduced resilience, financial problems, but not COVID-19 severity. Their confluence with depressive and anxiety symptoms predicted impairment and are associated with psychological and environmental factors.

Published
2023
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