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2023 International Conference on Electrical and Information Technology, IEIT 2023

作者： Sumari, Arwin Datumaya Wahyudi Saputra, Muhamad Andika Ayuningtyas, Astika Sudarmadi, Catur Adisutjipto Institute of Aerospace Technology Aerospace Intelligent System and Data Engineering Research Group Yogyakarta Indonesia Politeknik Negeri Malang Cognitive Artificial Intelligence Research Group Malang Indonesia Adisutjipto Institute of Aerospace Technology Informatics Study Program Faculty of Industrial Technology Yogyakarta Indonesia Adisutjipto Institute of Aerospace Technology Aerospace Health Informatics Research Group Faculty of Industrial Technology Yogyakarta Indonesia

ISBN: (纸本)9798350327298

This research aims to develop an application for selecting the best new tenant at PT Angkasa Pura II Sultan Thaha Jambi Airport Branch by utilizing Weighted Product (WP) method as the decision support system. Our approach is descriptive research with data analysis technique using four prospective tenants coded A-1 to A-4. The data was collected through observation, interviews, documentation, and literature study. The steps for selecting the best new tenant using the WP method include determining the criteria, sorting the suitability of alternatives and criteria, determining the weight of each criterion and its normalization, calculating the score, S value as an alternative preference, and calculating the ranking, V value as the final alternative preference. Based on the final calculation results, the new tenant coded A-4 is the best alternative based on the data collected from the Financial Management (FHFM) Unit and the Commercial Unit, with a value of V-4 is 0.294. © 2023 IEEE.

关键词： Decision support systems

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Whole genome sequencing analysis of body mass index identifies novel African ancestry-specific risk allele

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Nature communications 2025年第1期16卷 3470-3470页

作者： Zhang, Xinruo Brody, Jennifer A. Graff, Mariaelisa Highland, Heather M. Chami, Nathalie Xu, Hanfei Wang, Zhe Ferrier, Kendra R. Chittoor, Geetha Josyula, Navya Shilpa Meyer, Mariah Gupta, Shreyash Li, Xihao Li, Zilin Allison, Matthew A. Becker, Diane M. Bielak, Lawrence F. Bis, Joshua C. Boorgula, Meher Preethi Bowden, Donald W. Broome, Jai G. Buth, Erin J. Carlson, Christopher S. Chang, Kyong-Mi Chavan, Sameer Chiu, Yen-Feng Chuang, Lee-Ming Conomos, Matthew P. DeMeo, Dawn L. Du, Mengmeng Duggirala, Ravindranath Eng, Celeste Fohner, Alison E. Freedman, Barry I. Garrett, Melanie E. Guo, Xiuqing Haiman, Chris Heavner, Benjamin D. Hidalgo, Bertha Hixson, James E. Ho, Yuk-Lam Hobbs, Brian D. Hu, Donglei Hui, Qin Hwu, Chii-Min Jackson, Rebecca D. Jain, Deepti Kalyani, Rita R. Kardia, Sharon L R Kelly, Tanika N. Lange, Ethan M. LeNoir, Michael Li, Changwei Le Marchand, Loic McDonald, Merry-Lynn N McHugh, Caitlin P. Morrison, Alanna C. Naseri, Take Palmer, Nicholette D. Pankow, James S. Perry, James A. Peters, Ulrike Preuss, Michael H. Rao, D.C. Regan, Elizabeth A. Reupena, Sefuiva M. Roden, Dan M. Rodriguez-Santana, Jose Sitlani, Colleen M. Smith, Jennifer A. Tiwari, Hemant K. Vasan, Ramachandran S. Wang, Zeyuan Weeks, Daniel E. Wessel, Jennifer Wiggins, Kerri L. Wilkens, Lynne R. Wilson, Peter W F Yanek, Lisa R. Yoneda, Zachary T. Zhao, Wei Zöllner, Sebastian Arnett, Donna K. Ashley-Koch, Allison E. Barnes, Kathleen C. Blangero, John Boerwinkle, Eric Burchard, Esteban G. Carson, April P. Chasman, Daniel I. Ida Chen, Yii-Der Curran, Joanne E. Fornage, Myriam Gordeuk, Victor R. He, Jiang Heckbert, Susan R. Hou, Lifang Irvin, Marguerite R. Kooperberg, Charles Minster, Ryan L. Mitchell, Braxton D. Nouraie, Mehdi Psaty, Bruce M. Raffield, Laura M. Reiner, Alexander P. Rich, Stephen S. Rotter, Jerome I. Benjamin Shoemaker, M. Smith, Nicholas L. Taylor, Kent D. Telen, Marilyn J. Weiss, Scott T. Zhang, Yingze Heard-Costa, Nancy Sun, Yan V. Lin, Xihong Cupples, L Adrienne Lange, Leslie A. Liu, Ching-Ti Loos, Ruth J F North, K Department of Epidemiology Gillings School of Global Public Health University of North Carolina at Chapel Hill Chapel Hill NC United States Cardiovascular Health Research Unit Department of Medicine University of Washington Seattle WA United States The Charles Bronfman Institute for Personalized Medicine Icahn School of Medicine at Mount Sinai New York NY USA The Mindich Child Health and Development Institute Icahn School of Medicine at Mount Sinai New York NY USA Department of Biostatistics School of Public Health Boston University Boston MA United States Division of Biomedical Informatics and Personalized Medicine University of Colorado School of Medicine Anschutz Medical Campus Aurora CO United States Population Health Sciences Geisinger Danville PA United States Department of Biostatistics Harvard T.H. Chan School of Public Health Boston MA United States Department of Biostatistics Gillings School of Global Public Health University of North Carolina at Chapel Hill Chapel Hill NC United States Department of Genetics School of Medicine University of North Carolina at Chapel Hill Chapel Hill NC United States Biostatistics and Health Data Science Indiana University School of Medicine Indianapolis IN United States School of Mathematics and Statistics and KLAS Northeast Normal University Changchun Jilin China Department of Family Medicine Division of Preventive Medicine University of California San Diego La Jolla CA United States Department of Medicine General Internal Medicine Johns Hopkins University School of Medicine Baltimore MD United States Department of Epidemiology School of Public Health University of Michigan Ann Arbor MI United States Department of Medicine University of Colorado School of Medicine Aurora CO United States Department of Biochemistry Wake Forest School of Medicine Winston-Salem NC United States Department of Biostatistics School of Public Health University of Washington Seattle WA United States Department of

Obesity is a major public health crisis associated with high mortality rates. Previous genome-wide association studies (GWAS) investigating body mass index (BMI) have largely relied on imputed data from European individuals. This study leveraged whole-genome sequencing (WGS) data from 88,873 participants from the Trans-Omics for Precision Medicine (TOPMed) program, of which 51% were of non-European population groups. We discovered 18 BMI-associated signals (P < 5 × 10-9), including two secondary signals. Notably, we identified and replicated a novel low-frequency single nucleotide polymorphism (SNP) in MTMR3 that was common in individuals of African descent. Using a diverse study population, we further identified two novel secondary signals in known BMI loci and pinpointed two likely causal variants in the POC5 and DMD loci. Our work demonstrates the benefits of combining WGS and diverse cohorts in expanding current catalog of variants and genes confer risk for obesity, bringing us one step closer to personalized medicine. © 2025. The Author(s).

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Heightened COVID-19 Mortality in People With Severe Mental Illness Persists After Vaccination: A Cohort Study of Greater Manchester Residents (vol 49, pg 275, 2023)

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SCHIZOPHRENIA BULLETIN 2023年第5期49卷 1399-1403页

作者： Hassan, Lamiece Sawyer, Chelsea Peek, Niels Lovell, Karina Carvalho, Andre F. Solmi, Marco Tilston, George Sperrin, Matthew Firth, Joseph Division of Psychology and Mental Health The University of Manchester Manchester Academic Health Science Centre Manchester M13 9PL UK Centre for Health Informatics Division of Informatics Imaging and Data Sciences The University of Manchester M13 9PL UK NIHR Greater Manchester Patient Safety Translational Research Centre The University of Manchester Manchester UK Manchester Academic Health Science Centre National Institute for Health Research Manchester Biomedical Research Centre The University of Manchester Manchester M13 9PL UK Division of Nursing Midwifery and Social Work University of Manchester Manchester Academic Health Science Centre Manchester M13 9PL UK IMPACT (Innovation in Mental and Physical Health and Clinical Treatment) Strategic Research Centre School of Medicine Barwon Health Deakin University Geelong Victoria Australia Psychiatry Department University of Ottawa Ottawa ON Canada The Ottawa Hospital University of Ottawa Ottawa ON Canada Clinical Epidemiology Program Ottawa Hospital Research Institute (OHRI) University of Ottawa Ottawa ON Canada Greater Manchester Mental Health NHS Foundation Trust Manchester Academic Health Science Centre Manchester M13 9PL UK

Background and hypothesis Previous studies show that people with severe mental illness (SMI) are at higher risk of COVID-19 mortality, however limited evidence exists regarding risk post-vaccination. We investigated COVID-19 mortality among people with schizophrenia and other SMIs before, during and after the UK vaccine roll-out. Study Design Using the Greater Manchester (GM) Care Record to access routinely collected health data linked with death records, we plotted COVID-19 mortality rates over time in GM residents with schizophrenia/psychosis, bipolar disorder (BD) and/or recurrent major depressive disorder (MDD) from February 2020 to September 2021. Multivariable logistic regression was used to compare mortality risk (risk ratios;RRs) between people with SMI (N=190,188) and age-sex matched controls (N=760,752), adjusted for sociodemographic factors, pre-existing comorbidities and vaccination *** Results Mortality risks were significantly higher among people with SMI compared with matched controls, particularly among people with schizophrenia/psychosis (RR 3.14, CI 2.66-3.71) and/or BD (RR 3.17, CI 2.15-4.67). In adjusted models, the relative risk of COVID-19 mortality decreased, though remained significantly higher than matched controls for people with schizophrenia (RR 1.53, CI 1.24-1.88) and BD (RR 2.28, CI 1.49-3.49), but not recurrent MDD (RR 0.92, CI 0.78-1.09). People with SMI continued to show higher mortality rate ratios relative to controls throughout 2021, during vaccination roll-out. Conclusions People with SMI, notably schizophrenia and bipolar disorder, were at greater risk of COVID-19 mortality compared to matched controls. Despite population vaccination efforts that have prioritised people with SMI, disparities still remain in COVID-19 mortality for people with SMI.

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Base on GAN Combined with CNN Architecture to Generate Brain Stroke CT Images 24

Base on GAN Combined with CNN Architecture to Generate Brain...

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8th International Conference on Medical and health informatics, ICMHI 2024

作者： Shih, Guan-Yu Ting, Hsien-Wei Chen, Yi-Xiang Chien, Ting-Ying Department of Computer Science and Engineering Yuan Ze University Taoyuan City Taiwan Graduate Program inBiomedical Informatics Yuan Ze University Taoyuan City Taiwan Department of Neurosurgery Taipei Hospital Ministry of Health and Welfare New Taipei City242 Taiwan Innovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan City Taiwan

ISBN: (纸本)9798400716874

In recent years, many fields have expanded their research methods through the integration of artificial intelligence. In the current medical field, it is widely used in image recognition to diagnose patient symptoms, train clinical prediction models, and improve clinical workflow processes to avoid missing treatment opportunities. These methods have all achieved good results so far. Now, generative AI is also impacting the development of the medical field, not only generating prescriptions that match patient conditions but also being applied in generating medical images for data augmentation and simulating various pathological conditions, thereby aiding in training deep learning models. Stroke is a leading cause of death or disability, ranking as the second leading cause of death worldwide, next to cancer. If cancers were categorized individually, stroke would surpass all single diseases, making it a primary focus of research in brain disease treatment. There are already studies where generative AI has been used to create medical images, which are then manually evaluated by doctors to determine if they were generated by AI. In this project, we plan to generate CT images of strokes using generative AI and use a trained model (to diagnose stroke) to improve accuracy. Through this project, we hope to solve the problem of scarce stroke CT images while also providing support for training stroke-related models. © 2024 Copyright ACM.

关键词： Deep learning

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Constructing a Segmentation Model for Patients with Leukoaraiosis Using Deep Learning Algorithms 24

Constructing a Segmentation Model for Patients with Leukoara...

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8th International Conference on Medical and health informatics, ICMHI 2024

作者： Lo, Li-Ting Tsai, Yi-Chia Chang, Yi-Jou Ting, Hsien-Wei Chien, Ting-Ying Department of Computer Science and Engineering Yuan Ze University Taoyuan City Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan City Taiwan Department of Neurosurgery Taipei Hospital Ministry of Health and Welfare New Taipei City242 Taiwan Innovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan City Taiwan

ISBN: (纸本)9798400716874

Many patients with leukoaraiosis (LA) exhibit mild and difficult-to-detect symptoms in the early stages, and due to the lack of effective detection methods, the optimal timing for treatment is often missed. This not only makes later treatment more difficult but, in severe cases, can even lead to mild to severe dementia. Therefore, in addition to relying on radiologists to manually interpret magnetic resonance imaging (MRI) scans, finding more effective detection methods has become extremely urgent. This study aimed to combine image processing and deep learning technologies to develop a model capable of accurately identifying and segmenting white matter hyperintensities (WMHs) to assist doctors in the early diagnosis of LA. We first performed efficient preprocessing on MR image slices from two different series and used the ResNet18 model to identify key slices containing WMHs, ultimately achieving precise segmentation of WMH areas with the U-Net model. This research focused on developing innovative preprocessing techniques, improving the accuracy by 12.3% with a limited dataset through preprocessing. Ultimately, the ResNet18 model achieved an F1 score of 0.829, while the U-Net model’s intersection over union (IoU) and Dice coefficient reached 0.750 and 0.857, respectively, showing the potential of this architecture. We propose improvements in the final chapter to construct a more comprehensive model. © 2024 Copyright ACM.

关键词： Deep learning

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Constructing an Automated Method for Interpreting Alzheimer’s Disease Based on Deep Learning Models and Image Processing 24

Constructing an Automated Method for Interpreting Alzheimer...

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8th International Conference on Medical and health informatics, ICMHI 2024

作者： Yi, Chia-Pin Ting, Hsien-Wei Yang, Tzu-Fang Chien, Ting-Ying Department of Computer Science and Engineering Yuan Ze University Taoyuan City Taiwan Department of Neurosurgery Taipei Hospital Ministry of Health and Welfare New Taipei City242 Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan City Taiwan TaiwanInnovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan City Taiwan

ISBN: (纸本)9798400716874

Alzheimer’s disease (AD) often presents only mild symptoms in its early stages, and as there is no direct diagnostic method currently available, many patients are diagnosed only after the condition has worsened. Considering that magnetic resonance imaging (MRI) can provide key information in the diagnosis of AD, but the condition may go undiagnosed under manual interpretation due to the subtleness of symptoms and the reliance on radiologists to interpret MRI images, this study aimed to develop an automated method for interpreting brain MR images, hoping to screen for potential AD patients. By combining image processing, the U-Net model, YOLOv8 model, various CNN model architectures, and ensemble data statistics, our method was able to identify features related to AD and predict whether a patient has the disease. Our method can process a complete set of MR images and automatically extract important slices for prediction, making it practical for medical applications. Furthermore, we tested the accuracy and reliability of the method with additional datasets. © 2024 Copyright ACM.

关键词： Magnetic resonance imaging

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Population simulation modeling of disparities in US breast cancer mortality

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Journal of the National Cancer Institute - Monographs 2023年第62期2023卷 178-187页

作者： Mandelblatt, Jeanne S. Schechter, Clyde B. Stout, Natasha K. Huang, Hui Stein, Sarah Hunter Chapman, Christina Trentham-Dietz, Amy Jayasekera, Jinani Gangnon, Ronald E. Hampton, John M. Abraham, Linn O’Meara, Ellen S. Sheppard, Vanessa B. Lee, Sandra J. Department of Oncology Georgetown University Medical Center and Cancer Prevention and Control Program at Georgetown Lombardi Comprehensive Cancer Center WA United States Departments of Family and Social Medicine and of Epidemiology and Population Health Albert Einstein College of Medicine Bronx NY United States Department of Population Sciences Harvard Medical School and Harvard Pilgrim Health Care Institute Boston MA United States Department of Data Science Dana-Farber Cancer Institute and Harvard Medical School Boston MA United States Department of Radiation Oncology Section of Health Services Research Baylor College of Medicine and Health Policy Quality and Informatics Program at the Center for Innovations in Quality Effectiveness and Safety Michael E. DeBakey VA Medical Center Houston TX United States Department of Population Health Sciences and Carbone Cancer Center University of Wisconsin-Madison Madison WI United States Health Equity and Decision Sciences Research Lab National Institute on Minority Health and Health Disparities Intramural Research Program National Institutes of Health Bethesda MD United States Departments of Population Health Sciences and of Biostatistics and Medical Informatics and Carbone Cancer Center University of Wisconsin-Madison Madison WI United States Kaiser Permanente Washington Health Research Institute Seattle WA United States Department of Health Behavior and Policy and Massey Cancer Center Virginia Commonwealth University Richmond VA United States

BACKGROUND: Populations of African American or Black women have persistently higher breast cancer mortality than the overall US population, despite having slightly lower age-adjusted incidence. METHODS: Three Cancer Intervention and Surveillance Modeling Network simulation teams modeled cancer mortality disparities between Black female populations and the overall US population. Model inputs used racial group-specific data from clinical trials, national registries, nationally representative surveys, and observational studies. Analyses began with cancer mortality in the overall population and sequentially replaced parameters for Black populations to quantify the percentage of modeled breast cancer morality disparities attributable to differences in demographics, incidence, access to screening and treatment, and variation in tumor biology and response to therapy. RESULTS: Results were similar across the 3 models. In 2019, racial differences in incidence and competing mortality accounted for a net ‒1% of mortality disparities, while tumor subtype and stage distributions accounted for a mean of 20% (range across models = 13%-24%), and screening accounted for a mean of 3% (range = 3%-4%) of the modeled mortality disparities. Treatment parameters accounted for the majority of modeled mortality disparities: mean = 17% (range = 16%-19%) for treatment initiation and mean = 61% (range = 57%-63%) for real-world effectiveness. CONCLUSION: Our model results suggest that changes in policies that target improvements in treatment access could increase breast cancer equity. The findings also highlight that efforts must extend beyond policies targeting equity in treatment initiation to include high-quality treatment completion. This research will facilitate future modeling to test the effects of different specific policy changes on mortality disparities. © The Author(s) 2023. Published by Oxford University Press. All rights reserved. For permissions, please email: ***

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Discovery of novel ancestry specific genes for androgens and hypogonadism in Million Veteran program Men

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Nature Communications 2025年第1期16卷 1-12页

作者： Pagadala, Meghana S. Teerlink, Craig C. Jasuja, Guneet K. Palnati, Madhuri Anglin-Foote, Tori Chang, Nai-Chung N. Deka, Rishi Lee, Kyung M. Agiri, Fatai Y. Amariuta, Tiffany Seibert, Tyler M. Rose, Brent S. Pridgen, Kathryn M. Lynch, Julie A. Carter, Hannah K. Panizzon, Matthew S. Hauger, Richard L. Research Service VA San Diego Healthcare System San Diego CA United States Medical Scientist Training Program University of California San Diego La Jolla CA United States Biomedical Science Program University of California San Diego La Jolla CA United States VA Informatics and Computing Infrastructure (VINCI) VA Salt Lake City Healthcare System Salt Lake City UT United States Department of Internal Medicine Division of Epidemiology University of Utah School of Medicine Salt Lake City UT United States Center for Healthcare Organization and Implementation Research (CHOIR) VA Bedford Healthcare System Bedford MA United States Section of General Internal Medicine Boston University School of Medicine Boston MA United States Department of Health Law Policy and Management Boston University School of Public Health Boston MA United States Department of Radiation Medicine and Applied Sciences University of California San Diego La Jolla CA United States Department of Psychiatry University of California San Diego La Jolla CA United States Department of Medicine University of California San Diego La Jolla CA United States Halicioglu Data Science Institute University of California San Diego La Jolla CA United States Department of Radiology University of California San Diego La Jolla CA United States Department of Bioengineering University of California San Diego La Jolla CA United States Department of Urology University of California San Diego La Jolla CA United States Center for Behavior Genetics of Aging University of California San Diego La Jolla CA United States Center of Excellence for Stress and Mental Health (CESAMH) VA San Diego Healthcare System San Diego CA United States

Given the various roles of testosterone in men’s health, we conducted a multi-ancestral genetic analysis of total testosterone, free testosterone, SHBG, and hypogonadism in men within the Million Veteran program (MVP). Here we identified 157 significant testosterone genetic variants, of which 8 have significant ancestry-specific associations. These variants implicate several genes, including SERPINF2, PRPF8, BAIAP2L1, SHBG, PRMT6, and PPIF, related to liver function. Genetic regulators of testosterone have cell type-specific effects in the testes, liver, and adrenal gland and are associated with disease risk. We conducted a meta-analysis amongst ancestry groups to identify 188 variants significantly associated with testosterone, of which 22 are novel associations. We constructed genetic scores for total testosterone, SHBG levels, and hypogonadism and find that men with higher testosterone genetic scores have lower odds of diabetes, hyperlipidemia, gout, and cardiac disorders. These findings provide insight into androgen regulation and identify novel variants for disease risk stratification. © This is a U.S. Government work and not under copyright protection in the US;foreign copyright protection may apply 2025.

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Predicting Microsatellite Instability from Histology Images with Dilated Neighborhood Attention Transformer in Colorectal Cancer 24

Predicting Microsatellite Instability from Histology Images ...

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24th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2024

作者： Chen, Yun-Chi Chao, Tung Phandita, Wimaya Nitya Sun, Tzu-Yu Wang, Huai-Zhi Hsieh, Yi-Hsien Hsu, Le-Yin Lin, Che Taipei Taiwan College of Bioresources and Agriculture NTU Taipei Taiwan National Taiwan University of Science and Technology Department of Computer Science and Information Engineering Taipei Taiwan Graduate Institute of Communication Engineering NTU Taipei Taiwan NTU Data Science Degree Program Taipei Taiwan NTU Department of Electrical Engineering Taipei Taiwan Center for Advanced Computing and Imaging in Biomedicine NTU Taipei Taiwan NTU Smart Medicine and Health Informatics Program Taipei Taiwan

ISBN: (纸本)9798331518622

Microsatellite instability (MSI) is a pivotal genetic marker influencing the efficacy of immunotherapy in colorectal cancer. Traditional MSI examination often requires additional genetic or immunohistochemical tests, whereas histology images, widely available in colorectal cancer diagnosis, offer a valuable alternative for MSI prediction. Although Transformer-based models have demonstrated promising outcomes in predicting MSI from histology images, they are hampered by traditional local attention mechanisms that struggle to capture long-range interdependencies and establish a comprehensive global receptive field. In this study, we introduce DiNAT-MSI, a novel framework for histology-based MSI prediction that incorporates the Dilated Neighborhood Attention Transformer (DiNAT). This model enhances global context recognition and substantially expands receptive fields, all without additional computational burden. Our results demonstrate that DiNAT-MSI achieves a superior patientwise AUROC compared to ResNet18 and Swin Transformer, along with commendable explainability. Our work not only illustrates a more accessible diagnostic tool for leveraging histological data but also underscores the potential of Transformerbased models with sophisticated attention designs in advancing precision medicine for colorectal cancer patients. © 2024 IEEE.

关键词： Diseases

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O63: Leveraging global and local genomic data to guide equitable carrier screening: From Genome Aggregation database (gnomAD) to Qatar Genome

Genetics in Medicine Open

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Genetics in Medicine Open 2025年 3卷 102632-102632页

作者： Aarabi, Mahmoud Mbarek, Hamdi Bashar, Aryan Soman, Vishal Al-Mulla, Hajer Nkrumah, Esther Schmitz, Matthew Darabi, Helia Wang, John Kiehl, Paris Sethi, Rahil Saad, Chadi Rajkovic, Aleksandar Yatsenko, Svetlana Chandran, Uma Departments of Pathology and Obstetrics Gynecology and Reproductive Sciences University of Pittsburgh School of Medicine； Medical Genetics and Genomics Laboratories University of Pittsburgh Medical Center (UPMC) Pittsburgh PA Qatar Genome Program Qatar Foundation； Qatar Precision Health Institute Doha Qatar Data Science Graduate Program University of Colorado Boulder Boulder CO Department of Biomedical Informatics University of Pittsburgh School of Medicine Pittsburgh PA Joan H. Marks Graduate Program in Human Genetics Sarah Lawrence College New York NY Temerty Faculty of Medicine University of Toronto Toronto ON Canada Medical Genetics and Genomics Laboratories University of Pittsburgh Medical Center (UPMC) Department of Pediatrics University of Pittsburgh School of Medicine Pittsburgh PA Department of Microbiology Immunology and Pathology Colorado State University Fort Collins CO Department of Pathology and Institute of Human Genetics University of California San Francisco San Francisco CA Department of Pathology Stanford University School of Medicine Stanford CA

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