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Genome-wide analyses identify 30 loci associated with obsessive–compulsive disorder

Nature Genetics 2025年 1-13页

作者： Strom, Nora I. Gerring, Zachary F. Galimberti, Marco Yu, Dongmei Halvorsen, Matthew W. Abdellaoui, Abdel Rodriguez-Fontenla, Cristina Sealock, Julia M. Bigdeli, Tim Coleman, Jonathan R. Mahjani, Behrang Thorp, Jackson G. Bey, Katharina Burton, Christie L. Luykx, Jurjen J. Zai, Gwyneth Alemany, Silvia Andre, Christine Askland, Kathleen D. Bäckman, Julia Banaj, Nerisa Barlassina, Cristina Nissen, Judith Becker Bienvenu, O. Joseph Black, Donald Bloch, Michael H. Børte, Sigrid Bosch, Rosa Breen, Michael Brennan, Brian P. Brentani, Helena Buxbaum, Joseph D. Bybjerg-Grauholm, Jonas Byrne, Enda M. Cabana-Dominguez, Judit Camarena, Beatriz Camarena, Adrian Cappi, Carolina Carracedo, Angel Casas, Miguel Cavallini, Maria Cristina Ciullo, Valentina Cook, Edwin H. Crosby, Jesse Cullen, Bernadette A. De Schipper, Elles J. Delorme, Richard Djurovic, Srdjan Elias, Jason A. Estivill, Xavier Falkenstein, Martha J. Fundin, Bengt T. Garner, Lauryn Gironda, Christina Goes, Fernando S. Grados, Marco A. Grove, Jakob Guo, Wei Haavik, Jan Hagen, Kristen Harrington, Kelly Havdahl, Alexandra Höffler, Kira D. Hounie, Ana G. Hucks, Donald Hultman, Christina Janecka, Magdalena Jenike, Eric Karlsson, Elinor K. Kelley, Kara Klawohn, Julia Krasnow, Janice E. Krebs, Kristi Lange, Christoph Lanzagorta, Nuria Levey, Daniel Lindblad-Toh, Kerstin Macciardi, Fabio Maher, Brion Mathes, Brittany McArthur, Evonne McGregor, Nathaniel McLaughlin, Nicole C. Meier, Sandra Miguel, Euripedes C. Mulhern, Maureen Nestadt, Paul S. Nurmi, Erika L. O’Connell, Kevin S. Osiecki, Lisa Ousdal, Olga Therese Palviainen, Teemu Pedersen, Nancy L. Piras, Federica Potluri, Sriramya Rabionet, Raquel Ramirez, Alfredo Rauch, Scott Reichenberg, Abraham Riddle, Mark A. Ripke, Stephan Rosário, Maria C. Sampaio, Aline S. Schiele, Miriam A. Skogholt, Anne Heidi Sloofman, Laura G. Smit, Jan Artigas, María Soler Thomas, Laurent F. Tifft, Eric Vallada, Homero van Kirk, Nathanial Veenstra-VanderWeele, Jeremy Vulink, Nienke N. Walker, Christopher P. Wang, Ying Wendland, Jens R. Winsvold Department of Psychology Humboldt-Universität zu Berlin Berlin Germany Department of Psychiatric Phenomics and Genomics (IPPG) Ludwig-Maximilians University Munich Munich Germany Centre for Psychiatry Research Department of Clinical Neuroscience Karolinska Institutet and Stockholm Health Services Region Stockholm Stockholm Sweden Department of Biomedicine Aarhus University Aarhus Denmark University Hospital of Psychiatry and Psychotherapy University of Bern Bern Switzerland Department of Mental Health and Neuroscience Translational Neurogenomics QIMR Berghofer Medical Research Institute Brisbane QLD Australia Department of Population Health and Immunity Healthy Development and Ageing Walter and Eliza Hall Institute of Medical Research Melbourne VIC Australia Department of Psychiatry Human Genetics Yale University New Haven CT United States VA Connecticut Healthcare System West Haven CT United States Department of Center for Genomic Medicine Psychiatric and Neurodevelopmental Genetics Unit Massachusetts General Hospital Boston MA United States Stanley Center for Psychiatric Research Broad Institute of MIT and Harvard Cambridge MA United States Department of Genetics University of North Carolina at Chapel Hill Chapel Hill NC United States Department of Psychiatry Amsterdam University Medical Centers University of Amsterdam Amsterdam Netherlands Center for Research in Molecular Medicine and Chronic Diseases (CiMUS) Genomics and Bioinformatics University of Santiago de Compostela Santiago de Compostela Spain Grupo de Medicina Xenómica Genetics Instituto de Investigación Sanitaria de Santiago de Compostela (FIDIS) Santiago de Compostela Spain Vanderbilt Genetics Institute Vanderbilt University Nashville TN United States Department of Psychiatry and Behavioral Sciences SUNY Downstate Health Sciences University Brooklyn NY United States VA NY Harbor Healthcare System Brooklyn NY United States Social Genetic and Developmental Psychiatry Centre Insti

Obsessive–compulsive disorder (OCD) affects ~1% of children and adults and is partly caused by genetic factors. We conducted a genome-wide association study (GWAS) meta-analysis combining 53,660 OCD cases and 2,044,417 controls and identified 30 independent genome-wide significant loci. Gene-based approaches identified 249 potential effector genes for OCD, with 25 of these classified as the most likely causal candidates, including WDR6, DALRD3 and CTNND1 and multiple genes in the major histocompatibility complex (MHC) region. We estimated that ~11,500 genetic variants explained 90% of OCD genetic heritability. OCD genetic risk was associated with excitatory neurons in the hippocampus and the cortex, along with D₁ and D₂ type dopamine receptor-containing medium spiny neurons. OCD genetic risk was shared with 65 of 112 additional phenotypes, including all the psychiatric disorders we examined. In particular, OCD shared genetic risk with anxiety, depression, anorexia nervosa and Tourette syndrome and was negatively associated with inflammatory bowel diseases, educational attainment and body mass index. © The Author(s) 2025.

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AHR is a Zika virus host factor and a candidate target for antiviral therapy (vol 52, pg 941, 2020)

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NATURE NEUROSCIENCE 2020年第10期23卷 1307-1307页

作者： Giovannoni, Federico Bosch, Irene Polonio, Carolina Manganeli Torti, Maria F. Wheeler, Michael A. Li, Zhaorong Romorini, Leonardo Rodriguez Varela, Maria S. Rothhammer, Veit Barroso, Andreia Tjon, Emily C. Sanmarco, Liliana M. Takenaka, Maisa C. Modaresi, Seyed Mohamad Sadegh Gutierrez-Vazquez, Cristina Zanluqui, Nagela Ghabdan dos Santos, Nilton Barreto Munhoz, Carolina Demarchi Wang, Zhongyan Damonte, Elsa B. Sherr, David Gehrke, Lee Peron, Jean Pierre Schatzmann Garcia, Cybele C. Quintana, Francisco J. Ann Romney Center for Neurologic Diseases Brigham and Women’s Hospital Harvard Medical School Boston MA USA Laboratorio de Estrategias Antivirales Departamento de Química Biológica Facultad de Ciencias Exactas y Naturales Universidad de Buenos Aires. CONICET—Instituto de Química Biológica Buenos Aires Argentina Institute for Medical Engineering and Science Massachusetts Institute of Technology Cambridge MA USA Department of Medicine Mount Sinai School of Medicine New York NY USA Neuroimmune Interactions Laboratory Immunology Department—ICB IV University of São Paulo São Paulo Brazil Scientific Platform Pasteur—USP University of São Paulo São Paulo Brazil Laboratorio de Investigación aplicada a Neurociencias Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia Buenos Aires Argentina Immunopathology and Allergy Post Graduate Program School of Medicine University of São Paulo São Paulo Brazil Department of Pharmacology Institute of Biomedical Science University of São Paulo São Paulo Brazil Dept. of Environmental Health Boston University School of Public Health Boston MA USA Program in Virology Division of Medical Sciences Harvard Medical School Boston MA USA Broad Institute of MIT and Harvard Cambridge MA USA

Zika virus (ZIKV) is a flavivirus linked to multiple birth defects including microcephaly, known as congenital ZIKV syndrome. The identification of host factors involved in ZIKV replication may guide efficacious therapeutic interventions. In genome-wide transcriptional studies, we found that ZIKV infection triggers aryl hydrocarbon receptor (AHR) activation. Specifically, ZIKV infection induces kynurenine (Kyn) production, which activates AHR, limiting the production of type I interferons (IFN-I) involved in antiviral immunity. Moreover, ZIKV-triggered AHR activation suppresses intrinsic immunity driven by the promyelocytic leukemia (PML) protein, which limits ZIKV replication. AHR inhibition suppressed the replication of multiple ZIKV strains in vitro and also suppressed replication of the related flavivirus dengue. Finally, AHR inhibition with a nanoparticle-delivered AHR antagonist or an inhibitor developed for human use limited ZIKV replication and ameliorated newborn microcephaly in a murine model. In summary, we identified AHR as a host factor for ZIKV replication and PML protein as a driver of anti-ZIKV intrinsic immunity.

关键词： Immunology Microbiology

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Corrigendum to “Cell cycle dysregulation in cancer” [ Pharmacological Reviews 77 (2025) 100030]

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Pharmacological Reviews 2025年第3期77卷

作者： Antonino Glaviano Samarendra K. Singh E. Hui Clarissa Lee Elena Okina Hiu Yan Lam Daniela Carbone E. Premkumar Reddy Mark J. O’Connor Andrew Koff Garima Singh Justin Stebbing Gautam Sethi Karen Carmelina Crasta Patrizia Diana Khandan Keyomarsi Michael B. Yaffe Seth A. Wander Aditya Bardia Alan Prem Kumar Department of Biological Chemical and Pharmaceutical Sciences and Technologies University of Palermo Palermo Italy School of Biotechnology Institute of Sciences Banaras Hindu University Varanasi Uttar Pradesh India Department of Pharmacology Yong Loo Lin School of Medicine National University of Singapore Singapore Singapore NUS Center for Cancer Research Yong Loo Lin School of Medicine National University of Singapore Singapore Singapore Department of Oncological Sciences Icahn School of Medicine at Mount Sinai New York New York Discovery Centre AstraZeneca Cambridge United Kingdom Memorial Sloan Kettering Cancer Center New York New York Division of Cancer Imperial College London Hammersmith Campus London United Kingdom Healthy Longevity Translational Research Program Yong Loo Lin School of Medicine National University of Singapore Singapore Singapore Department of Physiology Yong Loo Lin School of Medicine National University of Singapore Singapore Singapore Centre for Healthy Longevity National University Health System Singapore Singapore Department of Experimental Radiation Oncology The University of Texas MD Anderson Cancer Center Houston Texas MIT Center for Precision Cancer Medicine Koch Institute for Integrative Cancer Research Broad Institute Massachusetts Institute of Technology Cambridge Boston Massachusetts Massachusetts General Hospital Harvard Medical School Boston Massachusetts

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Oncogenic RIT1 mutations in lung adenocarcinoma (vol 33, pg 4418, 2014)

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ONCOGENE 2022年第19期41卷 2788-2788页

作者： Berger, A. H. Imielinski, M. Duke, F. Wala, J. Kaplan, N. Shi, G. -X Andres, D. A. Meyerson, M. Cancer Program The Broad Institute of Harvard and M.I.T. 7 Cambridge Center Cambridge MA USA Department of Medical Oncology Dana Farber Cancer Institute Boston MA USA Department of Pathology Massachusetts General Hospital Boston MA USA Department of Pathology Harvard Medical School Boston MA USA Harvard-MIT Division of Health Sciences and Technology Massachusetts Institute of Technology Cambridge MA USA Department of Molecular and Cellular Biochemistry University of Kentucky College of Medicine Lexington KY USA

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Deep learning-based detection of intravenous contrast in computed tomography scans

arXiv

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arXiv 2021年

作者： Ye, Zezhong Qian, Jack M. Hosny, Ahmed Zeleznik, Roman Plana, Deborah Likitlersuang, Jirapat Zhang, Zhongyi Mak, Raymond H. Aerts, Hugo J.W.L. Kann, Benjamin H. Program Mass General Brigham Harvard Medical School BostonMA United States Department of Radiation Oncology Dana-Farber Cancer Institute Brigham and Women’s Hospital Harvard Medical School BostonMA United States Harvard-MIT Division of Health Sciences & Technology CambridgeMA United States Department of Radiology Brigham and Women’s Hospital Dana-Farber Cancer Institute Harvard Medical School BostonMA United States Radiology and Nuclear Medicine CARIM & GROW Maastricht University Maastricht Netherlands

Purpose: Identifying intravenous (IV) contrast use within CT scans is a key component of data curation for model development and testing. Currently, IV contrast is poorly documented in imaging metadata and necessitates manual correction and annotation by clinician experts, presenting a major barrier to imaging analyses and algorithm deployment. We sought to develop and validate a convolutional neural network (CNN)-based deep learning (DL) platform to identify IV contrast within CT scans. Methods: For model development and evaluation, we used independent datasets of CT scans of head, neck (HN) and lung cancer patients, totaling 133,480 axial 2D scan slices from 1,979 CT scans manually annotated for contrast presence by clinical experts. Five different DL models were adopted and trained in HN training datasets for slice-level contrast detection. Model performances were evaluated on a hold-out set and on an independent validation set from another institution. DL models was then fine-tuned on chest CT data and externally validated on a separate chest CT dataset. Results: Initial DICOM metadata tags for IV contrast were missing or erroneous in 1,496 scans (75.6%). The EfficientNetB4-based model showed the best overall detection performance. For HN scans, AUC was 0.996 in the internal validation set (n = 216) and 1.0 in the external validation set (n = 595). The fine-tuned model on chest CTs yielded an AUC: 1.0 for the internal validation set (n = 53), and AUC: 0.980 for the external validation set (n = 402). Conclusion: The DL model could accurately detect IV contrast in both HN and chest CT scans with near-perfect performance. © 2021, CC BY-NC-ND.

关键词： Computerized tomography

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Weakly Supervised AI for Efficient Analysis of 3D Pathology Samples

arXiv

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arXiv 2023年

作者： Song, Andrew H. Williams, Mane Williamson, Drew F.K. Jaume, Guillaume Zhang, Andrew Chen, Bowen Serafin, Robert Liu, Jonathan T.C. Baras, Alex Parwani, Anil V. Mahmood, Faisal Department of Pathology Brigham and Women’s Hospital Harvard Medical School BostonMA United States Department of Pathology Massachusetts General Hospital Harvard Medical School BostonMA United States Cancer Program Broad Institute of Harvard and MIT CambridgeMA United States Data Science Program Dana-Farber Cancer Institute BostonMA United States Department of Biomedical Informatics Harvard Medical School BostonMA United States Harvard-MIT Division of Health Sciences and Technology Massachusetts Institute of Technology CambridgeMA United States Department of Mechanical Engineering Bioengineering Laboratory Medicine & Pathology University of Washington SeattleWA United States Department of Pathology Johns Hopkins Hospital BaltimoreMD United States Department of Biomedical Engineering Johns Hopkins Hospital BaltimoreMD United States Department of Pathology The Ohio State University ColumbusOH United States

Human tissue consists of complex structures that display a diversity of morphologies, forming a tissue microenvironment that is, by nature, three-dimensional (3D). However, the current standard-of-care involves slicing 3D tissue specimens into two-dimensional (2D) sections and selecting a few for microscopic evaluation1,2, with concomitant risks of sampling bias and misdiagnosis3–6. To this end, there have been intense efforts to capture 3D tissue morphology and transition to 3D pathology, with the development of multiple high-resolution 3D imaging modalities7–18. However, these tools have had little translation to clinical practice as manual evaluation of such large data by pathologists is impractical and there is a lack of computational platforms that can efficiently process the 3D images and provide patient-level clinical insights. Here we present Modality-Agnostic Multiple instance learning for volumetric Block Analysis (MAMBA), a deep-learning-based platform for processing 3D tissue images from diverse imaging modalities and predicting patient outcomes. Archived prostate cancer specimens were imaged with open-top light-sheet microscopy12–14 or microcomputed tomography15,16 and the resulting 3D datasets were used to train risk-stratification networks based on 5-year biochemical recurrence outcomes via MAMBA. With the 3D block-based approach, MAMBA achieves an area under the receiver operating characteristic curve (AUC) of 0.86 and 0.74, superior to 2D traditional single-slice-based prognostication (AUC of 0.79 and 0.57), suggesting superior prognostication with 3D morphological features. Further analyses reveal that the incorporation of greater tissue volume improves prognostic performance and mitigates risk prediction variability from sampling bias, suggesting that there is value in capturing larger extents of spatially heterogeneous 3D morphology. With the rapid growth and adoption of 3D spatial biology and pathology techniques by researchers and clinicians, M

关键词： Pathology

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Multimodal Whole Slide Foundation Model for Pathology

arXiv

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arXiv 2024年

作者： Ding, Tong Wagner, Sophia J. Song, Andrew H. Chen, Richard J. Lu, Ming Y. Zhang, Andrew Vaidya, Anurag J. Jaume, Guillaume Shaban, Muhammad Kim, Ahrong Williamson, Drew F.K. Chen, Bowen Almagro-Perez, Cristina Doucet, Paul Sahai, Sharifa Chen, Chengkuan Komura, Daisuke Kawabe, Akihiro Ishikawa, Shumpei Gerber, Georg Peng, Tingying Le, Long Phi Mahmood, Faisal Department of Pathology Mass General Brigham Harvard Medical School BostonMA United States Data Science Program Dana-Farber Cancer Institute BostonMA United States Cancer Program Broad Institute of Harvard and MIT CambridgeMA United States John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA United States Helmholtz Munich German Research Center for Environment and Health Munich Germany School of Computation Information and Technology Technical University of Munich Munich Germany Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States Harvard-MIT Division of Health Sciences and Technology Massachusetts Institute of Technology CambridgeMA United States Department of Pathology Pusan National University Busan Korea Republic of Department of Pathology and Laboratory Medicine Emory University School of Medicine AtlantaGA United States Harvard Data Science Initiative Harvard University CambridgeMA United States Department of Systems Biology Harvard Medical School BostonMA United States Department of Preventive Medicine Graduate School of Medicine The University of Tokyo Tokyo Japan Division of Pathology National Cancer Center Exploratory Oncology Research & Clinical Trial Center Chiba Japan

The field of computational pathology has been transformed with recent advances in foundation models that encode histopathology region-of-interests (ROIs) into versatile and transferable feature representations via self-supervised learning (SSL). However, translating these advancements to address complex clinical challenges at the patient and slide level remains constrained by limited clinical data in disease-specific cohorts, especially for rare clinical conditions. We propose TITAN, a multimodal whole slide foundation model pretrained using 335,645 WSIs via visual self-supervised learning and vision-language alignment with corresponding pathology reports and 423,122 synthetic captions generated from a multimodal generative AI copilot for pathology. Without any finetuning or requiring clinical labels, TITAN can extract general-purpose slide representations and generate pathology reports that generalize to resource-limited clinical scenarios such as rare disease retrieval and cancer prognosis. We evaluate TITAN on diverse clinical tasks and find that TITAN outperforms both ROI and slide foundation models across machine learning settings such as linear probing, few-shot and zero-shot classification, rare cancer retrieval and cross-modal retrieval, and pathology report generation. The model is publicly accessible at https://***/mahmoodlab/TITAN © 2024, CC BY-NC-ND.

关键词： Self-supervised learning

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Multiple Doses of Exogenous FSTL1 Delivered with an Epicardial Reservoir Improve Cardiac Function, Healing and Angiogenesis after a Myocardial Infarction

SSRN

引用

SSRN 2023年

作者： Varela, Claudia E. Monahan, David S. Islam, Shahrin Whyte, William Bonnemain, Jean Ngoy, Souen Fisch, Sudeshna Duffy, Garry P. Roche, Ellen T. Institute for Medical Engineering and Science Massachusetts Institute of Technology CambridgeMA United States Harvard-MIT Program in Health Sciences and Technology CambridgeMA United States University of Galway Galway Ireland Department of Mechanical Engineering Massachusetts Institute of Technology CambridgeMA United States Department of Adult Intensive Care Medicine Lausanne University Hospital University of Lausanne Lausanne Switzerland Department of Medicine Brigham & Women's Hospital Harvard Medical School BostonMA United States Advanced Materials and BioEngineering Research Centre AMBER University of Galway Galway Ireland

Epicardial delivery of human follistatin-like 1 protein (FSTL1) induces significant cardiac benefit following a myocardial infarction (MI). However, the optimal dosing regimen for maximal therapeutic benefit has not yet been elucidated. To investigate the impact of multiple FSTL1 doses, without the confounding effects of multiple surgical procedures for multidose delivery, alternative delivery strategies are needed. Here, we use an epicardial reservoir that allows non-invasive delivery of additional doses after implantation to investigate the impact of single, double, and triple FSTL1 dose regimens in a rat model of MI. Multidose delivery of FSTL1 improves ejection fraction (3 doses), fractional shortening (1, 2 and 3 doses), and chamber stiffness (2 doses) 28 days after MI. Histologically, multiple FSTL1 doses increase ventricular wall thickness (2 and 3 doses) and reduce infarct size (1, 2, and 3 doses). We also demonstrate a dose-dependent increase in blood vessel number and density in the infarct zone, with three FSTL1 doses leading to the highest improvements. This study shows that multidose delivery of FSTL1 improves cardiac function, healing, and angiogenesis following MI. The epicardial delivery platform used here may be essential in optimizing dosing regimens of various bioagent combinations for a range of clinical indications. © 2023, The Authors. All rights reserved.

关键词： Controlled drug delivery

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Pale Red Dot: A Large, Robust Architecture for Human Settlements on Mars

Pale Red Dot: A Large, Robust Architecture for Human Settlem...

引用

Accelerating Space Commerce, Exploration, and New Discovery Conference, ASCEND 2023

作者： Lordos, George Hoying, Madelyn McKinney, Lanie Webb, Alisa Lin, Yuying Martin, Estelle Beilstein, John Miller, Duncan Villegas, David Sepulveda, Francisco AlSadah, Yousif Iyer, Divya O’Neill, Cormac Patel, Palak Garcia, Ignacio Arzuaga Azzouz, H. Chan, Wing Lam Eyre, Ezra Gleason, Dane Romero, Elizabeth Arias, Liliana Orozco, Omar Gentgen, Chloe Latyshev, Kir Hoffman, Jeffrey de Weck, Olivier Ikinci, Meltem Lordos, Alexandros Aeronautics and Astronautics Massachusetts Institute of Technology 33-409 CambridgeMA02139 United States Massachusetts Institute of Technology Harvard-MIT Health Sciences and Technology Program and MIT Aeronautics and Astronautics 33-409 CambridgeMA02139 United States Massachusetts Institute of Technology CambridgeMA02139 United States Aeronautics and Astronautics Massachusetts Institute of Technology 77 Mass Ave. 33-409 CambridgeMA02139 United States System Design and Management Massachusetts Institute of Technology 77 Mass Ave. E40-300 CambridgeMA02139 United States Mechanical Engineering Massachusetts Institute of Technology 77 Mass Ave. 33-409 CambridgeMA02139 United States Civil and Environmental Engineering Massachusetts Institute of Technology 77 Mass Ave. CambridgeMA02139 United States Electrical Engineering Massachusetts Institute of Technology 77 Mass Ave. CambridgeMA02139 United States Nuclear Science and Engineering Massachusetts Institute of Technology 77 Mass Ave. CambridgeMA02139 United States Computer Science and Engineering Massachusetts Institute of Technology 77 Mass Ave. CambridgeMA02139 United States Massachusetts Institute of Technology 77 Mass Ave. 37-219 CambridgeMA02139 United States Massachusetts Institute of Technology 77 Mass Ave. 33-410 CambridgeMA02139 United States Behavioral Economics University of Cyprus Nicosia Cyprus Department of Psychology University of Cyprus Nicosia Cyprus

ISBN: (数字)9781624107054

ISBN: (纸本)9781624107054

We propose a ten-yearMars mission to establish Earth-independence and support efficient, safe, and sustainable science and exploration goals. The campaign begins with a series of precursor missions, starting with the Artemis program, followed by robotic missions to performsite surveys and selection, de-risk technologies, and to start stockpiling in-situ water and propellant. Robotic Cargo Starships would depart first in 2035 to deliver and deploy approximately 5,800 tons of habitats, nuclear microreactors, farmmodules, manufacturing and ISRU systems. In 2040, two Crew Starships carrying 36 persons would land onMars to establish two close-proximity villages. The lifecycle cost is estimated at $81 billion, with a peak annual cost of $6.6 billion. To meet RASC-AL competition requirements, intended to motivate the exploration of highly self-reliant architecture concepts, resupply from Earth was constrained to only 5 metric tons every two years and minimum surface endurance was set at 7 years. The risks to human life and to critical systems increase exponentially over a minimally-resupplied, 7-10 year mission toMars. Our approach focused on identifying capabilities to mitigate known and unknown risks. Using a high-fidelity crew time model, we found that large architectures, with their economies of scale and productivity gains from specialization, are essential to robustly support the necessary capabilities. The model also shows that small 7-year missions of 12 persons or less cannot provide value in terms of free surface traverse time for science and exploration. Given the necessity of a large, complex architecture, we then sought out efficiencies and synergies (value engineering) in order to make the mission feasible and affordable. In the process, we identified more benefits that are enabled only by large missions, including the creation of a true community on Mars with sufficient social complexity for humans to thrive, and the geopolitically compelling option to inclu

关键词： Value engineering

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Ontology engineering

引用

Nature Biotechnology 2010年第2期28卷 128-130页

作者： Alterovitz, Gil Xiang, Michael Hill, David P. Lomax, Jane Liu, Jonathan Cherkassky, Michael Dreyfuss, Jonathan Mungall, Chris Harris, Midori A. Dolan, Mary E. Blake, Judith A. Ramoni, Marco F. Children's Hospital Informatics Program Harvard-MIT Division of Health Sciences and Technology Harvard Medical School Boston MA United States Partners Healthcare Center for Personalized Genetic Medicine Boston MA United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA United States Jackson Laboratory Bar Harbor ME United States EMBL-EBI Wellcome Trust Genome Campus Hinxton United Kingdom Department of Biology Massachusetts Institute of Technology Cambridge MA United States Lawrence Berkeley National Laboratory Berkeley CA United States

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