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现代生物医学进展 2017年第15期17卷 I0003-I0003页

作者： Huipeng Li, Elise T Courtois, Debarka Sengupta, Yuliana Tan Shyam Prabhakar Elise T Courtois, Yuliana Tan Paul Robson Debarka Sengupta Kok Hao Chen, Jolene Jie Lin Goh Paul Jongjoon Choi Say Li Kong, Axel M Hillmer Iain Beehuat Tan Clarinda Chua Iain Beehuat Tan Lim Kiat Hon Wah Siew Tan Mark Wong Lawrence J K Wee Iain Beehuat Tan Paul Robson Paul Robson Paul Robson Computational and Systems Biology Genome Institute of Singapore Singapore Developmental Cellomics Laboratory Genome Institute of Singapore Singapore Department of Computer Science and Engineering and Center for Computational Biology Indraprastha Institute of Information Technology Delhi India Synthetic Biology Genome Institute of Singapore Singapore Cancer Therapeutics and Stratified Oncology Genome Institute of Singapore Singapore Department of Medical Oncology National Cancer Centre Singapore Singapore Department of Pathology Singapore General Hospital Singapore Department of Colorectal Surgery Singapore General Hospital Singapore Data Analytics Department Institute for Infocomm Research Singapore Program in Cancer and Stem Cell Biology Duke-NUS Medical School Singapore The Jackson Laboratory for Genomic Medicine Farmington Connecticut USA Department of Genetics and Genome Sciences Institute for Systems Genomics University of Connecticut Farmington Connecticut USA Department of Biological Sciences National University of Singapore Singapore.

结合单细胞基因组学和计算机技术，一个研究团队（包括来自杰克逊实验室（JAx）单细胞生物学主任Paul Robson博士在内）鉴定出了11种结直肠癌肿瘤的癌细胞组成及邻近的非癌细胞。这对于更好的肿瘤靶向诊断和治疗很重要。”使用单细胞测... 详细信息

结合单细胞基因组学和计算机技术，一个研究团队（包括来自杰克逊实验室（JAx）单细胞生物学主任Paul Robson博士在内）鉴定出了11种结直肠癌肿瘤的癌细胞组成及邻近的非癌细胞。这对于更好的肿瘤靶向诊断和治疗很重要。”使用单细胞测序。”JAX科学家、这篇发表在Nature Genetics上的文章共同第一作者Elise Courtois说道。”我们可以根据肿瘤中的细胞组成将结直肠癌进一步分类。因为每种亚型的肿瘤病人生存率存在差别，我们的方法将为肿瘤医生提供更多的关于肿瘤预后和治疗的信息。”

关键词：单细胞分析结直肠癌癌细胞 Nat 细胞组成肿瘤病计算机技术细胞生物学

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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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Nanoinformatics workshop report: Current resources, community needs and the proposal of a collaborative framework for data sharing and information integration

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Computational Science and Discovery 2013年第1期6卷 14008页

作者： Harper, Stacey L. Hutchison, James E. Baker, Nathan Ostraat, Michele Tinkle, Sally Steevens, Jeffrey Hoover, Mark D. Adamick, Jessica Rajan, Krishna Gaheen, Sharon Cohen, Yoram Nel, Andre Cachau, Raul E. Tuominen, Mark Department of Environmental and Molecular Toxicology Oregon State University Corvallis OR United States School of Chemical Biological and Environmental Engineering Oregon State University Corvallis OR United States Department of Chemistry University of Oregon Eugene OR United States Pacific Northwest National Laboratory Richland WA United States RTI International Research Triangle Park NC United States Science and Technology Policy Institute Washington DC United States US Army Engineer Research and Development Center Vicksburg MS United States National Institute for Occupational Safety and Health Morgantown WV United States University Libraries University of Michigan Amherst MA United States Department of Materials Science and Engineering Iowa State University Ames IA United States SAIC Frederick Frederick MD United States Department of Chemical and Biomolecular Engineering University of California Los Angeles CA United States Center for Environmental Implications of Nanotechnology University of California Los Angeles CA United States Advanced Biomedical Computer Center Information Systems Program Frederick National Laboratory for Cancer Research Frederick MD United States Physics Department University of Massachusetts Amherst MA United States

The quantity of information on nanomaterial properties and behavior continues to grow rapidly. Without a concerted effort to collect, organize and mine disparate information coming out of current research efforts, the value and effective use of this information will be limited at best. Data will not be translated to knowledge. At worst, erroneous conclusions will be drawn and future research may be misdirected. Nanoinformatics can be a powerful approach to enhance the value of global information in nanoscience and nanotechnology. Much progress has been made through grassroots efforts in nanoinformatics resulting in a multitude of resources and tools for nanoscience researchers. In 2012, the nanoinformatics community believed it was important to critically evaluate and refine currently available nanoinformatics approaches in order to best inform the science and support the future of predictive nanotechnology. The Greener Nano 2012: Nanoinformatics Tools and Resources Workshop brought together informatics groups with materials scientists active in nanoscience research to evaluate and reflect on the tools and resources that have recently emerged in support of predictive nanotechnology. The workshop goals were to establish a better understanding of current nanoinformatics approaches and to clearly define immediate and projected informatics infrastructure needs of the nanotechnology community. The theme of nanotechnology environmental health and safety (nanoEHS) was used to provide real-world, concrete examples on how informatics can be utilized to advance our knowledge and guide nanoscience. The benefit here is that the same properties that impact the performance of products could also be the properties that inform EHS. From a decision management standpoint, the dual use of such data should be considered a priority. Key outcomes include a proposed collaborative framework for data collection, data sharing and information integration. © 2013 IOP Publishing Ltd.

关键词： Data integration

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Workshop on imaging science development for cancer prevention and preemption

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Cancer Biomarkers 2007年第1期3卷 1-33页

作者： Kelloff, Gary J. Sullivan, Daniel C. Baker, Houston Clarke, Lawrence P. Nordstrom, Robert Tatum, James L. Dorfman, Gary S. Jacobs, Paula Berg, Christine D. Pomper, Martin G. Birrer, Michael J. Tempero, Margaret Higley, Howard R. Petty, Brenda Gumbs Sigman, Caroline C. Maley, Carlo Sharma, Prateek Wax, Adam Ginsberg, Gregory G. Dannenberg, Andrew J. Hawk, Ernest T. Messing, Edward M. Grossman, H. Barton Harisinghani, Mukesh Bigio, Irving J. Griebel, Donna Henson, Donald E. Fabian, Carol J. Ferrara, Katherine Fantini, Sergio Schnall, Mitchell D. Zujewski, Jo Anne Hayes, Wendy Klein, Eric A. DeMarzo, Angelo Ocak, Iclal Ketterling, Jeffrey A. Tempany, Clare Shtern, Faina Parnes, Howard L. Gomez, Jorge Srivastava, Sudhir Szabo, Eva Lam, Stephen Seibel, Eric J. Massion, Pierre McLennan, Geoffrey Cleary, Kevin Suh, Robert Burt, Randall W. Pfeiffer, Ruth M. Hoffman, John M. Roy, Hemant K. Wang, Thomas Limburg, Paul J. El-Deiry, Wafik S. Papadimitrakopoulou, Vali Hittelman, Walter N. MacAulay, Calum Veltri, Robert W. Solomonzz, Diane Jeronimo, Jose Richards-Kortum, Rebecca Johnson, Karen A. Viner, Jaye L. Stratton, Steven P. Rajadhyaksha, Milind Dhawan, Atam NIH/NCI/DCTD Cancer Imaging Program Bethesda MD United States NIH/NCI/DCTD Cancer Imaging Program NCI-Frederick Frederick MD United States NIH/NCI/DCP Early Detection Research Group Bethesda MD United States The Johns Hopkins University School of Medicine Departments of Radiology Pharmacology and Oncology Division of Neuroradiology Baltimore MD United States NIH/NCI/CCR Cell and Cancer Biology Branch Bethesda MD United States University of California San Francisco Department of Medicine Division of Medical Oncology San Francisco CA United States CCS Associates Inc. Mountain View CA United States Wistar Institute Molecular and Cellular Oncogenesis Program Philadelphia PA United States University of Kansas School of Medicine VA Hospital Kansas City MO United States Duke University Department of Biomedical Engineering Medical Physics Program Durham NC United States University of Pennsylvania School of Medicine Department of Gastroenterology Philadelphia PA United States New York Presbyterian Hospital Weill Medical College of Cornell University New York NY United States NIH/NCI/OD Office of Centers for Training and Resources Bethesda MD United States University of Rochester Medical Center Department of Urology Rochester NY United States University of Texas MD Anderson Cancer Center Department of Urology Houston TX United States Massachusetts General Hospital Abdominal Imaging/Interventional Radiology Boston MA United States Boston University Biomedical Engineering Electrical and Computer Engineering and Physics Boston MA United States NIH/NCI/DCP Gastrointestinal and Other Cancers Research Group Bethesda MD United States The George Washington University Cancer Institute Office of Cancer Prevention and Control Washington DC United States University of Kansas Cancer Center Clinical Oncology Kansas City KS United States University of California Davis Department of Biomedical Engineering Davis CA United States Tufts University De

The concept of intraepithelial neoplasm (IEN) as a near-obligate precursor of cancers has generated opportunities to examine drug or device intervention strategies that may reverse or retard the sometimes lengthy process of carcinogenesis. Chemopreventive agents with high therapeutic indices, well-monitored for efficacy and safety, are greatly needed, as is development of less invasive or minimally disruptive visualization and assessment methods to safely screen nominally healthy but at-risk patients, often for extended periods of time and at repeated intervals. Imaging devices, alone or in combination with anticancer drugs, may also provide novel interventions to treat or prevent precancer.

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Technology Roadmap for Flexible Sensors

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ACS NANO 2023年第6期17卷 5211-5295页

作者： Luo, Yifei Abidian, Mohammad Reza Ahn, Jong-Hyun Akinwande, Deji Andrews, Anne M. Antonietti, Markus Bao, Zhenan Berggren, Magnus Berkey, Christopher A. Bettinger, Christopher John Chen, Jun Chen, Peng Cheng, Wenlong Cheng, Xu Choi, Seon-Jin Chortos, Alex Dagdeviren, Canan Dauskardt, Reinhold H. Di, Chong-an Dickey, Michael D. Duan, Xiangfeng Facchetti, Antonio Fan, Zhiyong Fang, Yin Feng, Jianyou Feng, Xue Gao, Huajian Gao, Wei Gong, Xiwen Guo, Chuan Fei Guo, Xiaojun Hartel, Martin C. He, Zihan Ho, John S. Hu, Youfan Huang, Qiyao Huang, Yu Huo, Fengwei Hussain, Muhammad M. Javey, Ali Jeong, Unyong Jiang, Chen Jiang, Xingyu Kang, Jiheong Karnaushenko, Daniil Khademhosseini, Ali Kim, Dae-Hyeong Kim, Il-Doo Kireev, Dmitry Kong, Lingxuan Lee, Chengkuo Lee, Nae-Eung Lee, Pooi See Lee, Tae-Woo Li, Fengyu Li, Jinxing Liang, Cuiyuan Lim, Chwee Teck Lin, Yuanjing Lipomi, Darren J. Liu, Jia Liu, Kai Liu, Nan Liu, Ren Liu, Yuxin Liu, Yuxuan Liu, Zhiyuan Liu, Zhuangjian Loh, Xian Jun Lu, Nanshu Lv, Zhisheng Magdassi, Shlomo Malliaras, George G. Matsuhisa, Naoji Nathan, Arokia Niu, Simiao Pan, Jieming Pang, Changhyun Pei, Qibing Peng, Huisheng Qi, Dianpeng Ren, Huaying Rogers, John A. Rowe, Aaron Schmidt, Oliver G. Sekitani, Tsuyoshi Seo, Dae-Gyo Shen, Guozhen Sheng, Xing Shi, Qiongfeng Someya, Takao Song, Yanlin Stavrinidou, Eleni Su, Meng Sun, Xuemei Takei, Kuniharu Tao, Xiao-Ming Tee, Benjamin C. K. Thean, Aaron Voon-Yew Trung, Tran Quang Wan, Changjin Wang, Huiliang Wang, Joseph Wang, Ming Wang, Sihong Wang, Ting Wang, Zhong Lin Weiss, Paul S. Wen, Hanqi Xu, Sheng Xu, Tailin Yan, Hongping Yan, Xuzhou Yang, Hui Yang, Le Yang, Shuaijian Yin, Lan Yu, Cunjiang Yu, Guihua Yu, Jing Yu, Shu-Hong Yu, Xinge Zamburg, Evgeny Zhang, Haixia Zhang, Xiangyu Zhang, Xiaosheng Zhang, Xueji Zhang, Yihui Zhang, Yu Zhao, Siyuan Zhao, Xuanhe Zheng, Yuanjin Zheng, Yu-Qing Zheng, Zijian Zhou, Tao Zhu, Bowen Zhu, Ming Zhu, Rong Zhu, Yangzhi Zhu, Yong Zou, Guijin Chen, Xiaodong 08-03 Innovis Singapore 138634 Republic of Singapore Innovative Centre for Flexible Devices (iFLEX) School of Materials Science and Engineering Nanyang Technological University Singapore 639798 Singapore Department of Biomedical Engineering University of Houston Houston Texas 77024 United States School of Electrical and Electronic Engineering Yonsei University Seoul 03722 Republic of Korea Department of Electrical and Computer Engineering The University of Texas at Austin Austin Texas 78712 United States Microelectronics Research Center The University of Texas at Austin Austin Texas 78758 United States Department of Chemistry and Biochemistry California NanoSystems Institute and Department of Psychiatry and Biobehavioral Sciences Semel Institute for Neuroscience and Human Behavior and Hatos Center for Neuropharmacology University of California Los Angeles Los Angeles California 90095 United States Colloid Chemistry Department Max Planck Institute of Colloids and Interfaces 14476 Potsdam Germany Department of Chemical Engineering Stanford University Stanford California 94305 United States Laboratory of Organic Electronics Department of Science and Technology Campus Norrköping Linköping University 83 Linköping Sweden Wallenberg Initiative Materials Science for Sustainability (WISE) and Wallenberg Wood Science Center (WWSC) SE-100 44 Stockholm Sweden Department of Materials Science and Engineering Stanford University Stanford California 94301 United States Department of Biomedical Engineering and Department of Materials Science and Engineering Carnegie Mellon University Pittsburgh Pennsylvania 15213 United States Department of Bioengineering University of California Los Angeles Los Angeles California 90095 United States School of Chemistry Chemical Engineering and Biotechnology Nanyang Technological University Singapore 637457 Singapore Nanobionics Group Department of Chemical and Biological Engineering Monash University Clayton Australia 3800 Monash

Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze continued...challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative

关键词： soft materials mechanics engineering flexible electronics conformable sensors bioelectronics human-machine interfaces body area sensor networks technology translation sustainable electronics

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Left ventricular hypertrophy detection using electrocardiographic signal

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Scientific reports 2023年第1期13卷 2556页

作者： Cheng-Wei Liu Fu-Hsing Wu Yu-Lun Hu Ren-Hao Pan Chuen-Horng Lin Yung-Fu Chen Guo-Shiang Tseng Yung-Kuan Chan Ching-Lin Wang Division of Cardiology Department of Internal Medicine Tri-Service General Hospital Songshan Branch National Defense Medical Center Taipei Taiwan. Bachelor Degree Program of Artificial Intelligence National Taichung University of Science and Technology Taichung Taiwan. Department of Management Information Systems National Chung-Hsing University Taichung Taiwan. La Vida Tec. Co. Ltd. Taichung Taiwan. Preventive Medicine Center National Yang Ming Chiao Tung University Taipei Taiwan. Department of Information Management Tunghai University Taichung Taiwan. Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taichung Taiwan. Department of Dental Technology and Materials Science Central Taiwan University of Science and Technology Taichung Taiwan. Division of Cardiology Department of Internal Medicine Taoyuan Armed Force General Hospital Hsinchu Branch Hsinchu Taiwan. Department of Management Information Systems National Chung-Hsing University Taichung Taiwan. ykchan@nchu.edu.tw. Department of Information Management National Chin-Yi University of Technology Taichung Taiwan. clwang@ncut.edu.tw.

Left ventricular hypertrophy (LVH) indicates subclinical organ damage, associating with the incidence of cardiovascular diseases. From the medical perspective, electrocardiogram (ECG) is a low-cost, non-invasive, and easily reproducible tool that is often used as a preliminary diagnosis for the detection of heart disease. Nowadays, there are many criteria for assessing LVH by ECG. These criteria usually include that voltage combination of RS peaks in multi-lead ECG must be greater than one or more thresholds for diagnosis. We developed a system for detecting LVH using ECG signals by two steps: firstly, the R-peak and S-valley amplitudes of the 12-lead ECG were extracted to automatically obtain a total of 24 features and ECG beats of each case (LVH or non-LVH) were segmented; secondly, a back propagation neural network (BPN) was trained using a dataset with these features. Echocardiography (ECHO) was used as the gold standard for diagnosing LVH. The number of LVH cases (of a Taiwanese population) identified was 173. As each ECG sequence generally included 8 to 13 cycles (heartbeats) due to differences in heart rate, etc., we identified 1466 ECG cycles of LVH patients after beat segmentation. Results showed that our BPN model for detecting LVH reached the testing accuracy, precision, sensitivity, and specificity of 0.961, 0.958, 0.966 and 0.956, respectively. Detection performances of our BPN model, on the whole, outperform 7 methods using ECG criteria and many ECG-based artificial intelligence (AI) models reported previously for detecting LVH.

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Got Air?

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Journal of Labor and Society 2004年第2期7卷

作者： Joan Greenbaum David Kotelchuck JOAN GREENBAUM is professor of computer information systems at LaGuardia Community College of the City University of New York (CUNY) and she is on the faculty in environmental psychology of the Graduate Center (CUNY). Greenbaum and David Kotelchuck are co‐chairs of Health and Safety for the Professional Staff Congress of CUNY (Local 2334 AFT). DAVID KOTELCHUCK is associate professor in the Urban Public Health Program at Hunter College (CUNY) and is director of Hunter College Center for Occupational and Environmental Health. He has been a member of the board of directors of the New York Committee for Occupational Safety and Health for many years.

For more than a century the industrial sector was center stage for safety and health struggles. While industrial issues are still vital, the scope of conflict has broadened to include white‐collar and service occupations, which now constitute the overwhelming majority of American workers. In many workplaces, the air quality may be as poor as–or worse than–it is outdoors. Unions are becoming increasingly aware of such issues, and the Health and Safety Committee of the Professional Staff Congress of the City University of New York is taking an innovative approach to the problem with its “Got Air?” campaign.

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computer AIDS FOR SHIP DESIGN, INTEGRATION AND CONTROL

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NAVAL ENGINEERS JOURNAL 1980年第2期92卷 73-87页

作者： CARLSON, CM JOHNSON, RA HELMING, FW Mr. Craig M. Carlson received his B.S. degree in Naval Architecture and Marine Engineering from the University of Michigan in 1970 and began his career with the Department of the Navy at the Naval Ship Engineering Center (NAVSEC). In 1972. he returned to the University of Michigan under the NAVSEC Long Term Training Program and received his M.S. degree in Naval Architecture and Marine Engineering. After returning to the Ship Arrangements Branch at NAVSEC. he was assigned as Task Leader for General Arrangements for the PGG PCG PHM. and MCM ship designs and was awarded Outstanding Performance Awards in 1974 and 1975. In addition he was Manager of the Arrangement Subsystem of the Navy's Computer-Aided Ship Design and Construction Program (CASDAC). In October 1979. he became Manager of the CASDAC Hull Design System. Currently. he also is enrolled in the M.S. of Computer Science Program at Johns Hopkins University. Mr. Carlson previously has presented technical papers at ASNE Day 1974 and 1978 as well as at the 1979 DOD Manufacturing Technology Advisory Group Conference. Besides ASNE. which he joined in 1972. he is a member of SNAME. ASE. and the U.S. Naval Institute. Mr. Robert A. Johnson is a Naval Architect in Surface Combatants Design (SEA 03D3). Ship Design Integration Directorate Naval Sea Systems Command. He received an Associate in Engineering degree in Drafting and Design Technology in 1959. his B.S. degree in Aerospace Engineering in 1965. and his M.S. degree in Engineering Mechanics in 1970. all from the Pennsylvania State University. In 1973. he was selected for the NA VSEC Hull Division s Long Term Training Program at the University of Michigan subsequently receiving his M.S.E. degree in Naval Architecture in 1974. Mr. Johnson began his career with the Ordnance Research Laboratory at Pennsylvania State University in 1959 where he worked. on the design of hydroelastic submarine models and conducted research in the area of flow induced structural vibrations. In 1967 he joined HRB-Singer at State Colle

This paper presents an integrated approach to computer-Aided Ship Design for U.S. Navy preliminary and contract design. An integrated Hull Design System (HDS), currently under development by the Hull Group of the Naval Sea systems Command (NAVSEA 32). is the vehicle for the discussion. This paper is directed toward practicing ship design professionals and the managers of the ship design process. Primary emphasis of this paper, and of the development effort currently under way, is on aiding ship design professionals in their work. Focus is on integration and management control of the extremely complex set of processes which make up naval ship design. The terminology of the Ship Designer and Design Manager is used. The reader needs no familiarity with the technologies of computer science.

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FUEL-CELL POWER-PLANTS FOR SURFACE FLEET APPLICATIONS

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NAVAL ENGINEERS JOURNAL 1994年第3期106卷 59-76页

作者： GOUBAULT, P GREENBERG, M HEIDENREICH, T WOERNER, J Philippe Goubault:graduated in 1983 from the “Ecole Nationale Superieure de Techniques Avancees” in Paris with a major in naval architecture. After one year of military service with the French navy he worked as naval architect and program director for the French navy between 1984 and 1988. He was in charge of the development of AGNES200 Surface Effect Ship design which completed its sea trials in 1992. He also was responsible for the construction of five ships (four hydrographic vessels and one experimental MCM vessel) which entered service between 1988 and 1991. He has been involved in a number of projects and studies for the U.S. Navy U.S. Coast Guard and other foreign and domestic customers. At Band Lavis & Associates Inc. Mr. Goubault has expanded the computer tools used to conduct parametric analysis of advanced hullforms and has developed cost-effectiveness assessment tools and methodologies for both commercial and military ships. Mr. Goubault is a member of ASNE. Marc Greenberg:is employed as a cost analyst at the cost and economic analysis branch systems assessment and engineering division Naval Surface Warfare Center. He provides cost estimates and analyses of Navy ship and submarine technologies and has assisted in the development of parametric cost models since 1991. Employed as an electronics engineer by the U.S. Army Information Systems Command from 1989 to 1991 he provided support in simulation design and construction of high frequency and microwave communication systems. Mr. Greenberg received his BS degree in ceramic science and engineering from the Pennsylvania State University May 1987. He is a member of MORS. Todd Heidenreich:is employed in the design analysis and tools branch systems assessment and engineering division of the Carde-rock Division Naval Surface Warfare Center. He is involved as a project naval architect in the conceptual design of future surface ship designs future technology impact assessments and the assessment of current domestic and foreign surface ship desig

This paper describes the results of a study undertaken to determine the impact of fuel cell technology on the design and effectiveness of future naval surface combatants. The study involved the collection of data to characterize four different fuel cell technologies: proton exchange membrane, molten carbonate, phosphoric acid, and solid oxide fuel cells. This information was used to expand current computer models to develop specific fuel cell plants that met the power requirements for several applications on a nominal 5000 Lton destroyer and a nominal 2000 Lton corvette. Each of the fuel cell technologies was incorporated into several applications aboard the destroyer and the corvette. These applications included combinations of centralized and distributed ship service power, and propulsion power. In addition, the impact of fuel cell technology was determined for a ship service power backfit option aboard a DDG-51 class destroyer. The results of the impact on the ship designs were analyzed and a military effectiveness assessment was conducted to address such issues as the impact of fuel cells on mobility, survivability, affordability, and on the environment. The paper identifies which aspects of the fuel cell technologies have the greatest impact upon the ship designs and their operational costs. Recommendations are given for future technology development efforts required to make fuel cells suitable for Navy service.

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PB-09Alpha-synuclein Binds Unconventional Microtubules That Have a Unique Function

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Microscopy 2017年第SUPPL_1期66卷 i37-i37页

作者： Toba, Shiori Jin, Mingyue Yamada, Masami Matsumoto, Sakiko Yasunaga, Takuo Fukunaga, Yuko Miyazawa, Atsuo Kojima, Hiroaki Arai, Yoshiyuki Nagai, Takeharu Hirotsune, Shinji Osaka City Univ. Grad. Sch. of Medicine Present address: Dept. of Emergency Medical Technology Hirosaki University of Health and Welfare Junior College Faculty of Computer Science and Systems Engineering Kyushu Inst. of Technology Fukuoka Japan JST-SENTAN JST-CREST Grad. Sch. of Life Science Univ. of Hyogo RSC-Univ. of Hyogo Leading Program Center RIKEN SPring-8 Center Advanced ICT Research Inst. National Inst. of Information and Communications Technology and Inst. of Scientific and Industrial Research Osaka Univ. Japan.

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