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7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE

作者： Alterovitz, Gil Lyashenko, Eugenia Xiang, Michael Ramoni, Marco F. Division of Health Sciences and Technology Harvard Medical School Massachusetts Institute of Technology Boston MA United States Children's Hospital Informatics Program at Health Sciences and Technology Harvard Medical School Boston MA United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA United States Harvard Partners Center for Genetics and Genomics Harvard Medical School Boston MA United States Department of Biomedical Informatics Columbia University New York NY United States

ISBN: (纸本)1424415098

With the human genome sequenced, attention has been shifting to proteins and their function. Several technologies including mass spectrometry and gel electrophoresis have traditionally been used to study proteins. These technologies rely on proteins' masses to characterize and/or identify them. Once identified, the discovered proteins' are often analyzed for their functional relevance. In this paper, we present an alternative approach to studying protein function directly, based on protein mass. By analyzing proteins with similar mass ranges (bins), we discovered that certain biological functions are associated with specific mass bins more frequently than would be expected by chance. Biological functional classes found in this manner could be seen across the three examined organisms: human, worm and fly. We found no sequence-based homology across significant mass-function proteins in the three different organisms. This investigation leads to useful property that the mass-based biological functional classes are preserved across the organisms. Thus, this paper describes a potential constraint for evolution of similar function based on mass constraints. Finally, this work yields a roadmap for experimental design for functional exploration of proteomes in mass-based technologies such as gel electrophoresis and mass spectrometry. ©2007 IEEE.

关键词： Proteins

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Universal designs versus assistive technologies: Research agendas and practical applications

Universal designs versus assistive technologies: Research ag...

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ASSETS 2005 - The Seventh International ACM SIGACCESS Conference on Computers and Accessibility

作者： Law, Chris Jacko, Julie Peterson, Bill Tobias, Jim Information Systems Department UMBC 1000 Hilltop Circle Baltimore MD 21227 United States Department of Biomedical Engineering Georgia Institute of Technology 313 Ferst Drive NW Atlanta GA 30332 United States Section 508 Program Management. Office Department of Homeland Security 301 7th Street SW Washington DC 20407 United States Inclusive Technologies 37 Miriam Drive Matawan NJ 07747 United States Georgia Tech. United States Laboratory for HCI and Health Care Informatics United States Section 508 Program Management Office United States Interagency Committee on Disability Research Subcommittee on Technology Inclusive Technologies United States United States

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ISBN: (纸本)1595931597

No abstract available

关键词： Assistive Technologies Research and Practice Universal Design

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Computer-based analysis of heart rate variability signal for detection of sleep disordered breathing in children

Computer-based analysis of heart rate variability signal for...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： H. Nazaren Y. Pamula A. Gradziel K. Ung S. Vijendra K. Behbehani Dept. of Electr. & Comput. Eng. Texas Univ. El Paso TX USA Department of Pulmonary Medicine Women's and Children's Hospital Adelaide Australia School of Informatics and Engineering Flinders University Adelaide Australia Biomedical Engineering Program University of Texas at Arlington USA

A computer-based analysis system was developed to display and analyze heart rate variability (HRV). ECG, oxygen saturation and respiratory signals (airflow, abdominal and thoracic movements), were used as raw data. The heart rate variability signal was derived from ECG by applying a Hilbert transform-based algorithm for reliable QRS complex detection. Following the guidelines suggested by the Task Force of the European Society of Cardiology and the North American Society for Pacing and Electrophysiology, appropriate time-domain and frequency-domain methods were used for HRV signal analysis. Autoregressive modeling of the HRV power spectrum was achieved by implementing the Burg algorithm. Three main spectral features were clearly distinguished in the heart rate variability signal spectrum from polysomnographic recordings of different sleep stages and were correlated with respiratory parameters. The integrated graphical user interface was developed using LabView and the signal processing algorithms were implemented using Matlab application programs. In this paper we present an overview of the system and analyze pilot data for two children undergoing nocturnal polysomnography. The pilot data demonstrated that the HRV analysis system may potentially distinguish between periods of normal and sleep disordered breathing (SDB) in children.

关键词： Signal analysis Heart rate variability Signal detection Heart rate detection Sleep Signal processing algorithms Computer displays Electrocardiography Abdomen Power system reliability

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Contributory presentations/posters

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Journal of Biosciences 1999年第1期24卷 33-198页

作者： N. Manoj V. R. Srinivas A. Surolia M. Vijayan K. Suguna R. Ravishankar R. Schwarzenbacher K. Zeth Diederichs G. M. Kostner A. Gries P. Laggner R. Prassl Madhusudan Pearl Akamine Nguyen-huu Xuong Susan S. Taylor M. Bidva Sagar K. Saikrishnan S. Roy K. Purnapatre P. Handa U. Varshney B. K. Biswal N. Sukumar J. K. Mohana Rao A. Johnson Vasantha Pattabhi S. Sri Krishna Mira Sastri H. S. Savithri M. R. N. Murthy Bindu Pillai Kannan M. V. Hosur Mukesh Kumar Swati Patwardhan K. K. Kannan B. Padmanabhaa S. Sasaki-Sugio M. Nukaga T. Matsuzaki S. Karthikevan S. Sharma A. K. Sharma M. Paramasivam P. Kumar J. A. Khan S. Yadav A. Srinivasan T. P. Singh S. Gourinath Neelima Alam A. Srintvasan Vikas Chandra Punit Kaur Ch. Betzel S. Ghosh A. K. Bera S. Bhattacharya S. Chakraborty A. K. Pal B. P. Mukhopadhyay I. Dey U. Haldar Asok Baneriee Jozef Sevcik Adriana Solovicova K. Sekar M. Sundaralingam N. Genov Dong-cai Liang Tao Jiang Ji-ping Zhang Wen-rui Chang Wolfgang Jahnke Marcel Blommers S. C. Panchal R. V. Hosur Bindu Pillay Puniti Mathur S. Srivatsun Ratan Mani Joshi N. R. Jaganathan V. S. Chauhan H. S. Atreya S. C. Sahu K. V. R. Chary Girjesh Govil Elisabeth Adjadj Éric Quinjou Nadia Izadi-Pruneyre Yves Blouquit Joël Mispelter Bernadette Heyd Guilhem Lerat Philippe Milnard Michel Desmadreil Y. Lin B. D. Nageswara Rao Vidva Raghunathan Mei H. Chau Prashant Pesais Sudha Srivastava Evans Coutinho Anil Saran Leizl F. Sapico Jayson Gesme Herbert Lijima Raymond Paxton Thamarapu Srikrishnan C. R. Grace G. Nagenagowda A. M. Lynn Sudha M. Cowsik Sarata C. Sahu S. Chauhan A. Bhattacharya G. Govil Anil Kumar Maurizio Pellecchia Erik R. P. Zuiderweg Keiichi Kawano Tomoyasu Aizawa Naoki Fujitani Yoichi Hayakawa Atsushi Ohnishi Tadayasu Ohkubo Yasuhiro Kumaki Kunio Hikichi Katsutoshi Nitta V. Rani Parvathy R. M. Kini Takumi Koshiba Yoshihiro Kobashigawa Min Yao Makoto Demura Astushi Nakagawa Isao Tanaka Kunihiro Kuwajima Jens Linge Seán O. Donoghue Michael Nilges G. Chakshusmathi Girish S. Ratnaparkhi P. K. Madhu R. Varadarajan C. Tetreau Molecular Biophysics Unit Indian Institute of Science Bangalore India Austrian Academy of Sciences Institute of Biophysics and X-ray Structure Research Graz Austria Faculty of Biology University of Konstanz Germany Institute of Medical Biochemistry University of Graz Austria Howard Huges Medical Institute Department of Chemistry & Biochemistry University of California La Jolla USA Department of Chemistry & Biochemistry University of California San Diego La Jolla USA Molecular Biophysics Unit India Department of Microbiology &Cell Biology Indian Institute of Science Bangalore India Macromolecular Structure Laboratory NCI - Frederick Cancer Research and Development Center ABL - Basic Research Program Frederick USA Department of Crystallography and Biophysics University of Madras Chennai Biochemistry Department Indian Institute of Science Bangalore India Condensed Matter Physics Division B.A.R.C. Trombay Mumbai Condensed Matter Physics Division Bhabha Atomic Research Centre Trombay Mumbai IMCB The University of Tokyo Tokyo Japan Mitsubishi Chemical Corporation Yokohama Research Center Yokohama Japan Faculty of Pharmaceutical Sciences Chiba University Chiba Japan Department of Biophysics All India Institute of Medical Sciences New Delhi India Department of Biophysics All India Institute of Medical sciences New Delhi India Institüt Physiologische Chemie Hamburg Germany Biophysics Department Bose Institute Calcutta India Slovak Academy of Sciences Institute of Molecular Biology Bratislava Slovakia Indian Institute of Science Bioinformatics Centre Bangalore Departments of Chemistry and Biochemistry Biological Macromolecular Structure Center USA Bulgarian Academy of Sciences Institute of Organic Chemistry Sofia Bulgaria Chinese Academy of Sciences Institute of Biophysics Beijing China Novartis Pharma AG Preclinical Research Basel Switzerland Department of Chemical Sciences Tata Institute of Fundamental Research Mumbai India Solid State Physics Div

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Non-rigid image alignment for layered cell representation and motion tracking

Non-rigid image alignment for layered cell representation an...

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Annual Northeast Bioengineering Conference

作者： S. Shemlon S. Hawkins Graduate Program in Biomedical Informatics SHRP Newark NJ USA Department of Biomedical Engineering Rutgers University Piscataway NJ USA

Mathematical modeling of the dynamic behavior of physical systems, gives computers that mimic the capabilities to intelligently monitor and predict their evolutionary characteristics. In this paper, we present a system for tracking the time-varying features of non-rigid objects in images of evolving scenes, using the elastic string model of planar contours, which permits the inference and prediction of the quantitative parameters that characterize evolutionary behavior. The goal of our work is to dynamically track non-rigid objects in video sequences, using object alignment techniques based on the properties of the elastic string. We present experimental results of growth cone and neurite tracking in cell growth and motion studies.

关键词： Tracking Deformable models Predictive models Mathematical model biomedical computing Layout Image motion analysis Motion analysis Equations Neurons

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Deforming post-mortem radiograph images to reconstruct a bullet path through stock CT data

Deforming post-mortem radiograph images to reconstruct a bul...

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Visualization in biomedical Computing 1994

作者： Boxwala, Aziz A. Oliver, William R. Program in Medical Informatics Department of Biomedical Engineering University of North Carolina Chapel HillNC27599-7575 United States Digital Image Processing Laboratory Department of Cellular Pathology Armed Forces Institute of Pathology WashingtonDC20306-6000 United States

An earlier study investigated a technique to reconstruct in three-dimensions the path of a bullet through a skull, using the post-mortem X-rays of the victim and stock computed tomography (CT) data. This paper describes the addition of image deformation methods in order to improve the accuracy of the reconstruction technique. The skull X-ray images of the victim were warped to match the shape of the skull in the CT. The warping was done by a thin-plate spline deformation algorithm. The bullet path was again reconstructed using the warped X-ray images as the source of information for the bullet entry and exit wound. The difference in the angle of the bullet paths in the two reconstructions was 6.9 degrees. © 1994 SPIE. All rights reserved.

关键词： Deformation

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Graph Artificial Intelligence in Medicine

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biomedical Data Science 1000年第1期7卷 345-368页

作者： Ruth Johnson Michelle M. Li Ayush Noori Owen Queen Marinka Zitnik 1Department of Biomedical Informatics Harvard Medical School Boston Massachusetts USA email: marinka@hms.harvard.edu 2Berkowitz Family Living Laboratory Harvard Medical School Boston Massachusetts USA 3Bioinformatics and Integrative Genomics Program Harvard Medical School Boston Massachusetts USA 4Department of Computer Science Harvard John A. Paulson School of Engineering and Applied Sciences Allston Massachusetts USA 5Broad Institute of MIT and Harvard Cambridge Massachusetts USA 7Kempner Institute for the Study of Natural and Artificial Intelligence Harvard University Allston Massachusetts USA 6Harvard Data Science Initiative Cambridge Massachusetts USA

In clinical artificial intelligence (AI), graph representation learning, mainly through graph neural networks and graph transformer architectures, stands out for its capability to capture intricate relationships and structures within clinical datasets. With diverse data—from patient records to imaging—graph AI models process data holistically by viewing modalities and entities within them as nodes interconnected by their relationships. Graph AI facilitates model transfer across clinical tasks, enabling models to generalize across patient populations without additional parameters and with minimal to no retraining. However, the importance of human-centered design and model interpretability in clinical decision-making cannot be overstated. Since graph AI models capture information through localized neural transformations defined on relational datasets, they offer both an opportunity and a challenge in elucidating model rationale. Knowledge graphs can enhance interpretability by aligning model-driven insights with medical knowledge. Emerging graph AI models integrate diverse data modalities through pretraining, facilitate interactive feedback loops, and foster human–AI collaboration, paving the way toward clinically meaningful predictions.

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