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NON-LINEAR WHEELSET FORCES IN FLANGE CONTACT .2. MEASUREMENTS USING DYNAMICALLY SCALED MODELS

JOURNAL OF DYNAMIC SYSTEMS MEASUREMENT AND CONTROL-TRANSACTIONS OF THE ASME 1979年第3期101卷 247-255页

作者： SWEET, LM SIVAK, JA PUTMAN, WF Transportation Program Department of Mechanical and Aerospace Engineering Princeton University Princeton N.J. 08540 Princeton University Princeton N.J. Applied Physics Laboratory Johns Hopkins University Baltimore Maryland 21205 Department of Mechanical and Aerospace Engineering Princeton University Princeton N.J. 08540

This paper presents new experimental methods for the study of rail vehicle dynamics through the use of scaled models on tangent track, and the application of these techniques to the measurement of nonlinear wheelset force/displacement relations in steady-state. These relations are important to the analysis of wheelset response to track inputs, curving performance of trucks, and wheelclimb derailment. A one-fifth scale model instrumented wheelset with new wheel profiles is used, with similitude scaling of the contact forces achieved through use of a polycarbonate resin for the contact surfaces. Data are presented for wheelset lateral force and yaw moment for the nonlinear range of wheelset lateral displacements and yaw angles, including flange contact. The measured data validate the analytical model presented in Part 1 of this paper, based on nonlinear wheel/rail contact geometry, creep forces with adhesion limits, and wheelset kinematics. A criterion for wheelclimb derailment is presented and verified experimentally.

关键词： Yaw Creep Trucks Flanges Wheelsets Rail vehicles Adhesion Displacement Dynamics (Mechanics) Kinematics Resins Wheels Experimental methods Steady state Rails Geometry

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Design of an oncology clinical information system 77

Design of an oncology clinical information system

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ACM Annual Conference, ACM 1977

作者： Blum, Bruce I. Lenhard, Raymond E. Applied Physics Laboratory Department of Biomedical Engineering JHU United States Johns Hopkins Oncology Center Johns Hopkins University BaltimoreMD United States

ISBN: (纸本)9781450339216

The use of complex multimodality treatment plans and the necessity for detailed, precisely timed collection of measurements of tumor response, drug toxicity, biochemical markers, and other specific parameters of therapeutic evaluation has created the need for a clinically oriented data system which can assist the oncologist in the organization, collection, and analysis of multiple pieces of research and treatment related data. To serve this need, a prototype computer~based system is in routine use at the Johns Hopkins Oncology Center, and an interactive Oncology Clinical Information System (OCIS) is being implemented to replace it. The OCIS will support the functions of therapy planning, data evaluation, and medical record keeping;it will also be integrated with the Johns Hopkins Hospital Medical Information System. This paper des=ribes the structure of the OCIS.

关键词： Medical information systems

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CONTROL PARAMETERS OF BLOOD-PRESSURE REGULATORY SYSTEM .2. OPEN-LOOP GAIN, REFERENCE PRESSURE AND BASAL HEARTRATE

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MEDICAL & BIOLOGICAL engineering 1972年第5期10卷 596-+页

作者： VALENTINUZZI, ME HOFF, HE GEDDES, LA POWELL, T POSEY, JA Department of Physiology Division of Biomedical Engineering Baylor College of Medicine Houston 77025 Texas USA Department of Physics Applied to Medicine The Middlesex Hospital Medical School W1P 6DB London England

The open-loop gainG 0 reference pressureP 0 and basal heartrateR 0 of the blood-pressure regulatory system have been estimated under four different experimental conditions by employing an aortic balloon and assuming a theoretical approach based on a 1st-order linear model. Mean values of 1·7–3·7, 75–130 mmHg, and 95–130 beats/min were obtained forG 0,P 0 andR 0, respectively. Significant variations in these parameters were observed when the anaesthetic conditions were changed. It is suggested thatG 0,P 0 andR 0 may have useful clinical applications both in characterising hypotensive and hypertensive syndromes and also in assessing the effect of anaesthetic agents.

关键词： Carotid Sinus Balloon Occlusion Reference Pressure Aortic Occlusion Basal Heartrate

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CONTROL PARAMETERS OF BLOOD-PRESSURE REGULATORY SYSTEM .1. HEARTRATE SENSITIVITY

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MEDICAL & BIOLOGICAL engineering 1972年第5期10卷 584-+页

作者： VALENTINUZZI, ME POWELL, T GEDDES, LA HOFF, HE Department of Physiology Division of Biomedical Engineering Baylor College of Medicine 77025 Houston Texas USA Department of Physics Applied to Medicine The Middlesex Hospital Medical School W1P 6DB London England

A method has been developed to evaluate the heartrate sensitivity (S 0) of the baroreceptor system, which is defined as the decrease in heartrate per unit increase in mean arterial pressure. Average values, for the intact system, of the order of 2·23–3·65 beats min−1 mmHg−1 were found in the dog, and of 0·5 beats min−1 mmHg−1 in one pony. The carotid sinus baroreceptors add considerable, but not preponderant, sensitivity to the cardiovascular regulatory system for blood pressure. Decreases inS 0 of the order of 40% were found when these receptors were essentially eliminated by occlusion of the carotid arteries. The type of anaesthesia used has a marked influence on the sensitivity of the baroreceptor system, suggesting the possibility of using this sensitivity coefficient to evaluate the influence of anaesthetics on the homeostatic mechanisms for blood pressure.

关键词： Carotid Sinus Carotid Sinus Nerve Arteriolar Smooth Muscle Carotid Sinus Baroreceptor Heartrate Sensitivity

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MEASUREMENT OF RELATIVE BLOOD-FLOW, TRANSIT-TIME DISTRIBUTIONS AND TRANSPORT-MODEL PARAMETERS BY RESIDUE DETECTION WHEN RADIOTRACER RECIRCULATES

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JOURNAL OF THEORETICAL BIOLOGY 1972年第3期37卷 503-&页

作者： LARSON, KB SNYDER, DL Biomedical Computer Laboratory and Division of Radiation Physics Mallinckrodt Institute of Radiology School of Medicine Washington University St. Louis Missouri 63110 USA Biomedical Computer Laboratory School of Medicine and Department of Electrical Engineering School of Engineering and Applied Science Washington University St. Louis Missouri 63110 USA

The interpretation of tracer kinetic data obtained by external monitoring of radioactively-labeled indicators is generally based on mathematical models that ignore recirculation. An important limitation of these models is that they can be used successfully only in those situations in which the major portion of the response curves are obtainable prior to the onset of the first recirculation. A further complication that limits the usefulness of the models is due to interference from radiotracers carried by recirculation into adjacent perfused regions also within the field of view of the detector. In this paper, we develop a mathematical model applicable for measuring blood flow per unit volume and determining transit-time distributions by external monitoring when recirculation is not a late event and must be taken into consideration. Additionally, the model accounts for interfering recirculation of tracer to adjacent perfused regions in the field of view of the detector. Central to the model is the use of two injections of tracer: one upstream (arterial) and one downstream (venous) of the particular organ or region of interest. Thus, two residue curves are obtained. We develop equations indicating how to employ the two residue curves in order to determine the mean transit time of tracer through the vascular system of interest, as well as higher moments of the transit-time distribution if they are desired. These equations are natural generalizations of Zierler's residue-detection method in that they do not depend for their validity on any model of mass transport within the system of interest. The numerical calculations implied by our equations need not rely on curve-fitting of the data. In addition to mean flow per unit volume, our method can yield compartmental parameters if these are appropriate and desired.

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Closure to "Discussion of ‘Analysis and Physiological Monitoring of the Human Left Ventricle’" (1971, ASME J. Basic Eng., 93, pp. 159–160)

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Journal of Fluids engineering, Transactions of the ASME 1971年第2期93卷 160-161页

作者： Ghista, D.N. Advani, S.H. Gaonkar, G.H. Balachandran, K. Brady, A.J. Biomedical Engineering Program Mechanical and Aerospace Engineering Department Washington University St. Louis Mo. Department of Theoretical and Applied Mechanics West Virginia University Morgantown W. Va. Mathematics Department University of Southern Illinois Edwardsville Ill. Department of Physiology School of Medicine University of California Los Angeles Calif.

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Computational Methods for Single-Particle Electron Cryomicroscopy

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biomedical Data Science 1000年第1期3卷 163-190页

作者： Amit Singer Fred J. Sigworth 1Department of Mathematics and Program in Applied and Computational Mathematics Princeton University Princeton New Jersey 08544 USA email: amits@math.princeton.edu 2Departments of Cellular and Molecular Physiology Biomedical Engineering and Molecular Biophysics and Biochemistry Yale University New Haven Connecticut 06520 USA email: fred.sigworth@yale.edu

Single-particle electron cryomicroscopy (cryo-EM) is an increasingly popular technique for elucidating the three-dimensional (3D) structure of proteins and other biologically significant complexes at near-atomic resolution. It is an imaging method that does not require crystallization and can capture molecules in their native states. In single-particle cryo-EM, the 3D molecular structure needs to be determined from many noisy 2D tomographic projections of individual molecules, whose orientations and positions are unknown. The high level of noise and the unknown pose parameters are two key elements that make reconstruction a challenging computational problem. Even more challenging is the inference of structural variability and flexible motions when the individual molecules being imaged are in different conformational states. This review discusses computational methods for structure determination by single-particle cryo-EM and their guiding principles from statistical inference, machine learning, and signal processing, which also play a significant role in many other data science applications.

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Erratum: The Nuclear Legacy Today of Fukushima

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Nuclear and Particle Science 1000年第1期

作者： Kai Vetter 2Applied Nuclear Physics Program Lawrence Berkeley National Laboratory Berkeley California 94720 USA 1Department of Nuclear Engineering University of California Berkeley California 94720 USA email: kvetter@berkeley.edu

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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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Computational Neuroscience: Mathematical and Statistical Perspectives

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Statistics and Its Application 1000年第1期5卷 183-214页

作者： Robert E. Kass Shun-Ichi Amari Kensuke Arai Emery N. Brown Casey O. Diekman Markus Diesmann Brent Doiron Uri T. Eden Adrienne L. Fairhall Grant M. Fiddyment Tomoki Fukai Sonja Grün Matthew T. Harrison Moritz Helias Hiroyuki Nakahara Jun-nosuke Teramae Peter J. Thomas Mark Reimers Jordan Rodu Horacio G. Rotstein Eric Shea-Brown Hideaki Shimazaki Shigeru Shinomoto Byron M. Yu Mark A. Kramer 1Department of Statistics Machine Learning Department and Center for the Neural Basis of Cognition Carnegie Mellon University Pittsburgh Pennsylvania 15213 USA email: kass@stat.cmu.edu 2Mathematical Neuroscience Laboratory RIKEN Brain Science Institute Wako Saitama Prefecture 351-0198 Japan 3Department of Mathematics and Statistics Boston University Boston Massachusetts 02215 USA 5Department of Anesthesia Harvard Medical School Boston Massachusetts 02115 USA 4Department of Brain and Cognitive Sciences Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA 6Department of Mathematical Sciences New Jersey Institute of Technology Newark New Jersey 07102 USA 8Department of Theoretical Systems Neurobiology Institute of Biology RWTH Aachen University 52062 Aachen Germany 7Institute of Neuroscience and Medicine Jülich Research Centre 52428 Jülich Germany 9Department of Mathematics University of Pittsburgh Pittsburgh Pennsylvania 15260 USA 10Department of Physiology and Biophysics University of Washington Seattle Washington 98105 USA 11Division of Applied Mathematics Brown University Providence Rhode Island 02912 USA 12Department of Integrated Theoretical Neuroscience Osaka University Suita Osaka Prefecture 565-0871 Japan 13Department of Mathematics Applied Mathematics and Statistics Case Western Reserve University Cleveland Ohio 44106 USA 14Department of Neuroscience Michigan State University East Lansing Michigan 48824 USA 15Department of Statistics University of Virginia Charlottesville Virginia 22904 USA 17Institute for Brain and Neuroscience Research New Jersey Institute of Technology Newark New Jersey 07102 USA 16Federated Department of Biological Sciences Rutgers University/New Jersey Institute of Technology Newark New Jersey 07102 USA 19Department of Physics Kyoto University Kyoto Kyoto Prefecture 606-8502 Japan 18Honda Research Institute Japan Wako Saitama Prefecture 351-0188 Japan 20Department of Electrical and Compute

Mathematical and statistical models have played important roles in neuroscience, especially by describing the electrical activity of neurons recorded individually, or collectively across large networks. As the field moves forward rapidly, new challenges are emerging. For maximal effectiveness, those working to advance computational neuroscience will need to appreciate and exploit the complementary strengths of mechanistic theory and the statistical paradigm.

关键词： neural data analysis neural modeling neural networks theoretical neuroscience

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