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Multiscale modeling in rodent ventricular myocytes

Multiscale modeling in rodent ventricular myocytes

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作者： Lu, Shaoying Michailova, Anushka Saucerman, Jeffrey Cheng, Yuhui Yu, Zeyun Kaiser, Timothy Li, Wilfred Bank, Randolph Holst, Michael McCammon, J. Hayashi, Takeharu Hoshijima, Masahiko Arzberger, Peter McCulloch, Andrew Bioengineering Department University of Illinois United States Bioengineering Department UCSD United States Department of Biomedical Engineering University of Virginia United States Department of Mathematics UCSD United States UCSD United States College of Computer Science and Technology Jilin University China Department of Mathematics Center for Computational Mathematics and the Computational Science Mathematics and Engineering Doctoral Program at UCSD Department of Pharmacology UCSD United States Center for Research in Biological Systems UCSD United States Department of Bioengineering UCSD United States Department of Bioengineering PFBH 241 University of California San Diego 9500 Gilman Drive San Diego CA 92093-0412 United States

There is a growing body of experimental evidence suggesting that the Ca2+ signaling in ventricular myocytes is characterized by a high gradient near the cell membrane and a more uniform Ca2+ distribution in the cell interior [1]--[7]. An important reason for this phenomenon might be that in these cells the t-tubular system forms a network of extracellular space, extending deep into the cell interior. This allows the electrical signal, that propagates rapidly along the cell membrane, to reach the vicinity of the sarcoplasmic reticulum (SR), where intracellular Ca2+ required for myofilament activation is stored [1], [8]--[11]. Early studies of cardiac muscle showed that the t-tubules are found at intervals of about 2 lm along the longitudinal cell axis in close proximity to the Z-disks of the sarcomeres [12]. Subsequent studies have demonstrated that the t-tubular system has also longitudinal extensions [9]--[11], [13].

关键词： Cells

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A CRCD experience: Integrating machine learning concepts into introductory engineering and science programming courses

A CRCD experience: Integrating machine learning concepts int...

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2003 ASEE Annual Conference and Exposition: Staying in Tune with engineering Education

作者： Georgiopoulos, Michael Russell, Ingrid Castro, Jose Wu, Annie Kysilka, Marcella Demara, Ronald Gonzalez, Avelino Gelenbe, Erol Mollaghasemi, Mansooreh Sch. of Elec. Eng. and Comp. Science University of Central Florida Department of Computer science University of Hartford Intelligent Systems Focus Group IEEE-CS/ACM Computer Engineering Program University of Central Florid Education Foundations Department University of Central Florida Sch. of Elec. Eng. and Comp. Science Coll. of Eng. and Computer Science University of Central Florida Indust. Eng. and Mgmt. Sci. Dept. University of Central Florida

Machine Learning has traditionally been a topic of research and instruction in computer science and computer engineering programs. Yet, due to its wide applicability in a variety of fields, its research use has expanded in other disciplines, such as electrical engineering, industrial engineering, civil engineering, and mechanical engineering. Currently, many undergraduate and first-year graduate students in the aforementioned fields do not have exposure to recent research trends in Machine Learning. This paper reports on a project in progress, funded by the National science Foundation under the program Combined Research and Curriculum Development (CRCD), whose goal is to remedy this shortcoming. The project involves the development of a model for the integration of Machine Learning into the undergraduate curriculum of those engineering and science disciplines mentioned above. The goal is increased exposure to Machine Learning technology for a wider range of students in science and engineering than is currently available. Our approach of integrating Machine Learning research into the curriculum involves two components. The first component is the incorporation of Machine Learning modules into the first two years of the curriculum with the goal of sparking student interest in the field. The second is the development of new upper level Machine Learning courses for advanced undergraduate students. The paper will describe the first phase of the project, that of the integration of Machine Learning concepts into introductory engineering and science programming courses through appropriately designed programming projects.

关键词： engineering education

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A neural model for sonar-based navigation in obstacle fields

A neural model for sonar-based navigation in obstacle fields

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IEEE International Symposium on Circuits and systems (ISCAS)

作者： T.K. Horiuchi Institute for Systems Research & The Neuroscience and Cognitive Science Program Electrical and Computer Engineering Department University of Maryland College Park MD USA

The rapid control of sonar-guided vehicles through obstacle fields has been a goal of robotics for decades. How sensory data is represented strongly affects how obstacles and goal information can be combined to select a direction of travel. While typical approaches combine attractive and repulsive effects to directly determine steering, we are investigating an algorithm that evaluates multiple directions simultaneously followed by a winner-take-all (WTA) function which then guides steering. In this paper we describe a neuromorphic VLSI implementation of this algorithm using the inherent echo delay to create a range-dependent gain in a 'race-to-first-spike' neural WTA circuit. The chip was fabricated in a commercially-available 0.5 mum CMOS process and in this paper we present preliminary test results

关键词： Sonar navigation Robot sensing systems Neurons Automotive engineering Neuroscience Cognitive science Educational institutions Control systems Vehicles Cognitive robotics

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Reports of the AAAI 2011 conference workshops

Reports of the AAAI 2011 conference workshops

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作者： Agmon, Noa Agrawal, Vikas Aha, David W. Aloimonos, Yiannis Buckley, Donagh Doshi, Prashant Geib, Christopher Grasso, Floriana Green, Nancy Johnston, Benjamin Kaliski, Burt Kiekintveld, Christopher Law, Edith Lieberman, Henry Mengshoel, Ole J. Metzler, Ted Modayil, Joseph Oard, Douglas W. Onder, Nilufer O'Sullivan, Barry Pastra, Katerina Precup, Doina Ramachandran, Sowmya Reed, Chris Sariel-Talay, Sanem Selker, Ted Shastri, Lokendra Singh, Satinder Smith, Stephen F. Srivastava, Siddharth Sukthankar, Gita Uthus, David C. Williams, Mary-Anne Department of Computer Science University of Texas at Austin United States Center for Knowledge Driven Information Systems Infosys Labs India Adaptive Systems Section Naval Research Laboratory Washington DC United States Department of Computer Science University of Maryland College Park United States EMC's Center of Excellence in Ireland Research Europe at EMC Ireland Department of Computer Science University of Georgia United States School of Informatics University of Edinburgh United Kingdom Department of Computer Science University of Liverpool United Kingdom Department of Computer Science University of North Carolina Greensboro United States Faculty of Engineering and Information Technology University of Technology Sydney Australia VeriSign Inc. United States Department of Computer Science University of Texas at El Paso United States Machine Learning Department Carnegie Mellon University United States Massachusetts Institute of Technology Media Laboratory United States Carnegie Mellon University's Silicon Valley Campus United States Darrell W. Hughes Program for Religion and Science Dialogue Oklahoma City University United States Department of Computing Science University of Alberta Canada College of Information Studies Institute for Advanced Computer Studies University of Maryland College Park United States Department of Computer Science Michigan Technological University United States Department of Computer Science University College Cork Ireland Cognitive Systems Research Insitute Athens Greece School of Computer Science McGill University Canada Stottler Henke Inc. United States School of Computing University of Dundee United Kingdom Department of Computer Engineering Istanbul Technical University Turkey Convergence Lab Infosys Technologies Ltd. United States Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor United States Robotics Institute Carnegie Mellon University United States University of Wisconsin

The AAAI-11 workshop program was held Sunday and Monday, August 7-18, 2011, at the Hyatt Regency San Francisco in San Francisco, California USA. The AAAI-11 workshop program included 15 workshops covering a wide range of topics in artificial intelligence. The titles of the workshops were Activity Context Representation: Techniques and Languages;Analyzing Microtext;Applied Adversarial Reasoning and Risk Modeling;Artificial Intelligence and Smarter Living: The Conquest of Complexity;Artifiicial Intelligence for Data Center Management and Cloud Computing;Automated Action Planning for Autonomous Mobile Robots;Computational Models of Natural Argument;Generalized Planning;Human Computation;Human-Robot Interaction in Elder Care;Interactive Decision Theory and Game Theory, 2010;Language-Action Tools for Cognitive Artificial Agents: Integrating Vision, Action, and Language;Lifelong Learning from Sensorimotor Experience;Plan, Activity, and Intent Recognition;and Scalable Integration of Analytics and Visualization. This article presents short summaries of those events. Copyright © 2012, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： Human robot interaction

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Predicting Probability of Mortality in the Neonatal Intensive Care Unit

Predicting Probability of Mortality in the Neonatal Intensiv...

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

作者： Dajie Zhou Monique Frize Program of Systems Science University of Ottawa Ottawa ONT Canada Department of Systems and Computer Engineering Carleton University and the School of Information Technology and Engineering University of Ottawa Ottawa ONT Canada

Artificial neural networks can be trained to predict outcomes in a neonatal intensive care unit (NICU). This paper expands on past research and shows that neural networks trained by the maximum likelihood estimation criterion will approximate the `a posteriori probability' of NICU mortality. A gradient ascent method for the weight update of three-layer feed-forward neural networks was derived. The neural networks were trained on NICU data and the results were evaluated by performance measurement techniques, such as the Receiver Operating Characteristic Curve and the Hosmer-Lemeshow test. The resulting models applied as mortality prognostic screening tools are presented

关键词： Pediatrics Neural networks Artificial neural networks Maximum likelihood estimation Measurement Testing Feedforward systems Feedforward neural networks Resource management Cities and towns

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A departmental reform strategy and the resultant national model for an undergraduate industrial engineering curriculum

A departmental reform strategy and the resultant national mo...

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114th Annual ASEE Conference and Exposition, 2007

作者： Furterer, Sandra Sharawi, Abeer Crumpton-Young, Lesia Rabelo, Luis Williams, Kent Gregg St John, H. East Carolina University United States Industrial Distribution and Logistics Program College of Technology and Computer Science East Carolina University United States University of Central Florida United States Industrial Engineering and Management Systems Department University of Central Florida United States EMG Consulting

This paper presents the results of an extensive research study to develop a national model for an undergraduate curriculum in Industrial engineering. A departmental reform strategy was developed and applied to reengineer the IE curriculum. The reform strategy began with identifying the voice of the customer, including both industry and academic institutions which employ or enroll IE students after they graduate with their IE undergraduate degree. The emerging topics and desired characteristics of undergraduate IE graduates were identified in an earlier research study. These emerging topics were used in a focus group to identify high-level knowledge clusters of information that future IE graduates would need to master based on possible future work scenarios. The emerging topics were mapped to the high level knowledge clusters to generate curriculum requirements of future progress. This research effort developed a revised IE curriculum that can be used as a national model for IE departments. This curriculum focuses on nontraditional industry sectors, incorporating enhanced instructional strategies that can improve learning and retention, as well as state-of-the art technologies that support these strategies. The national model also includes material to help engineers gain knowledge that will help to prepare them for roles of leadership and management in their careers, as well as provides for recruiting strategies to increase the numbers of underrepresented minorities and women in IE programs. This paper describes the approach used in this research effort to develop the national model, as well as details of the resulting model for undergraduate IE curriculum. © American Society for engineering Education, 2007.

关键词： Industrial engineering

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A soft expert system for the creative exploration of first principles of crystal-laser design

A soft expert system for the creative exploration of first p...

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2002 IEEE International Conference on systems, Man and Cybernetics

作者： Rubin, Stuart H. Rush, Robert J. Smith, Michael H. Jayaram Murthy, S.N. Trajkovic, Ljiljana Spawars Systems Center 53560 Hull Street San Diego CA United States BISC Program University of California Berkeley CA United States Department of Computer Science Central Michigan University Mt. Pleasant MI United States School of Engineering Science Simon Fraser University Burnaby BC Canada

A soft expert system is defined to be one that is qualitatively fuzzy. In this paper, we present such a system known as "KASER" which stands for 'Knowledge Amplification by Structural Expert Randomization". KASER facilitates reasoning using domain specific expert and commonsense knowledge. It accomplishes this through object-classed predicates and an associated novel inference engine. It addresses the high cost associated with the knowledge acquisition bottleneck. It also enables the entry of a basis of rules and provides for the automatic extension of that basis through domain symmetries. We demonstrate an application for KASER in the design of an intelligent tutoring system that teaches the basic science of crystal-laser design. It enables the student to experiment with various design concepts and receive feedback on the functionality of the proposed design. This is possible without a need to preprogram all possible scenarios.

关键词： Knowledge engineering

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A neuromorphic head direction cell system

A neuromorphic head direction cell system

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IEEE International Symposium on Circuits and systems (ISCAS)

作者： Tarek M. Massoud Timothy K. Horiuchi Electrical and Computer Engineering Department Institute of Systems Research Neuroscience and Cognitive Science Program University of Maryland College Park MD USA

ISBN: (纸本)9781424438273

The head direction (HD) cell system in the brain of mammals is thought to be part of the neural circuitry supporting their spatial navigation capabilities. In this paper we present a neuromorphic VLSI system that mimics the operation of the HD system. Relying on spiking neurons and attractor dynamics, this system can be used to represent and retain the current estimate of head orientation in the environment and integrate angular velocity to update this estimate. The presented system is a first step towards more general odometry systems that could be used for modeling biological navigation systems.

关键词： Neuromorphics Neurons High definition video Navigation Very large scale integration Biological system modeling Brain Neuroscience Cognitive science Educational institutions

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A summating, exponentially-decaying CMOS synapse for spiking neural systems

A summating, exponentially-decaying CMOS synapse for spiking...

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17th Annual Conference on Neural Information Processing systems, NIPS 2003

作者： Shi, Rock Z. Horiuchi, Timothy Electrical and Computer Engineering Department University of Maryland College Park MD 20742 United States Institute for Systems Research University of Maryland College Park MD 20742 United States Neuroscience and Cognitive Science Program University of Maryland College Park MD 20742 United States

ISBN: (纸本)0262201526

Synapses are a critical element of biologically-realistic, spike-based neural computation, serving the role of communication, computation, and modification. Many different circuit implementations of synapse function exist with different computational goals in mind. In this paper we describe a new CMOS synapse design that separately controls quiescent leak current, synaptic gain, and time-constant of decay. This circuit implements part of a commonly-used kinetic model of synaptic conductance. We show a theoretical analysis and experimental data for prototypes fabricated in a commercially-available 1.5μm CMOS process.

关键词： CMOS integrated circuits

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Arabic text sentiment analysis and emotion classification using transformers

Arabic text sentiment analysis and emotion classification us...

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Electronics, Communications, and Computations (JAC-ECC), International Japan-Africa Conference on

作者： Nada Mossad Youssef Mohamed A Fares Ahmed B. Zaky Egypt-Japan University of Science and Technology (E-JUST) Alexandria Egypt The Department of Electrical Engineering The Computer Systems Engineering Program Faculty of Engineering at Shoubra Benha University

Sentiment analysis and emotion classification are two crucial components of natural language processing (NLP), which have been widely explored in recent years due to their broad applications. Sentiment analysis aims to identify the polarity of written texts, ranging from positive to negative. Meanwhile, emotion classification is focused on recognizing and categorizing the emotional states expressed in the text. To achieve a deeper understanding of sentiments and emotions, it's essential to utilize models like BERT transformers that can effectively interpret the context. The process begins with data preprocessing, including tokenization and noise removal, followed by fine-tuning techniques to adapt the BERT model to the proposed tasks. We employed the BERT model on four datasets obtained from various sources, including Twitter, news websites, and restaurant reviews, where each dataset represents a distinct Arabic dialect. Our proposed model outperforms commonly used techniques like LSTM and CNN, yielding superior results. Despite the progress made, there are still challenges to overcome, such as dealing with Arabic diacritics, the new Arabic Arabizi, which uses Latin characters, and handling Arabic idioms. Further research is required to address these challenges adequately.

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