Cardiac electrical activities are varying in both space and time. Human heart consists of a fractal network of muscle cells, Purkinje fibers, arteries and veins. Whole-heart modeling of electrical wave conduction and propagation involves a greater level of complexity. Our previous work developed a computer model of the anatomically realistic heart and simulated the electrical conduction with the use of cellular automata and parallel computing. However, simplistic assumptions and rules limit its ability to provide an accurate approximation of real-world dynamics on the complex heart surface, due to sensitive dependence of nonlinear dynamical systems on initial conditions. In this paper, we propose new reaction-diffusion methods and pattern recognition tools to simulate and model spatiotemporal dynamics of electrical wave conduction and propagation on the complex heart surface, which include (i) whole heart model; (ii) 2D isometric graphing of 3D heart geometry; (iii) reaction diffusion modeling of electrical waves in 2D graph, and (iv) spatiotemporal pattern recognition. Experimental results show that the proposed numerical solution has strong potentials to model the space-time dynamics of electrical wave conduction in the whole heart, thereby achieving a better understanding of disease-altered cardiac mechanisms.
Making effective use of graphics processing units (GPUs) in computationsOregon State University
Graphics processing units (GPUs) are specialized computer processors used in computers and video game systems to accelerate the creation and display of images. Due to their inherent parallel structure, they also have great potential to speed up computations in many scientific and engineering applications. GPUs are attractive for their ability to perform a large number of computations in parallel at an attractive price. Many of the world¹s largest supercomputers use GPUs to achieve their high performance, and personal computers and laptops use them for graphics displays and image processing. This seminar will explore the use of GPUs in general, describe examples of the use of GPUs in computations, and introduce some best practices for GPU computing.
PowerPoint slides from a 2015 Guest Lecture in PSYCH-268A: Computational Neuroscience, Prof. Jeff Krichmar, University of California, Irvine (UCI).
Corresponding publication:
Beyeler*, M., Carlson*, K. D. , Chou*, T-S., Dutt, N., Krichmar, J. L. (2015). CARLsim 3: A user-friendly and highly optimized library for the creation of neurobiologically detailed spiking neural networks. Proceedings of IEEE International Joint Conference on Neural Networks (IJCNN), Killarney, Ireland. (*equal contribution)
Making effective use of graphics processing units (GPUs) in computationsOregon State University
Graphics processing units (GPUs) are specialized computer processors used in computers and video game systems to accelerate the creation and display of images. Due to their inherent parallel structure, they also have great potential to speed up computations in many scientific and engineering applications. GPUs are attractive for their ability to perform a large number of computations in parallel at an attractive price. Many of the world¹s largest supercomputers use GPUs to achieve their high performance, and personal computers and laptops use them for graphics displays and image processing. This seminar will explore the use of GPUs in general, describe examples of the use of GPUs in computations, and introduce some best practices for GPU computing.
PowerPoint slides from a 2015 Guest Lecture in PSYCH-268A: Computational Neuroscience, Prof. Jeff Krichmar, University of California, Irvine (UCI).
Corresponding publication:
Beyeler*, M., Carlson*, K. D. , Chou*, T-S., Dutt, N., Krichmar, J. L. (2015). CARLsim 3: A user-friendly and highly optimized library for the creation of neurobiologically detailed spiking neural networks. Proceedings of IEEE International Joint Conference on Neural Networks (IJCNN), Killarney, Ireland. (*equal contribution)
The Algorithms of Life - Scientific Computing for Systems Biologyinside-BigData.com
In this deck from ISC 2019, Ivo Sbalzarini from TU Dresden presents: The Algorithms of Life - Scientific Computing for Systems Biology. In his talk, Sbalzarini mainly discussed the rapidly growing importance and influence in the life sciences for scientific high-performance computing.
"Scientific high-performance computing is of rapidly growing importance and influence in the life sciences. Thanks to the increasing knowledge about the molecular foundations of life, recent advances in biomedical data science, and the availability of predictive biophysical theories that can be numerically simulated, mechanistic understanding of the emergence of life comes within reach. Computing is playing a pivotal and catalytic role in this scientific revolution, both as a tool of investigation and hypothesis testing, but also as a school of thought and systems model. This is because a developing tissue, embryo, or organ can itself be seen as a massively parallel distributed computing system that collectively self-organizes to bring about behavior we call life. In any multicellular organism, every cell constantly takes decisions about growth, division, and migration based on local information, with cells communicating with each other via chemical, mechanical, and electrical signals across length scales from nanometers to meters. Each cell can therefore be understood as a mechano-chemical processing element in a complexly interconnected million- or billion-core computing system. Mechanistically understanding and reprogramming this system is a grand challenge. While the “hardware” (proteins, lipids, etc.) and the “source code” (genetic code) are increasingly known, we known virtually nothing about the algorithms that this code implements on this hardware. Our vision is to contribute to this challenge by developing computational methods and software systems for high-performance data analysis, inference, and numerical simulation of computer models of biological tissues, incorporating the known biochemistry and biophysics in 3D-space and time, in order to understand biological processes on an algorithmic basis. This ranges from real-time approaches to biomedical image analysis, to novel simulation languages for parallel high-performance computing, to virtual reality and machine learning for 3D microscopy and numerical simulations of coupled biochemical-biomechanical models. The cooperative, interdisciplinary effort to develop and advance our understanding of life using computational approaches not only places high-performance computing center stage, but also provides stimulating impulses for the future development of this field."
Watch the video: https://wp.me/p3RLHQ-kBB
Learn more: https://www.isc-hpc.com/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Description of the quality-assessment and validation of the third release of the Individual Brain Charting (IBC) dataset, namely on naturalistic stimuli using a fastSRM encoding experiment.
Magnetic resonance imaging as a tool to assess reliability in simulating hemodynamics in cerebral aneurysms with a dedicated computational fluid dynamics prototype: preliminary results
Christof Karmonik, Y. Jonathan. Zhang, Orlando Diaz, Richard Klucznik, Sasan Partovi, Robert G. Grossman, Gavin W. Britz
The clinical indication of arrhythmia identifies specific aberrant circumstances in heart pumping that may be detected using electrical impulses during conduction or by allowing a little amount of current to travel through the electrodes, disrupting the cardiac muscle's resistance. The electrocardiogram (ECG) is one of the most important instruments for detecting cardiac arrhythmia since it is the most least intrusive and effective procedure. Physically or visually inspecting the heart is time-consuming and difficult, hence the development of computer aided diagnosis (CAD) is being developed to aid clinical decision-making. In this suggested research, a convolutional neural network (CNN)-based approach is used to automate the heartbeat classification process in order to identify cardiac arrhythmia. The improved enhancement of CNN structure has been implemented in this suggested research. The feature maps are then subjected to the max pooling process. Finally, feature maps are generated by concatenating kernels of different sizes and delivering them as an input to the fully linked layers. The MIT BIH arrhythmia database is used to implement this approach, and the total average accuracy is 99.21%. The proof of the suggested study's efficiency and efficacy in identifying cardiac arrhythmia has also been done via an experimental comparison.
The Advancement and Challenges in Computational Physics - PhdassistancePhD Assistance
For the last five decades, computational physics has been a valuable scientific instrument in physics. In comparison to using only theoretical and experimental approaches, it has enabled physicists to understand complex problems better. Computational physics was mostly a scientific activity at the time, with relatively few organised undergraduate study.
Ph.D. Assistance serves as an external mentor to brainstorm your idea and translate that into a research model. Hiring a mentor or tutor is common and therefore let your research committee know about the same. We do not offer any writing services without the involvement of the researcher.
Learn More: https://bit.ly/3AUvG0y
Contact Us:
Website: https://www.phdassistance.com/
UK NO: +44–1143520021
India No: +91–4448137070
WhatsApp No: +91 91769 66446
Email: info@phdassistance.com
The Algorithms of Life - Scientific Computing for Systems Biologyinside-BigData.com
In this deck from ISC 2019, Ivo Sbalzarini from TU Dresden presents: The Algorithms of Life - Scientific Computing for Systems Biology. In his talk, Sbalzarini mainly discussed the rapidly growing importance and influence in the life sciences for scientific high-performance computing.
"Scientific high-performance computing is of rapidly growing importance and influence in the life sciences. Thanks to the increasing knowledge about the molecular foundations of life, recent advances in biomedical data science, and the availability of predictive biophysical theories that can be numerically simulated, mechanistic understanding of the emergence of life comes within reach. Computing is playing a pivotal and catalytic role in this scientific revolution, both as a tool of investigation and hypothesis testing, but also as a school of thought and systems model. This is because a developing tissue, embryo, or organ can itself be seen as a massively parallel distributed computing system that collectively self-organizes to bring about behavior we call life. In any multicellular organism, every cell constantly takes decisions about growth, division, and migration based on local information, with cells communicating with each other via chemical, mechanical, and electrical signals across length scales from nanometers to meters. Each cell can therefore be understood as a mechano-chemical processing element in a complexly interconnected million- or billion-core computing system. Mechanistically understanding and reprogramming this system is a grand challenge. While the “hardware” (proteins, lipids, etc.) and the “source code” (genetic code) are increasingly known, we known virtually nothing about the algorithms that this code implements on this hardware. Our vision is to contribute to this challenge by developing computational methods and software systems for high-performance data analysis, inference, and numerical simulation of computer models of biological tissues, incorporating the known biochemistry and biophysics in 3D-space and time, in order to understand biological processes on an algorithmic basis. This ranges from real-time approaches to biomedical image analysis, to novel simulation languages for parallel high-performance computing, to virtual reality and machine learning for 3D microscopy and numerical simulations of coupled biochemical-biomechanical models. The cooperative, interdisciplinary effort to develop and advance our understanding of life using computational approaches not only places high-performance computing center stage, but also provides stimulating impulses for the future development of this field."
Watch the video: https://wp.me/p3RLHQ-kBB
Learn more: https://www.isc-hpc.com/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Description of the quality-assessment and validation of the third release of the Individual Brain Charting (IBC) dataset, namely on naturalistic stimuli using a fastSRM encoding experiment.
Magnetic resonance imaging as a tool to assess reliability in simulating hemodynamics in cerebral aneurysms with a dedicated computational fluid dynamics prototype: preliminary results
Christof Karmonik, Y. Jonathan. Zhang, Orlando Diaz, Richard Klucznik, Sasan Partovi, Robert G. Grossman, Gavin W. Britz
The clinical indication of arrhythmia identifies specific aberrant circumstances in heart pumping that may be detected using electrical impulses during conduction or by allowing a little amount of current to travel through the electrodes, disrupting the cardiac muscle's resistance. The electrocardiogram (ECG) is one of the most important instruments for detecting cardiac arrhythmia since it is the most least intrusive and effective procedure. Physically or visually inspecting the heart is time-consuming and difficult, hence the development of computer aided diagnosis (CAD) is being developed to aid clinical decision-making. In this suggested research, a convolutional neural network (CNN)-based approach is used to automate the heartbeat classification process in order to identify cardiac arrhythmia. The improved enhancement of CNN structure has been implemented in this suggested research. The feature maps are then subjected to the max pooling process. Finally, feature maps are generated by concatenating kernels of different sizes and delivering them as an input to the fully linked layers. The MIT BIH arrhythmia database is used to implement this approach, and the total average accuracy is 99.21%. The proof of the suggested study's efficiency and efficacy in identifying cardiac arrhythmia has also been done via an experimental comparison.
The Advancement and Challenges in Computational Physics - PhdassistancePhD Assistance
For the last five decades, computational physics has been a valuable scientific instrument in physics. In comparison to using only theoretical and experimental approaches, it has enabled physicists to understand complex problems better. Computational physics was mostly a scientific activity at the time, with relatively few organised undergraduate study.
Ph.D. Assistance serves as an external mentor to brainstorm your idea and translate that into a research model. Hiring a mentor or tutor is common and therefore let your research committee know about the same. We do not offer any writing services without the involvement of the researcher.
Learn More: https://bit.ly/3AUvG0y
Contact Us:
Website: https://www.phdassistance.com/
UK NO: +44–1143520021
India No: +91–4448137070
WhatsApp No: +91 91769 66446
Email: info@phdassistance.com
Flu Vaccine Alert in Bangalore Karnatakaaddon Scans
As flu season approaches, health officials in Bangalore, Karnataka, are urging residents to get their flu vaccinations. The seasonal flu, while common, can lead to severe health complications, particularly for vulnerable populations such as young children, the elderly, and those with underlying health conditions.
Dr. Vidisha Kumari, a leading epidemiologist in Bangalore, emphasizes the importance of getting vaccinated. "The flu vaccine is our best defense against the influenza virus. It not only protects individuals but also helps prevent the spread of the virus in our communities," he says.
This year, the flu season is expected to coincide with a potential increase in other respiratory illnesses. The Karnataka Health Department has launched an awareness campaign highlighting the significance of flu vaccinations. They have set up multiple vaccination centers across Bangalore, making it convenient for residents to receive their shots.
To encourage widespread vaccination, the government is also collaborating with local schools, workplaces, and community centers to facilitate vaccination drives. Special attention is being given to ensuring that the vaccine is accessible to all, including marginalized communities who may have limited access to healthcare.
Residents are reminded that the flu vaccine is safe and effective. Common side effects are mild and may include soreness at the injection site, mild fever, or muscle aches. These side effects are generally short-lived and far less severe than the flu itself.
Healthcare providers are also stressing the importance of continuing COVID-19 precautions. Wearing masks, practicing good hand hygiene, and maintaining social distancing are still crucial, especially in crowded places.
Protect yourself and your loved ones by getting vaccinated. Together, we can help keep Bangalore healthy and safe this flu season. For more information on vaccination centers and schedules, residents can visit the Karnataka Health Department’s official website or follow their social media pages.
Stay informed, stay safe, and get your flu shot today!
Explore natural remedies for syphilis treatment in Singapore. Discover alternative therapies, herbal remedies, and lifestyle changes that may complement conventional treatments. Learn about holistic approaches to managing syphilis symptoms and supporting overall health.
ARTIFICIAL INTELLIGENCE IN HEALTHCARE.pdfAnujkumaranit
Artificial intelligence (AI) refers to the simulation of human intelligence processes by machines, especially computer systems. It encompasses tasks such as learning, reasoning, problem-solving, perception, and language understanding. AI technologies are revolutionizing various fields, from healthcare to finance, by enabling machines to perform tasks that typically require human intelligence.
Tom Selleck Health: A Comprehensive Look at the Iconic Actor’s Wellness Journeygreendigital
Tom Selleck, an enduring figure in Hollywood. has captivated audiences for decades with his rugged charm, iconic moustache. and memorable roles in television and film. From his breakout role as Thomas Magnum in Magnum P.I. to his current portrayal of Frank Reagan in Blue Bloods. Selleck's career has spanned over 50 years. But beyond his professional achievements. fans have often been curious about Tom Selleck Health. especially as he has aged in the public eye.
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Introduction
Many have been interested in Tom Selleck health. not only because of his enduring presence on screen but also because of the challenges. and lifestyle choices he has faced and made over the years. This article delves into the various aspects of Tom Selleck health. exploring his fitness regimen, diet, mental health. and the challenges he has encountered as he ages. We'll look at how he maintains his well-being. the health issues he has faced, and his approach to ageing .
Early Life and Career
Childhood and Athletic Beginnings
Tom Selleck was born on January 29, 1945, in Detroit, Michigan, and grew up in Sherman Oaks, California. From an early age, he was involved in sports, particularly basketball. which played a significant role in his physical development. His athletic pursuits continued into college. where he attended the University of Southern California (USC) on a basketball scholarship. This early involvement in sports laid a strong foundation for his physical health and disciplined lifestyle.
Transition to Acting
Selleck's transition from an athlete to an actor came with its physical demands. His first significant role in "Magnum P.I." required him to perform various stunts and maintain a fit appearance. This role, which he played from 1980 to 1988. necessitated a rigorous fitness routine to meet the show's demands. setting the stage for his long-term commitment to health and wellness.
Fitness Regimen
Workout Routine
Tom Selleck health and fitness regimen has evolved. adapting to his changing roles and age. During his "Magnum, P.I." days. Selleck's workouts were intense and focused on building and maintaining muscle mass. His routine included weightlifting, cardiovascular exercises. and specific training for the stunts he performed on the show.
Selleck adjusted his fitness routine as he aged to suit his body's needs. Today, his workouts focus on maintaining flexibility, strength, and cardiovascular health. He incorporates low-impact exercises such as swimming, walking, and light weightlifting. This balanced approach helps him stay fit without putting undue strain on his joints and muscles.
Importance of Flexibility and Mobility
In recent years, Selleck has emphasized the importance of flexibility and mobility in his fitness regimen. Understanding the natural decline in muscle mass and joint flexibility with age. he includes stretching and yoga in his routine. These practices help prevent injuries, improve posture, and maintain mobilit
Lung Cancer: Artificial Intelligence, Synergetics, Complex System Analysis, S...Oleg Kshivets
RESULTS: Overall life span (LS) was 2252.1±1742.5 days and cumulative 5-year survival (5YS) reached 73.2%, 10 years – 64.8%, 20 years – 42.5%. 513 LCP lived more than 5 years (LS=3124.6±1525.6 days), 148 LCP – more than 10 years (LS=5054.4±1504.1 days).199 LCP died because of LC (LS=562.7±374.5 days). 5YS of LCP after bi/lobectomies was significantly superior in comparison with LCP after pneumonectomies (78.1% vs.63.7%, P=0.00001 by log-rank test). AT significantly improved 5YS (66.3% vs. 34.8%) (P=0.00000 by log-rank test) only for LCP with N1-2. Cox modeling displayed that 5YS of LCP significantly depended on: phase transition (PT) early-invasive LC in terms of synergetics, PT N0—N12, cell ratio factors (ratio between cancer cells- CC and blood cells subpopulations), G1-3, histology, glucose, AT, blood cell circuit, prothrombin index, heparin tolerance, recalcification time (P=0.000-0.038). Neural networks, genetic algorithm selection and bootstrap simulation revealed relationships between 5YS and PT early-invasive LC (rank=1), PT N0—N12 (rank=2), thrombocytes/CC (3), erythrocytes/CC (4), eosinophils/CC (5), healthy cells/CC (6), lymphocytes/CC (7), segmented neutrophils/CC (8), stick neutrophils/CC (9), monocytes/CC (10); leucocytes/CC (11). Correct prediction of 5YS was 100% by neural networks computing (area under ROC curve=1.0; error=0.0).
CONCLUSIONS: 5YS of LCP after radical procedures significantly depended on: 1) PT early-invasive cancer; 2) PT N0--N12; 3) cell ratio factors; 4) blood cell circuit; 5) biochemical factors; 6) hemostasis system; 7) AT; 8) LC characteristics; 9) LC cell dynamics; 10) surgery type: lobectomy/pneumonectomy; 11) anthropometric data. Optimal diagnosis and treatment strategies for LC are: 1) screening and early detection of LC; 2) availability of experienced thoracic surgeons because of complexity of radical procedures; 3) aggressive en block surgery and adequate lymph node dissection for completeness; 4) precise prediction; 5) adjuvant chemoimmunoradiotherapy for LCP with unfavorable prognosis.
These lecture slides, by Dr Sidra Arshad, offer a quick overview of physiological basis of a normal electrocardiogram.
Learning objectives:
1. Define an electrocardiogram (ECG) and electrocardiography
2. Describe how dipoles generated by the heart produce the waveforms of the ECG
3. Describe the components of a normal electrocardiogram of a typical bipolar leads (limb II)
4. Differentiate between intervals and segments
5. Enlist some common indications for obtaining an ECG
Study Resources:
1. Chapter 11, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 9, Human Physiology - From Cells to Systems, Lauralee Sherwood, 9th edition
3. Chapter 29, Ganong’s Review of Medical Physiology, 26th edition
4. Electrocardiogram, StatPearls - https://www.ncbi.nlm.nih.gov/books/NBK549803/
5. ECG in Medical Practice by ABM Abdullah, 4th edition
6. ECG Basics, http://www.nataliescasebook.com/tag/e-c-g-basics
These simplified slides by Dr. Sidra Arshad present an overview of the non-respiratory functions of the respiratory tract.
Learning objectives:
1. Enlist the non-respiratory functions of the respiratory tract
2. Briefly explain how these functions are carried out
3. Discuss the significance of dead space
4. Differentiate between minute ventilation and alveolar ventilation
5. Describe the cough and sneeze reflexes
Study Resources:
1. Chapter 39, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 34, Ganong’s Review of Medical Physiology, 26th edition
3. Chapter 17, Human Physiology by Lauralee Sherwood, 9th edition
4. Non-respiratory functions of the lungs https://academic.oup.com/bjaed/article/13/3/98/278874
Ethanol (CH3CH2OH), or beverage alcohol, is a two-carbon alcohol
that is rapidly distributed in the body and brain. Ethanol alters many
neurochemical systems and has rewarding and addictive properties. It
is the oldest recreational drug and likely contributes to more morbidity,
mortality, and public health costs than all illicit drugs combined. The
5th edition of the Diagnostic and Statistical Manual of Mental Disorders
(DSM-5) integrates alcohol abuse and alcohol dependence into a single
disorder called alcohol use disorder (AUD), with mild, moderate,
and severe subclassifications (American Psychiatric Association, 2013).
In the DSM-5, all types of substance abuse and dependence have been
combined into a single substance use disorder (SUD) on a continuum
from mild to severe. A diagnosis of AUD requires that at least two of
the 11 DSM-5 behaviors be present within a 12-month period (mild
AUD: 2–3 criteria; moderate AUD: 4–5 criteria; severe AUD: 6–11 criteria).
The four main behavioral effects of AUD are impaired control over
drinking, negative social consequences, risky use, and altered physiological
effects (tolerance, withdrawal). This chapter presents an overview
of the prevalence and harmful consequences of AUD in the U.S.,
the systemic nature of the disease, neurocircuitry and stages of AUD,
comorbidities, fetal alcohol spectrum disorders, genetic risk factors, and
pharmacotherapies for AUD.
MANAGEMENT OF ATRIOVENTRICULAR CONDUCTION BLOCK.pdfJim Jacob Roy
Cardiac conduction defects can occur due to various causes.
Atrioventricular conduction blocks ( AV blocks ) are classified into 3 types.
This document describes the acute management of AV block.
The prostate is an exocrine gland of the male mammalian reproductive system
It is a walnut-sized gland that forms part of the male reproductive system and is located in front of the rectum and just below the urinary bladder
Function is to store and secrete a clear, slightly alkaline fluid that constitutes 10-30% of the volume of the seminal fluid that along with the spermatozoa, constitutes semen
A healthy human prostate measures (4cm-vertical, by 3cm-horizontal, 2cm ant-post ).
It surrounds the urethra just below the urinary bladder. It has anterior, median, posterior and two lateral lobes
It’s work is regulated by androgens which are responsible for male sex characteristics
Generalised disease of the prostate due to hormonal derangement which leads to non malignant enlargement of the gland (increase in the number of epithelial cells and stromal tissue)to cause compression of the urethra leading to symptoms (LUTS
Evaluation of antidepressant activity of clitoris ternatea in animals
Whole Heart Modeling – Spatiotemporal Dynamics of Electrical Wave Conduction and Propagation
1. Whole Heart Modeling – Spatiotemporal
Dynamics of Electrical Wave Conduction and
Propagation
Hui Yang
杨 徽
Associate Professor
Complex Systems Monitoring, Modeling and Control Lab
The Pennsylvania State University
University Park, PA 16802
November 25, 2017
2. Outline
1 Introduction
2 Research Methodology
Fractal surface simulation
Isometric graphing for surface characterization
Reaction-diffusion modeling in the reduced dimension
Spatiotemporal pattern recognition
3 Experimental Results
4 Conclusions and Future Directions
3. Introduction Research Methodology Experiments Conclusions
Introduction
Computer Simulation
Describe complex phenomena
Predict system behaviors
Optimize control action
Improve system performance
Whole-heart Modeling and Simulation
Overcome many practical and ethical limitations in real-world
biomedical experiments
Offer greater flexibility to test their hypothesis and develop new
hypotheses
Circulation Research - “Biophysics-based cardiac modeling has the
potential to dramatically change the 21st century cardiovascular
research and the field of cardiology.”
Yang, Hui (Penn State) Whole-Heart Modeling November 25, 2017 3 / 22
4. Introduction Research Methodology Experiments Conclusions
Challenges
Spatiotemporal dynamics
Reaction process: dynamic variables are interacting with each other
Diffusion process: dynamic variables spread out in space
High dimensionality
Approximately 2 billion heart muscle cells
Complex geometry
Euclidean vs. Fractal
Yang, Hui (Penn State) Whole-Heart Modeling November 25, 2017 4 / 22
6. Introduction Research Methodology Experiments Conclusions
Literature - Fractal characterization and modeling
Mandelbrot - ”A rough or fragmented geometric shape that can be subdivided in
parts, each of which is (at least approximately) a reduced/size copy of the whole”
Characterization of fractal dimension
Monofractal - homogeneous self-similarity across scales, characterized by a single
fractal dimension.
Multifractal - non-homogeneous self-similarity across scales, singularity spectrum
to characterize scaling properties
Modeling the fractal object or process
Iterative or recursive function systems
Rough surfaces - shear displacement algorithm, diamond-square algorithm
Heart rate time series - random cascade model
Gaps
Little has been done to develop simulation model of spatiotemporal
dynamics on fractal geometry
Modeling differences between fractal and Euclidean geometry have not
been fully investigated before
Need to fill the gaps
Yang, Hui (Penn State) Whole-Heart Modeling November 25, 2017 6 / 22
7. Introduction Research Methodology Experiments Conclusions
Literature - Dimensionality reduction
High-dimensional data are difficult to visualize and interpret
Dimensionality reduction approaches
Principal component analysis (PCA)
Multidimensional scaling (MDS)
Self-organizing map (SOM)
Isometric feature mapping (ISOMAP)
Most of previous studies focused on the reduction of high-dimensional
data and then the extraction of useful information from the
low-dimensional data.
Gaps - Few previous approaches considered the construction of
simulation models in the low-dimensional space.
Need to fill the gaps
Yang, Hui (Penn State) Whole-Heart Modeling November 25, 2017 7 / 22
8. Introduction Research Methodology Experiments Conclusions
Fractal surface simulation
Random midpoint displacement algorithm
Yang, Hui (Penn State) Whole-Heart Modeling November 25, 2017 8 / 22
9. Introduction Research Methodology Experiments Conclusions
Isometric graphing for surface characterization
Isometric graphing algorithm*
Construct neighborhood graph
Compute shortest paths – geodesic distances: e.g., Dijkstra’s algorithm
Construct low dimensional embedding: classical MDS
Euclidean distance → Geodesic distance
*J. B.Tenenbaum et al., A Global Geometric Framework for Nonlinear Dimensionality Reduction, Science 290 (5500), 2000
Yang, Hui (Penn State) Whole-Heart Modeling November 25, 2017 9 / 22
10. Introduction Research Methodology Experiments Conclusions
Reaction-Diffusion Modeling
FitzHugh-Nagumo (FHN) model
∂u
∂t = c1u(1 − u)(u − a) − c2uv + 2u
∂v
∂t = b(u − dv)
where a = 0.13; b = 0.013; c1 = 0.26; c2 = 0.1; d = 1.0; u : membrane
voltage; v : recovery variable.
Yang, Hui (Penn State) Whole-Heart Modeling November 25, 2017 10 / 22
11. Introduction Research Methodology Experiments Conclusions
R-D Model on the Heart
Healthy heart
Near-periodic electrical impulse
Arrhythmia heart
Atrial fibrillation
Rapid, disorganized and irregular electrical impulse
https://youtu.be/bH vJfZzgOM
Yang, Hui (Penn State) Whole-Heart Modeling November 25, 2017 11 / 22
12. Introduction Research Methodology Experiments Conclusions
Spatiotemporal pattern recognition
Spatiotemporal data
Y (si, t), t = 1, ..., T
si - spatial location, i = 1, ..., N
Hyper-distance matrix: spatiotemporal dissimilarity
Yang, Hui (Penn State) Whole-Heart Modeling November 25, 2017 12 / 22
13. Introduction Research Methodology Experiments Conclusions
Self-Organizing Network Embedding
Spring-Electrical Model
Nodes − electrically charged particles
Edges − springs between nodes
The repulsive force exists between any pair of nodes
fr(l, m) = −
1
xl − xm
2
× eDT (l,m)
The attractive force exists only between two connected nodes
fa(l, m) = xl − xm
2
× e−DT (l,m)
, l ↔ m
The combined force at a node l: f(l, x)
l=m
−
(xl − xm)
xl − xm
3
×eDT (l,m)
+
l↔m
xl −xm ×(xl − xm)×e−DT (l,m)
Minimal energy network: x∗ = arg minx l=1,...,T f(l, x)2
Yang, Hui (Penn State) Whole-Heart Modeling November 25, 2017 13 / 22
14. Introduction Research Methodology Experiments Conclusions
Low-dimensional Pattern
Yang, Hui (Penn State) Whole-Heart Modeling November 25, 2017 14 / 22
15. Introduction Research Methodology Experiments Conclusions
GPGPU Acceleration
Computer Setting
Intel dual core i3-2100 CPU @ 3.10GHz with 16G DDR3 memory,
Nvidia Telsa C2075 graphic card with 6GB global memory, Window 7
(64 bit) operating system
GPU - NVidia CUDA platform and OpenGL - rendering loop iteration
and GUI mechanism
CPU-based simulation
The algorithm traverses through all cells in the 3D heart model to
finish one iteration, and determine the status (timer value) of each cell.
Then, OpenGL is called to render the whole heart according to the
status of cells.
GPU-based simulation
Instead of iterating through the heart, we issue every cell a thread,
which will increase the timer of the thread, look up the neighbors, and
calculate and store the states for the cell if applicable. We, then,
launch 148516 threads to the GPU multicore processors and run these
threads in parallel.
Yang, Hui (Penn State) Whole-Heart Modeling November 25, 2017 15 / 22
16. Introduction Research Methodology Experiments Conclusions
GPU vs. CPU
GPU Case 1 - OpenGL/CUDA coop
GPU Case 2 - fast data transferring within shared memory
728,321 cells in the whole-heart model,
Improve the speed of computing by approximately 30-fold
Yang, Hui (Penn State) Whole-Heart Modeling November 25, 2017 16 / 22
17. Introduction Research Methodology Experiments Conclusions
GPU Simulation Demo
https://youtu.be/7MGA9r9A- A
Yang, Hui (Penn State) Whole-Heart Modeling November 25, 2017 17 / 22
18. Introduction Research Methodology Experiments Conclusions
Summary
Challenges
Complex geometry: Fractal nature of high-dimensional systems
Geometric preservation: Geodesic distances vs. Euclidean distances
Large-scale simulation of complex biological systems
Spatiotemporal dynamics of electrical conduction and propagation
Methodology - spatiotemporal dynamics on fractal surfaces
Characterization and modeling of fractal geometry
Fractal-based simulation and modeling of spatiotemporal dynamics
Recognizing and quantifying spatiotemporal patterns.
Parallel computing - GPGPU
GPU yields 30 times faster than CPU-based simulation models
Yang, Hui (Penn State) Whole-Heart Modeling November 25, 2017 18 / 22
19. References
H. Yang*, Y. Chen, and F. M. Leonelli, “Whole Heart Modeling – Spatiotemporal
Dynamics of Electrical Wave Conduction and Propagation,”Proceedings of 2016
IEEE Engineering in Medicine and Biology Society Conference (EMBC), August
16-20, 2016, Orlando, FL, United States, DOI: 10.1109/EMBC.2016.7591990
Y. Chen and H. Yang*, “Numerical simulation and pattern characterization of
spatiotemporal dynamics on fractal surfaces for the whole-heart modeling
applications,”European Physical Journal B, p. 1-16, 2016, DOI:
10.1140/epjb/e2016-60960-6
D. Yu, D. Du, H. Yang*, and Y.C. Tu, “Parallel Computing Simulation of
Electrical Excitation and Conduction in the 3D Human Heart,”Proceedings of
2014 IEEE Engineering in Medicine and Biology Society Conference (EMBC), p.
4315-4319, August 26-30, 2014, Chicago, IL, United States. DOI:
10.1109/EMBC.2014.6944579
20. Introduction Research Methodology Experiments Conclusions
Acknowledgements
NSF CAREER Award
NSF CMMI-1617148
NSF CMMI-1646660
NSF CMMI-1619648
NSF IOS-1146882
James A. Haley Veterans’ Hospital
Yang, Hui (Penn State) Whole-Heart Modeling November 25, 2017 20 / 22
21. Introduction Research Methodology Experiments Conclusions
Contact Information
Hui Yang, PhD
Associate Professor
Complex Systems Monitoring Modeling and Control Laboratory
Harold and Inge Marcus Department of Industrial and Manufacturing
Engineering
The Pennsylvania State University
Tel: (814) 865-7397
Fax: (814) 863-4745
Email: huy25@psu.edu
Web: http://www.personal.psu.edu/huy25/
Yang, Hui (Penn State) Whole-Heart Modeling November 25, 2017 21 / 22
22. Introduction Research Methodology Experiments Conclusions
Questions?
Yang, Hui (Penn State) Whole-Heart Modeling November 25, 2017 22 / 22