UCSF Informatics Day 2014 - Keith R. Yamamoto, "Precision Medicine"CTSI at UCSF
Keith R. Yamamoto, PhD — Opening Remarks – Precision Medicine
Vice Chancellor for Research
Executive Vice Dean of the School of Medicine
Professor of Cellular and Molecular Pharmacology
UCSF
UCSF Informatics Day 2014 - Keith R. Yamamoto, "Precision Medicine"CTSI at UCSF
Keith R. Yamamoto, PhD — Opening Remarks – Precision Medicine
Vice Chancellor for Research
Executive Vice Dean of the School of Medicine
Professor of Cellular and Molecular Pharmacology
UCSF
Data sharing drivers in precision oncology, biomedical research, and healthcare. Accelerating discovery, innovation, providing credit for all stakeholders - patients, researchers, care providers, payers.
Future Health Challenges: Developing Global Norms for Data and Results Sharin...ExternalEvents
http://www.fao.org/about/meetings/wgs-on-food-safety-management/en/
Future Health Challenges: Developing Global Norms for Data and Results Sharing during Public Health Emergencies. Presentation from the Technical Meeting on the impact of Whole Genome Sequencing (WGS) on food safety management and GMI-9, 23-25 May 2016, Rome, Italy.
The reality of moving towards precision medicineElia Stupka
How do we move towards precision medicine? How can we deliver on the big data in health promise? Who will be the enablers and players? Pharma, Big Tech, or newcomers?
Transforming the NHS through genomic and personalised medicine, pop up uni, 1...NHS England
Expo is the most significant annual health and social care event in the calendar, uniting more NHS and care leaders, commissioners, clinicians, voluntary sector partners, innovators and media than any other health and care event.
Expo 15 returned to Manchester and was hosted once again by NHS England. Around 5000 people a day from health and care, the voluntary sector, local government, and industry joined together at Manchester Central Convention Centre for two packed days of speakers, workshops, exhibitions and professional development.
This year, Expo was more relevant and engaging than ever before, happening within the first 100 days of the new Government, and almost 12 months after the publication of the NHS Five Year Forward View. It was also a great opportunity to check on and learn from the progress of Greater Manchester as the area prepares to take over a £6 billion devolved health and social care budget, pledging to integrate hospital, community, primary and social care and vastly improve health and well-being.
More information is available online: www.expo.nhs.uk
Big data and better health outcomes, the journey to the Ministry of Health virtual information centre, viewed from a research perspective. Presented by Professor Tony Blakely, University of Otago, Wellington, at HINZ 2014, 12 November 2014, 8.30am, Plenary Room
Clinical Research Informatics Year-in-ReviewPeter Embi
Peter Embi's 2018 Clinical Research Informatics Year-in-Review. Presented as closing Keynote address at the 2018 AMIA Informatics Summit in San Francisco, CA.
Cancer in Asia and Role of IAEA in Offering Support to Member StatesAnja Nitzsche-Bell
Presentation delivered at Forum on Innovations and Actions Against Non-Communicable Diseases (IAAN), Asian Development Bank, Manila, Philippines, 18 July 2018
Hannes smarason next code-wuxi combined technologiesHannes Smárason
Lower-cost genome sequencing has reached a point of strong commercial viability. The remaining 2 legs of the “3-legged stool” of genomics-enabling technologies —genomic analysis tools database storage—are rapidly evolving to support the use of genomic information in medical care.
Early diagnosis and prevention enabled by big data geneva conference finale-Marefa
The presentation provides an overview of how digital health or use of data processing and telecommunication infrastructure can contribute to the early diagnosis and prevention of diseases.
Using real-world evidence to investigate clinical research questionsKarin Verspoor
Adoption of electronic health records to document extensive clinical information brings with it the opportunity to utilise that information to support clinical research, and ultimately to support clinical decision making. In this talk, I discuss both these opportunities and the challenges that we face when working with real-world clinical data, and introduce some of the strategies that we are adopting to make this data more usable, and to extract more value from it. I specifically discuss the use of natural language processing to transform clinical documentation into structured data for this purpose.
Work Package (WP) 12 – PEARL Barriers In search for an inventory and assessme...ExternalEvents
http://www.fao.org/about/meetings/wgs-on-food-safety-management/en/
International challenges regarding the future sharing of sequence data. Presentation from the Technical Meeting on the impact of Whole Genome Sequencing (WGS) on food safety management and GMI-9, 23-25 May 2016, Rome, Italy.
Towards Digitally Enabled Genomic Medicine: the Patient of The FutureLarry Smarr
12.02.22
Invited Speaker
Hacking Life
TTI/Vanguard Conference
Title: Towards Digitally Enabled Genomic Medicine: the Patient of The Future
San Jose, CA
Co-ordinated malaria research for better policy and practice: the role of res...ACT Consortium
Prof. David Schellenberg from the London School of Hygiene & Tropical Medicine presents on behalf of the ACT Consortium at the European Congress on Tropical Medicine and International Health in Basel, Switzerland, 8 September 2015
DOE-NCI Pilots presentation at the Frederick National Laboratory Advisory Com...Warren Kibbe
May 2016 FNLAC presentation of the DOE-NCI partnership around three pilots focused on existing projects in NCI and existing NSCI directives and activities in DOE.
Data sharing drivers in precision oncology, biomedical research, and healthcare. Accelerating discovery, innovation, providing credit for all stakeholders - patients, researchers, care providers, payers.
Future Health Challenges: Developing Global Norms for Data and Results Sharin...ExternalEvents
http://www.fao.org/about/meetings/wgs-on-food-safety-management/en/
Future Health Challenges: Developing Global Norms for Data and Results Sharing during Public Health Emergencies. Presentation from the Technical Meeting on the impact of Whole Genome Sequencing (WGS) on food safety management and GMI-9, 23-25 May 2016, Rome, Italy.
The reality of moving towards precision medicineElia Stupka
How do we move towards precision medicine? How can we deliver on the big data in health promise? Who will be the enablers and players? Pharma, Big Tech, or newcomers?
Transforming the NHS through genomic and personalised medicine, pop up uni, 1...NHS England
Expo is the most significant annual health and social care event in the calendar, uniting more NHS and care leaders, commissioners, clinicians, voluntary sector partners, innovators and media than any other health and care event.
Expo 15 returned to Manchester and was hosted once again by NHS England. Around 5000 people a day from health and care, the voluntary sector, local government, and industry joined together at Manchester Central Convention Centre for two packed days of speakers, workshops, exhibitions and professional development.
This year, Expo was more relevant and engaging than ever before, happening within the first 100 days of the new Government, and almost 12 months after the publication of the NHS Five Year Forward View. It was also a great opportunity to check on and learn from the progress of Greater Manchester as the area prepares to take over a £6 billion devolved health and social care budget, pledging to integrate hospital, community, primary and social care and vastly improve health and well-being.
More information is available online: www.expo.nhs.uk
Big data and better health outcomes, the journey to the Ministry of Health virtual information centre, viewed from a research perspective. Presented by Professor Tony Blakely, University of Otago, Wellington, at HINZ 2014, 12 November 2014, 8.30am, Plenary Room
Clinical Research Informatics Year-in-ReviewPeter Embi
Peter Embi's 2018 Clinical Research Informatics Year-in-Review. Presented as closing Keynote address at the 2018 AMIA Informatics Summit in San Francisco, CA.
Cancer in Asia and Role of IAEA in Offering Support to Member StatesAnja Nitzsche-Bell
Presentation delivered at Forum on Innovations and Actions Against Non-Communicable Diseases (IAAN), Asian Development Bank, Manila, Philippines, 18 July 2018
Hannes smarason next code-wuxi combined technologiesHannes Smárason
Lower-cost genome sequencing has reached a point of strong commercial viability. The remaining 2 legs of the “3-legged stool” of genomics-enabling technologies —genomic analysis tools database storage—are rapidly evolving to support the use of genomic information in medical care.
Early diagnosis and prevention enabled by big data geneva conference finale-Marefa
The presentation provides an overview of how digital health or use of data processing and telecommunication infrastructure can contribute to the early diagnosis and prevention of diseases.
Using real-world evidence to investigate clinical research questionsKarin Verspoor
Adoption of electronic health records to document extensive clinical information brings with it the opportunity to utilise that information to support clinical research, and ultimately to support clinical decision making. In this talk, I discuss both these opportunities and the challenges that we face when working with real-world clinical data, and introduce some of the strategies that we are adopting to make this data more usable, and to extract more value from it. I specifically discuss the use of natural language processing to transform clinical documentation into structured data for this purpose.
Work Package (WP) 12 – PEARL Barriers In search for an inventory and assessme...ExternalEvents
http://www.fao.org/about/meetings/wgs-on-food-safety-management/en/
International challenges regarding the future sharing of sequence data. Presentation from the Technical Meeting on the impact of Whole Genome Sequencing (WGS) on food safety management and GMI-9, 23-25 May 2016, Rome, Italy.
Towards Digitally Enabled Genomic Medicine: the Patient of The FutureLarry Smarr
12.02.22
Invited Speaker
Hacking Life
TTI/Vanguard Conference
Title: Towards Digitally Enabled Genomic Medicine: the Patient of The Future
San Jose, CA
Co-ordinated malaria research for better policy and practice: the role of res...ACT Consortium
Prof. David Schellenberg from the London School of Hygiene & Tropical Medicine presents on behalf of the ACT Consortium at the European Congress on Tropical Medicine and International Health in Basel, Switzerland, 8 September 2015
DOE-NCI Pilots presentation at the Frederick National Laboratory Advisory Com...Warren Kibbe
May 2016 FNLAC presentation of the DOE-NCI partnership around three pilots focused on existing projects in NCI and existing NSCI directives and activities in DOE.
SAMSI Precision Medicine Keynote, August 2018: Data: where Precision Oncology...Warren Kibbe
The promise of precision medicine in oncology is predicated on the availability of accurate, high quality data from the clinic and the laboratory. Likewise, a Learning Health System is one in which we use data to monitor that we are following guidelines and care pathways to deliver the best care and not revert to prior practices (regression testing for care!) and also provide real world evidence to determine effectiveness and identify populations that would benefit from novel therapies. Into this mix of clinical drivers are the rapidly changing capabilities in instrumentation, computing, computation, and the pervasive use of sensors and smart devices. I will highlight a few of the obvious and perhaps not as obvious opportunities in leveraging the increasingly digital landscape in healthcare and biomedical research as we move toward a national learning health system for cancer.
2016 Data Commons and Data Science Workshop June 7th and June 8th 2016. Genomic Data Commons, FAIR, NCI and making data more findable, publicly accessible, interoperable (machine readable), reusable and support recognition and attribution
National Cancer Data Ecosystem and Data SharingWarren Kibbe
Grand Rounds at the Siteman Cancer Center at Washington University. Highlighting the Genomic Data Commons and the National Cancer Data Ecosystem defined by the Cancer Moonshot Blue Ribbon Panel
NCI Cancer Genomics, Open Science and PMI: FAIR Warren Kibbe
Talk given to the NLM Fellows on July 8, 2016. Touches on Cancer Genomics, Open Science and PMI: FAIR in NCI genomics thinking and projects. Includes discussion of the Genomic Data Commons (GDC), Cancer Data Ecosystem, Data sharing, and the NCI cancer clinical trials open API.
US Federal Cancer Moonshot- One Year LaterJerry Lee
Presentation from former Cancer Moonshot Data and Technology Track Co-chairs Jerry S.H. Lee, PhD (NCI, former OVP) and Dimitri Kusnezov, PhD (DOE) to update on efforts that will help realize the Data/Tech Track's vision of a national learning healthcare system for cancer. These include NCI/DOE pilots, DOE/VA pilot, NCI GDC, DoD/VA/NCI APOLLO, NCI/GSK ATOM, and BloodPAC.
Overview of the NIH-funded RADx-UP - Rapid Acceleration of Diagnostics - Underserved Populations (RADx-UP) Coordination and Data Collection Center (CDCC) with a focus on the Common Data Elements used to gather data across the RADx-UP Consortium for COVID-19 testing.
RADx-UP CDCC presentation for the NIH Disaster Interest GroupWarren Kibbe
Presentation on the RADx-Underserved Populations Coordination and Data Collection Center with an emphasis on how it will help understand and reduce the disparities associated with the COVDI-19 pandemic
Data Harmonization for a Molecularly Driven Health SystemWarren Kibbe
Maximizing the value of data, computing, data science in an academic medical center, or 'towards a molecularly informed Learning Health System. Given in October at the University of Florida in Gainesville
Data Harmonization for a Molecularly Driven Health SystemWarren Kibbe
Seminar for Dr. Min Zhang's Purdue Bioinformatics Seminar Series. Touched on learning health systems, the Gen3 Data Commons, the NCI Genomic Data Commons, Data Harmonization, FAIR, and open science.
Drivers for data sharing in funding of biomedical research. Importance of data sharing on open science, innovation, reproducibility that is enabled by digital technologies and data science.
Data in precision oncology SAMSI Precision Medicine Meeting mar 2019Warren Kibbe
Talk at the March 14-15 2019 SAMSI Advances in Precision and Personalized Medicine held as part of the Program on Statistical, Mathematical, and Computational Methods for Precision Medicine (PMED) at NCSU, Raleigh, NC
Opportunities in technology and connected health for population science Warren Kibbe
AACR Modernizing Population Science in the Digital Age MEG meeting.Keynote on Opportunities in technology and connected health for population science from February 2019
Focus is on the cancer data science and informatics community, a sad farewell to our friend and colleague Paul Fearn, kudos to Frank Manion, Mia Levy, and Samir Courdy. A little bit of overall change in cancer therapies, informatics, technology, and of course data science. A few highlights from publications as well!
Pace of technology innovation, changes in publication, separating data generation from publishing insights. Given at the 2018 VIVO conference at Duke University.
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!
Recomendações da OMS sobre cuidados maternos e neonatais para uma experiência pós-natal positiva.
Em consonância com os ODS – Objetivos do Desenvolvimento Sustentável e a Estratégia Global para a Saúde das Mulheres, Crianças e Adolescentes, e aplicando uma abordagem baseada nos direitos humanos, os esforços de cuidados pós-natais devem expandir-se para além da cobertura e da simples sobrevivência, de modo a incluir cuidados de qualidade.
Estas diretrizes visam melhorar a qualidade dos cuidados pós-natais essenciais e de rotina prestados às mulheres e aos recém-nascidos, com o objetivo final de melhorar a saúde e o bem-estar materno e neonatal.
Uma “experiência pós-natal positiva” é um resultado importante para todas as mulheres que dão à luz e para os seus recém-nascidos, estabelecendo as bases para a melhoria da saúde e do bem-estar a curto e longo prazo. Uma experiência pós-natal positiva é definida como aquela em que as mulheres, pessoas que gestam, os recém-nascidos, os casais, os pais, os cuidadores e as famílias recebem informação consistente, garantia e apoio de profissionais de saúde motivados; e onde um sistema de saúde flexível e com recursos reconheça as necessidades das mulheres e dos bebês e respeite o seu contexto cultural.
Estas diretrizes consolidadas apresentam algumas recomendações novas e já bem fundamentadas sobre cuidados pós-natais de rotina para mulheres e neonatos que recebem cuidados no pós-parto em unidades de saúde ou na comunidade, independentemente dos recursos disponíveis.
É fornecido um conjunto abrangente de recomendações para cuidados durante o período puerperal, com ênfase nos cuidados essenciais que todas as mulheres e recém-nascidos devem receber, e com a devida atenção à qualidade dos cuidados; isto é, a entrega e a experiência do cuidado recebido. Estas diretrizes atualizam e ampliam as recomendações da OMS de 2014 sobre cuidados pós-natais da mãe e do recém-nascido e complementam as atuais diretrizes da OMS sobre a gestão de complicações pós-natais.
O estabelecimento da amamentação e o manejo das principais intercorrências é contemplada.
Recomendamos muito.
Vamos discutir essas recomendações no nosso curso de pós-graduação em Aleitamento no Instituto Ciclos.
Esta publicação só está disponível em inglês até o momento.
Prof. Marcus Renato de Carvalho
www.agostodourado.com
Couples presenting to the infertility clinic- Do they really have infertility...Sujoy Dasgupta
Dr Sujoy Dasgupta presented the study on "Couples presenting to the infertility clinic- Do they really have infertility? – The unexplored stories of non-consummation" in the 13th Congress of the Asia Pacific Initiative on Reproduction (ASPIRE 2024) at Manila on 24 May, 2024.
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.
Title: Sense of Taste
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the structure and function of taste buds.
Describe the relationship between the taste threshold and taste index of common substances.
Explain the chemical basis and signal transduction of taste perception for each type of primary taste sensation.
Recognize different abnormalities of taste perception and their causes.
Key Topics:
Significance of Taste Sensation:
Differentiation between pleasant and harmful food
Influence on behavior
Selection of food based on metabolic needs
Receptors of Taste:
Taste buds on the tongue
Influence of sense of smell, texture of food, and pain stimulation (e.g., by pepper)
Primary and Secondary Taste Sensations:
Primary taste sensations: Sweet, Sour, Salty, Bitter, Umami
Chemical basis and signal transduction mechanisms for each taste
Taste Threshold and Index:
Taste threshold values for Sweet (sucrose), Salty (NaCl), Sour (HCl), and Bitter (Quinine)
Taste index relationship: Inversely proportional to taste threshold
Taste Blindness:
Inability to taste certain substances, particularly thiourea compounds
Example: Phenylthiocarbamide
Structure and Function of Taste Buds:
Composition: Epithelial cells, Sustentacular/Supporting cells, Taste cells, Basal cells
Features: Taste pores, Taste hairs/microvilli, and Taste nerve fibers
Location of Taste Buds:
Found in papillae of the tongue (Fungiform, Circumvallate, Foliate)
Also present on the palate, tonsillar pillars, epiglottis, and proximal esophagus
Mechanism of Taste Stimulation:
Interaction of taste substances with receptors on microvilli
Signal transduction pathways for Umami, Sweet, Bitter, Sour, and Salty tastes
Taste Sensitivity and Adaptation:
Decrease in sensitivity with age
Rapid adaptation of taste sensation
Role of Saliva in Taste:
Dissolution of tastants to reach receptors
Washing away the stimulus
Taste Preferences and Aversions:
Mechanisms behind taste preference and aversion
Influence of receptors and neural pathways
Impact of Sensory Nerve Damage:
Degeneration of taste buds if the sensory nerve fiber is cut
Abnormalities of Taste Detection:
Conditions: Ageusia, Hypogeusia, Dysgeusia (parageusia)
Causes: Nerve damage, neurological disorders, infections, poor oral hygiene, adverse drug effects, deficiencies, aging, tobacco use, altered neurotransmitter levels
Neurotransmitters and Taste Threshold:
Effects of serotonin (5-HT) and norepinephrine (NE) on taste sensitivity
Supertasters:
25% of the population with heightened sensitivity to taste, especially bitterness
Increased number of fungiform papillae
NVBDCP.pptx Nation vector borne disease control programSapna Thakur
NVBDCP was launched in 2003-2004 . Vector-Borne Disease: Disease that results from an infection transmitted to humans and other animals by blood-feeding arthropods, such as mosquitoes, ticks, and fleas. Examples of vector-borne diseases include Dengue fever, West Nile Virus, Lyme disease, and malaria.
Pulmonary Thromboembolism - etilogy, types, medical- Surgical and nursing man...VarunMahajani
Disruption of blood supply to lung alveoli due to blockage of one or more pulmonary blood vessels is called as Pulmonary thromboembolism. In this presentation we will discuss its causes, types and its management in depth.
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Ve...kevinkariuki227
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Verified Chapters 1 - 19, Complete Newest Version.pdf
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Verified Chapters 1 - 19, Complete Newest Version.pdf
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
Tom Selleck Health: A Comprehensive Look at the Iconic Actor’s Wellness Journey
Super computing 19 Cancer Computing Workshop Keynote
1. Computing and Data in
Precision Oncology
Warren A. Kibbe, Ph.D.
Professor, Biostats & Bioinformatics
Chief Data Officer, Duke Cancer Institute
warren.kibbe@duke.edu
@wakibbe
@supercomputing
#sc19
#DataSharing
#ComputationalPhenomics
#PrecisionOncology
2. Personal & Professional Background
• PhD in Chemistry at Caltech, Postdoc in
molecular genetics of RAS
• Cancer research for 20+ years - cancer
informatics, data science, healthcare
• Faculty in the Feinberg School of Medicine at
Northwestern for 15+ years
• Director NCI CBIIT 2013-2017; NCI CIO
2013-2017; Acting NCI Deputy Director for
Data Science 2016-2017
• Lost three grandparents to cancer, father to
cancer in 2019
3. Take homes
• Biology, cancer is a grand challenge
• Computation, ML, High Dimensional
Modeling is crucial for understanding
cancer
• Advances in biology, human health,
and implementation science are all
needed
• Scaling is a hard problem
4. As of January 2019, there are
an estimated
16,900,000
cancer survivors in the U. S.
From https://cancercontrol.cancer.gov/ocs/statistics/statistics.html ,
based on Bluethmann SM, Mariotto AB, Rowland, JH. Anticipating the
''Silver Tsunami'': Prevalence Trajectories and Comorbidity Burden among
Older Cancer Survivors in the United States. Cancer Epidemiol Biomarkers
Prev. (2016) 25:1029-1036
5. In 2030, there will be an
estimated
22,000,000
cancer survivors in the U. S.
From https://doi.org/10.3322/caac.21565 Miller KD, Nogueira L, et al,
Cancer Treatment and Survivorship Statistics, 2019. CA Cancer J Clin
(2019) 0:1-23
Survival, incidence, and all-cause mortality rates were assumed to be
constant from 2016 through 2030.
6. (10,000+ patient tumors and increasing)
Courtesy of P. Kuhn (USC)
2006-2015:
A Decade of Illuminating the
Underlying Causes of Primary
Untreated Tumors Omics
Characterization
Cancer is a grand challenge
• Deep biological understanding
• Advances in scientific methods
• Advances in instrumentation
• Advances in technology
• Data and computation
• Mathematical models
Cancer Research and Care generate
detailed data that is critical to
create a learning health system for cancer
Requires:
7. Understanding Cancer
• Precision medicine will lead to fundamental
understanding of the complex interplay between
genetics, epigenetics, nutrition, environment and
clinical presentation and direct effective,
evidence-based prevention and treatment.
Ramifications across many aspects of health care
10. Health vs Disease
• What is ’normal’?
• Systematic and measurement error
• Biological heterogeneity
• Population Health
11. The promise
of precision
medicine:
How can we
meaningfull
y group
patients? signs and
symptoms,
demographics,
exposure, diet,
traits, etc.
Slide from Melissa Haendel
12. Biology and Medicine are now
data intensive enterprises
Scale is rapidly changing
Technology, data, computing and
IT are pervasive in the lab, the
clinic, in the home, and across the
population
13. Healthcare
• Evidence is not consistently
accessible and structured
• Outcomes are not connected to care
• Patient trajectories are not calculated
or accessible
14. Healthcare
• More data is ‘digital first’ every day
• Decision aids are needed
• Good UX and responsive computing
and analytics are critical for
improving health outcomes
15. LEARNING HEALTH SYSTEMS
“Science, informatics, incentives, and
culture are aligned for continuous
improvement and innovation, with best
practices seamlessly embedded in the
delivery process and new knowledge
captured as an integral by-product of the
delivery experience.”
—Institute of Medicine
18. EHRs are now ubiquitous
But evidence-driven decision support
remains a future vision
19. Real World Evidence
• Needs big data!
• Needs population representation
• Need epidemiologists and
statisticians to understand the
potential biases in representation
• EHRs, NLP, Machine Learning can
power real world evidence learning
• Critical for a Learning Health
System
22. Emergence of “population health
analytics” to measure quality
• Requires very similar infrastructure, tools, and data to
outcomes/pragmatic research with RWD
24. Team Science is critical
Clinical Trials
Biostatists
Bioinformatics
Clinical Care
Clinical Research
EHRs, Imaging, Lab Systems
Data Science, ML, BD, HPC
Analytics and Visualization
Open Data enhances collaboration and team science!
25. A brief history
• Eric Stahlberg and Jason Paragas
discussed the potential for a joint HPC-
focused collaboration between NCI and DOE
after SC14
• Winter – Spring of 2015 started fleshing out
a potential set of pilots
• First public presentation to the NIH HPC
Scientific Management workgroup in 8/2015
• Presented to FNLAC (NCI FACA) in 9/2015
• First Cancer Workshop at SC15
• Presented to NCI Director and DOE
Secretary 12/2015
26. 26
Integrated Precision Oncology
Joint Design of Advanced Computing Solutions for Cancer
NCI
National
Cancer
InstituteDOE
Department
of Energy Cancer driving
computing
advances
Exascale
technologies
driving advances
Initiatives Supported
NSCI and PMI
Molecular Domain – Multiscale biological models
Models for RAS-RAS complex interactions
Insight into RAS related cancers
Pre-clinical Domain – Improved predictive models
Computational/hybrid predictive models of drug response
Improved experimental design
Clinical Domain – Precision oncology surveillance
Expanded SEER database information capture
Modeling patient health trajectories
CANcer Distributed Learning Environment (CANDLE)
Scalable Deep Learning for Cancer
Molecular Pre-clinical Populatio
n
Frederick National Lab for
Cancer Research
JDACS4C
JDACS4C established June 27, 2016 with signed MOU between NCI and DOE
27. Chronology of Significant Events
27
Executive Order, July 29, 2015
January 20, 2015
January 12, 2016
December 11, 2015
From the Frontiers of Predictive Oncology and Computing meeting, July 2016
28. National Strategic Computing Initiative (NSCI)
28
Executive Order, July 29, 2015
It is the policy of the United States to sustain and enhance its scientific,
technological, and economic leadership position in HPC research, development, and
deployment through a coordinated Federal strategy guided by four principles:
1) The United States must deploy and apply new HPC technologies broadly for
economic competitiveness and scientific discovery.
2) The United States must foster public-private collaboration, relying on the
respective strengths of government, industry, and academia to maximize the
benefits of HPC.
3) The United States must adopt a "whole-of government" approach that draws
upon the strengths of and seek cooperation among all Federal departments
and agencies with significant expertise or equities in HPC in concert with
industry.
4) The United States must develop a comprehensive technical and scientific
approach to efficiently transition HPC research on hardware, system software,
development tools, and applications into development and, ultimately,
operations.
This order establishes the NSCI to implement this whole-of-government strategy, in
collaboration with industry and academia, for HPC research, development, and
deployment.
DOE is a Lead Agency for NSCI
NIH/NCI is a Broad Deployment Agency for NSCI
29. 29
Precision Medicine Initiative (PMI)
January 30, 2015
Objectives of the Precision Medicine Initiative:
1. More and better treatments for cancer NCI will accelerate the design and testing of
effective, tailored treatments for cancer by expanding genetically based clinical cancer trials,
exploring fundamental aspects of cancer biology, and establishing a national “cancer
knowledge network” that will generate and share new knowledge to fuel scientific discovery
and guide treatment decisions.
2. Creation of a voluntary national research cohort
3. Commitment to protecting privacy
4. Regulatory modernization
5. Public-private partnerships The Obama Administration will forge strong partnerships with
existing research cohorts, patient groups, and the private sector to develop the
infrastructure that will be needed to expand cancer genomics, and to launch a voluntary
million-person cohort. The Administration will call on academic medical centers,
researchers, foundations, privacy experts, medical ethicists, and medical product innovators
to lay the foundation for this effort, including developing new approaches to patient
participation and empowerment. The Administration will carefully consider and develop an
approach to precision medicine, including appropriate regulatory frameworks, that ensures
consumers have access to their own health data – and to the applications and services that
can safely and accurately analyze it – so that in addition to treating disease, we can
empower individuals and families to invest in and manage their health.
30. NCI Precision Oncology – Extending
the Frontiers
30
Identify promising new treatment options through the use of
advanced computation to rapidly develop, test and validate
predictive pre-clinical models for precision oncology.
Deepen understanding of cancer biology and identify new
drugs through the integrated development and use of new
simulations, predictive models and next-generation
experimental data.
Transform cancer care by applying advanced computational
capabilities to population-based cancer data to understand the
impact of new diagnostics, treatments and patient factors in
real world patients.
31. DOE Exascale Computing –
Extending the Frontiers
31
Broaden CORAL functionality through co-design of highly
scalable machine learning tools able to exploit node
coherence.
Explore how deep learning can define dynamic multi-scale
validation, uncertainty quantification and optimally guide
experiments and accelerate time-to-solution.
Shape the design of architectures for exascale simultaneously
optimized for big data, machine learning and large-scale
simulation.
32. 32
Cancer Moonshot
• Precision Medicine Initiative (PMI)
• National Strategic Computing Initiative (NSCI)
• Making data available: Genomic Data Commons
• Using the cloud: NCI Cloud Pilots
• Computation and data: DOE-NCI Pilots
• Audacious yet possible!
• Investigate, explore, predict using real-world
data!
SuperComputing 2016
38. JDACS4C – NCI-DOE
Collaboration Overview
38
• Shared Interests
– Cancer scientific challenges driving advances in computing
– Exascale technologies driving cancer advances
• Supports Two Primary Executive Office Initiatives
– Precision Medicine Initiative (Jan 2015)
– National Strategic Computing Initiative (July 2015)
• Three Pilot Efforts
– Molecular domain pilot
• NCI Lead: Frank McCormick (FNL/UCSF), Dwight Nissley (FNL)
• Lead DOE Leads: Fred Streitz (LLNL), Felice Lightstone (LLNL)
– Pre-clinical domain pilot
• NCI Lead: Jim Doroshow (DCTD) and Yvonne Evrard (FNL)
• Lead DOE Leads: Rick Stevens (ANL)
– Population/clinical domain pilot
• NCI Lead: Lynne Penberthy and Paul Fearn (DCCPS)
• Lead DOE Labs: Gina Tourassi (ORNL), Gil Weigand (ORNL)
NCI
National
Cancer
InstituteDOE
Department
of Energy Cancer driving
computing
advances
Exascale
technologies
driving advances
39. JDACS4C Collaboration Pilots:
Capabilities to Accelerate Precision Oncology
NCI Mission Impact:
Accelerating development of new
treatment options for precision
cohorts
Pilot 1:
Pre-clinical Models
Predictive patient drug response
models with advanced computing
Pilot 2:
Biological Models
Multi-scale computational
biological models
Pilot 3:
Cancer Surveillance
Computational insight into factors
impacting clinical response
40. Accelerating Computational and
Data-driven Cancer Research
Exascale in a nutshell:
• Millions of CPU cores contributing to a single task
• Nearly 1000 times faster than fastest computer today
• Focus of DOE Advanced Strategic Computing
Infrastructure: Networking, Data Transfer, Data Management, HPC Access
2015 2016 2017 2018 2019 2020
Training: Scientists, Developers, Support Personnel
Applications: Development, Libraries, Frameworks
Collaborative Pilot Investigations
Exascale Cancer Science
Co-Design
Efforts
44. Multi-modal experimental
data, image reconstruction,
analytics
Adaptive spatial
resolution
Adaptive time
stepping
High-fidelity subgrid modeling
Experiments
on nanodisc
CryoEM
imaging
X-ray/neutron
scattering
Protein structure
databases
Adaptive sampling molecular dynamics
simulation codes
Unsupervised deep
feature learning
Uncertainty quantification
Mechanistic
network models
RAS activation
experiments
(FNLCR)
Phase field
model
Coarse-
grain MD
Classical
MD
Machine learning guided
dynamic validation
Granular RAS
membrane
interaction
simulations
Atomic resolution
RAS-RAF interaction
RAS Activation
Predictive simulation
and analysis of RAS
Phase Field model of
lipid membrane
Cancer Moonshot Pilot 2
Dr. Fred Streitz, Lawrence Livermore National Lab
45. Machine learning enables a new dynamic
validation approach to high-fidelity
simulation
Hypothesis
generation
Machine learning
systems
HPC-enabled
simulation
Observed
molecular
properties
Machine learning systems uncover
relationships between computational
models and experimental data
This “active learning” approach can
be used to optimize solutions with
significant reduction in compute
requirements
Dr. Fred Streitz, Lawrence Livermore National Lab
46. Simulation of full system will incorporate a large
number of smaller simulations
O(103) 100,000-atom simulations
10-100 µm lipid patches
Dynamic membrane
Hundreds of Ras proteins
Mutant and wild-type
Many conformations
Many environments
Investigate diffusion and
aggregation in of Ras in
context of specific
membrane properties
Dr. Fred Streitz, Lawrence Livermore National Lab
47. Multi-disciplinary team from FNLCR, LLNL,
LANL, ORNL and ANL
Argonne National Lab: Prasanna Balaprakash, Tom Brettin, FangFang Xia
FNLCR / NCI Team: Frantz Jean-Francois, Frank McCormick, Dhirendra Simanshu,
Eric Stahlberg, Andy Stephen, Tommy Turbyville, Debanjan Goswami
Oak Ridge National Lab: , Pratul K. Agarwal, Debsindhu Bhowmik, Arvind
Ramanathan, Blake A. Wilson, Christopher B. Stanley
Lawrence Livermore National Lab: Harsh Bhatia, Barry Belmont, Tim Carpenter,
Francesco Di Natale, Jim Glosli, Helgi Ingolfsson, Piyush Karande, Felice
Lightstone, Tomas Oppelstrup, Liam Stanton, Michael Surh, Sachin Talathi, Brian Van
Essen, Yue Yang, Xiaohua Zhang
Los Alamos National Lab: Angel Garcia, Christoph Jungans, Cesar Lopez, Chris
Neale, Danny Perez, Sandrasegaram Gnanakaran, Tim Travers, Art Voter
Dr. Fred Streitz, Lawrence Livermore National Lab
48. 48
NCI-DOE collaboration: CANDLE
• CANcer Distributed Learning
Environment
• Expanding collaboration between NCI
and DOE
• CANDLE’s goal is to use deep/machine
learning to accelerate cancer research
image credit: www.globus.org
49. CANDLE: Cancer Distributed
Learning Environment
RAS
Biology
Treatment
Strategy
Drug
Response
CANDLE
Unsupervised learning inside
multiscale molecular
simulations steered by semi-
supervised learning
Supervised learning
augmented by stochastic
pathway modeling and
experimental design
Semi-supervised learning,
scalable data analysis and
agent based simulations on
population scale data
Building a machine learning framework
for the NCI-DOE pilots
57. Fundamental Changes
• Data generation is not the bottleneck
• Most data are now ‘digital first’
• Old statistical models assuming variable
independence are inadequate – systems
and pathways are not independent!
• Project management is critical in scaling
population science
Well-defined experiments are still key
58. Changes in the Society
• Perceptions of privacy
• GA4GH – right to benefit from
research
• Social media
• Familiarity with IT
• Tobacco control
• Health disparities
• Affordable Care Act
59. Cancer Moonshot
“…it is of critical national importance
that we …double the rate of progress
in the fight against cancer- and put
ourselves on a path to achieve in just
5 years research and treatment gains
that otherwise might take a decade or
more…”
60. WHAT IS “DATA SCIENCE”?
Knowledge among computer scientists
about how to think of and approach the
analysis of data is limited, just as the
knowledge of computing environments
by statisticians is limited. A merger of the
knowledge bases would produce a
powerful force for innovation.
—Bill Cleveland on Data Science (2001)
63. Cancer Genomics
• Several distinct molecular forms of
cancer at each organ site
• The genomic abnormalities of each
cancer are unique
• The same molecular abnormalities
are found in cancers that arise in
different organs
Our understanding of biology, cancer, and intervention is changing!
64. Genomics, Data resources, and
clinical trials are changing
• NCI Cancer Genomic Data Commons
• Umbrella and basket clinical trials
such as NCI MATCH and Pediatric
MATCH
65. 65
Keeping in mind cellular dynamics
On average across 375
tumor samples, ONLY
33% of RNA expression
differences correlated
with protein abundance
Zhang B et al, Proteogenomic characterization of human colon and rectal cancer, Nature, 2014, July 20.
66. Genomics is the beginning of
precision medicine, not the end!
We now have tools to let us both understand social determinants of health and build ‘early warning systems’ to identify people who are have acute medical issues
By the year 2020, ninety percent of clinical decisions will be supported by accurate, timely, and up-to-date clinical information, and will reflect the best available evidence.”
CharterIOM Roundtable on Value & Science-Driven Health Care
By the year 2020, ninety percent of clinical decisions will be supported by accurate, timely, and up-to-date clinical information, and will reflect the best available evidence.”
CharterIOM Roundtable on Value & Science-Driven Health Care
OECD Health Data, 2009. Life expectancy at birth in different countries versus per capita expenditures on health care in dollar terms, adjusted for purchasing power. The United States is a clear outlier on the curve, spending far more than any other country yet achieving less.
Overview slide to put pieces in context.
Joint Design for Advanced Computing Solutions for Cancer (JDACS4C)
Three year pilot collaboration between NCI and DOE involving to advance cancer research through application of large scale computing while providing design input for future large scale computing systems to DOE
Program is in its first year
Program enabled by National Strategic Computing Initiative and Precision Medicine Initiative
DOE brings computational expertise and capability with their infrastructure
NCI brings subject matter expertise and sources of data
Program involves four DOE national laboratories (Argonne, Lawrence Livermore, Los Alamos, and Oak Ridge)
Frederick National Laboratory, NCI Division of Cancer Treatment and Diagnostics, Division of Cancer Control and Population Sciences
Three representative/exploratory pilot projects selected, one in each of the molecular, pre-clinical and population domains.
Program builds on established national priorities in understanding RAS cancers, developing pre-clinical models, and building network of cancer patient information
Pilots explore direction and inform/guide future investments for using increasing amounts of advanced computing accelerate cancer and health research.
Insight being shared across NIH institutes and centers
CANDLE (Cancer Distributed Learning Environment) is a DOE Exascale Computing Project and will deliver an open source environment over the 4 years
Early engagement occurring on NIH campus to involve growing numbers of scientists and research challenges in the development of the environment
Or for #1 - machine learning tools including deep learning" or state only deep learning if everyone will focus exclusively on DL
Or for #2… alternative – “Integration of mechanistic biological simulations with deep learning to create quantified predictions”.
Maps JDACS4C pilots onto NCI efforts for advancing precision oncology. Demonstrates alignment with organizational interests.
Left this slide in to provide perspective and context for technical motivation – data, training, collaborative design, leading to exascale cancer science
Biochemical/biophysical properties of fully processed KRAS4b
RAS on nanodiscs
Lipid composition of RAS:membrane interaction
RAS:RAF binding in the context of nanodisc membranes
Structure of RAS on membranes
Crystallography of KRAS, and KRAS:effector complexes
NMR of KRAS bound to nanodiscs
X-ray/neutron scattering of KRAS on nanodiscs
Cryo-EM imaging of KRAS protein complexes (+effectors)
Dynamics of RAS in membranes
Supported bilayers in vitro
Live cell imaging with single molecule tracking
Adaptive spatial resolution (e.g., sub-grid modeling)
Propagating both coarse-grained and classical (atomistic) MD information, we aim to maintain the highest fidelity possible at the point of interactions while capturing long distance effects
Multiple time scales
By judiciously switching between spatial scales we enable investigation of timescales that are orders of magnitude longer than possible with fine-scale simulation alone.
Automated hypothesis generation and dynamic validation
We will efficiently and accurately explore, e.g., possible interaction sequences by coupling Machine Learning techniques with large-scale predictive simulation.
Extreme scale simulation
Requried novel computational algorithms and techniques will be developed for use on Sierra-class architectures, and will be designed for exascale.
Deep learning algorithms
Powerful pattern recognition tools will accelerate our predictive simulation capability by giving rapidly identifying, e.g., the time or region where a sub-grid model is needed or by logically exploring an intractably large decision tree.
Uncertainty quantification
Application of our extensive capability will be tested in the new (highly uncertain) world of biology and healthcare, leading to new insights and the development of new methods
Scalable statistical inference tools
The continued convergence of data analytics and predictive simulation as we approach exascale will require statistical tools that scale far beyond what is current, requiring the development of new strategies.
Be careful of the hype. The promise is solid, but the snakeoil is real
Be careful of the hype. The promise is solid, but the snakeoil is real