SKIING the SUN New Mexico Essays by Michael Waterman
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And Should Not, Change Toxic Tort Causation Doctrine
THE MORE WE KNOW, THE LESS INTELLIGENT WE ARE? - HOW GENOMIC INFORMATION SHOULD, AND SHOULD NOT, CHANGE TOXIC TORT CAUSATION DOCTRINE Steve C. Gold* Advances in genomic science are rapidly increasing our understanding of dis- ease and toxicity at the most fundamental biological level. Some say this heralds a new era of certainty in linking toxic substances to human illness in the courtroom. Others are skeptical. In this Article I argue that the new sciences of molecular epidemiology and toxicogenomics will evince both remarkable explanatory power and intractable complexity. Therefore, even when these sciences are brought to bear, toxic tort claims will continue to present their familiar jurisprudential problems. Nevertheless, as genomic information is used in toxic tort cases, the sci- entific developments will offer courts an opportunity to correct mistakes of the past. Courts will miss those opportunities, however, if they simply transfer attitudesfrom classical epidemiology and toxicology to molecular and genomic knowledge. Using the possible link between trichloroethyleneand a type of kidney cancer as an exam- ple, I describe several causation issues where courts can, if they choose, improve doctrine by properly understanding and utilizing genomic information. L Introduction ............................................... 370 II. Causation in Toxic Torts Before Genomics ................... 371 A. The Problem .......................................... 371 B. Classical Sources of Causation Evidence ................ 372 C. The Courts Embrace Epidemiology - Perhaps Too Tightly ................................................ 374 D. Evidence Meets Substance: Keepers of the Gate ......... 379 E. The Opposing Tendency ................................ 382 III. Genomics, Toxicogenomics, and Molecular Epidemiology: The M ore We Know ........................................ 383 A. Genetic Variability and "Background" Risk .............. 384 B. Genetic Variability and Exposure Risk ................... 389 C. -
Monte Carlo Simulation in Statistics with a View to Recent Ideas
(sfi)2 Statistics for Innovation Monte Carlo simulation in statistics, with a view to recent ideas Arnoldo Frigessi [email protected] THE JOURNAL OF CHEMICAL PHYSICS VOLUME 21, NUMBER 6 JUNE, 1953 Equation of State Calculations by Fast Computing Machines NICHOLAS METROPOLIS, ARIANNA W. ROSENBLUTH, MARSHALL N. ROSENBLUTH, AND AUGUSTA H. TELLER, Los Alamos Scientific Laboratory, Los Alamos, New Mexico AND EDWARD TELLER, * Department of Physics, University of Chicago, Chicago, Illinois (Received March 6, 1953) A general method, suitable for fast computing machines, for investigating such properties as equations of state for substances consisting of interacting individual molecules is described. The method consists of a modified Monte Carlo integration over configuration space. Results for the two-dimensional rigid-sphere system have been obtained on the Los Alamos MANIAC and are presented here. These results are compared to the free volume equation of state and to a four-term virial coefficient expansion. The Metropolis algorithm from1953 has been cited as among the top 10 algorithms having the "greatest influence on the development and practice of science and engineering in the 20th century." MANIAC The MANIAC II 1952 Multiplication took a milli- second. (Metropolis choose the name MANIAC in the hope of stopping the rash of ”silly” acronyms for machine names.) Some MCMC milestones 1953 Metropolis Heat bath, dynamic Monte Carlo 1970 Hastings Political Analysis, 10:3 (2002) Society for Political Methodology 1984 Geman & Geman Gibbs -
Plant Pangenome: Impacts on Phenotypes and Evolution Christine Tranchant-Dubreuil, Mathieu Rouard, Francois Sabot
Plant Pangenome: Impacts On Phenotypes And Evolution Christine Tranchant-Dubreuil, Mathieu Rouard, Francois Sabot To cite this version: Christine Tranchant-Dubreuil, Mathieu Rouard, Francois Sabot. Plant Pangenome: Im- pacts On Phenotypes And Evolution. Annual Plant Reviews, Wiley Online Library 2019, 10.1002/9781119312994.apr0664. hal-02053647 HAL Id: hal-02053647 https://hal.archives-ouvertes.fr/hal-02053647 Submitted on 1 Mar 2019 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Copyright Plant Pangenome: Impacts On Phenotypes And Evolution Christine Tranchant-Dubreuil1,3, Mathieu Rouard2,3, and Francois Sabot1,3 1DIADE University of Montpellier, IRD, 911 Avenue Agropolis, 34934 Montpellier Cedex 5, France 2Bioversity International, Parc Scientifique Agropolis II, 34397 Montpellier Cedex 5, France 3South Green Bioinformatics Platform, Bioversity, CIRAD, INRA, IRD, Montpellier, France With the emergence of low-cost high-throughput sequencing all the genes from a given species are not obtained using a technologies, numerous studies have shown that a single genome single genome (10–12). In plants, evidence first from maize is not enough to identify all the genes present in a species. Re- (13, 14) showed that only half of the genomic structure is cently, the pangenome concept has become widely used to in- conserved between two individuals. -
Algorithms for Computational Biology 8Th International Conference, Alcob 2021 Missoula, MT, USA, June 7–11, 2021 Proceedings
Lecture Notes in Bioinformatics 12715 Subseries of Lecture Notes in Computer Science Series Editors Sorin Istrail Brown University, Providence, RI, USA Pavel Pevzner University of California, San Diego, CA, USA Michael Waterman University of Southern California, Los Angeles, CA, USA Editorial Board Members Søren Brunak Technical University of Denmark, Kongens Lyngby, Denmark Mikhail S. Gelfand IITP, Research and Training Center on Bioinformatics, Moscow, Russia Thomas Lengauer Max Planck Institute for Informatics, Saarbrücken, Germany Satoru Miyano University of Tokyo, Tokyo, Japan Eugene Myers Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany Marie-France Sagot Université Lyon 1, Villeurbanne, France David Sankoff University of Ottawa, Ottawa, Canada Ron Shamir Tel Aviv University, Ramat Aviv, Tel Aviv, Israel Terry Speed Walter and Eliza Hall Institute of Medical Research, Melbourne, VIC, Australia Martin Vingron Max Planck Institute for Molecular Genetics, Berlin, Germany W. Eric Wong University of Texas at Dallas, Richardson, TX, USA More information about this subseries at http://www.springer.com/series/5381 Carlos Martín-Vide • Miguel A. Vega-Rodríguez • Travis Wheeler (Eds.) Algorithms for Computational Biology 8th International Conference, AlCoB 2021 Missoula, MT, USA, June 7–11, 2021 Proceedings 123 Editors Carlos Martín-Vide Miguel A. Vega-Rodríguez Rovira i Virgili University University of Extremadura Tarragona, Spain Cáceres, Spain Travis Wheeler University of Montana Missoula, MT, USA ISSN 0302-9743 ISSN 1611-3349 (electronic) Lecture Notes in Bioinformatics ISBN 978-3-030-74431-1 ISBN 978-3-030-74432-8 (eBook) https://doi.org/10.1007/978-3-030-74432-8 LNCS Sublibrary: SL8 – Bioinformatics © Springer Nature Switzerland AG 2021 This work is subject to copyright. -
Dean, School of Science
Dean, School of Science School of Science faculty members seek to answer fundamental questions about nature ranging in scope from the microscopic—where a neuroscientist might isolate the electrical activity of a single neuron—to the telescopic—where an astrophysicist might scan hundreds of thousands of stars to find Earth-like planets in their orbits. Their research will bring us a better understanding of the nature of our universe, and will help us address major challenges to improving and sustaining our quality of life, such as developing viable resources of renewable energy or unravelling the complex mechanics of Alzheimer’s and other diseases of the aging brain. These profound and important questions often require collaborations across departments or schools. At the School of Science, such boundaries do not prevent people from working together; our faculty cross such boundaries as easily as they walk across the invisible boundary between one building and another in our campus’s interconnected buildings. Collaborations across School and department lines are facilitated by affiliations with MIT’s numerous laboratories, centers, and institutes, such as the Institute for Data, Systems, and Society, as well as through participation in interdisciplinary initiatives, such as the Aging Brain Initiative or the Transiting Exoplanet Survey Satellite. Our faculty’s commitment to teaching and mentorship, like their research, is not constrained by lines between schools or departments. School of Science faculty teach General Institute Requirement subjects in biology, chemistry, mathematics, and physics that provide the conceptual foundation of every undergraduate student’s education at MIT. The School faculty solidify cross-disciplinary connections through participation in graduate programs established in collaboration with School of Engineering, such as the programs in biophysics, microbiology, or molecular and cellular neuroscience. -
A Selected Bibliography of Publications By, and About, J
A Selected Bibliography of Publications by, and about, J. Robert Oppenheimer Nelson H. F. Beebe University of Utah Department of Mathematics, 110 LCB 155 S 1400 E RM 233 Salt Lake City, UT 84112-0090 USA Tel: +1 801 581 5254 FAX: +1 801 581 4148 E-mail: [email protected], [email protected], [email protected] (Internet) WWW URL: http://www.math.utah.edu/~beebe/ 17 March 2021 Version 1.47 Title word cross-reference $1 [Duf46]. $12.95 [Edg91]. $13.50 [Tho03]. $14.00 [Hug07]. $15.95 [Hen81]. $16.00 [RS06]. $16.95 [RS06]. $17.50 [Hen81]. $2.50 [Opp28g]. $20.00 [Hen81, Jor80]. $24.95 [Fra01]. $25.00 [Ger06]. $26.95 [Wol05]. $27.95 [Ger06]. $29.95 [Goo09]. $30.00 [Kev03, Kle07]. $32.50 [Edg91]. $35 [Wol05]. $35.00 [Bed06]. $37.50 [Hug09, Pol07, Dys13]. $39.50 [Edg91]. $39.95 [Bad95]. $8.95 [Edg91]. α [Opp27a, Rut27]. γ [LO34]. -particles [Opp27a]. -rays [Rut27]. -Teilchen [Opp27a]. 0-226-79845-3 [Guy07, Hug09]. 0-8014-8661-0 [Tho03]. 0-8047-1713-3 [Edg91]. 0-8047-1714-1 [Edg91]. 0-8047-1721-4 [Edg91]. 0-8047-1722-2 [Edg91]. 0-9672617-3-2 [Bro06, Hug07]. 1 [Opp57f]. 109 [Con05, Mur05, Nas07, Sap05a, Wol05, Kru07]. 112 [FW07]. 1 2 14.99/$25.00 [Ber04a]. 16 [GHK+96]. 1890-1960 [McG02]. 1911 [Meh75]. 1945 [GHK+96, Gow81, Haw61, Bad95, Gol95a, Hew66, She82, HBP94]. 1945-47 [Hew66]. 1950 [Ano50]. 1954 [Ano01b, GM54, SZC54]. 1960s [Sch08a]. 1963 [Kuh63]. 1967 [Bet67a, Bet97, Pun67, RB67]. 1976 [Sag79a, Sag79b]. 1981 [Ano81]. 20 [Goe88]. 2005 [Dre07]. 20th [Opp65a, Anoxx, Kai02]. -
The Convergence of Markov Chain Monte Carlo Methods 3
The Convergence of Markov chain Monte Carlo Methods: From the Metropolis method to Hamiltonian Monte Carlo Michael Betancourt From its inception in the 1950s to the modern frontiers of applied statistics, Markov chain Monte Carlo has been one of the most ubiquitous and successful methods in statistical computing. The development of the method in that time has been fueled by not only increasingly difficult problems but also novel techniques adopted from physics. In this article I will review the history of Markov chain Monte Carlo from its inception with the Metropolis method to the contemporary state-of-the-art in Hamiltonian Monte Carlo. Along the way I will focus on the evolving interplay between the statistical and physical perspectives of the method. This particular conceptual emphasis, not to mention the brevity of the article, requires a necessarily incomplete treatment. A complementary, and entertaining, discussion of the method from the statistical perspective is given in Robert and Casella (2011). Similarly, a more thorough but still very readable review of the mathematics behind Markov chain Monte Carlo and its implementations is given in the excellent survey by Neal (1993). I will begin with a discussion of the mathematical relationship between physical and statistical computation before reviewing the historical introduction of Markov chain Monte Carlo and its first implementations. Then I will continue to the subsequent evolution of the method with increasing more sophisticated implementations, ultimately leading to the advent of Hamiltonian Monte Carlo. 1. FROM PHYSICS TO STATISTICS AND BACK AGAIN At the dawn of the twentieth-century, physics became increasingly focused on under- arXiv:1706.01520v2 [stat.ME] 10 Jan 2018 standing the equilibrium behavior of thermodynamic systems, especially ensembles of par- ticles. -
Precision Medicine Initiative: Building a Large US Research Cohort
Precision Medicine Initiative: Building a Large U.S. Research Cohort February 11-12, 2015 PARTICIPANT LIST Goncalo Abecasis, D. Phil. Philip Bourne, Ph.D. Professor of Biostatistics Associate Director for Data Science University of Michigan, Ann Arbor Office of the Director National Institutes of Health Christopher Austin, M.D. Director Murray Brilliant, Ph.D. National Center for Advancing Translational Sciences Director National Institutes of Health Center for Human Genetics Marshfield Clinic Research Foundation Vikram Bajaj, Ph.D. Chief Scientist Greg Burke, M.D., M.Sc. Google Life Sciences Professor and Director Wake Forest School of Medicine Dixie Baker, Ph.D. Wake Forest University Senior Partner Martin, Blanck and Associates Antonia Calafat, Ph.D. Chief Dana Boyd Barr, Ph.D. Organic Analytical Toxicology Branch Professor, Exposure Science and Environmental Health Centers for Disease Control and Prevention Rollins School of Public Health Emory University Robert Califf, M.D. Vice Chancellor for Clinical and Translational Research Jonathan Bingham, M.B.A. Duke University Medical Center Product Manager, Genomics Google, Inc. Rex Chisholm, Ph.D. Adam and Richard T. Lind Professor of Medical Eric Boerwinkle, Ph.D. Genetics Professor and Chair Vice Dean for Scientific Affairs and Graduate Studies Human Genetics Center Associate Vice President for Research University of Texas Health Science Center Northwestern University Associate Director Human Genome Sequencing Center Rick Cnossen, M.S. Baylor College of Medicine Director Global Healthcare Solutions Erwin Bottinger, M.D. HIMSS Board of Directors Professor PCHA/Continua Health Alliance Board of Directors The Charles Bronfman Institute for Personalized Intel Corporation Medicine Icahn School of Medicine at Mount Sinai - 1 - Francis Collins, M.D., Ph.D. -
Monte Carlo Sampling Methods
[1] Monte Carlo Sampling Methods Jasmina L. Vujic Nuclear Engineering Department University of California, Berkeley Email: [email protected] phone: (510) 643-8085 fax: (510) 643-9685 UCBNE, J. Vujic [2] Monte Carlo Monte Carlo is a computational technique based on constructing a random process for a problem and carrying out a NUMERICAL EXPERIMENT by N-fold sampling from a random sequence of numbers with a PRESCRIBED probability distribution. x - random variable N 1 xˆ = ---- x N∑ i i = 1 Xˆ - the estimated or sample mean of x x - the expectation or true mean value of x If a problem can be given a PROBABILISTIC interpretation, then it can be modeled using RANDOM NUMBERS. UCBNE, J. Vujic [3] Monte Carlo • Monte Carlo techniques came from the complicated diffusion problems that were encountered in the early work on atomic energy. • 1772 Compte de Bufon - earliest documented use of random sampling to solve a mathematical problem. • 1786 Laplace suggested that π could be evaluated by random sampling. • Lord Kelvin used random sampling to aid in evaluating time integrals associated with the kinetic theory of gases. • Enrico Fermi was among the first to apply random sampling methods to study neutron moderation in Rome. • 1947 Fermi, John von Neuman, Stan Frankel, Nicholas Metropolis, Stan Ulam and others developed computer-oriented Monte Carlo methods at Los Alamos to trace neutrons through fissionable materials UCBNE, J. Vujic Monte Carlo [4] Monte Carlo methods can be used to solve: a) The problems that are stochastic (probabilistic) by nature: - particle transport, - telephone and other communication systems, - population studies based on the statistics of survival and reproduction. -
Count Down: Six Kids Vie for Glory at the World's Toughest Math
Count Down Six Kids Vie for Glory | at the World's TOUGHEST MATH COMPETITION STEVE OLSON author of MAPPING HUMAN HISTORY, National Book Award finalist $Z4- 00 ACH SUMMER SIX MATH WHIZZES selected from nearly a half million EAmerican teens compete against the world's best problem solvers at the Interna• tional Mathematical Olympiad. Steve Olson, whose Mapping Human History was a Na• tional Book Award finalist, follows the members of a U.S. team from their intense tryouts to the Olympiad's nail-biting final rounds to discover not only what drives these extraordinary kids but what makes them both unique and typical. In the process he provides fascinating insights into the creative process, human intelligence and learning, and the nature of genius. Brilliant, but defying all the math-nerd stereotypes, these athletes of the mind want to excel at whatever piques their cu• riosity, and they are curious about almost everything — music, games, politics, sports, literature. One team member is ardent about water polo and creative writing. An• other plays four musical instruments. For fun and entertainment during breaks, the Olympians invent games of mind-boggling difficulty. Though driven by the glory of winning this ultimate math contest, in many ways these kids are not so different from other teenagers, finding pure joy in indulging their personal passions. Beyond the Olympiad, Steve Olson sheds light on such questions as why Americans feel so queasy about math, why so few girls compete in the subject, and whether or not talent is innate. Inside the cavernous gym where the competition takes place, Count Down reveals a fascinating subculture and its engaging, driven inhabitants. -
Interview with Melvin I. Simon
MELVIN I. SIMON (b. 1937) INTERVIEWED BY SHIRLEY K. COHEN May 24 and June 5, 2005 Melvin I. Simon ARCHIVES CALIFORNIA INSTITUTE OF TECHNOLOGY Pasadena, California Subject area Biology Abstract Interview in two sessions, May 24 and June 5, 2005, with Melvin I. Simon, Anne P. and Benjamin F. Biaggini Professor of Biological Sciences, emeritus, in the Division of Biology. Dr. Simon received his BS from City College of New York in 1959 and his PhD in biochemistry from Brandeis in 1963. After receiving his degree, he was a postdoctoral fellow with Arthur Pardee at Princeton for a year and a half and then joined the faculty of the University of California at San Diego. In 1978, Dr. Simon and his UCSD colleague John Abelson established the Agouron Institute in San Diego, focusing on problems in molecular biology. He (along with Abelson) joined the faculty of Caltech as a full professor in 1982, and he served as chair of the Biology Division from 1995 to 2000. http://resolver.caltech.edu/CaltechOH:OH_Simon_M In this interview, he discusses his education in Manhattan’s Yeshiva High School (where science courses were taught by teachers from the Bronx High School of Science), at CCNY, and as a Brandeis graduate student working working with Helen Van Vunakis on bacteriophage. Recalls his unsatisfactory postdoc experience at Princeton and his delight at arriving at UC San Diego, where molecular biology was just getting started. Discusses his work on bacterial organelles; recalls his and Abelson’s vain efforts to get UCSD to back a full-scale initiative in molecular biology and their subsequent founding of their own institute. -
The Markov Chain Monte Carlo Approach to Importance Sampling
The Markov Chain Monte Carlo Approach to Importance Sampling in Stochastic Programming by Berk Ustun B.S., Operations Research, University of California, Berkeley (2009) B.A., Economics, University of California, Berkeley (2009) Submitted to the School of Engineering in partial fulfillment of the requirements for the degree of Master of Science in Computation for Design and Optimization at the MASSACHUSETTS INSTITUTE OF TECHNOLOGY September 2012 c Massachusetts Institute of Technology 2012. All rights reserved. Author.................................................................... School of Engineering August 10, 2012 Certified by . Mort Webster Assistant Professor of Engineering Systems Thesis Supervisor Certified by . Youssef Marzouk Class of 1942 Associate Professor of Aeronautics and Astronautics Thesis Reader Accepted by . Nicolas Hadjiconstantinou Associate Professor of Mechanical Engineering Director, Computation for Design and Optimization 2 The Markov Chain Monte Carlo Approach to Importance Sampling in Stochastic Programming by Berk Ustun Submitted to the School of Engineering on August 10, 2012, in partial fulfillment of the requirements for the degree of Master of Science in Computation for Design and Optimization Abstract Stochastic programming models are large-scale optimization problems that are used to fa- cilitate decision-making under uncertainty. Optimization algorithms for such problems need to evaluate the expected future costs of current decisions, often referred to as the recourse function. In practice, this calculation is computationally difficult as it involves the evaluation of a multidimensional integral whose integrand is an optimization problem. Accordingly, the recourse function is estimated using quadrature rules or Monte Carlo methods. Although Monte Carlo methods present numerous computational benefits over quadrature rules, they require a large number of samples to produce accurate results when they are embedded in an optimization algorithm.