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Paul Morris

Bio: Paul Morris is an academic researcher from University of Sheffield. The author has contributed to research in topics: Fractional flow reserve & Coronary artery disease. The author has an hindex of 49, co-authored 252 publications receiving 10739 citations. Previous affiliations of Paul Morris include Johns Hopkins University & Center for Complex Systems and Brain Sciences.


Papers
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Journal ArticleDOI
TL;DR: The augmented infection model more closely resembles the natural situation and establishes the role of resident environmental microflora in the initiation of disease by an invading pathogen.
Abstract: All bacterial infections occur within a polymicrobial environment, from which a pathogen population emerges to establish disease within a host. Emphasis has been placed on prevention of pathogen dominance by competing microflora acting as probiotics1. Here we show that the virulence of the human pathogen Staphylococcus aureus is augmented by native, polymicrobial, commensal skin flora and individual species acting as 'proinfectious agents'. The outcome is pathogen proliferation, but not commensal. Pathogenesis augmentation can be mediated by particulate cell wall peptidoglycan, reducing the S. aureus infectious dose by over 1,000-fold. This phenomenon occurs using a range of S. aureus strains and infection models and is not mediated by established receptor-mediated pathways including Nod1, Nod2, Myd88 and the NLPR3 inflammasome. During mouse sepsis, augmentation depends on liver-resident macrophages (Kupffer cells) that capture and internalize both the pathogen and the proinfectious agent, leading to reduced production of reactive oxygen species, pathogen survival and subsequent multiple liver abscess formation. The augmented infection model more closely resembles the natural situation and establishes the role of resident environmental microflora in the initiation of disease by an invading pathogen. As the human microflora is ubiquitous2, its role in increasing susceptibility to infection by S. aureus highlights potential strategies for disease prevention.

76 citations

Journal ArticleDOI
TL;DR: In this article, two mathematical methods (steady and pseudotransient) were developed that accelerate the computation of vFFR from >36h to <36h using paired steady-state CFD analyses, without the need for computationally expensive, full transient CFD analysis.

75 citations

Journal ArticleDOI
TL;DR: The 'cause' versus 'effect' controversy, regarding low testosterone levels in men with coronary heart disease, as well as concerns over the use of testosterone replacement therapy in middle aged and elderly men are discussed.
Abstract: Despite regional variations in the prevalence of coronary artery disease (CAD), men are consistently more at risk of developing and dying from CAD than women, and the gender-specific effects of sex hormones are implicated in this inequality. This ‘Perspectives' article reviews the current evidence regarding the cardiovascular effects of testosterone in men including an examination of the age-related decline in testosterone, the relationship between testosterone levels and coronary disease, coronary risk factors and mortality. We also review the vaso-active effects of testosterone, and discuss how these have been used in men with heart failure and angina. We discuss the ‘cause' versus ‘effect' controversy, regarding low testosterone levels in men with coronary heart disease, as well as concerns over the use of testosterone replacement therapy in middle aged and elderly men. The article concludes with a discussion regarding the future direction for work in this interesting area, including the relative merits of screening for, and treating hypogonadism with testosterone replacement therapy in men with heart disease.

75 citations

Journal ArticleDOI
TL;DR: It is concluded that high heels exaggerate sex specific aspects of female gait and women walking in high heels could be regarded as a supernormal stimulus.

75 citations

Book ChapterDOI
19 May 2014
TL;DR: This work presents a fast algorithm for Dynamic Controllability, and notes a correspondence between the reduction steps in the algorithm and the operations involved in converting the projections to dispatchable form.
Abstract: An important issue for temporal planners is the ability to handle temporal uncertainty. Recent papers have addressed the question of how to tell whether a temporal network is Dynamically Controllable, i.e., whether the temporal requirements are feasible in the light of uncertain durations of some processes. We present a fast algorithm for Dynamic Controllability. We also note a correspondence between the reduction steps in the algorithm and the operations involved in converting the projections to dispatchable form. This has implications for the complexity for sparse networks.

69 citations


Cited by
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28 Jul 2005
TL;DR: PfPMP1)与感染红细胞、树突状组胞以及胎盘的单个或多个受体作用,在黏附及免疫逃避中起关键的作�ly.
Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1(PfPMP1)与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用,在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员,通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

18,940 citations

Book
01 Jan 1988
TL;DR: Probabilistic Reasoning in Intelligent Systems as mentioned in this paper is a complete and accessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty, and provides a coherent explication of probability as a language for reasoning with partial belief.
Abstract: From the Publisher: Probabilistic Reasoning in Intelligent Systems is a complete andaccessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty. The author provides a coherent explication of probability as a language for reasoning with partial belief and offers a unifying perspective on other AI approaches to uncertainty, such as the Dempster-Shafer formalism, truth maintenance systems, and nonmonotonic logic. The author distinguishes syntactic and semantic approaches to uncertainty—and offers techniques, based on belief networks, that provide a mechanism for making semantics-based systems operational. Specifically, network-propagation techniques serve as a mechanism for combining the theoretical coherence of probability theory with modern demands of reasoning-systems technology: modular declarative inputs, conceptually meaningful inferences, and parallel distributed computation. Application areas include diagnosis, forecasting, image interpretation, multi-sensor fusion, decision support systems, plan recognition, planning, speech recognition—in short, almost every task requiring that conclusions be drawn from uncertain clues and incomplete information. Probabilistic Reasoning in Intelligent Systems will be of special interest to scholars and researchers in AI, decision theory, statistics, logic, philosophy, cognitive psychology, and the management sciences. Professionals in the areas of knowledge-based systems, operations research, engineering, and statistics will find theoretical and computational tools of immediate practical use. The book can also be used as an excellent text for graduate-level courses in AI, operations research, or applied probability.

15,671 citations

Journal ArticleDOI
TL;DR: Authors/Task Force Members: Piotr Ponikowski* (Chairperson) (Poland), Adriaan A. Voors* (Co-Chair person) (The Netherlands), Stefan D. Anker (Germany), Héctor Bueno (Spain), John G. F. Cleland (UK), Andrew J. S. Coats (UK)

13,400 citations

Journal ArticleDOI
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

13,246 citations

Journal Article
Fumio Tajima1
30 Oct 1989-Genomics
TL;DR: It is suggested that the natural selection against large insertion/deletion is so weak that a large amount of variation is maintained in a population.

11,521 citations