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.

Low serum testosterone and increased mortality in men with coronary heart disease

Abstract: Background To examine the effect of serum testosterone levels on survival in a consecutive series of men with confirmed coronary disease and calculate the prevalence of testosterone deficiency. Design Longitudinal follow-up study. Setting Tertiary referral cardiothoracic centre. Patients 930 consecutive men with coronary disease referred for diagnostic angiography recruited between June 2000 and June 2002 and followed up for a mean of 6.962.1 years. Outcome All-cause mortality and vascular mortality. Prevalence of testosterone deficiency. Results The overall prevalence of biochemical testosterone deficiency in the coronary disease cohort using bio-available testosterone (bio-T) <2.6 nmol/l was 20.9%, using total testosterone <8.1 nmol/l was 16.9% and using either was 24%. Excess mortality was noted in the androgen-deficient group compared with normal (41 (21%) vs 88 (12%), p¼0.002). The only parameters found to influence time to all-cause and vascular mortality (HR 6 95% CI) in multivariate analyses were the presence of left ventricular dysfunction (3.85; 1.72 to 8.33), aspirin therapy (0.63; 0.38 to 1.0), b-blocker therapy (0.45; 0.31 to 0.67) and low serum bio-T (2.27; 1.45 to 3.6). Conclusions In patients with coronary disease testosterone deficiency is common and impacts significantly negatively on survival. Prospective trials of testosterone replacement are needed to assess the effect of treatment on survival.

MAPGEN: mixed-initiative planning and scheduling for the Mars Exploration Rover mission

Abstract: The Mars Exploration Rover mission is one of NASA's most ambitious science missions to date. Launched in the summer of 2003, each rover carries instruments for conducting remote and in site observations to elucidate the planet's past climate, water activity, and habitability. Science is MER's primary driver, so making best use of the scientific instruments, within the available resources, is a crucial aspect of the mission. To address this criticality, the MER project team selected MAPGEN (mixed initiative activity plan generator) as an activity-planning tool. MAPGEN combines two existing systems, each with a strong heritage: the APGEN activity-planning tool from the Jet Propulsion Laboratory and the Europa planning and scheduling system from NASA Ames Research Center. We discuss the issues arising from combining these tools in this mission's context. MAPGEN is the first AI-based system to control a space platform on another planet's surface.

A maximum entropy approach to nonmonotonic reasoning

Abstract: An approach to nonmonotonic reasoning that combines the principle of infinitesimal probabilities with that of maximum entropy, thus extending the inferential power of the probabilistic interpretation of defaults, is proposed. A precise formalization of the consequences entailed by a conditional knowledge base is provided, the computational machinery necessary for drawing these consequences is developed, and the behavior of the maximum entropy approach is compared to related work in default reasoning. The resulting formalism offers a compromise between two extremes: the cautious approach based on the conditional interpretations of defaults and the bold approach based on minimizing abnormalities. >

Cardiac biomarkers of acute coronary syndrome: from history to high-sensitivity cardiac troponin

Abstract: The role of cardiac troponins as diagnostic biomarkers of myocardial injury in the context of acute coronary syndrome (ACS) is well established. Since the initial 1st-generation assays, 5th-generation high-sensitivity cardiac troponin (hs-cTn) assays have been developed, and are now widely used. However, its clinical adoption preceded guidelines and even best practice evidence. This review summarizes the history of cardiac biomarkers with particular emphasis on hs-cTn. We aim to provide insights into using hs-cTn as a quantitative marker of cardiomyocyte injury to help in the differential diagnosis of coronary versus non-coronary cardiac diseases. We also review the recent evidence and guidelines of using hs-cTn in suspected ACS.

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

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。

Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference

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.

Machine learning

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.).