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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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Proceedings Article
30 Jul 2005
TL;DR: The MAPGEN system is described, focusing on the mixed-initiative planning aspect, to assist operators building daily plans for each of the rovers, maximizing science return, while maintaining rover safety and abiding by science and engineering constraints.
Abstract: One of the ground tools used to operate the Mars Exploration Rovers is a mixed-initiative planning system called MAPGEN. The role of the system is to assist operators building daily plans for each of the rovers, maximizing science return, while maintaining rover safety and abiding by science and engineering constraints. In this paper, we describe the MAPGEN system, focusing on the mixed-initiative planning aspect. We note important challenges, both in terms of human interaction and in terms of automated reasoning requirements. We then describe the approaches taken in MAPGEN, focusing on the novel methods developed by our team.

18 citations

Journal ArticleDOI
TL;DR: Reversible heart failure caused by toxic effects can be caused by drugs (prescribed and illicit) and by tachycardic arrhythmia (tachycardiomyopathy), and are caused by abnormalities of mitochondrial function and myocytic calcium processing.
Abstract: Heart failure is usually a relentless condition associated with a poor prognosis. Triggered by a physiological insult, maladaptive neurohumoral processes result in an ever-spiralling deterioration of cardiovascular function. However, there are certain underlying conditions which are associated with a temporary reduction in contractile function leading to reversible heart failure. These conditions affect a relatively small number of patients when compared with heart failure secondary to inherited cardiomyopathies and ischaemic heart disease. There are two broad mechanisms responsible for reversible myocyte dysfunction: acute inflammatory activation in which cytokines depress myocyte function, and toxic effects in which there is impairment of intra-cellular energetics. In this review, we discuss reversible heart failure caused by toxic effects. These effects can be caused by drugs (prescribed and illicit) and by tachycardic arrhythmia (tachycardiomyopathy), and are caused by abnormalities of mitochondrial function and myocytic calcium processing. The underlying pathological mechanisms, clinical features and management options are discussed, illustrated by clinical case studies.

17 citations

Journal ArticleDOI
TL;DR: Automated and manual annotation of the ATP binding cassette (ABC) superfamily in the Phytophthora ramorum and P. sojae genomes has identified 135 and 136 members, indicating that this family is comparable in size to the Arabidopsis thaliana and rice genomes, and significantly larger than that of two fungal pathogens.
Abstract: Automated and manual annotation of the ATP binding cassette (ABC) superfamily in the Phytophthora ramorum and P. sojae genomes has identified 135 and 136 members, respectively, indicating that this family is comparable in size to the Arabidopsis thaliana and rice genomes, and significantly larger than that of two fungal pathogens, Fusarium graminearum and Magnaporthe grisea. The high level of synteny between these oomycete genomes extends to the ABC superfamily, where 108 orthologues were identified by phylogenetic analysis. The largest subfamilies include those most often associated with multidrug resistance. The P. ramorum genome contains 22 multidrug resistance-associated protein (MRP) genes and 49 pleiotropic drug resistance (PDR) genes, while P. sojae contains 20 MRP and 49 PDR genes. Tandem duplication events in the last common ancestor appear to account for much of the expansion of these subfamilies. Recent duplication events in the PDR and ABCG families in both the P. ramorum and the P. sojae genomes indicate that selective expansion of ABC transporters may still be occurring. In other kingdoms, subfamilies define both domain arrangements and proteins having a common phylogenetic origin, but this is not the case for several subfamilies in oomycetes. At least one ABCG type transporter is derived from a PDR transporter, while transporters in the ABCB-half family cluster with transporters from bacterial, plant, and metazoan genomes. Additional examples of transporters that appear to be derived from horizontal transfer events from bacterial genomes include components of transporters associated with iron uptake and DNA repair.

17 citations

Journal ArticleDOI
TL;DR: The utility of an environmental omics approach to yield insights underlying phototrophic life as well as the interactions of the entire microbial community in an extreme environment is demonstrated.

16 citations

Journal ArticleDOI
TL;DR: In this article, the authors evaluated the utility of drug use forums as an early indicator or predictor of impending intoxications with potentially harmful or lethal outcomes prior to their occurrences and found that activity on Reddit can help predict changes in exposures associated with new or re-emerging NPS in the real world.

16 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