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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: Visualization of the embedding space reveals organization of structural and functional properties that aid binding prediction, and quantifies the observed increase in AUC on binding prediction tasks between classifiers trained on the translation embedding versus those using an untrained embedding.
Abstract: Cheminformatics aims to assist in chemistry applications that depend on molecular interactions, structural characteristics, and functional properties The arrival of deep learning and the abundance

22 citations

Journal ArticleDOI
TL;DR: Absolute coronary flow and MVR can be determined alongside FFR, in absolute units, during routine catheter laboratory assessment, without the need for additional catheters, wires or drug infusions.
Abstract: Aims Ischaemic heart disease is the reduction of myocardial blood flow, caused by epicardial and/or microvascular disease. Both are common and prognostically important conditions, with distinct guideline-indicated management. Fractional flow reserve (FFR) is the current gold-standard assessment of epicardial coronary disease, but is only a surrogate of flow and only predicts percentage flow changes. It cannot assess absolute (volumetric) flow or microvascular disease. The aim of this study was to develop and validate a novel method that predicts absolute coronary blood flow and microvascular resistance (MVR) in the catheter laboratory. Methods and Results A computational fluid dynamics (CFD) model was used to predict absolute coronary flow (QCFD) and coronary microvascular resistance (MVR) using data from routine invasive angiography and pressure-wire assessment. QCFD was validated in an in vitro flow circuit which incorporated patient-specific, 3-D printed coronary arteries; and then in vivo, in patients with coronary disease. In vitro, QCFD agreed closely with the experimental flow over all flow rates (bias +2.08 mL/min; 95% CI (error range) -4.7 to + 8.8 mL/min; R2=0.999, p < 0.001; variability coefficient <1%). In vivo, QCFD and MVR were successfully computed in all 40 patients under baseline and hyperaemic conditions, from which coronary flow reserve (CFR) was also calculated. QCFD-derived CFR correlated closely with pressure-derived CFR (R2=0.92, P < 0.001). This novel method was significantly more accurate than Doppler-wire-derived flow both in vitro (±6.7 vs ± 34 mL/min) and in vivo (±0.9 vs ± 24.4 mmHg). Conclusions Absolute coronary flow and MVR can be determined alongside FFR, in absolute units, during routine catheter laboratory assessment, without the need for additional catheters, wires or drug infusions. Using this novel method, epicardial and microvascular disease can be discriminated and quantified. This comprehensive coronary physiological assessment may enable a new level of patient stratification and management. Translational Perspective Current pressure wire-based methods of assessing coronary disease cannot assess absolute flow or microvascular disease. Our novel QCFD method, using only angiography-based CFD and a pressure wire, simultaneously measures FFR, absolute coronary blood flow rate, microvascular resistance and coronary flow reserve. QCFD is suitable for use in the catheter laboratory and requires no dedicated catheters, wires or infusions. QCFD measures blood flow and microvascular resistance in absolute units and allows microvascular and epicardial disease to be differentiated, quantified and separately assessed, with the potential to improve diagnostic accuracy and clinical management.

21 citations

Proceedings ArticleDOI
17 Jul 2006
TL;DR: The MAPGEN system was deployed in the Mars Exploration Rover mission as a mission-critical component of the ground operations system as mentioned in this paper, and it represents a successful mission infusion of planning technology.
Abstract: The MAPGEN system was deployed in the Mars Exploration Rover mission as a mission-critical component of the ground operations system. MAPGEN, which was jointly developed by ARC and JPL, represents a successful mission infusion of planning technology. The MER mission has operated spectacularly for over two years now, and we have learned valuable lessons regarding application of mixed-initiative planning technology to mission operations. These lessons have spawned new research in mixed-initiative planning and have influenced the design of a new ground operations system, called ENSEMBLE, that is base-lined for the Phoenix and Mars Science Laboratory missions. This paper discusses some of the lessons learned from the MER mission infusion experience and presents a preliminary report on these subsequent developments.

21 citations

Journal ArticleDOI
TL;DR: Cloned 22 full-length pectate lyase (PcPL) genes from a highly aggressive strain of Phytophthora capsici SD33 revealed that 12 PcPL genes were found to be highly induced during infection of pepper by SD33 but the induction level was twofold less in a mildly aggressive strain, YN07.
Abstract: Pectate lyases (PL) play a critical role in pectin degradation. PL have been extensively studied in major bacterial and fungal pathogens of a wide range of plant species. However, the contribution ...

21 citations

Patent
Robert Nado1, Paul Morris1
13 Dec 1988
TL;DR: In this article, a method is provided for representing a directed acyclic graph of worlds using an assumption-based truth maintenance system (ATMS) as a tool, in order to allow deletion of an assertion upon transition between worlds.
Abstract: In artificial intelligence, a method is provided for representing a directed acyclic graph of worlds using an assumption-based truth maintenance system (ATMS) as a tool The invention introduces the concepts of a nondeletion assumption and a deletion nogood, in order to allow deletion of an assertion upon transition between worlds The traditional (de Kleer) ATMS tool is augmented to allow distinction between two kinds of assumptions, namely the nondeletion assumption and the world assumption The nondeletion assumption is the elementary stipulation indicating the presence of an added assertion in a world The world assumption is the elementary stipulation representing existence of a world According to the invention, a method for testing assertions is provided for determining whether an assertion holds in a world The method involves taking into account the presence of deletion nogoods relevant to the tested assertion A deletion nogood is a nogood which indicates the contradiction between a world assumption and a nondeletion assumption which arises from a deletion of an assertion Deletion nogoods are introduced at a world to block any further inheritance of an assertion from an ancestor world The ATMS tool is further modified by replacing the traditional ATMS notion of inconsistency with a concept of inconsistency wherein only world assumptions are blamed for inconsistencies The present invention may be used in connection with planning systems and diagnosis systems as well as with other types of knowledge-based systems

21 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