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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: It is shown that the Phytophthora capsici genome contains five putative prolyl 4-hydroxylases, and in mycelia, all P4Hs were downregulated in response to hypoxia, but the expression of PcP4H1 was most affected.

1 citations

Journal ArticleDOI
29 Jun 2022-Heart
TL;DR: A post hoc evaluation of the subset of patients from SYNTAXES whose pattern of disease did (or did not) include the pLAD is presented, and an increasing awareness of the importance of adequate stent deployment and the adoption of physiological assessment, intravascular imaging, lesion preparation and stent optimisation have made PCI capable and durable.
Abstract: After the left main, the most important coronary artery is the left anterior descending (LAD), because it subtends the greatest proportion of myocardium. Disease in its proximal part confers the highest risk of myocardial infarction, mortality, left ventricular impairment and ischaemic burden. Therefore, revascularisation of this vessel may provide considerable benefits. Coronary artery bypass grafting (CABG), including an arterial conduit anastomosed beyond the proximal (p)LAD lesion, diverts blood past the region of vulnerability and obstruction, at the expense of invasiveness and competitive flow through the diseased segment. The internal mammary (thoracic) artery graft is both effective and durable, being virtually immune to atheroma, contributing to excellent surgical outcomes for the last 30 years. The basic operation has therefore remained largely unchanged. The main problems in the longer term relate to premature deterioration in venous grafts, and progression of atheroma and comorbidities. In contrast, percutaneous coronary intervention (PCI) restores vessel diameter and flow, at the expense of vascular trauma and leaving exposed any mild but potentially vulnerable disease. However, PCI techniques, adjunctive antithrombotic therapy and stents themselves have progressed enormously over the same period. Firstgeneration stents were bare metal and associated with a high rate of restenosis. Secondgeneration drugeluting stents had thick struts, thick polymer, a substantial drug load and an accompanying risk of stent thrombosis. But we now have thirdgeneration stents, with thin struts, thin (often only abluminal) polymer and a limited dose of drug, usually of the ‘limus’ family, virtually eliminating restenosis. Adjunctive therapy has progressed from warfarin and dextran, through aspirin and ticlopidine, to aspirin and clopidogrel or potent P2Y12 inhibitor, minimising the risk of thrombosis. In addition, an increasing awareness of the importance of adequate stent deployment, and the adoption of physiological assessment, intravascular imaging, lesion preparation and stent optimisation have made PCI capable and durable. This technological revolution in PCI poses a challenge for assessing historical studies comparing CABG and PCI. An isolated singlevessel lesion in a patient presenting with an acute or chronic coronary syndrome is unusual. When present, it is usually accompanied by disease elsewhere, and the whole ischaemic picture has to be considered when it comes to revascularisation decisions. In the case of onevessel or twovessel disease, the majority of patients are treated with PCI, whether or not one of the lesions is located in the pLAD, bearing in mind the efficacy of stenting in the current era; but threevessel disease, particularly that involving the left main or pLAD, generally stimulates a ‘Heart Team’ discussion about the relative merits of each form of revascularisation, and particularly an assessment of whether the patient fits the criteria of the ‘PCI versus CABG’ trials, which are largely based on multivessel disease. Of note, there is no largescale trial of CABG versus PCI for isolated pLAD disease. One of the most influential trials in the modern era is the SYNTAX (Synergy between PCI with Taxus and Cardiac Surgery) Study, now augmented by the ‘SYNTAXES’ (extended survival) Study of the same patients out to 10 years. In the original study, 1787 patients with de novo threevessel and/or left main coronary artery disease were randomised to CABG or PCI with Taxus Express paclitaxeleluting stents. In this journal, Ono et al present a post hoc evaluation of the subset of patients from SYNTAXES whose pattern of disease did (or did not) include the pLAD, but not the left main, with mortality outcomes to 10 years and major adverse cardiac and cardiovascular events (MACCE) to 5 years. There were 559 patients with multivessel disease including a pLAD lesion, of which 269 were treated with PCI and 290 with CABG. Five hundred and twentynine did not have a pLAD lesion, of which 274 were treated with PCI and 255 with CABG. There were two points of interest: first, any differences in outcomes between pLAD and nonpLAD patients as a whole; and second, any advantage of PCI over CABG, or vice versa, in each group. The main finding was that 10year allcause mortality was identical in the pLAD and nonpLAD groups (24% for each); and even 5year MACCE was very similar (29% vs 30%, respectively). In both pLAD and nonpLAD groups, mortality was higher after PCI than CABG (pLAD 29% vs 22%, p=0.06; and nonpLAD 29% vs 20%, p=0.03); and MACCE at 5 years was also higher, whether there was a pLAD lesion (42% vs 26%) or not (41% vs 28%). This study appears to show that revascularisation of patients with a pLAD is not associated with any different results from those without. There are, however, some important limitations of this study. There were generic issues relevant to the original SYNTAX Study. First, this was a rarefied group of patients who were deemed to be suitable for either form of revascularisation. In the ‘real world’, most patients with MVD tend to fall into one or the other group; an excess of comorbidity or poor ‘target’ vessels predisposing to PCI, and an excess of complex lesions with good targets predisposing to CABG. Second, this is now an old study (recruitment 2005–2008). The PCI group is therefore disadvantaged, with a thick strut, thick polymer, stent with an oldfashioned drug (paclitaxel). Third, physiological guidance was not used and we know that many cases of visually apparent disease are actually physiologically nonsignificant. Fourth, the rate of complete revascularisation was disappointing in both PCI and CABG groups, being 50%–53% in the former and 56%–59% in the latter. In addition, there were specific limitations imposed by a retrospective analysis. The location of a lesion in the pLAD was not prespecified, and therefore the findings are prone to bias. The large majority of patients in both pLAD and nonpLAD groups had triple vessel disease (95% vs 98%, respectively), but there were some potentially important differences; for pLAD versus nonpLAD, respectively, the SYNTAX score was 30 vs 24, the proportion in the lowest SYNTAX tertile was 21% vs 45%, the proportion in the highest tertile was 39% vs 19% (though this was partly a tautological reflection of the pLAD lesion itself), there was a Department of Infection, Immunity and Cardiovascular Disease, Sheffield Teaching Hospitals and University of Sheffield, Sheffield, UK Infection, Immunity and Cardiovascular Disease, The University of Sheffield, Sheffield, UK

1 citations

Proceedings ArticleDOI
01 Jul 2019
TL;DR: It is found that on average, across all datasets, rectified linear units perform better than any maxout activation when the number of convolutional filters is increased six times, and that maxout networks train relatively slower than networks comprised of traditional activation functions.
Abstract: Visual recognition is one of the most active research topics in computer vision due to its potential applications in self-driving cars, healthcare, social media, manufacturing, etc. For image classification tasks, deep convolutional neural networks have achieved state-of-the-art results, and many activation functions have been proposed to enhance the classification performance of these networks. We explore the performance of multiple maxout activation variants on image classification, facial recognition and verification tasks using convolutional neural networks. Our experiments compare rectified linear unit, leaky rectified linear unit, scaled exponential linear unit, and hyperbolic tangent to four maxout variants. Throughout the experiments, we find that maxout networks train relatively slower than networks comprised of traditional activation functions. We found that on average, across all datasets, rectified linear units perform better than any maxout activation when the number of convolutional filters is increased six times.

1 citations

Proceedings Article
11 Apr 2000

1 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