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.

The Complementary Value of Absolute Coronary Flow in the Assessment of Patients with Ischaemic Heart Disease (the COMPAC-Flow Study)

Abstract: Abstract Fractional flow reserve (FFR) is the current gold standard invasive assessment of coronary artery disease (CAD). FFR reports coronary blood flow (CBF) as a fraction of a hypothetical and unknown normal value. Although used routinely to diagnose CAD and guide treatment, how accurately FFR predicts actual CBF changes remains unknown. In this study, we compared fractional CBF with absolute CBF (aCBF, in ml min −1 ), measured with a computational method during standard angiography and pressure wire assessment, on 203 diseased arteries (143 patients). We found a substantial correlation between the two measurements ( r = 0.89 and Cohen’s kappa = 0.71). Concordance between fractional and absolute CBF reduction was high when FFR was >0.80 (91%) but reduced when FFR was ≤0.80 (81%), 0.70–0.80 (68%) and, particularly, 0.75–0.80 (62%). Discordance was associated with coronary microvascular resistance, vessel diameter and mass of myocardium subtended, all factors to which FFR is agnostic. Assessment of aCBF complements FFR and may be valuable to assess CBF, particularly in cases within the FFR ‘gray zone’.

021 Computer modelled coronary physiology and “virtual” fractional flow reserve from rotational angiography

Abstract: Background Percutaneous coronary intervention (PCI) guided by fractional flow reserve (FFR) measurement is superior to visual angiographic assessment alone. We have developed a workflow that takes a single rotational angiogram (RoCA), reconstructs the 3-dimensional arterial tree and applies computational fluid dynamics (CFD) to calculate the FFR without the need to induce hyperaemia or perform invasive pressure measurements. Methods 20 patients, scheduled for elective PCI underwent RoCA. The FFR was measured with a Combowire (Volcano), under resting and hyperaemic conditions. Physiologically significant lesions were stented and the measurements repeated. The arterial anatomy was reconstructed on a Philips 3DCA workstation. Generic boundary conditions for CFD were derived from the measured data. The calculated (“virtual”) and measured FFR values were then compared. Results There were 11 right coronary artery (RCA) cases (6 stented) and 12 left coronary artery (LCA) cases (8 stented). The anatomy was reconstructed, and the FFR computed in each case (pre- and post-stenting). The CFD model accurately predicted which lesions were physiologically significant (FFR 0.8) in all cases. The virtual FFR values deviated from the measured by ±6% (SD=6%) for both RCA and LCA cases. Conclusion We have developed a novel, user-friendly workflow, which has the potential to predict FFR without the need for invasive measurements or inducing hyperaemic conditions. Our model identified lesions requiring intervention in all cases. Further work will optimise and refine the model by better characterising the downstream generic boundary conditions. We aim to improve the accuracy of the optimised model with more complex patients and lesions.

Fitness for Use: The BDQIG aims for improved Stability and Consistency

Abstract: The process of choosing data for a project and then determining what subset of records are suitable for use has become one of the most important concerns for biodiversity researchers in the 21 century. The rise of large data aggregators such as GBIF (Global Biodiversity Information Facility), iDigBio (Integrated Digitized Biocollections), the ALA (Atlas of Living Australia) and its many clones, OBIS (Ocean Biogeographic Information System), SIBBr (Sistema de Informação sobre a Biodiversidade Brasileria), CRIA (Centro de Referência em Informação Ambiental) and many others has made access to large volumes of data easier, but choosing which data are fit for use remains a more difficult task. There has been no consistency between the various aggregators on how best to clean and document the quality – how tests are run, or how annotations are stored and reported. Feedback to data custodians on possible errors has been minimal, inconsistent, and adherence to recommendations and controlled vocabularies (where they exist) has been haphazard to say the least. ‡ § | ¶ # ¤ « » ˄

Limits of neuroscience

Abstract: Examining the relationship between jealous behaviour and the amygdala may be quite informative about the function of the amygdala, but the amygdala may be less helpful in informing us about jealous behaviour. Claims about the potential practical relevance of the results also require that the magnitude of the effects inform the relevant discussion. The dogs used in the study probably share some very important personality characteristics; this too limits the practical implications of Cook et al.’s findings for dogs in general. It is nevertheless a testament to the skill of the experimenters, and the amazing bond between dogs and humans, that such research could be conducted at all. Paul Morris is a psychologist interested in how intentions and emotions are embodied in behaviour. His work includes research on the manifestation and perception of emotions in human infants and non-human animals. Website It is a remarkable feat to have trained 13 dogs to tolerate an fMRI scanner, let alone get them to cooperate in an experiment in such a context. One cannot help but be deeply impressed by the experimental virtuosity of the researchers (Cook et al., 2018). However, I am certainly not the first to be uneasy about the real utility of much neuroscience research: There is something of a backlash against many of the claims of neuroscience (Satel & Lilienfeld, 2013), a backlash so well established that there is a backlash against the backlash (Marcus, 2013). At a broad philosophical level, my concern with the target article is that it is implicit in the title that we can take jealousy in dogs more seriously because of evidence from neuroscience. However, it is an uncomfortable truth for some scientists studying emotion that the primary data for our knowledge about emotions are subjective experience and human judgement. The plural of anecdote in this case is data. Our knowledge of the localisation of affect is ultimately derived from human experience and judgement. We think a particular area of the brain may be associated with a particular emotion because we have induced a particular emotion and then observed what the brain gets up to. We know what emotions are because we are emotional beings. We began investigating jealousy in dogs because our experience with dogs suggested that dogs were jealous. We did not start to investigate jealousy in dogs because of what was going on in their amygdala. Studying brain/behaviour relationships provides a rich source of information concerning brain function, but much less so concerning behaviour. There are several more technical issues that I would like to mention. I am not at all sure that amygdala function can provide really useful information. The amygdala is implicated in just about everything from emotion, to fundamental cognitive processes such as long-term memory, working memory and visual attention (Schaefer & Gray, 2007). The statistical analysis is also problematic, as interpreting the magnitude of effects from a mixed-effects model is by Animal Sentience 2018.132: Morris on Cook et al. on Dog Jealousy 2 no means straightforward. In the discussion, the authors make no mention of the magnitude of the effects, but simply state that there was a positive correlation between aggressive temperament and amygdala activation. The magnitude of any such relationship is crucial to any claims that the information from the study could inform behavioural interventions. My final comment is that regardless of the C-BARQ scores, given what the dogs were required to do, I cannot think that these dogs were anything but highly social, unaggressive and co-operative. These may be special dogs. In any study of individual differences, it is crucial to have sampled the range of the trait of interest. The findings of a study of individual differences using 13 very carefully trained and selected participants must be treated with great caution. My overwhelming feeling having written this commentary is social guilt (which I believe is thought to be localised to the anterior middle cingulate cortex) because being a critic is easy, and I remain amazed that the researchers managed to conduct this study at all.

Maxout Networks for Visual Recognition

Abstract: This study investigates the effectiveness of multiple maxout activation variants on image classification, facial identification and verification tasks using convolutional neural networks. A network with maxout activation has a higher number of trainable parameters compared to networks with traditional activation functions. However, it is not clear if the activation function itself or the increase in the number of trainable parameters is responsible for yielding the best performance on different entity recognition tasks. This article investigates if an increase in the number of convolutional filters on the rectified linear unit activation performs equal-to or better-than maxout 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 found that on average, across all datasets, the rectified linear unit networks perform better than any maxout activation when the number of convolutional filters is increased six times.

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.).