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 impact of question type and empathy on police interviews with suspects of homicide, filicide and child sexual abuse

Abstract: Conducting interviews with “high-stake” offenders, especially those accused of murder and sexual offences, represents a complex and emotive area of work for police officers. Using an English sample of 59 actual police interviews, the effects of empathy and question type on the amount of investigation-relevant information obtained from interviews with suspects of child murder, child sex offences and adult murder were analysed and compared. No direct effects of empathy on the amount of information elicited were found. However, in interviews classified as empathic, interviewers asked significantly more appropriate questions than they did in interviews classified as non-empathic, and significantly more items of information were elicited from appropriate questions. There was a significant effect of offence type on the number of inappropriate, questions asked, with significantly more inappropriate questions being asked in interviews with suspects of child sex offences than in interviews with suspects of child or adult murder.

Invasive coronary physiology in patients with angina and non-obstructive coronary artery disease: a consensus document from the coronary microvascular dysfunction workstream of the British Heart Foundation/National Institute for Health Research Partnership

Abstract: Nearly half of all patients with angina have non-obstructive coronary artery disease (ANOCA); this is an umbrella term comprising heterogeneous vascular disorders, each with disparate pathophysiology and prognosis. Approximately two-thirds of patients with ANOCA have coronary microvascular disease (CMD). CMD can be secondary to architectural changes within the microcirculation or secondary to vasomotor dysfunction. An inability of the coronary vasculature to augment blood flow in response to heightened myocardial demand is defined as an impaired coronary flow reserve (CFR), which can be measured non-invasively, using imaging, or invasively during cardiac catheterisation. Impaired CFR is associated with myocardial ischaemia and adverse cardiovascular outcomes. The CMD workstream is part of the cardiovascular partnership between the British Heart Foundation and The National Institute for Health Research in the UK and comprises specialist cardiac centres with expertise in coronary physiology assessment. This document outlines the two main modalities (thermodilution and Doppler techniques) for estimation of coronary flow, vasomotor testing using acetylcholine, and outlines a standard operating procedure that could be considered for adoption by national networks. Accurate and timely disease characterisation of patients with ANOCA will enable clinicians to tailor therapy according to their patients’ coronary physiology. This has been shown to improve patients’ quality of life and may lead to improved cardiovascular outcomes in the long term.

Direct perception of animal mind

Abstract: Kujalars (2017) target article is ostensibly focused on how everyday folk (fail to) make sense of canine emotions. However, the theories outlined in the article apply to making sense of all aspects of the mentality of both human and non-human animals. The target article neglects the...

Open Annotation Data Model: community draft

Abstract: The Open Annotation Core Data Model specifies an interoperable framework for creating associations between related resources, annotations, using a methodology that conforms to the Architecture of the World Wide Web. Open Annotations can easily be shared between platforms, with sufficient richness of expression to satisfy complex requirements while remaining simple enough to also allow for the most common use cases, such as attaching a piece of text to a single web resource.An Annotation is considered to be a set of connected resources, typically including a body and target, where the body is somehow about the target. The full model supports additional functionality, enabling semantic annotations, embedding content, selecting segments of resources, choosing the appropriate representation of a resource and providing styling hints for consuming clients.n

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.).