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.

Distinctive Expansion of Potential Virulence Genes in the Genome of the Oomycete Fish Pathogen Saprolegnia parasitica

Abstract: Oomycetes in the class Saprolegniomycetidae of the Eukaryotic kingdom Stramenopila have evolved as severe pathogens of amphibians, crustaceans, fish and insects, resulting in major losses in aquaculture and damage to aquatic ecosystems. We have sequenced the 63 Mb genome of the fresh water fish pathogen, Saprolegnia parasitica. Approximately 1/3 of the assembled genome exhibits loss of heterozygosity, indicating an efficient mechanism for revealing new variation. Comparison of S. parasitica with plant pathogenic oomycetes suggests that during evolution the host cellular environment has driven distinct patterns of gene expansion and loss in the genomes of plant and animal pathogens. S. parasitica possesses one of the largest repertoires of proteases (270) among eukaryotes that are deployed in waves at different points during infection as determined from RNA-Seq data. In contrast, despite being capable of living saprotrophically, parasitism has led to loss of inorganic nitrogen and sulfur assimilation pathways, strikingly similar to losses in obligate plant pathogenic oomycetes and fungi. The large gene families that are hallmarks of plant pathogenic oomycetes such as Phytophthora appear to be lacking in S. parasitica, including those encoding RXLR effectors, Crinkler's, and Necrosis Inducing-Like Proteins (NLP). S. parasitica also has a very large kinome of 543 kinases, 10% of which is induced upon infection. Moreover, S. parasitica encodes several genes typical of animals or animal-pathogens and lacking from other oomycetes, including disintegrins and galactose-binding lectins, whose expression and evolutionary origins implicate horizontal gene transfer in the evolution of animal pathogenesis in S. parasitica.

Activity planning for the Mars Exploration Rovers

Abstract: Operating the Mars Exploration Rovers is a challenging, time-pressured task. Each day, the operations team must generate a new plan describing the rover activities for the next day. These plans must abide by resource limitations, safety rules, and temporal constraints. The objective is to achieve as much science as possible, choosing from a set of observation requests that oversubscribe rover resources. In order to accomplish this objective, given the short amount of planning time available, the MAPGEN (Mixed-initiative Activity Plan GENerator) system was made a mission-critical part of the ground operations system. MAPGEN is a mixed-initiative system that employs automated constraint-based planning, scheduling, and temporal reasoning to assist operations staff in generating the daily activity plans. This paper describes the adaptation of constraint-based planning and temporal reasoning to a mixed-initiative setting and the key technical solutions developed for the mission deployment of MAPGEN.

Temporal constraint reasoning with preferences

Abstract: A number of reasoning problems involving the manipulation of temporal information can naturally be viewed as implicitly inducing an ordering of potential local decisions involving time (specifically, associated with durations or orderings of events) on the basis of preferences For example a pair of events might be constrained to occur in a certain order, and, in addition it might be preferable that the delay between them be as large, or as small, as possible This paper explores problems in which a set of temporal constraints is specified, where each constraint is associated with preference criteria for making local decisions about the events involved in the constraint, and a reasoner must infer a complete solution to the problem such that, to the extent possible, these local preferences are met in the best way A constraint framework for reasoning about time is generalized to allow for preferences over event distances and durations, and we study the complexity of solving problems in the resulting formalism It is shown that while in general such problems are NP-hard, some restrictions on the shape of the preference functions, and on the structure of the preference set, can be enforced to achieve tractability In these cases, a simple generalization of a single-source shortest path algorithm can be used to compute a globally preferred solution in polynomial time

Chemotropic and Contact Responses of Phytophthora sojae Hyphae to Soybean Isoflavonoids and Artificial Substrates

Abstract: We have investigated the role of the isoflavones daidzein and genistein on the chemotropic behavior of germinating cysts of Phytophthora sojae. Hyphal germlings were shown to respond chemotropically to daidzein and genistein, suggesting that hyphal tips from zoospores that have encysted adjacent to the root may use specific host isoflavones to locate their host. Observations of the contact response of hyphal germlings were made on several different substrates in the presence and absence of isoflavones. Hyphal tips of germlings detected and penetrated pores in membranes and produced multiple appressoria on smooth, impenetrable surfaces. Hyphae that successfully penetrated the synthetic membrane were observed to grow away from the membrane surface. The presence of isoflavones in the medium surrounding the hyphal germlings did not appear to alter any of those habits. Daidzein and genistein did not inhibit germination or initial hyphal growth at concentrations up to 20 μm.

Secondary emotions in non-primate species? Behavioural reports and subjective claims by animal owners

Abstract: A defining characteristic of primary emotions is that they occur in wide variety of species. Secondary emotions are thought to be restricted to humans and other primates. We report evidence from two studies investigating claims of primary and secondary emotions in non-primate species. Study 1. We surveyed 907 owners about emotions that they had observed in their animal. Participants reported primary emotions more frequently than secondary emotions and self-conscious emotions more frequently than self-conscious evaluative emotions. Jealousy was reported at very high levels (81% of dogs and 79% of horses), which was surprising as jealousy is generally defined as a secondary emotion. Study 2. Forty dog owners were interviewed about the contexts and behaviours that led them to claim their animal was jealous. There was coherence and consistency in the behavioural descriptions of jealousy. We claim that such reports provide evidence for the existence of secondary emotions in non-primate species as predicted by theorists such as Buck (1999).

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.).