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.

Issues in temporal reasoning for autonomous control systems

Abstract: Deep Space One will be the rst spacecraft to be controlled by an autonomous agent poten tially capable of carrying out a complete mission with minimal commanding from Earth The New Millennium Remote Agent NMRA includes a planner scheduler that produces plans and an executive that carries them out In this paper we discuss several issues arising at the interface between planning and execution including exe cution latency plan dispatchability and the dis tinction between controllable and uncontrollable events Temporal information in the plan is rep resented within the general framework of Simple Temporal Constraint networks as introduced by Dechter Meiri and Pearl However the execu tion requirements have a substantial impact on the topology of the links and the propagation through the network

Cooperative role for tetraspanins in adhesin-mediated attachment of bacterial species to human epithelial cells

Abstract: The tetraspanins are a superfamily of transmembrane proteins with diverse functions and can form extended microdomains within the plasma membrane in conjunction with partner proteins, which probably includes receptors for bacterial adhesins. Neisseria meningitidis, the causative agent of meningococcal disease, attaches to host nasopharyngeal epithelial cells via type IV pili and opacity (Opa) proteins. We examined the role of tetraspanin function in Neisseria meningitidis adherence to epithelial cells. Tetraspanins CD9, CD63, and CD151 were expressed by HEC-1-B and DETROIT 562 cells. Coincubation of cells with antibodies against all three tetraspanin molecules used individually or in combination, with recombinant tetraspanin extracellular domains (EC2), or with small interfering RNAs (siRNAs) significantly reduced adherence of Neisseria meningitidis. In contrast, recombinant CD81, a different tetraspanin, had no effect on meningococcal adherence. Antitetraspanin antibodies reduced the adherence to epithelial cells of Neisseria meningitidis strain derivatives expressing Opa and pili significantly more than isogenic strains lacking these determinants. Adherence to epithelial cells of strains of Staphylococcus aureus, Neisseria lactamica, Escherichia coli, and Streptococcus pneumoniae was also reduced by pretreatment of cells with tetraspanin antibodies and recombinant proteins. These data suggest that tetraspanins are required for optimal function of epithelial adhesion platforms containing specific receptors for Neisseria meningitidis and potentially for multiple species of bacteria.

MAPGEN : mixed initiative planning and scheduling for the Mars '03 MER mission

Abstract: The Mars Exploration Rovers Mars '03 mission is one of NASA's most ambitious science missions to date. The rovers will be launched in the summer of 2003 with each rover carrying instruments to conduct remote and in-situ observation to elucidate the planet's past climate, water activity, and habitability. Science is the primary driver of MER and, as a consequence, making best use of the scientific instruments, within the available resources, is a crucial aspect of the mission. To address this critically, the MER project has selected MAPGEN (Mixed-Initiative Activity Plan GENerator) as an activity planning tool. MAPGEN combines two exiting systems, each with a strong heritage: APGEN the Activity Planning tool from the Jet Propulsion Laboratory and the Europs Planning/Scheduling system from NASA Ames Research Center. This paper discusses the issues arising from combining these tools in the context of this mission.

Proximity and gaze influences facial temperature: a thermal infrared imaging study.

Abstract: Direct gaze and interpersonal proximity are known to lead to changes in psycho-physiology, behavior and brain function. We know little, however, about subtler facial reactions such as rise and fall in temperature, which may be sensitive to contextual effects and functional in social interactions. Using thermal infrared imaging cameras 18 female adult participants were filmed at two interpersonal distances (intimate and social) and two gaze conditions (averted and direct). The order of variation in distance was counterbalanced: half the participants experienced a female experimenter’s gaze at the social distance first before the intimate distance (a socially “normal” order) and half experienced the intimate distance first and then the social distance (an odd social order). At both distances averted gaze always preceded direct gaze. We found strong correlations in thermal changes between six areas of the face (forehead, chin, cheeks, nose, maxilliary, and periorbital regions) for all experimental conditions and developed a composite measure of thermal shifts for all analyses. Interpersonal proximity led to a thermal rise, but only in the “normal” social order. Direct gaze, compared to averted gaze, led to a thermal increase at both distances with a stronger effect at intimate distance, in both orders of distance variation. Participants reported direct gaze as more intrusive than averted gaze, especially at the intimate distance. These results demonstrate the powerful effects of another person’s gaze on psycho-physiological responses, even at a distance and independent of context.

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.).