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.

Data Quality Task Group 2: Tests and Assertions

Abstract: Task Group 2 of the TDWG Data Quality Interest Group aims to provide a standard suite of tests and resulting assertions that can assist with filtering occurrence records for as many applications as possible. Currently ‘data aggregators’ such as the Global Biodiversity Information Facility (GBIF), the Atlas of Living Australia (ALA) and iDigBio run their own suite of tests over records received and report the results of these tests (the assertions): there is, however, no standard reporting mechanisms. We reasoned that the availability of an internationally agreed set of tests would encourage implementations by the aggregators, and at the data sources (museums, herbaria and others) so that issues could be detected and corrected early in the process. All the tests are limited to Darwin Core terms. The ~95 tests refined from over 250 in use around the world, were classified into four output types: validations, notifications, amendments and measures. Validations test one of more Darwin Core terms, for example, that dwc:decimalLatitude is in a valid range (i.e. between -90 and +90 inclusive). Notifications report a status that a user of the record should know about, for example, if there is a user-annotation associated with the record. Amendments are made to one or more Darwin Core terms when the information across the record can be improved, for ‡ § | ¶ # ¤ © Belbin L et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. example, if there is no value for dwc:scientificName, it can be filled in from a valid dwc:taxonID. Measures report values that may be useful for assessing the overall quality of a record, for example, the number of validation tests passed. Evaluation of the tests was complex and time-consuming, but the important parameters of each test have been consistently documented. Each test has a globally unique identifier, a label, an output type, a resource type, the Darwin Core terms used, a description, a dimension (from the Framework on Data Quality from TG1), an example, references, implementations (if any), test-prerequisites and notes. For each test, generic code is being written that should be easy for institutions to implement – be they aggregators or data custodians. A valuable product of the work of TG2 has been a set of general principles. One example is “Darwin Core terms are either: 1. literal verbatim (e.g., dwc:verbatimLocality) and cannot be assumed capable of validation, 2. open-ended (e.g., dwc:behavior) and cannot be assumed capable of validation, or 3. bounded by an agreed vocabulary or extents, and therefore capable of validation (e.g., dwc:countryCode)”. Another is “criteria for including tests is that they are informative, relatively simple to implement, mandatory for amendments and have power in that they will not likely result in 0% or 100% of all record hits.” A third: “Do not ascribe precision where it is unknown.” GBIF, the ALA and iDigBio have committed to implementing the tests once they have been finalized. We are confident that many museums and herbaria will also implement the tests over time. We anticipate that demonstration code and a test dataset that will validate the code will be available on project completion.

Characterization of Membrane Transporters by Heterologous Expression in E. coli and Production of Membrane Vesicles

Abstract: Several methods have been developed to functionally characterize novel membrane transporters. Polyamines are ubiquitous in all organisms, but polyamine exchangers in plants have not been identified. Here, we outline a method to characterize polyamine antiporters using membrane vesicles generated from the lysis of Escherichia coli cells heterologously expressing a plant antiporter. First, we heterologously expressed AtBAT1 in an E. coli strain deficient in polyamine and arginine exchange transporters. Vesicles were produced using a French press, purified by ultracentrifugation and utilized in a membrane filtration assay of labeled substrates to demonstrate the substrate specificity of the transporter. These assays demonstrated that AtBAT1 is a proton-mediated transporter of arginine, γ-aminobutyric acid (GABA), putrescine and spermidine. The mutant strain that was developed for the assay of AtBAT1 may be useful for the functional analysis of other families of plant and animal polyamine exchangers. We also hypothesize that this approach can be used to characterize many other types of antiporters, as long as these proteins can be expressed in the bacterial cell membrane. E. coli is a good system for the characterization of novel transporters, since there are multiple methods that can be employed to mutagenize native transporters.

Solving and Learning Soft Temporal Constraints: Experimental Scenario and Examples

Abstract: Soft temporal constraint problems allow to describe in a natural way scenarios where events happen over time and preferences are associated to event distances and durations. However, sometimes such local preferences are difficult to set, and it may be easier instead to associate preferences to some complete solutions of the problem. To model everything in a uniform way via local preferences only, and also to take advantage of the existing constraint solvers which exploit only local preference use machine learning techniques which learn the local preferences from the global ones. In this paper we describe the existing framework for both solving and learning preferences in temporal constraint problems, the implemented modules, the experimental scenario, and preliminary results on some examples.

Learning to Be Streetwise: The Acquisition of Accurate Judgments of Aggression.

Abstract: The detection of potential danger is an important factor in avoiding harm that is even more important for vulnerable populations such as children. This study explores whether children can recognise the potential for a dangerous encounter from observing the gait of an approaching individual. The participants are divided into three age groups: 13- to 15-year-olds, 16- to 17-year-olds, and over 18s. Participants made judgments of nine, point light presentations of people walking on a treadmill. Ratings of intimidation made by participants were used to assess their ability to detect the walkers' trait aggression. The ability to detect trait aggression accurately was found to increase with age, as does the consistency in ratings between individuals within the same age group. The importance of experiential learning in the acquisition of aggression detection skills is highlighted.

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.).