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。

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

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.

Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference

2016 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure: The Task Force for the diagnosis and treatment of acute and chronic heart failure of the European Society of Cardiology (ESC). Developed with the special contribution of the Heart Failure Association (HFA) of the ESC.

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.).

Machine learning

Statistical method for testing the neutral mutation hypothesis by DNA polymorphism.

Simple Temporal Networks (STNs) have proved useful in applications that involve metric time However, many applications involve events whose timing is uncertain in the sense that it is not controlled by the execution agent In this paper we consider execution algorithms for temporal networks that include events of uncertain timing We present two such algorithms The first retains maximum flexibility, but requires potentially costly updates during execution The second surrenders some flexibility in order to obtain a fast execution comparable to that available for ordinary STNs

/pdf/execution-of-temporal-plans-with-uncertainty-2k0ta7f6kj.pdf

Execution of Temporal Plans with Uncertainty

This article addresses the influence of 2 personality traits on making hand movements during deception, namely public self-consciousness and ability to control behavior. It was hypothesized that especially individuals with high public self-consciousness and individuals who are skilled in controlling their behavior would make fewer hand movements during deception compared to truth-telling. A total of 56 participants were interviewed twice; in one interview they told the truth and in the other interview they lied. Before the interviews the participants completed a personality inventory to measure their levels of public self-consciousness and ability to control their behavior. The results supported the hypotheses. Some implications of these findings are discussed.

Individual Differences in Hand Movements During Deception

BACKGROUND: The life sentence population is growing older and increasing in number. Despite the potential negative physical and social environment in prisons little is known about the prevalence or aetiology of depression in elderly 'lifers'. AIMS: To determine the prevalence and associated risk factors of depression in elderly life sentence prisoners. METHOD: One hundred and twenty-one elderly life/indeterminate sentence prisoners from two category B prisons in the United Kingdom were interviewed using the Geriatric Depression Scale and the relationship with prison and non prison specific variables analysed. RESULTS: Over half of the prisoners scored above the threshold for mild depression. The length of sentence served and other prison related variables were not associated with the depression score. However, the imported chronic physical ill health was strongly related to depression score. CONCLUSIONS: Depression in long term prisoners is common and is related to the burden of imported chronic ill health as opposed to specific effects of imprisonment

Depression in elderly life sentence prisoners.

Key branchpoint enzymes of aromatic amino acid biosynthesis, 3-deoxy-d-arabino-heptulosonate 7-phosphate synthase (DS) and chorismate mutase (CM), have previously been shown to exist as separate compartmentalized isozymes in the chloroplasts and cytosol of tobacco, sorghum and spinach. Although additional examples of plants containing these isozyme pairs are accumulating, some studies in the literature report the presence of only the single plastidic DS or CM enzyme. Such apparent exceptions contradict the universality of pathway organization existing in higher plants that is implied by the dual-pathway hypothesis of aromatic biosynthesis. Since potato (Solanum tuberosum) exemplifies a case where only a single species of both DS and CM have been reported, we selected this system for further analysis. The DS-Mn and DS-Co isozyme pair, exhibiting all of the differential properties described in Nicotiana silvestris, have now been identified in S. tuberosum. Likewise, partial purification via DEAE-cellulose chromatography revealed two isozymes of CM in disks excised from tubers of S. tuberosum. The differential regulatory properties of these isozymes were comparable to the CM-1 and CM-2 isozymes of N. silvestris.

Evidence from Solanum tuberosum in Support of the Dual-Pathway Hypothesis of Aromatic Biosynthesis.

The Assumption-based Truth Maintenance System, introduced by de Kleer, is a powerful new tool for organizing a search through a space of alternatives. However, the ATMS is oriented towards inferential problem solving, and provides no special mechanisms for modeling actions or state changes. We describe an approach to applying the ATMS to the task of representing the effects of actions. The approach extends traditional tree-structured context mechanisms to allow context merges. It also takes advantage of the underlying ATMS to detect inconsistent contexts and to maintain derived results. Some results are presented concerning possible approaches to the treatment of merges in questionable circumstances. Finally, the analysis of actions in terms of a truth maintenance system suggests the need for a more elaborate treatment of contradiction in such systems than exists at present.

/pdf/representing-actions-with-an-assumption-based-truth-izns6ucg9l.pdf

Paul Morris

Papers

Execution of Temporal Plans with Uncertainty

Individual Differences in Hand Movements During Deception

Depression in elderly life sentence prisoners.

Evidence from Solanum tuberosum in Support of the Dual-Pathway Hypothesis of Aromatic Biosynthesis.

Representing actions with an assumption-based truth maintenance system