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.


 
 
 International guidelines mandate the use of fractional flow reserve (FFR) and/or non-hyperaemic pressure ratios to assess the physiological significance of moderate coronary artery lesions to guide revascularization decisions. However, they remain underused such that visual estimation of lesion severity continues to be the predominant decision-making tool. It would be pragmatic to have an improved understanding of the relationship between lesion morphology and haemodynamics. The aim of this study was to compute virtual FFR (vFFR) in idealized coronary artery geometries with a variety of stenosis and vessel characteristics.
 
 
 
 Coronary artery geometries were modelled, based upon physiologically realistic branched arteries. Common stenosis characteristics were studied, including % narrowing, length, eccentricity, shape, number, position relative to branch, and distal (myocardial) resistance. Computational fluid dynamics modelling was used to calculate vFFRs using the VIRTUheart™ system. Percentage lesion severity had the greatest effect upon FFR. Any ≥80% diameter stenosis in two views (i.e. concentric) was physiologically significant (FFR ≤ 0.80), irrespective of length, shape, or vessel diameter. Almost all eccentric stenoses and all 50% concentric stenoses were physiologically non-significant, whilst 70% uniform concentric stenoses about 10 mm long straddled the ischaemic threshold (FFR 0.80). A low microvascular resistance (MVR) reduced FFR on average by 0.05, and a high MVR increased it by 0.03.
 
 
 
 Using computational modelling, we have produced an analysis of vFFR that relates stenosis characteristics to haemodynamic significance. The strongest predictor of a positive vFFR was a concentric, ≥80% diameter stenosis. The importance of MVR was quantified. Other lesion characteristics have a limited impact.


http://academic.oup.com/ehjdh/advance-article-pdf/doi/10.1093/ehjdh/ztab075/40303647/ztab075.pdf

The relationship between coronary stenosis morphology and fractional flow reserve: a computational fluid dynamics modelling study

A formulation of planning based on multisets is introduced. Actions are represented by a simple well-behaved formalism which is related both to Petri nets and to predicate logic. It is inherently resource and quantity oriented and more accurately models constraints imposed by resource limitations.
The consequences of this for plan generation are explored. The formalism is simple enough to allow theoretical issues to be investigated. Parallel plans are represented by a concise graphical structure with the property that a syntactically complete plan is necessarily correct. Abstract algorithms for plan generation with provable completeness properties are exhibited. Techniques for recognizing and escaping from vicious circles are studied. These turn out to also have the effect of excluding certain correct but nonsensical plans, and allow the automatic derivation of planning-time checks on the appropriateness of specific operators, but cause incompleteness in some domains.
Novel methods for efficient planning that arise naturally from the formalism are described. Cut and splice operations on plan graphs permit the order of goal selection to be based on efficiency rather than correctness considerations. The multiset based formalism is particularly suited to developing the notion of an invariant, or conservation law. Invariants are exploited in a technique called goal targeting which provides an early outline of the final plan. This helps to eliminate misdirected search and facilitates certain intelligent goal selection and variable binding mechanisms.

A resource oriented formalism for plan generation (artificial intelligence, problem solving)

The extant literature has generally demonstrated that young adults can detect the trait aggression of another person with limited information. However, there is little research that investigates the life course persistence of aggression detection accuracy. Here, we aimed to explore the accuracy of older adults at detecting potential aggressors. Thirty-nine older adults (M = 71.49, SD = 7.59) and eighty-seven young adults (M = 20.24, SD = 1.74) made intimidation judgments, via video recordings, for nine people (targets). 'Aggression detection accuracy' was shown in the relationship between the intimidation judgments made by participants and the targets' responses to the Buss-Perry Aggression Questionnaire. Both age groups were highly accurate in their recognition of trait aggression and accuracy was maintained into older age, with no difference in accuracy between the older and young adults. There was, however, more variability in the ratings given by the older adults compared to the young adults, suggesting less consensus in judgment for the older compared to the young group. Overall, the participants in this study were highly accurate at detecting trait aggression. There was no difference in average aggression detection between older and young adults but there was in sample agreement. These results are discussed in the context of age effects on intimidation, as well as research in accurate aggression detection.

/pdf/staying-streetwise-accurate-judgments-of-approaching-256jqp2s83.pdf

Staying Streetwise: Accurate Judgments of Approaching Aggression in Older Age.

This paper describes the implementation of the ProTalk compiler for PrkAda. An important component of the ProKappa system is its multi-paradigm, mixed-rule/imperative language, ProTalk. ProTalk combines elements of backtracking AI languages such as Prolog with imperative constructs such as conditionals, iteration, assignment, and subprogram invocation. Our implementation illustrates the use of generators to achieve a Prolog-like semantics, and the possibility (and difficulties) of employing such mechanisms in Ada.

Compiling Knowledge-Based Systems to Ada: The PrkAda ProTalk Compiler

Abstract Aims Angiography-derived fractional flow reserve (angio-FFR) permits physiological lesion assessment without the need for an invasive pressure wire or induction of hyperaemia. However, accuracy is limited by assumptions made when defining the distal boundary, namely coronary microvascular resistance (CMVR). We sought to determine whether machine learning (ML) techniques could provide a patient-specific estimate of CMVR and therefore improve the accuracy of angio-FFR. Methods and results Patients with chronic coronary syndromes underwent coronary angiography with FFR assessment. Vessel-specific CMVR was computed using a three-dimensional computational fluid dynamics simulation with invasively measured proximal and distal pressures applied as boundary conditions. Predictive models were created using non-linear autoregressive moving average with exogenous input (NARMAX) modelling with computed CMVR as the dependent variable. Angio-FFR (VIRTUheart™) was computed using previously described methods. Three simulations were run: using a generic CMVR value (Model A); using ML-predicted CMVR based upon simple clinical data (Model B); and using ML-predicted CMVR also incorporating echocardiographic data (Model C). The diagnostic (FFR ≤ or >0.80) and absolute accuracies of these models were compared. Eighty-four patients underwent coronary angiography with FFR assessment in 157 vessels. The mean measured FFR was 0.79 (±0.15). The diagnostic and absolute accuracies of each personalized model were: (A) 73% and ±0.10; (B) 81% and ±0.07; and (C) 89% and ±0.05, P < 0.001. Conclusion The accuracy of angio-FFR was dependent in part upon CMVR estimation. Personalization of CMVR from standard clinical data resulted in a significant reduction in angio-FFR error.

/pdf/incorporating-clinical-parameters-to-improve-the-accuracy-of-18yo74t9.pdf

Paul Morris

Papers

The relationship between coronary stenosis morphology and fractional flow reserve: a computational fluid dynamics modelling study

A resource oriented formalism for plan generation (artificial intelligence, problem solving)

Staying Streetwise: Accurate Judgments of Approaching Aggression in Older Age.

Compiling Knowledge-Based Systems to Ada: The PrkAda ProTalk Compiler

Incorporating clinical parameters to improve the accuracy of angiography-derived computed fractional flow reserve