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.

We introduce methods for identifying operator preconditions that need not be expanded further. The methods are proved to be admissible, that is, they will not cause a solution to be missed when one exists. In certain cases, the methods also identify operator reformulations that increase the number of nonexpandable preconditions. This approach provides effective loop control in common situations. Moreover, the computation required can be performed during a precompilation of the operators in a domain; thus, there is no significant additional run-time overhead during planning.

Admissible criteria for loop control in planning

Computational techniques for binding-affinity prediction and molecular docking have long been considered in terms of their utility for drug discovery. With the advent of deep learning, new supervised learning techniques have emerged which can utilize the wealth of experimental binding data already available. Here we demonstrate the ability of a fully convolutional neural network to classify molecules from their Simplified Molecular-Input Line-Entry System (SMILES) strings for binding affinity to HIV proteins. The network is evaluated on two tasks to distinguish a set of molecules which are experimentally verified to bind and inhibit HIV-1 Protease and HIV-1 Reverse Transcriptase from a random sample of drug-like molecules. We report 98% and 93% classification accuracy on the respective tasks using a computationally efficient model which outperforms traditional machine learning baselines. Our model is suitable for virtually screening a large set of drug-like molecules for binding to HIV or other protein targets.

Convolutional Neural Networks for Predicting Molecular Binding Affinity to HIV-1 Proteins

: The objective of this project was to explore the usefulness of incorporating truth maintenance system (TMS) technology into the design of planning systems. In particular the research was aimed at exploitation of dependency-directed back-tracking in planning search and plan repair, as well as the development of mechanisms enabling use of revokable assumptions about the external world to support replanning in dynamic environments. This report describes a prototype planning/replanning system that incorporates TMS technology in the plan structure to support efficient backtracking in support of replanning in the context of violated or retracted plan assumptions. Techniques for reducing planning search using domain knowledge are also described in the report. The six appendices describe the underlying research contributing to the design of the prototype system. Keywords: Truth maintenance, Planning search, Replanning, Nonmonotonic reasoning, Defeasible reasoning.

Truth Maintenance in Automatic Planning.

Prevalence of hypogonadism in men with coronary artery disease

/pdf/coronary-physiological-assessment-in-the-catheter-laboratory-2tha4oqt.pdf

Paul Morris

Papers

Admissible criteria for loop control in planning

Convolutional Neural Networks for Predicting Molecular Binding Affinity to HIV-1 Proteins

Truth Maintenance in Automatic Planning.

Prevalence of hypogonadism in men with coronary artery disease

Coronary physiological assessment in the catheter laboratory: haemodynamics, clinical assessment and future perspectives