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.

The effects of testosterone on cardiac electrophysiology are poorly described. In this study we report the effect of physiologic testosterone therapy in 2 cohorts of men, the first with stable coronary disease and the second with congestive heart failure. Testosterone reduced QT dispersion in the heart failure cohort; no other effects were observed.

Effect of testosterone therapy on QT dispersion in men with heart failure.

The rice gene POLYAMINE UPTAKE TRANSPORTER1 (PUT1) was originally identified based on its homology to the polyamine uptake transporters LmPOT1 and TcPAT12 in Leishmania major and Trypanosoma cruzi, respectively. Here we show that five additional transporters from rice and Arabidopsis that cluster in the same clade as PUT1 all function as high affinity spermidine uptake transporters. Yeast expression assays of these genes confirmed that uptake of spermidine was minimally affected by 166 fold or greater concentrations of amino acids. Characterized polyamine transporters from both Arabidopsis thaliana and Oryza sativa along with the two polyamine transporters from L. major and T. cruzi were aligned and used to generate a hidden Markov model. This model was used to identify significant matches to proteins in other angiosperms, bryophytes, chlorophyta, discicristates, excavates, stramenopiles and amoebozoa. No significant matches were identified in fungal or metazoan genomes. Phylogenic analysis showed that some sequences from the haptophyte, Emiliania huxleyi, as well as sequences from oomycetes and diatoms clustered closer to sequences from plant genomes than from a homologous sequence in the red algal genome Galdieria sulphuraria, consistent with the hypothesis that these polyamine transporters were acquired by horizontal transfer from green algae. Leishmania and Trypansosoma formed a separate cluster with genes from other Discicristates and two Entamoeba species. We surmise that the genes in Entamoeba species were acquired by phagotrophy of Discicristates. In summary, phylogenetic and functional analysis has identified two clades of genes that are predictive of polyamine transport activity.

Kinetic and phylogenetic analysis of plant polyamine uptake transporters

The anomalous extension problem in default reasoning

Theta phase locking across the neocortex reflects cortico-hippocampal recursive communication during goal conflict resolution.

The three studies reported examine judgment about the attempts of footballers (soccer players) to deceptively exaggerate the effect of a tackle Study one reveals that non-professional participants agree about which players were attempting deception and those that were not; there was also agreement about the tackles in which the intentions were ambiguous Study two demonstrates that the intentions of tackled players match the judgment of their intentions by observers Study three provides a taxonomy of behaviors that are associated with deceptive and non deceptive intentions We conclude that deceptive intentions in this context are to a degree manifest in behavior and are observable

Paul Morris

Papers

Effect of testosterone therapy on QT dispersion in men with heart failure.

Kinetic and phylogenetic analysis of plant polyamine uptake transporters

The anomalous extension problem in default reasoning

Theta phase locking across the neocortex reflects cortico-hippocampal recursive communication during goal conflict resolution.

Tackling Diving: The Perception of Deceptive Intentions in Association Football (Soccer)