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.

Background International guidelines for the treatment of patients with pulmonary arterial hypertension (PAH) recommend the use of risk stratification to optimise therapy to achieve and maintain a low risk profile. However, recommended methods require hospital-based investigations. We sought to develop a method for daily, remote risk evaluation. Methods Consecutive patients (5820) with pulmonary hypertension (PH) were identified from the ASPIRE registry and stepwise Cox regression was applied to identify parameters associated with survival. A physiological risk score was applied to all patients and survival was assessed by the Kaplan-Meier method. Physical activity was measured in patients with PAH implanted with insertable cardiac monitors (ICM, 80) to provide a remote measure of exercise capacity. In patients with PAH and implanted pulmonary artery pressure (PAP) monitor and ICM (28) we undertook a time-stratified bidirectional case crossover study to determine the physiology of therapeutic escalation (TE) and clinical worsening and a remote physiological risk score applied to the data. Results Aage, male sex, PH aetiology, WHO functional class (FC), incremental shuttle walk distance (ISWD), heart rate reserve (HRR) and total pulmonary resistance (TPR) as independent predictors of survival. Mortality increased with each decile of baseline physiological risk. In patients with PAH, thresholds of physiological risk were used to classify patients into low-, intermediate low, intermediate high , and high risk groups for one-year mortality, which were well matched to COMPERA2.0 score-stratified groups. ICM-measured physical activity decreased with indicators of increased clinical risk (WHO-FC, NT-proBNP, ISWD, COMPERA2.0). Following TE, remote monitored mean PAP and TPR were reduced, and cardiac output (CO) and physical activity increased at days seven, four, 22 and 42 respectively. Clinical worsening events (CWE) were preceded by an increase remote monitored mean PAP and TPR and reduced CO and physical activity. Change in emote physiological risk score identifiable six days after TE and twelve days prior to a CWE. Conclusion Remote risk evaluation may facilitate personalised medicine and proactive management. The physiological risk score accurately stratifies patients with PH and may be applied to remote monitoring data for early evaluation of clinical efficacy and detection of clinical worsening.

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Remote evaluation of risk and physiological response to therapeutic escalation and clinical worsening in patients with pulmonary hypertension

Refining murray’s law in human epicardial coronary arteries

Acting upon clinical patient data, acquired in the pathway of percutaneous intervention, we deploy hierarchical, multi-stage, data-handling protocols and interacting low- and high-order mathematical models (chamber elastance, state-space system and CFD models), to establish and then validate a framework to quantify the burden of ischaemia. Our core tool is a compartmental, zero-dimensional model of the coupled circulation with four heart chambers, systemic and pulmonary circulations and an optimally adapted windkessel model of the coronary arteries that reflects the diastolic dominance of coronary flow. We guide the parallel development of protocols and models by appealing to foundational physiological principles of cardiac energetics and a parameterisation (stenotic Bernoulli resistance and micro-vascular resistance) of patients’ coronary flow. We validate our process first with results which substantiate our protocols and, second, we demonstrate good correspondence between model operation and patient data. We conclude that our core model is capable of representing (patho)physiological states and discuss how it can potentially be deployed, on clinical data, to provide a quantitative assessment of the impact, on the individual, of coronary artery disease.

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Modelling The Hemodynamics of Coronary Ischemia

/pdf/e-learning-collaboration-and-group-support-in-medical-4ekyqwtzty.pdf

E‑learning, collaboration, and group support in medical education.

Recognising intraspecies threat is essential for survival. However, this needs to be balanced against the undue avoidance of unknown others who may be useful to us. Research has shown that judgments of ‘aggression’ and ‘threat’ posed by an unknown person can accurately reflect that person’s general aggressive tendencies. To date, there has not been a within-sample comparison of the informativeness of static and walking stimuli for threat judgments. In this study, 193 participants rated the threat posed by 23 target people presented as both simplified gait presentations (point-light walkers) and still images. We analysed how threat judgments made by participants were predicted by the target’s self-reported aggression (accuracy), the sex of the targets and the medium of target presentation (point-light vs. still image). Our results showed that participants’ threat judgments accurately predicted targets’ aggression. Male targets received higher threat ratings than female targets and point-light displays were rated as more threatening than still images. There were no effects of target sex and presentation medium on accuracy of threat perception and no sex by medium interactions on judgments themselves. Overall, this study provides further evidence of the accuracy of threat judgments at detecting trait aggression. However, further research is needed to explain what features of the target people are enabling the accurate judgments of aggression.

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Paul Morris

Papers

Remote evaluation of risk and physiological response to therapeutic escalation and clinical worsening in patients with pulmonary hypertension

Refining murray’s law in human epicardial coronary arteries

Modelling The Hemodynamics of Coronary Ischemia

E‑learning, collaboration, and group support in medical education.

Is threat in the way they move? Influences of static and gait information on threat judgments of unknown people