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.

Dispatchability is an important property for the efficient execution of temporal plans where the temporal constraints are represented as a Simple Temporal Network (STN). It has been shown that every STN may be reformulated as a dispatch-able STN, and dispatchability ensures that the temporal constraints need only be satisfied locally during execution. Recently it has also been shown that Simple Temporal Networks with Uncertainty, augmented with wait edges, are Dynamically Controllable provided every projection is dispatchable. Thus, the dispatchability property has both theoretical and practical interest.

One thing that hampers further work in this area is the underdeveloped theory. The existing definitions are expressed in terms of algorithms, and are less suitable for mathematical proofs. In this paper, we develop a new formal theory of dispatchability in terms of execution sequences. We exploit this to prove a characterization of dispatchability involving the structural properties of the STN graph. This facilitates the potential application of the theory to uncertainty reasoning.

/pdf/the-mathematics-of-dispatchability-revisited-lcn8xr5kyl.pdf

The mathematics of dispatchability revisited

There is a clear association between low serum testosterone and coronary artery disease (CAD) in men. Hypotestosteronaemia is associated with accelerated atherosclerosis and a quarter of men with CAD are biochemically hypogonadal. Amongst those with CAD, hypotestosteronaemia is associated with increased mortality. Testosterone vasodilates coronary arteries, and exogenous testosterone reduces ischaemia. Whether hypotestosteronaemia is a cause or a consequence of CAD remains unanswered. The aim of this prospective observational study was to investigate whether coronary revascularization affected androgen status in men with stable angina pectoris. Twenty five men (mean age 62.7, SD 9.18) with angiographically significant CAD and symptomatic angina underwent full coronary revascularization by percutaneous coronary intervention. Androgen status and symptoms of angina, stress, depression and sexual function were assessed before, and at one and 6 months after the coronary revascularization. All patients underwent complete revascularization which was associated with a significant reduction in angina symptoms and ischaemia. No significant difference was seen in total testosterone (11.33 nmol/L baseline; 12.56, 1 month post; 13.04 at 6 months; p = 0.08). A significant and sustained rise in sex hormone-binding globulin was seen (33.99 nm/L baseline; 36.11 nm/L 1 month post PCI; 37.94 nm/L at 6 months; p = 0.03) Overall, there was no significant alteration in any other marker of androgen status including free testosterone or bioavailable testosterone. There was no change in symptoms of anxiety, depression or sexual function. Coronary revascularization has no sustained effect on androgen status. This supports the hypothesis that hypotestosteronaemia is not a consequence of angina pectoris or myocardial ischaemia.

https://www.onlinecjc.ca/article/S0828-282X(15)00565-6/pdf

Revascularization with percutaneous coronary intervention does not affect androgen status in males with chronic stable angina pectoris.

The current management of acute coronary syndromes (ACS) is with an invasive strategy to guide treatment. However, identifying the lesions which are physiologically significant can be challenging. Non-invasive imaging is generally not appropriate or timely in the acute setting, so the decision is generally based upon visual assessment of the angiogram, supplemented in a small minority by invasive pressure wire studies using fractional flow reserve (FFR) or related indices. Whilst pressure wire usage is slowly increasing, it is not feasible in many vessels, patients and situations. Limited evidence for the use of FFR in non-ST elevation (NSTE) ACS suggests a 25% change in management, compared with traditional assessment, with a shift from more to less extensive revascularisation. Virtual (computed) FFR (vFFR), which uses a 3D model of the coronary arteries constructed from the invasive angiogram, and application of the physical laws of fluid flow, has the potential to be used more widely in this situation. It is less invasive, fast and can be integrated into catheter laboratory software. For severe lesions, or mild disease, it is probably not required, but it could improve the management of moderate disease in 'real time' for patients with non-ST elevation acute coronary syndromes (NSTE-ACS), and in bystander disease in ST elevation myocardial infarction. Its practicability and impact in the acute setting need to be tested, but the underpinning science and potential benefits for rapid and streamlined decision-making are enticing.

/pdf/virtual-computed-fractional-flow-reserve-future-role-in-2vm3y0r9yi.pdf

Virtual (Computed) Fractional Flow Reserve: Future Role in Acute Coronary Syndromes.

Soft temporal constraints problems allow to describe in a natural way scenarios where events happen over time and preferences are associated to event distances and durations. However, sometimes such local preferences are difficult to set, and it may be easier instead to associate preferences to some complete solutions of the problem. Machine learning techniques can be useful in this respect. In this paper we describe two solvers (one more general and the other one more efficient) for tractable subclasses of soft temporal problems, and we show some experimental results. The random generator used to build the problems on which tests are performed is also described. We also compare the two solvers highlighting the tradeoff between performance and representational power. Finally, we present a learning module and we show its behavior on randomly-generated examples.

Solving and Learning Soft Temporal Constraints: Experimental Setting and Results

NASA plans call for long duration deep space missions with human crews. Because of light-time delay and other considerations, increased autonomy is needed. Crews on next-generation missions will likely be small, perhaps with as few as four members. A small crew is not likely to possess the full range of expertise needed to deal with unexpected failures and anomalies. Applied artificial intelligence technologies have developed decision support tools with the potential to fill the gap, but these tools need to be integrated to provide a smooth operational capability. In this paper we describe such an integration involving anomaly detection, diagnosis, system effect propagation, and plan repair.

/pdf/determining-mission-effects-of-equipment-failures-2wjli06yet.pdf

Paul Morris

Papers

The mathematics of dispatchability revisited

Revascularization with percutaneous coronary intervention does not affect androgen status in males with chronic stable angina pectoris.

Virtual (Computed) Fractional Flow Reserve: Future Role in Acute Coronary Syndromes.

Solving and Learning Soft Temporal Constraints: Experimental Setting and Results

Determining Mission Effects of Equipment Failures