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.

Ammonia production and assimilation(1) were examined in photorespiratory mutants of Arabidopsis thaliana L lacking ferredoxin-dependent glutamate synthase (Fd-GluS) activity Although photosynthesis was rapidly inhibited in these mutants in normal air, NH(4) (+) continued to accumulate The accumulation of NH(4) (+) was also seen after an initial lag of 30 minutes in 2% O(2), 350 microliters per liter of CO(2) and after 90 minutes in 2% O(2), 900 microliters per liter of CO(2) The accumulation of NH(4) (+) in normal air and low O(2) was also associated with an increase in the total pool of amino acid-N and glutamine, and a decrease in the pools of glutamate, aspartate, alanine, and serine Upon return to dark conditions, or to 21% O(2), 1% CO(2) in the light, the NH(4) (+) which had accumulated in the leaves was reassimilated into amino acids The addition of methionine sulfoximine (MSO) resulted in higher accumulations of NH(4) (+) in glutamate synthase mutants and prevented the reassimilation of NH(4) (+) upon return to the dark The addition of MSO also resulted in the accumulation of NH(4) (+) in glutamate synthase mutants in the light and in 21% O(2), 1% CO(2) These results indicate that glutamine synthetase is essential for the reassimilation of photorespiratory NH(4) (+) and for primary N assimilation in the leaves and strongly suggest that glutamate dehydrogenase plays only a minimal role in the assimilation of ammonia Levels of NADH-dependent glutamate synthase (NADH-GluS) appear to be sufficient to account for the assimilation of NH(4) (+) by a GS/NADH-GluS cycle

Ammonia Production and Assimilation in Glutamate Synthase Mutants of Arabidopsis thaliana.

In a recent paper, Hanks and McDermott presented a simple problem in temporal reasoning which showed that a seemingly natural representation of a frame axiom in non monotonic logic can give rise to an anomalous extension, i.e., one which is counter-intuitive in that it does not appear to be supported by the known facts.

An alternative, less formal approach to nonmonotonic reasoning uses the mechanism of a truth maintenance system (TMS). Surprisingly, when reformulated in terms of a TMS, the anomalous extension noted by Hanks and McDermott disappears. We analyze the reasons for this. First it is seen that anomalous extensions are not limited to temporal reasoning, but can occur in simple non-temporal default reasoning as well. In these cases also, the natural TMS representation avoids the problem. Exploring further, it is observed that the form of the TMS justifications resembles that of nonnormal default rules. Nonnormal rules have already been proposed as a means of avoiding anomalous extensions in some non-temporal reasoning situations. It appears that, suitably formulated, they can exclude the anomalous extension in the Hanks-McDermott case also, although the representation does not adjust smoothly to fresh information, as does the TMS. Some variant of nonnormal default appears to be required to provide a correct semantic basis for truth maintenance systems.

/pdf/curing-anomalous-extensions-3proz8t7t0.pdf

Curing anomalous extensions

Often we need to work in scenarios where events happen over time and preferences are associated with event distances and durations. Soft temporal constraints allow one to describe in a natural way problems arising in such scenarios.

In general, solving soft temporal problems requires exponential time in the worst case, but there are interesting subclasses of problems which are polynomially solvable. In this paper we identify one of such subclasses, that is, simple fuzzy temporal problems with semi-convex preference functions, giving tractability results. Moreover, we describe two solvers for this class 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 robustness.

Sometimes, however, temporal local preferences are difficult to set, and it may be easier instead to associate preferences to some complete solutions of the problem. To model everything in a uniform way via local preferences only, and also to take advantage of the existing constraint solvers which exploit only local preferences, we show that machine learning techniques can be useful in this respect. In particular, we present a learning module based on a gradient descent technique which induces local temporal preferences from global ones. We also show the behavior of the learning module on randomly-generated examples.

/pdf/solving-and-learning-a-tractable-class-of-soft-temporal-1h3uvpfgb4.pdf

Solving and learning a tractable class of soft temporal constraints: Theoretical and experimental results

Soft temporal constraints problems allow for a natural description of scenarios where events happen over time and preferences are associated with 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, and then to learn from them suitable preferences over distances and durations. In this paper, we describe our learning algorithm and we show its behaviour on classes of randomly generated problems. Moreover, we also describe two solvers (one more general and the other one more efficient) for tractable subclasses of soft temporal problems, and we give experimental results to compare them.

Learning and Solving Soft Temporal Constraints: An Experimental Study

Aims

The clinical reliability of echocardiographic surrogate markers of left ventricular filling pressures (LVFPs) across different cardiovascular pathologies remains unanswered. The main objective was to evaluate the evidence of how effectively different echocardiographic indices estimate true LVFP.


Methods and results

Design: this is a systematic review and meta‐analysis. Data source: Scopus, PubMed and Embase. Eligibility criteria for selecting studies were those that used echocardiography to predict or estimate pulmonary capillary wedge pressure or left ventricular end‐diastolic pressures. Twenty‐seven studies met criteria. Only eight studies (30%) reported both correlation coefficient and bias between non‐invasive and invasively measured LVFPs. The majority of studies (74%) recorded invasive pulmonary capillary wedge pressure as a surrogate for left ventricular end‐diastolic pressures. The pooled correlation coefficient overall was r = 0.69 [95% confidence interval (CI) 0.63–0.75, P < 0.01]. Evaluation by cohort demonstrated varying association: heart failure with preserved ejection fraction (11 studies, n = 575, r = 0.59, 95% CI 0.53–0.64) and heart failure with reduced ejection fraction (8 studies, n = 381, r = 0.67, 95% CI 0.61–0.72).


Conclusions

Echocardiographic indices show moderate pooled association to invasively measured LVFP; however, this varies widely with disease state. In heart failure with preserved ejection fraction, no single echocardiography‐based metric offers a reliable estimate. In heart failure with reduced ejection fraction, mitral inflow‐derived indices (E/e′, E/A, E/Vp, and EDcT) have reasonable clinical applicability. While an integrated approach of several echocardiographic metrics provides the most promise for estimating LVFP reliably, such strategies need further validation in larger, patient‐specific studies.

Paul Morris

Papers

Ammonia Production and Assimilation in Glutamate Synthase Mutants of Arabidopsis thaliana.

Curing anomalous extensions

Solving and learning a tractable class of soft temporal constraints: Theoretical and experimental results

Learning and Solving Soft Temporal Constraints: An Experimental Study

Meta-analysis of echocardiographic quantification of left ventricular filling pressure.