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.

This paper presents an approach to automatically providing explanations and recommendations for temporal inconsistencies within the context of constraint-based, mixed-initiative planning. In this style of planning, a user operation can cause the planner’s temporal constraint network to become inconsistent. For example, if a partial plan has been built and the user is trying to add another activity into the plan, an inconsistency will arise if the activity’s associated constraints conflict with the current plan’s constraints. In such cases, it is helpful if the system can explain to the user why the user’s operation led to the inconsistency, and it is even better if the system can give the user recommendations on how to fix the inconsistency. Our approach to explanation and recommendation is generally applicable to constraint-based reasoning systems; however, the motivation and validation of our approach derive from a deployed planning system for planetary rover missions.

Explanations and Recommendations for Temporal Inconsistencies

The role of ‘stand-alone’ coronary angiography (CAG) in the management of patients with chronic coronary syndromes is the subject of debate, with arguments for its replacement with CT angiography on the one hand and its confinement to the interventional cardiac catheter laboratory on the other. Nevertheless, it remains the standard of care in most centres. Recently, computational methods have been developed in which the laws of fluid dynamics can be applied to angiographic images to yield ‘virtual’ (computed) measures of blood flow, such as fractional flow reserve. Together with the CAG itself, this technology can provide an ‘all-in-one’ anatomical and functional investigation, which is particularly useful in the case of borderline lesions. It can add to the diagnostic value of CAG by providing increased precision and reduce the need for further non-invasive and functional tests of ischaemia, at minimal cost. In this paper, we place this technology in context, with emphasis on its potential to become established in the diagnostic workup of patients with suspected coronary artery disease, particularly in the non-interventional setting. We discuss the derivation and reliability of angiographically derived fractional flow reserve (CAG-FFR) as well as its limitations and how CAG-FFR could be integrated within existing national guidance. The assessment of coronary physiology may no longer be the preserve of the interventional cardiologist.

https://heart.bmj.com/content/heartjnl/107/10/783.full.pdf

The new role of diagnostic angiography in coronary physiological assessment

Polyamines are small aliphatic amines found in almost all organisms, ranging from bacteria to plants and animals. In most plants, putrescine, the metabolic precursor for longer polyamines, such as spermidine and spermine, is produced from arginine, with either agmatine or ornithine as intermediates. Here we show that Arabidopsis thaliana (Arabidopsis) arginine decarboxylase 1 (ADC1), one of the two known arginine decarboxylases in Arabidopsis, not only synthesizes agmatine from arginine, but also converts Nδ -acetylornithine to N-acetylputrescine. Phylogenetic analyses indicate that duplication and neofunctionalization of ADC1 and NATA1, the enzymes that synthesize Nδ -acetylornithine in Arabidopsis, co-occur in a small number of related species in the Brassicaceae. Unlike ADC2, which is localized in the chloroplasts, ADC1 is in the endoplasmic reticulum together with NATA1, an indication that these two enzymes have access to the same substrate pool. Together, these results are consistent with a model whereby NATA1 and ADC1 together provide a pathway for the synthesis of N-acetylputrescine in Arabidopsis.

Arabidopsis ADC1 functions as an Nδ -acetylornithine decarboxylase.

GOTO removal based on regular expressions

Purpose To date, the literature on perception of affective, pragmatic, and grammatical prosody abilities in autism spectrum disorders (ASD) has been sparse and contradictory. It is interesting to n...

/pdf/relations-among-detection-of-syllable-stress-speech-2lgkr32kon.pdf

Paul Morris

Papers

Explanations and Recommendations for Temporal Inconsistencies

The new role of diagnostic angiography in coronary physiological assessment

Arabidopsis ADC1 functions as an Nδ -acetylornithine decarboxylase.

GOTO removal based on regular expressions

Relations Among Detection of Syllable Stress, Speech Abnormalities, and Communicative Ability in Adults With Autism Spectrum Disorders.