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.

Heart failure with a preserved ejection fraction

Uridine phosphorylase (UP) is a key enzyme of pyrimidine salvage pathways that enables the recycling of endogenous or exogenous-supplied pyrimidines and plays an important intracellular metabolic role. Here, we biochemically and structurally characterized two evolutionarily divergent uridine phosphorylases, PcUP1 and PcUP2 from the oomycete pathogen Phytophthora capsici. Our analysis of other oomycete genomes revealed that both uridine phosphorylases are present in Phytophthora and Pythium genomes, but only UP2 is seen in Saprolegnia spp. which are basal members of the oomycetes. Moreover, uridine phosphorylases are not found in obligate oomycete pathogens such as Hyaloperonospora arabidopsidis and Albugo spp. PcUP1 and PcUP2 are upregulated 300 and 500 fold respectively, within 90 min after infection of pepper leaves. The crystal structures of PcUP1 in ligand-free and in complex with uracil/ribose-1-phosphate, 2′-deoxyuridine/phosphate and thymidine/phosphate were analyzed. Crystal structure of this uridine phosphorylase showed strict conservation of key residues in the binding pocket. Structure analysis of PcUP1 with bound ligands, and site-directed mutagenesis of key residues provide additional support for the “push-pull” model of catalysis. Our study highlights the importance of pyrimidine salvage during the earliest stages of infection.

/pdf/structural-and-catalytic-analysis-of-two-diverse-uridine-2df55grs3s.pdf

Structural and catalytic analysis of two diverse uridine phosphorylases in Phytophthora capsici.

: STAMMER was developed to serve as a demonstration of the applicability of rule-based inference techniques to the Tactical Situation Assessment (TSA) problem. In addition, it serves as a testbed for development of new inference rules and techniques. STAMMER serves as an organizer of information. It collects information by receiving messages and radar reports, and organizes this raw data into graphic displays and textual commentary to aid in tactical situation assessment. The organization that STAMMER performs is more than simple formatting, however. Through the use of conclusions about the situation in the vicinity of the home ship. These conclusions are reflected in both the display and commentary. The system data base is available for examination, and includes not only the original raw data, but also information about why and how the conclusions were reached. (Author)

STAMMER: System for Tactical Assessment of Multisource Messages, Even Radar.

: STAMMER 2 is a revised version of STAMMER, a System for Tactical Assessment of Multisource Messages, Even Radar. STAMMER was created as part of an investigation of new correlation methodologies, and served as a testbed for explorations of applications of rule-based inference systems to the tactical situation assessment (TSA) problem. STAMMER concentrated on the specific task of merchant detection from radar and external messages. Experience with STAMMER revealed areas for improvement, which have led to the creation of STAMMER2. In addition to several changes in the underlying rule mechanisms used, the enhancements found in STAMMER2 arose out of a desire for greater generality and flexibility in the demonstration system, the explanation system, and the range of acceptable inputs to the system. STAMMER2 should prove to be a more useful system for testing various rule/scenario collections. During the development that led to STAMMER2, further issues in the design of rule-based inference systems for use in support of C3 activities have become apparent and they are discussed. This volume consists of the code. Volume 1 consists of the design description. (Author

STAMMER2 Production System for Tactical Situation Assessment. Volume 2. Code.

Background: Measuring the extent to which renal artery stenosis (RAS) alters renal haemodynamics may permit precision medicine by physiologically guided revascularization. This currently requires invasive intra-arterial pressure measurement with associated risks and is rarely performed. The present proof-of-concept study investigates an in silico approach that uses computational fluid dynamic (CFD) modeling to non-invasively estimate renal artery haemodynamics from routine anatomical computed tomography (CT) imaging of RAS.

Methods: We evaluated 10 patients with RAS by CT angiography. Intra-arterial renal haemodynamics were invasively measured by a transducing catheter under resting and hyperaemic conditions, calculating the translesional ratio of distal to proximal pressure (Pd/Pa). The diagnostic and quantitative accuracy of the CFD-derived virtual Pd/Pa ratio (vPd/Pa) was evaluated against the invasively measured Pd/Pa ratio (mPd/Pa).

Results: Hyperaemic haemodynamics was infeasible and CT angiography in 4 patients had insufficient image resolution. Resting flow data is thus reported for 7 stenosed arteries from 6 patients (one patient had bilateral RAS). The comparison showed a mean difference of 0.015 (95% confidence intervals of ± 0.08), mean absolute error of 0.064, and a Pearson correlation coefficient of 0.6, with diagnostic accuracy for a physiologically significant Pd/Pa of ≤ 0.9 at 86%.

Conclusion: We describe the first in silico estimation of renal artery haemodynamics from CT angiography in patients with RAS, showing it is feasible and diagnostically accurate. This provides a methodological framework for larger prospective studies to ultimately develop non-invasive precision medicine approaches for studies and interventions of RAS and resistant hypertension.

/pdf/non-invasive-stenotic-renal-artery-haemodynamics-by-in-u5wf2368ag.pdf

Paul Morris

Papers

Heart failure with a preserved ejection fraction

Structural and catalytic analysis of two diverse uridine phosphorylases in Phytophthora capsici.

STAMMER: System for Tactical Assessment of Multisource Messages, Even Radar.

STAMMER2 Production System for Tactical Situation Assessment. Volume 2. Code.

Non-invasive Stenotic Renal Artery Haemodynamics by in silico Medicine