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.

Creativity has fascinated scholars for generations, and its identification as one of the key ‘twenty-first century skills' necessary for economic growth has led to renewed interest. This creates two challenges for the OECD: its flagship Programme of International Student Assessment (PISA) does not directly measure creativity. Secondly, the increased importance attached to creativity has highlighted claims that high performers on PISA are largely nations stereotyped as lacking creativity. This challenges PISA's self-proclaimed status as the premier global benchmark for evaluating and comparing the quality of school systems and weakens its capacity to deliver its core mission; to identify ‘best practices' which ensure economic prosperity. We explore these challenges and examine both how the OECD has responded to them and is moving to include creativity in PISA 2022. We argue that, while a precise definition of creativity has defied scholars for centuries, the indications are that the OECD's metric will focus on a narrow, convergent and easily-measured conception associated with cognitive competencies and linked to enhancing human capital. In this way, the ‘messiness’ around the polysemic concept will be simultaneously both exploited and threatened, as new, measurable versions displace alternatives. 

https://www.tandfonline.com/doi/pdf/10.1080/14767724.2022.2100981?needAccess=true

Capturing the spark: PISA, twenty-first century skills and the reconstruction of creativity

A mixed-initiative approach to activity planning for space mission operations was introduced in the Mars Exploration Rover mission, and has been extended and adapted to other missions. The approach involves a collaboration between a human planner and automated tools that reason about activities and constraints. One important class of constraints arises from state requirements and effects. The mixed-initiative framework passively detects and reports constraint violations. At the user's request, it can also offer suggestions, obtained through automated planning techniques, for actively fixing certain violations. Due to the need for a rapid response, active solving previously used a timeline insertion strategy that limited the types of violations that could be fixed, whereas the passive checking employed an encoding of the state constraints as resource constraints that identified all the violations. In this paper, we report on an extension of the active solver to handle resource problems, allowing a unification of the passive and active strategies.

https://ti.arc.nasa.gov/publications/2835/download/

State-Based Scheduling via Active Resource Solving

Assembly language code provides both a daunting challenge and a sterling opportunity for software reengineering. Ideally, one would like a system which, when fed assembly code, would automatically produce quality, maintainable high-level programs. That ideal, if not impossible, is certainly far beyond current technology. However, automation can profitably be applied to part of the task of reverse engineering assembly code, by producing a "draft" of a high-level language version, to be verified modified and polished by competent software reengineers. The authors describe their progress in developing a reengineer's apprentice to aid reverse engineering of handwritten IBM 370 assembly code by automatically translating it to a higher-level form. The paper explores the problems that arise and some potential solutions, and describes the implementation of Mandrake, a system that performs an interesting collection of the reengineer's apprentice tasks.

Mandrake: a tool for reverse-engineering IBM assembly code

/pdf/shear-stress-the-dark-energy-of-atherosclerotic-plaques-giwogb76td.pdf

Paul Morris

Papers

Capturing the spark: PISA, twenty-first century skills and the reconstruction of creativity

State-Based Scheduling via Active Resource Solving

Mandrake: a tool for reverse-engineering IBM assembly code

Shear Stress: The Dark Energy Of Atherosclerotic Plaques.

Complexity theoretic properties of recursively enumerable sets