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.

Wearable technology and the cardiovascular system: the future of patient assessment.

Robotics researchers often require inexpensive hardware that is freely distributable to the public domain in large numbers, yet reliable enough for use in different applications without fear of the hardware itself becoming a burden. In the past, researchers have moved towards robot simulations, in favor of the lack of hardware and ease of replication. In this paper we introduce an implementation of Q-Learning, a reinforcement learning technique, as a case study for a new open-source robotics platform, the Brookstone Rover 2.0. We utilize a Theano-based implementation of Google DeepMind’s Deep Q-Learning algorithm, as well as OpenCV for the purpose of state-reduction, and determine its effectiveness in our rovers with a color-seeking “rover-ina- box” task.

/pdf/q-learning-in-an-autonomous-rover-571k0j2ool.pdf

Q-Learning in an Autonomous Rover

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 dispatchable 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, dispatchability has considerable theoretical as well as practical significance. One thing that hampers further work in this area is the underdeveloped theory. Moreover, the existing foundation is inadequate in certain respects. In this paper, we develop a new mathematical theory of dispatchability and its relationship to execution. We also provide several characterizations of dispatchability, including characterizations in terms of the structural properties of the STN graph. This facilitates the potential application of the theory to other areas.

/pdf/the-mathematics-of-dispatchability-revisited-36fvo2bfl4.pdf

The Mathematics of Dispatchability, Revisited

Critical perspectives on internationalization in higher education: commercialization, global citizenship, or postcolonial imperialism?

PISA for Development (PISA-D) was a pioneering pilot project designed to make PISA, which compares the performance of 15-year-olds in school, more suitable for low- and middle-income countries. This would allow the OECD to move beyond its traditional focus on more affluent nations and to play a central role in monitoring the Sustainable Development Goals. PISA-D was declared a success by the OECD, and its most innovative feature was that, unlike PISA, its assessment included out-of-school youth (OOSY). We analyse that strand of the assessment focussing on who was assessed. We argue that the inclusion of OOSY has been misrepresented by the OECD. Building on the literature serving as the OECD’s sources of legitimacy and applying Suchman’s framework for analysing organisational legitimacy, we portray its shifting definitions of OOSY as a tactical move that allowed it to ensure the project’s success and resolve problems that challenged its sources of legitimacy. 

https://www.tandfonline.com/doi/pdf/10.1080/17508487.2023.2227227?needAccess=true&role=button

Paul Morris

Papers

Wearable technology and the cardiovascular system: the future of patient assessment.

Q-Learning in an Autonomous Rover

The Mathematics of Dispatchability, Revisited

Critical perspectives on internationalization in higher education: commercialization, global citizenship, or postcolonial imperialism?

Measuring and misrepresenting the missing millions: the OECD’s assessment of out-of-school youth in PISA for Development