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

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

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Feeling and thinking : Preferences need no inferences

A category of stimuli of great importance for primates, humans in particular, is that formed by actions done by other individuals. If we want to survive, we must understand the actions of others. Furthermore, without action understanding, social organization is impossible. In the case of humans, there is another faculty that depends on the observation of others' actions: imitation learning. Unlike most species, we are able to learn by imitation, and this faculty is at the basis of human culture. In this review we present data on a neurophysiological mechanism--the mirror-neuron mechanism--that appears to play a fundamental role in both action understanding and imitation. We describe first the functional properties of mirror neurons in monkeys. We review next the characteristics of the mirror-neuron system in humans. We stress, in particular, those properties specific to the human mirror-neuron system that might explain the human capacity to learn by imitation. We conclude by discussing the relationship between the mirror-neuron system and language.

The mirror-neuron system.

Part I. Experimental Studies: 2. Experiment in psychology 3. Experiments on perceiving III Experiments on imaging 4-8. Experiments on remembering: (a) The method of description (b) The method of repeated reproduction (c) The method of picture writing (d) The method of serial reproduction (e) The method of serial reproduction picture material 9. Perceiving, recognizing, remembering 10. A theory of remembering 11. Images and their functions 12. Meaning Part II. Remembering as a Study in Social Psychology: 13. Social psychology 14. Social psychology and the matter of recall 15. Social psychology and the manner of recall 16. Conventionalism 17. The notion of a collective unconscious 18. The basis of social recall 19. A summary and some conclusions.

Remembering. A Study in Experimental and Social Psychology, Cambridge (University Press) 1964.

Investigating memory and deferred imitation in 12-month-olds

Human beings are the most imitative creatures on the planet. Prior to language, human infants, use imitation to learn about physical objects and cultural practices [1]. Young children learn novel skills [2], abstract principles [3,4], and cultural customs from observing how other agents act upon the world. Imitation requires a mapping between action perception and action production. This mapping is now being delineated at both the psychological level [5] and at the neural level [6]. I will describe a theory of social-cognitive development-the "Like-Me" developmental theory [7]--that has emerged from studying how infants make sense of self, other, and their interaction. The theory proposes that one of the infant's first and most basic psychological acts is the recognition of others who act, move, and behave like the self. Preverbal children devote special attention to entities that are "Like-Me" and are predisposed to learn from "Like-Me" agents [7]. This theoretical framework, based on behavioral studies, is aligned with emerging findings in developmental neuroscience, including results using infant EEG [8], which is beginning to document the neural basis of "Like-Me" perceptions prior to language [5,9]. I will analyze infants' mapping between self and other, the role of experience in changing this initial state, and the impact of early self-other mapping on interpersonal functioning and cognitive development.Topics covered include how infants understand the goals and intentions of other agents [10], and how they use imitation, gestures, and behavioral patterns--not simply morphological features--to recognize a person as the "same one again" after a break in perceptual contact [11], called the "social identity function" of imitation [12]. I will also discuss how infants come to understand the visual gaze of others [13] and use gaze to bootstrap language learning [14].A foundation for infants coming to understand the gaze of others is their own self-experience with their own perceptual systems [15]. The "Like-Me" developmental theory describes this ontogenetic sequence, which is a crucial aspect of human interpersonal interaction, and a rare skill in the animal kingdom.All of these findings with human infants-imitation, intention/goal understanding, person recognition, gaze following, and action coding and representation-have implications for robotics and human-computer interaction (HCI). At our Institute we have fostered several collaborations with computer scientists and roboticists, which have specifically analyzed how studies of infant behavior, brain development, and computer modeling can be combined to illuminate critical aspects of social learning and interpersonal understanding [16-19]. Most recently I have collaborated with a French team, and we have used computer modeling and robotics to examine the "social identity function" of gestural imitation [20]. Members of the French team will be at this conference. We will provide specific examples of the computer modeling and algorithms that were used in our social-interaction experiments with robots, adults, and children with autism. The overarching goal of our research program is to build bridges between human infant development, cognitive neuroscience, computer science, and social/ interpersonal psychology.

Infants' Brains are Wired to Learn from Culture: Implications for Social Robots

This Introduction highlights the key point of this special issue on brain research, psychology, and learning. The issue discusses concrete ways in which neuroscience and experimental psychology, among other disciplines, can contribute to reducing educational inequities worldwide. The Introduction discusses common themes among the articles in this issue and outlines myriad benefits, as well as some unintended risks, of sharing scientific findings with educators and policymakers in order to induce educational change. The article also offers some novel ideas to help researchers bridge the gap between the science of learning and its implementation in educational settings, emphasizing the value of research-practice partnerships.

Andrew N. Meltzoff

Papers

Investigating memory and deferred imitation in 12-month-olds

Infants' Brains are Wired to Learn from Culture: Implications for Social Robots

Neuroscience, psychology, and society: Translating research to improve learning

Numerical Identity and the Development of Object Permanence

Perception, action, and cognition in early infancy.