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.

The focus of the current study is on a particular aspect of tactile perception: categorical segmentation on the body surface into discrete body parts. The MMN has been shown to be sensitive to categorical boundaries and language experience in the auditory modality. Here we recorded the somatosensory MMN (sMMN) using two tactile oddball protocols and compared sMMN amplitudes elicited by within- and across-boundary oddball pairs. Both protocols employed the identity MMN method that controls for responsivity at each body location. In the first protocol, we investigated the categorical segmentation of tactile space at the wrist by presenting pairs of tactile oddball stimuli across equal spatial distances, either across the wrist or within the forearm. Amplitude of the sMMN elicited by stimuli presented across the wrist boundary was significantly greater than for stimuli presented within the forearm, suggesting a categorical effect at an early stage of somatosensory processing. The second protocol was designed to investigate the generality of this MMN effect, and involved three digits on one hand. Amplitude of the sMMN elicited by a contrast of the third digit and the thumb was significantly larger than a contrast between the third and fifth digits, suggesting a functional boundary effect that may derive from the way that objects are typically grasped. These findings demonstrate that the sMMN is a useful index of processing of somatosensory spatial discrimination that can be used to study body part categories.

Neuropsychology of Human Body Parts: Exploring Categorical Boundaries of Tactile Perception Using Somatosensory Mismatch Responses.

The Case for Developmental Cognitive Science: Theories of People and Things

Two experiments systematically examined factors that influence infants' manual search for hidden objects (N=96). Experiment 1 used a new procedure to assess infants' search for partially versus totally occluded objects. Results showed that 8.75-month-old infants solved partial occlusions by removing the occluder and uncovering the object, but these same infants failed to use this skill on total occlusions. Experiment 2 used sound-producing objects to provide a perceptual clue to the objects' hidden location. Sound clues significantly increased the success rate on total occlusions for 10-month-olds, but not for 8.75-month-olds. An identity development account is offered for why infants succeed on partial occlusions earlier than total occlusions and why sound helps only the older infants. We propose a mechanism for how infants use object identity as a basis for developing a notion of permanence. Implications are drawn for understanding the dissociation between looking time and search assessments of object permanence.

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Factors affecting infants' manual search for occluded objects and the genesis of object permanence.

This chapter offers a framework for understanding human learning and psychological development as situated within a system that entails dynamic interplay between neurobiological processes and people’s participation in cultural practices. The chapter draws from multiple disciplines, including evolutionary biology, neuroscience, human development, anthropology, cognitive science, cultural and social psychology, and the learning sciences.
 Human development is a complex interchange that includes neurobiological processes, evolved over many millennia, that now get taken up and adapted as people engage in cultural practices, which themselves vary over communities and historical time. The chapter highlights intersections between neurobiological processes and people’s participation in cultural practices as these intersections shape human sense-making, influencing people’s goals, effort, and persistence—what people do and why. The chapter offers the following core propositions undergirding human learning and development:

 Proposition 1: Biology does not determine the endpoint of human ontogeny.
 Proposition 2: The primacy of learning from, with, and through others.
 Proposition 3: Diversity in developmental pathways is core to realistic accounts of human development.
 Proposition 4: Individuals belong to multiple, not single, cultural groups—“intersectionality.”
 Proposition 5: The social brain “expects” and is modified by social interactions.
 Proposition 6: The power and pervasiveness of implicit and observational learning.
 The chapter reviews empirical studies documenting how we can see in infancy developmental trajectories that are outgrowths of dispositions inherited from our evolutionary history that unfold in particular competencies because of our participation in cultural practices. The chapter then goes on to discuss the implications of these foundational principles for the design of learning environments in older children and adolescents.

The Braid of Human Learning and Development : Neuro-Physiological Processes and Participation in Cultural Practices

http://ilabs.washington.edu/meltzoff/pdf/10Kaipa_Meltzoff_Bongard_NN.pdf

Andrew N. Meltzoff

Papers

Neuropsychology of Human Body Parts: Exploring Categorical Boundaries of Tactile Perception Using Somatosensory Mismatch Responses.

The Case for Developmental Cognitive Science: Theories of People and Things

Factors affecting infants' manual search for occluded objects and the genesis of object permanence.

The Braid of Human Learning and Development : Neuro-Physiological Processes and Participation in Cultural Practices

2010 Special Issue: Self discovery enables robot social cognition: Are you my teacher?