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

/pdf/feeling-and-thinking-preferences-need-no-inferences-q4mw2hj8rx.pdf

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.

Children are selective and flexible imitators. They combine their own prior experiences and the perceived causal efficacy of the model to determine whether and what to imitate. In Experiment 1, children were randomly assigned to have either a difficult or an easy experience achieving a goal. They then saw an adult use novel means to achieve the goal. Children with a difficult prior experience were more likely to imitate the adult’s precise means. Experiment 2 showed further selectivity—children preferentially imitated causally efficacious versus nonefficacious acts. In Experiment 3, even after an easy prior experience led children to think their own means would be effective, they still encoded the novel means performed by the model. When a subsequent manipulation rendered the children’s means ineffective, children recalled and imitated the model’s means. The research shows that children integrate information from their own prior interventions and their observations of others to guide their imitation.

http://ilabs.washington.edu/meltzoff/pdf/08_Williamson_Meltzoff_Markman.pdf

Prior Experiences and Perceived Efficacy Influence 3-Year-Olds' Imitation.

The sensorimotor theory of infancy has been overthrown, but there is little consensus on a replacement. We hypothesize that a capacity for representation is the starting point for infant development, not its culmination. Logical distinctions are drawn between object representation, identity, and permanence. Modern experiments on early object permanence and deferred imitation suggest: (a) even for young infants, representations persist over breaks in sensory contact, (b) numerical identity of objects (Os) is initially specified by spatiotemporal criteria (place and trajectory), (c) featural and functional identity criteria develop, (d) events are analyzed by comparing representations to current perception, and (e) representation operates both prospectively, anticipating future contacts with an O, and retrospectively, reidentifying an O as the \"same one again.\" A model of the architecture and functioning of the early representational system is proposed. It accounts for young infants' behavior toward absent people and things in terms of their efforts to determine the identity of objects. Our proposal is developmental without denying innate structure and elevates the power of perception and representation while being cautious about attributing complex concepts to young infants.

Object representation, identity, and the paradox of early permanence

Long-term recall memory, as indexed by deferred imitation, was assessed in 12-month-old infants. Independent groups of infants were tested after retention intervals of 3 min, 1 week and 4 weeks. Deferred imitation was assessed using the ‘observation-only’ procedure in which infants were not allowed motor practice on the tasks before the delay was imposed. Thus, the memory could not have been based on re-accessing a motor habit, because none was formed in the first place. After the delay, memory was assessed either in the same or a different environmental context from the one in which the adult had originally demonstrated the acts. In Experiments 1 and 3, infants observed the target acts while in an unusual environment (an orange and white polka-dot tent), and recall memory was tested in an ordinary room. In Experiment 2, infants observed the target acts in their homes and were tested for memory in a university room. The results showed recall memory after all retention intervals, including the 4 week delay, with no effect of context change. Interestingly, the forgetting function showed that the bulk of the forgetting occurred during the first week. The findings of recall memory without motor practice support the view that infants as young as 12 months old use a declarative (nonprocedural) memory system to span delay intervals as long as 4 weeks.

/pdf/long-term-memory-forgetting-and-deferred-imitation-in-12-3d37rz1ygb.pdf

Long-term memory, forgetting, and deferred imitation in 12-month-old infants.

The sensorimotor theory of infancy has been overthrown, but there is little consensus on a replacement. We hypothesize that a capacity for representation is the starting point for infant development, not its culmination. Logical distinctions are drawn between object representation, identity, and permanence. Modern experiments on early object permanance and deferred imitation suggest: (a) even for young infants, representations persist over breaks in sensory contact, (b) numerical identity of objects ( O s) is initially specified by spatiotemporal criteria (place and trajectory), (c) featural and functional identity criteria develop, (d) events are analyzed by comparing representations to current perception, and (e) representation operates both prospectively, anticipating future contacts with an O , and retrospectively, reidentifying an O as the “same one again”. A model of the architecture and functioning of the early representational system is proposed. It accounts for young infants' behavior toward absent people and things in terms of their efforts to determine the identity of objects. Our proposal is developmental without denying innate structure and elevates the power of perception and representation while being cautious about attributing complex concepts to young infants.

/pdf/object-representation-identity-and-the-paradox-of-early-4onutpjgk9.pdf

Andrew N. Meltzoff

Papers

Prior Experiences and Perceived Efficacy Influence 3-Year-Olds' Imitation.

Object representation, identity, and the paradox of early permanence

Long-term memory, forgetting, and deferred imitation in 12-month-old infants.

OBJECT REPRESENTATION, IDENTITY, AND THE PARADOX OF EARLY PERMANENCE: Steps Toward a New Framework.

Measuring implicit attitudes of 4-year-olds: the preschool implicit association test.