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.

Brain responses to tactile stimulation have often been studied through the examination of ERPs elicited to touch on the body surface. Here, we examined two factors potentially modulating the amplitude of the somatosensory mismatch negativity (sMMN) and P300 responses elicited by touch to pairs of body parts: (a) the distance between the representation of these body parts in somatosensory cortex, and (b) the physical distances between the stimulated points on the body surface. The sMMN and the P300 response were elicited by tactile stimulation in two oddball protocols. One protocol leveraged a discontinuity in cortical somatotopic organization, and involved stimulation of either the neck or the hand in relation to stimulation of the lip. The other protocol involved stimulation to the third or fifth finger in relation to the second finger. The neck-lip pairing resulted in significantly larger sMMN responses (with shorter latencies) than the hand-lip pairing, whereas the reverse was true for the amplitude of the P300. Mean sMMN amplitude and latency did not differ between finger pairings. However, larger P300 responses were elicited to stimulation of the fifth finger than the third finger. These results suggest that, for certain combinations of body parts, early automatic somatosensory mismatch responses may be influenced by distance between the cortical representations of these body parts, whereas the later P300 response may be more influenced by the distance between stimulated body parts on the body surface. Future investigations can shed more light on this novel suggestion.

Using somatosensory mismatch responses as a window into somatotopic processing of tactile stimulation.

We explored whether 15-month-olds expect another person's emotional disposition to be stable across social situations. In three observation trials, infants watched two adults interact. Half the infants saw one of the adults (“Emoter”) respond negatively to the other adult's actions (Anger group); half saw the Emoter respond neutrally to the same actions (Neutral group). After a change in social context, infants participated in novel tasks with the (now-neutral) Emoter. Infants in the Anger group were significantly more likely to relinquish desirable toys to the Emoter. We hypothesize that, in the initial observation trials, infants learned that the Emoter was “anger-prone” and expected her to get angry again in a new social situation. Consequently, infants readily gave the Emoter what she wanted. These findings reveal three key features of infants' affective cognition: (1) infants track adults' emotional history across encounters; (2) infants learn from observing how people interact with others and use this to form expectations about how these people will treat them; and (3) more speculatively, infants use appeasement to cope with social threat. We hypothesize that infants form “trait-like” attributions about people's emotional dispositions and use this to formulate adaptive responses to adults in novel social contexts.

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Transfer of Social Learning Across Contexts: Exploring Infants' Attribution of Trait‐Like Emotions to Adults

Accurate perception of emotional (facial) expressions is an essential social skill. It is currently debated whether emotion categorization in infancy emerges in a "broad-to-narrow" pattern and the degree to which language influences this process. We used an habituation paradigm to explore (a) whether 14- and 18-month-old infants perceive different facial expressions (anger, sad, disgust) as belonging to a superordinate category of negative valence and (b) how verbal labels influence emotion category formation. Results indicated that infants did not spontaneously form a superordinate category of negative valence (Experiments 1 and 3). However, when a novel label ("toma") was added to each event during habituation trials (Experiments 2 and 4), infants formed this superordinate valance category when habituated to disgust and sad expressions (but not when habituated to anger and sadness). These labeling effects were obtained with two stimuli sets (Radboud Face Database and NimStim), even when controlling for the presence of teeth in the expressions. The results indicate that infants, at 14 and 18 months of age, show limited superordinate categorization based on the valence of different negative facial expressions. Specifically, infants only form this abstract emotion category when labels were provided, and the labeling effect depends on which emotions are presented during habituation. These findings have important implications for developmental theories of emotion. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

Superordinate categorization of negative facial expressions in infancy: The influence of labels.

Mothers of one-month-old infants were asked to provide information regarding their psychological and physical expectations about childbirth, and their actual experiences. This information was obtained by means of a questionnaire which was completed by 267 women who gave birth in the Seattle area. The results showed that primiparous women (n=139) had greater discrepancies between expectations and outcomes than did multiparous women (n=128). This discrepancy was largely due to poorer outcomes rather than differences in the initial level of expectations. Specifically, primiparas had many more obstetric interventions and had a more negative psychological experience. Psychological outcomes were also positively correlated with maternal attachment in this group. The clinical implications of these results are discussed, especially of the psychological care of mothers before and shortly after childbirth.

Andrew N. Meltzoff

Papers

Using somatosensory mismatch responses as a window into somatotopic processing of tactile stimulation.

Transfer of Social Learning Across Contexts: Exploring Infants' Attribution of Trait‐Like Emotions to Adults

Superordinate categorization of negative facial expressions in infancy: The influence of labels.

Expected and actual experience in labour and delivery and their relationship to maternal attachment

The Importance of Imitation for Theories of Social‐Cognitive Development