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.

Computer science has one of the largest gender disparities in science, technology, engineering, and mathematics. An important reason for this disparity is that girls are less likely than boys to enroll in necessary “pipeline courses,” such as introductory computer science. Two experiments investigated whether high-school girls’ lower interest than boys in enrolling in computer science courses is influenced by stereotypes of the field. We further tested whether these stereotypes can be communicated by the physical classroom environment, and whether changing this environment alters girls’ interest. In 2 experiments (N = 269), a computer science classroom that did not project current computer science stereotypes caused girls, but not boys, to express more interest in taking computer science than a classroom that made these stereotypes salient. The gender difference was mediated by girls’ lower sense of belonging in the course, even beyond the effects of negative stereotype concerns, expectations of success, and utility value. Girls’ lower sense of belonging could be traced to lower feelings of fit with computer science stereotypes. Individual differences in fit with stereotypes predicted girls’ belonging and interest in a stereotypical, but not a nonstereotypical, classroom. Adolescence is a critical time for career aspirations. Girls may avoid computer science courses because current prevailing stereotypes of the field signal to them that they do not belong. However, providing them with an educational environment that does not fit current computer science stereotypes increases their interest in computer science courses and could provide grounds for interventions to help reduce gender disparities in computer science enrollment. (PsycINFO Database Record (c) 2016 APA, all rights reserved)

http://depts.washington.edu/sibl/Publications/Master,%20Cheryan,%20&%20Meltzoff,%202015%20JEP.pdf

Computing Whether She Belongs: Stereotypes Undermine Girls’ Interest and Sense of Belonging in Computer Science

Objective To determine the television-, DVD-, and video-viewing habits of children younger than 2 years. Design A telephone survey of 1009 parents of children aged 2 to 24 months. Setting Parents in Minnesota and Washington state were surveyed. Participants A random sample of parents of children born in the previous 2 years was drawn from birth certificate records. Households in which English was not spoken were excluded, as were children with major disabilities. Main Outcome Measure The amount of regular television and DVD/video viewing by content, reasons for viewing, and frequency of parent-child coviewing. Results By 3 months of age, about 40% of children regularly watched television, DVDs, or videos. By 24 months, this proportion rose to 90%. The median age at which regular media exposure was introduced was 9 months. Among those who watched, the average viewing time per day rose from 1 hour per day for children younger than 12 months to more than 1.5 hours per day by 24 months. Parents watched with their children more than half of the time. Parents gave education, entertainment, and babysitting as major reasons for media exposure in their children younger than 2 years. Conclusions Parents should be urged to make educated choices about their children's media exposure. Parental hopes for the educational potential of television can be supported by encouraging those parents who are already allowing screen time to watch with their children.

/pdf/television-and-dvd-video-viewing-in-children-younger-than-2-2fpxazhxp6.pdf

Television and DVD/Video Viewing in Children Younger Than 2 Years

We found that infant gaze following and pointing predicts subsequent language development. At ages 0;10 or 0;11, infants saw an adult turn to look at an object in an experimental setting. Productive vocabulary was assessed longitudinally through two years of age. Growth curve modeling showed that infants who gaze followed and looked longer at the target object had significantly faster vocabulary growth than infants with shorter looks, even with maternal education controlled; adding infant pointing strengthened the model. We highlight the role of social cognition in word learning and emphasize the communicative-referential functions of early gaze following and pointing.

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

Infant gaze following and pointing predict accelerated vocabulary growth through two years of age: a longitudinal, growth curve modeling study.

Human adults and children effortlessly learn new behaviors from watching others. Parents provide their young with an apprenticeship in how to act as a member of their particular culture long before verbal instruction is possible. A wide range of behaviors—from tool use to social customs—are passed from one generation to another through imitative learning. In western cultures, toddlers hold telephones to their ears and babble into the receivers. The children of Australian aborigines would not do this, one suspects. There is no innate proclivity to treat pieces of plastic in this manner, nor is it due to Skinnerian learning. Imitation is chiefly responsible. Imitation evolved through Darwinian means but achieves Lamarckian ends. It provides a mechanism for the "inheritance" of acquired characteristics. Imitation is powerful and can lead to rapid learning; it is essentially no-trial learning.

/pdf/1-imitation-and-other-minds-the-like-me-hypothesis-3wodl2uldh.pdf

1 Imitation and Other Minds: The "Like Me" Hypothesis

http://ilabs.washington.edu/meltzoff/pdf/07Meltzoff_Intention_Agency.pdf

Andrew N. Meltzoff

Papers

Computing Whether She Belongs: Stereotypes Undermine Girls’ Interest and Sense of Belonging in Computer Science

Television and DVD/Video Viewing in Children Younger Than 2 Years

Infant gaze following and pointing predict accelerated vocabulary growth through two years of age: a longitudinal, growth curve modeling study.

1 Imitation and Other Minds: The "Like Me" Hypothesis

The 'like me' framework for recognizing and becoming an intentional agent