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.

Acting in the present in anticipation of the future is argued to be a behavioral correlate of mental time travel (MTT) Yet, it is important to consider how other future-directed behaviors – including planning, delay of gratification, and acts of prospective memory – figure into a theory of MTT and future thinking more broadly Developmental science can help in this formulation

How developmental science contributes to theories of future thinking

We previously reported preliminary data on infants' abilities to detect the cross‐modal correspondences between the visual and auditory concomittants of speech [Kuhl and Meltzoff, J. Acoust. Soc. Am. Suppl. 1 70, S96 (1981)]. We have now completed testing on 32 4.5‐ to 5‐month‐old infants. Each was shown a filmed display of two faces, one producing the articulatory movements corresponding to the vowel [a] and the other producing the articulatory movements corresponding to the vowel [i]. One of the sound tracks (either [a] or [i]) was played in synchrony with the faces. The infants' visual fixations to the faces were scored by an observer who could not hear the sound track presented to the infant nor see the faces. Results demonstrated that the infants looked longer at the face matching the sound being presented than at the nonmatching face (p < 0.01). We hypothesized that the recognition of these cross‐modal equivalences was based on the structural correspondence between a particular articulatory movement and a particular vowel sound, rather than on any temporal correspondence between a particular face‐sound pair. This hypothesis predicts that if the crucial spectral information is removed from the vowels while the temporal information is preserved, performance should drop to chance. Experiment II tested this hypothesis using pure‐tone stimuli. Performance fell to chance. [Supported by NSF.]

Bimodal speech perception in early infancy

Les As. argumentent leur position en faveur d'une conception evolutionniste du developpement cognitif chez l'enfant ou la construction des theories abstraites (qui permettent de prevoir les mouvements et les comportements) est consideree comme debutant des la naissance

Theories vs. Modules: To the Max and Beyond A Reply to Poulin-Dubois and to Stich and Nichols

Motivation and Identity

The current application is directed to developing and administering subject-appropriate ITAs. The various to developing and administering subject-appropriate ITAs evaluate many aspects of ITA content, content presentation, and administration with regard to particular categories of test subjects in order to develop subject-appropriate ITAs (“SAITAs”) and to administer SAITAs appropriately to the categories of test subjects for which the tests are developed. Aspects of SAITA presentation and administration that are evaluated include input devices, presentation formats, presentation language, presentation media, colors used in presentation of tutorials and stimuli, words, pictures, and symbols used in a presentation of tutorials and stimuli, the pace and length of various portions of SAITAs, human vs. computer administration of SAITAs, presentation of feedback to test subjects during SAITAs, ITAs and many other aspects of ITA content, content presentation, and administration. SAITAs employ systematic methods for both development and administration of SAITAs.

Andrew N. Meltzoff

Papers

How developmental science contributes to theories of future thinking

Bimodal speech perception in early infancy

Theories vs. Modules: To the Max and Beyond A Reply to Poulin-Dubois and to Stich and Nichols

Motivation and Identity

Method and system for developing and administering subject-appropriate implicit-association tests