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

Despite the concern that has been expressed about potential method biases, and the pervasiveness of research settings with the potential to produce them, there is disagreement about whether they really are a problem for researchers in the behavioral sciences. Therefore, the purpose of this review is to explore the current state of knowledge about method biases. First, we explore the meaning of the terms “method” and “method bias” and then we examine whether method biases influence all measures equally. Next, we review the evidence of the effects that method biases have on individual measures and on the covariation between different constructs. Following this, we evaluate the procedural and statistical remedies that have been used to control method biases and provide recommendations for minimizing method bias.

Sources of Method Bias in Social Science Research and Recommendations on How to Control It

I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

Learners rely on a combination of experience-independent and experience-dependent mechanisms to extract information from the environment. Language acquisition involves both types of mechanisms, but most theorists emphasize the relative importance of experience-independent mechanisms. The present study shows that a fundamental task of language acquisition, segmentation of words from fluent speech, can be accomplished by 8-month-old infants based solely on the statistical relationships between neighboring speech sounds. Moreover, this word segmentation was based on statistical learning from only 2 minutes of exposure, suggesting that infants have access to a powerful mechanism for the computation of statistical properties of the language input.

https://science.sciencemag.org/content/sci/274/5294/1926.full.pdf

Statistical Learning by 8-Month-Old Infants

Linguistic experience affects phonetic perception. However, the critical period during which experience affects perception and the mechanism responsible for these effects are unknown. This study of 6-month-old infants from two countries, the United States and Sweden, shows that exposure to a specific language in the first half year of life alters infants' phonetic perception.

https://science.sciencemag.org/content/sci/255/5044/606.full.pdf

Linguistic experience alters phonetic perception in infants by 6 months of age

Infants learn language with remarkable speed, but how they do it remains a mystery. New data show that infants use computational strategies to detect the statistical and prosodic patterns in language input, and that this leads to the discovery of phonemes and words. Social interaction with another human being affects speech learning in a way that resembles communicative learning in songbirds. The brain's commitment to the statistical and prosodic patterns that are experienced early in life might help to explain the long-standing puzzle of why infants are better language learners than adults. Successful learning by infants, as well as constraints on that learning, are changing theories of language acquisition.

/pdf/early-language-acquisition-cracking-the-speech-code-bf9adht4u8.pdf

Early language acquisition: cracking the speech code

Human speech and birdsong have numerous parallels. Both humans and songbirds learn their complex vocalizations early in life, exhibiting a strong dependence on hearing the adults they will imitate, as well as themselves as they practice, and a waning of this dependence as they mature. Innate predispositions for perceiving and learning the correct sounds exist in both groups, although more evidence of innate descriptions of species-specific signals exists in songbirds, where numerous species of vocal learners have been compared. Humans also share with songbirds an early phase of learning that is primarily perceptual, which then serves to guide later vocal production. Both humans and songbirds have evolved a complex hierarchy of specialized forebrain areas in which motor and auditory centers interact closely, and which control the lower vocal motor areas also found in nonlearners. In both these vocal learners, however, how auditory feedback of self is processed in these brain areas is surprisingly unclear. Finally, humans and songbirds have similar critical periods for vocal learning, with a much greater ability to learn early in life. In both groups, the capacity for late vocal learning may be decreased by the act of learning itself, as well as by biological factors such as the hormones of puberty. Although some features of birdsong and speech are clearly not analogous, such as the capacity of language for meaning, abstraction, and flexible associations, there are striking similarities in how sensory experience is internalized and used to shape vocal outputs, and how learning is enhanced during a critical period of development. Similar neural mechanisms may therefore be involved.

/pdf/birdsong-and-human-speech-common-themes-and-mechanisms-39gg3m47d4.pdf

BIRDSONG AND HUMAN SPEECH: Common Themes and Mechanisms

Infants acquire language with remarkable speed, although little is known about the mechanisms that underlie the acquisition process. Studies of the phonetic units of language have shown that early in life, infants are capable of discerning differences among the phonetic units of all languages, including native- and foreign-language sounds. Between 6 and 12 mo of age, the ability to discriminate foreign-language phonetic units sharply declines. In two studies, we investigate the necessary and sufficient conditions for reversing this decline in foreign-language phonetic perception. In Experiment 1, 9-mo-old American infants were exposed to native Mandarin Chinese speakers in 12 laboratory sessions. A control group also participated in 12 language sessions but heard only English. Subsequent tests of Mandarin speech perception demonstrated that exposure to Mandarin reversed the decline seen in the English control group. In Experiment 2, infants were exposed to the same foreign-language speakers and materials via audiovisual or audio-only recordings. The results demonstrated that exposure to recorded Mandarin, without interpersonal interaction, had no effect. Between 9 and 10 mo of age, infants show phonetic learning from live, but not prerecorded, exposure to a foreign language, suggesting a learning process that does not require long-term listening and is enhanced by social interaction.

https://www.pnas.org/content/pnas/100/15/9096.full.pdf

Foreign-language experience in infancy: Effects of short-term exposure and social interaction on phonetic learning

Many perceptual categories exhibit internal structure in which category prototypes play an important role. In the four experiments reported here, the internal structure of phonetic categories was explored in studies involving adults, infants, and monkeys. In Experiment 1, adults rated the category goodness of 64 variants of the vowel /i/ on a scale from 1 to 7. The results showed that there was a certain location in vowel space where listeners rated the /i/ vowels as best instances, or prototypes. The perceived goodness of Iii vowels declined systematically as stimuli were further removed from the prototypic Iii vowel. Experiment 2 went beyond this initial demonstration and examined the effect of speech prototypes on perception. Either the prototypic or a nonprototypic IM vowel was used as the referent stimulus and adults’ generalization to other members of the category was examined. Results showed that the typicality of the speech stimulus strongly affected perception. When the prototype of the category served as the referent vowel, there was significantly greater generalization to other /i/ vowels, relative to the situation in which the nonprototype served as the referent. The notion of aperceptual magnet was introduced. The prototype of the category functioned like a perceptual magnet for other category members; it assimilated neighboring stimuli, effectively pulling them toward the prototype. In Experiment 3, the ontogenetic origins of the perceptual magnet effect were explored by testing 6-month-old infants. The results showed that infants’ perception of vowels was also strongly affected by speech prototypes. Infants showed significantly greater generalization when the prototype of the vowel category served as the referent; moreover, their responses were highly correlated with those of adults. In Experiment 4, Rhesus monkeys were tested to examine whether or not the prototype’s magnet effect was unique to humans. The animals did not provide any evidence of speech prototypes; they did not exhibit the magnet effect. It is suggested that the internal organization of phonetic categories around prototypic members is an ontogenetically early, species-specific, aspect of the speech code

/pdf/human-adults-and-human-infants-show-a-perceptual-magnet-at9x5yq0o7.pdf

Patricia K. Kuhl

Papers

Linguistic experience alters phonetic perception in infants by 6 months of age

Early language acquisition: cracking the speech code

BIRDSONG AND HUMAN SPEECH: Common Themes and Mechanisms

Foreign-language experience in infancy: Effects of short-term exposure and social interaction on phonetic learning

Human adults and human infants show a "perceptual magnet effect" for the prototypes of speech categories, monkeys do not