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

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Co-Planar Stereotaxic Atlas of the Human Brain—3-Dimensional Proportional System: An Approach to Cerebral Imaging, J. Talairach, P. Tournoux. Georg Thieme Verlag, New York (1988), 122 pp., 130 figs. DM 268

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

NeuroVault.org: A web database for sharing statistical parametric maps

Over the past few decades, neuroscience research has illuminated the neural mechanisms supporting learning from reward feedback, demonstrating a critical role for the striatum and ascending midbrain dopamine system. However, in humans, short- term working memory can also play an important role in reward-based learning, particularly in common paradigms that are relatively compressed in time. Given the growing use of reward-based learning tasks in translational studies in computational psychiatry, it is important to further investigate the degree to which apparent reinforcement learning is driven by working memory contributions. In our experiments, we manipulated the spacing between repetitions and also the post-learning delay period duration. Supporting a significant role for working memory in reward-based learning, we found that learning was faster for massed-trained compared to spaced-trained stimuli, and that brief interruptions of massed learning led to drops in subsequent performance. Further, individual differences in working memory capacity positively correlated with massed learning performance. Critically, after a delay period but not immediately, relative performance decayed in the massed condition and increased in the spaced condition. Overall, our results provide further support that working memory plays a large role in temporally condensed reward learning designs. We suggest that spacing training across or between sessions is a promising direction to pursue in future studies investigating the mechanisms of reward-based learning, and particularly in translational research.

https://biorxiv.org/content/10.1101/2020.03.19.997098v1.full.pdf

Reward learning and working memory: effects of massed versus spaced training and post-learning delay period

The Neural Correlates of Multiple Forms of Self-Control

Interacting sets of nodes and fluctuations in their interaction are important properties of a dynamic network system. In some cases the edges reflecting these interactions are directly quantifiable from the data collected, however in many cases (such as functional magnetic resonance imaging (fMRI) data) the edges must be inferred from statistical relations between the nodes. Here we present a new method, called temporal communities by trajectory clustering (TCTC), that derives time-varying communities directly from time series data collected from the nodes in a network. First, we verify TCTC on resting and task fMRI data by showing that time-averaged results correspond with expected static connectivity results. We then show that the time-varying communities correlate and predict single-trial behaviour. This new perspective on temporal community detection of node-collected data identifies robust communities revealing ongoing spatial-temporal community configurations during task performance.

/pdf/the-identification-of-temporal-communities-through-eyz7lwb9iu.pdf

Russell A. Poldrack

Papers

NeuroVault.org: A web database for sharing statistical parametric maps

Reward learning and working memory: effects of massed versus spaced training and post-learning delay period

The Neural Correlates of Multiple Forms of Self-Control

The identification of temporal communities through trajectory clustering correlates with single-trial behavioural fluctuations in neuroimaging data

Handbook of Functional MRI Data Analysis: Preprocessing fMRI data