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

https://www2.econ.iastate.edu/tesfatsi/DeepLearningInNeuralNetworksOverview.JSchmidhuber2015.pdf

Deep learning in neural networks

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

Publisher Summary This chapter focuses on the modern notion of short-term memory, called working memory. Working memory refers to the temporary maintenance of information that was just experienced or just retrieved from long-term memory but no longer exists in the external environment. These internal representations are short-lived, but can be maintained for longer periods of time through active rehearsal strategies, and can be subjected to various operations that manipulate the information in such a way that makes it useful for goal-directed behavior. Working memory is a system that is critically important in cognition and seems necessary in the course of performing many other cognitive functions, such as reasoning, language comprehension, planning, and spatial processing. This chapter demonstrates the functional importance of dopamine to working memory function in several ways. Elucidation of the cognitive and neural mechanisms underlying human working memory is an important focus of cognitive neuroscience and neurology for much of the past decade. One conclusion that arises from research is that working memory, a faculty that enables temporary storage and manipulation of information in the service of behavioral goals, can be viewed as neither a unitary, nor a dedicated system. Data from numerous neuropsychological and neurophysiological studies in animals and humans demonstrates that a network of brain regions, including the prefrontal cortex, is critical for the active maintenance of internal representations.

Chapter 11 Working memory

This work introduces a novel method for the temporal deconvolution of the BOLD signal for multiecho functional MRI (fMRI) data: Multiecho Paradigm Free Mapping (ME-PFM). By solving an inverse problem with the Generalized LASSO, this method obtains voxelwise time-varying estimates of the changes in the transverse relaxation (R) and the net magnetization (So) of the fMRI signal without prior knowledge of the timings of the BOLD responses. Our results with experimental data demonstrate that the maps of R changes obtained with ME-PFM at the times of the stimulus trials closely resemble the maps obtained with a standard approach that is aware of the onsets and durations of the events. Compared with previous deconvolution approaches for BOLD fMRI, this method yields estimates of R changes in physiologically interpretable units, which is particularly relevant for the quantification of the dynamics of the BOLD fMRI signal during tasks and resting state.

A temporal deconvolution algorithm for multiecho functional MRI

It is commonly thought that improvements in image S/N translate directly to improvements in fMRI sensitivity. This study demonstrates that improvements in image S/N by means of increased field strength, and the use of surface coils, does not translate to similar gains in temporal S/N, due to physiological noise. An analysis of the S/N dependence on signal strength for both the imaginary and real noise components is presented. The real noise component, which is lower than the imaginary component, is a significant contributor to temporal variations in single-shot fMRI procedures. The imaginary component becomes an important contributor to time series fluctuations in multi-shot or 3D techniques. In the experiments presented here, the relationship between image and temporal S/N is examined by modulating the signal strength by means of echo time stepping, field strength modulation, and RF coil comparison. The spatial and temporal noise contributions of the resting brain are characterized by comparing phantom, subject, and thermal noise measurements. The sensitivity of both spiral and EPI single-shot acquisition methods to physiologic and systematic noise is characterized. The results suggest that the fMRI sensitivity plateaus as image S/N is increased unless physiological noise is filtered out.© (2002) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

S/N and fMRI sensitivity

The Use of Neurofeedback with Real-Time Functional MRI to Suppress Physiological Noise.

Blood volume fMRI with 3D-EPI-VASO: any benefits over SMS-VASO?

Searchlight-based brain morphological classification analysis

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