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

The Adolescent Brain Cognitive Development (ABCD) study is an unprecedented longitudinal neuroimaging sample that tracks the brain development of over 10,000 9-10 year olds through adolescence. At the core of this study are the three tasks that are completed repeatedly within the fMRI scanner, one of which is the stop-signal task. In analyzing the available stopping experimental code and data, we identified a set of design issues that we believe significantly limit its value. These issues include but are not limited to: variable stimulus durations that violate basic assumptions of dominant stopping models, trials in which stimuli are incorrectly not presented, and faulty stop-signal delays. We present eight issues, show their effect on the existing ABCD data, suggest prospective solutions to the study organizers including task changes for future data collection, and suggest retrospective solutions for data users who wish to make the most of the existing data.

https://www.biorxiv.org/content/biorxiv/early/2020/06/17/2020.05.08.084707.full.pdf

Design issues and solutions for stop-signal data from the Adolescent Brain Cognitive Development [ABCD] study

Reference anatomies of the brain and corresponding atlases play a central role in experimental neuroimaging workflows and are the foundation for reporting standardized results. The choice of such references -i.e., templates- and atlases is one relevant source of methodological variability across studies, which has recently been brought to attention as an important challenge to reproducibility in neuroscience. TemplateFlow is a publicly available framework for human and nonhuman brain models. The framework combines an open database with software for access, management, and vetting, allowing scientists to distribute their resources under FAIR -findable, accessible, interoperable, reusable- principles. TemplateFlow supports a multifaceted insight into brains across species, and enables multiverse analyses testing whether results generalize across standard references, scales, and in the long term, species, thereby contributing to increasing the reliability of neuroimaging results.

/pdf/templateflow-fair-sharing-of-multi-scale-multi-species-brain-3ewokwfimc.pdf

TemplateFlow: FAIR-sharing of multi-scale, multi-species brain models

There is compelling evidence that the human cerebellum is engaged in a wide array of motor and cognitive tasks. A fundamental question centers on whether the cerebellum is organized into distinct functional sub-regions. To address this question, we employed a rich task battery, designed to tap into a broad range of cognitive processes. During four functional magnetic resonance imaging (fMRI) sessions, participants performed a battery of 26 diverse tasks comprising 47 unique conditions. Using the data from this multi-domain task battery, we derived a comprehensive functional parcellation of the cerebellar cortex, and evaluated it by predicting functional boundaries in a novel set of tasks. The new parcellation successfully identified distinct functional sub-regions, providing significant improvements over existing parcellations derived from task-free data. Lobular boundaries, commonly used to summarize functional data, did not coincide with functional subdivisions. This multi-domain task approach offers novel insights into the functional heterogeneity of the cerebellar cortex.

https://www.biorxiv.org/content/biorxiv/early/2018/09/20/423509.full.pdf

A Multi-Domain Task Battery Reveals Functional Boundaries in the Human Cerebellum

A recent stream of alarming publications questions the validity of published neuroimaging findings. As a consequence, fMRI teams worldwide are encouraged to increase their sample sizes to reach higher power and thus increase the positive predictive value of their findings. However, an often-overlooked factor influencing power is the experimental design: by choosing the appropriate experimental design, the statistical power of a study can be increased within subjects. By optimizing the order and timing of the stimuli, power can be gained at no extra cost. To facilitate design optimization, we created a python package and web-based tool called Neurodesign to maximize the detection power or estimation efficiency within subjects, while controlling for psychological factors such as the predictability of the design. We implemented the genetic algorithm, introduced by Wager and Nichols (2003) and further improved by Kao et al. (2009), to optimize the experimental design. The toolbox allows more complex experimental setups than existing toolboxes, while the GUI provides a more user-friendly experience. The toolbox is accessible online at www.neuropowertools.org.

/pdf/neurodesign-optimal-experimental-designs-for-task-fmri-1gou0xycps.pdf

Russell A. Poldrack

Papers

Design issues and solutions for stop-signal data from the Adolescent Brain Cognitive Development [ABCD] study

TemplateFlow: FAIR-sharing of multi-scale, multi-species brain models

A Multi-Domain Task Battery Reveals Functional Boundaries in the Human Cerebellum

Neurodesign: Optimal Experimental Designs for Task fMRI

Appendix – Prospect Theory and the Brain *