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

We have developed a near-real-time computer system that can locate and track a subject's head, and then recognize the person by comparing characteristics of the face to those of known individuals. The computational approach taken in this system is motivated by both physiology and information theory, as well as by the practical requirements of near-real-time performance and accuracy. Our approach treats the face recognition problem as an intrinsically two-dimensional (2-D) recognition problem rather than requiring recovery of three-dimensional geometry, taking advantage of the fact that faces are normally upright and thus may be described by a small set of 2-D characteristic views. The system functions by projecting face images onto a feature space that spans the significant variations among known face images. The significant features are known as "eigenfaces," because they are the eigenvectors (principal components) of the set of faces; they do not necessarily correspond to features such as eyes, ears, and noses. The projection operation characterizes an individual face by a weighted sum of the eigenface features, and so to recognize a particular face it is necessary only to compare these weights to those of known individuals. Some particular advantages of our approach are that it provides for the ability to learn and later recognize new faces in an unsupervised manner, and that it is easy to implement using a neural network architecture.

/pdf/eigenfaces-for-recognition-1vpm5kz4jh.pdf

Eigenfaces for recognition

▪ Abstract The prefrontal cortex has long been suspected to play an important role in cognitive control, in the ability to orchestrate thought and action in accordance with internal goals. Its neural basis, however, has remained a mystery. Here, we propose that cognitive control stems from the active maintenance of patterns of activity in the prefrontal cortex that represent goals and the means to achieve them. They provide bias signals to other brain structures whose net effect is to guide the flow of activity along neural pathways that establish the proper mappings between inputs, internal states, and outputs needed to perform a given task. We review neurophysiological, neurobiological, neuroimaging, and computational studies that support this theory and discuss its implications as well as further issues to be addressed

/pdf/an-integrative-theory-of-prefrontal-cortex-function-2j9unprn5f.pdf

An integrative theory of prefrontal cortex function

A baseline or control state is fundamental to the understanding of most complex systems. Defining a baseline state in the human brain, arguably our most complex system, poses a particular challenge. Many suspect that left unconstrained, its activity will vary unpredictably. Despite this prediction we identify a baseline state of the normal adult human brain in terms of the brain oxygen extraction fraction or OEF. The OEF is defined as the ratio of oxygen used by the brain to oxygen delivered by flowing blood and is remarkably uniform in the awake but resting state (e.g., lying quietly with eyes closed). Local deviations in the OEF represent the physiological basis of signals of changes in neuronal activity obtained with functional MRI during a wide variety of human behaviors. We used quantitative metabolic and circulatory measurements from positron-emission tomography to obtain the OEF regionally throughout the brain. Areas of activation were conspicuous by their absence. All significant deviations from the mean hemisphere OEF were increases, signifying deactivations, and resided almost exclusively in the visual system. Defining the baseline state of an area in this manner attaches meaning to a group of areas that consistently exhibit decreases from this baseline, during a wide variety of goal-directed behaviors monitored with positron-emission tomography and functional MRI. These decreases suggest the existence of an organized, baseline default mode of brain function that is suspended during specific goal-directed behaviors.

/pdf/a-default-mode-of-brain-function-zwenkbmjw7.pdf

A default mode of brain function.

Recent advances in neuroscience are making it possible to describe the architecture and properties of some of the networks of neurones (brain cells) in brain structures, while neuronal network theory, including approaches related to the statistical mechanics of spin glasses, is becoming helpful in analysing the computational properties of these networks. These developments are illuminating processes of information storage in the brain, and this is illustrated in the case of one brain structure considered here, the hippocampus.

Neuronal networks in the brain

Brain-stimulation reward

The (primate and human) orbitofrontal cortex receives taste, olfactory, visual, somatosensory, and auditory inputs from cortical regions which are toward the end of the unimodal cortical processing streams that build representations of what stimulus is present in each of these sensory modalities. Neurophysiological studies in primates show that it is the reward value of taste, olfactory, oral texture, and visual stimuli that is represented, as shown, for example, by the reduction to zero of the neuronal responses after feeding to satiety on a particular food. The reward is partly specific to a particular food, as shown by sensory-specific satiety-related neuronal responses. Visual and olfactory stimuli become rewarding by learned association with a primary (unlearned) reinforcer such as the taste of food. This learning can be reversed (in as little as one trial for visual stimuli) and may be facilitated by error neurons that respond when an expected reward is not obtained. Complementary neuroimaging studies in humans show that abstract reinforcers such as monetary reward are also represented in the orbitofrontal cortex, and that the expected value of a reward may be represented in the orbitofrontal cortex.

Reward Neurophysiology and Orbitofrontal Cortex

There are five types of taste receptor cell, sweet, salt, bitter, sour, and umami (protein taste). There are 1000 olfactory receptor genes each specifying a different type of receptor each for a set of odors. Tastes are primary, unlearned, rewards and punishers, and are important in emotion. Pheromones and some other olfactory stimuli are primary reinforcers, but for many odors the reward value is learned by stimulus–reinforcer association learning. The primary taste cortex in the anterior insula provides separate and combined representations of the taste, temperature, and texture
(including fat texture) of food in the mouth independently of hunger and thus of reward value and pleasantness. One
synapse on, in the orbitofrontal cortex, these sensory inputs are for some neurons combined by learning with olfactory
and visual inputs, and these neurons encode food reward value in that they only respond to food when hungry, and in
that activations correlate with subjective pleasantness. Cognitive factors, including word-level descriptions, and
attention, modulate the representation of the reward value of taste, odor, and flavor in the orbitofrontal cortex and a
region to which it projects, the anterior cingulate cortex. Further, there are individual differences in the representation
of the reward value of food in the orbitofrontal cortex. Overeating and obesity are related in many cases to an increased reward value of the sensory inputs produced by foods, and their modulation by cognition and attention that override existing satiety signals

Edmund T. Rolls

Papers

Neuronal networks in the brain

Brain-stimulation reward

Reward Neurophysiology and Orbitofrontal Cortex

Taste and smell, psychology of

Competitive networks, including self-organizing maps