There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

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

Anomaly detection is an important problem that has been researched within diverse research areas and application domains. Many anomaly detection techniques have been specifically developed for certain application domains, while others are more generic. This survey tries to provide a structured and comprehensive overview of the research on anomaly detection. We have grouped existing techniques into different categories based on the underlying approach adopted by each technique. For each category we have identified key assumptions, which are used by the techniques to differentiate between normal and anomalous behavior. When applying a given technique to a particular domain, these assumptions can be used as guidelines to assess the effectiveness of the technique in that domain. For each category, we provide a basic anomaly detection technique, and then show how the different existing techniques in that category are variants of the basic technique. This template provides an easier and more succinct understanding of the techniques belonging to each category. Further, for each category, we identify the advantages and disadvantages of the techniques in that category. We also provide a discussion on the computational complexity of the techniques since it is an important issue in real application domains. We hope that this survey will provide a better understanding of the different directions in which research has been done on this topic, and how techniques developed in one area can be applied in domains for which they were not intended to begin with.

/pdf/anomaly-detection-a-survey-1x33g9m0a3.pdf

Anomaly detection: A survey

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

The study of decision making spans such varied fields as neuroscience, psychology, economics, statistics, political science, and computer science. Despite this diversity of applications, most decisions share common elements including deliberation and commitment. Here we evaluate recent progress in understanding how these basic elements of decision formation are implemented in the brain. We focus on simple decisions that can be studied in the laboratory but emphasize general principles likely to extend to other settings.

/pdf/the-neural-basis-of-decision-making-2p5agsty56.pdf

The Neural Basis of Decision Making

We present a large-scale anatomically constrained spiking neuron model of the lateral geniculate nucleus (LGN), which operates solely with retinal input, relay cells, and interneurons. We show that...

Dependence of response properties on sparse connectivity in a spiking neuron model of the lateral geniculate nucleus.

Objective. We investigated the neural correlates of workload buildup in a fine visuomotor task called the boundary avoidance task (BAT). The BAT has been known to induce naturally occurring failures of human–machine coupling in high performance aircraft that can potentially lead to a crash—these failures are termed pilot induced oscillations (PIOs). Approach. We recorded EEG and pupillometry data from human subjects engaged in a flight BAT simulated within a virtual 3D environment. Main results. We find that workload buildup in a BAT can be successfully decoded from oscillatory features in the electroencephalogram (EEG). Information in delta, theta, alpha, beta, and gamma spectral bands of the EEG all contribute to successful decoding, however gamma band activity with a lateralized somatosensory topography has the highest contribution, while theta band activity with a fronto-central topography has the most robust contribution in terms of real-world usability. We show that the output of the spectral decoder can be used to predict PIO susceptibility. We also find that workload buildup in the task induces pupil dilation, the magnitude of which is significantly correlated with the magnitude of the decoded EEG signals. These results suggest that PIOs may result from the dysregulation of cortical networks such as the locus coeruleus (LC)—anterior cingulate cortex (ACC) circuit. Significance. Our findings may generalize to similar control failures in other cases of tight man-machine coupling where gains and latencies in the control system must be inferred and compensated for by the human operators. A closed-loop intervention using neurophysiological decoding of workload buildup that targets the LC-ACC circuit may positively impact operator performance in such situations.

/pdf/neural-mechanisms-underlying-catastrophic-failure-in-human-2qebpy9q7s.pdf

Neural mechanisms underlying catastrophic failure in human-machine interaction during aerial navigation.

An apparatus for the calibration and quality assurance of a multichannel spectrophotometer, particularly an ELISA spectrophotometer, comprises a series of filters having a known first color and linearly increasing optical density. The response of the spectrophotometer is measured against the known color and linearly increasing optical density. Additional filters of at least one additional color permit checking the color response of the spectrophotmeter. An algorithm determines whether the response conforms to predetermined conditions. An output is produced to provide a record of the calibration and quality assurance of the spectrophotometer. The invention has particular utility for conducting calibration and quality assurance of ELISA spectrophotometers used in clinical laboratory screening for infectious diseases, such as Hepatitis B and the AIDS viruses.

Method and apparatus for providing quality assurance and calibration assurance in a spectrophotometer

Within the next 1.5 decades, 1 in 7 U.S. adults is anticipated to suffer from age-related macular degeneration (AMD), a degenerative retinal disease which leads to blindness if untreated. Optical coherence tomography angiography (OCTA) has become a prime technique for AMD diagnosis, specifically for late-stage neovascular (NV) AMD. Such technologies generate massive amounts of data, challenging to parse by experts alone, transforming artificial intelligence into a valuable partner. We describe a deep learning (DL) approach which achieves multi-class detection of non-AMD vs. non-neovascular (NNV) AMD vs. NV AMD from a combination of OCTA, OCT structure, 2D b-scan flow images, and high definition (HD) 5-line b-scan cubes; DL also detects ocular biomarkers indicative of AMD risk. Multimodal data were used as input to 2D-3D Convolutional Neural Networks (CNNs). Both for CNNs and experts, choroidal neovascularization and geographic atrophy were found to be important biomarkers for AMD. CNNs predict biomarkers with accuracy up to 90.2% (positive-predictive-value up to 75.8%). Just as experts rely on multimodal data to diagnose AMD, CNNs also performed best when trained on multiple inputs combined. Detection of AMD and its biomarkers from OCTA data via CNNs has tremendous potential to expedite screening of early and late-stage AMD patients. 

/pdf/a-multimodal-deep-learning-system-to-distinguish-late-stages-2pnjwbgr.pdf

A multimodal deep learning system to distinguish late stages of AMD and to compare expert vs. AI ocular biomarkers

For day-to-day decisions, multiple factors influence our choice between alternatives. Two dimensions of decision making that substantially affect choice are the objective perceptual properties of the stimulus (e.g., salience) and its subjective value. Here we measure EEGs in human subjects to relate their feedback-evoked EEG responses to estimates of prediction error given a neurally derived expected value for each trial. Unlike in traditional reinforcement learning paradigms, in our experiment the reward itself is not probabilistic; rather, it is a fixed value, which, when combined with the variable stimulus salience, yields uncertainty in the choice. We find that feedback-evoked event-related potentials (ERPs), specifically those classically termed feedback-related negativity, are modulated by both the reward level and stimulus salience. Using single-trial analysis of the EEG, we show stimulus-locked EEG components reflecting perceived stimulus salience can be combined with the level of reward to create an estimate of expected reward. This expected reward is used to form a prediction error that correlates with the trial-by-trial variability of the feedback ERPs for negative, but not positive, feedback. This suggests that the valence of prediction error is more important than the valence of the actual feedback, since only positive rewards were delivered in the experiment (no penalty or loss). Finally, we show that these subjectively defined prediction errors are informative of the riskiness of the subject's choice on the subsequent trial. In summary, our work shows that neural correlates of stimulus salience interact with value information to yield neural representations of subjective expected reward. Significance statement: How we make perceptual decisions depends on sensory evidence and the value of our options. These two factors often interact to yield subjective decisions; i.e., individuals integrate sensory evidence and value to form their own estimates of expected reward. Here, we use electroencephelography to identify trial-by-trial neural activity of perceived stimulus salience, showing that this activity can be combined with the value of choice options to form a representation of expected reward. Our results provide insight into the neural processing governing the interaction between salience and value and the formation of subjective expected reward and prediction error. This work is potentially important for identifying neural markers of abnormal sensory/value processing, as is seen in some cases of psychiatric illnesses.

https://www.jneurosci.org/content/jneuro/35/38/13064.full.pdf

Paul Sajda

Papers

Dependence of response properties on sparse connectivity in a spiking neuron model of the lateral geniculate nucleus.

Neural mechanisms underlying catastrophic failure in human-machine interaction during aerial navigation.

Method and apparatus for providing quality assurance and calibration assurance in a spectrophotometer

A multimodal deep learning system to distinguish late stages of AMD and to compare expert vs. AI ocular biomarkers

Perceptual Salience and Reward Both Influence Feedback-Related Neural Activity Arising from Choice