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.

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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.

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The Neural Basis of Decision Making

Age-related macular degeneration (AMD) is the major cause of blindness in the developed world. Though substantial work has been done to characterize the disease, it is difficult to predict how the state of an individual's retina will ultimately affect their high-level perceptual function. In this paper, we describe an approach that couples retinal imaging with computational neural modeling of early visual processing to generate quantitative predictions of an individual's visual perception. Using a patient population with mild to moderate AMD, we show that we are able to accurately predict subject-specific psychometric performance by decoding simulated neurodynamics that are a function of scotomas derived from an individual's fundus image. On the population level, we find that our approach maps the disease on the retina to a representation that is a substantially better predictor of high-level perceptual performance than traditional clinical metrics such as drusen density and coverage. In summary, our work identifies possible new metrics for evaluating the efficacy of treatments for AMD at the level of the expected changes in high-level visual perception and, in general, typifies how computational neural models can be used as a framework to characterize the perceptual consequences of early visual pathologies.

Decoding simulated neurodynamics predicts the perceptual consequences of age-related macular degeneration.

BACKGROUND The communication through coherence model posits that brain rhythms are synchronized across different frequency bands and that effective connectivity strength between interacting regions depends on their phase relation. Evidence to support the model comes mostly from electrophysiological recordings in animals while evidence from human data is limited. METHODS Here, an fMRI-EEG-TMS (fET) instrument capable of acquiring simultaneous fMRI and EEG during noninvasive single pulse TMS applied to dorsolateral prefrontal cortex (DLPFC) was used to test whether prefrontal EEG alpha phase moderates TMS-evoked top-down influences on subgenual, rostral and dorsal anterior cingulate cortex (ACC). Results in healthy volunteers (n=11) were compared to those from patients with major depressive disorder (MDD) (n=17) collected as part of a ongoing clinical trial investigation. RESULTS In both groups, TMS-evoked functional connectivity between DLPFC and subgenual ACC (sgACC) depended on the EEG alpha phase. TMS-evoked DLPFC to sgACC effective connectivity (EC) was moderated by EEG alpha phase in healthy volunteers, but not in the MDD patients. Top-down EC was inhibitory for TMS onsets during the upward slope of the alpha wave relative to TMS timed to the downward slope of the alpha wave. Prefrontal EEG alpha phase dependent effects on TMS-evoked fMRI BOLD activation of the rostral anterior cingulate cortex were detected in the MDD patient group, but not in the healthy volunteer group. DISCUSSION Results demonstrate that TMS-evoked top-down influences vary as a function of the prefrontal alpha rhythm, and suggest clinical applications whereby TMS is synchronized to the brain’s internal rhythms in order to more efficiently engage deep therapeutic targets.

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Functional and effective connectivity between dorsolateral prefrontal and subgenual anterior cingulate cortex depends on the timing of transcranial magnetic stimulation relative to the phase of prefrontal alpha EEG

In this paper we compare three linear methods, independent component analysis (ICA), common spatial patterns (CSP), and linear discrimination (LD) for recovering task relevant neural activity from high spatial density electroencephalography (EEG). Each linear method uses a different objective function to recover underlying source components by exploiting statistical structure across a large number of sensors. We test these methods using a dual-task event-related paradigm. While engaged in a primary task, subjects must detect infrequent changes in the visual display, which would be expected to evoke several well-known event-related potentials (ERPs), including the N2 and P3. We find that though each method utilizes a different objective function, they in fact yield similar components. We note that one advantage of the LD approach is that the recovered component is easily interpretable, namely it represents the component within a given time window which is most discriminating for the task, given a spatial integration of the sensors. Both ICA and CSP return multiple components, of which the most discriminating component may not be the first. Thus, for these methods, visual inspection or additional processing is required to determine the significance of these components for the task.

Comparison of supervised and unsupervised linear methods for recovering task-relevant activity in EEG

Whole-brain analysis of concurrent TMS-EEG-fMRI reveals brain-wide state-dependent TMS effects

Simultaneous EEG-fMRI is a multi-modal neuroimaging technique that provides complementary spatial and temporal resolution. Challenging has been developing principled and interpretable approaches for fusing the modalities, speciﬁcally approaches enabling inference of latent source spaces representative of neural activity. In this paper, we address this inference problem within the framework of transcoding – mapping from a speciﬁc encoding (modality) to a decoding (the latent source space) and then encoding the latent source space to the other modality. Speciﬁcally, we develop a symmetric method consisting of a cyclic convolutional transcoder that transcodes EEG to fMRI and vice versa. Without any prior knowledge of either the hemodynamic response function or lead ﬁeld matrix, the complete data-driven method exploits the temporal and spatial relationships between the modalities and latent source spaces to learn these mappings. We quantify, for both the simulated and real EEG-fMRI data, how well the modalities can be transcoded from one to another as well as the source spaces that are recovered, all evaluated on unseen data. In addition to enabling a new way to symmetrically infer a latent source space, the

Paul Sajda

Papers

Decoding simulated neurodynamics predicts the perceptual consequences of age-related macular degeneration.

Functional and effective connectivity between dorsolateral prefrontal and subgenual anterior cingulate cortex depends on the timing of transcranial magnetic stimulation relative to the phase of prefrontal alpha EEG

Comparison of supervised and unsupervised linear methods for recovering task-relevant activity in EEG

Whole-brain analysis of concurrent TMS-EEG-fMRI reveals brain-wide state-dependent TMS effects

Inferring latent neural sources via deep transcoding of simultaneously acquired EEG and fMRI