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

: This thesis applies neural network feature selection techniques to multivariate time series data to improve prediction of a target time series. Two approaches to feature selection are used. First, a subset enumeration method is used to determine which financial indicators are most useful for aiding in prediction of the S&P 500 futures daily price. The candidate indicators evaluated include RSI, Stochastics and several moving averages. Results indicate that the Stochastics and RSI indicators result in better prediction results than the moving averages. The second approach to feature selection is calculation of individual saliency metrics. A new decision boundary-based individual saliency metric, and a classifier independent saliency metric are developed and tested. Ruck's saliency metric, the decision boundary based saliency metric, and the classifier independent saliency metric are compared for a data set consisting of the RSI and Stochastics indicators as well as delayed closing price values. The decision based metric and the Ruck metric results are similar, but the classifier independent metric agrees with neither of the other metrics. The nine most salient features, determined by the decision boundary based metric, are used to train a neural network and the results are presented and compared to other published results. (AN)

/pdf/nonlinear-time-series-analysis-1j3a35n4y3.pdf

Nonlinear Time Series Analysis.

There are a number of ways in which a clinical diagnosis of dementia of the Alzheimer type can be made – the application of clinical criteria is the commonest but ancillary techniques such as neuroima

Clinical Diagnosis of Alzheimer's Disease

tical perspective. For instance, there are only three passing references to kuru in a book of 650 pages. This edition reflects the renewed interest in the immunological theories of multiple sclerosis. More than half the text is devoted to Professor Lumsden's analysis of the pathoIogy and, in particular, the chemical pathology of the immune response. There is a great deal of original work devoted to the chemistry and behaviour of the immunoglobulins. Much of this appears in specialist journals and one must be grateful for the critical summary provided here. Professor Lumsden unequivocally sees the key to the problem of multiple sclerosis in the study of its immunochemistry, relegating infection by a virus or a slow virus to a quite subsidiary role. The clinical studies drawing on wide practical experience help to get one's prejudices about the illness onto a more reasoned footing. The section on treatment is still sadly limited. Dr. McAlpine found little to add to the regime which he described in 1955. McAlpine, Lumsden, and Acheson's reappraisal is an essential reference for the practising neurologist and the new edition makes important modification of and changes in emphasis from the edition of 1965.

https://jnnp.bmj.com/content/jnnp/36/5/894.3.full.pdf

The assessment of aphasia and related disorders

Let X(t) be the number of tumor cells at time t, and Pr{X(t) = n} = pn(t) is the density of X. A “birth”, i.e., an increase of one of the total population of cancer cells, can occur either by mutation of a normal cell caused by the action of the carcinogen, consisting of randomly (Poisson) distributed hits, or by reproduction of existing cancer cells. A death of a tumor cell occurs as an additive combination of non-immunological and immunological elements. Once a tumor is initiated by carcinogenic action, it undergoes a birth and death process with infinitesimal birth rate linear and infinitesimal death rate composed of a linear and a nonlinear term, the former due to non-immunological deaths, the latter to immunological feedback. The death rate per tumor cell due to immunological response is proportional to the total number of antigen-producing (tumor) cells; thus, the total death rate is quadratic. Although this assumes a very simple mechanism for the action of immunological feedback, it is nevertheless a first step.

The Mathematical Model

The continued integration of technology into all
aspects of society stresses the need to identify and
understand the risk associated with assimilating new
technologies. This necessity is heightened when
technology is used for medical purposes like
ambulatory devices that monitor a patient’s vital signs.
This integration creates environments that are
conducive to malicious activities. The potential impact
presents new challenges for the medical community.
Hence, this research presents attack graph
modeling as a viable solution to identifying
vulnerabilities, assessing risk, and forming mitigation
strategies to defend ambulatory medical devices from
attackers. Common and frequent vulnerabilities and
attack strategies related to the various aspects of
ambulatory devices, including Bluetooth enabled
sensors and Android applications are identified in the
literature. Based on this analysis, this research
presents an attack graph modeling example on a
theoretical device that highlights vulnerabilities and
mitigation strategies to consider when designing
ambulatory devices with similar components

/pdf/attack-graph-threat-modeling-assessment-of-ambulatory-1eks0o6lbz.pdf

Attack-Graph Threat Modeling Assessment of Ambulatory Medical Devices

Predicting brain age from functional connectivity in symptomatic and preclinical Alzheimer disease

Background Frailty is an important clinical concern for the aging population of people living with HIV (PLWH). The objective of this study was to identify the combination of risk features that distinguish frail from nonfrail individuals. Setting Machine learning analysis of highly dimensional risk features was performed on a clinical cohort of PLWH. Methods Participants included 105 older (average age = 55.6) PLWH, with at least a 3-month history of combination antiretroviral therapy (median CD4 = 546). Predictors included demographics, HIV clinical markers, comorbid health conditions, cognition, and neuroimaging (ie, volumetrics, resting-state functional connectivity, and cerebral blood flow). Gradient-boosted multivariate regressions were implemented to establish linear and interactive classification models. Model performance was determined by sensitivity/specificity (F1 score) with 5-fold cross validation. Results The linear gradient-boosted multivariate regression classifier included lower current CD4 count, lower psychomotor performance, and multiple neuroimaging indices (volumes, network connectivity, and blood flow) in visual and motor brain systems (F1 score = 71%; precision = 84%; and sensitivity = 66%). The interactive model identified novel synergies between neuroimaging features, female sex, symptoms of depression, and current CD4 count. Conclusions Data-driven algorithms built from highly dimensional clinical and brain imaging features implicate disruption to the visuomotor system in older PLWH designated as frail individuals. Interactions between lower CD4 count, female sex, depressive symptoms, and neuroimaging features suggest potentiation of risk mechanisms. Longitudinal data-driven studies are needed to guide clinical strategies capable of preventing the development of frailty as PLWH reach advanced age.

Machine Learning Analysis Reveals Novel Neuroimaging and Clinical Signatures of Frailty in HIV.

In the realm of cybersecurity, rootkits pose a credible threat to individuals, corporations, and governments. Through various techniques, rootkits are not only able to infect computer systems, but often times are able to remain undetected in a host for an extended amount of time by manipulating system software. The purpose of this paper is to describe what a rootkit is, how they operate, and how they relate to other types of malware. Historical data and statistics will be presented in order to show how rootkits have been employed in cyber attacks. Different types of rootkits, including user, kernel, and hypervisor rootkits will be described, as well as the various methods used todefend against rootkits. We will then present a case study where neural networks were used to analyze the behavior of a system both not infected and infected with a rootkit, and categorize the resulting system calls as anomalous or not.

Neural Network Analysis of System Call Timing for Rootkit Detection

Rootkits are powerful and dangerous pieces of malware that use stealth and administrative privilege to maintain a persistent, covert foothold on a compromised system. These capabilities make them popular with a wide range of cyber attackers, including the instigators of advanced persistent threat (APT) attacks. With recent examples of APT attacks being used to disrupt critical infrastructure in Ukraine in 2015, the urgency to produce a robust detection framework for rootkits is increased. However, due to sophisticated active and passive defense techniques, rootkits are resistant to traditional detection and remediation techniques. An effective detector will sit outside of a rootkit's sphere of influence and use process-indicative data from a side channel that cannot be tampered with or spoofed. In this paper, we present preliminary results of experiments based on Oak Ridge National Laboratory's (ORNL) Beholder Project to detect rootkit execution out-of-band. We utilize power supply voltage measurements collected with a basic multimeter and current clamp, then extract time-serial system dynamics through the application of a novel nonlinear, phase-space algorithm. We examine variance of phase-space graph features between nominal and infected data, and then train our algorithm on threshold values to reach best possible prediction accuracy. Our results indicate that the algorithm can successfully detect a rootkit infection through power measurement analysis, at an accuracy rate that meets or exceeds the performance of other machine learning algorithms in a similar testing context.

Patrick Luckett

Papers

Attack-Graph Threat Modeling Assessment of Ambulatory Medical Devices

Predicting brain age from functional connectivity in symptomatic and preclinical Alzheimer disease

Machine Learning Analysis Reveals Novel Neuroimaging and Clinical Signatures of Frailty in HIV.

Neural Network Analysis of System Call Timing for Rootkit Detection

Rootkit detection through phase-space analysis of power voltage measurements