Assisted Diagnosis of Parkinsonism Based on the Striatal Morphology

Parkinsonism is a clinical syndrome characterized by the progressive loss of striatal dopamine. Its diagnosis is usually corroborated by neuroimaging data such as DaTSCAN neuroimages that allow visualizing the possible dopamine deficiency. During the last decade, a number of computer systems have been proposed to automatically analyze DaTSCAN neuroimages, eliminating the subjectivity inherent to the visual examination of the data. In this work, we propose a computer system based on machine learning to separate Parkinsonian patients and control subjects using the size and shape of the striatal region, modeled from DaTSCAN data. First, an algorithm based on adaptative thresholding is used to parcel the striatum. This region is then divided into two according to the brain hemisphere division and characterized with 152 measures, extracted from the volume and its three possible 2-dimensional projections. Afterwards, the Bhattacharyya distance is used to discard the least discriminative measures and, finally, the neuroimage category is estimated by means of a Support Vector Machine classifier. This method was evaluated using a dataset with 189 DaTSCAN neuroimages, obtaining an accuracy rate over 94%. This rate outperforms those obtained by previous approaches that use the intensity of each striatal voxel as a feature.This work was supported by the MINECO/ FEDER under the TEC2015-64718-R project, the Ministry of Economy, Innovation, Science and Employment of the Junta de Andaluc´ıa under the P11-TIC-7103 Excellence Project and the Vicerectorate of Research and Knowledge Transfer of the University of Granada

Open Problems on Central Simple Algebras

We provide a survey of past research and a list of open problems regarding central simple algebras and the Brauer group over a field, intended both for experts and for beginners.Comment: v2 has some small revisions to the text. Some items are re-numbered, compared to v

A tomographic analysis of reflectometry data I: Component factorization

Many signals in Nature, technology and experiment have a multi-component structure. By spectral decomposition and projection on the eigenvectors of a family of unitary operators, a robust method is developed to decompose a signals in its components. Different signal traits may be emphasized by different choices of the unitary family. The method is illustrated in simulated data and on data obtained from plasma reflectometry experiments in the tore Supra.Comment: 27 pages Latex, 17 figure

Transnational partisanship: idea and practice

That parties might successfully organize transnationally is an idea often met with scepticism. This article argues that while certain favourable conditions are indeed absent in the transnational domain, this implies not that partisanship is impossible but that it is likely to be marked by certain traits. Specifically, it will tend to be episodic, structured as a low-density network and delocalized in its ideational content. These tendencies affect the normative expectations one can attach to it. Transnational partisanship should be valued as a transitional phenomenon, e.g. as a pathway to transnational democracy, more than as a desirable thing in itself

First-principles calculations of the phase stability of TiO2

Published versio

HIV incidence estimate combining HIV/AIDS surveillance, testing history information and HIV test to identify recent infections in Lazio, Italy

<p>Abstract</p> <p>Background</p> <p>The application of serological methods in HIV/AIDS routine surveillance systems to identify persons with recently acquired HIV infection has been proposed as a tool which may provide an accurate description of the current transmission patterns of HIV. Using the information about recent infection it is possible to estimate HIV incidence, according to the model proposed by Karon et al. in 2008, that accounts for the effect of testing practices on the number of persons detected as recently infected.</p> <p>Methods</p> <p>We used data from HIV/AIDS surveillance in the period 2004-2008 to identify newly diagnosed persons. These were classified with recent/non-recent infection on the basis of an avidity index result, or laboratory evidence of recently acquired infection (i.e., previous documented negative HIV test within 6 months; or presence of HIV RNA or p24 antigen with simultaneous negative/indeterminate HIV antibody test). Multiple imputation was used to impute missing information. The incidence estimate was obtained as the number of persons detected as recently infected divided by the estimated probability of detection. Estimates were stratified by calendar year, transmission category, gender and nationality.</p> <p>Results</p> <p>During the period considered 3,633 new HIV diagnoses were reported to the regional surveillance system. Applying the model, we estimated that in 2004-2008 there were 5,465 new infections (95%CI: 4,538-6,461); stratifying by transmission category, the estimated number of infections was 2,599 among heterosexual contacts, 2,208 among men-who-have-sex-with-men, and 763 among injecting-drug-users. In 2008 there were 952 (625-1,229) new HIV infections (incidence of 19.9 per 100,000 person-years). In 2008, for men-who-have-sex-with-men (691 per 100,000 person-years) and injecting drug users (577 per 100,000 person-years) the incidence remained comparatively high with respect to the general population, although a decreasing pattern during 2004-2008 was observed for injecting-drug-users.</p> <p>Conclusions</p> <p>These estimates suggest that the transmission of HIV infection in Lazio remains frequent and men-who-have-sex-with men and injecting-drug-users are still greatly affected although the majority of new infections occurs among heterosexual individuals.</p

Quantitative Evaluation of Artifact Removal in Real Magnetoencephalogram Signals with Blind Source Separation

The magnetoencephalogram (MEG) is contaminated with undesired signals, which are called artifacts. Some of the most important ones are the cardiac and the ocular artifacts (CA and OA, respectively), and the power line noise (PLN). Blind source separation (BSS) has been used to reduce the influence of the artifacts in the data. There is a plethora of BSS-based artifact removal approaches, but few comparative analyses. In this study, MEG background activity from 26 subjects was processed with five widespread BSS (AMUSE, SOBI, JADE, extended Infomax, and FastICA) and one constrained BSS (cBSS) techniques. Then, the ability of several combinations of BSS algorithm, epoch length, and artifact detection metric to automatically reduce the CA, OA, and PLN were quantified with objective criteria. The results pinpointed to cBSS as a very suitable approach to remove the CA. Additionally, a combination of AMUSE or SOBI and artifact detection metrics based on entropy or power criteria decreased the OA. Finally, the PLN was reduced by means of a spectral metric. These findings confirm the utility of BSS to help in the artifact removal for MEG background activity