Building an Open Social Learning Community Around a DSpace Repository on Statistics

4th International Conference on Open RepositoriesThis presentation was part of the session : Conference PostersIn this paper we describe a project which aims to build an open social learning community around a learning object repository (LOR) based on DSpace containing learning resources about Statistics. We combine the preservation capabilities of DSpace with the facilities of a tagging mechanism such as Delicious. On top of this ensemble we intend to build a new browsing interface for improving users' learning experience when using the LOR. We also intend to gather and analyze usage data in order to better understand the real learning process in virtual learning environments.Spanish Government Grant under Refs. TIN2006-15107-C06 and EA2008-015

Automated quality control for proton magnetic resonance spectroscopy data using convex non-negative matrix factorization

Proton Magnetic Resonance Spectroscopy (1H MRS) has proven its diagnostic potential in a variety of conditions. However, MRS is not yet widely used in clinical routine because of the lack of experts on its diagnostic interpretation. Although data-based decision support systems exist to aid diagnosis, they often take for granted that the data is of good quality, which is not always the case in a real application context. Systems based on models built with bad quality data are likely to underperform in their decision support tasks. In this study, we propose a system to filter out such bad quality data. It is based on convex Non-Negative Matrix Factorization models, used as a dimensionality reduction procedure, and on the use of several classifiers to discriminate between good and bad quality data.Peer ReviewedPostprint (author's final draft

Pattern Recognition Analysis of MR Spectra

The need for multivariate analysis of magnetic resonance spectroscopy (MRS) data was recognized about 20 years ago, when it became evident that spectral patterns were characteristic of some diseases. Despite this, there is no generally accepted methodology for performing pattern recognition (PR) analysis of MRS data sets. Here, the data acquisition and processing requirements for performing successful PR as applied to human MRS studies are introduced, and the main techniques for feature selection, extraction, and classification are described. These include methods of dimensionality reduction such as principal component analysis (PCA), independent component analysis (ICA), non-negative matrix factorization (NMF), and feature selection. Supervised methods such as linear discriminant analysis (LDA), logistic regression (LogR), and nonlinear classification are discussed separately from unsupervised and semisupervised classification techniques, including k –means clustering. Methods for testing and metrics for gauging the performance of PR models (sensitivity and specificity, the ‘Confusion Matrix’, ‘k –fold cross-validation’, ‘Leave One Out’, ‘Bootstrapping’, the ‘Receiver Operating Characteristic curve’, and balanced error and accuracy rates) are briefly described. This article ends with a summary of the main lessons learned from PR applied to MRS to date

Automatic relevance source determination in human brain tumors using Bayesian NMF.

The clinical management of brain tumors is very sensitive; thus, their non-invasive characterization is often preferred. Non-negative Matrix Factorization techniques have been successfully applied in the context of neuro-oncology to extract the underlying source signals that explain different tissue tumor types, for which knowing the number of sources to calculate was always required. In the current study we estimate the number of relevant sources for a set of discrimination problems involving brain tumors and normal brain. For this, we propose to start by calculating a high number of sources using Bayesian NMF and automatically discarding the irrelevant ones during the iterative process of matrices decomposition, hence obtaining a reduced range of interpretable solutions. The real data used in this study come from a widely tested human brain tumor database. Simulated data that resembled the real data was also generated to validate the hypothesis against ground truth. The results obtained suggest that the proposed approach is able to provide a small range of meaningful solutions to the problem of source extraction in human brain tumors

Semi-supervised source extraction methodology for the nosological imaging of glioblastoma response to therapy.

Glioblastomas are one the most aggressive brain tumors. Their usual bad prognosis is due to the heterogeneity of their response to treatment and the lack of early and robust biomarkers to decide whether the tumor is responding to therapy. In this work, we propose the use of a semi-supervised methodology for source extraction to identify the sources representing tumor response to therapy, untreated/unresponsive tumor, and normal brain; and create nosological images of the response to therapy based on those sources. Fourteen mice were used to calculate the sources, and an independent test set of eight mice was used to further evaluate the proposed approach. The preliminary results obtained indicate that was possible to discriminate response and untreated/unresponsive areas of the tumor, and that the color-coded images allowed convenient tracking of response, especially throughout the course of therapy

Reconstructing past terrace fields in the Pyrenees: Insights into land management and settlement from the Bronze Age to the Early Modern era at Vilalta (1650 masl, Cerdagne, France)

© Trustees of Boston University 2015. The building of a solar power station at Thémis, at 1650 masl on the south-facing slope of the Carlit massif in the eastern Pyrenees, led to an archaeological evaluation from April-June 2009. This evaluation covered a surface of 10 ha that included a medieval village as well as the surrounding agricultural land in terraces. Non-destructive archaeological methods were used for the village. A detailed study of the 6 ha of terraces began with a fieldwalking survey, mapping every visible feature, followed by systematic trial trenches. Fifty-five trenches, 11 in the village and 44 in the fields, were opened. The stratigraphies were then compared with a series of 22 radiocarbon dates and eight relative dates provided by ceramic typologies. This combination of surface and buried evidence supported our preliminary hypothesis about the dynamics of the slope. The results suggest the existence of agrarian features beginning in the Bronze Age and reveal that the field patterns were frequently transformed, both in the Medieval and Early Modern periods. The transformations in the terrace fields after the village was abandoned are as interesting as those during occupation because, contrary to the idea of a fixed, unchanging landscape after the end of the Middle Ages, they challenge the idea that mountain zones are marginal spaces by nature, or were marginalized later.Peer Reviewe

A Novel Semi-Supervised Methodology for Extracting Tumor Type-Specific MRS Sources in Human Brain Data

BackgroundThe clinical investigation of human brain tumors often starts with a non-invasive imaging study, providing information about the tumor extent and location, but little insight into the biochemistry of the analyzed tissue. Magnetic Resonance Spectroscopy can complement imaging by supplying a metabolic fingerprint of the tissue. This study analyzes single-voxel magnetic resonance spectra, which represent signal information in the frequency domain. Given that a single voxel may contain a heterogeneous mix of tissues, signal source identification is a relevant challenge for the problem of tumor type classification from the spectroscopic signal.Methodology/Principal FindingsNon-negative matrix factorization techniques have recently shown their potential for the identification of meaningful sources from brain tissue spectroscopy data. In this study, we use a convex variant of these methods that is capable of handling negatively-valued data and generating sources that can be interpreted as tumor class prototypes. A novel approach to convex non-negative matrix factorization is proposed, in which prior knowledge about class information is utilized in model optimization. Class-specific information is integrated into this semi-supervised process by setting the metric of a latent variable space where the matrix factorization is carried out. The reported experimental study comprises 196 cases from different tumor types drawn from two international, multi-center databases. The results indicate that the proposed approach outperforms a purely unsupervised process by achieving near perfect correlation of the extracted sources with the mean spectra of the tumor types. It also improves tissue type classification.Conclusions/SignificanceWe show that source extraction by unsupervised matrix factorization benefits from the integration of the available class information, so operating in a semi-supervised learning manner, for discriminative source identification and brain tumor labeling from single-voxel spectroscopy data. We are confident that the proposed methodology has wider applicability for biomedical signal processing

Cardiovascular disease in immune-mediated inflammatory diseases: A cross-sectional analysis of 6 cohorts

To analyze in several immune-mediated inflammatory diseases (IMIDs) the influence of demographic and clinical-related variables on the prevalence of cardiovascular disease (CVD), and compare their standardized prevalences.Cross-sectional study, including consecutive patients diagnosed with rheumatoid arthritis, psoriatic arthritis, psoriasis, systemic lupus erythematosus, Crohn disease, or ulcerative colitis, from rheumatology, gastroenterology, and dermatology tertiary care outpatient clinics located throughout Spain, between 2007 and 2010. Our main outcome was defined as previous diagnosis of angina, myocardial infarction, peripheral vascular disease, and/or stroke. Bivariate and multivariate logistic and mixed-effects logistic regression models were performed for each condition and the overall cohort, respectively. Standardized prevalences (in subjects per 100 patients, with 95% confidence intervals) were calculated using marginal analysis.We included 9951 patients. For each IMID, traditional cardiovascular risk factors had a different contribution to CVD. Overall, older age, longer disease duration, presence of traditional cardiovascular risk factors, and male sex were independently associated with a higher CVD prevalence. After adjusting for demographic and traditional cardiovascular risk factors, systemic lupus erythematosus exhibited the highest CVD standardized prevalence, followed by rheumatoid arthritis, psoriasis, Crohn disease, psoriatic arthritis, and ulcerative colitis (4.5 [95% confidence interval (CI): 2.2, 6.8], 1.3 [95% CI: 0.8, 1.8], 0.9 [95% CI: 0.5, 1.2], 0.8 [95% CI: 0.2, 1.3], 0.6 [95% CI: 0.2, 1.0], and 0.5 [95% CI: 0.1, 0.8], respectively).Systemic lupus erythematosus, rheumatoid arthritis, and psoriasis are associated with higher prevalence of CVD compared with other IMIDs. Specific prevention programs should be established in subjects affected with these conditions to prevent CVD

Observation of room temperature photoluminescence from asymmetric CuGaO2/ZnO/ZnMgO multiple quantum well structures

Asymmetric (CuGaO2/ZnO/ZnMgO) and symmetric (ZnMgO/ZnO/ZnMgO) multiple quantum well (MQW) structures were successfully fabricated using pulsed laser deposition (PLD) and their comparison were made. Efficient room temperature photoluminescent (PL) emission was observed from these MQWs and temperature dependent luminescence of asymmetric and symmetric MQWs can be explained using the existing theories. A systematic blue shift was observed in both MQWs with decrease in the confinement layer thickness which could be attributed to the quantum confinement effects. The PL emission from asymmetric and symmetric MQW structures were blue shifted compared to 150 nm thick ZnO thin film grown by PLD due to quantum confinement effects