@neurIST infrastructure for advanced disease management through integration of heterogeneous data, computing, and complex processing services

The increasing volume of data describing humandisease processes and the growing complexity of understanding, managing, and sharing such data presents a huge challenge for clinicians and medical researchers. This paper presents the@neurIST system, which provides an infrastructure for biomedical research while aiding clinical care, by bringing together heterogeneous data and complex processing and computing services. Although @neurIST targets the investigation and treatment of cerebral aneurysms, the system’s architecture is generic enough that it could be adapted to the treatment of other diseases.Innovations in @neurIST include confining the patient data pertaining to aneurysms inside a single environment that offers cliniciansthe tools to analyze and interpret patient data and make use of knowledge-based guidance in planning their treatment. Medicalresearchers gain access to a critical mass of aneurysm related data due to the system’s ability to federate distributed informationsources. A semantically mediated grid infrastructure ensures that both clinicians and researchers are able to seamlessly access andwork on data that is distributed across multiple sites in a secure way in addition to providing computing resources on demand forperforming computationally intensive simulations for treatment planning and research.

Nonresponse to clozapine and ultrarapid CYP1A2 activity: clinical data and analysis of CYP1A2 gene.

Clozapine (CLO), an atypical antipsychotic, depends mainly on cytochrome P450 1A2 (CYP1A2) for its metabolic clearance. Four patients treated with CLO, who were smokers, were nonresponders and had low plasma levels while receiving usual doses. Their plasma levels to dose ratios of CLO (median; range, 0.34; 0.22 to 0.40 ng x day/mL x mg) were significantly lower than ratios calculated from another study with 29 patients (0.75; 0.22 to 2.83 ng x day/mL x mg; P < 0.01). These patients were confirmed as being CYP1A2 ultrarapid metabolizers by the caffeine phenotyping test (median systemic caffeine plasma clearance; range, 3.85; 3.33 to 4.17 mL/min/kg) when compared with previous studies (0.3 to 3.33 mL/min/kg). The sequencing of the entire CYP1A2 gene from genomic DNA of these patients suggests that the -164C > A mutation (CYP1A2*1F) in intron 1, which confers a high inducibility of CYP1A2 in smokers, is the most likely explanation for their ultrarapid CYP1A2 activity. A marked (2 patients) or a moderate (2 patients) improvement of the clinical state of the patients occurred after the increase of CLO blood levels above the therapeutic threshold by the increase of CLO doses to very high values (ie, up to 1400 mg/d) or by the introduction of fluvoxamine, a potent CYP1A2 inhibitor, at low dosage (50 to 100 mg/d). Due to the high frequency of smokers among patients with schizophrenia and to the high frequency of the -164C > A polymorphism, CYP1A2 genotyping could have important clinical implications for the treatment of patients with CLO.

Connections between signal processing and complex analysis

We describe one of the research lines of the Grup de Teoria de Funcions de la UAB UB, which deals with sampling and interpolation problems in signal analysis and their connections with complex function theory.

Multiple Kernel Learning of Environmental Data. Case study: Analysis and Mapping of Wind Fields

The paper presents the Multiple Kernel Learning (MKL) approach as a modelling and data exploratory tool and applies it to the problem of wind speed mapping. Support Vector Regression (SVR) is used to predict spatial variations of the mean wind speed from terrain features (slopes, terrain curvature, directional derivatives) generated at different spatial scales. Multiple Kernel Learning is applied to learn kernels for individual features and thematic feature subsets, both in the context of feature selection and optimal parameters determination. An empirical study on real-life data confirms the usefulness of MKL as a tool that enhances the interpretability of data-driven models.

Data Acquisition and Computer Plotting of Delamtect Data, HR-269, 1985

The overall system is designed to permit automatic collection of delamination field data for bridge decks. In addition to measuring and recording the data in the field, the system provides for transferring the recorded data to a personal computer for processing and plotting. This permits rapid turnaround from data collection to a finished plot of the results in a fraction of the time previously required for manual analysis of the analog data captured on a strip chart recorder. In normal operation the Delamtect provides an analog voltage for each of two channels which is proportional to the extent of any delamination. These voltages are recorded on a strip chart for later visual analysis. An event marker voltage, produced by a momentary push button on the handle, is also provided by the Delamtect and recorded on a third channel of the analog recorder.

ConnectomeViewer - Multi-Modal Multi-Level Network Visualization and Analysis.

Introduction: The field of Connectomic research is growing rapidly, resulting from methodological advances in structural neuroimaging on many spatial scales. Especially progress in Diffusion MRI data acquisition and processing made available macroscopic structural connectivity maps in vivo through Connectome Mapping Pipelines (Hagmann et al, 2008) into so-called Connectomes (Hagmann 2005, Sporns et al, 2005). They exhibit both spatial and topological information that constrain functional imaging studies and are relevant in their interpretation. The need for a special-purpose software tool for both clinical researchers and neuroscientists to support investigations of such connectome data has grown. Methods: We developed the ConnectomeViewer, a powerful, extensible software tool for visualization and analysis in connectomic research. It uses the novel defined container-like Connectome File Format, specifying networks (GraphML), surfaces (Gifti), volumes (Nifti), track data (TrackVis) and metadata. Usage of Python as programming language allows it to by cross-platform and have access to a multitude of scientific libraries. Results: Using a flexible plugin architecture, it is possible to enhance functionality for specific purposes easily. Following features are already implemented: * Ready usage of libraries, e.g. for complex network analysis (NetworkX) and data plotting (Matplotlib). More brain connectivity measures will be implemented in a future release (Rubinov et al, 2009). * 3D View of networks with node positioning based on corresponding ROI surface patch. Other layouts possible. * Picking functionality to select nodes, select edges, get more node information (ConnectomeWiki), toggle surface representations * Interactive thresholding and modality selection of edge properties using filters * Arbitrary metadata can be stored for networks, thereby allowing e.g. group-based analysis or meta-analysis. * Python Shell for scripting. Application data is exposed and can be modified or used for further post-processing. * Visualization pipelines using filters and modules can be composed with Mayavi (Ramachandran et al, 2008). * Interface to TrackVis to visualize track data. Selected nodes are converted to ROIs for fiber filtering The Connectome Mapping Pipeline (Hagmann et al, 2008) processed 20 healthy subjects into an average Connectome dataset. The Figures show the ConnectomeViewer user interface using this dataset. Connections are shown that occur in all 20 subjects. The dataset is freely available from the homepage (connectomeviewer.org). Conclusions: The ConnectomeViewer is a cross-platform, open-source software tool that provides extensive visualization and analysis capabilities for connectomic research. It has a modular architecture, integrates relevant datatypes and is completely scriptable. Visit www.connectomics.org to get involved as user or developer.

Diagnosing Patients Combining Principal Components Analysis and Case Based Reasoning

This paper addresses the application of a PCA analysis on categorical data prior to diagnose a patients data set using a Case-Based Reasoning (CBR) system. The particularity is that the standard PCA techniques are designed to deal with numerical attributes, but our medical data set contains many categorical data and alternative methods as RS-PCA are required. Thus, we propose to hybridize RS-PCA (Regular Simplex PCA) and a simple CBR. Results show how the hybrid system produces similar results when diagnosing a medical data set, that the ones obtained when using the original attributes. These results are quite promising since they allow to diagnose with less computation effort and memory storage

Self-Organising Maps and Correlation Analysis as a Tool to Explore Patterns in Excitation-Emission Matrix Data Sets and to Discriminate Dissolved Organic Matter Fluorescence Components

Dissolved organic matter (DOM) is a complex mixture of organic compounds, ubiquitous in marine and freshwater systems. Fluorescence spectroscopy, by means of Excitation-Emission Matrices (EEM), has become an indispensable tool to study DOM sources, transport and fate in aquatic ecosystems. However the statistical treatment of large and heterogeneous EEM data sets still represents an important challenge for biogeochemists. Recently, Self-Organising Maps (SOM) has been proposed as a tool to explore patterns in large EEM data sets. SOM is a pattern recognition method which clusterizes and reduces the dimensionality of input EEMs without relying on any assumption about the data structure. In this paper, we show how SOM, coupled with a correlation analysis of the component planes, can be used both to explore patterns among samples, as well as to identify individual fluorescence components. We analysed a large and heterogeneous EEM data set, including samples from a river catchment collected under a range of hydrological conditions, along a 60-km downstream gradient, and under the influence of different degrees of anthropogenic impact. According to our results, chemical industry effluents appeared to have unique and distinctive spectral characteristics. On the other hand, river samples collected under flash flood conditions showed homogeneous EEM shapes. The correlation analysis of the component planes suggested the presence of four fluorescence components, consistent with DOM components previously described in the literature. A remarkable strength of this methodology was that outlier samples appeared naturally integrated in the analysis. We conclude that SOM coupled with a correlation analysis procedure is a promising tool for studying large and heterogeneous EEM data sets.

Data collection and criticality analysis for cost effective maintenance during the life cycle of a flue gas line

Työn tarkoituksena oli kerätä käyttövarmuustietoa savukaasulinjasta kahdelta suomalaiselta sellutehtaalta niiden käyttöönotosta aina tähän päivään asti. Käyttövarmuustieto koostuu luotettavuustiedoista sekä kunnossapitotiedoista. Kerätyn tiedon avulla on mahdollista kuvata tarkasti laitoksen käyttövarmuutta seuraavilla tunnusluvuilla: suunnittelemattomien häiriöiden lukumäärä ja korjausajat, laitteiden seisokkiaika, vikojen todennäköisyys ja korjaavan kunnossapidon kustannukset suhteessa savukaasulinjan korjaavan kunnossapidon kokonaiskustannuksiin. Käyttövarmuustiedon keräysmetodi on esitelty. Savukaasulinjan kriittisten laitteiden määrittelyyn käytetty metodi on yhdistelmä kyselytutkimuksesta ja muunnellusta vian vaikutus- ja kriittisyysanalyysistä. Laitteiden valitsemiskriteerit lopulliseen kriittisyysanalyysiin päätettiin käyttövarmuustietojen sekä kyselytutkimuksen perusteella. Kriittisten laitteiden määrittämisen tarkoitus on löytää savukaasulinjasta ne laitteet, joiden odottamaton vikaantuminen aiheuttaa vakavimmat seuraukset savukaasulinjan luotettavuuteen, tuotantoon, turvallisuuteen, päästöihin ja kustannuksiin. Tiedon avulla rajoitetut kunnossapidon resurssit voidaan suunnata oikein. Kriittisten laitteiden määrittämisen tuloksena todetaan, että kolme kriittisintä laitetta savukaasulinjassa ovat molemmille sellutehtaille yhteisesti: savukaasupuhaltimet, laahakuljettimet sekä ketjukuljettimet. Käyttövarmuustieto osoittaa, että laitteiden luotettavuus on tehdaskohtaista, mutta periaatteessa samat päälinjat voidaan nähdä suunnittelemattomien vikojen todennäköisyyttä esittävissä kuvissa. Kustannukset, jotka esitetään laitteen suunnittelemattomien kunnossapitokustannusten suhteena savukaasulinjan kokonaiskustannuksiin, noudattelevat hyvin pitkälle luotettavuuskäyrää, joka on laskettu laitteen seisokkiajan suhteena käyttötunteihin. Käyttövarmuustiedon keräys yhdistettynä kriittisten laitteiden määrittämiseen mahdollistavat ennakoivan kunnossapidon oikean kohdistamisen ja ajoittamisen laitteiston elinaikana siten, että luotettavuus- ja kustannustehokkuusvaatimukset saavutetaan.

Self-Organising Maps and Correlation Analysis as a Tool to Explore Patterns in Excitation-Emission Matrix Data Sets and to Discriminate Dissolved Organic Matter Fluorescence Components

Dissolved organic matter (DOM) is a complex mixture of organic compounds, ubiquitous in marine and freshwater systems. Fluorescence spectroscopy, by means of Excitation-Emission Matrices (EEM), has become an indispensable tool to study DOM sources, transport and fate in aquatic ecosystems. However the statistical treatment of large and heterogeneous EEM data sets still represents an important challenge for biogeochemists. Recently, Self-Organising Maps (SOM) has been proposed as a tool to explore patterns in large EEM data sets. SOM is a pattern recognition method which clusterizes and reduces the dimensionality of input EEMs without relying on any assumption about the data structure. In this paper, we show how SOM, coupled with a correlation analysis of the component planes, can be used both to explore patterns among samples, as well as to identify individual fluorescence components. We analysed a large and heterogeneous EEM data set, including samples from a river catchment collected under a range of hydrological conditions, along a 60-km downstream gradient, and under the influence of different degrees of anthropogenic impact. According to our results, chemical industry effluents appeared to have unique and distinctive spectral characteristics. On the other hand, river samples collected under flash flood conditions showed homogeneous EEM shapes. The correlation analysis of the component planes suggested the presence of four fluorescence components, consistent with DOM components previously described in the literature. A remarkable strength of this methodology was that outlier samples appeared naturally integrated in the analysis. We conclude that SOM coupled with a correlation analysis procedure is a promising tool for studying large and heterogeneous EEM data sets.

Comparison of the Polar S810i monitor and the ECG for the analysis of heart rate variability in the time and frequency domains

The aim of the present study was to compare heart rate variability (HRV) at rest and during exercise using a temporal series obtained with the Polar S810i monitor and a signal from a LYNX® signal conditioner (BIO EMG 1000 model) with a channel configured for the acquisition of ECG signals. Fifteen healthy subjects aged 20.9 ± 1.4 years were analyzed. The subjects remained at rest for 20 min and performed exercise for another 20 min with the workload selected to achieve 60% of submaximal heart rate. RR series were obtained for each individual with a Polar S810i instrument and with an ECG analyzed with a biological signal conditioner. The HRV indices (rMSSD, pNN50, LFnu, HFnu, and LF/HF) were calculated after signal processing and analysis. The unpaired Student t-test and intraclass correlation coefficient were used for data analysis. No statistically significant differences were observed when comparing the values analyzed by means of the two devices for HRV at rest and during exercise. The intraclass correlation coefficient demonstrated satisfactory correlation between the values obtained by the devices at rest (pNN50 = 0.994; rMSSD = 0.995; LFnu = 0.978; HFnu = 0.978; LF/HF = 0.982) and during exercise (pNN50 = 0.869; rMSSD = 0.929; LFnu = 0.973; HFnu = 0.973; LF/HF = 0.942). The calculation of HRV values by means of temporal series obtained from the Polar S810i instrument appears to be as reliable as those obtained by processing the ECG signal captured with a signal conditioner.

Modelling and Analysis of Bivariate Lifetime Data using Reversed Hazard Rates

Department of Statistics, Cochin University of Science and Technology

Modeling and Analysis of Some Heavy Tailed Time Series

The thesis has covered various aspects of modeling and analysis of finite mean time series with symmetric stable distributed innovations. Time series analysis based on Box and Jenkins methods are the most popular approaches where the models are linear and errors are Gaussian. We highlighted the limitations of classical time series analysis tools and explored some generalized tools and organized the approach parallel to the classical set up. In the present thesis we mainly studied the estimation and prediction of signal plus noise model. Here we assumed the signal and noise follow some models with symmetric stable innovations.We start the thesis with some motivating examples and application areas of alpha stable time series models. Classical time series analysis and corresponding theories based on finite variance models are extensively discussed in second chapter. We also surveyed the existing theories and methods correspond to infinite variance models in the same chapter. We present a linear filtering method for computing the filter weights assigned to the observation for estimating unobserved signal under general noisy environment in third chapter. Here we consider both the signal and the noise as stationary processes with infinite variance innovations. We derived semi infinite, double infinite and asymmetric signal extraction filters based on minimum dispersion criteria. Finite length filters based on Kalman-Levy filters are developed and identified the pattern of the filter weights. Simulation studies show that the proposed methods are competent enough in signal extraction for processes with infinite variance.Parameter estimation of autoregressive signals observed in a symmetric stable noise environment is discussed in fourth chapter. Here we used higher order Yule-Walker type estimation using auto-covariation function and exemplify the methods by simulation and application to Sea surface temperature data. We increased the number of Yule-Walker equations and proposed a ordinary least square estimate to the autoregressive parameters. Singularity problem of the auto-covariation matrix is addressed and derived a modified version of the Generalized Yule-Walker method using singular value decomposition.In fifth chapter of the thesis we introduced partial covariation function as a tool for stable time series analysis where covariance or partial covariance is ill defined. Asymptotic results of the partial auto-covariation is studied and its application in model identification of stable auto-regressive models are discussed. We generalize the Durbin-Levinson algorithm to include infinite variance models in terms of partial auto-covariation function and introduce a new information criteria for consistent order estimation of stable autoregressive model.In chapter six we explore the application of the techniques discussed in the previous chapter in signal processing. Frequency estimation of sinusoidal signal observed in symmetric stable noisy environment is discussed in this context. Here we introduced a parametric spectrum analysis and frequency estimate using power transfer function. Estimate of the power transfer function is obtained using the modified generalized Yule-Walker approach. Another important problem in statistical signal processing is to identify the number of sinusoidal components in an observed signal. We used a modified version of the proposed information criteria for this purpose.