Differential diagnosis of CT focal liver lesions using texture features, feature selection and ensemble driven classifiers

The aim of the present study is to define an optimally performing computer-aided diagnosis (CAD) architecture for the classification of liver tissue from non-enhanced computed tomography (CT) images into normal liver (C1), hepatic cyst (C2), hemangioma (C3), and hepatocellular carcinoma (C4). To this end, various CAD architectures, based on texture features and ensembles of classifiers (ECs), are comparatively assessed.

Model Driven Architecture (MDA): Integration and Model Reuse for Open Source eLearning Platforms

Open Source (OS) community offers numerous eLearning platforms of both types: Learning Management Systems (LMS) and Learning Content Systems (LCS). General purpose OS intermediaries such as SourceForge, ObjectWeb, Apache or specialized intermediaries like CampusSource reduce the cost to locate such eLearning platforms. Still, it is impossible to directly compare the functionalities of those OS software products without performing detailed testing on each product. Some articles available from eLearning Wikipedia show comparisons between eLearning platforms which can help, but at the end they barely serve as documentation which are becoming out of date quickly [1]. The absence of integration activities between OS eLearning platforms - which are sometimes quite similar in terms of functionalities and implementation technologies - is sometimes critical since most of the OS projects possess small financial and human resources. This paper shows a possible solution for these barriers of OS eLearning platforms. We propose the Model Driven Architecture (MDA) concept to capture functionalities and to identify similarities between available OS eLearning platforms. This contribution evolved from a fruitful discussion at the 2nd CampusSource Developer Conference at the University of Muenster (27th August 2004).

IDH/MGMT-driven molecular classification of low-grade glioma is a strong predictor for long-term survival

BACKGROUND Low-grade gliomas (LGGs) are rare brain neoplasms, with survival spanning up to a few decades. Thus, accurate evaluations on how biomarkers impact survival among patients with LGG require long-term studies on samples prospectively collected over a long period. METHODS The 210 adult LGGs collected in our databank were screened for IDH1 and IDH2 mutations (IDHmut), MGMT gene promoter methylation (MGMTmet), 1p/19q loss of heterozygosity (1p19qloh), and nuclear TP53 immunopositivity (TP53pos). Multivariate survival analyses with multiple imputation of missing data were performed using either histopathology or molecular markers. Both models were compared using Akaike's information criterion (AIC). The molecular model was reduced by stepwise model selection to filter out the most critical predictors. A third model was generated to assess for various marker combinations. RESULTS Molecular parameters were better survival predictors than histology (ΔAIC = 12.5, P< .001). Forty-five percent of studied patients died. MGMTmet was positively associated with IDHmut (P< .001). In the molecular model with marker combinations, IDHmut/MGMTmet combined status had a favorable impact on overall survival, compared with IDHwt (hazard ratio [HR] = 0.33, P< .01), and even more so the triple combination, IDHmut/MGMTmet/1p19qloh (HR = 0.18, P< .001). Furthermore, IDHmut/MGMTmet/TP53pos triple combination was a significant risk factor for malignant transformation (HR = 2.75, P< .05). CONCLUSION By integrating networks of activated molecular glioma pathways, the model based on genotype better predicts prognosis than histology and, therefore, provides a more reliable tool for standardizing future treatment strategies.

Reanalysis-driven climate simulation over CORDEX North America domain using the Canadian Regional Climate Model, version 5: model performance evaluation

The performance of reanalysis-driven Canadian Regional Climate Model, version 5 (CRCM5) in reproducing the present climate over the North American COordinated Regional climate Downscaling EXperiment domain for the 1989–2008 period has been assessed in comparison with several observation-based datasets. The model reproduces satisfactorily the near-surface temperature and precipitation characteristics over most part of North America. Coastal and mountainous zones remain problematic: a cold bias (2–6 °C) prevails over Rocky Mountains in summertime and all year-round over Mexico; winter precipitation in mountainous coastal regions is overestimated. The precipitation patterns related to the North American Monsoon are well reproduced, except on its northern limit. The spatial and temporal structure of the Great Plains Low-Level Jet is well reproduced by the model; however, the night-time precipitation maximum in the jet area is underestimated. The performance of CRCM5 was assessed against earlier CRCM versions and other RCMs. CRCM5 is shown to have been substantially improved compared to CRCM3 and CRCM4 in terms of seasonal mean statistics, and to be comparable to other modern RCMs.

MiniCASCC - A battery driven fog collector for ecosystem research

We developed a small version of the Caltech active strand cloud water collector (CASCC) for biogeochemical investigations in ecological applications. The device is battery powered and thus allows operation at locations where mains power is not available. The collector is designed for sampling periods of up to one week, depending on fog frequency. Our new device is equipped with standard sensors for air temperature, relative humidity, wind, and horizontal visibility for fog detection with a low-cost optical sensor. In mountain areas and during times when clouds are thin the installation of the visibility sensor became a key issue, which limits the potential to estimate liquid water content of the sampled fog. Field tests with 5 devices at three different sites in the Swiss Alps (Niesen) and the Jura Mountains (Lägeren, Switzerland) during two extended summer seasons in 2006 and 2007 showed that in almost all cases it was possible to obtain sample volumes which were large enough for the examination of basic inorganic chemistry of the collected cloud water. Collection rates varied typically from 12 to 30 mL h− 1. The fog droplet cutoff diameter is ≈ 6 μm, which is low enough to include all droplet sizes that are relevant for the liquid water content of typical fog types in the collected samples. From theoretical assumptions of the collection efficiency and theoretical droplet spectra it is possible to estimate the liquid water content of the sampled fog or cloud. Our new fog collector can be constructed and operated at relatively low costs. In combination with chemical and isotopic analyses of the sampled water, this allows to quantify nutrient and pollutant fluxes as is typically needed in ecosystem biogeochemistry studies.

A tutorial on data-driven methods for statistically assessing ERP topographies

Dynamic changes in ERP topographies can be conveniently analyzed by means of microstates, the so-called "atoms of thoughts", that represent brief periods of quasi-stable synchronized network activation. Comparing temporal microstate features such as on- and offset or duration between groups and conditions therefore allows a precise assessment of the timing of cognitive processes. So far, this has been achieved by assigning the individual time-varying ERP maps to spatially defined microstate templates obtained from clustering the grand mean data into predetermined numbers of topographies (microstate prototypes). Features obtained from these individual assignments were then statistically compared. This has the problem that the individual noise dilutes the match between individual topographies and templates leading to lower statistical power. We therefore propose a randomization-based procedure that works without assigning grand-mean microstate prototypes to individual data. In addition, we propose a new criterion to select the optimal number of microstate prototypes based on cross-validation across subjects. After a formal introduction, the method is applied to a sample data set of an N400 experiment and to simulated data with varying signal-to-noise ratios, and the results are compared to existing methods. In a first comparison with previously employed statistical procedures, the new method showed an increased robustness to noise, and a higher sensitivity for more subtle effects of microstate timing. We conclude that the proposed method is well-suited for the assessment of timing differences in cognitive processes. The increased statistical power allows identifying more subtle effects, which is particularly important in small and scarce patient populations.