Data intensive scientific analysis with grid computing

At the end of September 2009, a new Italian GPS receiver for radio occultation was launched from the Satish Dhawan Space Center (Sriharikota, India) on the Indian Remote Sensing OCEANSAT-2 satellite. The Italian Space Agency has established a set of Italian universities and research centers to implement the overall processing radio occultation chain. After a brief description of the adopted algorithms, which can be used to characterize the temperature, pressure and humidity, the contribution will focus on a method for automatic processing these data, based on the use of a distributed architecture. This paper aims at being a possible application of grid computing for scientific research

Spectral Observations of Optical Emissions Associated With Terrestrial Gamma-Ray Flashes

The Atmosphere-Space Interactions Monitor measures Terrestrial Gamma-Ray Flashes (TGFs) simultaneously with optical emissions from associated lightning activity. We analyzed optical measurements at 180–230, 337, and 777.4 nm related to 69 TGFs observed between June 2018 and October 2019. All TGFs are associated with optical emissions and 90% of them are at the onset of a large optical pulse, suggesting that they are connected with the initiation of current surges. A model of photon delay induced by cloud scattering suggests that the sources of the optical pulses are from 0.7 ms before to 4.4 ms after the TGFs, with a median of −10 ± 80 µs, and 1–5 km below the cloud top. The pulses have rise times comparable to lightning but longer durations. Pulse amplitudes at 337 nm are ∼3 times larger than at 777.4 nm. The results support the leader-streamer mechanism for TGF generation.publishedVersio

Local and distant recurrences in rectal cancer patients are predicted by the nonspecific immune response; specific immune response has only a systemic effect - a histopathological and immunohistochemical study

BACKGROUND: Invasion and metastasis is a complex process governed by the interaction of genetically altered tumor cells and the immunological and inflammatory host reponse. Specific T-cells directed against tumor cells and the nonspecific inflammatory reaction due to tissue damage, cooperate against invasive tumor cells in order to prevent recurrences. Data concerning involvement of individual cell types are readily available but little is known about the coordinate interactions between both forms of immune response. PATIENTS AND METHODS: The presence of inflammatory infiltrate and eosinophils was determined in 1530 patients with rectal adenocarcinoma from a multicenter trial. We selected 160 patients to analyze this inflammatory infiltrate in more detail using immunohistochemistry. The association with the development of local and distant relapses was determined using univariate and multivariate log rank testing. RESULTS: Patients with an extensive inflammatory infiltrate around the tumor had lower recurrence rates (3.4% versus 6.9%, p = 0.03), showing the importance of host response against tumor cells. In particular, peritumoral mast cells prevent local and distant recurrence (44% versus 15%, p = 0.007 and 86% versus 21%, p < 0.0001, respectively), with improved survival as a consequence. The presence of intratumoral T-cells had independent prognostic value for the occurrence of distant metastases (32% versus 76%, p < 0.0001). CONCLUSIONS: We showed that next to properties of tumor cells, the amount and type of inflammation is also relevant in the control of rectal cancer. Knowledge of the factors involved may lead to new approaches in the management of rectal cancer

Elastic SCAD as a novel penalization method for SVM classification tasks in high-dimensional data

<p>Abstract</p> <p>Background</p> <p>Classification and variable selection play an important role in knowledge discovery in high-dimensional data. Although Support Vector Machine (SVM) algorithms are among the most powerful classification and prediction methods with a wide range of scientific applications, the SVM does not include automatic feature selection and therefore a number of feature selection procedures have been developed. Regularisation approaches extend SVM to a feature selection method in a flexible way using penalty functions like LASSO, SCAD and Elastic Net.</p> <p>We propose a novel penalty function for SVM classification tasks, Elastic SCAD, a combination of SCAD and ridge penalties which overcomes the limitations of each penalty alone.</p> <p>Since SVM models are extremely sensitive to the choice of tuning parameters, we adopted an interval search algorithm, which in comparison to a fixed grid search finds rapidly and more precisely a global optimal solution.</p> <p>Results</p> <p>Feature selection methods with combined penalties (Elastic Net and Elastic SCAD SVMs) are more robust to a change of the model complexity than methods using single penalties. Our simulation study showed that Elastic SCAD SVM outperformed LASSO (<it>L</it>₁) and SCAD SVMs. Moreover, Elastic SCAD SVM provided sparser classifiers in terms of median number of features selected than Elastic Net SVM and often better predicted than Elastic Net in terms of misclassification error.</p> <p>Finally, we applied the penalization methods described above on four publicly available breast cancer data sets. Elastic SCAD SVM was the only method providing robust classifiers in sparse and non-sparse situations.</p> <p>Conclusions</p> <p>The proposed Elastic SCAD SVM algorithm provides the advantages of the SCAD penalty and at the same time avoids sparsity limitations for non-sparse data. We were first to demonstrate that the integration of the interval search algorithm and penalized SVM classification techniques provides fast solutions on the optimization of tuning parameters.</p> <p>The penalized SVM classification algorithms as well as fixed grid and interval search for finding appropriate tuning parameters were implemented in our freely available R package 'penalizedSVM'.</p> <p>We conclude that the Elastic SCAD SVM is a flexible and robust tool for classification and feature selection tasks for high-dimensional data such as microarray data sets.</p

Development and Validation of a New Method to Measure Walking Speed in Free-Living Environments Using the Actibelt® Platform

Walking speed is a fundamental indicator for human well-being. In a clinical setting, walking speed is typically measured by means of walking tests using different protocols. However, walking speed obtained in this way is unlikely to be representative of the conditions in a free-living environment. Recently, mobile accelerometry has opened up the possibility to extract walking speed from long-time observations in free-living individuals, but the validity of these measurements needs to be determined. In this investigation, we have developed algorithms for walking speed prediction based on 3D accelerometry data (actibelt®) and created a framework using a standardized data set with gold standard annotations to facilitate the validation and comparison of these algorithms. For this purpose 17 healthy subjects operated a newly developed mobile gold standard while walking/running on an indoor track. Subsequently, the validity of 12 candidate algorithms for walking speed prediction ranging from well-known simple approaches like combining step length with frequency to more sophisticated algorithms such as linear and non-linear models was assessed using statistical measures. As a result, a novel algorithm employing support vector regression was found to perform best with a concordance correlation coefficient of 0.93 (95%CI 0.92–0.94) and a coverage probability CP1 of 0.46 (95%CI 0.12–0.70) for a deviation of 0.1 m/s (CP2 0.78, CP3 0.94) when compared to the mobile gold standard while walking indoors. A smaller outdoor experiment confirmed those results with even better coverage probability. We conclude that walking speed thus obtained has the potential to help establish walking speed in free-living environments as a patient-oriented outcome measure