C3C^{3} : A Command-line Catalogue Cross-matching tool for modern astrophysical survey data

In the current data-driven science era, it is needed that data analysis techniques has to quickly evolve to face with data whose dimensions has increased up to the Petabyte scale. In particular, being modern astrophysics based on multi-wavelength data organized into large catalogues, it is crucial that the astronomical catalog cross-matching methods, strongly dependant from the catalogues size, must ensure efficiency, reliability and scalability. Furthermore, multi-band data are archived and reduced in different ways, so that the resulting catalogues may differ each other in formats, resolution, data structure, etc, thus requiring the highest generality of cross-matching features. We present $C^{3}$ (Command-line Catalogue Cross-match), a multi-platform application designed to efficiently cross-match massive catalogues from modern surveys. Conceived as a stand-alone command-line process or a module within generic data reduction/analysis pipeline, it provides the maximum flexibility, in terms of portability, configuration, coordinates and cross-matching types, ensuring high performance capabilities by using a multi-core parallel processing paradigm and a sky partitioning algorithm.Comment: 6 pages, 4 figures, proceedings of the IAU-325 symposium on Astroinformatics, Cambridge University pres

Synthesis of the 4-aza cyclopentenone analogue of Δ12,14-15-deoxy-PGJ2 and S-cysteine adducts

Abstract The synthesis of a series of 4-aza cross-conjugated cyclopentenones, inspired by the natural prostaglandin Δ12,14-15-deoxy-PGJ2 (5) is described. Using the 4-aza cyclopentenone 7, the installation of the α-side chain was performed using N-functionalisation, following a Boc-deprotection. The ω-side chain was then installed through a Baylis-Hillman type aldol reaction with trans-2-octenal. This afforded 11, the aza-analogue of 5. With this prostaglandin analogue in hand, a series of thiol adducts (14–16) were prepared. Included are activities for compounds 11 and 14–16 in relation to inhibition of the transcription factor NF-κB

Do Memories Haunt You? An Automated Black Box Testing Approach for Detecting Memory Leaks in Android Apps

Memory leaks represent a remarkable problem for mobile app developers since a waste of memory due to bad programming practices may reduce the available memory of the device, slow down the apps, reduce their responsiveness and, in the worst cases, they may cause the crash of the app. A common cause of memory leaks in the specific context of Android apps is the bad handling of the events tied to the Activity Lifecycle. In order to detect and characterize these memory leaks, we present FunesDroid, a tool-supported black box technique for the automatic detection of memory leaks tied to the Activity Lifecycle in Android apps. FunesDroid implements a testing approach that can find memory leaks by analyzing unnecessary heap object replications after the execution of three different sequences of Activity Lifecycle events. In the paper, we present an exploratory study that shows the capability of the proposed technique to detect memory leaks and to characterize them in terms of their size, persistence and growth trend. The study also illustrates how memory leak causes can be detected with the support of the information provided by the FunesDroid tool

Machine learning based data mining for Milky Way filamentary structures reconstruction

We present an innovative method called FilExSeC (Filaments Extraction, Selection and Classification), a data mining tool developed to investigate the possibility to refine and optimize the shape reconstruction of filamentary structures detected with a consolidated method based on the flux derivative analysis, through the column-density maps computed from Herschel infrared Galactic Plane Survey (Hi-GAL) observations of the Galactic plane. The present methodology is based on a feature extraction module followed by a machine learning model (Random Forest) dedicated to select features and to classify the pixels of the input images. From tests on both simulations and real observations the method appears reliable and robust with respect to the variability of shape and distribution of filaments. In the cases of highly defined filament structures, the presented method is able to bridge the gaps among the detected fragments, thus improving their shape reconstruction. From a preliminary "a posteriori" analysis of derived filament physical parameters, the method appears potentially able to add a sufficient contribution to complete and refine the filament reconstruction.Comment: Proceeding of WIRN 2015 Conference, May 20-22, Vietri sul Mare, Salerno, Italy. Published in Smart Innovation, Systems and Technology, Springer, ISSN 2190-3018, 9 pages, 4 figure

A Command-line Cross-matching tool for modern astrophysical pipelines

The emerging need for efficient, reliable and scalable astronomical catalog cross-matching is becoming more pressing in the current data-driven science era, where the size of data has rapidly increased up to the Petabyte scale. C3 (Command-line Catalogue Cross-matching) is a multi-platform tool designed to efficiently cross-match massive catalogues from modern astronomical surveys, ensuring high-performance capabilities through the use of a multi-core parallel processing paradigm. The tool has been conceived to be executed as a stand-alone command-line process or integrated within any generic data reduction/analysis pipeline, providing the maximum flexibility to the end user, in terms of parameter configuration, coordinates and cross-matching types. In this work we present the architecture and the features of the tool. Moreover, since the modular design of the tool enables an easy customization to specific use cases and requirements, we present also an example of a customized C3 version designed and used in the FP7 project ViaLactea, dedicated to cross-correlate Hi-GAL clumps with multi-band compact sources...

Molecular phenotyping and biomarker development: Are we on our way towards targeted therapy for severe asthma?

Although different phenotypes of severe asthma can be identified, all are characterized by common symptoms. Due to their heterogeneity, they exhibit differences in pathogenesis, etiology and clinical responses to therapeutic approaches. The identification of distinct molecular phenotypes to define severe asthmatic patients will allow us to better understand the pathophysiology of the disease and thus to more precisely target the treatment for each patient. To achieve this goal, a systematic search for new, reliable and stable biomarkers specific for each phenotype is essential. This review focuses on the current known molecular phenotypes of severe asthma and highlights the need for biomarkers that could (either alone or in combination) be predictive of the treatment outcome

Command-line Cross-matching Tool for Modern Astrophysical Pipelines

The emerging need for efficient, reliable and scalable astronomical catalog cross-matching is becoming more pressing in the current data-driven science era, where the size of data has rapidly increased up to the Petabyte scale. C3 (Command-line Catalogue Cross-matching) is a multi-platform tool designed to efficiently cross-match massive catalogues from modern astronomical surveys, ensuring high-performance capabilities through the use of a multi-core parallel processing paradigm. The tool has been conceived to be executed as a stand-alone command-line process or integrated within any generic data reduction/analysis pipeline, providing the maximum flexibility to the end user, in terms of parameter configuration, coordinates and cross-matching types. In this work we present the architecture and the features of the tool. Moreover, since the modular design of the tool enables an easy customization to specific use cases and requirements, we present also an example of a customized C3 version designed and used in the FP7 project ViaLactea, dedicated to cross-correlate Hi-GAL clumps with multi-band compact sources...