Data as a Service (DaaS) for sharing and processing of large data collections in the cloud

Data as a Service (DaaS) is among the latest kind of services being investigated in the Cloud computing community. The main aim of DaaS is to overcome limitations of state-of-the-art approaches in data technologies, according to which data is stored and accessed from repositories whose location is known and is relevant for sharing and processing. Besides limitations for the data sharing, current approaches also do not achieve to fully separate/decouple software services from data and thus impose limitations in inter-operability. In this paper we propose a DaaS approach for intelligent sharing and processing of large data collections with the aim of abstracting the data location (by making it relevant to the needs of sharing and accessing) and to fully decouple the data and its processing. The aim of our approach is to build a Cloud computing platform, offering DaaS to support large communities of users that need to share, access, and process the data for collectively building knowledge from data. We exemplify the approach from large data collections from health and biology domains.Peer ReviewedPostprint (author's final draft

L'evoluzione dell'epidemia da coronavirus in Italia

Nel febbraio 2020 è stato identificato nel Nord Italia un focolaio di infezione da coronavirus SARS-nCOV-2, le cui proporzioni suggeriscono un notevole stato di avanzamento pregresso al momento dell’identificazione del primo paziente (20 febbraio). A partire da tale data è stato rilevato giornalmente un numero sempre crescente di casi di infezione, tanto da superare in dieci giorni il numero di mille soggetti trovati infetti. Contrariamente a quanto ventilato in qualche sede, l’epidemia in corso è ancora nella sua fase iniziale. Pertanto, lungi dall’abbandonare le misure di mitigazione necessarie, in questo momento è più che mai opportuno proseguire secondo alcune importanti raccomandazioni

Fractal parameters and vascular networks: facts & artifacts

<p>Abstract</p> <p>Background</p> <p>Several fractal and non-fractal parameters have been considered for the quantitative assessment of the vascular architecture, using a variety of test specimens and of computational tools. The fractal parameters have the advantage of being scale invariant, i.e. to be independent of the magnification and resolution of the images to be investigated, making easier the comparison among different setups and experiments.</p> <p>Results</p> <p>The success of several commercial and/or free codes in computing the fractal parameters has been tested on well known exact models. Based on such a preliminary study, we selected the code Frac-lac in order to analyze images obtained by visualizing the angiogenetic process occurring in chick Chorio Allontoic Membranes (CAM), assumed to be paradigmatic of a realistic 2D vascular network. Among the parameters investigated, the fractal dimension D_fproved to be the most robust estimator for CAM vascular networks. Moreover, only D_fwas able to discriminate between effective and elusive increases in vascularization after drug-induced angiogenic stimulations on CAMs.</p> <p>Conclusion</p> <p>The fractal dimension D_fis likely to be the most promising tool for monitoring the effectiveness of anti-angiogenic therapies in various clinical contexts.</p

Studies on the Relations between Molecular and Functional Properties of Hemoglobin V. THE INFLUENCE OF TEMPERATURE ON THE BOHR EFFECT IN HUMAN AND IN HORSE HEMOGLOBIN

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A Novel Gaussian Extrapolation Approach for 2D Gel Electrophoresis Saturated Protein Spots

Analysis of images obtained from two-dimensional gel electrophoresis (2D-GE) is a topic of utmost importance in bioinformatics research, since commercial and academic software available currently has proven to be neither completely effective nor fully automatic, often requiring manual revision and refinement of computer generated matches. In this work, we present an effective technique for the detection and the reconstruction of over-saturated protein spots. Firstly, the algorithm reveals overexposed areas, where spots may be truncated, and plateau regions caused by smeared and overlapping spots. Next, it reconstructs the correct distribution of pixel values in these overexposed areas and plateau regions, using a two-dimensional least-squares fitting based on a generalized Gaussian distribution. Pixel correction in saturated and smeared spots allows more accurate quantification, providing more reliable image analysis results. The method is validated for processing highly exposed 2D-GE images, comparing reconstructed spots with the corresponding non-saturated image, demonstrating that the algorithm enables correct spot quantificatio