University of Trento

About: University of Trento is a education organization based out in Trento, Italy. It is known for research contribution in the topics: Population & Context (language use). The organization has 10527 authors who have published 30978 publications receiving 896614 citations. The organization is also known as: Universitá degli Studi di Trento & Universita degli Studi di Trento.

Accessible, curated metagenomic data through ExperimentHub

Abstract: We present curatedMetagenomicData, a Bioconductor and command-line resource providing thousands of metagenomic profiles from the Human Microbiome Project and other publicly available datasets, and ExperimentHub, for convenient cloud-based distribution of data to the R desktop. curatedMetagenomicData provides standardized per-participant metadata linked to bacterial, fungal, archaeal, and viral taxonomic abundances, as well as quantitative metabolic functional profiles, generated by the HUMAnN2 and MetaPhlAn2 pipelines. The resulting datasets can be immediately analyzed with a wide range of statistical methods, requiring a minimum of bioinformatic expertise and no preprocessing of data. We demonstrate exploratory data analysis, an investigation of gut "enterotypes", and a comparison of the accuracy of disease classification from different data types. These documented analyses can be reproduced efficiently on a laptop, without the barriers of working with large-scale, raw sequencing data. The development of curatedMetagenomicData will continue with the addition, curation, and analysis of further microbiome datasets.

Planning and Monitoring Web Service Composition

Abstract: The ability to automatically compose web services, and to monitor their execution, is an essential step to substantially decrease time and costs in the development, integration, and maintenance of complex services. In this paper, we exploit techniques based on the “Planning as Model Checking” approach to automatically compose web services and synthesize monitoring components. By relying on such a flexible technology, we are able to deal with the difficulties stemming from the unpredictability of external partner services, the opaqueness of their internal status, and the presence of complex behavioral requirements. We test our approach on a simple, yet realistic example; the results provide a witness to the potentiality of this approach.

Complex-Weight Sparse Linear Array Synthesis by Bayesian Compressive Sampling

Abstract: An innovative method for the synthesis of maximally sparse linear arrays matching arbitrary reference patterns is proposed. In the framework of sparseness constrained optimization, the approach exploits the multi-task (MT) Bayesian compressive sensing (BCS) theory to enable the design of complex non-Hermitian layouts with arbitrary radiation and geometrical constraints. By casting the pattern matching problem into a probabilistic formulation, a Relevance-Vector-Machine (RVM) technique is used as solution tool. The numerical assessment points out the advances of the proposed implementation over the extension to complex patterns of and it gives some indications about the reliability, flexibility, and numerical efficiency of the MT-BCS approach also in comparison with state-of-the-art sparse-arrays synthesis methods.

The need for transparency and good practices in the qPCR literature

Abstract: Two surveys of over 1,700 publications whose authors use quantitative real-time PCR (qPCR) reveal a lack of transparent and comprehensive reporting of essential technical information. Reporting standards are significantly improved in publications that cite the Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) guidelines, although such publications are still vastly outnumbered by those that do not.