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Digital technologies ignited a revolution in the agrifood domain known as precision agriculture: a main question for enabling precision agriculture at scale is if accurate product quality control can be made available at minimal cost, leveraging existing technologies and agronomists' skills. As a contribution along this direction we demonstrate a tool for accurate fruit yield estimation from smartphone cameras, by adapting Deep Learning algorithms originally developed for crowd counting.

Publishing reproducible analyses is a long-standing and widespread challenge for the scientific community, funding bodies and publishers. Although a definitive solution is still elusive, the problem is recognized to affect all disciplines and lead to a critical system inefficiency. Here, we propose a blockchain-based approach to enhance scientific reproducibility, with a focus on life science studies and precision medicine. While the interest of encoding permanently into an immutable ledger all the study key information-including endpoints, data and metadata, protocols, analytical methods and all findings-has been already highlighted, here we apply the blockchain approach to solve the issue of rewarding time and expertise of scientists that commit to verify reproducibility. Our mechanism builds a trustless ecosystem of researchers, funding bodies and publishers cooperating to guarantee digital and permanent access to information and reproducible results. As a natural byproduct, a procedure to quantify scientists' and institutions' reputation for ranking purposes is obtained., Comment: 8 pages, 1 figure

A bioinformatics platform is introduced aimed at identifying models of disease-specific pathways, as well as a set of network measures that can quantify changes in terms of global structure or single link disruptions.The approach integrates a network comparison framework with machine learning molecular profiling. The platform includes different tools combined in one Open Source pipeline, supporting reproducibility of the analysis. We describe here the computational pipeline and explore the main sources of variability that can affect the results, namely the classifier, the feature ranking/selection algorithm, the enrichment procedure, the inference method and the networks comparison function. The proposed pipeline is tested on a microarray dataset of late stage Parkinsons' Disease patients together with healty controls. Choosing different machine learning approaches we get low pathway profiling overlapping in terms of common enriched elements. Nevertheless, they identify different but equally meaningful biological aspects of the same process, suggesting the integration of information across different methods as the best overall strategy. All the elements of the proposed pipeline are available as Open Source Software: availability details are provided in the main text.