Proton therapy is nowadays a major clinical modality in the fight against cancer due to the advantages offered by its peculiar depth dose profile, that allows to improve its efficacy on tumors while reducing damages to healthy tissues. The number of worldwide facilities and of treated patients is increasing every year. A challenge of proton therapy is that treatment planning systems require accurate dose computation, but the golden standard in accuracy are Monte Carlo algorithms that are slow. For this reason, accurate and faster dose calculation algorithms are needed. In this paper we use Deep Learning to achieve both speed and accuracy in dose calculation for proton therapy. The results positively compare with previous existing literature in the thorax cases, that are usually the most difficult to calculate by fast algorithms.

BACKGROUND: Hepatocellular carcinoma (HCC) is an aggressive epithelial tumor which shows very poor prognosis and high rate of recurrence, representing an urgent problem for public healthcare. MicroRNAs (miRNAs/miRs) are a class of small, non-coding RNAs that attract great attention because of their role in regulation of processes such as cellular growth, proliferation, apoptosis. Because of the thousands of potential interactions between a single miR and target mRNAs, bioinformatics prediction tools are very useful to facilitate the task for individuating and selecting putative target genes. In this study, we present a chemically-induced HCC mouse model to identify differential expression of miRNAs during the progression of the hepatic injury up to HCC onset. In addition, we describe an established bioinformatics approach to highlight putative target genes and protein interaction networks where they are involved. RESULTS: We describe four miRs (miR-125a-5p, miR-27a, miR-182, miR-193b) which showed to be differentially expressed in the chemically-induced HCC mouse model. The miRs were subjected to four of the most used predictions tools and 15 predicted target genes were identified. The expression of one (ANK3) among the 15 predicted targets was further validated by immunoblotting. Then, enrichment annotation analysis was performed revealing significant clusters, including some playing a role in ion transporter activity, regulation of receptor protein serine/threonine kinase signaling pathway, protein import into nucleus, regulation of intracellular protein transport, regulation of cell adhesion, growth factor binding, and regulation of TGF-beta/SMAD signaling pathway. A network construction was created and links between the selected miRs, the predicted targets as well as the possible interactions among them and other proteins were built up. CONCLUSIONS: In this study, we combined miRNA expression analysis, obtained by an in vivo HCC mouse model, with a bioinformatics-based workflow. New genes, pathways and protein interactions, putatively involved in HCC initiation and progression, were identified and explored.

We address the semiconductor industry problem of detecting microchips that escape production tests but are returned by customers as non-functional. This problem deals with analyzing high dimensional unbalanced databases collecting only a very small number of customer return samples. We show how to construct a model for effectively discriminating, based on wafer probe test data, potential customer returns from other good chips at the cost of a low overkill, where a model is a pair consisting of a selected set of wafer probe tests with minimal redundancy and a 1-class-SVM (Support Vector Machine) with optimal kernel parameters. We report about an experimentation on real data from EWS (Electronic Wafer Sort) test and customer returns showing the capability of predicting customer returns at cost of a relatively low overkill.

No abstract available.