An innovative solid liquid extraction technology: use of the naviglio extractor for the production of lemon liquor.

This document is a review on solid-liquid extractive techniques and describes an innovative solid-liquid extraction technology using the Naviglio Extractor®. Also explained is an application for the production of alcoholic extract from lemon peel. The alcoholic extract, mixed with a sugar and water solution in the right proportions is used to make a well known Italian lemon liquor commonly named "limoncello". Lemon liquor is obtained utilizing the Naviglio Extractor®; the procedure used is fast and efficient and uses about half the weight of lemon peel per volume of ethyl alcohol used for the extraction of the odorous and taste responsible compounds, compared to the commonly used extraction procedures. To test the consumer’s preference and compare the taste of the liquor obtained with that obtained by peel maceration from the same lot of lemons and obtained using the traditional recipe, a consumer test has been carried out. One hundred people, chosen from among frequent consumers of limoncello, tasted the two lemon liquors, and evaluated intensity of aroma, colour, alcohol taste and sweetness. In about 81% of the preferences, the liquor obtained using the Naviglio Extractor® was preferred. The extraction process used allows the ethanol from used up lemon peel to be totally recovered so that these can be disposed of as non toxic waste or used in agriculture or as cattle feed

Would the combination of everolimus with endocrine-therapy help in FGFR2 positive serous endometrial cancer?

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Convolutional Neural Networks for the segmentation of microcalcification in Mammography Imaging

Cluster of microcalcifications can be an early sign of breast cancer. In this paper we propose a novel approach based on convolutional neural networks for the detection and segmentation of microcalcification clusters. In this work we used 283 mammograms to train and validate our model, obtaining an accuracy of 98.22% in the detection of preliminary suspect regions and of 97.47% in the segmentation task. Our results show how deep learning could be an effective tool to effectively support radiologists during mammograms examination.Comment: 13 pages, 7 figure

Effect of tomato by-products in the diet of Comisana sheep on composition and conjugated linoleic acid (CLA) content of milk fat

To evaluate the effect of supplementing the diet of Comisana sheep with by-products from industrial tomato manifacture on the composition and conjugated linoleic acid (CLA) content of milk fat, two groups of 50 ewes each were fed either total mixed ration standard (TMRS) or total mixed ration with added tomato by-products (TMRA). Milk fat composition was determinated by HRGC. The milk fat content for animals fed the TMRA diet increased by 6,41% aafter six weeks, compared with the animals fed the TMRS diet. The CLA content in the milk fat for the group of animal fed the TMRA diet was 19,8% higher than for those fed the TMRS diet. The fatty acid composition showed an increase in the amount of PUFA; the n-3:n-6 ratio increased by 13% in the milk from sheep fed with the TMRA diet

A new bioinformatic pipeline allows the design of small, targeted gene panels for efficient TMB estimation

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