Effect of fresh pork meat conditioning on quality characteristics of salami

The aim of this work was to evaluate the effect of pork meat conditioning under different relative humidity (RH) values on salami quality characteristics. During a 6 days conditioning period at 0 \ub0C under two levels of RH (95% vs. 80%), meat pH and weight loss were measured. Salami characteristics (moisture, weight loss, texture, appearance properties) were evaluated during 20 days of ripening. Results showed that conditioning at 80% RH yielded a significantly drier meat, being the weight loss rate 1.6 times higher than at 95% RH. The lower water content of meat allowed a shorter salami ripening phase, guaranteeing an appropriate weight loss and the development of the desired texture, while maintaining good appearance properties. The acceleration of this production phase represents a clear economic advantage for producers and consumers, leading to higher profit margins and lower retail prices. The possibility of using FT-NIR spectroscopy as a valid tool for the rapid evaluation of salami ripening was also demonstrated

Complex Data Imputation by Auto-Encoders and Convolutional Neural Networks—A Case Study on Genome Gap-Filling

Missing data imputation has been a hot topic in the past decade, and many state-of-the-art works have been presented to propose novel, interesting solutions that have been applied in a variety of fields. In the past decade, the successful results achieved by deep learning techniques have opened the way to their application for solving difficult problems where human skill is not able to provide a reliable solution. Not surprisingly, some deep learners, mainly exploiting encoder-decoder architectures, have also been designed and applied to the task of missing data imputation. However, most of the proposed imputation techniques have not been designed to tackle \u201ccomplex data\u201d, that is high dimensional data belonging to datasets with huge cardinality and describing complex problems. Precisely, they often need critical parameters to be manually set or exploit complex architecture and/or training phases that make their computational load impracticable. In this paper, after clustering the state-of-the-art imputation techniques into three broad categories, we briefly review the most representative methods and then describe our data imputation proposals, which exploit deep learning techniques specifically designed to handle complex data. Comparative tests on genome sequences show that our deep learning imputers outperform the state-of-the-art KNN-imputation method when filling gaps in human genome sequences

A bit stickier, a bit slower, a lot stiffer: Specific vs. nonspecific binding of gal4 to dna

Transcription factors regulate gene activity by binding specific regions of genomic DNA thanks to a subtle interplay of specific and nonspecific interactions that is challenging to quantify. Here, we exploit Reflective Phantom Interface (RPI), a label-free biosensor based on optical reflectivity, to investigate the binding of the N-terminal domain of Gal4, a well-known gene regulator, to double-stranded DNA fragments containing or not its consensus sequence. The analysis of RPI-binding curves provides interaction strength and kinetics and their dependence on temperature and ionic strength. We found that the binding of Gal4 to its cognate site is stronger, as expected, but also markedly slower. We performed a combined analysis of specific and nonspecific binding— equilibrium and kinetics—by means of a simple model based on nested potential wells and found that the free energy gap between specific and nonspecific binding is of the order of one kcal/mol only. We investigated the origin of such a small value by performing all-atom molecular dynamics simulations of Gal4–DNA interactions. We found a strong enthalpy–entropy compensation, by which the binding of Gal4 to its cognate sequence entails a DNA bending and a striking conformational freezing, which could be instrumental in the biological function of Gal4