Protective Activity of Broccoli Sprout Juice in a Human Intestinal Cell Model of Gut Inflammation

Benefits to health from a high consumption of fruits and vegetables are well established and have been attributed to bioactive secondary metabolites present in edible plants. However, the effects of specific health-related phytochemicals within a complex food matrix are difficult to assess. In an attempt to address this problem, we have used elicitation to improve the nutraceutical content of seedlings of Brassica oleracea grown under controlled conditions. Analysis, by LC-MS, of the glucosinolate, isothiocyanate and phenolic compound content of juices obtained from sprouts indicated that elicitation induces an enrichment of several phenolics, particularly of the anthocyanin fraction. To test the biological activity of basal and enriched juices we took advantage of a recently developed in vitro model of inflamed human intestinal epithelium. Both sprouts’ juices protected intestinal barrier integrity in Caco-2 cells exposed to tumor necrosis factor under marginal zinc deprivation, with the enriched juice showing higher protection. Multivariate regression analysis indicated that the extent of rescue from stress-induced epithelial dysfunction correlated with the composition in bioactive molecules of the juices and, in particular, with a group of phenolic compounds, including several anthocyanins, quercetin-3-Glc, cryptochlorogenic, neochlorogenic and cinnamic acids

The “Right” recipes for security culture: a competing values model perspective

This study argues that the effect of perceived organizational culture on the formation of security-related subjective norms and the level of compliance pressure will vary based on how the employees perceive their organization’s cultural values. These perceptions reflect on the assumptions and principles that organizations use to guide their security-related behaviors. To make these arguments, we adopt the competing values model (CVM), which is a model used to understand the range of organizational values and resulting cultural archetypes

Distributions based Regression Techniques for Compositional Data

A systematic study of regression methods for compositional data, which are unique and rare are explored in this thesis. We start with the basic machine learning concept of regression. We use regression equations to solve a classification problem. With partial least squares discriminant analysis (PLS-DA), we follow regression algorithms and solve classification problems, like spam filtering and intrusion detection. After getting the basic understanding of how regression works, we move on to more complex algorithms of distributions based regression. We explore the uni-dimensional case of distributions, applied to regression, the beta-regression. This gives us an understanding of how, when the data to be predicted, or the outcome, is assumed to be of beta distribution, a prediction can be made with regression equations. To further enhance our understanding, we look into Dirichlet distribution, which is for a multi-dimensional case. Unlike traditional regression, here we are predicting a compositional outcome. Two novel regression approaches based on distributions are proposed for compositional data, namely generalized Dirichlet regression and Beta-Liouville regression. They are extensions of Beta regression in a multi-dimensional scenario, similar to Dirichlet regression. The models are learned by maximum likelihood estimation algorithm using Newton-Raphson approach. The performance comparison between the proposed models and other popular solutions is given and both synthetic and real data sets extracted from challenging applications such as market share analysis using Google-Trends and occupancy estimation in smart buildings are evaluated to show the merits of the proposed approaches. Our work will act as a tool for product based companies to estimate how their investments in advertising have yielded results in the market shares. Google-Trends gives an estimate of the popularity of a company, which reflects the effect of advertisements. This thesis bridges the gap between open source data from Google-Trends and market shares

Spectroscopic techniques applied to the real time monitoring and control of the production processes of the pharmaceutical and food industry

L'abstract è presente nell'allegato / the abstract is in the attachmen

Comparative Analysis of Student Learning: Technical, Methodological and Result Assessing of PISA-OECD and INVALSI-Italian Systems .

PISA is the most extensive international survey promoted by the OECD in the field of education, which measures the skills of fifteen-year-old students from more than 80 participating countries every three years. INVALSI are written tests carried out every year by all Italian students in some key moments of the school cycle, to evaluate the levels of some fundamental skills in Italian, Mathematics and English. Our comparison is made up to 2018, the last year of the PISA-OECD survey, even if INVALSI was carried out for the last edition in 2022. Our analysis focuses attention on the common part of the reference populations, which are the 15-year-old students of the 2nd class of secondary schools of II degree, where both sources give a similar picture of the students

Advanced Analysis of Plutonium: Pre- and Post-Detonation Scenarios

Pre- and post-detonation nuclear material require chemical analysis that is rapid, precise, and in some cases, portable. Special Nuclear Material (SNM) as well as nuclear debris is mostly radioactive, causing additional safety concerns and complexities in recent research. Literature research has provided a large number of conventional table top analysis techniques, focusing on the measurement of actinide ratios – most of which are destructive analysis. There is a need for the development of chemical characterization methods for pre- and post-detonation nuclear material that focuses on less destructive techniques for age and compositional analysis, as well as reduction in time of analysis (and thus, exposure to radiation). The research presented (i) investigates time- and temperature-dependent signatures of SNM (pre-detonation) through O isotope fractionation (ii) determines the effects of ion irradiation on SNM and how damage affects oxidation over time (iii) modifies data acquisition of HHLIBS in identification of nuclear debris through multivariate analysis (MVA) techniques. The application of MVA techniques to HHLIBS measurements produces quantitative compositional data from unknown samples and is expected to be a major contribution from this research, most notably for nuclear forensics. There is a large gap in the ability to use commercial HHLIBS for direct forensic analysis beyond qualitative relative abundances without any previous knowledge of sample composition. Due to the shared nature of nuclear weapon material to that of nuclear fuel, the results from these studies can be used to speculate the usefulness of MVA HHLIBS for routine non-destructive analysis inspections at nuclear energy facilities. The use of ammonium biflouride digestion (ABF) and ICP-OES for destructive methods will help to validate the MVA model and through iteration, create a successful method for infield analysis of unknown samples

ISBIS 2016: Meeting on Statistics in Business and Industry

This Book includes the abstracts of the talks presented at the 2016 International Symposium on Business and Industrial Statistics, held at Barcelona, June 8-10, 2016, hosted at the Universitat Politècnica de Catalunya - Barcelona TECH, by the Department of Statistics and Operations Research. The location of the meeting was at ETSEIB Building (Escola Tecnica Superior d'Enginyeria Industrial) at Avda Diagonal 647. The meeting organizers celebrated the continued success of ISBIS and ENBIS society, and the meeting draw together the international community of statisticians, both academics and industry professionals, who share the goal of making statistics the foundation for decision making in business and related applications. The Scientific Program Committee was constituted by: David Banks, Duke University Amílcar Oliveira, DCeT - Universidade Aberta and CEAUL Teresa A. Oliveira, DCeT - Universidade Aberta and CEAUL Nalini Ravishankar, University of Connecticut Xavier Tort Martorell, Universitat Politécnica de Catalunya, Barcelona TECH Martina Vandebroek, KU Leuven Vincenzo Esposito Vinzi, ESSEC Business Schoo