Six-Month Synbio Administration Affects Nutritional and Inflammatory Parameters of Older Adults Included in the PROBIOSENIOR Project.

The physiological changes associated with ageing contribute to the incidence of diseases, morbidity, and mortality. For modern society, it is essential to find solutions to improve elderly people's health and quality of life. Among promising strategies, the PROBIOSENIOR project proposed a daily six-month supplementation with new probiotic functional foods and nutraceuticals.

A Bayesian Network Approach to Explainable Reinforcement Learning with Distal Information.

Nowadays, Artificial Intelligence systems have expanded their competence field from research to industry and daily life, so understanding how they make decisions is becoming fundamental to reducing the lack of trust between users and machines and increasing the transparency of the model. This paper aims to automate the generation of explanations for model-free Reinforcement Learning algorithms by answering "why" and "why not" questions. To this end, we use Bayesian Networks in combination with the NOTEARS algorithm for automatic structure learning.

Joint selection of brain network nodes and edges for MCI identification.

Background And Objective: Functional brain graph (FBG), by describing the interactions between different brain regions, provides an effective representation of fMRI data for identifying mild cognitive impairment (MCI), an early stage of Alzheimer's Disease (AD). Prior to the identification task, selecting features from the estimated FBG is a necessary step for reducing computational cost, alleviating the risk of overfitting, and finding potential biomarkers of brain diseases. In practice, either node-based features (e.

Impact of a probiotic diet on well-being of healthy senior: THE PROBIOSENIOR PROJECT.

Aims: The aim of this work was to assess the effects of a probiotic diet on well-being of healthy seniors living in boarding and private homes in Marche Region, Italy. In particular, we focused on the modulation of high-sensitivity C-reactive protein (HsCRP), intestinal microbiota and short-chain fatty acids (SCFAs).

Methods And Results: Ninety-seven healthy seniors took part in a double-blind, placebo-controlled feeding study (59 fed probiotics, 38 fed placebo) for 6 months.

Estimating High-Order Brain Functional Networks in Bayesian View for Autism Spectrum Disorder Identification.

Brain functional network (BFN) has become an increasingly important tool to understand the inherent organization of the brain and explore informative biomarkers of neurological disorders. Pearson's correlation (PC) is the most widely accepted method for constructing BFNs and provides a basis for designing new BFN estimation schemes. Particularly, a recent study proposes to use two sequential PC operations, namely, correlation's correlation (CC), for constructing the high-order BFN.

Extracting BOLD signals based on time-constrained multiset canonical correlation analysis for brain functional network estimation and classification.

Brain functional network (BFN), usually estimated from blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI), has been proven to be a powerful tool to study the organization of the brain and discover biomarkers for diagnosis of brain disorders. Prior to BFN estimation and classification, extracting representative BOLD signals from brain regions of interest (ROIs) is a critical step. Traditional extraction methods include averaging, peaking operation and dimensionality reduction, often leading to signal cancellation and information loss.

Machine learning models predicting multidrug resistant urinary tract infections using "DsaaS".

Background: The scope of this work is to build a Machine Learning model able to predict patients risk to contract a multidrug resistant urinary tract infection (MDR UTI) after hospitalization. To achieve this goal, we used different popular Machine Learning tools. Moreover, we integrated an easy-to-use cloud platform, called DSaaS (Data Science as a Service), well suited for hospital structures, where healthcare operators might not have specific competences in using programming languages but still, they do need to analyze data as a continuous process.

Probiotic-enriched foods and dietary supplement containing SYNBIO positively affects bowel habits in healthy adults: an assessment using standard statistical analysis and Support Vector Machines.

A randomised, double-blind, placebo-controlled, parallel group study assessed in healthy adults how daily consumption of the probiotic combination SYNBIO®, administered in probiotic-enriched foods or in a dietary supplement, affected bowel habits. Primary and secondary outcomes gave the overall assessment of bowel well-being, while a Psychological General Well-Being Index compiled by participants estimated the health-related quality of life as well as the gastrointestinal tolerance determined with the Gastrointestinal Symptom Rating Scale. Support Vector Machine models for classification problems were used to validate the total outcomes on bowel well-being.

Integrating support vector machines and neural networks.

Support vector machines (SVMs) are a powerful technique developed in the last decade to effectively tackle classification and regression problems. In this paper we describe how support vector machines and artificial neural networks can be integrated in order to classify objects correctly. This technique has been successfully applied to the problem of determining the quality of tiles.