Analysis of Extra Virgin Olive Oils from Two Italian Regions by Means of Proton Nuclear Magnetic Resonance Relaxation and Relaxometry Measurements

The interest in development of new non-destructive methods for characterization of extra virgin olive oils (EVOOs) has been increasing in the recent years. Among different experimental techniques, nuclear magnetic resonance (NMR) relaxation measurements are very promising in the field of food characterization and authentication. In this study, we focused on relaxation times T-1 and T-2 measured at different magnetic field strengths (namely, 2, 100, and 400 MHz) and H-1 NMR T-1 relaxometry dispersions directly on olive oil samples without any chemical/physical treatments. A large set of EVOO samples produced in two regions of Italy, Tuscany and Apulia, were investigated by means of H-1 NMR relaxation techniques. The relaxation studies reported here show several common features between the two sets of EVOO samples, thus indicating that relaxation properties, namely, the ranges of values of T-1 and T-2 at 2 and 100 MHz, are characteristic of EVOOs, independently from the cultivars, climate, and geographic origin. This is a promising result in view of quality control and monitoring

Sensitivity of Proton NMR Relaxation and Proton NMR Diffusion Measurements to Olive Oil Adulterations with Vegetable Oils

Olive oils and, in particular, extra-virgin olive oils (EVOOs) are one of the most frauded food. Among the different adulterations of EVOOs, the mixture of high-quality olive oils with vegetable oils is one of the most common in the market. The need for fast and cheap techniques able to detect extra-virgin olive oil adulterations was the main motivation for the present research work based on H-1 NMR relaxation and diffusion measurements. In particular, the H-1 NMR relaxation times, T-1 and T-2, measured at 2 and 100 MHz on about 60 EVOO samples produced in Italy are compared with those measured on four different vegetable oils, produced from macadamia nuts, linseeds, sunflower seeds, and soybeans. Self-diffusion coefficients on this set of olive oils and vegetable oil samples were measured by means of the H-1 NMR diffusion ordered spectroscopy (DOSY) technique, showing that, except for the macadamia oil, other vegetable oils are characterized by an average diffusion coefficient sensibly different from extra-virgin olive oils. Preliminary tests based on both NMR relaxation and diffusometry methods indicate that eventual adulterations of EVOO with linseed oil and macadamia oil are the easiest and the most difficult frauds to be detected, respectively

Observing short-range orientational order in small-molecule liquids

AG and TA acknowledge the funding from Slovenian Research Agency, Basic core funding grant P1-0125. SNF, MGH acknowledge the FCT—Portuguese Foundation for Science and Technology projects PIDDAC (POCI-01-0145-FEDER-007688, Reference UIDB/50025/2020-2023). Publisher Copyright: © 2022, The Author(s).Local molecular ordering in liquids has attracted a lot of interest from researchers investigating crystallization, but is still poorly understood on the molecular scale. Classical nucleation theory (CNT), a macroscopic thermodynamic description of condensation, has shortcomings when dealing with clusters consisting of tens of molecules. Cluster formation and local order fluctuations in liquid media are difficult to study due to the limited spatial resolution of electron- and photon-imaging methods. We used NMR relaxometry to demonstrate the existence of dynamic clusters with short-range orientational order in nominally isotropic liquids consisting of elongated molecules. We observed clusters in liquids where the local ordering is driven by polar, steric, and hydrogen-bond interactions between the molecules. In the case of a liquid crystal, measuring the local orientational order fluctuations allowed us to observe the size of these clusters diverging when approaching the phase transition from the isotropic to the nematic phase. These fluctuations are described in terms of rotational elasticity as a consequence of the correlated reorientations of the neighbouring molecules. Our quantitative observations of the dynamic clusters in liquids, numbering about ten or fewer molecules, indicate that this is a general phenomenon in various types of liquids.publishersversionpublishe

Holistic health record for Hidradenitis suppurativa patients.

Hidradenitis suppurativa (HS) is a recurrent inflammatory skin disease with a complex etiopathogenesis whose treatment poses a challenge in the clinical practice. Here, we present a novel integrated pipeline produced by the European consortium BATMAN (Biomolecular Analysis for Tailored Medicine in Acne iNversa) aimed at investigating the molecular pathways involved in HS by developing new diagnosis algorithms and building cellular models to pave the way for personalized treatments. The objectives of our european Consortium are the following: (1) identify genetic variants and alterations in biological pathways associated with HS susceptibility, severity and response to treatment; (2) design in vitro two-dimensional epithelial cell and tri-dimensional skin models to unravel the HS molecular mechanisms; and (3) produce holistic health records HHR to complement medical observations by developing a smartphone application to monitor patients remotely. Dermatologists, geneticists, immunologists, molecular cell biologists, and computer science experts constitute the BATMAN consortium. Using a highly integrated approach, the BATMAN international team will identify novel biomarkers for HS diagnosis and generate new biological and technological tools to be used by the clinical community to assess HS severity, choose the most suitable therapy and follow the outcome.This work was supported by a Biomolecular Analyses for Tailored Medicine in AcneiNversa (BATMAN) project, funded by ERA PerMed (JTC_2018) through the Italian Ministry of Health, the “Fondazione Regionale per la Ricerca Biomedica” (FRRB), the Slovenian Ministry of Education, Science, and Sport (MIZŠ), the Austrian Science fund (I 4229), the Federal Ministry of Education and Research Germany (BMBF), and ANR automate (ANR-20-CE15-0018-01). This work was also supported by and by a grant from the Institute for Maternal and Child Health IRCCS ‘Burlo Garofolo/Italian Ministry of Health (RC16/2018) and by a Starting Grant (SG-2019-12369421) founded by the Italian Ministry of Health. Figures were created with BioRender.com

INLIFE - independent living support functions for the elderly : technology and pilot overview

In this paper, we present the European H2020 project INLIFE (INdependent LIving support Functions for the Elderly). The project brought together 20 partners from nine countries with the goal of integrating into a common ICT platform a range of technologies intended to assist community-dwelling older people with cognitive impairment. The majority of technologies existed prior to INLIFE and a key goal was to bring them together in one place along with a number of new applications to provide a comprehensive set of services. The range of INLIFE services fell into four broad areas: Independent Living Support, Travel Support, Socialization and Communication Support and Caregiver Support. These included security applications, services to facilitate interactions with formal and informal caregivers, multilingual conversation support, web-based physical exercises, teleconsultations, and support for transport navigation. In total, over 2900 people participated in the project; they included elderly adults with cognitive impairment, informal caregivers, healthcare professionals, and other stakeholders. The aim of the study was to assess whether there was improvement/stabilization of cognitive/emotional/physical functioning, as well as overall well-being and quality of life of those using the INLIFE services, and to assess user acceptance of the platform and individual services. The results confirm there is a huge interest and appetite for technological services to support older adults living with cognitive impairment in the community. Different services attracted different amounts of use and evaluation with some proving extremely popular while others less so. The findings provide useful information on the ways in which older adults and their families, health and social care services and other stakeholders wish to access technological services, what sort of services they are seeking, what sort of support they need to access services, and how these services might be funded

Improving the Chemical Selectivity of an Electronic Nose to TNT, DNT and RDX Using Machine Learning

We used a 16-channel e-nose demonstrator based on micro-capacitive sensors with functionalized surfaces to measure the response of 30 different sensors to the vapours from 11 different substances, including the explosives 1,3,5-trinitro-1,3,5-triazinane (RDX), 1-methyl-2,4-dinitrobenzene (DNT) and 2-methyl-1,3,5-trinitrobenzene (TNT). A classification model was developed using the Random Forest machine-learning algorithm and trained the models on a set of signals, where the concentration and flow of a selected single vapour were varied independently. It is demonstrated that our classification models are successful in recognizing the signal pattern of different sets of substances. An excellent accuracy of 96% was achieved for identifying the explosives from among the other substances. These experiments clearly demonstrate that the silane monolayers used in our sensors as receptor layers are particularly well suited to selecting and recognizing TNT and similar types of explosives from among other substances

Bumble bee nest thermoregulation

Careful control of brood temperature is important for successful colony development in social insects. Six bumble bee colonies of six common Central European species (B. hypnorum, B. hortorum, B. argillaceus, B. pascuorum, B. humilis, B. sylvarum) were continuously monitored for several weeks. We recorded the brood temperature as well as the air temperature in the nest and outside once per minute using a homemade electronic setup. Two colonies succeeded in producing new queens and males during the measurement period while the other four colonies were attacked by parasites at some point during the equilibrium stage. We discuss the nest thermoregulation in view of species and the number of workers in the colony. The results show that the strongest colonies were able to maintain very stable brood temperature over longer periods (standard deviations below 0.5 °C), which is in agreement with previous studies. Colonies with 25 or more workers typically kept the standard deviation below 1 °C. There are two main contributions to the paper. First, we discuss the applicability of a multi-sensor monitoring setup for an outdoor study. Second, due to fast temperature sampling, we were able to observe different thermoregulation strategies that colonies may apply, including keeping the temperature above 31 °C at night and letting it rise during the day in B. argillaceus and short heating cycles in B. sylvarum

Intelligent assistant carer for active aging

Abstract We present the concept of an Intelligent Assistant Carer system for the elderly, designed to help with active aging and to facilitate the interactions with carers. The system is modular, allowing the users to choose the appropriate functions according to their needs, and is built on an open platform in order to make it compatible with third-party products and services. Currently, the system consists of a wearable device (a smartwatch) and an internet portal that manages the data and takes care of the interactions between the user, the carers, and the support services. We present in detail one of the modules, i.e., fall detection, and the results of a pilot study for the system on 150 users over the course of 3 months

Wellbeing Forecasting in Postpartum Anemia Patients

Postpartum anemia is a very common maternal health problem and remains a persistent public health issue globally. It negatively affects maternal mood and could lead to depression, increased fatigue, and decreased cognitive abilities. It can and should be treated by restoring iron stores. However, in most health systems, there is typically a six-week gap between birth and the follow-up postpartum visit. Risks of postpartum maternal complications are usually assessed shortly after birth by clinicians intuitively, taking into account psychosocial and physical factors, such as the presence of anemia and the type of iron supplementation. In this paper, we investigate the possibility of using machine-learning algorithms to more reliably forecast three parameters related to patient wellbeing, namely depression (measured by Edinburgh Postnatal Depression Scale—EPDS), overall tiredness, and physical tiredness (both measured by Multidimensional Fatigue Inventory—MFI). Data from 261 patients were used to train the forecasting models for each of the three parameters, and they outperformed the baseline models that always predicted the mean values of the training data. The mean average error of the elastic net regression model for predicting the EPDS score (with values ranging from 0 to 19) was 2.3 and outperformed the baseline, which already hints at the clinical usefulness of using such a model. We further investigated what features are the most important for this prediction, where the EDPS score and both tiredness indexes at birth turned out to be by far the most prominent prediction features. Our study indicates that the machine-learning model approach has the potential for use in clinical practice to predict the onset of depression and severe fatigue in anemic patients postpartum and potentially improve the detection and management of postpartum depression and fatigue

Adult height prediction using the growth curve comparison method.

Understanding the growth pattern is important in view of child and adolescent development. Due to different tempo of growth and timing of adolescent growth spurt, individuals reach their adult height at different ages. Accurate models to assess the growth involve intrusive radiological methods whereas the predictive models based solely on height data are typically limited to percentiles and therefore rather inaccurate, especially during the onset of puberty. There is a need for more accurate non-invasive methods for height prediction that are easily applicable in the fields of sports and physical education, as well as in endocrinology. We developed a novel method, called Growth Curve Comparison (GCC), for height prediction, based on a large cohort of > 16,000 Slovenian schoolchildren followed yearly from ages 8 to 18. We compared the GCC method to the percentile method, linear regressor, decision tree regressor, and extreme gradient boosting. The GCC method outperformed the predictions of other methods over the entire age span both in boys and girls. The method was incorporated into a publicly available web application. We anticipate our method to be applicable also to other models predicting developmental outcomes of children and adolescents, such as for comparison of any developmental curves of anthropometric as well as fitness data. It can serve as a useful tool for assessment, planning, implementation, and monitoring of somatic and motor development of children and youth