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Microwave-assisted deep eutectic solvent (DES) has received attention as an ultrafast pretreatment method in lignocellulose fractionation. This study investigated the improvement of milled softwood mixture (MSM) fractionation with chlorine chloride-formic acid (ChCl:FA) to obtain residues with high glucan retention and purity while removing majority of the lignin and hemicelluloses. At the optimum pretreatment conditions i.e., ChCl:FA (1:4), 140 °C, 14 min, 800 W and 15 % (w/v), 96.2 % hemicellulose removal, 90.1 % delignification and 93.5 % glucan retention were achieved. About 85 % lignin was recovered with a 95 % purity when solid loading was 10–20 % (w/v). This study showed that microwave assisted ChCl:FA pretreatment was a suitable means to fractionate MSM to achieve high quality glucan and lignin at high solid loading.

The decentralized nature of federated learning, that often leverages the power of edge devices, makes it vulnerable to attacks against privacy and security. The privacy risk for a peer is that the model update she computes on her private data may, when sent to the model manager, leak information on those private data. Even more obvious are security attacks, whereby one or several malicious peers return wrong model updates in order to disrupt the learning process and lead to a wrong model being learned. In this paper we build a federated learning framework that offers privacy to the participating peers as well as security against Byzantine and poisoning attacks. Our framework consists of several protocols that provide strong privacy to the participating peers via unlinkable anonymity and that are rationally sustainable based on the co-utility property. In other words, no rational party is interested in deviating from the proposed protocols. We leverage the notion of co-utility to build a decentralized co-utile reputation management system that provides incentives for parties to adhere to the protocols. Unlike privacy protection via differential privacy, our approach preserves the values of model updates and hence the accuracy of plain federated learning; unlike privacy protection via update aggregation, our approach preserves the ability to detect bad model updates while substantially reducing the computational overhead compared to methods based on homomorphic encryption.

BACKGROUND: It has been previously shown that acutely administered insect Alphitobius diaperinus protein increases food intake in rats and modifies the ex vivo enterohormone secretory profile differently than beef or almond proteins. In this study, we aimed to evaluate whether these effects could be maintained for a longer period and determine the underlying mechanisms. RESULTS: We administered two different insect species to rats for 26 days and measured food intake at different time points. Both insect species increased food intake in the first week, but the effect was later lost. Glucagon-like peptide 1 (GLP-1) and ghrelin were measured in plasma and ex vivo, and no chronic effects on their secretion or desensitization were found. Nevertheless, digested A. diaperinus acutely modified GLP-1 and ghrelin secretion ex vivo. CONCLUSION: Our results suggest that increases in food intake could be explained by a local ghrelin reduction acting in the small intestine. © 2022 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Background: Child malnutrition is a major epidemiological problem in developing countries, especially in African countries. Nutrition education for mothers can alleviate this malnutrition in their young children. The objective of this study was to make a systematic review to assess the effect of intervention programs in nutrition education for African mothers on the nutritional status of their infants. Methods: A bibliographic search was carried out in the PubMed database for clinical trials between November 2012 and 2021. The studies should contain educational programs to evaluate the impact on the infant's nutritional indicators in children under 5 years (food consumption, anthropometry and/or knowledge of nutrition in caretakers). Results: A total of 20 articles were selected, of which 53% evaluated infant's food consumption, 82% anthropometric measurements and 30% nutritional knowledge. In general, nutritional education programs are accredited with some significant improvements in food and nutrient consumption, knowledge and dietary practices in complementary feeding, but only those studies that implemented strategies in agriculture, educational workshops and supplementation obtained reductions in chronic malnutrition figures. Limitations: There is high heterogeneity in the articles included, since the intervention programs have different approaches. Conclusions: Programs that implemented actions of national agriculture or nutritional supplementation reap the greatest benefits in curbing infant malnutrition.

The article uses passive mobile data to analyse the complex mobilities that occur in a coastal region characterised by seasonal patterns of tourism activity. A large volume of data generated by mobile phone users has been selected and processed to subsequently display the information in the form of visualisations that are useful for transport and tourism research, policy, and practice. More specifically, the analysis consisted of four steps: (1) a dataset containing records for four days-two on summer days and two in winter-was selected, (2) these were aggregated spatially, temporally, and differentiating trips by local residents, national tourists, and international tourists, (3) origin-destination matrices were built, and (4) graph-based visualisations were created to provide evidence on the nature of the mobilities affecting the study area. The results of our work provide new evidence of how the analysis of passive mobile data can be useful to study the effects of tourism seasonality in local mobility patterns.

This article presents the first results obtained from the use of high-resolution images from the SAR-X sensor of the PAZ satellite platform. These are in result of the application of various radar image-treatment techniques, with which we wanted to carry out a non-invasive exploration of areas of the archaeological site of Clunia (Burgos, Spain). These areas were analyzed and contrasted with other sources from high-resolution multispectral images (TripleSat), or from digital surface models obtained from Laser Imaging Detection and Ranging (LiDAR) data from the National Plan for Aerial Orthophotography (PNOA), and treated with image enhancement functions (Relief Visualization Tools (RVT)). Moreover, they were compared with multispectral images created from the Infrared Red Blue (IRRB) data contained in the same LiDAR points.

© 2020 American Chemical Society. All rights reserved. Protein functional interactions could explain the biological response of secoiridoids (SECs), main phenolic compounds in virgin olive oil (VOO). The aim was to assess protein-protein interactions (PPIs) of the aorta gap junction alpha-1 (GJA1) and the heart peptidyl-prolyl cis-trans isomerase (FKBP1A), plus the phosphorylated heart proteome, to describe new molecular pathways in the cardiovascular system in rats using nanoliquid chromatography coupled with mass spectrometry. PPIs modified by SECs and associated with GJA1 in aorta rat tissue were calpain, TUBA1A, and HSPB1. Those associated with FKBP1A in rat heart tissue included SUCLG1, HSPE1, and TNNI3. In the heart, SECs modulated the phosphoproteome through the main canonical pathways PI3K/mTOR signaling (AKT1S1 and GAB2) and gap junction signaling (GAB2 and GJA1). PPIs associated with GJA1 and with FKBP1A, the phosphorylation of GAB2, and the dephosphorylation of GJA1 and AKT1S1 in rat tissues are promising protein targets promoting cardiovascular protection to explain the health benefits of VOO.

Ageing is a multi-factorial physiological process and the development of novel IoT systems, tools and devices, specifically targeted to older people, must be based on a holistic framework built on robust scientific knowledge in different health domains. Furthermore, interoperability must be guaranteed using standardized frameworks or approaches. These aspects still largely lack in the specific literature. The main aim of the paper is to develop a new ontology (the NESTORE ontology) to extend the available ontologies provided by universAAL-IoT (uAAL-IoT). The ontology is based on a multidomain healthy ageing holistic model, structuring well-assessed scientific knowledge, specifically targeted to healthy older adults aged between 65 and 75. The tool is intended to support, and standardize heterogeneous data about ageing in compliance with the uAAL-IoT framework. The NESTORE ontology covers all the relevant concepts to represent 3 significant domains of ageing: (1) Physiological Status and Physical Activity Behaviour; (2) Nutrition; and (3) Cognitive and Mental Status and Social Behaviour. In total, 12 sub-ontologies were modelled with more than 60 classes and sub-classes referenced among them by using more than 100 relations and around 20 enumerations. The proposed ontology increases the uAAL collection by 40%. NESTORE ontology provides innovation both in terms of semantic content and technological approach. The thorough use of this ontology can support the development of a decision support system, to promote healthy ageing, with the capacity to do dynamic multi-scale modelling of user-specific data based on the semantic annotations of users' profile.

Estimation of the quality of food products is vital in determining the properties and validity of the food concerning the baking and other manufacturing processes. This article considers the quality estimation of the wheat bread that is baked under standard conditions. The sensory data are collected in real-time, and the obtained data are analysed using the efficient data analytics to predict the quality of the product. The dataset obtained consists of 300 bread samples prepared in 15 days whose vital physical, chemical, and rheological measures are sensed. The measures of the read are obtained through sensory tools and are gathered as a dataset. The obtained data are generally raw, and hence, the required features are obtained through dimensionality reduction using the Linear Discriminant Analysis (LDA). The processed data and the attributes are given as input to the classifier to obtain final estimation results. The efficient Fuzzy Weighted Relevance Vector Machine (FWRVM) classifier model is developed for this achieving this objective. The proposed quality estimation model is implemented using the MATLAB programming environment with the required setting for the FWRVM classifier. The model is trained and tested with the input dataset with data analysis steps. Some state-of-the-art classifiers are also implemented to compare the evaluated performance of the proposed model. The estimation accuracy is obtained by comparing the number of correctly detected bread classes with the wrongly classified breads. The results indicate that the proposed FWRVM-based classifier estimates the quality of the breads with 96.67% accuracy, 96.687% precision, 96.6% recall, and 96.6% F-measure within 8.96726 seconds processing time which is better than the compared Support vector machine (S

This article provides a critical analysis of the digitization of healthcare communication in Italy, Finland, Norway, and Spain. Particularly, we focus on organizational communication and interactions among institutions, providers, and patients. A qualitative content analysis was conducted on data collected between January and May 2019 from (a) documents and policies and (b) interviews in each country with health-related key experts. Results indicate that Finland and Norway are closer than Italy and Spain to the EU discourse concerning the digitization of healthcare communication. Despite what we see as two roads of innovation, all four countries share problems such as the transition toward patient-centered care and the standardization of e-services at different levels. Given that the COVID-19 pandemic has accelerated these practices since March 2020, this article suggests that European digitization of healthcare is undergoing rapid change that warrants broader analysis.