Influence of grilling pretreatment and optimization of sous vide processing parameters on the physicochemical and microbiological quality of pirarucu fillet

The demand for high-quality food products has promoted the study of techniques for its processing and conservation. The present research aimed to evaluate the influence of grilling pretreatment on the physical characteristics of pirarucu fillets and the heat transfer process by a computational modelling, and to optimize the sous vide process parameters. Before and after the sous vide process, the samples were analysed for microbiological, chemical and physical characteristics. There was no significant difference between the total experimental time of grilling and that obtained by computational modelling. Immersion in brine for 300s in combination with grilling at 200/120s was selected because of its water-holding capacity (%) 79.40±0.31, texture (N) 1.91±0.40 and value of L* 74.44±0.38 in the fillets. Cooking at 60 for 568.8s were the best sous vide parameters obtained, with highest water-holding capacity (%) 93.60, texture (N) 6.24, E* 7.43, and with microbiological loads below 6 log CFU/g and 7 log MPN/g in the final product. Useful information obtained from this study highlighted the brine and grill pretreatment in combination with sous vide proved it is a potential solution for developing pirarucu products even at an industrial scale.The author(s) disclosed receipt of the following financial support for the research, authorship, and/ or publication of this article: The work behind this paper was funded by the Coordenação de Aperfeiçoamento de Pessoal de Nivel Superior – CAPES, Brazil. Enrique Pino-Herna´ndez thanks to the Organization of American States (OAS – United States) and the Coimbra Group of Brazilian Universities (GCUB) under the scope of Partnerships Program for Education and Training (PAEC) to Latin America and the Caribbean, by the scholarship received for studying a Master program in the Federal University of Para´ (call Brazil, OASGCUB 2013).info:eu-repo/semantics/publishedVersio

Transformer-based normative modelling for anomaly detection of early schizophrenia

Despite the impact of psychiatric disorders on clinical health, early-stage diagnosis remains a challenge. Machine learning studies have shown that classifiers tend to be overly narrow in the diagnosis prediction task. The overlap between conditions leads to high heterogeneity among participants that is not adequately captured by classification models. To address this issue, normative approaches have surged as an alternative method. By using a generative model to learn the distribution of healthy brain data patterns, we can identify the presence of pathologies as deviations or outliers from the distribution learned by the model. In particular, deep generative models showed great results as normative models to identify neurological lesions in the brain. However, unlike most neurological lesions, psychiatric disorders present subtle changes widespread in several brain regions, making these alterations challenging to identify. In this work, we evaluate the performance of transformer-based normative models to detect subtle brain changes expressed in adolescents and young adults. We trained our model on 3D MRI scans of neurotypical individuals (N=1,765). Then, we obtained the likelihood of neurotypical controls and psychiatric patients with early-stage schizophrenia from an independent dataset (N=93) from the Human Connectome Project. Using the predicted likelihood of the scans as a proxy for a normative score, we obtained an AUROC of 0.82 when assessing the difference between controls and individuals with early-stage schizophrenia. Our approach surpassed recent normative methods based on brain age and Gaussian Process, showing the promising use of deep generative models to help in individualised analyses.Comment: 10 pages, 2 figures, 2 tables, presented at NeurIPS22@PAI4M

Inhalation of bacterial cellulose nanofibrils triggers an inflammatory response and changes lung tissue morphology of mice

In view of the growing industrial use of Bacterial cellulose (BC), and taking into account that it might become airborne and be inhaled after industrial processing, assessing its potential pulmonary toxic effects assumes high relevance. In this work, the murine model was used to assess the effects of exposure to respirable BC nanofibrils (nBC), obtained by disintegration of BC produced by Komagataeibacter hansenii. Murine bone marrow-derived macrophages (BMM) were treated with different doses of nBC (0.02 and 0.2 mg/mL, respectively 1 and 10 g of fibrils) in absence or presence of 0.2% Carboxymethyl Cellulose (nBCMC). Furthermore, mice were instilled intratracheally with nBC or nBCMC at different concentrations and at different time-points and analyzed up to 6 months after treatments. Microcrystaline Avicel-plus® CM 2159, a plant-derived cellulose, was used for comparison. Markers of cellular damage (lactate dehydrogenase release and total protein) and oxidative stress (hydrogen peroxidase, reduced glutathione, lipid peroxidation and glutathione peroxidase activity) as well presence of inflammatory cells were evaluated in brochoalveolar lavage (BAL) fluids. Histological analysis of lungs, heart and liver tissues was also performed. BAL analysis showed that exposure to nBCMC or CMC did not induce major alterations in the assessed markers of cell damage, oxidative stress or inflammatory cell numbers in BAL fluid over time, even following cumulative treatments. Avicel-plus® CM 2159 significantly increased LDH release, detected 3 months after 4 weekly administrations. However, histological results revealed a chronic inflammatory response and tissue alterations, being hypertrophy of pulmonary arteries (observed 3 months after nBCMC treatment) of particular concern. These histological alterations remained after 6 months in animals treated with nBC, possibly due to foreign body reaction and the organisms inability to remove the fibers. Overall, despite being a safe and biocompatible biomaterial, BC-derived nanofibrils inhalation may lead to lung pathology and pose significant health risks.The authors acknowledge Embrapa Tropical Agroindustry and Coordination for the Improvement of Higher Education Personnel (CAPES) and the project under the bilateral program FCT/CAPES: Bacterial Cellulose: a platform for the development of bionanoproducts for funding this research. This work was also financially supported by: European Investment Funds by FEDER/COMPETE/POCI - Operational Competitiveness and Internationalization Program, under Project POCI-01-0145-FEDER-006958, National Funds by FCT - Portuguese Foundation for Science and Technology, Project POCI-01-0145-FEDER-006939 (Laboratory for Process Engineering, Environment, Biotechnology and Energy - LEPABE funded by FEDER, funds through COMPETE2020 - Programa Operacional Competitividade e Internacionalização (POCI) - and by national funds through FCT. Rui Gil da Costa is supported by grant nº SFRH/BPD/85462/2012 from FCT, financed by the Portuguese Government and the Social European Fund. This study was supported by the Portuguese Foundation for Science and Technology (FCT) also under the scope of the strategic funding of UID/BIO/04469/2013 unit and COMPETE 2020 (POCI-01-0145-FEDER-006684) and BioTecNorte operation (NORTE-01-0145-FEDER-000004) funded by the European Regional Development Fund under the scope of Norte2020 - Programa Operacional Regional do Norte.info:eu-repo/semantics/publishedVersio

NIRS and multivariate methods for discrimination of morning glory species at different growth stages

Morning glory species are weeds very common in tropical crops, where they cause direct and indirect damage. The management of these species primarily relies on the application of herbicides, disregarding the growth stage and spatial distribution. Studies addressing new techniques for identifying these species may contribute to the development of proximal sensors for carrying out specific and rational management. Thus, the objective of this work was to use near infrared spectroscopy (NIRS) and multivariate analysis to discriminate two species of morning glory in three growth stages. NIRS spectra were collected from Ipomoea hederifolia and Merremia aegyptia were collected at three different stages in the spectral range of 4.000 to 10.000 cm-1. PCA and PC-LDA were used to analyze the entire spectrum and specific bands. NIRS associated with PCA and PC-LDA were sufficient to discriminate I. hederifolia and M. aegyptia species and their growth stages. PCA allowed a proper segregation of stages and species when applied individually PC-LDA correctly classified between 90.93 to 100% of species and stages. The best discrimination results were observed in the NIR spectra ranges from 4.500 to 6.000 cm-1 and 4.500 to 6.000 + 6.500 to 7.750 cm-1. This study represents an advance in the research and implementation of NIRS technology to discriminate weed species for the future development of equipment to assist in the adoption and/or performance of a specific management of weeds, capable of contributing to the reduction in the use of herbicides in crops

Acacia wood fractionation using deep eutectic solvents: extraction, recovery, and characterization of the different fractions

The selective extraction and recovery of different lignocellulosic molecules of interest from forestry residues is increasing every day not only to satisfy the needs of driving a society toward more sustainable approaches and materials (rethinking waste as a valuable resource) but also because lignocellulosic molecules have several applications. For this purpose, the development of new sustainable and ecologically benign extraction approaches has grown significantly. Deep eutectic solvents (DESs) appear as a promising alternative for the processing and manipulation of biomass. In the present study, a DES formed using choline chloride and levulinic acid (ChCl:LA) was studied to fractionate lignocellulosic residues of acacia wood (Acacia dealbata Link), an invasive species in Portugal. Different parameters, such as temperature and extraction time, were optimized to enhance the yield and purity of recovered cellulose and lignin fractions. DESs containing LA were found to be promising solvent systems, as the hydrogen bond donor was considered relevant in relation to lignin extraction and cellulose concentration. On the other hand, the increase in temperature and extraction time increases the amount of extracted material from biomass but affects the purity of lignin. The most promising DES system, ChCELA in a ratio of 1:3, was found to not significantly depolymerize the extracted lignin, which presented a similar molecular weight to a la-aft lignin. Additionally, the P-31 NMR results revealed that the extracted lignin has a high content of phenolic OH groups, which favor its reactivity. A mixture of ChCl:LA may be considered a fully renewable solvent, and the formed DES presents good potential to fractionate wood residues.info:eu-repo/semantics/publishedVersio

Using normative modelling to detect disease progression in mild cognitive impairment and Alzheimer’s disease in a cross-sectional multi-cohort study

Abstract Normative modelling is an emerging method for quantifying how individuals deviate from the healthy populational pattern. Several machine learning models have been implemented to develop normative models to investigate brain disorders, including regression, support vector machines and Gaussian process models. With the advance of deep learning technology, the use of deep neural networks has also been proposed. In this study, we assessed normative models based on deep autoencoders using structural neuroimaging data from patients with Alzheimer’s disease (n = 206) and mild cognitive impairment (n = 354). We first trained the autoencoder on an independent dataset (UK Biobank dataset) with 11,034 healthy controls. Then, we estimated how each patient deviated from this norm and established which brain regions were associated to this deviation. Finally, we compared the performance of our normative model against traditional classifiers. As expected, we found that patients exhibited deviations according to the severity of their clinical condition. The model identified medial temporal regions, including the hippocampus, and the ventricular system as critical regions for the calculation of the deviation score. Overall, the normative model had comparable cross-cohort generalizability to traditional classifiers. To promote open science, we are making all scripts and the trained models available to the wider research community