Os impactos da pandemia da Covid-19 nos trabalhadores de entrega de alimentos por intermédio das plataformas digitais / The impacts of the Covid-19 pandemic on food delivery workers through digital platforms

A tecnologia aliada aos serviços de entrega de alimentos gerou um aumento no número de serviços realizados por entregadores de plataformas digitais. Durante a Pandemia da Covid-19 observou-se o crescimento de colaboradores desde ramo de atividade. O presente artigo tem como objetivo analisar os impactos positivos e negativos nessas relações, destacando a falta de legislação para amparar esses trabalhadores. Trata-se de pesquisa exploratória e de análise qualitativa, que aplica a técnica do levantamento bibliográfico, normativo e documental, por meio do método dedutivo, para atingir o objetivo proposto. Ao final, destaca-se que será necessária uma modificação legislativa para proteger esses trabalhadores

Systematic study on the extraction of antioxidants from pinhão (araucaria angustifolia (bertol.) Kuntze) coat

Food by-products containing bioactive substances have attracted attention due to the possibility of adding values to residues of the food industry. In this work, the extraction of phenolic compounds from pinhão seed coats (Araucaria angustifolia (Bertol.) Kuntze) using a central composite rotatable design was applied to obtain prediction models for the extract volume yield, total phenolic content, total phenolic acids and total flavonoids. Principal component analysis and hierarchical cluster analysis were implemented showing an evident poor effect of the temperature on phenolic compounds extraction, which is in accordance with the prediction model obtained by the experimental design for total phenolic acids. Volume yield presented a high positive correlation with extraction temperature, followed by solvent composition. Scanning electron microscopy showed that higher temperatures and lower ethanol percentages resulted in highly defibrillated pinhão coats that retained more extract after the extraction process, leading to lower volume yield percentages.Authors thank the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) for the master scholarship and the ‘Centro Multiusuário de Caracterização de Materiais (CMCM) of UTFPR-Campus Curitiba’ for the SEM analysis. Authors are grateful to the Foundation for Science and Technology (FCT, Portugal) and FEDER under Programme PT2020 for financial support to CIMO (UID/AGR/00690/2013) and L. Barros contract. The authors are also grateful to FEDER-Interreg España-Portugal programme for financial support through the project 0377_Iberphenol_6_E. Also to POCI-01-0145-FEDER-006984 (LA LSRE-LCM), funded by FEDER, through POCICOMPETE2020 and FCT.info:eu-repo/semantics/publishedVersio

SARS-CoV-2 introductions and early dynamics of the epidemic in Portugal

Genomic surveillance of SARS-CoV-2 in Portugal was rapidly implemented by the National Institute of Health in the early stages of the COVID-19 epidemic, in collaboration with more than 50 laboratories distributed nationwide. Methods By applying recent phylodynamic models that allow integration of individual-based travel history, we reconstructed and characterized the spatio-temporal dynamics of SARSCoV-2 introductions and early dissemination in Portugal. Results We detected at least 277 independent SARS-CoV-2 introductions, mostly from European countries (namely the United Kingdom, Spain, France, Italy, and Switzerland), which were consistent with the countries with the highest connectivity with Portugal. Although most introductions were estimated to have occurred during early March 2020, it is likely that SARS-CoV-2 was silently circulating in Portugal throughout February, before the first cases were confirmed. Conclusions Here we conclude that the earlier implementation of measures could have minimized the number of introductions and subsequent virus expansion in Portugal. This study lays the foundation for genomic epidemiology of SARS-CoV-2 in Portugal, and highlights the need for systematic and geographically-representative genomic surveillance.We gratefully acknowledge to Sara Hill and Nuno Faria (University of Oxford) and Joshua Quick and Nick Loman (University of Birmingham) for kindly providing us with the initial sets of Artic Network primers for NGS; Rafael Mamede (MRamirez team, IMM, Lisbon) for developing and sharing a bioinformatics script for sequence curation (https://github.com/rfm-targa/BioinfUtils); Philippe Lemey (KU Leuven) for providing guidance on the implementation of the phylodynamic models; Joshua L. Cherry (National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health) for providing guidance with the subsampling strategies; and all authors, originating and submitting laboratories who have contributed genome data on GISAID (https://www.gisaid.org/) on which part of this research is based. The opinions expressed in this article are those of the authors and do not reflect the view of the National Institutes of Health, the Department of Health and Human Services, or the United States government. This study is co-funded by Fundação para a Ciência e Tecnologia and Agência de Investigação Clínica e Inovação Biomédica (234_596874175) on behalf of the Research 4 COVID-19 call. Some infrastructural resources used in this study come from the GenomePT project (POCI-01-0145-FEDER-022184), supported by COMPETE 2020 - Operational Programme for Competitiveness and Internationalisation (POCI), Lisboa Portugal Regional Operational Programme (Lisboa2020), Algarve Portugal Regional Operational Programme (CRESC Algarve2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF), and by Fundação para a Ciência e a Tecnologia (FCT).info:eu-repo/semantics/publishedVersio

Erratum to: The study of cardiovascular risk in adolescents – ERICA: rationale, design and sample characteristics of a national survey examining cardiovascular risk factor profile in Brazilian adolescents

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Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio