39 research outputs found
Arquiteturas da saúde na segunda metade do século XIX e os modelos de ensino nas academias portuguesas
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
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
ATLANTIC EPIPHYTES: a data set of vascular and non-vascular epiphyte plants and lichens from the Atlantic Forest
Epiphytes are hyper-diverse and one of the frequently undervalued life forms in plant surveys and biodiversity inventories. Epiphytes of the Atlantic Forest, one of the most endangered ecosystems in the world, have high endemism and radiated recently in the Pliocene. We aimed to (1) compile an extensive Atlantic Forest data set on vascular, non-vascular plants (including hemiepiphytes), and lichen epiphyte species occurrence and abundance; (2) describe the epiphyte distribution in the Atlantic Forest, in order to indicate future sampling efforts. Our work presents the first epiphyte data set with information on abundance and occurrence of epiphyte phorophyte species. All data compiled here come from three main sources provided by the authors: published sources (comprising peer-reviewed articles, books, and theses), unpublished data, and herbarium data. We compiled a data set composed of 2,095 species, from 89,270 holo/hemiepiphyte records, in the Atlantic Forest of Brazil, Argentina, Paraguay, and Uruguay, recorded from 1824 to early 2018. Most of the records were from qualitative data (occurrence only, 88%), well distributed throughout the Atlantic Forest. For quantitative records, the most common sampling method was individual trees (71%), followed by plot sampling (19%), and transect sampling (10%). Angiosperms (81%) were the most frequently registered group, and Bromeliaceae and Orchidaceae were the families with the greatest number of records (27,272 and 21,945, respectively). Ferns and Lycophytes presented fewer records than Angiosperms, and Polypodiaceae were the most recorded family, and more concentrated in the Southern and Southeastern regions. Data on non-vascular plants and lichens were scarce, with a few disjunct records concentrated in the Northeastern region of the Atlantic Forest. For all non-vascular plant records, Lejeuneaceae, a family of liverworts, was the most recorded family. We hope that our effort to organize scattered epiphyte data help advance the knowledge of epiphyte ecology, as well as our understanding of macroecological and biogeographical patterns in the Atlantic Forest. No copyright restrictions are associated with the data set. Please cite this Ecology Data Paper if the data are used in publication and teaching events. © 2019 The Authors. Ecology © 2019 The Ecological Society of Americ
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 understanding 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,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 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 underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities 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 organism 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 neglected 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 lost