TrainMiC® Presentations Translated in Portuguese

TrainMiC® is a European programme for life-long learning about how to interpret the metrological requirements in chemistry. It is operational across many parts of Europe via national teams. These teams use shareware pedagogic tools which have been harmonized at European level by a joint effort of many experts across Europe working in an editorial board. The material has been translated into fourteen different languages. In this publication, TrainMiC® presentations translated in Portuguese language by the Portuguese TrainMiC® team are published.JRC.D.3-Knowledge Transfer and Standards for Securit

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

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

Revisão sistematizada da literatura e opinião de peritos

Objective: The 3E (Evidence, Expertise, Exchange) Initiative is a multinational effort of rheumatologists aimed at developing evidence-based recommendations addressing specific questions relevant to clinical practice. The objective of the Portuguese contribution for the 3E Initiative was to develop evidence-based recommendations on how to investigate, follow-up and treat undifferentiated peripheral inflammatory arthritis (UPIA) adapted to local reality and develop additional recommendations considered relevant in the national context. Methods: An international scientific committee from 17 countries selected a set of questions concerning the diagnosis and monitoring of UPIA using a Delphi procedure. Evidence-based answers to each question were sought by a systematic literature search, performed in Medline, Embase, the Cochrane Library and ACR/EULAR 2007-2009 meeting abstracts. Relevant articles were reviewed for quality assessment, data extraction and synthesis. In a national meeting, a panel of 63 Portuguese rheumatologists used the evidence which was gathered to develop recommendations, and filled the gaps in the evidence with their expert opinion. Finally, national recommendations were formulated and agreement among the participants was assessed. Results: A total of 54754 references were identified, of which 267 were systematically reviewed. Thirteen national key recommendations about the investigation, follow-up and treatment of UPIA were formulated. One recommendation addressed differential diagnosis and investigations prior to the established operational diagnosis of UPIA, eight recommendations were related to the diagnostic and prognostic value of clinical and laboratory assessments in established UPIA (history and physical examination, acute phase reactants, serologies, autoantibodies, radiographs, magnetic resonance imaging and ultrasound, genetic markers and synovial biopsy), one recommendation highlighted predictors of persistence (chronicity), one addressed monitoring of clinical disease activity in UPIA, one aimed to find an useful method/score to predict a definitive diagnosis and the last one was related to treatment. Conclusion: Portuguese evidence-based recommendations for the management of UPIA in everyday practice were developed. Their dissemination and implementation in daily clinical practice should help to improve practice uniformity and optimize the management of UPIA patients.publishersversionpublishe