"O caminho se faz ao caminhar": processo de reativação de conselhos locais de saúde em Sobral, a partir do protagonismo cidadão

This article presents the process of political formation of popular leaders with the objective of reactivating the Local Councils and Health (CLS) in the municipality of Sobral, state of Ceará, Brazil. The 48 local popular leaders received a 40 hours intensive training, based on dialogical methodology; assessment of the workshops was made after each one of them, in order to adapt the planning to the needs identified by the group. Follow up actions in their territory were also developed, for technical and pedagogical back up. Some of the results achieved were the expansion of the number of CLSs, from five to twenty; greater engagement and better understanding between Municipal councilors and the local health teams; finally, greater support from the population to the CLDSS in many neighborhoods and districts, wider dissemination of information about CLDSS actions in community radios and various meeting spaces, for example in public squares.O presente artigo tem como objetivo descrever o processo de formação política de lideranças populares com vistas à reativação dos Conselhos Locais de Desenvolvimento Social e Saúde (CLDSS) no município de Sobral - CE. Os 48 mobilizadores locais, como foram denominados, obtiveram formação de 40h/aula intensas com metodologia dialógica, avaliada e planejada após cada oficina para contemplar as necessidades apontadas pelo grupo. Também foi desenvolvido acompanhamento das ações em território visando apoio técnico e pedagógico. . Entre os resultados obtidos ressaltamos: expansão de 5(cinco) para 20 (Vinte) CLDSS; maior envolvimento entre Conselheiros municipais e equipe de saúde, assim como maior apoio aos CLDSS nos diversos bairros e distritos;, maior divulgação das ações do CLDSS em rádios comunitárias e espaços diversificados de encontros, por exemplo em praças públicas

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

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

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

"O caminho se faz ao caminhar": processo de reativação de conselhos locais de saúde em Sobral, a partir do protagonismo cidadão "A path is made by walking": reactivation process of local health councils in Sobral through citizens' leadership

O presente artigo tem como objetivo descrever o processo de formação política de lideranças populares com vistas à reativação dos Conselhos Locais de Desenvolvimento Social e Saúde (CLDSS) no município de Sobral - CE. Os 48 mobilizadores locais, como foram denominados, obtiveram formação de 40h/aula intensas com metodologia dialógica, avaliada e planejada após cada oficina para contemplar as necessidades apontadas pelo grupo. Também foi desenvolvido acompanhamento das ações em território visando apoio técnico e pedagógico. . Entre os resultados obtidos ressaltamos: expansão de 5(cinco) para 20 (Vinte) CLDSS; maior envolvimento entre Conselheiros municipais e equipe de saúde, assim como maior apoio aos CLDSS nos diversos bairros e distritos;, maior divulgação das ações do CLDSS em rádios comunitárias e espaços diversificados de encontros, por exemplo em praças públicas.<br>This article presents the process of political formation of popular leaders with the objective of reactivating the Local Councils and Health (CLS) in the municipality of Sobral, state of Ceará, Brazil. The 48 local popular leaders received a 40 hours intensive training, based on dialogical methodology; assessment of the workshops was made after each one of them, in order to adapt the planning to the needs identified by the group. Follow up actions in their territory were also developed, for technical and pedagogical back up. Some of the results achieved were the expansion of the number of CLSs, from five to twenty; greater engagement and better understanding between Municipal councilors and the local health teams; finally, greater support from the population to the CLDSS in many neighborhoods and districts, wider dissemination of information about CLDSS actions in community radios and various meeting spaces, for example in public squares

NEOTROPICAL ALIEN MAMMALS: a data set of occurrence and abundance of alien mammals in the Neotropics

Biological invasion is one of the main threats to native biodiversity. For a species to become invasive, it must be voluntarily or involuntarily introduced by humans into a nonnative habitat. Mammals were among first taxa to be introduced worldwide for game, meat, and labor, yet the number of species introduced in the Neotropics remains unknown. In this data set, we make available occurrence and abundance data on mammal species that (1) transposed a geographical barrier and (2) were voluntarily or involuntarily introduced by humans into the Neotropics. Our data set is composed of 73,738 historical and current georeferenced records on alien mammal species of which around 96% correspond to occurrence data on 77 species belonging to eight orders and 26 families. Data cover 26 continental countries in the Neotropics, ranging from Mexico and its frontier regions (southern Florida and coastal-central Florida in the southeast United States) to Argentina, Paraguay, Chile, and Uruguay, and the 13 countries of Caribbean islands. Our data set also includes neotropical species (e.g., Callithrix sp., Myocastor coypus, Nasua nasua) considered alien in particular areas of Neotropics. The most numerous species in terms of records are from Bos sp. (n = 37,782), Sus scrofa (n = 6,730), and Canis familiaris (n = 10,084); 17 species were represented by only one record (e.g., Syncerus caffer, Cervus timorensis, Cervus unicolor, Canis latrans). Primates have the highest number of species in the data set (n = 20 species), partly because of uncertainties regarding taxonomic identification of the genera Callithrix, which includes the species Callithrix aurita, Callithrix flaviceps, Callithrix geoffroyi, Callithrix jacchus, Callithrix kuhlii, Callithrix penicillata, and their hybrids. This unique data set will be a valuable source of information on invasion risk assessments, biodiversity redistribution and conservation-related research. There are no copyright restrictions. Please cite this data paper when using the data in publications. We also request that researchers and teachers inform us on how they are using the data

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical science. © The Author(s) 2019. Published by Oxford University Press