É tradução ou não é tradução? Uma revisão (e uma reformulação) do conceito de tradução intralingual

Este estudo tem como propósito revisar o conceito de tradução intralingual (INTRA), de modo a caracterizar essa atividade como uma tarefa de tradução e como sendo objeto de interesse dos Estudos da Tradução. É a partir do debate sobre o lugar, o status e a definição da tradução intralingual (Zethsen & Hill-Madsen, 2016), que apresentamos critérios encontrados na literatura para caracterizar tradução que incluem a INTRA. Posteriormente, sugerimos uma reformulação do conceito de INTRA. Abdicando-nos do hiperônimo “Tradução”, apresentamos a proposta do conceito de Reformulação Intralingual como um hiperônimo que melhor abarca as diferentes tarefas de reformulação que podem existir entre dialetos, sistemas e representações de uma mesma língua. A oposição entre Reformulação Interlingual e Reformulação Intralingual capta a essência da tarefa de reformulação e ainda especifica a distinção entre interlingualidade e intralingualidade. Outro ganho com o emprego do termo Reformulação Intralingual é deixar claro que esse é, por si só, um hiperônimo e que não há um único tipo de atividade intralingual, mas diferentes tarefas que podem ser assim entendidas

PERFIL EPIDEMIOLÓGICO DOS ÓBITOS POR NEOPLASIA DE PRÓSTATA NO ESTADO DO TOCANTINS ENTRE 2014 A 2019 (SIM DATASUS).

INTRODUÇÃO: O câncer de próstata é o segundo tipo mais comum de neoplasia maligna entre os homens e com maior prevalência em idosos, a idade é um fator de risco notável para a incidência e mortalidade por essa doença. Considera-se que os hábitos de vida, genética e fatores ambientais também interferem para o desenvolvimento da doença, o que demanda estratégias de investigação precoce das neoplasias malignas para possibilitar sucesso no tratamento e sobrevida. OBJETIVO: Descrever e avaliar o perfil epidemiológico de óbitos por neoplasia maligna de próstata no Estado do Tocantins, Brasil, durante os anos de 2014-2019. METODOLOIGIA: Trata-se de um estudo epidemiológico utilizando dados disponibilizados na base de dados do DATASUS e pelo Sistema de Informação sobre Mortalidade (SIM). RESULTADOS: Foram retirados 752 casos no período do estudo. Demonstrou-se que homens brancos, maiores de 70 anos, com, no mínimo, 3 anos escolaridade, casados e moradores de microrregiões com maior concentração populacional ao apresentarem municípios com mais de 50 mil habitantes, com o local do óbito sendo os hospitais, compõem o perfil epidemiológico dos óbitos por neoplasia maligna de próstata no estado do Tocantins entre 2014 e 2019. Denotou-se pouca variação nas variáveis analisadas entre os anos estudados, o que evidencia pouca mudança do cenário estadual. CONCLUSÃO: Tendo em vista o cenário de desigualdade ao enfrentamento do câncer prostático no Tocantins, a detecção e o tratamento precoce a partir de programas de rastreio com profissionais capacitados em populações vulneráveis em áreas de menor acesso aos serviços de saúde compõe medidas eficazes de prevenção para alterar a situação de saúde.permitiu identificar a prevalência de casos e óbitos em indivíduos do sexo masculino sendo jovens a faixa etária com mais internações e adultos com mais óbitos.   Palavras-chave : Neoplasias, Câncer de Próstata, Óbito

MAMMALS IN PORTUGAL : A data set of terrestrial, volant, and marine mammal occurrences in P ortugal

Mammals are threatened worldwide, with 26% of all species being includedin the IUCN threatened categories. This overall pattern is primarily associatedwith habitat loss or degradation, and human persecution for terrestrial mam-mals, and pollution, open net fishing, climate change, and prey depletion formarine mammals. Mammals play a key role in maintaining ecosystems func-tionality and resilience, and therefore information on their distribution is cru-cial to delineate and support conservation actions. MAMMALS INPORTUGAL is a publicly available data set compiling unpublishedgeoreferenced occurrence records of 92 terrestrial, volant, and marine mam-mals in mainland Portugal and archipelagos of the Azores and Madeira thatincludes 105,026 data entries between 1873 and 2021 (72% of the data occur-ring in 2000 and 2021). The methods used to collect the data were: live obser-vations/captures (43%), sign surveys (35%), camera trapping (16%),bioacoustics surveys (4%) and radiotracking, and inquiries that represent lessthan 1% of the records. The data set includes 13 types of records: (1) burrowsjsoil moundsjtunnel, (2) capture, (3) colony, (4) dead animaljhairjskullsjjaws, (5) genetic confirmation, (6) inquiries, (7) observation of live animal (8),observation in shelters, (9) photo trappingjvideo, (10) predators dietjpelletsjpine cones/nuts, (11) scatjtrackjditch, (12) telemetry and (13) vocalizationjecholocation. The spatial uncertainty of most records ranges between 0 and100 m (76%). Rodentia (n=31,573) has the highest number of records followedby Chiroptera (n=18,857), Carnivora (n=18,594), Lagomorpha (n=17,496),Cetartiodactyla (n=11,568) and Eulipotyphla (n=7008). The data setincludes records of species classified by the IUCN as threatened(e.g.,Oryctolagus cuniculus[n=12,159],Monachus monachus[n=1,512],andLynx pardinus[n=197]). We believe that this data set may stimulate thepublication of other European countries data sets that would certainly contrib-ute to ecology and conservation-related research, and therefore assisting onthe development of more accurate and tailored conservation managementstrategies for each species. There are no copyright restrictions; please cite thisdata paper when the data are used in publications.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

Mammals in Portugal: a data set of terrestrial, volant, and marine mammal occurrences in Portugal

Mammals are threatened worldwide, with ~26% of all species being included in the IUCN threatened categories. This overall pattern is primarily associated with habitat loss or degradation, and human persecution for terrestrial mammals, and pollution, open net fishing, climate change, and prey depletion for marine mammals. Mammals play a key role in maintaining ecosystems functionality and resilience, and therefore information on their distribution is crucial to delineate and support conservation actions. MAMMALS IN PORTUGAL is a publicly available data set compiling unpublished georeferenced occurrence records of 92 terrestrial, volant, and marine mammals in mainland Portugal and archipelagos of the Azores and Madeira that includes 105,026 data entries between 1873 and 2021 (72% of the data occurring in 2000 and 2021). The methods used to collect the data were: live observations/captures (43%), sign surveys (35%), camera trapping (16%), bioacoustics surveys (4%) and radiotracking, and inquiries that represent less than 1% of the records. The data set includes 13 types of records: (1) burrows | soil mounds | tunnel, (2) capture, (3) colony, (4) dead animal | hair | skulls | jaws, (5) genetic confirmation, (6) inquiries, (7) observation of live animal (8), observation in shelters, (9) photo trapping | video, (10) predators diet | pellets | pine cones/nuts, (11) scat | track | ditch, (12) telemetry and (13) vocalization | echolocation. The spatial uncertainty of most records ranges between 0 and 100 m (76%). Rodentia (n =31,573) has the highest number of records followed by Chiroptera (n = 18,857), Carnivora (n = 18,594), Lagomorpha (n = 17,496), Cetartiodactyla (n = 11,568) and Eulipotyphla (n = 7008). The data set includes records of species classified by the IUCN as threatened (e.g., Oryctolagus cuniculus [n = 12,159], Monachus monachus [n = 1,512], and Lynx pardinus [n = 197]). We believe that this data set may stimulate the publication of other European countries data sets that would certainly contribute to ecology and conservation-related research, and therefore assisting on the development of more accurate and tailored conservation management strategies for each species. There are no copyright restrictions; please cite this data paper when the data are used in publications

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

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

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time, and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space. While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes, vast areas of the tropics remain understudied. In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity, but it remains among the least known forests in America and is often underrepresented in biodiversity databases. To worsen this situation, human-induced modifications 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, 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

Efeito da soja-grão moída no crescimento de novilhas leiteiras

Vinte e quatro novilhas leiteiras (altas mestiças holandesas), com idade de 12 a 18 meses, foram divididas em oito blocos de três animais e alimentadas por 112 dias com três diferentes concentrados, à base de 2kg por cabeça por dia, além de silagem de milho à vontade Os concentrados eram assim constituídos: A €” quirera de milho 80% e farelo de soja 20%; B €” milho desintegrado com palha e sabugo 78% e soja crua desintegrada 22%, e C milho desintegrado com palha e sabugo 78% e soja torrada desintegrada 22%. Os ganhos médios de peso no período foram: 97,75; 76,68 e 74.43kg. O tratamento A diferiu estatisticamente do B e do C em ganho de peso no período (P‰¤ 0,01) e em consumo de proteína bruta (P ‰¤ 0,05). As demais análises, ganho em altura, em comprimento, em perímetro torácico e consumo de matéria seca, não foram estatisticamente significativas. As conversões alimentares obtidas foram: 1:7,62; 1:9,41 e 1:9,74, respectivamente, para os tratamentos A, B e C