Definição lexicográfica em semântica descritiva

O autor analisa os vários aspectos do ato de definir uma palavra para um dicionário eos tipos de critério a serem adotados. Uma questão de relevância é a do tipo de meta/íngua que se deveadotar na redação de um dicionário e a metodologia empregada para definir o "definiendum", podendo-se empregar várias estratégias: o método analítico, o método sintético, o método denotativo,o método ostensivo ou de mostração, o método implicativo, ou contextual. O método escolhido dependeráda natureza do termo a ser definido: um referente concreto, uma noção abstrata, uma ação ou processoverbal, um instrumento gramatical etc. A sinonímia e a antonímia amplamente usadas nas definiçõestêm também grande importância lexicográfica. O lexicógrafo, ou a equipe de dicionaristas que tra-.balham na confecção de um dicionário, nunca se deve esquecer que as suas definições devem valer paratoda a comunidade lingüística a que ele se destina e assim usarem a linguagem comum a todos e não o(s)seu(s) idioleto(s) particular(es)

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

Worldwide trends in underweight and obesity from 1990 to 2022: a pooled analysis of 3663 population-representative studies with 222 million children, adolescents, and adults

Background Underweight and obesity are associated with adverse health outcomes throughout the life course. We estimated the individual and combined prevalence of underweight or thinness and obesity, and their changes, from 1990 to 2022 for adults and school-aged children and adolescents in 200 countries and territories. Methods We used data from 3663 population-based studies with 222 million participants that measured height and weight in representative samples of the general population. We used a Bayesian hierarchical model to estimate trends in the prevalence of different BMI categories, separately for adults (age ≥20 years) and school-aged children and adolescents (age 5–19 years), from 1990 to 2022 for 200 countries and territories. For adults, we report the individual and combined prevalence of underweight (BMI <18·5 kg/m2) and obesity (BMI ≥30 kg/m2). For schoolaged children and adolescents, we report thinness (BMI <2 SD below the median of the WHO growth reference) and obesity (BMI >2 SD above the median). Findings From 1990 to 2022, the combined prevalence of underweight and obesity in adults decreased in 11 countries (6%) for women and 17 (9%) for men with a posterior probability of at least 0·80 that the observed changes were true decreases. The combined prevalence increased in 162 countries (81%) for women and 140 countries (70%) for men with a posterior probability of at least 0·80. In 2022, the combined prevalence of underweight and obesity was highest in island nations in the Caribbean and Polynesia and Micronesia, and countries in the Middle East and north Africa. Obesity prevalence was higher than underweight with posterior probability of at least 0·80 in 177 countries (89%) for women and 145 (73%) for men in 2022, whereas the converse was true in 16 countries (8%) for women, and 39 (20%) for men. From 1990 to 2022, the combined prevalence of thinness and obesity decreased among girls in five countries (3%) and among boys in 15 countries (8%) with a posterior probability of at least 0·80, and increased among girls in 140 countries (70%) and boys in 137 countries (69%) with a posterior probability of at least 0·80. The countries with highest combined prevalence of thinness and obesity in school-aged children and adolescents in 2022 were in Polynesia and Micronesia and the Caribbean for both sexes, and Chile and Qatar for boys. Combined prevalence was also high in some countries in south Asia, such as India and Pakistan, where thinness remained prevalent despite having declined. In 2022, obesity in school-aged children and adolescents was more prevalent than thinness with a posterior probability of at least 0·80 among girls in 133 countries (67%) and boys in 125 countries (63%), whereas the converse was true in 35 countries (18%) and 42 countries (21%), respectively. In almost all countries for both adults and school-aged children and adolescents, the increases in double burden were driven by increases in obesity, and decreases in double burden by declining underweight or thinness. Interpretation The combined burden of underweight and obesity has increased in most countries, driven by an increase in obesity, while underweight and thinness remain prevalent in south Asia and parts of Africa. A healthy nutrition transition that enhances access to nutritious foods is needed to address the remaining burden of underweight while curbing and reversing the increase in obesit

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

Definição lexicográfica em semântica descritiva

<p>O autor analisa os vários aspectos do ato de definir uma palavra para um dicionário eos tipos de critério a serem adotados. Uma questão de relevância é a do tipo de meta/íngua que se deveadotar na redação de um dicionário e a metodologia empregada para definir o "definiendum", podendo-se empregar várias estratégias: o método analítico, o método sintético, o método denotativo,o método ostensivo ou de mostração, o método implicativo, ou contextual. O método escolhido dependeráda natureza do termo a ser definido: um referente concreto, uma noção abstrata, uma ação ou processoverbal, um instrumento gramatical etc. A sinonímia e a antonímia amplamente usadas nas definiçõestêm também grande importância lexicográfica. O lexicógrafo, ou a equipe de dicionaristas que tra-.balham na confecção de um dicionário, nunca se deve esquecer que as suas definições devem valer paratoda a comunidade lingüística a que ele se destina e assim usarem a linguagem comum a todos e não o(s)seu(s) idioleto(s) particular(es).</p&gt

Zika virus in the Americas: Early epidemiological and genetic findings

Submitted by sandra infurna ([email protected]) on 2016-06-21T16:53:42Z No. of bitstreams: 1 gonzalo2_bello_etal_IOC_2016.pdf: 1066180 bytes, checksum: d43c1cf1b828de79e634ed276cc62178 (MD5)Approved for entry into archive by sandra infurna ([email protected]) on 2016-06-21T17:27:43Z (GMT) No. of bitstreams: 1 gonzalo2_bello_etal_IOC_2016.pdf: 1066180 bytes, checksum: d43c1cf1b828de79e634ed276cc62178 (MD5)Made available in DSpace on 2016-06-21T17:27:43Z (GMT). No. of bitstreams: 1 gonzalo2_bello_etal_IOC_2016.pdf: 1066180 bytes, checksum: d43c1cf1b828de79e634ed276cc62178 (MD5) Previous issue date: 2016Submitted by Angelo Silva ([email protected]) on 2016-07-07T11:16:45Z No. of bitstreams: 3 gonzalo2_bello_etal_IOC_2016.pdf.txt: 51037 bytes, checksum: bebf604bcb5623ddff92fec2bebc02a5 (MD5) gonzalo2_bello_etal_IOC_2016.pdf: 1066180 bytes, checksum: d43c1cf1b828de79e634ed276cc62178 (MD5) license.txt: 2991 bytes, checksum: 5a560609d32a3863062d77ff32785d58 (MD5)Approved for entry into archive by sandra infurna ([email protected]) on 2016-07-07T11:43:23Z (GMT) No. of bitstreams: 3 license.txt: 2991 bytes, checksum: 5a560609d32a3863062d77ff32785d58 (MD5) gonzalo2_bello_etal_IOC_2016.pdf: 1066180 bytes, checksum: d43c1cf1b828de79e634ed276cc62178 (MD5) gonzalo2_bello_etal_IOC_2016.pdf.txt: 51037 bytes, checksum: bebf604bcb5623ddff92fec2bebc02a5 (MD5)Made available in DSpace on 2016-07-07T11:43:23Z (GMT). No. of bitstreams: 3 license.txt: 2991 bytes, checksum: 5a560609d32a3863062d77ff32785d58 (MD5) gonzalo2_bello_etal_IOC_2016.pdf: 1066180 bytes, checksum: d43c1cf1b828de79e634ed276cc62178 (MD5) gonzalo2_bello_etal_IOC_2016.pdf.txt: 51037 bytes, checksum: bebf604bcb5623ddff92fec2bebc02a5 (MD5) Previous issue date: 2016Ministério da Saúde. Instituto Evandro Chagas, Centro de Inovação tecnológica. Ananindeua, PA, Brasil / University of Oxford. Department of Zoology. Oxford, UK.Ministério da Saúde. Instituto Evandro Chagas. Departamento de Arbovirologia e Febres Hemorrágicas. Ananindeua, PA, Brasil.University of Oxford. Department of Zoology. Oxford, UK.Universidade de São Paulo. Instituto Adolfo Lutz. São Paulo, SP, Brasil.Universidade de São Paulo. Instituto Adolfo Lutz. São Paulo, SP, Brasil.University of Oxford. Department of Zoology. Oxford, UK.University of Oxford. Department of Zoology. Oxford, UK.University of Oxford. Department of Zoology. Oxford, UK.University of Oxford. Wellcome Trust Centre for Human Genetics. Oxford, UK.University of Oxford. Wellcome Trust Centre for Human Genetics. Oxford, UK.Universidade de São Paulo. Instituto Adolfo Lutz. São Paulo, SP, Brasil.Universidade de São Paulo. Instituto Adolfo Lutz. São Paulo, SP, Brasil.Universidade de São Paulo. Instituto Adolfo Lutz. São Paulo, SP, Brasil.Universidade de São Paulo. Instituto Adolfo Lutz. São Paulo, SP, Brasil.Universidade de São Paulo. Instituto Adolfo Lutz. São Paulo, SP, Brasil.Universidade de São Paulo. Instituto Adolfo Lutz. São Paulo, SP, Brasil.Ministério da Saúde. Instituto Evandro Chagas. Departamento de Arbovirologia e Febres Hemorrágicas. Ananindeua, PA, Brasil.Ministério da Saúde. Instituto Evandro Chagas. Departamento de Arbovirologia e Febres Hemorrágicas. Ananindeua, PA, Brasil.Ministério da Saúde. Instituto Evandro Chagas. Departamento de Arbovirologia e Febres Hemorrágicas. Ananindeua, PA, Brasil.Ministério da Saúde. Instituto Evandro Chagas. Departamento de Arbovirologia e Febres Hemorrágicas. Ananindeua, PA, Brasil.Ministério da Saúde. Instituto Evandro Chagas. Departamento de Arbovirologia e Febres Hemorrágicas. Ananindeua, PA, Brasil.Ministério da Saúde. Instituto Evandro Chagas. Departamento de Arbovirologia e Febres Hemorrágicas. Ananindeua, PA, Brasil.Ministério da Saúde. Instituto Evandro Chagas. Departamento de Arbovirologia e Febres Hemorrágicas. Ananindeua, PA, Brasil.Ministério da Saúde. Instituto Evandro Chagas. Departamento de Arbovirologia e Febres Hemorrágicas. Ananindeua, PA, Brasil.Ministério da Saúde. Instituto Evandro Chagas. Departamento de Arbovirologia e Febres Hemorrágicas. Ananindeua, PA, Brasil.Ministério da Saúde. Instituto Evandro Chagas. Departamento de Arbovirologia e Febres Hemorrágicas. Ananindeua, PA, Brasil.Ministério da Saúde. Instituto Evandro Chagas. Departamento de Arbovirologia e Febres Hemorrágicas. Ananindeua, PA, Brasil.Ministério da Saúde. Instituto Evandro Chagas. Departamento de Arbovirologia e Febres Hemorrágicas. Ananindeua, PA, Brasil.Ministério da Saúde. Instituto Evandro Chagas. Departamento de Arbovirologia e Febres Hemorrágicas. Ananindeua, PA, Brasil.Ministério da Saúde. Instituto Evandro Chagas. Departamento de Arbovirologia e Febres Hemorrágicas. Ananindeua, PA, Brasil.University of Oxford. Department of Zoology. Oxford, UK / Metabiota. San Francisco, CA 94104, USA.University of Oxford. Department of Zoology. Oxford, UK.University of Oxford. Department of Zoology. Oxford, UK.Fundação Oswaldo Cruz. Salvador, BA, Brasil.Universidade Estadual de Feira de Santana, Feira de Santana. Departamento de Saúde. Centro de Pós-Graduação em Saúde Coletiva. Feira de Santana, BA, Brasil.Fundação Oswaldo Cruz. Salvador, BA, Brasil.University of Washington. Institute for Health Metrics and Evaluation,. Seattle, WA, USA / University of Oxford. Wellcome Trust Centre for Human Genetics. Oxford, UK.Ministério da Saúde. Instituto Evandro Chagas, Centro de Inovação tecnológica. Ananindeua, PA, Brasil.Ministério da Saúde. Instituto Evandro Chagas, Centro de Inovação tecnológica. Ananindeua, PA, BrasilMinistério da Saúde. Instituto Evandro Chagas, Centro de Inovação tecnológica. Ananindeua, PA, BrasilMinistério da Saúde. Instituto Evandro Chagas, Centro de Inovação tecnológica. Ananindeua, PA, BrasilMinistério da Saúde. Instituto Evandro Chagas, Centro de Inovação tecnológica. Ananindeua, PA, BrasilMinistério da Saúde. Instituto Evandro Chagas, Centro de Inovação tecnológica. Ananindeua, PA, BrasilMinistério da Saúde. Instituto Evandro Chagas, Centro de Inovação tecnológica. Ananindeua, PA, BrasilMinistério da Saúde. Instituto Evandro Chagas, Centro de Inovação tecnológica. Ananindeua, PA, BrasilMinistério da Saúde. Instituto Evandro Chagas, Centro de Inovação tecnológica. Ananindeua, PA, BrasilFundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de AIDS e Imunologia Molecular. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de AIDS e Imunologia Molecular. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de AIDS e Imunologia Molecular. Rio de Janeiro, RJ, Brasil.Li Ka Shing Knowledge Institute. St. Michael’s Hospital. Toronto, Canada / University of Toronto. Department of Medicine. Division of Infectious Diseases. Toronto, Canada.University of Toronto.Dalla Lana School of Public Health. Toronto, Canada;Brasil. Ministério da Saúde. Brasília, DF, Brasil.Brasil. Ministério da Saúde. Brasília, DF, Brasil.University of Texas Medical Branch. Department of Pathology. Galveston, TX, USA.University of Oxford. Department of Zoology. Oxford, UK / Metabiota. San Francisco, CA 94104, USA.Ministério da Saúde. Instituto Evandro Chagas, Centro de Inovação tecnológica. Ananindeua, PA, Brasil / University of Texas Medical Branch. Department of Pathology. Galveston, TX, USA.Ministério da Saúde. Instituto Evandro Chagas. Departamento de Arbovirologia e Febres Hemorrágicas. Ananindeua, PA, Brasil.Brazil has experienced an unprecedented epidemic of Zika virus (ZIKV), with ~30,000 cases reported to date. ZIKV was first detected in Brazil in May 2015 and cases of microcephaly potentially associated with ZIKV infection were identified in November 2015. Using next generation sequencing we generated seven Brazilian ZIKV genomes, sampled from four self-limited cases, one blood donor, one fatal adult case, and one newborn with microcephaly and congenital malformations. Phylogenetic and molecular clock analyses show a single introduction of ZIKV into the Americas, estimated to have occurred between May-Dec 2013, more than 12 months prior to the detection of ZIKV in Brazil. The estimated date of origin coincides with an increase in air passengers to Brazil from ZIKV endemic areas, and with reported outbreaks in Pacific Islands. ZIKV genomes from Brazil are phylogenetically interspersed with those from other South American and Caribbean countries. Mapping mutations onto existing structural models revealed the context of viral amino acid changes present in the outbreak lineage; however no shared amino acid changes were found among the three currently available virus genomes from microcephaly cases. Municipality-level incidence data indicate that reports of suspected microcephaly in Brazil best correlate with ZIKV incidence around week 17 of pregnancy, although this does not demonstrate causation. Our genetic description and analysis of ZIKV isolates in Brazil provide a baseline for future studies of the evolution and molecular epidemiology in the Americas of this emerging virus

Data from: Zika virus in the Americas: early epidemiological and genetic findings

Brazil has experienced an unprecedented epidemic of Zika virus (ZIKV), with ~30,000 cases reported to date. ZIKV was first detected in Brazil in May 2015 and cases of microcephaly potentially associated with ZIKV infection were identified in November 2015. Using next generation sequencing we generated seven Brazilian ZIKV genomes, sampled from four self-limited cases, one blood donor, one fatal adult case, and one newborn with microcephaly and congenital malformations. Phylogenetic and molecular clock analyses show a single introduction of ZIKV into the Americas, estimated to have occurred between May-Dec 2013, more than 12 months prior to the detection of ZIKV in Brazil. The estimated date of origin coincides with an increase in air passengers to Brazil from ZIKV endemic areas, and with reported outbreaks in Pacific Islands. ZIKV genomes from Brazil are phylogenetically interspersed with those from other South American and Caribbean countries. Mapping mutations onto existing structural models revealed the context of viral amino acid changes present in the outbreak lineage; however no shared amino acid changes were found among the three currently available virus genomes from microcephaly cases. Municipality-level incidence data indicate that reports of suspected microcephaly in Brazil best correlate with ZIKV incidence around week 17 of pregnancy, although this does not demonstrate causation. Our genetic description and analysis of ZIKV isolates in Brazil provide a baseline for future studies of the evolution and molecular epidemiology in the Americas of this emerging virus