A gestão do design na concepção de novos produtos e a diferenciação mercadológica

Desde a Revolução Industrial que o mundo não sofria profundas transformações, no que refere ao mercado de produtos industriais, como a ocorrida mediante a globalização. Dos Australopithecus, passando por C. Colombo, até a sonda espacial Pathinder, desbravar novos mundos (ou mercados), talvez seja um dos principais desafios da humanidade

Transtorno de déficit de atenção/hiperatividade (TDAH): avanços na abordagem terapêutica para a população pediátrica

O transtorno de déficit de atenção/hiperatividade (TDAH) é um transtorno do neurodesenvolvimento com alta prevalência entre a população infantil, com problemas de aprendizado e linguagem sendo comorbidades comuns entre tais pacientes. O presente estudo de revisão buscou avaliar novas evidências na abordagem terapêutica do TDAH, documentadas por meio de estudos clínicos e randomizados. Trata-se de uma pesquisa de revisão integrativa realizada por meio da base de dados PubMed, que levou em consideração os seguintes critérios de inclusão: ensaios clínicos e testes controlados e randomizados; artigos publicados no último ano; que possuíam texto completo disponível e que abordassem acerca de novas evidências no manejo do TDAH. Ficou constatado que o dimesilato de lisdexanfetamina se mostrou mais eficaz na terapêutica do TDAH nas doses de 10 a 30 mg em crianças de 4 a 5 anos de idade do que o placebo, com segurança e tolerabilidade consistentes com estudos anteriormente publicados. Ademais, o uso da suspensão oral de liberação prolongada de anfetamina em pacientes com TDAH demonstrou efeitos robustos e consistentes em tais pacientes, com ótimo início pela manhã e continuando ao longo do dia na terapêutica dos sintomas do TDAH em crianças de 6 a 12 anos. Outro ponto verificado foi que o programa de exercícios de natação trouxe melhorias nos níveis cognitivos e comportamentais, com melhor desempenho acadêmico em compreensão de leitura e matemática entre os pacientes. Por fim, a terapêutica com a estimulação elétrica transcutânea de pontos de acupuntura trouxe melhoria clínica significativa nos sintomas associados ao TDAH na população infantil

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

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

Núcleos de Ensino da Unesp: artigos 2010: volume 4: as disciplinas escolares, os temas transversais e o processo de educação

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP