Cadastramento e mapa inteligente: divergência entre teoria e prática / Registration and intelligent map: divergence between theory and practice

Territorialização é um dos pressupostos da organização das práticas de saúde. O mapa inteligente mostra a situação atual da comunidade como um todo, por isso é um importante instrumento para melhorar a qualidade do serviço de saúde através de planejamento. No entanto, essa estratégia, muitas vezes, reduz o conceito de espaço, utilizado de uma forma meramente administrativa. Nos causou estranhamento o fato de não ter sido visualizado mapas inteligentes em uma Unidade Básica de Saúde de Itajaí, em roda de conversa com os Agentes Comunitários de Saúde (ACS), foi verificado que o mapa é atualizado apenas uma vez ao ano com o intuito de cumprir obrigações referentes ao PMAQ-AB (Programa Nacional de Melhoria do Acesso e da Qualidade da Atenção Básica). Os ACSs utilizam uma agenda não padronizada para os registros de dados, percebe-se, portanto, que, além do desconhecimento sobre a importância do uso do mapa para ampliação de ações de prevenção e manutenção da saúde, há um déficit na perspectiva da integralidade do território e seus atores sociais, desfavorecendo, consequentemente, a população. Sendo assim, os ACSs deveriam receber um treinamento antes do início de suas atividades com o intuito de elucidar e ressaltar a importância de um mapa inteligente para a melhora da qualidade do serviço da saúde

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

Agamermis sp. (Nematoda: Mermithidae) parasitizing Armadillidium vulgare (Crustacea: Isopoda) in Argentina

The Mermithidae is a family of nematodes parasitic in many kinds of insects, spiders, leeches, crustaceans and other invertebrates throughout the world. While conducting an assay with entomopathogenic nematodes, we found Armadillidium vulgare (Crustacea: Isopoda) individuals to be infected with Agamermis sp., marking the fourth known discovery of a mermithid infection in the order Isopoda. In this work, we contribute with an 18S rDNA sequence of the isolated nematode and the morphological and morphometrical characterization of the juveniles.Fil: Rusconi, José Matías. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Centro de Estudios Parasitológicos y de Vectores. Universidad Nacional de La Plata. Facultad de Ciencias Naturales y Museo. Centro de Estudios Parasitológicos y de Vectores; ArgentinaFil: Eliceche, Daiana Pamela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Centro de Estudios Parasitológicos y de Vectores. Universidad Nacional de La Plata. Facultad de Ciencias Naturales y Museo. Centro de Estudios Parasitológicos y de Vectores; ArgentinaFil: Salas, Augusto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Centro de Estudios Parasitológicos y de Vectores. Universidad Nacional de La Plata. Facultad de Ciencias Naturales y Museo. Centro de Estudios Parasitológicos y de Vectores; ArgentinaFil: Balcazar, Dario Emmanuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Centro de Estudios Parasitológicos y de Vectores. Universidad Nacional de La Plata. Facultad de Ciencias Naturales y Museo. Centro de Estudios Parasitológicos y de Vectores; ArgentinaFil: Ibañez Shimabukuro, M.. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Centro de Estudios Parasitológicos y de Vectores. Universidad Nacional de La Plata. Facultad de Ciencias Naturales y Museo. Centro de Estudios Parasitológicos y de Vectores; ArgentinaFil: Achinelly, Maria Fernanda. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Centro de Estudios Parasitológicos y de Vectores. Universidad Nacional de La Plata. Facultad de Ciencias Naturales y Museo. Centro de Estudios Parasitológicos y de Vectores; Argentin

Morphological and molecular identification of filamentous fungi isolated from cosmetic powders

Seven fungi were isolated from 50 samples of cosmetic powders. Morphological analyses and ribosomal DNA Internal Transcribed Spacers sequencing were performed which allowed the discrimination of the isolated fungi as Aspergillus fumigatus, Penicillium sp., and Cladosporium sp. which could have, among their species, potentially pathogenic microorganisms