Estudo comparativo entre ovitrampa e o método LIRAa para avaliação da presença de Aedes aegypti (Diptera:Culicidae) em Pedro II, Piauí, Brasil / Comparative study between ovitraps and LIRAa method for evaluating the presence of Aedes aegypti (Diptera:Culicidae) in Pedro II, Piauí, Brazil

O aumento no número de casos de dengue, Chikungunya e Zika vírus se deve a vários fatores, como aumento da urbanização, abastecimento de água irregular e controle ineficaz do vetor primário Aedes aegypti. Assim, estratégias de monitoramento desses vetores são necessárias para redução da circulação viral. O objetivo desse estudo foi avaliar comparativamente, o Levantamento Rápido do índice de Infestação por Ae. aegypti (LIRAa) e a ovoposição em armadilhas (ovitrampa) na cidade de Pedro II, PI- Brasil em três bairros: Boa Esperança, Cristo Rei e Vila Kolping.  As ovitrampas foram elaboradas com pote plástico escuro, contendo no seu interior água, uma palheta e o larvicida Pyriproxifen. No período de 2019 e 2020, colocou-se 31 ovitrampas em pontos estratégicos para contagem e identificação dos ovos presentes na palheta e a partir dos dados obtidos foi possível o cálculo dos Índices de Positividade de Ovitrampa (IPO) e Índices de Densidade de Ovos (IDO). Os dados obtidos foram comparados com o LIRAa realizado em 2019 nos meses de maio, agosto e outubro.  Coletou-se 23.704 ovos. No bairro Boa Esperança foram 9.020 ovos (38,1%) com IPO = 68,9% e IDO = 222,8. No Cristo Rei foram 9.659 ovos (40,7%), IPO = 72,4% e IDO = 109,6. No Vila Kolping, 5.025 ovos (21,2%), IPO = 47,4% e IDO = 81,3. Os resultados mostraram que o IPO variou muito (27,2% a 100%) entre os bairros amostrados, o que demonstra a grande flutuabilidade populacional do vetor. De acordo com o LIRAa, os bairros ofereceram risco alto de infestação do vetor, especialmente no mês de maio. No entanto, no mês de outubro os índices aferidos pelo LIRAa tiveram o valor entre 0,8% e 1,3% e os ovos ainda estavam com a densidade alta, como é o caso do bairro Boa Esperança com 203,6 de IDO.  O grupo de depósitos que predominou foi o A2, caracterizado por grandes reservatórios de água, vasos/frascos com água, pratos, garrafas e outros. Os resultados da pesquisa indicam que o monitoramento por ovitrampas e o método LIRAa são atividades complementares e instrumentos de grande utilidade para a vigilância vetorial, permitindo maior precisão na tomada de decisões

Análise de transcriptoma de experimentos de RNA- Seq com e sem repetições biológicas: revisão.

The discovery of nucleic acids opened new frontiers of knowledge, enablingresearchers to access an enormous amount of data, through large-scale sequencing methodologiesand bioinformatics tools. Amongst these new possibilities, RNA-Seq has been used to identify andquantify RNA molecules. To obtain more accurate biological responses from RNA-Seq data somequestions should be considered such as experimental design, type ofsynthesized library, size ofthefragments generated, number ofbiological replicates, depth, and coverage ofthe sequencing, speciesgenome availability and, the choice of software to properly perform the computational analyzes.Accurate bioinformatics analyzes allow the selection ofgenes with a lower error rate, increasing thevalidation assertiveness via RT-qPCR and thus, reducing costs. The objective of this review was topresent the analysis stages of RNA-Seq data, from experimental design to systems biology,considering relevant points, as well as to pointed out some software currently available to carry theseanalyzes out. Besides, with this review, we aimed to help the academic community to understand allsteps and biases involved in RNA-Seq data analysis, from experiments with or without biologicalreplicates.A descoberta de ácidos nucléicos abriu novas fronteiras de conhecimento, permitindoque os pesquisadores acessassem uma enorme quantidade de dados, através de metodologias desequenciamento em larga escala e ferramentas de bioinformática. Entre essas novas possibilidades,o RNA-Seq (sequenciamento de RNA) tem sido usado para identificar e quantificar moléculas deRNA. Para obter respostas biológicas mais precisas a partir dos dados de RNA-Seq, algumasquestões devem ser consideradas, como o desenho experimental, o tipo de biblioteca sintetizada, otamanho dos fragmentos gerados, o número de repetições biológicas, a profundidade e cobertura dosequenciamento, a disponibilidade do genoma da espécie e, a escolha dos softwares para executaradequadamente as análises computacionais. Análises bioinformáticas precisas permitem a seleçãode genes com menor taxa de erro, aumentando a assertividade da validação via RT-qPCR e, assim,reduzindo custos. O objetivo desta revisão foi apresentar as etapas de análise de dados de RNA-Seq,desde o projeto experimental até a biologia dos sistemas, considerando pontos relevantes, bemcomo apontar alguns softwares atualmente disponíveis para realizar essas análises. Além disso, comesta revisão, objetivamos ajudar a comunidade acadêmica a compreender todas as etapas e viesesenvolvidos na análise de dados de RNA-Seq, a partir de experimentos com ou sem réplicasbiológicas

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

Condições de saneamento básico em áreas hiperendêmicas para esquistossomose no estado de Pernambuco em 2012

OBJETIVO: caracterizar as localidades hiperendêmicas para esquistossomose no estado de Pernambuco quanto às condições de saneamento e recomendar medidas de intervenção ambiental que favoreçam a sustentabilidade das ações de enfrentamento da doença. MÉTODOS: trata-se de um estudo descritivo utilizando os dados obtidos no 'Relatório das condições de saneamento das localidades hiperendêmicas para a esquistossomose e geo-helmintíases em Pernambuco', elaborado pela Secretaria Executiva de Vigilância em Saúde do estado de Pernambuco em 2012. RESULTADOS: das 119 localidades hiperendêmicas identificadas, em 72 (60,5%) foi observada inexistência de água encanada, em 110 (92,4%) não havia coleta de esgoto, e em 116 (97,5%), relatou-se ausência de tratamento do esgoto. CONCLUSÃO: as localidades hiperendêmicas avaliadas apresentaram condições de saneamento básico precárias, que contribuem para as altas prevalências da esquistossomose; por isso, torna-se necessária a sensibilização dos gestores municipais para ações efetivas de melhorias sanitárias que permitam garantir sustentabilidade no controle da esquistossomose

Detection of an Allele Conferring Resistance to Bacillus sphaericus Binary Toxin in Culex quinquefasciatus Populations by Molecular Screening▿

The activity of the Bacillus sphaericus binary (Bin) toxin on Culex quinquefasciatus larvae depends on its specific binding to the Cqm1 receptor, a midgut membrane-bound α-glucosidase. A 19-nucleotide deletion in the cqm1 gene (cqm1REC) mediates high-level resistance to Bin toxin. Here, resistance in nontreated and B. sphaericus-treated field populations of C. quinquefasciatus was assessed through bioassays as well as a specific PCR assay designed to detect the cqm1REC allele in individual larvae. Resistance ratios at 90% lethal concentration, gathered through bioassays, were close to 1 and indicate that the selected populations had similar levels of susceptibility to B. sphaericus, comparable to that of a laboratory colony. A diagnostic PCR assay detected the cqm1REC allele in all populations investigated, and its frequency in two nontreated areas was 0.006 and 0.003, while the frequency in the B. sphaericus-treated population was significantly higher. Values of 0.053 and 0.055 were detected for two distinct sets of samples, and homozygote resistant larvae were found. Evaluation of Cqm1 expression in individual larvae through α-glucosidase assays corroborated the allelic frequency revealed by PCR. The data from this study indicate that the cqm1REC allele was present at a detectable frequency in nontreated populations, while the higher frequency in samples from the treated area is, perhaps, correlated with the exposure to B. sphaericus. This is the first report of the molecular detection of a biolarvicide resistance allele in mosquito populations, and it confirms that the PCR-based approach is suitable to track such alleles in target populations

Brazilian Flora 2020: Leveraging the power of a collaborative scientific network

International audienceThe shortage of reliable primary taxonomic data limits the description of biological taxa and the understanding of biodiversity patterns and processes, complicating biogeographical, ecological, and evolutionary studies. This deficit creates a significant taxonomic impediment to biodiversity research and conservation planning. The taxonomic impediment and the biodiversity crisis are widely recognized, highlighting the urgent need for reliable taxonomic data. Over the past decade, numerous countries worldwide have devoted considerable effort to Target 1 of the Global Strategy for Plant Conservation (GSPC), which called for the preparation of a working list of all known plant species by 2010 and an online world Flora by 2020. Brazil is a megadiverse country, home to more of the world's known plant species than any other country. Despite that, Flora Brasiliensis, concluded in 1906, was the last comprehensive treatment of the Brazilian flora. The lack of accurate estimates of the number of species of algae, fungi, and plants occurring in Brazil contributes to the prevailing taxonomic impediment and delays progress towards the GSPC targets. Over the past 12 years, a legion of taxonomists motivated to meet Target 1 of the GSPC, worked together to gather and integrate knowledge on the algal, plant, and fungal diversity of Brazil. Overall, a team of about 980 taxonomists joined efforts in a highly collaborative project that used cybertaxonomy to prepare an updated Flora of Brazil, showing the power of scientific collaboration to reach ambitious goals. This paper presents an overview of the Brazilian Flora 2020 and provides taxonomic and spatial updates on the algae, fungi, and plants found in one of the world's most biodiverse countries. We further identify collection gaps and summarize future goals that extend beyond 2020. Our results show that Brazil is home to 46,975 native species of algae, fungi, and plants, of which 19,669 are endemic to the country. The data compiled to date suggests that the Atlantic Rainforest might be the most diverse Brazilian domain for all plant groups except gymnosperms, which are most diverse in the Amazon. However, scientific knowledge of Brazilian diversity is still unequally distributed, with the Atlantic Rainforest and the Cerrado being the most intensively sampled and studied biomes in the country. In times of “scientific reductionism”, with botanical and mycological sciences suffering pervasive depreciation in recent decades, the first online Flora of Brazil 2020 significantly enhanced the quality and quantity of taxonomic data available for algae, fungi, and plants from Brazil. This project also made all the information freely available online, providing a firm foundation for future research and for the management, conservation, and sustainable use of the Brazilian funga and flora