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Lepidoptera (Insecta) of medical relevance of the state of Rio de Janeiro, Brazil.

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Museu Nacional/UFRJ, Departamento de Entomologia, Laboratório de Pesquisas em Lepidoptera (LaPeL), Campus de Ensino e Pesquisa do Museu Nacional/UFRJ, Av. Bartolomeu de Gusmão, 875, São Cristóvão, 20941-160 Rio de Janeiro, RJ, Brazil.

Fourteen families of Lepidoptera are considered of medical relevance, with six of them recorded in Brazil. However, basic information on medically relevant Lepidoptera in Brazil is limited, with most studies focused on the southern region. This study presents the first comprehensive list of Lepidoptera of medical relevance for the state of Rio de Janeiro based on data from literature, iNaturalist, and the Taxonomic Catalog of the Brazilian Fauna.

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Why participatory plant research now?

Quant Plant Biol

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CIRAD, UMR AGAP Institut, F-34398 Montpellier, France.

In the current polycrisis era, plant science, particularly when applied to agronomy, becomes instrumental: because our main substantial and renewable resource is plant biomass, many future solutions will depend on our ability to grow and transform plant material in a sustainable way. This also questions the way we conduct plant research and thus quantitative plant biology. In response to the increasing polarization between science and society, participatory plant research offers a pertinent framework.

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Ticks of the Genus Rhipicephalus occur worldwide. Especially members of Rh. sanguineus s.

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Using gut microbiome metagenomic hypervariable features for diabetes screening and typing through supervised machine learning.

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Department of Tropical Medicine, Institute of Tropical Medicine, Arthropods of Medical Importance Resource Bank, Yonsei University College of Medicine, Yonsei-ro 50-1, Seodaemun-gu, Seoul 03722, Republic of Korea.

Diabetes mellitus is a complex metabolic disorder and one of the fastest-growing global public health concerns. The gut microbiota is implicated in the pathophysiology of various diseases, including diabetes. This study utilized 16S rRNA metagenomic data from a volunteer citizen science initiative to investigate microbial markers associated with diabetes status (positive or negative) and type (type 1 or type 2 diabetes mellitus) using supervised machine learning (ML) models.

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There is limited understanding of how litter abundance, particularly plastic items, varies between coastal and inland regions with different population densities. The absence of standardised data collection methods further complicates comparisons. Moreover, the lack of data for inland areas hampers efforts to identify potential litter sources, pathways, and interventions.

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