Antileishmanial activity of Handroanthus serratifolius (Vahl) S. Grose (Bignoniaceae).

This study aimed to evaluate the leishmanicidal activity of ethanol extract, fractions, and isolated substance from Handroanthus serratifolius against Leishmania amazonensis. Furthermore, this activity was related to cytotoxicity, and the selectivity index was determined.The ethanol extract was obtained by maceration of the stem powder, and the extract was subjected to fractionation on chromatographic column. The lapachol was obtained by acid base extraction followed by purification in chromatographic column. The antipromastigote activity and cytotoxicity tests were carried out by the cell viabilitymethod (MTT).Modified THP-1 cells were infected with L. amazonensis promastigotes and treated for 24 h with different concentrations of the extract, fractions, and lapachol. Theethanol extract, dichloromethane, and ethyl acetate fractions were not active against promastigotes (IC50 > 200 ?g/mL) or cytotoxic (CC50 > 500 ?g/mL), and the selectivity index (SI)was greater than 2.5.The ethyl acetate fractionwas active only in promastigotes; it is not cytotoxic (CC50 > 500 ?g/mL, SI > 5).The lapachol was selectively active only against amastigote (IS > 2.5, CC50 > 500 ?g/mL). In summary, lapachol and ethyl acetate fraction are promising against amastigote and promastigote forms, respectively

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

The shortage of reliable primary taxonomic data limits the description of biological taxa and the understanding of biodiver sity patterns and processes, complicating biogeographical, ecological, and evolutionary studies. This deficit creates a significant taxo nomic 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 be yond 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 un equally distributed, with the Atlantic Rainforest and the Cerrado being the most intensively sampled and studied biomes in the coun try. 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.Fil: Gomes da Silva, Janaina. Jardim Botânico do Rio de Janeiro: Rio de Janeiro, BrasilFil: Filardi, Fabiana L.R. Jardim Botânico do Rio de Janeiro; BrasilFil: Barbosa, María Regina de V. Universidade Federal da Paraíba: Joao Pessoa; BrasilFil: Baumgratz, José Fernando Andrade. Jardim Botânico do Rio de Janeiro; BrasilFil: de Mattos Bicudo, Carlos Eduardo. Instituto de Botânica. Núcleo de Pesquisa em Ecologia; BrasilFil: Cavalcanti, Taciana. Empresa Brasileira de Pesquisa Agropecuária Recursos Genéticos e Biotecnologia; BrasilFil: Coelho, Marcus. Prefeitura Municipal de Campinas; BrasilFil: Ferreira da Costa, Andrea. Federal University of Rio de Janeiro. Museu Nacional. Department of Botany; BrasilFil: Costa, Denise. Instituto de Pesquisas Jardim Botanico do Rio de Janeiro; BrasilFil: Dalcin, Eduardo C. Rio de Janeiro Botanical Garden Research Institute; BrasilFil: Labiak, Paulo. Universidade Federal do Parana; BrasilFil: Cavalcante de Lima, Haroldo. Jardim Botânico do Rio de Janeiro; BrasilFil: Lohmann, Lucia. Universidade de São Paulo; BrasilFil: Maia, Leonor. Universidade Federal de Pernambuco; BrasilFil: Mansano, Vidal de Freitas. Instituto de Pesquisas Jardim Botânico do Rio de Janeiro; Brasil. Jardim Botânico do Rio de Janeiro; BrasilFil: Menezes, Mariângela. Federal University of Rio de Janeiro. Museu Nacional. Department of Botany; BrasilFil: Morim, Marli. Instituto de Pesquisas Jardim Botânico do Rio de Janeiro; BrasilFil: Moura, Carlos Wallace do Nascimento. Universidade Estadual de Feira de Santana. Department of Biological Science; BrasilFil: Lughadha, Eimear NIck. Royal Botanic Gardens; Reino UnidoFil: Peralta, Denilson. Instituto de Pesquisas Ambientais; BrazilFil: Prado, Jefferson. Instituto de Pesquisas Ambientais; BrasilFil: Roque, Nádia. Universidade Federal da Bahia; BrasilFil: Stehmann, Joao. Universidade Federal de Minas Gerais; BrasilFil: da Silva Sylvestre, Lana. Universidade Federal do Rio de Janeiro; BrasilFil: Trierveiler-Pereira, Larissa. Universidade Estadual de Maringá. Departamento de Análises Clínicas e Biomedicina; BrasilFil: Walter, Bruno Machado Teles. EMBRAPA Cenargen Brasília; BrasilFil: Zimbrão, Geraldo. Universidade Federal do Rio de Janeiro; BrasilFil: Forzza, Rafaela C. Jardim Botânico do Rio de Janeiro; BrasilFil: Morales, Matías. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Recursos Biológicos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Morón. Facultad de Agronomía y Ciencias Agroalimentarias; Argentin

Data for a meta-analysis of the adaptive layer in adaptive large neighborhood search.

Meta-analysis, a systematic statistical examination that combines the results of several independent studies, has the potential of obtaining problem- and implementation-independent knowledge and understanding of metaheuristic algorithms, but has not yet been applied in the domain of operations research. To illustrate the procedure, we carried out a meta-analysis of the adaptive layer in adaptive large neighborhood search (ALNS). Although ALNS has been widely used to solve a broad range of problems, it has not yet been established whether or not adaptiveness actually contributes to the performance of an ALNS algorithm. A total of 134 studies were identified through Google Scholar or personal e-mail correspondence with researchers in the domain, 63 of which fit a set of predefined eligibility criteria. The results for 25 different implementations of ALNS solving a variety of problems were collected and analyzed using a random effects model. This dataset contains a detailed comparison of ALNS with the non-adaptive variant per study and per instance, together with the meta-analysis summary results. The data enable to replicate the analysis, to evaluate the algorithms using other metrics, to revisit the importance of ALNS adaptive layer if results from more studies become available, or to simply consult the ready-to-use formulas in the summary file to carry out a meta-analysis of any research question. The individual studies, the meta-analysis and its results are described and interpreted in detail in Renata Turkeš, Kenneth Sörensen, Lars Magnus Hvattum, Meta-analysis of Metaheuristics: Quantifying the Effect of Adaptiveness in Adaptive Large Neighborhood Search, in the European Journal of Operational Research