Hexapoda Yearbook (Arthropoda: Mandibulata: Pancrustacea) Brazil 2020: the first annual production survey of new Brazilian species

This paper provided a list of all new Brazilian Hexapoda species described in 2020. Furthermore, based on the information extracted by this list, we tackled additional questions regarding the taxa, the specialists involved in the species descriptions as well as the journals in which those papers have been published. We recorded a total of 680 new Brazilian species of Hexapoda described in 2020, classified in 245 genera, 112 families and 18 orders. These 680 species were published in a total of 219 articles comprising 423 different authors residing in 27 countries. Only 30% of these authors are women, which demonstrates an inequality regarding sexes. In relation to the number of authors by species, the majority of the new species had two authors and the maximum of authors by species was five. We also found inequalities in the production of described species regarding the regions of Brazil, with Southeast and South leading. The top 10 institutions regarding productions of new species have four in the Southeast, two at South and with one ate North Region being the outlier of this pattern. Out of the total 219 published articles, Zootaxa dominated with 322 described species in 95 articles. The average impact factor was of 1.4 with only seven articles being published in Impact Factors above 3, indicating a hardship on publishing taxonomic articles in high-impact journals.The highlight of this paper is that it is unprecedent, as no annual record of Hexapoda species described was ever made in previous years to Brazil.Fil: Silva Neto, Alberto Moreira. Ministério da Ciência, Tecnologia, Inovações. Instituto Nacional de Pesquisas da Amazônia; BrasilFil: Lopes Falaschi, Rafaela. Universidade Estadual do Ponta Grossa; BrasilFil: Zacca, Thamara. Universidade Federal Do Rio de Janeiro. Museu Nacional; BrasilFil: Hipólito, Juliana. Universidade Federal da Bahia; BrasilFil: Costa Lima Pequeno, Pedro Aurélio. Universidade Federal de Roraima; BrasilFil: Alves Oliveira, João Rafael. Ministério da Ciência, Tecnologia, Inovações. Instituto Nacional de Pesquisas da Amazônia; BrasilFil: Oliveira Dos Santos, Roberto. Ministério da Ciência, Tecnologia, Inovações. Instituto Nacional de Pesquisas da Amazônia; BrasilFil: Heleodoro, Raphael Aquino. Ministério da Ciência, Tecnologia, Inovações. Instituto Nacional de Pesquisas da Amazônia; BrasilFil: Jacobina, Adaiane Catarina Marcondes. Universidade Federal do Paraná; BrasilFil: Somavilla, Alexandre. Ministério da Ciência, Tecnologia, Inovações. Instituto Nacional de Pesquisas da Amazônia; BrasilFil: Camargo, Alexssandro. Ministério da Ciência, Tecnologia, Inovações. Instituto Nacional de Pesquisas da Amazônia; BrasilFil: de Oliveira Lira, Aline. Universidad Federal Rural Pernambuco; BrasilFil: Sampaio, Aline Amanda. Ministério da Ciência, Tecnologia, Inovações. Instituto Nacional de Pesquisas da Amazônia; BrasilFil: da Silva Ferreira, André. Universidad Federal Rural Pernambuco; BrasilFil: Martins, André Luis. Universidade Federal do Paraná; BrasilFil: Figueiredo de Oliveira, Andressa. Universidade Federal do Mato Grosso do Sul; BrasilFil: Gonçalves da Silva Wengrat , Ana Paula. Universidade do Sao Paulo. Escola Superior de Agricultura Luiz de Queiroz; BrasilFil: Batista Rosa, Augusto Henrique. Universidade Estadual de Campinas; BrasilFil: Dias Corrêa, Caio Cezar. Universidade Federal Do Rio de Janeiro. Museu Nacional; BrasilFil: Costa De-Souza, Caroline. Museu Paraense Emilio Goeldi; BrasilFil: Anjos Dos Santos, Danielle. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Centro de Investigación Esquel de Montaña y Estepa Patagónica. Universidad Nacional de la Patagonia "San Juan Bosco". Centro de Investigación Esquel de Montaña y Estepa Patagónica; ArgentinaFil: Pacheco Cordeiro, Danilo. Instituto Nacional Da Mata Atlantica; BrasilFil: Silva Nogueira, David. Ministério da Ciência, Tecnologia, Inovações. Instituto Nacional de Pesquisas da Amazônia; BrasilFil: Almeida Marques, Dayse Willkenia. Ministério da Ciência, Tecnologia, Inovações. Instituto Nacional de Pesquisas da Amazônia; BrasilFil: Nunes Barbosa, Diego. Universidade Federal do Paraná; BrasilFil: Mello Mendes, Diego Matheus. Instituto de Desenvolvimento Sustentável Mamirauá; BrasilFil: Galvão de Pádua, Diego. Ministério da Ciência, Tecnologia, Inovações. Instituto Nacional de Pesquisas da Amazônia; BrasilFil: Silva Vilela, Diogo. Universidade Estadual Paulista Julio de Mesquita Filho; BrasilFil: Gomes Viegas, Eduarda Fernanda. Ministério da Ciência, Tecnologia, Inovações. Instituto Nacional de Pesquisas da Amazônia; BrasilFil: Carneiro dos Santos, Eduardo. Universidade Federal do Paraná; BrasilFil: Rodrigues Fernandes, Daniell Rodrigo. Ministério da Ciência, Tecnologia, Inovações. Instituto Nacional de Pesquisas da Amazônia; Brasi

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

Synthesis and characterization of microcapsules containing tung oil and application in alkyd coatings for corrosion protection

One of the biggest economic problems in the industry is the corrosion and the whole range of problems and secondary costs tied to this process. To avoid it commonly organic coatings are used to protect metal substrates. However, ordinary coatings are flawed and prone to weathering, losing their functionality quickly. An alternative to extend the life of the coatings is through the insertion of microcapsules in the coating matrix, transforming it into a smart coating. Smart coatings are coatings capable of responding to certain stimuli, acting independently without the need for human intervention, for repairs or maintenance. In this work microcapsules of ureaformaldehyde containing tung oil were synthesized by the in situ polymerization method. The microcapsules were characterized by the presence of Tungue oil by FTIR. Soon after the characterization they were incorporated into the matrix of a commercial monocomponent alkyd coating, and evaluations of the performance and the anticorrosive properties of this coating, which happened to be a smart coating, through the insertion of the microcapsules. These were added in the same proportion as the commercial corrosion inhibitor rich in zinc ions. These, together with a coating sample without anti-corrosive additives, were tested by means of corrosive saline 3.5% NaCl, simulating sea water. For this, the stimulus-responsive capacity of the microcapsules was demonstrated through mechanical disruption. It was observed that the additive coating presented an excellent performance for application in 1020 carbon steel specimens, maintaining properties such as gloss, adhesion and roughness improved or practically unchanged. The corrosion protection performance of the coating was evaluated by electrochemical analysis such as electrochemical impedance spectroscopy and open circuit potential and these tests showed that the microcapsules in the coating matrix caused a beneficial increase in the barrier property of the coating, besides protecting the metallic substrate when it undergoes a mechanical defect, through the release of the oil and its active protection conditioned to the coating.Um dos maiores problemas econômicos da indústria é a corrosão e toda gama de problemas e custos secundários atrelados a esse processo. Para evitá-la comumente se utiliza revestimentos orgânicos para proteger substratos metálicos. Porém, os revestimentos comuns são falhos e propensos a sofrer com intempéries, perdendo sua funcionalidade rapidamente. Uma alternativa para prolongar a vida útil dos revestimentos é através da inserção de microcápsulas na matriz do revestimento, transformando-o em um smart coating. Os smart coatings são revestimentos capazes de responder a certos estímulos, atuando de maneira independente sem a necessidade da intervenção humana, para reparos ou manutenções. Neste trabalho microcápsulas de ureia-formaldeído contendo óleo de tungue foram sintetizadas pelo método de polimerização in situ. As microcápsulas foram caracterizadas comprovando a presença do óleo de Tungue por FTIR. Logo após a caracterização elas foram incorporadas na matriz de um revestimento alquídico monocomponente comercial, e foram realizadas avaliações do desempenho e das propriedades anticorrosivas deste revestimento, que passou a ser um ―smart coating‖ ou revestimento inteligente, pela inserção das microcápsulas. Estas foram adicionadas na mesma proporção em que é adicionado o inibidor de corrosão comercial rico em íons zinco. Estes juntamente com uma amostra de revestimento sem aditivos anticorrosivo foram testados mediante meio salino corrosivo de NaCl 3,5%, simulando a água do mar. Para isso foi comprovada a capacidade estímulo-responsivas das microcápsulas mediante rompimento mecânico. Foi observado que o revestimento aditivado apresentou uma excelente performance para aplicação em corpos de prova de aço carbono 1020, mantendo propriedades como brilho, aderência e rugosidade melhorada ou praticamente inalteradas. O desempenho de proteção contra a corrosão do revestimento foi avaliada mediante análises eletroquímicas, como espectroscopia de impedância eletroquímica e potencial de circuito aberto e estes testes mostraram que as microcápsulas na matriz do revestimento ocasionaram um benéfico aumento na propriedade de barreira do mesmo, além de protegerem o substrato metálico quando este sofre um defeito mecânico, através da liberação do óleo e da sua proteção ativa condicionada ao revestimento

Performance Evaluation of Layered Double Hydroxides Containing Benzotriazole and Nitrogen Oxides as Autonomic Protection Particles against Corrosion

Layered double hydroxides (LDH) are lamellar structures with positively charged laminates and charge-compensating interlayer anions. The ion-exchange capacity of LDHs makes them as promising hosts for corrosion inhibitor anions with stimulus-responsive release and self-healing anticorrosion. In the current work, LDHs loaded with two different corrosion inhibitors (nitrogen oxides and benzotriazole) were evaluated for their ion-exchange capacity and autonomic protection against corrosion on carbon steel. Studies on nitrogen oxide-loaded LDH (NOx-LDH) showed that nitrogen oxides were successfully intercalated in LDH structure, which were released in chloride media. Open Circuit Potential (OCP) results showed that NOx-LDH extract shifted OCP to nobler values, indicating the protection of metal. For benzotriazole-loaded LDH (BTZ-LDH), the results indicated the presence of benzotriazole in the structure, but its release was not observed. OCP results showed no significant increase of carbon steel protection, corroborating with the conclusion that benzotriazole ions did not migrate to metal surface. Considering these results, the insertion of NOx-LDH in an automotive primer was proceeded, under three different concentrations (0.2. 1.0, and 3.0%). Electrochemical impedance spectroscopy (EIS) showed that the more effective NOx-LDH concentration on corrosion delay was 0.2%, which better balanced protection level conferred by LDH with a possible loss on effectiveness of coating due to increase in porosity

Evaluation of Corrosion Protection of Self-Healing Coatings Containing Tung and Copaiba Oil Microcapsules

The objective of the current research is to evaluate and compare the corrosion protection efficiency of the microcapsules containing tung oil and copaiba oil using stereoscopic images, electrochemical tests, open circuit potential (OCP), and polarization curves (Tafel analysis). Carbon steel plates were painted with three different coating systems: (a) a coating system with an automotive primer which served as the control, (b) a coating system with microcapsules containing 3% tung oil, and (c) a coating system with microcapsules containing 3% copaiba oil. A crosscut was performed using a scalpel on the coating surfaces to promote the release of the oils, and after drying, electrochemical cells were assembled using electrolyte 3% NaCl. From OCP analyses, it was verified that the coating system containing tung oil loaded microcapsules obtained more positive final values than the control system and the coating system containing copaiba oil loaded microcapsules. The stereoscope images corroborate the OCP results, and the polarization curve analyses also indicated that the microcapsules containing tung oil offer better corrosion protection than the other systems studied

Relationship of Leishmania-specific IgG levels and IgG avidity with parasite density and clinical signs in canine leishmaniasis.

The clinical status and tissue parasite burden of the skin and spleen of 40 dogs naturally infected with Leishmania chagasi (syn. Leishmania infantum), together with 5 uninfected control dogs, were assessed. On the basis of the clinical evaluation, infected dogs were classified as asymptomatic (AD) or symptomatic (SD). Infected animals were also grouped according to their parasite load as exhibiting low (LP), medium (MP) and high (HP) parasitism. The results indicated a high parasite load in the skin samples of SD animals in relation to the AD group. The serum immunoglobin isotype profiles of the studied animals revealed increased levels of IgG 1 in the AD and LP dogs, whereas high levels of IgG 2 were correlated with SD and HP dogs. The avidity index (AI) of IgG total in the SD group was high in comparison of that of the AD group. Moreover, animals with a larger parasite burden either in the spleen or skin showed higher AI values than animals with lower parasitism. Based on these findings, it is suggested that CVL commences with an asymptomatic clinical form with low parasitism, high production of IgG 1 and low affinity of IgG total molecules, and evolves into a symptomatic clinical form with higher parasitism intensity, higher IgG 2 levels, and high affinity of IgG tota

Relationship of Leishmania-specific IgG levels and IgG avidity with parasite density and clinical signs in canine leishmaniasis.

The clinical status and tissue parasite burden of the skin and spleen of 40 dogs naturally infected with Leishmania chagasi (syn. Leishmania infantum), together with 5 uninfected control dogs, were assessed. On the basis of the clinical evaluation, infected dogs were classified as asymptomatic (AD) or symptomatic (SD). Infected animals were also grouped according to their parasite load as exhibiting low (LP), medium (MP) and high (HP) parasitism. The results indicated a high parasite load in the skin samples of SD animals in relation to the AD group. The serum immunoglobin isotype profiles of the studied animals revealed increased levels of IgG 1 in the AD and LP dogs, whereas high levels of IgG 2 were correlated with SD and HP dogs. The avidity index (AI) of IgG total in the SD group was high in comparison of that of the AD group. Moreover, animals with a larger parasite burden either in the spleen or skin showed higher AI values than animals with lower parasitism. Based on these findings, it is suggested that CVL commences with an asymptomatic clinical form with low parasitism, high production of IgG 1 and low affinity of IgG total molecules, and evolves into a symptomatic clinical form with higher parasitism intensity, higher IgG 2 levels, and high affinity of IgG tota