Senhora, de José de Alencar: uma análise histórica da sociedade brasileira do final do século XIX através de sua personagem feminina

Resumo criado pelo pesquisador do Projeto PROGRAD/DIREN/UFU 2016-2017 Historiografia e pesquisa discente: as monografias dos graduandos em História da UFU.Trabalho de Conclusão de Curso (Graduação)Este trabalho visa compreender as relações históricas da sociedade brasileira no final do século XIX, através da análise da obra Senhora de José de Alencar, buscando uma visão através de sua personagem feminina. O romantismo no Brasil acontece na mesma época de sua firmação de identidade nacional e busca pela independência, bem como sua consciência de brasilidade. Privilegiando a visão nacional e buscando delinear seu estilo os românticos, mesmo antes de José de Alencar faziam do nacionalismo uma forma de luta e oposição às tradições europeias. Senhora apresenta um painel social bem definido de sua época e diante disto, delineia o comportamento dos dois amantes - que parecem se odiar – e os costumes da aristocracia e da sociedade urbana oitocentista. Considerando a discussão sobre o papel das mulheres no final do século XIX, o romance de José de Alencar proporciona um estudo histórico a partir das situações vividas pela personagem Aurélia

The steroid-hormone ecdysone coordinates parallel pupariation neuromotor and morphogenetic subprograms via epidermis-to-neuron Dilp8-Lgr3 signal induction

Funding Information: We thank Drs. Carlos Ribeiro, Christen Mirth, Elio Sucena, Filip Port, Frank Schnorrer, Julien Colombani, Maria Dominguez, Maria Luisa Vasconcelos, Pierre Leopold, Simon Bullock, Rita Teodoro, Gerald Rubin, Melissa Harrison, Kate O’Connor-Giles, Jill Wildonger, Mariana Melani, Pablo Wappner, and Christian Wegener for fly stocks and reagents. We thank Ryohei Yagi and Konrad Basler for the LHV2 plasmid and Brain McCabe for the mhc-Gateway destination plasmid. We thank Carlos Ribeiro and Dennis Goldschmidt for help in designing and constructing one of the pupariation arenas and Mariana Melani, Pablo Wappner, Arash Bashirullah, and Filip Port for sharing resources and unpublished data. We thank Arash Bashirullah, Fillip Port, and Carlos Ribeiro for discussions and/or comments on the manuscript, and Jim Truman for discussions on Fraenkel’s pupariation factors. Stocks obtained from the Bloomington Drosophila Stock Center (NIH P40OD018537) were used in this study. Work in the Integrative Biomedicine Laboratory was supported by the European Commission FP7 (PCIG13-GA-2013-618847), by the FCT (IF/00022/2012; Congento LISBOA-01-0145-FEDER-022170, cofinanced by FCT/Lisboa2020; UID/Multi/04462/2019; PTDC/BEXBCM/1370/2014; PTDC/MED-NEU/30753/2017; PTDC/BIA-BID/31071/2017; FCT SFRH/BPD/94112/ 2013; SFRH/BD/94931/2013), the MIT Portugal Program (MIT-EXPL/BIO/0097/2017), and FAPESP (16/09659-3, 16/10342-4, and 17/17904-0). AG is a CONICET researcher, YV holds a CONICET postdoctoral fellowship and FPS and MJD hold a PhD fellowship from CONICET. Work in the Garelli lab was supported by ANPCyT (Agencia Nacional para la Promoción de la Ciencia y la Tecnología, PICT 2014-2900 and PICT 2017-0254) and CONICET (PIP11220150100182CO). Publisher Copyright: © 2021, The Author(s). Copyright: Copyright 2021 Elsevier B.V., All rights reserved.Innate behaviors consist of a succession of genetically-hardwired motor and physiological subprograms that can be coupled to drastic morphogenetic changes. How these integrative responses are orchestrated is not completely understood. Here, we provide insight into these mechanisms by studying pupariation, a multi-step innate behavior of Drosophila larvae that is critical for survival during metamorphosis. We find that the steroid-hormone ecdysone triggers parallel pupariation neuromotor and morphogenetic subprograms, which include the induction of the relaxin-peptide hormone, Dilp8, in the epidermis. Dilp8 acts on six Lgr3-positive thoracic interneurons to couple both subprograms in time and to instruct neuromotor subprogram switching during behavior. Our work reveals that interorgan feedback gates progression between subunits of an innate behavior and points to an ancestral neuromodulatory function of relaxin signaling.publishersversionpublishe

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