Produção da bananeira nanica (1º ciclo) em função da aplicação de doses de biofertilizantes líquidos

Objetivou-se com este trabalho, estudar os efeitos de 5 tipos e 10 doses de biofertilizantes na produção da bananeira Nanica (1º ciclo). O experimento foi conduzido, em condições de campo, no CCHA, pertencente a Universidade Estadual da Paraíba-UEPB, Campus Catolé do Rocha-PB. O delineamento experimental adotado foi o de blocos casualizados, com 50 tratamentos, no esquema fatorial 5 x 10, com quatro repetições, totalizando 200 parcelas experimentais. O valor do número de frutos por planta aumentou com o incremento da dose do biofertilizante B4 até um limite ótimo; o peso total de pencas por cacho aumentou linearmente com o incremento da dose de biofertilizante, atingindo o valor maior máximo na dose máxima; o peso médio de penca aumentou linearmente com o incremento da dose do biofertilizante B2, atingindo o maior valor na dose máxima; o peso médio do fruto e o peso do fruto médio aumentaram linearmente com o aumento da dose do biofertilizante B5, atingindo os maiores valores na dose máxima; a aplicação de (B5) proporcionou maior peso médio do fruto e peso do fruto médio

Produção e caracterização de anticorpos policlonais contra Xanthomonas campestris pv. viticola

The objective of this work was to produce polyclonal antibodies against Xanthomonas campestris pv. viticola and characterize these antibodies through Elisa serological indirect method. Results indicate that polyclonal antibodies produced were highly reactive against bacterial cells, showing specificity at the pathovar level and potential to be used for diagnosis and certification purposes.O objetivo deste trabalho foi a produção de anticorpos policlonais contra Xanthomonas campestris pv. viticola e sua caracterização pelo método Elisa indireto. Os resultados apontaram a qualidade dos anticorpos policlonais produzidos, os quais mostraram-se altamente reativos e específicos para o patovar com potencial para ser empregado no diagnóstico da doença e em programas de certificação

Genome of Herbaspirillum seropedicae Strain SmR1, a Specialized Diazotrophic Endophyte of Tropical Grasses

The molecular mechanisms of plant recognition, colonization, and nutrient exchange between diazotrophic endophytes and plants are scarcely known. Herbaspirillum seropedicae is an endophytic bacterium capable of colonizing intercellular spaces of grasses such as rice and sugar cane. The genome of H. seropedicae strain SmR1 was sequenced and annotated by The Paraná State Genome Programme—GENOPAR. The genome is composed of a circular chromosome of 5,513,887 bp and contains a total of 4,804 genes. The genome sequence revealed that H. seropedicae is a highly versatile microorganism with capacity to metabolize a wide range of carbon and nitrogen sources and with possession of four distinct terminal oxidases. The genome contains a multitude of protein secretion systems, including type I, type II, type III, type V, and type VI secretion systems, and type IV pili, suggesting a high potential to interact with host plants. H. seropedicae is able to synthesize indole acetic acid as reflected by the four IAA biosynthetic pathways present. A gene coding for ACC deaminase, which may be involved in modulating the associated plant ethylene-signaling pathway, is also present. Genes for hemagglutinins/hemolysins/adhesins were found and may play a role in plant cell surface adhesion. These features may endow H. seropedicae with the ability to establish an endophytic life-style in a large number of plant species

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

Catálogo Taxonômico da Fauna do Brasil: setting the baseline knowledge on the animal diversity in Brazil

The limited temporal completeness and taxonomic accuracy of species lists, made available in a traditional manner in scientific publications, has always represented a problem. These lists are invariably limited to a few taxonomic groups and do not represent up-to-date knowledge of all species and classifications. In this context, the Brazilian megadiverse fauna is no exception, and the Catálogo Taxonômico da Fauna do Brasil (CTFB) (http://fauna.jbrj.gov.br/), made public in 2015, represents a database on biodiversity anchored on a list of valid and expertly recognized scientific names of animals in Brazil. The CTFB is updated in near real time by a team of more than 800 specialists. By January 1, 2024, the CTFB compiled 133,691 nominal species, with 125,138 that were considered valid. Most of the valid species were arthropods (82.3%, with more than 102,000 species) and chordates (7.69%, with over 11,000 species). These taxa were followed by a cluster composed of Mollusca (3,567 species), Platyhelminthes (2,292 species), Annelida (1,833 species), and Nematoda (1,447 species). All remaining groups had less than 1,000 species reported in Brazil, with Cnidaria (831 species), Porifera (628 species), Rotifera (606 species), and Bryozoa (520 species) representing those with more than 500 species. Analysis of the CTFB database can facilitate and direct efforts towards the discovery of new species in Brazil, but it is also fundamental in providing the best available list of valid nominal species to users, including those in science, health, conservation efforts, and any initiative involving animals. The importance of the CTFB is evidenced by the elevated number of citations in the scientific literature in diverse areas of biology, law, anthropology, education, forensic science, and veterinary science, among others

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

Produção da bananeira nanica, 1o ciclo, em função da aplicação de doses de biofertilizantes líquidos

The objective of this work is to study the effects of 5 types and 10 doses of biofertilizers in the production of banana Nanica (1st cycle). The experiment was conducted in field conditions in the CCHA , belonging to the Universidade Estadual da Paraíba- UEPB Campus Catolé do Rocha-PB. The experimental design was a randomized complete block design with 50 treatments in a factorial 5 x 10, with four replications, totaling 200 plots. The value of the number of fruits per plant increased with increasing dose of biofertilizer B4 to an optimum limit, the total weight of hands per bunch increased linearly with increasing dose of biofertilizer, reaching the highest peak at the maximum dose, weight average bunch increased linearly with increasing dose of biofertilizer B2, reaching the highest value at the maximum dose, the average fruit weight and average fruit weight increased linearly with increasing dose of biofertilizer B5, reaching the highest values at the maximum dose, applying (B5) increased average fruit weight and average fruit weight.Objetivou-se com este trabalho, estudar os efeitos de 5 tipos e 10 doses de biofertilizantes na produção da bananeira Nanica (1o ciclo). O experimento foi conduzido, em condições de campo, no CCHA, pertencente a Universidade Estadual da Paraíba-UEPB, Campus Catolé do Rocha-PB. O delineamento experimental adotado foi o de blocos casualizados, com 50 tratamentos, no esquema fatorial 5 x 10, com quatro repetições, totalizando 200 parcelas experimentais. O valor do número de frutos por planta aumentou com o incremento da dose do biofertilizante B4 até um limite ótimo; o peso total de pencas por cacho aumentou linearmente com o incremento da dose de biofertilizante, atingindo o valor maior máximo na dose máxima; o peso médio de penca aumentou linearmente com o incremento da dose do biofertilizante B2, atingindo o maior valor na dose máxima; o peso médio do fruto e o peso do fruto médio aumentaram linearmente com o aumento da dose do biofertilizante B5, atingindo os maiores valores na dose máxima; a aplicação de (B5) proporcionou maior peso médio do fruto e peso do fruto médio