AVALIAÇÃO DE SUBSTRATOS PECTOCELULÓSICOS PARA O CULTIVO DE COGUMELOS COMESTÍVEIS DO GÊNERO PLEUROTUS SP. (AGARICALES)

Os fungos do gênero Pleurotus são bastante solicitados no mercado internacional tendo aplicações gastronômicas, ecológicas e medicinais. Neste trabalho foram avaliados o crescimento micelial (cm), a eficiência biológica (%), produtividade (%) e perda de matéria orgânica (%) de substratos pectocelulósicos, feitos com cascas de maracujá e banana, em comparação ao lignocelulósico de serragem (controle). Os substratos foram desidratados, esterilizados e umedecidos em água destilada. Em cada substrato foi colocado um inóculo (10mm) retirado de uma cultura já estabelecida de Pleurotus. Posteriormente os fungos foram incubados (45 dias) para crescimento à temperatura média de 29,2ºC e a 92,2% de umidade relativa. Foi aplicado o teste de ANOVA One-way para avaliar diferenças nos parâmetros analisados e comparar os diferentes substratos. Os substratos de casca de banana apresentaram maiores raios de crescimento micelial (4,55 ± 0,05 cm,

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

The mechanism by which a distinguishing arabinofuranosidase can cope with internal di-substitutions in arabinoxylans

Abstract Background Arabinoxylan is an abundant polysaccharide in industrially relevant biomasses such as sugarcane, corn stover and grasses. However, the arabinofuranosyl di-substitutions that decorate the xylan backbone are recalcitrant to most known arabinofuranosidases (Abfs). Results In this work, we identified a novel GH51 Abf (XacAbf51) that forms trimers in solution and can cope efficiently with both mono- and di-substitutions at terminal or internal xylopyranosyl units of arabinoxylan. Using mass spectrometry, the kinetic parameters of the hydrolysis of 33-α-l-arabinofuranosyl-xylotetraose and 23,33-di-α-l-arabinofuranosyl-xylotetraose by XacAbf51 were determined, demonstrating the capacity of this enzyme to cleave arabinofuranosyl linkages of internal mono- and di-substituted xylopyranosyl units. Complementation studies of fungal enzyme cocktails with XacAbf51 revealed an increase of up to 20% in the release of reducing sugars from pretreated sugarcane bagasse, showing the biotechnological potential of a generalist GH51 in biomass saccharification. To elucidate the structural basis for the recognition of internal di-substitutions, the crystal structure of XacAbf51 was determined unveiling the existence of a pocket strategically arranged near to the − 1 subsite that can accommodate a second arabinofuranosyl decoration, a feature not described for any other GH51 Abf structurally characterized so far. Conclusions In summary, this study reports the first kinetic characterization of internal di-substitution release by a GH51 Abf, provides the structural basis for this activity and reveals a promising candidate for industrial processes involving plant cell wall depolymerization