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

Simultaneous optimization of coffee quality variables during storage

ABSTRACT The objective of the study was to use methodology of simultaneous optimization of multiple responses applied to an experimental design to determine the best combination of storage period and conditions for preservation of coffee beans. Coffea arabica L. fruits were harvested in the ripe stage of maturation, processed using wet and dry methods, and dried to 11% (wet basis) moisture content. Part of the beans was hulled, while the other part was hulled only after the beans were stored under two different environmental conditions: cooled air at 10 ºC with 50% relative humidity; and at 25 ºC without controlling the relative humidity. Samples were taken at 3, 6, and 12 months intervals in order to evaluate quality. The data were submitted to the simultaneous optimization of responses for each processing and hulling condition separately, in a completely randomized design and 2 x 3 factorial scheme (two storage conditions and three storage periods). In conclusion, the use of the simultaneous optimization of responses is viable to be applied for determining the ideal storage conditions in a refrigerated condition

FERM domain interaction with myosin negatively regulates FAK in cardiomyocyte hypertrophy

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Focal adhesion kinase (FAK) regulates cellular processes that affect several aspects of development and disease. The FAK N-terminal FERM (4.1 protein-ezrin-radixin-moesin homology) domain, a compact clover-leaf structure, binds partner proteins and mediates intramolecular regulatory interactions. Combined chemical cross-linking coupled to MS, small-angle X-ray scattering, computational docking and mutational analyses showed that the FAK FERM domain has a molecular cleft (similar to 998 angstrom(2)) that interacts with sarcomeric myosin, resulting in FAK inhibition. Accordingly, mutations in a unique short amino acid sequence of the FERM myosin cleft, FP-1, impaired the interaction with myosin and enhanced FAK activity in cardiomyocytes. An FP-1 decoy peptide selectively inhibited myosin interaction and increased FAK activity, promoting cardiomyocyte hypertrophy through activation of the AKT-mammalian target of rapamycin pathway. Our findings uncover an inhibitory interaction between the FAK FERM domain and sarcomeric myosin that presents potential opportunities to modulate the cardiac hypertrophic response through changes in FAK activity.81102110Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq

FERM domain interaction with myosin negatively regulates FAK in cardiomyocyte hypertrophy

Focal adhesion kinase (FAK) regulates cellular processes that affect several aspects of development and disease. The FAK N-terminal FERM (4.1 protein-ezrin-radixin-moesin homology) domain, a compact clover-leaf structure, binds partner proteins and mediates intramolecular regulatory interactions. Combined chemical cross-linking coupled to MS, small-angle X-ray scattering, computational docking and mutational analyses showed that the FAK FERM domain has a molecular cleft (similar to 998 angstrom(2)) that interacts with sarcomeric myosin, resulting in FAK inhibition. Accordingly, mutations in a unique short amino acid sequence of the FERM myosin cleft, FP-1, impaired the interaction with myosin and enhanced FAK activity in cardiomyocytes. An FP-1 decoy peptide selectively inhibited myosin interaction and increased FAK activity, promoting cardiomyocyte hypertrophy through activation of the AKT-mammalian target of rapamycin pathway. Our findings uncover an inhibitory interaction between the FAK FERM domain and sarcomeric myosin that presents potential opportunities to modulate the cardiac hypertrophic response through changes in FAK activity.Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP) [2006/54878-3, 2007/55930-1, 2007/59442-1, 2008/53519-5, 2008/57805-2, 2010/02628-9]Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP)Conselho Nacional de Pesquisa (CNPq) [304366/2009-9, 475158/2010-5, 573672/2008-3, 559698/2009-7]Conselho Nacional de Pesquisa (CNPq