Effect of fruits maturation stage and vegetal regulators in the physiological quality of seeds sweet passion fruit cv. BRS Mel do Cerrado

Abstract This study aimed to evaluate the effect of fruit maturation stages and pre-germination treatments with plant regulators on the physiological quality of seeds of cv. BRS Mel do Cerrado. Two experiments were carried out, germination and emergence, installed in a completely randomized design in a 3 x 2 factorial arrangement, with three stages maturation (1-partially ripe fruits; 2-fully ripe fruits and 3-senescent fruits) and two pre-germination treatments (1. [GA4+7 +N-(phenylmethyl)-aminopurine (300 ppm)] and 2. distilled water), forming six treatments with four replications of 50 seeds. The evaluated characteristics were germination percentage at 14 and 35 days, germination speed index (GSI), emergence percentage and emergence speed index (ESI). Germination at 14 days, GSI and seedling emergence were not affected by the maturation stage, however germination and GSI were higher when the plant regulator was used. The final percentage of germination was higher in seeds taken from partially ripe fruits and treated with regulators and a higher ESI was observed for seeds from senescent fruits and treated with regulator. Seeds treated with plant regulators are more vigorous and have a higher rate of seedling emergence

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

Cowpea and abiotic stresses: identification of reference genes for transcriptional profiling by qPCR

Abstract Background Due to cowpea ability to fix nitrogen in poor soils and relative tolerance to drought and salt stresses, efforts have been directed to identifying genes and pathways that confer stress tolerance in this species. Real-time quantitative PCR (qPCR) has been widely used as the most reliable method to measure gene expression, due to its high accuracy and specificity. In the present study, nine candidate reference genes were rigorously tested for their application in normalization of qPCR data onto roots of four distinct cowpea accessions under two abiotic stresses: root dehydration and salt (NaCl, 100 mM). In addition, the regulation of four target transcripts, under the same referred conditions was also scrutinized. Results geNorm, NormFinder, BestKeeper, and ΔCt method results indicated a set of three statistically validated RGs for each stress condition: (I) root dehydration (actin, ubiquitin-conjugating enzyme E2 variant 1D, and a Phaseolus vulgaris unknown gene—UNK), and (II) salt (ubiquitin-conjugating enzyme E2 variant 1D, F-box protein, and UNK). The expression profile of the target transcripts suggests that flavonoids are important players in the cowpea response to the abiotic stresses analyzed, since chalcone isomerase and chalcone synthase were up-regulated in the tolerant and sensitive accessions. A lipid transfer protein also participates in the cowpea tolerance mechanisms to root dehydration and salt stress. The referred transcript was up-regulated in the two tolerant accessions and presented no differential expression in the sensitive counterparts. Chitinase B, in turn, generally related to plant defense, was an important target transcript under salt stress, being up-regulated at the tolerant, and down-regulated in the sensitive accession. Conclusions Reference genes suitable for qPCR analyses in cowpea under root dehydration and salt stress were identified. This action will lead to a more accurate and reliable analysis of gene expression on this species. Additionally, the results obtained in this study may guide future research on gene expression in cowpea under other abiotic stress types that impose osmotic imbalance. The target genes analyzed, in turn, deserve functional evaluation due to their transcriptional regulation under stresses and biotechnological potential