Análise da expressão dos genes da subfamília DREB em resposta à seca em plantas de Coffea canephora Conilon

Coffea canephora is one of the most economically important coffee species and in Brazil, Conilon is the most widely cultivated plant of this species. Abiotic stresses such as temperature variations and drought periods are factors that significantly affect their production and tend to worsen with globally recognized climate changes. In an attempt to understand the molecular responses of coffee plants in water deficit conditions, recent studies have identified candidate genes (CGs) as CcDREB1D. This gene showed increased expression in response to drought in the leaves of clone 14 (drought tolerant) in relation to the clone 22 (sensitive to drought) of C. canephora Conilon. Based on these results, the identification of DREB genes and their subgroups (SGs) of C. canephora, the objective is to analyze in silico and also in vivo these genes expression in leaf and root of tolerant (14, 73 and 120) and sensitive clones (22) of C. canephora Conilon submitted or not to a water deficit. In silico expressions of all DREB genes were analyzed from the Coffee Genome Hub Database and in vivo expression was performed by the technique "reverse transcription-quantitative PCR" (RT-qPCR). In silico gene expression analysis was possible to identify DREB genes with potential responses to abiotic stresses, corroborating some validated in vivo results. In this analysis, several genes showed differential expression in response to drought among the SGs (IIV), the tolerant and sensitive clones and the leaf and root. These differentially expressed genes were identified as potential CGs and among them, it was found that most tolerant clones showed increased expression in relation to sensitive in response to drought, with higher expression levels for clones 14 and 73. These highest levels were observed in leaves compared to the roots and SG-I stood at greater number of genes expressed in response to drought. These results suggest that DREB CGs, as Cc05_g06840, Cc02_g03420 e Cc08_g13960, play an important role in the regulatory mechanisms of response to drought in C. canephora Conilon.

SIRCo: uma arquitetura para Sensoriamento Inteligente de Rádios Cognitivos

The increased demand for using the Industrial, Scientific and Medical (ISM) unlicensed frequency spectrum has caused interference problems and lack of resource availability for wireless networks. Cognitive radio (CR) have emerged as an alternative to reduce interference and intelligently use the spectrum. Several protocols were proposed aiming to mitigate these problems, but most have not been implemented in real devices. This work presents an architecture for Intelligent Sensing for Cognitive Radios (ISCRa), and a spectrum decision model (SDM) based on Artificial Neural Networks (ANN), which uses as input a database with local spectrum behavior and a database with primary users information. For comparison, a spectrum decision model based on AHP, which employs advanced techniques in its spectrum decision method was implemented. Another spectrum decision model that considers only a physical parameter for channel classification was also implemented. Spectrum decision models evaluated, as well as ISCRa's architecture were developed in GNU-Radio framework and implemented on real nodes. Evaluation of SDMs considered metrics of: delivery rate, latency (Round Trip Time - RTT) and handoff. Experiments on real nodes showed that ISCRa architecture with ANN based SDM increased packet delivery rate and presented fewer frequency variation (handoff) while maintaining latency. Considering higher bandwidth as application's Quality of Service requirement, ANN-SDM obtained the best results when compared to other SDM for cognitive radio networks (CRN).