RESUMEN
BACKGROUND: In Mexico, coffee leaf rust (CLR) is the main disease that affects the Arabica coffee crop. In this study, the local response of two Mexican cultivars of Coffea arabica (Oro Azteca and Garnica) in the early stages of Hemileia vastatrix infection was evaluated. METHODS: We quantified the development of fungal structures in locally-infected leaf disks from both cultivars, using qRT-PCR to measure the relative expression of two pathogenesis recognition genes (CaNDR1 and CaNBS-LRR) and three genes associated with the salicylic acid (SA)-related pathway (CaNPR1, CaPR1, and CaPR5). RESULTS: Resistance of the cv. Oro Azteca was significantly higher than that of the cv. Garnica, with 8.2% and 53.3% haustorial detection, respectively. In addition, the non-race specific disease resistance gene (CaNDR1), a key gene for the pathogen recognition, as well as the genes associated with SA, CaNPR1, CaPR1, and CaPR5, presented an increased expression in response to infection by H. vastatrix in cv. Oro Azteca if comparing with cv. Garnica. Our results suggest that Oro Azteca's defense mechanisms could involve early recognition of CLR by NDR1 and the subsequent activation of the SA signaling pathway.
RESUMEN
Classification of electromyographic signals has a wide range of applications, from clinical diagnosis of different muscular diseases to biomedical engineering, where their use as input for the control of prosthetic devices has become a hot topic of research. The challenge of classifying these signals relies on the accuracy of the proposed algorithm and the possibility of its implementation in hardware. This paper considers the problem of electromyography signal classification, solved with the proposed signal processing and feature extraction stages, with the focus lying on the signal model and time domain characteristics for better classification accuracy. The proposal considers a simple preprocessing technique that produces signals suitable for feature extraction and the Burg reflection coefficients to form learning and classification patterns. These coefficients yield a competitive classification rate compared to the time domain features used. Sometimes, the feature extraction from electromyographic signals has shown that the procedure can omit less useful traits for machine learning models. Using feature selection algorithms provides a higher classification performance with as few traits as possible. The algorithms achieved a high classification rate up to 100% with low pattern dimensionality, with other kinds of uncorrelated attributes for hand movement identification.
