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 nonrace 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.

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.

Resource use and management Sympatric species develop more efficient ectomycorrhizae in the Pinus-Laccaria symbiosis Las especies simpátricas desarrollan ectomicorrizas más eficientes en la simbiosis Pinus-Laccaria

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 control of prosthetic devices has become a hot topic of research. Challenge of classifying this signals relies on the accuracy of the proposed algorithm and the possibility of its implementation on hardware. This paper consider the problem of electromyography signal classification, solved with the proposed signal processing and feature extraction stages, with 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 used time domain features. Sometimes, the feature extraction from electromyographic signals showed that procedure can omit less useful traits for machine learning models. Using feature selection algorithms provides a higher classification performance with as fewer traits as possible. Algorithms achieved a high classification rate up to 100% with low pattern dimensionality, with other kinds of uncorrelated attributes for hand movement identification.

The formal verification of a Spiking Neural P System (SN P Systems, for short) designed for solving a given problem is usually a hard task. Basically, the verification process consists of the search of invariant formulae such that, once proved their validity, show the right answer to the problem. Even though there does not exist a general methodology for verifying SN P Systems, in (Pa ˘un et al., Int J Found Comput Sci 17(4): 2006) a new tool based on the transition diagram of the P system has been developed for helping the researcher in the search of invariant formulae. In this paper we show a software tool which allows to generate the transition diagram of an SN P System in an automatic way, so it can be considered as an assistant for the formal verification of such computational devices.