Nematode endoparasites do not codiversify with their stick insect hosts.

Host-parasite coevolution stems from reciprocal selection on host resistance and parasite infectivity, and can generate some of the strongest selective pressures known in nature. It is widely seen as a major driver of diversification, the most extreme case being parallel speciation in hosts and their associated parasites. Here, we report on endoparasitic nematodes, most likely members of the mermithid family, infecting different Timema stick insect species throughout California. The nematodes develop in the hemolymph of their insect host and kill it upon emergence, completely impeding host reproduction. Given the direct exposure of the endoparasites to the host's immune system in the hemolymph, and the consequences of infection on host fitness, we predicted that divergence among hosts may drive parallel divergence in the endoparasites. Our phylogenetic analyses suggested the presence of two differentiated endoparasite lineages. However, independently of whether the two lineages were considered separately or jointly, we found a complete lack of codivergence between the endoparasitic nematodes and their hosts in spite of extensive genetic variation among hosts and among parasites. Instead, there was strong isolation by distance among the endoparasitic nematodes, indicating that geography plays a more important role than host-related adaptations in driving parasite diversification in this system. The accumulating evidence for lack of codiversification between parasites and their hosts at macroevolutionary scales contrasts with the overwhelming evidence for coevolution within populations, and calls for studies linking micro- versus macroevolutionary dynamics in host-parasite interactions

Physicochemical and sensorial properties of grapefruit jams as affected by processing

Jam is an effective and tasty way of preserving fruit. Jam processing procedures as well as storage conditions and duration are important factors for jam quality. Traditional jam processing involves the application of severe thermal treatments that imply undesirable changes in the product quality characteristics such as colour, texture, flavour and nutritional and functional value. In this work, osmotic dehydration (OD) and/or microwave energy (MW) was proven as adequate to obtain jam with the typical characteristics of water content, degree Brix, pH and water activity of jam obtained by conventional thermal heating. The sensory evaluation carried out to compare the product showed that samples submitted to more intense heating treatments (conventional or MW) had significantly higher scores in colour saturation, brightness, grapefruit taste and extensibility than OD or OD+MW ones. As deduced from the obtained results, OD treatment prevents grapefruit colour changes, and mild MW heating contributes to increase the consistency and decrease the extensibility of the obtained jam. In this way, OD+MW jam was preferred by assessors mainly due to its higher consistency. The sample obtained by this procedure was stable during storage.The authors would like to thank the Ministerio de Educacion y Ciencia for the financial support given throughout the Project AGL 2005-05994. The language revision of this paper was funded by the Universidad Politecnica de Valencia, Spain.Igual Ramo, M.; García Martínez, EM.; Camacho Vidal, MM.; Martínez Navarrete, N. (2013). Physicochemical and sensorial properties of grapefruit jams as affected by processing. Food and Bioprocess Technology. 6(1):177-185. https://doi.org/10.1007/s11947-011-0696-2S17718561AENOR (2009). Sensory analysis. Methodology. Paired comparison test. UNE-EN-ISO 5495.AOAC. (2000). 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Stabilizing of Subspaces Based on DPGA and Chaos Genetic Algorithm for Optimizing State Feedback Controller

The main purpose of the paper is to optimize state feedback parameters using intelligent method, GA, Hermite-Biehler, and chaos algorithm. GA is implemented for local search but it has some deficiencies such as trapping into a local minimum and slow convergence, so the combination of Hermite-Biehler and chaos algorithm has been added to GA to avoid its deficiencies. Dividing search space is usually done by distributed population genetic algorithm (DPGA). Moreover, using generalized Hermite-Biehler Theorem can find the domain of parameters. In order to speed up the convergence at the first step, Hermite-Biehler method finds some intervals for controller, in the next step the GA will be added, and, finally, chaos disturbance will help the algorithm to reach a global minimum. Therefore, the proposed method can optimize the parameters of the state feedback controller