However, the phloem phase was reached earlier and more frequently when the mealybugs were reared on the same host used for monitoring. This result indicates the presence of host conditioning so far not reported for mealybugs. This study aimed to detect the effect of the previous experience of the citrus mealybug, Planococcus citri (Risso) (Hemiptera: Pseudococcidae) on host choice, probing behavior and development. The citrus

mealybug was reared on coffee (Coffea arabica L.), citrus (Citrus sinensis L.) and squash (Cucurbita maxima L.) whose were named as source hosts, and transferred to coffee Ruboxistaurin inhibitor or citrus as receptor host plants. The study included choice tests, electrical penetration graphs (EPG) and development studies. The choice test between coffee and citrus in the first 72 hours showed that mealybugs reared on coffee showed a preference to settle on coffee. When the source plant was citrus the insects showed a trend, even not significant, to select citrus over coffee. On the other side, those mealybugs taken from a squash culture did not show any preference neither for coffee nor citrus. The probing behavior monitoring showed that the phloem phase, considered important in plant acceptance, was more

frequent in coffee plants, regardless selleck screening library using coffee or citrus as source plants. Those insects transferred from squash to coffee or citrus showed none or a very short phloem phase. Transferring the mealybugs, from any host to coffee or citrus did not modify the development time, fecundity or mortality. However, those reared

and transferred to squash presented a higher fecundity. Thus, even showing some preference for the source plants in the choice test, the transferring from coffee or citrus does not modify significantly the mealybug development or probing behavior.”
“The paper deals with the SCH727965 solubility dmso implementation of optimized neural networks (NNs) for state variable estimation of the drive system with an elastic joint. The signals estimated by NNs are used in the control structure with a state-space controller and additional feedbacks from the shaft torque and the load speed. High estimation quality is very important for the correct operation of a closed-loop system. The precision of state variables estimation depends on the generalization properties of NNs. A short review of optimization methods of the NN is presented. Two techniques typical for regularization and pruning methods are described and tested in detail: the Bayesian regularization and the Optimal Brain Damage methods. Simulation results show good precision of both optimized neural estimators for a wide range of changes of the load speed and the load torque, not only for nominal but also changed parameters of the drive system. The simulation results are verified in a laboratory setup.”
“Acute or chronic kidney inflammation is closely related to the progress of kidney diseases.