Pollen tubes extend through pistil tissues and are guided to ovules where they release sperm for fertilization. Although pollen tubes can germinate and elongate in a synthetic medium, their trajectory is random and their growth rates are slower compared to growth in pistil tissues. Furthermore, interaction with the pistil renders pollen tubes competent to respond to guidance cues secreted by specialized cells within the ovule. The molecular basis for this potentiation of the pollen tube by the pistil remains uncharacterized. Using microarray analysis in Arabidopsis, we show that pollen tubes that have grown through stigma and style tissues of a pistil have a distinct gene expression profile and express a substantially larger fraction of the Arabidopsis genome than pollen grains or pollen tubes grown in vitro. Genes involved in signal transduction, transcription, and pollen tube growth are overrepresented in the subset of the Arabidopsis genome that is enriched in pistil-interacted pollen tubes, suggesting the possibility of a regulatory network that orchestrates gene expression as pollen tubes migrate through the pistil. Reverse genetic analysis of genes induced during pollen tube growth identified seven that had not previously been implicated in pollen tube growth. Two genes are required for pollen tube navigation through the pistil, and five genes are required for optimal pollen tube elongation in vitro. Our studies form the foundation for functional genomic analysis of the interactions between the pollen tube and the pistil, which is an excellent system for elucidation of novel modes of cell–cell interaction.

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Prediction of user traffic in cellular networks has attracted profound attention for improving resource utilization. In this paper, we study the problem of network traffic traffic prediction and classification by employing standard machine learning and statistical learning time series prediction methods, including long short-term memory (LSTM) and autoregressive integrated moving average (ARIMA), respectively. We present an extensive experimental evaluation of the designed tools over a real network traffic dataset. Within this analysis, we explore the impact of different parameters to the effectiveness of the predictions. We further extend our analysis to the problem of network traffic classification and prediction of traffic bursts. The results, on the one hand, demonstrate superior performance of LSTM over ARIMA in general, especially when the length of the training time series is high enough, and it is augmented by a wisely-selected set of features. On the other hand, the results shed light on the circumstances in which, ARIMA performs close to the optimal with lower complexity.

The power control of wireless networks is formulated using a stochastic optimal control framework, in which the evolution of the channel is described by stochastic differential equations (SDE's). The latter capture the spatio-temporal variations of the communication link as well as the randomness. This class of models is more realistic than the static models usually encountered in the literature. Under this scenario, average and probabilistic Quality of Service (QoS) measures are introduced to evaluate the performance of any control strategy by using Chernoff bounds. Moreover, the Chernoff bound is computed explicitly, while the solution of the stochastic optimal power control is obtained through pathwise optimization. The pathwise optimization can be solved using linear programming if predictable control strategies are introduced. Finally, if predictable control strategies do not hold, it is shown that the proposed power control problem reduces to particular convex optimizations.

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