Coordination of growth and drought responses by GA-ABA signaling in rice.

The drought caused by global warming seriously affects the crop growth and agricultural production. Plants have evolved distinct strategies to cope with the drought environment. Under drought stress, energy and resources should be diverted from growth toward stress management. However, the molecular mechanism underlying coordination of growth and drought response remains largely elusive. Here, we discovered that most of the gibberellin (GA) metabolic genes were regulated by water scarcity in rice, leading to the lower GA contents and hence inhibited plant growth. Low GA contents resulted in the accumulation of more GA signaling negative regulator SLENDER RICE 1, which inhibited the degradation of abscisic acid (ABA) receptor PYL10 by competitively binding to the co-activator of anaphase-promoting complex TAD1, resulting in the enhanced ABA response and drought tolerance. These results elucidate the synergistic regulation of crop growth inhibition and promotion of drought tolerance and survival, and provide useful genetic resource in breeding improvement of crop drought resistance.

Turing-Hopf Bifurcation Analysis in a Diffusive Ratio-Dependent Predator-Prey Model with Allee Effect and Predator Harvesting.

This paper investigates the complex dynamics of a ratio-dependent predator-prey model incorporating the Allee effect in prey and predator harvesting. To explore the joint effect of the harvesting effort and diffusion on the dynamics of the system, we perform the following analyses: (a) The stability of non-negative constant steady states; (b) The sufficient conditions for the occurrence of a Hopf bifurcation, Turing bifurcation, and Turing-Hopf bifurcation; (c) The derivation of the normal form near the Turing-Hopf singularity. Moreover, we provide numerical simulations to illustrate the theoretical results. The results demonstrate that the small change in harvesting effort and the ratio of the diffusion coefficients will destabilize the constant steady states and lead to the complex spatiotemporal behaviors, including homogeneous and inhomogeneous periodic solutions and nonconstant steady states. Moreover, the numerical simulations coincide with our theoretical results.

Emotion Recognition From Multi-Channel EEG via Deep Forest.

Recently, deep neural networks (DNNs) have been applied to emotion recognition tasks based on electroencephalography (EEG), and have achieved better performance than traditional algorithms. However, DNNs still have the disadvantages of too many hyperparameters and lots of training data. To overcome these shortcomings, in this article, we propose a method for multi-channel EEG-based emotion recognition using deep forest. First, we consider the effect of baseline signal to preprocess the raw artifact-eliminated EEG signal with baseline removal. Secondly, we construct 2 D frame sequences by taking the spatial position relationship across channels into account. Finally, 2 D frame sequences are input into the classification model constructed by deep forest that can mine the spatial and temporal information of EEG signals to classify EEG emotions. The proposed method can eliminate the need for feature extraction in traditional methods and the classification model is insensitive to hyperparameter settings, which greatly reduce the complexity of emotion recognition. To verify the feasibility of the proposed model, experiments were conducted on two public DEAP and DREAMER databases. On the DEAP database, the average accuracies reach to 97.69% and 97.53% for valence and arousal, respectively; on the DREAMER database, the average accuracies reach to 89.03%, 90.41%, and 89.89% for valence, arousal and dominance, respectively. These results show that the proposed method exhibits higher accuracy than the state-of-art methods.