Cryptochrome 1b represses gibberellin signaling to enhance lodging resistance in maize.

Maize (Zea mays L.) is one of the most important crops worldwide. Photoperiod, light quality, and light intensity in the environment can affect the growth, development, yield, and quality of maize. In Arabidopsis (Arabidopsis thaliana), cryptochromes are blue-light receptors that mediate the photocontrol of stem elongation, leaf expansion, shade tolerance, and photoperiodic flowering. However, the function of maize cryptochrome ZmCRY in maize architecture and photomorphogenic development remains largely elusive. The ZmCRY1b transgene product can activate the light signaling pathway in Arabidopsis and complement the etiolation phenotype of the cry1-304 mutant. Our findings show that the loss-of-function mutant of ZmCRY1b in maize exhibits more etiolation phenotypes under low blue light and appears slender in the field compared with wild-type plants. Under blue and white light, overexpression of ZmCRY1b in maize substantially inhibits seedling etiolation and shade response by enhancing protein accumulation of the bZIP transcription factors ELONGATED HYPOCOTYL 5 (ZmHY5) and ELONGATED HYPOCOTYL 5-LIKE (ZmHY5L), which directly upregulate the expression of genes encoding gibberellin (GA) 2-oxidase to deactivate GA and repress plant height. More interestingly, ZmCRY1b enhances lodging resistance by reducing plant and ear heights and promoting root growth in both inbred lines and hybrids. In conclusion, ZmCRY1b contributes blue-light signaling upon seedling de-etiolation and integrates light signals with the GA metabolic pathway in maize, resulting in lodging resistance and providing information for improving maize varieties.

Online Passive-Aggressive Active Learning for Trapezoidal Data Streams.

The idea of combining the active query strategy and the passive-aggressive (PA) update strategy in online learning can be credited to the PA active (PAA) algorithm, which has proven to be effective in learning linear classifiers from datasets with a fixed feature space. We propose a novel family of online active learning algorithms, named PAA learning for trapezoidal data streams (PAATS) and multiclass PAATS (MPAATS) (and their variants), for binary and multiclass online classification tasks on trapezoidal data streams where the feature space may expand over time. Under the context of an ever-changing feature space, we provide the theoretical analysis of the mistake bounds for both PAATS and MPAATS. Our experiments on a wide variety of benchmark datasets have confirm that the combination of the instance-regulated active query strategy and the PA update strategy is much more effective in learning from trapezoidal data streams. We have also compared PAATS with online learning with streaming features (OLSF)-the state-of-the-art approach in learning linear classifiers from trapezoidal data streams. PAATS could achieve much better classification accuracy, especially for large-scale real-world data streams.

Physiological and iTRAQ-based proteomic analyses reveal that melatonin alleviates oxidative damage in maize leaves exposed to drought stress.

To explain the underlying mechanism of melatonin-mediated drought stress responses in maize, maize pre-treated with or without melatonin was subjected to 20% PEG nutrient solution to induce drought stress. We found that exogenous melatonin significantly improved drought tolerance, demonstrated by improved photosynthesis, reduced ROS accumulation, enhanced activities of superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT), and increased content of glutathione (GSH). Comparative iTRAQ proteomic analyses revealed a higher abundance of differentially expressed proteins (DEPs) in melatonin-treated maize under drought stress for carbon fixation in photosynthetic organisms, photosynthesis, biosynthesis of amino acids, and biosynthesis of secondary metabolites, compared to untreated plants. Changes in the above molecular mechanisms could explain the melatonin-induced physiological effects associated with drought tolerance. In summary, this study provides a more integrated picture about the effects of melatonin on the physiological and molecular mechanisms in maize seedlings responding to drought stress.

Modeling cognitive loads for evolving shared mental models in human-agent collaboration.

Recent research on human-centered teamwork highly demands the design of cognitive agents that can model and exploit human partners' cognitive load to enhance team performance. In this paper, we focus on teams composed of human-agent pairs and develop a system called Shared Mental Models for all--SMMall. SMMall implements a hidden Markov model (HMM)-based cognitive load model for an agent to predict its human partner's instantaneous cognitive load status. It also implements a user interface (UI) concept called shared belief map, which offers a synergic representation of team members' information space and allows them to share beliefs. An experiment was conducted to evaluate the HMM-based load models. The results indicate that the HMM-based load models are effective in helping team members develop a shared mental model (SMM), and the benefit of load-based information sharing becomes more significant as communication capacity increases. It also suggests that multiparty communication plays an important role in forming/evolving team SMMs, and when a group of agents can be partitioned into subteams, splitting messages by their load status can be more effective for developing subteam SMMs.