No abstract

Leaf rust caused by Puccinia triticina is one of the main diseases affecting wheat (Triticum aestivum) production worldwide. Calmodulin (CaM) was found involved in the early stage of signal transduction pathway in response to P. triticina in wheat. To study the function and molecular mechanism of calmodulin (CaM) in signal transduction of wheat against P. triticina, we cloned a putative calmodulin-binding transcription activator (TaCAMTA4), and characterized its molecular structure and functions by using the CaM-encoding gene (TaCaM4-1) as a bait to screen the cDNA library from P. triticina infected wheat leaves. The open reading frame of TaCAMTA4 was 2505 bp encoding a protein of 834 aa, which contained all the four conserved domains of family (CG-1 domain, TIG domain, ANK repeats and CaM-binding domain). TaCaM4-1 bound to TaCAMTA4 by the C-terminal CaM-binding domain in Ca2+-dependent manner in the electrophoretic mobility shift assay (EMSA). Bimolecular fluorescence complementation (BiFC) analysis indicated that the interaction of TaCAMTA4 and TaCaM4-1 took place in the cytoplasm and nucleus of epidermal leaf cells in N. benthamiana. The expression level of TaCAMTA4 genes was down-regulated in incompatible combination after P. triticina infection. Furthermore, virus-induced gene silencing (VIGS)-based knockdown of TaCAMTA4 and disease assays verified that silencing of TaCAMTA4 resulted in enhanced resistance to P. triticina race 165. These results suggested that TaCAMTA4 function as negative regulator of defense response against P. triticina.

Abstract:In this paper, we present a novel approach for automatically detecting buildings from multiple heterogeneous and uncalibrated very high-resolution (VHR) satellite images for a rapid response to natural disasters. In the proposed method, a simple and efficient visual attention method is first used to extract built-up area candidates (BACs) from each multispectral (MS) satellite image. After this, morphological building indices (MBIs) are extracted from all the masked panchromatic (PAN) and MS images with BACs to characterize the structural features of buildings. Finally, buildings are automatically detected in a hierarchical probabilistic model by fusing the MBI and masked PAN images. The experimental results show that the proposed method is comparable to supervised classification methods in terms of recall, precision and F-value.

We present novel two-stage dynamic scheduling of earth observation satellites to provide emergency response by making full use of the duration of the imaging task execution. In the first stage, the multiobjective genetic algorithm NSGA-II is used to produce an optimal satellite imaging schedule schema, which is robust to dynamic adjustment as possible emergent events occur in the future. In the second stage, when certain emergent events do occur, a dynamic adjusting heuristic algorithm (CTM-DAHA) is applied to arrange new tasks into the robust imaging schedule. Different from the existing dynamic scheduling methods, the imaging duration is embedded in the two stages to make full use of current satellite resources. In the stage of robust satellite scheduling, total task execution time is used as a robust indicator to obtain a satellite schedule with less imaging time. In other words, more imaging time is preserved for future emergent events. In the stage of dynamic adjustment, a compact task merging strategy is applied to combine both of existing tasks and emergency tasks into a composite task with least imaging time. Simulated experiments indicate that the proposed method can produce a more robust and effective satellite imaging schedule.

Earth observation satellites play a significant role in rapid responses to emergent events on the Earth’s surface, for example, earthquakes. In this paper, we propose a robust satellite scheduling model to address a sequence of emergency tasks, in which both the profit and robustness of the schedule are simultaneously maximized in each stage. Both the multiobjective genetic algorithm NSGA2 and rule-based heuristic algorithm are employed to obtain solutions of the model. NSGA2 is used to obtain a flexible and highly robust initial schedule. When every set of emergency tasks arrives, a combined algorithm called HA-NSGA2 is used to adjust the initial schedule. The heuristic algorithm (HA) is designed to insert these tasks dynamically to the waiting queue of the initial schedule. Then the multiobjective genetic algorithm NSGA2 is employed to find the optimal solution that has maximum revenue and robustness. Meanwhile, to improve the revenue and resource utilization, we adopt a compact task merging strategy considering the duration of task execution in the heuristic algorithm. Several experiments are used to evaluate the performance of HA-NSGA2. All simulation experiments show that the performance of HA-NSGA2 is significantly improved.