TaMYB44-5A reduces drought tolerance by repressing transcription of TaRD22-3A in the abscisic acid signaling pathway.

MAIN CONCLUSION: TaMYB44-5A identified as a transcription factor negatively regulates drought tolerance in transgenic Arabidopsis. Drought can severely reduce yields throughout the wheat-growing season. Many studies have shown that R2R3-MYB transcription factors are involved in drought stress responses. In this study, the R2R3-MYB transcription factor MYB44-5A was identified in wheat (Triticum aestivum L.) and functionally analyzed. Three homologs of TaMYB44 were isolated, all of which localized to the nucleus. Overexpression of TaMYB44-5A reduced drought tolerance in Arabidopsis thaliana. Further analysis showed that TaMYB44-5A reduced the sensitivity of transgenic Arabidopsis to ABA. Genetic and transcriptional regulation analyses demonstrated that the expression levels of drought- and ABA-responsive genes were downregulated by TaMYB44-5A, and TaMYB44-5A directly bound to the MYB-binding site on the promoter to repress the transcription level of TaRD22-3A. Our results provide insights into a novel molecular pathway in which the R2R3-MYB transcription factor negatively regulates ABA signaling in response to drought stress.

TaFDL2-1A confers drought stress tolerance by promoting ABA biosynthesis, ABA responses, and ROS scavenging in transgenic wheat.

Plants have developed various protective mechanisms to survive drought stress. Previously, it was shown that a wheat bZIP transcription factor gene TaFD-Like2-1A (TaFDL2-1A) can confer drought tolerance in Arabidopsis. However, the biological functions related to drought stress tolerance of TaFDL2-1A in wheat (Triticum aestivum L.) remain unclear. In the present study, overexpression of TaFDL2-1A in the wheat cultivar Fielder improved drought resistance and conferred abscisic acid (ABA) hypersensitivity. Further analysis showed that overexpression of TaFDL2-1A increased the hypersensitivity of stomata to drought stress and endogenous ABA content under drought conditions. Genetic analysis and transcriptional regulation analysis indicated that TaFDL2-1A binds directly to the promoter fragments of TaRAB21s and TaNCED2s via ACGT core cis-elements, thereby activating their expression, leading to enhanced ABA responses and endogenous ABA accumulation. In addition, our results demonstrate that overexpression of TaFDL2-1A results in higher SOD and GPX activities in wheat under drought conditions by promoting the expression of TaSOD1 and TaGPx1-D, indicating enhanced reactive oxygen species (ROS) scavenging. These results imply that TaFDL2-1A positively regulates ABA biosynthesis, ABA responses, and ROS scavenging to improve drought stress tolerance in transgenic wheat. Our findings improve our understanding of the mechanisms that allow the wheat bZIP transcription factor to improve drought resistance and provide a useful reference gene for breeding programs to enhance drought resistance.

Link-State Aware Hybrid Routing in the Terrestrial-Satellite Integrated Network.

In this paper, we study data transmission in the Terrestrial-Satellite Integrated Network (TSIN), where terrestrial networks and satellites are combined together to provide seamless global network services for ground users. However, efficiency of the data transmission is limited by the time-varying inter-satellite link connection and intermittent terrestrial-satellite link connection. Therefore, we propose a link-state aware hybrid routing algorithm, which selects the integrated data transmission path adaptively in this paper. First of all, a space-time topology model is constructed to represent the dynamic link connections in TSIN. Thus, the transmission delay can be analyzed accordingly, and the data transmission problem can then be formulated. To balance the effectiveness and accuracy of searching a hybrid path, we carefully discuss the optimization of space-time topology updating, and propose an inter-satellite link selection algorithm. For the terrestrial-satellite link in hybrid routing, the data transmission problem is transformed into a weighted bipartite graph matching problem and solved with a Kuhn-Munkres-based link selection algorithm. To verify the effectiveness of our proposed routing algorithm, extensive simulations are conducted based on a realistic Hongyun constellation project. Results show that the network performance is improved with respect to data transmission delay, packet loss rate, and throughput.

Bayesian Learning-Based Clustered-Sparse Channel Estimation for Time-Varying Underwater Acoustic OFDM Communication.

Orthogonal frequency division multiplexing (OFDM) has been widely adopted in underwater acoustic (UWA) communication due to its good anti-multipath performance and high spectral efficiency. For UWA-OFDM systems, channel state information (CSI) is essential for channel equalization and adaptive transmission, which can significantly affect the reliability and throughput. However, the time-varying UWA channel is difficult to estimate because of excessive delay spread and complex noise distribution. To this end, a novel Bayesian learning-based channel estimation architecture is proposed for UWA-OFDM systems. A clustered-sparse channel distribution model and a noise-resistant channel measurement model are constructed, and the model hyperparameters are iteratively optimized to obtain accurate Bayesian channel estimation. Accordingly, to obtain the clustered-sparse distribution, a partition-based clustered-sparse Bayesian learning (PB-CSBL) algorithm was designed. In order to lessen the effect of strong colored noise, a noise-corrected clustered-sparse channel estimation (NC-CSCE) algorithm was proposed to improve the estimation accuracy. Numerical simulations and lake trials are conducted to verify the effectiveness of the algorithms. Results show that the proposed algorithms achieve higher channel estimation accuracy and lower bit error rate (BER).

Double-Scale Adaptive Transmission in Time-Varying Channel for Underwater Acoustic Sensor Networks.

The communication channel in underwater acoustic sensor networks (UASNs) is time-varying due to the dynamic environmental factors, such as ocean current, wind speed, and temperature profile. Generally, these phenomena occur with a certain regularity, resulting in a similar variation pattern inherited in the communication channels. Based on these observations, the energy efficiency of data transmission can be improved by controlling the modulation method, coding rate, and transmission power according to the channel dynamics. Given the limited computational capacity and energy in underwater nodes, we propose a double-scale adaptive transmission mechanism for the UASNs, where the transmission configuration will be determined by the predicted channel states adaptively. In particular, the historical channel state series will first be decomposed into large-scale and small-scale series and then be predicted by a novel k-nearest neighbor search algorithm with sliding window. Next, an energy-efficient transmission algorithm is designed to solve the problem of long-term modulation and coding optimization. In particular, a quantitative model is constructed to describe the relationship between data transmission and the buffer threshold used in this mechanism, which can then analyze the influence of buffer threshold under different channel states or data arrival rates theoretically. Finally, numerical simulations are conducted to verify the proposed schemes, and results show that they can achieve good performance in terms of channel prediction and energy consumption with moderate buffer length.

Dynamic Divide Grouping Non-Orthogonal Multiple Access in Terrestrial-Satellite Integrated Network.

Non-orthogonal multiple access (NOMA) has been extensively studied to improve the performance of the Terrestrial-Satellite Integrated Network (TSIN) on account of the shortage of frequency band resources. In this paper, the terrestrial network and satellite network synergistically provide complete coverage for ground users, and based on the architecture, we first formulate a constrained optimization problem to maximize the sum rate of the TSIN under the limited spectrum resources. As the terrestrial networks and the satellite network will cause interference to each other, we first investigate the capacity performance of the terrestrial networks and the satellite networks separately, in which the optimal power control factor expression is derived. Then, by constructing the relationship model between user elevation angle, beam angle and distance, we develop a dynamic group pairing schemes to ensure the effective pairing of NOMA users. Based on the user pairing, to obtain the optimal resource allocation, a joint optimization algorithm of power allocation, beam channel and base station channel resource is proposed. Finally, simulation results are provided to evaluate the user paring scheme as well as the total system performance, in comparison with the existing works.

Real-Time Compact Environment Representation for UAV Navigation.

Recently, unmanned aerial vehicles (UAVs) have attracted much attention due to their on-demand deployment, high mobility, and low cost. For UAVs navigating in an unknown environment, efficient environment representation is needed due to the storage limitation of the UAVs. Nonetheless, building an accurate and compact environment representation model is highly non-trivial because of the unknown shape of the obstacles and the time-consuming operations such as finding and eliminating the environmental details. To overcome these challenges, a novel vertical strip extraction algorithm is proposed to analyze the probability density function characteristics of the normalized disparity value and segment the obstacles through an adaptive size sliding window. In addition, a plane adjustment algorithm is proposed to represent the obstacle surfaces as polygonal prism profiles while minimizing the redundant obstacle information. By combining these two proposed algorithms, the depth sensor data can be converted into the multi-layer polygonal prism models in real time. Besides, a drone platform equipped with a depth sensor is developed to build the compact environment representation models in the real world. Experimental results demonstrate that the proposed scheme achieves better performance in terms of precision and storage as compared to the baseline.

Ocean Surface Drifting Buoy System Based on UAV-Enabled Wireless Powered Relay Network.

We design an ocean surface drifting buoy system based on an unmanned aerial vehicle (UAV)-enabled wireless powered relay network in which the UAV acts as mobile hybrid access point that broadcasts energy to all buoys in the downlink and forwards information from the buoys to a ship signal tower (ST) in the uplink. In order to maximize the resource allocation efficiency of the system, due to the different initial energy reserve of the buoys, a novel communication mode selection strategy is proposed. In the direct transmission mode (DT mode), an energy-sufficient buoy transmits information directly to the ST, and in the relay transmission mode (RT mode), an energy-insufficient buoy relays information to the ST through the UAV. By applying the block coordinate descent and successive convex optimization, a joint UAV trajectory and resource allocation algorithm is proposed to maximize the minimum throughput of the buoys to work in the RT mode. Simulation results show that the proposed algorithm can significantly improve the minimum throughput of the ocean surface drifting buoys.

TaFDL2-1A interacts with TabZIP8-7A protein to cope with drought stress via the abscisic acid signaling pathway.

Drought has negative effects on cereal production. Studies have shown that many basic leucine zipper transcription factors (bZIP TFs) help to cope with drought stress. In this study, bZIP TF wheat (Triticum aestivum L.) FD-Like2 (TaFDL2) was isolated and functionally analyzed. Three homologs of TaFDL2 were identified and their expression was induced by drought and abscisic acid (ABA) treatment. TaFDL2-1A has transactivation activity and two activation domains, and the domain D region has different effects on the transcriptional activity of the two domains. Analysis of TaFDL2-1A overexpression plants indicated their enhanced drought tolerance and greater sensitivity to ABA. TabZIP8-7A was identified as a protein that interacts with TaFDL2-1A in the nucleus, and the overexpression of TabZIP8-7A conferred greater drought resistance and ABA sensitivity in Arabidopsis. Surprisingly, TaFDL2-1A × TabZIP8-7A double overexpression lines exhibited the highest drought resistance. Genetic and transcriptional regulation analyses demonstrated that stress-response gene transcription was initiated by TaFDL2-1A or TabZIP8-7A via the ABA signaling pathway. Importantly, TaFDL2-1A and TabZIP8-7A synergistically promoted ABA-inducible gene expression in a more efficient manner to form the transcriptional activation complex. Our findings provide new insights into the molecular mechanisms that allow bZIP TFs to regulate ABA signaling in response to drought stress.