Location-based Robust Beamforming Design for Cellular-enabled UAV Communications

Cellular communications have been regarded as promising approaches to deliver high-broadband communication links for Unmanned Aerial Vehicles (UAVs), which have been widely deployed to conduct various missions, e.g. precision agriculture, forest monitoring and border patrol. However, the unique features of aerial UAVs including high-altitude manipulation, three-dimension (3D) mobility, and rapid velocity changes, pose challenging issues to realize reliable cellular-enabled UAV communications, especially with the severe inter-cell interference generated by UAVs. To deal with this issue, we propose a novel position-based robust beamforming algorithm through complementarily integrating the navigation information and wireless channel information to improve the performance of cellular-enabled UAV communications. Specifically, in order to achieve the optimal beam weight vector, the navigation information of the UAV system is innovatively exploited to predict the changes of Direction-of-arrival (DoA) angle. To fight against the high mobility of UAV operations, an optimization problem is formed by considering the tapered surface of DoA angle and solved to correct the inherent position error. Comprehensive simulation experiments are conducted and the results show that the proposed robust beamforming algorithm could achieve over 90% DoA estimation error reduction and up to 14dB SINR gain compared with five benchmark beamforming algorithms, including Linearly Constrained Minimum Variance (LCMV), Position-based beamforming, Diagonal Loading (DL), Robust Capon Beamforming (RCB) and Robust LCMV algorithm

RF signal-based UAV detection and mode classification: a joint feature engineering generator and multi-channel deep neural network approach

With the proliferation of Unmanned Aerial Vehicles (UAVs) to provide diverse critical services, such as surveillance, disaster management, and medicine delivery, the accurate detection of these small devices and the efficient classification of their flight modes are of paramount importance to guarantee their safe operation in our sky. Among the existing approaches, Radio Frequency (RF) based methods are less affected by complex environmental factors. The similarities between UAV RF signals and the diversity of frequency components make accurate detection and classification a particularly difficult task. To bridge this gap, we propose a joint Feature Engineering Generator (FEG) and Multi-Channel Deep Neural Network (MC-DNN) approach. Specifically, in FEG, data truncation and normalization separate different frequency components, the moving average filter reduces the outliers in the RF signal, and the concatenation fully exploits the details of the dataset. In addition, the multi-channel input in MC-DNN separates multiple frequency components and reduces the interference between them. A novel dataset that contains ten categories of RF signals from three types of UAVs is used to verify the effectiveness. Experiments show that the proposed method outperforms the state-of-the-art UAV detection and classification approaches in terms of 98.4% and F1 score of 98.3%

Engineering the Ultrasensitive Transcription Factors by Fusing a Modular Oligomerization Domain

The dimerization and high-order oligomerization of transcription factors has endowed them with cooperative regulatory capabilities that play important roles in many cellular functions. However, such advanced regulatory capabilities have not been fully exploited in synthetic biology and genetic engineering. Here, we engineered a C-terminally fused oligomerization domain to improve the cooperativity of transcription factors. First, we found that two of three designed oligomerization domains significantly increased the cooperativity and ultrasensitivity of a transcription factor for the regulated promoter. Then, seven additional transcription factors were used to assess the modularity of the oligomerization domains, and their ultrasensitivity was generally improved, as assessed by their Hill coefficients. Moreover, we also demonstrated that the allosteric capability of the ligand-responsive domain remained intact when fusing with the designed oligomerization domain. As an example application, we showed that the engineered ultrasensitive transcription factor could be used to significantly improve the performance of a “stripe-forming” gene circuit. We envision that the oligomerization modules engineered in this study could act as a powerful tool to rapidly tune the underlying response profiles of synthetic gene circuits and metabolic pathway controllers

The miR167-OsARF12 module regulates grain filling and grain size downstream of miR159

Grain weight and quality are always determined by the grain filling. Plant miRNAs have drawn attention as key targets for regulating grain size and yield. Yet the mechanisms underlying the regulation of grain size are largely unclear due to the complex networks controlling this trait. Our earlier studies proved that the suppressed expression of miR167 (STTM/MIM167) substantially increased grain weight. In a field test, the increased yield up to 12.90%-21.94% due to the significantly enhanced grain filling rate. Biochemical and genetic analyses reveal the regulatory effects of miR159 on miR167 expression. Further analysis indicates that OsARF12 is the major mediator of miR167 in regulating rice grain filling. Expectedly, over expressing OsARF12 could resemble the phenotype of STTM/MIM167 plants with respect to grain weight and grain filling rate. Upon in-depth analysis, we found that OsARF12 activates OsCDKF;2 expressions by directly binding to the TGTCGG motif in the promoter region. Flow cytometric analysis in young panicles of plants overexpressing OsARF12 and cell number examination of cdkf;2 mutants verify that OsARF12 positively regulates grain filling and grain size by targeting OsCDKF;2. Moreover, RNA-seq result suggests that miR167-OsARF12 module is involved in the cell development process and hormone pathways. Additionally, plants overexpressing OsARF12 or cdkf;2 mutants present enhanced or reduced sensitivity to exogenous auxin and brassinosteroid (BR) treatments, confirming that OsCDKF;2 targeting by OsARF12 mediates auxin and BR signaling. Our results reveal that miR167-OsARF12 module works downstream of miR159 to regulate rice grain filling and grain size by OsCDKF;2 through controlling cell division and mediating auxin and BR signals

Engineering Translational Activators with CRISPR-Cas System

RNA parts often serve as critical components in genetic engineering. Here we report a design of translational activators which is composed of an RNA endoribonuclease (Csy4) and two exchangeable RNA modules. Csy4, a member of Cas endoribonuclease, cleaves at a specific recognition site; this cleavage releases a cis-repressive RNA module (crRNA) from the masked ribosome binding site (RBS), which subsequently allows the downstream translation initiation. Unlike small RNA as a translational activator, the endoribonuclease-based activator is able to efficiently unfold the perfect RBS-crRNA pairing. As an exchangeable module, the crRNA-RBS duplex was forwardly and reversely engineered to modulate the dynamic range of translational activity. We further showed that Csy4 and its recognition site, together as a module, can also be replaced by orthogonal endoribonuclease-recognition site homologues. These modularly structured, high-performance translational activators would endow the programming of gene expression in the translation level with higher feasibility