Molecular Cloning and Functional Characterization of FOUR LIPS in Pomegranate, a Protein Involved in Regulating the Gravitropic Set-Point Angle of Adventitious Roots

Wang Zenghui,Li Jialin,Wang Chuanzeng,Feng Lijuan,Yin Yanlei 한국식물학회 2022 Journal of Plant Biology Vol.65 No.5

Self-rooted pomegranate seedlings are widely used in the horticultural industry to cut costs and time. However, these seedlings produce shallow roots that exhibit poor cold resistance. Thus, deeper adventitious roots generated through gravitropism are imperative for seedling survival, and understanding the molecular mechanisms of gravitropism can facilitate improved breeding techniques. We hypothesized that gravitropism in pomegranate is partially controlled by pomegranate FOUR LIPS (PgFLP), an R2R3-MYB protein that interacts with and controls the transcriptional expression of PgPIN10, which facilitates transmembrane auxin signaling. We studied subcellular localization of PgFLP, quantified auxin levels, and measured gravitropic set-point angle (GSA) to investigate the underlying mechanisms regulating PgPIN10 expression during the formation of GSA in pomegranate adventitious roots. We found that PgFLP was localized to the nucleus based on use of green florescent proteins, suggesting that this protein is a transcription factor. When using the tractable 35S::PgFLP, we observed stronger gravitational response in overexpression lines leading to a narrower GSA than in the wild-type Arabidopsis, and the expression of PgFLP and PgPIN10 in ‘Lanbaoshi’ (LBS; a deep-rooted cultivar) was higher than that in ‘Taishanhong’ (TSH; a shallow-rooted cultivar), which indicates that PgFLP may participate in regulating the GSA of adventitious roots via PgPIN10 in pomegranate. Taken together, our results indicate that the pomegranate R2R3-MYB transcription factor, PgFLP, plays a vital role in setting the GSA of adventitious roots in this crop species.

Model Free Control Based on GIMC Structure

Zenghui Wang,Yanxia Sun,Guoyuan Qi,Barend Jacobus Van Wyk 제어·로봇·시스템학회 2012 International Journal of Control, Automation, and Vol.10 No.1

Many control researches for complicated and uncertain system are model-dependent and therefore require some prior knowledge for the complex systems. To avoid this problem, a number of model-free controllers are proposed. However, it is difficult to determine the control performance as the controller is not designed according certain system model especially when there are uncertainties and/or nonlinear dynamics in the system. To get over this problem, the model free controller (MFC) based on generalized internal model control (GIMC) structure is proposed in this paper. The MFC is used to attenuate the disturbance or uncertainty, and the system performance is determined by the nominal model and the nominal model controller. The parameters of nominal-model controller can be easily changed for meeting the change of the desired requirements. Moreover, the robust controller in the original GIMC is disassembled and rearranged to make the proposed methods easier to use, and the proposed method makes the controller be more flexible and greatly improves the system performance. Finally, the experiment results show that the MFC can be used to control the nonlinear systems and get the expected performance. The statistical analysis of performance for servo and regulatory behaviors also shows that the proposed method can achieve a better control performance than just using model free controller.

One-class models for validation of miRNAs and ERBB2 gene interactions based on sequence features for breast cancer scenarios

Juan Gutiérrez-Cárdenas,Zenghui Wang 한국통신학회 2021 ICT Express Vol.7 No.4

One challenge in miRNA–genes–diseases interaction studies is that it is challenging to find labeled data that indicate a positive or negative relationship between miRNA and genes. The use of one-class classification methods shows a promising path for validating them. We have applied two one-class classification methods, Isolation Forest and One-class SVM, to validate miRNAs interactions with the ERBB2 gene present in breast cancer scenarios using features extracted via sequence-binding. We found that the One-class SVM outperforms the Isolation Forest model, with values of sensitivity of 80.49% and a specificity of 86.49% showing results that are comparable to previous studies. Additionally, we have demonstrated that the use of features extracted from a sequence-based approach (considering miRNA and gene sequence binding characteristics) and one-class models have proven to be a feasible method for validating these genetic molecule interactions.

Quantitative Analysis of Critical Limitation in Using Extended State Observer

Mingwei Sun,Yi Li,Zenghui Wang,Zengqiang Chen 제어·로봇·시스템학회 2016 International Journal of Control, Automation, and Vol.14 No.3

Active disturbance rejection control (ADRC) has been successfully widely applied. The extended stateobserver (ESO) is a crucial component of ADRC to deal with uncertainties in many control systems. Althoughthe nominal stability of ADRC was proved by adopting a sufficiently fast observer with a performance recoveryprinciple, it is difficult to be employed in practice because severe oscillation or even instability might be triggeredif the fast observer was used. This paper investigates the reason behind this phenomenon within the frameworkof input time-delay sensitivity for a typical first-order system, which can provide an insightful understanding ofADRC. The positive root of the polynomial which determines the maximal input time-delay maintaining closed loopstability is quantitatively analyzed and the relationship among the bandwidth of ESO, other control parametersand the allowable input time-delay is thoroughly studied. Finally, numerical examples are presented to validate thecorresponding theoretical results.

Structural response reconstruction based on the information fusion of multi-source particle filters

Yonghe Shi,Hong Yin,Zhenrui Peng,Zenghui Wang,Yu Bai 대한기계학회 2023 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.37 No.2

Aiming at the problems that the lack of theoretical basis for the selection of particle set sampling variance and the resampling methods in traditional particle filter algorithms, and sampling process is easily disturbed by noise, an uncertainty structural response reconstruction method based on the information fusion of multi-source particle filters is proposed. Firstly, the sampling variance of particle set is analogous to the accuracy index of sensors, and a number of independent particle filtering samples from different sources are performed to ensure the independence of particles. Then, abnormal filters are screened and eliminated according to relative percentage error (RPE) threshold of preliminary reconstruction, and the state estimation results of remained particle filters are fused by the multi-source sensors information fusion technique to approximate to the real state values with high accuracy. Finally, the fused state values and the state space models are employed to reconstruct the responses of key positions, and the effectiveness of the proposed method is verified by numerical example of the space truss structure and the cantilever beam test. The results show that the proposed method can reduce the influence of the above uncertainties on reconstruction results, effectively improve the particle impoverishment problem, the filtering stability is good and the reconstruction accuracy is high.

Practical Integrated Guidance and Control for the High-Speed Anti-Ship Missile to Counter Switching Targets Under a Short Time-to-go Condition

Yunxi Zhang,Yu Fan,Mingwei Sun,Zenghui Wang,Zengqiang Chen 한국항공우주학회 2022 International Journal of Aeronautical and Space Sc Vol.23 No.1

The high-speed anti-ship missile might encounter a challenge in the terminal phase if there are mixed targets, including false and true ones. There is only a quite short time to take action after the onboard radar can distinguish which targets are true or false. In this study, a tunable acceleration feedback gain is used to realize a unique integrated guidance and control scheme in a natural way by maintaining advantages of the traditionally practical proportional navigation and three-loop acceleration autopilot without introducing additional dynamics as the existing methods in the literature. The gain range can be determined using a numerical calculation scheme. The adjoint method is employed to reveal the advantage of the tunable gain and can then obtain the optimal gain. The proposed method can achieve a trade-off between the effective navigation gain and the settling time of the autopilot during the integration. Extensive simulations are performed to validate the appealing discovery. This philosophy offers guideline for the renovation of existing anti-ship missiles.

Grey Wolf Optimization based Active Disturbance Rejection Control Parameter Tuning for Ship Course

Jia Ren,Zengqiang Chen,Yikang Yang,Mingwei Sun,Qinglin Sun,Zenghui Wang 제어·로봇·시스템학회 2022 International Journal of Control, Automation, and Vol.20 No.3

It is important to control the ship course in complicated ocean environment. In this paper, a Grey Wolf Optimization (GWO) based Active Disturbance Rejection Control (ADRC) tuning method is proposed in the application of the ship course. Here, GWO is used to tune the parameters of ADRC. To validate the performance of the proposed method, some simulations have been carried out and the results are compared with the results of other tuning methods, such as, Harris Hawks Optimization (HHO), Particle Swarm Optimization (PSO), Q-learning and manual tuning. To test the stability of different tuning methods, the cases of no disturbance, constant value disturbance, second-order wave force disturbance, white noise disturbance and mixed disturbance are considered. The robustness of the system for parameters perturbation is analyzed. The research indicates that the GWO based ADRC can achieve better performance than other methods.

Associations between AT-rich Interactive Domain 5B gene Polymorphisms and Risk of Childhood Acute Lymphoblastic Leukemia: a Meta-analysis

Zeng, Hui,Wang, Xue-Bin,Cui, Ning-Hua,Nam, Seungyoon,Zeng, Tuo,Long, Xinghua Asian Pacific Journal of Cancer Prevention 2014 Asian Pacific journal of cancer prevention Vol.15 No.15

Previous genome-wide association studies (GWAS) have implicated several single nucleotide polymorphisms (SNPs) in the AT-rich interactive domain 5B (ARID5B) gene with childhood acute lymphoblastic leukemia (ALL). However, replicated studies reported some inconsistent results in different populations. Using meta-analysis, we here aimed to clarify the nature of the genetic risks contributed by the two polymorphisms (rs10994982, rs7089424) for developing childhood ALL. Through searches of PubMed, EMBASE, and manually searching relevant references, a total of 14 articles with 16 independent studies were included. Odds ratios (ORs) with 95% confidence intervals (95%CI) were calculated to assess the associations. Both SNPs rs10994982 and rs7089424 showed significant associations with childhood ALL risk in all genetic models after Bonferroni correction. Furthermore, subtype analyses of B-lineage ALL provided strong evidence that SNP rs10994982 is highly associated with the risk of developing B-hyperdiploid ALL. These results indicate that SNPs rs10994982 and rs7089424 are indeed significantly associated with increased risk of childhood ALL.

Convolutional Neural Network Based Multi-feature Fusion for Non-rigid 3D Model Retrieval

Zeng, Hui,Liu, Yanrong,Li, Siqi,Che, JianYong,Wang, Xiuqing Korea Information Processing Society 2018 Journal of information processing systems Vol.14 No.1

This paper presents a novel convolutional neural network based multi-feature fusion learning method for non-rigid 3D model retrieval, which can investigate the useful discriminative information of the heat kernel signature (HKS) descriptor and the wave kernel signature (WKS) descriptor. At first, we compute the 2D shape distributions of the two kinds of descriptors to represent the 3D model and use them as the input to the networks. Then we construct two convolutional neural networks for the HKS distribution and the WKS distribution separately, and use the multi-feature fusion layer to connect them. The fusion layer not only can exploit more discriminative characteristics of the two descriptors, but also can complement the correlated information between the two kinds of descriptors. Furthermore, to further improve the performance of the description ability, the cross-connected layer is built to combine the low-level features with high-level features. Extensive experiments have validated the effectiveness of the designed multi-feature fusion learning method.

Learning-Based Multiple Pooling Fusion in Multi-View Convolutional Neural Network for 3D Model Classification and Retrieval

Zeng, Hui,Wang, Qi,Li, Chen,Song, Wei Korea Information Processing Society 2019 Journal of information processing systems Vol.15 No.5

We design an ingenious view-pooling method named learning-based multiple pooling fusion (LMPF), and apply it to multi-view convolutional neural network (MVCNN) for 3D model classification or retrieval. By this means, multi-view feature maps projected from a 3D model can be compiled as a simple and effective feature descriptor. The LMPF method fuses the max pooling method and the mean pooling method by learning a set of optimal weights. Compared with the hand-crafted approaches such as max pooling and mean pooling, the LMPF method can decrease the information loss effectively because of its "learning" ability. Experiments on ModelNet40 dataset and McGill dataset are presented and the results verify that LMPF can outperform those previous methods to a great extent.