Plant root systems are essential for many physiological processes, including water and nutrient absorption. MADS-box transcription factor (TF) genes have been characterized as the important regulators of root development in plants; however, the underlying mechanism is largely unknown, including chrysanthemum. Here, it was found that the overexpression of CmANR1, a chrysanthemum MADS-box TF gene, promoted both adventitious root (AR) and lateral root (LR) development in chrysanthemum. Whole transcriptome sequencing analysis revealed a series of differentially expressed unigenes (DEGs) in the roots of CmANR1-transgenic chrysanthemum plants compared to wild-type plants. Functional annotation of these DEGs by alignment with Gene Ontology (GO) terms and biochemical pathway Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis indicated that CmANR1 TF exhibited “DNA binding” and “catalytic” activity, as well as participated in “phytohormone signal transduction”. Both chromatin immunoprecipitation–polymerase chain reaction (ChIP-PCR) and gel electrophoresis mobility shift assays (EMSA) indicated the direct binding of CmPIN2 to the recognition site CArG-box motif by CmANR1. Finally, a firefly luciferase imaging assay demonstrated the transcriptional activation of CmPIN2 by CmANR1 in vivo. Overall, our results provide novel insights into the mechanisms of MADS-box TF CmANR1 modulation of both AR and LR development, which occurs by directly regulating auxin transport gene CmPIN2 in chrysanthemum.

Sense of touch is a major part of man’s communication with their environment. Artificial skins can help robots to have the same sense of touch, especially for their social interactions. This paper presents a pressure mapping sensing using piezo-resistive fabric to represent aspects of the sense of touch. In past few years’ electrical impedance tomography (EIT) is considered to be able offer a good alternative for artificial skin in particular for its ease of adaptation for large area skin compared to individual matrix based sensors. The EIT has also very good temporal performance in data collection allowing for monitoring of fast responses to touch stimulation, enabling a truly real time touch sensing. Electromechanical responses of a conductive fabric can be exploited using EIT to create a low cost and large area touch sensing. Such electromechanical properties are often very complex, so to improve the imaging resolution and touch visibility an artificial intelligent (AI) was used in addition to the state of the art spatio-temporal imaging algorithm. This work demonstrates a step towards an integrated seamless skin with large area sensing in dynamical settings, closer to natural human skin’s behaviour. For the first time a dynamical touch sensing are studies by means of a spatio-temporal based electrical impedance tomography (EIT) imaging on a conductive fabric. The experimental results demonstrated the successful results by a combined AI with dynamical EIT imaging results in single and multiple points of touch.

Root rot caused by Fusarium solani, is one of the most severe diseases in pepper (Capsicum annuum L.). Grafting has been attempted as an effective means to control the disease, but little is known about the disease resistance mechanism in grafted pepper. Therefore, we investigated the changes of biomass, cell structure, and the secondary metabolism in roots of control (non-grafted pepper) and grafted peppers using cvs. Weishi and Buyeding as rootstocks and the cv. Xinfeng 2 as a scion. After a manual inoculation, less F. solani invaded grafted pepper roots and consequently less serious injury to the root cell ultra-structure compared with the control was found. The roots of grafted pepper infected with F. solani exhibited greater biomass production and root activity than the roots of infected controls. Grafting led to an increased content of salicylic acid, benzoic acid, vanillin, lignin, and polyamines, as well as activities of phenylalanine ammonia lyase, polyphenoloxidase, and peroxidase. These results suggest that grafting improved the resistance of peppers to root rot.

Recent relation extraction models’ architecture are evolved from the shallow neural networks to natural language model, such as convolutional neural networks or recurrent neural networks to Bert. However, these methods did not consider the semantic information in the sequence or the distance dependence problem, the internal semantic information may contain the useful knowledge which can help relation classification. Focus on these problems, this paper proposed a BERT-based relation classification method. Compare with the existing Bert-based architecture, the proposed model can obtain the internal semantic information between entity pair and solve the distance semantic dependence better. The pre-trained BERT model after fine tuning is used in this paper to abstract the semantic representation of sequence, then adopt the piecewise convolution to obtain semantic information which influence the extraction results. Compare with the existing methods, the proposed method can achieve a better accuracy on relational extraction task because of the internal semantic information extracted in the sequence. While, the generalization ability is still a problem that cannot be ignored, and the numbers of the relationships are difference between different categories. In this paper, the focal loss function is adopted to solve this problem by assigning a heavy weight to less number or hard classify categories. Finally, comparing with the existing methods, the F1 metric of the proposed method can reach a superior result 89.95% on the SemEval-2010 Task 8 dataset.

Three-phase material imaging such as oil, gas and water is a critical problem in many processes. Monitoring and separation of each phase before corresponding process can be a key to realise cost efficiency production. In the oil and gas industry such a tool can generate great environmental benefits. In recent years, several multi-modality imaging systems are being adapted for such an application. It is not possible to gain information on three phase flow using a single imaging modality that can be deployed in production field. For a three-phase flow dominated by water phase, this is more challenging. The capability of the majority of current dual-modality systems was only demonstrated under limited flow regime conditions. This paper presents a novel combined imaging system including electrical impedance tomography (EIT), and ultrasound transmission tomography (UTT) for mapping three-phase flow. In water dominate mode, the EIT is able to image the nonconductive phases of oil and gas in a water dominate background phase, but not able to separate oil and gas. The EIT two-phase imaging then complimented with the speed of sound imaging of the UTT, which can separate liquid (oil and water) and gas. A dual modality EIT-UTT system combining a 32-electrode EIT array and a 32-transducer UTT sensor array is developed to demonstrate this novel three-phase material imaging. The concept was demonstrated using simulation study and then shown with experimental lab tests. Measurement principles and method are described, experiment based on several three-phase flow scenarios are established, and the results show successful distinguishably for all three phases. INDEX TERMS Three phase flow, electrical impedance tomography, ultrasound travel time tomography.