BnMs3 is required for tapetal differentiation and degradation, microspore separation, and pollen-wall biosynthesis in Brassica napus

7365AB, a recessive genetic male sterility system, is controlled by BnMs3 in Brassica napus, which encodes a Tic40 protein required for tapetum development. However, the role of BnMs3 in rapeseed anther development is still largely unclear. In this research, cytological analysis revealed that anther development of a Bnms3 mutant has defects in the transition of the tapetum to the secretory type, callose degradation, and pollen-wall formation. A total of 76 down-regulated unigenes in the Bnms3 mutant, several of which are associated with tapetum development, callose degeneration, and pollen development, were isolated by suppression subtractive hybridization combined with a macroarray analysis. Reverse genetics was applied by means of Arabidopsis insertional mutant lines to characterize the function of these unigenes and revealed that MSR02 is only required for transport of sporopollenin precursors through the plasma membrane of the tapetum. The real-time PCR data have further verified that BnMs3 plays a primary role in tapetal differentiation by affecting the expression of a few key transcription factors, participates in tapetal degradation by modulating the expression of cysteine protease genes, and influences microspore separation by manipulating the expression of BnA6 and BnMSR66 related to callose degradation and of BnQRT1 and BnQRT3 required for the primary cell-wall degradation of the pollen mother cell. Moreover, BnMs3 takes part in pollen-wall formation by affecting the expression of a series of genes involved in biosynthesis and transport of sporopollenin precursors. All of the above results suggest that BnMs3 participates in tapetum development, microspore release, and pollen-wall formation in B. napus

An NUTSF at Sub-Region for Suppression of Torque Ripple in Switched Reluctance Motors

The conventional torque sharing function (TSF) control strategy in a switched reluctance motor (SRM) has higher torque ripple due to the weak torque tracking ability with an increase in speed. A non-unity torque sharing function (NUTSF) is proposed in order to minimize the torque ripple. Firstly, the working principle of the conventional TSF is introduced, and the causes of higher torque ripple are analyzed. Secondly, the NUTSF control strategy at each sub-region, where the two-phase exchange region is further divided into region 1 and region 2 based on the inductance characteristics, is proposed, and an optimization algorithm at each sub-region is applied so that the TSF is more suitable for the inductance and torque characteristics of the motor. Finally, a three-phase 6/20 SRM is taken as an example for simulation analysis and a prototype experiment. The results show that the proposed control strategy can effectively reduce the torque ripple of an SRM at a wide speed range

Damage Location Diagnosis of Frame Structure Based on a Novel Convolutional Neural Network

In the case of strong noise, when the damage occurs at different locations of the frame structure, the fault vibration signals generated are relatively close. It is difficult to accurately diagnose the specific location of the damage by using the traditional convolution neural network method. In order to solve this problem, this paper proposes a novel convolutional neural network. The method first uses wavelet decomposition and reconstruction to filter out the noise signal in the original vibration signal, then uses CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise Analysis) to decompose the filtered signal to highlight the feature information in the filtered signal. Finally, a convolution neural network combined with WDCNN (First Layer Wide Convolution Kernel Deep Convolution Neural Network) and LSTM (Long Short-Term Memory Network) is used to achieve the accurate classification of the signal, so as to achieve the accurate diagnosis of the damage location of the frame structure. Taking the four-story steel structure frame of Columbia University as the research object, the fault diagnosis method proposed in this paper is used to carry out experimental research under strong noise conditions. The experimental results show that the accuracy of the fault diagnosis method proposed in this paper can reach 99.97% when the signal-to-noise ratio is −4 dB and the objective function value is reduced to 10−4. Therefore, the fault diagnosis method proposed in this paper has a high accuracy in the strong noise interference environment; it can realize a high precision diagnosis of the damage location of the frame structure under a strong noise environment. The contribution and innovation of this paper is to propose a novel fault diagnosis method based on the convolutional neural network, which solves the problem of accurate damage location diagnosis of frame structures under strong noise environment

Grey Box Modeling of Gas Temperature for a High-Speed and High-Temperature Heat-Airflow Test System

Aiming at a problem that it is difficult for accurately obtaining the mathematical model of a gas temperature for a high-speed, high-temperature heat-airflow test system, a grey box modeling method combining first principle of modeling, recursive least squares identification with auxiliary variable and correlation analysis is proposed, and the precise mathematical model of gas temperature is established. Firstly, the preliminary mathematical model of gas temperature is obtained by using the first principle modeling method, and the dynamic behaviors of the system are analyzed; the prior knowledge of the system is obtained, and the parameters that need to be identified are pointed out. Secondly, the basic principles of recursive least square identification with auxiliary variables and correlation analysis are introduced, and the uncertain parameters of the gas temperature model are identified by using the introduced methods. Finally, the precise mathematical model of gas temperature is obtained by simulation and experimental research. The research results show that the mathematical model of gas temperature established by the grey box modeling method put forward in this article has satisfactory accuracy

Damage Location Diagnosis of Frame Structure Based on a Novel Convolutional Neural Network

In the case of strong noise, when the damage occurs at different locations of the frame structure, the fault vibration signals generated are relatively close. It is difficult to accurately diagnose the specific location of the damage by using the traditional convolution neural network method. In order to solve this problem, this paper proposes a novel convolutional neural network. The method first uses wavelet decomposition and reconstruction to filter out the noise signal in the original vibration signal, then uses CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise Analysis) to decompose the filtered signal to highlight the feature information in the filtered signal. Finally, a convolution neural network combined with WDCNN (First Layer Wide Convolution Kernel Deep Convolution Neural Network) and LSTM (Long Short-Term Memory Network) is used to achieve the accurate classification of the signal, so as to achieve the accurate diagnosis of the damage location of the frame structure. Taking the four-story steel structure frame of Columbia University as the research object, the fault diagnosis method proposed in this paper is used to carry out experimental research under strong noise conditions. The experimental results show that the accuracy of the fault diagnosis method proposed in this paper can reach 99.97% when the signal-to-noise ratio is −4 dB and the objective function value is reduced to 10−4. Therefore, the fault diagnosis method proposed in this paper has a high accuracy in the strong noise interference environment; it can realize a high precision diagnosis of the damage location of the frame structure under a strong noise environment. The contribution and innovation of this paper is to propose a novel fault diagnosis method based on the convolutional neural network, which solves the problem of accurate damage location diagnosis of frame structures under strong noise environment

Fault diagnosis of gearbox based on adaptive wavelet de-noising and convolution neural network

In this paper, in order to solve the problem that it is difficult to carry out accurate fault diagnosis for gearbox under noise environment, complete ensemble imperial mode decomposition with adaptive noise analysis (CEEMDAN) is used to solve the sample entropy of the original signal and each intrinsic mode function (IMF) component, adaptive wavelet is adopted to decompose and reconstruct IMF with large sample entropy for noise reduction, and first layer wide convolution kernel deep convolution neural network (WDCNN) and long short term memory (LSTM) are used to extract the basic digital features of the reconstructed signal and the correlation features between the features. Therefore, a new fault diagnosis method for gearbox under noise environment is proposed. Taking the public data set of Jiangsu Qiangpeng Diagnostic Engineering Co., Ltd as the research object, the experiments were carried out with the method proposed in this paper. The experimental results show that the proposed method has high accuracy and strong anti-noise ability. Under the environment of no noise and low noise, the fault diagnosis accuracy of the gearbox is 100%; even if the signal to noise ratio is −4 dB, the fault diagnosis accuracy of the gearbox can still reach 99.97%. Therefore, this paper provides a method support for gearbox fault diagnosis under noise environment

Exploring the rumen microbial community in Guizhou White goats at different ages

This study evaluated the changes in rumen microbiome during the process of age development of farming Guizhou White goats from Southwest China. We conducted high-throughput 16S rRNA gene sequencing to investigate the diversity, structure, and composition of goat rumen microbiota (RM) of 21 goats of different age groups (1, 6, and 12 months). We found that volatile fatty acids (i.e., acetate, propionate, and butyrate) fermented by microbes were found to increase significantly in the sixand one-month-old goats. Results of the genera abundance analysis showed that abundance of eight and seven taxa decreased in six- and one-month-old goats, respectively, compared with that in 12-month-old goats. Additionally, differences in six taxa in six-month-old goats and in one taxon in one-month-old goats were found. In addition, specific gut microbiome was found, which was significantly correlated with rumen fermentation parameters in Guizhou White goats. These results revealed the signature microbiota in RM during various developmental stages in goats raised in Southwest China and can also provide a guiding tool for evaluating rumen health of ruminants worldwid

Morphological description of the mouthparts of the Asian citrus psyllid, Diaphorina citri Kuwayama (Hemiptera: Psyllidae)

Scanning (SEM) and transmission (TEM) electron microscopy were used to elucidate the morphology of the rostrum, as well as the mandibular and maxillary stylets of the psyllid Diaphorina cirri, vector of phloem-inhabiting bacteria associated with citrus huanglongbing (HLB) disease. D. cirri has a cone-shaped rostrum that extends behind the pair of prothoracic coxae. The stylet bundle comprises a pair of mandibular (Md) and maxillary (Mx) stylets with a mean length of 513.3 mu m; when retracted, their proximal portions form a loop and are stored in the crumena (Cr). Serial cross-sections of the rostrum revealed that the mandibles are always projected in front of the maxillary stylets. The two maxillary stylets form the food and salivary canals, with diameters of 0.9 mu m and 0.4 mu m respectively. These two canals merge at the end of the stylets forming a common duct with a length of 4.3 mu m and a mean diameter of 0.9 mu m. The acrostyle, a distinct anatomical structure present in the common duct of aphid maxillary stylets, was not observed by TEM in the ultrathin cross-sections of the common duct (CD) of D. citri. This study provides new information on D. citri mouthparts that may help to understand the behaviour of this important vector of HLB-associated bacteria. (C) 2011 Elsevier Ltd. All rights reserved.Spanish Ministry of Science and InnovationSpanish Ministry of Science and Innovation [AGL2007-66399-C03-02]National Council for Scientific and Technological Development (CNPq/Brazil)National Council for Scientific and Technological Development (CNPq), Brazi