Functional Characterization of Soybean Glyma04g39610 as a Brassinosteroid Receptor Gene and Evolutionary Analysis of Soybean Brassinosteroid Receptors

Brassinosteroids (BR) play important roles in plant growth and development. Although BR receptors have been intensively studied in Arabidopsis, the BR receptors in soybean remain largely unknown. Here, in addition to the known receptor gene Glyma06g15270 (GmBRI1a), we identified five putative BR receptor genes in the soybean genome: GmBRI1b, GmBRL1a, GmBRL1b, GmBRL2a, and GmBRL2b. Analysis of their expression patterns by quantitative real-time PCR showed that they are ubiquitously expressed in primary roots, lateral roots, stems, leaves, and hypocotyls. We used rapid amplification of cDNA ends (RACE) to clone GmBRI1b (Glyma04g39160), and found that the predicted amino acid sequence of GmBRI1b showed high similarity to those of AtBRI1 and pea PsBRI1. Structural modeling of the ectodomain also demonstrated similarities between the BR receptors of soybean and Arabidopsis. GFP-fusion experiments verified that GmBRI1b localizes to the cell membrane. We also explored GmBRI1b function in Arabidopsis through complementation experiments. Ectopic over-expression of GmBRI1b in Arabidopsis BR receptor loss-of-function mutant (bri1-5 bak1-1D) restored hypocotyl growth in etiolated seedlings; increased the growth of stems, leaves, and siliques in light; and rescued the developmental defects in leaves of the bri1-6 mutant, and complemented the responses of BR biosynthesis-related genes in the bri1-5 bak1-D mutant grown in light. Bioinformatics analysis demonstrated that the six BR receptor genes in soybean resulted from three gene duplication events during evolution. Phylogenetic analysis classified the BR receptors in dicots and monocots into three subclades. Estimation of the synonymous (Ks) and the nonsynonymous substitution rate (Ka) and selection pressure (Ka/Ks) revealed that the Ka/Ks of BR receptor genes from dicots and monocots were less than 1.0, indicating that BR receptor genes in plants experienced purifying selection during evolution

Recognition Method for Broiler Sound Signals Based on Multi-Domain Sound Features and Classification Model

In view of the limited number of extracted sound features, the lack of in-depth analysis of applicable sound features, and the lack of in-depth study of the selection basis and optimization process of classification models in the existing broiler sound classification or recognition research, the author proposes a recognition method for broiler sound signals based on multi-domain sound features and classification models. The implementation process is divided into the training stage and the testing stage. In the training stage, the experimental area is built, and multiple segments of broiler sound signals are collected and filtered. Through sub-frame processing and endpoint detection, the combinations of start frames and end frames of multiple sound types in broiler sound signals are obtained. A total of sixty sound features from four aspects of time domain, frequency domain, Mel-Frequency Cepstral Coefficients (MFCC), and sparse representation are extracted from each frame signal to form multiple feature vectors. These feature vectors are labeled manually to build the data set. The min-max standardization method is used to process the data set, and the random forest is used to calculate the importance of sound features. Then, thirty sound features that contribute more to the classification effect of the classification model are retained. On this basis, the classification models based on seven classification algorithms are trained, the best-performing classification model based on k-Nearest Neighbor (kNN) is obtained, and its inherent parameters are optimized. Then, the optimal classification model is obtained. The test results show that the average classification accuracy achieved by the decision-tree-based classifier (abbreviated as DT classifier) on the data set before and after min–max standardization processing is improved by 0.6%, the average classification accuracy achieved by the DT classifier on the data set before and after feature selection is improved by 3.1%, the average classification accuracy achieved by the kNN-based classification model before and after parameter optimization is improved by 1.2%, and the highest classification accuracy is 94.16%. In the testing stage, for a segment of the broiler sound signal collected in the broiler captivity area, the combinations of the start frames and end frames of multiple sound types in the broiler sound signal are obtained through signal filtering, sub-frame processing, endpoint detection, and other steps. Thirty sound features are extracted from each frame signal to form the data set to be predicted. The optimal classification model is used to predict the labels of each piece of data in the data set to be predicted. By performing majority voting processing on the predicted labels of the data combination corresponding to each sound type, the common labels are obtained; that is, the predicted types are obtained. On this basis, the definition of recognition accuracy for broiler sound signals is proposed. The test results show that the classification accuracy achieved by the optimal classification model on the data set to be predicted is 93.57%, and the recognition accuracy achieved on the multiple segments of the broiler sound signals is 99.12%

An Active Indoor Noise Control System Based on CS Algorithm

Environmental noise causes enormous harm to human health. This research suggests an active noise control (ANC) system based on the cuckoo search (CS) algorithm to reduce indoor noise pollution. The indoor environment’s ANC is more complicated than the conventional linear ANC system used in cars, aircraft, and other confined spaces. The suggested system architecture considers noise from many directions in order to reduce the impact of external noise on persons within a given environment. The effective search strategy of the CS algorithm enhances the approach of updating filter coefficients based on the LMS/NLMS algorithm in the conventional ANC systems. The results from the simulations demonstrate that the suggested strategy successfully validates the hypothesis and provides significant noise reduction. Additionally, we developed the system’s hardware, which is based on a digital signal processor (DSP). The experimental results show that the proposed technology could perform well with respect to ANC