Here, we conducted the first large-scale leaf phosphoproteome analysis of two bread wheat cultivars by liquid chromatography-tandem mass spectrometry. Altogether, 1802 unambiguous phosphorylation sites representing 1175 phosphoproteins implicated in various molecular functions and cellular processes were identified by gene ontology enrichment analysis. Among the 1175 phosphoproteins, 141 contained 3-10 phosphorylation sites. The phosphorylation sites were located more frequently in the N- and C-terminal regions than in internal regions, and ∼70% were located outside the conserved regions. Conservation analysis showed that 90.5% of the phosphoproteins had phosphorylated orthologs in other plant species. Eighteen significantly enriched phosphorylation motifs, of which six were new wheat phosphorylation motifs, were identified. In particular, 52 phosphorylated transcription factors (TFs), 85 protein kinases (PKs), and 16 protein phosphatases (PPs) were classified and analyzed in depth. All the Tyr phosphorylation sites were in PKs such as mitogen-activated PKs (MAPKs) and SHAGGY-like kinases. A complicated cross-talk phosphorylation regulatory network based on PKs such as Snf1-related kinases (SnRKs), calcium-dependent PKs (CDPKs), and glycogen synthase kinase 3 (GSK3) and PPs including PP2C, PP2A, and BRI1 suppressor 1 (BSU1)-like protein (BSL) was constructed and was found to be potentially involved in rapid leaf growth. Our results provide a series of phosphoproteins and phosphorylation sites in addition to a potential network of phosphorylation signaling cascades in wheat seedling leaves.
Accurate influent characteristic prediction is vital to maintain the stable performance of wastewater treatment processes. In this work, an associated approach based on the wavelet packet decomposition (WPD) and adaptive network‐based fuzzy inference system (ANFIS) is proposed to address this issue. In this method, the WPD is first adopted to decompose the historical data of the influent characteristic into wavelet coefficients in different scales. The time sub‐series, which are obtained with a single branch reconstruction of the wavelet coefficients in each scale, are then utilized to build the ANFIS regression model. The predicted sub‐results in each scale are finally summarized into an eventual predicted result. Moreover, a particle swarm optimization (PSO) algorithm is employed to acquire the optimal parameters of the multi‐scale ANFIS, and chaos theory is utilized to determine the input variables of the multi‐scale ANFIS. The reported approach is investigated by the influent characteristic data, including the chemical oxygen and biochemical oxygen demands from a wastewater treatment plant (WTP) in southwest of China. Two peer models are introduced for a comparison study. The results show that the developed approach has superior performance in terms of the mean absolute error (3.346 and 1.384), mean absolute percentage error (1.804% and 1.800%), root mean square error (3.988 and 1.788), and correlation coefficient (0.960 and 0.964), and can accurately predict the influent characteristic of the WTP.
When using array detector to measure,it requires to synthesize the spectrums measured by multiple detectors.The paper designs a software of γ-spectrum synthesis based on LabVIEW and MATLAB,which embeds the digital signal processing method for γ-spectrum synthesis.The software can dispose the spectrum documents automatically and accomplish the synthesis of γ-spectrum,which can be applied in the synthesis of γ-spectrum measured by NaI(Tl) or HPGe array detector.
In this study, we aimed to identify differentially accumulated proteins (DAPs) involved in PEG mock osmotic stress, cadmium (Cd2+) stress, and their combined stress responses in Brachypodium distachyon seedling roots. The results showed that combined PEG and Cd2+ stresses had more significant effects on Brachypodium seedling root growth, physiological traits, and ultrastructures when compared with each individual stress. Totally, 106 DAPs were identified that are responsive to individual and combined stresses in roots. These DAPs were mainly involved in energy metabolism, detoxification and stress defense and protein metabolism. Principal component analysis revealed that DAPs from Cd2+ and combined stress treatments were grouped closer than those from osmotic stress treatment, indicating that Cd2+ and combined stresses had more severe influences on the root proteome than osmotic stress alone. Protein-protein interaction analyses highlighted a 14-3-3 centered sub-network that synergistically responded to osmotic and Cd2+ stresses and their combined stresses. Quantitative real-time polymerase chain reaction (qRT-PCR) analysis of 14 key DAP genes revealed that most genes showed consistency between transcriptional and translational expression patterns. A putative pathway of proteome metabolic changes in Brachypodium seedling roots under different stresses was proposed, which revealed a complicated synergetic responsive network of plant roots to adverse environments.
Abstract Precise influent property forecasting is very important to maintain the stable operation of sewage treatment procedure. A big data analysis method of combining the wavelet packet transform (WPT) and adaptive network-based fuzzy inference system (ANFIS) is reported to solve this problem. In this approach, the WPT is used to decompose the influent property data in different cycles. The time sub-series, which are results of wavelet coefficients reconstruction, are employed to establish the forecasting system. The forecasting sub-results of each cycle are eventually integrated into an overall forecasting result. Furthermore, chaos theory is introduced to obtain the input structure of the multi-cycle regression models. The reported approach is verified by the historical influent property. A back propagation neural network and the standard ANFIS are used for a comparison test. The results demonstrate that the reported method has best ability in the peer models.
Traffic scene perception is key of autonomous driving. Computer vision and deep learning are popular basis of algorithms in this field. Current researches are mainly on single task, leading to high computing power demands and low operational efficiency. And algorithms may be trained on a small dataset, which means easier overfitting and more susceptible to noises. To overcome above shortcomings, we propose an end-to-end multi-task deep convolutional network model named ShuDA-RFBNet for multiple object detection and drivable area segmentation simultaneously. ShuDA-RFBNet adopts RFBNet for multiple object detection and DenseASPP for drivable area segmentation task. Both networks share a light-weight ShuffleNet V2 as the base net to extract features. ShuDA-RFBNet uses BDD100K to train, which is the current largest and diverse driving video dataset. Test results show that the mAP of multiple object detection task is 32.71% and the MOU of drivable area segmentation task is 82.67%. ShuDA-RFBNet can inference in real time.
K nearest neighbor (KNN) algorithm has been widely used as a simple and effective classification algorithm.The traditional KNN classification algorithm will find k nearest neighbors, it is necessary to calculate the distance from the test sample to all training samples.When the training sample data is very large, it will produce a high computational overhead, resulting in a decline in classification speed.Therefore, we optimize the distance calculation of the KNN algorithm.Since KNN only considers the k samples of the shortest distance from the test sample to the nearest training sample point, the large distance training has no effect on the classification of the algorithm.The improved method is to sample the training data around the test data, which reduces the number of distance calculation of the test data to each training data, and reduces the time complexity of the algorithm.The experimental results show that the optimized KNN classification algorithm is superior to the traditional KNN algorithm.
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