Search

Kinases play important roles in cell signaling, and adenosine monophosphate (AMP) is known to modulate cellular energy homeostasis through AMP-activated protein kinase (AMPK). Here, we explored novel AMP-binding kinases by employing a desthiobiotin-conjugated AMP acyl-phosphate probe to enrich efficiently AMP-binding proteins. Together with a parallel-reaction monitoring-based targeted proteomic approach, we uncovered 195 candidate AMP-binding kinases. We also enriched desthiobiotin-labeled peptides from adenine nucleotide-binding sites of kinases and analyzed them using LC-MS/MS in the multiple-reaction monitoring mode, which resulted in the identification of 44 peptides derived from 43 kinases displaying comparable or better binding affinities toward AMP relative to adenosine triphosphate (ATP). Moreover, our proteomic data revealed a potential involvement of AMP in the MAPK pathway through binding directly to the relevant kinases, especially MEK2 and MEK3. Together, we revealed the AMP-binding capacities of a large number of kinases, and our work built a strong foundation for understanding how AMP functions as a second messenger to modulate cell signaling.

When using near‐infrared (NIR) techniques for analysis, model construction and maintenance updates are essential. When model construction is performed in machine learning, the sample set is usually divided into the calibration set and the validation set. The representativeness of the calibration set and the reasonable distribution of the validation set affects the accuracy of the established model. In addition, when maintaining and updating models, selecting the most informative updated sample not only improves the model prediction accuracy but also reduces sample preparation. In this paper, the spectral information entropy (SIE) is proposed to be used as a similarity criterion for dividing the sample set and use this criterion to select updated samples. The Kennard–Stone (KS) and the sample set portioning based on joint x–y distance (SPXY) methods were used for comparison to verify the superiority of the proposed method. The results showed that the model built after dividing the sample set using the SIE method has good prediction effect compared with KS and SPXY method. When predicting soluble solid content (SSC) and hardness, the prediction determination coefficient (RP2) was improved by more than 15%, and the root mean square error (RMSE) of prediction was reduced by 50%. In terms of model updating, selecting a small number of updated samples using the SIE method can improve the correlation coefficient (RP) to more than 80%, and updated models' prediction accuracy is higher than that of KS and SPXY method. It is confirmed that the SIE method can make the NIR analysis technique more reliable. [ABSTRACT FROM AUTHOR]

Da-Yuan-Yin is a Chinese traditional prescription. This study explores the therapeutic effects of the Da-Yuan-Yin decoction polyphenol fraction (DYY-4) on acute lung injury (ALI) in mice induced by lipopolysaccharide (LPS). The mice (n = 10) were orally administrated with DYY-4 (15, 30, and 60 mg/kg) or DXM (5 mg/kg), half an hour after LPS (2 mg/kg) instilled intratracheally. The protein content and the levels of inflammatory factors, the levels of complements, the mRNA expression of inducible nitric oxide synthase (iNOS) and cyclooxygenase-2 (COX-2), the level of myeloperoxidase (MPO) and superoxide dismutase (SOD), the expression of the IkB kinase (IKK) and nuclear factor-kappa B (NF-κB), the lung wet-to-dry weight (W/D) ratio and lung tissue were evaluated, 24 h after LPS challenge. Network pharmacology predicted potential targets. DYY-4 (30, 60 mg/kg, p < 0.01, p < 0.01) decreased the lung W/D ratio, total protein concentration, the levels of C3, C3c and C5a, the levels of TNF-α, IL-6, and IL-1β, while increased the levels of IL-4 and IL-10. DYY-4 (60 mg/kg) decreased the levels of C5aR1, C5b-9 and COX-2 mRNA (p < 0.05), the levels of MPO and iNOS mRNA, the activation of the IKK/NF-κB pathway (p < 0.01), and increased the levels of IL-13 and SOD (p < 0.01). DYY-4 (60 mg/kg) relieved the lung tissue pathological changes and reduced the C3c deposition. Network pharmacology combined with animal experiments revealed the targets of DYY-4 alleviating ALI. [ABSTRACT FROM AUTHOR]

Deep learning algorithms represented by convolutional neural networks bring new opportunities for spectral analysis technology. Convolutional neural networks are more straightforward than traditional chemometric algorithms for detecting the quality of agricultural products, reducing the procedures of spectral preprocessing and band selection, and with higher prediction accuracy. However, there are few research papers on the relevance of the explanation of the convolutional neural networks model mechanism, and the reader cannot fully understand convolutional neural networks feature learning. In this study, convolutional neural networks combined with the subregional voting method were used to predict the moisture content of semen ziziphi spinosae. Firstly, 10 regions of interest were divided using the subregional voting method, and the results of network models were compared. It was found that the average spectrum of the fifth region of interest had the best prediction of moisture content because it was closest to the central region of semen ziziphi spinosae. Based on this, a convolutional neural network containing three convolutional layers, three pooling layers, and one fully connected layer is proposed. Partial least squares regression, backpropagation neural network, and convolutional neural networks were established to predict the moisture content of semen ziziphi spinosae. The correlation coefficient of the prediction set of the partial least squares regression is 0.98 after the multiplicative scatter correction preprocessed the spectra, and correlation coefficient of the prediction set of the backpropagation neural network is 0.83 after the standard normal variate preprocessed the spectra. The correlation coefficient of the prediction set of the convolutional neural networks established by using the raw spectra reached 0.99. The spectral preprocessing method can improve the prediction set correlation coefficient of partial least squares regression and backpropagation neural network. Still, it will reduce the prediction ability of convolutional neural networks. This study also analyzed the effect of different learning rates on the performance of convolutional neural networks, and it was found that the training loss and training accuracy performed most consistently when the learning rate was 0.01. Secondly, this study also visualized the output feature maps of the three convolutional layers of convolutional neural networks and verified the effectiveness of convolutional neural networks feature band extraction. This study provides new ideas for deep learning in the online detection of seed moisture content. The determination of moisture content of semen zizyphi spinosae by convolutional neural networks combined with subregional voting was investigated.A convolutional neural network containing three convolutional layers, three pooling layers, and one fully connected layer is proposed.We use the subregional voting method to divide the 10 regions of interests and compare the effects of modeling of the average spectrum of different regions of interests.Spectral preprocessing methods can improve the prediction accuracy of partial least squares regression and back propagation neural network models but reduce the prediction ability of Convolutional Neural Networks models.The feature maps of convolutional neural networks convolutional layers were visualized and studied to verify the effectiveness of convolutional neural networks model feature band extraction. [ABSTRACT FROM AUTHOR]