CNN-LSTM Neural Network for Identification of Pre-Cooked Pasta Products in Different Physical States Using Infrared Spectroscopy.

Infrared (IR) spectroscopy is nondestructive, fast, and straightforward. Recently, a growing number of pasta companies have been using IR spectroscopy combined with chemometrics to quickly determine sample parameters. However, fewer models have used deep learning models to classify cooked wheat food products and even fewer have used deep learning models to classify Italian pasta. To solve these problems, an improved CNN-LSTM neural network is proposed to identify pasta in different physical states (frozen vs. thawed) using IR spectroscopy. A one-dimensional convolutional neural network (1D-CNN) and long short-term memory (LSTM) were constructed to extract the local abstraction and sequence position information from the spectra, respectively. The results showed that the accuracy of the CNN-LSTM model reached 100% after using principal component analysis (PCA) on the Italian pasta spectral data in the thawed state and 99.44% after using PCA on the Italian pasta spectral data in the frozen form, verifying that the method has high analytical accuracy and generalization. Therefore, the CNN-LSTM neural network combined with IR spectroscopy helps to identify different pasta products.

Arctigenin inhibits abnormal germinal center reactions and attenuates murine lupus by inhibiting IFN-I pathway.

Interferon-I (IFN-I) signaling pathway plays a vital role in the differentiation of germinal center B cells and the pathogenesis of systemic lupus erythematosus (SLE). Therefore, targeting the IFN-I signaling pathway could serve as an effective treatment strategy in SLE. Arctigenin is an active ingredient present in the seeds of Arctium lappa L. It has been reported to act as a negative regulator of inflammatory responses. However, the role of arctigenin remains unknown in the regulation process of the IFN-I-mediated differentiation of germinal center B cells and the pathogenesis of SLE. In the present study, we demonstrated that arctigenin alleviated the progression of spontaneous lupus in MRL/lpr mice and imiquimod-mediated lupus mice. Especially, arctigenin significantly reduced the proportions of germinal center B cells (7.1%, vs. 5.12%, p < 0.01), follicular helper T cells (11.49%, vs. 5.53%, p < 0.05), and plasma cells (2.44%, vs. 1.39%, p < 0.01) in the lupus-prone mice. In vitro studies have shown that arctigenin significantly inhibited the IFN-α-induced CD69 and interferon-stimulated gene (ISG) expressions along with the phosphorylation of JAK1 and STAT1 by nearly half in murine B cells via activating PP2A. Overall, these data highlighted the role of arctigenin in regulating the IFN-I-mediated differentiation of germinal center B cells and the pathogenesis of SLE. Thus, arctigenin may be used as a potentially effective therapeutic target for the treatment of SLE.

Composite Multivariate Multi-Scale Permutation Entropy and Laplacian Score Based Fault Diagnosis of Rolling Bearing.

As a powerful tool for measuring complexity and randomness, multivariate multi-scale permutation entropy (MMPE) has been widely applied to the feature representation and extraction of multi-channel signals. However, MMPE still has some intrinsic shortcomings that exist in the coarse-grained procedure, and it lacks the precise estimation of entropy value. To address these issues, in this paper a novel non-linear dynamic method named composite multivariate multi-scale permutation entropy (CMMPE) is proposed, for optimizing insufficient coarse-grained process in MMPE, and thus to avoid the loss of information. The simulated signals are used to verify the validity of CMMPE by comparing it with the often-used MMPE method. An intelligent fault diagnosis method is then put forward on the basis of CMMPE, Laplacian score (LS), and bat optimization algorithm-based support vector machine (BA-SVM). Finally, the proposed fault diagnosis method is utilized to analyze the test data of rolling bearings and is then compared with the MMPE, multivariate multi-scale multiscale entropy (MMFE), and multi-scale permutation entropy (MPE) based fault diagnosis methods. The results indicate that the proposed fault diagnosis method of rolling bearing can achieve effective identification of fault categories and is superior to comparative methods.

[CD11b agonist leukadherin-1 inhibits activation of TLR7 and TLR9 in mouse bone marrow-derived dendritic cells by blocking NF-κB p65 pathway].

Objective To study the effect of CD11b agonist leukadherin-1 (LA1) on Toll-like receptor 7 (TLR7)- and TLR9-induced activation of mouse bone marrow-derived dendritic cells (BMDCs) and its specific mechanism. Methods BMDCs were successfully induced and the concentrations of LA1 used in the study were determined by CCK-8 assay and annexin V-FITC/PI double staining. BMDCs were treated with LA1 for 2 hours followed by stimulation of TLR7 agonist R837 and TLR9 agonist CpG1826. The expression of BMDCs surface markers CD40, CD86 and MHC-II were detected by flow cytometry; IL-6, IL-12p40 and tumor necrosis factor α (TNF-α) in the cell culture supernatant were detected by ELISA; the phosphorylation of NF-κB p65 in BMDCs was detected by Western blotting. Results LA1 concentration below 20 µmol/L had no effect on the viability and apoptosis of BMDCs. LA1 pretreatment significantly inhibited R837- and CpG 1826-induced expression of CD40, CD86 and MHC-II , and the secretion of IL-6, IL-12p40 and TNF-α in BMDCs. Moreover, LA1 pretreatment significantly inhibited the phosphorylation of NF-κB p65 activated by R837 and CpG1826 in BMDCs. Conclusion CD11b agonist LA1 can significantly inhibit the activation of TLR7 and TLR9 in BMDCs by blocking the NF-κB p65 signaling pathway.

Time-Shift Multi-scale Weighted Permutation Entropy and GWO-SVM Based Fault Diagnosis Approach for Rolling Bearing.

Multi-scale permutation entropy (MPE) is an effective nonlinear dynamic approach for complexity measurement of time series and it has been widely applied to fault feature representation of rolling bearing. However, the coarse-grained time series in MPE becomes shorter and shorter with the increase of the scale factor, which causes an imprecise estimation of permutation entropy. In addition, the different amplitudes of the same patterns are not considered by the permutation entropy used in MPE. To solve these issues, the time-shift multi-scale weighted permutation entropy (TSMWPE) approach is proposed in this paper. The inadequate process of coarse-grained time series in MPE was optimized by using a time shift time series and the process of probability calculation that cannot fully consider the symbol mode is solved by introducing a weighting operation. The parameter selections of TSMWPE were studied by analyzing two different noise signals. The stability and robustness were also studied by comparing TSMWPE with TSMPE and MPE. Based on the advantages of TSMWPE, an intelligent fault diagnosis method for rolling bearing is proposed by combining it with gray wolf optimized support vector machine for fault classification. The proposed fault diagnostic method was applied to two cases of experimental data analysis of rolling bearing and the results show that it can diagnose the fault category and severity of rolling bearing accurately and the corresponding recognition rate is higher than the rate provided by the existing comparison methods.