ABSTRACT Digital welding machine (DWM) is an advanced tool for material forming. The lifespan and health status of DWMs are closely related to the safety and reliability. To address the problem of low accuracy in the lifespan prediction of DWMs, a model based on immune algorithm (IA) and long short-term memory network (LSTM) with attention mechanism is proposed. First, the degradation characteristic indicators of the lifespan of DWMs are evaluated and selected. Then, a health index is constructed using linear regression to quantitatively reflect the lifespan status of DWMs. The optimized model is used to predict the remaining lifespan, and compared with various models using 5 indicators. Finally, predictive maintenance of DWMs is carried out based on product inspection and production scheduling. the optimal solution for the objective function is obtained to calculate the best predictive maintenance method for the digital welding machine.During the lifespan prediction process, the optimized model has a 20% decrease in root mean square error and a 35.8% decrease in mean square error compared to the traditional LSTM model. The average absolute error is decreased by 14.2% and the average absolute percentage error is closer to 0, while the coefficient of determination increases by 23%. By combining with actual production line arrangements, maintenance of DWMs can be performed at the most appropriate time to minimize maintenance costs.

Abstract Background: Hyperhomocysteinemia is associated with autoimmune diseases such as ankylosing spondylitis (AS), systemic lupus erythematosus (SLE), and rheumatoid arthritis (RA). Current findings regarding plasma/serum homocysteine (HCY) levels in AS patients are inconsistent. This study aims to systematically evaluate the association between circulating HCY levels and AS. Methods: Online electronic databases (PubMed, Web of Science, Embase, ScienceDirect, China National Knowledge Infrastructure (CNKI), and Wanfang data) were used to retrieve all relevant articles published up to May 7, 2020. The pooled standardized mean difference (SMD) with 95% confidence interval (CI) was calculated using the random-effect model, Stata16 software. Results: Nine articles containing 778 AS patients and 522 controls were included in this meta-analysis. No significant differences in HCY levels were found between AS and control groups (pooled SMD = 0.46, 95% CI = − 0.30 to 1.23, P = 0.23). However, subgroup analysis suggested that HCY levels were significantly higher (P < 0.05) in the AS group treated with methotrexate (MTX) compared with the control group. In contrast, HCY levels were significantly (P < 0.05) lower in the AS group receiving anti-TNF-α treatment compared with the control group. No significant differences were detected between HCY levels and disease activity scores (Bath AS disease activity index, BASDAI), and methylenetetrahydrofolate reductase (MTHFR) C677T genotype. Conclusion: This meta-analysis indicates that HCY levels are similar between AS and controls, and do not correlate with disease activity. However, different medical treatments cause fluctuations of circulating HCY levels in AS patients. Further and larger-scale studies are needed to confirm these findings. Trial registration: This study was registered at international prospective register of systematic reviews (PROSPERO), registration number: CRD42020184426.

Green tea is one of the main teas in China, which is unfermented and retains more natural substances of fresh tea leaves. This is the preliminary study of application of'fingerprints' based on differences in component composition of green tea. Five green teas from different areas in eastern China are analyzed, which are processed by microwave-assisted solvent (ethanol) extraction method to obtain tea polyphenols, flavonoids, polysaccharides, pigments (thearubigins, theaflavins, theabrownins). The results show that the component composition of five green teas are varied from each other; based on these contents varieties, we have constructed a 'fingerprint' and applied linear discriminant analysis (LDA) and hierarchical cluster analysis (HCA) to assist in the identification of these five green teas. This method does not require large, expensive instruments (such as high performance liquid chromatograph, gas chromatograph, mass spectrometer, etc.), and is easy to use, which provides a new avenue for the identification of tea.

O novo ecdisteróide 7,8beta-di-hidroponasterona A e a ponasterona A, foram isolados a partir do extrato metanólico das agulhas do teixo japonês Taxus cuspidata. As estruturas foram elucidadas por meios espectroscópicos, nomeadamente, RMN 13C e ¹H, COSY ¹H-¹H, NOESY, HMQC e HMBC, e confirmadas por HR-FABMS.

A new plant ecdysteroid 7,8beta-dihydroponasterone A, together with ponasterone A, were isolated from the methanol extract of the needles of the Japanese yew, Taxus cuspidata. Their structures were elucidated on the basis of spectroscopic analysis including ¹H NMR, 13C NMR, ¹H-¹H COSY, NOESY, HMQC and HMBC and confirmed by high-resolution FABMS data.