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Sudden failures of measurement and control circuits in hydropower plants may lead to unplanned shutdowns of generating units. Therefore, the diagnosis of hydropower station measurement and control system poses a great challenge. Existing fault diagnosis methods suffer from long fault identification time, inaccurate positioning, and low diagnostic efficiency. In order to improve the accuracy of fault diagnosis, this paper proposes a fault diagnosis method for hydropower station measurement and control system that combines variational modal decomposition (VMD), Pearson’s correlation coefficient, a one-dimensional convolutional neural network, and a bi-directional long and short-term memory network (1DCNN-BiLSTM). Firstly, the VMD parameters are optimised by the Improved Sparrow Search Algorithm (ISSA). Secondly, signal decomposition of the original fault signals is carried out by using ISSA-VMD, and meanwhile, the optimal intrinsic modal components (IMFs) are screened out by using Pearson’s correlation coefficient, and the optimal set of components is subjected to signal reconstruction in order to obtain the new signal sequences. Then, the 1DCNN-BiLSTM-based fault diagnosis model is proposed, which achieves accurate diagnosis of the faults of hydropower station measurement and control system. Finally, experimental verification reveals that, in comparison with other methods such as 1DCNN, BiLSTM, ELM, BP neural network, SVM, and DBN, the proposed approach in this paper achieves an exceptionally high average recognition accuracy of 99.8% in both simulation and example analysis. Additionally, it demonstrates faster convergence speed, indicating not only its superior diagnostic precision but also its high application value.

Abstract Prenylated and reverse-prenylated indolines are privileged scaffolds in numerous naturally occurring indole alkaloids with a broad spectrum of important biological properties. Development of straightforward and stereoselective methods to enable the synthesis of structurally diverse prenylated and reverse-prenylated indoline derivatives is highly desirable and challenging. In this context, the most direct approaches to achieve this goal generally rely on transition-metal-catalyzed dearomative allylic alkylation of electron-rich indoles. However, the electron-deficient indoles are much less explored, probably due to their diminished nucleophilicity. Herein, a photoredox-catalyzed tandem Giese radical addition/Ireland–Claisen rearrangement is disclosed. Diastereoselective dearomative prenylation and reverse-prenylation of electron-deficient indoles proceed smoothly under mild conditions. An array of tertiary α-silylamines as radical precursors is readily incorporated in 2,3-disubstituted indolines with high functional compatibility and excellent diastereoselectivity (>20:1 d.r.). The corresponding transformations of the secondary α-silylamines provide the biologically important lactam-fused indolines in one-pot synthesis. Subsequently, a plausible photoredox pathway is proposed based on control experiments. The preliminary bioactivity study reveals a potential anticancer property of these structurally appealing indolines.

Abstract Background Recent studies have shown that circulating microRNAs (miRNAs) can be used as diagnostic biomarkers for melanoma. This study aimed to evaluate the diagnostic value of circulating miRNAs for melanoma. Methods A comprehensive literature search was conducted and the quality of the included literature was evaluated using QUADAS-2 (Quality Assessment for diagnostic accuracy studies), and the diagnostic accuracy was assessed by pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and area under the curve (AUC). We used Deeks’ funnel plot to evaluate publication bias. Results The meta-analysis included 10 articles covering 16 studies, and the results showed that circulating miRNAs provide high diagnostic accuracy for melanoma. The overall pooled sensitivity was 0.87 (95% CI: 0.82–0.91), specificity was 0.81 (95% CI: 0.77–0.85), PLR was 4.6 (95% CI: 3.7–5.8), NLR was 0.16 (95% CI: 0.11–0.23), DOR was 29 (95% CI: 18–49), and AUC was 0.90 (95% CI: 0.87–0.92), respectively. Subgroup analysis showed better diagnostic value in miRNA clusters, European population, plasma miRNAs, and upregulated miRNAs compared to other subgroups. Conclusions The results indicated that circulating microRNAs can be used as a non-invasive biomarker for the diagnosis of melanoma.

A large number of prefabricated components are composed of different types of houses. The on-site assembly sequence of components is also the key link of the construction organization of the project. The assembly sequence obtained by manual experience is easy to omit components and interfere with each other. According to the weight and assembly process of construction components, the evaluation index of mechanical assembly process is introduced to clarify the relationship between the number of components and the assembly process. In the aspect of intelligent optimization algorithm, the simulated annealing program module is introduced into the existing genetic algorithm to form a new simulated annealing genetic algorithm. The comparative study of the calculation results shows that the simulated annealing genetic algorithm can search the global optimal assembly sequence scheme more accurately and efficiently, and meet the evaluation indexes at the same time. The mechanical simulation technology is innovatively used to simulate the assembly of building components, and the simulation process is used to guide the on-site construction process.

With the continuous operation of analog circuits, the component degradation problem gradually comes to the forefront, which may lead to problems, such as circuit performance degradation, system stability reductions, and signal quality degradation, which could be particularly evident in increasingly complex electronic systems. At the same time, due to factors, such as continuous signal transformation, the fluctuation of component parameters, and the nonlinear characteristics of components, traditional fault localization methods are still facing significant challenges when dealing with large-scale complex circuit faults. Based on this, this paper proposes a fault-diagnosis method for analog circuits using the ECWGEO algorithm, an enhanced version of the GEO algorithm, to de-optimize the 1D-CNN with an attention mechanism to handle time–frequency fusion inputs. Firstly, a typical circuit-quad op-amp dual second-order filter circuit is selected to construct a fault-simulation model, and Monte Carlo analysis is used to obtain a large number of samples as the dataset of this study. Secondly, the 1D-CNN network structure is improved for the characteristics of the analog circuits themselves, and the time–frequency domain fusion input is implemented before inputting it into the network, while the attention mechanism is introduced into the network. Thirdly, instead of relying on traditional experience for network structure determination, this paper adopts a parameter-optimization algorithm for network structure optimization and improves the GEO algorithm according to the problem characteristics, which enhances the diversity of populations in the late stage of its search and accelerates the convergence speed. Finally, experiments are designed to compare the results in different dimensions, and the final proposed structure achieved a 98.93% classification accuracy, which is better than other methods.

In order to solve low-quality problems such as data anomalies and missing data in the condition monitoring data of hydropower units, this paper proposes a monitoring data quality enhancement method based on HDBSCAN-WSGAIN-GP, which improves the quality and usability of the condition monitoring data of hydropower units by combining the advantages of density clustering and a generative adversarial network. First, the monitoring data are grouped according to the density level by the HDBSCAN clustering method in combination with the working conditions, and the anomalies in this dataset are detected, recognized adaptively and cleaned. Further combining the superiority of the WSGAIN-GP model in data filling, the missing values in the cleaned data are automatically generated by the unsupervised learning of the features and the distribution of real monitoring data. The validation analysis is carried out by the online monitoring dataset of the actual operating units, and the comparison experiments show that the clustering contour coefficient (SCI) of the HDBSCAN-based anomaly detection model reaches 0.4935, which is higher than that of the other comparative models, indicating that the proposed model has superiority in distinguishing between the valid samples and anomalous samples. The probability density distribution of the data filling model based on WSGAIN-GP is similar to that of the measured data, and the KL dispersion, JS dispersion and Hellinger’s distance of the distribution between the filled data and the original data are close to 0. Compared with the filling methods such as SGAIN, GAIN, KNN, etc., the effect of data filling with different missing rates is verified, and the RMSE error of data filling with WSGAIN-GP is lower than that of other comparative models. The WSGAIN-GP method has the lowest RMSE error under different missing rates, which proves that the proposed filling model has good accuracy and generalization, and the research results in this paper provide a high-quality data basis for the subsequent trend prediction and state warning.

Vibration monitoring and analysis play a crucial role in the fault diagnosis of hydroelectric units. However, accurate extraction and identification of fault features from vibration signals are challenging because of noise interference. To address this issue, this study proposes a novel denoising method for vibration signals based on improved complementary ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), permutation entropy (PE), and singular value decomposition (SVD). The proposed method is applied for the analysis of hydroelectric unit sway monitoring. Firstly, the ICEEMDAN method is employed to process the signal and obtain several intrinsic mode functions (IMFs), and then the PE values of each IMF are calculated. Subsequently, based on a predefined threshold of PE, appropriate IMFs are selected for reconstruction, achieving the first denoising effect. Then, the SVD is applied to the signal after the first denoising effect, resulting in the SVD spectrum. Finally, according to the principle of the SVD spectrum and the variation in the singular value and its energy value, the signal is reconstructed by choosing the appropriate reconstruction order to achieve the secondary noise reduction effect. In the simulation and case analysis, the method is better than the commonly used wavelet threshold, SVD, CEEMDAN–PE, and ICEEMDAN–PE, with a signal-to-noise ratio (SNR) improvement of 6.9870 dB, 4.6789 dB, 8.9871 dB, and 4.3762 dB, respectively, and where the root-mean-square error (RMSE) is reduced by 0.1426, 0.0824, 0.2093 and 0.0756, respectively, meaning that our method has a better denoising effect and provides a new way for denoising the vibration signal of hydropower units.

BackgroundSeveral studies have reported an association between the occurrence of immune-related adverse events (irAEs) and prognosis in patients with melanoma treated with immune checkpoint inhibitors (ICIs), but the results remain controversial. We conducted a systematic review and meta-analysis to investigate the association between irAEs and survival in patients with melanoma treated with ICIs.MethodsWe searched the PubMed, Web of Science, and China National Knowledge Infrastructure databases through May 5, 2022 for clinical studies evaluating the association between irAEs and in melanoma patients treated with ICIs. Combined hazard ratios (HRs) for overall survival (OS) and progression-free survival (PFS) were calculated using fixed- or random-effects models based on heterogeneity. ResultsA total of 60 articles were included, with 16,520 patients. In patients with melanoma treated with ICIs, the occurrence of irAEs was significantly associated with better OS (HR, 0.58; 95% confidence interval [CI], 0.51–0.66; P

Zearalenone (ZEN), a prevalent mycotoxin contaminating food and known for its intestinal toxicity, has been suggested as a potential risk factor for inflammatory bowel disease (IBD), although the exact relationship between ZEN exposure and IBD remains unclear. In this study, we established a rat model of colon toxicity induced by ZEN exposure to investigate the key targets of ZEN-induced colon toxicity and explore the underlying connection between ZEN exposure and IBD. Histological staining of the rat colon revealed significant pathological changes resulting from ZEN exposure (p < 0.01). Furthermore, the proteomic analysis demonstrated a notable upregulation of protein expression levels, specifically STAT2 (0.12 ± 0.0186), STAT6 (0.36 ± 0.0475) and ISG15 (0.43 ± 0.0226) in the rat colon (p < 0.05). Utilizing bioinformatics analysis, we combined ZEN exposure and IBD clinical sample databases to reveal that ZEN exposure may increase the risk of IBD through activation of the STAT-ISG15 pathway. This study identified novel targets for ZEN-induced intestinal toxicity, providing the basis for further study of ZEN exposure to IBD.

BackgroundGlucocorticoid-induced osteoporosis (GIOP) is a common form of secondary osteoporosis caused by the protracted or a large dosage of glucocorticoids (GCs). Total flavonoids of Drynariae rhizoma (TFDR) have been widely used in treating postmenopausal osteoporosis (POP). However, their therapeutic effects and potential mechanism against GIOP have not been fully elucidated.MethodsUltra-high-performance liquid chromatography coupled with electrospray ionization quadrupole time-of-flight mass spectrometry (UHPLC-ESIQ-TOF-MS) experiments were performed for qualitative analysis. We performed hematoxylin-eosin (HE) staining and microcomputed tomography (micro-CT) analysis to detect the changes in bone microstructure. The changes in biochemical parameters in the serum samples were determined by performing an enzyme-linked immunosorbent assay (ELISA). The prediction results of network pharmacology were verified via quantitative real-time polymerase chain reaction (qRT-PCR) to elucidate the potential mechanism of TFDR against GIOP.ResultsA total of 191 ingredients were identified in vitro and 48 ingredients in vivo. In the in-vivo experiment, the levels of the serum total cholesterol (TC), the serum triglyceride (TG), Leptin (LEP), osteocalcin (OC), osteoprotegerin (OPG), bone morphogenetic protein-2 (BMP-2), propeptide of type I procollagen (PINP), tartrate-resistant acid phosphatase (TRACP) and type-I collagen carboxy-terminal peptide (CTX-1) in the TFDR group significantly changed compared with those in the GIOP group. Moreover, the TFDR group showed an improvement in bone mineral density and bone microstructure. Based on the results of network pharmacology analysis, 67 core targets were selected to construct the network and perform PPI analysis as well as biological enrichment analysis. Five of the targets with high “degree value” had differential gene expression between groups using qRT-PCR.ConclusionTFDR, which may play a crucial role between adipose metabolism and bone metabolism, may be a novel remedy for the prevention and clinical treatment of GIOP.

ZeXie Decoction (ZXD) is one of the traditional Chinese medicine formulas (TCMFs) comprising Alisma orientalis (Sam.) Juzep. (ZX) and Atractylodes macrocephala Koidz. (BZ) in 5:2 ratios and is widely employed in clinical applications since ancient times. In this study, UHPLC-QE-Orbitrap-MS was used for qualitative analysis of ZXD in rats’ plasma after a single oral dose of 750 mg/kg body weight. Afterward, UHPLC-Q-TRAP-MS/MS was used for simultaneous analysis of three bioactive chemical compounds including alisol A, alisol B, and alisol A 24-acetate in ZXD’s ethanol extract. Subsequently, the pharmacokinetic profiles of the three analytes were investigated in rat plasma utilizing UHPLC-Q-TRAP-MS/MS. The multiple reaction monitoring (MRM) mode for the three analytes were at m/z 508.4→383.2 for alisol A, m/z 490.4→365.2 for alisol B, and m/z 550.4→515.5 for alisol A 24-acetate. The analysis method was validated in terms of its accuracy, stability, repeatability, linearity, spiked recovery and matrix effect. As a result, twenty-five chemical constituents of ZXD were putatively identified in plasma, and rapid, sensitive, and accurate methods were established for the quantitative analysis and pharmacokinetic study of ZXD. The findings of this study can provide a scientific base for further study of in vivo pharmacokinetics of TCMFs.

ZeXie Decoction (ZXD) is a traditional Chinese medicine composed of Alisma orientalis (Sam.) Juzep. and Atractylodes macrocephala Koidz. ZXD has been widely used to treat non-alcoholic fatty liver disease (NAFLD). The mechanistic basis for the pharmacological activity of ZXD, however, remains poorly understood. In this study, we used a network pharmacology approach and investigated the association between ZXD and NAFLD. We identified the active ingredients of ZXD and screened the potential targets of these ingredients, after which a database of relevant NAFLD-related targets were constructed and several enrichment analyses were performed. Furthermore, the ethanol and aqueous extracts of ZXD were prepared and experimental pharmacology validation was conducted using RT-qPCR of the non-alcoholic fatty liver disease (NAFLD) model in Sprague-Dawley (SD) rats. As a result, a herb-compound-target-pathway network model was developed, and HMGCR, SREBP-2, MAPK1, and NF-κBp65 targets were validated. The gene expression results of these four targets were consistent with those of the network pharmacology prediction. Using an integration strategy, we revealed that ZXD could treat NAFLD by targeting HMGCR, SREBP-2, MAPK1, and NF-κBp65.

As one of the most important conjugated mycotoxins, zearalenone-14-glucoside (Z14G) has received widespread attention from researchers. Although the metabolism of Z14G in animals has been extensively studied, the intracellular toxicity and metabolic process of Z14G are not fully elucidated. In this study, the cytotoxicity of Z14G to human ovarian granulosa cells (KGN) and the metabolism of Z14G in KGN cells were determined. Furthermore, the experiments of co-administration of β-glucosidase and pre-administered β-glucosidase inhibitor (Conduritol B epoxide, CBE) were used to clarify the mechanism of Z14G toxicity release. Finally, the human colon adenocarcinoma cell (Caco-2) metabolism model was used to verify the toxicity release mechanism of Z14G. The results showed that the IC50 of Z14G for KGN cells was 420 μM, and the relative hydrolysis rate of Z14G on ZEN was 35% (25% extracellular and 10% intracellular in KGN cells). The results indicated that Z14G cannot enter cells, and Z14G is only hydrolyzed extracellularly to its prototype zearalenone (ZEN) by β-glucosidase which can exert toxic effects in cells. In conclusion, this study demonstrated the cytotoxicity of Z14G and clarified the toxicity release mechanism of Z14G. Different from previous findings, our results showed that Z14G cannot enter cells but exerts cytotoxicity through deglycosylation. This study promotes the formulation of a risk assessment and legislation limit for ZEN and its metabolites.

Oil-immersed transformer is one of the most important components in the power system. The dissolved gas concentration prediction in oil is vital for early incipient fault detection of transformer. In this paper, a model for predicting the dissolved gas concentration in power transformer based on the modified grey wolf optimizer and least squares support vector machine (MGWO-LSSVM) with grey relational analysis (GRA) and empirical mode decomposition (EMD) is proposed, in which the influence of transformer load, oil temperature and ambient temperature on gas concentration is taken into consideration. Firstly, GRA is used to analyze the correlation between dissolved gas concentration and transformer load, oil temperature and ambient temperature, and the optimal feature set affecting gas concentration is extracted and selected as the input of the prediction model. Then, EMD is used to decompose the non-stationary series data of dissolved gas concentration into stationary subsequences with different scales. Finally, the MGWO-LSSVM is used to predict each subsequence, and the prediction values of all subsequences are combined to get the final result. DGA samples from two transformers are used to verify the proposed method, which shows high prediction accuracy, stronger generalization ability and robustness by comparing with LSSVM, particle swarm optimization (PSO)-LSSVM, GWO-LSSVM, MGWO-LSSVM, EMD-PSO-LSSVM, EMD-GWO-LSSVM, EMD-MGWO-LSSVM, GRA-EMD-PSO-LSSVM and GRA-EMD-GWO-LSSVM.

Background: Several studies have reported an association between the occurrence of immune-related adverse events (irAEs) and prognosis in patients with melanoma treated with immune checkpoint inhibitors (ICIs), but the results remain controversial. We conducted a systematic review and meta-analysis to investigate the association between irAEs and survival in patients with melanoma treated with ICIs. Methods: We searched the PubMed, Web of Science, and China National Knowledge Infrastructure databases through May 5, 2022 for clinical studies evaluating the association between irAEs and in melanoma patients treated with ICIs. Combined hazard ratios (HRs) for overall survival (OS) and progression-free survival (PFS) were calculated using fixed- or randomeffects models based on heterogeneity. Results: A total of 60 articles were included, with 16,520 patients. In patients with melanoma treated with ICIs, the occurrence of irAEs was significantly associated with better OS (HR, 0.58; 95% confidence interval [CI], 0.51-0.66; P<0.00001) and PFS (HR, 0.61; 95%CI, 0.51-0.72; P<0.00001). Endocrine irAEs (OS, HR, 0.81; 95%CI, 0.72-0.92; P=0.001; PFS: HR, 0.84; 95%CI, 0.73-0.96, P=0.009), skin irAEs (OS, HR, 0.59; 95%CI, 0.41-0.85; P=0.004; PFS: HR, 0.43; 95%CI, 0.36-0.52; P<0.00001), vitiligo (OS, HR, 0.22; 95%CI, 0.15-0.31; P<0.00001; PFS, HR, 0.33; 95%CI, 0.25-0.44; P<0.00001), and grade 1-2 irAEs (OS, HR, 0.67; 95%CI, 0.58-0.78; P<0.00001; PFS, HR, 0.62; 95%CI, 0.51-0.76; P<0.00001) showed similar results. However, thyroid, lung, gastrointestinal, liver, and grade 3-4 irAEs were not significantly associated with OS and PFS. The occurrence of non-thyroid endocrine irAEs was significantly associated with better OS (HR, 0.22; 95%CI, 0.15-0.31; P<0.00001). In patients with melanoma treated with anti-programmed cell death protein 1 (OS, HR, 0.61; 95%CI, 0.51-0.72; P<0.00001; PFS, HR, 0.59; 95%CI, 0.47-0.74; P<0.00001), the association between irAEs and clinical benefit was clearer than in patients treated with anti-cytotoxic Tlymphocyte-associated protein 4 (OS, HR, 0.68; 95%CI, 0.52-0.89; P=0.005; PFS, HR, 0.93; 95%CI, 0.49-1.78; P=0.83). Conclusion: Among patients with melanoma treated with ICIs, those who developed non-thyroid endocrine irAEs and cutaneous irAEs have better prognosis. This suggests that non-thyroid endocrine irAEs and cutaneous irAEs may be a prognostic biomarker for patients with melanoma treated with ICIs. [ABSTRACT FROM AUTHOR]

Experimental studies of chemical-vapor-deposited diamond films indicate an enhancement of surface energies with substrate temperature. This surface energy for diamond is lower than that for graphite. There is a higher number of cubic (100) and octahedral (111) facets on the surface than the (110) facets. Increasing the substrate temperature leads to conversion of (100) planes to (111) planes.

Carbon emissions have drawn widely attention due to the worsening climate changes. Off-site precast and cast-in-place are two different approaches to the industrialized construction of concrete buildings. A reasonable selection of building methods can not only save the construction cost and time, but also reduce the construction carbon emissions. However, Off-site precast concrete faces many uncertainties and constraints due to the highly fragmented management of design, construction, and supply chain. Thus, in order to overcome this limitation, cast-in-place construction technologies with industrialized methods is developed. Firstly, this study assesses four critical success factors (i.e. concrete, formwork, rebars, and scaffolding) for concrete construction according to the project subdivisions of a concrete production. Then, based on the AHP method, the four decisive factors were used to evaluated cast-in-place concrete in comparison with the off-site precast concrete. Finally, a comparison of two different approaches is analyzed to demonstrate the advantages of the cast-in-place. The results show that (i) Buildings with cast-inplace construction technologies have good structural integrity; (ii) Through the strategy of graded assembly, cast-in-place construction technologies raises the factory-based manufacturing efficiency; (iii) Cast-in-place construction technologies can reduce the transportation cost of large-scale prefabricated components. Therefore, cast-in-place concrete with industrialized methods is applicable to high-rise buildings in China, and contributes to promoting the sustainable development of human living environment. [ABSTRACT FROM AUTHOR]