Previous studies reported inconsistent results regarding association between diabetes mellitus (DM), prediabetes and risk, disease progression of Parkinson's disease (PD). The meta-analysis was made ...to investigate association between DM, prediabetes and risk, disease progression of PD.
Literatures investigating association between DM, prediabetes and risk, disease progression of PD were searched in these databases: PubMed and Web of Science. Included literatures were published before October 2022. STATA 12.0 software was used to compute odds ratios (ORs)/relative risks (RRs) or standard mean differences (SMDs).
DM was associated with a higher risk of PD, compared to non-diabetic participants with a random effects model (OR/RR = 1.23, 95% CI 1.12-1.35,
= 90.4%,
< 0.001). PD with DM (PD-DM) was associated with a faster motor progression compared to PD without DM (PD-noDM) with a fixed effects model (RR = 1.85, 95% CI 1.47-2.34,
= 47.3%,
= 0.091). However, meta-analysis for comparison in change rate of United Rating Scale (UPDRS) III scores from baseline to follow-up time between PD-DM and PD-noDM reported no difference in motor progression between PD-DM and PD-noDM with a random effects model (SMD = 2.58, 95% CI = -3.11 to 8.27,
= 99.9%,
< 0.001). PD-DM was associated with a faster cognitive decline compared to PD-noDM with a fixed effects model (OR/RR = 1.92, 95% CI 1.45-2.55,
= 50.3%,
= 0.110).
In conclusion, DM was associated with a higher risk and faster disease decline of PD. More large-scale cohort studies should be adopted to evaluate the association between DM, prediabetes and PD.
Mechanical equipment is composed of several parts, and the interaction between parts exists throughout the whole life cycle, leading to the widespread phenomenon of fault coupling. The diagnosis of ...independent faults cannot meet the requirements of the health management of mechanical equipment under actual working conditions. In this paper, the dynamic vertex interpretable graph neural network (DIGNN) is proposed to solve the problem of coupling fault diagnosis, in which dynamic vertices are defined in the data topology. First, in the date preprocessing phase, wavelet transform is utilized to make input features interpretable and reduce the uncertainty of model training. In the fault topology, edge connections are made between nodes according to the fault coupling information, and edge connections are established between dynamic nodes and all other nodes. Second the data topology with dynamic vertices is used in the training phase and in the testing phase, the time series data are only fed into dynamic vertices for classification and analysis, which makes it possible to realize coupling fault diagnosis in an industrial production environment. The features extracted in different layers of DIGNN interpret how the model works. The method proposed in this paper can realize the accurate diagnosis of independent faults in the dataset with an accuracy of 100%, and can effectively judge the coupling mode of coupling faults with a comprehensive accuracy of 88.3%.
To tackle the problems of over-reliance on traditional experience, poor troubleshooting robustness, and slow response by maintenance personnel to changes in faults in the current aircraft health ...management field, this paper proposes the use of a knowledge graph. The knowledge graph represents troubleshooting in a new way. The aim of the knowledge graph is to improve the correlation between fault data by representing experience. The data source for this study consists of the flight control system manual and typical fault cases of a specific aircraft type. A knowledge graph construction approach is proposed to construct a fault knowledge graph for aircraft health management. Firstly, the data are classified using the ERNIE model-based method. Then, a joint entity relationship extraction model based on ERNIE-BiLSTM-CRF-TreeBiLSTM is introduced to improve entity relationship extraction accuracy and reduce the semantic complexity of the text from a linguistic perspective. Additionally, a knowledge graph platform for aircraft health management is developed. The platform includes modules for text classification, knowledge extraction, knowledge auditing, a Q&A system, and graph visualization. These modules improve the management of aircraft health data and provide a foundation for rapid knowledge graph construction and knowledge graph-based fault diagnosis.
Zinc-dependent histone deacetylase 8 (HDAC8) catalyzes the removal of acetyl moieties from histone tails, and is critically involved in regulating chromatin structure and gene expression. The ...detailed knowledge of its catalytic process is of high importance since it has been established as a most promising target for the development of new antitumor drugs. By employing Born−Oppenheimer ab initio QM/MM molecular dynamics simulations and umbrella sampling, a state-of-the-art approach to simulate enzyme reactions, we have provided further evidence against the originally proposed general acid−base catalytic pair mechanism for Zinc-dependent histone deacetylases. Instead, our results indicated that HDAC8 employs a proton-shuttle catalytic mechanism, in which a neutral His143 first serves as the general base to accept a proton from the zinc-bound water molecule in the initial rate-determining nucleophilic attack step, and then shuttles it to the amide nitrogen atom to facilitate the cleavage of the amide bond. During the deacetylation process, the Zn2+ ion changes its coordination mode and plays multiple catalytic roles. For the K+ ion, which is located about 7 Å from the catalytic Zn2+ ion and conserved in class I and II HDACs, our simulations indicated that its removal would lead to the different transition state structure and a higher free energy reaction barrier for the rate-determining step. It is found that the existence of this conserved K+ ion would enhance the substrate binding, increase the basicity of His143, strengthen the catalytic role of zinc ion, and improve the transition state stabilization by the enzyme environment.
Long short-term memory (LSTM) is widely applied in both academic and industrial fields. However, there is no reliable criterion on selecting hyperparameters of LSTM. Currently, although some widely ...used classic methods such as random search and grid search have obtained success to some extent, the problems in local optimum and convergence still exist. In this research, we propose to use grey wolf optimizer (GWO) to search for the hyperparameters of LSTM. Through the method, the superiority of metaheuristic in global optimization and the strength of LSTM in predicting are combined. In this model, number of hidden layer nodes and learning rate of LSTM are set as preys, and grey wolf pack has a simple but efficient mechanism to search for the optimal hyperparameters. The benchmark tests on several basic functions were utilized, and the results were verified by a comparative study with random search, support vector regression and several other regression methods. Specifically, we applied this algorithm in predicting the degradation trend of the airborne fuel pump. As a result, the ergodicity and convergence of the algorithm are proved mathematically based on Markov processes theory. The benchmark tests show that the GWO-LSTM model holds for predicting data with low overall slope and high partial fluctuation. The application in airborne fuel pump shows that, trained by dataset with 5700 points, the proposed model could predict sequence of 300 points with root mean square error 0.617 after 30 iterations of optimizing, which is 2.512 previously. The result further demonstrates that the proposed algorithm is applicable to make prediction with high accuracy. Overall, the effectiveness of GWO-LSTM model is verified from theoretical proof to benchmark tests and then to actual product application.
Parameter-Efficient Fine-Tuning is widely used to transfer models between different domains. However, for some high-reliability-equipment, the degradation is at a slow rate and continually ...fluctuates, making it difficult to extract features effectively. Moreover, collecting an integrated source domain for high-reliability-equipment is tough due to the small sample of related datasets. Aiming at the transfer problem of the time-series prediction model, this research proposed an LSTM-fine-tune model, where the parameters of the model are explicitly trained and partly frozen, such that a small number of gradient steps with a small amount of training data from a new task will produce good generalization performance on that task. The algorithm is then benchmarked on sinusoidal functions, where data were randomly generated with different phases and amplitudes. The results show that the LSTM-fine-tune model can learn knowledge from different sinusoidal data and fit a new one quickly with high accuracy. This paper also considers solving two actual problems. One is transferring oxygen concentrator data from the experimental condition to the actual service condition, the results show that the accuracy is largely improved. Moreover, this paper tried to extract more general degradation knowledge from the Wiener process and then transferred it to the degradation data of the oxygen concentrator. The results show that the model quickly achieved higher prediction accuracy with the knowledge. The code and data from the test bed are accessible at https://github.com/panjinxin123/Adaptive-finetuning-in-degradation-time-series-forecasting-via-generating-source-domain .
Modified Sijunzi Decoction (MSJZD) is an empirical prescription of Traditional Chinese Medicine (TCM) and has been corroborated to be effective in multiple human diseases, but its role in non-small ...cell lung cancer (NSCLC) is enigmatic. Here we mainly analyze the function and mechanism of MSJZD in NSCLC. In this study, we used a method that coupled ultra-performance liquid chromatography to quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS) to investigate the major constituents in MSJZD with positive and negative ion modes. Additionally, in
experiments, the effects of serum-containing MSJZD on the biological behavior of NSCLC cells induced by TGF-β1 were assessed by cell function experiments. Then, the influences of serum-containing MSJZD on epithelial-mesenchymal transition (EMT)-related markers were examined by immunofluorescence and western blot assays. Also, the AKT/GSK3β pathway and apoptosis-related markers were estimated by western blotting. Tumor xenografts were generated by subcutaneously injecting A549 cells into BALB/c nude mice to determine the effects of MSJZD
. We first analyzed the composition of MSJZD. In positive ion mode, 47 kinds of components were identified. In negative ion mode, 45 kinds of components were identified. We also found that TGF-β1 contributed to inducing cell morphological changes and EMT progression.
, surprisingly, cell proliferation, migration as well as invasion in NSCLC cells induced by TGF-β1, could be weakened by serum-containing MSJZD, and apoptosis was intensified. Moreover, serum-containing MSJZD weakened EMT passage and AKT/GSK3β pathway activation and induced apoptosis-related markers in NSCLC cells triggered by TGF-β1.
, we discovered that MSJZD attenuated the tumor growth, promoted histopathological damage, and induced apoptosis in A549 tumor-bearing mice. Importantly, MSJZD has also restrained the development of EMT, AKT/GSK3β pathway, and TGF-β1 expression levels in nude mice. These findings demonstrated that MSJZD significantly weakened NSCLC progression by modulating EMT and AKT/GSK3β pathway.
The remarkable cyclization mechanism of the formation of the 6‐6‐6‐5 tetracyclic lanosterol (a key triterpenoid intermediate in the biosynthesis of cholesterol) from the acyclic 2,3‐oxidosqualene ...catalyzed by oxidosqualene cyclase (OSC) has stimulated the interest of chemists and biologists for over a half century. Herein, the elaborate, state‐of‐the‐art two‐dimensional (2D) QM/MM MD simulations have clearly shown that the cyclization of the A–C rings involves a nearly concerted, but highly asynchronous cyclization, to yield a stable intermediate with “6‐6‐5” rings followed by the ring expansion of the C‐ring concomitant with the formation of the D‐ring to yield the “6‐6‐6‐5” protosterol cation. The calculated reaction barrier of the rate‐limiting step (≈22 kcal mol−1) is comparable to the experimental kinetic results. Furthermore all previous experimental mutagenic evidence is highly consistent with the identified reaction mechanism.
Biocyclization: Lanosterol is the precursor of all animal sterols and its biocyclization mechanism has been debated over half a century. Herein it has been elucidated by state‐of‐the‐art simulations. The cyclization involves two concerted (or near‐concerted) reactions through two metastable states and one stable intermediate under oxidosqualane cyclase enzyme catalysis.
The goal of local community detection algorithms is to explore the optimal community with a reference to a given node. Such algorithms typically include two primary processes: seed selection and ...community expansion. This study develops and tests a novel local community detection algorithm called
that is based on the optimization of interaction relationships between nodes and the community. First, we introduce an improved seed selection method to solve the seed deviation problem. Second, this study uses a series of similarity indices to measure the interaction relationship between nodes and community. Third, this study uses a series of algorithms based on different similarity indices, and designs experiments to reveal the role of the similarity index in algorithms based on relationship optimization. The proposed algorithm was compared with five existing local community algorithms in both real-world networks and artificial networks. Experimental results show that the optimization of interaction relationship algorithms based on node similarity can detect communities accurately and efficiently. In addition, a good similarity index can highlight the advantages of the proposed algorithm based on interaction optimization.
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