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Background: Previous studies evaluating the association between prediabetes the prognosis of patients with acute myocardial infarction (AMI) showed inconsistent results. The aim of the meta-analysis was to compare the long-term incidence of major adverse cardiovascular events (MACEs) between AMI patients with prediabetes and normoglycemia. Methods: Relevant prospective cohort studies were obtained by searching Medline, Web of Science, and Embase databases. Only studies with follow-up duration of at least one year were included. A random-effects model was utilized to pool the results by incorporating the influence of heterogeneity. Results: Twelve studies with 6972 patients with AMI were included. Among them, 2998 were with prediabetes and 3974 were with normoglycemia. During a mean follow-up of 52.6 months, 2100 patients developed MACEs. Compared to those with normoglycemia, AMI patients with prediabetes were associated with a higher incidence of MACEs (risk ratio [RR]: 1.30, 95% confidence interval: 1.07 to 1.58, p = 0.008; I2 = 67%). Subgroup analysis showed a stronger association between prediabetes and MACEs in studies of patients with mean age ≥ 60 years compared to < 60 years (RR: 1.66 versus 1.10, p for subgroup difference = 0.04), with proportion of men < 75% compared to ≥ 75% (RR: 1.87 versus 1.08, p for subgroup difference = 0.01), and in prediabetes evaluated at or after discharge compared to that evaluated within three days of AMI onset (RR: 1.39 versus 0.78, p for subgroup difference = 0.01). Conclusions: Prediabetes may be associated with a higher risk of MACEs in patients with AMI. [ABSTRACT FROM AUTHOR]

Rapid climate variability and intense human activities generate obvious impacts on the Qilian Mountains ecosystem. The time series of fractional vegetation coverage (FVC) from 1986 to 2021 were used to quantify the impact of climate variability and human activities on vegetation variations in the Qilian Mountain National Nature Reserve (QMNNR), using 3147 land satellite images based on the Google Earth Engine cloud platform. The contributions of climate variability and human activities to FVC were quantified using multiple regression residual analysis. Partial correlation and correlation methods were used to quantify the impact of temperature, precipitation, and human activity footprints on FVC. The results showed that from 1986 to 2021, the increase rate of FVC was 1.7 × 10−3 y−1, and the high vegetation coverage of the FVC was mainly distributed in the southeastern part of the reserve. In contrast, the low vegetation coverage was mainly distributed in the northwest part of the reserve. The Mann–Kendall mutation test found that the year of 2009 was the year of the mutation. The growth rate of FVC from 2010 to 2021 was greater than that from 1986 to 2009. In addition, climate variability and human activities exhibited a remarkable spatial heterogeneity in FVC changes. Climate variability and human activities contributed 49% and 51% to the increase in FVC in the reserve, respectively, and the contribution of human activities was greater than that of climate variability. The warming and humidification phenomena in the reserve were obvious. However, precipitation was the dominant factor affecting the dynamic changes in FVC. This study improves our understanding of the response of vegetation dynamics to the climate and human activities in the QMNNR. [ABSTRACT FROM AUTHOR]

The reliability of liquid rocket engines (LREs), which are the main propulsion device of launch vehicles, cannot be overemphasised. The development of fault detection and diagnosis (FDD) technology for LREs can effectively improve the safety and reliability of launch vehicles, which has important theoretical and engineering significance. With the rapid development of artificial intelligence technologies such as machine learning and artificial neural network, data-driven FDD methods have gained increasing attention. However, the scarcity of engine fault samples limits the application of this methods. We proposed a method combining Wasserstein generative adversarial nets (WGANs) and multilayer perceptron (MLP) to perform FDD for LREs with sample imbalance. Wasserstein generative adversarial nets were trained using the fault data from the actual hot-firing ground test of a large LRE. Considerable fault data were generated to expand the data set to balance the ratio of positive and negative samples. Subsequently, the expanded data set was used to train the MLP for FDD of a large LRE. The results showed that the samples generated by the WGAN were authentic, confirming the application of the proposed method as a novel and effective tool for establishing a complete LRE fault database. Furthermore, the diagnosis times of the proposed method on five fault tests were advanced by 0.66, 15.82, 0.24, 0.14 and 1.08 s in relation to those of the conventional red-line cut-off system. Compared with support vector machine and adaptive threshold algorithm, the proposed method also performed better. [ABSTRACT FROM AUTHOR]

Etlingera velutina belongs to the Zingiberaceae family. The plant is known as a potent agent for snakebite and food flavoring in the east of Asia. In this study, the essential oil from the rhizomes of E. velutina was obtained using the hydro-distillation assay. The chemical composition of the oil was identified by chromatographic methods of GC/FID and GC/MS. The MTT assay was run to evaluate the cytotoxicity effect of the essential oil. The oil consists of sesquiterpene hydrocarbon. α-Cubebene (57.4%) and 2-undecanone (28.6%) were the major compounds for the rhizome oil. The MTT assay was run to treat the cell lines with essential oil for evaluation of the cytotoxicity and anti-human colorectal carcinoma properties. The IC50 of essential oil was 241 and 214 μg/mL against ileocecal colorectal adenocarcinoma (HCT-8 [HRT-18]) and Burkitt's lymphoma (Ramos.2G6.4C10) cell lines, respectively. The results showed a dose-dependently on The viability of malignant colorectal cell lines in the presence of essential oil. The obtained results revealed the essential oil of the rhizomes of E. velutina is a potent agent against colorectal carcinoma. [ABSTRACT FROM AUTHOR]

The development of health monitoring technology for liquid rocket engines (LREs) can effectively improve the safety and reliability of launch vehicles, which has important theoretical and engineering significance. Therefore, we propose a fault detection and diagnosis (FDD) method for a large LOX/kerosene rocket engine based on long short-term memory (LSTM) and generative adversarial networks (GANs). Specifically, we first modeled a large LOX/kerosene rocket engine using MATLAB/Simulink and simulated the engine's normal and fault operation states involving various startup and steady-state stages utilizing fault injection. Second, we created an LSTM-GAN model trained with normal operating data using LSTM as the generator and a multilayer perceptron (MLP) as the discriminator. Third, the test data were input into the discriminator to obtain the discrimination results and realize fault detection. Finally, the test data were input into the generator to obtain the predicted samples and calculate the absolute error between the predicted and the real value of each parameter. Then the fault diagnosis index, standardized absolute error (SAE), was constructed. SAE was analyzed to realize fault diagnosis. The simulated results highlight that the proposed method effectively detects faults in the startup and steady-state processes, and diagnoses the faults in the steady-state process without missing an alarm or being affected by false alarms. Compared with the conventional redline cut-off system (RCS), adaptive threshold algorithm (ATA), and support vector machine (SVM), the fault detection process of LSTM-GAN is more concise and more timely. [ABSTRACT FROM AUTHOR]