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Abstract In the context of global climate change and sustainable development, enterprise digital transformation has become key to improving efficiency and competitiveness. Digital twin technology, as an emerging tool, enables real-time monitoring, prediction, and optimization by creating dynamic virtual models of real-world processes. This paper explores the impact of digital twin-based transformation on renewable energy investment decisions. Through empirical analysis of over 200 companies globally, the study finds that companies using digital twin technology exhibit higher accuracy and efficiency in renewable energy investment decisions. These companies show improved forecasting of energy consumption and investment returns, gaining a competitive edge. On average, these companies experience a 15% ROI increase for their renewable energy investments and enjoy a 20% acceleration in the decision-making process. Furthermore, the study delves into how the adoption of digital twin technology differs across various company sizes and industries, providing actionable insights and guidance for enterprises embarking on their digital transformation journey.

Vegetation phenology plays a pivotal role in ecological processes on terrestrial surfaces and the interactions between the biosphere and atmospheric feedback. Current attempts to retrieve vegetation phenology have primarily depended on vegetation indices extracted from satellite remote sensing imagery. These approaches often neglect the underlying physical mechanisms associated with climatic factors, and there is a notable absence of evaluations and comparisons with field-observed phenology inventory data. To address these limitations, this paper proposes an innovative physical constraint neural networks (PCNNs) model that combines machine learning techniques with physical mechanisms to enhance the accuracy of vegetation phenology predictions. By incorporating meteorological variables into a machine learning model and by using the Moderate-Resolution Imaging Spectroradiometer (MODIS) dataset to identify the vegetation phenology of four vegetation types in North America, this study delved into the relationship between vegetation phenology and climate factors as well as its impacts on ecosystems. Our model demonstrated high accuracy compared to machine learning methods without physical mechanisms when validated by field observations from PhenoCam and the USA National Phenology Network (USA-NPN) spanning from 2001 to 2021. The results show that the overall root mean square error (RMSE) with physical constraints is reduced to 12.37 days, higher by 2.6 days than the machine learning method without physical constraints. We compared four vegetation types using different machine learning and traditional rule-based methods, deciduous vegetation (DV) exhibited the most favorable prediction results, with the RMSE and mean bias error (MBE) as low as 5.71 days and 4.06 days in PCNNs model, respectively. This was followed by evergreen needle-leaved forests and mixed forests with RMSE of 12.32 and 13.28 days, respectively. The stressed deciduous vegetation type had the worst prediction result of 19.86 days (RMSE), and the weighted index of agreement (WIA) attained a value of 0.68. These findings suggest that the model embedded with physical constraints significantly boosted prediction accuracy for four common vegetation types, particularly for DV, compared to the unconstrained ML model. It offers valuable insights into the incorporation of physical mechanisms within machine learning models. This research paves the way for substantial advancements in the field of land surface phenology, enabling more accurate and reliable predictions of vegetation phenology in various ecological and climatic contexts.

Abstract Endometriosis, a prevalent condition, has long been recognized as a chronic and debilitating ailment affecting an estimated 1790 million women worldwide. Observational studies have established a correlation between endometriosis and ovarian cancer. Thus, we endeavored to employ Two-Sample Mendelian Randomization, utilizing summary statistics from a Genome-Wide Association Study of endometriosis and epithelial ovarian cancer, with genetic markers serving as proxies for epithelial ovarian cancer. The analysis revealed a significant correlation between these entities, with an odds ratio (OR) of 1.23 (95% CI 1.11–1.36). Upon histotype-specific examination, robust evidence emerged for an association of endometriosis with the risk of endometrioid carcinoma (OR 1.49, 95% CI 1.24–1.81), clear cell carcinoma (OR 2.56, 95% CI 1.75–3.73), and low malignant potential tumors (OR 1.28, 95% CI 1.08–1.53). These findings provide a theoretical framework for prospective investigations aimed at enhancing the potential therapeutic efficacy of managing endometriosis in averting the onset and progression of ovarian cancer.

Background: Secreted frizzled-related protein 2 (sFRP2) is involved in various cardiovascular diseases. However, its relevance in left ventricular (LV) remodeling in patients with hypertension (HTN) is obscure. Methods: In this study, 196 patients with HTN were included, 59 with echocardiographic LV remodeling. A total of 100 healthy subjects served as normal controls. The serum-sFRP2 level was measured by enzyme-linked immunosorbent assay (ELISA). Data were collected from medical records for baseline characteristics, biochemistry tests, and echocardiography. Receiver operating characteristic (ROC) curves were used to assess the distinguishing value of sFRP2 for LV remodeling in patients with HTN. Spearman rank correlation analysis was utilized to identify factors correlated with sFRP2. Cardiac sFRP2 was determined by Western blot and quantitative polymerase chain reaction (qPCR). Results: The level of serum-sFRP2 was higher in HTN patients with echocardiographic LV remodeling than their non-remodeling counterparts. ROC analysis showed that the area under the curve (AUC) for sFRP2 in distinguishing echocardiographic LV remodeling in HTN patients was 0.791 (95% confidence interval (CI): 0.714–0.869). The sFRP2 was negatively correlated with LV dimension and positively correlated with relative wall thickness (RWT). The expression of sFRP2 was higher in hypertrophic hearts, which could be reversed by myricetin. Conclusions: The serum level and cardiac sFRP2 increased in the setting of LV remodeling and decreased by myricetin. Serum sFRP2 may be a promising distinguishing factor for LV remodeling in HTN patients.

Abstract Long-term use of antipsychotics is a common cause of myocardial injury and even sudden cardiac deaths that often lead to drug withdrawn or discontinuation. Mechanisms underlying antipsychotics cardiotoxicity remain largely unknown. Herein we performed RNA sequencing and found that NLRP3 inflammasome-mediated pyroptosis contributed predominantly to multiple antipsychotics cardiotoxicity. Pyroptosis-based small-molecule compound screen identified cannabinoid receptor 1 (CB1R) as an upstream regulator of the NLRP3 inflammasome. Mechanistically, antipsychotics competitively bond to the CB1R and led to CB1R translocation to the cytoplasm, where CB1R directly interacted with NLRP3 inflammasome via amino acid residues 177–209, rendering stabilization of the inflammasome. Knockout of Cb1r significantly alleviated antipsychotic-induced cardiomyocyte pyroptosis and cardiotoxicity. Multi-organ-based investigation revealed no additional toxicity of newer CB1R antagonists. In authentic human cases, the expression of CB1R and NLRP3 inflammasome positively correlated with antipsychotics-induced cardiotoxicity. These results suggest that CB1R is a potent regulator of the NLRP3 inflammsome-mediated pyroptosis and small-molecule inhibitors targeting the CB1R/NLRP3 signaling represent attractive approaches to rescue cardiac side effects of antipsychotics.

Pathological hypertrophic myocardium under consistent adverse stimuli eventually can cause heart failure. This study aims to explore the role of BACH2, a member of the basic region leucine zipper transcription factor family, in cardiac hypertrophy and failure. Transverse aortic constriction surgery was operated to induce cardiac hypertrophy and failure in mice. BACH2 was overexpressed in mice through tail vein injection of AAV9-Bach2. Mice with systemic or cardiac-specific knockdown of Bach2 were adopted. Neonatal rat ventricular myocytes (NRVMs) were isolated and infected with lentivirus to overexpress Bach2 or transfected with siRNA to knock down Bach2. Our data showed that overexpression of BACH2 ameliorated TAC-induced cardiac hypertrophy and failure in mice and decreased isoproterenol (ISO)-triggered myocyte hypertrophy in NRVMs. Systemic or cardiac-specific knockdown of Bach2 worsened the cardiac hypertrophy and failure phenotype in mice. Further assays showed that BACH2 bound to the promotor region of Akap6 at the -600 to -587 site and repressed its expression, which functioned as a crucial scaffold for cardiac hypertrophy and failure signaling pathways. Small molecular natural product library screening suggested that myricetin could up-regulate expression of Bach2 and simultaneously suppress the transcriptional levels of hypertrophic marker genes Bnp and Myh7. Further studies showed that myricetin exerted a BACH2-dependent protective effect against cardiac hypertrophy in vivo and in vitro. Taken together, our findings demonstrated that BACH2 plays a crucial role in the regulation of cardiac hypertrophy and failure and can be a potential therapeutic target in the future.

Aims: Although previous studies have linked short-term exposure to fine particulate matter (PM2.5) air pollution with various molecular biomarkers of cardiovascular system, limited evidence is available for indicators at clinical or subclinical levels. We examined the associations between short-term PM2.5 exposure and a range of clinical or subclinical indicators of cardiovascular health in general population. Methods and results: A longitudinal repeated-measure study was conducted among 247,640 participants who repeatedly visited health examination centers in 15 typical cities across China from 2013 to 2020. A total of 19 well-established indicators of cardiovascular risk or injury were evaluated and air quality data at nearest fixed-site monitors were collected. Linear mixed-effects models with distributed lag models were used to analyze the potentially lagged effects of PM2.5. The average daily PM2.5 concentration was 48 μg/m3 during the study period. PM2.5 exposure was associated with significant changes of 16 indicators with the effects generally peaked on lag 0 to 3 day. For an interquartile range (IQR) elevation (37 μg/m3) in PM2.5 concentrations over lag 0–7 day, the cumulative percentage changes were 0.50% to 1.27% in heart rates and blood pressure, 0.10% to 5.04% in inflammatory markers, −0.29% to 1.39% in blood viscosity parameters, −0.67% to 3.45% in blood lipids, 0.89% in blood homocysteine, 0.13% to 0.78% in myocardial enzymes, and 3.03% in pulse wave velocity. These associations were not substantially changed after adjusting concomitant exposures to gaseous pollutants. Conclusion: Short-term exposure to PM2.5 may induce early cardiovascular effects in general population, including acute inflammation, myocardial injury, increased blood viscosity, vascular stiffness and hyperlipidemia.

Cardiovascular disease is an essential comorbidity in patients with non-small cell lung cancer (NSCLC) and represents an independent risk factor for increased mortality. Therefore, careful monitoring of cardiovascular disease is crucial in the healthcare of NSCLC patients. Inflammatory factors have previously been associated with myocardial damage in NSCLC patients, but it remains unclear whether serum inflammatory factors can be utilized to assess the cardiovascular health status in NSCLC patients. A total of 118 NSCLC patients were enrolled in this cross-sectional study, and their baseline data were collected through a hospital electronic medical record system. Enzyme-linked immunosorbent assay (ELISA) was used to measure the serum levels of leukemia inhibitory factor (LIF), interleukin (IL)-18, IL-1β, transforming growth factor-β1 (TGF-β1), and connective tissue growth factor (CTGF). Statistical analysis was performed using the SPSS software. Multivariate and ordinal logistic regression models were constructed. The data revealed an increased serum level of LIF in the group using tyrosine kinase inhibitor (TKI)-targeted drugs compared to non-users (p < 0.001). Furthermore, serum TGF-β1 (area under the curve, AUC: 0.616) and cardiac troponin T (cTnT) (AUC: 0.720) levels were clinically evaluated and found to be correlated with pre-clinical cardiovascular injury in NSCLC patients. Notably, the serum levels of cTnT and TGF-β1 were found to indicate the extent of pre-clinical cardiovascular injury in NSCLC patients. In conclusion, the results suggest that serum LIF, as well as TGFβ1 together with cTnT, are potential serum biomarkers for the assessment of cardiovascular status in NSCLC patients. These findings offer novel insights into the assessment of cardiovascular health and underscore the importance of monitoring cardiovascular health in the management of NSCLC patients.

Background: Secreted frizzled-related protein 2 (sFRP2) plays an important role in metabolic syndrome and cardiovascular diseases (CVDs); However, its relevance with cardiometabolic diseases remains to be elucidated. We aimed to determine the serum levels of sFRP2 in patients at different stages of heart failure (HF) with or without type 2 diabetes mellitus (T2DM), and assess the correlation between circulating sFRP2 levels and cardiometabolic risk factors.Methods: In this study, serum samples from 277 patients visiting Zhongshan Hospital affiliated to Fudan University were collected. These patients were clinically diagnosed and categorized as five groups, including the control group, pre-clinical HF group, pre-clinical HF+T2DM group, HF group and HF+T2DM group. Serum sFRP2 levels were measured with enzyme-linked immunosorbent assay (ELISA) tests and the clinical characteristics of each patient were recorded. Spearman rank correlation analysis and multiple stepwise linear regression analysis were conducted. Univariate and multivariate logistic regression analysis were performed to screen risk factors for HF in patients with CVDs.Results: Serum sFRP2 levels were significantly lower in the HF+T2DM group compared with the other four groups. Spearman rank correlation analysis showed that sFRP2 was negatively correlated with parameters including patients' age, fasting plasma glucose (FPG), glycated hemoglobin A1c (HbA1c), cardiac troponin T (cTNT), N-terminal pro-B-type natriuretic peptide (NT-proBNP), high-sensitivity C-reactive protein (hs-CRP), left atrial dimension (LAD) and left ventricular posterior wall (LVPW), and positively correlated with hemoglobin, estimated glomerular filtration rate (eGFR), albumin, total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), and left ventricular ejection fraction (LVEF). However, in multiple regression analysis, significant associations with ln(sFRP2) were observed only in FPG, hs-CRP and LAD. Higher serum sFRP2 was significantly linked to lower odds of HF in patients with CVDs.Conclusion: sFRP2 progressively decreased when glucose homeostasis and cardiac function deteriorated. sFRP2 acted as a risk factor for HF in patients with CVDs, especially in those with concomitant T2DM.

This paper focuses on the control problem of wind energy conversion systems (WECSs) with direct-driven permanent magnet synchronous generator (PMSG) working in variable power output stage. A novel compound control scheme combined the active disturbance rejection controller (ADRC) and speed sensorless technology is proposed. In order to achieve maximum power point tracking (MPPT), a speed control loop is designed based on ADRC to improve the speed tracking ability and anti-disturbance ability of system. Moreover, considering the large amount of system model information, a model-assisted ADRC is designed on the nominal ADRC to improve the response speed of system and reduce the energy consumption for the system control. Additionally, to solve the contradiction between the requirement of speed sensor precision and the economy of system design, a speed observer based on current model is designed in α - β coordinate system. Furthermore, a resistance observer is introduced to improve the convergence rate of the observer. And a position sensorless observer structure based on the speed observer is proposed. Finally, simulation studies are conducted to evaluate power tracking performances of the proposed speed-observer-based model assisted ADRC technology. It is shown that the proposed compound scheme exhibits significant improvements in both control performance and anti-disturbance ability with high observation precision compared with the normal ADRC method.

Accurate building footprint polygons provide essential data for a wide range of urban applications. While deep learning models have been proposed to extract pixel-based building areas from remote sensing imagery, the direct vectorization of pixel-based building maps often leads to building footprint polygons with irregular shapes that are inconsistent with real building boundaries, making it difficult to use them in geospatial analysis. In this study, we proposed a novel deep learning-based framework for automated extraction of building footprint polygons (DLEBFP) from very high-resolution aerial imagery by combining deep learning models for different tasks. Our approach uses the U-Net, Cascade R-CNN, and Cascade CNN deep learning models to obtain building segmentation maps, building bounding boxes, and building corners, respectively, from very high-resolution remote sensing images. We used Delaunay triangulation to construct building footprint polygons based on the detected building corners with the constraints of building bounding boxes and building segmentation maps. Experiments on the Wuhan University building dataset and ISPRS Vaihingen dataset indicate that DLEBFP can perform well in extracting high-quality building footprint polygons. Compared with the other semantic segmentation models and the vector map generalization method, DLEBFP is able to achieve comparable mapping accuracies with semantic segmentation models on a pixel basis and generate building footprint polygons with concise edges and vertices with regular shapes that are close to the reference data. The promising performance indicates that our method has the potential to extract accurate building footprint polygons from remote sensing images for applications in geospatial analysis.

Vegetation phenology plays a key role in influencing ecosystem processes and biosphere-atmosphere feedbacks. Digital cameras such as PhenoCam that monitor vegetation canopies in near real-time provide continuous images that record phenological and environmental changes. There is a need to develop methods for automated and effective detection of vegetation dynamics from PhenoCam images. Here we developed a method to predict leaf phenology of deciduous broadleaf forests from individual PhenoCam images using deep learning approaches. We tested four convolutional neural network regression (CNNR) networks on their ability to predict vegetation growing dates based on PhenoCam images at 56 sites in North America. In the one-site experiment, the predicted phenology dated to after the leaf-out events agree well with the observed data, with a coefficient of determination (R2) of nearly 0.999, a root mean square error (RMSE) of up to 3.7 days, and a mean absolute error (MAE) of up to 2.1 days. The method developed achieved lower accuracies in the all-site experiment than in the one-site experiment, and the achieved R2 was 0.843, RMSE was 25.2 days, and MAE was 9.3 days in the all-site experiment. The model accuracy increased when the deep networks used the region of interest images rather than the entire images as inputs. Compared to the existing methods that rely on time series of PhenoCam images for studying leaf phenology, we found that the deep learning method is a feasible solution to identify leaf phenology of deciduous broadleaf forests from individual PhenoCam images.

The effect of integrated construction of green port construction is evaluated accurately and effectively, and the sustainable development level of green port construction is further improved. An evaluation model of integrated construction effect of green port construction based on BIM construction information is put forward. The green building feature information mining model of green port construction integration construction effect is constructed, the index of green port construction integration construction effect evaluation is constructed by using multi-parameter joint recurrent analysis modeling method, and the correlation evaluation and detection of green port construction integration construction effect is carried out by using association rule analysis method. The rough set quantitative evaluation model is used to calculate the global optimal solution of the green port construction integration construction effect evaluation, to improve the adaptability of the green port construction integration construction effect evaluation, to extract the statistical characteristic quantity of the green port construction integration construction effect, and to realize the construction of the green port construction integration evaluation system. Big data analysis is carried out by using SPSS software. The results of empirical analysis show that the confidence level of the integrated construction effect evaluation of green port construction using this model is high, and the reliability of the integrated evaluation system of green port construction is better. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the effect of tetramethyl pyrazine (TMP) on MPTP)-mediated neuronal apoptosis and oxidative imbalance in mice, and the mechanism of action involved. Methods: Forty-five mice were assigned evenly to blank control, MPTP and TMP groups. The protein concentrations of Bax, Bcl-2, cytochrome C (Cyt c), Nrf2, GCLC and cleaved caspase-3; and levels of glutathione (GSH) and thiobarbituric acid reactive products (TBARS) were evaluated and compared amongst the groups. Results: Cyt c, Bax, and cleaved caspase-3 protein levels in TMP group were significantly lower than those in MPTP group, while Bcl-2 protein expression was higher in TMP group than in MPTP mice (p < 0.05). Furthermore, TBARS was lower in TMP group than in MPTP group, while GSH level increased, relative to MPTP mice. The levels of Nrf2 and GCLC were significantly higher in TMP group than in MPTP group (p < 0.05). Conclusion: Tetramethyl pyrazine exerts anti-apoptotic and antioxidant effects on MPTP-mediated Parkinsonism via regulation of the expressions of Bax, Bcl-2, Nrf2 and glutamate-cysteine ligase catalytic subunit. Thus, TMP has potential for use in the treatment Parkinson’s disease. [ABSTRACT FROM AUTHOR]