Can EAT be an INOCA goalkeeper

Ischemia with non-obstructive coronary artery (INOCA) is a blind spot of coronary artery disease (CAD). Such patients are often reassured but offered no specific care, that lead to a heightened risk of adverse cerebrovascular disease (CVD) outcomes. Epicardial adipose tissue (EAT) is proven to correlate independently with CAD and its severity, but it is unknown whether EAT is a specific and sensitive indicator of INOCA. This review focuses on the INOCA epidemiology and related factors, as well as the association between EAT

Modeling Multi-wavelength Pulse Profiles of Millisecond Pulsar PSR B1821-24

PSR B1821$-$24 is a solitary millisecond pulsar (MSP) which radiates multi-wavelength pulsed photons. It has complex radio, X-ray and $\gamma$-ray pulse profiles with distinct peak phase-separations that challenge the traditional caustic emission models. Using the single-pole annular gap model with suitable magnetic inclination angle ($\alpha=40^\circ$) and viewing angle ($\zeta=75^\circ$), we managed to reproduce its pulse profiles of three wavebands. It is found that the middle radio peak is originated from the core gap region at high altitudes, and the other two radio peaks are originated from the annular gap region at relatively low altitudes. Two peaks of both X-ray and $\gamma$-ray wavebands are fundamentally originated from annular gap region, while the $\gamma$-ray emission generated from the core gap region contributes somewhat to the first $\gamma$-ray peak. Precisely reproducing the multi-wavelength pulse profiles of PSR B1821$-$24 enables us to understand emission regions of distinct wavebands and justify pulsar emission models.Comment: Accepted for publication in Ap

PRDM6 is enriched in vascular precursors during development and inhibits endothelial cell proliferation, survival, and differentiation

The mechanisms that regulate the differentiation program of multipotential stem cells remain poorly understood. In order to define the cues that delineate endothelial commitment from precursors, we screened for candidate regulatory genes in differentiating mouse embryoid bodies. We found that the PR/SET domain protein, PRDM6, is enriched in flk1(+) hematovascular precursor cells using a microarray-based approach. As determined by 5′ RACE, full length PRDM6 protein contains a PR domain and four Krüpple-like zinc fingers. In situ hybridization in mouse embryos demonstrates staining of the primitive streak, allantois, heart, outflow tract, para-aortic splanchnopleura (P-Sp)/aorto-gonadal-mesonephric (AGM) region and yolk sac, all sites known to be enriched in vascular precursor cells. PRDM6 is also detected in embryonic and adult-derived endothelial cell lines. PRDM6 is co-localized with histone H4 and methylates H4-K20 (but not H3) in vitro and in vivo, which is consistent with the known participation of PR domains in histone methyltransferase activity. Overexpression of PRDM6 in mouse embryonic endothelial cells induces apoptosis by activating caspase-3 and inducing G1 arrest. PRDM6 inhibits cell proliferation as determined by BrdU incorporation in endothelial cells, but not in rat aortic smooth muscle cells. Overexpression of PRDM6 also results in reduced tube formation in cultured endothelial cells grown in Matrigel. Taken together, our data indicate that PRDM6 is expressed by vascular precursors, has differential effects in endothelial cells and smooth muscle cells, and may play a role in vascular precursor differentiation and survival by modulating local chromatin-remodeling activity within hematovascular subpopulations during development

Knowledge of the Historical and Contemporary Status of Dong Ethnic Group's "Da Ge" Folk Music in Liuzhou, China

This research uses the qualitative research method, with the objectives of investigating the historical and contemporary status of the Dong ethnic group's "Da Ge" folk music. Using the research conceptual frameworks of Chinese art and music theory and western art and music theory, and the methodology of participant observations, experiments, simulations, questionnaires, and interviews, the three most representative types of "Da Ge" folk music of the Dong ethnic group in Liuzhou, China, selects two key informants. The study's findings are as follows: Many studies on musical score examples have revealed: The mode and scale of Dong “Da Ge” folk music are usually the Chinese national mode, and most of them are "Yu" mode. The melody is usually sung in several parts, usually two. The melody is sung in the high part of a song, and the continuous bass is sung in the low part. In melody, Dong “Da Ge” folk music primarily employs arpeggios and vibrato. The use of eighth and sixteenth notes, as well as dot rhythms that frequently alternate between two and three beats. Most songs have a paragraph structure. Dong ethnicity "Da Ge" music is sung in the most natural way possible, with a light breath

Knowledge of the Historical and Contemporary Status of Dong Ethnic Group's "Da Ge" Folk Music in Liuzhou, China

This research uses the qualitative research method, with the objectives of investigating the historical and contemporary status of the Dong ethnic group's "Da Ge" folk music. Using the research conceptual frameworks of Chinese art and music theory and western art and music theory, and the methodology of participant observations, experiments, simulations, questionnaires, and interviews, the three most representative types of "Da Ge" folk music of the Dong ethnic group in Liuzhou, China, selects two key informants. The study's findings are as follows: Many studies on musical score examples have revealed: The mode and scale of Dong “Da Ge” folk music are usually the Chinese national mode, and most of them are "Yu" mode. The melody is usually sung in several parts, usually two. The melody is sung in the high part of a song, and the continuous bass is sung in the low part. In melody, Dong “Da Ge” folk music primarily employs arpeggios and vibrato. The use of eighth and sixteenth notes, as well as dot rhythms that frequently alternate between two and three beats. Most songs have a paragraph structure. Dong ethnicity "Da Ge" music is sung in the most natural way possible, with a light breath

Multi-Level Support Technology and Application of Deep Roadway Surrounding Rock in the Suncun Coal Mine, China

To solve these problems of poor supporting effect and serious deformation and failure of surrounding rock of mining roadway under deep mining stress, a FLAC-3D numerical calculation model is established with −800 m level no. 2424 upper roadway in the Suncun Coal Mine as the background to compare the stress, deformation, and failure law of surrounding rock of mining roadway under once support and multi-level support with the same support strength. It is found that the multi-level support technology has obvious advantages in the surrounding rock of the horizontal roadway on the 2424 working face. From this, the key parameters of multi-level support are determined, and the field industrial test is carried out. The results show that the overall deformation of the surrounding rock is obviously reduced after multi-level support. The displacement of the two sides is reduced by about 40%, the displacement of the roof and floor is reduced by about 30%, and the plastic zone of the roadway is reduced by about 75%. The peak value of concentrated stress decreases from 98.7 MPa to 95.8 MPa, which decreases slightly. The integrity and stability of the surrounding rock are excellent, and the support effect is satisfactory. The research can provide reference and technical support for surrounding rock control of deep high-stress mining roadways

W₁ output by meta-heuristic algorithms.

Debris flow is a sudden natural disaster in mountainous areas, which seriously threatens the lives and property of nearby residents. Therefore, it is necessary to predict the volume of debris flow accurately and reliably. However, the predictions of back propagation neural networks are unstable and inaccurate due to the limited dataset. In this study, the Cubic map optimizes the initial population position of the whale optimization algorithm. Meanwhile, the adaptive weight adjustment strategy optimizes the weight value in the shrink-wrapping mechanism of the whale optimization algorithm. Then, the improved whale optimization algorithm optimizes the final weights and thresholds in the back propagation neural network. Finally, to verify the performance of the final model, sixty debris flow gullies caused by earthquakes in Longmenshan area are selected as the research objects. Through correlation analysis, 4 main factors affecting the volume of debris flow are determined and inputted into the model for training and prediction. Four methods (support vector machine regression, XGBoost, back propagation neural network optimized by artificial bee colony algorithm, back propagation neural network optimized by grey wolf optimization algorithm) are used to compare the prediction performance and reliability. The results indicate that loose sediments from co-seismic landslides are the most important factor influencing the flow of debris flows in the earthquake area. The mean absolute percentage error, mean absolute error and R2 of the final model are 0.193, 29.197 × 104 m3 and 0.912, respectively. The final model is more accurate and stable when the dataset is insufficient and under complexity. This is attributed to the optimization of WOA by Cubic map and adaptive weight adjustment. In general, the model of this paper can provide reference for debris flow prevention and machine learning algorithms.</div

The minimum data set.

Debris flow is a sudden natural disaster in mountainous areas, which seriously threatens the lives and property of nearby residents. Therefore, it is necessary to predict the volume of debris flow accurately and reliably. However, the predictions of back propagation neural networks are unstable and inaccurate due to the limited dataset. In this study, the Cubic map optimizes the initial population position of the whale optimization algorithm. Meanwhile, the adaptive weight adjustment strategy optimizes the weight value in the shrink-wrapping mechanism of the whale optimization algorithm. Then, the improved whale optimization algorithm optimizes the final weights and thresholds in the back propagation neural network. Finally, to verify the performance of the final model, sixty debris flow gullies caused by earthquakes in Longmenshan area are selected as the research objects. Through correlation analysis, 4 main factors affecting the volume of debris flow are determined and inputted into the model for training and prediction. Four methods (support vector machine regression, XGBoost, back propagation neural network optimized by artificial bee colony algorithm, back propagation neural network optimized by grey wolf optimization algorithm) are used to compare the prediction performance and reliability. The results indicate that loose sediments from co-seismic landslides are the most important factor influencing the flow of debris flows in the earthquake area. The mean absolute percentage error, mean absolute error and R2 of the final model are 0.193, 29.197 × 104 m3 and 0.912, respectively. The final model is more accurate and stable when the dataset is insufficient and under complexity. This is attributed to the optimization of WOA by Cubic map and adaptive weight adjustment. In general, the model of this paper can provide reference for debris flow prevention and machine learning algorithms.</div

CA-WOA-BPNN model.

Debris flow is a sudden natural disaster in mountainous areas, which seriously threatens the lives and property of nearby residents. Therefore, it is necessary to predict the volume of debris flow accurately and reliably. However, the predictions of back propagation neural networks are unstable and inaccurate due to the limited dataset. In this study, the Cubic map optimizes the initial population position of the whale optimization algorithm. Meanwhile, the adaptive weight adjustment strategy optimizes the weight value in the shrink-wrapping mechanism of the whale optimization algorithm. Then, the improved whale optimization algorithm optimizes the final weights and thresholds in the back propagation neural network. Finally, to verify the performance of the final model, sixty debris flow gullies caused by earthquakes in Longmenshan area are selected as the research objects. Through correlation analysis, 4 main factors affecting the volume of debris flow are determined and inputted into the model for training and prediction. Four methods (support vector machine regression, XGBoost, back propagation neural network optimized by artificial bee colony algorithm, back propagation neural network optimized by grey wolf optimization algorithm) are used to compare the prediction performance and reliability. The results indicate that loose sediments from co-seismic landslides are the most important factor influencing the flow of debris flows in the earthquake area. The mean absolute percentage error, mean absolute error and R2 of the final model are 0.193, 29.197 × 104 m3 and 0.912, respectively. The final model is more accurate and stable when the dataset is insufficient and under complexity. This is attributed to the optimization of WOA by Cubic map and adaptive weight adjustment. In general, the model of this paper can provide reference for debris flow prevention and machine learning algorithms.</div

S1 Appendix -

Debris flow is a sudden natural disaster in mountainous areas, which seriously threatens the lives and property of nearby residents. Therefore, it is necessary to predict the volume of debris flow accurately and reliably. However, the predictions of back propagation neural networks are unstable and inaccurate due to the limited dataset. In this study, the Cubic map optimizes the initial population position of the whale optimization algorithm. Meanwhile, the adaptive weight adjustment strategy optimizes the weight value in the shrink-wrapping mechanism of the whale optimization algorithm. Then, the improved whale optimization algorithm optimizes the final weights and thresholds in the back propagation neural network. Finally, to verify the performance of the final model, sixty debris flow gullies caused by earthquakes in Longmenshan area are selected as the research objects. Through correlation analysis, 4 main factors affecting the volume of debris flow are determined and inputted into the model for training and prediction. Four methods (support vector machine regression, XGBoost, back propagation neural network optimized by artificial bee colony algorithm, back propagation neural network optimized by grey wolf optimization algorithm) are used to compare the prediction performance and reliability. The results indicate that loose sediments from co-seismic landslides are the most important factor influencing the flow of debris flows in the earthquake area. The mean absolute percentage error, mean absolute error and R2 of the final model are 0.193, 29.197 × 104 m3 and 0.912, respectively. The final model is more accurate and stable when the dataset is insufficient and under complexity. This is attributed to the optimization of WOA by Cubic map and adaptive weight adjustment. In general, the model of this paper can provide reference for debris flow prevention and machine learning algorithms.</div