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Block building is a popular play activity among young children and is also used by psychologists to assess their intelligence. However, little research has attempted to systematically explore the cognitive bases of block-building ability. The current study (N = 66 Chinese preschoolers, 32 boys and 34 girls; mean age = 4.7 years, SD = 0.29, range = 3.4 to 5.2 years) investigated the relationships between six measures of spatial skills (shape naming, shape recognition, shape composition, solid figure naming, cube transformation, and mental rotation, with the former four representing form perception and the latter two representing visualization) and block-building complexity. Correlation results showed that three of the four measures of form perception (shape naming, shape recognition, and shape composition) were significantly and positively correlated with block-building complexity, whereas the two measures of visualization were not. Results from regression models indicated that shape recognition and shape composition, as well as shape-recognition-by-gender interaction, were unique predictors of children's block-building complexity. These findings provide preliminary evidence for the basic spatial skills underlying children's block-building complexity and have implications for classroom instructions aimed at improving preschoolers' block-building complexity.

Background Music therapy, as an emerging treatment, has a gradually evolving role in the rehabilitation of chronic diseases and is gaining increasing attention. But there are few studies on the effects of music therapy on diabetes rehabilitation. Objective To observe the effects of music assisted anagliptin on glycolipid metabolism in patients with type 2 diabetes. Methods Sixty five type 2 diabetic patients with poor glycemic control on oral agents who visited the outpatient clinic of Department of Endocrinology, Boai Hospital, China Rehabilitation Research Center, from October 2019 to February 2021 were selected. Patients were divided into group A (n=35) and group B (n=30) by random numbers, and patients in both groups maintained their original diet and exercise habits. Group A was treated with anagliptin tablets in combination with the original treatment (drug type, dose unchanged) , while group B was given music adjuvant therapy on the basis of group A. Patients in both groups were treated continuously for 12 weeks. Fasting plasma glucose (FPG) , glycated hemoglobin (HbA1c) , low-density lipoprotein cholesterol (LDL-C) , total cholesterol (TC) , triacylglycerol (TG) , high-density lipoprotein cholesterol (HDL-C) , uric acid (UA) , 2 h postprandial plasma glucose (2 hPG) , hypersensitivity C-reactive protein (hs-CRP) , free fatty acids (FFA) , insulin resistance index (HOMA-IR) , β Cell function secretion index (HOMA-β) before and 12 weeks after treatment were compared between the two groups. The patients were observed for the occurrence of adverse reactions during the treatment. Results One patient was eliminated from the trial because of adverse drug reactions during the course of the trial in both groups, and 34 and 29 patients finally completed the trial in groups A and B, respectively. Patients in group B had lower 2 hPG than those in group A after 12 weeks of treatment (P

In the process of intelligent coal mine construction, there are problems such as the 'data island' phenomenon, low data quality, lack of data governance system, and insufficient data empowerment. In order to solve the above problems, this paper analyzes the basic requirements of intelligent coal mine big data governance. This paper studies the key technologies of intelligent coal mines such as data acquisition and storage, data cleaning and standardization, data asset planning, data sharing and exchange. Combined with the field practice of data governance in Xiaobaodang Coal Mine, the overall technical architecture of intelligent coal mine big data governance based on the Industrial Internet system is proposed. The architecture functions correspond to the basic requirements of intelligent coal mine big data governance. It realizes the access, integration and fusion of multi-source heterogeneous perception data downward, provides data services for the development of various coal mine intelligent applications upward, and sediment various business indicators and model algorithms of coal mines in the middle, forming important data assets for coal mines. Unified access to to data form various coal mine systems is achieved through data access storage services based on different data access protocols. The necessary protocol conversion and data preprocessing are realized during the access process. The data processing is achieved through data cleaning and standardization services to improve data quality. The data is transformed into systematic data assets by adopting a hierarchical governance architecture. Finally, data assets are provided to other systems through standard interfaces through data sharing services, achieving the implementation of data value. The practical application of intelligent coal mine data governance achievements in different business scenarios is demonstrated from the perspective of coal mine single system application, mine-level application and company-level system application. After unified data governance, intelligent coal mine big data can achieve data fusion applications. It can break data islands, improve data quality, form coal mine unique data assets, and provide important value for coal mine production and operation.

Biological control of Verticillium wilt caused by Verticillium dahliae is a green and sustainable strategy. However, the antifungal activities of different amounts of biocontrol bacterial are still not understand. This study investigates the antifungal activity of different amount of Bacillus amyloliquefaciens FH1-RS1 and Bacillus cereus R19 against Verticillium dahliae V991 by dual culture plate. The results showed that both the fungal inhibition rate (FIR) and far fungal inhibition rate (FFIR) of B. amyloliquefaciens FH1-RS1 elevated with the increasing volume. The highest FIR (77.78%) and FFIR (58.33%) of B. amyloliquefaciens FH1-RS1 were both in 22 μL volume (7.2 × 108 CFU/mL). The highest FIR of B. cereus R19 was 48.15% in 12, 18 and 20 μL volume (1.2 × 108 CFU/mL). The highest FFIR of B. cereus R19 was 38.89% in 6 μL volume. This suggests that not all biocontrol bacteria have higher antifungal activity in higher amounts.

Objective: Classroom teachers can play an important role in supporting the implementation of a Comprehensive School Physical Activity Programme (CSPAP) by integrating physical activity (PA) into academic lessons, providing PA opportunities at recess and becoming role models for school-aged children. Grounded in the expectancy-value model, the goal of this study was to explore preservice classroom teachers' perspectives towards the programme in terms of their expectancy-related beliefs and subjective task values. Setting: A large public research university in the southwestern region of the USA. Method: Participants were 192 preservice classroom teachers (M[subscript age] = 19.85 years, SD = 0.6; female = 92%) enrolled in a public university. Participants responded to open-ended questions about their beliefs and values related to the CSPAP after learning about the programme model. Using thematic analysis, initial codes were generated from participants' responses, and three researchers who were knowledgeable in the CSPAP approach identified the final main themes. Results: The results revealed several overarching themes for understanding preservice classroom teachers' expectancy-related beliefs and subjective task values towards implementing CSPAP in schools, including the essential role of CSPAP in the family and community, innovation in pedagogy and the influence of the CSPAP on daily PA. Conclusion: Our study adds to the literature on preservice classroom teachers' beliefs and values towards the CSPAP using an expectancy-value model. Potential applications and suggestions for future practice are discussed.

ObjectiveTo determine the association between air pollutants （PM2.5， PM10， SO2， NO2） and death from respiratory diseases in Wuhan.MethodsDaily air pollutants， meteorological data and mortality from respiratory disease between 2014 and 2019 were collected for a descriptive analysis. A time series semi-parametric generalized additive model （GAM） was used to determine the exposure-effect relationship between atmospheric pollutants and daily mortality from respiratory diseases，and the excess risk （ER） was used to quantify the effects of air pollutants on death from respiratory diseases.ResultsThere was significant effect of PM2.5， PM10， SO2 and NO2 on respiratory diseases mortality. In the period with strongest effect， the ER of death from respiratory diseases were 2.803%（95%CI：2.151%‒3.460%）， 1.878%（95%CI：1.477%‒2.281%）， 10.210%（95%CI：7.922%‒12.549%）， 4.564%（95%CI：3.530%‒5.608%）， along with an incremental 10 μg·m-3 of PM2.5，PM10，SO2 and NO2， respectively. Furthermore， females were more sensitive to PM2.5， SO2 and NO2， while males were more sensitive to PM10. Residents aged less than 65 years were more sensitive to PM2.5 and NO2， and those older than 65 years were more sensitive to PM10 and SO2.ConclusionAir pollutants （PM2.5， PM10， SO2， and NO2） in Wuhan are associated with the death from respiratory diseases. Therefore， at-risk groups should be considered for formulating local policies against air pollution.

We investigated the role of the interaction between the Notch and Toll-like receptor 4 (TLR4) pathways in septic myocardial injury. The sepsis model was induced in rats with lipopolysaccharide (LPS). Rats were divided into control, LPS, LPS + TAK242 ((6R)-6-[N-(2-chloro-4-fluorophenyl)sulfamoyl]cyclohex-1-ene-1-carboxylate) and LPS + DAPT (N-[N-(3,5-difluorophenacetyl)-l-alanyl]-s-phenylglycinetbutylester) groups. Heart function was evaluated with a Cardiac Doppler ultrasound. Myocardial morphological changes were detected by hematoxylin-eosin staining (H&E). Apoptosis was assessed by a TUNEL assay. The mRNA and protein levels were detected with real-time PCR, Western blot, and immunohistochemistry analysis. We found that heart function in the LPS + TAK242 group was significantly improved, but not in the LPS + DAPT group. LPS + TAK242 had a lower level of degeneration and necrosis of cardiomyocytes and inflammatory cell infiltration, as well as lower apoptosis and caspase-3 expression than the LPS group. Compared with the LPS group, the inflammatory cell infiltration was reduced in the LPS + DAPT group, while the degeneration and necrosis of cardiomyocytes were not obviously improved. Additionally, the expression levels of tumor necrosis factor-α and Interleukin-6, the protein contents of Notch intracellular domain and Hes1, and the P65 nuclear factor kappa-B (NF-κB) to P-P65 NF-κB ratio in LPS + TAK242 group and LPS + DAPT group were significantly lower than those in LPS group. Conclusively, the interaction between TLR4 and Notch signaling pathways enhances the inflammatory response in the septic heart by activating NF-κB. Blocking the TLR4 pathway with TAK242 can improve heart dysfunction and myocardial damage in sepsis, while blocking the Notch pathway with DAPT cannot effectively prevent heart dysfunction and myocardial damage in sepsis.

Relevance. Under the dual background of the deepening global population aging and the rapid development of the digital economy, the study of the impact of digital literacy on the labor income of the elderly is of social significance in guiding the «young» elderly to continue to participate in social labor, making up for the shortage of labor force, alleviating the pressure of social pension and reducing the burden of family pension. In this regard, it is important to study international cases reflecting the impact of digital literacy on labor income. This paper introduces the influence of digital literacy on the labor income of «young» elderly people from China. Objective: to study the impact of digital literacy on the income of Chinese elderly people who have just reached retirement age and to make development recommendations for the interests of all stakeholders in this process: the State, the market, society, the elderly and their families. Methods: analysis and synthesis of scientific literature regarding the concept of digital literacy of the elderly and the influence mechanism of digital literacy on labor income. Using Chinese Social Survey 2017 (CGSS-2017) data, Poisson regression method is used to empirically analyze the impact of digital literacy on labor income of the «young» elderly in China. Results. This paper confirms that digital literacy has a positive impact on the labor income of the «young» elderly, and analyzes the digital divide formed by the differences in education level and living area of the «young» elderly and the differences in labor income. Conclusions. Protecting the labor participation of the «young» elderly by developing their digital literacy is important not only for increasing personal income but also for reallocating family budgets between generations; it is also important for macroeconomics, since the employment of the «young» elderly contributes to reducing labor shortages in the labor market, as a result of changes in the age structure of the working population in the context of global ageing. Maintaining one 's labor activities after reaching working age limits increases budgeted tax revenues, reduces government spending on additional social benefits for low-income older persons and reduces individual and family dependency on social pensions. Equally important is the social benefits of narrowing the intergenerational digital divide. This paper puts forward specific suggestions to improve the digital literacy of the «young» elderly and make up for the digital divide. It is suggested to actively build a 'five-in-one' digital literacy improvement governance system for the elderly with (1) government guidance, (2) market initiative, (3) social linkage, (4) family support and (5) elderly participation.

This study aimed to examine physical activity (PA) levels and mental health status (i.e., anxiety and depression) among parents of children with autism spectrum disorder (ASD). Secondary data analysis was conducted using the 2018 National Health Interview Survey. We identified 139 parents of children with ASD and 4,470 parents of children with no disability. Their PA levels, anxiety, and depression were analyzed. Compared with parents of children with no disability, parents of children with ASD were significantly less likely to meet the PA guideline for Americans and had lower odds of vigorous PA (aOR = 0.702), strengthening PA (aOR = 0.885), and light to moderate PA (aOR = 0.994). Parents of children with ASD reported significantly higher odds of anxiety (aOR = 1.559) and depression (aOR = 1.885). This study revealed lower PA levels and higher risks of anxiety and depression in parents of children with ASD. [ABSTRACT FROM AUTHOR]

The coal mine massive data has problems such as 'data island', weak correlation, poor data quality due to lack of data management system. It is difficult to make full use of the data and provide analysis and decision-making support for coal mine intelligence. The data warehouse can meet the requirements of multi-source heterogeneous data integration in coal mine, and provide data basis for intelligent application in coal mine. By analyzing the coal mine data types, characteristics and intelligent application requirements of actual data, the intelligent coal mine data warehouse modeling method is studied. Firstly, the layered architecture of intelligent coal mine data warehouse is constructed, and the characteristics of data model of original data layer, detailed data layer, basic index layer, service data layer and public dimension layer are analyzed. Secondly, taking the data of fully mechanized working face as an example, the modeling process of data warehouse is expounded from the aspects of business data analysis, application demand analysis and layered architecture design. Thirdly, the construction method of data model in coal mine data warehouse is introduced. The original data is transformed into data warehouse dimensional model through dimension alignment, dimension association and dimensional index aggregation. The method solves the application problem of coal mine data association in different dimensions. Finally, in order to solve the problem of portability of coal mine data warehouse, the design idea of coal mine parametric data warehouse based on general data warehouse in coal mine industry + parametric ETL (extraction-transformation-load) method is proposed. The platform of coal mine data warehouse in the laboratory environment is set up to process the data of fully mechanized working face of Shanxi Tiandi Wangpo Coal Industry Co., Ltd. The auxiliary mechanism model analysis and visual management cockpit are realized based on the processing data, which verifies the practicability of intelligent coal mine data warehouse. The performance indexes of the original data model and the intelligent coal mine data warehouse are compared. The results show that the data organization, model reuse and iteration difficulty of the intelligent coal mine data warehouse are better than those of the original data model, and the data query response time is shortened by more than 50%.