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Objective: COVID-19 has evolved into a seasonal coronavirus disease, characterized by prolonged infection duration and repeated infections, significantly increasing the risk of patients developing long COVID. Our research focused on the immune responses in asymptomatic and mild cases, particularly the critical factors influencing serum immunoglobulin G (IgG) levels and their predictive value. Methods: We conducted a retrospective analysis on data from 1939 asymptomatic or mildly symptomatic COVID-19 patients hospitalized between September 2022 and June 2023. Spearman methods were used to test the correlation between serum IgG and age, immunoglobulin M (IgM), procalcitonin (PCT), interleukin-6 (IL-6), nucleic acid conversion time, and BMI. Univariate and multivariate logistic regression analyses identified independent key factors influencing serum IgG levels, which were integrated and visualized in a nomogram. Finally, receiver operating characteristic (ROC) curves were plotted to predict the model's diagnostic performance by calculating the AUC. Results: Mild patients showed higher levels of IgG, IgM, and longer nucleic acid conversion times than asymptomatic patients, and a lower proportion of them had received ≥ 3 COVID-19 vaccine doses. Serum IgG was positively correlated with serum IgM and negatively correlated with age, PCT, IL-6, and BMI. Notably, it showed a moderate negative correlation with nucleic acid conversion time (r = -0.578, P < 0.001). Logistic regression results showed that younger age, lower IL-6 levels, ≥ 3 doses of vaccine, and no comorbidities were independent predictors of serum IgG levels ≥ 21.08 g/L. We used age, IL-6 levels, vaccine doses, and comorbidities to create a nomogram for predicting serum IgG levels, with the area under the ROC curve reaching 0.772. Conclusion: Age, IL-6 levels, vaccination status, and comorbidities were independent predictors of serum IgG levels in asymptomatic or mild COVID-19 patients, facilitating risk stratification and clinical decision-making. Notably, receiving ≥3 doses of the COVID-19 vaccine was the most beneficial factor for elevated serum IgG levels. [ABSTRACT FROM AUTHOR]

The nucleotide excision repair (NER) system is one of the main ways to protect organisms from DNA damage caused by endogenous and exogenous carcinogens. NER deficiency increases genome mutations, chromosomal aberrations, and cancer viability. However, the genetic association between Wilms tumor and NER pathway gene polymorphisms needs to be further validated. We assessed the associations between 19 NER gene polymorphisms and Wilms tumor susceptibility in 416 cases and 936 controls from East China via the TaqMan method. We found that xeroderma pigmentosum group D (XPD) rs238406 and rs13181 significantly decreased the risk of Wilms tumor [adjusted odds ratio (OR) = 0.59, 95% confidence interval (CI) = 0.46–0.75, p <.0001; adjusted OR = 0.63, 95% CI = 0.44–0.89, p =.009, respectively]. Furthermore, the rs751402 and rs2296147 polymorphisms in the xeroderma pigmentosum group G (XPG) gene were significantly correlated with an increased risk for Wilms tumor (adjusted OR = 1.47, 95% CI = 1.03–2.09, p =.034; adjusted OR = 2.14, 95% CI = 1.29–3.56, p =.003, respectively). Expression quantitative trait loci (eQTL) analysis revealed that these four polymorphisms may affect the expression of genes that are adjacent to XPD and XPG. Our study provides evidence that XPD and XPG gene polymorphisms are associated with Wilms tumor risk. Nonetheless, these findings should be confirmed in a larger sample size. [ABSTRACT FROM AUTHOR]

The article provides a comprehensive review of the development history, preparation principles and production process, factors affecting particle formation, trends in functionality, supporting systems, typical film defects and their solutions of waterborne multicolor coatings. Waterborne multicolor coatings are characterized by their environmental friendliness, high color tolerance, strong decorative and simulation effects as well as significant application prospects. China is regared as the largest and fastest-growing market for stone-like coatings, provides a vast market space for their development. However, current waterborne multicolor coatings face several issues such as unstable viscosity, color bleeding of particles, low fullness, poor initial water resistance, high cost and limited application methods. These are critical issues that need to be addressed. Therefore, the development of cost-effective, stable, and high performance waterborne multicolor coatings compatible with various application methods such as knifing, rolling, and onetime spraying has become a current research focus. Additionally, developing functional waterborne multicolor coatings with high weather resistance, self-cleaning properties and thermal insulation will be the future trend. [ABSTRACT FROM AUTHOR]

In order to solve the problem of large-scale power grid, complex connection relationship, and large demand for electricity carbon factor calculation resources, this paper proposes an algorithm that is easy to model the power grid topology and has high computational efficiency to implement the application of "One Electricity Carbon Chart". In this paper, the graph computing component is used to realize the graph modelling, distributed storage, and high-performance calculation of the electric carbon factor for the data representing the topology of the power grid, such as the power grid table, AC line segment table, and AC line end table. This paper realizes the rapid grid topology graph modelling of EMS dispatching system data, so that the grid structure and active power flow can be intuitively displayed and dynamically studied. This paper also realizes the distributed parallel calculation of electric carbon factor in the case of a ring network, which solves the problem of difficult analysis and calculation of large-scale power grid topology. We conduct experiments on real datasets, and the experimental results demonstrate the effectiveness of the proposed method. In addition, the research on the "One Electricity Carbon Chart" makes the carbon emission of power plants depend on the power flow of the power grid and transfers the accounting method, realizing the calculation of the carbon emission factor of the power grid at the plant level, and provides a new perspective for the sharing of carbon emission responsibilities in the power system. [ABSTRACT FROM AUTHOR]

In the field of electric carbon, the mapping relationship between carbon emission flow calculation and power flow calculation was studied by combining techniques such as current trajectory tracking, carbon flow trajectory analysis, power system flow calculation methods, and electric network analysis theory. By delving into the mechanism between these two factors, a better understanding of the correlation between them can be achieved. In addition, by using time series data, graph attention neural networks (GNN), distributed computing technology, and spatiotemporal computing engines, carbon emission fluctuations can be decomposed and a high-frequency "energy-electricity-carbon" integrated dynamic emission factor can be achieved. Through the spatiotemporal distribution patterns of this dynamic factor in multiple dimensions, the carbon emissions from key industries in cities can be accurately calculated. In this paper, the LSTM-GAT model is used as the core to construct a key carbon emission prediction model for cities. The study focuses on the power plant, chemical industry, steel, transportation industry, and construction industry, which are high energy-consuming industries with an annual electricity consumption of more than 100 million kWh in a major city of China. By analyzing the entire life cycle from power generation to electricity consumption and conducting current flow analysis, monthly, weekly, and daily carbon emission calculations were performed. Additionally, other factors such as the industrial development index, GDP, coverage area of power generation enterprises, regional population, size, and type of power-consuming units were included in the comprehensive calculation to build a measurement system. By conducting experiments and analyzing historical data, we have found that the LSTM-GAT model outperforms the single models of GCN, GAT, LSTM, GRU, and RNN in terms of lower error values and higher accuracy. The LSTM-GAT model is better suited for predicting carbon emissions and related indicators with an accuracy rate of 89.5%. Our predictions show that the carbon emissions will exhibit a slow growth trend in the future, while the carbon emission intensity will decrease. This information can provide a scientific basis for government decision-making. [ABSTRACT FROM AUTHOR]

Energy conservation and carbon reduction in buildings have become important concerns and, at the same time, the value of low-tech approaches employed in indigenous architecture is increasingly acknowledged as a pertinent reference for contemporary design practices. The research on vernacular dwellings in Jiangxi has many perspectives and fruitful results, but not enough attention has been paid to the research on climate adaptation. This article verifies the vernacular dwellings' climate adaptation and summarizes the low-tech methods embedded in vernacular dwellings, aiming to provide guidelines for future exploration of energy-saving and carbon-reducing practices in architecture. By selecting different types of vernacular dwellings in Nanfeng County, this article verifies three aspects of the ecological characteristics of vernacular dwellings: the light environment, wind environment, and energy consumption, by comparing them with those of local modern residential buildings. It is concluded that the average daylight factor of the hall area of vernacular dwellings is better than that of the modern residential buildings in rural areas, and the vernacular dwellings regulate the indoor wind environment and maintain indoor comfort through natural ventilation in winter and summer seasons. Also, the annual energy consumption of the vernacular dwellings per unit area per year can be reduced by up to about 32% in comparison with modern residential buildings. Subsequently, the article concludes that patio space has a positive impact on the indoor physical environment through comparative experiments. Vernacular dwellings are well adapted to the local climate in terms of form, structure, and materials, and these low-tech methods should be applied to the design of rural dwellings in the future. [ABSTRACT FROM AUTHOR]