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Abstract A body Shape index (ABSI), defined as waist circumference (WC)/(body mass index (BMI)2/3 × height1/2), is unique in that the ABSI is positively associated with visceral obesity and is independent of BMI. The ABSI has also been shown to be positively and linearly correlated with the amount of visceral adiposity in the general population and with all-cause and cardiovascular disease (CVD). However, it is uncertain whether ABSI is associated with periodontitis in hypertensive patients. A cross-sectional study of 2,482 hypertensive patients (mean age 65.2 ± 9.1 years) was conducted using the ABSI Z score, periodontitis as a dichotomous variable, the ABSI Z score as a continuous variable, and multiple covariates such as age, gender, and race. The data were analyzed using descriptive statistics, multiple regression model, subgroup analysis with EmpowerStats (version 4.2), and curve transformation plots were generated. In multiple regression model analysis, ABSI Z score showed a significant positive association with periodontitis in the hypertensive population in all three models. When ABSI Z score was divided into low, moderate, and high three groups, we observed that those with the highest ABSI Z score had a 20% increased risk of periodontitis among people with hypertension (OR 1.2; 95% CI 1.0, 1.5; p = 0.046). We used smooth curve fitting to confirm the results and found that these results were still supported, and additional subgroup analyses showed that the positive association between ABSI Z score and periodontitis in hypertensive population was stable and consistent in the general population. The present cross-sectional investigation showed a positive association between ABSI Z score and periodontitis in a hypertensive population.

Abstract Background Red blood cell (RBC) folate is an indicator of long-term folate nutrition. Whether there is an association between RBC folate and periodontitis is unclear. This study aimed to use the NHANES database to determine whether RBC folate is associated with moderate/severe periodontitis. Methods A cross-sectional analysis of 10,151 participants in the NHANES database from 2009 to 2014 was performed. Multivariate logistic regression was used to analyze the independent relationship between RBC folate and moderate/severe periodontitis. The generalized additive model (GAM), restricted cubic splines (RCS), smooth curve fitting, and threshold effect analysis were used to explore the dose–response relationship and the potential nonlinear relationship between RBC folate and periodontitis. Finally, subgroup analysis and interaction tests were performed to determine the effect of covariates on the relationship between RBC folate and moderate/severe periodontitis. Results After adjusting for all confounders, there was a negative association between RBC folate concentration and moderate/severe periodontitis. The lowest fraction Q1 ( 733 ng/mL) categories were 0.88 (0.77, 1.01), 0.83 (0.72, 0.96), 0.77 (0.67, 0.90), 0.65 (0.56, 0.77) respectively. Additionally, a threshold nonlinear association was found between RBC folate (ng/mL) log2 transformation and moderate/severe periodontitis. Conclusion This cross-sectional study revealed a negative relationship between RBC folate and moderate/severe periodontitis within a certain threshold range. Dentists and policymakers should pay closer attention to oral hygiene and health care for people with low or high RBC folate levels. Further causal and longitudinal research mechanisms are needed to validate our findings.

Abstract Objective Explore the therapeutic mechanism of Coptidis Rhizome (CR) in periodontitis using network pharmacology, and validate it through molecular docking and in vitro experiments. Methods Screened potential active components and target genes of CR from TCMSP and Swiss databases. Identified periodontitis-related target genes using GeneCards. Found common target genes using Venny. Conducted GO and KEGG pathway analysis. Performed molecular docking and in vitro experiments using Berberine, the main active component of CR, on lymphocytes from healthy and periodontitis patients. Assessed effects on inflammatory factors using CCK-8, flow cytometry, and ELISA. Results Fourteen active components and 291 targets of CR were identified. 30 intersecting target genes with periodontitis were found. GO and KEGG analysis revealed oxidative stress response and IL-17 signaling pathway as key mechanisms. Molecular docking showed strong binding of Berberine with ALOX5, AKT1, NOS2, and TNF. In vitro experiments have demonstrated the ability of berberine to inhibit the expression of Th17 + and other immune related cells in LPS stimulated lymphocytes, and reduce the secretion of IL-6, IL-8, and IL-17. Conclusion CR treats periodontitis through a multi-component, multi-target, and multi-pathway approach. Berberine, its key component, acts through the IL-17 signaling pathway to exert anti-inflammatory effects.

Electronic components are the main components of PCBs (printed circuit boards), so the detection and classification of ECs (electronic components) is an important aspect of recycling used PCBs. However, due to the variety and quantity of ECs, traditional target detection methods for EC classification still have problems such as slow detection speed and low performance, and the accuracy of the detection needs to be improved. To overcome these limitations, this study proposes an enhanced YOLO (you only look once) network (EC-YOLOv7) for detecting EC targets. The network uses ACmix (a mixed model that enjoys the benefits of both self-attention and convolution) as a substitute for the 3 × 3 convolutional modules in the E-ELAN (Extended ELAN) architecture and implements branch links and 1 × 1 convolutional arrays between the ACmix modules to improve the speed of feature retrieval and network inference. Furthermore, the ResNet-ACmix module is engineered to prevent the leakage of function data and to minimise calculation time. Subsequently, the SPPCSPS (spatial pyramid pooling connected spatial pyramid convolution) block has been improved by replacing the serial channels with concurrent channels, which improves the fusion speed of the image features. To effectively capture spatial information and improve detection accuracy, the DyHead (the dynamic head) is utilised to enhance the model’s size, mission, and sense of space, which effectively captures spatial information and improves the detection accuracy. A new bounding-box loss regression method, the WIoU-Soft-NMS method, is finally suggested to facilitate prediction regression and improve the localisation accuracy. The experimental results demonstrate that the enhanced YOLOv7 net surpasses the initial YOLOv7 model and other common EC detection methods. The proposed EC-YOLOv7 network reaches a mean accuracy (mAP@0.5) of 94.4% on the PCB dataset and exhibits higher FPS compared to the original YOLOv7 model. In conclusion, it can significantly enhance high-density EC target recognition.

A set of methods for predicting the favorable reservoir of deep shale gas based on machine learning is proposed through research of parameter correlation feature analysis principle, intelligent prediction method based on convolution neural network (CNN), and integrated fusion characterization method based on kernel principal component analysis (KPCA) nonlinear dimension reduction principle. (1) High-dimensional correlation characteristics of core and logging data are analyzed based on the Pearson correlation coefficient. (2) The nonlinear dimension reduction method of KPCA is used to characterize complex high-dimensional data to efficiently and accurately understand the core and logging response laws to favorable reservoirs. (3) CNN and logging data are used to train and verify the model similar to the underground reservoir. (4) CNN and seismic data are used to intelligently predict favorable reservoir parameters such as organic carbon content, gas content, brittleness and in-situ stress to effectively solve the problem of nonlinear and complex feature extraction in reservoir prediction. (5) KPCA is used to eliminate complex redundant information, mine big data characteristics of favorable reservoirs, and integrate and characterize various parameters to comprehensively evaluate reservoirs. This method has been used to predict the spatial distribution of favorable shale reservoirs in the Ordovician Wufeng Formation to the Silurian Longmaxi Formation of the Weirong shale gas field in the Sichuan Basin, SW China. The predicted results are highly consistent with the actual core, logging and productivity data, proving that this method can provide effective support for the exploration and development of deep shale gas.

Background: Youth football in schools has experienced rapid growth in China. Despite the increase of players engaging in more frequent, intensive, and organized sports training at their early ages, the controversy over early specialization (ES) still exists. This study aims to: a) investigate the training situation of players in the Chinese School Football Programme and b) examine the associations of early specialization, sports volume, and maturity status with musculoskeletal injury.Methods: A cross-sectional survey was used. Players who participated in the National School Football Winter Camp were invited to fill out a questionnaire that included the data of maturity, ES, sports volume, and injury history (n = 88 boys and n = 90 girls).Results: The results have shown that 80.3% of the athletes were classified as ES, while 19.7% of them were classified as non-ES. Almost all athletes (96%) participated in a sport for more than 8 months in a year. Most athletes (75.8%) spent more than twice of the time on organized sports than leisure activities. 30.3% of the athletes trained on average more hours per week than the number of their ages. Binomial logistic regression models reflected the significant differences in the odds ratios (OR) of reporting a history of injury among athletes with different levels of specialization (p = 0.024) and the OR of reporting a history of leg injury among players with different weekly sports volumes (p = 0.038). Significant differences were also shown in the OR of players reporting foot injuries between players with different maturity states (p = 0.046), and the Chi-squared test showed significant differences in the OR of reporting acute injuries between players with different levels of specialization (p = 0.048) and weekly activity (p = 0.022). No significant differences were found between the remaining variables.Conclusion: Most school football elite players follow the ES pathway even though ES increases the risk of injury, especially acute injury. Pre-pubertal and early pubertal players have a higher incidence of foot injuries. Players who train more hours per week than their ages have more leg injuries and acute injuries. Therefore, priority protection and intervention should be carried out for populations with a high risk of injury.

Microalgal-bacterial granular sludge (MBGS) serves as an environmentally friendly wastewater treatment technology with CO2 capture potential. Accordingly, this study intended to investigate the lipid and biodiesel production of MBGS with CO2 addition. The results showed that CO2 addition could increase not only the total lipid content in MBGS but also the biodiesel quality. The total lipid content in MBGS typically increased from 725.92 mg/L to 1056.55 mg/L with CO2 addition during the 30 days experiment. Furthermore, when CO2 was added, the C18:1 content of MBGS increased significantly from 0.17 (μg/mg)·FW to 0.22 (μg/mg)·FW, indicating that higher quality of biodiesel might be produced. Saturated fatty acid and polyunsaturated fatty acids contents of MBGS with CO2 addition were respectively 1.30 (μg/mg)·FW and 0.13 (μg/mg)·FW, while the monounsaturated fatty acids content of MBGS with CO2 addition was higher than without CO2 addition. This indicated that the MBGS system with CO2 addition appeared to be better suited for producing biodiesel. Microbial community further analysis revealed that CO2 addition might promote the growth of Chlorophyta, Cyanobacteria and Chloroflexi. This background information would aid in promoting the further application of MBGS for carbon capture and resource recovery towards carbon neutral or negative wastewater treatment.

Unveiling the variation mechanism of vegetation net primary productivity (NPP) and elucidating the underlying drivers of these changes is highly necessitated for terrestrial carbon cycle research and global carbon emission control. Taking Henan Province, renowned as the anciently central China and current China’s foremost grain producer, as an example, this study employed the Theil–Sen Median Trend Analysis to evaluate the spatiotemporal characteristics and trends of NPP. Correlation Analysis and Residual Analysis were used to explain the drivers of NPP dynamics. To deepen the inquiry, the Geodetector method was employed to scrutinize the multifaceted effects and interplay among diverse variables influencing NPP. The result showed demonstrated that approximately 85.72% of the area showed an increase in NPP, covering a broad geographical distribution. Notably, 89.31% of the province has witnessed a positive human-driven NPP change. It means human activities emerged as a driving force with a positive effect on vegetation NPP, consequently fostering an increasing trend of NPP. Among climatic factors, the correlation between NPP and precipitation was stronger than that between the temperature and NPP, the determined power of factors in Henan Province was population density, (0.341) > GDP (0.326) > precipitation (0.255) > elevation (0.167) > slope (0.136) > temperature (0.109), and a single factor had a lesser interaction effect than two factors. The implications of these findings extend beyond the realms of research, potentially offering valuable insights into the formulation of targeted ecosystem restoration measures tailored to the distinct context of Henan Province, and also expect to provide crucial references for carbon emission control in China and across the world.

Deep learning methods have exhibited the great capacity to process object detection tasks, offering a practical and viable approach in many applications. When researchers have advanced deep learning models to improve their performance, the model derived from the algorithmic improvement may itself require complementary increases in computational and power demands. Recently, model compression and pruning techniques have received more attention to promote the wide employment of the DNN model. Although these techniques have achieved a remarkable performance, the class imbalance issue during the mode compression process does not vanish. This paper exploits the Autonomous Binarized Focal Loss Enhanced Model Compression (ABFLMC) model to address the issue. Additionally, our proposed ABFLMC can automatically receive the dynamic difficulty term during the training process to improve performance and reduce complexity. A novel hardware architecture is proposed to accelerate inference. Our experimental results show that the ABFLMC can achieve higher accuracy, faster speed, and smaller model size.

Ex situ catalytic pyrolysis of biomass using char-supported nanoparticles metals (Fe and Ni) catalyst for syngas production and tar decomposition was investigated. The characterizations of fresh Fe-Ni/char catalysts were determined by TGA, SEM–EDS, Brunauer–Emmett–Teller (BET), and XPS. The results indicated that nanoparticles metal substances (Fe and Ni) successfully impregnated into the char support and increased the thermal stability of Fe-Ni/char. Fe-Ni/char catalyst exhibited relatively superior catalytic performance, where the syngas yield and the molar ratio of H2/CO were 0.91 Nm3/kg biomass and 1.64, respectively. Moreover, the lowest tar yield (43.21 g/kg biomass) and the highest tar catalytic conversion efficiency (84.97 wt.%) were also obtained under the condition of Ni/char. Ultimate analysis and GC–MS were employed to analyze the characterization of tar, and the results indicated that the percentage of aromatic hydrocarbons appreciably increased with the significantly decrease in oxygenated compounds and nitrogenous compounds, especially in Fe-Ni/char catalyst, when compared with no catalyst pyrolysis. After catalytic pyrolysis, XPS was employed to investigate the surface valence states of the characteristic elements in the catalysts. The results indicated that the metallic oxides (MexOy) were reduced to metallic Me0 as active sites for tar catalytic pyrolysis. The main reactions pathway involved during ex situ catalytic pyrolysis of biomass based on char-supported catalyst was proposed. These findings indicate that char has the potential to be used as an efficient and low-cost catalyst toward biomass pyrolysis for syngas production and tar decomposition.

Background: Dysregulation of microRNAs (miRNAs) is actively involved in the pathogenesis and tumorigenicity of colorectal cancer (CRC). miR-296 was found to play either oncogenic or tumor suppressive role in human cancers. However, the status of miR-296 and its function in CRC remain unknown.Methods: The expression of miR-296 was confirmed by qRT-PCR in CRC tissues and cells, and its level was altered by corresponding miRNA vectors. Wound healing and Transwall assays were performed to detect the migration and invasion of CRC cells. The levels of proteins were measured using immunoblotting, immunohistochemistry and immunofluorescence.Results: Underexpression of miR-296 was disclosed in CRC tissues and cells. Its decreased level was evidently correlated with adverse clinical parameters and poor prognosis of CRC patients. In vitro experiments indicated that miR-296 inhibited CRC cell migration and invasion. Mechanically, miR-296 inhibited the epithelial-mesenchymal transition (EMT) of CRC cells. A negative correlation between miR-296 and S100A4 expression was observed in CRC tissues. Luciferase reporter assays indicated that miR-296 inversely regulated the luciferase activity of 3'-UTR of S100A4. Herein, S100A4 was found to be a downstream molecule of miR-296 in CRC. Furthermore, S100A4 mediated the anti-metastatic effects of miR-296 on EMT, migration and invasion of CRC cells.Conclusions: miR-296 functions as an anti-metastatic factor mainly by suppressing S100A4 in CRC. It potentially acts as a prognostic predictor and a drug-target for CRC patients. [ABSTRACT FROM AUTHOR]