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IntroductionImmunity is involved in a variety of bone metabolic processes, especially osteoporosis. The aim of this study is to explore new bone immune-related markers by bioinformatics method and evaluate their ability to predict osteoporosis.MethodsThe mRNA expression profiles were obtained from GSE7158 in Gene expression Omnibus (GEO), and immune-related genes were obtained from ImmPort database (https://www.immport.org/shared/). immune genes related to bone mineral density(BMD) were screened out for differential analysis. protein-protein interaction (PPIs) networks were used to analyze the interrelationships between different immune-related genes (DIRGs). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of DIRGs function were performed. A least absolute shrinkage and selection operation (LASSO) regression model and multiple Support Vector Machine-Recursive Feature Elimination (mSVM-RFE) model were constructed to identify the candidate genes for osteoporosis prediction The receiver operator characteristic (ROC) curves were used to validate the performances of predictive models and candidate genes in GEO database (GSE7158,GSE13850).Through the RT - qPCR verify the key genes differentially expressed in peripheral blood mononuclear cells Finally, we constructed a nomogram model for predicting osteoporosis based on five immune-related genes. CIBERSORT algorithm was used to calculate the relative proportion of 22 immune cells.ResultsA total of 1158 DEGs and 66 DIRGs were identified between high-BMD and low-BMD women. These DIRGs were mainly enriched in cytokine−mediated signaling pathway, positive regulation of response to external stimulus and the cellular components of genes are mostly localized to external side of plasma membrane. And the KEGG enrichment analysis were mainly involved in Cytokine−cytokine receptor interaction, PI3K−Akt signaling pathway, Neuroactive ligand−receptor interaction,Natural killer cell mediated cytotoxicity. Then five key genes (CCR5, IAPP, IFNA4, IGHV3-73 and PTGER1) were identified and used as features to construct a predictive prognostic model for osteoporosis using the GSE7158 dataset.ConclusionImmunity plays an important role in the development of osteoporosis.CCR5, IAPP, IFNA4, IGHV3-73 and PTGER1were play an important role in the occurrences and diagnosis of OP.

In the study, polydopamine and oleylamine cooperated to modify the surface of anhydrous calcium sulfate prepared from phosphogypsum, and HDPE matrix composites with different filler content were prepared by filling HDPE before and after modification. The results show that when the filler content is 5 wt%, the elongation at break of mCSA/HDPE composite is 31.7% and 89% higher than that of HDPE and CSA/HDPE. Respectively, when the filler content is as high as 25 wt%, the flexural strength is still 16.8% higher than that of HDPE. After modification, the compatibility and dispersibility of anhydrous calcium sulfate in HDPE were improved, and the crystal structure of anhydrous calcium sulfate was not significantly changed, thus the mechanical properties and thermal stability of mCSA/HDPE composites were improved. Overall, after modification, not only the mechanical properties of the composites are improved, but also the content of inorganic powder as filler added to HDPE is increased, which can be regarded as a method for effective utilization of phosphogypsum. Highlights: Calcium sulfate was modified by polydopamine and oleylamineThe mechanical and thermal properties of the modified calcium sulfate/HDPE composites are good.The resource utilization of phosphogypsum was carried out [ABSTRACT FROM AUTHOR]

Sustainable development goals challenge the carbon lock-in dilemma of the industrial economy, and identifying the motivation and mechanism behind carbon unlocking has become an urgent priority. With its inclusive and precise advantages, digital finance (DF) provides a new impetus for the economy's low-carbon transformation, while reasonable environmental regulation (ER) acts as an important guiding constraint. We focus on the carbon unlocking performance of DF under ER constraints. After constructing and calculating the industrial carbon unlocking efficiency (ICUE), we observe the trends of ICUE fluctuating positively, clustering towards the eastern region, and polarization. Subsequently, based on theoretical analyses, we explore the marginal and configuration effects of DF and ER in improving ICUE using panel data from 30 Chinese provinces between 2011 and 2021 and adopt a mixed research method with regression analysis (Tobit hierarchical regression and quantile regression for panel data (QRPD)) and dynamic fuzzy-set qualitative comparative analysis (fsQCA). The regression analysis results show that DF can notably enhance China's provincial ICUE, with ER generally serving as a positive moderator; however, the unlocking potential of informal environmental regulations needs further exploration. As ICUE improves in a specific location or time, the positive contribution of DF to ICUE also increases, whereas the moderating effect of ER exhibits an optimal range and follows an inverted U-shape. The dynamic fsQCA results support the findings of the regression analysis and further emphasize that effective cooperation between DF and ER is crucial for high ICUE, while inadequate DF support and the absence of formal environmental regulations remain bottlenecks in industrial carbon lock-in. Moreover, configuration paths demonstrate clear path dependency in both time and space, indicating a prolonged unlocking endeavor. [ABSTRACT FROM AUTHOR]

Porcine circovirus type 2 (PCV2) infection can cause immunosuppressive diseases in pigs. Vascular endothelial cells (VECs), as the target cells for PCV2, play an important role in the immune response and inflammatory regulation. Endothelial IL-8, which is produced by porcine hip artery endothelial cells (PIECs) infected with PCV2, can inhibit the maturation of monocyte-derived dendritic cells (MoDCs). Here, we established a co-culture system of MoDCs and different groups of PIECs to further investigate the PCV2-induced endothelial IL-8 signaling pathway that drives the inhibition of MoDC maturation. The differentially expressed genes related to MoDC maturation were mainly enriched in the NF-κB and JAK2-STAT3 signaling pathways. Both the NF-κB related factor RELA and JAK2-STAT3 signaling pathway related factors (IL2RA, JAK, STAT2, STAT5, IL23A, IL7, etc.) decreased significantly in the IL-8 up-regulated group, and increased significantly in the down-regulated group. The expression of NF-κB p65 in the IL-8 up-regulated group was reduced significantly, and the expression of IκBα was increased significantly. Nuclear translocation of NF-κB p65 was inhibited, while the nuclear translocation of p-STAT3 was increased in MoDCs in the PCV2-induced endothelial IL-8 group. The results of treatment with NF-κB signaling pathway inhibitors showed that the maturation of MoDCs was inhibited and the expression of IL-12 and GM-CSF at mRNA level were lower. Inhibition of the JAK2-STAT3 signaling pathway had no significant effect on maturation, and the expression of IL-12 and GM-CSF at mRNA level produced no significant change. In summary, the NF-κB signaling pathway is the main signaling pathway of MoDC maturation, and is inhibited by the PCV2-induced up-regulation of endothelial-derived IL-8. [ABSTRACT FROM AUTHOR]

This study introduces a novel methodology for the dynamic extraction of information on cotton growth in terms of height utilizing the DJI Zenmuse L1 LiDAR sensor mounted onto a DJI Matrice 300 RTK Unmanned Aerial Vehicle (UAV), aimed at enhancing the precision and efficiency of growth monitoring within the realm of precision agriculture. Employing the Progressive TIN Densification (PTD) and Cloth Simulation Filter (CSF) algorithms, combined with Kriging interpolation, we generated Canopy Height Models (CHMs) to extract the cotton heights at two key agricultural sites: Zengcheng and Tumxuk. Our analysis reveals that the PTD algorithm significantly outperforms the CSF method in terms of accuracy, with its R2 values indicating a superior model fit for height extraction across different growth stages (Zengcheng: 0.71, Tumxuk: 0.82). Through meticulous data processing and cluster analysis, this study not only identifies the most effective algorithm for accurate height extraction but also provides detailed insights into the dynamic growth patterns of cotton varieties across different geographical regions. The findings highlight the critical role of UAV remote sensing in enabling large-scale, high-precision monitoring of crop growth, which is essential for the optimization of agricultural practices such as precision fertilization and irrigation. Furthermore, the study demonstrates the potential of UAV technology to select superior cotton varieties by analyzing their growth dynamics, offering valuable guidance for cotton breeding and cultivation. [ABSTRACT FROM AUTHOR]

Improving energy efficiency is vital for addressing climate change and reducing carbon emissions in emerging economies. Financial development (FD) is crucial for economic growth, and its environmental impact and the adjustment of the industrial structure (IND) is a crucial lever in China's economic transition period. This study explored the relationship between FD, IND, and China's green total factor productivity (GTFEE) from 2000 to 2020 using the super-efficiency SBM-undesirable model, which estimates China's GTFEE. The ARDL results suggest that FD and IND enhance GTFEE in the long term, with FD promoting GTFEE by facilitating industrial structure adjustments. The Dumitrescu–Hurlin panel causality tests supported this finding. The QRPD panel quantile regression and heterogeneity analysis revealed significant heterogeneity in the effects. With increasing GTFEE, FD exerts a restraining effect, gradually weakening and transitioning into a promoting effect, while the IND consistently plays a promoting role. [ABSTRACT FROM AUTHOR]

The photocatalytic performance of nano-TiO2 photocatalysts in air pollutant degradation greatly depends on the adsorption of water, substrates, and intermediates. Especially under excessive humidity, substrate concentration, and intermediate concentration, the competitive adsorption of water, substrates, and intermediates can seriously inhibit the photocatalytic performance. In the past few years, extensive studies have been performed to investigate the influence of humidity, substrate concentration, and intermediates on the photocatalytic performance of TiO2, and significant advances have been made in the area. However, to the best of our knowledge, there is no review focusing on the effects of water, substrate, and intermediate adsorption to date. A comprehensive understanding of their mechanisms is key to overcoming the limited application of nano-TiO2 photocatalysts in the photocatalytic decomposition of air pollutants. In this review, the progress in experimental and theoretical fields, including a recent combination of photocatalytic experiments and adsorption and photocatalytic simulations by density functional theory (DFT), to explore the impact of adsorption of various reaction components on nano-TiO2 photocatalysts is comprehensively summarized. Additionally, the mechanism and broad perspective of the impact of their adsorption on the photocatalytic activity of TiO2 in air treatment are also critically discussed. Finally, several solutions are proposed to resolve the current problems related to environmental factors. In general, this review contributes a comprehensive perspective of water, substrate, and intermediate adsorption toward boosting the photocatalytic application of TiO2 nanomaterials. [ABSTRACT FROM AUTHOR]