Changes in Blood Pressure is Associated with Bone Loss in US Adults: A Cross-Sectional Study from NHANES 2005-2018.

Hypertension and osteoporosis are common geriatric diseases, sharing similar risk factors. This study aims to investigate this association and explore relatively mixed variables. Our study included 12,787 eligible participants from the National Health and Nutrition Examination Survey (NHANES) 2005-2018. Included participants had valid data on hypertension and osteoporosis, without tumors, liver diseases, gout or thyroid diseases. We explored the association between hypertension and osteoporosis by logistic regression and examined blood pressure and BMD/BMC by linear and non-linear regression. Moreover, we used machine learning models to predict the importance of various factors in the occurrence of osteoporosis and evaluated causality by mendelian randomization. Our study found that osteoporosis is significantly associated with hypertension [OR 2.072 (95% CI 2.067-2.077), p < 0.001]. After adjusting for co-variances, the association remained significant [OR 1.223 (95% CI 1.220-1.227), p < 0.001]. Our study showed that osteoporosis is positively associated with hypertension in the US population. A variety of factors influence this relationship. Specific regulatory mechanisms and confounding factors need to be further investigated.

Mediating oxidative stress through the Palbociclib/miR-141-3p/STAT4 axis in osteoporosis: a bioinformatics and experimental validation study.

Osteoporosis is a common bone disease characterized by loss of bone mass, reduced bone strength, and deterioration of bone microstructure. ROS-induced oxidative stress plays an important role in osteoporosis. However, the biomarkers and molecular mechanisms of oxidative stress are still unclear. We obtained the datasets from the Gene Expression Omnibus (GEO) database, and performed differential analysis, Venn analysis, and weighted correlation network analysis (WGCNA) analysis out the hub genes. Then, the correlation between inflammatory factors and hub genes was analyzed, and a Mendelian randomization (MR) analysis was performed on cytokines and osteoporosis outcomes. In addition, "CIBERSORT" was used to analyze the infiltration of immune cells and single-cell RNA-seq data was used to analyze the expression distribution of hub genes and cell-cell communications. Finally, we collected human blood samples for RT-qPCR and Elisa experiments, the miRNA-mRNA network was constructed using the miRBase database, the 3D structure was predicted using the RNAfold, Vfold3D database, and the drug sensitivity analysis was performed using the RNAactDrug database. We obtained three differentially expressed genes associated with oxidative stress: DBH, TAF15, and STAT4 by differential, WGCNA clustering, and Venn screening analyses, and further analyzed the correlation of these 3 genes with inflammatory factors and immune cell infiltration and found that STAT4 was significantly and positively correlated with IL-2. Single-cell data analysis showed that the STAT4 gene was highly expressed mainly in dendritic cells and monocytes. In addition, the results of RT-qPCR and Elisa experiments verified that the expression of STAT4 was consistent with the previous analysis, and a significant causal relationship between IL-2 and STAT4 SNPs and osteoporosis was found by Mendelian randomization. Finally, through miRNA-mRNA network and drug sensitivity analysis, we analyzed to get Palbociclib/miR-141-3p/STAT4 axis, which can be used for the prevention and treatment of osteoporosis. In this study, we proposed the Palbociclib/miR-141-3p/STAT4 axis for the first time and provided new insights into the mechanism of oxidative stress in osteoporosis.

Performance evaluation, enzymatic activity change and metagenomic analysis of sequencing batch reactor under divalent zinc stress.

Divalent zinc (Zn2+) are widely detected in domestic and industrial wastewater, and it is essential to evaluate the effect of Zn2+ on wastewater biological treatment process due to its bio-toxicity. In this study, the nitrogen removal rates and their corresponding enzymatic activities of sequencing batch reactor decreased with the increase of Zn2+ concentration. The Zn2+ accumulation in activated sludge caused significant antioxidant response, and the reactive oxygen species (ROS) production and antioxidant enzymatic activities were positively correlated with Zn2+ concentration. The presence of Zn2+ inhibited the metabolic pathways related to energy production and electron transport. The abundance decreases of nitrification and denitrification functional genes led to the deterioration of nitrogen removal performance under Zn2+ stress. The correlation analysis between functional gene modules and microbial genera revealed that Zoogloea had obvious Zn2+ resistance. This study can provide the insights into the influencing mechanism of Zn2+ on the biological nitrogen removal process.

Comparative evaluation of four Chlorella species treating mariculture wastewater under different photoperiods: Nitrogen removal performance, enzyme activity, and antioxidant response.

The nitrogen removal performance, nitrogen metabolism enzyme activities, and antioxidant response of four Chlorella species (Chlorella sp., Chlorella vulgaris, Chlorella sorokiniana, and Chlorella protothecoides) were compared under different light: dark (L:D) photoperiods during treating mariculture wastewater. The increase of light duration in the range of 8L:16D to 16L:8D was beneficial to the chlorophyll synthesis of selected four Chlorella species. Chlorella vulgaris was the most effective to treat mariculture wastewater than Chlorella sp., Chlorella sorokiniana, and Chlorella protothecoides. and its microalgae density, photosynthetic activity, and nitrogen metabolism enzyme activity were higher than those of the other three Chlorella species. An obvious oxidative stress in microalgal cells was under 20L:4D photoperiod, which led to a decrease in photosynthetic activity and nitrogen metabolizing enzyme activity. Among the four Chlorella species, Chlorella protothecoides had the highest degree of light-induced stress and ROS accumulation. This study can provide suitable microalgae and optimal photoperiod for treating mariculture wastewater.

Platymonas helgolandica-driven nitrogen removal from mariculture wastewater under different photoperiods: Performance evaluation, enzyme activity and transcriptional response.

The nitrogen removal performance and biological mechanism of Platymonas helgolandica var. Tsingtaoensis (P. helgolandica) were investigated in treating mariculture wastewater under different light: dark (L:D) photoperiods. The growth of P. helgolandica was positively correlated with the photoperiods from 6L:18D to 15L:9D, and the highest photosynthetic activity appeared under 6L:18D photoperiod on day 3. P. helgolandica exhibited the highest removal efficiencies of total nitrogen and COD at 89 % and 93 % under 15L:9D photoperiod, respectively. NH4+-N assimilation was proportional to the photoperiods from 6L:18D to 15L:9D and longer illumination promoted NO2--N removal. However, the highest NO3--N reduction rate was achieved under 12L:12D photoperiod. The different nitrogen-transformed enzymatic activities were affected by photoperiod. Transcriptome revealed that unigenes were enriched in nitrogen metabolism and photosynthesis pathways, of which the functional gene expression was up-regulated significantly. This study provides insights into the optimization of photoperiod for mariculture wastewater treatment by P. helgolandica.

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Developing outer crown profile prediction models of typical urban greening tree species will lay a foundation for the spatial allocation optimization of urban greening. In this study, Pinus tabuliformis, a typical greening tree species in Shenyang, was selected as the research object. Based on the Crown Window device, a total of 60 sample trees were selected to measure the crown shape, with power equation, segmented polynomial equation, and modified Kozak equation as the basic models. By introducing crown structure variables (the maximum crown radius) and neighbour competition variables (mean tree height, mean diameter at breast height, mean crown width, number for the neighbour trees, and mean crown contact height between sample trees and neighbour trees) through reparameterization, we constructed an outer crown shape model of P. tabuliformis that incorporates neighbour tree competition and maximum crown radius. The results showed that modified Kozak equation had the largest Ra2 and the smallest RMSE, as well as good stability. After introducing the maximum crown radius and the mean DBH of neighbour trees into the basic model through reparameterization, the Ra2 of the model increased by 0.0693 and the MSER was 14.4%. The maximum crown radius had a great influence on the crown shape, while the crown radius increased with the increases of the maximum crown radius. The influence of mean DBH of neighbour trees on crown shape was weaker than that of maximum crown radius. The upper part of crown increased and the lower part of crown decreased with increasing neighbour tree competition. In this study, the marginal regression outer crown profile model of P. tabuliformis coupled with neighbour tree competition and the maximum crown radius showed good goodness of fit and could reasonably simulate and predict the crown shape of planted P. tabuliformis.

Metagenomic analysis and nitrogen removal performance evaluation of activated sludge from a sequencing batch reactor under different salinities.

The effect of salinity on the nitrogen removal performance and microbial community of activated sludge was investigated in a sequencing batch reactor. The NH4+-N removal efficiency was over 95% at 0-4% salinity, indicating that the nitrification performance of activated sludge was slightly affected by lower salinity. The obvious nitrite accumulation was observed with the increment of the salinity to 5%, followed by a notable decline in the nitrogen removal performance at 6% salinity. The salinity inhibited the microbial activity, and the specific rate of nitrification and denitrification was decreased by the increasing salinity obviously. Additionally, the lower activity of superoxide dismutase and peroxidase and higher reactive oxygen species content in activated sludge might account for the deteriorative nitrogen removal performance at 6% salinity. Metagenomics analysis revealed that the genes encoding the ABC-type quaternary amine transporter in the ABC transporter pathway were abundant in the activated sludge at 2% and 4% salinity, and the higher salinity of 6% led to the loss of the genes encoding the p-type Na+ transporter in the ABC transporter pathway. These results indicated that the salinity could weaken the ABC transporter pathway for the balance of osmotic pressure in activated sludge. The microbial activity and nitrogen removal performance of activated sludge were decreased due to the unbalanced osmotic pressure at higher salinity.

Micro-Expression Recognition Using Uncertainty-Aware Magnification-Robust Networks.

A micro-expression (ME) is a kind of involuntary facial expressions, which commonly occurs with subtle intensity. The accurately recognition ME, a. k. a. micro-expression recognition (MER), has a number of potential applications, e.g., interrogation and clinical diagnosis. Therefore, the subject has received a high level of attention among researchers in affective computing and pattern recognition communities. In this paper, we proposed a straightforward and effective deep learning method called uncertainty-aware magnification-robust networks (UAMRN) for MER, which attempts to address two key issues in MER including the low intensity of ME and imbalance of ME samples. Specifically, to better distinguish subtle ME movements, we reconstructed a new sequence by magnifying the ME intensity. Furthermore, a sparse self-attention (SSA) block was implemented which rectifies the standard self-attention with locality sensitive hashing (LSH), resulting in the suppression of artefacts generated during magnification. On the other hand, for the class imbalance problem, we guided the network optimization based on the confidence about the estimation, through which the samples from rare classes were allotted greater uncertainty and thus trained more carefully. We conducted the experiments on three public ME databases, i.e., CASME II, SAMM and SMIC-HS, the results of which demonstrate improvement compared to recent state-of-the-art MER methods.