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Background The importance of the gut microbiota in maintaining bone homeostasis has been increasingly emphasized by recent research. This study aimed to identify whether and how the gut microbiome of postmenopausal women with osteoporosis and osteopenia may differ from that of healthy individuals. Methods Fecal samples were collected from 27 individuals with osteoporosis (OP), 44 individuals with osteopenia (ON), and 23 normal controls (NC). The composition of the gut microbial community was analyzed by 16S rRNA gene sequencing. Results No significant difference was found in the microbial composition between the three groups according to alpha and beta diversity. At the phylum level, Proteobacteria and Fusobacteriota were significantly higher and Synergistota was significantly lower in the ON group than in the NC group. At the genus level, Roseburia, Clostridia_UCG.014, Agathobacter, Dialister and Lactobacillus differed between the OP and NC groups as well as between the ON and NC groups (p < 0.05). Linear discriminant effect size (LEfSe) analysis results showed that one phylum community and eighteen genus communities were enriched in the NC, ON and OP groups, respectively. Spearman correlation analysis showed that the abundance of the Dialister genus was positively correlated with BMD and T score at the lumbar spine (p < 0.05). Functional predictions revealed that pathways relevant to amino acid biosynthesis, vitamin biosynthesis, and nucleotide metabolism were enriched in the NC group. On the other hand, pathways relevant to metabolites degradation and carbohydrate metabolism were mainly enriched in the ON and OP groups respectively. Conclusions Our findings provide new epidemiologic evidence regarding the relationship between the gut microbiota and postmenopausal bone loss, laying a foundation for further exploration of therapeutic targets for the prevention and treatment of postmenopausal osteoporosis (PMO).

In response to the complexity of the Jacobian matrix inversion process in the power flow algorithm for AC/DC microgrids, leading to large memory requirements and susceptibility to convergence issues, a novel power flow algorithm based on an improved unified iteration method for AC/DC microgrids is proposed. Firstly, the fundamental equations of the unified iteration method and the characteristics of DC systems are analyzed. The reactive power correction terms and voltage phase correction differences are removed from the modified equations of the unified iteration method, and result in a reduction in the order of the Jacobian matrix in the power flow algorithm. Subsequently, the improved IEEE 11-node system is subjected to simulation verification to attain precise power flow solutions for hybrid AC/DC microgrids. The theoretical analysis identifies the main influencing parameters of active and reactive power errors and assesses their impact factors. Finally, experimental validation of the improved power flow algorithm is carried out on a physical platform, clarifying the applicability range of the proposed method. The research results indicate that within allowable error margins, the proposed approach reduces the difficulty of Jacobian matrix inversion, resulting in an 80% increase in computational speed compared to the unified iteration method. It is suitable for microgrid systems with short electrical distances and small magnitudes of node voltage amplitudes and phase differences.

Children's unexpected cardiac arrest warrants cardiopulmonary resuscitation (CPR) by kindergarten teachers. Yet, recent research revealed trained personnel reporting poor transfer of acquired skills during real-world emergencies with conventional CPR training. Immersive virtual reality (IVR) training induces a greater sense of presence and agency than conventional CPR training and may be more effective in terms of increasing trainees' intention and initiative to perform CPR in real-world emergencies. A quasi-experiment was conducted to assess the effectiveness of the IVR-based CPR training method in terms of enhancing the intention to perform CPR. The trial enrolled 50 participants, 25 of whom examined a child patient in an IVR virtual scenario, using an AED, and performed two rounds of two-minute chest compressions. The remaining 25 participants were trained using a video with consistent content on a monitor. A generalised estimating equation analysis demonstrated that the IVR training method significantly increased prospective kindergarten teachers' self-efficacy for performing CPR, positive attitudes towards CPR, and CPR knowledge. This advantage was also maintained after the five-week follow-up. Thus, CPR teaching via IVR looks to be an excellent way to enhance the intention to perform CPR and may be of great value in improving existing CPR training systems.

In order to obtain the static strength of small modulus plastic gears, a small modulus plastic gear static strength test bench is developed, which can accurately measure the static strength of plastic gears under different modulus, speeds and materials. The working principle, constitution and the design of tooling parts of the test bed are mainly introduced. The test bench is composed of the mechanical system, the electric control system and the measuring software. The electric control system and the measuring software mainly adopt the horizontal structure, which is of the compact structure, with small space occupation, uses simple fixture design and is easy to measure. The static strength test is carried out, and the static strength using different materials is briefly tested. The experimental equipment can realize the measurement of the static strength of the small module plastic gear, which solves the problems of inaccurate static strength test of the small module gear and the difficulty of test clamping, and provides theoretical value and practical application value for the design of the plastic gear in the future.

Abstract Background Bladder cancer (BLCA) is a prevalent urinary system malignancy. Understanding the interplay of immunological and metabolic genes in BLCA is crucial for prognosis and treatment. Methods Immune/metabolism genes were extracted, their expression profiles analyzed. NMF clustering found prognostic genes. Immunocyte infiltration and tumor microenvironment were examined. Risk prognostic signature using Cox/LASSO methods was developed. Immunological Microenvironment and functional enrichment analysis explored. Immunotherapy response and somatic mutations evaluated. RT-qPCR validated gene expression. Results We investigated these genes in 614 BLCA samples, identifying relevant prognostic genes. We developed a predictive feature and signature comprising 7 genes (POLE2, AHNAK, SHMT2, NR2F1, TFRC, OAS1, CHKB). This immune and metabolism-related gene (IMRG) signature showed superior predictive performance across multiple datasets and was independent of clinical indicators. Immunotherapy response and immune cell infiltration correlated with the risk score. Functional enrichment analysis revealed distinct biological pathways between low- and high-risk groups. The signature demonstrated higher prediction accuracy than other signatures. qRT-PCR confirmed differential gene expression and immunotherapy response. Conclusions The model in our work is a novel assessment tool to measure immunotherapy’s effectiveness and anticipate BLCA patients’ prognosis, offering new avenues for immunological biomarkers and targeted treatments.

Cold/hot forming wrought Al components with the apparent advantage of lightweight, outstanding formability and strength-ductility synergy are promising in structural materials field. Recrystallization is an indispensable process during deformation or heat treatment, whose driving force and microstructures have great relevance with the mechanical properties. In recent years, reinforcement and intermetallic particles have attracted great interests in precise intervening and controlling of recrystallization microstructures due to their immense potentials in regulating the driving and retarding force of recrystallization. Nevertheless, optimizing recrystallization microstructures of particle tailored wrought Al alloys is highly challenging due to the numerous affecting factors of particles (e.g., particle addition method, size, morphology, spatial distribution, coherency, and content, etc.) and the different driving force of static and dynamic recrystallization. To further impel the progress of recrystallization process of wrought Al alloy, this review gives an in-depth cognition of the roles of intermetallic and ceramic particles in recrystallization process of wrought Al alloys. The potential relevance between the particle characteristics, recrystallization driving force, and recrystallization microstructures in wrought Al alloys are summarized. The influences of regulated recrystallization microstructures on mechanical properties of wrought Al alloy are also overviewed. This review contributes to the comprehensive understanding of mechanisms of particle manipulated recrystallization evolution, and the future research orientations and development opportunities are prospected.

There is an increasingly urgent need of lightweight components in aerospace industry, among which aluminum (Al) alloys have been the optimal materials of choice for aircraft structural parts since being used in the Junkers F.13 aircraft in the 1920s. Compared to other metal materials, Al alloys have a lower density, and the use of Al alloys reduces the total weight of the aircraft and improves fuel efficiency and load capacity. Meanwhile, the strength and hardness of Al alloys with alloying and heat treatment can be significantly enhanced for uses in high loads and vibration environments. Furthermore, in the harsh aerospace environment, aircraft may receive various climatic conditions and chemical corrosion. Due to good corrosion and fatigue resistance, Al alloys demonstrate excellent performance under these conditions, ensuring the long–term service life of aircraft. In addition, Al alloys have good recyclability, and they can be recycled to reduce resource consumption and environmental load, in line with the principle of sustainable development. In recent years, although composites have been widely used in aerospace, high–strength Al alloys are still in an indispensable position. Therefore, this article reviews the progress and applications of Al alloys commonly used in aerospace. The common strengthening methods and advanced manufacturing and processing technologies of Al alloy are also discussed, which can provide references for the development of advanced high–performance aviation Al alloys in the future.

This study aimed to assess the effect of novel thermal glycation, utilizing microwave processing (100−150 °C) combined with sugars (glucose and lactose), on the in vitro protein digestibility, peptides, secondary structures, microstructures, and allergenic properties of Atlantic cod. The research demonstrated that microwave heating at 150 °C with glucose significantly reduced cod allergenicity by up to 16.16%, while also enhancing in vitro protein digestibility to 69.05%. Glucose was found to be more effective than lactose in conjunction with microwave heating in reducing the allergenicity of Atlantic cod. Moreover, treatments conducted at 150 °C were more effective in increasing in vitro protein digestibility and peptide content compared to those at 100 °C. This study revealed that the novel processing technique of thermal glycation effectively reduced the allergenicity of Atlantic cod. It also offered fresh insights into the potential benefits of combining microwave heating with sugars.

In the domain of multi-label classification, label correlations play a crucial role in enhancing prediction precision. However, traditional methods heavily depend on ground-truth label sets, which can be incompletely tagged due to the diverse backgrounds of annotators and the significant cost associated with procuring extensive labeled datasets. To address these challenges, this paper introduces a novel multi-label classification method called updating Correlation-enhanced Feature Learning (uCeFL), which extracts label correlations directly from the data instances, circumventing the dependency on potentially incomplete label sets. uCeFL initially computes a revised label matrix by multiplying the incomplete label matrix with the label correlations extracted from the data matrix. This revised matrix is then utilized to enrich the original data features, enabling a neural network to learn correlation-enhanced representations that capture intricate relationships between data features, labels, and their interactions. Notably, label correlations are not static; they are dynamically updated during the neural network’s training process. Extensive experiments carried out on various datasets emphasize the effectiveness of the proposed approach. By leveraging label correlations within data instances, along with the hierarchical learning capabilities of neural networks, it offers a significant improvement in multi-label classification, even in scenarios with incomplete labels.