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BackgroundWith increasing gaps between the rich and poor, potential risk factors for class conflict have attracted increasing attention from researchers. Although cognitive factors are known to be significant predictors of class-conflict behavior, limited attention has been paid to competence stereotypes of the upper class. When considering economic inequality, people pay more attention to competence stereotypes of the upper class, which may have adverse effects. This study aimed to investigate the relationship between competence stereotypes held by the lower class about the upper class and class conflict, and to test the mediating role of intergroup envy in this relationship and the moderating role of upward social mobility belief.MethodsData were collected from a convenience sample from a comprehensive university in China. Based on scores on subjective and objective class scales, 284 lower-class college students (103 males and 181 females) aged 18–24 were selected to participate (both their subjective and objective scores were lower than 3 points). Their endorsement of upper-class competence stereotypes, intergroup envy, upward social mobility beliefs, and class conflict were measured using a well-validated self-report questionnaire.ResultsThe main data were analyzed using correlation analysis, the SPSS macro PROCESS (Model 7), and simple slope analysis. The results show a significant positive correlation between competence stereotypes held by lower-class college students toward the higher class and class conflict, and this connection was mediated by intergroup envy. Moreover, the indirect effect of intergroup envy on this link was moderated by upward social mobility beliefs; this effect was stronger for college students with lower upward social mobility beliefs.ConclusionThis study broadens our understanding of how and when competence stereotypes among the lower class concerning the upper class are related to class conflict. Researchers and policymakers should pay special attention to competence stereotypes of the upper class, especially intergroup envy and class conflict among lower-class individuals with lower levels of upward social mobility beliefs.

Background: GC is a highly heterogeneous tumor with different responses to immunotherapy, and the positive response depends on the unique interaction between the tumor and the tumor microenvironment (TME). However, the currently available methods for prognostic prediction are not satisfactory. Therefore, this study aims to construct a novel model that integrates relevant gene sets to predict the clinical efficacy of immunotherapy and the prognosis of GC patients based on machine learning. Methods: Seven GC datasets were collected from the Gene Expression Omnibus (GEO) database, The Cancer Genome Atlas (TCGA) database and literature sources. Based on the immunotherapy cohort, we first obtained a list of immunotherapy related genes through differential expression analysis. Then, Cox regression analysis was applied to divide these genes with prognostic significancy into protective and risky types. Then, the Single Sample Gene Set Enrichment Analysis (ssGSEA) algorithm was used to score the two categories of gene sets separately, and the scores differences between the two gene sets were used as the basis for constructing the prognostic model. Subsequently, Weighted Correlation Network Analysis (WGCNA) and Cytoscape were applied to further screen the gene sets of the constructed model, and finally COX7A1 was selected for the exploration and prediction of the relationship between the clinical efficacy of immunotherapy for GC. The correlation between COX7A1 and immune cell infiltration, drug sensitivity scoring, and immunohistochemical staining were performed to initially understand the potential role of COX7A1 in the development and progression of GC. Finally, the differential expression of COX7A1 was verified in those GC patients receiving immunotherapy. Results: First, 47 protective genes and 408 risky genes were obtained, and the ssGSEA algorithm was applied for model construction, showing good prognostic discrimination ability. In addition, the patients with high model scores showed higher TMB and MSI levels, and lower tumor heterogeneity scores. Then, it is found that the COX7A1 expressions in GC tissues were significantly lower than those in their corresponding paracancerous tissues. Meanwhile, the patients with high COX7A1 expression showed higher probability of cancer invasion, worse clinical efficacy of immunotherapy, worse overall survival (OS) and worse disease-free survival (DFS). Conclusions: The ssGSEA score we constructed can serve as a biomarker for GC patients and provide important guidance for individualized treatment. In addition, the COX7A1 gene can accurately distinguish the prognosis of GC patients and predict the clinical efficacy of immunotherapy for GC patients. [ABSTRACT FROM AUTHOR]

Background: The previous effects of acupuncture-related interventions in improving chemotherapy-induced peripheral neuropathy (CIPN) symptoms and quality of life (QoL) remain unclear in terms of pairwise comparisons. Aims: This systematic review and network meta-analysis aimed to determine the hierarchical effects of acupuncture-related interventions on symptoms, pain, and QoL associated with CIPN in cancer patients undergoing chemotherapy. Methods: Nine electronic databases were searched, including PubMed, Embase, Cochrane Library, EBSCO, Medline Ovid, Airiti Library, China National Knowledge Infrastructure (CNKI), China Journal full-text database (CJFD), and Wanfang. Medical subject heading terms and text words were used to search for eligible randomized controlled trials published from database inception to May 2023. Results: A total of 33 studies involving 2,027 participants were included. Pairwise meta-analysis revealed that acupuncture-related interventions were superior to usual care, medication, or dietary supplements in improving CIPN symptoms, CIPN pain, and QoL. Furthermore, network meta-analysis indicated that acupuncture plus electrical stimulation (acupuncture-E) had the greatest overall effect among the various interventions. The surface under the cumulative ranking curve (SUCRA) revealed that acupuncture-E ranked the highest in improving CINP symptoms. Acupuncture alone was most effective in reducing CIPN pain, and acupuncture plus moxibustion (acupuncture-M) ranked highest in enhancing QoL. Conclusion: This finding suggests that acupuncture-related interventions can provide patients with benefits in improving CIPN symptoms, pain, and QoL. In particular, acupuncture-E could be the most effective approach in which the provided evidence offers diverse options for cancer patients and healthcare professionals. Implication for the profession and/or patient care: These findings provide valuable insights into the potential benefits of acupuncture-related interventions for managing symptoms, pain, and QoL associated with CIPN in patients undergoing chemotherapy. Among the various interventions studied, overall, acupuncture-E had the most significant impact and was effective for a minimum duration of 3 weeks. On the other hand, transcutaneous electrical acupoint/nerve stimulation (TEAS) was identified as a noninvasive and feasible alternative for patients who had concerns about needles or the risk of bleeding. It is recommended that TEAS interventions should be carried out for a longer period, preferably lasting 4 weeks, to achieve optimal outcomes. Trial registration: The study protocol was registered in the International Prospective Register of Systematic Reviews. Registration Number: CRD42022319871. [ABSTRACT FROM AUTHOR]

The southwestern region of China is a global biodiversity hotspot. Understanding the environmental mechanisms behind treeline formation in high-altitude areas is crucial for predicting ecosystem changes, such as the upward movement of the treeline due to climate warming and the disappearance of high-altitude rocky beach and shrub ecosystems. Globally, observations show that growing seasonal temperatures at treelines are typically 6–7 °C, but trees do not always reach the predicted elevations. Spatial heterogeneity exists in the deviation (Dtreeline) between actual treeline elevation and the thermal treeline; however, the main driving factors for Dtreeline in many areas remain unclear. This study uses Yulong Snow Mountain as an example, employing machine learning methods like Support Vector Machine (SVM) to precisely identify actual treeline elevation and Extreme Gradient Boosting Tree (XGBoost) to explore the main environmental factors driving the spatial heterogeneity of Dtreeline. Our research found that (1) more than half of the treelines deviated from the thermal treeline, with the average elevation of the thermal treeline (3924 ± 391 m) being about 56 m higher than the actual treeline (3863 ± 223 m); (2) Dtreeline has a complex relationship with environmental factors. In addition to being highly correlated with temperature, precipitation and wind speed also significantly influence the treeline in this region; and (3) the influence of individual variables such as precipitation and wind speed on the spatial variation of Dtreeline is limited, often nonlinear, and involves threshold effects. This knowledge is essential for developing comprehensive protection strategies for Yunnan's high-altitude ecological systems in response to climate warming. Furthermore, it plays a significant role in understanding the changes in biological communities and the response of high-altitude areas to climate change. [ABSTRACT FROM AUTHOR]

Deformation monitoring data provide a direct representation of the structural behavior of reservoir bank rock slopes, and accurate deformation prediction is pivotal for slope safety monitoring and disaster warning. Among various deformation prediction models, hybrid models that integrate field monitoring data and numerical simulations stand out due to their well-defined physical and mechanical concepts, and their ability to make effective predictions with limited monitoring data. The predictive accuracy of hybrid models is closely tied to the precise determination of rock mass mechanical parameters in structural numerical simulations. However, rock masses in rock slopes are characterized by intersecting geological structural planes, resulting in reduced strength and the creation of multiple fracture flow channels. These factors contribute to the heterogeneous, anisotropic, and size-dependent properties of the macroscopic deformation parameters of the rock mass, influenced by the coupling of seepage and stress. To improve the predictive accuracy of the hybrid model, this study introduces the theory of equivalent continuous media. It proposes a method for determining the equivalent deformation parameters of fractured rock mass considering the coupling of seepage and stress. This method, based on a discrete fracture network (DFN) model, is integrated into the hybrid prediction model for rock slope deformation. Engineering case studies demonstrate that this approach achieves a high level of prediction accuracy and holds significant practical value. [ABSTRACT FROM AUTHOR]

Urban land use provides essential information about how land is utilized within cities, which is critical for land planning, urban renewal, and early warnings for natural disasters. Although existing studies have utilized multi-source perception data to acquire land use information quickly and at low cost, and some have integrated urban morphological indicators to aid in land use identification, there is still a lack of systematic discussion in the literature regarding the potential of three-dimensional urban morphology to enhance identification effectiveness. Therefore, this paper aims to explore how urban three-dimensional morphology can be used to improve the identification of urban land use types. This study presents an innovative approach called the UMH–LUC model to enhance the accuracy of urban land use identification. The model first conducts a preliminary classification using points of interest (POI) data. It then improves the results with a dynamic reclassification based on floor area ratio (FAR) measurements and a variance reclassification using area and perimeter metrics. These methodologies leverage key urban morphological features to distinguish land use types more precisely. The model was validated in the Pearl River Delta urban agglomeration using random sampling, comparative analysis and case studies. Results demonstrate that the UMH–LUC model achieved an identification accuracy of 81.7% and a Kappa coefficient of 77.6%, representing an 11.9% improvement over a non-morphology-based approach. Moreover, the overall disagreement for UMH–LUC is 0.183, a reduction of 0.099 compared to LUC without urban morphology and 0.19 compared to EULUC-China. The model performed particularly well in identifying residential land, mixed-use areas and marginal lands. This confirms urban morphology's value in supporting low-cost, efficient land use mapping with applications for sustainable planning and management. [ABSTRACT FROM AUTHOR]

One new cyclonerane sesquiterpene, 9,11-cycloneren-3,7-diol (1), along with four known ones, 11- cycloneren-3,7,10-triol (2), 9-cycloneren-3,7,11-triol (3), 11-methoxy-9-cycloneren-3,7-diol (4), and cyclonerodiol (5), were obtained from the extract of marine-derived fungus Trichoderma harzianum WH-22. The structure of 9,11-cycloneren-3,7-diol (1) was elucidated by comprehensive spectroscopic analysis, including 1D/2D NMR and HRESIMS data. 9,11-Cycloneren-3,7-diol (1) exhibited weak cytotoxicity against HeLa and HepG2 cancer cell lines with IC50 values of 68.2 and 59.7 μM, respectively. [ABSTRACT FROM AUTHOR]

Background: Metabolic syndrome (MetS), a cluster of metabolic and cardiovascular risk factors is influenced by environmental, lifestyle, and genetic factors. We explored whether coffee consumption and the rs301 variant of the lipoprotein lipase (LPL) gene are related to MetS. Methods: We conducted multiple logistic regression analyses using data gathered from 9523 subjects in Taiwan Biobank (TWB). Results: Our findings indicated that individuals who consumed coffee had a reduced odds ratio (OR) for MetS (0.750 (95% confidence interval [CI] 0.653–0.861) compared to non-coffee drinkers. Additionally, the risk of MetS was lower for individuals with the 'TC' and 'CC' genotypes of rs301 compared to those with the 'TT' genotype. Specifically, the OR for MetS was 0.827 (95% CI 0.721–0.949) for the 'TC' genotype and 0.848 (95% CI 0.610–1.177) for the 'CC' genotype. We observed an interaction between coffee consumption and the rs301 variant, with a p-value for the interaction of 0.0437. Compared to the reference group ('no coffee drinking/TT'), the ORs for MetS were 0.836 (95% CI 0.706–0.992) for 'coffee drinking/TT', 0.557 (95% CI 0.438–0.707) for 'coffee drinking/TC', and 0.544 (95% CI 0.319–0.927) for 'coffee drinking/CC'. Notably, MetS was not observed in non-coffee drinkers regardless of their rs301 genotype. Conclusion: Our findings suggest that rs301 genotypes may protect against MetS in Taiwanese adults who consume coffee compared to non-coffee drinkers. [ABSTRACT FROM AUTHOR]

Compared to conventional actuators, the soft ionic polymer–metal composite (IPMC) actuator has significant advantages in specific applications, and the mathematical model of IPMC actuators is essential to comprehending and applying IPMCs. Due to the inherent characteristics of IPMCs and the impact of the manufacturing and measurement processes, it is challenging to developa reliable model. This article provides a comprehensive overview of the developments in IPMC actuator modeling. In particular, three types of models are examined and contrasted: the nonphysical identification model, the partial‐physical model, and the physical‐based model. In order to comprehend the current state of numerous IPMC actuator models, the characteristics, evolution, and functions of each type of model are discussed. Afterward, the evolution of the IPMC actuators' applications is discussed. Finally, promising research directions for IPMC actuator models are identified that can more effectively facilitate the development of IPMC‐based devices. [ABSTRACT FROM AUTHOR]

OBJECTIVE: To measure the association between ambient heat and hypoglycemia-related emergency department visit or hospitalization in insulin users. RESEARCH DESIGN AND METHODS: We identified cases of serious hypoglycemia among adults using insulin aged ≥65 in the U.S. (via Medicare Part A/B/D-eligible beneficiaries) and Taiwan (via National Health Insurance Database) from June to September, 2016–2019. We then estimated odds of hypoglycemia by heat index (HI) percentile categories using conditional logistic regression with a time-stratified case-crossover design. RESULTS: Among ∼2 million insulin users in the U.S. (32,461 hypoglycemia case subjects), odds ratios of hypoglycemia for HI >99th, 95–98th, 85–94th, and 75–84th percentiles compared with the 25–74th percentile were 1.38 (95% CI, 1.28–1.48), 1.14 (1.08–1.20), 1.12 (1.08–1.17), and 1.09 (1.04–1.13) respectively. Overall patterns of associations were similar for insulin users in the Taiwan sample (∼283,000 insulin users, 10,162 hypoglycemia case subjects). CONCLUSIONS: In two national samples of older insulin users, higher ambient temperature was associated with increased hypoglycemia risk. [ABSTRACT FROM AUTHOR]

This article discusses two methods for reconstituting injectable poly-D,L-lactic acid (PDLLA) using vacuum-assisted hydration (VAH). The first method, known as the "wet" VAH method, involves attaching a syringe containing sterile water for injection (SWFI) to a syringe housing PDLLA lumps and creating negative pressure to draw the SWFI into the lumps. The second method, known as the "dry" VAH method, involves creating a vacuum within both syringes before introducing the SWFI. Both methods are based on Boyle's law and can be completed in less than 1 minute. However, practitioners must follow aseptic procedures and have a thorough understanding of the process to avoid contamination or process failure. [Extracted from the article]