Search Results (24,029)

Multiple myeloma (MM) is an incurable malignancy characterized by the proliferation of abnormal plasma cells within the bone marrow, followed by potential dissemination to extramedullary sites. The bone marrow barrier (BMB) plays a pivotal role in plasma cell homing and disease progression. Bone marrow endothelial cells (BMECs) and bone marrow stromal cells (BMSCs), through their interactions with MM cells, secrete adhesion molecules, angiogenic cytokines, anti-apoptotic factors, and growth-promoting signals that support MM cell survival and proliferation. This review examines the components of the BMB and the major pathways involved in MM pathogenesis. Targeting the interactions between MM cells and the BMB may offer novel therapeutic opportunities. Full article

Drought is one of the most complex natural hazards, characterized by its slow onset, persistent nature, diverse sectoral impacts (e.g., agriculture, water resources, ecosystems), and dependence on meteorological, hydrological, and socioeconomic factors. Over the years, significant scientific effort has been devoted to developing methodologies that address its multifaceted nature, reflecting the interdisciplinary challenges of drought analysis. However, previous reviews have typically focused on individual methods, while this study presents a unified, multidisciplinary framework that integrates multiple drought analysis methods and links them to key factors guiding method selection. To address this gap, five widely used methods—index-based, remote sensing, threshold-level methods (TLM), impact-based methods, and the storyline approach—are critically evaluated from a multidisciplinary perspective. In addition, the study examines spatial and temporal trends in scientific publications, illustrating how the application of these methods has evolved over time and across regions. The primary objective of this review is twofold: (1) to provide a holistic, state-of-the-art synthesis of these methods, their applications, and their limitations; and (2) to evaluate and prioritize the critical decision-making factors, including drought type, data type/availability, study scale, and management objectives that influence method selection. By bridging this gap, the paper offers a conceptual decision-support framework for selecting context-appropriate drought analysis methods. However, challenges remain, including the vast diversity of methods beyond the scope of this review and the limited consideration of less influential factors such as user expertise, computational resources, and policy context. The paper concludes with insights and recommendations for optimizing method selection under varying circumstances, aiming to support both drought research and effective policy implementation. Full article

Background: This study aimed to assess the prevalence of SARS-CoV-2 infection, vaccination, and immune status among a population, both Dentists and University Professors, within a clinical setting at one and at 12 months after COVID-19 vaccination. Methods: A cross-sectional study involving 47 professionals (aged 27–52) was conducted in the University Fernando Pessoa. Participants completed an online survey on SARS-CoV-2 infection status and vaccination, received and provided plasma samples for serological analysis. The protocol was approved by the UFP-Ethics Committee. Anti-S1-RBD SARS-CoV-2 IgM and IgG antibody titration values (AU/mL) were measured, by enzyme-linked-immunosorbent assay (ELISA), with reactive immunoglobulins (Ig) seropositivity for values ≥1 AU/mL. Results: SARS-CoV-2 infection rate increased from 8.5% in July 2021 to 48.9% in June 2022, with 8.5% experiencing reinfection. Vaccination rate was 91.5% by July 2021 and increased slightly to 93.6% by June 2022; 72.3% of the sample received a third dose. IgG seropositivity increased from 91.5% to 95.7% in June 2022. After one-year, significant associations were found between IgG seropositivity and both participant’s age (p = 0.009; <50 years) and vaccine doses (p = 0.003; 1–3 doses) received. Conclusions: SARS-CoV-2 infection rate, vaccination, and IgG seropositivity rates were high and increased over one year. The age and vaccination status were associated with the immunity status at 12th month follow-up. Findings highlight variability in IgG seroprevalence due to multiple influencing factors, which justifies future studies. Full article

The global atmospheric concentration of carbon dioxide (CO₂) has exceeded 420 ppm. Adsorption-based carbon capture technologies, offer energy-efficient, sustainable solutions. Relying on classical adsorption models like Langmuir to predict CO₂ uptake presents limitations due to the need for case-specific parameter fitting. To address this, the present study introduces a universal machine learning (ML) framework using multiple algorithms—Generalized Linear Model (GLM), Feed-forward Multilayer Perceptron (DL), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Gradient Boosted Trees (GBT)—to reliably predict CO₂ adsorption capacities across diverse zeolite structures and conditions. By compiling over 5700 experimentally measured adsorption data points from 71 independent studies, this approach systematically incorporates critical factors including pore size, Si/Al ratio, cation type, temperature, and pressure. Rigorous Cross-Validation confirmed superior performance of the GBT model (R² = 0.936, RMSE = 0.806 mmol/g), outperforming other ML models and providing comparable performance with classical Langmuir model predictions without separate parameter calibration. Feature importance analysis identified pressure, Si/Al ratio, and cation type as dominant influences on adsorption performance. Overall, this ML-driven methodology demonstrates substantial promise for accelerating material discovery, optimization, and practical deployment of zeolite-based CO₂ capture technologies. Full article

Rapid urbanization and escalating disaster risks necessitate resilient urban governance systems. Smart city initiatives that leverage digital technologies—such as the internet of things (IoT), big data analytics, and artificial intelligence (AI)—demonstrate transformative potential in enhancing emergency management capabilities. However, empirical evidence regarding their causal impact and underlying mechanisms remains limited, particularly in developing economies. Drawing on panel data from 275 Chinese prefecture-level cities over the period 2006–2021 and using China’s smart city pilot policy as a quasi-natural experiment, this study applies a multi-period difference-in-differences (DID) approach to rigorously assess the effects of smart city construction on emergency management capabilities. Results reveal that smart city construction produced a statistically significant improvement in emergency management capabilities, which remained robust after conducting multiple sensitivity checks and controlling for potential confounding policies. The benefits exhibit notable heterogeneity: emergency management capability improvements are most pronounced in central China and in cities at the extremes of population size—megacities (>10 million residents) and small cities (<1 million residents)—while effects remain marginal in medium-sized and eastern cities. Crucially, mechanism analysis reveals that digital technology application fully mediates 86.7% of the total effect, whereas factor allocation efficiency exerts only a direct, non-mediating influence. These findings suggest that smart cities primarily enhance emergency management capabilities through digital enablers, with effectiveness contingent upon regional infrastructure development and urban scale. Policy priorities should therefore emphasize investments in digital infrastructure, interagency data integration, and targeted capacity-building strategies tailored to central and western regions as well as smaller cities. Full article

Individual willingness to pay (WTP) for green energy plays a vital role in mitigating climate change. Based on social cognitive theory (SCT), which emphasizes the dynamic interaction among individual cognition, behavior and the environment, this study develops a theoretical model to identify factors influencing green energy WTP. The study is based on 585 valid questionnaire responses from urban areas in China and uses Structural Equation Modeling (SEM) to reveal the linear causal path. Meanwhile, fuzzy-set Qualitative Comparative Analysis (fsQCA) is utilized to identify the combined paths of multiple conditions leading to a high WTP, making up for the limitations of SEM in explaining complex mechanisms. The SEM analysis shows that social trust, social networks, and social norms have a significant positive impact on individual green energy WTP. And this influence is further transmitted through the mediating role of environmental self-efficacy and expectations of environmental outcomes. The FsQCA results identified three combined paths of social capital and environmental cognitive conditions, including the Netong–Norm path, the Netong–efficacy path and the Netong–Outcome path, all of which can achieve a high level of green energy WTP. Among them, the social networks are a core condition in every path and a key element for enhancing the high green energy WTP. This study promotes the expansion of SCT, from emphasizing the linear role of individual cognition to focusing on the configuration interaction between social structure and psychological cognition, provides empirical evidence for formulating differentiated social intervention strategies and environmental education policies, and contributes to sustainable development and the green energy transition. Full article

Background: Vascular risk factors (VRFs) are known to influence cerebral small vessel disease (cSVD) burden and progression. However, their specific impact on the presence and distribution of each cSVD imaging marker (white matter hyperintensity [WMH], perivascular spaces [PVSs], lacunes, and cerebral microbleeds [CMBs]) and their spatial distribution remains unclear. Methods: We conducted a retrospective analysis of 93 patients with lacunar stroke with a standardized investigational magnetic resonance imaging protocol using a 3T scanner. WMH and PVSs were segmented semi-automatically, and lacunes and CMBs were manually segmented. We assessed the univariable associations of four common VRFs (hypertension, hyperlipidemia, diabetes, and smoking) with the load of each cSVD marker. Then, we assessed the independent associations of these VRFs in multivariable regression models adjusted for age and sex. Spatial lesion patterns were explored with regional volumetric comparisons using Pearson’s coefficient analysis, which was adjusted for multiple comparisons, and by visually examining heatmap lesion distributions. Results: Hypertension was the VRF that exhibited stronger associations with the cSVD markers in the univariable analysis. In the multivariable analysis, only lacunes (p = 0.009) and PVSs in the basal ganglia (p = 0.014) and white matter (p = 0.016) were still associated with hypertension. In the regional analysis, hypertension showed a higher WMH load in deep structures and white matter, particularly in the posterior periventricular regions. In patients with hyperlipidemia, WMH was preferentially found in hippocampal regions. Conclusions: Hypertension was confirmed to be the VRF with the most impact on cSVD load, especially for lacunes and PVSs, while the lesion topography was variable for each VRF. These findings shed light on the complexity of cSVD expression in relation to factors detrimental to vascular health. Full article

Background: Digital platforms and inclusive leadership are pivotal in modern healthcare, influencing organizational performance and patient outcomes. Despite the growing adoption of these factors, their combined impact on leadership effectiveness and patient care remains insufficiently understood. Prior research has primarily examined digital technology or leadership inclusivity separately, lacking integrative studies that address their joint effect on healthcare outcomes. There is a need to explore how these variables interact to improve leadership and patient-related metrics. Methods: This cross-sectional study surveyed 250 participants, including healthcare leaders, professionals, and patients, using structured questionnaires. The data analysis involved multiple regression, structural equation modeling (SEM), and hierarchical linear modeling (HLM) to examine the direct and hierarchical relationships among digital platform usage, leadership inclusivity, leadership effectiveness, and patient outcomes. Results: Leadership inclusivity showed a significant positive effect on leadership effectiveness (β = 0.16, p < 0.01) and patient satisfaction (β = 0.09, p < 0.05). Digital platform usage demonstrated a smaller but positive association with leadership effectiveness (β = 0.04) and patient satisfaction (β = 0.03). Leadership effectiveness was found to correlate moderately with patient safety (β = 0.23) and treatment efficacy (β = 0.25), with minimal organizational-level effects. Conclusions: This study uniquely integrates the adoption of digital technology with inclusive leadership, highlighting their synergistic influence on healthcare delivery. It advances the existing literature by providing quantitative evidence on how these elements interact to shape leadership and patient care outcomes. Full article

Investigating land cover patterns, changes in carbon stocks, and forecasting future conditions are essential for formulating regional sustainable development strategies and enhancing ecological and environmental quality. This study centers on Guiyang, a mountainous urban area in southwestern China, to analyze the dynamic changes in land cover and their effects on carbon stocks from 2000 to 2035. A carbon stocks assessment framework was developed using a cellular automaton-based artificial neural network model (CA-ANN), the InVEST model, and the geographical detector model to predict future land cover changes and identify the primary drivers of variations in carbon stocks. The results indicate that (1) from 2000 to 2020, impervious surfaces expanded significantly, increasing by 199.73 km². Compared to 2020, impervious surfaces are projected to increase by 1.06 km², 13.54 km², and 34.97 km² in 2025, 2030, and 2035, respectively, leading to further reductions in grassland and forest areas. (2) Over time, carbon stocks in Guiyang exhibited a general decreasing trend; spatially, carbon stocks were higher in the western and northern regions and lower in the central and southern regions. (3) The level of greenness, measured by the normalized vegetation index (NDVI), significantly influenced the spatial variation of carbon stocks in Guiyang. Changes in carbon stocks resulted from the combined effects of multiple factors, with the annual average temperature and NDVI being the most influential. These findings provide a scientific basis for advancing low-carbon development and constructing an ecological civilization in Guiyang. Full article

As an energy-intensive industry, the aviation sector’s carbon emissions have drawn significant attention. Against the backdrop of the “dual carbon” goals, how to enhance the carbon emission efficiency of airlines has become an urgent issue to be addressed for both industry development and low-carbon targets. This paper constructs an evaluation system for the carbon emission efficiency of airlines and uses the SBM-DDF model under the global production possibility set, combined with the bootstrap-DEA method, to calculate the efficiency values. On this basis, the fuzzy-set qualitative comparative analysis method is employed to analyze the synergistic effects of multiple influencing factors in three dimensions: economic benefits, transportation benefits, and energy consumption on improving carbon emission efficiency. The research findings reveal that, first, a single influencing factor does not constitute a necessary condition for achieving high carbon emission efficiency; second, there are four combinations that enhance carbon emission efficiency: “load volume-driven type”, “scale revenue-driven type”, “high ticket price + technology-driven type”, and “passenger and cargo synergy mixed type”. These discoveries are of great significance for promoting the construction of a carbon emission efficiency system by Chinese airlines and achieving high-quality development in the aviation industry. Full article

Photovoltaic panels have become a promising solution for generating renewable energy and reducing our reliance on fossil fuels by capturing solar energy and converting it into electricity. The effectiveness of this conversion depends on several factors, such as the quality of the solar panels and the amount of solar radiation received in a specific region. This makes accurate solar irradiance forecasting essential for planning and managing efficient solar power systems. This study examines the application of machine learning (ML) models for accurately predicting global horizontal irradiance (GHI) using a three-year dataset from six distinct photovoltaic stations: NELHA, ULL, HSU, RaZON+, UNLV, and NWTC. The primary aim is to identify optimal shared features for GHI prediction across multiple sites using a 30 min time shift based on autocorrelation analysis. Key features identified for accurate GHI prediction include direct normal irradiance (DNI), diffuse horizontal irradiance (DHI), and solar panel temperatures. The predictions were performed using tree-based algorithms and ensemble learners, achieving $R^2$ values exceeding 95% at most stations, with NWTC reaching 99%. Gradient Boosting Regression (GBR) performed best at NELHA, NWTC, and RaZON, while Multi-Layer Perceptron (MLP) excelled at ULL and UNLV. CatBoost was optimal for HSU. The impact of time-shifting values on performance was also examined, revealing that larger shifts led to performance deterioration, though MLP performed well under these conditions. The study further proposes a stacking ensemble approach to enhance model generalizability, integrating the strengths of various models for more robust GHI prediction. Full article

GOLDEN2-LIKEs (GLKs) are important transcription factors for the chloroplast development influencing photosynthesis, nutrition, senescence, and stress response in plants. Sunflower (Helianthus annuus) is a highly photosynthetic plant; here, a GLK-homologues gene HaGLK was identified from the sunflower genome by bioinformatics. To analyze the bio-function of HaGLK, transgenic rice plants overexpressing HaGLK (HaGLK-OE) were constructed and characterized via phenotype. Compared to the wild-type control rice variety Zhonghua 11 (ZH11), the HaGLK-OE lines exhibited increased photosynthetic pigment contents, higher net photosynthetic rates, and enlarged chloroplast area; meanwhile, genes involved in both photosynthesis and chlorophyll biosynthesis were also significantly up-regulated. Significantly, the HaGLK-OE plants showed a 12–13% increase in yield per plant. Additionally, the HaGLK-OE plants were demonstrated to have improved salt and drought tolerance compared to the control ZH11. Our results indicated that the HaGLK gene could play multiple roles in photosynthesis and stress response in rice, underscoring its potential value for improving crop productivity and environmental adaptability in breeding. Full article

As an industrial waste, slag powder can be processed and incorporated into cement-based materials as an additive, significantly improving the engineering properties of cement–soil. The strength of slag–cement-stabilized soil is subject to nonlinear interactions among multiple factors, including cement content, slag powder dosage, curing age, and moisture content, forming a complex influence mechanism. To achieve accurate strength prediction and mix proportion optimization for slag–cement-stabilized soil, this study prepared cement-stabilized soil specimens with different slag powder contents using typical sandy soil and clay from the Nantong region, and obtained sample data through unconfined compressive strength tests. A Back Propagation (BP) neural network prediction model was also established. Addressing the limitations of traditional BP neural networks in prediction accuracy caused by random initial weight thresholds and susceptibility to local optima, the sparrow search algorithm (SSA) was introduced to optimize initial network parameters, constructing an SSA-BP model that effectively enhances convergence speed and generalization capability. Research results demonstrated that the SSA-BP model reduced prediction error by 53.4% compared with the traditional BP model, showing superior prediction accuracy and effective characterization of multifactor nonlinear relationships. This study provides theoretical support and an efficient prediction tool for industrial waste recycling and environmentally friendly solidified soil engineering design. Full article

Background: Temporomandibular disorders (TMD) are common musculoskeletal conditions affecting the temporomandibular joints, masticatory muscles, and associated structures. Their etiology is complex and multifactorial, involving anatomical, behavioral, and psychosocial contributors. Parafunctional habits such as clenching, grinding, and abnormal jaw positioning have been proposed as contributing factors, yet their individual and cumulative contributions remain unclear. This exploratory cross-sectional study aimed to evaluate the prevalence and severity of parafunctional habits and their association with TMD in medical students—a group exposed to elevated stress levels. Subjects were examined in Krakow, Poland, using the Diagnostic Criteria for Temporomandibular Disorders (DC/TMD) protocol. Methods: Participants completed a 21-item Oral Behavior Checklist (OBC) assessing the frequency of oral behaviors on a 0–4 scale. A self-reported total parafunction load was calculated by summing individual item scores (range: 0–84). Logistic regression was used to evaluate associations between individual and total parafunction severity scores and TMD presence. Results: The study included 66 individuals aged 19–30. TMD was diagnosed in 55 participants (83.3%). The most commonly reported habits were resting the chin on the hand (90.9%) and sleeping in a jaw-compressing position (86.4%). Notably, jaw tension (OR = 14.5; p = 0.002) and daytime clenching (OR = 4.7; p = 0.027) showed significant associations with TMD in the tested population. Each additional point in the total parafunction score increased TMD odds by 13.6% (p = 0.004). Conclusions: These findings suggest that parafunctional behaviors—especially those involving chronic muscle tension or abnormal mandibular positioning—may meaningfully contribute to the risk of TMD in high-stress student populations. Moreover, the cumulative burden of multiple low-intensity habits was also significantly associated with increased TMD risk. Early screening for these behaviors may support prevention strategies, particularly among young adults exposed to elevated levels of stress. Full article

Forest mortality (FM) is influenced by several independent factors, including forest fragmentation (FF) at different spatial scales and multi-scales, site conditions, and stand characteristics. The aim of this study was to investigate the relationship and effect of FF at various spatial scales on the probability of Scots pine FM. The presented study also analyzed the relationship of the multi-scale fragmentation index effect on forest dieback. The relationship between multiple stressors emphasizes the distinct role of FF in influencing pine FM probability. Data on forest cover, deadwood volume of Scots pine forest, and environmental variables were obtained from the Forest Information System for Europe, the Polish National Forest Inventory, and existing databases, respectively. A generalized additive model approach was used to develop models. The results showed that, at small (50–600 m), large (800–3000 m), and multi spatial scales, the FF effect on Scots pine FM probabilities was statistically significant. There is a partial effect of multi-scale fragmentation on the probability of Scots pine FM, given a holistic view of the fragmentation effect that captures both small and large-scale effects. The study concludes that to calculate FF for a particular area, analyzing different scales and capturing multi-scale level fragmentation indices is crucial to studying the cumulative effect of fragmentation on the probability of Scots pine FM. Full article