Search Results (6,025)

Background: Varicella zoster virus (VZV) remains a significant cause of pediatric morbidity in populations in Lebanon, yet comprehensive data on population immunity and vaccination uptake are limited. This study aimed to estimate VZV seroprevalence and identify factors associated with immunity and vaccine uptake among children and adolescents in Northern Lebanon. Methods: A cross-sectional study was conducted among 180 participants aged 1–18 years recruited from urban and rural settings in North Lebanon. After receiving informed parental consent, sociodemographic and clinical information were collected via structured questionnaires. Anti-VZV IgG and IgM antibodies were measured using validated Enzyme-Linked Immunosorbent Assays (ELISA). Associations with seropositivity and vaccination uptake were analyzed using multivariable logistic regression. Results: IgG seroprevalence was 79.4% (95% CI: 72.7–85.1), indicating prior exposure or immunization, while IgM antibodies, reflecting recent infection, were detected in 5.0% (95% CI: 2.3–9.4) of participants. Among vaccinated participants, IgG seropositivity was 63.6% (95% CI: 43.5–83.7) in the one-dose group and 89.5% (95% CI: 83.0–96.0) in the two-dose group. Completing the two-dose regimen was significantly associated with a higher IgG seropositivity (OR = 0.110, 95% CI: 3.2–52.4, p = 0.002). Parental reporting of history of varicella showed high sensitivity (99.0%) and overall accuracy (90.8%) in predicting seropositivity. Primary vaccination barriers included preference for natural infection (67%), perceived non-necessity (19%), and cost (10%). Regular pediatric follow-up strongly predicted vaccination (OR = 15.239, p < 0.001), whereas low parental awareness was associated with decreased vaccine uptake (OR = 0.027, p = 0.015). Conclusions: Suboptimal VZV vaccination coverage and persistent susceptibility underscore the need to integrate varicella vaccination into Lebanon’s national immunization schedule. Targeted educational efforts and enhanced pediatric healthcare engagement are critical to increasing vaccine uptake and reducing disease burden. Full article

The gut microbiota plays a crucial role in the health and well-being of primates. This study applied 16S rRNA high-throughput sequencing technology, aiming to investigate the differences in gut microbiota composition and function between captive ring-tailed lemurs (Lemur catta) in different zoos across the north–south demarcation line in China. Results revealed significant differences in gut microbiota composition between northern and southern groups. Alpha diversity indices were higher in the southern group (p < 0.05), while beta diversity analysis showed distinct clustering based on geographic location (p < 0.001). Bacteroidetes were more abundant in the northern group (49.22% vs. 28.44%), while Firmicutes predominated in the southern group (59.10% vs. 32.78%). Functional prediction analysis indicated higher levels of membrane transport and lipid metabolism pathways in the southern group, suggesting differences in nutrient absorption and energy metabolism. These findings suggest that geographic location and associated environmental factors significantly influence the gut microbiota of captive ring-tailed lemurs, even under similar dietary and husbandry conditions. Our study provides insights into the impact of geographic location on gut microbiota in captive primates, highlighting the importance of considering regional factors in zoo animal management and informing future strategies for optimizing the care and conservation of captive primates across different geographic regions. Full article

Following the Mw 5.0 earthquake of 1 August 2024, which struck the Ionian sector of north-central Calabria (southern Italy), the SISMIKO emergency group of INGV deployed a temporary seismic network to enhance coverage of the National Seismic Network. This improved configuration enabled the relocation of over 1300 aftershocks and the identification of a second swarm near Cirò, active since May and reactivated after mid-August. A machine learning workflow was applied for automatic phase picking, event association, and relocation, producing a high-resolution catalogue. The seismicity of both sequences aligns with the NW-trending Rossano–San Nicola shear zone but reveals distinct rupture patterns: the Pietrapaola sequence, targeted by the SISMIKO deployment, shows compact hypocentral clustering, while the Cirò swarm displays more scattered seismicity. The analysis of the catalogue reveals a two-slope temporal decay of aftershocks and relatively low completeness magnitudes. Source geometry inferred from hypocentre distributions and INGV focal mechanisms supports the structural interpretation. Overall, the machine learning-based catalogue proved effective for near-real-time analysis and offers new insights into the active tectonic framework of the Calabrian Arc. Full article

This paper examines the impact of country risk on banking sector stability, employing the CAMELS framework, within 13 Middle Eastern and North African (MENA) countries for 1984–2024. The analysis exploits the impact of political, economic, and financial risk dimensions on 102 publicly listed banks using two-way random effects models and one-step dynamic panel data estimations. The findings reflected a significant inverted U-shaped nexus between country risk and the stability of the banking sector, addressing how high-country risk deteriorates banking resilience, whereas low country risk improves it. Political risk has the strongest impact with a similar nonlinear relationship. Conversely, economic and financial risks consistently have reverse linear effects. These findings signify the structural vulnerability of MENA banks to political, economic, and financial turmoil and address the urgent need for robust frames of risk management and fiscal discipline. This investigation extends sovereign risk theory, which explains the ability to maintain financial stability by integrating three core dimensions—political, economic, and financial risk—into a comprehensive empirical model that directly relates them to MENA banking stability and provides crucial insights for banking institutions, policymakers, and regulators in a highly volatile atmosphere. Full article

Ambiguity Resolution (AR) is regarded as an effective technique for enhancing positioning accuracy and reducing convergence time in Precise Point Positioning (PPP). However, the Wide-Lane Fractional Cycle Bias (WL FCB) and Narrow-Lane Fractional Cycle Bias (NL FCB) needed for AR are generated from network solutions based on numerous globally distributed stations, leading to considerable computational load and processing time. A prediction model for FCB is proposed using the Genetic Algorithm Optimized Backpropagation Neural Network (GA-BPNN), and high-precision predictions of WL and NL FCB for Day of Year (DOY) 321 in 2023 are successfully achieved. Comparisons with iGMAS products show that predicted WL FCB deviations are within 0.01 cycles, and predicted NL FCB over 12 h deviates within 0.1 cycles (excluding satellite C20). The performance of three PPP schemes, Float, Fixed (based on FCB from iGMAS), and BP-Fixed (based on FCB predicted by GA-BPNN), is compared through experiments. For GPS + BDS-3, the accuracies of the BP-Fixed scheme are 0.0034 m, 0.0039 m, and 0.0100 m in the east, north, and up directions, respectively. The ambiguity fixed rates reach 98.62% for BP-Fixed. These outcomes confirm that the positioning performance using the predicted FCB of GA-BPNN is highly consistent with that using FCB products. Full article

Mesozooplankton are pivotal for Black Sea food webs, yet they are highly vulnerable to hydrographic variability, eutrophication, and human pressures. This study analysed mesozooplankton dynamics along the Romanian coast (2013–2020) across three sectors (north, central, and south) and two distinct periods (cold and warm seasons), integrating Abundance–Biomass Comparison (ABC) curves with Fuzzy Cognitive Mapping (FCM). Results revealed a clear disturbance gradient: the Danube-influenced north supported high abundances of small-bodied taxa; the central sector maintained the most resilient and functionally diverse assemblages; and the southern sector showed chronic degradation with Noctiluca scintillans dominance. ABC curves quantified disturbance, with curve convergence in the north and near overlap in the south during summer, while FCM highlighted network simplification and reduced functional redundancy. Climate scenario simulations projected further declines in cladocerans and meroplankton under warming and freshening, whereas copepods showed relative resilience. Collectively, the findings demonstrate progressive simplification of mesozooplankton and declining energy transfer efficiency, underscoring the need to integrate zooplankton-based indicators into Black Sea monitoring and management frameworks. Full article

Indoor Air Quality (IAQ) in educational environments is a critical determinant of students’ health, well-being, and learning performance, with inadequate ventilation and pollutant accumulation consistently associated with respiratory symptoms, fatigue, and impaired cognitive outcomes. Conventional monitoring approaches—based on periodic inspections or subjective perception—provide only fragmented insights and often underestimate exposure risks. Artificial intelligence (AI) offers a transformative framework to overcome these limitations through sensor calibration, anomaly detection, pollutant forecasting, and the adaptive control of ventilation systems. This review critically synthesizes the state of AI applications for IAQ management in educational environments, drawing on twenty real-world case studies from North America, Europe, Asia, and Oceania. The evidence highlights methodological innovations ranging from decision tree models integrated into large-scale sensor networks in Boston to hybrid deep learning architectures in New Zealand, and regression-based calibration techniques applied in Greece. Collectively, these studies demonstrate that AI can substantially improve predictive accuracy, reduce pollutant exposure, and enable proactive, data-driven ventilation management. At the same time, cross-case comparisons reveal systemic challenges—including sensor reliability and calibration drift, high installation and maintenance costs, limited interoperability with legacy building management systems, and enduring concerns over privacy and trust. Addressing these barriers will be essential for moving beyond localized pilots. The review concludes that AI holds transformative potential to shift school IAQ management from reactive practices toward continuous, adaptive, and health-oriented strategies. Realizing this potential will require transparent, equitable, and cost-effective deployment, positioning AI not only as a technological solution but also as a public health and educational priority. Full article

The Tarim River Basin, a core region for economic development and ecological security in China’s inland arid areas, faces the pressing challenge of synergistically improving land use efficiency to resolve human-land conflicts under water resource constraints and achieve sustainable development. Based on the “economic-social-ecological” benefit coordination theory, this study constructs a land use efficiency evaluation system with 16 indicators and integrates the coupling coordination degree model and gravity model to quantitatively analyze the spatiotemporal differentiation patterns and coupling mechanisms of land use efficiency in the basin from 1990 to 2020. Results show that economic and social benefits of land use increased during this period, exhibiting a “high-north, low-south” spatial pattern, while ecological benefits remained relatively high but declined gradually. The coupling coordination degree of subsystem benefits displayed significant spatial heterogeneity, with an overall upward trend, where composite factors emerged as the primary constraint. Spatially, land use efficiency coupling coordination evolved from “core polarization” to “axial expansion” and finally “networked synergy,” with stronger linkages concentrated in oasis irrigation districts. These findings provide theoretical support for ecological conservation, water management, and policy-making in southern Xinjiang, offering pathways to synergize the “economic-social-ecological” system and promote sustainable development in arid regions. Full article

Background/Objectives: Lung cancer is the leading cause of cancer death in the United States (U.S.). Virginia, South Carolina, and North Carolina are among the U.S. states with extraordinarily high rates of lung cancer mortality, particularly among Black residents. The current lung cancer screening guidelines, revised in 2021, support screening for younger, non-Medicare age-eligible individuals who smoke. However, their health insurance, if any, may not cover their screening. This lack of access could create more disparities in lung cancer mortality rates. Methods: To address this concern, the Virginia Commonwealth University Massey Comprehensive Cancer Center, the Medical University of South Carolina Hollings Cancer Center, and the University of North Carolina Chapel Hill Lineberger Comprehensive Cancer Center secured a four-year Stand Up To Cancer^® (SU2C) grant titled “Southeastern Consortium for Lung Cancer Screening (SC3) Study” with a novel aim to test the effectiveness of a multimodal, multilevel, barrier-focused patient navigation intervention to promote lung cancer screening among Black patients from federally qualified health centers. Results: A total of 170/675 Black participants have been recruited to date. The majority of participants (n = 134; 78.82%) were aged 55–74 years. Most participants were unmarried (n = 100; 58.82%), more than half had a high school education/GED or less (n = 111; 65.29%), most currently smoked (n = 142; 83.53%), and more males than females participated (n = 107; 62.94% male). Their reported lung cancer screening barriers, addressed by the patient navigators, were cost concerns, insurance coverage issues, and recent medical history precluding screening. Conclusions with Relevance to Cancer Health Equity: This SC3 study includes a unique lung cancer screening cohort that is in direct contrast to the predominantly White cohort in the National Lung Screening Trial. The SU2C study has created a novel, community-engaged approach to lung cancer screening navigation that could become the gold standard in high-risk medically underserved populations. Full article

The Xishan–Yongding River cultural belt is a key component of the three major cultural belts of Beijing and its water heritage; as a representative of the intensive distribution of semi-arid climate, analyzing its spatial and temporal distribution characteristics is crucial for the development of systematic conservation strategies. This study is based on a dataset developed from field surveys and historical documentation and has been spatially analyzed using visual analytical methods and using a Geographic Information System (GIS). In this study, kernel density estimation was used to identify areas of high density, standard elliptic deviation was used to assess the distribution of water heritage sites over time, and the mean nearest neighbor index was used to determine the spatial clustering pattern of these sites. Regarding type and quantity, water heritage in the cultural belt is diverse, with non-water heritage sites, such as temples and inscriptions, being the most prevalent. In terms of temporal distribution, water heritage spans a long period, with the largest number dating to the Qing Dynasty. The centers of distribution across different periods exhibit a trend from south to north and from mountainous regions to plains, exhibiting a gradual concentration. Spatially, water heritage within the cultural belt follows a “multi-core, contiguous distribution” pattern, with three high-density zones, two medium-density zones, and six low-density zones. The distribution of water heritage is influenced by a combination of natural factors, such as river systems, settlements, elevation, and slope, alongside human factors, including historical culture and the political environment. The findings of this research offer a detailed analysis of the regional characteristics and underlying mechanisms of the temporal and spatial distribution of water heritage within the Xishan–Yongding River cultural belt. Full article

Autonomous vehicles (AVs), including privately owned self-driving cars and shared autonomous vehicles (SAVs), hold great potential to transform urban mobility by enhancing safety, accessibility, efficiency, and sustainability. However, their widespread deployment also carries the risk of significantly increasing vehicle miles traveled (VMT), a phenomenon known as the rebound effect. This paper examines the VMT rebound effects resulting from AV and SAV deployment, drawing on recent studies and global case insights. We conducted a systematic narrative review of 48 studies published between 2019 and 2025, drawing on academic sources and credible agency reports. We do not conduct a meta analysis. We quantify how different automation levels (SAE Levels 3, 4, 5) impact VMT and identify the primary factors driving VMT growth, namely: reduced perceived travel time cost, induced demand from new user groups, modal shifts away from transit, and empty VMT. Global case studies from North America, Europe, Asia, and the Middle East are reviewed alongside regional policy responses. Quantitative analyses indicate moderate to significant VMT increases under most scenarios—for example, approximately 10 to 20% increases with conditional automation and potentially over 50% with high/full automation, under the circumstances of no effective policy interventions. Meanwhile, aggressive ride-sharing and policy interventions, including road pricing and transit integration, can mitigate or even reverse these increases. The discussion provides a critical assessment of policy strategies such as mileage pricing, SAV incentives, and integrated land-use/transport planning to manage VMT growth. We conclude that without proactive policies, widespread AV adoption is likely to induce a rise in VMT, but that a suite of well-designed measures can steer automated mobility towards sustainable outcomes. These findings help policymakers and planners balance AV benefits with congestion, energy use, and climate goals. Full article

Global coastal reclamation areas face significant land subsidence, threatening infrastructure and sustainable development. China’s large-scale projects show particularly severe subsidence. For example, Tianjin’s Binhai New Area contains 413.6 km² of reclaimed land, and subsidence is driven by soft soil consolidation, industrial loads, and dynamic land use changes. This study addresses the unique geology of coastal reclamation zones: thick, soft clay layers; high porosity; and low soil strength. We employed optimized Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technology using 48 Sentinel-1A radar images (2019–2022), which generated high-resolution annual deformation rate maps revealing a north-high, south-low subsidence gradient. Crucially, validation against leveling data confirmed reliability. The systematically quantified results demonstrate built areas and the bare ground intensifies subsidence through structural loads and soil compression. Land use transitions also exacerbate differential settlement. For coastal cities and reclamation zones, key strategies emerge, including regulating structural loads in high-subsidence areas, managing soft soil consolidation, and implementing dynamic monitoring. Aligning development intensity with geological capacity is essential, and adopting adaptive spatial planning can mitigate subsidence hazards. This approach offers a scientific framework for enhancing global coastal resilience. Full article

Avian influenza (AI) is a highly contagious viral disease affecting both domestic and wild birds, posing a significant threat to poultry farming worldwide. This study aims to analyze the key landscape and population factors associated with H7 avian influenza outbreaks across the Euro-Asian continent and to identify high-risk areas in Russia for the virus’s introduction and subsequent spread. Two models were developed using the Maximum Entropy algorithm (MaxEnt): An introduction model predicting the likelihood of avian influenza presence based on climatic, landscape, wild waterfowl and semiaquatic bird population density data; and a spread model estimating outbreak risk in poultry farms using data on synanthropic birds, poultry flock density, and proximity to wild bird habitats. The first model was trained via maximum likelihood using data from H7 avian influenza outbreaks in Europe (Italy, Germany, France, Denmark, Lithuania, the Netherlands) and Southeast Asia (China, Hong Kong, Taiwan, Japan, Cambodia, North Korea, South Korea, Vietnam). The second model was trained using output from the first model. Specifically, areas with a predicted probability of H7 outbreak between 0.9 and 1.0 were used as occurrence points for the model in Russia. The results demonstrated that both models achieved high predictive reliability for avian influenza outbreaks in the Russian Federation: the introduction model (AUC = 0.855) and the spread model (AUC = 0.993). Areas with a high probability of disease occurrence were identified in the Central, Southern, North Caucasian, and Volga Federal Districts. These findings underscore the necessity of enhanced disease surveillance in these regions, as well as in the border areas of the Ural, Siberian, and Far Eastern Federal Districts. The authors recommend strengthening biosecurity measures, enhancing wild bird monitoring in high-risk areas, and maintaining stocks of relevant vaccines to timely contain the outbreaks. Full article

Human immunodeficiency virus (HIV), comprising two distinct types, HIV-1 and HIV-2, remains one of the most significant global health challenges, originating from multiple cross-species transmissions of simian immunodeficiency viruses (SIVs) in the early 20th century. This review traces the evolutionary trajectory of HIV from zoonotic spillover to its establishment as a global pandemic. HIV-1, the principal strain responsible for AIDS, emerged from SIVcpz in Central African chimpanzees, with phylogenetic evidence indicating initial human transmission between the 1920s and 1940s in present day Democratic Republic of Congo. The virus disseminated through colonial trade networks, reaching the Caribbean by the 1960s before establishing endemic transmission in North America and Europe. HIV’s extraordinary genetic diversity—driven by high mutation rates (~10⁻⁵ mutations per base per replication cycle) and frequent recombination events—has generated multiple groups, subtypes, and circulating recombinant forms (CRFs) with distinct epidemiological patterns. HIV-1 Group M, comprising subtypes A through L, accounts for over 95% of global infections, with subtype C predominating in sub-Saharan Africa and Asia, while subtype B dominates in Western Europe and North America. The extensive genetic heterogeneity of HIV significantly impacts diagnostic accuracy, antiretroviral therapy efficacy, and vaccine development, as subtypes exhibit differential biological properties, transmission efficiencies, and drug resistance profiles. Contemporary advances, including next-generation sequencing (NGS) for surveillance, broadly neutralizing antibodies for cross-subtype prevention and therapy, and long-acting antiretroviral formulations to improve adherence, have transformed HIV management and prevention strategies. NGS enables near real-time surveillance of drug resistance mutations and inference of transmission networks where it is available, although access and routine application remain uneven across regions. Broadly neutralizing antibodies demonstrate cross-subtype efficacy, while long-acting formulations have the potential to improve treatment adherence. This review synthesizes recent evidence and offers actionable recommendations to optimize clinical and public health responses—including the routine use of genotypic resistance testing where feasible, targeted use of phylogenetic analysis for outbreak investigation, and the development of region-specific diagnostic and treatment algorithms informed by local subtype prevalence. While the understanding of HIV’s evolutionary dynamics has substantially improved and remains essential, translating this knowledge into universally implemented intervention strategies remains a key challenge for achieving the UNAIDS 95-95-95 targets and the goal of ending AIDS as a public health threat by 2030. Full article

Background/Objectives: Epitope-based matching has emerged as a refined approach for assessing donor–recipient compatibility in renal transplantation. However, limited data are available on HLA Class I epitope distribution among Indian patients, particularly from northern India, where substantial allelic diversity is known to influence immunological risk. Methods: This retrospective analysis evaluated HLA Class I single-antigen bead (SAB) antibody data from 218 consecutive renal-transplant candidates who tested positive for anti-HLA antibodies between July 2018 and September 2024. HLA Class I epitopes were identified and analyzed using MATCH IT Antibody Software (Immucor, version 1.5.0). Demographic variables and sensitization history (previous transplant, transfusion, pregnancy) were reviewed. Results: A total of 504 distinct epitopes were identified, with 65GK and 163LG emerging as the most frequent motifs. The predominance of these epitopes mirrors the high prevalence of alleles such as HLA-A*24 and HLA-B*35 reported in North-Indian populations. The data suggest a strong influence of regional allele architecture on the immunogenic epitope landscape. Conclusions: This study provides the first baseline characterization of HLA Class I epitope distribution among northern-Indian renal-transplant candidates. The findings emphasize the need for establishing population-specific HLA epitope databases and highlight the potential of epitope-based matching to enhance donor selection and minimize immunological risk in Indian transplantation programs. Full article