Search Results (1,404)

Objectives: To establish normative data for anterior segment parameters in healthy pediatric and adult populations using swept-source optical coherence tomography (SS-OCT), and to evaluate the influence of age and sex on these parameters. Methods: This retrospective study included the right eyes of 390 healthy participants. Subjects were divided into three age groups: Group 1 (6–17 years, n = 97), Group 2 (18–45 years, n = 144), and Group 3 (46–77 years, n = 149). All patients were categorized according to their biological sex as female and male. Exclusion criteria were corneal pathology, prior intraocular/refractive surgery, recent contact lens use, severe dry eye, ectatic disorders, low-quality imaging, and refractive error of ±2.0 D or greater. Measurements of anterior and posterior keratometry, total corneal power (TCP), central corneal thickness (CCT), thinnest corneal thickness (TCT), pupil diameter (PD), lens thickness (LT), and white-to-white distance (WTW) were obtained using the Anterion^® SS-OCT system. Data were analyzed using SPSS software. Results: Group 1 demonstrated the highest PD and CCT values, whereas LT was lowest. In adults, LT increased with age and was significantly higher in males older than 45 years. Keratometric analysis revealed greater anterior and total steep astigmatism in the pediatric group, independent of sex. Adult females had significantly higher anterior and posterior keratometry values compared with males. In the pediatric cohort, females exhibited greater CCT, while WTW varied with age. PD decreased with age, whereas LT increased. Conclusions: Anterior segment parameters measured with SS-OCT show significant variations across different age groups and between sexes. Normative data, particularly for pediatric and adult populations, may serve as valuable reference values in keratorefractive surgical planning and corneal pathology assessment. Future studies with larger cohorts, especially in pediatric populations, are warranted. Full article

This paper presents the experimental implementation and validation of the two-chamber method presented in Part I for the high-precision determination of the temperature coefficient of resistance (TCR) of current shunts. The two-chamber approach enables improved thermal isolation and independent temperature control of the reference and test shunts, which significantly reduces the measurement uncertainty. In this part, the complete experimental setup is described, including the thermoelectric temperature control, the current generation and the data acquisition system with synchronized high-resolution digital multimeters (DMMs). The experimental measurements were carried out for different resistance ratios ranging from 0.1 to 10. The results confirm the theoretical predictions and the uncertainty analysis from Part I. The influences of the stability of the current source, the temperature uniformity and the synchronization accuracy on the measurement results are evaluated. The two-chamber method shows high repeatability, ease of use and suitability for laboratory and interlaboratory tests, and thus represents a robust alternative to classical TCR determination methods. Full article

Sporothrix schenckii is a pathogenic fungus with both clinical and environmental origins that was traditionally described as a single species but is increasingly recognized as being genetically diverse. In this study, we analyzed multiple isolates recovered from human sporotrichosis cases and environmental sources across Latin America (Mexico, Guatemala, Colombia). We conducted a polyphasic analysis of 16 isolates, integrating morphological data with multilocus sequence analysis (MLSA) targeting the internal transcribed spacer (ITS), calmodulin (CAL), β-tubulin (BT2), and translation elongation factor 1-α (TEF) gene regions. Phylogenetic relationships were resolved via maximum likelihood, and genetic structure was corroborated via four independent clustering methods: minimum spanning tree, principal component analysis, multidimensional scaling, and self-organizing maps. MLSA reidentified six isolates as S. globosa and confirmed the absence of S. brasiliensis in the cohort. The remaining S. schenckii s. str. isolates were resolved into three clades (A, B, and C). Notably, clade B (EH748, EH194, and EH257) formed a genetically divergent cluster with the highest nucleotide diversity (π = 0.03556) and was consistently segregated by all clustering algorithms. Clinical and environmental isolates were phylogenetically intermingled, supporting an active environmental reservoir for human infections. Phenotypic data, including colony size and conidial and yeast dimensions, varied but did not clearly distinguish between clinical and environmental origins. Our study provides compelling molecular evidence for a previously unrecognized, highly divergent clade within S. schenckii s. str., indicative of ongoing cryptic speciation. These findings refine the taxonomy of medically important Sporothrix species and reveal a distinct epidemiological profile for sporotrichosis in the studied regions, separate from the S. brasiliensis-driven epizootic. This highlights the critical role of molecular surveillance for accurate diagnosis, treatment, and public health strategies. Full article

The relationship between climate and independent glacier masses is now understood, but what is not understood is how ice sheets respond during times of rapid climate change. At its maximum extent the southern Laurentide Ice Sheet (LIS) was sourced from two domes that terminated in multiple lobes across central North America. The extent and timing of the eastern lobes, which were sourced from the Labrador Dome are relatively well constrained. Although the extent of the lobes sourced from the western Keewatin Dome is better understood, there is little chronologic data on them. Twenty-six radiocarbon ages recovered from within the drift of the James Lobe from South Dakota are used to reconstruct the timing of late-glacial fluctuations of the James Lobe. Lithologic logs from 21 South Dakota counties were analyzed and provide stratigraphic context for the radiocarbon ages. Analysis of the stratigraphy reveals two distinct glacial till units with a distinct, widespread layer of silt between them. The silt is interpreted here as evidence for interstadial conditions between two separate advances of the James Lobe. Radiocarbon ages of organics from this silt layer and from within the uppermost oxidized till indicate that interstadial conditions persisted from ~15.8 to 13.7 ka, followed by an advance of the James Lobe of at least 230 km to its maximum position at the Missouri River. Comparison to other locations in Wisconsin, northern lower Michigan, and western New York reveals a similar period of interstadial conditions followed by ice margin advance. We correlate this advance across ~1000 km and suggest that the simplest explanation is reduced summer ablation caused by widespread climatic cooling. Full article

The Advanced Very High-Resolution Radiometer (AVHRR) onboard NOAA-7 through NOAA-19 satellites has been the primary data source for two Climate Data Records (CDRs) that were developed specifically for Arctic and Antarctic studies: the AVHRR Polar Pathfinder (APP) and Extended AVHRR Polar Pathfinder (APP-x). With the decommissioning of these satellites and the loss of the AVHRR, a method for extending the CDRs with the Visible Infrared Imaging Radiometer Suite (VIIRS) on NOAA’s recent satellites is presented. The goal is to produce long-term, continuous, consistent, and traceable CDRs for polar climate research. As a result, APP and APP-x can now be continued as the VIIRS Polar Pathfinder (VPP) and Extended VIIRS Polar Pathfinder (VPP-x) CDRs. To ensure consistency, a VIIRS Global Area Coverage (VGAC) dataset that is comparable to AVHRR GAC data was used to develop an analogous VIIRS Polar Pathfinder suite. Five VIIRS bands (I1, I2, M12, M15, and M16) were selected to correspond to AVHRR Channels 1, 2, 3b, 4, and 5, respectively. A multivariate regression approach was used to intercalibrate these VIIRS bands to AVHRR channels based on data from overlapping AVHRR and VIIRS observations from 2013 to 2018. The data from 2012 and 2019 were reserved for independent validation. For the Arctic region north of 60°N at 14:00/04:00 Local Solar Time (LST) during 2012–2019, mean biases between APP and VPP composites at a spatial resolution of 5 km are −0.85%/3.03% (Channel 1), −1.22%/3.65% (Channel 2), −0.18 K/0.81 K (Channel 3b), 0.01 K/0.24 K (Channel 4), and 0.07 K/0.19 K (Channel 5). Mean biases between APP-x and VPP-x at a spatial resolution of 25 km for the same region and period are −1.52%/−1.48% for surface broadband albedo, 0.69 K/0.61 K for surface skin temperature, and −0.011 m/−0.017 m for sea ice thickness. Similar results were observed for the Antarctic region south of 60°S at 14:00/02:00 LST, indicating strong agreement between APP and VPP, and between APP-x and VPP-x. Full article

Background: Child stunting and wasting persist at alarmingly high rates in Mozambique, yet little is known about whether the improved sources of water and sanitation affect these outcomes differently. This study aims to disentangle the distinct contributions of improved water sources and sanitation facilities to child stunting and wasting at the national level, addressing a critical evidence gap in the WASH–nutrition literature in Mozambique. Methods: Using data from 3690 children under five in the 2022–2023 Mozambique Demographic and Health Survey, we applied stepwise logistic regression models to estimate the independent and combined associations of improved drinking water and sanitation facilities with child stunting and wasting, adjusting for child-, household-, and region-level factors. Results: Improved water access was significantly associated with a lower risk of stunting (odds ratio = 0.80, 95% CI: 0.67–0.94, p < 0.01), while sanitation showed only weak and inconsistent associations with stunting. In the fully adjusted model, neither improved water nor sanitation was associated with wasting. Wealth, gender, religion, and region were also significant predictors of stunting as well as wasting. Conclusions: These findings indicate that WASH components protect against child malnutrition through different pathways, with water being more protective against chronic undernutrition and sanitation less clearly linked to acute malnutrition. Broader socioeconomic and cultural factors—such as wealth, religion, and geography—play critical roles, highlighting the need for integrated, context-specific interventions. Full article

Background/Objectives: Social media use has become ubiquitous, with governmental bodies and researchers expressing a growing concern about its impact on mental health. This review aims to examine the relationship between social media use and disordered eating in individuals aged 18–30. Specifically, we aim to identify specific patterns of use (including addictive use) that are associated with increased risk for disordered eating. Methods: A search was conducted in March 2025 using PubMed and PsycINFO. Keywords were based on social media platforms and eating behaviors. Inclusion criteria were published studies in peer-reviewed journals from 2015–2025, written in English, with participants aged 18–30, whose disordered eating outcomes were assessed using validated measures. Conclusions: 637 articles were screened, with 28 studies meeting the inclusion criteria. Most studies assessed general social media use, without specifying the platform type. The EAT-26 and EDE-Q scales were used in most research to assess disordered eating. Data were narratively synthesized based on the type of social media variables assessed. Our findings demonstrate a complex relationship between social media use and disordered eating, with more consistent associations being found when the type of content (fitspiration and thinspiration) was the independent variable. These findings align with qualitative findings, which highlighted ambivalence in relation to the effect of social media: it is viewed as both a source of support and social comparison. Avenues for future research include longitudinal studies to understand the interaction between individual factors and social media patterns of use, as well as the utilization of platform-generated data on online engagement patterns. Full article

Accurate precipitation forecasting plays a crucial role in sustainable water resource management, especially in arid regions like Konya, one of Turkey’s driest areas. Reliable forecasts support effective water budgeting, agricultural planning, and climate adaptation efforts in the region. This study investigates the performance of different deep learning training algorithms in forecasting monthly precipitation using Long Short-Term Memory (LSTM) networks, a method tailored for time-series prediction. A comprehensive dataset comprising 39 years (1984–2022) of precipitation records was utilized, obtained from the Turkish State Meteorological Service (MGM) as ground-based observations and from NASA’s POWER database as remote sensing data, and was split into 80% for training and 20% for testing. A comparative analysis of three widely used optimization algorithms, Adaptive Moment Estimation (ADAM), Root Mean Square Propagation (RMSProp), and Stochastic Gradient Descent with Momentum (SGDM), revealed that ADAM consistently outperformed the others in forecasting accuracy. Model performance was evaluated with statistical metrics, and the LSTM-ADAM combination achieved the best results. In the final phase, cross-validation was applied using MGM and NASA data sources in a crosswise manner to test model generalizability and data source independence. The best performance was observed when the model was trained with MGM data and tested with NASA data, achieving a remarkably low RMSE of 3.62 mm, MAE of 2.93 mm, R² of 0.9966, and NSE of 0.9686. When trained with NASA data and tested with MGM data, the model still demonstrated strong performance, with an RMSE of 4.48 mm, MAE of 3.22 mm, R² of 0.9921, and NSE of 0.9678. These results demonstrate that satellite and ground-based data can be used interchangeably under suitable conditions, while also confirming the superiority of the ADAM optimizer in LSTM-based precipitation forecasting. Full article

Recent advances in hyperspectral imaging (HSI) and multimodal deep learning have opened new opportunities for crop health analysis; however, most existing models remain limited by dataset scope, lack of interpretability, and weak cross-domain generalization. To overcome these limitations, this study introduces Agri-DSSA, a novel Dual Self-Supervised Attention (DSSA) framework that simultaneously models spectral and spatial dependencies through two complementary self-attention branches. The proposed architecture enables robust and interpretable feature learning across heterogeneous data sources, facilitating the estimation of spectral proxies of chlorophyll content, plant vigor, and disease stress indicators rather than direct physiological measurements. Experiments were performed on seven publicly available benchmark datasets encompassing diverse spectral and visual domains: three hyperspectral datasets (Indian Pines with 16 classes and 10,366 labeled samples; Pavia University with 9 classes and 42,776 samples; and Kennedy Space Center with 13 classes and 5211 samples), two plant disease datasets (PlantVillage with 54,000 labeled leaf images covering 38 diseases across 14 crop species, and the New Plant Diseases dataset with over 30,000 field images captured under natural conditions), and two chlorophyll content datasets (the Global Leaf Chlorophyll Content Dataset (GLCC), derived from MERIS and OLCI satellite data between 2003–2020, and the Leaf Chlorophyll Content Dataset for Crops, which includes paired spectrophotometric and multispectral measurements collected from multiple crop species). To ensure statistical rigor and spatial independence, a block-based spatial cross-validation scheme was employed across five independent runs with fixed random seeds. Model performance was evaluated using $R^2$, RMSE, F1-score, AUC-ROC, and AUC-PR, each reported as mean ± standard deviation with 95% confidence intervals. Results show that Agri-DSSA consistently outperforms baseline models (PLSR, RF, 3D-CNN, and HybridSN), achieving up to $R^2=0.86$ for chlorophyll content estimation and F1-scores above 0.95 for plant disease detection. The attention distributions highlight physiologically meaningful spectral regions (550–710 nm) associated with chlorophyll absorption, confirming the interpretability of the model’s learned representations. This study serves as a methodological foundation for UAV-based and field-deployable crop monitoring systems. By unifying hyperspectral, chlorophyll, and visual disease datasets, Agri-DSSA provides an interpretable and generalizable framework for proxy-based vegetation stress estimation. Future work will extend the model to real UAV campaigns and in-field spectrophotometric validation to achieve full agronomic reliability. Full article

Determining a high-precision national geoid is a fundamental step in modernizing Kazakhstan’s vertical reference system. However, the country’s vast territory, complex topography, and limited coverage of modern terrestrial and airborne gravimetric surveys present significant challenges. In this context, Soviet-era gravimetric maps at a 1:200,000 scale remain the only consistent nationwide data source, yet their reliability has not previously been rigorously assessed within modern gravity standards. This study presents the first comprehensive validation of Soviet-era gravimetric surveys using two independent approaches. The first approach is about the comparison of gravity anomalies with the global geopotential models EGM2008, EIGEN-6C4 and XGM2019e_2159. The second approach is about the direct evaluation against absolute gravity measurements from the newly established Qazaqstan Gravity Reference Frame (QazGRF). The analysis demonstrates that, after applying systematic corrections, the Soviet-era gravimetric survey retains high information content. The mean discrepancy with QazGRF measurements is 0.7 mGal with a standard deviation of 2.5 mGal, and more than 90% of the evaluated points deviate by less than ±5 mGal. Larger inconsistencies, up to 20 mGal, are confined to mountainous and geophysically complex regions. In addition, several artifacts inherent to the global models were identified, suggesting that the integration of validated regional gravimetric data can also support future improvements of global gravity models. A key finding was the detection of an artifact in the global models on sheet M43. Its presence was confirmed by comparison with terrestrial gravimetric data and inter-model differences. It was established that the anomaly is caused by inaccuracies in the terrestrial “fill-in” component of the EGM2008 model, which subsequently inherited by later global solutions. The results confirm that Soviet gravimetric maps, once critically re-evaluated and tied to absolute observations, can be effectively integrated with global models. This integration delivers reliable, high-resolution inputs for regional gravity-field modeling. It establishes a robust scientific and practical foundation for constructing the first national geoid of Kazakhstan and for implementing a unified state coordinate and height system. It also helps enhance the accuracy of global geopotential models. Full article

Functional Near-Infrared Spectroscopy is (fNIRS) a non-invasive neuroimaging technique that monitors cerebral hemodynamic responses by measuring near-infrared (NIR) light absorption caused by changes in oxygenated and deoxygenated hemoglobin concentrations. While fNIRS has been widely used in cognitive and clinical neuroscience, a key challenge persists: the lack of practical tools required for calibrating source-detector separation (SDS) to maximize sensitivity at depth (SAD) for monitoring specific cortical regions of interest to neuroscience and neuroimaging studies. This study presents DrSVision version 1.0, a standalone software developed to address this limitation. Monte Carlo (MC) simulations were performed using segmented magnetic resonance imaging (MRI) data from eight cadaveric heads to realistically model light attenuation across anatomical layers. SAD of 10–20 mm with SDS of 19–39 mm was computed. The dataset was used to train a Gaussian Process Regression (GPR)-based machine learning (ML) model that recommends optimal SDS for achieving maximal sensitivity at targeted depths. The software operates independently of any third-party platforms and provides users with region-specific calibration outputs tailored for experimental goals, supporting more precise application of fNIRS. Future developments aim to incorporate subject-specific calibration using anatomical data and broaden support for diverse and personalized experimental setups. DrSVision represents a step forward in fNIRS experimentation. Full article

Bacillus anthracis is the etiological agent of the zoonotic disease anthrax. The pathogen has colonized many regions of all inhabited continents. Increasing evidence points to a strong contribution of anthropogenic activities (trade) in this almost global spread. This article contributes further genomic data from 21 B. anthracis strains, including 19 isolated in Germany, aiming to support and detail the human role in anthrax dispersal. The newly sequenced genomes belong to the B. anthracis lineage predominant in China. This lineage is remarkable because of its phylogenetic structure. A polytomy with nine branches radiating from a central node was identified by whole-genome single-nucleotide polymorphism (wgSNP) analysis. Strains from Germany populate two among the nine branches. Detailed analysis of the polytomy indicates that it most likely emerged in China. We propose that the polytomy is the result of the import of contaminated animal products in a limited spatiotemporal frame, followed by the distribution of these products to different locations within China, where new B. anthracis lineages then became independently established. Currently available data point to Bengal as a likely geographic source of the original contamination, and the history of trade exchanges between Bengal and China agrees with the early fifteenth century as a likely time period. The subsequent exports to Germany would have occurred during the 19th century according to German trade history. Notably, Germany has been experiencing localized anthrax outbreaks from this trade heritage up into the 21st century. Full article

The COVID-19 pandemic forced European Union member states to accelerate the digitalization of public services, turning a gradual policy priority into an urgent necessity. This study examines the pandemic’s impact on the digital transformation of public administrations, assessing the effectiveness of digital-oriented interventions implemented during this period. Using a Difference-in-Differences (DiDs) methodology, the analysis compares treatment and control groups based on 2019 Digital Economy and Society Index (DESI) scores, with digital public services as the dependent variable. Independent variables include pre-filled forms, service transparency, design and data protection, e-government usage, internet penetration, total population, and governance quality, covering all 27 EU member states from 2016 to 2023. Data sources include DESI, Eurostat, and the World Bank. The analysis shows that countries with lower digitalization achieved the largest post-pandemic gains, with transparency, service design, and data protection significantly enhancing digital service quality. Pre-existing governance and infrastructure shaped the magnitude of these improvements, highlighting the combined role of preparedness and reactive policy measures. The findings underscore the critical role of citizens as end-users and accountability drivers in digital governance. By providing empirical evidence on pandemic-driven digitalization trends, this study contributes to policy discussions on resilience, strategic planning, and the future of inclusive, transparent e-government services in the EU. Full article

Introduction: While low back pain (LBP) is most often associated with musculoskeletal issues, in a minority of cases, it can be caused by serious underlying conditions such as cancer. Recognizing malignancy early remains a major clinical challenge, as the warning signs, known as red flags (RFs), are often vague and inconsistent. Methods: A comprehensive search of six databases (PubMed, Scopus, Google Scholar, Web of Science, Cochrane Library, and SciELO) and grey literature was conducted for studies published from January 1999 to March 2025. Eligible sources included studies describing adult patients with cancer presenting with LBP. Study selection and data extraction were independently performed by two reviewers. Results: We included 70 studies, most of which were case-based, along with reviews and observational research. In these studies, cancer prevalence among patients with LBP ranged from 0.1% to 1.6%, with metastatic disease being the most common finding. A prior history of cancer emerged as the most reliable red flag (specificity up to 0.99), while other signs and symptoms were less consistent. Notably, combining multiple RFs, such as a history of cancer and unexplained weight loss, significantly improved the diagnostic accuracy (LR+ = 10.25 in one study). Conclusions: While current evidence is limited and largely based on case-based studies, some RFs, particularly a history of cancer, show greater diagnostic value. In patients with LBP associated with underlying malignancy, RFs seem to be more useful for ruling in rather than ruling out (i.e., screening) serious pathologies. Most RFs have poor standalone accuracy; however, considering combinations of RFs within the broader clinical context may improve early detection of spinal malignancy in patients with LBP. Full article

National-scale long-term forest ecosystem surveys based on systematic sampling offer a robust framework for detecting temporal growth trends of specific tree species across regions. The National Forest Inventory (NFI) of the Republic of Korea serves as a vital source for analyzing long-term forest dynamics on a national scale by providing regularly collected large-scale forest data. However, various limitations, such as the lack of individual-level and spatial interaction data, restrict the development of reliable individual tree growth models. To overcome this, distance-independent models, compatible with the structure and data resolution of the NFI, provide a practical alternative for simulating individual tree and stand-level growth by utilizing straightforward attributes, such as diameter at breast height (DBH). This study aimed to analyze the growth patterns and construct species-specific models for two major plantation species in South Korea, Pinus koraiensis and Larix kaempferi, using data from the 5th (2006–2010), 6th (2011–2015), and 7th (2016–2020) NFI survey cycles. The sampling points included 117 and 171 plots for P. koraiensis and L. kaempferi, respectively. An additional matching process was implemented to improve species identification and tracking across multiple survey years. The final models were parameterized using a distance-independent model, integrating the estimation of potential diameter growth (PG) and a modifier (MOD) function to adjust for species- and site-specific variabilities. Consequently, the models for each species demonstrated strong performance, with P. koraiensis showing an R² of 0.98 and RMSE of 1.15 (cm), and L. kaempferi showing an R² of 0.98 and RMSE of 1.14 (cm). This study provides empirical evidence for the development of generalized and scalable growth models using NFI data. As the NFI increases in volume, the framework can be expanded to underrepresented species to improve the accuracy of underperforming models. Ultimately, this study lays a scientific foundation for the future development of tree-level simulation algorithms for forest dynamics, encompassing mortality, harvesting, and regeneration. Full article