Search Results (25,978)

Astragalus root rot is a soil-borne disease primarily caused by Fusarium spp., which severely hampers the sustainable development of the Astragalus industry. F. acuminatum is a predominant pathogen causing this disease. To elucidate the genetic variation and adaptive evolutionary characteristics of F. acuminatum from different geographical origins, this study conducted whole-genome resequencing analysis on 28 isolates of F. acuminatum collected from four major Astragalus production regions. Approximately 124.9 Gb of high-quality sequencing data were obtained, and a large number of single-nucleotide polymorphisms (SNPs) were detected. Population genetic analysis revealed that strains from different regions did not form strictly geographically specific clusters, exhibiting a complex mixed distribution pattern. Nucleotide polymorphism analysis indicated that the Dingxi, Gansu (GD) population possessed the highest nucleotide diversity (π) value, reflecting the richest genetic diversity. Fixation index (Fst) analysis revealed significant genetic differentiation (Fst > 0.15) among populations from different provinces, suggesting that geographic isolation may be a contributing factor to restricted gene flow between pathogenic isolates in these regions. Tajima’s D positive values suggest a deviation from neutrality, consistent with balancing selection or population contraction. Ka/Ks analysis further revealed that the majority of genes exhibited Ka/Ks > 1, differing from the typical pattern of purifying selection dominance. This study revealed the genetic variation and selection signals of F. acuminatum isolates from different geographical origins, observed significant genetic differentiation between the Gansu and Ningxia populations, and identified a large number of genes that may be subject to positive selection. Full article

Understanding the spatial organization of the new materials industry chain is essential for promoting sustainable industrial development. However, existing research rarely examines it as an integrated intercity network spanning multiple segments and specialized sub-sectors. To address this gap, this study constructs the New Materials City Association Network (NM-CityNet) using firm-level cross-regional equity investment data for 294 Chinese cities from 2010 to 2024. NM-CityNet includes two dimensions: segment networks (upstream, midstream, downstream) and sub-sector networks (advanced basic materials, critical strategic materials, and frontier new materials). A chain-lock model is applied, combined with social network analysis and the quadratic assignment procedure. Location quotients are integrated with weighted degree to capture specialized division-of-labour patterns. Using these methods, this study reveals the regional distribution, network structure, specialization patterns, and formation mechanisms of NM-CityNet. Results show that: (1) upstream core cities cluster in eastern China, midstream activities diffuse toward central and western regions, and downstream activities concentrate along the south-eastern coast; (2) NM-CityNet remains sparse and shows clear community structures, while different segments form differentiated spatial organization mechanisms; (3) sub-sectors exhibit clear specialization, with critical strategic materials showing broader spatial coverage; (4) drivers are heterogeneous: administrative proximity promotes link formation; government S&T financial-support differences are positively associated with link formation, although this association may partly reflect selective investment effects; economic and transport disparities inhibit link formation; innovation differences matter only in the midstream segment; and resource-endowment differences matter upstream and downstream. Full article

Existing radio environment map(REM)-based emitter-detection methods suffer from high false positives and missed detections in blurred or conjoined structures, or require large annotated datasets and heavy computation. We propose an unsupervised method, persistent homology with agglomerative clustering (PH-AC), based on an improved persistent-homology algorithm. A simulated spaceborne-REM dataset is constructed via synthetic-aperture passive interferometric imaging, covering isolated, adjacent-pair, and complex-emitter distributions. Persistent homology tracks the birth, death, and merging of zero-dimensional connected components as the intensity threshold varies. To address missed detections for spatially proximate emitters, multidimensional topological features are constructed via feature-contribution analysis. Agglomerative clustering with Ward linkage then adaptively separates emitters from noise without supervision. Experimental results show that PH-AC achieves a perfect F1 score of 1.000 in isolated scenarios; for adjacent emitters, it improves F1 by 15.7% over the best image-processing method and stays within 4% of supervised deep learning methods, while requiring no annotations. In complex environments, it attains an F1 of 0.937, outperforming all compared methods. Its computational complexity is only $2.25\times {10}^6$ FLOPs, three orders lower than YOLO-based detectors. This work offers a lightweight, annotation-free topological paradigm for spaceborne-REM-emitter detection. Full article

Early detection of infectious disease outbreaks is critical in densely populated, resource-limited settings. This study aimed to describe the community-based surveillance (CBS) system and its application of the Data–Information–Knowledge–Wisdom (DIKW) framework in Bangladesh. CBS was implemented in 12 urban wards across Dhaka South, Rajshahi, and Sylhet, where trained community volunteers conducted routine household visits to identify five priority syndromes. Data were collected through a mobile application integrated with an automated pipeline for cleaning, geocoding, cluster detection, and alert generation. Between January and June 2025, 38,489 households were visited, enrolling 128,626 individuals. The system generated 10,191 alerts and 577 clusters, predominantly for suspected dengue (58.7%), followed by acute watery diarrhea (24.1%) and influenza-like illness (10.7%). Rajshahi contributed the majority of alerts and clusters. Spatiotemporal analysis identified ward-level outbreak signals, including localized dengue peaks across all three cities. Over 98% of records were synchronized within 24 h, and more than 99% of data entry errors were automatically corrected, ensuring timely and high-quality analytics. These findings demonstrate that digital CBS can effectively transform community-level data into actionable public health intelligence, supporting early outbreak detection and response. This translation enabled timely public health actions, including targeted outbreak investigations and localized vector control measures in identified hotspots. Integration with national surveillance platforms may further strengthen health system responsiveness and epidemic preparedness. Full article

Introduction/Objectives: Wall irregularity is a known risk factor in the evaluation of intracranial aneurysms, but the prognostic value of its subtypes remains unclear. Materials and methods: In this retrospective single-center cross-sectional study (2023–2025), we reviewed consecutive adult patients with intracranial aneurysms. Morphology was classified as daughter sac, multilobulated, or complex irregularity. We compared rupture status and calculated PHASES, ELAPSS, and UIATS scores. Principal Component Analysis (PCA), and logistic and linear regression were applied. Results: A total of 180 patients with 180 index aneurysms were included; mean age was 67.2 ± 12.1 years, and 72.2% were women. Overall, 43.3% of aneurysms were irregular, specifically: daughter sac (25.0%), multilobulated (36.1%), and complex irregularity (11.1%). SAH occurred in 40 patients (22.2%). Ruptured aneurysms had larger maximum diameter, size ratio, and aspect ratio (all p < 0.0001), plus higher 5-year PHASES (p = 0.0091) and ELAPSS growth scores (p < 0.0001). PCA identified three clusters with differing 5-year rupture risks; Cluster 3 had the highest risk (5.71 ± 5.25%) and was characterized by a higher proportion of daughter sac and multilobulated morphology (p = 1.65 × 10⁻⁷ and 8.80 × 10⁻¹⁶). Linear models showed each irregular subtype was associated with significantly larger aneurysm size. Conclusions: Irregular wall patterns were common and associated with larger aneurysm dimensions and higher risk scores. These findings support further investigation of refined morphological descriptors in rupture risk stratification. Full article

Background: Chronic obstructive pulmonary disease (COPD) and tuberculosis (TB) are among the most prevalent respiratory disorders worldwide and frequently coexist in the same patient. However, the contribution of active TB and post-tuberculosis lung disease to COPD exacerbations and long-term prognosis remains incompletely defined. Objective: To evaluate the prevalence, clinical correlates, and prognostic significance of tuberculosis and its sequelae in patients with COPD. Materials and methods: We conducted a population-based retrospective cohort study using de-identified data from the regional healthcare information system. The cohort included all adults aged 18 years or older with a recorded diagnosis of COPD (ICD-10 code J44). Tuberculosis was identified by codes A15–A19 and B90. The primary outcomes were COPD exacerbations and all-cause mortality. Group comparisons, cluster analysis, Kaplan–Meier survival analysis, Cox proportional hazards modeling, and multivariable logistic regression were performed. Results: Tuberculosis and/or its sequelae were identified in 267 of 16,714 patients (1.60%): post-TB sequelae (B90) in 197 (73.8%), active TB (A15–A19) in 22 (8.2%), and both in 48 (18.0%). Compared with patients without TB, those with COPD-TB were younger (63.5 ± 14.2 vs. 65.7 ± 14.7 years; p = 0.018), more often male (75.3% vs. 52.0%; p < 0.001), and had higher mortality (16.5% vs. 10.6%; p = 0.003). COPD-TB was associated with bronchiectasis (OR = 6.07; 95% CI, 3.03–12.16), pulmonary fibrosis (OR = 5.67; 95% CI, 3.40–9.45), and pneumonia (OR = 2.01; 95% CI, 1.50–2.71), but with lower prevalences of obesity, diabetes mellitus, and hypertension. Patients with TB experienced more COPD exacerbations, including recurrent exacerbations. In multivariable models, tuberculosis was associated with COPD exacerbations after adjustment for age and sex (adjusted OR = 1.43; 95% CI, 1.05–1.96); this association was attenuated and lost significance after further adjustment for post-tuberculosis structural lung disease, indicating that it is largely mediated by post-TB sequelae. Tuberculosis remained associated with mortality after adjustment for available covariates, both in logistic regression (adjusted OR = 1.61; 95% CI, 1.14–2.28) and in Cox analysis (hazard ratio = 1.37; 95% CI, 1.01–1.85). Conclusions: Tuberculosis and post-tuberculosis lung disease are clinically accessible risk markers associated with COPD exacerbations and mortality. These findings support recognizing patients with COPD and a history of TB as a high-risk subgroup requiring intensified follow-up, proactive exacerbation prevention, and prioritized vaccination counseling. In the context of personalized medicine, a documented history of tuberculosis and post-tuberculosis lung changes represents a clinically accessible marker that can be used to stratify individual risk and to tailor monitoring and prevention in patients with COPD. Full article

Multidrug-resistant (MDR) Enterococcus faecium is an opportunistic pathogen. Its resistance and virulence genes can spread through the food chain, posing risks to public health. This study investigated the antimicrobial resistance and genomic characteristics of MDR E. faecium isolated from milk samples from cows with mastitis in Ningxia between 2019 and 2024. From 2019 to 2024, 1341 milk samples were collected in Yinchuan, Yinnan, and Yinbei. MDRE. faecium was identified using plate screening, mass spectrometry, broth microdilution, and hemolysis detection. Whole-genome sequencing enabled SNP, MLST, pan-genome, and COG analyses, focusing on ARGs and MGEs. MRPP, AMOVA and PCoA were applied to compare gene communities and identify driver genes. Ninety-one E. faecium strains were isolated. Resistance to florfenicol, ceftiofur, and chloramphenicol exceeded 60%, while resistance to vancomycin and linezolid showed an overall increasing trend over the study period. Phylogenetic clustering revealed two subtypes, three clades, and 10 novel STs. Spearman correlation analysis revealed strong positive correlations among the resistance genes optrA, cfr(A), and vanF. Antibiotic resistance, particularly MDR, increased over time, and strains carried diverse ARGs and MGEs. Overall, strengthened surveillance of mastitis-derived E. faecium is warranted to support the control of bovine mastitis and safeguard public health. Full article

Soybean (Glycine max) germination is highly sensitive to neutral salt stress. Although sodium chloride (NaCl) and sodium sulfate (Na₂SO₄) co-exist in nature, their distinct phytotoxic mechanisms remain severely under-investigated. In this study, 50 germplasm accessions were systematically screened, identifying R014 as highly salt-tolerant and R120 as highly sensitive. Phenotypic and dynamic antioxidant monitoring (0–72 h) established 48 h as the critical tolerance window, revealing that Na₂SO₄ induces complex physical damage (crystallization) and osmotic injury, with its ionic toxicity significantly exceeding that induced by NaCl. Crucially, R014 effectively maintained peak activities of antioxidant enzymes (SOD, POD, CAT) to combat these specific stressors. By integrating deep RNA sequencing with weighted gene co-expression network analysis (WGCNA) using 48 h radicle data, significant transcriptomic reprogramming was revealed. WGCNA robustly isolated 35 functional modules, located five key phenotypic clusters, and defined three major hub genes (Glyma.11G101900, Glyma.17G185000, and Glyma.20G247850) that regulate calcium signaling. Verified by qRT-PCR, this study suggests the differential physiological and molecular architectural characteristics between chloride and sulfate toxicities, providing precisely targeted genetic loci for the breeding of salt-tolerant soybean. Full article

Metropolitan hinterlands are zones of intense urban–rural factor flows and spatial restructuring, where understanding rural settlement evolution is crucial for revealing human–land relationship transformations. Taking Changsha County, the core hinterland of the Changsha–Zhuzhou–Xiangtan metropolitan area in central China, as a case study, we integrated landscape pattern indices, kernel density estimation, centroid migration, the Optimal Parameters-based Geographical Detector (OPGD), and Geographically Weighted Random Forest (GWRF) to analyze the spatiotemporal evolution of rural settlements from 1990 to 2020 and identify the factors associated with the spatial differentiation of rural settlement scale in 2020. The results showed that: (1) The scale of rural settlements continuously expanded, with the total area increasing by 69.7% while patch density declined by 26.7%, exhibiting a “dense south, sparse north” pattern. High-value kernel density zones progressively clustered toward the southwestern concentric zone, and the settlement centroid persistently migrated toward the urban core. (2) The output value of secondary and tertiary industries per unit area, NDVI, and living facility adequacy were identified as the core driving factors; GDP per capita, distance to cropland, and distance to major roads also exerted notable effects, and strong synergistic interactions were detected among these factors. (3) GWRF-SHAP analysis revealed pronounced spatial heterogeneity: NDVI exhibited a south-promotion, north-suppression bidirectional effect; distance to cropland showed the most stable positive influence; road proximity was significant only at transportation hubs; the output value of secondary and tertiary industries displayed a polarized “central driving, north–south suppression” pattern; and socioeconomic factors generally stimulated expansion in suburban areas while inhibiting it in remote hinterlands. This spatial divergence can be interpreted through the “south-industry, north-agriculture” structure: suburban industrial corridors are associated with externally oriented attraction, whereas remote agricultural hinterlands are more closely related to endogenous, resource-based upgrading. The study proposes a compound explanatory framework of “natural baseline constraints–locational guidance–socioeconomic dominance,” providing a scientific basis for differentiated spatial governance of rural settlements in metropolitan hinterlands. Full article

Nuclear fusion represents a transformative solution for global energy systems, offering a carbon-free, inherently safe, and virtually inexhaustible power source. As the field transitions from experimental reactors like ITER to demonstration power plants (DEMO) capable of delivering net electricity to the grid (300–500 MW), the computational demands for plasma control have escalated. Modern fusion diagnostics, particularly soft X-ray (SXR) systems, generate massive data volumes that require high-throughput processing to ensure plasma stability and optimize energy gain. Recent breakthroughs in record-breaking plasma durations have further exposed the critical latency bottlenecks in traditional analytical workflows. This work addresses these challenges by introducing advanced computational strategies optimized towards next-generation reactors. Firstly, we present new data-processing algorithms in C++ and CUDA, achieving significant reductions in computation time. This allowed for more efficient analysis of collected experimental data for plasma confinement studies. Secondly, we discuss hardware architectures that will allow, in the future, up-scaling and parallel runtime processing of data with a feedback signal to the reactor control systems. We present a detailed analysis of the computational workflows underlying soft X-ray diagnostics, followed by a presentation of the proposed optimized algorithms. Their impact on prospective hardware system designs is then evaluated in terms of scalability, latency, and throughput. Performance evaluations demonstrated substantial speedups of both the sequential CPU-based and the parallel GPU-based algorithms, highlighting the potential of these methods for future real-time plasma control for energetically stable and efficient fusion power generation. The sequential and parallel algorithms were 18.8 and 89.1 times faster, respectively, versus the baseline implementation. The processing rate was increased from 31.8 MiB/s to 4.32 GiB/s. The results show the effectiveness of massively parallel computation for plasma diagnostics and pave the way towards further research to produce a cluster-based distributed system. The demand for such high-performance, real-time data processing methodologies extends beyond the plasma confinement domain and is expected to grow across energy systems as they become increasingly complex and data-driven. Full article

The increasing penetration of renewable generation and flexible loads has made modern power systems operate under highly variable and diverse conditions. For power-system planning studies, static power-system analysis plays an important role in characterizing the security and stability behavior of these operating conditions. In such planning tasks, annual or long-term hourly datasets are often needed to capture temporal variations in renewable generation, load, and power-flow patterns, but performing power-flow-based static analysis for every operating condition can be computationally expensive, especially for targets that require repeated power-flow-based calculations. Therefore, an effective operating-condition reduction framework is needed to select a compact yet representative subset and reconstruct the overall static-analysis profile required for variation trends and distribution analysis. To address this problem, this paper proposes a problem-driven scenario reduction framework based on batch-attention-based self-supervision feature selection (A-SFS) for simplifying large-scale power-flow-based static analysis. Instead of clustering operating conditions only according to their geometric similarity in the original feature space, the proposed framework incorporates the downstream static-analysis target into the reduction process. Target values are first computed for only a small portion of the operating-condition dataset, and A-SFS is then used to learn target-relevant features and their importance weights. Based on the learned weighted feature space, all operating conditions are clustered using weighted K-means++, and the actual operating condition closest to each cluster centroid is selected as the representative scenario. The downstream target evaluation is then performed only on these representative scenarios, and their target values are assigned to the operating conditions within the same clusters to reconstruct the overall target-value profile of the full dataset. The proposed framework is validated on a yearly RTS-GMLC operating-condition dataset using two representative static-analysis targets, namely load margin and the minimum singular value of the power-flow Jacobian, ${\sigma }_{min}$. The results show that the proposed target-aware clustering framework can effectively reconstruct the overall static-analysis profile of the full operating-condition dataset while preserving the relative ranking of different operating conditions. In the best-M comparison, the proposed method achieves MAPEs of 19.56% for load margin and 12.85% for ${\sigma }_{min}$, with corresponding Spearman coefficients of 0.8380 and 0.8755, respectively. Full article

The Rio Santiago basin is one of the most environmentally stressed river systems in Mexico, with persistent organic, nutrient, microbial, surfactant, and metal contamination. This study develops a near-term quantum machine learning workflow for environmental monitoring and water-pollution profiling using multivariate records from 13 stations between 2009 and 2022. QML is evaluated here because quantum feature maps can define nonlinear, interaction-rich kernels that remain executable on present quantum hardware, providing an alternative representation to compare with classical PCA, RBF, UMAP, and HDBSCAN baselines rather than a presumed computational advantage. After quality screening, log transformation, standardization, and domain-guided feature selection, pollution profiles are evaluated across PCA, RBF spectral clustering, UMAP/KMeans, UMAP/HDBSCAN, a simulated ZZ-style quantum feature-map kernel, and Qiskit Runtime hardware evaluations of the same kernel concept. The initial cleaned-data results show that classical PCA clustering identifies broad lower-load, high organic/surfactant, and rain-season solids/microbial profiles. UMAP/HDBSCAN provides the strongest cleaned full-sample nonlinear baseline, with a silhouette score of 0.568 after excluding 177 noise samples. The simulated quantum-kernel representation separates station-linked gradients, while matched n = 650 stability diagnostics show near-identical quantum-kernel clustering across random initializations (mean ARI = 0.994 for cleaned data) but retain the RBF kernel as the strongest nonlinear comparator. Two 24-sample Qiskit hardware runs and two matched 8-record hardware checks provide proof-of-execution evidence. The analysis is framed as a controlled representation study, not as a claim of quantum advantage. Full article

Background: Network medicine has reshaped how systemic comorbidities are quantified, but the internal comorbidity structure of oral diseases remains undescribed at four-character ICD-10 granularity. Methods: A total of 2,863,671 outpatient visit records from 583,614 patients (2011–2025) were analyzed. Using ICD-10 four-character codes (75 disease nodes), comorbidity networks were constructed for five age strata, with edges selected by relative risk (RR) > 1.5 and Bonferroni-corrected Fisher’s exact tests. Patient-level longitudinal sequences were mined for progression trajectories, and quasi-causal analyses—Cox regression, negative outcome controls, and Baron–Kenny mediation—were used to evaluate pathway directionality and specificity. Results: The all-age network contained 75 nodes and 167 edges (modularity = 0.53), forming eight communities. Network complexity peaked at 18–29 years and declined with age. Dental caries emerged as the strongest hub in the 60+ stratum (degree = 9). Cox regression adjusted for age, sex, and healthcare utilization confirmed pathway directionality (pulpitis → tooth defect: hazard ratio (HR) = 2.65; caries → pulpitis: HR = 2.25), and negative outcome controls confirmed biological specificity. Mediation analysis showed that pulpitis completely mediated the caries → tooth defect association (proportion mediated ≈ 100%; 95% confidence interval (CI), 90–128%). An oral mucosal immune cluster (burning mouth syndrome, lichen planus, candidiasis, and xerostomia) emerged as a clinically actionable community. Conclusions: Oral diseases form biologically coherent, age-evolving comorbidity communities, and pulpitis is the critical mediating intervention point in the caries-to-tooth-defect cascade. The framework provides a reusable network-medicine substrate for age- and sex-specific risk-stratified oral disease management. Full article

Soybean (Glycine max) is an important model crop for studying plant functional genes, such as the NAC transcription factor (TF) gene family. The NAC transcription factor (TF) family is one of the largest plant-specific TF families and plays critical roles in plant growth, development, and stress responses. In this study, we performed a pan-genome-wide analysis of NAC genes using 29 soybean genomes. A total of 5051 NAC genes were identified and clustered into 245 orthologous gene groups (OGGs), including 58 core, 88 soft-core, 32 shell, and 67 cloud groups. Based on phylogenetic relationships, the representative NAC OGGs were assigned to 18 subfamilies, 17 of which contained soybean NAC genes. Gene duplication analysis indicated that whole-genome duplication (WGD)/segmental duplication was the predominant driver of NAC family expansion, accounting for 90.88% of duplication events. Approximately 39.30% of NAC genes carried at least one intact transposable element (TE) within 2 kb upstream or downstream regions. NAC genes with copy number variation (CNV) harbored more nearby TEs than non-CNV genes (1.54 vs. 1.31 TEs per gene), and dispensable NAC genes contained more nearby TEs than core NAC genes (1.59 vs. 1.33 TEs per gene). These results indicate a significant association between local TE abundance and NAC gene CNV or dispensability. Selection pressure analysis showed that dispensable NAC genes had higher Ka, Ks, and Ka/Ks values than core genes, suggesting relatively relaxed evolutionary constraints. Expression profiling across six tissues revealed distinct transcriptional patterns among NAC subfamilies. Structurally conserved subfamilies generally showed broader expression, whereas structurally divergent subfamilies displayed greater expression variability. Regulatory network and Gene Ontology (GO) enrichment analyses suggested that conserved subfamilies were mainly associated with stress responses, while divergent subfamilies were related to cell wall regulation, signal transduction, and ion homeostasis. Further analysis of Wm82 drought RNA-seq data prioritized several putative drought-responsive NAC candidates, including Glyma.16G043200, Glyma.06G248900, Glyma.07G050600, Glyma.12G206900, and Glyma.18G261300. Overall, these findings elucidate the mechanisms of expansion and the functional divergence of the NAC gene family at the soybean pan-genome level, providing a theoretical basis for understanding NAC gene evolution and facilitating future crop improvement. Full article

Background: Spatial analysis methods, including model-based geostatistics (MBG), small-area estimation (SAE), and cluster detection, are increasingly used to map subnational immunisation coverage and identify geographic inequalities in low- and middle-income countries. However, the extent to which these methods capture the multidimensional determinants of immunisation uptake, and whether their outputs inform programme decisions in practice, remains unclear. Methods: We conducted a critical scoping review following the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews guidelines, systematically searching PubMed and Google Scholar for studies applying spatial statistical methods to childhood immunisation coverage or equity. Findings were synthesised using a combination of descriptive summary and thematic and interpretive synthesis. Results: We included 50 studies from the 421 papers identified. Spatial methods have successfully revealed subnational coverage inequalities that national averages obscure, and studies developed in collaboration with national programme teams, integrating routine health system data alongside household surveys, produced the most operationally relevant outputs. However, most studies relied exclusively on survey data with a limited incorporation of supply-side determinants, and few discussed how uncertainty in estimates should constrain downstream use. Although a growing number of studies articulated clear implementation pathways, confirmed programmatic uptake of spatial outputs remained largely undocumented. The emergence of machine learning approaches (8 of 50 studies) offers predictive gains but introduces additional challenges around transparency and quality assurance for governance use. Conclusions: Spatial methods are becoming more frequently used for immunisation but are more likely to contribute to immunisation equity goals when co-produced with programme teams, matched to decision-relevant geographies, and accompanied by transparent documentation of model assumptions and limitations. Future research should prioritise quality frameworks for algorithm-assisted health estimates and systematic evaluation of whether spatial outputs improve decision-making relative to existing data sources. Full article