Search Results (77)

Geographic Information Systems (GIS) are central to spatial evidence in Environmental Impact Assessment (EIA). In this review, GIS is used in a broad, integrative sense to refer to an ecosystem of geospatial technologies—such as remote sensing (RS) and GPS—where GIS serves as the core platform for managing, analyzing, and communicating spatial data throughout the EIA process. GIS plays a crucial role at each stage of EIA, from baseline data collection to spatial analysis, ecological sensitivity mapping, impact prediction, scenario simulation, and landscape connectivity assessment. These capabilities support alternatives analysis, risk communication, and decision-making in EIA. This paper synthesizes thematic evidence and presents case studies to illustrate the synergies between GIS, remote sensing, GeoAI, and multisource data fusion. It highlights operational workflows and key deliverables for EIA applications, including urban expansion, transport corridors, and protected-area management. We identify persistent challenges in data quality and standardization, interoperability, model uncertainty, and policy gaps. To address them, we propose a minimum geospatial dataset with clear metadata standards, interpretable GeoAI paired with formal sensitivity analysis, IoT–GIS pipelines for real-time monitoring and adaptive management, and the systematic inclusion of cumulative effects and climate scenarios. By linking GIS methods to typical decision points and reporting standards in EIA, this review clarifies where GIS adds value, how to quantify and communicate uncertainty, and how to align analytical outputs with regulatory requirements and stakeholder expectations. The study offers a practical framework and implementation checklist for standardized, transparent, and reproducible EIA processes, contributing to evidence-based ecological governance. Full article

The Lactobacillaceae family encompasses microorganisms of exceptional ecological and biotechnological importance, serving as central agents in food fermentations, health applications, and nutrient cycling across diverse environments. Despite their broad functional and phylogenetic diversity, the global distribution and ecological specialization of Lactobacillaceae are not yet fully understood. In this study, we performed a comprehensive analysis of over 2 million records from the NCBI database to survey and trace the ecological landscape of Lactobacillaceae across thousands of distinct habitats. Our results reveal that food products and animal hosts represent the primary ecological niches for members of this family. The examined taxa exhibit a broad spectrum of ecological strategies, ranging from generalists with wide environmental adaptability to specialists with strict niche preferences. Notably, our findings highlight a profound geographical and ecological sampling bias, with unclassified taxids frequent in animal gastrointestinal tracts, soils, and especially in living plant tissues—habitats identified as promising frontiers for discovering novel biodiversity. The obtained results emphasize the urgent need for expanded sampling efforts in underexplored geographic regions such as Africa, Antarctica, the Arctic, South America, and Central Asia to capture a more complete picture of Lactobacillaceae diversity. The study underscores the necessity of implementing standardized, metadata-rich data deposition practices to enable unbiased, large-scale ecological and evolutionary analyses. Ultimately, these insights not only deepen our fundamental knowledge of Lactobacillaceae diversity but also provide a strategic framework for future bioprospecting, fostering the discovery of novel strains and expanding the biotechnological potential of this influential bacterial family. Full article

Vector geographic data require strict preservation of coordinate precision and topological integrity. However, their open transmission poses simultaneous challenges for copyright protection and data security. To address these issues, this study proposes a reversible watermarking framework that integrates difference expansion (DE) for lossless coordinate recovery, the Arnold transform for watermark encryption, and a metadata-assisted dual restoration mechanism to ensure geometric and topological consistency after embedding. Experimental evaluations on multiple vector datasets—including administrative boundaries, hydrographic networks, and road layers—demonstrate that the proposed method achieves near-zero distortion (RMSE ≈ 10⁻¹⁶), complete reversibility, and strong robustness against geometric and noise attacks, outperforming conventional DFT- and QIM-based schemes in terms of imperceptibility and restoration accuracy. The approach provides an efficient and verifiable solution for secure sharing and copyright protection of vector geographic data, contributing to reliable data provenance and trustworthy spatial information management. Full article

In this paper, we present a unique method to determine the level of bot contamination of web-based user agents. It is common practice for bots and robotic agents to masquerade as human-like to avoid content and performance limitations. This paper continues our previous work, using over 600 million web log entries collected from over 4000 domains to derive and generalize how the prominence of specific web browser versions progresses over time, assuming genuine human agency. Here, we introduce a parametric model capable of reproducing this progression in a tunable way. This simulation allows us to tag human-generated traffic in our data accurately. Along with the highest confidence self-tagged bot traffic, we train a Transformer-based classifier that can determine the bot contamination—a botness metric of user-agents without prior labels. Unlike traditional syntactic or rule-based filters, our model learns temporal patterns of raw and heuristic-derived features, capturing nuanced shifts in request volume, response ratios, content targeting, and entropy-based indicators over time. This rolling window-based pre-classification of traffic allows content providers to bin streams according to their bot infusion levels and direct them to several specifically tuned filtering pipelines, given the current load levels and available free resources. We also show that aggregated traffic data from multiple sources can enhance our model’s accuracy and can be further tailored to regional characteristics using localized metadata from standard web server logs. Our ability to adjust the heuristics to geographical or use case specifics makes our method robust and flexible. Our evaluation highlights that 65% of unclassified traffic is bot-based, underscoring the urgency of robust detection systems. We also propose practical methods for independent or third-party verification and further classification by abusiveness. Full article

Background: Population persistence for organisms to survive in a world with a rapidly changing climate will require either dispersal to suitable areas, evolutionary adaptation to altered conditions and/or sufficient phenotypic plasticity to withstand it. Given the slow growth and geographically isolated populations of many tree species, there is a high likelihood of local adaption or the acclimation of functional traits in these populations across the UK. Objectives: Given the slow growth and often isolated populations of Tilia cordata (lime tree), we hypothesised that there is a high likelihood of local adaptation or the acclimation of metabolic traits in these populations across the UK. Our aim was to test if the functional metabolomic traits of Tilia cordata (lime tree), collected in situ from natural populations, varied within and between populations and to compare this to neutral allele variation in the population. Methods: We used a metabolic fingerprinting approach to obtain a snapshot of the metabolic status of leaves collected from T. cordata from six populations across the UK. Environmental metadata, longer-term functional traits (specific leaf area) and neutral allelic variation in the population were also measured to assess the plastic capacity and local adaptation of the species. Results: The metabolic fingerprints derived from leaf material collected and fixed in situ from individuals in six populations of T. cordata across its UK range were similar, despite contrasting environmental conditions during sampling. Neutral allele frequencies showed almost no significant group structure, indicating low differentiation between populations. The specific leaf area did vary between sites. Conclusions: The low metabolic variation between UK populations of T. cordata despite contrasting environmental conditions during sampling indicates high levels of phenotypic plasticity. Full article

This research addresses the long-standing dependence on printed maps for navigation and highlights the limitations of existing digital services like Google Street View and Google Street View Player in providing comprehensive solutions for route analysis and understanding. The absence of a systematic approach to route analysis, issues related to insufficient street view images, and the lack of proper image mapping for desired roads remain unaddressed by current applications, which are predominantly client-based. In response, we propose an innovative automatic system designed to generate videos depicting road routes between two geographic locations. The system calculates and presents the route conventionally, emphasizing the path on a two-dimensional representation, and in a multimedia format. A prototype is developed based on a cloud-based client–server architecture, featuring three core modules: frames acquisition, frames analysis and elaboration, and the persistence of metadata information and computed videos. The tests, encompassing both real-world and synthetic scenarios, have produced promising results, showcasing the efficiency of our system. By providing users with a real and immersive understanding of requested routes, our approach fills a crucial gap in existing navigation solutions. This research contributes to the advancement of route planning technologies, offering a comprehensive and user-friendly system that leverages cloud computing and multimedia visualization for an enhanced navigation experience. Full article

Accurate estimation of long-term average (LTA) discharge is fundamental for water resource assessment, infrastructure planning, and hydrological modeling, yet it remains a significant challenge, particularly in data-scarce or ungauged basins. This study introduces an advanced machine learning framework to estimate long-term average discharge using globally available hydrological station metadata from the Global Runoff Data Centre (GRDC). The methodology involved comprehensive data preprocessing, extensive feature engineering, log-transformation of the target variable, and the development of multiple predictive models, including a custom deep neural network with specialized pathways and gradient boosting machines (XGBoost, LightGBM, CatBoost). Hyperparameters were optimized using Bayesian techniques, and a weighted Meta Ensemble model, which combines predictions from the best individual models, was implemented. Performance was rigorously evaluated using R², RMSE, and MAE on an independent test set. The Meta Ensemble model demonstrated superior performance, achieving a Coefficient of Determination (R²) of 0.954 on the test data, significantly surpassing baseline and individual advanced models. Model interpretability analysis using SHAP (Shapley Additive explanations) confirmed that catchment area and geographical attributes are the most dominant predictors. The resulting model provides a robust, accurate, and scalable data-driven solution for estimating long-term average discharge, enhancing water resource assessment capabilities and offering a powerful tool for large-scale hydrological analysis. Full article

Objectives: The aim of this study was to assess the taxonomic diversity of the microbiota associated with periapical lesions of endodontic origin and to determine whether microbial profiles vary across different populations and clinical characteristics using a unified in silico analysis of next-generation sequencing (NGS) data. Methods: Raw 16S rRNA sequencing data from three published studies were retrieved from the NCBI Sequence Read Archive and reprocessed using a standardized bioinformatics pipeline. Amplicon sequence variants were inferred using DADA2, and taxonomic assignments were performed using BLASTN against a curated 16S rRNA reference database. Alpha and beta diversity analyses were conducted using QIIME 2 and R, and differential abundance was assessed with ANCOM-BC2. Statistical comparisons were made based on population, sex, symptomatology, and other clinical metadata. Results: A total of 38 periapical lesion samples yielded 566,223 high-confidence reads assigned to 347 bacterial species. Significant differences in microbial composition were observed between geographic regions (China vs. Spain), sexes, and symptoms. Core species such as Fretibacterium sp. HMT 360 and Porphyromonas endodontalis were prevalent across datasets. Porphyromonas gingivalis and Fusobacterium nucleatum were found in abundance across all three studies. Beta diversity metrics revealed distinct clustering by study and country. Symptomatic lesions were associated with higher abundance of Alloprevotella tannerae and Prevotella oris. Conclusions: The periapical lesion microbiota is taxonomically diverse and varies significantly by geographic and clinical features. Full article

Background: Residual blood specimens offer a cost- and time-efficient alternative for conducting serological surveys. However, their use is often criticized due to potential issues with the representativeness of the target population and/or limited availability of associated metadata. We conducted a scoping review to examine where, when, how, and why residual blood specimens have been used in serological surveys for vaccine-preventable diseases (VPDs) and how potential selection biases are addressed. Methods: The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines and identified relevant papers published in 1990–2022. Results: A total of 601 articles met the inclusion criteria after title, abstract screening, and full-text review. The most studied VPDs using residual blood specimens were COVID-19 (27%), hepatitis E (16%), hepatitis B (10%), influenza (9%), HPV (7%), and measles (7%). Residual blood specimens were primarily sourced from diagnostic specimens (61%) or blood and plasma donations (37%). Almost all articles used specimens linked to basic demographic data (e.g., age and sex), with 47% having access to extended demographic data (e.g., geographic location). Common strategies to address potential biases included comparing results with published estimates (78%) and performing stratified analyses (71%). Conclusions: Residual blood specimens are widely used in seroprevalence studies, particularly during emerging disease outbreaks when rapid estimates are critical. However, this review highlighted inconsistencies in how researchers analyze and report the use of residual specimens. We propose a set of recommendations to improve the analysis, reporting, and ethical considerations of serological surveys using residual specimens. Full article

Next Generation Sequencing technologies are essential in public health surveillance for tracking pathogen evolution, spread, and the emergence of new variants. However, the extensive sequencing of viral genomes during recent pandemics has highlighted the limitations of traditional molecular phylogenetic algorithms in capturing fine-grained evolutionary details when analyzed sequences are highly similar and datasets are large-scale. VirNA (Viral Network Analyzer) addresses this challenge by reconstructing detailed mutation patterns and tracing pathogen evolutionary routes in specific geographical regions through Minimum Spanning Networks. It enables users to analyze thousands of sequences, generating networks where nodes represent genomic sequences linked to their metadata, while edges represent potential evolutionary pathways. VirNA is a powerful tool for analyzing large, high-quality datasets, providing detailed insights into rapid pathogen evolution over short time periods, with potential applications in pandemic surveillance. Full article

Background: Tumor organoid and tumor-on-chip (ToC) platforms replicate aspects of the anatomical and physiological states of tumors. They, therefore, serve as models for investigating tumor microenvironments, metastasis, and immune interactions, especially for precision drug testing. To map the changing research diversity and focus in this field, we performed a quality-controlled text analysis of categorized academic publications and clinical studies. Methods: Previously, we collected metadata of academic publications on organoids or organ-on-chip platforms from PubMed, Web of Science, Scopus, EMBASE, and bioRxiv, published between January 2011 and June 2023. Here, we selected documents from this metadata corpus that were computationally determined as relevant to tumor research and analyzed them using an in-house text analysis algorithm. Additionally, we collected and analyzed metadata from ClinicalTrials.gov of clinical studies related to tumor organoids or ToC as of March 2023. Results and Discussion: From 3551 academic publications and 139 clinical trials, we identified 55 and 24 tumor classes modeled as tumor organoids and ToC models, respectively. The research was particularly active in neural and hepatic/pancreatic tumor organoids, as well as gastrointestinal, neural, and reproductive ToC models. Comparative analysis with cancer statistics showed that lung, lymphatic, and cervical tumors were under-represented in tumor organoid research. Our findings also illustrate varied research topics, including tumor physiology, therapeutic approaches, immune cell involvement, and analytical techniques. Mapping the research geographically highlighted the focus on colorectal cancer research in the Netherlands, though overall the specific research focus of countries did not reflect regional cancer prevalence. These insights not only map the current research landscape but also indicate potential new directions in tumor model research. Full article

Soil salinization is a significant global ecological issue that leads to soil degradation and is recognized as one of the primary factors hindering the sustainable development of irrigated farmlands and deserts. The integration of remote sensing (RS) and machine learning algorithms is increasingly employed to deliver cost-effective, time-efficient, spatially resolved, accurately mapped, and uncertainty-quantified soil salinity information. We reviewed articles published between January 2016 and December 2023 on remote sensing-based soil salinity prediction and synthesized the latest research advancements in terms of innovation points, data, methodologies, variable importance, global soil salinity trends, current challenges, and potential future research directions. Our observations indicate that the innovations in this field focus on detection depth, iterations of data conversion methods, and the application of newly developed sensors. Statistical analysis reveals that Landsat is the most frequently utilized sensor in these studies. Furthermore, the application of deep learning algorithms remains underexplored. The ranking of soil salinity prediction accuracy across the various study areas is as follows: lake wetland (R² = 0.81) > oasis (R² = 0.76) > coastal zone (R² = 0.74) > farmland (R² = 0.71). We also examined the relationship between metadata and prediction accuracy: (1) Validation accuracy, sample size, number of variables, and mean sample salinity exhibited some correlation with modeling accuracy, while sampling depth, variable type, sampling time, and maximum salinity did not influence modeling accuracy. (2) Across a broad range of scales, large sample sizes may lead to error accumulation, which is associated with the geographic diversity of the study area. (3) The inclusion of additional environmental variables does not necessarily enhance modeling accuracy. (4) Modeling accuracy improves when the mean salinity of the study area exceeds 30 dS/m. Topography, vegetation, and temperature are relatively significant environmental covariates. Over the past 30 years, the global area affected by soil salinity has been increasing. To further enhance prediction accuracy, we provide several suggestions for the challenges and directions for future research. While remote sensing is not the sole solution, it provides unique advantages for soil salinity-related studies at both regional and global scales. Full article

Urban settlements can support significant biodiversity and provide a wide range of ecosystem services. Remote sensing (RS) offers valuable tools for monitoring and conserving urban biodiversity. Our research, funded by the Italian Recovery and Resilience Plan (National Biodiversity Future Centre—Urban Biodiversity), undertakes a systematic scientific review to assess the current status and future prospects of urban biodiversity evaluation using RS. An extensive literature search of indexed peer-reviewed papers published between 2008 and 2023 was conducted on the Scopus database, using a selective choice of keywords. After screening the titles, abstracts, and keywords of 500 articles, 117 relevant papers were retained for meta-data analysis. Our analysis incorporated technical (e.g., sensor, platform, algorithm), geographic (e.g., country, city extent, population) and ecological (biodiversity target, organization level, biome) meta-data, examining their frequencies, temporal trends (Generalized Linear Model—GLM), and covariations (Cramer’s V). The rise in publications over time is linked to the increased availability of imagery, enhanced computing power, and growing awareness of the importance of urban biodiversity. Most research focused on the Northern Hemisphere and large metropolitan areas, with smaller cities often overlooked. Consequently, data coverage is predominantly concentrated on Mediterranean and temperate habitats, with limited attention given to boreal, desert, and tropical biomes. A strong association was observed between the source of RS data (e.g., satellite missions), pixel size, and the purpose of its use (e.g., modeling, detection). This research provides a comprehensive summary of RS applications for evaluating urban biodiversity with a focus on the biomes studied, biodiversity targets, and ecological organization levels. This work can provide information on where future studies should focus their efforts on the study of urban biodiversity using remote sensing instruments in the coming years. Full article

Efforts to establish Pakistan’s National Spatial Data Infrastructure (NSDI) have been underway for the past 15 years, and therefore it is necessary to gauge the current progress to channelize efforts into areas that need improvement. This article assessed Pakistan’s NSDI implementation efforts through well-established approaches, including the SDI readiness model, organizational aspects, and state of play. The data were collected from Spatial Data Infrastructure (SDI) and Geographic Information System (GIS) experts. The findings underscored challenges related to human resources, SDI education/culture, long-term vision, lack of awareness of geoinformation (GI), sustainable funding, metadata availability, online geospatial services, and geospatial standards hindering NSDI development in Pakistan. However, certain factors exhibit favorable standings, such as the legal framework for NSDI establishment, web connectivity, geospatial software availability, the unavailability of core spatial datasets, and institutional leadership. Thus, to enhance the development of NSDI in Pakistan, recommendations include bolstering financial and human resources, improving online geospatial presence, and fostering a long-term vision for NSDI. Full article

Geospatial data, especially remote sensing (RS) data, are of significant importance for public services and production activities. Expertise is critical in processing raw data, generating geospatial information, and acquiring domain knowledge and other remote sensing applications. However, existing geospatial service platforms are more oriented towards the professional users in the implementation process and final application. Building appropriate geographic applications for non-professionals remains a challenge. In this study, a geospatial data service architecture is designed that links desktop geographic information system (GIS) software and cloud-based platforms to construct an efficient user collaboration platform. Based on the scalability of the platform, four web apps with different themes are developed. Data in the fields of ecology, oceanography, and geology are uploaded to the platform by the users. In this pilot phase, the gap between non-specialized users and experts is successfully bridged, demonstrating the platform’s powerful interactivity and visualization. The paper finally evaluates the capability of building spatial data infrastructures (SDI) based on GeoNode and discusses the current limitations. The support for three-dimensional data, the improvement of metadata creation and management, and the fostering of an open geo-community are the next steps. Full article