Search Results (202)

This study presents the first systematic investigation of ancient Egyptian votive animal mummy wrappings, based on the analysis of an extensive dataset encompassing specimens from various museum collections and archaeologicalcontexts. The research addresses the long-standing neglect and fragmented understanding of the wrapping chaîne opératoire and aims to establish a consistent terminology, as the different stages of the wrapping sequence, bundle shapes, and decorative patterns have often been described vaguely. Through an interdisciplinary methodology that integrates photogrammetry, colorant identification, textile analysis, and experimental archeology, the study explores the complexity of wrapping practices across their different stages. This approach offers new insights into the structural logic, raw material selection, and design conventions behind this production. The analysis reveals that the bundles exhibit standardized shapes and decorative patterns grounded in well-established visual criteria and manufacturing sequences. These findings demonstrate that the wrappings reflect a codified visual language and a high level of technical knowledge, deeply rooted in Egyptian tradition. The study also emphasizes its economic implications: the wrapping significantly enhanced the perceived value of the offering, becoming the primary element influencing both its material and symbolic worth. Ultimately, this work provides an interpretative framework for understanding wrapping as an essential medium of ritual sacralization for votive animal mummies, allowing the individual prayer to be effectively conveyed to the intended deity. Consequently, this research marks a significant step forward in advancing the technical, aesthetic, and ritual insight of wrapping practices, which preserve a wealth of still-overlooked information. Full article

Crop yield prediction (CYP) has become increasingly critical in addressing the adverse effects of abnormal climate and enhancing agricultural productivity. This review investigates the application of advanced Artificial Intelligence (AI) techniques including Machine Learning (ML), Deep Learning (DL), Ensemble Learning, and Explainable AI (XAI) to CYP. It also explores the use of remote sensing and imaging technologies, identifies key environmental factors, and analyzes the primary causes of yield reduction. A wide diversity of input features was observed across studies, largely influenced by data availability and specific research goals. Stepwise feature selection was found to be more effective than increasing feature volume in improving model accuracy. Frequently used algorithms include Random Forest (RF) and Support Vector Machines (SVM) for ML, Artificial Neural Networks (ANNs) and Convolutional Neural Networks (CNNs) for DL, as well as stacking-based ensemble methods. Although XAI remains in the early stages of adoption, it shows strong potential for interpreting complex, multi-dimensional CYP models. Hyperspectral imaging (HSI) and multispectral imaging (MSI), often collected via drones, were the most commonly used sensing techniques. Major factors contributing to yield reduction included atmospheric and soil-related conditions under abnormal climate, as well as pest outbreaks, declining soil fertility, and economic constraints. Providing a comprehensive overview of AI-driven CYP frameworks, this review offers insights that support the advancement of precision agriculture and the development of data-informed agricultural policies. Full article

Molecular imaging and precision therapies are transforming ophthalmology, enabling earlier and more accurate diagnosis and targeted treatment of sight-threatening diseases. This review focuses on age-related macular degeneration, diabetic retinopathy, glaucoma, and uveitis, examining high-resolution imaging techniques such as optical coherence tomography (OCT), OCT angiography, MALDI-MSI, and spatial transcriptomics. Artificial intelligence supports these methods by improving image interpretation and enabling personalized analysis. The review also discusses therapeutic advances, including gene therapies (e.g., AAV-mediated RPE65 delivery), stem cell-based regenerative approaches, and biologics targeting inflammatory and neovascular processes. Targeted molecular therapies targeting specific signaling pathways, such as MAPK, are also explored. The combination of single-cell transcriptomics, proteomics, and machine learning facilitates the development of personalized treatment strategies. Although these technologies hold enormous potential, their implementation in routine clinical care requires further validation, regulatory approval, and long-term safety assessment. This review highlights the potential and challenges of integrating molecular imaging and advanced therapies in the future of precision ophthalmic medicine. Full article

Water hyacinth (Eichhornia crassipes), native to South America, is a highly invasive aquatic plant threatening freshwater ecosystems worldwide. Its rapid proliferation negatively impacts water quality, biodiversity, and navigation. Remote sensing offers an effective means to monitor such aquatic environments by providing extensive spatial and temporal coverage with improved resolution. This systematic review examines remote sensing applications for monitoring water hyacinth and water quality in studies published from 2014 to 2024. Seventy-eight peer-reviewed articles were selected from the Web of Science, Scopus, and Google Scholar following strict criteria. The research spans 25 countries across five continents, focusing mainly on lakes (61.5%), rivers (21%), and wetlands (10.3%). Approximately 49% of studies addressed water quality, 42% focused on water hyacinth, and 9% covered both. The Sentinel-2 Multispectral Instrument (MSI) was the most used sensor (35%), followed by the Landsat 8 Operational Land Imager (OLI) (26%). Multi-sensor fusion, especially Sentinel-2 MSI with Unmanned Aerial Vehicles (UAVs), was frequently applied to enhance monitoring capabilities. Detection accuracies ranged from 74% to 98% using statistical, machine learning, and deep learning techniques. Key challenges include limited ground-truth data and inadequate atmospheric correction. The integration of high-resolution sensors with advanced analytics shows strong promise for effective inland water monitoring. Full article

Background: Colorectal liver metastasis (CRLM) represents a major clinical challenge in oncology, affecting 25–50% of colorectal cancer patients and significantly impacting survival. While multimodal therapies—including surgical resection, systemic chemotherapy, and local ablative techniques—have improved outcomes, prognosis remains heterogeneous due to variations in tumor biology, patient factors, and institutional practices. Methods: This review synthesizes current evidence on prognostic factors influencing CRLM management, encompassing clinical (e.g., tumor burden, anatomic distribution, timing of metastases), biological (e.g., CEA levels, inflammatory markers), and molecular (e.g., RAS/BRAF mutations, MSI status, HER2 alterations) determinants. Results: Key findings highlight the critical role of molecular profiling in guiding therapeutic decisions, with RAS/BRAF mutations predicting resistance to anti-EGFR therapies and MSI-H status indicating potential responsiveness to immunotherapy. Emerging tools like circulating tumor DNA (ctDNA) and radiomics offer promise for dynamic risk stratification and early recurrence detection, while the gut microbiome is increasingly recognized as a modulator of treatment response. Conclusions: Despite advancements, challenges persist in standardizing resectability criteria and integrating multidisciplinary approaches. Current guidelines (NCCN, ESMO, ASCO) emphasize personalized strategies but lack granularity in terms of incorporating novel biomarkers. This exhaustive review underscores the imperative for the development of a unified, biomarker-integrated framework to refine CRLM management and improve long-term outcomes. Full article

Mangroves form the dominant coastal plant community of low-energy tropical intertidal habitats and provide critical ecosystem services to humans and the environment. However, more frequent and increasingly powerful hurricanes and storm surges are creating additional pressure on the natural resilience of these threatened coastal ecosystems. Advances in remote sensing techniques and approaches are critical to providing robust quantitative monitoring of post-storm mangrove forest recovery to better prioritize the often-limited resources available for the restoration of these storm-damaged habitats. Here, we build on previously utilized spatial and temporal ranges of European Space Agency (ESA) Sentinel satellite imagery to monitor and map the recovery of the mangrove forests of the British Virgin Islands (BVI) since the occurrence of back-to-back category 5 hurricanes, Irma and Maria, on September 6 and 19 of 2017, respectively. Pre- to post-storm changes in coastal mangrove forest health were assessed annually using the normalized difference vegetation index (NDVI) and moisture stress index (MSI) from 2016 to 2023 using Google Earth Engine. Results reveal a steady trajectory towards forest health recovery on many of the Territory’s islands since the storms’ impacts in 2017. However, some mangrove patches are slower to recover, such as those on the islands of Virgin Gorda and Jost Van Dyke, and, in some cases, have shown a continued decline (e.g., Prickly Pear Island). Our work also uses a linear ANCOVA model to assess a variety of geospatial, environmental, and anthropogenic drivers for mangrove recovery as a function of NDVI pre-storm and post-storm conditions. The model suggests that roughly 58% of the variability in the 7-year difference (2016 to 2023) in NDVI may be related by a positive linear relationship with the variable of population within 0.5 km and a negative linear relationship with the variables of northwest aspect vs. southwest aspect, island size, temperature, and slope. Full article

Remote sensing techniques that use spectral indices (SIs) are essential for monitoring vegetation recovery after wildfires. However, there is a critical gap in the comparability of SI responses across ecoregions due to ecological variability. In this study, a meta-analysis was conducted to evaluate the capacity of different SIs (GCI, MSI, NBR, NDVI, NDII, and EVI2) to reflect the effect of post-wildfire emergency works on early recovery of vegetation in four Spanish ecoregions. It compared vegetation regrowth between treated and untreated sites, identifying the most sensitive SI for monitoring this recovery. All indices except EVI2 detected significantly better recovery in treated areas; among these, GCI was the most sensitive and NDII the least. The effect of treatment on recovery measured through SI is influenced by site covariates (fire severity, physiography, post-fire action period, post-fire climate, and edaphic characteristics). Finally, random mixed models showed that annual precipitation lower than 700 mm, diurnal temperature over 21 °C, soils with finer texture, and water content under 33% are quantitative limits of the treatment effectiveness on vegetation recovery. Overall, the study highlighted the importance of immediate interventions after fires, especially in the first six months, and advocated context-specific management strategies based on fire severity, ecoregion, soil properties, and climate to optimize vegetation recovery. Full article

Chemical endodontic irritants can lead to the demineralization of the inorganic tooth structure, its loss of integrity, microhardness changes, erosion, and an increased risk of fractures. We investigated the action of iron oxide nanomagnet particles (IONPs) as an irrigant solution for improving hardness and identifying the concentration of element ions in the root canal. There were six groups in total: a control group (no treatment) and experimental groups (UN: ultrasound agitation normal saline, UI: ultrasound agitation IONPs, MSI: magnetic field and endodontic needle with syringe agitation IONPs, MUI: magnetic field and ultrasound agitation IONPs, and EDTA: ethylenediaminetetraacetic acid). We hypothesized that IONPs with magnetic agitation would preserve microhardness better than EDTA. Vickers hardness testing was used to evaluate microhardness, which was then analyzed using energy-dispersive X-ray spectroscopy (EDS) to investigate the calcium/phosphorus ratio and the presence of iron. The IONP groups exhibit a higher VHN value than the EDTA group (p < 0.05). These results support our hypothesis, indicating that utilizing an IONP irrigant solution with an external magnetic field does not change microhardness but enhances it compared to the EDTA group, suggesting that employing an external magnetic field to deliver nanoparticles to the root canal wall does not affect the properties of the tooth structure compared to conventional instrumentation techniques, which lead to unnecessary loss of root structure. Full article

In this article, a multidisciplinary methodological approach for studying a wooden panel painting is applied. The theoretical framework, within which this research has arisen, is the application of state-of-the-art non-destructive techniques for addressing issues concerning the constituting parts and composing materials of the artwork. Hereby, a post-Byzantine icon was studied, which was dated back to 1836. It is a painting executed on a wooden panel, with a decorated wooden frame attached. The artifact was thoroughly investigated through the application of infrared thermography (IRT), multispectral imaging (MSI), and macroscopic X-ray fluorescence spectrometry (MA-XRF). These analyses provided crucial information about the verso of the painting (i.e., the wooden panel and the frame) and allowed for the revelation of important details of the recto of the painting, which were not visible due to the presence of an old, decayed varnish. Additionally, through the detailed mapping of the distribution of various chemical elements on the recto of the painting and the frame, it was possible to identify the materials used and techniques employed. It is therefore shown that, when combined, the non-destructive methodologies in consideration can provide adequate information referring to the materiality and state of preservation of panel paintings, permitting the conservator to proceed to a tailored conservation treatment. Full article

Microplastic (MP) contamination is a growing environmental concern with significant impacts on ecosystems, the economy, and potentially human health. However, accurately detecting and characterizing MPs in biological samples remains a challenge due to the complexity of biological matrices and analytical limitations. This study presents a novel, non-destructive visible near-infrared multispectral imaging (Vis-NIR-MSI) method combined with a supervised self-organizing map (SOM) to enable rapid qualitative and quantitative analysis of MPs in seafood. We specifically aimed to identify and differentiate four types of microplastics, namely PET, PE, PP, and PS, in the range 1–4 mm, present on the surface of minced shrimp and shrimp shell. For quantification, MPs were incorporated into minced shrimp surface at concentrations ranging from 0.04% to 1% w/w. The modified model achieved a high coefficient of determination (R² > 0.99) for PE and PP quantification. Unlike conventional techniques, this approach eliminates the need for pre-sorting or chemical processing, offering a cost-effective and efficient solution for large-scale monitoring of MPs in seafood, with potential applications in food safety and environmental protection. Full article

Background: Mass spectrometry imaging (MSI) lipidomics is a subset of spatially resolved techniques wherein lipids are detected by mass spectrometry, allowing their multiplexed detection and acquiring position-correlated spectra along a tissue section. Rapid advances in the field provide solid evidence demonstrating how specific and regulated lipid distribution is in any biological context. Objectives: Herein, we describe the MSI, particularly matrix-assisted laser desorption/ionization (MALDI-MSI), challenges and advantages in defining human lung pathophysiology, particularly in lung cancer and chronic obstructive pulmonary disease, leading causes of death. Methods: MALDI-MSI analysis of lung tissue sections at 25 μm of lateral resolution allowed associating specific lipid profiles with the main tissues present and independently assessing the impact on lipid composition of smoking, chronic inflammation, and lung cancer. Results: Consistent with MALDI-MSI studies in tumor epithelia, arachidonic acid-containing phospholipids increased, agreeing with its role as a precursor of numerous bioactive molecules participating in cell differentiation and malignization. Next, a gene expression dataset of epithelial human non-small cell lung cancer samples was analyzed using system biology approaches, revealing that, consistent with the most relevant changes in lipid profiles, the network dominated by the tumor-associated module included genes tightly involved in phosphatidylinositol and sphingolipid metabolism. Hence, despite the intrinsic difficulties entailed by lung tissue handling, the results strongly encourage future analysis at higher lateral resolutions so that the lipidome changes associated with each lung cellular type, even subtype, could be fully mapped. Therefore, MALDI-MSI lipidomics definitively broadens the options, some still rather unexplored, to delve into pathophysiology at the cell-type level. Full article

With the continuous advancement of remote sensing technology and its growing importance, the need for ready-to-use data has increased exponentially. Satellite platforms such as Sentinel-2, which carries the Multispectral Instrument (MSI) sensor, known for their cost-effectiveness, capture valuable information about Earth in the form of images. However, they encounter a significant challenge in the form of clouds and their shadows, which hinders the data acquisition and processing for regions of interest. This article undertakes a comprehensive literature review to systematically analyze the critical cloud-related challenges. It explores the need for accurate cloud detection, reviews existing datasets, and evaluates contemporary cloud detection methodologies, including their strengths and limitations. Additionally, it highlights the inaccuracies introduced by varying atmospheric and environmental conditions, emphasizing the importance of integrating advanced techniques that can utilize local and global semantics. The review also introduces a structured intercomparison framework to enable standardized evaluation across binary and multiclass cloud detection methods using both qualitative and quantitative metrics. To facilitate fair comparison, a conversion mechanism is highlighted to harmonize outputs across methods with different class granularities. By identifying gaps in current practices and datasets, the study highlights the importance of innovative, efficient, and scalable solutions for automated cloud detection, paving the way for unbiased evaluation and improved utilization of satellite imagery across diverse applications. Full article

The Flexible Combined Imager (FCI) instrument aboard the Meteosat Third Generation (MTG-I) geostationary satellite, launched in December 2022 and operational since September 2024, by providing shortwave infrared (SWIR), medium infrared (MIR) and thermal infrared (TIR) data, with an image refreshing time of 10 min and a spatial resolution ranging between 500 m in the high-resolution (HR) and 1–2 km in the normal-resolution (NR) mode, may represent a very promising instrument for monitoring thermal volcanic activity from space, also in operational contexts. In this work, we assess this potential by investigating the recent Mount Etna (Italy, Sicily) eruption of February–March 2025 through the analysis of daytime and night-time SWIR observations in the NR mode. The time series of a normalized hotspot index retrieved over Mt. Etna indicates that the effusive eruption started on 8 February at 13:40 UTC (14:40 LT), i.e., before information from independent sources. This observation is corroborated by the analysis of the MIR signal performed using an adapted Robust Satellite Technique (RST) approach, also revealing the occurrence of less intense thermal activity over the Mt. Etna area a few hours before (10.50 UTC) the possible start of lava effusion. By analyzing changes in total SWIR radiance (TSR), calculated starting from hot pixels detected using the preliminary NHI algorithm configuration tailored to FCI data, we inferred information about variations in thermal volcanic activity. The results show that the Mt. Etna eruption was particularly intense during 17–19 February, when the radiative power was estimated to be around 1–3 GW from other sensors. These outcomes, which are consistent with Multispectral Instrument (MSI) and Operational Land Imager (OLI) observations at a higher spatial resolution, providing accurate information about areas inundated by the lava, demonstrate that the FCI may provide a relevant contribution to the near-real-time monitoring of Mt. Etna activity. The usage of FCI data, in the HR mode, may further improve the timely identification of high-temperature features in the framework of early warning contexts, devoted to mitigating the social, environmental and economic impacts of effusive eruptions, especially over less monitored volcanic areas. Full article

Background/Objectives: Accurate detection of periprosthetic joint infection (PJI) in patients with inflammatory arthritis (IA), including rheumatoid arthritis (RA), remains challenging due to overlapping inflammatory parameters and the influence of immunosuppressive regimens. Methods: A narrative review was conducted using PubMed/MEDLINE (2010–2025). Search terms included “periprosthetic joint infection”, “inflammatory arthritis”, “rheumatoid arthritis”, “diagnosis”, “biomarkers”, “synovial fluid”, and “immunosuppression”. Eventually, 50 studies were included. Results: IA patients diagnosed with PJI are more frequently younger, female, and present with a higher burden of comorbidities and an increased rate of false-positive histological findings and culture-negative infections. Standard biomarkers, such as serum C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), as well as synovial fluid white blood cell count and polymorphonuclear leukocyte percentage, have a low to moderate value for diagnosing PJI in patients with IA. Optimal thresholds for these tests differ from those recommended by the Musculoskeletal Infection Society (MSIS). Alpha-defensin has demonstrated superior diagnostic performance among synovial fluid biomarkers included in MSIS criteria. Novel markers, such as serum bactericidal permeability-increasing protein (BPI) and neutrophil elastase-2 (ELA-2), as well as synovial C-reactive protein and calprotectin, along with molecular techniques like polymerase chain reaction (PCR), are showing increasing potential. Conclusions: Disease and treatment-related confounders hinder PJI diagnosis in IA. Adjusted thresholds and IA-specific approaches are needed. Further research should validate emerging biomarkers, among which BPI, ELA-2, and synovial CRP show the greatest diagnostic potential and guide perioperative immunosuppressive strategies. Full article

Accurate classification of salt marsh vegetation is vital for conservation efforts and environmental monitoring, particularly given the critical role these ecosystems play as carbon sinks. Understanding and quantifying the extent and types of habitats present in Ireland is essential to support national biodiversity goals and climate action plans. Unmanned Aerial Vehicles (UAVs) equipped with optical sensors offer a powerful means of mapping vegetation in these areas. However, many current studies rely on single-sensor approaches, which can constrain the accuracy of classification and limit our understanding of complex habitat dynamics. This study evaluates the integration of Red-Green-Blue (RGB), Multispectral Imaging (MSI), and Hyperspectral Imaging (HSI) to improve species classification compared to using individual sensors. UAV surveys were conducted with RGB, MSI, and HSI sensors, and the collected data were classified using Random Forest (RF), Spectral Angle Mapper (SAM), and Support Vector Machine (SVM) algorithms. The classification performance was assessed using Overall Accuracy (OA), Kappa Coefficient (k), Producer’s Accuracy (PA), and User’s Accuracy (UA), for both individual sensor datasets and the fused dataset generated via band stacking. The multi-camera approach achieved a 97% classification accuracy, surpassing the highest accuracy obtained by a single sensor (HSI, 92%). This demonstrates that data fusion and band reduction techniques improve species differentiation, particularly for vegetation with overlapping spectral signatures. The results suggest that multi-sensor UAV systems offer a cost-effective and efficient approach to ecosystem monitoring, biodiversity assessment, and conservation planning. Full article