Search Results (1,616)

Coastal lagoons are fragile and dynamic ecosystems that are particularly vulnerable to climate change and anthropogenic pressures such as urbanization and eutrophication. These vulnerabilities highlight the need for frequent and spatially extensive monitoring of water quality (WQ). While satellite remote sensing offers a valuable tool to support this effort, the optical complexity and shallow depths of lagoons pose major challenges for retrieving water column biogeochemical parameters such as chlorophyll-a ([chl-a]) and suspended particulate matter ([SPM]) concentrations. In this study, we develop and evaluate a robust satellite-based processing chain using Sentinel-2 MSI imagery over two French Mediterranean lagoon systems (Berre and Thau), supported by extensive in situ radiometric and biogeochemical datasets. Our approach includes the following: (i) a comparative assessment of six atmospheric correction (AC) processors, (ii) the development of an Optically Shallow Water Probability Algorithm (OSWPA), a new semi-empirical algorithm to estimate the probability of bottom contamination (BC), and (iii) the evaluation of several [chl-a] and [SPM] inversion algorithms. Results show that the Sen2Cor AC processor combined with a near-infrared similarity correction (NIR-SC) yields relative errors below 30% across all bands for retrieving remote-sensing reflectance R_rs(λ). OSWPA provides a spatially continuous and physically consistent alternative to binary BC masks. A new [chl-a] algorithm based on a near-infrared/blue R_rs ratio improves the retrieval accuracy while the 705 nm band appears to be the most suitable for retrieving [SPM] in optically shallow lagoons. This processing chain enables high-resolution WQ monitoring of two coastal lagoon systems and supports future large-scale assessments of ecological trends under increasing climate and anthropogenic stress. Full article

Salt stress is a significant environmental factor that inhibits maize growth and development, severely affecting yield formation. Interestingly, nanomaterials, particularly ZnONPs, can enhance resistance to various stresses and support healthy crop growth. However, the effects of ZnONPs on maize under salt stress remain unclear. This study investigates the effect of foliar and seed exposure to zinc oxide nanoparticles (ZnONPs) on reducing NaCl-induced salt stress in two maize inbred lines (NKY298-1 and NKY211). Over a period of seven days, under 120 mM NaCl, we measured growth, reactive oxygen species (ROS), malondialdehyde (MDA), membrane stability index (MSI), water status (relative water content, RWC), photosynthetic pigments and parameters, selected photosynthetic enzymes, and antioxidant enzyme activities. Then, we propose four composite indices, including stress improvement index (SII), alleviation capacity index (ACI), comprehensive improvement effects (CIE), and comprehensive alleviation capacity (CAC), to rank the effectiveness of ZnONP doses. The findings suggested that 50–100 μM ZnONPs significantly mitigate salt damage, with optimal doses varying by genotype (50 μM for NKY211 and 100 μM for NKY298-1). Notably, the study’s originality lies in its side-by-side composite scoring across 26 traits in two maize genotypes’ seedlings. In conclusion, the findings will provide a new idea for research on the molecular mechanism by which exogenous ZnONPs application improves the salt tolerance of maize seedlings. Full article

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 archaeological contexts. 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 archaeology, 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

Rapid evaluations of farmland soil quality can provide data support for farmland protection and utilization. This study focuses on the soda–saline soil region of Da’an City, Jilin Province, covering an area of 4879 km²; it proposes a framework for evaluating farmland soil quality based on multi-source remote sensing data (Sentinel-2 MSI, GF-5 AHSI hyperspectral and field hyperspectral data). Soil organic matter content, salt content, and pH were selected as indicators of cultivated land soil quality in soda–saline soil areas. A threshold of 20% crop residue cover was set to mask high-cover areas, extracting bare soil information. The spectral indices SI1 and SI2 were utilized to predict the comprehensive grade of soil organic matter + salinity based on the cloud model ($M{E}_c$ = 0.74 and $M{E}_v$ = 0.68). The pH grade was predicted using the red-edge ratio vegetation index (RVIre) ($M{E}_c$ = 0.95 and $M{E}_v$ = 0.98). The short-board method was used to construct a soil quality evaluation system. The results indicate that 13.73% of the cultivated land in Da’an City is of high quality (grade 1), 80.63% is of medium quality (grades 2–3), and 5.65% is of poor quality (grade 4). This study provides a rapid assessment tool for the sustainable management of cultivated land in saline–alkali areas at the county level. Full article

This study numerically investigates the effect of hydrofoil installation on the motion responses and ride comfort of a 20 m monohull vessel operating at 10 knots in regular waves. Linear seakeeping analysis (Maxsurf Motions) and nonlinear computational fluid dynamics (CFD) simulations (STAR-CCM+) are performed to compute response-amplitude operators (RAOs); for the bare hull, the two methods agree within 5%, confirming methodological reliability. The CFD results show that hydrofoils reduce heave and pitch amplitudes by approximately 16% on average. Motion Sickness Incidence (MSI) analysis indicates negligible seasickness under Gentle Breeze conditions, even during prolonged exposure; under Moderate conditions, no seasickness is predicted within 30 min across all encounter frequencies. Although linear analysis cannot directly estimate MSI for hydrofoil-fitted cases, the observed reductions in RAOs imply improved ride comfort. Overall, these findings demonstrate that hydrofoils can enhance motion stability and passenger comfort in small, low-speed vessels, providing quantitative evidence to support design applications. Full article

Accurate rice yield prediction is essential for optimizing water management and supporting decision-making in agricultural systems, particularly in arid environments where irrigation efficiency is critical. This study assessed five machine learning algorithms—Multiple Linear Regression (MLR), Support Vector Regression (SVR, linear and RBF), Partial Least Squares Regression (PLSR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost)—for plot-scale rice yield estimation using Sentinel-2 vegetation indices (VIs) during the 2022 and 2023 seasons in the Chancay–Lambayeque Valley, Peru. VIs sensitive to canopy vigor, water status, and structure were derived in Google Earth Engine and optimized via Sequential Forward Selection to identify the most relevant predictors per phenological stage. Models were trained and validated against field yields using leave-one-out cross-validation (LOOCV). Intermediate stages (Flowering, Milk, Dough) yielded the strongest relationships, with water-sensitive indices (NDMI, MSI) consistently ranked as key predictors. MLR and PLSR achieved the highest generalization (R²_CV up to 0.68; RMSE_CV ≈ 1.3 t ha⁻¹), while RF and XGBoost showed high training accuracy but lower validation performance, indicating overfitting. Model accuracy decreased in 2023 due to climatic variability and limited satellite observations. Findings confirm that Sentinel-2–based VI modeling offers a cost-effective, scalable alternative to UAV data for operational rice yield monitoring, supporting water resource management and decision-making in data-scarce agricultural regions. Full article

Universal tumor screening (UTS) of all newly diagnosed colorectal cancers (CRCs) for the identification of Lynch syndrome (LS) is recommended. We explored the impact of the COVID-19 pandemic on the UTS process in a safety-net university hospital to identify areas of vulnerability and opportunities for improvement. Patients undergoing resection of a primary CRC were categorized into three cohorts based on surgery date relative to the pandemic (pre-[2018,2019], early-[2020,2021] and late-[2022]). Data regarding (1) UTS performance of immunohistochemistry (IHC) for LS genes and microsatellite instability (MSI) testing; (2) referrals to cancer genetic counseling (CGC) based on mismatch repair deficient (dMMR) status and/or age < 50 years at diagnosis; (3) attendance at CGC; and (4) reasons for not attending CGC were extracted. Between 2018 and 2022, 342 patients underwent resection of a CRC. During the three time periods (pre-, early- and late-pandemic), 93%, 94% and 96% of cases were screened with at least MMR IHC, respectively. Of the patients eligible for referral to CGC in each time period, 60%, 71% and 63% had a referral submitted. Of these, 23%, 36% and 20% in each time period did not attend CGC, with the most common reason for not attending being the inability of schedulers to reach the patient. Although the COVID-19 pandemic did not cause significant variation in the different steps of the UTS process, CGC utilization remained suboptimal throughout the three time periods. Further research on barriers preventing physicians from referring patients to CGC as well as schedulers inability to reach eligible patients should be pursued. Full article

The study presents a spectral assessment and analysis of various data and methods for snow cover analysis in different ranges of the electromagnetic spectrum through a differentiated approach applied to the territory of Bansko, Pirin Mountain. The aim of the presented research is to assess the effectiveness and accuracy of satellite observations together with field (in situ) measurements and to create a model of an integrated methodology. To achieve this goal, several indices, such as land surface temperature (LST), optical indices, Tasseled Cap Transformation (TCT) with wetness component (TCW), High-Resolution (HR) imagery, and Synthetic Aperture Radar (SAR) measurements, were analyzed. The results of the analysis proved that combining satellite and field data through a mobile thermal camera provides an accurate and comprehensive picture of snow conditions in high mountain regions for powder, hard-packed and wet snow. As the most important, there is the verification and validation of the results through the so-called regression analysis of the different data types, through which multiple correlations (over 10) were established, both in data from Sentinel 1SAR, Sentinel 2MSI, Sentinel 3 SLSTR, and PlanetScope. The results showed the effectiveness of optical indices for hard and fresh snow and radar and LST data for wet snow. The results can be used to improve snow surveys, event prediction (e.g., avalanches), and the interpretation of spectral analysis of snow. The study does not aim to perform a temporal analysis; all satellite data is from the temporal period 30 December 2024–5 January 2025. Full article

Colorectal cancer (CRC) remains a leading global health challenge, with early and accurate diagnosis crucial for effective treatment. Histopathological evaluation, the current diagnostic gold standard, faces limitations including subjectivity, delayed results, and reliance on well-prepared tissue slides. Mass spectrometry imaging (MSI) offers a complementary approach by providing molecular-level information, but its high dimensionality and the scarcity of labeled data present unique challenges for traditional supervised learning. In this study, we present the first implementation of foundation models for MSI-based cancer classification using desorption electrospray ionization (DESI) data. We evaluate multiple architectures adapted from other domains, including a spectral classification model known as FACT, which leverages audio–language pretraining. Compared to conventional machine learning approaches, these foundation models achieved superior performance, with FACT achieving the highest cross-validated balanced accuracy ($93.27\%\pm 3.25\%$) and AUROC ($98.4\%\pm 0.7\%$). Ablation studies demonstrate that these models retain strong performance even under reduced data conditions, highlighting their potential for generalizable and scalable MSI-based cancer diagnostics. Future work will explore the integration of spatial and multi-modal data to enhance clinical utility. Full article

Primary ovarian large cell neuroendocrine carcinoma is an extremely rare and aggressive gynecologic malignancy with poorly defined molecular characteristics and no standard treatment protocols. We present a case of pure ovarian LCNEC in a postmenopausal woman who underwent optimal cytoreductive surgery followed by platinum-based chemotherapy. Histopathologic and immunohistochemical analyses confirmed the diagnosis. Next-generation sequencing (NGS) revealed a pathogenic BRCA2 frameshift mutation (c.7177dupA), an ATM nonsense mutation, and Tier II mutations in TP53 and PTEN. The tumor exhibited homologous recombination deficiency (HRD), microsatellite instability-high (MSI-H), and an exceptionally high tumor mutational burden (TMB) of 277.49 mutations/Mb. These molecular alterations closely resemble those observed in high-grade neuroendocrine carcinomas of cervical and endometrial origin, suggesting a convergent genomic profile across gynecologic neuroendocrine carcinomas (NECs). Our findings underscore the potential of comprehensive genomic profiling in rare tumors such as ovarian LCNEC to refine diagnosis and identify candidates for biomarker-driven therapies, including PARP inhibitors and immune checkpoint inhibitors. This case supports the integration of molecular diagnostics into clinical practice and highlights the need for prospective studies incorporating molecular stratification to inform treatment strategies for rare and aggressive neuroendocrine tumors. Full article

MSI-H/dMMR endometrial cancer (EC) is closely linked to the mismatch repair (MMR) pathway, and its pathogenesis is associated with microsatellite instability (MSI) caused by abnormalities in the core genes of the conventional MMR system. This cancer exhibits a distinct immune microenvironment, which makes it suitable for treatment with immune checkpoint inhibitors (ICIs). This cancer type demonstrates heterogeneity, encompassing Lynch syndrome (LS)-associated EC (characterized by germline mutations), sporadic EC (attributed to MLH1 promoter hypermethylation), and Lynch-like EC (driven by somatic mutations). Research indicates that these three dMMR EC subtypes possess different immune microenvironments, which may influence the therapeutic efficacy of ICIs. However, the impact of somatic mutations in traditional MMR genes on EC has often been overlooked. Furthermore, over 50% of patients with MSI exhibit no response to ICIs, potentially due to abnormalities in nontraditional MMR genes. This review discusses the role of traditional and nontraditional MMR genes in dMMR EC and related treatment strategies, highlights key issues in the current diagnosis and treatment of dMMR EC, and aims to enhance understanding of its heterogeneity and advance precision diagnosis and treatment. Full article

Background/Objectives: FAM72B (Family with sequence similarity 72 member B) is a gene whose function is not yet fully elucidated and which belongs to the FAM72 gene family. Recent studies have indicated that it is involved in the regulation of stem cell proliferation and DNA repair and serves as a valuable prognostic biomarker for a few types of cancer. This study aimed to systematically investigate the expression profile of FAM72B in pan-cancer, its role in the tumor immune microenvironment, and its potential as a prognostic and immunotherapeutic biomarker. Methods: Using bioinformatics tools such as SangerBox3.0, GEPIA2.0, Kaplan–Meier Plotter, and cBioPortal, we systematically analyzed the correlation of FAM72B expression levels with various cancer types, clinical pathological parameters, prognostic value, genetic mutations, genomic heterogeneity, immune checkpoint genes, immune cell infiltration levels, and single-cell-level characteristics. Results:FAM72B was found to be overexpressed in most cancers and significantly associated with poor prognosis, although it may exert a protective effect in some cancers like thymoma (THYM). Its expression level was positively correlated with tumor mutation burden (TMB), microsatellite instability (MSI), neoantigen (NEO) levels, and expression of immune checkpoint genes in most cancers, suggesting that patients with high FAM72B expression may respond better to immune checkpoint inhibitors. Moreover, FAM72B expression was significantly correlated with the infiltration levels of various immune cells in the tumor immune microenvironment across pan-cancer. Single-cell sequencing results also demonstrated a significant correlation between FAM72B and the biological functional states of multiple cancers. Conclusions:FAM72B holds promise as a potential pan-cancer prognostic biomarker and therapeutic target, providing a novel basis for the development of personalized treatment strategies. Full article

The Surface Cool Island (SCI) refers to localized reductions in land surface temperature (LST) produced by features that enhance evapotranspiration, shading, and energy flux regulation. In arid urban areas, vegetated parks play a key role in mitigating heat through these mechanisms. This study evaluates how park vegetation structure and spatial configuration influence SCI intensity (ΔTmax) and extent (Lmax) using multi-seasonal, day–night satellite observations in Mexicali, Mexico. A total of 435 parks were analyzed using Landsat 8/9 TIRS (30 m) for LST and Sentinel-2 MSI (10 m) for vegetation mapping via NDVI thresholding and supervised random forest (RF) classification. On average, parks lowered daytime LST by 0.81 °C (max: 6.41 °C), with a mean Lmax of 120 m; nighttime cooling was weaker (avg. ΔTmax: 0.37 °C; Lmax: 48 m). RF-derived metrics explained SCI variability more effectively (R² up to 0.64 for ΔTmax; 0.48 for Lmax) than NDVI-based metrics (R² < 0.35), highlighting the value of object-based land cover classification in capturing vegetation structure. This remote sensing framework offers a scalable method for assessing urban cooling performance and supports climate-adaptive green space design in hot-arid cities. Full article

Precision oncology is witnessing an increasing number of molecular targets fueled by the continuous improvement of cancer genomics and drug development. Tumor genomic profiling is nowadays (August 2025) part of routine cancer patient care, guiding therapeutic decisions day by day. Nevertheless, implementing and distilling the increasing number of potential gene targets and possible precision drugs into therapeutically relevant actions is a challenge. The availability of prescreening programs for clinical trials has expanded the description of the genomic landscape of gastrointestinal tumors. The selection of the genomic test to use in each clinical situation, the correct interpretation of the results, and ensuring clinically meaningful implications in the context of diverse geographical drug accessibility, economic cost, and access to clinical trials are daily challenges of personalized medicine. In this context, well-established negative predictive biomarkers, such as extended RAS extended mutations for anti-EGFR therapy in colorectal cancer, and positive predictive biomarkers, such as MSI status, BRAF p.V600E hotspot mutation, ERBB2 amplification, or even NTRK1, NTRK2, NTRK3, RET, and NRG1 fusions across gastrointestinal cancers, are mandatory to provide tailored clinical care, improve patient selection for treatment and clinical trials, maximize therapeutic benefit, and minimize unnecessary toxicity. In this review, we provide an updated overview of actionable genomic alterations in GI cancers and discuss their implications for clinical decision making. Full article

Conventional thermospheric density models are limited in their ability to capture solar-geomagnetic coupling dynamics and lack probabilistic uncertainty estimates. We present MSIS-UN (NRLMSISE-00 with Uncertainty Quantification), an innovative framework integrating sparse principal component analysis (sPCA) with heteroscedastic neural networks. Our methodology leverages multi-satellite density measurements from the CHAMP, GRACE, and SWARM missions, coupled with MSIS-00-derived exospheric temperature (tinf) data. The technical approach features three key innovations: (1) spherical harmonic decomposition of T∞ using spatiotemporally orthogonal basis functions, (2) sPCA-based extraction of dominant modes from sparse orbital sampling data, and (3) neural network prediction of temporal coefficients with built-in uncertainty quantification. This integrated framework significantly enhances the temperature calculation module in MSIS-00 while providing probabilistic density estimates. Validation against SWARM-C measurements demonstrates superior performance, reducing mean absolute error (MAE) during quiet periods from MSIS-00’s 44.1% to 23.7%, with uncertainty bounds (1σ) achieving an MAE of 8.4%. The model’s dynamic confidence intervals enable rigorous probabilistic risk assessment for LEO satellite collision avoidance systems, representing a paradigm shift from deterministic to probabilistic modeling of thermospheric density. Full article