Search Results (27,873)

This study presents a multi-resolution and multi-temporal remote sensing approach to assess human-induced changes in cultural landscapes, with a focus on the archaeological site of Amrit (Syria) within the MapDam project. By integrating satellite archives (KH, Landsat series, NASADEM) with ancillary geospatial data (OpenStreetMap) and advanced analytical methods, four decades (1984–2024) of land-use/land-cover (LULC) change and shoreline dynamics were reconstructed. Machine learning classification (Random Forest) achieved high accuracy (Test Accuracy = 0.94; Kappa = 0.89), enabling robust LULC mapping, while predictive modelling of urban expansion, calibrated through a Gradient Boosting Machine, attained a Figure of Merit of 0.157, confirming strong predictive reliability. The results reveal path-dependent urban growth concentrated on low-slope terrains (≤5°) and consistent with proximity to infrastructure, alongside significant shoreline regression after 1974. A Business-as-Usual projection for 2024–2034 estimates 8.676 ha of new anthropisation, predominantly along accessible plains and peri-urban fringes. Beyond quantitative outcomes, this study demonstrates the replicability and scalability of open-source, data-driven workflows using Google Earth Engine and Python 3.14, making them applicable to other high-risk heritage contexts. This transparent methodology is particularly critical in conflict zones or in regions where cultural assets are neglected due to economic constraints, political agendas, or governance limitations, offering a powerful tool to document and safeguard endangered archaeological landscapes. Full article

Nowadays, the explosive growth of knowledge in the epigenetics field has highlighted DNA methyltransferase 1 (DNMT1) as a key regulator of genomic methylation patterns and a promising therapeutic target in several diseases. In light of the increasing clinical interest in epigenetic enzymes, the present study aimed to develop a robust computational framework for the discovery of novel DNMT1 inhibitors, merging both structure and data-driven strategies. Particularly, the study compiled a dataset of established DNMT1 inhibitors and calculated a series of molecular properties, thus enabling the training of a machine learning model to capture critical structure–activity relationships (SARs). When benchmarked against known active compounds, the model effectively discriminated between putative inhibitors and non-inhibitors with high accuracy. In parallel, molecular docking was conducted to screen additional uncharacterized compounds, estimating their binding affinity to human DNMT1. Their respective properties were then extracted and fed into the aforementioned model to predict their inhibitory potential. Our comparative evaluation against known human DNMT1 inhibitors demonstrated high predictive accuracy, confirming the reliability of the proposed integrated approach. By uniting molecular docking with data-driven SAR modelling, this workflow offers an expedited fast-track avenue for identifying promising human DNMT1 inhibitors while reducing experimental overhead. The results highlight the effectiveness of combining cheminformatics, machine learning, and in silico techniques to guide rational drug design, and accelerate the discovery of novel epigenetic inhibitors. Full article

Flooding poses a major hazard to rapidly urbanising cities in Southeast Asia, and risks are projected to intensify under climate change. Accurate risk assessment, however, is hindered by scarcity of hydrological and topographic data. Focusing on the Lower Prek Thnot River Basin, a peri-urban catchment of Phnom Penh, Cambodia, the study applied the Rainfall–Runoff–Inundation model and systematically augmented inputs: hourly satellite rainfall data, field-surveyed river cross-sections and representation of hydraulic infrastructure such as weirs and pumping. Validation used Sentinel-1 SAR-derived flood-extent maps for the October 2020 event. Scenario comparison shows that rainfall input and channel geometry act synergistically: omitting either degrades performance and spatial realism. The best configuration (Sim. 5) Accuracy = 0.891, Hit Ratio = 0.546 and True Ratio = 0.701 against Sentinel-1, and reproduced inundation upstream of weirs while reducing overestimation in urban districts through pumping emulation. At the study’s 500 m grid, updating land use from 2002 to 2020 had only a minor effect relative to rainfall, geometry and infrastructure. The results demonstrate that targeted data augmentation—combining satellite products, field surveys and operational infrastructure—can deliver robust inundation maps under data scarcity, supporting hazard mapping and resilience-oriented flood management in rapidly urbanising basins. Full article

Synthetic aperture radar (SAR), as a high-resolution microwave remote sensing imaging technology, plays an indispensable role in both military and civilian applications. However, in complex electromagnetic countermeasure environments, radio frequency interference (RFI) severely degrades SAR imaging quality. SAR anti-interference, as a countermeasure method, has significantly practical values. Although deep learning-based anti-interference techniques have demonstrated notable advantages, two key issues remain unresolved: 1. Strong coupling between interference suppression and SAR imaging—most existing methods rely on raw echo data, leading to a complex processing pipeline and error accumulation. 2. Scarcity of labeled data—the lack of high-quality labeled data severely restricts model deployment. To address these challenges, this work constructs a standardized dataset and conducts comprehensive validation experiments based on this dataset. The main contributions are as follows: Firstly, this work establishes the mathematical model for block-type interference, laying a theoretical foundation for the subsequent construction of RFI-polluted data. Secondly, this work constructs a block-type interference dataset, which includes the labeled data constructed by our laboratory and open-source data from the Sentinel-1 satellites, providing reliable data support for deep learning. Thirdly, this work proposes an image subspace-based interference suppression method, which eliminates the dependence on raw echo data and significantly simplifies the processing pipeline. Finally, this work makes a fair comparison of the current works, summarizes the existing problems, and looks forward to possible future research directions. Full article

The SARS-CoV-2 pandemic caused tremendous loss of life and long-term health effects for many. The virus continues to evolve, and new variants have the potential to cause widespread physical and economic impacts. Long-chain carboxylic acids featuring two conjugated acetylenes midway along the chain easily self-assemble onto various substrates, particularly polyvinylidene fluoride, and then polymerize to form a deep blue film. COVID-19 nucleocapsid or spike protein antibodies can be conjugated to the film, and upon exposure to appropriate trigger proteins, they turn pink or red. Certain additives commonly found in commercial preparations of COVID-19 proteins can trigger false positives. The addition of small amounts of surfactants can increase detector sensitivity, though this must be carefully controlled to avoid false positives. Sensing systems based on both nucleocapsid and ACE2 antibodies can detect authentic samples of the virus in human saliva. The platform is readily adaptable to antibodies from new variants. Full article

COVID-19 is associated with gut microbiome alterations that may influence disease outcomes through immune and inflammatory pathways. This systematic review and meta-analysis evaluated global evidence on gut dysbiosis in COVID-19. We searched PubMed/MEDLINE, Embase, Web of Science, Scopus, and Cochrane Library up to 5 October 2025 (PROSPERO CRD420251160970). Alpha-diversity indices and microbial taxa log-fold changes (logFC) were analyzed using random-effects models. The pooled standardized mean difference (SMD) for the Shannon index was −0.69 (95% CI −0.84 to −0.54; I² = 42%), confirming reduced microbial diversity. Faecalibacterium prausnitzii showed a significant pooled depletion (logFC = −1.24; 95% CI −1.68 to −0.80; k = 10; I² = 74%), while Enterococcus spp. was increased (logFC = 1.45; 95% CI 1.12–1.78). Egger’s test did not suggest publication bias (p = 0.32). Gut dysbiosis was consistently associated with reduced microbial diversity and enrichment of pathogenic taxa, correlating with increased disease severity and mortality (HR = 1.67). These findings highlight the potential of microbiome profiling as a prognostic tool in COVID-19, although clinical translation requires further validation. Full article

Global coastal reclamation areas face significant land subsidence, threatening infrastructure and sustainable development. China’s large-scale projects show particularly severe subsidence. For example, Tianjin’s Binhai New Area contains 413.6 km² of reclaimed land, and subsidence is driven by soft soil consolidation, industrial loads, and dynamic land use changes. This study addresses the unique geology of coastal reclamation zones: thick, soft clay layers; high porosity; and low soil strength. We employed optimized Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technology using 48 Sentinel-1A radar images (2019–2022), which generated high-resolution annual deformation rate maps revealing a north-high, south-low subsidence gradient. Crucially, validation against leveling data confirmed reliability. The systematically quantified results demonstrate built areas and the bare ground intensifies subsidence through structural loads and soil compression. Land use transitions also exacerbate differential settlement. For coastal cities and reclamation zones, key strategies emerge, including regulating structural loads in high-subsidence areas, managing soft soil consolidation, and implementing dynamic monitoring. Aligning development intensity with geological capacity is essential, and adopting adaptive spatial planning can mitigate subsidence hazards. This approach offers a scientific framework for enhancing global coastal resilience. Full article

Forest height is a critical parameter for understanding ecosystem functions, assessing carbon stocks, and supporting sustainable forest management. Its accurate measurement is essential for climate change mitigation and understanding the global carbon cycle. While traditional methods like field surveys and airborne LiDAR provide accurate measurements, their high costs and limited spatial coverage make them impractical for the large-scale, dynamic monitoring required for effective sustainability initiatives. This research presents a multi-source remote sensing fusion approach to tackle this problem. For regional forest height inversion, it includes Sentinel-1 SAR, Sentinel-2 multispectral images, ICESat-2 lidar, and SRTM DEM data. Sentinel-1 + ICESat-2 + SRTM, Sentinel-2 + ICESat-2 + SRTM, and Sentinel-1 + Sentinel-2 + ICESat-2 + SRTM were the three data combination methods built using Shangri-La Second-class Category Resource Survey data as ground truth. An accuracy assessment was performed using three machine learning models: Light Gradient Boosting (LightGBM), Extreme Gradient Boosting (XGBoost), and Random Forest (RF). Based on the results, the ideal configuration using the LightGBM model and the following sensors: Sentinel-1, Sentinel-2, ICESat-2, and SRTM yields a correlation coefficient of 0.72, an <!-- MathType@Translator@5@5@MathML2 (no namespace).tdl@MathML 2.0 (no namespace)@ --> Full article

The accuracy of historical landslide data is a key factor affecting the precision of landslide susceptibility mapping. The degree of conformity between mathematical models and disaster-prone environments cannot be predetermined, and the optimal model needs to be determined through comparative studies. In this paper, SBAS-InSAR and the object-oriented classification method were integrated to provide data for landslide susceptibility mapping: SBAS-InSAR was used to process Sentinel-1 images, while the object-oriented classification method was applied to interpret Landsat 8 images. Eleven hazard factors were selected for landslide susceptibility modeling, and the best-performing model was determined. The influences of single and multiple hazard factors on landslide susceptibility were analyzed using Geodetector. The results showed that 246 potential landslides were identified, with a total area of 0.427 km² and a total volume of 2.161 × 10⁶ m³. The Blending-XGBoost-CNN model achieved the highest AUC and Precision, outperforming the XGBoost model and CNN model. The extreme high susceptible areas, high susceptible areas, moderate susceptible areas, minor susceptible areas and extreme minor susceptible areas accounted for 6.24% (91.4 km²), 15.07% (220.6 km²), 29.15% (426.8 km²), 30.58% (447.7 km²), and 18.96% (277.8 km²) of the total area, respectively. NDVI and gradient were key factors determining landslide occurrence. Elevation, slope aspect, distance from river, and land use also played significant roles in landslide occurrence. The contributions of TWI and lithology to landslide occurrence were relatively small, while those of plane curvature and distance from road were minimal. The interaction of hazard factors exhibited NE or BE relationships, not only increasing landslide risk but also potentially leading to more complex disaster patterns. This study can provide a theoretical basis for landslide prevention-oriented land use planning. Full article

Terahertz synthetic aperture radar (SAR) can achieve high-resolution imaging of the target area through a large bandwidth, while squint imaging can flexibly detect the target area by adjusting the beam direction. However, the two-dimensional coupling effect intensifies under squint conditions, making it challenging for traditional frequency domain algorithms for high-resolution imaging. This paper analyzes the Doppler variations and proposes a two-dimensional decoupling algorithm for squint SAR imaging in the terahertz band. The proposed algorithm decouples in the time domain and combines the improved frequency scaling operation with the azimuthal nonlinear frequency scaling operation to obtain the focused SAR image. Compared to the Range Doppler algorithm and nonlinear frequency scaling algorithm, the simulation and experimental results verified the effectiveness of the proposed algorithm, which demonstrates the application potential for squint SAR imaging in the terahertz band. Full article

Background: Numerous studies have proved the efficacy of vaccination in reducing Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) transmission and the coronavirus disease (COVID-19) burden. However, even though the COVID-19 vaccination coverage is high for primary doses, a booster dose is needed to sustain protection. Continuing our previous research, this study evaluates the immunogenicity and safety of full and half doses of two COVID-19 booster vaccines, ChAdOx1-S (AstraZeneca) and BNT162b2 (Pfizer-BioNTech), in individuals primed with ChAdOx1-S. Methods: This study was an observer-blind randomized controlled trial to evaluate the immunogenicity and safety of half and full doses of two COVID-19 booster vaccine types, BNT162b2 and ChAdOx1-S, among fully vaccinated, ChAdOx1-S-primed individuals in Jakarta, Indonesia. A total of 329 participants were randomized to receive either full or half doses of the booster vaccines, namely the ChAdOx1-S and BNT162b2 COVID-19 vaccines. Immunogenicity was assessed through SARS-CoV-2 antibody titers and neutralizing antibodies (NAbs) at 28 days post-booster, while safety was monitored via adverse event reporting. Results: The results showed that both vaccines demonstrated increased geometric mean titers (GMTs) post-booster. In the ChAdOx1-S booster group, at the baseline visit (day 0) and third visit (day 28), no statistically significant differences in GMT between the half- and full-dose groups were observed (p = 0.970 and 0.539, respectively). In the BNT162b2 group, no statistically significant difference was noted at the baseline visit, while the full dose was higher than the half dose at 28 days (Day 28, p = 0.011). Surrogate virus neutralization tests (sVNTs) and NAbs assays also revealed no significant differences between the half and full dose groups for both the Wuhan strain and the Delta variant. The BNT162b2 group compared to the ChAdOx1-S group revealed a statistically significant increase in IgG levels compared to ChAdOx1-S, with p-values of <0.001 and <0.001 for the half dose and full dose, respectively. This was also reflected in the NAbs test results, where BNT162b2 showed significantly higher levels against both the Wuhan strain and Delta variant. Adverse events were predominantly mild: 79.6% (n = 86/108) in the ChAdOx1-S full-dose group, 75.4% (n = 43/57) in the ChAdOx1-S half-dose group, 84.2% (n = 101/120) in the BNT162b2 full-dose group, and 92.6% (n = 88/95) in the BNT162b2 half-dose group, with pain at the injection site being the most common local reaction and myalgia and headache the most frequent systemic reactions. One serious adverse event was reported, assessed as unrelated to the vaccine. Conclusions: This study confirms that half doses of ChAdOx1-S and BNT162b2 are as immunogenic and safe as full doses, and a heterologous booster is more immunogenic than a homologous booster. Full article

Background: The Delta variant of SARS-CoV-2 virus, one of the alarming variants of concern (VOC) with a distinct mutation characteristic, was immensely detrimental and a significant cause of the prolonged pandemic waves. This study aimed to analyze the genetic characteristics of the predominant Delta variant in acute febrile illness (AFI) patients in Ethiopia. Method: Nasopharyngeal swab samples were collected from AFI patients in four hospitals from February 2021 to June 2022 and tested for SARS-CoV-2 by using RT-qPCR. Of 101 positive samples, 48 stored specimens were re-tested, and 26 with sufficient RNA quality (Ct < 30) were sequenced using whole-genome sequencing to identify variants of concern, specific virus lineages and mutation features. Result: Delta variants (21J clade) were found predominant among all the sequenced SARS-CoV-2 isolate (80.8%, 21/26). AY.120 (46.2%) and B.1.617.2 (26.9%) were the predominant sub-lineages of the Delta variant. Omicron (21k, Pango BA.1.1/BA.1.17/BA.1) and Alpha (20I, Pango B.1.1.7) variants accounted for 11.5% and 7.7% of the total sequenced samples. Phylogenetic analysis showed evidence of local transmission and possible multiple introductions of SARS-CoV-2 VOCs in Ethiopia. The number of mutations increases dramatically from Alpha (~35 avg) to Delta (~42 avg) to Omicron (~56 avg). The Delta variant revealed a spike mutation on L452R and T478K and P681R, and was characterized by the double deletion E156-F157- in Spike protein. Conclusions: The findings are indicative of a gradual change in the genetic coding of the virus underscoring the importance of ongoing genomic surveillance to track the evolution and spread of SARS-CoV-2 and other emerging virus. Full article

The COVID-19 pandemic caused by the SARS-CoV-2 virus has exposed the urgency of research on rapid and efficient virus detection and strategies to inhibit its replication. Previous studies have mostly focused on traditional immunoassay or optical methods, but they have limitations in terms of sensitivity, timeliness, and in-depth analysis of molecular interaction mechanisms. Solid-state nanopore single-molecule detection methods, which can monitor molecular conditions in real time at the single-molecule level, bring new opportunities to solve this problem. The nucleocapsid protein (N protein) of SARS-CoV-2 was systematically investigated under different conditions, such as external drive voltage, pH, nanopore size, and N protein concentration. The translocation of the N protein through the nanopore was then analyzed. Subsequently, we analyzed the translocation characteristics of the N protein, RNA, and N protein–RNA complexes. With the aid of EMSA experiments, we conclusively confirmed that RNA binds to the N protein. Building on this finding, we further explored small molecules that could affect the nanopore translocation of N protein–RNA complexes, such as gallocatechin gallate (GCG), epigallocatechin gallate (EGCG), and the influenza A viral inhibitor Nucleozin. The results show that GCG can disrupt the liquid-phase condensation of the N protein–RNA complex and inhibit the replication of the N protein. Meanwhile, the structural isomer EGCG of GCG and the small molecule Nucleozin can also block RNA-triggered N protein liquid–liquid phase separation (LLPS). Our results confirmed that GCG, EGCG, and Nucleozin exhibit antagonistic effects on the N protein, with differences in their effective concentrations and the potency of their antagonism. Herein, using solid-state nanopore single-molecule detection technology, we developed an experimental method that can effectively detect RNA-induced changes in N protein properties and the regulatory effects of small molecules on the LLPS of N protein–RNA complexes. These findings not only provide highly valuable insights for in-depth research on the molecular interactions involved in viral replication, but also open up promising new avenues for future responses to similar viral outbreaks, the development of a rapid and effective detection method based on solid-state nanopores and single-molecule detection, and antiviral therapies targeting N protein–RNA interactions. Full article

Standardized, one-size-fits-all COVID-19 booster schedules may be suboptimal due to individual variation in immune backgrounds, particularly prior infection, which induces robust hybrid immunity. This study estimated optimal booster timing by modeling antibody decay in relation to surrogate correlates of protection (CoP). In a prospective cohort of 177 Japanese healthcare workers, we longitudinally monitored anti-spike receptor-binding domain (S-RBD) antibody titers following BNT162b2 vaccination. Participants were stratified into SARS-CoV-2-naïve and previously infected groups. Mixed-effects models were developed to predict when antibody titers would decline below predefined CoP thresholds. The model estimated optimal booster timing after a two-dose primary series to be 3–5 months for naïve individuals and approximately one year for those with prior infection. Following a third dose, the estimated interval extended to 8–12 months for the naïve group and 1.5–2 years for the previously infected group. These substantial differences underscore the limitations of uniform booster schedules. Our findings provide a quantitative framework for personalized vaccination strategies based on individual antibody profiles and immune status, thereby optimizing protection. Full article

Glacial lakes in the Upper Indus Basin (UIB) are rapidly evolving due to accelerated glacier retreat driven by climate change. Here we present a comprehensive inventory of glacial lakes using Sentinel-1 SAR data with adaptive backscatter thresholding, enabling consistent detection under challenging conditions and improving delineation accuracy. In August 2023, we identified 6019 glacial lakes at scales from 0.001 to 5.80 km², covering a cumulative area of 266 km² (~0.06% of the basin). Although more than 90% of the lakes are smaller than 0.1 km², large lakes (>0.1 km²) account for over 57% of the total lake area. Most lakes are concentrated between 4000 and 4600 m, coinciding with the main glacierized zone. Regional patterns reveal that the Hindu Kush and Himalayas are dominated by glacier erosion lakes (GELs) and moraine-dammed lakes (MDLs), reflecting widespread glacier retreat, whereas the Karakoram is characterized by numerous supraglacial lakes (SGLs) associated with extensive debris-covered glaciers. Compared to previous optical-based inventories, our SAR-based approach captures more lakes and better represents small and transient features such as SGLs. These findings provide a more accurate baseline for assessing cryospheric change and glacial lake hazards in one of the world’s most heavily glacierized basins. Full article