Search Results (193)

Background and Objectives: A thorough comprehension of the essential molecules and related processes underlying the carcinogenesis, proliferation, and recurrence of acute myeloid leukemia (AML) is crucial. This study aimed to investigate the expression levels, diagnostic and prognostic significance and biological roles of Bcl-2-associated athanogene 4 (BAG4) in AML carcinogenesis. Materials and Methods: Gene expression profiles were analyzed using publicly available datasets, particularly GSE9476 and TCGA, using tools such as GEO2R, GEPIA2, UALCAN and TIMER2.0. The immune infiltration correlation was examined using the GSCA platform, while the function of BAG4 at the single-cell level was analyzed via CancerSEA. Protein–protein and gene–gene interaction networks were constructed using STRING and GeneMANIA, and enrichment analyses were performed using GO, KEGG and DAVID. Expression validation was performed using RT-qPCR in HL-60 (AML) and HaCaT (normal) cells, and ROC curve analysis evaluated the diagnostic accuracy. Results: BAG4 was significantly overexpressed in AML tissues and cell lines compared with healthy controls. High BAG4 expression was associated with poor overall survival and strong diagnostic power (AUC = 0.944). BAG4 was positively associated with immune cell infiltration and negatively associated with CD4+/CD8+ T and NK cells. At the single-cell level, BAG4 was associated with proliferation, invasion, and DNA repair functions. Functional network analysis showed that BAG4 interacted with apoptosis and necroptosis-related genes such as BCL2, BAG3 and TNFRSF1A and was enriched in pathways such as NF-κB, TNF signaling and apoptosis. Conclusions: BAG4 is overexpressed in AML and is associated with adverse clinical outcomes and immune modulation. It may play an important role in leukemogenesis by affecting apoptotic resistance and immune evasion. BAG4 has potential as a diagnostic biomarker and treatment target in AML, but further in vivo and clinical validation is needed. Full article

Mangrove forests serve as critical ecological barriers in coastal zones and play a vital role in global blue carbon sequestration strategies. In recent decades, China’s mangrove ecosystems have experienced complex interactions between degradation and restoration under intense coastal urbanization and systematic conservation efforts. However, the long-term spatiotemporal patterns and driving mechanisms of mangrove ecosystem health changes remain insufficiently quantified. This study developed a multi-temporal analytical framework using Landsat imagery (1986–2021) to derive kernel normalized difference vegetation index (kNDVI) time series—an advanced phenological indicator with enhanced sensitivity to vegetation dynamics. We systematically characterized mangrove growth patterns along China’s southeastern coast through integrated Theil–Sen slope estimation, Mann–Kendall trend analysis, and Hurst exponent forecasting. A Deep Forest regression model was subsequently applied to quantify the relative contributions of environmental drivers (mean annual sea surface temperature, precipitation, air temperature, tropical cyclone frequency, and relative sea-level rise rate) and anthropogenic pressures (nighttime light index). The results showed the following: (1) a nationally significant improvement in mangrove vitality (p < 0.05), with mean annual kNDVI increasing by 0.0072/yr during 1986–2021; (2) spatially divergent trajectories, with 58.68% of mangroves exhibiting significant improvement (p < 0.05), which was 2.89 times higher than the proportion of degraded areas (15.10%); (3) Hurst persistence analysis (H = 0.896) indicating that 74.97% of the mangrove regions were likely to maintain their growth trends, while 15.07% of the coastal zones faced potential degradation risks; and (4) Deep Forest regression id the relative rate of sea-level rise (importance = 0.91) and anthropogenic (nighttime light index, importance = 0.81) as dominant drivers, surpassing climatic factors. This study provides the first national-scale, 30 m resolution assessment of mangrove growth dynamics using kNDVI, offering a scientific basis for adaptive management and blue carbon strategies in subtropical coastal ecosystems. Full article

Sea level rise due to climate change poses an increasing threat to coastal ecosystems, infrastructure, and human settlements. However, accurately estimating sea level changes in regions without tide gauge observations remains a major challenge. While satellite altimetry provides wide spatial coverage, its accuracy diminishes near coastlines. In contrast, tide gauges offer high precision but are spatially limited. This study aims to develop an artificial neural network-based model for estimating relative sea level changes in coastal regions where tide gauge data are unavailable. Unlike conventional forecasting approaches focused on future time series prediction, the proposed model is designed to learn spatial distribution patterns and temporal rates of sea level change from a fusion of satellite altimetry and tide gauge data. A normalization scheme is applied to reduce inconsistencies in reference levels, and Bayesian optimization is employed to fine-tune hyperparameters. A case analysis is conducted in two coastal regions in South Korea, Busan and Ansan, using data from 2018 to 2023. The model demonstrates strong agreement with observed tide gauge records, particularly in estimating temporal trends of sea level rise. This approach effectively compensates for the limitations of satellite altimetry in coastal regions and fills critical observational gaps in ungauged areas. The proposed method holds substantial promise for coastal hazard mitigation, infrastructure planning, and climate adaptation strategies. Full article

Four new minor monosulfated triterpene penta- and hexaosides, cladolosides S (1), S₁ (2), T (3), and T₁ (4), were isolated from the Vietnamese sea cucumber Cladolabes schmeltzii (Sclerodactylidae, Dendrochirotida). The structures of the compounds were established based on extensive analysis of 1D and 2D NMR spectra as well as HR-ESI-MS data. Cladodosides S (1), S₁ (2) and T (3), T₁ (4) are two pairs of dehydrogenated/hydrogenated compounds that share identical carbohydrate chains. The oligosaccharide chain of cladolosides of the group S is new for the sea cucumber glycosides due to the presence of xylose residue attached to C-4 Xyl1 in combination with a sulfate group at C-6 MeGlc4. The oligosaccharide moiety of cladolosides of the group T is unique because of the position of the sulfate group at C-3 of the terminal sugar residue instead of the 3-O-Me group. This suggests that the enzymatic processes of sulfation and O-methylation that occur during the biosynthesis of glycosides can compete with each other. This can presumably occur due to the high level of expression or activity of the enzymes that biosynthesize glycosides. The mosaicism of glycoside biosynthesis (time shifting or dropping out of some biosynthetic stages) may indicate a lack of compartmentalization inside the cells of organism producers, leading to a certain degree of randomness in enzymatic reactions; however, this also offers the advantage of providing chemical diversity of the glycosides. Analysis of the hemolytic activity of a series of 26 glycosides from C. schmeltzii revealed some patterns of structure–activity relationships: the presence or absence of 3-O-methyl groups has no significant impact, hexaosides, which are the final products of biosynthesis and predominant compounds of the glycosidic fraction of C. schmeltzii, are more active than their precursors, pentaosides, and the minor tetraosides, cladolosides of the group A, are weak membranolytics and therefore are not synthesized in large quantities. Two glycosides from C. schmeltzii, cladolosides D (18) and H₁ (26), display selectivity of cytotoxic action toward triple-negative breast cancer cells MDA-MB-231, while remaining non-toxic in relation to normal mammary cells MCF-10A. Quantitative structure–activity relationships (QSAR) were calculated based on the correlational analysis of the physicochemical properties and structural features of the glycosides and their hemolytic and cytotoxic activities against healthy MCF-10A cells and cancer MCF-7 and MDA-MB-231 cell lines. QSAR highlighted the complexity of the relationships as the cumulative effect of many minor contributions from individual descriptors can have a significant impact. Furthermore, many structural elements were found to have different effects on the activity of the glycosides against different cell lines. The opposing effects were especially pronounced in relation to hormone-dependent breast cancer cells MCF-7 and triple-negative MDA-MB-231 cells. Full article

A reliable ocean surface current (OSC) estimate is difficult to retrieve from synthetic aperture radar (SAR) data due to the challenge of accurately partitioning the Doppler shifts induced by wind waves and OSC. Recent research on SAR-based OSC retrieval is typically based on the assumption that the SAR Doppler shifts caused by wind waves and OSC are linearly superimposed. However, this assumption may lead to large errors in regions where nonlinear wave–current interactions are significant. To address this issue, we developed a novel deep learning model, OSCNet, for OSC retrieval. The model leverages Sentinel-1 Interferometric Wide (IW) Level 2 Ocean products collected from July 2023 to September 2024, combined with wave data from the European Centre for Medium-Range Weather Forecasts (ECMWF) and geostrophic currents from newly available SWOT Level 3 products. The OSCNet model is optimized by refining input ocean surface physical parameters and introducing a ResNet structure. Moreover, the Normalized Radar Cross-Section (NRCS) is incorporated to account for wave breaking and backscatter effects on Doppler shift estimates. The retrieval performance of the OSCNet model is evaluated using SWOT data. The mean absolute error (MAE) and root mean square error (RMSE) are found to be 0.15 m/s and 0.19 m/s, respectively. This result demonstrates that the OSCNet model enhances the retrieval of OSC from SAR data. Furthermore, a mesoscale eddy detected in the OSC map retrieved by OSCNet is consistent with the collocated sea surface chlorophyll-a observation, demonstrating the capability of the proposed method in capturing the variability of mesoscale eddies. Full article

Marine litter threatens ocean ecosystems, and nautical tourism, as a source of litter, contributes significantly. This paper presents a qualitative and quantitative study of seafloor litter in the Bay of Selehovica in the northern Adriatic Sea. The bay is accessible only by sea and is attractive to nautical tourism vessels. The survey was conducted using a remotely operated vehicle across 22,100 m² of seafloor, before and after the tourist season (summer) in 2024. The analysis shows a 25.90% increase in litter items after one season. The predominant litter category is plastic, followed by glass, metal, rubber, and textiles. The abundance of marine litter increased from 1.3 to 1.7 items per 100 m² in the post-season, reflecting a measurable rise in litter density. Due to non-normal data distribution (Shapiro–Wilk test, p < 0.001), the Wilcoxon Signed-Rank Test was used, revealing a statistically significant increase in marine litter (W = 0, p < 0.001) with a large effect size (Cohen’s d = 0.89). A strong positive correlation between the pre- and post-season values was observed (Spearman’s r = 0.96, p < 0.001), suggesting that areas with higher initial litter levels tend to accumulate more over time. The results point to the necessity of targeted management strategies to reduce the pressure of nautical tourism on marine ecosystems and to protect the marine environment. Full article

Using multi-source precipitation datasets including NASA GPM (IMERG), GPCP, ECMWF ERA5, and station precipitation data from the China Meteorological Administration (CMA), along with ERA5 reanalysis fields for atmospheric circulation analysis, this study investigates the extreme precipitation events during the “Dragon-Boat Precipitation” period from 20 May to 21 June over South China in 2022 using the synoptic diagnostic method. The results indicate that the total precipitation during this period significantly exceeded the climatological average, with multiple large-scale extreme rainfall events characterized by high intensity, extensive coverage, and prolonged duration. The spatial distribution of precipitation exhibited a north-more-south-less pattern, with the maximum rainfall center located in the Nanling Mountains, particularly in the Shaoguan–Qingyuan–Heyuan region of Guangdong Province, where peak precipitation exceeded 1100 mm, and the mean precipitation was approximately 1.7 times the climatology from the GPM data. The average daily precipitation throughout the period was 17.5 mm/day, which was 6 mm/day higher than the climatological mean, while the heaviest rainfall on 13 June reached 39 mm/day above the average, exceeding two standard deviations. The extreme precipitation during the “Dragon-Boat Precipitation” period in 2022 was associated with an anomalous deep East Asian trough, an intensified South Asian High, a stronger-than-usual Western Pacific Subtropical High, an enhanced South Asian monsoon and South China Sea monsoon, and the dominance of a strong Southwesterly Low-Level Jet (SLLJ) over South China. Two major moisture transport pathways were established: one from the Bay of Bengal to South China and another from the South China Sea, with the latter contributing a little higher amount of water vapor transport than the former. The widespread extreme precipitation on 13 June 2022 was triggered by the anomalous atmospheric circulation conditions. In the upper levels, South China was located at the northwestern periphery of the slightly stronger-than-normal Western Pacific Subtropical High, intersecting with the base of a deep trough associated with an anomalous intense Northeast China Cold Vortex (NCCV). At lower levels, the region was positioned along a shear line formed by anomalous southwesterly and northerly winds, where exceptionally strong southwesterly moisture transport, significant moisture convergence, and intense vertical updraft led to the widespread extreme rainfall event on that day. Full article

Sea surface wind retrieval methods using synthetic aperture radar (SAR) are generally classified into two categories: the direct inversion method and the variational analysis method (VAM). Traditional VAM retrieves wind fields by integrating background wind information with SAR normalized radar cross-section (NRCS). Recent studies have shown that incorporating SAR Doppler centroid anomaly (DCA) as an additional observation for variational analysis can improve the accuracy of wind speed and direction retrieval. However, this method has yet to be systematically evaluated, particularly with respect to its applicability to Sentinel-1 SAR data. This study presents a comprehensive assessment based on 1803 Sentinel-1 vertical–vertical (VV) polarization level-2 Ocean (OCN) product scenes collocated with in situ measurements from the National Data Buoy Center (NDBC), yielding a total of 2826 matched data pairs. We systematically evaluate the performance of three distinct VAM configurations: VAM1 (J_NRCS), utilizing only NRCS; VAM2 (J_DCA), employing solely DCA; and VAM3 (J_NRCS+DCA), which combines both NRCS and DCA. The results demonstrate that VAM3 (J_NRCS+DCA) achieves the best performance, with the lowest root mean square error (RMSE) of 1.42 m/s for wind speed and 26.00° for wind direction across wind speeds up to 23.2 m/s, outperforming both VAM1 (J_NRCS) and VAM2 (J_DCA). Furthermore, the accuracy of background wind speed is identified as a critical factor affecting VAM performance. After correcting the background wind speed, the RMSE and bias of the retrieved wind speed decreased significantly across all VAMs. The most notable bias reduction was observed at wind speeds exceeding 10 m/s. These findings provide essential theoretical support for the operational application of Sentinel-1 OCN products in sea surface wind retrieval. Full article

Soil–pile interaction damping plays a crucial role in reducing wind turbine loads and fatigue damage in monopile foundations, thus aiding in the optimized design of offshore wind structures and lowering construction and installation costs. Investigating the damping properties at the element level is essential for studying monopole–soil damping. Given the widespread distribution of silty clay in China’s seas, it is vital to conduct targeted studies on its damping characteristics. The damping ratio across the entire strain range is measured using a combination of resonant column and cyclic simple shear tests, with the results compared to predictions from widely used empirical models. The results indicate that the damping ratio–strain curve for silty clay remains “S”-shaped, with similar properties observed between overconsolidated and normally consolidated silty clay. While empirical models accurately predict the damping ratio at low strain levels, they tend to overestimate it at medium-to-high strain levels. This discrepancy should be considered when using empirical models in the absence of experimental data for engineering applications. The results in this study are significant for offshore wind earthquake engineering and structural optimization. Full article

The Pliensbachian–Toarcian boundary and early Toarcian events indicate significant environmental and oceanographic instabilities attributed to the emplacement of the Karoo–Ferrar large igneous province and subsequent greenhouse gas emissions. These geologic processes influenced carbon cycle perturbations and global warming, consistent with phases of a sea level rise. This study presents a high-resolution dataset of total organic carbon (TOC) and bulk rock geochemistry and mineralogy from a complete upper Pliensbachian–Toarcian interval of the Quse Formation at the Qixiangcuo section in the Southern Qiangtang Basin. The Qixiangcuo section consists of carbonate and siliciclastic organic carbon-poor sediments deposited in a shallow-shelf setting in the eastern Tethys Ocean. Chemostratigraphic data, including Ti, Zr, U, Ca, Mn, and Sr and their ratios normalized to Al, record characteristic changes linked to sea level evolution and resulting depositional sequences. Trends in these geochemical data allow for the subdivision of the Quse Formation into nine complete third-order transgressive–regressive sequences, referred to as Pliensbachian sequences PSQ1 and PSQ2, Toarcian sequences TSQ1 to TSQ7, and one incomplete sequence. Elemental proxies indicative of terrigenous detrital input and sediment grain size along with a mineralogical composition of quartz, plagioclase, and clay minerals exhibit similar trends. Increased values of these proxies suggest a sea level fall and the deposition of regressive systems tract (RST) sediments, with peak values indicating a maximum regressive surface (MRS), and vice versa for transgressive systems tract (TST) sediments and the maximum flooding surface (MFS). On the contrary, rising trends in calcite content and carbonate-bound elements indicate phases of a relative sea level transgression, reaching maximum values at the MFS, while declining trends mark a sea level regression. The Sr/Ca ratio exhibited inverse patterns to the carbonate proxies, in part, with rising values indicating a sea level fall and vice versa. Full article

In the western Tarim Basin, Carboniferous granular dolostones deposited on a carbonate platform contain a small amount of terrigenous materials of sand-size fraction, agglomerated clay minerals, or similar phases. However, the role of terrigenous materials on dolomitization is still unclear. The aim of this study was to reveal the dolomitization mechanism. The granular dolomites have small crystal size, earthy yellow color, and fabric-retentive texture, with relatively good order. These features indicate dolomites precipitated during early diagenesis. The ratio of rare earth elements (RREs) abundance of the stable isotopes ⁸⁷Sr/⁸⁶Sr relative to Post-Archean Australian Shale (PAAS) normalized patterns was used to study the source of the dolomitizing fluids. The composition of REEs is characterized by heavy rare earth (HREE) enrichment (average Nd_SN/Yb_SN = 0.83). There is a positive (La/La*)_SN anomaly and slightly positive (Gd/Gd*)_SN and (Y/Y*)_SN anomaly; δ¹⁸O of seawater in fractionation equilibrium with granular dolostones was from −2.8‰ to 1.7‰ PDB, implying the dolomitizing fluid was contemporary, slightly evaporated seawater. The granular dolostones on the relatively thick shoals were subject to subaerial exposure before pervasive dolomitization, with evidence that the input of detrital kaolinite predated the formation of dolomites. Higher ⁸⁷Sr/⁸⁶Sr values and ∑REE in granular dolostones than the values in equivalent limestones indicate that dolomitization was related to terrigenous materials. Within the terrigenous materials, the negative-charged clay minerals may have catalyzed the dolomitization, resulting in dramatically decreased induction time for precipitation of proto-dolomites. A greater amount of terrigenous materials occurred on the shoals at the sea level fall, resulting from enhanced river entrenchment and downcutting. As a result, after subaerial exposure, the penesaline water flow through the limy allochems sediments lead to dolomitization, with the catalysis of illite on relatively thick shoals. Full article

Existing research concentrates on analyzing the propagation and recovery of complex network risk or failure under a single model, which makes it difficult to effectively deal with the chain reaction. Concerning the recovery delay caused by the risk–failure interactions, this paper proposes a model for the propagation and recovery of risk–failure interactions. This model not only considers the network risk–failure interactive propagation mechanism but also introduces the load-balancing strategy and repair mechanism. The study quantifies the impact of the station on network resilience after different attack modes. In addition, the resilience metrics based on the station failure are established to accurately represent the resilience evolution of the network during propagation and recovery. Finally, focusing on the Belt and Road transportation network, we explore the evolution of network resilience under the variation of failure station repair time, station risk state recovery rate, and hub station allocation parameters. The simulation results showed that the model reduced the resilience loss through resilience recovery and accelerated the network back to normal in the face of attacks, shortening the station repair time and increasing the station risk recovery rate significantly improved the overall resilience level of the network, and increasing the proportion of hub station balancing based on the residual capacity effectively improved the minimum resilience of the sea–rail intermodal transportation network. Full article

Climate change manifests itself in rising temperatures across the continent and affects the El Niño–Southern Oscillation (ENSO) by changing sea surface temperatures and atmospheric circulation. This affects precipitation and temperature patterns, with South Africa normally experiencing drier conditions during El Niño events. These weather anomalies influence crop yields and food prices. Spatial price transmission indicates the extent to which prices of agricultural goods are linked across different geographical areas and how quickly price signals from one area are passed on to another. Although numerous studies explore spatial price transmission between various countries, there is a gap in the literature on price transmission between the US and South African maize markets during ENSO events. Therefore, we investigate how ENSO-related events impacted maize price transmission between the Chicago Mercantile Exchange and the Johannesburg Stock Exchange from 1997 to 2024. The empirical analysis starts with a correlation analysis, followed by tests for cointegration and error correction models. The results confirm the dominating impact of US maize prices on South African prices, but also how this relationship changes based on the nature of the ENSO event. There is some indication of lower levels of integration and higher levels of price diversion during El Niño periods. Full article

The response of large temporary working platforms for cross-sea bridges under the action of strong wind and waves with large tidal ranges is one of the key issues in offshore engineering. Based on a grand offshore bridge project in Fujian Province of China, on-site monitoring tests were carried out on a temporary working platform. A high-precision and fully automatic monitoring system was adopted to conduct the all-weather and high-frequency monitoring on vibrations, responses, and sea conditions of the platform, enabling us to grasp its structural mechanical characteristic and ensuring the platform safety. The results show that, under the severe sea conditions of typhoons, the stress of the platform structure increases significantly with the increase in the tidal range and reaches its maximum value at the high tide level. The inclination angle changes violently at the high tide level, while the amplitude of inclination angle change is relatively small at the low tide level. The effective value of the platform displacement under the severe sea conditions of typhoon meteorology is much larger than that under normal sea conditions. Compared with the low tide level, the acceleration of the offshore temporary work platform changes more drastically at the high tide level under severe sea conditions. Under severe sea conditions, the tidal level has a significant impact on the frequency corresponding to the peak value of the acceleration power spectrum of the offshore temporary platform. Full article

Climate change significantly stresses cold-adapted and stenothermic plant species in high-altitude mountain ecosystems. The diverse plant species at elevations ranging from 1324 to 2527 m above mean sea level (AMSL) provide an ideal setting for investigating these impacts in the Al-Baha Highlands, Saudi Arabia. Therefore, this study has three aims: first, to estimate vegetation cover in 2023 and its relationship with environmental factors; second, to analyze long-term trends (1991–2023) in key spectral indices, including the normalized difference vegetation index (NDVI), normalized difference vegetation water index (NDWI), normalized difference open water index (NDWIw), and land surface temperature (LST), using the Kendall tau-b method; and third, to model ecological stress via a generalized additive model (GAM) and assess its impact on vegetation. We utilized Landsat 5/7/8 (C2 SR T1) for spectral indices and the Copernicus DEM for topographic and hydrological analysis. The results indicate significant roles of LST, elevation, and distance from seasonal streams in shaping vegetation patterns (p < 2 × 10⁻¹⁶). There were negative trends in the NDVI (91.66 km²), NDWI (138 km²), and NDWIw (804 km²) (p < 0.05), whereas the LST exhibited positive trends (116.15 km²) (p < 0.05). The GAM achieved high predictive accuracy (R² = 0.979), capturing nonlinear relationships between the predictors and the stress score. Severe ecological stress occurred in high-altitude zones (>1700 m AMSL) on south-facing slopes due to increased LST and declining NDWI, impacting species such as Juniperus procera. Hypothesis testing was used to assess variations in the NDVI, its long-term trends, and ecological stress between highland and lower-elevation areas, revealing highly significant differences (p < 2.2 × 10⁻¹⁶). This study provides novel insights into ecological stress dynamics in relation to altitude and slope aspects, offering actionable recommendations for sustainable ecosystem management, including targeted reforestation and water resource optimization to mitigate stress and preserve biodiversity. Full article