Search Results (225)

Aberrant activation of c-MET signaling contributes to tumor progression and resistance to therapy in non-small-cell lung cancer (NSCLC), yet its therapeutic significance remains incompletely understood. In this study, we evaluated c-MET expression and its association with AKT activation and clinical outcomes using a tissue microarray cohort and publicly available datasets. c-MET overexpression was significantly associated with increased p-AKT expression and showed a trend toward poorer overall survival in the tissue microarray cohort, while analysis of the TCGA LUAD dataset confirmed a significant association with reduced survival (log-rank p = 0.0223; HR = 1.234, 95% CI: 1.029–1.480). Functional studies demonstrated that pharmacological inhibition of c-MET suppressed cell proliferation and induced caspase-dependent mitochondrial apoptosis in NSCLC cell lines. Mechanistically, c-MET inhibition resulted in AKT inactivation, identifying AKT as a key downstream mediator of c-MET signaling. Notably, combined inhibition of c-MET (PHA665752) and AKT (MK2206) exhibited strong synergistic effects, significantly enhancing apoptosis and reducing cell viability compared to single-agent treatments. These findings were further validated in vivo, where combination therapy markedly delayed tumor growth without significant toxicity. Collectively, our results highlight c-MET-driven AKT activation as a key oncogenic mechanism and support dual c-MET/AKT targeting as a promising therapeutic strategy for NSCLC. Full article

Under global warming, understanding regional drought evolution is essential for drought early warning, food security, climate adaptation, and sustainable water-resource management. This study analyzed meteorological drought in Jiangxi Province during 1961–2022 using SPI-12, SPEI-12, and EDDI-12 from the CHM_Drought high-resolution multi-index dataset. The Mann–Kendall (MK) test, Theil–Sen slope estimator, three-threshold run theory, Morlet wavelet analysis, wavelet coherence (WTC), and cross-wavelet transform (XWT) were used to examine drought trends, event characteristics, periodicity, and inter-index relationships. Results showed a widespread drying tendency. EDDI-12 exhibited a highly significant increase in 99.86% of valid resampled raster pixels, indicating enhanced atmospheric evaporative demand, while SPEI-12 and SPI-12 showed significant decreasing trends in 97.96% and 93.24% of valid pixels, respectively. Stronger drying signals were mainly distributed in central and northern Jiangxi. Run-theory analysis indicated longer-duration cumulative droughts in southern mountainous areas and frequent short-duration drought events in the Poyang Lake Plain and central-northern Jiangxi. Wavelet analysis identified a dominant interdecadal periodicity of approximately 20–21 years. WTC and XWT revealed strong in-phase coherence between SPI and SPEI, whereas SPI/SPEI and EDDI mainly showed anti-phase statistical phase relationships. From a sustainability perspective, these findings provide scientific support for multi-index drought monitoring, adaptive agricultural water allocation, drought early warning, and climate-resilient water-resource management in humid monsoon regions. Full article

Understanding spatio-temporal rainfall variability is critical for water resource management, especially for climate-sensitive river basins. This study examines rainfall trends and variability in the Baitarani River Basin (eastern India) using high-resolution gridded data for 1979–2020. Rainfall trends were investigated using non-parametric Mann–Kendall test (MK test) and Sen’s slope estimator (SSE). The shift point was detected using multiple homogeneity tests [Pettitt test, Standard Normal Homogeneity Test (SNHT), and Buishand test], while rainfall variability was quantified using an entropy-based Marginal Disorder Index (MDI). The analyses were performed at annual and seasonal scales. MK Z-statistic indicates the increasing or decreasing nature of a series, whereas Sen’s β slope provides the rate of change in that particular series. The MK test and SSE were applied again to examine trends before and after the identified change point. Finally, maps illustrating spatial trends and percentage changes were produced using ArcGIS 10.6. Over the 42-year period, the MK test revealed significant increasing annual trends in both districts, Keonjhar (Z = +2.4, β = 0.7 mm/year), with a percentage change of around +21.8%, and Mayurbhunj (Z = +2.4, β = 0.7 mm/year), with a percentage change of around +19.2%. During 1979–2020 post-monsoon rainfall showed the highest increase (62–70%) while, post 2001, monsoon rainfall declined substantially (1.7–3.3 mm/year) across all districts, with Balasore showing the largest decrease (−3.3 mm/year). The earlier period (1979–2001) had stable monsoon rainfall but greater variability in retreating monsoon, especially in northern regions. Entropy-based variability analysis indicated the Bhadrak and Balasore districts as having maximum variability with an MDI value of 1.44 and 1.35, respectively, for monsoon and annual rainfall series. These findings underscore the importance of incorporating changing seasonal dynamics into water-resource planning and flood-risk management for the Baitarani River Basin in the context of climate change. Full article

The accurate prediction of Arctic sea ice concentration is essential for polar ecological protection and shipping safety. However, existing prediction methods suffer from insufficient feature representation, which limits their ability to capture the complex spatiotemporal distribution of sea ice. Furthermore, they cannot effectively integrate multi-source, heterogeneous sea ice-related data, resulting in limited prediction accuracy. To address these issues, this paper proposes a Multimodal Feature and Trend analysis (MFT) method for sea ice concentration prediction. In the feature extraction stage, MFT combines a Convolutional Neural Network with a Convolutional Block Attention Module to deeply extract global deep semantic features while also employing the Scale-Invariant Feature Transform algorithm to accurately capture local stable features. To improve processing efficiency for high-dimensional remote sensing data, a coarse-resolution dimensionality reduction strategy is developed to select core spatial features, thereby preserving key spatial distribution information while optimizing computational efficiency. For temporal analysis, the Mann–Kendall (MK) non-parametric test and Sen’s slope method are integrated to quantitatively analyze long-term evolution trends in Arctic sea ice concentration. Experimental results show that the proposed MFT model outperforms random forest (RF), LSTM, and traditional MK methods in both prediction accuracy and computational efficiency. Full article

Analysis of the hydroclimatic variations in complex topographic and climatic regimes is important in determining the freshwater availability and its response. Although several previous studies have assessed the changing patterns of hydroclimatic variables in South Asian River basins, most of them have considered traditional statistical methods, which may inadequately reflect potential non-linear hydroclimatic trends. This study determines long-term variations in precipitation, temperature, and streamflow in the Soan River Basin of Pakistan, using three decades of in situ records (1991–2020). A non-parametric (Mann–Kendall) trend test along with an artificial neural network (ANN) approach was used to check the linear and non-linear trends. The results exhibited that the basin was getting warmer at a consistent rate, although the amount of precipitation varied significantly with location and season. The annual average amount of precipitation over the entire basin was decreasing at the rate of −7.33 mm/year. As compared to the westerly season, the trend of monsoon precipitation was less certain. Changes in streamflow patterns generally demonstrated the consequences of changing precipitation and rising temperature patterns. The annual average streamflow was decreasing at the rate of −0.47 (−1.30) m³/year, as per the results of MK (ANN). A moderate positive correlation between precipitation and streamflow indicates that precipitation mainly governed the flows in the basin. The results of the MK test and the machine-learning approach demonstrated the similar decreasing tendencies of hydroclimatic variables. However, the ANN approach more precisely demonstrates the non-linear behavior of hydroclimatic variables. It was concluded that the streamflow patterns were considerably responsive to the warming of the Soan River Basin, as well as to the changing behavior of precipitation. These findings emphasized the significance of integrating statistical and machine-learning approaches to enhance the comprehension of hydroclimatic trends. Results of this research could be applicable in sustainable management and planning of the water resources within the basin. Full article

The Shaanxi section of the Qinling Mountains serves as a critical ecological transition zone and security barrier between northern and southern China. Monitoring the dynamics of its vegetation Net Primary Productivity (NPP) is essential for understanding regional carbon cycling and informing ecological management strategies. This study integrates three complementary analytical frameworks: the Mann–Kendall test combined with the Theil–Sen slope for linear trend extrapolation (MK-Theil-Sen), mechanistic simulation (CASA model), and machine learning (random forest). First, we analyzed the spatiotemporal evolution of NPP from 2000 to 2023. Then, based on three CMIP6 scenarios (SSP119, SSP245, SSP585), we projected NPP changes for 2030–2050 and compared results across different models and scenarios. The key findings are as follows: ① From 2000 to 2023, NPP in the Shaanxi section of the Qinling Mountains exhibited a fluctuating upward trend with a cumulative increase of 16.7%. Spatially, it showed a pattern of “higher in the south, lower in the north; higher in the west, lower in the east”. ② Multiple models predict continued NPP growth, though the magnitude remains uncertain. Mechanistic models, incorporating climate stress factors, yield relatively conservative projections. ③ Emission scenarios significantly influence future trends, with low-emission pathways (SSP119) favoring NPP enhancement and extended growing seasons. ④ Different vegetation types exhibit varying responses to scenario changes: broadleaf forests show the highest sensitivity, while grasslands and meadows demonstrate strong climate stability across models, with cultivated vegetation exhibiting intermediate sensitivity. This study provides comprehensive scientific references for regional ecological security assessment and adaptive management through historical analysis and multi-model, multi-scenario projections of NPP in the Shaanxi section of the Qinling Mountains. Full article

Vegetation cover characteristics underpin the understanding of regional ecosystem status and guide sustainable development. While extensive research has documented long-term vegetation dynamics in Qinghai Province, critical gaps remain in identifying driving factors, quantifying their thresholds, and uncovering nonlinear relationships governing vegetation cover. In view of this, based on the MOD13Q1V6 dataset from the Google Earth Engine (GEE) platform, this study constructed a kernel normalized difference vegetation index (kNDVI) dataset for Qinghai Province spanning the period 2001–2023. Furthermore, the spatiotemporal characteristics and future evolution trends of vegetation cover were revealed by employing methods including the Theil–Sen–Mann–Kendall (Theil–Sen–MK) trend test, Hurst exponent, and centroid migration model. At a grid scale of 5 km × 5 km, based on the combined model of Extreme Gradient Boosting and SHapley Additive exPlanations (XGBoost-SHAP), this study integrated 10 multi-source remote sensing variables related to natural conditions, socioeconomic factors, and geographical accessibility to reveal the nonlinear effects between driving factors and kNDVI and identify the key threshold inflection points. The results showed the following: (1) From 2001 to 2023, the kNDVI of Qinghai Province exhibited a fluctuating growth trend with an annual growth rate of 0.0016 per year, presenting a spatial pattern of being higher in the southeast and lower in the northwest. Specifically, the kNDVI of unused land achieved the highest growth rate (65.96%), which was significantly higher than that of other land use types. (2) The kNDVI in Qinghai Province was dominated by stable areas, accounting for 52.75%. Future trend analysis indicated that the region was primarily characterized by sustainable improvement zones (39.91%), while areas with uncertain future trends accounted for 39.70%. (3) The XGBoost-SHAP model revealed that the annual mean precipitation (AMP) (47.26%) and Digital Elevation Model (DEM) (20.40%) exerted substantial impacts on the kNDVI. Marginal effect curves identified distinct threshold inflection points for the major characteristic factors: AMP = 363.2 mm (95%CI: 361.2–365.2 mm), DEM = 4463.9 m (95%CI: 4446.0–4481.1 m), grazing intensity = 1.8 SU (Stocking Unit)·ha⁻¹ (95%CI: 1.8–1.9 SU·ha⁻¹), and slope = 2.8° (95%CI: 2.7–3.0°) and 19.0° (95%CI: 18.8–19.3°). The interaction combinations of AMP × DEM and DEM × distance to construction land exerted a strong positive effect on the kNDVI in the study area, which was conducive to enhancing vegetation cover. These findings verified the effectiveness of ecological projects implemented in Qinghai Province to a certain extent and provided data support for subsequent differentiated restoration and management. Full article

Climate-induced drought increasingly threatens water security in semi-arid regions, as rising temperatures become the primary driver of hydro-climatic variability. This study assessed long-term drought dynamics in the Nzhelele River Catchment (NRC), through Mann–Kendall (MK) trend analysis, Sen’s slope estimation, and the Standardized Precipitation Evapotranspiration Index (SPEI) for the period from October 1994 to September 2024. Aggregated in situ weather station data and 0.25° × 0.25° gridded climate node (GCN) datasets were used to quantify trends in mean annual temperature, potential evapotranspiration (PET), and precipitation. The results revealed a statistically significant warming trend of 0.037 °C/yr. and an increase in PET at an average of 6.343 mm/yr., while precipitation showed a weak, non-significant decline (–0.568 mm/yr.). SPEI analysis identified recurrent severe droughts between 2003 and 2009; 2010–2013; 2014–2016; and 2018–2020, with the 2014–2016 period as the most extreme climatic stress. Gridded SPEI aligns closely with station-derived SPEI across all accumulation scales (R² = 0.76–0.87; p-value < 0.001), supporting the use of ERA5-based climate products for drought monitoring in data-scarce regions. Due to the limited number of in situ stations and spatial averaging inherent in gridded datasets, the results provide an approximate representation of hydro-climatic conditions across the catchment. Overall, the findings indicate a shift toward temperature-driven drought regimes, growing climate risks to water availability, and the need for climate-resilient water resource planning. Full article

Seasonal groundwater (GW) pumping and climatic variability significantly impact the dynamics of greenhouse-dominated agricultural systems, yet quantitative evaluations at the local scale remain limited. This study explores non-parametric statistical and deep learning (DL) models for analyzing seasonal GW trends and predicting GW levels near greenhouse agriculture systems in Gyeongsangnam-do, South Korea. The modified Mann–Kendall (MK) test and Sen’s slope estimator were used to estimate long-term seasonal trends for the summer (wet season) and winter (dry season), based on monthly GW-level time series from six monitoring wells. Findings indicate that seasonal asymmetry is strong (winter trends have greater magnitudes and greater variability than summer trends), and that winter trends are negative (ranging from −0.45 to +1.70 m year⁻¹) and summer trends are positive (ranging from −0.02 to +0.31 m year⁻¹). At Jinju1 and Jinju4, statistically significant increasing trends were observed in both seasons (p < 0.05), but at other stations, weak or non-significant trends were observed due to short records or high variance. Long short-term memory (LSTM) and spatio-temporal graph neural network (STGNN) models were deployed and compared to predict at the GW level. The STGNN was found to be superior to LSTM in terms of R² (0.799–0.994) and reduced RMSE of up to 64.6, especially in winter, when spatially synchronized pumping is dominant in GW behavior. Despite advanced modeling, there is a serious concern about data limitations. Findings show that combining seasonal trend analysis with spatiotemporal modeling of DLs can significantly enhance knowledge and forecasting of GW dynamics in intensive greenhouse farming. Full article

Background/Objectives: The underlying molecular mechanisms of deep vein thrombosis (DVT), which continues to be a major global public health concern, remain unclear. A key component of anticoagulant therapy, heparin (HP) interacts with heparin-binding growth factors including pleiotrophin (PTN) and midkine (MK), both of which have basic amino acid-rich domains that have a strong affinity for HP. The purpose of this study was to determine if changes in the levels of circulating HP, MK, and PTN are linked to the onset of acute DVT. Methods: Thirty patients diagnosed with acute DVT by venous Doppler ultrasonography (VDU) and 28 healthy controls with normal VDU findings were enrolled. Serum HP, MK, and PTN concentrations were measured using ELISA. In DVT patients, blood samples were obtained before and after routine subcutaneous low-molecular-weight heparin treatment; controls provided a single blood sample. ROC curve analysis was used to assess diagnostic performance. Results: Prior to treatment, patients with acute DVT exhibited significantly lower serum HP levels (p < 0.05) and significantly higher MK and PTN levels compared with healthy controls (both p < 0.05). Following heparin administration, serum HP levels increased significantly (p < 0.05), while MK and PTN levels showed a decreasing trend that did not reach statistical significance (p > 0.05). ROC curve analysis demonstrated limited diagnostic performance for HP (sensitivity 10.3%, specificity 68.8%), PTN (62.1%, 54.2%), and MK (82.8%, 35.4%). Conclusions: Decreased circulating HP and increased MK and PTN levels are characteristics of acute DVT that may indicate endogenous HP sequestration through binding to these growth factors. This imbalance could lead to less free HP being available, which would encourage the formation of thrombus. Therapeutic approaches that target MK- and PTN-mediated HP interactions may constitute a unique approach for the therapy of acute DVT, as evidenced by the partial normalization seen after exogenous heparin delivery. Full article

Climate change and river flow alterations in the Mekong River have significantly exacerbated drought conditions in the Vietnamese Mekong Delta (VMD). Understanding the temporal dynamics and propagation mechanisms of drought, coupled with the compounded impacts of human activities, is crucial. This study analyzed meteorological (1992–2021) and hydrological (2000–2021) drought trends in the Lower Mekong River Basin (LMB) using the Standardized Precipitation Index (SPI) and the Streamflow Drought Index (SDI), respectively, complemented by Mann–Kendall (MK) trend analysis. The results show an increasing trend of meteorological drought in Cambodia and Lao PDR, with mid-Mekong stations exhibiting a strong positive correlation with downstream discharge, particularly Tan Chau (Pearson r ranging from 0.60 to 0.70). A key finding highlights the complexity of flow regulation by the Tonle Sap system, evidenced by a very strong correlation (r = 0.71) between Phnom Penh and the 12-month SDI lagged by one year. Crucially, the comparison revealed a shift in drought severity since 2010: hydrological drought has exhibited greater severity (reaching severe levels in 2020–2021) compared to meteorological drought, which remained moderate. This escalation is substantiated by a statistically significant discharge reduction (95% confidence level) at the Chau Doc station during the wet season, indicating a decline in peak flow due to upstream dam operations. These findings provide a robust database on the altered hydrological regime, underlining the increasing vulnerability of the VMD and motivating the urgent need for comprehensive, adaptive water resource management strategies. Full article

Understanding long-term variations in temperature and precipitation is essential for interpreting regional hydroclimatic behavior and detecting potential shifts in water availability. This study analyzes annual and seasonal temperature–precipitation trends in the Yeşilırmak Basin, Türkiye, using data from seven meteorological stations over a 38-year period (1975–2012). The Randomness Test, Mann–Kendall (MK), and Innovative Trend Analysis (ITA) were applied to detect trends. In addition, a climograph was constructed to characterize seasonal climatic patterns. The climograph for Tokat and Dökmetepe stations shows May precipitation to be 40–50% higher than in winter, while August precipitation is nearly 89% lower than in May. Temperatures rise by approximately 20 °C from January to July, reflecting continental climatic characteristics influenced by the semi-arid transition between northern and central Türkiye. Results indicate statistically significant warming trends at confidence levels above 90%, particularly during summer and autumn, with autumn temperatures increasing by approximately 0.03–0.05 °C per year (Z = 2.3–2.5) at most stations. Precipitation exhibited moderate increases at certain stations, while overall patterns remained steady. While MK and ITA yielded largely consistent results, ITA proved advantageous in weak or borderline cases by detecting structural patterns across value zones. Across all seasonal and annual analyses, ITA identified additional trends in approximately 83% of the cases where MK detected no significant change, corresponding to 25 out of 30 seasonal comparisons. Moreover, in over 92% of statistically significant MK results, ITA outcomes were fully consistent, reinforcing its robustness. Full article

Understanding the persistence and stability of hydroclimatic trends is essential for climate adaptation and sustainable water resource management, particularly in Mediterranean regions characterized by irregular precipitation regimes. This study examines long-term rainfall variability (1974–2021) at six meteorological stations along the southern coasts of Türkiye using three complementary non-parametric techniques: the Mann-Kendall (MK) test, the Wilcoxon Signed-Rank Test (WT), and the Innovative Trend Analysis (ITA). The three tests were applied with their respective methodological extensions to enhance sensitivity and better capture trend stability. Results show that while most stations exhibit generally stable rainfall regimes, period- and location-specific variations with non-monotonic or oscillatory tendencies are present, revealing patterns that standard trend tests often fail to detect. The WT method was more responsive to short-term fluctuations, whereas ITA and its three-dimensional version (3D-ITA) provided valuable insights into trend persistence and stability. Overall, the findings highlight that trend stability assessment enables the distinction between transient climate variability and sustained hydroclimatic change, offering a stronger scientific basis for adaptive water management and regional sustainability planning under climate uncertainty. Full article

Understanding long-term rainfall variability is essential for addressing Saudi Arabia’s growing challenges of water scarcity, climate resilience, and sustainable resource management in its arid to hyper-arid environment. This study analyzes the spatiotemporal variations and long-term rainfall trends across the 13 administrative regions of the Kingdom of Saudi Arabia (KSA) using four decades of observed data (1982–2021) from the National Center for Meteorology (NCM). The non-parametric Mann–Kendall (M–K) test and Sen’s slope estimator were applied to detect and quantify rainfall trends. Results reveal that 10 of the 13 regions show statistically significant negative trends, excluding the Eastern, Mecca, and Tabuk regions, with declines ranging from −4 to −16 mm/yr. The most pronounced decreases occurred in Hail, Al-Qassim, Riyadh, Medina, and Asir, while Mecca and Tabuk exhibited weak positive signals during the last decade, likely linked to Red Sea Trough dynamics. Seasonal analysis indicates the largest declines during winter and spring, crucial periods for groundwater recharge and agriculture, whereas summer rainfall remains localized in the southwestern highlands with a slight decreasing trend. Overall, rainfall variability in Saudi Arabia reflects both long-term drying and short-term oscillations. The findings provide a robust rainfall baseline to support water security, climate adaptation, and sustainable management strategies in one of the world’s driest regions. Full article

The fragile karst ecosystem in Southwest China faces severe water scarcity. Since 2000, large-scale ecological restoration programs (e.g., the “Grain for Green” Program) have substantially increased vegetation coverage. Concurrently, climate change has manifested as a distinct warming trend and heightened drought risk in recent decades. Therefore, understanding the synergistic and competing effects of climate change and vegetation restoration on regional evapotranspiration (ET) is critical for projecting water budgets and ensuring the sustainability of ecosystems and water resources within this vital ecological barrier region. This study employs a dual-scenario PT-JPL model (simulating natural vegetation dynamics versus constant coverage) integrated with Sen + MK trend analysis to quantify the spatiotemporal patterns of ET and its components—canopy transpiration (ETc), interception evaporation (ETi), and soil evaporation (ETs)—in Southwest China’s karst region (2000–2018). Furthermore, multiple regression analysis and SEM were utilized to investigate the driving mechanisms of vegetation and climatic factors (temperature, precipitation, radiation, and relative humidity) on changes in ET and its components. The key results demonstrate the following: (1) Vegetation restoration exerted a net positive effect on total ET (+0.44 mm/a) through enhanced ETi (+0.22 mm/a) and ETs (+0.37 mm/a), despite reducing ETc (−0.08 mm/a), revealing trade-offs in water allocation. (2) Radiation dominated ET variability (66.45% of the area exhibiting >50% contribution), while temperature exhibited the most extensive spatial dominance (44.02% of the region), and relative humidity exhibited drought-mediated dual effects (promoting ETi while suppressing ETc). (3) Precipitation exhibited minimal direct influence. Vegetation restoration and climate change collectively drive ET dynamics, with ETc declines indicating potential water stress. These findings elucidate the synergistic regulation of vegetation restoration and climate change on karst ecohydrology, providing critical insights for water resource management in fragile ecosystems globally. Full article