Search Results (1,450)

Background/Objectives: Our prior epigenome-wide association study (EWAS) on multiple sclerosis (MS) identified myeloid-associated methylation signatures and an association with enhancer regions. Here we compared differential DNA methylation across three central nervous system inflammatory disorders: MS, neuromyelitis optica (NMO), and neurologic post-acute sequelae of COVID-19 (neuro-PASC). Methods: Whole-blood DNA was profiled on Infinium MethylationEPIC arrays. Analyses included EWAS at the CpG level, differentially methylation region (DMR) analysis, and gene regulatory-element enrichment using Locus Overlap Analysis (LOLA). Limma linear models were adjusted for race, EPIC array version, age, sex, disease-modifying treatment class, and blood cell composition. Results: All three diseases were associated with broad CpG-level differential methylation. The most robust findings were disease-specific DMR signatures in gene regulatory regions. All three diseases shared Lamin B1-anchored chromatin states as an architectural genomic feature but differed in immune regulatory transcription factor binding sites (TFBS), RNA polymerase (Pol II) occupancy, and DNase accessibility. MS was enriched for TFBS in myeloid CEBPB and SPI1/PU.1 and lymphocyte-associated RUNX3, EBF1, and BATF. MS hypomethylated DMRs were concentrated at active enhancers and myeloid TFBS, suggestive of chronic myeloid activation. NMO showed the clearest promoter and B lymphocyte associated profile. Neuro-PASC was associated with hematopoietic DNase accessibility and TFBS for BATF and EBF1. Conclusions: These results suggest that DNA methylation in MS, NMO, and neuro-PASC differ meaningfully in regulatory architecture rather than conforming to a single shared disease-associated methylation model. A long-term goal is to develop immune therapies for newly recognized diseases such as neuro-PASC. Full article

Rainfall variability strongly influences both flood and drought hazards, especially in climatic transition zones where precipitation is highly seasonal and spatially heterogeneous. This study assessed long-term changes in rainfall patterns and compound flood–drought hazard in the Huaihe River Basin, China, using ERA5-Land-derived daily precipitation series at 174 spatial sampling locations during 1950–2025. Rainfall pattern indicators, flood-related rainfall extremes, and SPI-3-based drought indicators were calculated to characterize rainfall amount, frequency, intensity, dry–wet persistence, heavy rainfall events, and meteorological drought conditions. The Mann–Kendall test and Sen’s slope estimator were used to detect long-term trends, and a compound flood–drought hazard classification framework was developed based on a flood-related rainfall hazard index (FHI) and a drought-related hazard index (DHI). The results showed that annual total precipitation, wet days, and consecutive wet days decreased significantly, indicating reduced rainfall occurrence and wet spell persistence. Flood-related rainfall indicators generally showed decreasing tendencies, with more evident declines in persistent multi-day extremes than in single-day rainfall. In contrast, mean SPI-3 showed a significant drying tendency, although drought frequency, severe drought frequency, and drought intensity did not exhibit significant monotonic trends. Spatially, rainfall pattern, flood-related, and drought-related indicators showed clear heterogeneity across the basin. The compound hazard classification identified flood-dominated and drought-dominated areas as the two major hazard types, each accounting for 31.03% of the spatial sampling locations, while low compound hazard and compound flood–drought hazard areas each accounted for 18.97%. These findings indicate that flood- and drought-related hazards coexist but vary spatially across the Huaihe River Basin. The proposed framework provides preliminary rainfall-based information for differentiated flood–drought hazard assessment, climate-adaptive water resources planning, and the sustainable management of water resources in regions facing spatially heterogeneous hydroclimatic hazards. Full article

Recurrent meteorological droughts, projected to intensify under climate change, affect the cross-border Lima River Basin shared between Portugal and Spain, highlighting the need for robust early warning systems to support proactive water management. Within the EU-funded RISC_PLUS project—aimed at strengthening resilience to hydro-climatic risks in the cross-border Minho–Lima River Basins—this study develops a regionalised forecasting framework to evaluate meteorological drought forecast skill using precipitation forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) Seasonal Forecasting System 5 (SEAS5) for the Portuguese section of the Lima River Basin. A precipitation-only 12-month Standardized Precipitation Index (SPI12) is employed to isolate the contribution of seasonal precipitation forecasts. SPI12 is computed from hybrid 12-month accumulations combining observed monthly precipitation (October 1979 to February 2025) and SEAS5 forecasts (October 2018 to February 2025). Four hybrid configurations (1 to 6 months lead time) are evaluated: 11 obs + 1 fcst, 10 obs + 2 fcsts, 9 obs + 3 fcsts, and 6 obs + 6 fcsts. Forecast performance is assessed from October 2018 to February 2025. Deterministic SPI12 forecasts and categorical drought classifications are evaluated using regression-based metrics (e.g., Pearson correlation and RMSE) and contingency-table metrics (e.g., FAR and F1-score), across SEAS5 ensemble members, percentiles, and spread-based indicators. The 11 obs + 1 fcst configuration, particularly when using the Dry Spread (SpD; Q10 + Q25 percentiles) and the Q75 percentile, exhibits the highest skill, achieving a Pearson correlation coefficient of $r=0.97$ and an RMSE of approximately 0.17, alongside near-perfect categorical performance (POD = 1.00; FAR = 0.00), although these scores are partly conditioned by the shared observed accumulation window. Conversely, longer lead-time configurations exhibit degraded performance, with the 6 obs + 6 fcsts configuration showing weak or negative skill relative to climatology, indicating that 6-month lead forecasts should be interpreted with caution. These results demonstrate that SEAS5 precipitation forecasts can provide skilful drought predictions at lead times of several months in the Lima River Basin within the SPI12 framework. The proposed blending methodology provides a transparent benchmark and a technical basis for the early-warning system being developed under the RISC_PLUS project to support drought risk management in the Minho–Lima region and complement data-driven drought forecasting approaches. Full article

Microglia are central regulators of Alzheimer’s disease pathogenesis, but their roles cannot be reduced to a simple protective-versus-harmful dichotomy. Genetic, single-cell, and spatial studies have shown that Alzheimer ’s-associated microglia occupy diverse disease-linked states shaped by amyloid plaques, tau pathology, lipid stress, complement activation, astrocyte signaling, aging, and immune genetic risk. Among the regulatory nodes controlling these states, SPI1, which encodes the myeloid transcription factor PU.1, has emerged as a key determinant of microglial identity and disease responsiveness. Human genetic studies suggest that reduced SPI1 expression may be protective, whereas experimental data indicate that excessive PU.1 suppression can impair essential microglial functions. This review examines the emerging concept that partial, plaque-associated reduction in PU.1 may enable a distinct lymphoid-like immunoregulatory microglial program marked by CD28 expression. Recent evidence suggests that PU.1-low CD28-positive microglia may restrain neuroinflammation and amyloid pathology, raising the possibility that Alzheimer’s plaques induce not only inflammatory and phagocytic microglial responses, but also endogenous suppressive programs that limit tissue damage. We discuss this proposed PU.1/CD28 regulatory axis in relation to disease-associated microglia, TREM2–APOE signaling, complement-mediated synapse loss, antigen-presentation pathways, plaque-niche biology, and therapeutic microglial reprogramming. We also highlight major unresolved questions, including whether PU.1-low CD28-positive microglia are present and functional in human Alzheimer’s disease, whether they are specific to amyloid-rich niches or extend to tau and mixed pathologies, and how such states could be safely manipulated without disrupting essential immune surveillance. We propose that lymphoid-like suppressive microglia represent a promising but still unproven framework for understanding protective neuroimmune regulation in Alzheimer’s disease and for developing state-specific microglial therapies. Full article

This study investigates recent changes in the surface area of Lake Marmara, a shallow lake located in western Türkiye under Mediterranean climate conditions, and their relationship with hydrometeorological variability. Lake surface area dynamics were quantified using the Modified Normalized Difference Water Index (MNDWI) derived from Landsat satellite imagery processed on the Google Earth Engine (GEE) platform. Climatic conditions were characterized by using precipitation, air temperature, and potential evapotranspiration data obtained from the ERA5-Land reanalysis dataset, from which drought indices including the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) were derived. Temporal analyses covering the period 2000–2025 were conducted to identify long-term tendencies and seasonal variability in lake area and climatic indicators. The results indicate that the rapid post-2015 lake desiccation cannot be explained by a statistically significant monotonic meteorological drought trend alone, highlighting the likely contribution of basin-scale hydrological pressures. 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

Taking the Yellow River Basin (YRB) as an example, this study explores the non-stationary drought evolution features in large river basins under climate change. This study utilized precipitation and multiple climate factor data to establish the non-stationary standardized precipitation index (NSPI) through the GAMLSS model. Combined with the run theory, Copula function and a cascaded RF-LSTM machine learning model, the drought characteristics and retrospective predictive patterns were systematically assessed. The results show that: (1) The Arctic Oscillation, the Pacific Decadal Oscillation, the Southern Oscillation and the North Pacific Index are the primary climate drivers of non-stationary precipitation variation in the YRB, with the former three being selected most frequently and NPI additionally influencing April–June and September, and their effects are both different and lagging. Compared with the traditional SPI, the NSPI assigned higher drought grades and greater severity to typical drought years (e.g., the 1974 event was rated D3 with a severity of 17.935 by NSPI versus D2 with 11.733 by SPI), and thus better captured non-stationary drought evolution. (2) The duration of droughts exhibited a decreasing trend that was not statistically significant (p > 0.05), whereas drought intensity and severity decreased significantly (p < 0.05); the peak severity showed a significant upward trend (p = 0.0078). Spatially, the northwest of the Loess Plateau was a compound core area with high severity, high frequency and long duration of droughts, while the upper reaches were mainly characterized by low severity, short duration and sudden droughts. (3) The drought risk in the YRB shows a higher frequency in the lower reaches and a lower frequency in the upper reaches. The middle and lower reaches were high-risk areas, with shorter AND-type joint exceedance return periods for moderate drought (2.46–5.83 years) and severe drought (3.77–9.15 years). The upper reaches were low-risk areas, with longer return periods reaching up to 5.83 years for moderate drought and 9.15 years for severe drought. The study shows that the NSPI, considering the driving of multiple climate factors, can more effectively identify and assess non-stationary drought risks, providing a scientific basis for drought prevention and control in river basins. Full article

Ensuring functional correctness in digital circuitry is arguably the most labor-intensive stage of hardware creation, routinely accounting for upwards of 70% of a project’s total resource allocation. While traditional coverage-driven verification (CDV) attempts to validate every operational state, reaching full coverage closure via manual intervention or constrained–random techniques requires significant engineering time and domain knowledge. To overcome this bottleneck, this study introduces an automated testing architecture that leverages the Advantage Actor–Critic (A2C) Reinforcement Learning (RL) algorithm. This agent intelligently navigates functional coverage closure across five diverse hardware designs: an Advanced Peripheral Bus Universal Asynchronous Receiver-Transmitter (APB UART), an Serial Peripheral Interface (SPI) Memory unit, a synchronous First-In First-Out (FIFO) queue, an APB RAM, and an Advanced High-performance Bus (AHB) Slave interface. By interfacing QuestaSim 2024.1 with a Python-based intelligent agent via a SystemVerilog DPI-C socket, the system dynamically produces test vectors informed by real-time coverage metrics. Based on evaluations across five distinct random seeds, the methodology successfully attains 95.1% to 100% coverage across all testbenches, with three designs achieving 100% and two reaching 95–98%. Notably, the RL-guided system achieved target coverage using approximately 35% fewer simulation cycles than an unguided random baseline, and 22% fewer cycles compared to a traditional constrained–random setup utilizing expert-defined rules. Ultimately, this framework bypasses the necessity for manual constraint formulation and seamlessly scales to novel hardware environments with negligible setup overhead. Full article

Climate change is expected to significantly alter hydrological regimes in high-altitude tropical basins, where water availability strongly depends on precipitation variability and groundwater processes. The Ramis River basin, a major tributary of Lake Titicaca in the Peruvian Altiplano, is particularly vulnerable to hydroclimatic variability due to its dependence on seasonal water resources. This study evaluates the impacts of climate change on runoff, groundwater recharge, percolation, and renewable water resources using the SWAT hydrological model calibrated and validated for the period 1981–2024. Future projections were developed using the MPI-ESM-MR and ACCESS1-0 global climate models under RCP 4.5 and RCP 8.5 scenarios for the period 2025–2100, applying bias correction through CMhyd. The results indicate a strong sensitivity of basin hydrology to climate forcing. Under the MPI-ESM-MR model, runoff decreases by up to 68% under RCP 4.5, while extreme increases exceeding 130% are projected under RCP 8.5. In contrast, ACCESS1-0 shows moderate reductions in most scenarios. Renewable water resources exhibit a general declining trend (−23% to −41%), suggesting increasing water scarcity conditions. Additionally, the Standardized Precipitation Index (SPI) reveals a higher frequency and persistence of drought events toward the end of the century, particularly under high-emission scenarios. Overall, the findings indicate that the Ramis River basin may face a dual hydroclimatic risk characterized by reduced water availability and increased hydrological extremes. These results highlight the need to integrate climate projections into water resource management and to implement adaptive strategies to reduce future water vulnerability in high-Andean basins. Full article

With global warming continuously worsening drought hazards, the Fuhe River Basin urgently requires insight into drought evolution laws to support resilient water resources management. However, traditional univariate indices such as the Standardized Precipitation Index (SPI) and Standardized Soil Moisture Index (SSI) are limited by their inability to capture the coupled meteorological-agricultural drought process and the time-lag effects between precipitation and soil moisture response. Therefore, a multivariate drought index—which integrates both precipitation and soil moisture information—is needed as a core tool for drought early warning and precise regulation. In this study, the calibrated SWAT model was used to simulate monthly soil moisture content in the Fuhe River Basin over the past 60 years. On a 3-month time scale, a Multivariate Standardized Drought Index (MSDI) was established by coupling the Standardized Precipitation Index (SPI) and Standardized Soil Moisture Index (SSI) using the Copula function. The main findings are as follows: (1) The Nash–Sutcliffe efficiency coefficient (NS) of the SWAT (Soil and Water Assessment Tool) model during the validation period reached above 0.70, indicating favorable performance in monthly runoff simulation. (2) The MSDI revealed frequent drought events in two periods, namely 1960–1979 and 2000–2019, demonstrating the periodic fluctuation pattern of droughts in the basin. (3) Wavelet analysis showed that compared with the previous two periods, the frequency of droughts in the basin increased significantly after 2000, with weakened periodic characteristics, intensified extreme drought events, and a further rise in drought risks. This study deepens the understanding of drought dynamics in the Fuhe River Basin and provides a scientific basis for regional sustainable water resource management and the formulation of climate adaptation strategies. Full article

Climate change and rapid urbanization increasingly threaten water security in large river basins, yet existing assessments often fail to capture the multi-scale interactions between hydroclimatic extremes and human activities. To address this gap, we developed an integrated framework combining risk assessment, multi-method driver diagnosis (Geodetector, Multi-Scale Geographically Weighted Regression (MGWR), and Structural Equation Modeling (SEM)), and Zoned Management. Using a landscape-derived Ecological Risk Index (ERI) as a proxy indicator of runoff and non-point source potential, based on established empirical linkages between landscape metrics and hydrological processes, we applied the framework to three major urban agglomerations in the Yangtze River Basin from 2000 to 2020. Our results reveal three distinct risk mechanisms: in the Chengdu–Chongqing area (CYUA), a 165.8% increase in impervious surfaces drives altered runoff; in the Middle Reaches (MRC), the q-value of the Standardized Precipitation Index (SPI) rose from 0.017 in 2000 to 0.146 in 2020, corresponding to a 759% relative increase. Although the absolute q-value of SPI remains moderate at around 0.15, its rapid rise suggests increasing hydrological sensitivity of the MRC’s river–lake system to precipitation extremes; in the Yangtze River Delta (YRD), socioeconomic activities exert overriding pressure. Based on these diagnostics, we propose tailored strategies for water environment management, adaptive planning, and disaster mitigation. This framework offers a scientific basis for differentiated water governance in large river basins facing coupled anthropogenic and hydroclimatic pressures. Full article

Background: Health accessibility is a key determinant of equitable cancer care. In many countries, specialized oncology services are concentrated in urban and socioeconomically advantaged regions, forcing many patients to travel long distances for treatment. Consequently, geographic and social characteristics may be impactful in determining cancer healthcare outcomes. Objective: The aim of this study was to evaluate the association between the municipal-level Social Progress Index (SPI) and geographic travel burden, stage at diagnosis, treatment, and survival in patients with pancreatic cancer in São Paulo state, Brazil. Methods: We conducted a population-based study using data from “Fundação Oncocentro” on adults with pancreatic adenocarcinoma (2005–2025). The SPI (0–100 scale), a composite measure of municipal social and environmental development, was the primary exposure. It is structured into 3 dimensions and 12 components: Basic Human Needs (nutrition, medical care, water and sanitation, housing, safety); Foundations of Well-being (education, information access, health, environmental quality); and Opportunity (rights, freedom of choice, social inclusion, higher education). Municipal residence and cancer center locations were geocoded, and travel distance (km) was estimated. Multivariable Cox, logistic, and linear regression models assessed associations between SPI and overall survival, stage IV at diagnosis, surgery, and travel distance. Results: A total of 13,478 patients were included (mean follow-up 15.1 ± 27.2 months; mean age 62.3 years; 50.4% male). Stage IV disease was frequent (46.3%), and surgery was performed in 33% of cases. Over half of patients (53.2%) traveled more than 10 km for treatment. Increasing SPI was strongly associated with shorter travel distance (β −62.6 km per SPI unit; p < 0.001) and higher odds of surgery (OR 1.04; p < 0.001) and remained independently associated with a higher likelihood of undergoing surgical treatment (adjusted OR 1.04; p < 0.001). The proportion of stage IV disease did not decrease with increasing SPI and was slightly higher in the highest quartile (49.3%). In survival analysis, SPI demonstrated a protective effect in univariate modeling (HR 0.987; p < 0.001), but lost significance in multivariable analysis (p = 0.125). Travel burden was not retained as an independent predictor of survival after adjustment. Conclusions: Municipal-level SPI was a strong determinant of healthcare access and the likelihood of receiving surgical treatment for pancreatic cancer. Social and geographic vulnerability directly influence care pathways, revealing structural inequities in access to treatment. SPI-based stratification may serve as a practical tool to identify priority regions for transport support and equitable allocation of oncology services. Full article

Drought and related climate features (aridity, water balance) in Romania since 1961 are well documented, but studies spanning longer periods are limited and typically rely on modelled or sparse observational data. This study presents an analysis of drought, water balance and aridity in Romania over 123 years (1901–2023), using monthly data from 156 weather stations included in the RoCliHom dataset. Drought evolution is analyzed using the Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI). Aridity is examined with the De Martonne Aridity Index. The non-parametric Mann–Kendall test is used for trend detection, which allows a fair comparison with previous studies on drought and aridity in Romania. Trend magnitude is calculated with Sen’s slope estimator. Our results show a clear increase in evapotranspiration as a sign of climate warming over the country since the beginning of the 20th century. Annual precipitation amount presents no major changes. Water balance has decreased in July and August at 40% and 85% of the locations, respectively. During the growing season, drought has intensified within the last seven, six and five decades, but there are no significant changes over the full period of study in this respect. We found strong negative correlations between SPEI and North Atlantic Oscillation, Northern Annular Mode and Arctic Oscillation teleconnection indices. The evolution over the 123-year period shows that the drought episodes that occurred in recent decades are not without precedent in the long-term climatic context. Full article

Accurate long-horizon drought forecasting is essential for water resource management and early warning systems in semi-arid regions. This study evaluates five state-of-the-art Transformer architectures—Vanilla Transformer, Informer, Autoformer, Temporal Fusion Transformer (TFT), and PatchTST—for 24-month forecasting of the Standardized Precipitation Index (SPI-12) across four climatically homogeneous regions of Zacatecas, Mexico (Semi-arid, Highlands, Mountains, and Canyons). Models were trained on monthly precipitation data from 1965–2022 and evaluated on an independent test period (2023–2024) using MAE, RMSE, Pearson correlation, and the Diebold–Mariano test. The results show that PatchTST achieved the best overall performance in three of the four regions, significantly outperforming the other models in most cases. The Vanilla Transformer performed best in the less variable Highlands region. These findings demonstrate that the model’s suitability is strongly dependent on regional climatic characteristics. PatchTST’s patch-based approach proved particularly effective for capturing complex temporal dependencies in highly variable semi-arid environments. This study highlights the potential of Transformer architectures, especially PatchTST, to improve long-horizon SPI forecasting and strengthen operational drought monitoring systems in water-scarce regions. Full article

The upper Sebou River occupies a strategic territory draining varied mountain reaches in northern Morocco. As such, it is rich in surface water resources and karst springs with important downstream uses. However, the variability of rainfall threatens its water potential, making it highly vulnerable and at risk of desiccation. This study explores rainfall trends and their effects on streamflow and water resource availability. Data from three stations representing the upstream section of the watershed, along with two streamflow series—one for the upper Sebou River (Pont Medz) and the other for the Aïn Timdrine karst spring—cover the period from 1956 to 2018. The methodology employs Mann–Kendall trend tests, Sen’s Slope test, and the Standardized Precipitation Index (SPI) for rainfall series, as well as the Streamflow Drought Index (SDI) for hydrological series. The results demonstrate a decline in rainfall since 1979, significant at the 5% threshold. This trend has an immediate impact on the flow rates of the area’s rivers and karst springs, which have also tended to decline, with a succession of dry years and seasons since 1980. This observation highlights the depletion of water resources of the fragile upper Sebou region in the face of decreasing rainfall and snowfall, compounded by the rampant and unsustainable exploitation of groundwater resources linked to the development of irrigated cash crops in the Middle Atlas Mountains. Full article