Search Results (33,476)

Background: Despite significant advances in genetics, immunology, and endometrial research, the underlying cause of nearly half of recurrent pregnancy loss (RPL) cases remains unknown. This highlights the limitations of conventional diagnostic approaches and underscores the need for methods that can detect complex, subtle biological patterns. Objectives: To summarize and critically assess how artificial intelligence (AI) is changing our knowledge of, ability to predict, and future therapeutic management of RPL, with a focus on machine learning (ML) approaches that identify latent biological pathways and multifactorial contributors to pregnancy loss. Methods: This narrative review summarizes contemporary research on AI applications in reproductive medicine. Research using imaging, proteomic, genomic, clinical, and multi-omics information to create predictive or mechanistic models associated with RPL provided evidence. Results: AI-based approaches are increasingly demonstrating the ability to detect complex interactions among environmental, immunological, biochemical, and genetic factors associated with RPL. ML and deep learning (DL) models enhance prognostic accuracy, identify novel candidate biomarkers, and provide insights into the systemic and molecular mechanisms underlying pregnancy loss. Integrating heterogeneous data through AI supports the development of personalized reproductive profiles and can improve prediction and counseling. Conclusions: AI has the potential to improve both personalized prediction and mechanistic understanding of RPL. However, clinical translation is currently hampered by a number of important issues, including small and diverse datasets, conflicting diagnostic definitions, limited external validation, and a lack of prospective clinical trials. To responsibly integrate AI tools into reproductive care, these limitations must be addressed. Full article

Addressing the growing threat of climate change requires urgent and sustainable solutions for managing carbon dioxide (CO₂) emissions. This review investigates the latest advancements in technologies for capturing and converting CO₂, with a focus on approaches that prioritize energy efficiency, environmental compatibility, and economic viability. Emerging strategies in CO₂ capture are discussed, with attention to low-carbon-intensity materials and scalable designs. In parallel, innovative CO₂ conversion pathways, such as thermocatalytic, electrocatalytic, and photochemical processes, are evaluated for their potential to transform CO₂ into valuable chemicals and fuels. A growing body of research now focuses on integrating capture and conversion into unified systems, eliminating energy-intensive intermediate steps like compression and transportation. These integrated carbon capture and conversion/utilization (ICCC/ICCU) technologies have gained significant attention as promising strategies for sustainable carbon management. By bridging the gap between CO₂ separation and reuse, these sustainable technologies are poised to play a transformative role in the transition to a low-carbon future. Full article

Gross Primary Productivity (GPP) plays a vital role in the terrestrial carbon cycle and ecosystem functioning. Understanding its spatio-temporal dynamics and driving mechanisms is critical for predicting ecosystem responses to climate change. China’s GPP has experienced complex responses due to heterogeneous climate, environment, and human activities, yet their impacts and interactions across ecosystems remain unquantified. This study used the Mann–Kendall test and SHapley Additive exPlanations to quantify the contributions and interactions of climate, vegetation, topography, and human factors using GPP data (2001–2020). Nationally, GPP showed a significant upward trend, particularly in deciduous broadleaf forests, croplands, grasslands, and savannas. Leaf area index (LAI) is identified as the primary contributor to GPP variations, while climate factors exhibit nonlinear interactive effects on the modeled GPP. Ecosystem-specific sensitivities were evident: forest GPP is predominantly associated with climate–vegetation coupling. Additionally, in coniferous forests, the interaction between anthropogenic factors and topography shows a notable association with productivity patterns. Grassland GPP is primarily linked to topography, while cropland GPP is mainly related to management practices and environmental conditions. In contrast, the GPP of savannas and shrublands is less influenced by factor interactions. These findings high-light the necessity of ecosystem-specific management and restoration strategies and provide a basis for improving carbon cycle modeling and climate change adaptation planning. Full article

This follow-up study investigates the long-term environmental sustainability and remediation outcomes of the UPPER (‘Urban Productive Parks for Sustainable Urban Regeneration’-UIA04-252) project in Latina, Italy, focusing on Nature-Based Solutions (NbS) applied to urban green infrastructure. By integrating proximal and satellite-based remote sensing methodologies, the research evaluates persistent improvements in vegetation health, soil moisture dynamics, and overall environmental quality over multiple years. Building upon the initial monitoring framework, this case study incorporates updated data and refined techniques to quantify temporal changes and assess the ecological performance of NbS interventions. In more detail, ground-based data from meteo-climatic, air quality stations and remote satellite data from the Sentinel-2 mission are adopted. Ground-based measurements such as temperature, humidity, radiation, rainfall intensity, PM10 and PM2.5 are carried out to monitor the overall environmental quality. Updated satellite imagery from Sentinel-2 is analyzed using advanced band ratio indices, including the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Water Index (NDWI) and the Normalized Difference Moisture Index (NDMI). Comparative temporal analysis revealed consistent enhancements in vegetation health, with NDVI values significantly exceeding baseline levels (NDVI 2022–2024: +0.096, p = 0.024), demonstrating successful vegetation establishment with larger gains in green areas (+27.0%) than parking retrofits (+11.4%, p = 0.041). However, concurrent NDWI decline (−0.066, p = 0.063) indicates increased vegetation water stress despite irrigation infrastructure. NDMI improvements (+0.098, p = 0.016) suggest physiological adaptation through stomatal regulation. Principal Component Analysis (PCA) of meteo-climatic variables reveals temperature as the dominant environmental driver (PC2 loadings > 0.8), with municipality-wide NDVI-temperature correlations of r = −0.87. These multi-scale findings validate sustained NbS effectiveness in enhancing vegetation density and ecosystem services, yet simultaneously expose critical water-limitation trade-offs in Mediterranean semi-arid contexts, necessitating adaptive irrigation management and continued monitoring for long-term urban climate resilience. The integrated monitoring approach underscores the critical role of continuous, multi-scale assessment in ensuring long-term success and adaptive management of NbS-based interventions. Full article

Climate change has exacerbated flood risks for urban infrastructure, rendering sewage treatment facilities (STFs) particularly vulnerable due to their typical low-lying topographic placement. However, conventional flood risk assessment methodologies often rely solely on physical hazard parameters such as inundation depth, neglecting the functional interdependencies and operational criticality of individual treatment units. To address this limitation, this study proposes the Integrated Hydro-Operational Risk Assessment (IHORA) framework. The IHORA framework synthesizes 2D hydrodynamic modeling with a modified Hazard and Operability Study(HAZOP) study to systematically identify unit-specific physical failure thresholds and employs the Analytic Hierarchy Process (AHP) to quantify the relative operational importance of each process based on expert elicitation. The framework was applied to an underground STF under both fluvial flooding and internal structural breach scenarios. The results revealed a significant risk misalignment in traditional assessments; vital assets like electrical facilities were identified as high-risk hotspots despite moderate physical exposure, due to their high operational weight. Furthermore, Cause–Consequence Analysis (CCA) was utilized to trace cascading failure modes, bridging the gap between static risk metrics and dynamic emergency response protocols. This study demonstrates that the IHORA framework provides a robust scientific basis for prioritizing mitigation resources and enhancing the operational resilience of environmental facilities. Full article

Wetlands are a critical component of the global biogeochemical cycle and have great potential for carbon sequestration under the changing climate. However, previous studies have mainly focused on the dynamics of soil organic carbon while paying little attention to the vegetation carbon storage in wetlands. Poyang Lake is the largest freshwater lake in China, where intra-annual and inter-annual variations in water levels significantly affect the vegetation carbon storage in the floodplain wetland. Therefore, we assessed the seasonal distribution and carbon storage of six typical plant communities (Arundinella hirta, Carex cinerascens, Miscanthus lutarioriparius, Persicaria hydropiper, Phalaris arundinacea, and Phragmites australis) in Poyang Lake wetlands from 2019 to 2024 based on field surveys, the literature, and remote sensing data. Then, we used 16 preseason meteorological and hydrological variables for two growing seasons to investigate the impacts of environmental factors on vegetation carbon storage based on four correlation and regression methods (including Pearson and partial correlation, ridge, and elastic net regression). The results show that the C. cinerascens community was the most dominant contributor to vegetation carbon storage, occupying 12.68% to 44.22% of the Poyang Lake wetland area. The vegetation carbon storage in the Poyang Lake wetland was significantly (p < 0.01) higher in spring (87.75 × 10⁴ t to 239.10 × 10⁴ t) than in autumn (77.32 × 10⁴ t to 154.78 × 10⁴ t). Water body area emerged as a key explanatory factor, as it directly constrains the spatial extent available for vegetation colonization and growth by alternating inundation and exposure. In addition, an earlier start or end to floods could both enhance vegetation carbon storage in spring or autumn. However, preseason precipitation and temperature are negative to carbon storage in spring but exhibited opposite effects in autumn. These results assessed the seasonal dynamics of dominant vegetation communities and helped understand the response of the wetland carbon cycle under the changing climate. Full article

Adherent-invasive E. coli (AIEC) is closely related to inflammatory bowel disease (IBD). However, its pathogenic mechanism has not yet been fully elucidated. Using a BLASTP search, we discovered that the amino acid sequence of a putative protein (UFP37798.1) in the AIEC LF82 strain is highly homologous to some regulators in the SlyA family. We named it EruA. We displayed the secondary structures of EruA using bioinformatics, overexpressed the His₆-tagged EruA protein using SDS-PAGE, and dissected the genetic organization of the eruA chromosomal region using 5′RACE. We constructed an eruA deletion mutant (ΔeruA) and a complementary strain (CΔeruA) of the LF82 strain. The transcriptomes of wild-type (WT) and ΔeruA bacteria were compared using RNA sequencing and qRT-PCR, thereby identifying 32 differentially expressed genes (DEGs). Based on YASARA software and EMSA analysis, EruA directly binds to the consensus sequences (P_fimA and P_tnaB) in the promoter region of the fimA and tnaB genes from these DEGs. By using a super-resolution confocal microscope (SCM), counting CFUs of colonies on plates, indole quantification, and crystal violet staining of biofilms adhered to tubes or 96-well plates, we found that EruA activates the fimA to promote bacterial adhesion to intestinal epithelial cells and activates the tnaB to enhance bacterial indole production and biofilm formation. Moreover, EruA helps AIEC resist environmental stress and enhances bacterial survival within macrophages as well as loading in mouse tissues. Notably, EruA promotes AIEC colonization in the colons of mice and exacerbates intestinal inflammation caused by bacterial infection in mice with DSS-induced inflammatory colitis, manifested by weight loss, colon length shortening, and pathological changes in colon tissues. Therefore, EruA plays a key role in the pathogenicity of AIEC. Full article

The escalating threat of wildfires under global climate change necessitates rigorous monitoring to mitigate environmental and socio-economic risks. Burned area (BA) mapping is crucial for understanding fire dynamics, assessing ecosystem impacts, and supporting sustainable land management under increasing fire frequency. This study aims to develop a high-resolution detection framework specifically calibrated for Mediterranean environmental conditions, ensuring the production of consistent and accurate annual BA maps. Using Sentinel-2 MSI time series over Sardinia (Italy), the research objectives were to: (i) integrate field surveys with high-resolution photointerpretation to build a robust, locally tuned training dataset; (ii) evaluate the discriminative power of multi-temporal spectral indices; and (iii) implement a Random Forest classifier capable of providing higher spatial precision than current operational products. Validation results show a Dice Coefficient (DC) of 91.8%, significantly outperforming the EFFIS Burnt Area product (DC = 79.9%). The approach proved particularly effective in detecting small and rapidly recovering fires, often underrepresented in existing datasets. While inaccuracies persist due to cloud cover and landscape heterogeneity, this study demonstrates the effectiveness of a machine learning approach for long-term monitoring, for generating multi-year wildfire inventories, offering a vital tool for data-driven forest policy, vegetation recovery assessment and land-use change analysis in fire-prone regions. Full article

The IPCC has emphasised the increasing impacts of climate change across multiple sectors, including cultural heritage. In response, UNESCO launched the Policy Document on Climate Action for World Heritage in 2023, offering guidance on mitigation strategies for historic sites. Cultural heritage faces risks not only from sudden catastrophic events—such as floods, droughts, and wildfires—but also from the gradual deterioration of buildings and artefacts due to shifting environmental conditions. Climate change further affects the indoor microclimate of heritage sites, including museums, archives, and libraries, which are critical to the long-term preservation of cultural assets. Heritage, including heritage buildings and both tangible and intangible heritages, are subject to changes; therefore, their conservation should be assessed to identify sustainable approaches. This study investigates how climate change and microclimate alterations impact the conservation of historic buildings without modern climate control, using the Malatestiana Library—a UNESCO Memory of the World site—as a case study. The library has preserved a remarkably stable indoor environment for centuries, without the introduction of heating, cooling, or major restorations. A monitoring campaign during the summer of 2024 assessed the effects of extreme heat events on the library’s microclimate, comparing two internal spaces to examine the attic’s role in mitigating thermal stress. Data from the 2024 heatwave are also compared with similar data collected in 2013. Results show a marked shift toward a more tropical indoor climate over the past decade, signalling new threats to the preservation of historic materials. These findings highlight the urgent need for adaptive conservation strategies to address the evolving challenges posed by climate change. Full article

Agricultural land represents a fundamental production resource and one of the key factors of ecological and economic stability in rural and peri-urban areas. In the municipality of Grocka, the impacts of urbanization, demographic decline, and changes in the agrarian production structure have led to spatial degradation and reduced economic sustainability. To assess the current state and potential of agriculture at the settlement level, a multi-criteria analysis (MCA) integrated with Geographic Information Systems (GIS) was applied. The analysis encompassed demographic, production, environmental, and spatial indicators, normalized using the min–max scaling method and aggregated through a weighted sum. Criteria weights were defined based on a combination of literature review and expert judgment. The results reveal spatial variations in the level of sustainability and enable the identification of priority zones for agro-economic improvement, areas of moderate stability, and spaces suitable for developing sustainable agricultural models. Sensitivity testing (±20% variation in weights) confirmed the robustness of the results. The identified zones and proposed measures aim to revitalize degraded areas, preserve permanent crops, and strengthen production and institutional capacities. The applied methodological framework can serve as a tool for planning and policymaking in sustainable agricultural development, particularly in peri-urban contexts. Full article

Floods and wildfires are two extreme environmental events with significant yet different impacts on soil health and on two particularly important soil pollutants, heavy metals (HMs) and polycyclic aromatic hydrocarbons (PAHs), which are directly associated with ishytoxic properties and their ability to enter the food chain. The present study includes a methodological approach that was based on a literature review of published studies conducted worldwide regarding these two phenomena. The main forms of both pollutants, their possible sources and inevitable deposition onto the soil surface, along with their behavior–transport–mobility, and their residence time in soil were investigated. Furthermore, the changes that both HMs and PAHs induce in the physicochemical properties of post-flood and post-fire soils (in soil pH, Cation Exchange Capacity (CEC), organic matter content, porosity, mineralogical alterations, etc.), are investigated after a literature review of various case studies. Wildfires, in contrast to floods, can more easily remove large quantities of heavy metals into the soil ecosystem, most likely due to the intense erosion they cause. At the same time, floods appear to significantly burden soils with PAHs. In wildfires, the largest mean increases were observed for Mn (386%), Zn (300%), and Cu (202%). In floods, Pb showed the highest mean increase (534%), with Cd also rising substantially (236%). Regarding total PAHs, mean post-event concentrations reached 482.3 μg/kg after wildfires, compared to 4384 μg/kg after floods. Changes in the structure and chemical composition of flooded and burned soils may also affect the mobility and bioavailability of the pollutants under study. Overall, these two phenomena significantly alter soil quality, affecting both ecological processes and potential health impacts. Full article

Climate change and increasing groundwater demand underscore the urgency of sustainable water resource planning. Managed Aquifer Recharge (MAR) represents a promising strategy, yet its implementation depends on accurately identifying locations suited for specific MAR techniques. This study presents a GIS-based methodology developed under the DEEPWATER-CE project for identifying suitable locations for six MAR techniques in Central Europe. The methodology integrates environmental, hydrological, and land use criteria in a two-stage approach: an initial screening to delineate potentially suitable areas, followed by a detailed classification of those areas into high, moderate, and low suitability categories. The approach was tested in the Polish part of the Dunajec River catchment (4835 km²), revealing that river or lake bank filtration, infiltration ditches, and underground dams are the most viable MAR options, suitable for 12.6%, 13%, and 15.6% of the catchment area, respectively. A focused analysis within the Tarnów agglomeration, identified as highly vulnerable to climate change and with intensive groundwater use, demonstrated that 83–87% of the area is moderately suitable for infiltration ditches and riverbank filtration techniques. This decision-support tool can inform water managers and planners regarding the best locations for implementing MAR to enhance aquifer resilience, ensure water availability, and mitigate the impacts of extreme weather events. The methodology is transferable to other regions facing similar hydroclimatic challenges. Full article

Late-onset sporadic Alzheimer’s disease (LOAD) is the most common form of dementia. The disease is characterized by progressive loss of memory and behavioral changes followed by neurodegeneration of all cortical areas. While the contribution of genetic and environmental factors is important, advanced aging remains the most important disease risk factor. Because LOAD does not naturally occur in most animal species, except humans, studies have traditionally relied on the use of transgenic mouse models recapitulating early-onset familial Alzheimer’s disease (EOAD). Hence, the development of more representative LOAD models through reprograming of patient-derived cells into neuronal, glial, and immune cells became a necessity to better understand the disease’s origin and pathophysiology. Herein, and focusing on neurons, we review current work in the field and compare results obtained with two different reprograming methods to generate LOAD patient’s neuronal cells: the induced pluripotent stem cell and induced neuron technologies. We also evaluate if these models can faithfully mimic cellular and molecular pathologies observed in LOAD patients’ brains. Full article

Gut microbes play a crucial role in regulating physiological processes such as host energy metabolism, nutrient absorption, and environmental adaptation. The predicted functions of gut microbes can be influenced by many factors, both extrinsic and intrinsic to the hosts. The plateau pika is a key species in the alpine ecosystem of the Qinghai–Tibet Plateau. Previous research on the plateau pika primarily examined how extrinsic factors affected its gut microbiota. However, studies on intrinsic factors are scarce. Here, we used live-trapping to capture plateau pikas and collect cecum contents. Using metagenomic sequencing of cecum content samples, we characterized and compared the gut microbial composition and predicted function of plateau pika in adult (n = 9) and juvenile (n = 9) populations. The results indicated that Bacillota and Bacteroidete were the major bacterial phyla. The core gut microbial genera were the same, but the relative abundance of Oscillospira in juveniles was significantly lower than that in adults. The changes in the proportion of cellulose-degradation-related bacterial communities in juveniles suggest that they tend to choose low-fiber diets. In this study, we found no significant differences in the gut microbial composition and diversity, KEGG level 1 metabolic pathways, or CAZy class level between adult and juvenile plateau pikas. In total, the composition and predicted functions of cecal microorganisms in juvenile and adult male plateau pikas were not different. Regarding KEGG level 2 metabolic pathways, the juvenile group had a higher relative abundance of metabolic pathways for cofactors and vitamins, terpenoids, and polyketides, whereas the adult group had a higher relative abundance of energy metabolism. However, the resulting differences remain unclear. Therefore, future research should validate the above findings on a broader spatio-temporal scale and conduct cross-species comparisons to construct a microbial ecological framework for the health management of plateau wild animals. Full article

Chinese fir [Cunninghamia lanceolata (Lamb.) Hook.] is a fast-growing species widely utilized in construction, industrial raw materials. Owing to its broad application scope, research on Chinese fir is fragmented across multiple disciplines, making it difficult to grasp the overall research context and trends. Following the PRISMA guidelines, we retrieved articles related to Chinese fir published between 1942 and 2024 from Chinese databases (i.e., CNKI, Wanfang Data, and VIP Chinese Journal Database) and the Web of Science Core Collection (WOSCC). After removing duplicate and irrelevant records, a total of 7174 valid records were retained, including 5862 from Chinese databases and 1312 from WOSCC. The PRISMA-screened literature was imported into CiteSpace V.6.2.R4 for bibliometric analysis. Through keyword clustering, burst detection, and timeline mapping, we focused on analyzing the domestic resource distribution, research hotspots, and evolutionary trends of Chinese fir research. The results showed that research publications on Chinese fir have increased year by year, and international research started earlier and is more in-depth, while Chinese research covers a wider scope. Both follow two stages (germination and growth). Chinese research focuses on basic application areas such as seedling cultivation and plantation management; international research emphasizes ecological functions and biomass development. Global research exhibits convergence in the field of eco-environmental interactions; specifically, both domestic and international studies investigate the impacts of climate change (e.g., drought and global warming) and nitrogen deposition on the growth and functional evolution of Chinese fir. This study provides references for researchers, forestry policymakers, and planters. Full article