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Background and Objectives: Brain metastases frequently evolve over time in multiple waves, especially in patients with prolonged survival. Despite repeated imaging and targeted therapies, lesion-level continuity is fragmented in clinical practice, as follow-up is typically limited to pairwise MRI comparisons. The aim of the study is to assess the ability of routine narrative MRI follow-up reports to preserve longitudinal lesion identity and to reconstruct a coherent trajectory of disease evolution. Materials and Methods: We conducted a single-center, retrospective, observational study of all brain MRI examinations performed between June 2024 and June 2025 (n = 731 scans, 616 patients). All imaging reviews and longitudinal lesion tracking were performed by one board-certified neuroradiologist. Adult patients with confirmed brain metastases and at least three MRI examinations (including external studies) were included. We assessed the concordance of routine narrative MRI follow-up reports against a longitudinal review of all available MRIs and treatment timelines, which served as the reference standard. Lesion identity was considered preserved when reports explicitly recognized and linked lesions across time points, and lost when identity was omitted or ambiguous in at least one report. Results: The final cohort comprised 73 patients (477 tracked lesions). More than half of monitored lesions disappeared (42.9%) or evolved into post-treatment sequelae (9.9%), and were omitted from narrative reports, limiting retrospective recognition without prior imaging. The ability of routine reports to preserve lesion identity declined as cases became more complex. Concordance was higher in uniform evolution patterns (≈60%) but dropped to 18.2% in mixed evolution. A similar decline was seen with sequential metastatic waves, defined as new metastases appearing at distinct time points: 65.2% (1 wave), 46.7% (2 waves), 18.2% (3 waves), and complete loss of continuity when >3 waves occurred. Conclusions: Routine narrative MRI follow-up reports generally provide adequate information in simple cases with uniform lesion behavior, but tend to lose critical details as disease trajectories become more complex, particularly in heterogeneous or multi-wave disease. Even when individual lesions are identified across examinations, documentation remains fragmented and reflects only a snapshot of the disease course rather than an integrated longitudinal perspective. These findings highlight a critical vulnerability in current follow-up practices. Improving lesion-level continuity, potentially through AI-assisted tools, may enhance the accuracy, consistency, and clinical utility of MRI surveillance in patients with brain metastases. Full article

Glioblastoma (GBM) remains one of the most treatment-resistant human malignancies, largely due to the interplay between disrupted fluid dynamics, immune evasion, and the structural complexity of the tumor microenvironment; in addition to these, treatment resistance is also driven by intratumoral heterogeneity, glioma stem cell persistence, hypoxia-induced metabolic and epigenetic plasticity, adaptive oncogenic signaling, and profound immunosuppression within the tumor microenvironment. Emerging evidence shows that dysfunction of the glymphatic system, mislocalization of aquaporin-4, and increased intracranial pressure compromise cerebrospinal fluid–interstitial fluid exchange and impair antigen drainage to meningeal lymphatics, thereby weakening immunosurveillance. GBM simultaneously remodels the blood–brain barrier into a heterogeneous and permeable blood–tumor barrier that restricts uniform drug penetration yet enables tumor progression. These alterations intersect with profound immunosuppression mediated by pericytes, tumor-associated macrophages, and hypoxic niches. Advances in imaging, including DCE-MRI, DTI-ALPS, CSF-tracing PET, and elastography, now allow in vivo characterization of glymphatic function and interstitial flow. Therapeutic strategies targeting the fluid-immune interface are rapidly expanding, including convection-enhanced delivery, intrathecal and intranasal approaches, focused ultrasound, nanoparticle systems, and lymphatic-modulating immunotherapies such as VEGF-C and STING agonists. Integrating barrier modulation with immunotherapy and nanomedicine holds promise for overcoming treatment resistance. Our review synthesizes the mechanistic, microenvironmental, and translational advances that position the glymphatic–immune axis as a new frontier in glioblastoma research. Full article

This study critically examines the sectoral dynamics of renewable energy (RE) adoption across the EU-27 from 1990 to 2023, addressing the persistent gap between electricity generation and end-use sectors. Utilizing Eurostat energy balance data, the research employs a robust multi-methodological framework. We apply the Logarithmic Mean Divisia Index (LMDI) decomposition to isolate driving factors, and the Self-Organizing Maps (SOM) of Kohonen to cluster countries with similar transition structures. Furthermore, the Method of Moments Quantile Regression (MMQR) is used to estimate heterogeneous drivers across the distribution of RE shares. The empirical findings reveal a sharp dichotomy: while the share of renewables in the electricity generation mix (RES-E-Renewable Energy Share in Electricity) reached approximately 53.8% in leading member states, the aggregated share in the transport sector (RES-T) remains significantly lower at 9.1%. This distinction highlights that while power generation is decarbonizing rapidly, end-use electrification lags behind. The MMQR analysis indicates that economic growth drives renewable adoption more effectively in countries with already high renewable shares (upper quantiles) due to established market mechanisms and grid flexibility. Conversely, in lower-quantile countries, regulatory stability and direct infrastructure investment prove more critical than market-based incentives, highlighting the need for differentiated policy instruments. While EU policy milestones (RED I–III-) align with progress in power generation, they have failed to accelerate transitions in lagging sectors. This study concludes that achieving climate neutrality requires moving beyond aggregate targets to implement distinct, sector-specific interventions that address the unique structural barriers in transport and thermal applications. Full article

Micro-expression recognition (MER) is challenged by a brief duration, low intensity, and heterogeneous spatial frequency patterns. This study introduces a novel MER architecture that reduces computational cost by fine-tuning a large feature extraction model with LoRA, while integrating frequency-domain transformation and graph-based temporal modeling to minimize preprocessing requirements. A Spatial Frequency Adaptive (SFA) module decomposes high- and low-frequency information with dynamic weighting to enhance sensitivity to subtle facial texture variations. A Dynamic Graph Attention Temporal (DGAT) network models video frames as a graph, combining Graph Attention Networks and LSTM with frequency-guided attention for temporal feature fusion. Experiments on the SAMM, CASME II, and SMIC datasets demonstrate superior performance over existing methods. On the SAMM 5-class setting, the proposed approach achieves an unweighted F1 score (UF1) of 81.16% and an unweighted average recall (UAR) of 85.37%, outperforming the next best method by 0.96% and 2.27%, respectively. Full article

The response of Arctic vegetation to climate warming exhibits pronounced spatial heterogeneity, driven partly by widespread permafrost degradation. However, the role of thermokarst lake development in mediating vegetation-climate interactions remains poorly understood, particularly across heterogeneous landscapes of northeastern Siberia. This study integrated multi-source remote sensing data (2001–2021) with trend analysis, partial correlation, and a Shapley Additive Explanation (SHAP)-interpreted random forest model to examine the drivers of normalized difference vegetation index (NDVI) variability across five levels of thermokarst lake coverage (none, low, moderate, high, very high) and two vegetation types (forest, tundra). The results show that although greening dominates the region, browning is disproportionately observed in areas with high thermokarst lake coverage (>30%), highlighting the localized reversal of regional greening trends under intensified thermokarst activity. Air temperature was identified as the dominant driver of NDVI change, whereas soil temperature and soil moisture exerted secondary but critical influences, especially in tundra ecosystems with extensive thermokarst lake development. The relative importance of these factors shifted across thermokarst lake coverage gradients, underscoring the modulatory effect of thermokarst processes on vegetation-climate feedbacks. These findings emphasize the necessity of incorporating thermokarst dynamics and landscape heterogeneity into predictive models of Arctic vegetation change, with important implications for understanding cryospheric hydrology and ecosystem responses to ongoing climate warming. Full article

Driven by the national policy of total water resources control and efficiency improvement, the behavior of water resource utilization reduction by firms is widespread, which may have an impact on the value of firms. This study integrates dynamic capability theory and signaling theory to construct a dual-path analytical framework, systematically investigating the impact of water utilization reduction on firm value and its intrinsic mechanisms. Based on data from Chinese A-share listed companies spanning 2012–2023, fixed-effect models, mediation-effect tests, and heterogeneity analysis are employed for empirical verification. The results reveal that water utilization reduction exerts a significant dual-path promoting effect on firm value: it enhances financial performance (ROA) primarily through technological innovation, reflecting the process of resource orchestration and dynamic capability construction; concurrently, it boosts market performance (Tobin’s Q) mainly by improving ESG performance as a signaling channel, mirroring the capital market’s positive pricing of green signals. Further heterogeneity analysis indicates that these effects are more pronounced during the policy deepening stage, in non-water-intensive industries, and in humid/sub-humid regions. This study contributes theoretical support and empirical evidence for firms’ green transformation and the formulation of differentiated water resource policies by the government, highlighting the synergistic development of high-quality economic growth and ecological civilization construction. Full article

Reliable forecasting of karst spring discharge is critical for sustainable groundwater resource management under the dual pressures of climate change and intensified anthropogenic activities. This study proposes a Heterogeneous Spatiotemporal Graph Attention Network (H-STGAT) to predict spring discharge dynamics at Shentou Spring, Shanxi Province, China. Unlike conventional spatiotemporal networks that treat all relationships uniformly, our model derives its heterogeneity from a graph structure that explicitly categorizes spatial, temporal, and periodic dependencies as unique edge classes. Specifically, a dual-layer attention mechanism is designed to independently extract hydrological features within each relational channel while dynamically assigning importance weights to fuse these multi-source dependencies. This architecture enables the adaptive capture of spatial heterogeneity, temporal dependencies, and multi-year periodic patterns in karst hydrological processes. Results demonstrate that H-STGAT outperforms both traditional statistical and deep learning models in predictive accuracy, achieving an RMSE of 0.22 m³/s and an NSE of 0.77. The model reveals a long-distance recharge pattern dominated by high-altitude regions, a finding validated by independent isotopic evidence, and accurately identifies an approximately 4–6 month lag between precipitation and spring discharge, which is consistent with the characteristic hydrological lag identified through statistical cross-covariance analysis. This research enhances the understanding of complex mechanisms in karst hydrological systems and provides a robust predictive tool for sustainable groundwater management and ecological conservation, while offering a generalizable methodological framework for similar complex karst hydrological systems. Full article

Maize is one of the top three crops globally, ranking only behind rice and wheat, making it an important crop of interest. Aboveground biomass is a key indicator for assessing maize growth and its yield potential. This study developed an efficient and stable biomass prediction model to estimate the aboveground biomass (AGB) of spring maize (Zea mays L.) under subsurface drip irrigation in arid regions, based on UAV multispectral remote sensing and machine learning techniques. Focusing on typical subsurface drip-irrigated spring maize in arid Xinjiang, multispectral images and field-measured AGB data were collected from 96 sample points (selected via stratified random sampling across 24 plots) over four key phenological stages in 2024 and 2025. Sixteen vegetation indices were calculated and 40 texture features were extracted using the gray-level co-occurrence matrix method, while an integrated feature-selection strategy combining Elastic Net and Random Forest was employed to effectively screen key predictor variables. Based on the selected features, six machine learning models were constructed, including Elastic Net Regression (ENR), Gradient Boosting Decision Trees (GBDT), Gaussian Process Regression (GPR), Partial Least Squares Regression (PLSR), Random Forest (RF), and Extreme Gradient Boosting (XGB). Results showed that the fused feature set comprised four vegetation indices (GRDVI, RERVI, GRVI, NDVI) and five texture features (R_Corr, NIR_Mean, NIR_Vari, B_Mean, B_Corr), thereby retaining red-edge and visible-light texture information highly sensitive to AGB. The GPR model based on the fused features exhibited the best performance (test set R² = 0.852, RMSE = 2890.74 kg ha⁻¹, MAE = 1676.70 kg ha⁻¹), demonstrating high fitting accuracy and stable predictive ability across both the training and test sets. Spatial inversions over the two growing seasons of 2024 and 2025, derived from the fused-feature GPR optimal model at four key phenological stages, revealed pronounced spatiotemporal heterogeneity and stage-dependent dynamics of spring maize AGB: the biomass accumulates rapidly from jointing to grain filling, slows thereafter, and peaks at maturity. At a constant planting density, AGB increased markedly with nitrogen inputs from N0 to N3 (420 kg N ha⁻¹), with the high-nitrogen N3 treatment producing the greatest biomass; this successfully captured the regulatory effect of the nitrogen gradient on maize growth, provided reliable data for variable-rate fertilization, and is highly relevant for optimizing water–fertilizer coordination in subsurface drip irrigation systems. Future research may extend this integrated feature selection and modeling framework to monitor the growth and estimate the yield of other crops, such as rice and cotton, thereby validating its generalizability and robustness in diverse agricultural scenarios. Full article

The impacts of land use on stormwater runoff quality and Best Management Practices to mitigate these impacts have been investigated since the 1970s, yet challenges remain in providing a modeling approach that concomitantly considers contributions from different land use types. In densely developed urban areas, a buildup/washoff approach is often applied, while in rural areas, some type of erosion modeling is employed, as the processes of detachment, entrainment, and transport are fundamentally different. This study presents a coupled modeling approach within PCSWMM, integrating exponential buildup/washoff for impervious surfaces with the Modified Universal Soil Loss Equation (MUSLE) for pervious areas, including construction sites, to characterize water quality in the large mixed urban–rural Sparrovale catchment in Geelong, Australia. The watershed includes an innovative cascading system of 12 online NbS wetlands along one of the main tributaries, Armstrong Creek, to manage runoff quantity and quality, as well as 16 offline NbS wetlands that are tributary to the online system. A total of 78 samples for Total Suspended Solids (TSS), Total Phosphorus (TP), and Total Nitrogen (TN) were collected from six monitoring sites along Armstrong Creek during wet- and dry-weather events between May and July 2024 for model validation. The data were supplemented with six other catchment stormwater quality datasets collected during earlier studies, which provided an understanding of water quality status for the broader Geelong region. Results showed that average nutrient concentrations across all the sites ranged from 0.44 to 2.66 mg/L for TP and 0.69 to 5.7 mg/L for TN, spanning from within to above the ecological threshold ranges for eutrophication risk (TP: 0.042 to 1 mg/L, TN: 0.3 to 1.5 mg/L). In the study catchment, upstream wetlands reduced pollutant levels; however, downstream wetlands that received runoff from agriculture, residential areas, and, importantly, construction sites, showed a substantial increase in sediment and nutrient concentration. Water quality modeling revealed washoff parameters primarily influenced concentrations from established urban neighborhoods, whereas erosion parameters substantially impacted total pollutant loads for the larger system, demonstrating the importance of integrated modeling for capturing pollutant dynamics in heterogeneous, urbanizing catchments. The study results emphasize the need for spatially targeted management strategies to improve stormwater runoff quality and also show the potential for cascading wetlands to be an important element of the Nature-based Solution (NbS) runoff management system. Full article

Promoting the value realization of ecological products is a central issue in practicing the concept that “lucid waters and lush mountains are invaluable assets.” This is particularly urgent for alpine regions, which are vital ecological security barriers but face stringent developmental constraints. This study takes Golog Tibetan Autonomous Prefecture in Qinghai Province as a case study. It establishes a Gross Ecosystem Product (GEP) accounting framework tailored to the characteristics of alpine ecosystems and conducts continuous empirical accounting for the period 2020–2023. The findings reveal that: (i) The total GEP of Golog is immense (reaching 655.586 billion yuan in 2023) but exhibits significant dynamic non-stationarity driven by climatic fluctuations, with a coefficient of variation as high as 11.48%. (ii) The value structure of the GEP is highly unbalanced, with regulatory services contributing over 97.6%. Water conservation and biodiversity protection are the two pillars, highlighting its role as a supplier of public ecological products and the predicament of market failure. (iii) The spatial distribution of GEP is highly heterogeneous. Maduo County, comprising 34% of the prefecture’s land area, contributes 48% of its total GEP, with its value per unit area being 1.68 times that of Gande County, revealing the spatial agglomeration of key ecosystem services. To address the dynamic, structural, and spatial constraints identified by these quantitative features, this paper proposes synergistic realization pathways centered on “monetizing regulatory services,” “precision policy regulation,” and “capacity and institution building”. The aim is to overcome the systemic bottlenecks—“difficulties in measurement, trading, coarse compensation, and weak incentives”—in alpine ecological functional zones. This provides a systematic theoretical and practical solution for fostering a virtuous cycle between ecological conservation and regional sustainable development. Full article

Strengthening their supply chain resilience constitutes a strategic priority for Chinese enterprises to respond to evolving globalization patterns and sustain long-term competitiveness in an increasingly sustainability-oriented market. This research systematically measures enterprise supply chain resilience by analyzing panel data from Chinese listed firms (2010–2022) through the tri-dimensional constructs of resistance capacity, recovery resilience, and adaptive creativity. Regression analyses demonstrate that digital transformation significantly improves enterprise supply chain resilience, exhibiting dual-threshold characteristics in nonlinear relationships. Mediation tests reveal that information sharing and resource integration capabilities serve as the critical transmission channels. Digital transformation demonstrates strong predictive validity for both the resistance capacity and adaptive creativity of supply chains. The seemingly paradoxical findings on enterprise operational efficiency highlight the need for a more layered and dynamic understanding of its underlying mechanisms. The positive impact of digital transformation on supply chain resilience demonstrates heterogeneity across state-owned versus non-state-owned enterprises, regions, and industry types. The findings offer actionable insights for orchestrating digital transformation initiatives and designing tiered supply chain resilience frameworks that support enterprises’ sustainability goals. Full article

Neurodegenerative disorders are characterized by progressive neuronal loss and dysfunction, yet increasing evidence indicates that glial cells are central mediators of both disease initiation and progression. Astrocytes, microglia, and oligodendrocyte lineage cells modulate neuronal survival by regulating neuroinflammation, metabolic support, synaptic maintenance, and proteostasis. However, dysregulated glial responses, including chronic microglial activation, impaired phagocytosis, altered cytokine production, and mitochondrial dysfunction, contribute to persistent inflammation and structural degeneration observed across Alzheimer’s disease, Parkinson’s disease, amyotrophic lateral sclerosis, Huntington’s disease and multiple sclerosis. Recent advances in single-cell and spatial omics have revealed extensive glial heterogeneity and dynamic shifts between neuroprotective and neurotoxic phenotypes, emphasizing the context-dependent nature of glial activity. This review summarizes current knowledge regarding the multifaceted involvement of glial cells in neurodegenerative disorders. Full article

This paper examines the contrasting yet complementary approaches of the Constrained Disorder Principle (CDP) and Stefan Hell’s deterministic optical nanoscopy for managing noise in complex systems. The CDP suggests that controlled disorder within dynamic boundaries is crucial for optimal system function, particularly in biological contexts, where variability acts as an adaptive mechanism rather than being merely a measurement error. In contrast, Hell’s recent breakthrough in nanoscopy demonstrates that engineered diffraction minima can achieve sub-nanometer resolution without relying on stochastic (random) molecular switching, thereby replacing randomness with deterministic measurement precision. Philosophically, these two approaches are distinct: the CDP views noise as functionally necessary, while Hell’s method seeks to overcome noise limitations. However, both frameworks address complementary aspects of information extraction. The primary goal of microscopy is to provide information about structures, thereby facilitating a better understanding of their functionality. Noise is inherent to biological structures and functions and is part of the information in complex systems. This manuscript achieves integration through three specific contributions: (1) a mathematical framework combining CDP variability bounds with Hell’s precision measurements, validated through Monte Carlo simulations showing 15–30% precision improvements; (2) computational demonstrations with N = 10,000 trials quantifying performance under varying biological noise regimes; and (3) practical protocols for experimental implementation, including calibration procedures and real-time parameter optimization. The CDP provides a theoretical understanding of variability patterns at the system level, while Hell’s technique offers precision tools at the molecular level for validation. Integrating these approaches enables multi-scale analysis, allowing for deterministic measurements to accurately quantify the functional variability that the CDP theory predicts is vital for system health. This synthesis opens up new possibilities for adaptive imaging systems that maintain biologically meaningful noise while achieving unprecedented measurement precision. Specific applications include cancer diagnostics through chromosomal organization variability, neurodegenerative disease monitoring via protein aggregation disorder patterns, and drug screening by assessing cellular response heterogeneity. The framework comprises machine learning integration pathways for automated recognition of variability patterns and adaptive acquisition strategies. Full article

Metal–organic frameworks (MOFs) and porous organic frameworks (POFs) have been extensively explored in recent years as lubricant additives for various systems due to their structural designability, pore storage capacity, and tunable surface chemistry. These materials are utilized to construct low-friction, low-wear interfaces and investigate the potential for superlubricity. This paper systematically reviews the tribological behavior and key mechanisms of MOFs/POFs in oil-based, water-based, and solid coating systems. In oil-based systems, MOFs/POFs primarily achieve friction reduction and wear resistance through third-body particles, layer slip, and synergistic friction-induced chemical/physical transfer films. However, limitations in achieving superlubricity stem from the multi-component heterogeneity of boundary films and the dynamic evolution of shear planes. In water-based systems, MOFs/POFs leverage hydrophilic functional groups to induce hydration layers, promote polymer thickening, and soften gels through interfacial anchoring. Under specific conditions, a few cases exhibit superlubricity with coefficients of friction entering the 10⁻³ range. In solid coating systems, two-dimensional MOFs/COFs with controllable orientation leverage interlayer weak interactions and incommensurate interfaces to reduce potential barriers, achieving structural superlubricity at the 10⁻³–10⁻⁴ level on the micro- and nano-scales. However, at the engineering scale, factors such as roughness, contamination, and discontinuities in the lubricating film still constrain performance, leading to amplified energy dissipation and degradation. Finally, this paper discusses key challenges in achieving superlubricity with MOFs/POFs and proposes future research directions, including the design of shear-plane structures. Full article

The tumor immune microenvironment (TIME) is closely involved in tumor initiation, malignant progression, immune escape, and response to immunotherapy. With the continued development of high-throughput sequencing technologies, transcriptomic approaches have become essential for examining the cellular and molecular features of the TIME. Bulk RNA sequencing offers tissue-level gene expression profiles and allows the estimation of immune cell composition through computational deconvolution. Single-cell RNA sequencing provides finer resolution, revealing cellular heterogeneity, lineage relationships, and functional states. Spatial transcriptomics (ST) retains the native anatomical context, making it possible to localize gene expression patterns and cell–cell interactions within intact tissues. These approaches, when considered together, have shifted TIME research from averaged measurements toward a more detailed and mechanistic understanding. This review summarizes the principles, applications and limitations of bulk, single-cell and spatial transcriptomic methods, highlighting emerging strategies for integrative analysis. Such multi-scale frameworks are increasingly important for studying immune dynamics and may contribute to the development of more precise biotechnological and immunotherapeutic strategies. Full article