Search Results (842)

Breast cancer remains the most common and leading cause of cancer deaths among women worldwide. The efficacy of conventional therapies is often hampered by off-target effects and multidrug resistance. Phototherapy, encompassing photodynamic therapy (PDT) and photothermal therapy (PTT), has gained significant attention due to its non-invasiveness, high spatiotemporal selectivity, and minimal side effects. However, its application is hindered by several obstacles, including the tumor hypoxic microenvironment, insufficient light penetration depth, and acquired heat resistance. Metal–organic frameworks (MOFs) have adjustable structures, enormous specific surfaces, and facile functionalization, providing an ideal platform to overcome these limitations. This review summarizes the latest research progress in the application of MOFs for precision phototherapy in breast cancer treatment. It emphasizes their role as a direct photosensitizer (PS), photothermal agent (PTA), or multifunctional nanocarrier for PDT, PTT, and synergistic phototherapy (including PDT/PTT, chemo/phototherapy, and immunotherapy/phototherapy). The design strategy and therapeutic effect of MOFs for phototherapy of breast cancer are critically discussed. In addition, the current bottlenecks and future perspectives are outlined to facilitate the clinical translation of MOF-based breast cancer treatment platforms. Full article

Global river water quality degradation severely impairs aquatic ecosystem stability and human health, highlighting the urgency of spatiotemporal analysis for management guidance. Based on 2014–2024 monitoring data from the Quzhou Section of Qiantang River Basin, this study adopted the Water Quality Index (WQI) and statistical methods (PCA, Mann–Kendall test) to explore the spatiotemporal characteristics of water quality across the basin. Results showed an overall mean WQI of 79.26 (classified as “Good”), with general stability, localized fluctuations, and a stable-then-declining trend, mirroring an imbalance between governance effects and emerging pollution pressures. It identifies a critical governance phase focused on securing the current good water quality and curbing the trend of further deterioration. Water quality exhibited distinct variations: upper reaches > lower reaches, tributaries > mainstreams, with priority required for the Wuxi River’s declining WQI and the Qu River’s persistently low WQI. TN, TP, and NH₃-N were identified as key factors coupled with land use patterns. A differentiated strategy prioritizing nitrogen control, synergizing phosphorus–oxygen management, and reducing organics is thus proposed. This study provides scientific references for water quality assessment and targeted aquatic ecological governance in the basin and similar river networks. Full article

Polar sea ice is undergoing rapid change, with recent record-low extents in both hemispheres, raising the demand for skillful predictions from days to seasons for navigation, ecosystem management, and climate risk assessment. Accurate sea ice prediction is essential for understanding coupled climate processes, supporting safe polar operations, and informing adaptation strategies. Physics-based numerical models remain the backbone of operational forecasting, but their skill is limited by uncertainties in coupled ocean–ice–atmosphere processes, parameterizations, and sparse observations, especially in the marginal ice zone and during melt seasons. Statistical and empirical models can provide useful baselines for low-dimensional indices or short lead times, yet they often struggle to represent high-dimensional, nonlinear interactions and regime shifts. This review synthesizes recent progress of DL for key sea ice prediction targets, including sea ice concentration/extent, thickness, and motion, and organizes methods into (i) sequential architectures (e.g., LSTM/GRU and temporal Transformers) for temporal dependencies, (ii) image-to-image and vision models (e.g., CNN/U-Net, vision Transformers, and diffusion or GAN-based generators) for spatial structures and downscaling, and (iii) spatiotemporal fusion frameworks that jointly model space–time dynamics. We further summarize hybrid strategies that integrate DL with numerical models through post-processing, emulation, and data assimilation, as well as physics-informed learning that embeds conservation laws or dynamical constraints. Despite rapid advances, challenges remain in generalization under non-stationary climate conditions, dataset shift, and physical consistency (e.g., mass/energy conservation), interpretability, and fair evaluation across regions and lead times. We conclude with practical recommendations for future research, including standardized benchmarks, uncertainty-aware probabilistic forecasting, physics-guided training and neural operators for long-range dynamics, and foundation models that leverage self-supervised pretraining on large-scale Earth observation archives. Full article

This study evaluates the spatiotemporal variations in surface water quality in An Giang province, a key upstream region of the Vietnamese Mekong Delta (VMD), under the influence of hydrological alterations and climate change impacts. Water quality data from 2010 to 2023 were collected from 10 monitoring stations along the Tien and Hau Rivers, focusing on key parameters including pH, temperature, Dissolved Oxygen (DO), Total Suspended Solids (TSS), Biological Oxygen Demand (BOD), Chemical Oxygen Demand (COD), Ammonium (N-NH₄⁺), Nitrate (NO₃⁻), orthophosphate (P-PO₄³⁻), and Coliforms. The Mann–Kendall test and Sen’s slope estimator were employed to detect long-term trends and quantify the magnitude of changes. The findings indicated that the Hau River exhibits significant organic pollution, evidenced by elevated levels of BOD and COD, alongside diminished levels of DO. The Tien River exhibits elevated concentrations of NH₄⁺ and total suspended solids (TSS). The MK test indicated that BOD, COD, and NH₄⁺ levels were increasing at most locations in a statistically significant manner. This indicates that the water quality deteriorated over time. The study revealed that the majority of pollutants exhibited statistically significant increasing trends (p ≤ 0.05). The Tien River’s COD is increasing by 1.6 mg/L annually, whereas the Hau River’s COD is escalating by 1.7 mg/L per year. The biochemical oxygen demand on both rivers is increasing by 0.5 mg/L each year. The diminishing quantities of dissolved oxygen indicated a decline in water quality. Pollutant concentrations demonstrated significant positive associations with maximum temperature (r = 0.47–0.64) and hours of sunshine (r ≈ 0.50–0.64). A significant negative correlation with river discharge was observed, particularly during the dry season (r = −0.79 to −0.88), when diminished flows resulted in elevated pollution concentrations. The findings offer measurable evidence that increasing temperatures and decreasing river flows significantly affect water quality, underscoring the necessity of adapting water resource management in the Mekong Delta. Full article

Meteorological measurements from Unmanned Aircraft Systems (UASs) increase the volume of observations available for validating and improving high-spatiotemporal-resolution models. Accurate model forecasts for UAS operations are essential to the successful development of the low-altitude economy (LAE). In this study, two UAS test flights were analyzed to assess the consistency between UAS measurements and Regional Atmospheric Modeling System model outputs, thereby evaluating model forecast skill. UAS measurements were compared with ground-based anemometer and radiosonde observations to meet the World Meteorological Organization observational requirements at both the Threshold and Goal levels. Model-forecast turbulence exhibited strong agreement with atmospheric turbulence derived from high-frequency UAS wind data. The numerical weather prediction model at high spatial and temporal resolution is found to have sufficiently accurate forecasts to support UAS operation. A computational fluid dynamics model was also tested for high-resolution wind and turbulence forecasting; however, it did not yield improvements over the meteorological model. This work represents the first study of its kind for LAE applications in Hong Kong, and further statistical analyses are planned. Full article

Background: The zinc finger-homeodomain (ZF-HD) transcription factor family exerts pivotal regulatory functions in plant development and stress responses, yet a systematic genome-wide survey is lacking for Rosa chinensis. Methods: In this study, we performed a comprehensive genome-wide identification and analysis of RcZF-HD genes in R. chinensis using bioinformatics approaches. Nine RcZF-HD loci were mined from the rose genome and comprehensively profiled for physicochemical parameters, phylogenetic affiliations, chromosomal positions, exon–intron architectures, conserved motifs, and spatiotemporal expression landscapes. Results: The results showed that RcZF-HD genes were unevenly distributed across four chromosomes (Chr2, Chr4, Chr6, and Chr7), with tandem duplication events detected on chromosomes 2 and 7, suggesting their contribution to gene family expansion. Maximum-likelihood phylogeny placed RcZF-HD proteins within nine well-supported sub-clades alongside Arabidopsis orthologs, implying both evolutionary conservation and lineage-specific divergence. All members retain canonical zinc-finger domains, yet acquire unique motif signatures predictive of functional specialization. Gene structure analysis revealed considerable diversity in exon–intron organization. Expression profiling across six different tissues (root, stem, leaf, bud, flower, and fruit) demonstrated remarkable tissue-specific expression patterns. Notably, RchiOBHm_Chr2g0168531 exhibited extremely high expression in stem tissue, while RchiOBHm_Chr7g0181371 showed preferential expression in flower tissue, suggesting specialized roles in stem development and floral organ formation, respectively. The cold-stress challenge of ‘Old Blush’ petals further disclosed pronounced up-regulation of seven RcZF-HD genes, attesting to their critical contribution to low-temperature tolerance. Conclusions: Integrative analyses furnish a multidimensional blueprint of the rose RcZF-HD repertoire, providing molecular landmarks for future functional dissection and ornamental trait engineering. Full article

Heatwaves pose increasing risks to human health and socio-economic systems, yet their spatiotemporal organization and underlying synergistic mechanisms remain insufficiently understood, particularly with respect to daytime and nighttime processes. Using a dual identification framework combining absolute and relative temperature thresholds, this study systematically investigates the spatiotemporal evolution of daytime and nighttime heatwaves across China during 1961–2022. A complex network approach is further introduced to characterize the interannual co-variability and interdecadal structural evolution of heatwave activity from a system-level perspective. Results reveal a pronounced interdecadal transition in the early 1990s, accompanied by a fundamental reorganization of heatwave co-occurrence networks. Heatwave frequency exhibits a clear post-transition desynchronization, characterized by a sharp decline in network connectivity and fragmented local clustering, indicating a shift from large-scale, circulation-dominated coherence toward increasingly localized and heterogeneous heatwave occurrences. In contrast, heatwave duration shows an opposite evolution, with significantly enhanced spatial synchronization after the transition. Degree centrality and clustering coefficients increase markedly, and high-connectivity cores expand from coastal regions into inland areas, including North, Central, and Northwest China. This coexistence of desynchronized heatwave occurrence and strongly synchronized persistence suggests an emerging high-risk regime in which heatwaves occur more randomly but, once initiated, tend to persist coherently across large regions. Furthermore, a dual-layer network analysis reveals previously undocumented cross-temporal coupling between daytime and nighttime heatwaves, with pronounced regional differences. The middle and lower reaches of the Yangtze River are more strongly influenced by local processes, whereas northern China is increasingly governed by large-scale circulation control and enhanced regional clustering after the transition. These findings demonstrate that complex network analysis provides a powerful framework for uncovering hidden structural changes in extreme heat events and offer new insights into the evolving risks of compound and persistent heatwaves under climate change. Full article

As distributed generators (DGs) and flexible adjustable loads (FALs) further penetrate distribution networks (DNs), to reduce regulation complexity compared with traditional centralized control frameworks, DGs and FALs in DNs should be packed in several clusters to enable their dispatch to become standard in the industry. To mitigate the negative influence of DGs’ and FALs’ spatiotemporal distribution and uncertain output characteristics on dispatch, this paper proposes an intelligent dynamic cluster partitioning strategy for DNs, from which the DN’s resources and loads can be intelligently aggregated, organized, and regulated in a dynamic and optimal way with relatively high implementation efficiency. An environmental model based on the Markov decision process (MDP) technique is first developed for DN cluster partitioning, in which a continuous state space, a discrete action space, and a dispatching performance-oriented reward are designed. Then, a novel random forest Q-learning network (RF-QN) is developed to implement dynamic cluster partitioning by interacting with the proposed environmental model, from which the generalization and robust capability to estimate the Q-function can be improved by taking advantage of combining deep learning and decision trees. Finally, a modified IEEE-33-node system is adopted to verify the effectiveness of the proposed intelligent dynamic cluster partitioning and regulation strategy; the results also indicate that the proposed RF-QN is superior to the traditional deep Q-learning (DQN) model in terms of renewable energy accommodation rate, training efficiency, and portioning and regulation performance. Full article

The Jiangsu Coastal Thermal Front (JCF), a persistent feature in Chinese marginal seas, plays a significant role in modulating phytoplankton dynamics and carbon cycling. However, the multi-scale spatiotemporal variability of the persistent JCF and the underlying mechanisms driving its ecological effects remain limited. Using satellite observations and reanalysis data, this study systematically investigates the JCF’s distribution and its regulatory impact on phytoplankton chlorophyll-a (Chla) and particulate organic carbon (POC). Results show the persistent JCF is most active in summer and winter, primarily in Haizhou Bay and the Jiangsu Shoal. In summer, stratification-induced nutrient limitation within the Haizhou Bay thermal front decreases Chla and POC (by ~−20% and ~−40%, respectively), whereas nutrient-replete non-frontal waters support higher biomass. In the Jiangsu Shoal, the thermal front blocks the southward transport of POC, helping to maintain stable POC levels in the nearshore non-frontal region; meanwhile, the shift from southward to northward transport leaves the offshore non-frontal area without sufficient replenishment, resulting in a ~35% decrease in POC. In winter, the Haizhou Bay thermal frontal barrier effect restricts suspended particulate matter, alleviating light limitation inside the front and enhancing Chla (up to 15%) while reducing POC due to diminished resuspension. We elucidate that the JCF shapes ecological patterns through two primary pathways: by directly acting as a barrier to material transport and by interacting with ancillary processes like upwelling. These findings advance the mechanistic understanding of frontal impacts on coastal ecosystems and provide a mechanistic basis for understanding synergistic coastal carbon sinks. Full article

Population activity drives urban development, and high-spatiotemporal-resolution population distribution provides critical insights for refined urban management and social services. However, mixed population activity patterns and spatial heterogeneity make simultaneous high-temporal- and -spatial-resolution estimation difficult. Therefore, we propose the High-Spatiotemporal-Resolution Population Distribution Estimation Based on the Strong and Weak Perception of Population Activity Patterns (SWPP-HSTPE) method to estimate hourly population distribution at the building scale. During the weak-perception period, we construct a Modified Dual-Environment Feature Fusion model using building features within small-scale grids to estimate stable nighttime populations. During the strong-perception period, we incorporate activity characteristics of weakly perceived activity populations (minors and older people). Then, the Self-Organizing Map algorithm and spatial environment function purity are used to decompose mixed patterns of strongly perceived activity populations (young and middle-aged) and to extract fundamental patterns, combined with building types, for population calculation. Results demonstrated that the SWPP-HSTPE method achieved high-spatiotemporal-resolution population distribution estimation. During the weak-perception period, the estimated population correlated strongly with actual household counts (r = 0.72) and outperformed WorldPop and GHS-POP by 0.157 and 0.133, respectively. During the strong-perception period, the SWPP-HSTPE model achieves a correlation with hourly population estimates that is approximately 4% higher than that of the baseline model, while reducing estimation errors by nearly 2%. By jointly accounting for temporal dynamics and population activity patterns, this study provides valuable data support and methodological insights for fine-grained urban management. Full article

Understanding the spatiotemporal dynamics, interactions, and drivers of ecosystem services (ESs) is critical for ecological conservation and sustainable management in fragile sandy ecosystems. This study assessed five key ESs (water conservation, vegetation carbon sequestration, biodiversity, soil conservation, sand fixation) in the Hunshandake Sandy Land during 2000–2020, using Spearman correlation, geographically weighted regression, self-organizing maps (SOMs), and Structural Equation Modeling (SEM) to quantify trade-offs/synergies, identify ES bundles (ESBs), and clarify natural/social drivers. Results showed that all ESs fluctuated temporally with distinct spatial heterogeneity (higher in wetter, vegetated east; lower in arid, wind-erosion-prone west). Synergies dominated most ES pairs (e.g., WC-VS, WC-SC), with VS-BD showing a trade-off, WC-SF/VS-SC synergies strengthened, and WC-BD shifted from synergy to trade-off. SOMs identified six ESBs with consistent spatial patterns across decades. SEM revealed precipitation enhanced WC, evapotranspiration reduced SF/BD, temperature promoted SC but suppressed VS, elevation strongly benefited SC, NDVI was the primary driver of VS, and GDP had a slight negative effect. These findings provide insights for targeted ecological management in the study area and sustainable ES promotion in global fragile sandy landscapes. Full article

The Lower Liaohe River Plain (LLRP) is a core grain production base in Northeast China. Monitoring the dynamics and changing rates of soil organic carbon (SOC) in cultivated lands is essential for regulating soil fertility, safeguarding food production, and maintaining the regional carbon balance. Based on soil survey data from three periods, 1980, 2008, and 2019, this study investigated the spatiotemporal dynamics of SOC content and its changing rate (SOC_r) using geospatial analysis. Results showed that SOC content declined significantly from 11.19 g kg⁻¹ to 10.47 g kg⁻¹ during 1980–2008, then recovered slightly to 10.58 g kg⁻¹ in 2019. Moreover, SOC_r varied temporally in the period of 2008–2019, exhibiting a positive mean rate of 0.01 g kg⁻¹ yr⁻¹, which was significantly higher than that of the period of 1980–2008 (−0.03 g kg⁻¹ yr⁻¹). A significant negative correlation was examined between the initial SOC content and SOC_r, showing an identification of the SOC equilibrium point (SOC_ep). The SOC_ep in the period of 2008–2019 was 9.69% higher than that in the period of 1980–2008. These findings provide a scientific basis for formulating regional policies and optimizing spatially differentiated management strategies to improve cropland SOC in the study area. Full article

Major depressive disorder (MDD), identified by the World Health Organization as the leading cause of disability worldwide, remains underdiagnosed due to the lack of objective diagnostic tools. Electroencephalogram (EEG) signals offer potential biomarkers, yet conventional analyses often overlook the brain’s nonlinear dynamics. In this study, we analyzed resting-stage EEG data to identify four microstate types in MDD patients. Symmetrical microstate–brain networks were then constructed for each microstate by using time series of four types of microstates as dynamic windows. Then, we compared microstate features (duration, occurrence, coverage, transition probability) and brain network parameters (clustering coefficient, characteristic path length, local and global efficiency) between MDD patients and healthy controls to analyze the characteristics of the changes in the brain activities of the patients with MDD and the topological patterns of the functional connectivity. The comparative analysis showed that MDD patients showed more frequent microstate transitions and reduced network efficiency, suggesting elevated energy consumption and impaired neural integration, which may imply a cognitive shift in MDD patients toward internal focus and psychological withdrawal from external stimuli. By integrating microstate and brain network features, we captured the temporal and spatial characteristics of MDD-related brain activity and validated their diagnostic utility using our previously proposed multiscale spatiotemporal convolutional attention network (MSCAN). Our MSCAN achieved an accuracy of 98.64% for MDD detection, outperforming existing approaches. Our study can offer promising implications for the intelligent diagnosis of MDD and a deeper understanding of its neurophysiological underpinnings. Full article

Using MODIS-Aqua satellite observations, this study analyzes the spatiotemporal distribution characteristics of particulate organic carbon (POC) in China’s marginal seas from 2003 to 2024. The statistical relationships between various marine environmental variables, including sea surface temperature (SST), nutrients, and primary production (PP), and POC concentrations are explored using partial least squares path modeling (PLS-PM). Finally, a box model approach is conducted to assess the POC budget in the study area. The results indicate that the POC concentration in the marginal seas of China generally exhibits a characteristic of being high in spring and low in summer. The highest concentration of POC is observed in the Bohai Sea, followed by the Yellow Sea, and the lowest in the East China Sea, with coastal waters exhibiting higher POC concentrations compared to the central areas. The spatial distribution and seasonal changes in POC are jointly influenced by PP, water mass exchange, resuspended sediments, and terrestrial inputs. Large-scale climate modes show statistical associations with POC concentration in the open waters of China’s marginal seas. PP and respiratory consumption are identified as the predominant input and output fluxes, respectively, in China’s marginal seas. This study enriches the understanding of carbon cycling processes and carbon sink mechanisms in marginal seas. Full article

Landslide disasters are complex spatiotemporal phenomena. Existing deep learning (DL) models for remote sensing (RS) image analysis primarily exploit shallow visual features, inadequately incorporating critical geological, geographical, and environmental knowledge. This limitation impairs detection accuracy and generalization, especially in complex terrains and diverse vegetation conditions. We propose Knowledge Graph Embedding in Swin Feature Pyramid Networks (KGE–SwinFpn), a novel RS landslide segmentation framework that integrates explicit domain knowledge with deep features. First, a comprehensive landslide knowledge graph is constructed, organizing multi-source factors (e.g., lithology, topography, hydrology, rainfall, land cover, etc.) into entities and relations that characterize controlling, inducing, and indicative patterns. A dedicated KGE Block learns embeddings for these entities and discretized factor levels from the landslide knowledge graph, enabling their fusion with multi-scale RS features in SwinFpn. This approach preserves the efficiency of automatic feature learning while embedding prior knowledge guidance, enhancing data–knowledge–model coupling. Experiments demonstrate significant outperformance over classic segmentation networks: on the Yuan-yang dataset, KGE–SwinFpn achieved 96.85% pixel accuracy (PA), 88.46% mean pixel accuracy (MPA), and 82.01% mean intersection over union (MIoU); on the Bijie dataset, it attained 96.28% PA, 90.72% MPA, and 84.47% MIoU. Ablation studies confirm the complementary roles of different knowledge features and the KGE Block’s contribution to robustness in complex terrains. Notably, the KGE Block is architecture-agnostic, suggesting broad applicability for knowledge-guided RS landslide detection and promising enhanced technical support for disaster monitoring and risk assessment. Full article