Changes In Spatiotemporal Patterns Research Articles

Characterizing ground ice content and origin to better understand the seasonal surface dynamics of the Gruben rock glacier and the adjacent Gruben debris-covered glacier (southern Swiss Alps)

Abstract. Over the recent years, there have been focused international efforts to coordinate the development and compilation of rock glacier inventories. Nevertheless, in some contexts, identifying and characterizing rock glaciers can be challenging as complex conditions and interactions, such as glacier–rock-glacier interactions, can yield landforms or landform assemblages that are beyond a straightforward interpretation and classification through ordinary visual means alone. To gain a better understanding of the spatial and temporal complexity of the ongoing processes where glacier–permafrost interactions have occurred, the characterization of the subsurface of the Gruben rock glacier and its adjacent complex contact zone with the then more extended Little Ice Age Gruben glacier is quantitatively assessed using a petrophysical joint inversion (PJI) scheme, based on electrical resistivity (ERT) and refraction seismic (RST) data. Surface dynamics are assessed using both in situ and close-range remote sensing techniques to monitor daily and seasonal displacements and to monitor landform-wide surface changes at high spatial resolution, respectively. Both the geophysical and geodetic surveys allowed two zones to be identified: the rock glacier zone and the complex contact zone where both permafrost and embedded surface ice are present. In the complex contact zone extremely high ice contents (estimated up to 85 %) were found. Widespread supersaturated permafrost conditions were found in the rock glacier zone. Surface displacement rates in this zone are typical of permafrost creep behaviour, with a gradual acceleration in late spring and a gradual deceleration in winter. Moreover, the coherent nature of the rock glacier zone surface deformation contrasts with the back-creeping and slightly chaotic surface deformation of the complex contact zone. Favouring a multi-method approach allowed a detailed representation of the spatial distribution of ground ice content and origin, which enabled us to discriminate glacial from periglacial processes as their spatio-temporal patterns of surface change and geophysical signatures are (mostly) different.

Exploration of the Spatiotemporal Characteristics and Driving Mechanisms of Vegetation Greenness Changes in Laos

In the context of climate change, vegetation changes in Laos have attracted widespread attention, especially the profound impact of its greenness changes on ecosystems, water cycles, and climate feedback. However, our understanding of the driving factors of vegetation greenness changes in different latitudes is still limited. This study utilized EVI and climate factor data from 2001 to 2023, employing trend analysis, correlation analysis, and machine learning methods to investigate the spatiotemporal patterns of vegetation greenness changes across Laos and their responses to climate factors. Results revealed an overall increasing trend in vegetation greenness, with 75% of the area exhibiting annual increases, primarily in northern, central, and parts of the southern regions. Conversely, 24.8% of the area experienced declines, concentrated near Vientiane and certain southern regions. Seasonal trends during the wet season largely aligned with annual patterns, although reduced rainfall negatively impacted some areas. The dry season exhibited the most pronounced changes, with 70% of the area showing increased greenness, especially in northern and central regions, despite localized rainfall constraints. Minimum temperature (TMMN) emerged as the most influential factor, with importance values of 0.42 for annual changes and 0.37 for dry season changes, while precipitation impacts varied across space and time. High temperatures affected vegetation more significantly in low-latitude regions, whereas high-latitude areas relied on changes in DSR. This significant finding underscores the differential impact of climate factors on vegetation greenness across latitudes, which is crucial for understanding the complex dynamics of tropical inland ecosystems under climate change and for developing targeted conservation and adaptation strategies.

Basin-scale spatio-temporal development of glacial lakes in the Hindukush-Karakoram-Himalayas

Glacial lakes are expanding exponentially in the cryospheric environment of the Hindukush-Karakoram-Himalayas (HKH). Rapid glacier melting due to an above mean global annual temperature increase in HKH is attributed as the main reason for the expansion of the glacial lakes. The rapid expansion of glacial lakes increases the risk of future Glacial Lake Outburst Floods (GLOFs) events in the HKH.In the present study, glacial lake inventories for the Indus, Ganga and Brahmaputra (IGB) river basins in the HKH were generated for 1990, 2000, 2010 and 2020 using Landsat (TM & OLI) at the sub-basin level to understand the spatio-temporal and regional patterns of glacial lakes dynamics, elevational evolution, and changes in the typology. We mapped 17,641 glacial lakes (area: 1082.57 ± 192.601 km2) in 1990, 18,206 (area: 1120.95 ± 198.49 km2) in 2000, 18,399 (area: 1147.12 ± 201.26 km2) in 2010, and 19,284 (area: 1191.81 ± 209.21 km2) in 2020. Between 1990 and 2020, IGB basins showed an increase of 9.31 % in total number and 10.09 % in total area of glacial lakes. In 2020, the Brahmaputra basin had the maximum total area (area: 763.59 ± 132.14 km2), followed by Indus basin (area: 217.47 ± 43.39 km2) and the Ganga basin (area: 210.74 ± 33.66 km2). However, between 1990 and 2020, glacial lakes in the Ganga basin (n: 22.08 %) had the highest growth rate, followed by the Indus basin (n: 14.73 %) and the Brahmaputra basin (n: 4.41 %). In 2020, 76.11 % of glacial lakes were end-moraine-dammed M(e) lakes, followed by other bedrock-dammed B(o) lakes (16.45 %), supraglacial lakes (2.79 %), lateral moraine-dammed M(l) lakes (2 %), cirque B(c) lakes (1.06 %), other moraine-dammed M(o) lakes (0.38 %), and other glacial (O) lakes (1.18 %). Given the rapid growth of glacial lakes in the region along with their likely flood volumes and damage potential in case of their failures, the present study will be of importance for disaster management authorities, an important input for detection of potentially hazardous glacial lakes and for development of mitigation strategies to minimize the impact of potential future GLOF events.

Spatio-Temporal Heterogeneity of Ecological Quality in a Typical Dryland of Northern China Driven by Climate Change and Human Activities.

With the intensification of climate change and anthropogenic impacts, the ecological environment in drylands faces serious challenges, underscoring the necessity for regionally adapted ecological quality evaluation. This study evaluates the suitability of the original Remote Sensing Ecological Index (oRSEI), modified RSEI (mRSEI), and adapted RSEI (aRSEI) in a typical dryland region of northern China. Spatio-temporal changes in ecological quality from 2000 to 2022 were analyzed using Theil-Sen median trend analysis, the Mann-Kendall test, and the Hurst exponent. Multiple regression residual analysis quantified the relative contributions of climate change and human activities to ecological quality changes. Results showed that (1) the aRSEI was the most suitable index for the study area; (2) observed changes exhibited significant spatial heterogeneity, with improvements generally in the inner areas of the Yellow River and declines in the outer areas; and (3) changes in ecological quality were primarily driven by climate change and human activities, with human activities dominating from 2000 to 2011 and the influence of climate change increasing from 2012 to 2022. This study compares the efficacy of RSEIs in evaluating dryland ecological quality, identifies spatio-temporal change patterns, and elucidates driving mechanisms, offering scientific evidence and policy recommendations for targeted conservation and restoration measures to address future changes in dryland regions.

Spatio-temporal patterns of land use and land cover change in Kibwezi West, Eastern Kenya

Kenyan drylands have over the years undergone extensive land use and land cover (LULC) changes due to population increase, urbanization, agricultural expansion, industrialization and infrastructural developments. There is however limited information on their historical and future spatio-temporal patterns. This study assessed the spatio-temporal LULC change patterns in Kibwezi West for the period 1990–2021 and predicted the LULC map of 2051. Six LULC classes (Forested land, shrubland, grassland, cropland, water body and other lands) covering 1,040.9 Km2 were examined. Landsat imageries (1990, 2000, 2011 and 2021) were classified using Random Forest algorithm in R software, while LULC change was analyzed using ERDAS Imagine. The 2051 LULC map was predicted using Artificial Neural Network and Cellular Automata algorithms. OpenLand software was used for visualization of LULC patterns using Sankey diagrams. Overall classification accuracy of 78.04% was obtained with 0.61 kappa coefficient. A net loss in forested land (−112.8 km2), shrubland (−54.48 km2) and water body (−0.688 km2) had occurred, with a net gain in cropland (146.03 km2), grassland (20.24 km2) and other lands (1.66 km2) between 1990–2021. Further, a net loss in shrubland (−110.48 km2), forested land (−89.1 km2), water body (−0.38 km2) and other lands (−0.32 km2) was predicted in 2051 while a net gain was predicted in cropland (176.90 km2) and grassland (23.39 km2). The study pointed out historical and future encroachment into natural ecosystems like forested lands and shrublands. The findings of this study contribute to the body of knowledge on LULC dynamics in drylands. These results will inform evidence-based decision-making processes for sustainable land use planning, natural resource management and environmental conservation efforts in Kibwezi West and other similar landscapes.

Assessing the Interaction Impacts of Multi-Scenario Land Use and Landscape Pattern on Water Ecosystem Services in the Greater Bay Area by Multi-Model Coupling

Water ecosystem services (WESs) are intrinsically associated with the livelihood of urban residents and are frequently disrupted by human activities. Land use and landscape patterns are key driving factors of alterations in WESs. However, existing research primarily quantifies single-factor influences and often overlooks the interactions between these factors. This study addresses this gap by employing a multi-model coupling approach, integrating the Patch-generating Land Use Simulation (PLUS), Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model, and Geographical Detector (GD) models alongside various indicators to analyse the evolution of land use, landscape patterns and WESs in the Greater Bay Area from 2000 to 2020, and to simulate spatio-temporal change patterns in different scenarios from 2030 to 2050. Additionally, this study examines the multi-factorial interactions between land use, landscape patterns, and WESs. The results indicate that (1) urbanisation steadily increased, leading to intensified landscape fragmentation, and water yield (WY) and total phosphorus (TP) consistently increased, while total nitrogen (TN) in water gradually decreased; (2) urban areas exerted the most significant impact on WY in the Greater Bay Area while Patch density (PD) had a stronger influence on WY, and Shannon’s diversity index (SHDI) had the most pronounced effect on TN and TP; (3) the interaction between any two land-use types or landscape indices exerted a greater impact on WESs compared with the impact of individual factors alone. The interaction between urban areas and cropland substantially influenced WY (q¯ = 0.634) and most strongly affected TN and TP in water (q¯ = 0.74 and 0.73, respectively). SHDI and PD had the most significant impact on WY in the economic development scenario (q¯ = 0.19) and exhibited the greatest influence on the TN and TP levels in the ecological priority scenario (q¯ = 0.12 and 0.15, respectively). Our findings can provide theoretical and technical support for the integrated scientific planning of regional water ecosystems and the development of comprehensive land use policies in the future.

Spatiotemporal dynamics of LULC change and implications for sustainable urban growth in Saudi Arabia

This study investigates the spatiotemporal dynamics of land use and land cover (LULC) change in Saudi cities between 2013 and 2023. Using Landsat satellite imagery and a Maximum Likelihood Classifier, LULC changes are analyzed in nine cities to understand the impact of Saudi Arabia’s rapid urbanization following the oil crisis. The objectives are: (1) to evaluate the LULC changes in nine cities between 2013 and 2023; (2) to explore the spatiotemporal patterns of LULC changes in this period; and (3) to interpret the urban expansion that occurs in Saudi Arabia in the light of the kingdom sustainable development plan. The results identifiy four primary LULC change models: Urbanization, Vegetation Recovery, Land Degradation, and Ecological Transition Models. The Urbanization and Land Degradation Models determine Saudi Arabia’s urban growth trend, posing significant challenges to sustainable development. Most cities experienced substantial increases in impermeable surfaces and developed lands, while concurrently losing high terrain and barren areas. However, the small cities showed lower urbanization and land degradation rates than major cities, showing an opportunity to sustainably grow by adopting sustainable planning approaches. The study’s findings show that more targeted and comprehensive policies are needed to address the challenges of urbanization and land degradation, mitigate environmental impacts, and ensure long-term resilience. It underscores the importance of considering Ecological Urbanism in urban development plans.

Comprehensive Representations of Subpixel Land Use and Cover Shares by Fusing Multiple Geospatial Datasets and Statistical Data with Machine-Learning Methods

Land use and cover change (LUCC) is a key factor influencing global environmental and socioeconomic systems. Many long-term geospatial LUCC datasets have been developed at various scales during the recent decades owing to the availability of long-term satellite data, statistical data and computational techniques. However, most existing LUCC products cannot accurately reflect the spatiotemporal change patterns of LUCC at the regional scale in China. Based on these geospatial LUCC products, normalized difference vegetation index (NDVI), socioeconomic data and statistical data, we developed multiple procedures to represent both the spatial and temporal changes of the major LUC types by applying machine-learning, regular decision-tree and hierarchical assignment methods using northeastern China (NEC) as a case study. In this approach, each individual LUC type was developed in sequence under different schemes and methods. The accuracy evaluation using sampling plots indicated that our approach can accurately reflect the actual spatiotemporal patterns of LUC shares in NEC, with an overall accuracy of 82%, Kappa coefficient of 0.77 and regression coefficient of 0.82. Further comparisons with existing LUCC datasets and statistical data also indicated the accuracy of our approach and datasets. Our approach unfolded the mixed-pixel issue of LUC types and integrated the strengths of existing LUCC products through multiple fusion processes. The analysis based on our developed dataset indicated that forest, cropland and built-up land area increased by 17.11 × 104 km2, 15.19 × 104 km2 and 2.85 × 104 km2, respectively, during 1980–2020, while grassland, wetland, shrubland and bare land decreased by 26.06 × 104 km2, 4.24 × 104 km2, 3.97 × 104 km2 and 0.92 × 104 km2, respectively, in NEC. Our developed approach accurately reconstructed the shares and spatiotemporal patterns of all LUC types during 1980–2020 in NEC. This approach can be further applied to the entirety of China, and worldwide, and our products can provide accurate data supports for studying LUCC consequences and making effective land use policies.

The effects of locus coeruleus optogenetic stimulation on global spatiotemporal patterns in rats

Abstract Whole-brain intrinsic activity as detected by resting-state fMRI can be summarized by three primary spatiotemporal patterns. These patterns have been shown to change with different brain states, especially arousal. The noradrenergic locus coeruleus (LC) is a key node in arousal circuits and has extensive projections throughout the brain, giving it neuromodulatory influence over the coordinated activity of structurally separated regions. In this study, we used optogenetic-fMRI in rats to investigate the impact of LC stimulation on the global signal and three primary spatiotemporal patterns. We report small, spatially specific changes in global signal distribution as a result of tonic LC stimulation, as well as regional changes in spatiotemporal patterns of activity at 5 Hz tonic and 15 Hz phasic stimulation. We also found that LC stimulation had little to no effect on the spatiotemporal patterns detected by complex principal component analysis. We hypothesize that localized effects could be due to engagement of LC modules that support behaviors induced by our specific stimulation parameters, in addition to noradrenergic receptor profile distributions. Nonetheless, these results show that the effects of LC activity on the BOLD signal in rats may be small and regionally concentrated, as opposed to widespread and globally acting, further supporting emerging evidence of a modular LC.

Remote Sensing Mapping and Analysis of Spatiotemporal Patterns of Land Use and Cover Change in the Helong Region of the Loess Plateau Region (1986–2020)

Land use and cover change (LUCC) is directly linked to the sustainability of ecosystems and the long-term well-being of human society. The Helong Region in the Loess Plateau has become one of the areas most severely affected by soil and water erosion in China due to its unique geographical location and ecological environment. The long-term construction of terraces and orchards is one of the important measures for this region to combat soil erosion. Despite the important role that terraces and orchards play in this region, current studies on their extraction and understanding remain limited. For this reason, this study designed a land use classification system, including terraces and orchards, to reveal the patterns of LUCC and the effectiveness of ecological restoration projects in the area. Based on this system, this study utilized the Random Forest classification algorithm to create an annual land use and cover (LUC) dataset for the Helong Region that covers eight periods from 1986 to 2020, with a spatial resolution of 30 m. The validation results showed that the maps achieved an average overall accuracy of 87.54% and an average Kappa coefficient of 76.94%. This demonstrates the feasibility of the proposed design and land coverage mapping method in the study area. This study found that, from 1986 to 2020, there was a continuous increase in forest and grassland areas, a significant reduction in cropland and bare land areas, and a notable rise in impervious surface areas. We emphasized that the continuous growth of terraces and orchards was an important LUCC trend in the region. This growth was primarily attributed to the conversion of grasslands, croplands, and forests. This transformation not only reduced soil erosion but also enhanced economic efficiency. The products and insights provided in this study help us better understand the complexities of ecological recovery and land management.

Spatio-temporal pattern change of LULC and its response to climate in the Loess Plateau, China

Exploring land use/cover (LULC) change is essential for the sustainable development of ecologically fragile areas. The main objective of this study was to clarify the characteristics and differences in the spatiotemporal changes of LULC on the Loess Plateau (LP) based on the transfer matrix and land use dynamics and to quantitatively describe the impact of natural factors on LULC using a geodetector. The results indicated that the overall LULC change in the LP was characterized by a decrease in the area of cropland, grassland, and bare land, and an increase in the area of woodland and built-up land. This trend shows a clear phase-change characteristic around 2000. LULC changes were primarily affected by human activities in the southeastern agricultural region. The project of returning farmland to forest and grassland had a great impact on LULC change in the central region. Vegetation was sensitive to temperature and precipitation, and the impact of LULC change was significantly higher than that in the humid region in the northwest arid region. NDVI, PRE, and TEM were determined to be the main contributors to LULC changes in the LP. These results provide a scientific basis for the sustainable development of LP.

Unraveling Projected Changes in Spatiotemporal Patterns and Drought Events across Mainland China Using CMIP6 Models and an Intensity–Area–Duration Algorithm

In the context of global warming, temperature increases have led to frequent drought events and a sharp increase in economic losses and social risks. In this study, five medium- and high-emission scenario models, the SSP245 and SSP585, CMIP6 monthly scale temperature and precipitation datasets under different global warming contexts (1.5 °C and 2 °C), and the 1984–2014 weather station observations were selected. The latter dataset was used to improve the ability of the CMIP6 to simulate surface drought accuracy. A standardized precipitation–evapotranspiration index dataset was generated. The latest intensity–area–duration framework was adopted to identify regional drought events by considering their continuity and spatial dynamic characteristics. The parameters of intensity, area, and duration were used to characterize the dynamic evolution of drought events. Under the medium- to high-emission scenario model, with a continuous increase in global temperature to 1.5 °C, in the southeastern Qinghai–Tibet Plateau (QTP) and southern Xinjiang (XJ) there is a significant increase in intensity, extent, and duration of drought events and some drought exacerbation in northeastern China. Under the high-emission SSP585 scenario model, the severity of these drought events is reduced when compared with the SSP245 scenario model, but this also shows an increasing trend, especially with the 2 °C global warming background. Significant drought aggravation trends were observed in southern XJ, northern QTP, and northern Northwest. In contrast, a small but significant drought-weakening trend was observed in southwestern south China. The results of this study provide a reference for society and government departments to make decisions in response to future drought events.

Assessing the impact of land use and land cover change on the Densu Delta wetland using Markov chain modeling and artificial neural networks

This study investigates the dynamics of land use and land cover (LULC) changes in the Densu Delta wetlands, a critical ecosystem in Ghana. Here, satellite images spanning from 1998 to 2023 were used to analyse the spatio-temporal patterns of LULC changes and their implications for water bodies, wetlands, vegetations, bare lands and urban areas in the Densu Delta wetland. Employing advanced techniques such as Markov chain modelling and artificial neural networks (ANNs), the research assesses and predicts LULC alterations. Significantly, the largest loss of LULC is observed in the Densu Delta wetland, where wetlands transition to waterbody cover type (14.02 km²). Model validation for 2023 attests to the accuracy of the model, boasting a correctness percentage of 70% and a kappa value of 0.74. In-depth analyses explore regional variations in the Densu Delta wetlands, revealing distinct patterns in the rates of LULC change before and after 2013. Notably, urbanization emerges as a prominent factor post-2013, with urban areas experiencing remarkable rates of change in the wetland. Transition matrices underscore the intricate interplay of different land cover classes over the years. Simulated LULC predictions for 2033 and 2043 highlight the urban land cover type as having the highest positive change, recording approximately 0.39% for the Densu Delta wetland. The wetland land cover in the Densu Delta wetland exhibit negative changes of about −0.52%. The synthesis of LULC data enhances our understanding of the complex interactions shaping these critical ecosystems. This research offers valuable insights for sustainable environmental conservation, emphasizing the pivotal role of informed urban planning policies. It also unveils potential challenges posed by climate change, advocating for a holistic approach to preserve these vital wetland ecosystems.

Changes in Spatiotemporal Pattern and Its Driving Factors of Suburban Forest Defoliating Pest Disasters

Forest defoliating pests are significant global forest disturbance agents, posing substantial threats to forest ecosystems. However, previous studies have lacked systematic analyses of the continuous spatiotemporal distribution characteristics over a complete 3–5 year disaster cycle based on remote sensing data. This study focuses on the Dendrolimus superans outbreak in the Changbai Mountain region of northeastern China. Utilizing leaf area index (LAI) data derived from Sentinel-2A satellite images, we analyze the extent and dynamic changes of forest defoliation. We comprehensively examine the spatiotemporal patterns of forest defoliating pest disasters and their development trends across different forest types. Using the geographical detector method, we quantify the main influencing factors and their interactions, revealing the differential impacts of various factors during different growth stages of the pests. The results show that in the early stage of the Dendrolimus superans outbreak, the affected area is extensive but with mild severity, with newly affected areas being 23 times larger than during non-outbreak periods. In the pre-hibernation stage, the affected areas are smaller but more severe, with a cumulative area reaching up to 8213 hectares. The spatial diffusion characteristics of the outbreak follow a sequential pattern across forest types: Larix olgensis, Pinus sylvestris var. mongolica, Picea koraiensis, and Pinus koraiensis. The most significant influencing factor during the pest development phase was the relative humidity of the year preceding the outbreak, with a q-value of 0.27. During the mitigation phase, summer precipitation was the most influential factor, with a q-value of 0.12. The combined effect of humidity and the low temperatures of 2020 had the most significant impact on both the development and mitigation stages of the outbreak. This study’s methodology achieves a high-precision quantitative inversion of long-term disaster spatial characteristics, providing new perspectives and tools for real-time monitoring and differentiated control of forest pest infestations.

Impacts of forest cover change on local temperature in Yangtze River Delta and Pearl River Delta urban agglomerations of China

The continuous economic and ecological construction in the Yangtze River Delta (YRD) and Pearl River Delta (PRD) has caused frequent temporal and spatial changes in local forests, thus affecting the regional climate. Yet few studies have addressed the temperature feedback through biophysical mechanisms due to forest change in two urban agglomerations of China. We compared MODIS and Landsat-based land cover data to detect a more accurate forest cover change. We then used the moving window strategy and spatiotemporal pattern change analysis method to quantify and compare the actual impact of forest cover change on temperature and the differences in driving factors (e.g., evapotranspiration (ET), albedo, and precipitation) from 2010 to 2020. The results showed that Landsat-based land cover data performed well. The conversion from forest to cropland was dominated in YRD and PRD, followed by the conversion of cropland to forest, with a small proportion of forest converting to impervious surface. The afforested areas in the two regions showed a diurnal cooling effect (-0.18 ± 0.07 °C and -0.10 ± 0.13 °C, respectively), which was greater than the air temperature. Forest converting to impervious surfaces led to stronger warming (0.39 ± 0.37 °C in YRD) than that of cropland (0.05 ± 0.03 °C in YRD and 0.07 ± 0.06 °C in PRD). The daytime LST variations can be explained by ET and inconsistent albedo effects. Seasonally, the cooling effects induced by afforestation predominated during the growing season (spring and summer), accompanied by the relatively high ET. This study shows that rational afforestation and control of deforestation are helpful to achieve sustainable forest management in urban agglomerations and to regulate climate warming.

Deciphering the fate of granulation tissue in the human inflammatory alveolar microenvironment using single-cell RNA sequencing

Objective: To investigate the cellular composition and heterogeneity of granulation tissue in human alveolar sockets and construct single-cell transcriptomic maps to elucidate the potential outcomes of natural resolution in the inflammatory microenvironment. Methods: Granulation tissues from 12 alveolar sockets undergoing tooth extraction due to periodontitis and scheduled for delayed site preservation or autologous tooth transplantation were collected in the Department of Oral and Maxillofacial Surgery, School of stomatology Fourth Military Medical University from September 2022 to August 2023. This study used HE staining and single-cell RNA sequencing (scRNA-seq) to observe the cellular composition and morphological changes of different types of granulation tissues. This approach enabled us to identify cellular sequence variations in the inflammatory dental alveolar granulation tissue within specific microenvironments, construct single-cell atlases for different types of human dental alveolar granulation tissues, and explore the spatiotemporal patterns of cell type distribution and key gene changes during the resolution process of inflammatory granulation tissue. Results: HE staining revealed extensive infiltration of inflammatory cells in the dental alveolar inflammatory granulation tissue. After allowing the inflammatory granulation tissue to naturally resolve for three weeks, its histological morphology was essentially consistent with that of reparative granulation tissue. ScRNA-seq captured a total of 20 448 cells from granulation tissues, and the gene expression quantification analysis revealed total gene counts of 33 835 for inflammatory granulation tissue, 36 058 for naturally resolved granulation tissue, and 34 719 for reparative granulation tissue. At the single-cell level, granulation tissue was annotated into ten cell subgroups, including vascular endothelial cells, mesenchymal stem cells, and fibroblasts. Differences were observed in the proportion of cell compositions between inflammatory and naturally resolved granulation tissues. Pseudo-temporal analysis illustrated two main outcomes in tissue resolution and healing, involving immune responses and angiogenesis. Among these, genes associated with inflammation regulation and immune response, such as Igbp5, Zfp36, and Hspa1b, as well as genes involved in extracellular matrix secretion and the formation of vessels and fibers such as Timp3, Postn, and Rgs5, showed significant differences in expression between the two types of granulation tissues. Conclusions: Inflammatory granulation tissue exhibits heterogeneity in cell composition, gene expression, and biological functions compared to naturally resolved granulation tissue. When the inflammatory granulation tissue in the alveolar socket is left undisturbed to undergo natural resolution, it displays a cellular composition similar to that of reparative granulation tissue at both the histological and single-cell levels. Moreover, it modulates the inflammatory response and the healing process through immune reactions and tissue remodeling.

Past and future joint return period of precipitation extremes over South Asia and Southeast Asia

Climate change is one of the major reasons for the increased intensity and frequency of precipitation extremes in tropical regions. This will have a significant impact on underdeveloped countries in South Asia and Southeast Asia. Therefore, this study analyzes changes in spatiotemporal patterns and the joint behavior of precipitation extremes across South Asian and Southeast Asian countries for the historical period (1975 to 2014) and future periods (2021–2060 (F1) and 2061–2100 (F2)). This study develops bias-corrected precipitation data and ranks General Circulation Models (GCMs) using the Empirical Quantile Mapping method and TOPSIS method, respectively. A multi-model ensemble of precipitation extremes is created using the top five GCMs. The Mann Kendall test is used to analyze trends in precipitation extremes. Joint probabilistic analysis is also conducted for different combinations of ETCCDI precipitation-based indices using Archimedes and Elliptical copulas. The results of the trend analysis indicate significant positive trends for R20mm (19.26%), R95pTOT (18.40%), Rx5Day (11.53%), and CWD (10.46%), while CDD (5.07%) shows a significant negative trend across the study area during historical period (1975–2014). The results of R20mm, R95pTOT, and Rx5Day under SSP585 shows almost 19 to 39% of area comes under significant positive trend during the F1 period. This becomes more evident in F2, with trends under SSP585 rising between 32% and 61%. The high-emissions scenario SSP585 reveals an increase in these positive trends compared to SSP126 in both the future periods (F1 and F2). These results indicating a noticeable rise in extreme precipitation events. The analysis of changes in the joint return period of precipitation extremes indicates that the South Peninsular India, North East India, West Central India and Central Northeast India, as well as most parts of Southeast Asian countries, will experience very heavy intensify precipitations in the future. In addition, persistent co-occurrence of dry and wet conditions will be observed in Pakistan, North West India, and Central Northeast India. This study provides useful information on the distribution of precipitation extremes in different regions of the study area.

The Effects of Locus Coeruleus Optogenetic Stimulation on Global Spatiotemporal Patterns in Rats.

Whole-brain intrinsic activity as detected by resting-state fMRI can be summarized by three primary spatiotemporal patterns. These patterns have been shown to change with different brain states, especially arousal. The noradrenergic locus coeruleus (LC) is a key node in arousal circuits and has extensive projections throughout the brain, giving it neuromodulatory influence over the coordinated activity of structurally separated regions. In this study, we used optogenetic-fMRI in rats to investigate the impact of LC stimulation on the global signal and three primary spatiotemporal patterns. We report small, spatially specific changes in global signal distribution as a result of tonic LC stimulation, as well as regional changes in spatiotemporal patterns of activity at 5 Hz tonic and 15 Hz phasic stimulation. We also found that LC stimulation had little to no effect on the spatiotemporal patterns detected by complex principal component analysis. These results show that the effects of LC activity on the BOLD signal in rats may be small and regionally concentrated, as opposed to widespread and globally acting.

Spatiotemporal variation and prediction of NPP in Beijing-Tianjin-Hebei region by coupling PLUS and CASA models

Vegetation productivity is crucial for human production and livelihoods. Understanding net primary productivity (NPP) in historical contexts and predicting its future fluctuations is imperative for assessing the environmental sustainability of a region. However, relatively few researches have been conducted on predicting NPP, requiring further development and refinement of NPP prediction methods and models. This study introduces a novel approach that discretely couples the PLUS and CASA models for NPP prediction, and it validates the applicability of this approach in the research area. The objective of our study is to analyze the spatiotemporal patterns of NPP change in the Beijing-Tianjin-Hebei (BTH) region from 2001 to 2020, predict NPP under three different climate scenarios (SSP 1-2.6, SSP 2-4.5, SSP 5-8.5) in 2030, and identify an appropriate path for the future development of this region. The results indicate:(1) From 2001 to 2020, NPP in the research area has shown a gradual improvement trend and maintained a certain spatial distribution pattern in general. (2) The study discovered a correlation coefficient of 0.83 and an RMSE of 102.86 between predicted and actual NPP for 2020. This suggests that the method introduced in our study is suitable for predicting NPP in the research area. (3) NPP in the study area is predicted to decline in 2030 compared with 2020 under all three scenarios. Moreover, the SSP 1-2.6 scenario, representing a low-emission scenario, is suitable for the BTH region compared with other climate scenarios. This research sheds light on NPP variations in the BTH region over the past 20 years and the next 10 years, offering a scientific basis for relevant departments to formulate future policies.

Quantifying the air pollution impacts on solar photovoltaic capacity factors and potential benefits of pollution control for the solar sector in China

Solar photovoltaic (PV) plays a crucial role in China's energy transition. However, air pollution diminishes solar radiation resources, thereby reducing PV power generation efficiency. This study aims to quantify the impacts of air pollution on PV capacity factors in China while emphasizing the geographically specific potential benefits of improved air quality for the future PV sector. Using a PV power generation evaluation model, this study estimated solar PV capacity factors from 1961 to 2016 under both all-sky and clear-sky scenarios. Also, the spatiotemporal patterns of air pollution-induced capacity factor changes were analyzed. The results indicate that the all-sky and clear-sky PV capacity factors showed consistent annual anomalies, suggesting that air pollution due to aerosol emissions drove PV capacity factor temporal evolutions and trends in China. From 1961 to 1990, the national average clear-sky PV capacity factors for fixed modules with optimal tilt angle showed a significant decreasing trend of −0.0053 per decade, whereas from 1990 to 2016, an upward trend of 0.0030 per decade was observed. Compared with fixed modules, tracking systems suffered from greater productivity losses due to air pollution. In comparison to 1961–1965 averages, the provincial PV capacity factors for 2012–2016 decreased by 0.48–13.54%, 1.15–11.40%, 1.06–14.83%, and 1.31–16.51% for fixed modules with optimal tilt angle, horizontal fixed, one-axis horizontal tracking, and two-axis tracking modules, respectively, with decreases of 7–17% in the central and southeast. Furthermore, according to the provincial PV installation targets projected in China's 14th Five-Year Plan, national solar PV power generation was expected to increase by 81.333 TWh to 1069.997 TWh in 2025 with average PV capacity factors for 1961–1965, compared to 2012–2016 averages. In a scenario of keeping grid parity, additional power generation would offer an economic benefit of approximately 30.102 billion CNY.