Changes In Temperature Patterns Research Articles

Revealing the effects of environmental and spatio-temporal variables on changes in Japanese sardine (Sardinops melanostictus) high abundance fishing grounds based on interpretable machine learning approach

The construction of accurate and interpretable predictive model for high abundance fishing ground is conducive to better sustainable fisheries production and carbon reduction. This article used refined statistical maps to visualize the spatial and temporal patterns of catch changes based on the 2014-2021 fishery statistics of the Japanese sardine Sardinops melanostictus fishery in the Northwest Pacific Ocean. Three models (XGBoost, LightGBM, and CatBoost) and two variable importance visualization methods (model built-in (split) and SHAP methods) were used for comparative analysis to determine the optimal modeling and visualization strategies. Results: 1) From 2014 to 2021, the annual catch showed an overall increasing trend and peaked at 220,009.063 tons in 2021; the total monthly catch increased and then decreased, with a peak of 76, 033.4944 tons (July), and the catch was mainly concentrated in the regions of 39.5°-43°N and 146.75°-155.75°E; 2) Catboost model predicted better than LightGBM and XGBoost models, with the highest values of accuracy and F1-score, 73.8% and 75.31%, respectively; 3) the overall importance ranking of the model’s built-in method differed significantly from that in the SHAP method, and the overall importance ranking of the spatial variables in the SHAP method increased. Compared to the built-in method, the SHAP method informs the magnitude and direction of the influence of each variable at the global and local levels. The results of the research help us to select the optimal model and the optimal visualization method to construct a prediction model for the Japanese sardine fishing grounds in the Northwest Pacific Ocean, which will provide a scientific basis for the Japanese sardine fishery to achieve environmental and economically sustainable fishery development.

Satellite Observations Reveal Widespread Color Variations in Global Lakes Since the 1980s

AbstractThe color of lakes is an essential indicator of the local ecological state, and the corresponding changes can reflect the physical and biochemical processes of lakes. However, worldwide changes in lake color and their drivers remain largely unknown. Here, we analyze the long‐term color distributions and changes of 67,579 lakes worldwide from 1984 to 2021 by utilizing 32 million consistent satellite observations. Blue lakes (<495 nm) were primarily located in high‐latitude and high‐elevation areas. Green lakes (495–560 nm) were more prevalent in densely populated middle‐latitude regions, while most red and yellow colors (≥560 nm) were located in the Southern Hemisphere. Our findings reveal distinct temporal patterns of lake color changes, with the majority of global lakes shifted toward shorter wavelengths. This phenomenon is more common in Warm Temperate and Boreal zones. Lake color changes are closely linked to basin vegetation conditions, population, water volume change, and lake area. Our study provides essential references for monitoring the ecological status of global lakes, further supporting the sustainable development of water resources in the future.

Links Between Internal Variability and Forced Climate Feedbacks: The Importance of Patterns of Temperature Variability and Change

AbstractUnderstanding the relationships between internal variability and forced climate feedbacks is key for using observations to constrain future climate change. Here we probe and interpret the differences in these relationships between the climate change projections provided by the CMIP5 and CMIP6 experiment ensembles. We find that internal variability feedbacks better predict forced feedbacks in CMIP6 relative to CMIP5 by over 50%, and that the increased predictability derives primarily from the slow (>20 years) response to climate change. A key novel result is that the increased predictability is consistent with the higher resemblance between the patterns of internal and forced temperature changes in CMIP6, which suggests temperature pattern effects play a key role in predicting forced climate feedbacks. Despite the increased predictability, emergent constraints provided by observed internal variability are weak and largely unchanged from CMIP5 to CMIP6 due to the shortness of the observational record.

Dynamic Changes and Driving Force of Ecosystem Services Values Under Land Use/Land Cover Scenarios in Nanning, China

A comprehensive understanding of the spatial and temporal patterns of changes in ecosystem services, along with their driving factors, is crucial for managing ecosystems effectively and ensuring sustainable development in the area. Intense human activities and rapid urbanization have led to frequent land use/cover changes. Nanning, as a rapidly developing city, has received increasing attention for its ecosystem services. This research assesses the valuation of ecosystem services by employing the equivalent factor approach, utilizing land use data from Nanning City spanning the period from 2000 to 2020. It analyzes the spatial and temporal dynamics of these services and identifies key drivers through the application of a GeoDetector model. The results show the following: (1) Between 2000 and 2020, forests were the predominant ecosystem in Nanning City. During this period, significant land type conversions occurred, with the largest outflow from arable land and the largest inflow to construction land. (2) Over the 20 years, the aggregate value of ecosystem services (ESV) in Nanning exhibited a declining trend, culminating in a net reduction of USD 1056.8 million. Forest land ESVs constituted the largest proportion of the total value, exceeding 59%. Among the individual ESVs, hydrological regulation and climate regulation were the predominant services, collectively accounting for over 48% of the total value, with hydrological regulation contributing 20.7% and climate regulation 27.7%. (3) The ESVs in Nanning City exhibit pronounced spatial differentiation, with higher values concentrated in the central regions and lower values in peripheral zones. Notably, the high-value zones are experiencing a reduction in size, while the low-value zones are progressively expanding. (4) The findings from the GeoDetector analysis indicate that soil organic matter, urbanization rate, annual sunshine, financial expenditure, and population density are the primary determinants affecting ESV. Furthermore, the explanatory power of these influencing factors is substantially increased following interactive detection. This research provides a scientific basis for developing land management strategies and policies in Nanning City and holds substantial significance for advancing sustainable regional development.

Consequences of Early Maternal Deprivation on Neuroinflammation and Mitochondrial Dynamics in the Central Nervous System of Male and Female Rats.

Early life stress (ELS) is associated with an increased risk for neuropsychiatric disorders, and both neuroinflammation and mitochondrial dysfunction seem to be central to mental health. Herein, using an animal model of ELS, a single episode of maternal deprivation (MD, 24 h on pnd 9) extensively documented to elicit behavioural anomalies in male and female Wistar rats, we investigated its consequences in terms of neuroinflammation and mitochondrial dynamics in the prefrontal cortex (PFC) and the hippocampal formation (HCF). MD differentially affected the brain content of cytokines: MD induced a transient increase in pro-inflammatory cytokines (IL-1β and IL-6) in the PFC, as well as in the levels of the anti-inflammatory cytokine IL-10 in the HCF. MD also induced a significant decrease mitochondria citrate synthase activity, but MD did not exert significant changes in mitochondria Complex IV activity, revealing a generalized decrease in mitochondrial density without any change in mitochondrial respiration. In the present study, we demonstrate that MD induces neuroinflammatory processes in specific brain regions. Additional research is needed to better understand the temporal pattern of such changes, their impact on the developing brain, and their participation in the already well-known behavioural consequences of MD.

Changing characteristics, driving factors and future predictions of land use in the Weigan-Kuqa River Delta Oasis, China

The oasis serves as the central component of the arid ecosystem and plays a crucial role in supporting human activities. However, the ecological environment in the oasis region is fragile, and even a minor alteration in land use (LU) can significantly impact the stability of the ecosystem. Therefore, it is imperative to undertake comprehensive research on the spatio–temporal patterns of LU change in the oasis, reveal its driving factors, and predict future development. This is crucial for devising scientifically and logically sound land management strategies, upholding the equilibrium between humans and land in arid areas, and attaining sustainable development of the regional ecology and economy. This study focuses on the Weigan–Kuqa River Delta Oasis in China as the research area, analyzes the changes in LU in the oasis from 2010 to 2022 using various methods such as transition matrix, dynamic degree, intensity analysis, and center of gravity shift. The study also investigates the factors influencing these changes using the optimal parameters–based geographical detector (OPGD). Additionally, it predicts the future trends in LU development under four different scenarios using the mixed–cell cellular automata (MCCA), and illustrates distribution characteristics by combining Moran’s I index and hotspot analysis. The results suggest that: (1) Between 2010 and 2022, the LU in the oasis changed rapidly, with consistent increase in the amount of construction land, arable land, and garden land, while the amount of forest-grassland and unused land decreased overall. (2) Population density played a leading role in the changes, but soil type also had a significant impact. Over the course of time, the influence of roads and transportation has progressively increased. (3) Compared with 2022, the acreage of arable land, garden land, and construction land increases under the four future scenarios: natural development scenario (NDS), economic development scenario (EDS), cropland development scenario (CDS), and ecological protection scenario (EPS). However, the acreage of forest–grassland and unused land decrease. From a spatial perspective, large towns, the downstream of alluvial fans, and the central oasis are key areas where the distribution of hot spots and sub–hot spots of each LU type varies significantly among the four scenarios. The EPS provides a certain level of protection for forest-grassland areas and water bodies, making it the most appropriate development model for oases. These findings have the potential to offer valuable academic guidance for oasis land resource management and are crucial for achieving coordinated development at regional level.

Quantifying the temporal pattern of shape change in trigoniids through the Middle Jurassic–Early Cretaceous of Kutch: A geometric morphometric approach

Quantifying the temporal pattern of shape change in trigoniids through the Middle Jurassic–Early Cretaceous of Kutch: A geometric morphometric approach

Comprehensive comparative analysis of reconstructed sea level datasets in the China Seas: insights from tide gauge and satellite altimetry

At present, there are many reconstructed datasets at the global scale. To test the applicability of these datasets in the China seas, the study comprehensively analyzes the reliability and accuracy of reconstructed sea level datasets in capturing nuanced temporal patterns of sea level changes in the China Seas. This study applied analysis methods or indicators such as time series, Taylor plots, correlation coefficients, growth rates, and standard deviations. Ocean Data Assimilations (ODAs) outperform Tide Gauge Reconstructions (TGRs) in terms of correlation with measured data in the nearshore, while TGRs exhibit superior capability in capturing oceanic sea level variability. Although the ODAs and TGRs both suffer from the underestimation of sea level variability in China as well as in neighboring seas, the TGRs perform better than the former. ODAs show inconsistency in reflecting the rate of sea level rise, but they, particularly the China Ocean Reanalysis (CORA), demonstrate a better correlation with satellite altimetry datasets. Meanwhile, both of them can reflect the Pacific Decadal Oscillation (PDO) well. TGRs, relying on oceanic tide gauge stations, suffer from poor correlation with tide gauge stations due to limited coverage. Reconstruction discrepancies are attributed to methodological differences and data assimilation techniques. Future studies should explore alternative variables like sea surface temperature and so on to enhance sea-level reconstruction, especially in regions with sparse tide gauge coverage.

Long-Term Spatiotemporal Trends in Precipitation, Temperature, and Evapotranspiration Across Arid Asia and Africa

This study examines trends in precipitation (PRE), maximum temperature (TMAX), minimum temperature (TMIN), and potential evapotranspiration (PET) using the Modified Mann-Kendall test and Sen’s slope estimator between 1901 and 2022 in the arid lands of Central Asia, West Asia and North Africa. The results reveal complex spatial and temporal climate change patterns across the study area. Annual PRE shows a slight negative trend (Z = −0.881, p = 0.378), with significant decreases from 1951–2000 (Z = −3.329, p = 0.001). The temperatures exhibit strong warming trends (TMIN: Z = 9.591, p < 0.001; TMAX: Z = 8.405, p < 0.001). PET increased significantly (Z = 6.041, p < 0.001), with acceleration in recent decades. Spatially, precipitation decreased by 10% in maximum annual values, while PET increased by 10–15% in many areas. Temperature increases of 2–3 °C were observed, with TMAX rising from 36–39 °C to 39–42 °C in some MENA regions. Seasonal analysis shows winter precipitation decreasing significantly in recent years (Z = −1.974, p = 0.048), while summer PET shows the strongest increasing trend (Z = 5.647, p < 0.001). Spatial analysis revealed clear latitudinal gradients in temperature and PET, with higher values in southern regions. PRE patterns were more complex, with coastal and mountainous areas receiving more precipitation. The combination of rising temperatures, increasing PET, and variable PRE trends suggest an overall intensification of aridity in many parts of the region. This analysis provides crucial insights into the climate variability of these water-scarce areas, emphasizing the need for targeted adaptation strategies in water resource management, agriculture, and ecosystem conservation.

Skin graft monitoring using forward-looking infrared thermal imaging.

This study examined the feasibility of non-invasive infrared thermography to monitor skin graft viability. Sixty-three patients with skin defects attending a single institution from May 2022 to August 2023 were included. Patients underwent full-thickness or split-thickness skin grafts based on clinical indication. Infrared thermal images were obtained on postoperative days 0, 2, 4, 6 and 8. The temperature difference between the skin graft and surrounding normal skin was assessed using image analysis software. All 33 patients with full-thickness skin grafts showed successful healing. Among the 30 patients with split-thickness skin grafts, 7 experienced failure. The groups with successful full-thickness and split-thickness skin grafts exhibited a gradual increase in graft temperature, peaking on postoperative day 6 and decreasing on postoperative day 8. Temporal temperature changes were significant in each patient group (p < 0.001), and the differences in temperature change patterns between the two groups with successful grafts and the group with graft failure were also significant (p < 0.001). On postoperative day 6, the temperature difference was highest in the full-thickness skin graft group (0.197 ± 0.335°C), followed by the successful split-thickness skin graft group (0.426 ± 0.428°C), and the split-thickness skin graft group with graft failure (-2.100 ± 0.361°C). In conclusion, infrared thermal imaging can provide a non-invasive real-time assessment of graft status and predict graft success or failure.

Nonlinear Radiative Response to Patterned Global Warming due to Convection Aggregation and Nonlinear Tropical Dynamics

Abstract Climate responses to global warming exhibit large dependence on the spatial pattern of sea surface temperature (SST) changes. A Green’s function (GF) approach—predicting the global climate response to a complex SST pattern change as the linear superposition of the response to finite-area SST perturbations evaluated in isolation—has been used to systematically evaluate and understand the top of atmosphere (TOA) radiation response to patterned warming. However, in general, the linear GF approach fails for TOA radiation reconstruction under future global warming scenarios in coupled models. Here, we show that the linear superposition of global mean TOA radiation responses to large SST warming perturbations at multiple patches (the GF approach prediction) overestimates the actual response to simultaneous multipatch perturbation. This is because the linear superposition overestimates tropical large-scale convection aggregation strengthening upon localized heating that enhances longwave radiative cooling, which is further explained by the overestimation of circulation response and associated horizontal water vapor transport. The nonadditivity of TOA radiation response is caused by the nonadditivity of convection aggregation, ultimately rooted in nonlinear tropical dynamics. We also demonstrate that the prediction error of the GF approach grows with decreasing patch size (equivalently, increasing patch number). We conclude that using the GF approach to predict future climate change could overestimate longwave radiative cooling and underestimate (effective) climate sensitivity. Numerical experiments may be used to identify specific perturbation patterns where the errors are smaller than the signal. Our research also highlights that an increase in the degree of large-scale convection aggregation has a nonlinear and negative feedback for mean warming.

Linking land use and precipitation changes to water quality changes in Lake Victoria using earth observation data

Due to the continued increase in land use changes and changing climatic patterns in the Lake Victoria basin, understanding the impacts of these changes on the water quality of Lake Victoria is imperative for safeguarding the integrity of the freshwater ecosystem. Thus, we analyzed spatial and temporal patterns of land cover, precipitation, and water quality changes in the Lake Victoria basin between 2000 and 2022 using global satellite products. Focusing on chlorophyll-a (Chl-a) and turbidity (TUR) in Lake Victoria, we used statistical metrics (correlation coefficient, trend analysis, change budget, and intensity analysis) to understand the relationship between land use and precipitation changes in the basin with changes in Chl-a and TUR at two major pollution hotspots on the lake, i.e., Winam Gulf and Inner Murchison Bay (IMB). Results show that the Chl-a and TUR concentrations in the Winam gulf increase with increases in precipitation. Through increases in precipitation, the erosion risks are increased and transport of nutrients from land to the lake system, promoting algal growth and turbidity. In the IMB, Chl-a and TUR concentrations decrease with an increase in precipitation, possibly due to dilution, but peak during moderate rainfall. Interestingly, changes in land use and land cover (LULC) at 5-year intervals showed no substantial correlation with water quality changes at selected hotspots even though a broader LULC change analysis over the past two decades indicated a notable 300% increase in built-up areas across the Lake Victoria basin. These findings underscore the dominant influence of precipitation changes over LULC changes on the water quality of Lake Victoria for the selected hotspot areas.

Dynamics of tumor in situ fluid circulating tumor DNA in recurrent glioblastomas forecasts treatment efficacy of immune checkpoint blockade coupled with low-dose bevacizumab

PurposeImmune checkpoint blockade (ICB) therapies have shown efficacy in various tumors, but long-term responses in glioblastoma are less than 10%. Quantifying tumor in situ fluid circulating tumor DNA (TISF-ctDNA) and therapeutic dynamics may enable real-time GBM disease burden evaluation. This study explores the potential of tumor in situ fluid circulating tumor DNA (TISF-ctDNA) dynamics in predicting treatment efficacy.MethodsTISF and peripheral blood samples were collected from patients with recurrent glioblastoma (rGBM) undergoing tislelizumab (a programmed death 1 inhibitor) combined with low-dose bevacizumab (an anti-vascular endothelial growth factor antibody) treatment before and during each immunotherapy cycle. Biomarkers evaluated included TISF-ctDNA, measured using Next Generation Sequencing (NGS), and host inflammation markers such as the platelet-to-lymphocyte ratio (PLR).ResultsAll 32 patients received tislelizumab plus low-dose bevacizumab regularly. The median progression-free survival (PFS) was 4.0 months, and overall survival (OS) was 22.3 months. An analysis of 19 patients with continuous evaluable TISF showed baseline TISF-ctDNA abundance did not correlate with OS (p = 0.23) or PFS (p = 0.23). However, a change in TISF-ctDNA maximal Somatic Variant Allelic Frequency (MVAF) after six treatment cycles predicted both PFS (p = 0.02) and OS (p < 0.0001). Lower baseline PLR also correlated with better survival outcomes.ConclusionThe combination of tislelizumab and low-dose bevacizumab therapy appears to be effective in extending both OS and PFS in rGBM patients. Continuous TISF-ctDNA testing shows potential utility in complementing radiological monitoring. The temporal change pattern of TISF MVAF is more predictive of immunotherapy response than imaging. PLR before immunotherapy can screen patients likely to benefit from tislelizumab plus low-dose bevacizumab therapy.Trial registrationThe trial registration number: NCT05502991; Date of registration: 2022-08-14.

Influence of climate change on flowering phenology of Yoshino cherry at its southern distribution limit.

To clarify the influence of climate change on the flowering phenology of Yoshino cherry at its southern distribution limit, we examined the relationship between cold exposure for endodormancy release (chilling requirement) and heat requirement for bud growth on Hachijojima Island, Japan, from 1948 to 2024. Cold exposure and heat requirement had a significant relationship approximated by linear or log-linear functions. In years with less cold exposure, the first flowering dates were much later than normal, in accordance with the higher heat requirement. Our results indicate that the variation in the balance between cold exposure and heat requirement depending on the pattern of annual air temperature change is likely to vary the first flowering date greatly at the distribution limit of Yoshino cherry.

A new deep learning-based model for reconstructing high-quality NDVI time-series data in heavily cloudy areas: fusion of Sentinel 1 and 2 data

ABSTRACT Reconstructing high-quality Normalized Difference Vegetation Index time series data is essential for ecological and agricultural applications but remains challenging in heavily cloudy areas. Fusing Sentinel SAR and optical data with deep learning could be helpful but is also challenging for stable models due to unstable SAR-NDVI relationships caused by imaging mechanism differences and environmental complexities. In this study, we developed a new Bidirectional Recurrent Imputation for Optical-SAR fusion (BRIOS) model to reconstruct high-quality Sentinel-2 NDVI time series data. BRIOS designs a two-layer recurrent architecture that integrates the autocorrelation of discrete, cloud-free NDVI observations into the model for a more stable SAR-NDVI relationship. Evaluating BRIOS against three baseline methods (GF-SG spatiotemporal fusion, Harmonic regression interpolation, and MCNN-Seq deep learning) across three full Sentinel-2 tiles in reconstructing 8-day NDVI time series, BRIOS consistently outperformed in scenarios of either random or continuously missing data, as evidenced by lower RMSE values (e.g. 0.075 for BRIOS vs. 0.108 for GF-SG vs. 0.143 for Harmonic regression vs. 0.303 for MCNN-Seq), better Edge index, and high linear correlation coefficients (R values up to 0.97). Further ablation experiments revealed that deep integration of NDVI autocorrelation features and SAR temporal change patterns has improved the stability and generalization of BRIOS. Discussions on the model's scalability across various cloud sizes and training dataset sizes affirm its practicality for broad-scale application in vegetation monitoring under challenging cloudy conditions.

Study of patterns of PM2.5 and PM10 changes in the atmospheric air of Ivano-Frankivsk region

The purpose of this research is to analyse the temporal patterns of changes in concentrations of dust particles – aerosols, regression modeling of the interdependence of PM2.5 and PM10 at the level of instantaneous, average hourly, average daily and average weekly concentrations, and assessment of the impact of the anthropogenic component of dust pollution of atmospheric air in the Ivano-Frankivsk region on regularities of the temporal distribution of these shares.The database for the study included measurements of the concentration of PM10, PM2.5 every hour at four Ecocity public monitoring stations: in the central part of the city of Ivano-Frankivsk, in the village of Bovshiv within the influence of the Burshtyn TPP, in the village of Broshniv-Osada within the influence of the woodworking enterprise "SVISS KRONO" and within the recreational territory of the village of Mykulychyn, Nadvirnyan district. The general regularity of the temporal distribution of PM10, PM2.5 was established for all posts for daily fluctuations – the highest concentrations are observed at night, the lowest – during the period of maximum daily temperatures. It has been proven by actual data that the greater the level of atmospheric air pollution (the greater the anthropogenic component of PM), the higher the daily concentrations of PM10, PM2.5 and the more often the one-time standards of solid suspended particles are exceeded.According to the data of monitoring stations from the territories with different anthropogenic influence, functional dependences of PM2.5 content on PM10 content were obtained for instantaneous values, hourly average values, daily average values, and weekly average values. The scientific novelty of the conducted research consists in the establishment of identical functional interdependence of temporal changes in concentrations of PM2.5 and PM10 dust particles within a conditionally clean area and within the limits of the influence of stationary sources of pollution with a high level of direct regression dependence and a coefficient of determination greater than 0.9 in all cases. This allows us to conclude that the temporal patterns of changes in both PM2.5 and PM10 will not differ in clean and polluted air conditions for the Ivano-Frankivsk region. In conditions of incomplete data on the temporal distribution of the concentration of PM10, the obtained equations can be used to forecast the temporal distribution of the concentration of PM2.5.

Identification of Spatial Distribution of Afforestation, Reforestation, and Deforestation and Their Impacts on Local Land Surface Temperature in Yangtze River Delta and Pearl River Delta Urban Agglomerations of China

Forest change affects local and global climate by altering the physical properties of the land surface. Accurately assessing urban forest changes in local land surface temperature (LST) is a scientific and crucial strategy for mitigating regional climate change. Despite this, few studies have attempted to accurately characterize the spatial and temporal pattern of afforestation, reforestation, and deforestation to optimize their effects on surface temperature. We used the China Land Cover Dataset and knowledge criterion-based spatial analysis model to map urban forestation (e.g., afforestation and reforestation) and deforestation. We then analyzed the impacts of these activities on LST from 2010 to 2020 based on the moving window strategy and the spatial–temporal pattern change analysis method in the urban agglomerations of the Yangtze River Delta (YRD) and Pearl River Delta (PRD), China. The results showed that forest areas declined in both regions. Most years, the annual deforestation area is greater than the yearly afforestation areas. Afforestation and reforestation had cooling effects of −0.24 ± 0.19 °C and −0.47 ± 0.15 °C in YRD and −0.46 ± 0.10 °C and −0.86 ± 0.11 °C in PRD. Deforestation and conversion of afforestation to non-forests led to cooling effects in YRD and warming effects of 1.08 ± 0.08 °C and 0.43 ± 0.19 °C in PRD. The cooling effect of forests is more evident in PRD than in YRD, and it is predominantly caused by reforestation. Moreover, forests demonstrated a significant seasonal cooling effect, except for December in YRD. Two deforestation activities exhibited seasonal warming impacts in PRD, mainly induced by deforestation, while there were inconsistent effects in YRD. Overall, this study provides practical data and decision-making support for rational urban forest management and climate benefit maximization, empowering policymakers and urban planners to make informed decisions for the benefit of their communities.

The Temporal Change in Ionised Calcium, Parathyroid Hormone and Bone Metabolism Following Ingestion of a Plant-Sourced Marine Mineral + Protein Isolate in Healthy Young Adults.

Background: An increase in plant-sourced (PS) nutrient intake is promoted in support of a sustainable diet. PS dietary minerals and proteins have bioactive properties that can affect bone health and the risk of fracture. Methods: In a group randomised, cross-over design, this study evaluated the post-ingestion temporal pattern of change in arterialised ionised calcium (iCa), parathyroid hormone (PTH), C-terminal crosslinked telopeptide of type I collagen (CTX) and procollagen type 1 amino-terminal propeptide (P1NP) for 4 h following ingestion of a novel supplement (SUPP) containing a PS marine multi-mineral + PS protein isolate. A diurnally matched intake of mineral water was used as a control (CON). Results: Compared to baseline, the change in iCa concentration was 0.022 (95% CI, 0.006 to 0.038, p = 0.011) mmol/l greater in SUPP than CON, resulting in a -4.214 (95% CI, -8.244 to -0.183, p = 0.042) pg/mL mean reduction in PTH, a -0.64 (95% CI, -0.199 to -0.008, p = 0.029) ng/mL decrease in the biomarker of bone resorption, CTX, and no change in the biomarker of bone formation, P1NP. Conclusions: When used as a dietary supplement, or incorporated into a food matrix, the promotion of PS marine multi-mineral and PS protein isolates may contribute to a more sustainable diet and overall bone health.

Heat pipe for thermally coupled fuel cell and metal hydride as well as for cooling metal hydride hydrogen charging

ABSTRACT In this paper, a metal hydride (MH) canister and a PEM fuel cell are coupled using heat pipes to study the minimum number of heat pipes required, the operating range of the fuel cell, and the temperature change patterns of the MH. We account for temperature difference limitations between the fuel cell and the MH (∆T = 35–55 K) on the heat pipe dynamics, which has been neglected in previous similar studies. Since the hydrogen absorption rate of the MH canisters exhibits an initial increase followed by a decrease with increasing temperature, an analysis was conducted to select the appropriate heat pipe under different hydrogen filling pressures. This analysis ensured that the MH canisters remained at the optimal charging temperature (326 K) and achieved the maximum charging rate (6.64 slpm).

Analysis of Temporal and Spatial Carbon Stock Changes and Driving Mechanism in Xinjiang Region by Coupled PLUS-InVEST-Geodector Model

Based on the goal of "dual-carbon" strategy, it is important to explore the impacts of land use change on carbon stock and the drivers of spatial differentiation of carbon stock in Xinjiang. Here, we predicted the land use types in Xinjiang in 2035 under different scenarios and analyzed the impacts of land use on carbon stock, which is of great theoretical and practical importance for policy formulation, land use structure adjustment, and carbon neutrality target achievement in Xinjiang. The coupled PLUS-InVEST-Geodector model was used to explore the spatial and temporal patterns of carbon stock change under the scenarios of rapid development, natural change, arable land protection, and ecological protection in Xinjiang in 2035 and to quantitatively reveal the attribution of influences on the changes in carbon stock from the perspectives of land use change and the combination of nature-socioeconomic-accessibility. The results showed that： ① From 1990 to 2020, the area of arable land and construction land in Xinjiang increased, and in terms of the transfer direction, it was mainly shifted from unutilized land to grassland. ② On the time scale, the carbon stock in Xinjiang showed the fluctuation of "decrease-increase-decrease," with an overall increasing trend. The transfer of unutilized land to grassland was the main reason for the increase in carbon stock； on the spatial scale, the carbon stock in the Altai Mountains in the north, the Tianshan Mountains in the middle, and the Kunlun Mountains in the south was higher, whereas the carbon stock in the Tarim Basin and the Junggar Basin was lower. ③ In 2035, the carbon stock of the natural development and rapid development scenarios decreased by 27.24 Tg and 71.17 Tg compared with 2020, respectively, and the ecological protection and arable land protection scenarios increased by 492.55 Tg and 46.67 Tg. The ecological protection scenario could significantly increase the carbon stock of the Xinjiang Region compared with that in the other scenarios, and the distribution pattern of the carbon stock in the four scenarios was more or less the same as that in 2020. In addition to land transformation, soil erosion intensity was the main driver of spatial differentiation of carbon stocks in Xinjiang （q value of 0.3501）, followed by net primary productivity of vegetation. The results of multifactor interactions showed that the spatial differentiation of carbon stocks in Xinjiang was the result of the joint action of multiple factors. All the factors had a synergistic enhancement under the interactions. The interaction between soil erosion intensity and the net primary productivity of vegetation was the main driver of the spatial differentiation of carbon stocks in Xinjiang.