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In recent decades, alpine meadows have experienced severe degradation owing to external disturbances. Although soil microorganisms are critical for ecosystem services, little is known about their responses to soil degradation and the potential patterns in alpine meadows. To solve this question, we collected and analyzed soil samples from three degraded alpine meadows situated on the Qinghai-Tibet Plateau. We aimed to examine the effects of degradation on soil microbial diversity and identify the ecological predictors for the diversity of bacteria and fungi. Our results showed that alpine meadow degradation significantly changed soil bacterial and fungal diversity and community composition. Specifically, the relationship between bacterial and fungal diversity and degradation intensity was a hump-shaped, with the highest diversity observed at a moderate degradation level. Additionally, alpine meadow degradation-induced changes in microbial diversity were strongly correlated with decreased plant production, with fungal diversity showing a closer link with below-ground biomass (BGB) than with bacterial diversity. Our findings offer empirical evidence that intermediate disturbance (i.e., moderate degradation) may be beneficial for supporting soil biodiversity. This has important implications for informing policy and management strategies meant to conserve soil biodiversity and ecosystem services when facing anthropogenic change.

ABSTRACTJiangxi Province boasts the second-highest forest coverage in China. Its forests play a crucial role in providing essential ecosystem services and maintaining the ecological health of the region. High-resolution and high-precision forest mapping are significant in the timely and accurate monitoring of dynamic forest changes to support sustainable forest management. This study used Sentinel-2 images from four seasons in the Google Earth Engine (GEE) platform to map forest distribution. Moreover, the classification results were compared and analyzed using different classification algorithms and feature-variable combinations. Based on the overall accuracy, the optimal image seasonality, feature combinations and classification algorithms were selected, and the forest maps of Jiangxi Province were mapped from 2019 to 2021. The accuracy evaluation showed that the winter image classification results had the highest accuracy (above 0.88). The red edge bands carried by Sentinel-2 could effectively improve the classification accuracy. The Random Forest classifier is the optimal classification algorithm for forest mapping in Jiangxi Province. The forest mapping obtained can be used for ecological health assessment and ecosystem function. The study provides a scientific basis for accurate and timely extraction of forest cover and can serve as a valuable resource for forest management planning and future research.

The alpine grasslands of the Qinghai-Tibetan Plateau play an important role in multiple ecosystem functions, all of which are key in regulating regional climate influences and providing pristine headwaters for millions of people downstream from this basin. Alpine grasslands act as a carbon sink, storing carbon dioxide and keeping heat-trapping greenhouse gases out of the atmosphere, but it is not clear how this will change in a warming climate in the future. In this paper, the net ecosystem productivity (NEP) of alpine grasslands in Qinghai province, on the Qinghai-Tibetan Plateau, was predicted for the period from 2010 to 2099. Trends and stability were analyzed under two climate scenarios, Representative Concentration Pathway 4.5 (RCP4.5) and 8.5 (RCP8.5) representing the lower and higher emission scenarios for greenhouse gases. The results suggest that grasslands will continue to contribute as a carbon sink, with a positive NEP through this century. Almost the same magnitude (38 Tg C a-1, 1 T g = 1012 g) of contribution was projected under both scenarios. Grasslands are projected to be the major contributor to NEP in Qinghai province, with more than 89% of NEP in the future. The carbon sink function will increase over more than 69% of the grasslands and peak around 2069 (RCP4.5) or 2066 (RCP8.5). Then the carbon sink function will begin to decrease and it will decrease more quickly and become more variable under the RCP8.5 than the RCP4.5. The impacts of temperature and precipitation changes were analyzed and NEP was found to be more sensitive to temperature than precipitation change. The trend of increasing contribution to NEP is driven by a warming climate, while the stability of NEP is mainly influenced by the precipitation, which results in an upward trend before the peak and a decline due to stresses from limits in available water in a continued warming climate.

Vegetation response to climate change can be characterized as long-term trends, but with periodic oscillations. Revealing vegetation response trends, with oscillations, is essential for formulating climate change mitigation and adaptation planning, as well as adopting ecological protection and restoration measures. Here, the long-term trend and periodic oscillations in vegetation changes were explored through a measure of gross primary production (GPP), an indicator of vegetation photosynthesis, using data of both high spatial resolution (1 km2) and a long-term of nearly 4 decades, from 1980 to 2018. The GPP data were estimated for China’s terrestrial ecosystem and validated against the eddy covariance-based observations of 70 site-year in China and compared to other GPP data products. The results showed the estimated GPP can explain 76% of spatial–temporal variance with the same data accuracy as other products, but with the advantage of both high spatial resolution and long time periods that other products do not have. From the estimated GPP data, Chinese terrestrial vegetation growth was found to have a periodic oscillation of close to 2.79 years and a long-term increasing trend of 59.8 Mt C per year over 92% of all vegetated land in China from 1980 to 2018. Moreover, it was found that vegetation growth accelerated significantly in the two recent decades over the previous two decades. The growth rate was 83.5 Mt C a−1 from 2000 to 2018, which was 2.31 times faster than the rate from 1980 to 2000. The underlying mechanism of the vegetation responses was analyzed by ridge regression, a method to decrease the effects of multicollinearity. Long-term variability and trends in vegetation could be attributed to a warming minimum air temperature when examined with precipitation, maximum air temperature, and solar shortwave radiation. Though more influences, such as those from freezing and thawing and human activities, should be explored in the future, this study offers insight that warming, with increasing daily minimum temperatures, is likely to be a vital contributor to ecosystem changes, and will be essential knowledge for mitigation of effects and adaptation to future climate.

BackgroundSystemic lupus erythematosus (SLE) and primary Sjögren’s syndrome (pSS) are common systemic autoimmune diseases that share a wide range of clinical manifestations and serological features. This study investigates genes, signaling pathways, and transcription factors (TFs) shared between SLE and pSS.MethodsGene expression profiles of SLE and pSS were obtained from the Gene Expression Omnibus (GEO). Weighted gene co-expression network analysis (WGCNA) and differentially expressed gene (DEG) analysis were conducted to identify shared genes related to SLE and pSS. Overlapping genes were then subject to Gene Ontology (GO) and protein-protein interaction (PPI) network analyses. Cytoscape plugins cytoHubba and iRegulon were subsequently used to screen shared hub genes and predict TFs. In addition, gene set variation analysis (GSVA) and CIBERSORTx were used to calculate the correlations between hub genes and immune cells as well as related pathways. To confirm these results, hub genes and TFs were verified in microarray and single-cell RNA sequencing (scRNA-seq) datasets.ResultsFollowing WGCNA and limma analysis, 152 shared genes were identified. These genes were involved in interferon (IFN) response and cytokine-mediated signaling pathway. Moreover, we screened six shared genes, namely IFI44L, ISG15, IFIT1, USP18, RSAD2 and ITGB2, out of which three genes, namely IFI44L, ISG15 and ITGB2 were found to be highly expressed in both microarray and scRNA-seq datasets. IFN response and ITGB2 signaling pathway were identified as potentially relevant pathways. In addition, STAT1 and IRF7 were identified as common TFs in both diseases.ConclusionThis study revealed IFI44L, ISG15 and ITGB2 as the shared genes and identified STAT1 and IRF7 as the common TFs of SLE and pSS. Notably, the IFN response and ITGB2 signaling pathway played vital roles in both diseases. Our study revealed common pathogenetic characteristics of SLE and pSS. The particular roles of these pivotal genes and mutually overlapping pathways may provide a basis for further mechanistic research.

Elymus nutans is a perennial grass of the Gramineae family. Due to its cold-resistance and nutrition deficiency tolerance, it has been applied to the ecological restoration of degraded alpine grassland on the Qinghai-Tibet Plateau. As an important symbiotic microorganism, arbuscular mycorrhizal fungi (AMF) have been proven to have great potential in promoting the growth and stress resistance of Gramineae grasses. However, the response mechanism of the AMF needs to be clarified. Therefore, in this study, Rhizophagus irregularis was used to explore the mechanism regulating cold resistance of E. nutans. Based on pot experiments and metabolomics, the effects of R. irregularis were investigated on the activities of antioxidant enzyme and metabolites in the roots of E. nutans under cold stress (15/10°C, 16/8 h, day/night). The results showed that lipids and lipid molecules are the highest proportion of metabolites, accounting for 14.26% of the total metabolites. The inoculation with R. irregularis had no significant effects on the activities of antioxidant enzyme in the roots of E. nutans at room temperature. However, it can significantly change the levels of some lipids and other metabolites in the roots. Under cold stress, the antioxidant enzyme activities and the levels of some metabolites in the roots of E. nutans were significantly changed. Meanwhile, most of these metabolites were enriched in the pathways related to plant metabolism. According to the correlation analysis, the activities of antioxidant enzyme were closely related to the levels of some metabolites, such as flavonoids and lipids. In conclusion, AMF may regulate the cold-resistance of Gramineae grasses by affecting plant metabolism, antioxidant enzyme activities and antioxidant-related metabolites like flavonoids and lipids. These results can provide some basis for studying the molecular mechanism of AMF regulating stress resistance of Gramineae grasses.

Abstract Background An accurate assessment of the carbon budget is a crucial part of projecting future climate change and its impact on ecosystems. Grasslands foster multiple ecological functions including support for wild animals and livestocks. Herbivores intake forage biomass carbon, then digest and metabolize, and finally retain some carbon. The carbon processes have not been well quantified, resulting in uncertainties in the estimation of regional carbon budgets for grassland ecosystems. Methods An animal metabolic carbon flux model was developed for herbivores in the Three‐Rivers Headwaters region of China. The forage intake and metabolic carbon rates were estimated through metabolic body weight and daily digested measures for the main herbivore species. Results The carbon intake was 5.52 Tg C year−1 (45%) from partial aboveground biomass (12.2 Tg C year−1), in which 39.31% was released into the atmosphere by respiration CO2, 43.77% was returned to the ecosystem as feces and urine, and 16.96% was retained in herbivores for population regeneration or for human well‐being. Conclusions This study, as the first research on this topic, quantified the carbon flux of herbivores and found livestock accounts for a major part of consumed carbon on grasslands, which is important for understanding regional carbon budgets to mitigate and adapt to climate change over grasslands worldwide.

Forest resilience is crucial to the mitigation of climate change, due to the enormous potential of forests to reduce atmospheric carbon dioxide concentrations and the possible conversion of forests from net carbon sinks into carbon sources following external disturbances. Subtropical forests are suffering the highest rates of forest change, but how they are evolving in response to climate change is little known. In this study, we estimated the spatial pattern and temporal trend of the resilience of subtropical evergreen forests in China by applying the lag-one autocorrelation (AC1) method to satellite kernel normalized difference vegetation index (kNDVI) data over the past two decades and identified the influential environmental factors that affect the ecosystem resilience by developing random forest (RF) regression models. The computed long-term AC1 based on kNDVI for the 2001–2020 period depicts considerable spatial variability in the resilience of the subtropical evergreen forests in China, with lower resilience at lower latitudes. The RF regression analysis suggests that the spatial variability in the forest resilience can be re-established by forest and climatic variables, and is largely affected by climate, with the three most influential variables being solar radiation (SR, %incMSE = 20.7 ± 1.8%), vapor pressure deficit (VPD, %incMSE = 13.8 ± 0.2%) and minimum temperature (Tmin, %incMSE = 13.3 ± 1.2%). Higher forest resilience is more likely to be located in areas with less radiation stress, adequate water availability, and less warming. Trend analysis shows a declining trend for the resilience of subtropical evergreen forests in China since the 2000s but an increasing forest resilience in the last decade, which is mainly dominated by temperature changes, including average and minimum temperatures. Considering the expected warming-dominated period in times of rapid climatic change, we suggest potential critical responses for subtropical forest productivity to the disturbances should be of greater concern in the future.

Osteoarthritic (OA) is characterized by progressive destruction of joint cartilage. Here, the authors show that microRNA-17 plays a dual role in maintaining cartilage homeostasis and in the prevention of osteoarthritis, by targeting hypoxia-inducible factor-1α as well as multiple matrix-degrading enzymes.

Associations between grasses and soil microorganisms can strongly influence plant community structures. However, the associations between grass productivity and diversity and soil microbes, as well as the patterns of co-occurrence between grass and microbes remain unclear. Here, we surveyed grass productivity and diversity, determined soil physicochemical, and sequenced soil archaea, bacteria and fungi by metabarcoding technology at 16 alpine grasslands. Using the Distance-decay relationship, Inter-Domain Ecological Network (IDEN), and Mantel tests, we investigated the relationship between grass productivity, diversity and microbial diversity, and the patterns of co-occurrence between grass and microbial inter-domain network in alpine grassland. We found the archaea richness, bacteria richness and Shannon, and fungi α-diversity were significantly negatively correlation with grass diversity, but archaea and bacteria diversity were positively correlation with grass productivity. Moreover, an increase in microbial β-diversity was observed along with increased discrepancy in grass diversity and productivity and soil variables. Variance partitioning analysis suggested that the contribution of grass productivity on microbial community was higher than that of soil variables and grass diversity, which implies that microbial community was more related to grass productivity. Inter-Domain Ecological Network showed that the grass species formed complex and stable ecological networks with some bacterial, archaeal, and fungal species, and the grass-fungal ecological networks showed the highest robustness, which indicated that soil fungi could better co-coexist with aboveground grass in alpine grasslands. Besides, the connectivity degrees of the grass-microbial network were significantly positively correlated with grass productivity, suggesting that the coexistence pattern of grasses and microbes had a positive feedback effect on the grass productivity. The results are important for establishing the regulatory mechanisms between plants and microorganisms in alpine grassland ecosystems.

Abstract Large-scale COVID-19 vaccinations are currently underway in many countries in response to the COVID-19 pandemic. Here, we report, besides generation of neutralizing antibodies, consistent alterations in hemoglobin A1c, serum sodium and potassium levels, coagulation profiles, and renal functions in healthy volunteers after vaccination with an inactivated SARS-CoV-2 vaccine. Similar changes had also been reported in COVID-19 patients, suggesting that vaccination mimicked an infection. Single-cell mRNA sequencing (scRNA-seq) of peripheral blood mononuclear cells (PBMCs) before and 28 days after the first inoculation also revealed consistent alterations in gene expression of many different immune cell types. Reduction of CD8+ T cells and increase in classic monocyte contents were exemplary. Moreover, scRNA-seq revealed increased NF-κB signaling and reduced type I interferon responses, which were confirmed by biological assays and also had been reported to occur after SARS-CoV-2 infection with aggravating symptoms. Altogether, our study recommends additional caution when vaccinating people with pre-existing clinical conditions, including diabetes, electrolyte imbalances, renal dysfunction, and coagulation disorders.

Grassland is not only the natural habitats for the survival of wild animals, but also the material guarantees for livestock husbandry. However, the previous studies on grassland carrying capacity are mostly only considering livestock but wild herbivores were ignored. In this study, the population of large wild herbivores, Tibetan wild ass, Tibetan gazelle and Tibetan antelope, and livestock were surveyed, and the grassland forage yield was estimated by a remote sensing-process coupled model in Qumahe Township in Three-River Headwaters, Qinghai-Tibetan in 2020. The actual carrying capacity (AC) was calculated from the both wildlife and livestock and the theoretical carrying capacity (TC) in the term on edible herbage and crude protein was estimated. Grazing pressure index was measured by the ratio of AC to TC in this region. The results showed the AC was contributed by about 20,700 standard sheep units (SHU) from wild large herbivores, and 256,500 SHU from the livestock. The theoretical carrying capacity of edible herbage, which is 274,800 SHU, was lower than the theoretical carrying capacity of crude protein in the region. If only considering livestock, the grazing capacity pressure index is 0.93, but the index will be 1.01 if the both wildlife and livestock were considered, and wildlife contributes 7.5% to the grazing pressure. This study suggested the wildlife population should be calculated in grassland ecological carrying capacity estimation though domestic livestock contributed much more to grazing pressure than wild herbivores. The new indicator would be useful in grassland management to maintain ecosystem and biodiversity balance, and society-economy sustainability.

The fraction of absorbed photosynthetically active radiation (FPAR) is a key biophysiological parameter of terrestrial ecosystems. However, due to a lack of data with adequate spatial resolution and in long enough time series, there have been limitations in exploring the spatiotemporal changes of vegetation and response to climate change. In this study, a 1 km spatial resolution and 8-day period length dataset (FPARANN) was developed covering the years 1980 to 2018 and evaluated on spatiotemporal change consistency by validating with Gross Primary Production (GPP) observations from the Chinese Flux Observation and Research Network (ChinaFLUX), and comparison with other FPAR products. FPARANN provided a comparable performance in capturing seasonal change observed through GPP, according to the coefficient of determination (R2): 0.50, 0.51, 0.70 and 0.74 averaged for all sites, forest sites, grassland sites and cropland flux sites, respectively. The new data had more spatial similarity to the MODIS FPAR product (FPARMCD15A2) with a greater R2 (0.77) and a lower RMSE (0.12) than other products. With a newly developed dataset, combined with FPARANN (1980–2003) and FPARMCD15A2 (2004–2018), an overall increasing trend in FPAR was found for over 81% of the vegetated area of China from 1980 to 2018. FPAR increased more rapidly for over 83.7% of the area in the 2010s, and at a slower pace for over 62.1% of the area in the early 2000s, which was attributed to a decadal shifting of climate change. This study implies the new dataset is useful in quantifying vegetation changes and would be an important data source for future study of the carbon cycle, soil erosion, or evapotranspiration, with great application potential.

The coarse spatial resolutions of satellite-based soil moisture (SM) products restrict their applications at smaller spatial scales. In this study, the monthly European Space Agency Climate Change Initiative SM data (ESA CCI SM) from 2000 to 2016 was downscaled from 25- to 1-km resolution in the Taihu Lake Basin, a typical humid area with complex terrain and land uses. The normalized difference vegetation index (NDVI) and land surface temperature (LST) were used as auxiliary data. The regional monthly mean ESA CCI SM values were classified into low value (0.24–0.30 m3m–3), mid-value (0.30–0.33 m3m–3) and high value (0.33–0.39 m3m–3) months by the K-means clustering algorithm. The linear (multiple linear regression) and non-linear (support vector machine) downscaling models were compared. In addition, whether building downscaling models based on wetness conditions could improve the accuracies was tested. Results showed that without considering wetness conditions, the linear method was slightly better than the non-linear method. However, linear models constructed based on wetness conditions performed the best, which demonstrated that wetness conditions should be considered in the downscaling process. Results of this study would improve the accuracies in downscaling satellite-based SM data, facilitating their applications at regional scales. HIGHLIGHTS The quality of ESA CCI SM was evaluated in Taihu Basin Lake.; Linear and non-linear downscaling methods were compared.; Wetness conditions should be considered in the downscaling process.;

The sun-induced chlorophyll fluorescence (SIF) is directly related to the photosynthesis of winter wheat and has a good linear relationship with the gross primary production (GPP). However, the relationship of SIF and GPP is still uncertain under different soil water content conditions. The sun-induced chlorophyll fluorescence, GPP and non-photochemical quenching (NPQ) are the three ways of photosynthetically active radiation in plants. The photochemical reflectance index (PRI) can be used as a proxy indicator of NPQ to characterize the change in heat dissipation during photosynthesis process of winter wheat. To detect the impact of soil water content on winter wheat photosynthesis and SIF and GPP relationship, based on the ground measurement data at Zhaoxian Experimental Station in northern China during the growing season in 2018 and the simulated data by the SCOPE model, we use PRI to improve the GPP model estimated by SIF. The results indicate that: (1) PRI is sensitive to soil water content (R2 = 0.64), so it can be used to improve the estimation of GPP based on SIF under different soil water conditions; (2) For the ground measurement data, PRIobv can improve the estimation accuracy of GPPobv by SIFobv. Under different soil water content (SWC) conditions, the relationship between GPPobv and SIFobv is significantly correlated (R2 = 0.33–0.45, p

To clarify the global status and research hotspots of heavy metal pollution phytoremediation, we used Web of Science, Cite Space software, and VOS viewer to analyse 1123 publications from the period of 2000–2020. Literature categories, research hotpots, and the most prolific publications by country, institution, and author were analysed separately. Around 34% of the articles are contributed from five countries: China (29.37%), India (11.00%), Spain (6.29%), Italy (6.20%), and Pakistan (5.67%). The hot research topic keywords were “diversity”, “translocation”, and “enhanced phytoremediation”. Cadmium was the most highly concerned heavy metal in the phytoremediation. Twenty-three articles were highly cited, and they mainly focused on 1) enhancing the remediation ability of plants in heavy metal contaminated soil by microbial and chemical additives; 2) the molecular effect and mechanism of heavy metals on plant growth and development; 3) discovering novel heavy metal hyper-enriched plants which can remediate mixed heavy metal pollution. From the above analysis, we concluded that the future research directions should be 1) strengthening the plant remediation ability by biochemical means; 2) studying the molecular mechanism underlying heavy metal damage to plants; 3) studying the enrichment principle of plants for heavy metals. The present study provides a further understanding of the trends in phytoremediation of heavy metal pollution, and the data analysed can be used as a guide for future research directions.

The authors of this study compile data on spatial and temporal dynamics of surface water bodies across China, covering a time span from 1989 – 2016. The study describes hot-spot areas with strongly decreasing trends in surface water area and terrestrial water storage in North China and discusses implications of water resources and security in China.

Abstract Biomass temporal stability plays a key role in maintaining sustainable ecosystem functions and services of grasslands, and climate change has exerted a profound impact on plant biomass. However, it remains unclear how the community biomass stability in alpine meadows responds to changes in some climate factors (e.g., temperature and precipitation). Long‐term field aboveground biomass monitoring was conducted in four alpine meadows (Haiyan [HY], Henan [HN], Gande [GD], and Qumalai [QML]) on the Qinghai‐Tibet Plateau. We found that climate factors and ecological factors together affected the community biomass stability and only the stability of HY had a significant decrease over the study period. The community biomass stability at each site was positively correlated with both the stability of the dominant functional group and functional groups asynchrony. The effect of dominant functional groups on community stability decreased with the increase of the effect of functional groups asynchrony on community stability and there may be a ‘trade‐off’ relationship between the effects of these two factors on community stability. Climatic factors directly or indirectly affect community biomass stability by influencing the stability of the dominant functional group or functional groups asynchrony. Air temperature and precipitation indirectly affected the community stability of HY and HN, but air temperature in the growing season and nongrowing season had direct negative and direct positive effects on the community stability of GD and QML, respectively. The underlying mechanisms varied between community composition and local climate conditions. Our findings highlighted the role of dominant functional group and functional groups asynchrony in maintaining community biomass stability in alpine meadows and we highlighted the importance of the environmental context when exploring the stability influence mechanism. Studies of community stability in alpine meadows along with different precipitation and temperature gradients are needed to improve our comprehensive understanding of the mechanisms controlling alpine meadow stability.

Monitoring and evaluating ecological quality and changes are crucial for policy formulation to guide ecosystem management and socioeconomic sustainable development. However, evaluation of ecological quality is still very challenging due to difficulties in determination of its associated indicators and weights. This paper proposes supporting, providing and regulating ecosystems services-based indicators to describe ecological quality, and applies a Projection Pursuit Model to eliminate redundant indicators and objectively determine weights for an ecological quality index (EQI) on a regional scale. Taking Jiangxi Province, China, as a demonstration area, the data for indicator measures were retrieved from satellite remote sensing and ecosystem modelling with a spatial resolution of 1 km for the three years 2005, 2010 and 2015. The results suggest that Normalized Difference Vegetation Index (NDVI) and water use efficiency (WUE) should be weighed higher and leaf area index (LAI) and Bowen ratio should be weighed lowest in the calculation of an EQI for Jiangxi Province. For 2015, the regional EQI was calculated to be 55.32 on a scale from 0 as the worst to 100 as the best, with higher values ascribed to the hills and mountains and the lower values existing near urban areas. The EQI increased from 52.26 in 2005 to 55.32 in 2015 with an increased area of good-and-above grade from 25.47% to 36.8% for the whole province. The changes in EQI could be attributed to a warmer and wetter climate trend playing a positive dominant effect, while urbanization and afforestation have negative and positive effects, respectively. This study demonstrates that it is feasible to evaluate ecological quality based on a comprehensive set of indicators and PPM-based weight determination, which could be further applied in regular ecological quality monitoring and evaluation on the regional, or even the national scale.

Forest ecosystems play a significant role in climate change mitigation and uptake a larger amount of atmospheric CO2 than other terrestrial ecosystems via photosynthesis process in form of gross primary production (GPP). The photosynthesis or GPP is largely determined by the photosynthetic capacity of vegetation (i.e., maximum rate of carboxylation, Vcmax) in ecosystem models. However, considerable uncertainties of Vcmax estimates may limit our potential to address scientific issues of GPP related to the increasing emission of atmospheric CO2. Recently, solar-induced chlorophyll fluorescence (SIF) signals have been used as a proxy for resolving photosynthesis. In this study, the biochemical and structural parameters were retrieved from hyperspectral reflectance and fluorescence quantum efficiency (FQEs) was retrieved from ground-based SIF. Then, retrieved parameters were incorporated into the Soil Canopy Observation Photosynthesis and Energy (SCOPE) model to explore the potential of ground-based SIF to track Vcmax variability for a subtropical evergreen mixed forest. Then, SIF-derived Vcmax was used to parameterize Boreal Ecosystem Production Simulator (BEPS) model to simulate the GPP. With retrieved vegetative parameters and FQEs, the ground-based SIF was strongly correlated with the model-based SIF simulation at O2-B and O2-A bands, demonstrating that the coefficient of determination (R2) improved from 0.15 (constant values) to 0.60 (retrieved values) for SIFB and from 0.79 to 0.94 for SIFA simulation. Using SIF-derived Vcmax, the R2 value of simulated GPP against eddy covariance-based measurements substantially increased from 0.18 (constant Vcmax) to 0.38 (SIF-derived Vcmax) for dry season and from 0.56 to 0.67 for wet season respectively. The utilization of SIF-derived Vcmax with its corrected temperature response function reduced the relative error in annual GPP simulations by 24.9%. Our results support the significant references toward reducing unbiased SIF simulation and highlighting the potential of ground SIF in deriving Vcmax at the site scale for defining forest management options.

South Asia, one of the most important food producing regions in the world, is facing a significant threat to food grain production under the influence of extreme high temperatures. Furthermore, the probability of simultaneous trends in extreme precipitation patterns and extreme heat conditions, which can have compounding effects on crops, is a likelihood in South Asia. In this study, we found complex relationships between extreme heat and precipitation patterns, as well as compound effects on major crops (rice and wheat) in South Asia. We also employed event coincidence analysis (ECA) to quantify the likelihood of simultaneous temperature and crop extremes. We used the Enhanced Vegetation Index (EVI) as the primary data to evaluate the distinct responses of major crops to weather extremes. Our results suggest that while the probability of simultaneous extreme events is small, most regions of South Asia (more than half) have experienced extreme events. The regulatory effect of precipitation on heat stress is very unevenly distributed in South Asia. The harm caused by a wet year at high temperature is far greater than that during a dry year, although the probability of a dry year is greater than that of a wet year. For the growing seasons, the highest significant event coincidence rates at a low EVI were found for both high- and low-temperature extremes. The regions that responded positively to EVI at extreme temperatures were mainly concentrated in irrigated farmland, and the regions that responded negatively to EVI at extreme temperatures were mostly in the mountains and other high-altitude regions. Implications can guide crop adaptation interventions in response to these climate influences.

BackgroundChimeric antigen receptor (CAR) T-cell therapy has emerged as a novel treatment modality for hematologic malignancies and is predicted to experience widespread use in the near future. However, not all risks associated with this novel approach are well defined. There are few data in the risk of HBV reactivation and limited experience in management in patients with resolved HBV infection who undergo CAR-T cell therapy.MethodsWe performed a post-hoc analysis of a prospective clinical trial of anti-CD19 CAR-T (CART19) cell therapy in patients with relapsed or refractory (r/r) B-cell malignancies, and aimed at exploring the actual risk of HBV reactivation in a cohort of patients with resolved HBV infection receiving CART19 cell therapy in the absence of antiviral prophylaxis.ResultsIn this study, we investigated the risk of HBV reactivation after CART19 cell therapy in 30 consecutive patients with B-cell malignancies and resolved HBV infection without antiviral prophylaxis, in the Tongji Hospital of Tongji University. In this cohort, two patients developed HBV reactivation 2 months and 14 months after CAR-T cell infusion, respectively, the latter of whom developed severe hepatitis. These findings showed that the incidence of HBV reactivation was 6.67% (95% CI, 0.8–22.1). Specifically, none of the 21 patients who were HBsAb positive (0.0%) versus two of nine patients who were HBsAb negative (22.2%) experienced HBV reactivation (p = 0.03), suggesting HbsAb seronegativity at baseline is a possible risk factor in this population. Although use of tocilizumab or corticosteroids has been associated with increased risk of HBV reactivation, none of the patients who received these agents had HBV reactivation in this study.ConclusionThis is the first and largest study to assess the true incidence of HBV reactivation in patients with resolved HBV infection receiving CART19 cell therapy without antiviral prophylaxis. This study highlights that this population are at risk of developing HBV reactivation and indicates that close monitoring of HBV DNA is required in the absence of antiviral prophylaxis. In addition, antiviral prophylaxis is recommended in the HBsAb-negative subpopulation.

Abstract Terrestrial ecosystems provide multiple services interacting in complex ways. However, most ecosystem services (ESs) models (e.g., InVEST and ARIES) ignored the relationships among ESs. Process‐based models can overcome this limitation, and the integration of ecological models with remote sensing data could greatly facilitate the investigation of the complex ecological processes. Therefore, based on the Carbon and Exchange between Vegetation, Soil, and Atmosphere (CEVSA) models, we developed a process‐based ES model (CEVSA‐ES) integrating remotely sensed leaf area index to evaluate four important ESs (i.e., productivity provision, carbon sequestration, water retention, and soil retention) at annual timescale in China. Compared to the traditional terrestrial biosphere models, the main innovation of CEVSA‐ES model was the consideration of soil erosion processes and its impact on carbon cycling. The new version also improved the carbon‐water cycle algorithms. Then, the Sobol and DEMC methods that integrated the CEVSA‐ES model with nine flux sites comprising 39 site‐years were used to identify and optimize parameters. Finally, the model using the optimized parameters was validated at 26 field sites comprising 135 site‐years. Simulation results showed good fits with ecosystem processes, explaining 95%, 92%, 76%, and 65% interannual variabilities of gross primary productivity, ecosystem respiration, net ecosystem productivity, and evapotranspiration, respectively. The CEVSA‐ES model performed well for productivity provision and carbon sequestration, which explained 96% and 81% of the spatial‐temporal variations of the observed annual productivity provision and carbon sequestration, respectively. The model also captured the interannual trends of water retention and soil erosion for most sites or basins.

Parkinson's disease (PD) is a common neurodegenerative disease with movement and balance impairments. Although studies have reported improvement of motor symptoms with physical exercise, the mechanisms by which exercise is beneficial remains poorly understood. Our study addresses the exercise-induced changes to peripheral immune cells by interrogating the transcriptome of blood-derived leukocytes in PD patients before and after exercise. Patients attended 1 h exercise classes twice a week for 12 weeks. Leukocytes were collected at the beginning and end of the study for gene expression analysis by RNA-seq or quantitative real-time PCR. We correlated differentially expressed genes after exercise with clinical measures and analyzed the potential functions of gene changes with Kyoto Encyclopedia of Genes and Genomes pathway and Gene Ontology analysis. Exercise improved measures of movement and balance when compared with scores before the exercise program. Among the gene changes, Kyoto Encyclopedia of Genes and Genomes and Gene Ontology analysis suggests that T-cell receptor signaling, T-cell activation, and T-cell migration pathways were downregulated, while the T-cell receptor signaling pathway was the most significantly correlated with clinical measures. To further investigate T-cell-related changes in PD leukocytes, we reanalyzed the differentially expressed genes from publicly available microarray data and found that genes in the T-cell activation, differentiation, and migration pathways were upregulated in PD samples compared to controls in a time-dependent manner. Together, our findings suggest that exercise rehabilitation may improve movement and balance in PD patients by reversing the upregulated T-cell activation pathways associated with PD. This study was registered with the Chinese Clinical Trial Registry under ChiCTR-TRC-14004707. Registered on May 27, 2014.

Neural repair within the central nervous system (CNS) has been extremely challenging due to limited abilities of adult CNS neurons to regenerate, particularly in a highly inflammatory injury environment that is also filled with myelin debris. Spinal cord injury (SCI) is a serious medical condition that often leads to paralysis and currently has no effective treatment. Here we report the construction of a novel biocompatible and biodegradable material, Bio-C, through coating of acid-desalted-collagen (ADC) tube with pre-modified hyaluronic acid, which, after implantation, can elicit quite robust neural regeneration and functional recovery after complete spinal-cord transection with a 2 mm–spinal-cord-segment removal in mice. We combined morphological, electrophysiological, and objective transcriptomic analyses, in addition to behavioral analyses, to demonstrate neural tissue regeneration and functional recovery through the establishment of Bio-C-induced anti-inflammatory, neurogenic, and neurotrophic microenvironment. Through this study, we unveiled the underlying logic for CNS neural repair.

Lake Qinghai has shrunk and then expanded over the past few decades. Quantifying the contributions of climate change and human activities to lake variation is important for water resource management and adaptation to climate change. In this study, we calculated the water volume change of Lake Qinghai, analyzed the climate and land use changes in Lake Qinghai catchment, and distinguished the contributions of climate change and local human activities to water volume change. The results showed that lake water volume decreased by 9.48 km3 from 1975 to 2004 and increased by 15.18 km3 from 2005 to 2020. The climate in Lake Qinghai catchment is becoming warmer and more pluvial, and the changes in land use have been minimal. Based on the Soil and Water Assessment Tool (SWAT), land use change, climate change and interaction effect of them contributed to 7.46%, 93.13% and −0.59%, respectively, on the variation in surface runoff into the lake. From the perspective of the water balance, we calculated the proportion of each component flowing into and out of the lake and found that the contribution of climate change to lake water volume change was 97.55%, while the local human activities contribution was only 2.45%. Thus, climate change had the dominant impact on water volume change in Lake Qinghai.

Solar-induced chlorophyll fluorescence (SIF) is considered as a prospective indicator of vegetation photosynthetic activity and the ecosystem carbon cycle. The current coarse spatial-temporal resolutions of SIF data from satellite missions and ground measurements still cannot satisfy the corroboration of its correlation with photosynthesis and carbon flux. Practical approaches are needed to be explored for the supplementation of the SIF measurements. In our study, we clarified the diurnal variations of leaf and canopy chlorophyll fluorescence for a subtropical evergreen coniferous forest and evaluated the performance of the canopy chlorophyll concentration (CCC) approach and the backward approach from gross primary production (GPP) for estimating the diurnal variations of canopy SIF by comparing with the Soil Canopy Observation Photosynthesis Energy (SCOPE) model. The results showed that the canopy SIF had similar seasonal and diurnal variations with the incident photosynthetically active radiation (PAR) above the canopy, while the leaf steady-state fluorescence remained stable during the daytime. Neither the CCC nor the raw backward approach from GPP could capture the short temporal dynamics of canopy SIF. However, after improving the backward approach with a correction factor of normalized PAR incident on leaves, the variation of the estimated canopy SIF accounted for more than half of the diurnal variations in the canopy SIF (SIF687: R2 = 0.53, p < 0.001; SIF760: R2 = 0.72, p < 0.001) for the subtropical evergreen coniferous forest without water stress. Drought interfered with the utilization of the improved backward approach because of the decoupling of SIF and GPP due to stomatal closure. This new approach offers new insight into the estimation of diurnal canopy SIF and can help understand the photosynthesis of vegetation for future climate change studies.

Land use and land cover (LULC) change influences many issues such as the climate, ecological environment, and economy. In this study, the LULC transitions in the Yellow River Basin (YRB) were analyzed based on the GlobeLand30 land use data in 2000, 2010, and 2020. The intensity analysis method with hypothetical errors calculation was used, which could explain the deviations from uniform land changes. The strength of the evidence for the deviation was revealed even though the confusion matrixes of the LULC data at each time point for the YRB were unavailable. The results showed that at the interval scale, the land transition rate increased from the first to the second period for all of the upper, middle, and lower reaches. The exchange component was larger than the quantity and shift component, and the gross change was 4.1 times larger than the net change. The size of cultivated land decreased during both intervals. The artificial surfaces gains were active for all three reaches and had strong evidence. A hypothetical error in 93% of the 2000 data and 58% of the 2010 data can explain deviations from uniform transition given woodland gain during 2000–2010 and 2010–2020. Ecological restoration projects such as Grain for Green implemented in 2000 in the upper reaches resulted in the woodland increase.

ABSTRACT The mammalian brain is heterogeneous, containing billions of neurons and trillions of synapses forming various neural circuitries, through which sense, movement, thought, and emotion arise. The cellular heterogeneity of the brain has made it difficult to study the molecular logic of neural circuitry wiring, pruning, activation, and plasticity, until recently, transcriptome analyses with single cell resolution makes decoding of gene regulatory networks underlying aforementioned circuitry properties possible. Here we report success in performing both electrophysiological and whole-genome transcriptome analyses on single human neurons in culture. Using Weighted Gene Coexpression Network Analyses (WGCNA), we identified gene clusters highly correlated with neuronal maturation judged by electrophysiological characteristics. A tight link between neuronal maturation and genes involved in ubiquitination and mitochondrial function was revealed. Moreover, we identified a list of candidate genes, which could potentially serve as biomarkers for neuronal maturation. Coupled electrophysiological recording and single cell transcriptome analysis will serve as powerful tools in the future to unveil molecular logics for neural circuitry functions.

As a newly emerging satellite form of data, solar-induced chlorophyll fluorescence (SIF) provides a direct measurement of photosynthetic activity. The potential of SIF for drought assessment in different grassland ecosystems is not yet clear. In this study, the correlations between spaceborne SIF and nine drought indices were evaluated. Standardized precipitation evapotranspiration index (SPEI) at a 1, 3, 6, 9, 12 month scale, Palmer drought severity index (PDSI), soil moisture, temperature condition index (TCI), and vapor pressure deficit (VPD) were evaluated. The relationships between different grassland types and different seasons were compared, and the driving forces affecting the sensitivity of SIF to drought were explored. We found that the correlations between SIF and drought indices were different for temperate grasslands and alpine grasslands. The correlation coefficients between SIF and soil moisture were the highest (the mean value was 0.72 for temperate grasslands and 0.69 for alpine grasslands), followed by SPEI and PDSI at a three month scale, and the correlation coefficient between SIF and TCI was the lowest (the mean value was 0.38 for both temperate and alpine grasslands). Spaceborne SIF is more effective for drought monitoring during the peak period of the growing season (July and August). Temperature and radiation are important factors affecting the sensitivity of SIF to drought. The results from this study demonstrated the importance of SIF in drought monitoring especially for temperate grasslands in the peak growing season.

Gross primary production (GPP) plays an important role in the net ecosystem exchange of CO2 between the atmosphere and terrestrial ecosystems. It is particularly important to monitor GPP in Southeast Asia because of increasing rates of tropical forest degradation and deforestation in the region in recent decades. The newly available, improved, third generation Normalized Difference Vegetation Index (NDVI3g) from the Global Inventory Modelling and Mapping Studies (GIMMS) group provides a long temporal dataset, from July 1981 to December 2011, for terrestrial carbon cycle and climate response research. However, GIMMS NDVI3g-based GPP estimates are not yet available. We applied the GLOPEM-CEVSA model, which integrates an ecosystem process model and a production efficiency model, to estimate GPP in Southeast Asia based on three independent results of the fraction of photosynthetically active radiation absorbed by vegetation (FPAR) from GIMMS NDVI3g (GPPNDVI3g), GIMMS NDVI1g (GPPNDVI1g), and the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD15A2 FPAR product (GPPMOD15). The GPP results were validated using ground data from eddy flux towers located in different forest biomes, and comparisons were made among the three GPPs as well as the MOD17A2 GPP products (GPPMOD17). Based on validation with flux tower derived GPP estimates the results show that GPPNDVI3g is more accurate than GPPNDVI1g and is comparable in accuracy with GPPMOD15. In addition, GPPNDVI3g and GPPMOD15 have good spatial-temporal consistency. Our results indicate that GIMMS NDVI3g is an effective dataset for regional GPP simulation in Southeast Asia, capable of accurately tracking the variation and trends in long-term terrestrial ecosystem GPP dynamics.

China’s terrestrial ecosystems play an important role in the global carbon cycle. Regional contributions to the interannual variation (IAV) of China’s terrestrial carbon sink and the attributions to climate variations are not well understood. Here we have investigated how terrestrial ecosystems in the four climate zones with various climate variabilities contribute to the IAV in China’s terrestrial net ecosystem productivity (NEP) using modeled carbon fluxes data from six ecosystems models. Model results show that the monsoonal region of China dominates national NEP IAV with a contribution of 86% (69%–96%) on average. Yearly national NEP changes are mostly driven by gross primary productivity IAV and half of the annual variation results from NEP changes in summer. Regional contributions to NEP IAV in China are consistent with their contributions to the magnitude of national NEP. Rainfall variability dominates the NEP annual variability in China. Precipitation in the temperate monsoon climate zone makes the largest contribution (23%) to the IAV of NEP in China because of both the high sensitivity of terrestrial ecosystem carbon uptake to rainfall and the large fluctuation in the precipitation caused by the East Asian summer monsoon anomalies. Our results suggest that NEP IAV can be mainly attributed to ecosystems with larger productivity and response to precipitation, and highlight the importance of monsoon climate systems with high seasonal and interannual variability in driving internannual variation in the land carbon sink.

As the biggest carbon flux of terrestrial ecosystems from photosynthesis, gross primary productivity (GPP) is an important indicator in understanding the carbon cycle and biogeochemical process of terrestrial ecosystems. Despite advances in remote sensing-based GPP modeling, spatial and temporal variations of GPP are still uncertain especially under extreme climate conditions such as droughts. As the only official products of global spatially explicit GPP, MOD17A2H (GPPMOD) has been widely used to assess the variations of carbon uptake of terrestrial ecosystems. However, systematic assessment of its performance has rarely been conducted especially for the grassland ecosystems where inter-annual variability is high. Based on a collection of GPP datasets (GPPEC) from a global network of eddy covariance towers (FluxNet), we compared GPPMOD and GPPEC at all FluxNet grassland sites with more than five years of observations. We evaluated the performance and robustness of GPPMOD in different grassland biomes (tropical, temperate, and alpine) by using a bootstrapping method for calculating 95% confident intervals (CI) for the linear regression slope, coefficients of determination (R2), and root mean square errors (RMSE). We found that GPPMOD generally underestimated GPP by about 34% across all biomes despite a significant relationship (R2 = 0.66 (CI, 0.63⁻0.69), RMSE = 2.46 (2.33⁻2.58) g Cm−2 day−1) for the three grassland biomes. GPPMOD had varied performances with R2 values of 0.72 (0.68⁻0.75) (temperate), 0.64 (0.59⁻0.68) (alpine), and 0.40 (0.27⁻0.52) (tropical). Thus, GPPMOD performed better in low GPP situations (e.g., temperate grassland type), which further indicated that GPPMOD underestimated GPP. The underestimation of GPP could be partly attributed to the biased maximum light use efficiency (εmax) values of different grassland biomes. The uncertainty of the fraction of absorbed photosynthetically active radiation (FPAR) and the water scalar based on the vapor pressure deficit (VPD) could have other reasons for the underestimation. Therefore, more accurate estimates of GPP for different grassland biomes should consider improvements in εmax, FPAR, and the VPD scalar. Our results suggest that the community should be cautious when using MODIS GPP products to examine spatial and temporal variations of carbon fluxes.

In ecologically vulnerable regions with intricate land use dynamics, such as ecotones, frequent and intense land use transitions unfold. Therefore, the precise and timely mapping of land use becomes imperative. With that goal, by using principal component analysis, we integrated Sentinel-1 and Sentinel-2 data, using an object-oriented methodology to craft a 10-meter-resolution land use map for the forest–grassland ecological zone of the Greater Khingan Mountains spanning the years 2019 to 2021. Our research reveals a substantial enhancement in classification accuracy achieved through the integration of synthetic aperture radar–optical remote sensing data. Notably, our products outperformed other land use/land cover data sets, excelling particularly in delineating intricate riverine wetlands. The 10-meter land use product stands as a pivotal guide, offering indispensable support for sustainable development, ecological assessment, and conservation endeavors in the Greater Khingan Mountains region. [ABSTRACT FROM AUTHOR]

Under global change and climate variations, determining the impacts of climate change and human activities on cropland net primary productivity (NPP) in Bangladesh, India and Myanmar (BIM) is of great significance for identifying yield-limiting factors, making adaptive agricultural management plans, and improving yields. Based on the GLOPEM-CEVSA model, through an integration of remote sensing data and LAI simulation, we investigated the impacts and spatiotemporal changes of water and human activities on BIM from 1982 to 2015. Three types of cropland NPPs were considered: actual NPP (NPPA), NPP affected by temperature and water (NPPWT), and NPP only affected by temperature (NPPT). Our analysis revealed that the water factor plays a predominant role in determining the NPP level in the BIM. Temperature variability was found to be conducive to NPPT, exhibiting an increasing trend of 10.66 g C m–2 yr–1. However, this trend was partially offset by precipitation variability, resulting in a net increase of 0.96 g C m–2 yr–1. In comparing temperature-driven NPP to temperature and water-driven NPP, water stress caused NPPT to decrease by 65.46% compared to NPPWT for the entire region. Cropland NPP in northwestern India and the central Deccan Plateau were significantly affected by water stress. Moreover, the influence of water on NPP in the BIM exhibited a substantial upward trend from 1982 to 2015, with Myanmar experiencing the most significant increase. The gap between NPPWT and NPPA in BIM demonstrated a notable decreasing trend during the same period, underscoring the positive impact of human activities on NPP. Inferences drawn from our findings suggest that with the implementation of rational and efficient crop management practices, there is a 36.80% potential improvement in NPPA compared to NPPWT in the BIM region, with India and Myanmar showing potential increases of 39.20% and 38.29%, respectively. These insights provide guidance for practical measures aimed at water resource management to enhance cropland productivity in the BIM, and they present a methodology for quantifying the effects of climatic changes and human activities at a regional scale. [ABSTRACT FROM AUTHOR]