Explore the vast range of titles available.

During the past several decades, the carbon budget in the dryland ecosystem has experienced great variation under the joint impact of climate change and anthropogenic interference. How the net ecosystem productivity (NEP) responds to climate change and human interference in the Qilian Mountains (QLM), Northwest China, remains unclear. To fill these gaps, we first estimated the NEP in the QLM and then quantified the independent and interactive influences of natural environment factors, climatic factors, and human activity intensity on the NEP change from 2000 to 2020 by linking the Geodetector and structural equation models. The NEP of the QLM showed a significant increase during the recent 20 years, and 78.93% of the QLM experienced a significant increase in NEP; while only 4.83% of the area in the QLM experienced a decreasing trend, which is dominantly located on the southeast edge, surrounding the Qinghai Lake, and the midland of the QLM. The area percentage of the carbon sink region increased from 47% in 2000 to 62% in 2020. The natural environment factors (e.g., altitude and soil type) and climate factors (e.g., temperature and precipitation) were the dominant factors that determine the spatial distribution of NEP. Compared with a single factor, the interaction of pairs of factors enhanced the influence strength on NEP. The natural environment factors indirectly affected NEP change through influencing human activities intensity and climatic factors. Human activities intensity played a medium indirectly negative effect on NEP, while climatic factors exerted strong direct and indirect positive influences on NEP. The contributions of human activity intensity, climatic factors, and natural environment on NEP change in the QLM were 33.5%, 62.3%, and 38.3%, respectively. Overall, warming and wetting shifts in meteorological conditions offset the negative impact of human activities on NEP in the QLM, and the QLM has acted as a growing carbon sink in the past 20 years.

Background Micronutrient malnutrition affects the well-being of both adults and children. Dietary diversity score (DDS) is a useful evaluation index with a relatively well-developed guideline by FAO. It’s meaningful to assess and predict inadequate micronutrient intakes using DDS in Chinese children, after ruling out the risk of obesity coming with more dietary diversity. Methods Data for evaluation were extracted from the Nutrition Study of Preschool Children and School Children, which is a cross-sectional study covering 8 cities of China, including 1694 children in kindergartens and primary schools. This study applied DDS to Chinese children to test the validity for micronutrient inadequacy, and then explored the relationship between dietary diversity and obesity. Results It reveals that dietary diversity varied with age and place of residence; the older ones and the ones living in rural areas tend to have poorer dietary diversity. Another discovery is that DDS is positively correlated with indicators of micronutrient adequacy, with a score of 6–8 indicating the lowest risk of micronutrient inadequacy in different groups of children. In our study population, dietary diversity is not related with obesity. Conclusions Dietary diversity score is a valid indicator to evaluate micronutrient inadequacy in Chinese children, though there is still room for improvement of the method. Besides, the relationship between increase of dietary diversity and risk of obesity should be treated circumspectly.

Gas chromatography-mass spectrometry (GC-MS) has been applied to the analysis of arsenic forms in water, plants, and other samples; however, it has not been used to determine the form of arsenic in soil due to the complex soil matrix. The purpose of this study was to develop an analytical method for the simultaneous determination of inorganic arsenic species (As (III) and As (V)) and monomethylarsonic acid (MMA) in soil using GC-MS. The arsenic compounds were subjected to derivatization with 2,3-dimercapto-1-propanol (BAL) and subsequently analyzed using GC-MS. The BAL volume, derivatization reaction time, and temperature were optimized using standard added soil extracts. A reaction with 150 μL of BAL at 40°C for 30 min was selected as the optimal condition for quantitative derivatization of both inorganic arsenic (iAs) and MMA. The calibration curves exhibited linearity within the range of 5–100 ng/mL for each arsenic species, with correlation coefficients exceeding 0.997. The limits of detection for each arsenic species were determined to be 0.24 ng/mL and 1.31 ng/mL, respectively. The accuracy of the method was verified by the recovery of reference samples. The recovery experiments for reference samples showed that the recovery rates for As (III), As (V), and MMA were 89.5–93.7%, 88.5–105.6%, and 90.2–95.8% respectively, with precision ranging from 4.6 to 6.5%, 2.3 to 3.8%, and 2.4 to 6.3%. These results indicate good accuracy and precision. The accuracy of this method is not significantly different from that of liquid chromatography-inductively coupled plasma mass spectrometry (p = 0.05). The optimized method was sensitive, convenient and reliable for the extraction and analysis of different arsenic species in soil samples.

Objective The transcription factor SRY-related HMG-box 4 (SOX4) has been implicated in intervertebral disc diseases. This study aimed to investigate the role of SOX4 in intervertebral disc degeneration (IDD) and explore the underlying molecular mechanisms. Methods We established an IDD rat model via surgery and analyzed SOX4 expression using qRT-PCR and Western blotting. Histological evaluation, immunohistochemistry, and Safranin O staining assessed IDD progression. In vitro, an IDD cellular model was constructed using IL-1β-stimulated nucleus pulposus (NP) cells. SOX4 knockdown and overexpression experiments in NP cells examined SOX4 effects on ECM degradation, NLRP3-mediated pyroptosis, and mitochondrial ROS-dependent NLRP3 inflammasome activation. The involvement of the EZH2/NRF2 pathway in SOX4-mediated NLRP3 activation was also examined. Results SOX4 expression was significantly increased in IDD rats and promoted IDD progression. Knockdown of SOX4 inhibited ECM degradation and NLRP3-mediated pyroptosis in NP cells. In vitro experiments showed that SOX4 promoted ECM degradation by upregulating MMPs and ADAMTS-5 expression, and suppressed collagen II and aggrecan synthesis. SOX4 knockdown inhibited NLRP3-mediated pyroptosis, while overexpression accelerated it in NP cells. Additionally, SOX4 was found to exacerbate mitochondrial ROS-dependent NLRP3 inflammasome activation in NP cells. Further investigation revealed that SOX4 enhanced NLRP3 inflammasome activation by upregulating EZH2 expression and modulating the EZH2/NRF2 pathway, with EZH2 inhibition attenuating SOX4-induced NLRP3 activation. Conclusion Our findings suggest that SOX4 accelerates IDD progression by promoting NLRP3 inflammasome activation via modulating the EZH2/NRF2 pathway, leading to NP cell pyroptosis and ECM degradation. Targeting SOX4 may represent a potential therapeutic strategy for treating IDD.

Timely and accurate agricultural information is essential for food security assessment and agricultural management. Synthetic aperture radar (SAR) systems are increasingly available in crop mapping, as they provide all-weather imagery. In particular, the Sentinel-1 sensor provides dense time-series data, thus offering a unique opportunity for crop mapping. However, in most studies, the Sentinel-1 complex backscatter coefficient was used directly which limits the potential of the Sentinel-1 in crop mapping. Meanwhile, most of the existing methods may not be tailored for the task of crop classification in time-series polarimetric SAR data. To solve the above problem, we present a novel deep learning strategy in this research. To be specific, we collected Sentinel-1 time-series data in two study areas. The Sentinel-1 image covariance matrix is used as an input to maintain the integrity of polarimetric information. Then, a depthwise separable convolution recurrent neural network (DSCRNN) architecture is proposed to characterize crop types from multiple perspectives and achieve better classification results. The experimental results indicate that the proposed method achieves better accuracy in complex agricultural areas than other classical methods. Additionally, the variable importance provided by the random forest (RF) illustrated that the covariance vector has a far greater influence than the backscatter coefficient. Consequently, the strategy proposed in this research is effective and promising for crop mapping.

Crop rotations, the farming practice of growing crops in sequential seasons, occupy a core position in agriculture management, showing a key influence on food security and agro-ecosystem sustainability. Despite the improvement in accuracy of identifying mono-agricultural crop distribution, crop rotation patterns remain poorly mapped. In this study, a hybrid convolutional neural network (CNN) and long short-term memory (LSTM) architecture, namely crop rotation mapping (CRM), were proposed to synergize the synthetic aperture radar (SAR) and optical time series in a rotational mapping task. The proposed end-to-end architecture had reasonable accuracies (i.e., accuracy > 0.85) in mapping crop rotation, which outperformed other state-of-the-art non-deep or deep-learning solutions. For some confusing rotation types, such as fallow-single rice and crayfish-single rice, CRM showed substantial improvements from traditional methods. Furthermore, the deeply synergistic SAR-optical, time-series data, with a corresponding attention mechanism, were effective in extracting crop rotation features, with an overall gain of accuracy of four points compared with ablation models. Therefore, our proposed method added wisdom to dynamic crop rotation mapping and yields important information for the agro-ecosystem management of the study area.

The adsorption and desorption of phosphorus (P) in soil constitute a crucial internal cycle that is closely associated with soil fertility, exerting direct influence on the quantity, form, and availability of P within the soil. The vertical spatial variation characteristics of soil adsorption and desorption were investigated for the 0–100 cm soil layer in the northeast black soil region in this study. The maximum adsorption capacity (Q max ) and maximum adsorption buffer capacity (MBC) of black soil in the study area ranged from 313.8 to 411.9 mg kg -1 and from 3.1 to 28.8 L kg -1 , respectively, within the soil layer of 0–100 cm depth, exhibiting an increasing trend with greater soil depth. The degree of P adsorption saturation (DPS) exhibited a contrasting trend with the variations in Q max and MBC, ranging from 3.8% to 21.6%. The maximum desorption capacity (D max ) and desorption rate (D r ) of soil P ranged from 112.8 to 215.7 mg kg -1 and 32.1% to 52.5%, respectively, while the readily desorbable P (RDP) in soil was within the range of 1.02 to 3.35 mg kg -1 . Both D max , D r , and RDP exhibited a decreasing trend with increasing soil depth before showing an upward trend. These research findings not only provide essential background data for the systematic investigation of soil P in the black soil region but also serve as a valuable reference for assessing soil quality in this area.

Although China is the largest producer of rice, accounting for about 25% of global production, there are no high-resolution maps of paddy rice covering the entire country. Using time-weighted dynamic time warping (TWDTW), this study developed a pixel- and phenology-based method to identify planting areas of double-season paddy rice in China, by comparing temporal variations of synthetic aperture radar (SAR) signals of unknown pixels to those of known double-season paddy rice fields. We conducted a comprehensive evaluation of the method’s performance at pixel and regional scales. Based on 145,210 field surveyed samples from 2018 to 2020, the producer’s and user’s accuracy are 88.49% and 87.02%, respectively. Compared to county-level statistical data from 2016 to 2019, the relative mean absolute errors are 34.11%. This study produced distribution maps of double-season rice at 10 m spatial resolution from 2016 to 2020 over nine provinces in South China, which account for more than 99% of the planting areas of double-season paddy rice of China. The maps are expected to contribute to timely monitoring and evaluating rice growth and yield.

Human activities have dramatically changed ecosystems. As an irreplaceable ecological barrier in western China, the Qilian Mountains (QLM) provide various ecosystem services for humans. To evaluate the changes in the intensity of human activities in the QLM and their impact on the ecosystem, the human footprint (HF) method was used to conduct a spatial dataset of human activity intensity. In our study, the NDVI was used to characterize the growth of vegetation, and six categories of human pressures were employed to create the HF map in the QLM for 2000–2015 at a 1-km scale. The results showed that the mean NDVI during the growing season showed a significant increasing trend over the entire QLM in the period 2000–2015, while the NDVI showed a significant declining trend of more than 70% concentrated in Qinghai. Human pressure throughout the QLM occurred at a low level during 2000–2015, being greater in the eastern region than the western region, while the Qinghai area had greater human pressure than the Gansu area. Due to the improvement in traffic facilities, tourism, overgrazing, and other illegal activities, grasslands, shrublands, forests, wetlands, and bare land were the vegetation types most affected by human activities (in decreasing order). As the core area of the QLM, the Qilian Mountains National Nature Reserve (NR) has effectively reduced the impact of human activities. However, due to the existence of many ecological historical debts caused by unreasonable management in the past, the national park established in 2017 is facing great challenges to achieve its goals. These data and results will provide reference and guidance for future protection and restoration of the QLM ecosystem.

Soil phosphorus exists in various forms, but only minimal soluble phosphorus is bioavailable. While phosphate fertilizers address deficiency, low efficiency leads to insoluble phosphate accumulation, wasting resources. Enhancing phosphorus availability by converting non-available forms is a critical research priority. Under phosphorus-deficient conditions, plants enhance soil phosphorus availability by secreting organic acids. This study aims to explore phosphorus activation in Alfisol and Mollisol soils through maize root-secreted organic acids. Semi-hydroponic maize cultivation under low/medium/high phosphorus levels and sterilized soil (Alfisol/Mollisol) incubation with root-secreted organic acids (0.5–2%). Exudates analyzed via UHPLC-MS/MS; soil phosphorus dynamics monitored over 60 days. Under phosphorus limitation, maize root exudates showed marked increases in malic, citric, tartaric, and trans-aconitic acids. Soil incubation revealed peak phosphorus release during 0–20 d, with Alfisol exhibiting 95.8% (tartaric), 91.1% (citric), 81.8% (malic), and 67.7% (trans-aconitic) increases in available phosphorus, while Mollisol showed 94.0%, 119.6%, 83.1%, and 75.2% increments, respectively. Tartaric and citric acids outperformed others, boosting H 2 O-P and NaHCO 3 -P i , whereas malic and trans-aconitic acids mainly elevated NaHCO 3 -P i . Fractionation analysis highlighted distinct mechanisms: citric acid primarily mobilizes HCl-P; malic acid mainly targets NaOH-P i and HCl-P; tartaric acid mobilizes NaOH-P o , HCl-P, and NaOH-P i ; while trans-aconitic acid influences NaOH-P o , HCl-P, and Res-P. These findings demonstrate acid-specific phosphorus pool mobilization, identifying tartaric and citric acids as most effective, supporting optimized phosphorus management strategies in black soils.