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Crop growth monitoring and yield estimate information can be obtained via appropriate metrics such as the leaf area index (LAI) and biomass. Such information is crucial for guiding agricultural production, ensuring food security, and maintaining sustainable agricultural development. Traditional methods of field measurement and monitoring typically have low efficiency and can only give limited untimely information. Alternatively, methods based on remote sensing technologies are fast, objective, and nondestructive. Indeed, remote sensing data assimilation and crop growth modeling represent an important trend in crop growth monitoring and yield estimation. In this study, we assimilate the leaf area index retrieved from Sentinel-2 remote sensing data for crop growth model of the simple algorithm for yield estimation (SAFY) in wheat. The SP-UCI optimization algorithm is used for fine-tuning for several SAFY parameters, namely the emergence date (D 0 ), the effective light energy utilization rate (ELUE), and the senescence temperature threshold (STT) which is indicative of biological aging. These three sensitive parameters are set in order to attain the global minimum of an error function between the SAFY model predicted values and the LAI inversion values. This assimilation of remote sensing data into the crop growth model facilitates the LAI, biomass, and yield estimation. The estimation results were validated using data collected from 48 experimental plots during 2014 and 2015. For the 2014 data, the results showed coefficients of determination (R 2 ) of the LAI, biomass and yield of 0.73, 0.83 and 0.49, respectively, with corresponding root-mean-squared error (RMSE) values of 0.72, 1.13 t/ha and 1.14 t/ha, respectively. For the 2015 data, the estimated R 2 values of the LAI, biomass, and yield were 0.700, 0.85, and 0.61, respectively, with respective RMSE values of 0.83, 1.22 t/ha, and 1.39 t/ha, respectively. The estimated values were found to be in good agreement with the measured ones. This shows high applicability of the proposed data assimilation scheme in crop monitoring and yield estimation. As well, this scheme provides a reference for the assimilation of remote sensing data into crop growth models for regional crop monitoring and yield estimation.

The number of wheat ears is an essential indicator for wheat production and yield estimation, but accurately obtaining wheat ears requires expensive manual cost and labor time. Meanwhile, the characteristics of wheat ears provide less information, and the color is consistent with the background, which can be challenging to obtain the number of wheat ears required. In this paper, the performance of Faster regions with convolutional neural networks (Faster R-CNN) and RetinaNet to predict the number of wheat ears for wheat at different growth stages under different conditions is investigated. The results show that using the Global WHEAT dataset for recognition, the RetinaNet method, and the Faster R-CNN method achieve an average accuracy of 0.82 and 0.72, with the RetinaNet method obtaining the highest recognition accuracy. Secondly, using the collected image data for recognition, the R2 of RetinaNet and Faster R-CNN after transfer learning is 0.9722 and 0.8702, respectively, indicating that the recognition accuracy of the RetinaNet method is higher on different data sets. We also tested wheat ears at both the filling and maturity stages; our proposed method has proven to be very robust (the R2 is above 90). This study provides technical support and a reference for automatic wheat ear recognition and yield estimation.

Acute kidney injury (AKI) is a major health burden with a high risk of progression to chronic kidney disease (CKD). Renal fibrosis is the ultimate outcome of CKD progression, with M2 macrophages playing a critical role by secreting pro-fibrotic factors. Chemokines can influence the progression of renal fibrosis by modulating macrophage polarization during the course of AKI. An integrative analysis of single-cell transcriptomic data from kidneys of mice 7 days after AKI was performed to investigate ligand-receptor (LR) interactions between macrophages and to explore gene co-expression patterns during macrophage differentiation under AKI conditions. The AKI model was induced by unilateral ischemia-reperfusion injury (uIRI), and kidney samples were harvested at day 7. qPCR and WB were employed to measure the transcriptional levels of Ccl6, Ccr2, and M2 polarization markers in macrophages. Transwell assays were performed to evaluate the effect of Ccl6 on BMDMs migration. Cell proliferation was assessed using the Cell Counting Kit-8 (CCK-8) assay. Histological analysis was performed to assess the extent of kidney injury and fibrosis. Multiplex immunofluorescence analysis was conducted to assess the co-localization of Ccl6, Ccr2, and Arg1 expression. Through integrated analysis of multiple single-cell transcriptomic datasets from AKI, we identified strong interactions between Ccl6 and Ccr2 in renal macrophages at 7 days post-AKI. Additionally, co-expression of Ccl6, Ccr2, and Arg1 was observed in renal macrophages, and the abundance of Ccl6+Ccr2+Arg1+ cells was positively correlated with the severity of renal interstitial fibrosis. Ccl6 promoted the migration and M2 polarization of bone marrow-derived macrophages (BMDMs). Inhibition of Ccr2 in AKI mice reduced the infiltration of Arg1+ macrophages and attenuated the progression of renal fibrosis. Targeting the Ccl6/Ccr2 axis may attenuate fibrotic progression and offers potential therapeutic insights for preventing the transition from AKI to CKD, a possibility that warrants further validation through future functional experiments.

Arid zone grassland is a crucial component of terrestrial ecosystems and plays a significant role in ecosystem protection and soil erosion prevention. However, accurately mapping grassland spatial information in arid zones presents a great challenge. The accuracy of remote sensing grassland mapping in arid zones is affected by spectral variability caused by the highly diverse landscapes. In this study, we explored the potential of a rectangular tile classification model, constructed using the random forest algorithm and integrated images from Sentinel-1A (synthetic aperture radar imagery) and Sentinel-2 (optical imagery), to enhance the accuracy of grassland mapping in the semiarid to arid regions of Ordos, China. Monthly Sentinel-1A median value images were synthesised, and four MODIS vegetation index mean value curves (NDVI, MSAVI, NDWI and NDBI) were used to determine the optimal synthesis time window for Sentinel-2 images. Seven experimental groups, including 14 experimental schemes based on the rectangular tile classification model and the traditional global classification model, were designed. By applying the rectangular tile classification model and Sentinel-integrated images, we successfully identified and extracted grasslands. The results showed the integration of vegetation index features and texture features improved the accuracy of grassland mapping. The overall accuracy of the Sentinel-integrated images from EXP7-2 was 88.23%, which was higher than the accuracy of the single sensor Sentinel-1A (53.52%) in EXP2-2 and Sentinel-2 (86.53%) in EXP5-2. In all seven experimental groups, the rectangular tile classification model was found to improve overall accuracy (OA) by 1.20% to 13.99% compared to the traditional global classification model. This paper presents novel perspectives and guidance for improving the accuracy of remote sensing mapping for land cover classification in arid zones with highly diverse landscapes. The study presents a flexible and scalable model within the Google Earth Engine framework, which can be readily customized and implemented in various geographical locations and time periods.

Limited research into the tumor immune microenvironment (TIME) for bladder urothelial carcinoma (BUC), particularly the neglect of the intratumoral microbiota, has hindered the development of immunotherapies targeting BUC. Here, we collect 401 patients with BUC with host transcriptome samples and matched tumor microbiome samples from The Cancer Genome Atlas database. Besides, two independent BUC cohorts receiving immunotherapy were obtained. First, we find that the TIME profile is closely related to the prognosis of patients with BUC. Additionally, the genus Lachnoclostridium in tumors could regulate the accumulation of chemokines to recruit immune cell populations into bladder tumors. Among them, chemokines include CCL3 , CCL4 , CXCL9 , CXCL10 , and CXCL11 , and immune cells mainly involve macrophages and CD8 + T cells. Analyses based on two independent immunotherapy cohorts suggest that these immune-related chemokines strongly influence the immunotherapeutic efficacy of BUC. Furthermore, drug predictive analyses show that immune-related chemokines impact patients' sensitivity to diverse drugs. These results suggest a dual role of immune-related chemokines in combination therapy against BUC. Collectively, our study provides new insights into the regulation of TIME by intratumoral microbiota and provides guidance for improving immunotherapy against BUC.

Leaf area index (LAI) is highly related to crop growth, and the traditional LAI measurement methods are field destructive and unable to be acquired by large-scale, continuous, and real-time means. In this study, fractional order differential and continuous wavelet transform were used to process the canopy hyperspectral reflectance data of winter wheat, the fractional order differential spectral bands and wavelet energy coefficients with more sensitive to LAI changes were screened by correlation analysis, and the optimal subset regression and support vector machine were used to construct the LAI estimation models for different growth stages. The precision evaluation results showed that the LAI estimation models constructed by using wavelet energy coefficients combined with a support vector machine at the jointing stage, fractional order differential combined with support vector machine at the booting stage, and wavelet energy coefficients combined with optimal subset regression at the flowering and filling stages had the best prediction performance. Among these, both flowering and filling stages could be used as the best growth stages for LAI estimation with modeling and validation R2 of 0.87 and 0.71, 0.84 and 0.77, respectively. This study can provide technical reference for LAI estimation of crops based on remote sensing technology.

The timely and accurate acquisition of winter wheat acreage is crucial for food security. This study investigated the feasibility of extracting the spatial distribution map of winter wheat in Henan Province by using synthetic aperture radar (SAR, Sentinel-1A) and optical (Sentinel-2) images. Firstly, the SAR images were aggregated based on the growth period of winter wheat, and the optical images were aggregated based on the moderate resolution imaging spectroradiometer normalized difference vegetation index (MODIS-NDVI) curve. Then, five spectral features, two polarization features, and four texture features were selected as feature variables. Finally, the Google Earth Engine (GEE) cloud platform was employed to extract winter wheat acreage through the random forest (RF) algorithm. The results show that: (1) aggregated images based on the growth period of winter wheat and sensor characteristics can improve the mapping accuracy and efficiency; (2) the extraction accuracy of using only SAR images was improved with the accumulation of growth period. The extraction accuracy of using the SAR images in the full growth period reached 80.1%; and (3) the identification effect of integrated images was relatively good, which makes up for the shortcomings of SAR and optical images and improves the extraction accuracy of winter wheat.

Warming can affect soil microbial respiration by changing microbial biomass and community composition. The responses of soil microbial respiration to warming under experimental conditions are also related to background conditions and the experimental setup, such as warming magnitude, duration, and methods. However, the global pattern of soil microbial respiration in response to warming and underlying mechanisms remain unclear. Here, we conducted a global meta-analysis of the response of soil microbial respiration to warming by synthesizing data from 187 field experiments. We found that experimental warming significantly increased soil microbial respiration and microbial biomass carbon by 11.8% and 6.4%, respectively. The warming-induced increase in microbial carbon decomposition was positively correlated with increased microbial biomass carbon, but not community composition. Moreover, the positive response of soil microbial respiration marginally increased with warming magnitude, particularly in short-term experiments, but soil microbial respiration adapted to higher warming at longer timescales. Warming method did not significantly affect the response of microbial respiration, except for a significant effect with open top chamber warming. In addition, the impact of warming on soil microbial respiration was more pronounced in wetter sites and in sites with lower soil pH and higher soil organic carbon. Our findings suggest that warming stimulates microbial respiration mainly by increasing microbial biomass carbon. We also highlight the importance of the combination of warming magnitude and duration in regulating soil microbial respiration responses, and the dependence of warming effects upon background precipitation and soil conditions. These findings can advance our understanding of soil carbon losses and carbon-climate feedbacks in a warm world.

Soil nitrogen (N) mineralization is crucial for the sustainability of available soil N and hence ecosystem productivity and functioning. Metabolic quotient of N mineralization (Qmin), which is defined as net soil N mineralization per unit of soil microbial biomass N, reflects the efficiency of soil N mineralization. However, it is far from clear how soil Qmin changes and what are the controlling factors at the global scale. We compiled 871 observations of soil Qmin from 79 published articles across terrestrial ecosystems (croplands, forests, grasslands, and wetlands) to elucidate the global variation of soil Qmin and its predictors. Soil Qmin decreased from the equator to two poles, which was significant in the North Hemisphere. Soil Qmin correlated negatively with soil pH, total soil N, the ratio of soil carbon (C) to N, and soil microbial biomass C, and positively with mean annual temperature and C:N ratio of soil microbial biomass at a global scale. Soil microbial biomass, climate, and soil physical and chemical properties in combination accounted for 41% of the total variations of global soil Qmin. Among those predictors, C:N ratio of soil microbial biomass was the most important factor contributing to the variations of soil Qmin (the standardized coefficient = 0.39) within or across ecosystem types. This study emphasizes the critical role of microbial stoichiometry in soil N cycling, and suggests the necessity of incorporating soil Qmin into Earth system models to better predict N cycling under environmental change.

The alpine meadow is one of the most important ecosystems on the Qinghai-Tibet Plateau (QTP) due to its huge carbon storage and wide distribution. Evaluating the carbon fluxes in alpine meadow ecosystems is crucial to understand the dynamics of carbon storage in high-altitude areas. Here, we investigated the carbon fluxes at seasonal and inter-annual timescales based on 5 years of observations of eddy covariance fluxes in the Zoige alpine meadow on the eastern Tibetan Plateau. We found that the Zoige alpine meadow acted as a faint carbon source of 94.69 ± 86.44 g C m −2 y −1 during the observation periods with large seasonal and inter-annual variations (IAVs). At the seasonal scale, gross primary productivity (GPP) and ecosystem respiration (Re) were positively correlated with photosynthetic photon flux density (PPFD), average daily temperature (Ta), and vapor pressure (VPD) and had negative relationships with volumetric water content (VWC). Seasonal variations of net ecosystem carbon dioxide (CO 2 ) exchange (NEE) were mostly explained by Ta, followed by PPFD, VPD, and VWC. The IAVs of GPP and Re were mainly attributable to the IAV of the maximum GPP rate (GPP max ) and maximum Re rate (Re max ), respectively, both of which increased with the percentage of Cyperaceae and decreased with the percentage of Polygonaceae changes across years. The IAV of NEE was well explained by the anomalies of the maximum CO 2 release rate (MCR). These results indicated that the annual net CO 2 exchange in the alpine meadow ecosystem was controlled mainly by the maximum C release rates. Therefore, a better understanding of physiological response to various environmental factors at peak C uptake and release seasons will largely improve the predictions of GPP, Re, and NEE in the context of global change.