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Nitrogen is one of the most important macronutrients for plant growth and timely estimation of canopy nitrogen content (CNC) is crucial for agricultural applications. Remote sensing has emerged as an important tool to quantify CNC using either empirically or physically based methods. Most empirical methods use chlorophyll related spectral indices and are dependent on the relationship between nitrogen and chlorophyll, which varies with vegetation types and growth stages. In contrast, physically based methods use the full-range reflectance data and retrieve CNC from coupled leaf and canopy radiative transfer models (such as PROSPECT-PRO + 4SAIL, PROSAIL-PRO). However, the subtle absorption features of nitrogen and protein in fresh leaves hinder the accurate estimation of CNC. Therefore, this study proposed an efficient and mechanistic framework to estimate CNC (PROSAIL-NAM) by coupling PROSAIL-PRO with a nitrogen allocation model, which divided the total nitrogen into non-photosynthetic nitrogen (NPN) and photosynthetic nitrogen (PN). At the canopy level, PN and NPN are assumed to be proportional to canopy chlorophyll content (CCC) and canopy dry matter content (CDM), respectively. The PROSAIL-PRO model was first used to estimate CCC and CDM, and then the resulting CCC and CDM were fed to the nitrogen allocation model to estimate CNC. The estimation accuracy of CNC was assessed with six diverse datasets: four from field crop experiments across geographic sites, one from multiple ecosystems, and one from a satellite-ground joint experiment. Our results showed that satisfactory estimations of CNC were obtained when CCC and CDM were estimated using a model inversion method (RMSE = 0.54–1.56 g/m2) and a hybrid retrieval method (RMSE = 0.49–2.25 g/m2). The model inversion method was comparable with the hybrid retrieval method for ground platforms, but performed better for airborne and satellite platforms. In addition, the traditional protein-nitrogen conversion model obtained CNC from the canopy protein content and led to clear overestimations of CNC with RMSE > 1.95 g/m2. This study represents a first attempt to develop a robust approach by coupling PROSAIL-PRO with a nitrogen allocation model for accurate estimation of CNC across geographic sites, ecosystems, and platforms. These finding will advance the monitoring of CNC from regional to global scales.

Voltage-gated calcium (Cav) channels mediate Ca2+ influx in response to membrane depolarization, playing critical roles in diverse physiological processes. Dysfunction or aberrant regulation of Cav channels can lead to life-threatening consequences. Cav-targeting drugs have been clinically used to treat cardiovascular and neuronal disorders for several decades. This review aims to provide an account of recent developments in the structural dissection of Cav channels. High-resolution structures have significantly advanced our understanding of the working and disease mechanisms of Cav channels, shed light on the molecular basis for their modulation, and elucidated the modes of actions (MOAs) of representative drugs and toxins. The progress in structural studies of Cav channels lays the foundation for future drug discovery efforts targeting Cav channelopathies.

The contribution of spike photosynthesis to grain yield (GY) has been overlooked in the accurate spectral prediction of yield. Thus, it’s essential to construct and estimate a yield-related phenotypic trait considering spike photosynthesis. Based on field and spectral reflectance data from 19 wheat cultivars under two nitrogen fertilization conditions in two years, our objectives were to (i) construct a yield-related phenotypic trait (spike–leaf composite indicator, SLI) accounting for the contribution of the spike to photosynthesis, (ii) develop a novel spectral index (enhanced triangle vegetation index, ETVI3) sensitive to SLI, and (iii) establish and evaluate SLI estimation models by integrating spectral indices and machine learning algorithms. The results showed that SLI was sensitive to nitrogen fertilizer and wheat cultivar variation as well as a better predictor of yield than the leaf area index. ETVI3 maintained a strong correlation with SLI throughout the growth stage, whereas the correlations of other spectral indices with SLI were poor after spike emergence. Integrating spectral indices and machine learning algorithms improved the estimation accuracy of SLI, with the most accurate estimates of SLI showing coefficient of determination, root mean square error (RMSE), and relative RMSE values of 0.71, 0.047, and 26.93%, respectively. These results provide new insights into the role of fruiting organs for the accurate spectral prediction of GY. This high-throughput SLI estimation approach can be applied for wheat yield prediction at whole growth stages and may be assisted with agronomical practices and variety selection.

Abstract Background The Daur people are one of the 55 minority ethnic groups in China and have lived in Northern China for 300 years. In traditional Daur medicine, medicinal and edible plants (MEPs) are utilised for health benefits and therapeutic purposes; however, related ethnobotanical knowledge is rarely reported, which is disadvantageous for the sustainable development of these MEPs. Methods Semi-structured interviews with 122 informants, six focus group discussions, and a resource survey were conducted in a Daur minority nationality area in Inner Mongolia from 2015 to 2020, and the data statistics were analysed. In this study, we simulated a system dynamics model aimed at understanding the multiple feedback mechanisms involved in the relationships between the cultural influences and socioeconomic factors, sustainable environment, and development of MEPs. Results A total of 52 species of MEPs were identified and relevant ethnobotanical knowledge was assessed using Daur medicinal species data from Inner Mongolia and the Xinjiang region, with the literature and Ewenki ethnic group data used for comparison. The most commonly used medicinal plant species by the Daur were found to be Betula pendula subsp. mandshurica, Artemisia integrifolia, Crataegus pinnatifida, Saposhnikovia divaricata, Artemisia argyi, and Jacobaea cannabifolia. The MEPs most frequently targeted the digestive and rheumatic immunity systems, as well as infectious diseases or parasitic infections and other common diseases and basic health issues. MEP knowledge was primarily limited to older generations; thus, the valuable ethnobotanical knowledge on traditional medicines must be protected from future losses. Conclusions Our findings provide insights for future research aimed at exploiting the rich phytochemical diversity in traditional medicine and promote its use in modern lifestyles. Effective assessment and management of plant resources will lead to their application for the improvement of dietary diversity, nutrition, and health care.

Rapid, accurate, and nondestructive estimation of grain number per panicle (GNPP) in winter wheat is crucial to accelerate smart breeding, improve precision crop management, and ensure food security. As two (panicle number per unit ground area and GNPP) of three commonly used yield components, GNPP was much less quantified with remotely sensed data than the former through visual counting. The limited research suffered from either low accuracies with ground canopy spectra or low efficiency with proximal panicle imaging systems. No studies have been reported on estimating GNPP with unmanned aerial vehicle (UAV) imagery, underscoring its strong advantages in high-resolution and efficient monitoring. To address these issues, this study proposed a practical approach for estimating GNPP in winter wheat by integrating UAV imagery and meteorological data with meta-learning ensemble regression. The potential contributions of different variables were examined for understanding the improvement in the spectral estimation of GNPP, including spectral indices (SIs), the optimal canopy height (CH) metric, and absorbed photosynthetic active radiation (APAR).The results demonstrated that CHP99 (CH at the 99th percentile in the region of interest) derived from red-green-blue (RGB) imagery exhibited the strongest correlation with measured plant height among all RGB- or multispectral (MS)-derived CH metrics. The incorporation of remotely sensed APAR and RGB-derived CHP99 improved the accuracy of GNPP estimation over using merely color indices or SIs. Among all feature combinations, Comb. #6 (SIs + APARMS + CHP99) yielded the highest overall accuracies in estimating GNPP for individual and multiple stages. Compared with the best anthesis models for Combs. #5–7 (Rval2 = 0.52–0.64, RMSE = 2.85–2.47, RRMSE = 6.01–5.21 %), the multi-stage (heading + anthesis) models exhibited higher accuracies in independent validation (Rval2 = 0.60–0.65, RMSE = 2.60–2.42, RRMSE = 5.48–5.10 %). The findings suggest this study has opened a new avenue for estimating GNPP with UAV remote sensing. The proposed method for the synergistic use of UAV imagery and meteorological data has great potential in the prediction of GNPP and grain yield over various regions with satellite imagery and climate datasets.

High-throughput phenotypic analysis plays an increasingly important role in crop breeding. In such research, the breeder usually establishes hundreds to thousands of plots, with each plot having its independent genetic breeding sources. The breeding plot extraction of genetic sources is usually outlined manually on RGB UAV imagery, which is time-consuming and subject to human bias. Therefore, a rapid method to extract the breeding plot for each genetic source in high-throughput phenotypic analysis would be very significant. In this paper, we propose a transferable method for extracting breeding plots from UAV RGB imagery. We utilized the fully convolutional neural network model A-UNet with an attention gate. After obtaining binary raster data from deep learning, we introduced post-processing. Subsequently, the raster data after post-processing were converted to vector data to obtain geographical coordinates. Finally, the UAV imagery was masked by the vector data to obtain the extraction results for each plot. The results showed that A-UNet achieved accuracies of over 90 % in precision, recall, and F1 score. Post-processing resulted in a 93 % average IoU in breeding plot extraction in the main study area. The average IOU achieved over 86 % in different spatial resolutions (1.6 cm and 0.4 cm), plot sizes (1 m × 1.5 and 2 m × 5 m), and crop types (rice). In summary, this study developed a method for extracting breeding plots in high-throughput phenotype analysis, which would help to be used as a high-throughput screening technique for accelerating crop breeding.

The timely and accurate acquisition of crop-growth information is a prerequisite for implementing intelligent crop-growth management, and portable multispectral imaging devices offer reliable tools for monitoring field-scale crop growth. To meet the demand for obtaining crop spectra information over a wide band range and to achieve the real-time interpretation of multiple growth characteristics, we developed a novel portable snapshot multispectral imaging crop-growth sensor (PSMICGS) based on the spectral sensing of crop growth. A wide-band co-optical path imaging system utilizing mosaic filter spectroscopy combined with dichroic mirror beam separation is designed to acquire crop spectra information over a wide band range and enhance the device’s portability and integration. Additionally, a sensor information and crop growth monitoring model, coupled with a processor system based on an embedded control module, is developed to enable the real-time interpretation of the aboveground biomass (AGB) and leaf area index (LAI) of rice and wheat. Field experiments showed that the prediction models for rice AGB and LAI, constructed using the PSMICGS, had determination coefficients (R²) of 0.7 and root mean square error (RMSE) values of 1.611 t/ha and 1.051, respectively. For wheat, the AGB and LAI prediction models had R² values of 0.72 and 0.76, respectively, and RMSE values of 1.711 t/ha and 0.773, respectively. In summary, this research provides a foundational tool for monitoring field-scale crop growth, which is important for promoting high-quality and high-yield crops.

Leaf chlorophyll content (LCC) is an important indicator of nitrogen content, and therefore, the accurate monitoring of LCC will benefit agronomists in guiding fertilizer applications. Remote sensing techniques have been widely used to estimate LCC from canopy reflectance spectra. However, there is no sensor specific to the estimation of LCC from canopy reflectance spectra, and it is unclear whether LCC estimation is sensitive to the reflectance quality (such as the noise level) or sensor settings (such as spectral resolution). To help design a sensor specific to the estimation of LCC, this study calibrated a semiempirical model based on the leaf area index-insensitive chlorophyll index (LICI) and evaluated its sensitivity to reflectance quality and sensor settings using simulated, measured, and artificial datasets. Our results indicated that random Gaussian noise in reflectance has limited effects on LCC estimation when the level of Gaussian noise is less than 2%, while the negative systematic bias in reflectance has clear effects on the LCC estimation. The LCC estimation accuracy is nearly independent of the spectral resolution (

Abstract The number of seedlings is an important indicator that reflects the size of the wheat population during the seedling stage. Researchers increasingly use deep learning to detect and count wheat seedlings from unmanned aerial vehicle (UAV) images. However, due to the small size and diverse postures of wheat seedlings, it can be challenging to estimate their numbers accurately during the seedling stage. In most related works in wheat seedling detection, they label the whole plant, often resulting in a higher proportion of soil background within the annotated bounding boxes. This imbalance between wheat seedlings and soil background in the annotated bounding boxes decreases the detection performance. This study proposes a wheat seedling detection method based on a local annotation instead of a global annotation. Moreover, the detection model is also improved by replacing convolutional and pooling layers with the Space-to-depth Conv module and adding a micro-scale detection layer in the YOLOv5 head network to better extract small-scale features in these small annotation boxes. The optimization of the detection model can reduce the number of error detections caused by leaf occlusion between wheat seedlings and the small size of wheat seedlings. The results show that the proposed method achieves a detection accuracy of 90.1%, outperforming other state-of-the-art detection methods. The proposed method provides a reference for future wheat seedling detection and yield prediction.

Abstract Background The metrics for assessing the yield of crops in the field include the number of ears per unit area, the grain number per ear, and the thousand-grain weight. Typically, the ear number per unit area contributes the most to the yield. However, calculation of the ear number tends to rely on traditional manual counting, which is inefficient, labour intensive, inaccurate, and lacking in objectivity. In this study, two novel extraction algorithms for the estimation of the wheat ear number were developed based on the use of terrestrial laser scanning (TLS) in conjunction with the density-based spatial clustering (DBSC) algorithm based on the normal and the voxel-based regional growth (VBRG) algorithm. The DBSC involves two steps: (1) segmentation of the point clouds using differences in the normal vectors and (2) clustering of the segmented point clouds using a density clustering algorithm to calculate the ear number. The VBRG involves three steps: (1) voxelization of the point clouds, (2) construction of the topological relationships between the voxels as a connected region using the k-dimensional tree, and (3) detection of the wheat ears in the connected areas using a regional growth algorithm. Results The results demonstrated that DBSC and VBRG were promising in estimating the number of ears for different cultivars, planting densities, N fertilization rates, and growth stages of wheat (RMSE = 76 ~ 114 ears/m2, rRMSE = 18.62 ~ 27.96%, r = 0.76 ~ 0.84). Comparing the performance of the two algorithms, the overall accuracy of the DBSC (RMSE = 76 ears/m2, rRMSE = 18.62%, r = 0.84) was better than that of the VBRG (RMSE = 114 ears/m2, rRMSE = 27.96%, r = 0.76). It was found that with the DBSC, the calculation in points as units permitted more detailed information to be retained, and this method was more suitable for estimation of the wheat ear number in the field. Conclusions The algorithms adopted in this study provide new approaches for non-destructive measurement and efficient acquisition of the ear number in the assessment of the wheat yield phenotype.

Abstract Background Detecting and counting wheat spikes is essential for predicting and measuring wheat yield. However, current wheat spike detection researches often directly apply the new network structure. There are few studies that can combine the prior knowledge of wheat spike size characteristics to design a suitable wheat spike detection model. It remains unclear whether the complex detection layers of the network play their intended role. Results This study proposes an interpretive analysis method for quantitatively evaluating the role of three-scale detection layers in a deep learning-based wheat spike detection model. The attention scores in each detection layer of the YOLOv5 network are calculated using the Gradient-weighted Class Activation Mapping (Grad-CAM) algorithm, which compares the prior labeled wheat spike bounding boxes with the attention areas of the network. By refining the multi-scale detection layers using the attention scores, a better wheat spike detection network is obtained. The experiments on the Global Wheat Head Detection (GWHD) dataset show that the large-scale detection layer performs poorly, while the medium-scale detection layer performs best among the three-scale detection layers. Consequently, the large-scale detection layer is removed, a micro-scale detection layer is added, and the feature extraction ability in the medium-scale detection layer is enhanced. The refined model increases the detection accuracy and reduces the network complexity by decreasing the network parameters. Conclusion The proposed interpretive analysis method to evaluate the contribution of different detection layers in the wheat spike detection network and provide a correct network improvement scheme. The findings of this study will offer a useful reference for future applications of deep network refinement in this field.

The rapid and accurate estimation of leaf area index (LAI) through remote sensing holds significant importance for precise crop management. However, the direct construction of a vegetation index model based on multi-spectral data lacks robustness and spatiotemporal expansibility, making its direct application in practical production challenging. This study aimed to establish a simple and effective method for LAI estimation to address the issue of poor accuracy and stability that is encountered by vegetation index models under varying conditions. Based on seven years of field plot trials with different varieties and nitrogen fertilizer treatments, the Kalman filter (KF) fusion method was employed to integrate the estimated outcomes of multiple vegetation index models, and the fusion process was investigated by comparing and analyzing the relationship between fixed and dynamic variances alongside the fusion accuracy of optimal combinations during different growth stages. A novel multi-model integration fusion method, KF-DGDV (Kalman Filtering with Different Growth Periods and Different Vegetation Index Models), which combines the growth characteristics and uncertainty of LAI, was designed for the precise monitoring of LAI across various growth phases of rice. The results indicated that the KF-DGDV technique exhibits a superior accuracy in estimating LAI compared with statistical data fusion and the conventional vegetation index model method. Specifically, during the tillering to booting stage, a high R2 value of 0.76 was achieved, while at the heading to maturity stage, it reached 0.66. In contrast, within the framework of the traditional vegetation index model, the red-edge difference vegetation index (DVIREP) model demonstrated a superior performance, with an R2 value of 0.65, during tillering to booting stage, and 0.50 during the heading to maturity stage, respectively. The multi-model integration method (MME) yielded an R2 value of 0.67 for LAI estimation during the tillering to booting stage, and 0.53 during the heading to maturity stage. Consequently, KF-DGDV presented an effective and stable real-time quantitative estimation method for LAI in rice.

The accuracy of leaf nitrogen accumulation (LNA) estimation is often compromised by the vertical heterogeneity of crop nitrogen. In this study, an estimation model of LNA considering vertical heterogeneity of wheat was developed based on unmanned aerial vehicle (UAV) multispectral data and near-ground hyperspectral data, both collected at different view zenith angles (e.g., 0°, −30°, and −45°). Winter wheat plants were evenly divided into 3 layers from top to bottom, and LNA was obtained for the upper, middle, and lower leaf layers, as well as for various combinations of these layers (upper and middle, middle and lower, and the entire canopy, referred to as LNACanopy). The linear regression (LR) and random forest regression (RF) models were constructed to estimate the LNA for each individual leaf layer. Subsequently, models for estimating LNACanopy that considered the impact of vertical heterogeneity (namely, LR-LNASum and RF-LNASum) were established based on the relationships between LNACanopy and LNA in different leaf layers. Meanwhile, LNA models that did not consider the effect of vertical heterogeneity (LR-LNAnon and RF-LNAnon) were used for comparative validation. The validation datasets consisted of UAV-simulated data from hyperspectral reflectance and UAV-measured data. Results showed that LNASum models had markedly higher accuracy compared to LNAnon. The optimal scheme for estimating LNACanopy was the combination of the upper, middle, and lower layers based on the normalized difference red edge index. Among these models, RF-LNASum demonstrated higher accuracy than LR-LNASum, with a validation relative root mean square error of 19.3% and 17.8% for the UAV-measured and simulated dataset, respectively.

Objective: Bispecific antibodies (BsAbs) have demonstrated significant therapeutic impacts for the treatment of a broad spectrum of diseases that include oncology, auto-immune, and infectious diseases. However, the large-scale production of clinical batches of bispecific antibodies still has many challenges that include having low yield, poor stability, and laborious downstream purification processes. To address such challenges, we describe the optimization of the controlled Fab arm exchange (cFAE) process for the efficient generation of BsAbs.Methods: The process optimization of a large-scale good manufacturing practice (GMP) cFAE strategy to prepare BsAbs was based on screening the parameters of temperature, reduction, oxidation, and buffer exchange. We include critical quality standards for the reducing agent cysteamine hydrochloride.Results: This large-scale production protocol enabled the generation of bispecific antibodies with >90% exchange yield and at >95% purity. The subsequent downstream processing could use typical mAb procedures. Furthermore, we demonstrated that the bispecific generation protocol can be scaled up to ∼60 L reaction scale using parental monoclonal antibodies that were expressed in a 200 L bioreactor.Conclusion: We presented a robust development strategy for the cFAE process that can be used for a larger scale GMP BsAb production.

Efficient and accurate acquisition of the rice grain protein content (GPC) is important for selecting high-quality rice varieties, and remote sensing technology is an attractive potential method for this task. However, the majority of multispectral sensors are poor predictors of GPC due to their broad spectral bands. Hyperspectral technology provides a new analytical technology for bridging the gap between phenomics and genomics. However, the small size of typical datasets is a constraint for model construction for estimating GPC, limiting their accuracy and reducing their ability to generalize to a wide range of varieties. In this study, we used hyperspectral data of rice grains from 515 japonica varieties and deep convolution generative adversarial networks (DCGANs) to generate simulated data to improve the model accuracy. Features sensitive to GPC were extracted after applying a continuous wavelet transform (CWT), and the estimated GPC model was constructed by partial least squares regression (PLSR). Finally, a genome-wide association study (GWAS) was applied to the measured and generated datasets to detect GPC loci. The results demonstrated that the simulated GPC values generated after 8,000 epochs were closest to the measured values. The wavelet feature (WF1743, 2), obtained from the data with the addition of 200 simulated samples, exhibited the highest GPC estimation accuracy (R2 = 0.58 and RRMSE = 6.70%). The GWAS analysis showed that the estimated values based on the simulated data detected the same loci as the measured values, including the OsmtSSB1L gene related to grain storage protein. This study provides a new technique for the efficient genetic study of phenotypic traits in rice based on hyperspectral technology.

The impact-rubbing dynamic characteristics of the power turbine rotor with the hollow shaft and offset discs for aircraft engine are investigated, and the impact-rubbing analytical method for the complex rotor based on MDOP Timoshenko beam theory is proposed in this paper. Compared with the traditional approach, the novel method can obtain more data to satisfy the need of engineering. The Lagrange equation is adopted to derive the equations of motion for the rotor system, and the Newmark-β method is applied to solve the equations. The diagrams such as the bifurcation, axis trajectory, spectrum, and Poincaré map are obtained to research on the effect of the rotating speed, gap, and eccentricity on the vibration response. The finite element analysis was carried out to validate the correctness of the theoretical modeling method. The research results indicate that the power turbine rotor with the hollow shaft on operation shows the various nonlinear dynamic behaviors including the multiperiod, quasi-period, jumping phenomenon, and chaotic motions; there exists an optimal gap between the rotor and the stator from the perspective of the efficiency and the dynamics; the optimal gap should make system avoid the resulting chaos or the quasi-period motion for the stability and safety of the machinery.

Voltage-gated Ca2+ channel subtype Cav2.3 represents a potential drug target for neurological diseases. Here, authors report cryo-EM structures of Cav2.3 and its mutant to reveal the crosstalk of intracellular segments, which may facilitate future drug discovery.

Vegetation phenology changes caused by urbanization could lead to shifts in ecosystem services in urban areas and impact on human health. The characteristics of urbanization affect vegetation phenology need to be emphasized, especially in China with a complex natural environment and rapid urbanization background. In this study, we used remote sensing-based phenological data (MODIS MCD12Q2) to analyze the spatial heterogeneity of vegetation phenology caused by urbanization between urban and non-urban areas in 320 cities across China. We found a significant difference between vegetation phenology in urban and its corresponding non-urban area at national and the regional scale. For national scale, the start of the growing season (SOS) was significantly advanced by 2.53 days (P

Summary: Hedgehog (Hh) signaling mediated by transcription factor Ci/Gli plays a vital role in embryonic development and adult tissue homeostasis in invertebrates and vertebrates, whose dysregulation leads to many human disorders, including cancer. However, till now, cofactors of Ci/Gli which can affect tumorigenesis are not well known. Here, through genetic screen, we find overexpression of active Ci alone is not sufficient to generate tumor-like eye phenotype in Drosophila, however, its overexpression combined with knockdown of hib causes a striking tumor-like big eye phenotype. Mechanistically, HIB/SPOP inhibits Ci/Gli-mediated tumorigenesis by modulating the RNA polymerase II (RNAPII) components Rpb3/Rpb7 stabilities in E3 ligase dependent manner. In addition, Ci/Gli can promote HIB/SPOP-mediated Rpb7/Rpb3 degradation. Taken together, our results indicate Ci/Gli needs to hook up with suitable RNAPII together to achieve the tumor-like eye phenotype and HIB/SPOP plays dual roles through controlling Ci/Gli and Rpb3/Rpb7 protein stabilities to temper Ci/Gli/RNAPII-mediated tumorigenesis.

Mapping crop distribution using satellite technology is an effective approach for gaining information about food production over broad, regional scales. However, crop classification in high altitude regions from satellite platforms remains challenging, due to the spatial heterogeneity caused by the complex planting patterns. Moreover, the frequent cloud cover makes it difficult to collect time-series imagery for these regions. Thus, this study used a mosaic of single images of Gaofen-6 data to map the crop distribution in high altitude regions of Xining City and Haidong City prefectures of Qinghai Province, China. To improve the accuracy of the crop classification, random forest-recursive feature elimination (RF-RFE) was used to determine an optimal feature subset from existing spectral, texture and topographic features. Then, a two-layer stacking generalization ensemble model, incorporating Random Forest, XGBoost and AdaBoost, was trained. The results reveal that the stacking algorithm outperformed the other single classifiers, with overall accuracy higher than 85% (87.89% for the optimal feature subset and 85.38% for the original spectral band subset). In addition, the user’s and producer’s accuracies for wheat, rape and maize field all exceeded 90%. Elevation was the variable with the highest importance score, illustrating its importance in crop classification of high altitude regions. Overall, the framework, combining RF-RFE and a stacking algorithm, can improve the accuracy of the crop classification in high altitude regions.

The timely and robust prediction of wheat yield is very significant for grain trade and food security. In this study, the yield prediction model was developed by coupling an ensemble model with multi-source data, including vegetation indices (VIs) and meteorological data. The results showed that green chlorophyll vegetation index (GCVI) is the optimal remote sensing (RS) variable for predicting wheat yield compared with other VIs. The accuracy of the adaptive boosting- long short-term memory (AdaBoost-LSTM) ensemble model was higher than the LSTM model. AdaBoost-LSTM coupled with optimal input data had the best performance. The AdaBoost-LSTM model had strong robustness for predicting wheat yield under different irrigation and extreme weather events in general. Additionally, the accuracy of AdaBoost-LSTM for rainfed counties was higher than that for irrigation counties in most years except extreme years. The yield prediction model developed with the characteristic variables of the window from February to April had higher accuracy and smaller data requirements, which was the best prediction window. Therefore, wheat yield can be accurately predicted by the AdaBoost-LSTM model one to two months of lead time before maturity in the HHHP. Overall, the AdaBoost-LSTM model can achieve accurate and robust yield prediction in large-scale regions.

This study presents an electrolysis system utilizing a novel self-circulation process of sulfate (SO42−) and persulfate (S2O82−) ions based on a boron-doped diamond (BDD) anode and an activated carbon fiber (ACF) cathode, which is designed to enable electrochemical remediation of environmental contaminants with reduced use of chemical reagents and minimized residues. The production of S2O82− and hydrogen peroxide (H2O2) on the BDD anode and ACF cathode, respectively, is identified as the source of active radicals for the contaminant degradation. The initiator, sulfate, is identified by comparing the degradation efficiency in NaSO4 and NaNO3 electrolytes. Quenching experiments and electron paramagnetic resonance (EPR) spectroscopy confirmed that the SO4−· and ·OH generated on the ACF cathode are the main reactive radicals. A comparison of the degradation efficiency and the generated S2O82−/H2O2 of the divided/undivided electrolysis system is used to demonstrate the superiority of the synergistic effect between the BDD anode and ACF cathode. This work provides evidence of the effectiveness of the philosophy of “catalysis in lieu of supplementary chemical agents” and sheds light on the mechanism of the generation and transmission of reactive species in the BDD and ACF electrolysis system, thereby offering new perspectives for the design and optimization of electrolysis systems.

The purpose of this study is to examine the influence of different parameters on the legitimacy rate and effective efficiency of crowd decision-making and to guide decision-making in real life. In this paper, a crowd decision representation method based on the preference domain is proposed for the large-scale simulation implementation of crowd decision in a crowd intelligence network, a simulation modeling is performed for the members participating in the decision, and a formal propulsion algorithm is perfected. Lastly, the influence of key parameters on the decision results is analyzed through a large-scale simulation experiment. This study analyzes the influence of key parameters, such as the number of candidates, number of voters, and voting legitimacy rate reference value, on the decision-making results and summarizes the selection range of key parameters under different results. Through the simulation experiment of crowd decision-making, this paper provides inspiration for researchers to explore the parameter sensitivity of crowd decision-making and provides guidance for crowd decision-making in social life.

Respiratory syncytial virus (RSV) is a significant viral pathogen that causes respiratory infections in infants, the elderly, and immunocompromised individuals. RSV-related illnesses impose a substantial economic burden worldwide annually. The molecular structure, function, and in vivo interaction mechanisms of RSV have received more comprehensive attention in recent times, and significant progress has been made in developing inhibitors targeting various stages of the RSV replication cycle. These include fusion inhibitors, RSV polymerase inhibitors, and nucleoprotein inhibitors, as well as FDA-approved RSV prophylactic drugs palivizumab and nirsevimab. The research community is hopeful that these developments might provide easier access to knowledge and might spark new ideas for research programs.

Yield prediction is essential in food security, food trade, and field management. However, due to the associated complex formation mechanisms of yield, accurate and timely yield prediction remains challenging in remote sensing-based crop monitoring domains. In this study, a framework of soybean yield prediction integrating extreme gradient boosting (XGBoost) and multidimensional feature engineering was developed at the county level in the United States using publicly available datasets. Excellent accuracy values were obtained for over 959 counties in 12 states throughout the midwestern U.S., with a test coefficient of determination (R2) of 0.82 and a root-mean-square error (RMSE) of 0.246 t/ha, using our approach. Following a “train–validate–test” assessment strategy, our study shows that XGBoost outperforms other county-level soybean yield prediction models with identical inputs, including linear regression (LR), random forest (RF), k-nearest neighbor (KNN), artificial neural network (ANN), support vector regression (SVR), long short-term memory (LSTM), and deep neural network (DNN). The results show that accurate results of soybean yield prediction can be obtained as early as the pod-setting stage. We implemented the feature importance and Shapley additive explanations (SHAP) algorithms to quantify the impact of input features on the XGBoost model in the training and prediction stages, respectively. The enhanced vegetation index (EVI) at the pod-setting period is the most crucial factor, but the yield prediction is not dependent on only a few key features. Yields were detrended using longer-term historical yield data, and R2 increased from 0.58 to 0.82 while RMSE decreased from 0.374 t/ha to 0.246 t/ha. We employed multidimensional feature engineering to generate phenology-based features, and R2 improved from 0.79 to 0.82 while RMSE decreased from 0.268 t/ha to 0.246 t/ha using this approach. The framework can be easily implemented and extended in the future in combination with early crop identification.

The growth of the fusarium head blight (FHB) pathogen at the grain formation stage is a deadly threat to wheat production through disruption of the photosynthetic processes of wheat spikes. Real-time nondestructive and frequent proxy detection approaches are necessary to control pathogen propagation and targeted fungicide application. Therefore, this study examined the ch\lorophyll-related phenotypes or features from spectral and chlorophyll fluorescence for FHB monitoring. A methodology is developed using features extracted from hyperspectral reflectance (HR), chlorophyll fluorescence imaging (CFI), and high-throughput phenotyping (HTP) for asymptomatic to symptomatic disease detection from two consecutive years of experiments. The disease-sensitive features were selected using the Boruta feature-selection algorithm, and subjected to machine learning-sequential floating forward selection (ML-SFFS) for optimum feature combination. The results demonstrated that the biochemical parameters, HR, CFI, and HTP showed consistent alterations during the spike–pathogen interaction. Among the selected disease sensitive features, reciprocal reflectance (RR=1/700) demonstrated the highest coefficient of determination (R2) of 0.81, with root mean square error (RMSE) of 11.1. The multivariate k-nearest neighbor model outperformed the competing multivariate and univariate models with an overall accuracy of R2 = 0.92 and RMSE = 10.21. A combination of two to three kinds of features was found optimum for asymptomatic disease detection using ML-SFFS with an average classification accuracy of 87.04% that gradually improved to 95% for a disease severity level of 20%. The study demonstrated the fusion of chlorophyll-related phenotypes with the ML-SFFS might be a good choice for crop disease detection.

The organ-specific critical nitrogen (Nc) dilution curves are widely thought to represent a new approach for crop nitrogen (N) nutrition diagnosis, N management, and crop modeling. The Nc dilution curve can be described by a power function (Nc = A1·W−A2), while parameters A1 and A2 control the starting point and slope. This study aimed to investigate the uncertainty and drivers of organ-specific curves under different conditions. By using hierarchical Bayesian theory, parameters A1 and A2 of the organ-specific Nc dilution curves for wheat were derived and evaluated under 14 different genotype × environment × management (G × E × M) N fertilizer experiments. Our results show that parameters A1 and A2 are highly correlated. Although the variation of parameter A1 was less than that of A2, the values of both parameters can change significantly in response to G × E × M. Nitrogen nutrition index (NNI) calculated using organ-specific Nc is in general consistent with NNI estimated with overall shoot Nc, indicating that a simple organ-specific Nc dilution curve may be used for wheat N diagnosis to assist N management. However, the significant differences in organ-specific Nc dilution curves across G × E × M conditions imply potential errors in Nc and crop N demand estimated using a general Nc dilution curve in crop models, highlighting a clear need for improvement in Nc calculations in such models. Our results provide new insights into how to improve modeling of crop nitrogen–biomass relations and N management practices under G × E × M.

Rapid and accurate estimation of panicle number per unit ground area (PNPA) in winter wheat before heading is crucial to evaluate yield potential and regulate crop growth for increasing the final yield. The accuracies of existing methods were low for estimating PNPA with remotely sensed data acquired before heading since the spectral saturation and background effects were ignored. This study proposed a spectral-textural PNPA sensitive index (SPSI) from unmanned aerial vehicle (UAV) multispectral imagery for reducing the spectral saturation and improving PNPA estimation in winter wheat before heading. The effect of background materials on PNPA estimated by textural indices (TIs) was examined, and the composite index SPSI was constructed by integrating the optimal spectral index (SI) and TI. Subsequently, the performance of SPSI was evaluated in comparison with other indices (SI and TIs). The results demonstrated that green-pixel TIs yielded better performances than all-pixel TIs apart from TI[HOM], TI[ENT], and TI[SEM] among all indices from 8 types of textural features. SPSI, which was calculated by the formula DATT[850,730,675] + NDTICOR[850,730], exhibited the highest overall accuracies for any date in any dataset in comparison with DATT[850,730,675], TINDRE[MEA], and NDTICOR[850,730]. For the unified models assembling 2 experimental datasets, the RV2 values of SPSI increased by 0.11 to 0.23, and both RMSE and RRMSE decreased by 16.43% to 38.79% as compared to the suboptimal index on each date. These findings indicated that the SPSI is valuable in reducing the spectral saturation and has great potential to better estimate PNPA using high-resolution satellite imagery.

Accurate wheat spike detection is crucial in wheat field phenotyping for precision farming. Advances in artificial intelligence have enabled deep learning models to improve the accuracy of detecting wheat spikes. However, wheat growth is a dynamic process characterized by important changes in the color feature of wheat spikes and the background. Existing models for wheat spike detection are typically designed for a specific growth stage. Their adaptability to other growth stages or field scenes is limited. Such models cannot detect wheat spikes accurately caused by the difference in color, size, and morphological features between growth stages. This paper proposes WheatNet to detect small and oriented wheat spikes from the filling to the maturity stage. WheatNet constructs a Transform Network to reduce the effect of differences in the color features of spikes at the filling and maturity stages on detection accuracy. Moreover, a Detection Network is designed to improve wheat spike detection capability. A Circle Smooth Label is proposed to classify wheat spike angles in drone imagery. A new micro-scale detection layer is added to the network to extract the features of small spikes. Localization loss is improved by Complete Intersection over Union to reduce the impact of the background. The results show that WheatNet can achieve greater accuracy than classical detection methods. The detection accuracy with average precision of spike detection at the filling stage is 90.1%, while it is 88.6% at the maturity stage. It suggests that WheatNet is a promising tool for detection of wheat spikes.

Background: Natural ecosystems provide necessary services for human beings, including ecosystem service values (ESVs) and socioeconomic service values (SSVs). The value orientations of ESVs and SSVs are mainly related to people’s interaction with nature. This study reclassified greenspace from a perspective of exposed and non-exposed greenspace based on the level of interaction by people and greenspace. We applied an expert questionnaire to survey the SSVs value orientations of forestland, grassland, wetland, and water bodies, and quantitatively compared the value orientations between the ESVs and SSVs of greenspace in China. Result: (1) The values of exposed greenspace were relatively far higher than non-exposed greenspace, as it had both ESVs and SSVs. (2) The forestland and grassland had relatively high ESVs and SSVs, and are the priority for both the exposed and non-exposed greenspace. (3) Wetland had relatively high ESVs but low SSVs, which was unpopular for exposed greenspace. (4) The ESVs and SSVs of water body were relatively balanced. Conclusion: Greenspace had both ESVs and SSVs when they are exposed to human. Our study provided an innovative perspective to explore the value orientations of greenspace, which provides an actionable scientific basis for greenspace planning, design and construction in human habitat. .

PurposeTo explore the changes of blood lipid and serum inflammatory factors in pregnant women with hypertensive disorder complicating pregnancy (HDP) and the relationship with disease development.Methods107 pregnant women with HDP who had regular prenatal examination in our hospital from July 2018 to July 2021 were selected as the research objects. According to the severity of the disease, they were divided into gestational hypertension group, mild preeclampsia group and severe preeclampsia group. 30 healthy pregnant women who underwent prenatal examination in the same period were selected as the healthy group. Serum total cholesterol (TC), triglyceride (TG), high density lipoprotein cholesterol (HDL-C), low density lipoprotein cholesterol (LDL-C), lipoprotein-associated phospholipaseA2 (Lp-PLA2), C- reactive protein (CRP), interleukin -6 (IL-6), tumor necrosis factor-α (TNF-α) were measured. Receiver operating characteristic curve (ROC) was used to analyze the predictive value of blood lipid and serum inflammatory factors in pregnant women with HDP.ResultsThe levels of serum TC, TG and LDL-C increased with the progression of HDP, the level of serum HDL-C decreased with the progression of HDP (P

Accurate and timely monitoring of leaf nitrogen concentration (LNC) in rice is crucial to optimize nitrogen fertilizer management and reduce environmental pollution. Existing vegetation indices (VIs) often perform well for high canopy cover conditions, but their performance becomes poor at early growth stages due to the significant exposure of background materials and the induced spectral mixing effect. This study proposed a novel approach to estimate the LNC at early and middle growth stages of paddy rice by using abundance adjusted VIs (AAVIs) from unmanned aerial vehicle (UAV) multispectral imagery. An AAVI was constructed by combining the traditional VI and the rice abundant from linear spectral mixture analysis of UAV imagery. Subsequently, the performance of AAVIs was evaluated in comparison with traditional VIs derived from all pixels or green pixels for individual growth stages or multiple stages. The results demonstrated that AAVIs exhibited better performance in LNC estimation, regardless of individual stages or across the entire early season. Specially, AACIred-edge showed the best performance among the AAVIs evaluated for LNC estimation. For universal modeling across early stages, the combination of AACIred-edge and AAEVI yielded the highest accuracy (R2 = 0.78, RMSE = 0.26%, and rRMSE = 10.4%) performed remarkably better than the traditional VIs from all pixels or green pixels (R2

The synthetic aperture radar (SAR) interferometric coherence can complement optical data for the estimation of crop growth parameters, but it has not been yet investigated for predicting crop yield. Many studies have used machine-learning methods, such as neural networks, random forest, and Gaussian process regression, to estimate crop yield from remotely sensed data. However, their performance depends on the amount of available ground truth data. This study proposed Gaussian kernel regression for rice yield estimation from optical and SAR imagery using a limited amount of ground truth data. The main objective was to investigate the synergetic use of Sentinel-2 vegetation indices and Sentinel-1 interferometric coherence data through Gaussian kernel regression for estimating rice grain yield. The prediction accuracy was assessed using in situ measured yield data collected in 2019 and 2020 over Xinghua county in Jiangsu Province, China. In all cases, Gaussian kernel regression outperformed the probabilistic Gaussian regression and Bayesian linear inference. With the independently used optical and SAR data, a better prediction accuracy was achieved with the optical red edge difference vegetation index (RDVI1) (r2 = 0.65, RMSE = 0.61 t/ha) than with the interferometric coherence (r2 = 0.52 and RMSE = 0.79 t/ha).The highest prediction accuracy can be achieved by combining RDVI1 with interferometric coherence at the heading stage (r2 = 0.81 and RMSE = 0.55 t/ha). The results suggest the value of synergy between satellite interferometric coherence and optical indices for crop yield mapping with Gaussian kernel regression.

Abstract Background Liver fibrosis resulting from chronic liver injury is one of the major causes of mortality worldwide. Stem cell-secreted secretome has been evaluated for overcoming the limitations of cell-based therapy in hepatic disease, while maintaining its advantages. Methods In this study, we investigated the effect of human fetal skin-derived stem cell (hFSSC) secretome in the treatment of liver fibrosis. To determine the therapeutic potential of the hFSSC secretome in liver fibrosis, we established the CCl4-induced rat liver fibrosis model and administered hFSSC secretome in vivo. Moreover, we investigated the anti-fibrotic mechanism of hFSSC secretome in hepatic stellate cells (HSCs). Results Our results showed that hFSSC secretome effectively reduced collagen content in liver, improved the liver function and promoted liver regeneration. Interestingly, we also found that hFSSC secretome reduced liver fibrosis through suppressing the epithelial-mesenchymal transition (EMT) process. In addition, we found that hFSSC secretome inhibited the TGF-β1, Smad2, Smad3, and Collagen I expression, however, increased the Smad7 expression. Conclusions In conclusions, our results suggest that hFSSC secretome treatment could reduce CCl4-induced liver fibrosis via regulating the TGF-β/Smad signal pathway.

Abstract Background The tiller number per unit area is one of the main agronomic components in determining yield. A real-time assessment of this trait could contribute to monitoring the growth of wheat populations or as a primary phenotyping indicator for the screening of cultivars for crop breeding. However, determining tiller number has been conventionally dependent on tedious and labor-intensive manual counting. In this study, an automatic tiller-counting algorithm was developed to estimate the tiller density under field conditions based on terrestrial laser scanning (TLS) data. The novel algorithm, which is named ALHC, involves two steps: (1) the use of an adaptive layering (AL) algorithm for cluster segmentation and (2) the use of a hierarchical clustering (HC) algorithm for tiller detection among the clusters. Three field trials during the 2016–2018 wheat seasons were conducted to validate the algorithm with twenty different wheat cultivars, three nitrogen levels, and two planting densities at two ecological sites (Rugao & Xuzhou) in Jiangsu Province, China. Result The results demonstrated that the algorithm was promising across different cultivars, years, growth stages, planting densities, and ecological sites. The tests from Rugao and Xuzhou in 2016–2017 and Rugao in 2017–2018 showed that the algorithm estimated the tiller number of the wheat with regression coefficient (R2) values of 0.61, 0.56 and 0.65, respectively. In short, tiller counting with the ALHC generally underestimated the tiller number and performed better for the data with lower plant densities, compact plant types and the jointing stage, which were associated with overlap and noise between plants and inside the dense canopy. Conclusions Differing from the previous methods, the ALHC proposed in this paper made full use of 3D crop information and developed an automatic tiller counting method that is suitable for the field environment.

PurposeThe purpose of this paper is to aim mainly at social public decision-making problems, studies the corresponding relationship between different voting rule combinations and the final results, and discusses the quantitative relationships between group intelligence (final votes) and individual intelligence (everyone) to defend democracy under the circumstance of rapid development of network technology, and crowd intelligence becomes more complicated and universal.Design/methodology/approachAfter summarizing the crowd co-decisions of related studies, the standards, frameworks, techniques, methods and tools have been discussed according to the characteristics of large-scale simulations.FindingsThe contributions of this paper will be useful for both academics and practitioners for formulating VV&A in large-scale simulations.Originality/valueThis paper will help researchers solve the social public decision-making problems in large-scale simulations.

Abstract. To study the relationship between miR-148a and preeclampsia (PE), and clarify that miR-148a can regulate the endoplasmic reticulum stress (ERS) of placental trophoblasts by targeting the ERS protein X box binding protein 1 (XBP1). Fifty patients with hypertension during pregnancy, patients with mild PE, patients with severe PE, and normal pregnant women were selected, and their placental tissues were collected. RT-polymerase chain reaction was used to detect the expression of miR-148a in placental tissues, and Western blot was used to detect XBP1 in placental tissues. Compare the expression differences of miR-148a and XBP1 in each group, and analyze the correlation between the expressions of the two. Compared with the Neg-miR group, MTT experiment result in pre-miR-148a group was decreased. MTT experiment result in anti-miR-148a group was increased. Cell cycle test result in pre-miR-148a group [G1 (%)] was increased. Cell cycle test result in anti-miR-148a group [S (%)] was increased. Apoptosis test result in pre-miR-148a group [early apoptotic cells (%), late apoptotic cells (%)] was increased. Apoptosis test result in anti-miR-148a group [early apoptotic cells (%), late apoptotic cells (%)] was decreased. XBP1 expression result in pre-miR-148a group was increased. XBP1 expression result in anti-miR-148a group was decreased. Compared with the normal population, XBP1 is expressed in hypertension, mild eclampsia, severe eclampsia increased. GRP78, CHOP, and caspase-12 expression result in pre-miR-148a group was increased. GRP78, CHOP, and caspase-12 expression result in anti-miR-148a group was decreased. miR-148a can regulate the ERS-mediated apoptosis by targeting XBP1, thereby intervening in the occurrence and development of PE.

Plant potassium accumulation (PKA) plays an important role in evaluating the production capacity of crops. Remote sensing from unmanned aerial vehicles (UAVs) is increasingly used in precision agriculture, but relatively little is known about its use for determining the potassium (K) nutritional status of rice (Oryza sativa L.). This study compares the performance of different sets of spectral and textural indices derived from UAV data for estimating rice K nutritional status. A UAV equipped with three cameras (RGB, color-infrared (CIR) and multispectral cameras) was used to acquire imagery of rice canopies at different key growth stages. Immediately following the overflights, rice canopies were sampled for rice PKA. Regression models were then built to predict rice PKA with spectral and textural indices as predictor variables. Finally, stepwise multiple linear regression (SMLR) was used to determine if fusing spectral and textural indices significantly improved UAV-based PKA estimates. The renormalized difference vegetation index (RDVI) calculated from multispectral imagery proved to be the best predictor of rice PKA (R2 = 0.72, RMSE = 5.42 g m−2), though spectral vegetation indices calculated from RGB and CIR imagery also showed moderately strong prediction capability (R2 ≤ 0.56, RMSE ≥ 7.33 g m−2). Among the textural indices, the renormalized difference texture index [RDTI (MEA800, MEA680)] calculated by mean texture (MEA) of the multispectral imagery was the best predictor of rice PKA (R2 = 0.74, RMSE = 5.57 g m−2) while textural indices calculated from the RGB and CIR imagery showed only weak relationships with rice PKA (R2 ≤ 0.40, RMSE ≥ 8.11 g m−2). Fusing textural and spectral vegetation indices improved our ability to remotely sense PKA, with the SMLR model combination of RDTI (MEA800, MEA680) and spectral vegetation index DATT performing best (R2 increased by 11.11% to 0.80 and RMSE decreased by 7.5% to 5.15 g m−2). Our findings suggest that spectral and textural indices derived from UAV data allow for accurate mapping of PKA and that fusion of the two further improves the accuracy.

Timely and accurate prediction of crop yield prior to harvest is vital for precise agricultural management. Unmanned aerial vehicles (UAVs) provide a fast and convenient approach to crop yield prediction, but most existing crop yield models have rarely been tested across different years, cultivars and sensors. This has limited the ability of these yield models to be transferred to other years or regions or to be potentially used with data from other sensors. In this study, UAV-based multispectral imagery was used to predict rice grain yield at the booting and filling stages from four field experiments, involving three years, two rice cultivars, and two UAV sensors. Reflectance and texture features were extracted from the UAV imagery, and vegetation indices (VIs) and normalized difference texture indices (NDTIs) were computed. The models were independently validated to test the stability and transferability across years, rice cultivars, and sensors. The results showed that the red edge normalized difference texture index (RENDTI) was superior to other texture indices and vegetation indices for model regression with grain yield in most cases. However, the green normalized difference texture index (GNDTI) achieved the highest prediction accuracy in model validation across rice cultivars and sensors. The yield prediction model of Japonica rice achieved stronger transferability to Indica rice with root mean square error (RMSE), bias, and relative RMSE (RRMSE) of 1.16 t/ha, 0.08, and 11.04%, respectively. Model transferability was improved significantly between different sensors after band correction with a decrease of 15.05–59.99% in RRMSE. Random forest (RF) was found to be a good solution to improve the model transferability across different years and cultivars and obtained the highest prediction accuracy with RMSE, bias, and RRMSE of 0.94 t/ha, −0.21, and 9.37%, respectively. This study provides a valuable reference for crop yield prediction when existing models are transferred across different years, cultivars and sensors.

Ticagrelor is the first reversibly binding, direct-acting, oral P2Y12 receptor inhibitor. The contribution of UDP-glucuronosyltransferases (UGTs) enzymes to the metabolism of ticagrelor to its glucuronide conjugation, ticagrelor-O-glucuronide, in human liver microsomes (HLM) and human intestinal microsomes (HIM), was well characterized in the current study. The inhibition potential of human major UGTs by ticagrelor and ticagrelor-O-glucuronide was explored. The inhibitory effects of ticagrelor-O-glucuronide on cytochrome P450s (CYPs) enzymes were investigated as well. Ticagrelor glucuronidation exhibits substrate inhibition kinetics in both HLM and HIM with apparent Km values of 5.65 and 2.52 μM, Vmax values of 8.03 and 0.90 pmol min−1·mg protein−1, Ksi values of 1,343.0 and 292.9 respectively. The in vitro intrinsic clearances (Vmax/Km) for ticagrelor glucuronidation by HLM and HIM were 1.42 and 0.36 μl min−1·mg protein−1, respectively. Study with recombinant human UGTs suggested that multiple UGT isoforms including UGT1A9, UGT1A7, UGT1A3, UGT1A4, UGT1A1, UGT2B7 and UGT1A8 are involved in the conversion of ticagrelor to ticagrelor-O-glucuronide with UGT1A9 showing highest catalytic activity. The results were further supported by the inhibition studies on ticagrelor glucuronidation with typical UGT inhibitors in pooled HLM and HIM. Little or no inhibition of UGT1A1, UGT1A3, UGT1A4, UGT1A6, UGT1A9 and UGT2B7 by ticagrelor and ticagrelor-O-glucuronide was noted. Ticagrelor-O-glucuronide also exhibited limited inhibitory effects toward CYP2C8, CYP2D6 and CYP3A4. In contrast, ticagrelor-O-glucuronide weakly inhibited CYP2B6, CYP2C9 and CYP2C19 activity with apparent IC50 values of 45.0, 20.0 and 18.8 μM, respectively. The potential of ticagrelor-O-glucuronide to cause drug-drug interactions warrant further study.

This work explored potassium nutrient retrieval in wheat blades using reflectance spectra. Spectral data were collected from wheat blades at different growth stages, in different cultivars, and following different fertilisation treatments from 2016 to 2019 using a leaf clip and halogen bulb with an ASD spectrometer. Reflectance data from 350 to 2500 nm were collected, and data of 400 to 2400 nm were used in the retrieval. Using a leaf clip to measure the reflectance of a narrow blade can cause bias, which can be corrected using a normalisation method, i.e. the reflectance of each band was divided by the average reflectance of all bands. Three such methods were employed: vegetation index (VI), partial least squares (PLS), and random forest (RF). The approach yielded leaf potassium content (LKC, %) and leaf potassium per area (LKA, g/m2). The results showed that newly developed VIs outperformed previously published indices. The model using a modified ratio spectral index, mRSI(2275, 1875), yielded LKC with a coefficient of determination (R2) of 0.61 and a root mean square error (RMSE) of 0.57%. Normalisation methods can eliminate multiplicative error in blade spectra, thereby correcting the underestimated reflectance of narrow blades, and improving the accuracy of potassium retrieval models. Among the three methods, PLS achieved the highest accuracy. The retrieval of LKC and LKA based on normalised spectra and the PLS method yielded R2 values of 0.74 and 0.65, respectively, and their corresponding RMSE values were 0.46% and 0.21 g/m2. LKC retrieval models had higher R2 values than LKA models. This comprehensive analysis of different methods revealed the importance of reflectance at 1883 nm and 2305 nm. In conclusion, it is feasible to retrieve wheat leaf potassium levels using spectral data.

Accurate and timely acquisition of crop spatial distribution is a prerequisite for growth monitoring and yield forecasting. Currently, the automatic acquisition of crop distribution at large scales is still a challenge due to the time-consuming processing of remotely sensed imagery and manual collection of sufficient training samples. Although the advent of cloud computing platforms has proved to improve the efficiency and automation of crop type classification, how to obtain sufficient training samples in an efficient and cost-effective way remains unclear. In this research, we developed a new approach integrating the automatic generation of training samples and one-class machine learning classification (AGTOC) for mapping winter wheat over Jiangsu Province, China on Google Earth Engine (GEE). After extracting spatial objects from Sentinel-2 imagery in the season of 2017–2018, this method performed recognition of winter wheat objects based on the unique phenology and spectral features of winter wheat. Then the generated winter wheat objects were further refined and regarded as training samples for provincial winter wheat classification with one-class support vector machine (OCSVM). Furthermore, the transferability of AGTOC was evaluated by applying the classification approach to different seasons (2016–2017 & 2019–2020) and a different sensor (Landsat-8 OLI). According to independent ground truth data, the winter wheat mapping with AGTOC achieved an overall accuracy (OA) of 92.61%. When compared with agricultural census data, the winter wheat area accounted for 99% and 90% of the variability at the municipal and county levels. Furthermore, the OA achieved 88.94% and 90.17% while transferring the AGTOC from 2017–2018 to 2016–2017 and 2019–2020. The transferability of the AGTOC model to Landsat-8 OLI imagery of the same season yielded an OA of 85.98%. These results demonstrated that AGTOC exhibited high efficiency and accuracy across the province, different seasons and sensors without the need of extensive field visits for training sample collection. This proposed approach has great potential in the automatic mapping of winter wheat on GEE at country or global levels.

Satellite-based time-series crop monitoring at the subfield level is essential to the efficient implementation of precision crop management. Existing spatiotemporal image fusion techniques can be helpful, but they were often proposed to generate medium-resolution images. This study proposed a high-resolution spatiotemporal image fusion method (HISTIF) consisting of filtering for cross-scale spatial matching (FCSM) and multiplicative modulation of temporal change (MMTC). In FCSM, we considered both point spread function effect and geo-registration errors between fine and coarse resolution images. Subsequently, MMTC used pixel-based multiplicative factors to estimate the temporal change between reference and prediction dates without image classification. The performance of HISTIF was evaluated using both simulated and real datasets with one from real Gaofen-1 (GF-1) and simulated Landsat-like/Sentinel-like images, and the other from real GF-1 and real Landsat/Sentinel-2 data on two sites. HISTIF was compared with the existing methods spatial and temporal adaptive reflectance fusion model (STARFM), FSDAF, and Fit-FC. The results demonstrated that HISTIF produced substantial reduction in the fusion error from cross-scale spatial mismatch and accurate reconstruction in spatial details within fields, regardless of simulated or real data. The images predicted by STARFM exhibited pronounced blocky artifacts. While the images predicted by HISTIF and Fit-FC both showed clear within-field variability patterns, HISTIF was able to reduce the spectral distortion more significantly than Fit-FC. Furthermore, HISTIF exhibited the most stable performance across sensors. The findings suggest that HISTIF could be beneficial for the frequent and detailed monitoring of crop growth at the subfield level.

Abstract Background Mesenchymal stem cells (MSCs) are increasingly being applied as a therapy for liver fibrosis. Exosomes possess similar functions to their parent cells; however, they are safe and effective cell-free reagents with controllable and predictable outcomes. In this study, we investigated the therapeutic potential and underlying molecular mechanism for human bone mesenchymal stem cells-derived exosomes (hBM-MSCs-Ex) in the treatment of liver fibrosis. Methods We established an 8-week CCl4-induced rat liver fibrosis model, after which, we administered hBM-MSCs-Ex in vivo for 4 weeks. The resulting histopathology, liver function, and inflammatory cytokines were analyzed. In addition, we investigated the anti-fibrotic mechanism of hBM-MSCs-Ex in hepatic stellate cells (HSCs) and liver fibrosis tissue, by western blotting for the expression of Wnt/β-catenin signaling pathway-related genes. Results In vivo administration of hBM-MSCs-Ex effectively alleviated liver fibrosis, including a reduction in collagen accumulation, enhanced liver functionality, inhibition of inflammation, and increased hepatocyte regeneration. Moreover, based on measurement of the collagen area, Ishak fibrosis score, MDA levels, IL-1, and IL-6, the therapeutic effect of hBM-MSCs-Ex against liver fibrosis was significantly greater than that of hBM-MSCs. In addition, we found that hBM-MSCs-Ex inhibited the expression of Wnt/β-catenin pathway components (PPARγ, Wnt3a, Wnt10b, β-catenin, WISP1, Cyclin D1), α-SMA, and Collagen I, in both HSCs and liver fibrosis tissue. Conclusions These results suggest that hBM-MSCs-Ex treatment could ameliorate CCl4-induced liver fibrosis via inhibition of HSC activation through the Wnt/β-catenin pathway.

Abstract Background Aboveground biomass (AGB) is a widely used agronomic parameter for characterizing crop growth status and predicting grain yield. The rapid and accurate estimation of AGB in a non-destructive way is useful for making informed decisions on precision crop management. Previous studies have investigated vegetation indices (VIs) and canopy height metrics derived from Unmanned Aerial Vehicle (UAV) data to estimate the AGB of various crops. However, the input variables were derived either from one type of data or from different sensors on board UAVs. Whether the combination of VIs and canopy height metrics derived from a single low-cost UAV system can improve the AGB estimation accuracy remains unclear. This study used a low-cost UAV system to acquire imagery at 30 m flight altitude at critical growth stages of wheat in Rugao of eastern China. The experiments were conducted in 2016 and 2017 and involved 36 field plots representing variations in cultivar, nitrogen fertilization level and sowing density. We evaluated the performance of VIs, canopy height metrics and their combination for AGB estimation in wheat with the stepwise multiple linear regression (SMLR) and three types of machine learning algorithms (support vector regression, SVR; extreme learning machine, ELM; random forest, RF). Results Our results demonstrated that the combination of VIs and canopy height metrics improved the estimation accuracy for AGB of wheat over the use of VIs or canopy height metrics alone. Specifically, RF performed the best among the SMLR and three machine learning algorithms regardless of using all the original variables or selected variables by the SMLR. The best accuracy (R 2 = 0.78, RMSE = 1.34 t/ha, rRMSE = 28.98%) was obtained when applying RF to the combination of VIs and canopy height metrics. Conclusions Our findings implied that an inexpensive approach consisting of the RF algorithm and the combination of RGB imagery and point cloud data derived from a low-cost UAV system at the consumer-grade level can be used to improve the accuracy of AGB estimation and have potential in the practical applications in the rapid estimation of other growth parameters.

Surface thermal environment (STE) is closely related to the comfort and health of residents, affecting regional livability, and its spatial and temporal changes are deeply affected by the urbanization process. Considering there is a lack of effective comparative analysis on STE in different urbanized inhabited islands, the special geographical unit and vital human settlement environment, long-term spatiotemporal characteristics and impact factor quantitative analyses were performed in two inhabited islands via the RS and GIS methods. The results suggest that the surface heat amplitude of the highly urbanized Xiamen Island decreases, with the surface heat intensity continuing to increase from 2000 to 2020, while that of the lowly urbanized Kinmen Island is reversed. Although the land surface temperature (LST) of the two inhabited islands shows similar spatial distribution characteristics with evident cold/hot spots, the geographical distribution characteristics of high LST zones are significantly different, and the thermal landscape of Xiamen Island is more fragmented, discrete, and simple in shape, as revealed by the landscape metrics. We demonstrate that the area proportion between cooling land (water body and greenland) and warming land (bare land and impervious surface) is the most influential factor of LST in the two islands while the marine environment is a unique contributor to STE of inhabited islands compared with inland cities, where the seawater around the island can reduce LST over a range of distances, and the influence of elevation on LST is mostly indirect. These results provide a scientific basis and case support for understanding the STE situation of inhabited islands with different urbanization levels.

Tools for robust identification of crop diseases are crucial for timely intervention by farmers to minimize yield losses. Visual diagnosis of crop diseases is time-consuming and laborious, and has become increasingly unsuitable for the needs of modern agricultural production. Recently, deep convolutional neural networks (CNNs) have been used for crop disease diagnosis due to their rapidly improving accuracy in labeling images. However, previous CNN studies have mostly used images of single leaves photographed under controlled conditions, which limits operational field use. In addition, the wide variety of available CNNs and training options raises important questions regarding optimal methods of implementation of CNNs for disease diagnosis. Here, we present an assessment of seven typical CNNs (VGG-16, Inception-v3, ResNet-50, DenseNet-121, EfficentNet-B6, ShuffleNet-v2 and MobileNetV3) based on different training strategies for the identification of wheat main leaf diseases (powdery mildew, leaf rust and stripe rust) using field images. We developed a Field-based Wheat Diseases Images (FWDI) dataset of field-acquired images to supplement the public PlantVillage dataset of individual leaves imaged under controlled conditions. We found that a transfer-learning method employing retuning of all parameters produced the highest accuracy for all CNNs. Based on this training strategy, Inception-v3 achieved the highest identification accuracy of 92.5% on the test dataset. While lightweight CNN models (e.g., ShuffleNet-v2 and MobileNetV3) had shorter processing times (

In the context of global warming, how to measure summer climate suitability at a local scale is important for meteorological services. Considering meteorological and ecological conditions, body comfort, and the atmospheric environment, an assessment method for summer climate suitability for Zhejiang Province is proposed. In this paper, a summer suitable index (SSI) for Zhejiang is calculated, including four secondary indices: a summer cool index (SCI), a comfort days index (CDI), a good air days index (GADI) and a vegetation cover index (VCI). Using a local evaluation criterion, summer climate suitable areas are distinguished objectively according to the SSI. The results show that especially suitable regions account for 4.97% of Zhejiang Province, very suitable regions account for 22.2%, suitable regions account for 39.58%, and general regions account for 33.25%. The summer climate suitable areas are located mainly in high mountains and hills and coastal island areas while plain areas cannot be considered a suitable destination for summer tourism. By comparison and discussion, the SSI is demonstrated to capture summer climate suitability well. In contrast to a fixed evaluation index, benchmark values obtained for the SSI depend on the local climate and the index is straightforward to apply.

Every year, a large amount of tobacco waste liquid (TWL) is discharged into the environment, resulting in serious pollution for the environment. In this work, a TWL-based particle (OACT) was fabricated by CaO, attapulgite (ATP), and TWL, and, then, OACT was coated by amino silicon oil (ASO) to form OACT@ASO. Therein, OACT@ASO had high controlled-release ability for fulvic acid (FA), because of the nanonetworks structure for ATP and the high content of FA in TWL. The release ratio (RR) of FA from OACT@ASO reached 94% at 75 h in deionized water, and 23% at 32 d in silica sand. Furthermore, the release mechanism of FA from OACT@ASO was consistent with the First-order law. Additionally, OACT@ASO also possessed high immobilization capacity for Cu(II), Cd(II), and Pb(II) (CCP) in soil. Notably, a pot experiment indicated that OACT@ASO could facilitate the growth of pakchoi seedlings and decrease the absorption of CCP by pakchoi seedlings. Thus, this study provides a new kind of organic fertilizer which could not only release FA, but also immobilize CCP in soil.