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Metal Nanoclusters (MNCs) have shown great potential applications in the field of food safety due to their unique physical and chemical properties. Through the template synthesis method, the specific size, shape and dispersion of MNCs can be regulated, thereby improving their functional characteristics. Based on the recent studies on MNCs, the mechanism of template-assisted synthesis of MNCs and their applications in the detection of food contaminants, food packaging materials, antimicrobial agents and food additives are reviewed. It provides a scientific basis for template synthesis of MNCs, and to provide insights and directions for MNCs in food safety monitoring.

Abstract China, as the world’s biggest soybean importer and fourth-largest producer, needs accurate mapping of its planting areas for global food supply stability. The challenge lies in gathering and collating ground survey data for different crops. We proposed a spatiotemporal migration method leveraging vegetation indices’ temporal characteristics. This method uses a feature space of six integrals from the crops’ phenological curves and a concavity-convexity index to distinguish soybean and non-soybean samples in cropland. Using a limited number of actual samples and our method, we extracted features from optical time-series images throughout the soybean growing season. The cloud and rain-affected data were supplemented with SAR data. We then used the random forest algorithm for classification. Consequently, we developed the 10-meter resolution ChinaSoybean10 maps for the ten primary soybean-producing provinces from 2019 to 2022. The map showed an overall accuracy of about 93%, aligning significantly with the statistical yearbook data, confirming its reliability. This research aids soybean growth monitoring, yield estimation, strategy development, resource management, and food scarcity mitigation, and promotes sustainable agriculture.

Abstract Objective To investigate the effect of bone cement on the vertebral body and biomechanical properties in percutaneous cement discoplasty (PCD) for degenerative lumbar disc disease. Methods Three-dimensional reconstruction of L2 ~ L3 vertebral bodies was performed in a healthy volunteer, and the corresponding finite element model of the spine was established. Biomechanical analysis was performed on the changes in stress distribution in different groups of models by applying quantitative loads. Results Models with percutaneous discoplasty (PCD) showed improved stability under various stress conditions, and intervertebral foraminal heights were superior to models without discoplasty. Conclusion Cement discoplasty can improve the stability of the vertebral body to a certain extent and restore a certain height of the intervertebral foramen, which has a good development prospect and potential.

Timely and accurate crop yield estimation is crucial for managing crops, trade, and food security. The combination of remote sensing technology with machine learning methods is increasingly popular for global yield prediction. However, traditional machine learning methods rely on data correlation rather than causality, leading to poor interpretability. To address this issue, we propose a novel approach that combines a structural causal model (SCM) with deep learning to develop a causal graph attention network model (SCM-GAT) based on causal relationships for soybean yield prediction at the county level of 10 major soybean-producing states in the United States. The SCM-GAT model considers not only conventional vegetation indices and weather variables but also the causal relationships between variables as inputs. Using independent validation and five-fold cross-validation strategies, our results show that the predictive performance of the SCM-GAT model is not only superior to traditional prediction models, such as selection operator regression (LASSO), random forest, but also superior to the common deep learning models based on correlation relationships, such as long short-term memory network, and transformer. It has also proved to be more robust than other models under extreme weather events. In addition, we identify branching to blooming and blooming to setting pods as the key growth phases for soybean yield, and adding these phases as inputs to the model further improves prediction accuracy. Our findings suggest that incorporating causal relationships in deep learning models can improve prediction accuracy. This study provides a novel approach for remote sensing prediction of crop yield.

Selecting the appropriate unmanned aerial vehicle flight height is beneficial for increasing the monitoring efficiency. We firstly used an unmanned aerial vehicle to explore the scale effect on monitoring rice aboveground biomass. The results confirmed the feasibility of using vegetation indices and textures from hyperspectral images to improve the estimations at different spatial resolutions. The monitoring accuracy of combining vegetation indices and textures was the highest, and exhibited a decreasing trend as the spatial resolution decreased with the greatest accuracy appearing at 13 cm. Two new concepts were proposed: “appropriate monitoring scale domain” to define the range of spatial resolution where the monitoring accuracy was less affected by scale effect, and “appropriate monitoring scale threshold” to define the spatial resolution where accuracy dropped noticeably. The appropriate monitoring scale domains varied at different growth stages and the appropriate monitoring scale thresholds of using vegetation indices and textures were lower than those using textures: 39 cm, 52 cm, and 65 cm at the pre-heading, post-heading, and entire growth stages, respectively when using textures, and 52 cm, 65 cm, and 78 cm at the corresponding growth stages when combining vegetation indices and textures. In terms of aboveground biomass level, growth stage and error value, the relatively lower aboveground biomass levels, earlier growth stages of the multi-temporal models, and overestimations were more likely to yield notable accuracy changes when the spatial resolution converted to lower level on both sides of appropriate monitoring scale threshold. Vegetation indices containing red-edge or near-infrared bands were effective for estimation. Yellow/green band textures and vegetation indices containing green bands with near-infrared/red-edge bands also obtained inspiring performances. MEA was indispensable in estimation while more diverse textures were incorporated into the models of the entire growth stages and models established at lower spatial resolutions. These findings are essential for understanding the scale effect in estimating rice aboveground biomass, facilitating efficient monitoring at field scale.

Identifying the sex pheromone systems of local pest populations facilitates their management, especially for moth species that show significant geographic variation in sex pheromone communication. We investigated the pheromone production and behavioral responses of the Asian corn borer (Ostrinia furnacalis Guenée; ACB) in Xinjiang, China. The ACB produces three compounds: (Z)-12-tetradecenyl acetate (Z12-14:Ac) and (E)-12-tetradecenyl acetate (E12-14:Ac) which are two sex pheromone compounds, and n-tetradecyl acetate (14:Ac) which has variable roles in mediating behavioral responses. The ratios of these three compounds produced in female gland are geographically distinct among different populations. Quantitative analysis of pheromone production showed that the proportions of Z12-14:Ac in the E/Z isomers (i.e., Z and E12-14:Ac) and the proportions of 14:Ac in the ternary blend respectively averaged 60.46% (SD=5.26) and 25.00% (SD=7.37), with their probabilities normally or near-normally distributed. Trapping experiments in a cornfield indicated that deploying the E/Z isomers and the three compounds in rubber septa close to their gland ratios yielded the most captured males, while other ratios that deviated from the gland ratios showed reduced field captures. The ternary blend was significantly more attractive to males than the E/Z isomers in the field, indicating a functional role of 14:Ac as the third pheromone component used by the local population. Additionally, the dose-response test demonstrated that the application of the three compounds at dosages between 200 and 350 μg attracted significantly more males compared to other dosages. Therefore, the characterization of this local ACB pheromone system provides additional information about its geographic variation and serves as a basis for optimizing the pheromone-mediated control of this pest in Xinjiang.

High-sensitivity fluorescence monitoring has been widely used in agriculture and environmental science. However, the active fluorescence detection information of leaf segments mainly focuses on total chlorophyll, and the fluorescence information of chlorophyll a, chlorophyll b, and some other pigments has not been explored. This only considers the fluorescence spectrum characteristics at a single wavelength or the fluorescence integral from a range of wavelength regions and does not completely consider the linkage relation between the excitation, emission, and interference information. In this paper, the three-dimensional fluorescence spectrum, containing the excitation and emission fluorescence spectra, and the corresponding multiple pigment characteristics from the upgraded LOPEX_ZJU database were collected. The linkages of excitation and emission of the three-dimensional fluorescence spectra of these pigments were analyzed for the newly built multiple pigment 3-D fluorescence spectral indices (3-D FSIs), including those of chlorophyll a, chlorophyll b, carotenoid, anthocyanin, and flavonoid 3-D FSIs. Then, these pigment inversion models were established and validated. The results show that the 3-D FSIs performances for the photosynthetic pigment content inversion (including chlorophyll a and b, and carotenoids) were much better than those for the photo-protective pigments (including anthocyanins and flavonoids) from the 3-D fluorescence spectra of these plant leaves. Here, the 3-D fluorescence normalization index (FNI ((F430,690 − F430,763)/(F430,690 + F430,763))) for the chlorophyll a inversion model has a high accuracy, the RMSE is 2.96 μg/cm2, and the 3-D fluorescence reciprocal difference index (FRI (F650,704/F650,668) for the chlorophyll b model has an encouraging RMSE (2.01 μg/cm2). The RMSE of the 3-D fluorescence ratio index (FRI (F500,748/F500,717)) for the carotenoid inversion is 3.77 μg/cm2 RMSE. Only FRI (F370,615/F370,438) was selected for the modeling and validating evaluation of the leaf Flas content inversion, but the evaluation metrics were not good, with an RMSE (151.13 μg/cm2). For Ants, although there was a 3-D FSI (FRDI (1/F540,679 − 1/F540,557)), and its evaluation metrics, with an RMSE (2.8 μg/cm2), were at or over 0.05 level, the validating evaluation metric VC (98.3577%) was not encouraging. These results showed that fluorescence, as a nondestructive and efficient detection method, could determine the contents of chlorophyll a, chlorophyll b, and carotenoid in plant leaves, providing a new method to detect plant information. It can also provide a potential chance for the fluorescence images of fine photo-protective pigments, especially chlorophyll a and b, using the special active fluorescence excitation light source and a few fluorescence imaging channels from the optimal FSIs.

The study of the adsorption behavior of C, CO and Cl2 on the surface of ZrSiO4 is of great significance for the formulation of the technological parameters in the carbochlorination reaction process. Based on first principles, the adsorption structure, adsorption energy, Barder charge, differential charge density, partial density of states and energy barrier were calculated to research the adsorption and reaction mechanism of C and Cl2 on ZrSiO4 surfaces. The results indicated that when C, CO and Cl2 co-adsorbed on the surface of ZrSiO4, they interacted with surface atoms and the charge transfer occurred. The Cl2 molecules dissociated and formed Zr-Cl bonds, while C atoms formed C1=O1 bonds with O atoms. Compared with CO, the co-adsorption energy and reaction energy barrier of C and Cl2 are lower, and the higher the C content, the lower the adsorption energy and energy barrier, which is beneficial for promoting charge transfer and the dissociation of Cl2. The 110-2C-2Cl2 has the lowest adsorption energy and the highest reaction activity, with adsorption energy and energy barriers of −13.45 eV and 0.02 eV. The electrons released by C are 2.30 e, while the electrons accepted by Cl2 are 2.37 e.

Abstract Background Carotid vulnerable plaque is an important risk factor for stroke occurrence and recurrence. However, the relationship between risk parameters related to carotid vulnerable plaque (plaque size, echogenicity, intraplaque neovascularization, and plaque stiffness) and neurological outcome after ischemic stroke or TIA is unclear. This study investigates the value of multimodal ultrasound-based carotid plaque risk biomarkers to predict poor short-term functional outcome after ischemic stroke or TIA. Methods This study was a single-center, prospective, continuous, cohort study to observe the occurrence of adverse functional outcomes (mRS 2–6/3–6) 90 days after ischemic stroke or TIA in patients, where the exposure factors in this study were carotid plaque ultrasound risk biomarkers and the risk factors were sex, age, disease history, and medication history. Patients with ischemic stroke or TIA (mRS ≤3) whose ipsilateral internal carotid artery stenosis was ≥50% within 30 days were included. All patients underwent multimodal ultrasound at baseline, including conventional ultrasound, superb microvascular imaging (SMI), and shear wave elastography (SWE). Continuous variables were divided into four groups at interquartile spacing for inclusion in univariate and multifactorial analyses. After completion of a baseline ultrasound, all patients were followed up at 90 days after ultrasound, and patient modified neurological function scores (mRSs) were recorded. Multivariate Cox regression and ROC curves were used to assess the risk factors and predictive power for predicting poor neurological function. Results SMI revealed that 20 (30.8%) patients showed extensive neovascularization in the carotid plaque, and 45 (69.2%) patients showed limited neovascularization in the carotid plaque. SWE imaging showed that the mean carotid plaque stiffness was 51.49 ± 18.34 kPa (23.19–111.39 kPa). After a mean follow-up of 90 ± 14 days, a total of 21 (32.3%) patients had a mRS of 2–6, and a total of 10 (15.4%) patients had a mRS of 3–6. Cox regression analysis showed that the level of intraplaque neovascularization and plaque stiffness were independent risk factors for a mRS of 2–6, and the level of intraplaque neovascularization was an independent risk factor for a mRS of 3–6. After correcting for confounders, the HR of intraplaque neovascularization level and plaque stiffness predicting a mRS 2–6 was 3.06 (95% CI 1.05–12.59, P = 0.041) and 0.51 (95% CI 0.31–0.83, P = 0.007), respectively; the HR of intraplaque neovascularization level predicting a mRS 3–6 was 6.11 (95% CI 1.19–31.45, P = 0.031). For ROC curve analysis, the mRSs for intraplaque neovascularization level, plaque stiffness, and combined application to predict 90-day neurological outcome ranged from 2 to 6, with AUCs of 0.73 (95% CI 0.59–0.87), 0.76 (95% CI 0.64–0.89) and 0.85 (95% CI 0.76–0.95), respectively. The mRSs for the intraplaque neovascularization level to predict 90-day neurological outcome ranged from 3 to 6, with AUCs of 0.79 (95% CI 0.63–0.95). Conclusion Intraplaque neovascularization level and plaque stiffness may be associated with an increased risk of poor short-term functional outcome after stroke in patients with recent anterior circulation ischemic stroke due to carotid atherosclerosis. The combined application of multiple parameters has efficacy in predicting poor short-term functional outcome after stroke.

Abstract Background Moyamoya disease can lead to stroke with devastating consequences, it is necessary to find a non-invasive and effective approach to identify the occurrence of stroke. In this study, we aim to analyze the association between ultrasound parameters and ipsilateral cerebral hemisphere stroke in patients with moyamoya disease by logistic regression analysis. Methods In this retrospective case–control study, 88 patients with MMD (153 cerebral hemispheres) hospitalized in Beijing Tiantan Hospital, Capital Medical University from November 2020 to October 2021 were analyzed. According to the occurrence of stroke, the 153 cerebral hemispheres were divided into a stroke group and a non-stroke group. Clinical data and ultrasound parameters of the ipsilateral internal carotid artery, superficial temporal artery, maxillary artery, and posterior cerebral artery were recorded. The ultrasound parameters were divided into four groups according to interquartile range, and then they were compared between the stroke group and the non-stroke group. Those with significant differences were scored by multivariate logistic regression analysis. Results There were 75 cerebral hemispheres (49.0%) in the stroke group and 78 cerebral hemispheres (51.0%) in the non-stroke group. Logistic regression analysis showed that the internal diameter of the internal carotid artery, peak systolic velocity of the internal carotid artery and peak systolic velocity of the posterior cerebral artery were independently correlated factors for stroke in patients with MMD. The fourth quartile group of the above three ultrasound parameters was taken as the reference group, and the odds ratio of the first quartile group were 11.679 (95% CI 2.918–46.749, P = 0.001), 19.594 (95% CI 4.973–77.193, P

Growth monitoring of rice is of great significance to food security of human society. Rice flowering is an important growing stage for grain formation, and flower intensity is the dominant factor in determining rice yield. The estimation of flower intensity helps us to know the rice yield in advance. This research proposed a series of the flower index (FI) to monitor status of rice flowers by developing flower intensity estimation models using stepwise multiple linear regression (SMLR) and random forest (RF), and their performance was compared to the models developed by vegetation indices (VI) of some key growth stages. The FI that in types of normalization (NDFI), ratio (RFI) and differences (DFI) were tested. The involved FIs in the FI-based models were those in type of difference (DFI) that obtained by difference of reflectance before and after flowering (DR) and their first derivative (DR’). The FIs of ten consecutive days during the flowering were obtained and their correlations with flower intensity showed that FIs of the late flowering (the 8th day, 9th day and 10th day) were more significantly correlated to flower intensity than those at the early or mid-flowering, with the maximum correlation coefficient of 0.702 given by FIs in difference type formed by DR’. The accuracy assessment of flower intensity estimation models showed that FI-based models had the equivalent accuracies to VI-based models, especially for the SMLR model that based on four FIs, which had R2 = 0.707, MAPE = 10.54%, rRMSE = 11.39%, was comparable to the model that developed by three VIs of the booting, heading and jointing stages (R2 = 0.751, MAPE = 8.99%, rRMSE = 10.31%). The promising results of FIs in estimating flower intensity make the simple data acquisition possible and provide an alternative way to get information about rice yield.

Objective: To investigate whether mineralized collagen modified polymethyl methacrylate (MC-PMMA) bone cement impacts the implanted vertebral body and adjacent segments and the feasibility of biomechanical properties compared with common bone cement in the treatment of osteoporotic vertebral compression fractures (OVCF).Methods: A healthy volunteer was selected to perform a three-dimensional reconstruction of the T11-L1 vertebral body to establish the corresponding finite element model of the spine, and the changes in the stress distribution of different types of cement were biomechanically analyzed in groups by applying quantitative loads.Results: The stress distribution of the T11-L1 vertebral body was similar between the two bone types of cement under various stress conditions.Conclusion: Mineralized collagen modified bone cement had the advantages of promoting bone regeneration, good biocompatibility, good transformability, and coupling, and had support strength not inferior to common PMMA bone cement, indicating it has good development prospects and potential.

The digitalization of information is crucial for the upgrading of the bayberry digital agriculture industry, while the low-cost information detection sensing equipment for bayberry are a bottleneck for the digital development of the industry. The existing rapid and non-destructive detection devices for fruit acidity and sugar content mainly use near-infrared and mid-infrared spectral characteristic for detection. These devices use expensive InGaAs sensor, which are difficult to promote and apply in the bayberry digital industry. This study is based on the high-spectral range of 454–998 nm in bayberry fruit to study the mechanism of fruit sugar and acidity content detection and to develop a portable bayberry fruit sugar and acidity detection device using Si-sensor in order to achieve low-cost quality parameter detection of bayberry fruit. The research results show that: Based on the hyperspectral of bayberry fruit, the sensitive wavelength for sugar content inversion is 610 nm, and the inversion accuracy (RMSE) is 1.399Brix; the sensitive wavelength for pH inversion is 570 nm, and the inversion accuracy (RMSE) is 0.1329. Based on the above spectroscopic detection mechanism and spectral dimension reduction methods, combined with low-cost Si-sensor (400–1000 nm), a low-cost non-destructive portable bayberry fruit sugar and acidity detection device has been developed, with detection accuracies of 94.74% and 97.14%, respectively. This bayberry fruit sugar and acidity detector provides a low-cost portable non-destructive quality detection instrument of bayberry, which is in line with the industrial group of low consumption in which the bayberry is mainly cultivated on a small scale, accelerating the digitalization process of the bayberry industry.

IntroductionGreen sea turtles are endangered marine reptiles. Carapacial ulcers will develop on juvenile green sea turtles during artificial rescue, seriously affecting their health and potentially leading to death.MethodsTo determine the pathogens causing ulcerative carapacial disease, we performed 16S and ITS high-throughput sequencing, and microbial diversity analysis on samples from carapacial ulcers, healthy carapaces, feces, and seawater of juvenile green sea turtles.ResultsOur analysis showed that changes in microbial diversity of green sea turtle feces and seawater were not significantly associated with ulcerative carapacial disease.DiscussionPsychrobacter sp. is the dominant species in the carapacial ulcers of green sea turtles. The bacterium is present in both healthy turtles and seawater where carapacial ulcers did not occur and decreasing seawater temperatures are likely responsible for the infection of juvenile green turtles with Psychrobacter sp. This is the first study on carapacial ulcers in captive juvenile green sea turtles. Our research provides theoretical guidance for the prevention and control of carapacial ulcers in captive juvenile green sea turtles.

Amylose content (AC) is an important indicator for rice quality grading. The rapid development of unmanned aerial vehicle (UAV) technology provides rich spectral and spatial information on observed objects, making non-destructive monitoring of crop quality possible. To test the potential of UAV-based hyperspectral images in AC estimation, in this study, observations on five rice cultivars were carried out in eastern China (Zhejiang province) for four consecutive years (from 2017 to 2020). The correlations between spectral and textural variables of UAV-based hyperspectral images at different growth stages (booting, heading, filling, and ripening) and AC (%) were analyzed, and the linear regression models based on spectral variables alone, textural variables alone, and combined spectral and textural variables were established. The results showed that the sensitive bands (P< 0.001) to AC were mainly centered in the green (536∽568 nm) and red regions (630∽660nm), with spectral and textural variables at the ripening stage giving the highest negative correlation coefficient of -0.868 and -0.824, respectively. Models based on combined spectral and textural variables give better estimation than those based on spectral or textural variables alone, characterized by less variables and higher accuracy. The best models using spectral or textural variables alone both involved three growth stages (heading, filling, and ripening), with root mean square error (RMSE) of 1.01% and 1.04%, respectively, while the models based on combined spectral and textural variables have RMSE of 1.04% 0.844% with only one (ripening stage) or two (ripening and filling stages) growth stages involved. The combination of spectral and textural variables of UAV-based hyperspectral images is expected to simplify data acquisition and enhance estimation accuracy in remote sensing of rice AC.

Infrared thermography is a widely utilized nondestructive testing technique in the field of artwork inspection. However, raw thermograms often suffer from problems, such as limited quantity and high background noise, due to limitations inherent in the acquisition equipment and experimental environment. To overcome these challenges, there is a growing interest in developing thermographic data enhancement methods. In this study, a defect inspection method for artwork based on principal component analysis is proposed, incorporating two distinct deep learning approaches for thermographic data enhancement: spectral normalized generative adversarial network (SNGAN) and convolutional autoencoder (CAE). The SNGAN strategy focuses on augmenting the thermal images, while the CAE strategy emphasizes enhancing their quality. Subsequently, principal component thermography (PCT) is employed to analyze the processed data and improve the detectability of defects. Comparing the results to using PCT alone, the integration of the SNGAN strategy led to a 1.08% enhancement in the signal-to-noise ratio, while the utilization of the CAE strategy resulted in an 8.73% improvement.

Several machine learning regression models have been advanced for the estimation of crop biophysical parameters with optical satellite imagery. However, literature on the comparative performances of such models is still limited in range and scope, especially under multiple data sources, despite the potential of multi-source imagery to improving crop monitoring in cloudy areas. To fill in this knowledge gap, this study explored the synergistic use of Landsat-8, Sentinel-2A, China’s environment and disaster monitoring and forecasting satellites (HJ-1 A and B) and Gaofen-1 (GF-1) data to evaluate four machine learning regression models that include Random Forest (RF), Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), and Gradient Boosting Decision Tree (GBDT), for rice dry biomass estimation and mapping. Taking a major rice cultivation area in southeast China as case study during the 2016 and 2017 growing seasons, a cross-calibrated time series of the Enhanced Vegetation Index (EVI) was obtained from the quad-source optical imagery and on which the aforementioned models were applied, respectively. Results indicate that in the before rice heading scenario, the most accurate dry biomass estimates were obtained by the GBDT model (R2 of 0.82 and RMSE of 191.8 g/m2) followed by the RF model (R2 of 0.79 and RMSE of 197.8 g/m2). After heading, the k-NN model performed best (R2 of 0.43 and RMSE of 452.1 g/m2) followed by the RF model (R2 of 0.42 and RMSE of 464.7 g/m2). Whist the k-NN model performed least in the before heading scenario, SVM performed least in the after heading scenario. These findings may suggest that machine learning regression models based on an ensemble of decision trees (RF and GBDT) are more suitable for the estimation of rice dry biomass, at least with optical satellite imagery. Studies that would extend the evaluation of these machine learning models, to other parameters like leaf area index, and to microwave imagery, are hereby recommended.

Paddy rice is one of the most widely planted crops in Asia. Yield estimation of paddy rice is crucial to food security. Gross primary productivity or net primary productivity (NPP) models are some of the most commonly used methods for crop yield estimation as they have a theoretical basis and are simple to use. The harvest index (HI) of paddy rice, one of the input parameters in yield estimation models, has been increasingly used with the improvement of paddy rice cultivars over the past four decades. However, in many previous researches, this parameter was arbitrarily determined to fit the observed yield data values, without considering its changing trend. In the present study, we developed an NPP-based rice yield estimation model with HI being a changing parameter. The accuracy of the proposed method is tested over the Jiangsu Province, Southeast China. The results showed that the rice HI increases linearly in study area. Compared with yield estimation with a fixed HI, yield estimations using a changing HI are greatly improved, with the average estimation accuracy greater than 96%, and the relative errors within ±5%. The results proved that the NPP-based yield estimation model with a changing HI can be a promising alternative for rice yield estimation.

The status of crop growth under the influence of COVID-19 is an important information for evaluating the current food security in China. This article used the cloud computing platform of Google Earth Engine, to access and analyze Sentinel-2, MODIS, and other multisource remote sensing data in the last five years to monitor the growth of crops in China, especially in Hubei province, during the period of the rapid spread of COVID-19 (i.e., from late January to mid-March 2020), and compared with the growth over the same period under similar climate conditions in the past four years. We further analyzed the indirect effects of COVID-19 on crop growth. The results showed that: the area of the crops with better growth (51%) was much more than that with worse growth (22%); the crops with better and worse growth were mainly distributed in the North China Plain (the main planting areas of winter wheat in China) and the South China regions (such as Guangxi, Guangdong province), respectively. The area of the crops with a similar growth occupied 27%. In Hubei province, the area of the crops with better growth (61%) was also more than that with worse growth (27%). It was found that there was no obvious effect from COVID-19 on the overall growth of crops in China during the period from late January to mid-March 2020 and the growth of crops was much better than that during the same period in previous years. The findings in this study are helpful in evaluating the impact of the COVID-19 on China's agriculture, which are conducive to serve the relevant agricultural policy formulation and to ensure food security.

At present, spring tea yield is mainly estimated through a manual sampling survey. Obtaining yield information is time consuming and laborious for the whole spring tea industry, especially at the regional scale. Remote sensing yield estimation is a popular method used in large-scale grain crop fields, and few studies on the estimation of spring tea yield from remote sensing data have been reported. This is a similar spectrum of fresh tea yield components to that of the tea tree canopy. In this study, two types of unmanned aerial vehicle (UAV) hyperspectral images from the unpicked and picked Anji white tea tree canopies are collected, and research on the estimation of the spring tea fresh yield is performed using the differences identified in the single and combined chlorophyll spectral indices (CSIs) or leaf area spectral indices (LASIs) while also considering the changes in the green coverage of the tea tree canopy by way of a linear or piecewise linear function. The results are as follows: (1) in the linear model with a single index variable (LMSV), the accuracy of spring tea fresh yield models based on the selected CSIs was better than that based on the selected LASIs as a whole, in which the model based on the curvature index (CUR) was the best with regard to the accuracy metrics; (2) compared to the LMSVs, the accuracy performance of the piecewise linear model with the same index variables (PLMSVs) was obviously improved, with an encouraging root mean square error (RMSE) and validation determination coefficient (VR2); and (3) in the piecewise model with the combined index variables (PLMCVs), its evaluation metrics are also improved, in which the best performance of them was the CUR&CUR model with a RMSE (124.602 g) and VR2 (0.625). It showed that the use of PLMSVs or PLMCVs for fresh tea yield estimation could reduce the vegetation index saturation of the tea tree canopy. These results show that the spectral difference discovered through hyperspectral remote sensing can provide the potential capability of estimating the fresh yield of spring tea on a large scale.

Tomato produces a number of terpenes in their glandular trichomes that contribute to host plant resistance against pests. While glandular trichomes of cultivated tomato Solanum lycopersicum primarily accumulate a blend of monoterpenes, those of the wild tomato species Solanum habrochaites produce various sesquiterpenes. Recently, we have identified two groups of sesquiterpenes in S. habrochaites accessions that negatively affect the performance and choice behavior of the potato aphid (Macrosiphum euphorbiae). Aphids are piercing-sucking herbivores that use their mouthpart to penetrate and probe plant tissues in order to ultimately access vascular tissue and ingest phloem sap. Because secondary metabolites produced in glandular trichomes can affect the initial steps of the aphid feeding behavior, introducing the formation of defensive terpenes into additional plant tissues via metabolic engineering has the potential to reduce tissue penetration by aphids and in consequence virus transmission. Here, we have developed two multicistronic expression constructs based on the two sesquiterpene traits with activity toward M. euphorbiae previously identified in S. habrochaites. Both constructs are composed of sequences encoding a prenyl transferase and a respective S. habrochaites terpene synthase, as well as enhanced green fluorescent protein as a visible marker. All three coding sequences were linked by short nucleotide sequences encoding the foot-and-mouth disease virus 2A self-processing oligopeptide which allows their co-expression under the control of one promoter. Transient expression of both constructs under the epidermis-specific Arabidopsis CER5-promoter in tomato leaves demonstrated that formation of the two sets of defensive sesquiterpenes, β-caryophyllene/α-humulene and (−)-endo-α-bergamotene/(+)-α-santalene/(+)-endo-β-bergamotene, can be introduced into new tissues in tomato. The epidermis-specific transgene expression and terpene formation were verified by fluorescence microscopy and tissue fractionation with subsequent analysis of terpene profiles, respectively. In addition, the longevity and fecundity of M. euphorbiae feeding on these engineered tomato leaves were significantly reduced, demonstrating the efficacy of this novel aphid control strategy.

Pincer ligand supported RuII chloride complexes may be used for acetylene hydrochlorination as non-mercury molecular catalysts. Based on theoretical calculations, the catalytic mechanism and the interaction between catalysts and reactants has been evaluated, indicating that the (pincer)RuIICl2 platform supports electrophilic proton-ruthenation of C2H2. Energy decomposition studies further illustrate the electron-rich property of the RuII center, which can increase the negative charge of C2H2 via 4d-electron backdonation. Thus, the electrophilic reaction mechanism is favored due to lower energetic barriers. By improving the electron-donating ability of ligands, this lowering of energetic barriers can be enhanced. Therefore, non-mercury catalysts for acetylene hydrochlorination with milder reaction conditions and higher catalytic activity can be designed.

Infrared thermography techniques with thermographic data analysis have been widely applied to non-destructive tests and evaluations of subsurface defects in practical composite materials. However, the performance of these methods is still restricted by limited informative images and difficulties in feature extraction caused by inhomogeneous backgrounds and noise. In this work, a novel generative manifold learning thermography (GMLT) is proposed for defect detection and the evaluation of composites. Specifically, the spectral normalized generative adversarial networks serve as an image augmentation strategy to learn the thermal image distribution, thereby generating virtual images to enrich the dataset. Subsequently, the manifold learning method is employed for the unsupervised dimensionality reduction in all images. Finally, the partial least squares regression is presented to extract the explicit mapping of manifold learning for defect visualization. Moreover, probability density maps and quantitative metrics are proposed to evaluate and explain the obtained defect detection performance. Experimental results on carbon fiber-reinforced polymers demonstrate the superiorities of GMLT, compared with other methods.

The speedy development of UAV (Unmanned Aerial Vehicle) has provided more data choices for crop yield estimation. In most cases, spectral information derived from multispectral or hyperspectral images is used alone for yield estimation, while variables that contain spatial information such as textural measures were not considered. As UAV can acquire images with spatial resolution at centimeter-level, which contain rich spatial information of observed objects, the test of textural measures in improving yield estimation becomes possible. In this study, UAV-based hyperspectral images of rice in the east of China were acquired in 2017, 2018 and 2019 three consecutive year during rice growing seasons. Eight gray level co-occurrence matrix (GLCM) based texture measures were derived, and all two band combinations of these textural measures as well as spectral reflectance in three types (normalized type, differential type and simple ratio type) were calculated to developing textural and spectral index. Grain yield estimation models were established using spectral indices alone and spectral indices combining textural measures, respectively. The results show that models based on spectral indices combining textural measures all performed better than these using spectral indices alone, with coefficient of determination R2 of the best model greater than 0.8, RMSE (Root Mean Square Error) of 0.421 Mg ha−1 and MAPE (Mean Absolute Percentage Error) of 4.66% for calibration results, and 0.521 Mg ha−1 and 6.63% for validation results. Meanwhile, spectral indices combining textural measures can adjust the problems of overestimation and underestimation in models developed with spectral indices alone. This kind of improvement is especially obvious when grain yield is below 5.0 Mg ha−1 or above 8.0 Mg ha−1. Besides, the estimation accuracy of the model based on spectral index combining textural measure at the booting stage was equivalent to the model based on spectral indices at the booting and heading stages, indicating that textural measures may be helpful in monitoring grain yield at an earlier stage.

Over the last decade, the Allegheny National Forest (ANF) in the USA has experienced issues with the regeneration of black cherry (Prunus serotina). This study was conducted to investigate the effects of silvicultural treatment on the insect communities that may affect black cherry pollination and regeneration. We conducted a 2-year study to compare the abundance, richness, and diversity of insects in unmanaged, shelterwood seed-tree, and shelterwood clear-cut stands. Using pan traps, we sampled insects at the ground level and in the canopies of flowering mature black cherry trees. The results of this study showed significant increases in the abundance of insects captured in shelterwood seed-tree stands and in species richness and diversity of insects captured in the canopy of black cherry in shelterwood removal stands, indicating that silvicultural treatment affected the insect community significantly. The dominant insect order was Diptera (true flies, 72.91%, n = 12,668), and Anthalia bulbosa (Diptera: Hybotidae) was the dominant species comprising 33% of all insects found in the canopy of flowering black cherry. The findings in this study could help land managers in managing black cherry for its pollination and natural regeneration.

Optical satellite images are prone to cloud contamination in the tropics and subtropics especially during the monsoon season which is contemporaneous with rice cultivation. Producing rice field maps in such areas using a single optical satellite source is therefore very challenging. To obtain an adequate number of usable optical images that captures the seasonal profile of rice fields for their discrimination, this study explored an operational approach to combining quad-source optical satellite data that include Landsat-8, Sentinel-2A, China’s environment and disaster monitoring and forecasting satellite constellation (HJ-1 A and B), and Gaofen-1 over a test site located in southeast China at two rice growing seasons (2016 and 2017). To minimise inter-sensor differences, a spectral index (SI) dataset containing cross-calibrated Enhanced Vegetation Index (EVI) images from all optical sensors and Modified Normalised Difference Water Index (MNDWI) images from Landsat-8 and Sentinel-2, was derived. As more accurate rice maps have been obtained with the synergistic use of optical and microwave data, the aforementioned datasets were combined with the vertical transmitted and horizontal received (VH) and vertical transmitted and vertical received (VV) polarisation images of Sentinel-1A. The resultant optical, microwave, and combinations of optical and microwave data were used as inputs to the Support Vector Machine (SVM) and Random Forest (RF) algorithms available in ENVI 5.3 and ENMAP Box 2.0, respectively to discriminate rice fields from other land-cover types. Results showed that Sentinel-1A data produced higher mapping accuracies than the quad-source optical datasets. This is attributed to the higher number of Sentinel-1A images at the early stages of rice growth (vegetative phase), a period where changes in rice satellite signals are most abrupt and therefore most diagnostic for discriminating the rice crop from other land-cover types. Additionally, combinations of optical and microwave data produced higher overall accuracies than when used separately, and the highest overall accuracies for both years were achieved with the application of RF on a combination of VH, VV and SI (VHVVSI), yielding 98.43% in 2016 and 96.73% in 2017. Moreover, combining VH with SI (VHSI) produced overall mapping accuracies that are more equivalent to the above (98.40% in 2016 and 96.53% in 2017) than with the combination of VV and SI (VVSI) which yielded 97.22% in 2016 and 93.92% in 2017. This indicates that VH is not only more complementary to optical satellite imagery in rice discrimination, but its combination with SI alone can produce rice distribution maps of the highest possible accuracies in cloud-prone environments.

Abstract Background Acupuncture is effective in functional constipation (FC) treatment, but the central mechanism has not been well investigated. This trial will combine functional magnetic resonance imaging (fMRI) and positron emission tomography-computed tomography (PET-CT) to investigate the potential central mechanism of acupuncture treatment for FC. Methods This is a multimodal neuroimaging randomized controlled trial. In total, 140 FC patients will be randomly allocated into four groups: the verum acupuncture group; the sham acupuncture group; the PEG 4000 group; and the waiting-list group. This trial will include a two-week baseline period and a two-week treatment period. Patients will receive 10 sessions of acupuncture, sham acupuncture, PEG 4000, or no intervention during the treatment period. The stool diary, Cleveland Constipation Score (CCS), Patient Assessment of Constipation Symptom (PAC-SYM), and Patient Assessment of Constipation Quality of Life Questionnaire (PAC-QoL) will be used to assess the clinical efficacy of different interventions. The MRI and PET-CT scans will be performed to detect cerebral functional changes in 15 patients in each group at baseline and at the end of treatment/waiting. Multimodal imaging data will be associated with clinical data to investigate possible correlation between brain activity changes elicited by different interventions and symptoms improvement. Discussion We hypothesize that acupuncture can treat FC through normalizing the pathological alteration of the cerebral activity. The results of this trial will allow us to re-testify the therapeutic effects of acupuncture treating for FC and to investigate the potential central mechanism of acupuncture treatment for FC from direct (cerebral glucose metabolism) and indirect (contrast of oxyhemoglobin and deoxyhemoglobin) approaches. Trial registration Chinese Clinical Trial Registry, ChiCTR1800016658. Registered on 14 June 2018.

Rice is one of the most important food crops around the world. Remote sensing technology, as an effective and rapidly developing method, has been widely applied to precise rice management. To observe the current research status in the field of rice remote sensing (RRS), a bibliometric analysis was carried out based on 2680 papers of RRS published during 1980–2021, which were collected from the core collection of the Web of Science database. Quantitative analysis of the number of publications, top countries and institutions, popular keywords, etc. was conducted through the knowledge mapping software CiteSpace, and comprehensive discussions were carried out from the aspects of specific research objects, methods, spectral variables, and sensor platforms. The results revealed that an increasing number of countries and institutions have conducted research on RRS and a great number of articles have been published annually, among which, China, the United States of America, and Japan were the top three and the Chinese Academy of Sciences, Zhejiang University, and Nanjing Agricultural University were the first three research institutions with the largest publications. Abundant interest was paid to “reflectance”, followed by “vegetation index” and “yield” and the specific objects mainly focused on growth, yield, area, stress, and quality. From the perspective of spectral variables, reflectance, vegetation index, and back-scattering coefficient appeared the most frequently in the frontiers. In addition to satellite remote sensing data and empirical models, unmanned air vehicle (UAV) platforms and artificial intelligence models have gradually become hot topics. This study enriches the readers’ understanding and highlights the potential future research directions in RRS.

The phenology indicator of vegetation green-up dates (GUD) is prone to being affected by changes in temperature. However, the influencing degree of urbanization-induced temperature warming on vegetation GUDs among different vegetation species along the urban-rural gradient remains inadequately described. In this study, based on the long-term (2001–2019) satellite-derived vegetation GUDs and nighttime land surface temperature (LST) of forests, grasslands, and croplands along the urban-rural gradient with Beijing (China) as a case study area, the responses of vegetation GUDs to temperature changes were quantitatively analyzed, taking into account the vegetation types and distances away from the urban domain. The results show that (1) long-term GUDs and LST are significantly negatively correlated, characterized by a weaker significant correlation near the urban area when compared with its surrounding areas, with the greatest absolute linear correlation coefficients (r) happening at rings 32 km (rmax = −0.93, forests), 20 km and 48 km (rmax = −0.83, grasslands), and 34 km (rmax = −0.82, croplands), respectively; (2) the magnitude of change in GUDs over the past 19 year (2001–2019) are significantly positively correlated with these in LST near the urban area, demonstrating a distance-decay trend, with the greatest advance in GUDs occurring at the ring nearest the urban area, by about 20 days (forests), 24.5 days (grasslands), and 15.6 days (croplands), respectively; (3) the spatial pattern of the response rate of GUDs change to LST change (days K−1) also showed a declining trend with distance, with GUD advanced by 6.8 days K−1 (forests), 7.5 days K−1 (grasslands), and 4.9 days K−1 (croplands) at the closest ring to the urban, decreasing to about 2.3 days K−1 (48 km), 4.1 days K−1 (18 km), and 1 day K−1 (18 km), respectively, indicating a notable influence of temperature warming on vegetation GUDs near the urban domains.

Abstract There is an urgent need for enhancing climate change resilience in rice production. Here, we report a reflective algal mulching‐based culture practice to attain a simple and low‐cost biological method for enhancement of climate change resilience. The dense mulching of Rhizoclonium sp. in field water highly reflected sunlight in the visible and infrared regions, thus significantly decreasing the root zone temperature, and significantly and simultaneously mitigated CO2, CH4, and N2O emissions through gas retention and removal in the field water layer, as well as reducing gas production. The algae could also enhance rice growth and soil fertility.

The accurate and rapid estimation of the aboveground biomass (AGB) of rice is crucial to food security. Unmanned aerial vehicles (UAVs) mounted with hyperspectral sensors can obtain images of high spectral and spatial resolution in a quick and effective manner. Integrating UAV-based spatial and spectral information has substantial potential for improving crop AGB estimation. Hyperspectral remote-sensing data with more continuous reflectance information on ground objects provide more possibilities for band selection. The use of band selection for the spectral vegetation index (VI) has been discussed in many studies, but few studies have paid attention to the band selection of texture features in rice AGB estimation. In this study, UAV-based hyperspectral images of four rice varieties in five nitrogen treatments (N0, N1, N2, N3, and N4) were obtained. First, multiple spectral bands were used to identify the optimal bands of the spectral vegetation indices, as well as the texture features; next, the vegetation index model (VI model), the vegetation index combined with the corresponding-band textures model (VI+CBT model), and the vegetation index combined with the full-band textures model (VI+FBT model) were established to compare their respective rice AGB estimation abilities. The results showed that the optimal bands of the spectral and textural information for AGB monitoring were inconsistent. The red-edge and near-infrared bands demonstrated a strong correlation with the rice AGB in the spectral dimension, while the green and red bands exhibited a high correlation with the rice AGB in the spatial dimension. The ranking of the monitoring accuracies of the three models, from highest to lowest, was: the VI+FBT model, then the VI+CBT model, and then the VI model. Compared with the VI model, the R2 of the VI+FBT model and the VI+CBT model increased by 1.319% and 9.763%, respectively. The RMSE decreased by 2.070% and 16.718%, respectively, while the rRMSE decreased by 2.166% and 16.606%, respectively. The results indicated that the integration of vegetation indices and textures can significantly improve the accuracy of rice AGB estimation. The full-band textures contained richer information that was highly related to rice AGB. The VI model at the tillering stage presented the greatest sensitivity to the integration of textures, and the models in the N3 treatment (1.5 times the normal nitrogen level) gave the best AGB estimation compared with the other nitrogen treatments. This research proposes a reliable modeling framework for monitoring rice AGB and provides scientific support for rice-field management.

Currently, analyses related the status of soybeans, a major oil crop, as well as the related climate drivers, are based on on-site data and are generally focused on a particular country or region. This study used remote sensing, meteorological, and statistical data products to analyze spatiotemporal variations at the end of the growing season (EOS) for soybeans in the world’s major soybean-growing areas. The ridge regression estimation model calculates the average annual temperature, precipitation, and total radiation contributions to phenological changes. A systematic analysis of the spatiotemporal changes in the EOS and the associated climate drivers since the beginning of the 21st century shows the following: (1) in India, soybean EOS is later than in China and the United States. The main soybean-growing areas in the southern hemisphere are concentrated in South America, where two crops are planted yearly. (2) In most of the world’s soybean-growing regions, the rate change of the EOS is ±2 days/year. In the Mississippi River Valley, India, and South America (the first quarter), the soybean EOS is generally occurring earlier, whereas, in northeast China, it is generally occurring later. (3) The relative contributions of different meteorological factors to the soybean EOS vary between soybean-growing areas; there are also differences within the individual areas. This study provides a solid foundation for understanding the spatiotemporal changes in soybean crops in the world’s major soybean-growing areas and spatiotemporal variations in the effects of climate change on soybean EOS.

The PROSPECT leaf optical radiative transfer models, including PROSPECT-MP, have addressed the contributions of multiple photosynthetic pigments (chlorophyll a and b, and carotenoids) to leaf optical properties, but photo-protective pigment (anthocyanins), another important indicator of vegetation physiological and ecological functions, has not been simultaneously combined within a leaf optical model. Here, we present a new calibration and validation of PROSPECT-MP+ that separates the contributions of multiple photosynthetic and photo-protective pigments to leaf spectrum in the 400–800 nm range using a new empirical dataset that contains multiple photosynthetic and photo-protective pigments (LOPEX_ZJU dataset). We first provide multiple distinct in vivo individual photosynthetic and photo-protective pigment absorption coefficients and leaf average refractive index of the leaf interior using the LOPEX_ZJU dataset. Then, we evaluate the capabilities of PROSPECT-MP+ for forward modelling of leaf directional hemispherical reflectance and transmittance spectra and for retrieval of pigment concentrations by model inversion. The main result of this study is that the absorption coefficients of chlorophyll a and b, carotenoids, and anthocyanins display the physical principles of absorption spectra. Moreover, the validation result of this study demonstrates the potential of PROSPECT-MP+ for improving capabilities in remote sensing of leaf photosynthetic pigments (chlorophyll a and b, and carotenoids) and photo-protective pigment (anthocyanins).

Black cherry is an ecologically important high-value wood. A decline of its regeneration has been reported in the USA, which could be associated with a lack of pollination. This study was conducted to identify insects visiting black cherry flowers, to determine whether insects captured on the flowers carry black cherry pollen and to identify the volatile organic compounds (VOCs) emitted by flowers of black cherry. A two-year insect survey was conducted before, during and after the black cherry bloom. A total of 9533 insects were captured in traps and Diptera was the most abundant (64.1%). Significantly more insects in Diptera, Lepidoptera and Thysanoptera were captured in the traps installed in the canopy than those on the ground, and Anthalia bulbosa (Diptera: Hybotidae) was the dominant species. Electron microscopy analyses demonstrated that insects captured in the canopy indeed carried black cherry pollen. Black cherry flowers emitted a VOC blend that is composed of 34 compounds and dominated by β-ocimene and several phenylpropanoids/benzenoids. This floral VOC profile is similar to that of other pollinator-dependent Prunus species. This study reports pollinator insects and associated VOCs, for the first time, that could play a significant role in the pollination and regeneration of black cherry.

Rice floret number per unit area as one of the key yield structure parameters is directly related to the final yield of rice. Previous studies paid little attention to the effect of the variations in vegetation indices (VIs) caused by rice flowering on rice yield estimation. Unmanned aerial vehicles (UAV) equipped with hyperspectral cameras can provide high spatial and temporal resolution remote sensing data about the rice canopy, providing possibilities for flowering monitoring. In this study, two consecutive years of rice field experiments were conducted to explore the performance of florescence spectral information in improving the accuracy of VIs-based models for yield estimates. First, the florescence ratio reflectance and florescence difference reflectance, as well as their first derivative reflectance, were defined and then their correlations with rice yield were evaluated. It was found that the florescence spectral information at the seventh day of rice flowering showed the highest correlation with the yield. The sensitive bands to yield were centered at 590 nm, 690 nm and 736 nm–748 nm, 760 nm–768 nm for the first derivative florescence difference reflectance, and 704 nm–760 nm for the first derivative florescence ratio reflectance. The florescence ratio index (FRI) and florescence difference index (FDI) were developed and their abilities to improve the estimation accuracy of models basing on vegetation indices at single-, two- and three-growth stages were tested. With the introduction of florescence spectral information, the single-growth VI-based model produced the most obvious improvement in estimation accuracy, with the coefficient of determination (R2) increasing from 0.748 to 0.799, and the mean absolute percentage error (MAPE) and the root mean squared error (RMSE) decreasing by 11.8% and 10.7%, respectively. Optimized by flowering information, the two-growth stage VIs-based model gave the best performance (R2 = 0.869, MAPE = 3.98%, RMSE = 396.02 kg/ha). These results showed that introducing florescence spectral information at the flowering stage into conventional VIs-based yield estimation models is helpful in improving rice yield estimation accuracy. The usefulness of florescence spectral information for yield estimation provides a new idea for the further development and improvement of the crop yield estimation method.

Abstract The PROSPECT leaf optical model has, to date, well-separated the effects of total chlorophyll and carotenoids on leaf reflectance and transmittance in the 400–800 nm. Considering variations in chlorophyll a:b ratio with leaf age and physiological stress, a further separation of total plant-based chlorophylls into chlorophyll a and chlorophyll b is necessary for advanced monitoring of plant growth. In this study, we present an extended version of PROSPECT model (hereafter referred to as PROSPECT-MP) that can combine the effects of chlorophyll a, chlorophyll b and carotenoids on leaf directional hemispherical reflectance and transmittance (DHR and DHT) in the 400–800 nm. The LOPEX93 dataset was used to evaluate the capabilities of PROSPECT-MP for spectra modelling and pigment retrieval. The results show that PROSPECT-MP can both simultaneously retrieve leaf chlorophyll a and b, and also performs better than PROSPECT-5 in retrieving carotenoids concentrations. As for the simulation of DHR and DHT, the performances of PROSPECT-MP are similar to that of PROSPECT-5. This study demonstrates the potential of PROSPECT-MP for improving capabilities of remote sensing of leaf photosynthetic pigments (chlorophyll a, chlorophyll b and carotenoids) and for providing a framework for future refinements in the modelling of leaf optical properties.

Time-series Vegetation Indices (VIs) are usually used for estimating grain yield. However, multi-temporal VIs may be affected by different background, illumination, and atmospheric conditions, so the absolute differences among time-series VIs may include the effects induced from external conditions in addition to vegetation changes, which will pose a negative effect on the accuracy of crop yield estimation. Therefore, in this study, the parcel-based relative vegetation index (ΔVI) and the parcel-based relative yield are proposed and further used to estimate rice yield. Hyperspectral images at key growth stages, including tillering stage, jointing stage, booting stage, heading stage, filling stage, and ripening stage, as well as rice yield, were obtained with Rikola hyperspectral imager mounted on Unmanned Aerial Vehicle (UAV) in 2017 growing season. Three types of parcel-level relative vegetation indices, including Relative Normalized Difference Vegetation Index (RNDVI), Relative Ratio Vegetation Index (RRVI), and Relative Difference Vegetation Index (RDVI) are created by using all possible two-band combinations of discrete channels from 500 to 900 nm. The optimal VI type and its band combinations at different growth stages are identified for rice yield estimation. Furthermore, the optimal combinations of different growth stages for yield estimation are determined by F-test and validated using leave-one-out cross validation (LOOCV) method. The comparison results show that, for the single-growth-stage model, RNDVI[880,712] at booting stage has the best correlation with rice yield with a R2-value of 0.75. For the multiple-growth-stage model, RNDVI[808,744] at jointing stage, RNDVI[880,712] at booting stage and RNDVI[808,744] at filling stage gain a higher R2-value of 0.83 with the mean absolute percentage error of estimated rice yield of 3%. The study demonstrates that the proposed method with parcel-level relative vegetation indices and relative yield can achieve higher yield estimation accuracy because it can make full use of the advantage that remote sensing can monitor relative changes accurately. The new method will further enrich the technology system for crop yield estimation based on remotely sensed data.

The in vivo features of the absorption of leaf photosynthetic and photo-protective pigments are closely linked to the leaf spectrum in the 400–800 nm regions. However, this information is difficult to obtain because the overlapping leaf pigments can mask the contribution of individual pigments to the leaf spectrum. Here, to limit the masking phenomenon between these pigments, the separation technology for leaf spectral overlapping was employed in the PROSPECT model with the ZJU dataset. The main results of this study include the following aspects: (1) the absorption coefficients of separated chlorophyll a and b, carotenoids and anthocyanins in the leaf in vivo display the physical principles of forming an absorption spectrum similar to those in an organic solution; (2) the differences in the position of each absorption peak of pigments between the leaf in vivo and in an organic solution can be described by a spectral displacement parameter; and (3) the overlapping characteristics between the separated pigments in the leaf in vivo are clearly drawn by a range of absorption feature (RAF) parameter. Moreover, the absorption coefficients of the separated pigments were successfully applied in leaf spectral modeling and pigment retrieval. The results show that the separated multiple pigment absorption coefficients from the leaf spectrum in vivo are effective and provide a framework for future refinements in describing leaf optical properties.

Pangolins are unique placental mammals with eight species existing in the world, which have adapted to a highly specialized diet of ants and termites, and are of significance in the control of forest termite disaster. Besides their ecological value, pangolins are extremely important economic animals with the value as medicine and food. At present, illegal hunting and habitat destruction have drastically decreased the wild population of pangolins, pushing them to the edge of extinction. Captive breeding is an important way to protect these species, but because of pangolin’s specialized behaviors and high dependence on natural ecosystem, there still exist many technical barriers to successful captive breeding programs. In this paper, based on the literatures and our practical experience, we reviewed the status and existing problems in captive breeding of pangolins, including four aspects, the naturalistic habitat, dietary husbandry, reproduction and disease control. Some recommendations are presented for effective captive breeding and protection of pangolins.

Since April 2014, new infections of H5N6 avian influenza virus (AIV) in humans and domestic poultry have caused considerable economic losses in the poultry industry and posed an enormous threat to human health worldwide. In previous research using gene sequence and phylogenetic analysis, we reported that H5N6 AIV isolated in February 2015 (ZH283) in Pallas’s sandgrouse was highly similar to that isolated in a human in December 2015 (A/Guangdong/ZQ874/2015), whereas a virus (i.e., SW8) isolated in oriental magpie-robin in 2014 was highly similar to that of A/chicken/Dongguan/2690/2013 (H5N6). However, the pathogenicity, transmissibility, and host immune-related response of chickens infected by those wild bird-origin H5N6 AIVs remain unknown. In response, we examined the viral distribution and mRNA expression profiles of immune-related genes in chickens infected with both viruses. Results showed that the H5N6 AIVs were highly pathogenic to chickens and caused not only systemic infection in multiple tissues, but also 100% mortality within 3–5 days post-infection. Additionally, ZH283 efficiently replicated in all tested tissues and transmitted among chickens more rapidly than SW8. Moreover, quantitative real-time polymerase chain reaction analysis showed that following infection with H5N6, AIVs immune-related genes remained active in a tissue-dependent manner, as well as that ZH283 induced mRNA expression profiles such as TLR3, TLR7, IL-6, TNF-α, IL-1β, IL-10, IL-8, and MHC-II to a greater extent than SW8 in the tested tissues of infected chickens. Altogether, our findings help to illuminate the pathogenesis and immunologic mechanisms of H5N6 AIVs in chickens.

Measuring forest aboveground biomass (AGB) at local to regional scales is critical to understanding their role in regional and global carbon cycles. The Three-North Shelterbelt Forest Program (TNSFP) is the largest ecological restoration project in the world, and has been ongoing for over 40 years. In this study, we developed models to estimate the planted forest aboveground biomass (PF_AGB) for Yulin, a typical area in the project. Surface reflectances in the study area from 1978 to 2013 were obtained from Landsat series images, and integrated forest z-scores were constructed to measure afforestation and the stand age of planted forest. Normalized difference vegetation index (NDVI) was combined with stand age to develop an initial model to estimate PF_AGB. We then developed additional models that added environment variables to our initial model, including climatic factors (average temperature, total precipitation, and total sunshine duration) and a topography factor (slope). The model which combined the total precipitation and slope greatly improved the accuracy of PF_AGB estimation compared to the initial model, indicating that the environmental variables related to water distribution indirectly affected the growth of the planted forest and the resulting AGB. Afforestation in the study area occurred mainly in the early 1980s and early 21st century, and the PF_AGB in 2003 was 2.3 times than that of 1998, since the fourth term TNSFP started in 2000. The PF_AGB in 2013 was about 3.33 times of that in 2003 because many young trees matured. The leave-one-out cross-validation (LOOCV) approach showed that our estimated PF_AGB had a significant correlation with field-measured data (correlation coefficient (r) = 0.89, p < 0.001, root mean square error (RMSE) = 6.79 t/ha). Our studies provided a method to estimate long time series PF_AGB using satellite repetitive measures, particularly for arid or semi-arid areas.

Long-term monitoring of regional and global environment changes often depends on the combined use of multi-source sensor data. The most widely used vegetation index is the normalized difference vegetation index (NDVI), which is a function of the red and near-infrared (NIR) spectral bands. The reflectance and NDVI data sets derived from different satellite sensor systems will not be directly comparable due to different spectral response functions (SRF), which has been recognized as one of the most important sources of uncertainty in the multi-sensor data analysis. This study quantified the influence of SRFs on the red and NIR reflectances and NDVI derived from 31 Earth observation satellite sensors. For this purpose, spectroradiometric measurements were performed for paddy rice grown under varied nitrogen levels and at different growth stages. The rice canopy reflectances were convoluted with the spectral response functions of various satellite instruments to simulate sensor-specific reflectances in the red and NIR channels. NDVI values were then calculated using the simulated red and NIR reflectances. The results showed that as compared to the Terra MODIS, the mean relative percentage difference (RPD) ranged from −12.67% to 36.30% for the red reflectance, −8.52% to −0.23% for the NIR reflectance, and −9.32% to 3.10% for the NDVI. The mean absolute percentage difference (APD) compared to the Terra MODIS ranged from 1.28% to 36.30% for the red reflectance, 0.84% to 8.71% for the NIR reflectance, and 0.59% to 9.32% for the NDVI. The lowest APD between MODIS and the other 30 satellite sensors was observed for Landsat5 TM for the red reflectance, CBERS02B CCD for the NIR reflectance and Landsat4 TM for the NDVI. In addition, the largest APD between MODIS and the other 30 satellite sensors was observed for IKONOS for the red reflectance, AVHRR1 onboard NOAA8 for the NIR reflectance and IKONOS for the NDVI. The results also indicated that AVHRRs onboard NOAA7-17 showed higher differences than did the other sensors with respect to MODIS. A series of optimum models were presented for remote sensing data assimilation between MODIS and other sensors.

Sparganosis is a zoonotic disease caused by the spargana of Spirometra, and snake is one of the important intermediate hosts of spargana. In some areas of China, snake is regarded as popular delicious food, and such a food habit potentially increases the prevalence of human sparganosis. To understand the prevalence of Spirometra in snakes in food markets, we conducted a study in two representative cities (Guangzhou and Shenzhen), during January–August 2013. A total of 456 snakes of 13 species were examined and 251 individuals of 10 species were infected by Spirometra, accounting for 55.0% of the total samples. The worm burden per infected snake ranged from 1 to 213, and the prevalence in the 13 species was 0∼96.2%. More than half (58.1%) of the spargana were located in muscular tissue, 25.6% in subcutaneous tissue, and 16.3% in coelomic cavity. The results indicated that Spirometra severely infected snakes in food markets in Guangzhou and Shenzhen, implying that eating snakes has great health risk and improper cooking methods may increase the risk of Spirometra infection in humans in China. Additional steps should be considered by the governments and public health agencies to prevent the risk of snake-associated Spirometra infections in humans.

As an important piece of equipment in industrial production, the health of an air compressor directly impacts the success of production. Therefore, researching fault diagnosis methods for air compressors is of significant importance in improving the continuity, reliability, and safety of production. Traditional fault diagnosis methods, however, struggle to obtain accurate fault features. The measurement of feature distribution differences between various working conditions lacks sufficient domain adaptability, making it challenging to achieve high recognition accuracy. Additionally, the operation of air compressors generates background noise, which can introduce interference and impact the accuracy in fault detection. In order to overcome these limitations, a fault diagnosis method for air compressors based on feature fusion is proposed. Firstly, the Mel-frequency cepstral coefficients (MFCC) features and wavelet transform features of the air compressor are extracted separately. Then, late fusion is applied at the decision level to combine confidence scores and predicted bounding boxes. The best network model is determined based on evaluation metrics to complete the classification. Based on the experimental results analysis, the feature fusion method demonstrated superior recognition performance. [ABSTRACT FROM AUTHOR]