Appropriate Site Specific Weed Management (SSWM) is crucial to ensure the crop yields. Within SSWM of large-scale area, remote sensing is a key technology to provide accurate weed distribution ...information. Compared with satellite and piloted aircraft remote sensing, unmanned aerial vehicle (UAV) is capable of capturing high spatial resolution imagery, which will provide more detailed information for weed mapping. The objective of this paper is to generate an accurate weed cover map based on UAV imagery. The UAV RGB imagery was collected in 2017 October over the rice field located in South China. The Fully Convolutional Network (FCN) method was proposed for weed mapping of the collected imagery. Transfer learning was used to improve generalization capability, and skip architecture was applied to increase the prediction accuracy. After that, the performance of FCN architecture was compared with Patch_based CNN algorithm and Pixel_based CNN method. Experimental results showed that our FCN method outperformed others, both in terms of accuracy and efficiency. The overall accuracy of the FCN approach was up to 0.935 and the accuracy for weed recognition was 0.883, which means that this algorithm is capable of generating accurate weed cover maps for the evaluated UAV imagery.
Citrus huanglongbing (HLB) is a highly destructive disease currently threatening citrus production worldwide. In China, the disease is exclusively associated with 'Candidatus Liberibacter asiaticus', ...a nonculturable proteobacterium. HLB was observed in Guangdong of China over a hundred years ago. Researchers and citrus growers have been battling with the disease through vigorous research and have exercised various control practices. Much of the early work was not well known outside China. This review is intended to fill in gaps of historical information by reviewing selected literature records. Along the way, the HLB system within southern China was evaluated. Emphases were on comparison of symptomatology, evolution of etiology, control practices, and impacts of using next-generation sequencing technology for 'Ca. L. asiaticus' research and detection.
Core shell structured magnetoelectric multiferroics have attracted great attentions due to their improved multiferroic properties and potential applications. In this paper, CoFe2O4 @BaTiO3 core shell ...structured composites with different core size were prepared by hydrothermal method and the effect of core size on the microstructure and multiferroic properties have been systematically studied. The results indicate that with the decrease of the core size, the dielectric, ferroelectric properties as well as the magnetoelectric coupling effect were increased dramatically. Improved interface area and the perfect distribution of CoFe2O4 particles into BaTiO3 matrix are ascribed to be the result of this enhancement of magnetoelectric properties. Our result may shield light on the improvement of multiferroic properties for composite multiferroic materials.
•Scoparone improves hepatic inflammation and autophagy in mice with MCD diet.•Scoparone has an effect on autophag in macrophages, but not hepatocytes.•Scoparone inhibited the up-regulation of p62 ...transcription by ROS/P38/Nrf2 axis.•Scoparone enhances autophagy flux by promoting autolysosome formation.•Scoparone regulates autophagy by inhibiting PI3K/AKT/mTOR pathway.
Scoparone has been shown to ameliorate many forms of liver disease, and several underlying molecular mechanisms involved have been previously revealed. However, the potential role of scoparone in autophagy, which is dysregulated in nonalcoholic fatty liver disease-nonalcoholic steatohepatitis (NAFLD-NASH), has not been evaluated. In the current study, we investigated the effect and potential mechanisms of scoparone in hepatic autophagy in mice with NASH.
In vivo, mice were fed a methionine–choline deficient (MCD) diet to establish a NASH model and then subjected to treatment with or without scoparone for 4 weeks. In vitro, scoparone was applied in a hepatocellular lipid overload model in AML12 cells challenged with palmitic acid (PA) and in lipopolysaccharide (LPS)-induced RAW264.7 cells.
Scoparone improved impaired autophagy and several key features of NASH in mice fed an MCD diet. In vitro, scoparone had an effect on the autophagy of macrophages but not hepatocytes. In RAW264.7 cells, scoparone reduced the LPS-induced accumulation of autophagosomes and autophagy substrates, the production of reactive oxygen species (ROS) and the inflammatory response. Scoparone inhibited the upregulation of p62 transcription, which is mediated by the ROS/P38/Nrf2 axis. Chloroquine (CQ), an inhibitor of autophagic flux, significantly inhibited scoparone-mediated protection against inflammation. In addition, scoparone suppressed activation of the PI3K/AKT/mTOR pathway, and MHY1485 (an mTOR activator that inhibits autophagy) inhibited the anti-inflammatory effect of scoparone.
In LPS-induced macrophages, scoparone regulates autophagy and further suppresses inflammation by inhibiting the ROS/P38/Nrf2 axis and PI3K/AKT/mTOR pathway and enhancing autophagic flux. Scoparone may improve hepatic autophagy and NASH partly through enhancing autophagy in macrophages but not hepatocytes. Scoparone is expected to become a novel therapeutic drug for NASH or diseases associated with dysregulated autophagy in macrophages.
Online monitoring data of atmospheric environmental quality often deviate or are missing, causing a great impact on regional atmospheric quality analysis. In this study, a deep learning method to ...repair atmospheric environmental quality data based on Gaussian diffusion and gate recurrent unit (GD-GRU) was developed to improve repair accuracy. A multi-source Gaussian diffusion model was developed to estimate PM2.5 based on the pollutant diffusion law and the data of 61 stations in Guilin. The root mean square error (RMSE) of the estimated and observed value was extracted as the error sequence. The error value was regarded as output of gate recurrent unit (GRU) with the inputs of weather and pollutant parameters. Missing data were calculated by Gaussian diffusion estimated value and the error predicted by GRU. The established GD-GRU model was applied to repair the long-sequence missing data. The analytical results indicated that the GD-GRU model had higher prediction accuracy of extreme values than Gaussian diffusion model and GRU model, because GD-GRU based on Gaussian diffusion can calculate the extreme value by simulating the diffusion and transmission mechanism. The established model predicted PM2.5 concentration in the next hours with an RMSE of 12.561, which was approximately 21.02% better, on average, than methods like autoregressive integrated moving average model (ARIMA), support vector regression (SVR), recurrent neural network (RNN), long short-term memory model (LSTM), and GRU. The established GD-GRU model demonstrated good performance on extreme values prediction and air quality data repair, thus providing a new method for air quality long-sequence missing data repair.
Magnetoelectric multiferroic fluids composed of BaTiO
3
@CoFe
2
O
4
composite nanoparticles dispersed in a highly insulating nonpolar oleic acid/silicone oil mixture have been developed. The effects ...of the particle volume fraction and a magnetic field, as well as an electric field, on the ferroelectric and magnetic properties, as well as the magnetoelectric coupling effect, have been systematically studied and discussed in this paper. Magnetic characterization shows an approximation to superparamagnetism, and both the remanent magnetization (
M
r
) and the coercive field (
H
c
) increase with increases in the volume fraction and applied electric field. Similarly, a superparaelectric state has been observed in the multiferroic fluids, in which both the remanent polarization (
P
r
) and the coercive field (
E
c
) are near zero, whereas they increase with increases in the applied magnetic field and volume fraction. High converse and direct magnetoelectric coupling coefficients are estimated to be
α
H
= 8.16 × 10
−4
(Oe cm) V
−1
and
α
E
= 1.58 × 10
4
V (cm Oe)
−1
, respectively. Further analysis indicates that the composite particles can be aligned under an external magnetic/electric field so that their magnetic/electric moments can be parallel to the external field, which in turn results in changes in the magnetization/polarization directions. These results imply that besides magnetoelectric fluids that consist of core/shell-structured nanoparticles, conventional multiferroic fluids based on composite particles may provide an opportunity to gain electrical control of magnetization and
vice versa
, which implies potential applications.
Magnetoelectric multiferroic fluids composed of BaTiO
3
@CoFe
2
O
4
composite nanoparticles dispersed in a highly insulating nonpolar oleic acid/silicone oil mixture have been developed.
The SARS-CoV-2 Delta variant has spread rapidly worldwide. To provide data on its virological profile, we here report the first local transmission of Delta in mainland China. All 167 infections could ...be traced back to the first index case. Daily sequential PCR testing of quarantined individuals indicated that the viral loads of Delta infections, when they first become PCR-positive, were on average ~1000 times greater compared to lineage A/B infections during the first epidemic wave in China in early 2020, suggesting potentially faster viral replication and greater infectiousness of Delta during early infection. The estimated transmission bottleneck size of the Delta variant was generally narrow, with 1-3 virions in 29 donor-recipient transmission pairs. However, the transmission of minor iSNVs resulted in at least 3 of the 34 substitutions that were identified in the outbreak, highlighting the contribution of intra-host variants to population-level viral diversity during rapid spread.
Pine wilt disease causes huge economic losses to pine wood forestry because of its destructiveness and rapid spread. This paper proposes a detection and location method of pine wood nematode disease ...at a large scale adopting UAV (Unmanned Aerial Vehicle) remote sensing and artificial intelligence technology. The UAV remote sensing images were enhanced by computer vision tools. A Faster-RCNN (Faster Region Convolutional Neural Networks) deep learning framework based on a RPN (Region Proposal Network) network and the ResNet residual neural network were used to train the pine wilt diseased dead tree detection model. The loss function and the anchors in the RPN of the convolutional neural network were optimized. Finally, the location of pine wood nematode dead tree was conducted, which generated the geographic information on the detection results. The results show that ResNet101 performed better than VGG16 (Visual Geometry Group 16) convolutional neural network. The detection accuracy was improved and reached to about 90% after a series of optimizations to the network, meaning that the optimization methods proposed in this paper are feasible to pine wood nematode dead tree detection.
Citrus is a globally important, perennial fruit crop whose rhizosphere microbiome is thought to play an important role in promoting citrus growth and health. Here, we report a comprehensive analysis ...of the structural and functional composition of the citrus rhizosphere microbiome. We use both amplicon and deep shotgun metagenomic sequencing of bulk soil and rhizosphere samples collected across distinct biogeographical regions from six continents. Predominant taxa include Proteobacteria, Actinobacteria, Acidobacteria and Bacteroidetes. The core citrus rhizosphere microbiome comprises Pseudomonas, Agrobacterium, Cupriavidus, Bradyrhizobium, Rhizobium, Mesorhizobium, Burkholderia, Cellvibrio, Sphingomonas, Variovorax and Paraburkholderia, some of which are potential plant beneficial microbes. We also identify over-represented microbial functional traits mediating plant-microbe and microbe-microbe interactions, nutrition acquisition and plant growth promotion in citrus rhizosphere. The results provide valuable information to guide microbial isolation and culturing and, potentially, to harness the power of the microbiome to improve plant production and health.
Weed control is necessary in rice cultivation, but the excessive use of herbicide treatments has led to serious agronomic and environmental problems. Suitable site-specific weed management (SSWM) is ...a solution to address this problem while maintaining the rice production quality and quantity. In the context of SSWM, an accurate weed distribution map is needed to provide decision support information for herbicide treatment. UAV remote sensing offers an efficient and effective platform to monitor weeds thanks to its high spatial resolution. In this work, UAV imagery was captured in a rice field located in South China. A semantic labeling approach was adopted to generate the weed distribution maps of the UAV imagery. An ImageNet pre-trained CNN with residual framework was adapted in a fully convolutional form, and transferred to our dataset by fine-tuning. Atrous convolution was applied to extend the field of view of convolutional filters; the performance of multi-scale processing was evaluated; and a fully connected conditional random field (CRF) was applied after the CNN to further refine the spatial details. Finally, our approach was compared with the pixel-based-SVM and the classical FCN-8s. Experimental results demonstrated that our approach achieved the best performance in terms of accuracy. Especially for the detection of small weed patches in the imagery, our approach significantly outperformed other methods. The mean intersection over union (mean IU), overall accuracy, and Kappa coefficient of our method were 0.7751, 0.9445, and 0.9128, respectively. The experiments showed that our approach has high potential in accurate weed mapping of UAV imagery.
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