Inversion of chlorophyll content under the stress of leaf mite for jujube based on model PSO-ELM method.

During the growth season, jujube trees are susceptible to infestation by the leaf mite, which reduces the fruit quality and productivity. Traditional monitoring techniques for mites are time-consuming, difficult, subjective, and result in a time lag. In this study, the method based on a particle swarm optimization (PSO) algorithm extreme learning machine for estimation of leaf chlorophyll content (SPAD) under leaf mite infestation in jujube was proposed. Initially, image data and SPAD values for jujube orchards under four severities of leaf mite infestation were collected for analysis. Six vegetation indices and SPAD value were chosen for correlation analysis to establish the estimation model for SPAD and the vegetation indices. To address the influence of colinearity between spectral bands, the feature band with the highest correlation coefficient was retrieved first using the successive projection algorithm. In the modeling process, the PSO correlation coefficient was initialized with the convergent optimal approximation of the fitness function value; the root mean square error (RMSE) of the predicted and measured values was derived as an indicator of PSO goodness-of-fit to solve the problems of ELM model weights, threshold randomness, and uncertainty of network parameters; and finally, an iterative update method was used to determine the particle fitness value to optimize the minimum error or iteration number. The results reflected that significant differences were observed in the spectral reflectance of the jujube canopy corresponding with the severity of leaf mite infestation, and the infestation severity was negatively correlated with the SPAD value of jujube leaves. The selected vegetation indices NDVI, RVI, PhRI, and MCARI were positively correlated with SPAD, whereas TCARI and GI were negatively correlated with SPAD. The accuracy of the optimized PSO-ELM model (R 2 = 0.856, RMSE = 0.796) was superior to that of the ELM model alone (R 2 = 0.748, RMSE = 1.689). The PSO-ELM model for remote sensing estimation of relative leaf chlorophyll content of jujube shows high fault tolerance and improved data-processing efficiency. The results provide a reference for the utility of UAV remote sensing for monitoring leaf mite infestation of jujube.

Construction and Analysis of Disuse Atrophy Model of the Gastrocnemius Muscle in Chicken.

Disuse muscle atrophy is identified as the physiological, biochemical, morphological, and functional changes during restricted movement, immobilization, or weightlessness. Although its internal mechanism has been extensively studied in mammals and was thought to be mainly related to oxidative stress, it was unclear whether it behaved consistently in non-mammals such as chickens. In this study, we tried to construct a disuse atrophy model of the gastrocnemius muscle in chickens by limb immobilization, and collected the gastrocnemius muscles of the fixed group and the control group for RNA sequencing. Through analysis of muscle loss, HE staining, immunohistochemistry, and oxidative stress level, we found that limb immobilization could lead to loss of muscle mass, decrease in muscle fiber diameter, decrease in the proportion of slow muscle fibers, and increase in the proportion of fast muscle fibers, and also cause elevated levels of oxidative stress. In addition, a total of 565 different expression genes (DEGs) were obtained by RNA sequencing, which was significantly enriched in the biological processes such as cell proliferation and apoptosis, reactive oxygen species metabolism, and fast and slow muscle fiber transformation, and it showed that the FOXO signaling pathway, closely related to muscle atrophy, was activated. In brief, we initially confirmed that limb immobilization could induce disuse atrophy of skeletal muscle, and oxidative stress was involved in the process of disuse muscle atrophy.

Research on Lightweight Citrus Flowering Rate Statistical Model Combined with Anchor Frame Clustering Optimization.

At present, learning-based citrus blossom recognition models based on deep learning are highly complicated and have a large number of parameters. In order to estimate citrus flower quantities in natural orchards, this study proposes a lightweight citrus flower recognition model based on improved YOLOv4. In order to compress the backbone network, we utilize MobileNetv3 as a feature extractor, combined with deep separable convolution for further acceleration. The Cutout data enhancement method is also introduced to simulate citrus in nature for data enhancement. The test results show that the improved model has an mAP of 84.84%, 22% smaller than that of YOLOv4, and approximately two times faster. Compared with the Faster R-CNN, the improved citrus flower rate statistical model proposed in this study has the advantages of less memory usage and fast detection speed under the premise of ensuring a certain accuracy. Therefore, our solution can be used as a reference for the edge detection of citrus flowering.

Single-cell RNA sequencing of preadipocytes reveals the cell fate heterogeneity induced by melatonin.

Obesity is a global epidemic health disorder and associated with several diseases. Body weight-reducing effects of melatonin have been reported; however, no investigation toward examining whether the beneficial effects of melatonin are associated with preadipocyte heterogeneity has been reported. In this study, we profiled 25 071 transcriptomes of normal and melatonin-treated preadipocytes using scRNA-seq. By tSNE analysis, we present a cellular-state landscape for melatonin-treated preadipocytes that covers multiple-cell subpopulations, defined as cluster 0 to cluster 13. Cluster 0 and cluster 1 were the largest components of normal and melatonin-treated preadipocytes, respectively. G0S2, an inhibitor of adipose triglyceride lipase (ATGL), was significantly upregulated in cluster 0 and downregulated in cluster 1. We redefined cluster 0 as the G0S2-positive cluster (G0S2+ ) and cluster 1 as the G0S2-negative cluster (G0S2- ). Through pseudotime analysis, the G0S2- cluster cell differentiation trajectory was divided into three major structures, that is, the prebranch, the lipid catabolism branch, and the cell fate 2 branch. In vitro, G0S2 knockdown enhanced the expression levels of ATGL, BAT markers and fatty acid oxidation-related genes, but inhibited C/EBPα and PPARγ expression. In vivo, knockdown of G0S2 reduced the body weight gain in high-fat-fed mice. The beneficial effects of the G0S2- cell cluster in promoting lipolysis and inhibiting adipogenesis are dependent on two major aspects: first, downregulation of the G0S2 gene in the G0S2- cluster, resulting in activation of ATGL, which is responsible for the bulk of triacylglycerol hydrolase activity; and second, upregulation of FABP4 in the G0S2- cluster, resulting in inhibition of PPARγ and further reducing adipogenesis.

Novel recyclable deep eutectic solvent boost biomass pretreatment for enzymatic hydrolysis.

Deep eutectic solvent (DES) with protonic acid shows the great potential for biomass valorization. However, the acid corrosion and recycling are still severe challenges in biorefinery. Herein, a novel DES by coordinating FeCl3 in choline chloride/glycerol DES was designed for effective and recyclable pretreatment. As compared to DESs with FeCl2, ZnCl2, AlCl3 and CuCl2, DES with FeCl3 approvingly retained most of cellulose in pretreated Hybrid Pennisetum (95.2%). Meanwhile, the cellulose saccharification significantly increased to 99.5%, which was six-fold higher than that of raw biomass. The excellent pretreatment performance was mainly attributed to the high removal of lignin (78.88 wt%) and hemicelluloses (93.63 wt%) under the synergistic effect of Lewis acid and proper hydrogen-bond interaction of DES with FeCl3. Furthermore, almost all cellulose still can be converted into glucose after five recycling process. Overall, the process demonstrated designed pretreatment was great potential for the low-cost biorefinery and boost the biofuel development.

Controllable reduction of graphene oxide by electron-beam irradiation.

The oxygen content of graphene oxide (GO) is directly related to its physical and chemical properties, such as hydrophilicity, suspension stability, adsorption, and ion-sieving ability of GO membranes. Here, a series of reduced GO (rGO) with C/O atomic ratios from 1.6 to 4.8 were prepared conveniently by electron-beam irradiation (EBI) with irradiation-dose control. Moreover, a single oxygen-containing group, i.e., epoxy or carbonyl, could be retained mainly in the rGO. The interlayer spacing of rGO could be changed from 9.6 Å to 7.4 Å through control of the oxygen content. The prepared rGO exhibited an excellent adsorption effect on Pb(ii) ions, and the max adsorption capacity reached 194.76 mg g-1 for rGO with a low irradiation dose (5 kGy), which showed that the ratio of oxygen-containing groups is important for improving the adsorption of rGO in aqueous solution. These results indicated that highly efficient, environmentally friendly, and advanced EBI technology has good potential prospects for use in the large-scale production of rGO with precise control of the oxygen content.