PpNAC187 Enhances Lignin Synthesis in ‘Whangkeumbae’ Pear (Pyrus pyrifolia) ‘Hard-End’ Fruit

A disorder in pears that is known as ‘hard-end’ fruit affects the appearance, edible quality, and market value of pear fruit. RNA-Seq was carried out on the calyx end of ‘Whangkeumbae’ pear fruit with and without the hard-end symptom to explore the mechanism underlying the formation of hard-end. The results indicated that the genes in the phenylpropanoid pathway affecting lignification were up-regulated in hard-end fruit. An analysis of differentially expressed genes (DEGs) identified three NAC transcription factors, and RT-qPCR analysis of PpNAC138, PpNAC186, and PpNAC187 confirmed that PpNAC187 gene expression was correlated with the hard-end disorder in pear fruit. A transient increase in PpNAC187 was observed in the calyx end of ‘Whangkeumbae’ fruit when they began to exhibit hard-end symptom. Concomitantly, the higher level of PpCCR and PpCOMT transcripts was observed, which are the key genes in lignin biosynthesis. Notably, lignin content in the stem and leaf tissues of transgenic tobacco overexpressing PpNAC187 was significantly higher than in the control plants that were transformed with an empty vector. Furthermore, transgenic tobacco overexpressing PpNAC187 had a larger number of xylem vessel elements. The results of this study confirmed that PpNAC187 functions in inducing lignification in pear fruit during the development of the hard-end disorder. View Full-Tex

Overexpression of Pear (Pyrus pyrifolia) CAD2 in Tomato Affects Lignin Content

PpCAD2 was originally isolated from the ‘Wangkumbae’ pear (Pyrus pyrifolia Nakai), and it encodes for cinnamyl alcohol dehydrogenase (CAD), which is a key enzyme in the lignin biosynthesis pathway. In order to verify the function of PpCAD2, transgenic tomato (Solanum lycopersicum) ‘Micro-Tom’ plants were generated using over-expression constructs via the agrobacterium-mediated transformation method. The results showed that the PpCAD2 over-expression transgenic tomato plant had a strong growth vigor. Furthermore, these PpCAD2 over-expression transgenic tomato plants contained a higher lignin content and CAD enzymatic activity in the stem, leaf and fruit pericarp tissues, and formed a greater number of vessel elements in the stem and leaf vein, compared to wild type tomato plants. This study clearly indicated that overexpressing PpCAD2 increased the lignin deposition of transgenic tomato plants, and thus validated the function of PpCAD2 in lignin biosynthesis

Channel Estimation Based on Statistical Frames and Confidence Level in OFDM Systems

Channel estimation is an important module for improving the performance of the orthogonal frequency division multiplexing (OFDM) system. The pilot-based least square (LS) algorithm can improve the channel estimation accuracy and the symbol error rate (SER) performance of the communication system. In pilot-based channel estimation, a certain number of pilots are inserted at fixed intervals between OFDM symbols to estimate the initial channel information, and channel estimation results can be obtained by one-dimensional linear interpolation. The minimum mean square error (MMSE) and linear minimum mean square error (LMMSE) algorithms involve the inverse operation of the channel matrix. If the number of subcarriers increases, the dimension of the matrix becomes large. Therefore, the inverse operation is more complex. To overcome the disadvantages of the conventional channel estimation methods, this paper proposes a novel OFDM channel estimation method based on statistical frames and the confidence level. The noise variance in the estimated channel impulse response (CIR) can be largely reduced under statistical frames and the confidence level; therefore, it reduces the computational complexity and improves the accuracy of channel estimation. Simulation results verify the effectiveness of the proposed channel estimation method based on the confidence level in time-varying dynamic wireless channels

Layer-by-Layer Heterostructure of MnO2@Reduced Graphene Oxide Composites as High-Performance Electrodes for Supercapacitors

In this paper, δ-MnO2 with layered structure was prepared by a facile liquid phase method, and exfoliated MnO2 nanosheet (e-MnO2) was obtained by ultrasonic exfoliation, whose surface was negatively charged. Then, positive charges were grafted on the surface of MnO2 nanosheets with a polycation electrolyte of polydiallyl dimethylammonium chloride (PDDA) in different concentrations. A series of e-MnO2@reduced graphene oxide (rGO) composites were obtained by electrostatic self-assembly combined with hydrothermal chemical reduction. When PDDA was adjusted to 0.75 g/L, the thickness of e-MnO2 was ~1.2 nm, and the nanosheets were uniformly adsorbed on the surface of graphene, which shows layer-by-layer morphology with a specific surface area of ~154 m2/g. On account of the unique heterostructure, the composite exhibits good electrochemical performance as supercapacitor electrodes. The specific capacitance of e-MnO2-0.75@rGO can reach 456 F/g at a current density of 1 A/g in KOH electrolyte, which still remains 201 F/g at 10 A/g. In addition, the capacitance retention is 98.7% after 10000 charge-discharge cycles at 20 A/g. Furthermore, an asymmetric supercapacitor (ASC) device of e-MnO2-0.75@rGO//graphene hydrogel (GH) was assembled, of which the specific capacitance achieves 94 F/g (1 A/g) and the cycle stability is excellent, with a retention rate of 99.3% over 10000 cycles (20 A/g)

Low-Altitude Remote Sensing Inversion of River Flow in Ungauged Basins

Runoff is closely related to human production, the regional environment, and hydrological characteristics. It is also an important basis for water cycle research and regional water resource development and management. However, obtaining hydrological information for uninformed river sections is complicated by harsh environments, limited transportation, sparse populations, and a low density of hydrological observation stations in the inland arid zone. Here, low-altitude remote sensing technology was introduced to combine riverbed characteristics through unmanned aerial vehicle (UAV) inversion with classical hydraulic equations for ungauged basins in the middle and lower reaches of the Keriya River, northwest China, and investigate the applicability of this method on wide and shallow riverbeds of inland rivers. The results indicated that the estimated average error of the low-altitude remote sensing flow was 8.49% (ranging 3.26–17.00%), with a root mean square error (RMSE) of 0.59 m3·s−1 across the six selected river sections, suggesting that this method has some applicability in the study area. Simultaneously, a method for estimating river flow based on the water surface width– and water depth–flow relationship curves for each section was proposed whereas the precise relationships were selected based on actual section attributes to provide a new method for obtaining runoff data in small- and medium-scale river areas where information is lacking

Low-Altitude Remote Sensing Inversion of River Flow in Ungauged Basins

Runoff is closely related to human production, the regional environment, and hydrological characteristics. It is also an important basis for water cycle research and regional water resource development and management. However, obtaining hydrological information for uninformed river sections is complicated by harsh environments, limited transportation, sparse populations, and a low density of hydrological observation stations in the inland arid zone. Here, low-altitude remote sensing technology was introduced to combine riverbed characteristics through unmanned aerial vehicle (UAV) inversion with classical hydraulic equations for ungauged basins in the middle and lower reaches of the Keriya River, northwest China, and investigate the applicability of this method on wide and shallow riverbeds of inland rivers. The results indicated that the estimated average error of the low-altitude remote sensing flow was 8.49% (ranging 3.26–17.00%), with a root mean square error (RMSE) of 0.59 m3·s−1 across the six selected river sections, suggesting that this method has some applicability in the study area. Simultaneously, a method for estimating river flow based on the water surface width– and water depth–flow relationship curves for each section was proposed whereas the precise relationships were selected based on actual section attributes to provide a new method for obtaining runoff data in small- and medium-scale river areas where information is lacking

UAV-based remote sensing using visible and multispectral indices for the estimation of vegetation cover in an oasis of a desert

Natural vegetation is an important indicator for the maintenance of symbiosis in an oasis in extremely arid zones. Unmanned aerial vehicles have advantages of high resolution and multiple wavebands to obtain details of sparse vegetation cover. So far, studies on the selection of machine learning methods are relatively limited and usually focus on only a few selected methods. In this study, the natural vegetation of the Dariyabui Oasis in the hinterland of the Taklamakan Desert in China was mapped using 2,550 samples of data and 14 visible and multispectral vegetation indices as model variables. Six machine learning methods were used to construct fractional vegetation cover (FVC) predictive regression models. Coefficient of determination (R2), root-mean-square error (RMSE), and mean-absolute error (MAE) were used to evaluate the models. The regression models were divided into four components: visible (RF: R2 = 0.65, RMSE = 0.59 %, MAE = 0.41 %), multispectral (RF: R2 = 0.71, RMSE = 0.54 %, MAE = 0.36 %), visible and multispectral (RF: R2 = 0.69, RMSE = 0.55 %, MAE = 0.37 %), and the product of visible and multispectral vegetation indices (RF: R2 = 0.68, RMSE = 0.57 %, MAE = 0.39 %). Besides, the visible vegetation index results were validated using different years and different aerial height data. The results show that these four regression models can effectively obtain the FVC of sparse vegetation of the desert. This study applied the Random Forest model, which was selected based on a comparison of other models, to predict the status of desert vegetation cover based on spectral data to provide information for its conservation and management

Aqueous extract of Platycodon grandiflorus attenuates lipopolysaccharide-induced apoptosis and inflammatory cell infiltration in mouse lungs by inhibiting PI3K/Akt signaling

Abstract Background Acute lung injury (ALI), an acute inflammatory lung disease, can cause a rapid inflammatory response in clinic, which endangers the patient's life. The components of platycodon grandiflorum, such as platycodins have a wide range of pharmacological activities such as expectorant, anti-apoptotic, anti-inflammatory, anti-tumor and anti-oxidant properties, and can be used for improving human immunity. Previous studies have shown that aqueous extract of platycodon grandiflorum (PAE) has a certain protective effect on ALI, but the main pharmacodynamic components and the mechanism of action are not clear. Methods The anti-inflammatory properties of PAE were studied using the lipopolysaccharide (LPS)-induced ALI animal model. Hematoxylin and eosin stains were used to assess the degree of acute lung damage. Changes in RNA levels of pro-inflammatory cytokines in the lungs were measured using quantitative RT-qPCR. The potential molecular mechanism of PAE preventing ALI was predicted by lipidomics and network pharmacology. To examine the anti-apoptotic effects of PAE, TdT-mediated dUTP nick-end labelling (TUNEL) was employed to determine apoptosis-related variables. The amounts of critical pathway proteins and apoptosis-related proteins were measured using Western blotting. Results Twenty-six chemical components from the PAE were identified, and their related pathways were obtained by the network pharmacology. Combined with the analysis of network pharmacology and literature, it was found that the phosphatidylinositol 3 kinase (PI3K)/protein kinase B (AKT) signaling pathway is related to ALI. The results of lipidomics show that PAE alleviates ALI via regulating lung lipids especially phosphatidylinositol (PI). Finally, the methods of molecular biology were used to verify the mechanism of PAE. It can be found that PAE attenuates the inflammatory response to ALI by inhibiting apoptosis through PI3K/Akt signaling pathway. Conclusion The study revealed that the PAE attenuates lipopolysaccharide-induced apoptosis and inflammatory cell infiltration in mouse lungs by inhibiting PI3K/Akt signaling. Furthermore, our findings provide a novel strategy for the application of PAE as a potential agent for preventing patients with ALI