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Urban tailwater typically has a low carbon-to-nitrogen ratio and adding external carbon sources can effectively improve the denitrification performance of wastewater. However, it is difficult to determine the dosage of additional carbon sources, leading to insufficient or excessive addition. Therefore, it is necessary to prepare solid slow-release carbon source (SRC) materials to solve the difficulty in determining the dosage of carbon sources. This study selected two SRCs of slow-release carbon source 1 (SRC1) and slow-release carbon source 2 (SRC2), with good slow-release performance after static carbon release and batch experiments. The composition of SRC1 was: hydroxypropyl methylcellulose/disodium fumarate/polyhydroxy alkanoate (HPMC/DF/PHA) at a ratio of 3:2:4, with an Fe3O4 mass fraction of 3%. The composition of SRC2 was: HPMC/DF/PHA with a ratio of 1:1:1 and an Fe3O4 mass fraction of 3%. The fitted equations of carbon release curves of SRC1 and SRC2 were y = 61.91 + 7190.24e−0.37t and y = 47.92 + 8770.42e−0.43t, respectively. The surfaces of SRC1 and SRC2 had a loose and porous morphological structure, which could increase the specific surface area of materials and be more conducive to the adhesion and metabolism of microorganisms. The experimental nitrogen removal by denitrification with SRCs showed that when the initial total nitrogen concentration was 40.00 mg/L, the nitrate nitrogen (NO3−-N) concentrations of the SRC1 and SRC2 groups on the 10th day were 2.57 and 2.66 mg/L, respectively. On the 20th day, the NO3−-N concentrations of the SRC1 and SRC2 groups were 1.67 and 2.16 mg/L, respectively, corresponding to removal efficiencies of 95.83% and 94.60%, respectively. The experimental results indicated that SRCs had a good nitrogen removal effect. Developing these kinds of materials can provide a feasible way to overcome the difficulty in determining the dosage of carbon sources in the process of heterotrophic denitrification. [ABSTRACT FROM AUTHOR]

Distributed generation (DG) needs to be connected to the microgrid (MG) through an inverter. The power quality of MG is impacted due to the characteristics of DGs and access to many types of loads. Traditionally, robust control or secondary regulation is used in MG inverters to solve power quality problems. However, there are issues in which the controller order is too high or the design is too complicated. A novel adaptive control strategy based on Narendra theory for voltage source inverter in MGs is proposed in this paper to solve the above problems. This strategy improves the control performance by designing an adaptive law. The MG inverter can adjust the parameters adaptively to change the output voltage quality under complex working conditions. The example shows that the inverter with the proposed adaptive control strategy can maintain good voltage control performance under complex conditions of MG, thus ensure the power quality in the MG. [ABSTRACT FROM AUTHOR]

A reasonable assessment of microgrid power quality (MGPQ) is essential for ensuring the safe and stable operation of the system. However, due to the complex and variable operating conditions of microgrid (MG), the results of power quality (PQ) assessments are often discrete. Therefore, further research is needed to determine how to accurately estimate the overall PQ of a MG based on these discrete evaluation results. To address this issue, a model for evaluating MGPQ based on confidence estimation using Chebyshev inequality is proposed in this paper. Firstly, Chebyshev inequality is utilized to describe the discreteness of PQ evaluation results in MG. Secondly, the multi-scale adaptive phase number selection CRITIC method and probabilistic statistics method are employed to evaluate the PQ index of the MG under multiple working conditions. Furthermore, sample standard deviation (SD) is used to quantify the dispersion of evaluation results, and a 90% confidence level is used to estimate the confidence interval of multiple evaluation results. Finally, the example presented in this paper demonstrates that at least 90% probability exists for an evaluation result to fall within ±3.16 SDs from its mean. Compared with traditional methods, this paper comprehensively reflects the overall PQ status of MG from three aspects by considering index data characteristics, different time scales, and confidence intervals—providing clear and practical guidance for MG users and managers to ensure safe and stable operation of the MG. [ABSTRACT FROM AUTHOR]

The stochasticity of power flow of distributed generations (DGs) and load in the microgrid has great influence on power flow distribution and voltage quality of the distribution network. For improving the voltage quality of the distribution network, the questions need to be further studied, which include the description of the stochasticity of the power flow in the microgrid and the impact of the microgrid into the distribution network on the power flow. Therefore, a novel stochastic power flow calculation and optimal control method for the microgrid based on multivariate stochastic factors fusion-sensitivity (MSFF-sensitivity) is proposed in this paper. Firstly, the multivariate stochastic factors fusion (MSFF) function is developed by using the probability density function to extract the stochasticity and correlation of power flow among different stochastic factors in the microgrid, which are effectively unified. Furthermore, the fusion-sensitivity (F-sensitivity) of the power flow in the microgrid integrated into the distribution network is constructed to accurately characterize the influence degree of various stochastic factors in the microgrid on the power flow of the distribution network. Based on this, the output power of the stochastic factor is adjusted to optimally control the power flow of the distribution network. Finally, the algorithm verification suggests that, compared with the conventional power flow methods, the method proposed in this paper is more suitable for the microgrid. The influence of stochastic power flow on the distribution network can be effectively reduced and the voltage quality of the distribution network can be improved by optimizing control of the power flow in the microgrid integrated into the distribution network. [ABSTRACT FROM AUTHOR]

Accurate prediction of food crop yield is of great significance for global food security and regional trade stability. Since remote sensing data collected from unmanned aerial vehicle (UAV) platforms have the features of flexibility and high resolution, these data can be used as samples to develop regional regression models for accurate prediction of crop yield at a field scale. The primary objective of this study was to construct regional prediction models for winter wheat yield based on multi-spectral UAV data and machine learning methods. Six machine learning methods including Gaussian process regression (GPR), support vector machine regression (SVR) and random forest regression (RFR) were used for the construction of the yield prediction models. Ten vegetation indices (VIs) extracted from canopy spectral images of winter wheat acquired from a multi-spectral UAV at five key growth stages in Xuzhou City, Jiangsu Province, China in 2021 were selected as the variables of the models. In addition, in situ measurements of wheat yield were obtained in a destructive sampling manner for prediction algorithm modeling and validation. Prediction results of single growth stages showed that the optimal model was GPR constructed from extremely strong correlated VIs (ESCVIs) at the filling stage (R2 = 0.87, RMSE = 49.22 g/m2, MAE = 42.74 g/m2). The results of multiple stages showed GPR achieved the highest accuracy (R2 = 0.88, RMSE = 49.18 g/m2, MAE = 42.57 g/m2) when the ESCVIs of the flowering and filling stages were used. Larger sampling plots were adopted to verify the accuracy of yield prediction; the results indicated that the GPR model has strong adaptability at different scales. These findings suggest that using machine learning methods and multi-spectral UAV data can accurately predict crop yield at the field scale and deliver a valuable application reference for farm-scale field crop management. [ABSTRACT FROM AUTHOR]

[Display omitted] To test the hypothesis that transient nonischemic stimulation of hypertrophy would render the heart resistant to subsequent ischemic stress, short-term transverse aortic constriction (TAC) was performed in mice and then withdrawn for several days by aortic debanding, followed by subsequent myocardial exposure to ischemia/reperfusion (I/R). Following I/R injury, the myocardial infarct size and apoptosis were markedly reduced, and contractile function was significantly improved in the TAC preconditioning group compared with the control group. Mechanistically, hypertrophic preconditioning remarkably alleviated I/R-induced oxidative stress, as evidenced by the increased reduced nicotinamide adenine dinucleotide phosphate (NADPH)/nicotinamide adenine dinucleotide phosphate (NADP) ratio, increase in the reduced glutathione (GSH)/oxidized glutathione (GSSH) ratio, and reduced mitochondrial reactive oxygen species (ROS) production. Moreover, TAC preconditioning inhibited caspase-3 activation and mitigated the mitochondrial impairment by deacetylating isocitrate dehydrogenase 2 (IDH2) via a sirtuin 3 (SIRT3)-dependent mechanism. In addition, the expression of a genetic deacetylation mimetic IDH2 mutant (IDH2 K413R) in cardiomyocytes, which increased IDH2 enzymatic activity and decreased mitochondrial ROS production, and ameliorated I/R injury, whereas the expression of a genetic acetylation mimetic (IDH2 K413Q) in cardiomyocytes abolished these protective effects of hypertrophic preconditioning. Furthermore, both the activity and expression of the SIRT3 protein were markedly increased in preconditioned mice exposed to I/R. Treatment with an adenovirus encoding SIRT3 partially emulated the actions of hypertrophic preconditioning, whereas genetic ablation of SIRT3 in mice blocked the cardioprotective effects of hypertrophic preconditioning. The present study identifies hypertrophic preconditioning as a novel endogenous self-defensive and cardioprotective strategy for cardiac I/R injury that induces IDH2 deacetylation through a SIRT3-dependent mechanism. A therapeutic strategy targeting IDH2 may be a promising treatment for cardiac ischemic injury. [ABSTRACT FROM AUTHOR]

This research designs an intelligent parking system including service application layer, perception layer, data analysis layer, and management layer. The network system adopts opm15 system, and the parking space recognition adopts improved convolution neural networks (CNNs) algorithm and image recognition technology. Firstly, the parking space is occupied and located, and the shortest path (Dynamic Programming, DP) is selected. In order to describe the path algorithm, the parking system model is established. Aiming at the problems of DP low power and adjacent path interference in the path detection system, a method of combining interference elimination technology with enhanced detector technology is proposed to effectively eliminate the interference path signal and improve the performance of the intelligent parking system. In order to verify whether the CNNs system designed in this study has advantages, the simulation experiments of CNNs, ZigBee, and manual parking are carried out. The results show that the parking system designed in this study can control the parking error, has smaller parking error than ZigBee, and has more than 25.64% less parking time than ZigBee, and more than 34.83% less time than manual parking. In terms of parking energy consumption, when there are less free parking spaces, CNNs have lower energy consumption. [ABSTRACT FROM AUTHOR]

The power quality assessment provides a reference for power quality management and control of microgrid operation. In terms of reflecting the correlation of power quality indexes and the dynamic changes of microgrid operating conditions, the traditional power quality assessment methods need to be improved. A power quality comprehensive evaluation based on CRITIC and dynamic coefficient is proposed in this paper. In this method, the objective weight of power quality indicators in single node is determined by using the intensity of conflict and contrast firstly. For the node weight calculation, the dynamic coefficient is proposed to reflect the different influence degree of node with different connected load. The proposed method in this paper can reflect both the internal characteristic of data sequence and the relationship between different data sequences. In addition, it also can reflect the dynamic changes of microgrid. Finally, an example is used to verify the feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

• Model-based polarimetric decomposition method for soil moisture estimation (SME). • Dihedral scattering component is neglected in bare soil and sparse vegetation areas. • SME results of C- and L-band full polarimetric data are analyzed. • SME accuracies of bare soil, sparse, and dense grassland are evaluated. Polarimetric synthetic aperture radar (PolSAR), as an advanced active sensor, can penetrate vegetation to detect ground information. The multimode observation data can provide more information to interpret different scattering mechanisms and have great application potential in soil moisture estimation (SME). In this paper, a modified method of soil moisture inversion from PolSAR data based on a two-component polarimetric decomposition model is introduced. The proposed method neglects the dihedral scattering component in the case of sparse vegetation cover and no-building areas, compensates the orientation angle by considering the influence of surface roughness and terrain slope, and calculates the dielectric constant with the hybrid X-Bragg/Fresnel model. Gaofen-3 (C-band) data from Ordos, Inner Mongolia, and Soil Moisture Experiment 2003 (SMEX03) airborne synthetic aperture radar (AIRSAR) (C- and L-band) data from Southern Oklahoma were applied in the experimental research. Furthermore, the performance and validity limits were assessed by comparing the retrieval results with in situ measurements and Soil Moisture Active Passive (SMAP) soil moisture products. The results reveal a root-mean-square error (RMSE) of 8.66% with an inversion rate of up to 61% when using the Gaofen-3 data from Ordos. For the Southern Oklahoma study area, the inversion RMSE of the AIRSAR C- and L-band data is 8.57% and 8.95%, respectively. The tendency of the inversion results is also consistent with the change trend of the site observations. In the bare soil and sparse vegetation covered areas, the retrieval error is relatively small, while in dense vegetation covered areas, soil moisture is underestimated in both the C- and L-band data, and the former is more underestimated than the latter. [ABSTRACT FROM AUTHOR]

A single crystal, (110)-oriented dilute antiferromagnet (AF) Fe[SUB0.82]Zn[SUB0.18]F[SUB2] film was grown via molecular beam epitaxy on a (110)-MgF[SUB2] substrate by codepositing FeF[SUB2] and ZnF[SUB2], followed by 1.0 nm pure FeF[SUB2] and 18 nm Co layers. The exchange bias (H[SUB E]) and coercivity (H[SUBC]) of the Co film strongly depend on the cooling field (H[SUBCF]) and temperature. For 0≤H[SUBCF]<2 kOe, H[SUBE]<0 in the whole temperature range before reaching zero at the blocking temperature (T[SUBB]). For(H[SUBCF])&ges;15 kOe, H[SUBE]>0 for T

The exchange bias (H[sub E]) was measured in Fe[sub χ]Z[sub 1-χ]F[sub 2]/Co bilayers grown on MgO (100) with and without a 1.0 nm layer of pure FeF[sub 2] at the interface between the Fe[sub χ]Zn[sub 1-χ]F[sub 2] and the Co layers. For χ = 0.83 samples with an additional 1.0 nm FeF[sub 2] interface layer H[sub E] increases by 65% when compared to the FeF[sub 2]/Co bilayer. The FeF[sub 2] interface layer also causes a significant H[sub E] to appear for χ < 0.5. This enhancement of H[sub E] with an FeF[sub 2] interlayer cannot be explained by simply scaling the exchange bias with the Fe concentration. Presumably this is due to the formation of domain states inside the dilute antiferromagnetic film, and the pure interface layer increases the coupling between the ferromagnet and the antiferromagnet. [ABSTRACT FROM AUTHOR]

Although studies have shown that Urotensin II (UII) can induce cardiomyocyte hypertrophy and UII-induced cardiomyocyte hypertrophy model has been widely used for hypertrophy research, but its precise mechanism remains unknown. Recent researches have demonstrated that UII-induced cardiomyocyte hypertrophy has a relationship with the changes of intracellular Ca 2 + concentration. Therefore, the aim of this study was to investigate the mechanisms of cardiomyocyte hypertrophy induced by UII and to explore whether the calcium/calmodulin-dependent protein kinase II (CaMKII)-mediated up-regulating of phospholamban (PLN) Thr17–phosphorylation signaling pathway contributed to UII-induced cardiomyocyte hypertrophy. Primary cultures of neonatal rat cardiomyocytes were stimulated for 48 h with UII. Cell size, protein/DNA contents and intracellular Ca 2 + were determined. Phosphorylated and total forms of CaMKII, PLN and the total amount of serco/endo-plasmic reticulum ATPases (SERCA 2a) were quantified by western blot. The responses of cardiomyocytes to UII were also evaluated after pretreatment with the CaMKII inhibitor, KN-93. These results showed that UII increased cell size, protein/DNA ratio and intracellular Ca 2 + , consistent with a hypertrophic response. Furthermore, the phosphorylation of CaMKII and its downstream target PLN (Thr17), SERCA 2a levels were up-regulated by UII treatment. Conversely, treatment with KN-93 reversed all those effects of UII. Taken together, the results suggest that UII can induce cardiomyocyte hypertrophy through CaMKII-mediated up-regulating of PLN Thr17–phosphorylation signaling pathway. [ABSTRACT FROM AUTHOR]

Weather-dependent photovoltaic (PV) system power variation may become frequent and rapid, which could be regarded as perturbation for the microgrid (MG). This perturbation would lead to voltage and frequency fluctuations and dramatically degrade the MG performances. Conventional secondary control strategy could restore the voltage and frequency after a certain time delay, but it cannot satisfy the speed demands when frequent and rapid perturbations appear. To overcome the limitation, this paper proposes a novel real-time voltage and frequency compensation strategy to suppress the fluctuations. The proposed strategy realizes voltage regulation by an adaptive virtual impedance loop and realizes frequency regulation by a virtual frequency-impedance loop. The novel real-time strategy is simple to be implemented and needless of communication; therefore, it is much more suitable for the PV-based MG. Finally, a PV-based MG is established as laboratory prototype, and experimental results validate the effectiveness of the proposed method. [ABSTRACT FROM PUBLISHER]

Polycrystalline ZnO thin films were deposited on pure Al foils at 80 °C using electrochemical deposition (ECD) in solutions containing zinc nitrate hexahydrate and hexamethylenetetramine. Photoluminescence (PL) spectra between 20 and 200 K showed intense UV emissions near the band edge along with weak broad bands due to defects. When the source solution was slightly doped with Mg2+ ions, the defect-induced band was suppressed along with a redshift, whereas the UV peak position and intensity remained the same. Surprisingly, Auger electron spectroscopy (AES) revealed no Mg contents in the films within the detection limit. Based on the solubility products of Mg(OH)2 and Zn(OH)2, we put forward a mechanism to describe how metal impurities could be incorporated into ZnO in such an aqueous process, which was strongly supported by samples grown in Ca- or Cu-doped solutions. To further corroborate, we also fabricated, for the first time via ECD, MgZnO alloys in Mg-doped solutions with very high nitrate concentrations. Blue shift of the UV emission peak was clearly observed from as-prepared samples on Al. MgZnO films, deposited on Si substrates under similar supersaturated conditions and annealed in ambient air at 350 °C for 30 min, showed a similar peak shift. This electrochemical route could therefore be used as an effective approach to enhance the optical properties of compound semiconductors, demonstrating its potential for deep UV applications as well. [ABSTRACT FROM AUTHOR]

Surface soil moisture (SM) retrieval over agricultural areas from polarimetric synthetic aperture radar (PolSAR) has long been restricted by vegetation attenuation, simplified polarimetric scattering modelling, and limited SAR measurements. This study proposes a modified polarimetric decomposition framework to retrieve SM from multi-incidence and multitemporal PolSAR observations. The framework is constructed by combining the X-Bragg model, the extended double Fresnel scattering model and the generalised volume scattering model (GVSM). Compared with traditional decomposition models, the proposed framework considers the depolarisation of dihedral scattering and the diverse vegetation contribution. Under the assumption that SM is invariant for the PolSAR observations at two different incidence angles and that vegetation scattering does not change between two consecutive measurements, analytical parameter solutions, including the dielectric constant of soil and crop stem, can be obtained by solving multivariable nonlinear equations. The proposed framework is applied to the time series of L-band uninhabited aerial vehicle synthetic aperture radar data acquired during the Soil Moisture Active Passive Validation Experiment in 2012. In this study, we assess retrieval performance by comparing the inversion results with in-situ measurements over bean, canola, corn, soybean, wheat and winter wheat areas and comparing the different performance of SM retrieval between the GVSM and Yamaguchi volume scattering models. Given that SM estimation is inherently influenced by crop phenology and empirical parameters which are introduced in the scattering models, we also investigate the influence of surface depolarisation angle and co-pol phase difference on SM estimation. Results show that the proposed retrieval framework provides an inversion accuracy of RMSE<6.0% and a correlation of R ≥0.6 with an inversion rate larger than 90%. Over wheat and winter wheat fields, a correlation of 0.8 between SM estimates and measurements is observed when the surface scattering is dominant. Specifically, stem permittivity, which is retrieved synchronously with SM also shows a linear relationship with crop biomass and plant water content over bean, corn, soybean and wheat fields. We also find that a priori knowledge of surface depolarisation angle, co-pol phase difference and adaptive volume scattering could help to improve the performance of the proposed SM retrieval framework. However, the GVSM model is still not fully adaptive because the co-pol power ratio of volume scattering is potentially influenced by ground scattering. • A new framework for soil moisture (SM) retrieval from multiple PolSAR observations. • The soil and crop stem permittivity are derived from the proposed framework. • SM estimates over selected crop fields show an accuracy of RMSE≤6.0%. • The surface depolarization angle influences the SM inversion performance. • The optimal selection of co-pol phase difference improves the SM estimation. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) technology has become an important means of flood monitoring because of its large coverage, repeated observation, and all-weather and all-time working capabilities. The commonly used thresholding and change detection methods in emergency monitoring can quickly and simply detect floods. However, these methods still have some problems: (1) thresholding methods are easily affected by low backscattering regions and speckle noise; (2) changes from multi-temporal information include urban renewal and seasonal variation, reducing the precision of flood monitoring. To solve these problems, this paper presents a new flood mapping framework that combines semi-automatic thresholding and change detection. First, multiple lines across land and water are drawn manually, and their local optimal thresholds are calculated automatically along these lines from two ends towards the middle. Using the average of these thresholds, the low backscattering regions are extracted to generate a preliminary inundation map. Then, the neighborhood-based change detection method combined with entropy thresholding is adopted to detect the changed areas. Finally, pixels in both the low backscattering regions and the changed regions are marked as inundated terrain. Two flood datasets, one from Sentinel-1 in the Wharfe and Ouse River basin and another from GF-3 in Chaohu are chosen to verify the effectiveness and practicality of the proposed method. [ABSTRACT FROM AUTHOR]

• Strain DNB-S2 suffered membrane damage and oxidative stress from DBP. • AQDS enhances the capture of strain DNB-S2 to hydrophobic pollutant DBP. • Maintaining cell membrane integrity is the key to AQDS promoting DBP biodegradation. • AQDS enhances the degrading strains scavenging ability of OH and H 2 O 2. Plasticizer dibutyl phthalate (DBP) pollution has received more and more attention. In this study, a DBP degrading bacteria Enterobacter sp. DNB-S2 was found to suffer membrane damage and oxidative stress during DBP degradation. Physiological and transcriptome analysis showed that 100 μmol L−1 anthraquinone-2,6-disulfonate (AQDS) could enhance the ability of strain DNB-S2 for biodegradation of DBP. AQDS adjusted the cell surface structure, including increase levels of hydrophobic and unsaturated fatty acids. These changes increased the chemotactic ability of the strain DNB-S2 to the hydrophobic pollutant DBP and the fluidity of the cell membrane. The expression of methyl chemotactic protein and genes associated with cell membrane-fixed components were up-regulated. AQDS also improved the scavenging ability of ·OH and H 2 O 2 of DNB-S2 by promoting expression genes related to glutathione metabolism, thereby reducing oxidative stress. These results will provide new insights into the biodegradation of DBP. [ABSTRACT FROM AUTHOR]

Soil phthalic contamination has received more and more attention due to the widespread use of plastic mulching films. Humic acid (HA) is a natural antidote. The effects of HA on the biodegradation of dibutyl phthalate (DBP) in mollisol was investigated in this study. Through the calculation by bi-exponential model, the half-life of DBP was effectively shortened after adding HA, from 11.65 days to 3.36 days, and soil bulk density decreased. The enhancement mechanism for DBP removal by HA was analyzed by fluorescence spectrometry, two-dimensional FTIR correlation spectroscopy (2D-FTIR-COS) and PLFA analysis. Two major functional groups (aryl C–O and alkyl ester C O) were found at the binding site between DBP and HA. By mediating the transportation of DBP, HA could provide more time for soil microorganisms to degrade DBP. Meanwhile, HA effectively stabilized the mollisol microbial community composition and promote the growth of aerobic bacteria, which contributes to the degradation of DBP. The results are valuable for relieving DBP pollution caused by agricultural production and promoting sustainable use of mollisol. • HA shorten the half-life of DBP in black soil. • HA and DBP are bound by static quenching and have only one binding site. • HA stabilizes microbial activity and promotes the growth of aerobic microorganisms. [ABSTRACT FROM AUTHOR]

• Tea seedling growth is monitored by hyperspectral imaging. • Spectrum is extracted by Mask R-CNN network with high precision. • UVE is the best among the feature band selection methods. • The CNN-GRU regression model is proposed, and the effect is better than single-layer deep learning and machine learning models. The shoot and root growth not only serve as important indicators for measuring the quality of tea cutting seedlings, but also can provide scientific basis for agricultural production and fine seedling management. Currently, manual weighing is the main method used to measure shoot and root growth, which is both destructive and inefficient. The study proposes a method for monitoring the shoot and root growth in tea cutting seedlings using hyperspectral imaging. First, mature leaves and shoots spectra were extracted from tea cutting seedlings using Mask R-CNN (Epoch=20 and Learning-rate=0.001). The spectra were then preprocessed by MSC, S-G, 1-D filtering techniques, with feature bands screened by UVE (Optimal factor number=5), CARS (Monte Carlo sampling times=300), and SPA (Epoch=25). Finally, a CNN-GRU (Epoch=100 and Learning-rate=0.01) network was employed to predict shoot and root biomass, and compared with machine learning methods of SVR (Kernel function=Polynomial), RFR (Ntrees=200), and PLSR (Latent variable=16) and deep learning methods (Epoch=100 and Learning-rate=0.01) of CNN and LSTM. The results show that (i) Mask R-CNN can accurately extract the spectral of mature leaves (precision=97.8 %) and shoots (precision=91.5 %), (ii) The spectral feature bands of shoot (number=212) and root (number=105) biomass screened by UVE were more abundant than CARS and SPA, (iii) The UVE+CNN-GRU model (Rp2=0.90, RMSEP = 0.12, RPD=2.43) based on shoots spectrum provided the optimal estimation of shoot biomass. The SPA+LSTM model (Rp2 = 0.65, RMSEP = 0.05, RPD=1.67) based on the mature leaves spectrum provided the optimal estimation of root biomass. These findings demonstrate that hyperspectral imaging technology combined with deep learning algorithms can accurately monitor the growth of cutting seedlings quickly without causing damage. This not only provides a new data sources along with technical means for efficient screening excellent tea varieties, but also improves agricultural production efficiency and resource utilization. [ABSTRACT FROM AUTHOR]

Multisource remote sensing images have rich features and high interpretability and are widely employed in many applications. However, highly unbalanced category distributions and complex backgrounds have created some difficulties in the application of remote sensing image semantic segmentation tasks, such as low accuracy of small target segmentation and inaccurate edge extraction. To solve these problems, in this paper, a feature map segmentation reconstruction module and an attention upsampling module are proposed. In the encoder part, the input feature map is equally segmented, and the segmented feature map is enlarged to effectively improve the small target feature information expression ability in the model. In the decoder part, the key segmentation and location information of shallow features are obtained using the global view. The deep semantic information and shallow spatial location information are fully combined to achieve a more refined upsampling operation. In addition, the attention mechanism of the spatial and channel squeeze and excitation block (scSE) is applied to pay more attention to important features and to suppress irrelevant background and redundant information. To verify the effectiveness of the proposed method, the WHU-OPT-SAR dataset and six state-of-the-art algorithms are utilized in comparative experiments. The experimental results show that our model has demonstrated the best performance and low computational complexity. With only approximately half the floating-point operation count and the number of model parameters of the MCANet model, which is specially designed for the dataset, our model surpasses MCANet by 1.52% and 1.53% in terms of mean intersection over union (mIoU) and F1 score, respectively. In particular, for small object regions such as roads and other categories, compared to the baseline model, the IoU and F1 score of our model are improved by 5.27% and 3.99% and by 5.68% and 5.65%, respectively. These results demonstrate the superior performance of our model in terms of accuracy and efficiency. [ABSTRACT FROM AUTHOR]

Worldwide, myocardial infarction (MI) is the leading cause of death and disability-adjusted life years lost. Recent researches explored new methods of detecting biomarkers that can predict the risk of developing myocardial infarction, which includes identifying genetic markers associated with increased risk. We induced myocardial infarction in mice by occluding the left anterior descending coronary artery and performed TTC staining to assess cell death. Next, we performed ChIP assays to measure the enrichment of histone modifications at the promoter regions of key genes involved in mitochondrial fission. We used qPCR and western blot to measure expression levels of relative apoptotic indicators. We report that miR-181a inhibits myocardial ischemia-induced apoptosis and preserves left ventricular function after MI. We show that programmed cell death protein 4 (PDCD4) is the target gene involved in miR-181a-mediated anti-ischemic injury, which enhanced BID recruitment to the mitochondria. In addition, we discovered that p53 inhibits the expression of miR-181a via transcriptional regulation. Here, we discovered for the first time a mitochondrial fission and apoptosis pathway which is controlled by miR-181a and involves PDCD4 and BID. This pathway may be controlled by p53 transcriptionally, and we presume that miR-181a may lead to the discovery of new therapeutic and preventive targets for ischemic heart diseases. [ABSTRACT FROM AUTHOR]

Cardiac hypertrophy mainly predicts heart failure and is highly linked with sudden loss of lives. MicroRNAs (miRNAs) play essential roles in the development of cardiac hypertrophy through binding to corresponding mRNA targets. In this study, in order to investigate the roles of two mature forms of miRNA-195, miR-195-3p, and miR-195-5p, in vitro and in vivo models of cardiac hypertrophy were established by applying angiotensin II (Ang II) to H9c2 cardiomyocytes and infusing chronic Ang II to mice, respectively. We found that miR-195-5p was evidently equally upregulated in the in vitro and in vivo studies of cardiac hypertrophy induced by Ang II. High expressed miR-195-5p could adequately promote hypertrophy, whereas the suppression of miR-195-5p prevented hypertrophy of H9c2 cardiomyocytes under Ang II treatment. Furthermore, the luciferase reporter system demonstrated that MFN2 and FBWX7 were target genes of miR-195-5p, which negatively regulated the expression of these two genes in H9c2 cells. By contrast, in both models, expression of miR-195-3p was only slightly changed without statistical significance. In addition, we observed a trend towards decreased expression of hypertrophic markers by overexpressing miR-195-3p in AngII-treated H9c2 cardiomyocytes in vitro. Taken together, our study indicates that miR-195-5p promotes cardiac hypertrophy via targeting MFN2 and FBXW7 and may provide promising therapeutic strategies for interfering cardiac hypertrophy. [ABSTRACT FROM AUTHOR]

Quorum quenching (QQ) is a novel anti-biofouling strategy for membrane bioreactors (MBRs) used in wastewater treatment. However, actual operation of QQ-MBR systems for wastewater treatment needs to be systematically studied to evaluate the comprehensive effects of QQ on wastewater treatment engineering applications. In this study, a novel QQ strain, Acinetobacter pittii HITSZ001, was encapsulated and applied to a MBR system to evaluate the effects of this organism on real wastewater treatment. To verify the effectiveness of immobilized QQ beads in the MBR system, we examined the MBR effluent quality and sludge characteristics. We also measured the extracellular polymeric substances (EPS) and soluble microbial products (SMP) in the system to determine the effects of the organism on membrane biofouling inhibition. Additionally, changes in microbial communities in the system were analyzed by high-throughput sequencing. The results indicated that Acinetobacter pittii HITSZ001 is a promising strain for biofouling reduction in MBRs treating real wastewater, and that immobilization does not affect the biofouling control potential of QQ bacteria. [ABSTRACT FROM AUTHOR]

The interferometric synthetic aperture radar (InSAR) technique based on time–frequency (TF) analysis has great potential for mapping the forest canopy height model (CHM) at regional and global scales, as it benefits from the additional InSAR observations provided by the sublook decomposition. Meanwhile, due to the wider swath and higher spatial resolution of single-polarization data, InSAR has a higher observation efficiency in comparison with PolInSAR. However, the accuracy of the CHM inversion obtained by the TF-InSAR method is attenuated by its inaccurate coherent scattering modeling and uncertain parameter calculation. Hence, a new approach for CHM estimation based on single-baseline InSAR data and sublook decomposition is proposed in this study. With its derivation of the coherent scattering modeling based on the scattering matrix of sublook observations, a time–frequency based random volume over ground (TF-RVoG) model is proposed to describe the relationship between the sublook coherence and the forest biophysical parameters. Then, a modified three-stage method based on the TF-RVoG model is used for CHM retrieval. Finally, the two-dimensional (2-D) ambiguous error of pure volume coherence caused by residual ground scattering and temporal decorrelation is alleviated in the complex unit circle. The performance of the proposed method was tested with airborne L-band E-SAR data at the Krycklan test site in Northern Sweden. Results show that the modified three-stage method provides a root-mean-square error (RMSE) of 5.61 m using InSAR and 14.3% improvement over the PolInSAR technique with respect to the classical three-stage inversion result. An inversion accuracy of RMSE = 2.54 m is obtained when the spatial heterogeneity of CHM is considered using the proposed method, demonstrating a noticeable improvement of 32.8% compared with results from the existing method which introduces the fixed temporal decorrelation factor. [ABSTRACT FROM AUTHOR]

Alpha-Lipoic acid (ALA) plays a protective role in a variety of vascular diseases, however, its effect on aortic aneurysm and dissection (AAD) has not been reported. In this study, we found that Alpha-Lipoic Acid treatment significantly improved the AAD and AAA development, which was demonstrated by ameliorated aneurysmal dilation, decreased aortic dissection and aneurysm incidence, improved aortic morphology and inhibited elastin degradation. ALA blunted extra-cellular matrix degradation, vascular smooth muscle cell (VSMC) loss and phenotype transformation. Moreover, the protective effect of ALA on VSMCs may be related to the amelioration of mitochondrial dysfunction. In conclusion, our study revealed that ALA exerts inhibitory effects against progression of AAD, thus suggesting that ALA may be a novel therapeutic molecule for AAD. [ABSTRACT FROM AUTHOR]

Alpha-Lipoic acid (ALA) plays a protective role in a variety of vascular diseases, however, its effect on aortic aneurysm and dissection (AAD) has not been reported. In this study, we found that Alpha-Lipoic Acid treatment significantly improved the AAD and AAA development, which was demonstrated by ameliorated aneurysmal dilation, decreased aortic dissection and aneurysm incidence, improved aortic morphology and inhibited elastin degradation. ALA blunted extra-cellular matrix degradation, vascular smooth muscle cell (VSMC) loss and phenotype transformation. Moreover, the protective effect of ALA on VSMCs may be related to the amelioration of mitochondrial dysfunction. In conclusion, our study revealed that ALA exerts inhibitory effects against progression of AAD, thus suggesting that ALA may be a novel therapeutic molecule for AAD. [ABSTRACT FROM AUTHOR]

Quorum quenching (QQ) has proven its potential as an innovative approach for biofouling control in membrane bioreactor (MBR) for wastewater treatment. In this study, a novel quorum-quenching bacterium, Acinetobacter pittii HITSZ001, was isolated from activated sludge. In contrast to previous studies, in which QQ bacteria were directly applied in the MBR system, in this investigation, this strain was co-cultured with Pseudomonas aeruginosa to illustrate the inhibitory effect of the QQ strain on the key genes involved in biofilm formation. The types of QQ enzymes secreted by A. pittii HITSZ001 and its functional ahlm gene was also identified. To better apply this strain, it was encapsulated in the QQ beads with great activities, which could well adapt to a complex real activated sludge environment and effectively controlled biofilm formation on the membrane. The results of this study suggest that A. pittii HITSZ001 represents a novel and promising strain for biofilm mitigation to promote MBR performance and that it would be economically and technically feasible to expand biofouling control with this strain to plant-scale MBRs and similar environmental engineering systems. [Display omitted] • A quorum quenching strain was isolated and its mechanism was identified. • The QS genes in Pseudomonas aeruginosa were downregulated by QQ strain. • The encapsulated QQ strain can inhibit biofilm formation effectively. • A new biological method to detect the quantity of AHL was improved. [ABSTRACT FROM AUTHOR]

Accurate and effective monitoring of environmental parameters in tea seedling greenhouses is an important basis for regulating the seedling environment, which is crucial for improving the seedling growth quality. This study proposes a tea seedling growth simulation (TSGS) model based on deep learning. The Internet of Things system was used to measure environmental change during the whole seedling process. The correlation between the environmental parameters and the biomass growth of tea seedlings in various varieties was analyzed. A CNN-LSTM network was proposed to build the TSGS model of light, temperature, water, gas, mineral nutrition, and growth biomass. The results showed that: (1) the average correlation coefficients of air temperature, soil temperature, and soil moisture with the biomass growth of tea seedlings were 0.78, 0.84, and −0.63, respectively, which were three important parameters for establishing the TSGS model. (2) For evaluating the TSGS model of a single variety, the accuracy of ZM's TSGS based on the CNN-LSTM network was the highest (Rp2 = 0.98, RMSEP = 0.14). (3) For evaluating the TSGS model of multiple varieties, the accuracy of TSGS based on the CNN-LSTM network was the highest (Rp2 = 0.96, RMSEP = 0.17). This study provided effective technical parameters for intelligent control of tea-cutting growth and a new method for rapid breeding. [ABSTRACT FROM AUTHOR]

The leaf area index (LAI) is critical for the respiration, transpiration, and photosynthesis of crops. Color indices (CIs) and vegetation indices (VIs) extracted from unmanned aerial vehicle (UAV) imagery have been widely applied to the monitoring of the crop LAI. However, when the coverage of the crop canopy is large and only spectral data are used to monitor the LAI of the crop, the LAI tends to be underestimated. The canopy height model (CHM) data obtained from UAV-based point clouds can represent the height and canopy structure of the plant. However, few studies have been conducted on the use of the CHM data in the LAI modelling. Thus, in this study, the feasibility of combining the CHM data and CIs and VIs, respectively, to establish LAI fitting models for winter wheat in four growth stages was investigated, and the impact of image resolution on the extraction of remote sensing variables (the CHM data, CIs, and VIs) and on the accuracy of the LAI models was evaluated. Experiments for acquiring remote sensing images of wheat canopies during the four growth stages from the RGB and multispectral sensors carried by a UAV were carried out. The partial least squares regression (PLSR), random forest regression (RFR), and support vector machine regression (SVR) were used to develop the LAI fitting models. Results showed that the accuracy of the wheat LAI models can be improved in the entire growth stages by the use of the additional CHM data with the increment of 0.020–0.268 in R2 for three regression methods. In addition, the improvement from the Cis-based models was more noticeable than the Vis-based ones. Furthermore, the higher the spatial resolution of the CHM data, the better the improvement made by the use of the additional CHM data. This result provides valuable insights and references for UAV-based LAI monitoring. [ABSTRACT FROM AUTHOR]

The timely and accurate detection of wheat lodging at a large scale is necessary for loss assessments in agricultural insurance claims. Most existing deep-learning-based methods of wheat lodging detection use data from unmanned aerial vehicles, rendering monitoring wheat lodging at a large scale difficult. Meanwhile, the edge feature is not accurately extracted. In this study, a semantic segmentation network model called the pyramid transposed convolution network (PTCNet) was proposed for large-scale wheat lodging extraction and detection using GaoFen-2 satellite images with high spatial resolutions. Multi-scale high-level features were combined with low-level features to improve the segmentation's accuracy and to enhance the extraction sensitivity of wheat lodging areas in the proposed model. In addition, four types of vegetation indices and three types of edge features were added into the network and compared to the increment in the segmentation's accuracy. The F1 score and the intersection over union of wheat lodging extraction reached 85.31% and 74.38% by PTCNet, respectively, outperforming other compared benchmarks, i.e., SegNet, PSPNet, FPN, and DeepLabv3+ networks. PTCNet can achieve accurate and large-scale extraction of wheat lodging, which is significant in the fields of loss assessment and agricultural insurance claims. [ABSTRACT FROM AUTHOR]

The spatial distribution of oxide abundances and Mg# (Mg/(Mg + Fe)) on the lunar surface is of great significance for in-depth understanding the origin and evolution of the Moon. China's Chang'E−5 (CE-5) mission returned young lunar soils for the first time, providing a new ground truth for the inversion of oxide abundances. In this study, the relationship between multi-source remote sensing data (including Chang'E−1 Interference Imaging Spectrometer (CE-1 IIM) data and the new global Christiansen feature (CF) product, named IIM-CF data), and the abundances of six oxides (FeO, TiO 2 , MgO, SiO 2 , Al 2 O 3 and CaO) measured at 40 lunar sampling sites including CE-5 were analyzed. The use of IIM-CF data as the input features of the selected inversion models for obtaining the abundances of oxides, and the oxide abundances measured at the 40 sampling sites were used as the ground truth. The models selected for this investigation contain three typical algorithms − random forest (RF), extreme gradient boosting (XGBoost) and partial least squares regression (PLSR), and a new method integrates RF, XGBoost and PLSR together named RXP was developed in this study. The modeling results of the abundances of the six oxides derived from the above four algorithms show that the RXP algorithm outperforms the other three algorithms. The distributions of the six oxides and Mg# on the lunar surface covering the range from 70° N to 70° S (70° N/S) with a resolution of about 200 m/pixel were generated using the proposed RXP algorithm. Our results indicate that, compared with the result of a single data source, the use of IIM-CF data improved the accuracy of the modeling of oxide abundances and Mg#. It is expected that the CE-5 samples can bring additional references to the studies of the inversion for the oxides of the lunar surface and deepen our understanding over this issue. • The changes in inversion results of the Moon due to the utilization of CE-5 samples. • RXP outperforms other three models since RXP take advantages of the three algorithms. • IIM-CF data avoid the limitation of single data and enhance accuracy of the inversion. • Mg# calculated from inverted FeO and MgO more accurate than Mg# directly inverted. [ABSTRACT FROM AUTHOR]

The recovery of nutrients from residual fish sludge in aquaponics could achieve zero solid waste discharge and supplement additional nutrients to hydroponics, thus yielding environmental and economic benefits. In this study, novel biological aerated filters (BAFs) with ceramsite plus different lignocellulosic materials (corn straw, wheat straw, and sawdust) are designed and installed in aquaponic systems. The BAFs performed the fish sludge separation and nutrient conversion in a single equipment without the necessity for backwashing. The potential for nutrient recovery from fish sludge is evaluated by comparing four BAFs filled with different media. The BAF with ceramsite plus sawdust achieves the highest mineralization capacity based on nitrification and dissolved total phosphorus. The average concentrations of N, P, K, Ca, Mg, and S macronutrients reach 12.3%, 11.8%, 4.5%, 26.4%, 22.6%, and 46.0% of those of Hoagland nutrient solution, respectively. The recovered Fe, Mn, and B micronutrients are 10.0%, 12.0%, and 18.0%, respectively, whereas Cu, Zn, and Mo are 1.5, 1.8, and 1.0 times, respectively, of those of Hoagland nutrient solution. The estimated nutrient recoveries of Ca, Mg, and S have good performance efficiencies, followed by K and Cu; those of N, P, Fe, Mn, and Zn have potentials for further improvement. The modified aquaponics with BAFs are proposed for the recovery of both dissolved and solid nutrients. Image 10130 • A novel biological aerated filter is designed for nutrient recovery in aquaponics. • Ceramsites mixed with corn straw, wheat straw, and sawdust are tested as filter media. • The highest mineralization capacity is achieved by the use of ceramsite plus sawdust. • The nutrient recovery efficiencies of macronutrients and micronutrients are evaluated. • The modified aquaponics is proposed for the recovery of both dissolved and solid nutrients. [ABSTRACT FROM AUTHOR]

The combination of coagulation and addition of skeleton builder is a popular pretreatment method to improve the dewaterability of sludge. In this study, a novel bifunctional inorganic/organic hybrid coagulant (CS–Si@ATP) was designed and obtained by chemically coupling a cationic starch (CS) with a popular clay, that is, attapulgite (ATP), via a silane coupling agent (APTES) for one-step conditioning of sludge. CS-Si@ATP can evidently enhance the sludge dewatering performance compared with CS, ATP, and their simple combination due to the distinct dual functions of this hybrid coagulant. The tentacle-like cationic CS in CS-Si@ATP shows efficient charge neutralization effect to aggregate and precipitate the suspended solids for further formation of compact sludge cakes. Meanwhile, the internal ATP with a stable and rigid structure acts as the skeleton builder to notably improve the filterability and permeability of the sludge cakes. The synergistic effects of CS and ATP in CS-Si@ATP, i.e., the charge neutralization of CS and the skeleton construction of ATP, cause the evidently enhanced sludge dewaterability, with a filter cake moisture content approximately 78.30% after the mechanical dewatering at 0.05 MPa. In comparison with the traditional two-step combination process by separated addition of CS and ATP, the one-step addition of CS-Si@ATP can reduce the required ATP dose nearly an order of magnitude. Thus, CS-Si@ATP has the notable advantages of simple operation, efficient utilization of ATP and evident reduction of disposal cost. This study provides an environmentally friendly and cost-effective coagulant to further improve the dewaterability of sludge. [Display omitted] • A novel bifunctional hybrid coagulant shows high sludge dewatering performance • This coagulant has good charge-neutralization effect and also act as skeleton builder • 78.30% of water content was obtained after mechanical dewatering at 0.05 MPa • The originally required skeleton builder was reduced nearly an order of magnitude • One-step conditioning of this coagulant is simple operation and cost saving [ABSTRACT FROM AUTHOR]

In order to efficiently remove commonly used herbicide atrazine in farmland, an iron-modified biochar (FeMBC) was fabricated via chemical co-precipitation of Fe3+ onto corn stalks biochar. The composites of FeMBC and Acinetobacter lwoffii DNS32 (bFeMBC) effectively accelerated the degradation rate of atrazine (100 mg L−1) in inorganic salt culture solution. TEM，XRD，XPS and FTIR were used to study the basic properties of the Materials. FeMBC promoted the formation of bacterial biofilm, -NH functional group on the surface of bacterial extracellular polymers (EPS) and FeMBC could interact with the aromatic ring of atrazine through Hbonding, which were conducive for microbial capture of atrazine. Meanwhile, the pores (2–10 μm) of FeMBC facilitated the passage of the DNS32 strain and the atrazine molecule, which contributed to the efficient capture and degradation of atrazine by DNS32 strain. BFeMBC amendment helped to maintain the bacterial diversity in the atrazine contaminated soil. The increase of rare bacteria (relative abundance of 0.01%–0.05%) richness plays a certain role in stabilizing nutrient cycling, thereby promoting microbial nutrient utilization activities and has the function of pollutant degradation. This may contribute to the digestion of atrazine and its intermediate metabolites，reducing the stress of microbial in atrazine contaminated soil. bFeMBC amendment may be a promising in situ remediation technique for soil atrazine contamination. Unlabelled Image • A FeMBC with high adsorption efficiency for atrazine was fabricated. • FeMBC could promote the biofilm formation of atrazine degrading bacteria. • bFeMBC could contribute to the effective capture and degradation of atrazine. • bFeMBC could stabilize the rare bacteria genera in atrazine contaminated soil. [ABSTRACT FROM AUTHOR]

Abstract Aquaponics is considered as a promising approach to mass produce both fish meat and vegetable at high rates without discharge pollutants. A novel aquaponics system is proposed to control water quality and enhance nutrition efficiency with high rate immobilized biofilm units, including mass-bio system (MBS) and fiber biofilter (FB), installed between upstream hydroponics units, which are composed of the media filled beds (MFD) and nutrient film technique (NFT), and downstream aquaculture unit for fish rearing. A pilot test was conducted over 130 d to characterize the performance of proposed system which converted residual nutrients from aquaculture unit into ammonium and nitrate at balanced ratio for aquatic plant growth and then removed residual ammonium or/and nitrite to ensue recycled water quality for fish rearing. The kinetics of organic matter removal and nutrient conversion in hydroponics unit and immobilized biofilm units were determined via batch assays over 24 h in the system. The estimated daily pollutant increments for TAN, NO 2 −-N, NO 3 −-N, TN, DTP and TOC in the system were 0.28, 0.17, 0.20, 0.64, 0.56 and 2.19 mg/L, respectively. The results demonstrated that total ammonia nitrogen (TAN), nitrite (NO 2 −-N), nitrate (NO 3 −-N), total nitrogen (TN), dissolved total phosphorus (DTP) and total organic carbon (TOC) were maintained at low levels in the system for a long-term. Aquatic plants in MFD and NFT contributed to the removal of TAN, NO 2 −-N, NO 3 −-N, TN and DTP. The removal efficiencies of MFD versus NFT for TAN were 71.4% vs 43.5% for TAN and 62.5% vs 52.3% for DTP. MBS and FB performed the removal of TAN, NO 2 −-N and TOC. The 24 h removal efficiencies of TAN, NO 2 −-N and TOC were 71.8%, 58.5% and 3.7% for MBS and 15.7%, 7.7% and 27.2% for FB. Graphical abstract Image Highlights • A novel process of aquaponics system combined with immobilized biofilm units was tested at pilot scale. • This system controlled water quality of aquaponics for fish rearing over 130 d. • Aquatic plants in media filled beds showed better nutrient removal than nutrient film technique. • More total ammonia was removed by immobilized biofilm units than hydroponics units. [ABSTRACT FROM AUTHOR]

The composition and function of the intestinal microbiota play important roles in digestion and degradation of herbal medicines (HMs). However, few studies have examined the relationship between the fecal microbiota and HMs. In this study the effect of unfermented Astragalus (UA) and fermented Astragalus (FA) on growth performance, serum biochemical parameters, and fecal microbiota was evaluated in broiler chickens. In total, 180 one-day-old broiler chickens (Avian breeds) were randomly assigned to a control (C) group fed a basal diet, an unfermented (U) group fed a basal diet containing 0.5% UA, or a fermented (F) group fed a basal diet containing 0.5% FA, for 42 days. The F/G ratio was lower in F and U groups than in C group from 22 to 42 days (P < 0.05). Glutathione superoxide dismutase, antioxidant capacity, and total superoxide dismutase were higher, whereas malondialdehyde was lower in F group than in C and U groups from 1 to 21 days and from 22 to 42 days (P < 0.05). Fecal microbiota were profiled on an Illumina MiSeq platform following PCR amplification of the V4 region of the 16S rRNA gene. At the genus level Lactobacillus was the most abundant genus on day 7 in F group. Importantly, a potentially pathogenic genus, Enterococcus, was less abundant in the U and F groups than in the C group on day 35 (P < 0.05). These results indicate that dietary supplementation with 0.5% FA has beneficial effects on growth performance, serum biochemical parameters and fecal microbiota of broiler chickens. [ABSTRACT FROM AUTHOR]

We investigated if fermentation with probiotic cultures could improve the production of health-promoting biological compounds in Astragalus membranaceus. We tested the probiotics Enterococcus faecium, Lactobacillus plantarum and Enterococcus faecium + Lactobacillus plantarum and applied PacBio single molecule, real-time sequencing technology (SMRT) to evaluate the quality of Astragalus fermentation. We found that the production rates of acetic acid, methylacetic acid, aethyl acetic acid and lactic acid using E. faecium + L. plantarum were 1866.24 mg/kg on day 15, 203.80 mg/kg on day 30, 996.04 mg/kg on day 15, and 3081.99 mg/kg on day 20, respectively. Other production rates were: polysaccharides, 9.43%, 8.51%, and 7.59% on day 10; saponins, 19.6912 mg/g, 21.6630 mg/g and 20.2084 mg/g on day 15; and flavonoids, 1.9032 mg/g, 2.0835 mg/g, and 1.7086 mg/g on day 20 using E. faecium, L. plantarum and E. faecium + L. plantarum, respectively. SMRT was used to analyze microbial composition, and we found that E. faecium and L. plantarum were the most prevalent species after fermentation for 3 days. E. faecium + L. plantarum gave more positive effects than single strains in the Astragalus solid state fermentation process. Our data demonstrated that the SMRT sequencing platform is applicable to quality assessment of Astragalus fermentation. [ABSTRACT FROM AUTHOR]

The gut microbiota play important roles in the degradation of chemical compounds of herbal medicines (HMs). However, little information regarding the interplay between HMs and the gut microbiota is available. Thus, the aim of this study was to investigate the composition of the fecal microbiota of young (age, 11 weeks) hens fed a conventional diet containing a crude Astragalus (0.5%) additive for 21 days (group A) vs. controls (group B) that were fed only conventional feed. The fecal contents of 14-week-old hens were collected for DNA extraction, and then the V3 and V4 hyper-variable regions of the 16S rRNA gene were amplified and analyzed using high-throughput sequencing technology. A distinctive difference in microbial diversity was observed between the two groups. The microbial composition of hens fed a diet supplemented with Astragalus was greater than that of the control group. At the genus level, Lactobacillus was more abundant in group A than group B (p < 0.05). Importantly, this study is the first to report the observation of a novel Romboutsia sp. in the feces of hens. However, Romboutsia was less abundant in group A than group B (17.94 vs. 33.98%, respectively, p < 0.05). The microbial community differed significantly between the two groups at the genus level, suggesting that Astragalus modulates the composition of the fecal microbiota. Based on these differences, these findings provide fresh insights into the application of Astragalus in the poultry industry, as well as a better understanding of the interplay between HMs and the gut microbiota. [ABSTRACT FROM AUTHOR]

The traditional unsupervised change detection methods based on the pixel level can only detect the changes between two different times with same sensor, and the results are easily affected by speckle noise. In this paper, a novel method is proposed to detect change based on time-series data from different sensors. Firstly, the overall difference image of the time-series PolSAR is calculated by omnibus test statistics, and difference images between any two images in different times are acquired by Rj test statistics. Secondly, the difference images are segmented with a Generalized Statistical Region Merging (GSRM) algorithm which can suppress the effect of speckle noise. Generalized Gaussian Mixture Model (GGMM) is then used to obtain the time-series change detection maps in the final step of the proposed method. To verify the effectiveness of the proposed method, we carried out the experiment of change detection using time-series PolSAR images acquired by Radarsat-2 and Gaofen-3 over the city of Wuhan, in China. Results show that the proposed method can not only detect the time-series change from different sensors, but it can also better suppress the influence of speckle noise and improve the overall accuracy and Kappa coefficient. [ABSTRACT FROM AUTHOR]

Background: In this study, we tested the hypothesis that miR-181a levels increase during acute myocardial infarction. We investigated circulating miR-181a as a potential novel biomarker for early diagnosis of acute myocardial infarction (AMI). Methods: From June 2014 to June 2016, 120 consecutive eligible patients with AMI (n = 60) or unstable angina (UA; n = 60) and 60 control subjects were enrolled. Plasma miR-181a levels were determined by quantitative reverse transcriptase-polymerase chain reaction. Results: Circulating miR-181a expression levels detected immediately after admission were higher in the AMI group than in the UA and control groups. Relative miR-181a levels in AMI patients were positively correlated with the concentrations of the creatine kinase-MB fraction and cardiac troponin I. Correlation analysis showed that plasma miR-181a was positively correlated with coronary Gensini score (r = 0.573, P < 0.05) and negatively correlated with left ventricular ejection fraction (r = -0.489, P < 0.05). Receiver operating characteristic curve analyses showed that plasma miR-181a was of significant diagnostic value for AMI (AUC, 0.834; 95% CI, 0.756-0.912, P < 0.05). Conclusion: Circulating miR-181a levels in patients with AMI were significantly changed in a time-dependent manner, indicating the value of plasma miR-181a as a novel biomarker for diagnosing AMI. [ABSTRACT FROM AUTHOR]

Background: Here, we determined miR-499 involvement in the protective effect of ischemic postconditioning (IPC) against myocardial ischemia/reperfusion (I/R) injury and identified the underlying mechanisms. Methods: To investigate the cardioprotective effect of IPC-induced miR-499, rats were divided into the following five groups: sham, I/R, IPC, IPC + scramble, and IPC + antagomiR-499. Hemodynamic indexes were measured by carotid-artery intubation to assess left ventricular function . Ischemia and infarction areas of rat hearts were determined by Evans blue and triphenyltetrazolium chloride staining, and cardiomyocyte apoptosis was detected by terminal deoxynucleotidyl transferase dUTP nick-end-labeling assay. Results: IPC attenuated I/R-induced infarct size of the left ventricle (45.28 ± 5.40% vs. 23.56 ± 6.20%, P < 0.05), myocardial apoptosis, and decreased creatine kinase (1867.31 ± 242.41% vs. 990.21 ± 172.39%, P < 0.05), lactate dehydrogenase (2257.50 ± 305.11% vs. 1289.11 ± 347.28%, P < 0.05), and malondialdehyde levels (7.18 ± 1.63% vs. 4.85 ± 1.52%, P < 0.05). Additionally, left ventricular systolic pressure, +dp/dtmax, and -dp/dtmax were elevated, and left ventricular end diastolic pressure was significantly reduced in the IPC group. Furthermore, IPC-mediated cardiac protection against I/R injury was inhibited in vivo and in vitro by knockdown of cardiac miR-499, suggesting that miR-499 may participate in the protective function of IPC against I/R injury through targeting programmed cell death 4 (PDCD4). Conclusion: Our data revealed that IPC-regulated miR-499 plays an important role in IPC-mediated cardiac protection against I/R injury by targeting PDCD4. [ABSTRACT FROM AUTHOR]

• Proposal of a two-component model-based PolSAR scheme for soil moisture retrieval. • A physical constraint algorithm of the dielectric constant is incorporated. • The performance of 3 different generalized volume scattering models is evaluated. • Factors affecting the accuracy of soil moisture retrieval in crops are analyzed. Model-based polarimetric decomposition can separate to some extent the backscattered radar signals from the vegetation canopy and the underlying ground, hence enabling a strategy for soil moisture retrieval in vegetated agricultural fields. However, the volume scattering models used in previous studies are only applicable to specific cases, making it difficult to completely remove the volume component induced by the vegetation layer. In this paper, three generalized volume scattering models (i.e., generalized volume scattering model (GVSM), simplified adaptive volume scattering model (SAVSM), and simplified Neumann volume scattering model (SNVSM)) are incorporated in the decomposition and evaluated for soil moisture retrieval. Considering the complexity and descriptive ability of the available physical models, a modified two-component model-based decomposition is proposed as the basic decomposition framework. This decomposition is also based on the physical constraint of the dielectric constants included in the model. The employed models combine an X-Bragg surface scattering model with three continuous generalized volume scattering models. The analytic solution of the parameters is obtained, and the minimum residual power criterion is used to determine the optimal solution to fit the model. By using the proposed model-based decomposition framework, the performance of the three models to simulate the canopy scattering and, as a result, to later estimate soil moisture under agricultural vegetation is evaluated. Fully polarimetric RADARSAT-2 C-band images acquired on eight dates in 2013 and 2015 over fields covered by two crops (wheat and soybean) were employed for validation. Results show that the proposed decomposition method, using any of the three volume scattering models, can provide promising inversion results of soil moisture, with RMSEs ranging from 2.89 to 7.43 [vol.%]. Compared with the other two models, the SNVSM simulates the vegetation contribution more accurately in this framework, and it provides a stable soil moisture inversion performance at different crop phenological stages, with an optimal overall accuracy of RMSE = 4.99 [vol.%] and a correlation coefficient of R = 0.78. [ABSTRACT FROM AUTHOR]

[Display omitted] • To improve water flux by simultaneous using interstice and pores of large sizes CNTs arrays (CNTA). • 100% salt desalination achieved by modified interstice of CNTA. • The interstitial water flux of modified CNTA is 3 ∼ 5 times higher than that of CNTs pores. • Results imply that CNTA membrane is a promising candidate for RO water desalination. Like single-wall carbon nanotubes (SWNTs), multi-wall carbon nanotubes (MWNTs) have channels for selective separation of ions. Moreover, MWNTs have the advantages of simpler preparation and lower cost, potentially allowing wider applications in the desalination industry. In this study, the transport of water and ions inside two types of MWNTs, i.e., double-wall carbon nanotubes (DWNTs) and tri-wall carbon nanotubes (TWNTs) were investigated using non-equilibrium molecular dynamics simulations. Modification groups, including carbon chains of different lengths and types with hydrophilic groups like carboxylate anion (COO–), and ammonium (NH 3 +), were used to improve the salt rejection performance. The simulation results suggest that 100% salt rejection could be achieved by adding different modification groups to various MWNTs ranging from 13 to 20 Å. More importantly, this study demonstrates that MWNT membranes with 100% desalination have water fluxes that are 10–30% greater than that of SWNT membranes with 100% desalination, implying the greater potential of MWNTs as building blocks towards next-generation desalination membranes. Additionally, even though all real-world carbon nanotubes (CNTs) have a certain amount of defects from the production process, most previous molecular model studies of water and ions transport in CNTs assumed that CNTs were perfect and defect-free. This study shows for the first time that CNTs with defects have improved salt rejection rates than perfect carbon nanotubes, although the former have lower water fluxes than the latter. [ABSTRACT FROM AUTHOR]

Background: Atherosclerosis is widely recognized as a complex inflammatory disease involving pathogenic immune response of T cells and antigen-presenting cells (APCs) such as dendritic cells (DCs) and macrophages. Accumulating evidence has revealed that mature DCs play critical roles in the differentiation of effector T cells into CD4+ T cells, which effectively participate in the onset of acute coronary syndrome (ACS). IFN regulatory factor (IRF)-1 has been shown to be involved in various immune processes. The role of IRF-1 in DCs in the pathogenesis of ACS has not been investigated. Methods and Results: We examined the relative mRNA and protein expression of IRF-1 in human monocyte-derived DCs in patients with coronary artery disease (CAD). The overexpression or silencing of IRF-1 expression in DCs in patients with ACS was performed to explore the possible role of IRF-1 in the maturation and function of DCs involved in ACS. The results showed that the relative expression of IRF-1 in DCs is obviously increased in patients with ACS. The overexpression or silencing of IRF-1 expression could effectively promote or attenuate the maturation and function of DCs. In addition, we revealed that the MAPK pathway (phosphorylation of JNK, p38 and ERK1/2) might be downstream of IRF-1 signalling pathway in activation of circulating DCs in ACS patients. Conclusion: The present data demonstrate that IRF-1 could effectively promote the immune maturation and function of DCs in ACS patients. [ABSTRACT FROM AUTHOR]

The convolutional neural network (CNN) method has been widely used in the classification of hyperspectral images (HSIs). However, the efficiency and accuracy of the HSI classification are inevitably degraded when small samples are available. This study proposes a multidimensional CNN model named MDAN, which is constructed with an attention mechanism, to achieve an ideal classification performance of CNN within the framework of few-shot learning. In this model, a three-dimensional (3D) convolutional layer is carried out for obtaining spatial–spectral features from the 3D volumetric data of HSI. Subsequently, the two-dimensional (2D) and one-dimensional (1D) convolutional layers further learn spatial and spectral features efficiently at an abstract level. Based on the most widely used convolutional block attention module (CBAM), this study investigates a convolutional block self-attention module (CBSM) to improve accuracy by changing the connection ways of attention blocks. The CBSM model is used with the 2D convolutional layer for better performance of HSI classification purposes. The MDAN model is applied for classification applications using HSI, and its performance is evaluated by comparing the results with the support vector machine (SVM), 2D CNN, 3D CNN, 3D–2D–1D CNN, and CBAM. The findings of this study indicate that classification results from the MADN model show overall classification accuracies of 97.34%, 96.43%, and 92.23% for Salinas, WHU-Hi-HanChuan, and Pavia University datasets, respectively, when only 1% HSI data were used for training. The training and testing times of the MDAN model are close to those of the 3D–2D–1D CNN, which has the highest efficiency among all comparative CNN models. The attention model CBSM is introduced into MDAN, which achieves an overall accuracy of about 1% higher than that of the CBAM model. The performance of the two proposed methods is superior to the other models in terms of both efficiency and accuracy. The results show that the combination of multidimensional CNNs and attention mechanisms has the best ability for small-sample problems in HSI classification. [ABSTRACT FROM AUTHOR]

At present, the soil is facing serious problems of reduced nutrient utilization and organic pollution. In this study, a straw-based biochar BC550 with four phosphate-solubilizing bacteria and Acinetobacter lwoffii DNS32, a degrading bacteria of herbicide atrazine were co-cultured as a combined application to improve soil available phosphorus while repairing the atrazine contaminated soil. The porous structure and surface characteristics of BC550 ensures its ability to adsorb atrazine and is beneficial to the phosphorus solubilization of phosphate-solubilizing bacteria. Phosphate-solubilizing bacteria could use the intermediate metabolites produced during the degradation of atrazine by strain DNS32 to grow and secrete low molecular weight organic acids to promote the expression of atrazine-degrading genes trz N, atz B, and atz C in strain DNS32. The addition of BC550 directly increases the total and available phosphorus content in the soil, and may further stimulate degradation by concentrating of atrazine in proximity of the degrading bacteria on the biochar surface. The combined application showed 49% higher ability to degrade 100 mg L−1 atrazine within 24 h and 27% higher ability to degrade 20 mg kg−1 atrazine within 3 days than pure degrading bacteria DNS32, in liquid and soil experiments, respectively. This research will help provide a promising in situ remediation technique to improve the quality of the soil environment. • Biochar BC550 could increase the activity of phosphate-solubilizing bacteria. • Phosphate-solubilizing bacteria use atrazine metabolites to growth. • Low molecular weight organic acids increased the degradation ability of strain DNS32. [ABSTRACT FROM AUTHOR]

Most of the traditional supervised classification methods using full-polarimetric synthetic aperture radar (PolSAR) imagery are dependent on sufficient training samples, whereas the results of pixel-based supervised classification methods show a high false alarm rate due to the influence of speckle noise. In this paper, to solve these problems, an object-based supervised classification method with an active learning (AL) method and random forest (RF) classifier is presented, which can enhance the classification performance for PolSAR imagery. The first step of the proposed method is used to reduce the influence of speckle noise through the generalized statistical region merging (GSRM) algorithm. A reliable training set is then selected from the different polarimetric features of the PolSAR imagery by the AL method. Finally, the RF classifier is applied to identify the different types of land cover in the three PolSAR images acquired by different sensors. The experimental results demonstrate that the proposed method can not only better suppress the influence of speckle noise, but can also significantly improve the overall accuracy and Kappa coefficient of the classification results, when compared with the traditional supervised classification methods. [ABSTRACT FROM AUTHOR]