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The return of crop residues to cultivated fields has numerous agronomic and soil quality benefits and, therefore, the areal extent of crop residue cover (CRC) could provide a rapid measure of the sustainability of agricultural production systems in a region. Recognizing the limitations of traditional CRC methods, a new method is proposed for estimating the spatial and temporal distribution of maize residue cover (MRC) in the Jilin Province, NE China. The method used random forest (RF) algorithms, 13 tillage indices and 9 textural feature indicators derived from Sentinel-2 data. The tillage indices with the best predictive performance were STI and NDTI (R2 of 0.85 and 0.84, respectively). Among the texture features, the best-fitting was Band8AMean-5∗5 (R2 of 0.56 and 0.54 for the line-transect and photographic methods, respectively). Based on MSE and InNodePurity, the optimal combination of RF algorithm for the line-transect method was STI, NDTI, NDI7, NDRI5, SRNDI, NDRI6, NDRI7 and Band3Mean-3∗3. Likewise, the optimal combination of RF algorithm for the photographic method was STI, NDTI, NDI7, SRNDI, NDRI6, NDRI5, NDRI9 and Band3Mean-3∗3. Regional distribution of MRC in the Jilin Province, estimated using the RF prediction model, was higher in the central and southeast sections than in the northwest. That distribution was in line with the spatial heterogeneity of maize yield in the region. These findings showed that the RF algorithm can be used to map regional MRC and, therefore, represents a useful tool for monitoring regional-scale adoption of conservation agricultural practices.

Tylosin (TYL) pollution has aroused widespread concern, and its existence poses a serious threat to the environment and human health. Microbial degradation of antibiotics is considered to be an effective strategy to reduce the environmental impact of antibiotics, but its degradation mechanism is still unclear. In this study, transcriptome analysis was combined to explore the response mechanism of K. gibsonii strain TYL-A1 under TYL stress. The results showed that the strain showed a significant antioxidant response under TYL stress to cope with TYL-induced cell damage. TYL also increased the level of intracellular reactive oxygen species (ROS), damaged the integrity of the cell membrane, and inhibited the growth of strain TYL-A1. Transcriptome sequencing showed that under TYL exposure conditions, 1650 DEGs in strain TYL-A1 showed expression changes, of which 806 genes were significantly up-regulated and 844 genes were significantly down-regulated. Differentially expressed DEGs were significantly enriched in pathways related to metabolism, biosynthesis, and stress response, and tricarboxylic acid cycle, oxidative phosphorylation, and carbon metabolism genes were significantly up-regulated. In conclusion, this study provides novel insights regarding the degradation of TYL by K. gibsonii TYL-A1.

Microbial degradation of tylosin (TYL) is a safe and environmentally friendly technology for remediating environmental pollution. Kurthia gibsonii (TYL-A1) and Klebsiella pneumonia (TYL-B2) were isolated from wastewater; degradation efficiency of the two strains combined was significantly greater than either alone and resulted in degradation products that were less toxic than TYL. With Polyvinyl alcohol (PVA)-sodium alginate (SA)-activated carbon (AC) used to form a bacterial immobilization carrier, the immobilized bacterial alliance reached 95.9% degradation efficiency in 1 d and could be reused for four cycles, with > 93% degradation efficiency per cycle. In a wastewater application, the immobilized bacterial alliance degraded 67.0% TYL in 9 d. There were significant advantages for the immobilized bacterial alliance at pH 5 or 9, with 20 or 40 g/L NaCl, or with 10 or 50 mg/L doxycycline. In summary, in this study, a bacterial consortium with TYL degradation ability was constructed using PVA-SA-AC as an immobilized carrier, and the application effect was evaluated on farm wastewater with a view to providing application guidance in environmental remediation.

Abstract Inner product arguments allow a prover to prove that the inner product of two committed vectors equals a public scalar. They are used to reduce the complexity of many cryptographic primitives, such as range proofs. Range proofs are deployed in numerous applications to prove that a committed value lies in a certain range. As core building blocks, their complexity largely determines the performance of corresponding applications. In this paper, we have optimised the inner product argument with statement including two vector commitments (IPAtvc) and range proof of Daza et al. (PKC’20), the inner product argument with statement including only one vector commitment (IPAovc) of Bünz et al. (S&P′18). For IPAtvc, we reduce the concrete communication complexity by 2 log2n field elements, where n is the vector dimension. For range proofs, we reduce the concrete communication and prover complexities by about 2 log2m field elements and 11m field multiplications, respectively, where m is the bit length of range. For IPAovc, we exponentially reduce the asymptotic verifier complexity from linear to logarithmic. Due to the asymptotic characteristics, our protocols are highly competitive when the vector dimension or bit length of range is large.

The J-TEXT capability is enhanced compared to two years ago with several upgrades of its diagnostics and the increase of electron cyclotron resonance heating (ECRH) power to 1 MW. With the application of electron cyclotron wave (ECW), the ECW assisted plasma startup is achieved; the tearing mode is suppressed; the toroidal injection of 300 kW ECW drives around 24 kA current; fast electrons are generated with toroidal injected ECW and the runaway current conversion efficiency increases with ECRH power. The mode coupling between 2/1 and 3/1 modes are extensively studied. The coupled 2/1 and 3/1 modes usually lead to major disruption. Their coupling can be either suppressed or avoided by external resonant magnetic perturbation fields and hence avoids the major disruption. It is also found that the 2/1 threshold of external field is significantly reduced by a pre-excited 3/1 mode, which can be either a locked island or an external kink mode. The disruption control is studied by developing prediction methods capable of cross tokamak application and by new mitigation methods, such as the biased electrode or electromagnetic pellet injector. The high-density operation and related disruptions are studied from various aspects. Approaching the density limit, the collapse of the edge shear layer is observed and such collapse can be prevented by applying edge biasing, leading to an increased density limit. The density limit is also observed to increase, if the plasma is operated in the poloidal divertor configuration or the plasma purity is increased by increasing the pre-filled gas pressure or ECRH power during the start-up phase.

Arbuscular mycorrhizal (AM) fungi form a mutual association with the majority of land plants, including most angiosperms of the dicotyledon and monocotyledon lineages. The symbiosis is based upon bidirectional nutrient exchange between the host and symbiont that occurs between inner cortical cells of the root and branched AM hyphae called arbuscules that develop within these cells. Lipid transport and its regulation during the symbiosis have been intensively investigated in dicotyledon plants, especially legumes. Here, we characterize OsRAM2 and OsRAM2L, homologs of Medicago truncatula RAM2, and found that plants defective in OsRAM2 were unable to be colonized by AM fungi and showed impaired colonization by Magnaporthe oryzae. The induction of OsRAM2 and OsRAM2L is dependent on OsRAM1 and the common symbiosis signaling pathway pathway genes CCaMK and CYCLOPS, while overexpression of OsRAM1 results in increased expression of OsRAM2 and OsRAM2L. Collectively, our data show that the function and regulation of OsRAM2 is conserved in monocot and dicot plants and reveals that, similar to mutualistic fungi, pathogenic fungi have recruited RAM2-mediated fatty acid biosynthesis to facilitate invasion.[Graphic: see text] Copyright © 2022 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Reyes et al. identified and characterized dominant mutations in the MutL homolog MLH2 (in S. cerevisiae) and PMS1 (in humans) causing an increased mutator phenotype. Based on their findings, they propose that Mlh2/hPMS1-Mlh1 mutant complexes can act as roadblocks on DNA preventing mismatch repair function.

Raman spectroscopy is a type of inelastic scattering spectroscopy that is widely used in determining and analyzing molecular structure. It also has a number of practical applications in evaluating food safety, medicine, and forensics. The Raman spectral signal is weak, but the development of the surface-enhanced Raman scattering (SERS) technique has overcome this problem and led to further developments in Raman spectroscopy. This paper describes a fundamental study of the use of focused ion beam (FIB) direct writing for preparing gold substrates for SERS. Molecular dynamics and Monte Carlo simulation methods are used to investigate the damage induced by gallium ion implantation of a gold substrate. Based on characterization by x-ray photoelectron spectroscopy (XPS) and scanning electron microscopy, the mechanism by which ion implantation and annealing influence the damage induced by a gallium FIB is analyzed. After annealing at 350 °C, a mixture of metallic gallium, its oxide Ga2O3 conforming to the stoichiometric ratio, and its sub-stable oxide (Ga2Ox) in sub-stoichiometric ratio precipitated on the surface are detected by XPS. Annealing treatment can effectively reduce the effect of gallium ion implantation on a SERS substrate fabricated by FIB direct writing.

In the remote sensing monitoring of conservation tillage, the acquisition of remote sensing data with high spatial and temporal resolution is critical. The current optical remote sensing images cannot realize both temporal and spatial resolution, especially under cloud and rain interference. Thus, this study employs the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) to obtain the normalized difference tillage index (NDTI) with both temporal and spatial resolution estimated by Sentinel−2 and MODIS using the Index−then−Blend (IB) and Blend−then−Index (BI) fusion schemes. After comparison, the IB scheme was better than the BI scheme in predicting results and prediction efficiency. The NDTI predicted by ESTARFM and Sentinel−2 on June 12, 2020 was compared. A coefficient of determination R2 of 0.73 and RMSE of 0.000117 was obtained, indicating a high prediction accuracy, which meets the prediction requirements. Based on the predicted ESTARFM NDTI of the study area on May 17, 2021, the maize residue cover (MRC) of the study area was estimated using the previously constructed MRC unary linear regression model. The MRC of the sampling points of the remote sensing images was estimated by verifying the predicted ESTARFM NDTI with the MRC of the sampling points taken in the field extracted by the maximum likelihood classifier, which has a coefficient of determination R2 of 0.78 and RMSE of 0.00676, signifying better prediction results. The proposed method provides considerable data sources for the remote sensing monitoring studies of conservation tillage.

Pasteurella multocida is one of the primary pathogens of bovine respiratory disease (BRD), and causes huge losses in the cattle industry. The Pm3 strain was a natural isolate, which is a strong form of pathogen and is sensitive to fluoroquinolones antibiotics. A high fluoroquinolone resistant strain, Pm64 (MIC = 64 μg/mL), was formed after continuous induction with subinhibitory concentration (1/2 MIC) of enrofloxacin, with the enhanced growth characteristics and large attenuation of pathogenicity in mice. This study reports the whole genome sequence and the transcription profile by RNA-Seq of strain Pm3/Pm64. The results showed an ineffective difference between the two strains at the genome level. However, 32 genes could be recognized in the gene islands (GIs) of Pm64, in which 24 genes were added and 8 genes were lost. Those genes are involved in DNA binding, trehalose metabolism, material transportation, capsule synthesis, prophage, amino acid metabolism, and other functions. In Pm3 strain, 558 up-regulated and 568 down-regulated genes were found compared to Pm64 strain, from which 20 virulence factor-related differentially expressed genes (DEGs) were screened. Mainly differentially transcribed genes were associated with capsular polysaccharide (CPS), lipopolysaccharide (LPS), lipooligosaccharide (LOS). Iron utilization, and biofilm composition. We speculated that the main mechanism of virulence attenuation after the formation of resistance of Pm64 comes from the change of the expression profile of these genes. This report elucidated the toxicity targets of P. multocida serogroup A which provide fundamental information toward the understanding of the pathogenic mechanism and to decreasing antimicrobial drugs resistance.

As an important medicinal plant, we still do not know the effect of exogenous hormones on absorption of elements and accumulation of secondary metabolites in Artemisia argyi leaves. In this work, we analyzed the difference in 21 elements absorbed by A. argyi leaves under three exogenous hormone (MeJA, SA and ABA) treatments, and also clarified the correlation between 21 elements and eight bioactive components. Different hormone treatments changed the absorption and enrichment of elements, and the composition also changed significantly. The contents of eight bioactive components changed significantly under different hormone treatments. When A. argyi was stimulated by exogenous hormones, the content of secondary metabolites was adjusted in the leaves through changes in the absorption and enrichment of elements. The widely untargeted metabolomic analysis further confirmed that ABA changes the metabolic direction of secondary metabolites in A. argyi leaves and stimulates the biosynthesis of multiple secondary metabolites including phenylpropanoids, flavonoids, terpenoids, alkaloids and others. These results provide a new perspective for the changes in element absorption and the mechanism of secondary metabolic components in A. argyi leaves under exogenous hormone treatments, and also deepen people’s understanding of the interaction mechanism between medicinal plants and hormones.

Controlling straw burning is important for ensuring the ambient air quality and for sustainable agriculture. Detecting burning straw is vital for managing and controlling straw burning. Existing methods for detecting straw combustion mainly look for combustion products, especially smoke. In this study, the improved You Only Look Once version 5 (YOLOv5s) algorithm was used to detect smoke in Sentinel-2 images captured by remote sensing. Although the original YOLOv5s model had a faster detection speed, its detection accuracy was poor. Thus, a convolutional block attention module was added to the original model. In addition, in order to speed up the convergence of the model, this study replaced the leaky Rectified Linear Unit (leaky ReLU) activation function with the Mish activation function. The accuracy of the improved model was approximately 4% higher for the same detection speed. The improved YOLOv5s had a higher detection accuracy and speed compared to common target detection algorithms, such as RetinaNet, mask Region-Based Convolutional Neural Network (R-CNN), Single-Shot Multibox Detector (SSD), and faster R-CNN. The improved YOLOv5s analyzed an image in 2 ms. In addition, mAP50 exceeded 94%, demonstrating that with this study’s improved method, smoke can be quickly and accurately identified. This work may serve as a reference for improving smoke detection, and for the effective management and control of straw burning.

Sea surface partial pressure of CO2 (pCO2) is a critical parameter in the quantification of air–sea CO2 flux, which plays an important role in calculating the global carbon budget and ocean acidification. In this study, we used chlorophyll-a concentration (Chla), sea surface temperature (SST), dissolved and particulate detrital matter absorption coefficient (Adg), the diffuse attenuation coefficient of downwelling irradiance at 490 nm (Kd) and mixed layer depth (MLD) as input data for retrieving the sea surface pCO2 in the North Atlantic based on a remote sensing empirical approach with the Categorical Boosting (CatBoost) algorithm. The results showed that the root mean square error (RMSE) is 8.25 μatm, the mean bias error (MAE) is 4.92 μatm and the coefficient of determination (R2) can reach 0.946 in the validation set. Subsequently, the proposed algorithm was applied to the sea surface pCO2 in the North Atlantic Ocean during 2003–2020. It can be found that the North Atlantic sea surface pCO2 has a clear trend with latitude variations and have strong seasonal changes. Furthermore, through variance analysis and EOF (empirical orthogonal function) analysis, the sea surface pCO2 in this area is mainly affected by sea temperature and salinity, while it can also be influenced by biological activities in some sub-regions.

Gear reliability assessment of vehicle transmission has been a challenging issue of determining vehicle safety in the transmission industry due to a significant amount of classification errors with high-coupling gear parameters and insufficient high-density data. In terms of the preprocessing of gear reliability assessment, this paper presents a representation generation approach based on generative adversarial networks (GAN) to advance the performance of reliability evaluation as a classification problem. First, with no need for complex modeling and massive calculations, a conditional generative adversarial net (CGAN) based model is established to generate gear representations through discovering inherent mapping between features with gear parameters and gear reliability. Instead of producing intact samples like other GAN techniques, the CGAN based model is designed to learn features of gear data. In this model, to raise the diversity of produced features, a mini-batch strategy of randomly sampling from the combination of raw and generated representations is used in the discriminator, instead of using all of the data features. Second, in order to overcome the unlabeled ability of CGAN, a Wasserstein labeling (WL) scheme is proposed to tag the created representations from our model for classification. Lastly, original and produced representations are fused to train classifiers. Experiments on real-world gear data from the industry indicate that the proposed approach outperforms other techniques on operational metrics.