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In recent years, noticeable subsidence depressions have occurred along the coastal zone of the Yellow River Delta. In some coastal areas, the average annual subsidence varies from tens of millimeters to hundreds of millimeters. Although some studies have discovered a significant land subsidence funnel in the coastal zone of the Yellow River Delta, it has rarely been reported in recent years. Using Sentinel-1A/1B images from the last five years and permanent scatterers interferometric synthetic aperture radar technology, we found a typical subsidence bowl in the northeastern part of the delta, with a cumulative settlement of nearly 1 m over five years. In addition, we used the approach of soil mechanics to simulate the settlement of confined brine layers with different thicknesses under drainage conditions. We found that the 15 m thick confined aquifer can produce 1 m of settlement for every 15% decrease in water content. The simulation results explain the large settlement of the brine industrial area. This study can provide guidance for brine mining in the future. If we continue to overexploit underground brine, there will be more severe land subsidence in the delta in the future. [ABSTRACT FROM AUTHOR]

The inversion method based on the reflection loss-grazing angle curve is an effective tool to obtain local underwater acoustic parameters. Because geoacoustic parameters vary in sensitivity to grazing angle, it is difficult to get accurate results in geoacoustic parameter inversion based on small-grazing-angle data in shallow water. In addition, the normal-mode model commonly used in geoacoustic parameter inversion fails to meet the needs of accurate local sound field simulation as the influence of the secant integral is ignored. To solve these problems, an acoustic data acquisition scheme was rationally designed based on a sparker source, a fixed vertical array, and ship drifting with the swell, which could balance the trade-off among signal transmission efficiency and signal stability, and the actual local acoustic data at low-to-mid frequencies were acquired at wide grazing angles in the South Yellow Sea area. Furthermore, the bottom reflection coefficients (bottom reflection losses) corresponding to different grazing angles were calculated based on the wavenumber integration method. The local seafloor sediment parameters were then estimated using the genetic algorithm and the bottom reflection loss curve with wide grazing angles, obtaining more accurate local acoustic information. The seafloor acoustic velocity inverted is c p = 1659   m / s and the sound attenuation is α p = 0.656   dB / λ in the South Yellow Sea. Relevant experimental results indicate that the method described in this study is feasible for local inversion of geoacoustic parameters for seafloor sediments. Compared with conventional large-scale inversion methods, in areas where there are significant changes in the seabed sediment level, this method can obtain more accurate local acoustic features within small-scale areas. [ABSTRACT FROM AUTHOR]

This paper proposed an MSC-Transformer model based on the Transformer's neural network, which was applied to seabed sediment classification. The data came from about 2900 km2 of seabed area on the northern slope of the South China Sea. Using the submarine backscattering intensity and depth data obtained by the sub-bottom profiler, combined with latitude and longitude information, a seabed dataset of the slope area of the South China Sea was constructed. Moreover, using the MSC-Transformer, the accurate identification and judgment of sediment types such as calcareous bio-silt, calcareous bio-clay silt, silty sand, medium sand and gravel sand were realized. Compared with the conventional deep neural network CNN, RNN, etc., the model shows advantages when applied to the sediment dataset of the shallow sea slope region of the South China Sea. This confirms the feasibility and validity of the model and provides a reliable and accurate tool for seabed sediment classification in the field of marine science. The completeness and accuracy of the dataset and the good performance of the model provide a solid foundation for the scientificalness and practicability of the study. [ABSTRACT FROM AUTHOR]

The accurate semantic segmentation of point cloud data is the basis for their application in the inspection of extra high-voltage transmission lines (EHVTL). As deep learning evolves, point-wise-based deep neural networks have shown great potential for the semantic segmentation of EHVTL point clouds. However, EHVTL point cloud data are characterized by a large data volume and significant class imbalance. Therefore, the down-sampling method and point cloud feature extraction method used in current point-wise-based deep neural networks hardly meet the needs of computational accuracy and efficiency. In this paper, we proposed a two-step down-sampling method and a point cloud feature extraction method based on local feature aggregation of the point clouds after down-sampling in each layer of the model (LFAPAD). We then established a deep neural network named PowerLine-Net for the semantic segmentation of the EHVTL point clouds. Furthermore, in order to test and analyze the performance of PowerLine-Net, we constructed a point cloud dataset for the EHVTL scenes. Using this dataset and the Semantic3D dataset, we implemented network parameter testing, semantic segmentation, and an accuracy comparison of different networks based on PowerLine-Net. The results illustrate that the semantic segmentation model proposed in this paper has a high computational efficiency and accuracy in the semantic segmentation of EHVTL point clouds. Compared with conventional deep neural networks, including PointCNN, KPConv, SPG, PointNet++, and RandLA-Net, PowerLine-Net also achieves a higher accuracy in the semantic segmentation of EHVTL point clouds. Moreover, based on the results predicted by PowerLine-Net, the risk point detection for EHVTL point clouds has been achieved, which demonstrates the important value of this network in practical applications. In addition, as shown by the results of Semantic3D, PowerLine-Net also achieves a high segmentation accuracy, which proves its powerful capability and wide applicability in semantic segmentation for the point clouds of large-scale scenes. [ABSTRACT FROM AUTHOR]

Barley is one of the world's oldest cereal crops forming an important component of many traditional diets. Barley is rich in a variety of bioactive phytochemicals with potentially health-promoting effects. However, its beneficial nutritional attributes are not being fully realized because of the limited number of foods it is currently utilized in. It is therefore crucial for the food industry to produce novel barley-based foods that are healthy and cater to customers' tastes. This article reviews the nutritional and functional characteristics of barley, with an emphasis on its ability to improve glucose/lipid metabolism. Then, recent trends in barley product development are discussed. Finally, current limitations and future research directions in glucolipid modulation mechanisms and barley bioprocessing are discussed. [ABSTRACT FROM AUTHOR]

Endosulfan, as an effective broad-spectrum insecticide, has been banned in agricultural areas because of the potential harmful effects on human health. This study aimed to develop an indirect competitive enzyme-linked immunosorbent assay (ic-ELISA) and colloidal gold immunochromatographic (ICA) strip based on a prepared monoclonal antibody (mAb) for quantitative and qualitative detection of endosulfan. A new mAb with high sensitivity and affinity was designed and screened. The ic-ELISA showed a 50% inhibition concentration (IC50) value of 5.16 ng/mL for endosulfan. Under optimum conditions, the limit of detection (LOD) was determined to be 1.14 ng/mL. The average recoveries of endosulfan in spiked pear and apple samples ranged from 91.48–113.45% and 92.39–106.12% with an average coefficient of variation (CV) of less than 7%, respectively. The analysis of colloidal gold ICA strip could be completed within 15 min by naked eye and the visual limit of detection (vLOD) was both 40 ng/mL in pear and apple samples. In conclusion, both developed immunological methods were suitable and reliable for the on-site detection of endosulfan in real samples at trace levels. [ABSTRACT FROM AUTHOR]

Penicillium expansum is the causal agent of blue mold decay on apple fruits and is also known to be the major producer of patulin, a mycotoxin that represents serious hazard to human health. Several mechanisms have been suggested to explain the pathogenesis of P. expansum in host plants. Secreted effector proteins are vital for the pathogenicity of many fungal pathogens through manipulating their hosts for efficient colonization. In this study, we performed a RNA-Seq analysis followed by computational prediction of effector proteins from P. expansum during infection of the host apple fruits, and a total of 212 and 268 candidate effector protein genes were identified at 6 and 9 h after inoculation (hai), respectively. One of the candidate effector protein genes was identified as a concanavalin A-like lectin/glucanase (Peclg), which was dramatically induced during the pathogen–host interaction. Targeted knockout of Peclg resulted in significant reduction in conidial production and germination relative to the wild type. Further studies showed that in addition to salt stress, the mutant was much more sensitive to SDS and Congo red, suggesting a defect in cell wall integrity. Pathogenicity assays revealed that the ΔPeclg mutant showed significant decrease in virulence and infectious growth on apple fruits. All these results suggest that Peclg is required for fungal growth, stress response, and the virulence of P. expansum. [ABSTRACT FROM AUTHOR]

Gravity anomalies (GAs) and vertical gravity gradient anomalies (VGGs) are regularly used to predict bathymetry. However, few studies have explored the combination of the GAs and VGGs to predict bathymetry. We introduce the back propagation (BP) neural network into bathymetric prediction field and propose a method to predict depth from GAs and VGGs. The method was tested in the Mariana Trench region, and a neural network bathymetry model was constructed using feature data obtained from GAs and VGGs as the input of the BP neural network. Additionally, single‐beam sounding data were used as label data. By comparing the neural network and the gravity‐geologic method (GGM), the neural network was found to provide better performance with an accuracy improvement of 19%. The root‐mean‐square of the absolute difference between the neural network bathymetry model and the single‐beam sounding data was 72.40 m, with a relative accuracy of 1.71%. Approximately 50% of the differences were distributed within ±20 m, and 90% were distributed within ±100 m. The neural network bathymetry model was also compared with the GGM bathymetry model for different depths and topographies, and the results verified the feasibility and effectiveness of the BP neural network method. Plain Language Summary: The gravity data currently used to predict bathymetry include gravity anomalies and vertical gravity gradient anomalies. We propose a new method that introduces the back propagation neural network into bathymetric prediction, based on two kinds of gravity data, which are used to predict depth. The results show that the depths predicted by the neural network are closer to the shipborne depths than are those estimated with the current method. We also test the adaptability of the neural network method for different depths and topographies (seamount, trench, and plain). Finally, we find that the BP neural network method is competent for various inversion tasks. Key Points: A new method is proposed to predict depth using gravity data derived from a back propagation neural networkBathymetric prediction using gravity anomalies and vertical gravity gradient anomaliesThe BP neural network method displays outstanding performance compared to the gravity‐geologic method for different depths and topographies [ABSTRACT FROM AUTHOR]

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A rapid colloidal gold immunochromatography assay (GICA) for the detection of pefloxacin (PEF) was established and optimized. The anti-PEF monoclonal antibody (mAb) was used to target PEF as a colloidal gold-mAb conjugate. The mAb belonged to the IgG2b subtype, lambda light chain, the affinity constant (Ka) was 5.21 × 109 L·mol−1, and its half maximal inhibitory concentration (IC50) was 0.23 ng·mL−1. No obvious cross-reactivity (CR) was observed with other common fluoroquinolone antibiotics, including ciprofloxacin (CIP), norfloxacin (NOR), lomefloxacin (LOM) and ofloxacin (OFL). The visual limit of detection (vLOD) of the optimized GICA was 2 ng·g−1 under the conventional pretreatment method, and the assay was completed in 15 min. Liquid chromatography tandem-mass spectrometry (LC–MS/MS) was employed to confirm the performance of the strip. In addition, a novel pretreatment was established and compared with conventional pretreatment. Without the removal of organic solvents, the novel pretreatment method reduced the sample pretreatment time (more than 10 min). The vLOD of the optimized GICA was also 2 ng·g−1 when applying the novel pretreatment method. In conclusion, the proposed PEF-GICA could detect samples containing PEF rapidly and accurately, and the novel pretreatment method saved the time of sample pretreatment and improved the efficiency of detection. [ABSTRACT FROM AUTHOR]

Calibration and validation (Cal/Val) of the sea surface height as measured by satellite radar altimeters is essential to understand altimeter biases, observation trends, and instrument aging. It also supports the long-term stability of the produced climate change records of sea level as determined by altimetry. In this article, we report the calibration of HY-2B and Jason-2/3 using the established research infrastructure and data sharing initiative introduced by the Altimetry Calibration Cooperation Plan (ACCP) of China. Currently, three ACCP calibration sites encompass the Wanshan Islands, and two national oceanic sites are located along the China coastline. For each Cal/Val site, the components of the facilities—the geodetic, sea level, and Global Navigation Satellite System (GNSS) infrastructure—and the followed monitoring procedures and calibration methods are described. The HY-2B performance was primarily evaluated using about two years data, which indicated a mean bias of −0.2 ± 4.2 cm. Confidence in the results is strong, because the HY-2B biases were cross compared and confirmed by all the three independent sites and the three satellite ground tracks. Compared with its predecessor HY-2A, HY-2B shows very stable observations with no linear drift at present. In addition, Jason-2 and Jason-3 were mainly assessed using Qianliyan site. Our results indicate that the Jason-3 sea-surface height bias is approximately 2–3 cm smaller than that of Jason-2 and that the long-term stability of Jason-2/3 shows no significant trend, which in good agreement with the international dedicated sites. The instrument noises of Jason-2/3 and HY-2B were estimated based on the ACCP sites. The results show that the instrument noise in the previous literature is underestimated. This was also consolidated by the result from wavenumber spectrum and global crossover point analysis. The code and Wanshan data used in these Cal/Val experiments are publicly available to facilitate further work in this domain (https://github.com/GenericAltimetryTools/CalAlti). [ABSTRACT FROM AUTHOR]