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Lomefloxacin (LOM), an antibiotic crucial for preventing various animal diseases in animal husbandry, can pose serious health risks when found in excessive amounts in meat products. The development of highly specific and sensitive colloidal gold immunochromatographic test strips is essential for the accurate detection of this class of antibiotics. Our study utilized a monoclonal antibody (mAb) assay and immunochromatographic strips to detect lomefloxacin residues in meat products. The results showed minimal cross-reactivity with other structural analogs, with a maximum half inhibitory concentration (IC50) of 0.93 ng/mL and a linear range of 0.38 to 2.3 ng/mL for the indirect competitive enzyme-linked immunosorbent assay (ic-ELISA). The recovery of LOM was 80% to 120%, with an average coefficient of variation below 5%. The immunochromatographic strip test results showed a visual detection limit of 2.5 ng/g, meeting the market requirements for the test. This study highlights the significance of specific and sensitive testing methods for detecting lomefloxacin, ensuring consumers’ safety and health.

Abstract Background Biomedical researchers are strongly encouraged to make their research outputs more Findable, Accessible, Interoperable, and Reusable (FAIR). While many biomedical research outputs are more readily accessible through open data efforts, finding relevant outputs remains a significant challenge. Schema.org is a metadata vocabulary standardization project that enables web content creators to make their content more FAIR. Leveraging Schema.org could benefit biomedical research resource providers, but it can be challenging to apply Schema.org standards to biomedical research outputs. We created an online browser-based tool that empowers researchers and repository developers to utilize Schema.org or other biomedical schema projects. Results Our browser-based tool includes features which can help address many of the barriers towards Schema.org-compliance such as: The ability to easily browse for relevant Schema.org classes, the ability to extend and customize a class to be more suitable for biomedical research outputs, the ability to create data validation to ensure adherence of a research output to a customized class, and the ability to register a custom class to our schema registry enabling others to search and re-use it. We demonstrate the use of our tool with the creation of the Outbreak.info schema—a large multi-class schema for harmonizing various COVID-19 related resources. Conclusions We have created a browser-based tool to empower biomedical research resource providers to leverage Schema.org classes to make their research outputs more FAIR.

Abstract Biomedical datasets are increasing in size, stored in many repositories, and face challenges in FAIRness (findability, accessibility, interoperability, reusability). As a Consortium of infectious disease researchers from 15 Centers, we aim to adopt open science practices to promote transparency, encourage reproducibility, and accelerate research advances through data reuse. To improve FAIRness of our datasets and computational tools, we evaluated metadata standards across established biomedical data repositories. The vast majority do not adhere to a single standard, such as Schema.org, which is widely-adopted by generalist repositories. Consequently, datasets in these repositories are not findable in aggregation projects like Google Dataset Search. We alleviated this gap by creating a reusable metadata schema based on Schema.org and catalogued nearly 400 datasets and computational tools we collected. The approach is easily reusable to create schemas interoperable with community standards, but customized to a particular context. Our approach enabled data discovery, increased the reusability of datasets from a large research consortium, and accelerated research. Lastly, we discuss ongoing challenges with FAIRness beyond discoverability.

Abstract From in situ growth to invasive dissemination is the most lethal attribute of various tumor types. This transition is majorly mediated by the dynamic interplay between two cancer hallmarks, EMT and cell cycle. In this study, we applied nonlinear association analysis in 33 cancer types and found that most signaling receptors simultaneously associating with EMT and cell cycle are potential tumor suppressors. Here we find that a top co-associated receptor, Neogenin (NEO1), inhibits colorectal cancer (CRC) and Glioma in situ growth and metastasis by forming a complex with Merlin (NF2), and subsequent simultaneous promoting the phosphorylation of YAP. Furthermore, Neogenin protein level is associated with good prognosis and correlates with Merlin status in CRC and Glioma. Collectively, our results define Neogenin as a tumor suppressor in CRC and Glioma that acts by restricting oncogenic signaling by the Merlin-YAP pathway, and suggest Neogenin as a candidate therapeutic agent for CRC and Glioma.

BackgroundMalignant pleural mesothelioma (MPM) is a highly invasive malignant tumor. Ultrasound guidance has the advantages of real-time, convenience and nonradiative. We sought to identify diagnostic value and its influenced factors of ultrasound-guided percutaneous pleural needle biopsy (US-PPNB) for MPM.MethodsPatients who underwent US-PPNB between March 2014 and March 2020 and were finally diagnosed with MPM were retrospectively analyzed. We retrospectively analyzed the US-PPNBs pathological results of all patients clinically confirmed as MPM, and divided US-PPNBs into correctly and incorrectly diagnosed groups. Patient, thoracic, and biopsy variables that affected diagnostic accuracy were assessed. All variables significant on univariate analyses were subjected to multivariate logistic regression to identify significant predictors of diagnostic accuracy. We derived cutoffs for all significant continuous variables and used the Mantel–Haenszel test to determine whether the diagnostic accuracy of US-PPNB for MPM increased with pleural thickness.ResultsIn total, 49 patients with clinically confirmed MPM underwent US-PPNB; 37 diagnoses were correct and 12 were incorrect (accuracy = 75.5%). The pleura was significantly thicker in the correctly diagnosed group (p

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 cp=1659 m/s and the sound attenuation is αp=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.

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.

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.

Northern snakehead, Channa argus (C. argus), is an important economic and ecological fish species. The wild population of the species was sharply declined in the last decade. A high-quality reference genome could lay a solid foundation for the genetic and conservation studies for C. argus. In this work, we report a chromosomal genome assembly with PacBio and Hi-C technology using the albino northern snakehead, a color variety of C. argus. A 644.1-Mb genome with 24 chromosomes was obtained with a contig and scaffold N50 of 11.78 and 27.8 Mb, respectively. We inferred that C. argus diverged from A. testudineus around 85.6 million years ago. 514 expanded gene families and 214 positively selected genes were identified in the C. argus genome. The chromosome-level genome provides a valuable high-quality genomic resource for population, as well as genetic and evolutionary studies for C. argus and other species in Channidae.

Abstract In the process of synthetic aperture radar image noise suppression by the bi‐dimensional empirical mode decomposition (BEMD) algorithm, the edge effect is a key problem in the BEMD operation. To weaken this effect, an improved BEMD‐kernel principal component analysis (BEMD‐KPCA) method of image denoising is proposed in this study. Experimental results show that the BEMDKPCA algorithm has a good capability of improving edge effects in the BEMD decomposition process and satisfying the requirement of the reliable decomposition results. Compared with the traditional BEMD method, the proposed approach has a good effect on suppressing speckle noise. Additionally, the denoised image from the decomposed components of the IMFs processed by the BEMD‐KPCA method sufficiently preserves the edge and detail information, confirming its high coherency with the original image.

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.

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 In the past decade, mesenchymal stem cells (MSCs) have been widely used for the treatment of osteoarthritis (OA), and exosomes may play a major role. Here, we acquired a special kind of MSCs from the bone marrow of surgically resected tissue from the hand of a patient with polydactyly. Experiments were focused on the role of polydactyly bone marrow-derived MSCs (pBMSCs) in osteoarthritis. The results showed that the pBMSCs had a greater ability than the BMSCs to differentiate into chondrocytes. Mechanistically, the expression of BMP4 was significantly higher in the pBMSCs than it was in the BMSCs. Furthermore, we showed that the migration and proliferation of chondrocytes were stimulated by exosomes secreted by pBMSC (pBMSC-EXOs). Notably, the downregulation of BMP4 in pBMSCs by siRNA inhibited both the chondrogenic differentiation potential of the MSCs and the function of the chondrocytes. In addition, the injection of pBMSC-EXOs and BMSC-EXOs attenuated OA in an OA mouse model, but the pBMSC-EXOs had a superior therapeutic effect compared with that of the BMSC-EXOs. Taken together, the data indicate that pBMSCs have greater ability to differentiate into chondrocytes and regulate chondrocyte formation through BMP4 signaling. Therefore, pBMSC-EXOs may represent a novel treatment for OA.

Ocean tides in polar regions play an important role in the study of sea ice dynamics and floating ice shelves. The accuracy of existing ocean tide models in shallow waters and polar seas is much lower than that in open deep oceans. This study summarized the advances of state-of-the-art global tide models in the Antarctic Ocean, the construction of tide models around Circum-Antarctica, and five typical regions: Antarctic Peninsula, Ross Sea, Filchner–Ronne Ice Shelf (FRIS), Weddell Sea, and Amery Ice Shelf (AIS). The accuracy of FES 2014, TPXO9, EOT20, CATS 2008, and regional tide models in the Antarctic Ocean and typical areas was evaluated using tidal records and satellite altimetry data. EOT20 and ANTPEN04.01 models have higher accuracy in the Antarctic Peninsula, and the root sum square (RSS) values are 8.29 and 7.46 cm, respectively. TPXO9 has the highest accuracy in the Weddell Sea and FRIS and AIS and RSS values are 18.33 and 12.77 cm, respectively. TPXO9 and RIS_Optimal models have higher accuracy in the Ross Sea and Ross Ice Shelf (RIS), RSS values are 5.62 and 6.21 cm, respectively. The accuracy of FES 2014, TPXO9, CATS 2008, and the regional tide model in the Drake Passage, Kerguelen Islands, and Adelie–Mertz was evaluated using satellite altimetry data. The RSS values are less than 4 cm. By using the altimetry data at Sentinel-3A single-satellite crossovers in terms of the STD of the SLA, the comparison of the STDs show that FES2014 is the best.

A new Mean Sea Surface (MSS) model called Shandong University of Science and Technology Antarctic Mean Sea Surface model (SDUST_ANT MSS) in the Antarctic Ocean is presented and validated in this paper. The SDUST_ANT MSS updates the DTU18 MSS with 6 years of Exact Repeat Mission (ERM) and Geodetic Mission (GM) data from HY-2A. Collinear adjustment was applied to all the ERM data to obtain the along-track mean sea surface height. Oceanic variability has been removed from the GM data. Crossover adjustment was applied to both the ERM and GM data. We constructed the HY-2A_MSS using HY-2A altimetry data based on optimal interpolation method. Several types of errors (such as white noise, residual effect of oceanic variability, and long wavelength bias) have been taken into account for the determination of MSS using optimal interpolation method. The SDUST_ANT MSS was constructed by mapping HY-2A_MSS onto the DTU18 MSS. The SDUST_ANT MSS was compared with DTU18 MSS and CNES_CLS15 MSS. At wavelengths below 150 km, differences between models are consistent with seafloor topographic gradient. At wavelengths above 150 km, differences are affected by the mesoscale activities and the altimetry errors in coastal areas. The errors of the three models, as indicated by their power spectral densities (PSDs), are of similar orders of magnitude. The absolute error is slightly smaller in SDUST_ANT than in CNES_CLS15 or DTU18.

Shore zone information is essential for coastal habitat assessment, environmental hazard monitoring, and resource conservation. However, traditional coastal zone classification mainly relies on in situ measurements and expert knowledge interpretation, which are costly and inefficient. This study classifies a shore zone area using satellite remote sensing data only and investigates the effect of the statistical indicators from Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) information with the Sentinel-2 data-derived spectral variables on the prediction results. Google Earth Engine was used to synthesize long time-series Sentinel-2 images, and different features were calculated for this synthetic image. Then, statistical indicators reflecting the characteristics of the shore zone profile were extracted from ICESat-2. Finally, a random forest algorithm was used to develop characteristics and shore zone classification. Comparing the results with the data measured shows that the proposed method can effectively classify the shore zone; it has an accuracy of 83.61% and a kappa coefficient of 0.81.

Carbendazim, as a fungicide, was commonly used to control fungal diseases in agriculture, forestry, and veterinary medicines. In this study, the acute and reproductive toxicity of carbendazim was assessed using Caenorhabditis elegans (C. elegans) as a model in order to preliminarily evaluate the potential risks of this fungicide in agricultural production and application. The results showed that the growth of C. elegans was inhibited by 0.01 μg/L carbendazim. The treatment of 0.1 μg/L carbendazim caused a significant decrease in locomotion behavior and significant damage to the reproductive and antioxidant system, causing the lifespan of nematodes to be drastically shortened. These results provide a better understanding of the environmental risk of carbendazim and raise new concerns about safety.

In recent years, noticeable subsidence depressions have occurred along the coastal zone of the Yellow River Delta. Consistent with these changes, dramatic human modifications within the coastal zone stand out, and the coastline is altered from an undisturbed natural area to an artificial coastline. However, very few studies have attempted to quantitatively analyze the relationship between subsidence depression and human activities. Here, the subsidence characteristics of the different land-use types in the Yellow River Delta are examined, and their spatiotemporal trends are quantified using a long-term satellite-observed time series of 30 years (1984–2017) regarding the land use map in combination with the InSAR-derived vertical ground deformations during three typical periods (P1: 1992–2000, P2: 2007–2010, and P3: 2016–2017). Noticeably, the highest subsidence rates were observed in areas where substantial human activities were observed, such as the subsidence in the salt fields ranging from 13 mm/year to 32 mm/year to 453 mm/year, respectively. Moreover, through the land-use prediction of Land Change Modeler (LCM), it is found that the salt field area will be further expanded in the future. The ecological vulnerability of the Yellow River Delta coastal zone should receive more attention in the future in terms of planning environmental protection strategies.

Shallow-water depth information is essential for ship navigation and fishery farming. However, the accurate acquisition of shallow-water depth has been a challenge for marine mapping. Combining Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) bathymetry data with multispectral data, satellite-derived bathymetry is a promising solution through which to obtain bathymetric information quickly and accurately. This study proposes a photon refraction correction method considering sea-surface undulations to address errors in the underwater photons obtained by the ICESat-2. First, the instantaneous sea surface and beam emission angle are integrated to determine the sea-surface incidence angle. Next, the distance of photon propagation in water is determined using sea-surface undulation and Snell’s law. Finally, position correction is performed through geometric relationships. The corrected photons were combined with the multispectral data for bathymetric inversion, and a bathymetric map of the Yongle Atoll area was obtained. A bathymetric chart was created using the corrected photons and the multispectral data in the Yongle Atoll. Comparing the results of different refraction correction methods with the data measured shows that the refraction correction method proposed in this paper can effectively correct bathymetry errors: the root mean square error is 1.48 m and the R2 is 0.86.

Calibration/Validation (Cal/Val) of satellite altimeters is fundamental for monitoring onboard sensor performance and ensuring long-term data quality. As altimeter technology has been evolving rapidly from profile to wide swath and interferometric altimetry, different requirements regarding Cal/Val have emerged. Most current Cal/Val technology has been developed for conventional profile altimeters, whereby satellite observations are compared against measurements at one point along orbit lines. However, the application of this type of Cal/Val technique to swath interferometric altimeters with two-dimensional measurements is difficult. Here, we propose a new strategy for the evaluation of interferometric altimeters based on comparison of wave-induced sea surface elevation (WSSE) spectra from one- and two-dimensional measurements. This method assumes that the WSSE variance of an equilibrium wave field is uniform and can be measured equivalently in the space or time domains. The method was first tested with simulated data and then used to evaluate the performance of an airborne interferometric radar altimeter system (AIRAS) using Global Navigation Satellite System (GNSS) buoy measurements. The differences between the WSSE variances from the AIRAS and two GNSS buoys were below 8 cm2, corresponding to a standard deviation of 2.8 cm, which could serve as a reference for the WSSE error over the scale range of waves. The correlation coefficient between the AIRAS and GNSS buoys was approximately 0.90, indicating that the error was small relative to the WSSE signals. In addition, the sea surface height (SSH) difference measured by the AIRAS was compared with that derived from the GNSS buoys at two sites. The results indicated that the error of the SSH difference was 3 cm. This approach represents a possible technique for the Cal/Val of future spaceborne/airborne interferometric altimeters; however, additional experiments and applications are needed to verify the feasibility of this method.

The antidiabetic activities of bitter melon powders produced with lyophilization/superfine grinding and hot air drying/normal grinding were investigated in vivo for selecting a suitable bitter melon processing procedure. After a five-week treatment, bitter melon lyophilized superfine grinding powder (BLSP) had a higher antidiabetic activity with reducing fasting blood glucose levels from 21.40 to 12.54 mmol/L, the serum insulin levels from 40.93 to 30.74 mIU/L, and restoring activities of SOD compared with those in the bitter melon hot air drying powder (BAP) treated group. Furthermore, BLSP protected pancreatic tissues including islet beta cells and reduced the loss of islet cells. Combined with the difference of compositions in BLSP and BAP, it could be concluded that superfine grinding and lyophilization processes were beneficial for presenting the antidiabetic activity, which will provide a reference for direct utilization of bitter melon as a suitable functional food to relieve symptoms of diabetes.