Antibiotic resistance genes in lakes from middle and lower reaches of the Yangtze River, China: Effect of land use and sediment characteristics

Freshwater lakes provided an ideal media for the accumulation and propagation of antibiotic resistance genes (ARGs), because they were susceptible to anthropogenic impacts. Land reclamation and urbanization exerted severe anthropogenic impacts on lakes from middle and lower reaches of the Yangtze River, China over the past decades. In this study,15 lakes in the region were selected to understand the level and variability of ARGs. Proportion of different land use types was applied to display the land reclamation and urbanization around each lake. For sulfonamide resistance (sul) genes, still had the highest relative abundance in sediments, with maximum 2.11 x 10(-1) copies/16SrRNA copy in Gehu Lake. For tetracycline resistance (tet) genes, tetG had the highest average value of relative abundance (4.74 x 10(-3) copies/16SrRNA copy), followed by tetB, tetA, tetQ and tetM. Class I integron (intl1) played an important role in acquisition and dissemination of sul1 and tetG. Sediment characteristics (moisture, density, total nitrogen, total carbon, ammonium, and nitrate) were found to have no significant effect on ARG distribution. Taihu Lake and Yangcheng Lake which exhibited high sul and tet genes had the high proportion of built-up land use. (C) 2017 Elsevier Ltd. All rights reserved

A Scraper Conveyor Coal Flow Monitoring Method Based on Speckle Structured Light Data

Aiming at the problem of serious shutdowns of conveyors caused by abnormal coal flow of scraper conveyors, a coal flow monitoring method based on speckle structured light is proposed. The point cloud data of the coal body on the scraper conveyor is collected through the speckle structured light acquisition system. Then, the proposed PDS-Algorithm (Planar Density Simplification Algorithm) is used to complete the simplification and differentiation of the collected point cloud data, which provides a basis for constructing geometric characteristics of coal flow lineament. This paper uses the processed point cloud data to calculate the volume of the coal mass and monitor the coal flow of the scraper conveyor. Finally, this method is used in the detection of abnormal coal flow of a coal mine scraper conveyor, and the results show that the proposed abnormal flow monitoring method can meet the accuracy and real-time requirements of coal mine abnormal alarms

A Scraper Conveyor Coal Flow Monitoring Method Based on Speckle Structured Light Data

Aiming at the problem of serious shutdowns of conveyors caused by abnormal coal flow of scraper conveyors, a coal flow monitoring method based on speckle structured light is proposed. The point cloud data of the coal body on the scraper conveyor is collected through the speckle structured light acquisition system. Then, the proposed PDS-Algorithm (Planar Density Simplification Algorithm) is used to complete the simplification and differentiation of the collected point cloud data, which provides a basis for constructing geometric characteristics of coal flow lineament. This paper uses the processed point cloud data to calculate the volume of the coal mass and monitor the coal flow of the scraper conveyor. Finally, this method is used in the detection of abnormal coal flow of a coal mine scraper conveyor, and the results show that the proposed abnormal flow monitoring method can meet the accuracy and real-time requirements of coal mine abnormal alarms

Exploring latent weight factors and global information for food-oriented cross-modal retrieval

Food-oriented cross-modal retrieval aims to retrieve relevant recipes given food images or vice versa. The modality semantic gap between recipes and food images (text and image modalities) is the main challenge. Though several studies are introduced to bridge this gap, they still suffer from two major limitations: 1) The simple embedding concatenation only can capture the simple interactions rather than complex interactions between different recipe components. 2) The image feature extraction based on convolutional neural networks only considers the local features and ignores the global features of an image, as well as the interactions between different extracted features. This paper proposes a novel method based on Latent Component Weight Factors and Global Information (LCWF-GI) to learn the robust recipe and image representations for food-oriented cross-modal retrieval. This proposed method integrates the textual embeddings of different recipe components into a compact embedding to represent the recipes with the latent component-specific weight factors. A transformer encoder is utilised to capture the intra-modality interactions and the importance of different extracted image features for enhanced image representations. Finally, the bi-directional triplet loss is further used to perform retrieval learning. Experimental results on the Recipe 1M dataset show that our LCWF-GI method achieves competent improvements

Radiocarbon isotope technique as a powerful tool in tracking anthropogenic emissions of carbonaceous air pollutants and greenhouse gases: A review

Air pollution and climate change are two important threats facing in our planet and are tightly linked to carbonaceous components in the atmosphere. A better understanding of the emission sources and environmental fate/sink of carbonaceous components is critical for improving our knowledge of the global carbon cycle and mitigating the negative environmental impacts of air pollution and climate change on human well-being. Radiocarbon (14C), which is decayed completely in fossil fuel (e.g. coal and petroleum), is an ideal tool for quantifying the carbon flow in various carbon reservoirs. This study reviews the current knowledge of 14C in organic carbon (OC), elemental carbon (EC), individual organic compounds, methane (CH4), carbon dioxide (CO2), annual plants, and tree rings. The impacts of fossil and non-fossil sources on the atmosphere can be quantified by measuring 14C. We also report on the influence of nuclear power plants and sea-air gas exchange on the abundance of 14C in the atmosphere. The increasing fossil fuel emissions indicated by the depletion of 14CO2 under IPCC RCP scenarios, support the urgent need to devise ambitious strategies of reducing carbonaceous components to achieve sustainable development on Earth. This review summarizes the challenges and perspectives of 14C studies of the atmosphere

A Big Coal Block Alarm Detection Method for Scraper Conveyor Based on YOLO-BS

With the aim of solving the problem of coal congestion caused by big coal blocks in underground mine scraper conveyors, in this paper we proposed the use of a YOLO-BS (YOLO-Big Size) algorithm to detect the abnormal phenomenon of coal blocks on scraper conveyors. Given the scale of the big coal block targets, the YOLO-BS algorithm replaces the last layer of the YOLOv4 algorithm feature extraction backbone network with the transform module. The YOLO-BS algorithm also deletes the redundant branch which detects small targets in the PAnet module, which reduces the overall number of parameters in the YOLO-BS algorithm. As the up-sampling and down-sampling operations in the PAnet module of the YOLO algorithm can easily cause feature loss, YOLO-BS improves the problem of feature loss and enhances the convergence performance of the model by adding the SimAM space and channel attention mechanism. In addition, to solve the problem of sample imbalance in big coal block data, in this paper, it was shown that the YOLO-BS algorithm selects focal loss as the loss function. In view of the problem that the same lump coal in different locations on the scraper conveyor led to different congestion rates, we conducted research and proposed a formula to calculate the congestion rate. Finally, we collected 12,000 image datasets of coal blocks on the underground scraper conveyor in Daliuta Coal Mine, China, and verified the performance of the method proposed in this paper. The results show that the processing speed of the proposed method can reach 80 fps, and the correct alarm rate can reach 93%. This method meets the real-time and accuracy requirements for the detection of abnormal phenomena in scraper conveyors

Development of a nomogram to predict the incidence of acute kidney injury among ischemic stroke individuals during ICU hospitalization

Background: Limited clinical prediction models exist to assess the likelihood of acute kidney injury (AKI) occurrence in ischemic stroke individuals. In this retrospective study, our aim was to construct a nomogram that utilizes commonly available clinical features to predict the occurrence of AKI during intensive care unit hospitalization among this patient population. Methods: In this study, the MIMIC-IV database was utilized to investigate potential risk factors associated with the incidence of AKI among ischemic stroke individuals. A predictive nomogram was developed based on these identified risk factors. The discriminative performance of the constructed nomogram was assessed. Calibration analysis was utilized to evaluate the calibration performance of the constructed model, assessing the agreement between predicted probabilities and actual outcomes. Furthermore, decision curve analysis (DCA) was employed to assess the clinical net benefit, taking into account the potential risks and benefits associated with different decision thresholds. Results: A total of 2089 ischemic stroke individuals were included and randomly allocated into developing (n = 1452) and verification cohorts (n = 637). Risk factors for AKI incidence in ischemic stroke individuals, determined through LASSO and logistic regression. The constructed nomogram had good performance in predicting the occurrence of AKI among ischemic stroke patients and provided significant improvement compared to existing scoring systems. DCA demonstrated satisfactory clinical net benefit of the constructed nomogram in both the validation and development cohorts. Conclusions: The developed nomogram exhibits robust predictive performance in forecasting AKI occurrence in ischemic stroke individuals

Fluorescence in situ hybridization is superior for monitoring Epstein Barr viral load in infectious mononucleosis patients

Abstract Background Epstein Barr virus (EBV) plays a causal role in some diseases, including infectious mononucleosis, lymphoproliferative diseases and nasopharyngeal carcinoma. Detection of EBV infection has been shown to be a useful tool for diagnosing EBV-related diseases. In the present study, we compared the performance of molecular tests, including fluorescence in situ hybridization (FISH) and EBV real-time PCR, to those of serological assays for the detection of EBV infection. Methods Thirty-eight patients with infectious mononucleosis (IM) were enrolled, of whom 31 were diagnosed with a mild type, and seven were diagnosed with IM with haemophagocytic lymphohistiocytosis and chronic active EBV infection. Twenty healthy controls were involved in the study. The atypical lymphocytes in peripheral blood were detected under a microscope and the percentage of positive cells was calculated. EBV DNA load in peripheral blood was detected using real-time PCR. The FISH assay was developed to detect the EBV genome from peripheral blood mononuclear cells (PBMC). Other diagnosis methods including the heterophil agglutination (HA) test and EBV-VCA-IgM test, to detect EBV were also compared. SPSS17.0 was used for statistical analysis. Results In all, 5–41% atypical lymphocytes were found among the PBMC in mild IM patients, whereas 8–51% atypical lymphocytes were found in IM patients with haemophagocytic lymphohistiocytosis and chronic active EBV infection patients. There was no significant difference in the ratios of atypical lymphoma between patients of the different types. We observed that 71.2% of mild IM patients and 85.7% of IM patients with haemophagocytic lymphohistiocytosis and chronic active EBV infection patients were positive for EBV-VCA-IgM. EBV-VCA-IgM was negative in all healthy control subjects. In addition, 67.1% of mild IM patients tested heterophile antibody positive, whereas 71.4% of IM patients with haemophagocytic lymphohistiocytosis and chronic active EBV infection tested positive. EBV DNA detected using real-time PCR was observed in 89.5% of these IM patients. The EBV genome was detected by the FISH assay in 97.4% of the IM patients. The EB viral loads detected by FISH and real-time PCR increased with the severity of IM. The EBV genome was detected in almost all the PBMC of IM with haemophagocytic lymphohistiocytosis and chronic active EBV infection patients. Conclusion Molecular tests, including FISH and EBV real-time PCR, are more sensitive than serological assays for the detection of EBV infection. The FISH assay detecting EBV copies in unfractionated whole blood is preferable and superior to plasma real-time PCR in its reflection of the absolute viral burden circulating in the patients