Combined turnover of carbon and soil aggregates using rare earth oxides and isotopically labelled carbon as tracers

This work was granted by the China-UK jointed Red Soil Critical Zone project from National Natural Science Foundation of China (NSFC: 41571130053, 41371235) and from Natural Environmental Research Council (NERC: Code: NE/N007611/1).Peer reviewedPublisher PD

Reconstruction of Cardiac Cine MRI under Free-breathing using Motion-guided Deformable Alignment and Multi-resolution Fusion

Objective: Cardiac cine magnetic resonance imaging (MRI) is one of the important means to assess cardiac functions and vascular abnormalities. However, due to cardiac beat, blood flow, or the patient's involuntary movement during the long acquisition, the reconstructed images are prone to motion artifacts that affect the clinical diagnosis. Therefore, accelerated cardiac cine MRI acquisition to achieve high-quality images is necessary for clinical practice. Approach: A novel end-to-end deep learning network is developed to improve cardiac cine MRI reconstruction under free breathing conditions. First, a U-Net is adopted to obtain the initial reconstructed images in k-space. Further to remove the motion artifacts, the Motion-Guided Deformable Alignment (MGDA) method with second-order bidirectional propagation is introduced to align the adjacent cine MRI frames by maximizing spatial-temporal information to alleviate motion artifacts. Finally, the Multi-Resolution Fusion (MRF) module is designed to correct the blur and artifacts generated from alignment operation and obtain the last high-quality reconstructed cardiac images. Main results: At an 8$\times$ acceleration rate, the numerical measurements on the ACDC dataset are SSIM of 78.40%$\pm$4.57%, PSNR of 30.46$\pm$1.22 dB, and NMSE of 0.0468$\pm$0.0075. On the ACMRI dataset, the results are SSIM of 87.65%$\pm$4.20%, PSNR of 30.04$\pm$1.18 dB, and NMSE of 0.0473$\pm$0.0072. Significance: The proposed method exhibits high-quality results with richer details and fewer artifacts for cardiac cine MRI reconstruction on different accelerations under free breathing conditions.Comment: 28 pages, 5 tables, 11 figure

The Governance of Distributor in Different Supply Chains

The importance of governance is discussed in this paper at first, and the phenomenon of distributors in different supply chains is analyzed. Some frangible factors are found in supply chain, and then we discuss how to improve the robustness of the distribution channel. The relationship between the factors of distributors and the robustness of distribution channel is analyzed. The aim is to improve the robustness of supply chain and make the distribution channels operate efficiently

EDMAE: An Efficient Decoupled Masked Autoencoder for Standard View Identification in Pediatric Echocardiography

This paper introduces the Efficient Decoupled Masked Autoencoder (EDMAE), a novel self-supervised method for recognizing standard views in pediatric echocardiography. EDMAE introduces a new proxy task based on the encoder-decoder structure. The EDMAE encoder is composed of a teacher and a student encoder. The teacher encoder extracts the potential representation of the masked image blocks, while the student encoder extracts the potential representation of the visible image blocks. The loss is calculated between the feature maps output by the two encoders to ensure consistency in the latent representations they extract. EDMAE uses pure convolution operations instead of the ViT structure in the MAE encoder. This improves training efficiency and convergence speed. EDMAE is pre-trained on a large-scale private dataset of pediatric echocardiography using self-supervised learning, and then fine-tuned for standard view recognition. The proposed method achieves high classification accuracy in 27 standard views of pediatric echocardiography. To further verify the effectiveness of the proposed method, the authors perform another downstream task of cardiac ultrasound segmentation on the public dataset CAMUS. The experimental results demonstrate that the proposed method outperforms some popular supervised and recent self-supervised methods, and is more competitive on different downstream tasks.Comment: 15 pages, 5 figures, 8 tables, Published in Biomedical Signal Processing and Contro

Accuracy of epidemiological inferences based on publicly available information: retrospective comparative analysis of line lists of human cases infected with influenza A(H7N9) in China

Background: Appropriate public health responses to infectious disease threats should be based on best-available evidence, which requires timely reliable data for appropriate analysis. During the early stages of epidemics, analysis of ‘line lists’ with detailed information on laboratory-confirmed cases can provide important insights into the epidemiology of a specific disease. The objective of the present study was to investigate the extent to which reliable epidemiologic inferences could be made from publicly-available epidemiologic data of human infection with influenza A(H7N9) virus. Methods: We collated and compared six different line lists of laboratory-confirmed human cases of influenza A(H7N9) virus infection in the 2013 outbreak in China, including the official line list constructed by the Chinese Center for Disease Control and Prevention plus five other line lists by HealthMap, Virginia Tech, Bloomberg News, the University of Hong Kong and FluTrackers, based on publicly-available information. We characterized clinical severity and transmissibility of the outbreak, using line lists available at specific dates to estimate epidemiologic parameters, to replicate real-time inferences on the hospitalization fatality risk, and the impact of live poultry market closure. Results: Demographic information was mostly complete (less than 10% missing for all variables) in different line lists, but there were more missing data on dates of hospitalization, discharge and health status (more than 10% missing for each variable). The estimated onset to hospitalization distributions were similar (median ranged from 4.6 to 5.6 days) for all line lists. Hospital fatality risk was consistently around 20% in the early phase of the epidemic for all line lists and approached the final estimate of 35% afterwards for the official line list only. Most of the line lists estimated >90% reduction in incidence rates after live poultry market closures in Shanghai, Nanjing and Hangzhou. Conclusions: We demonstrated that analysis of publicly-available data on H7N9 permitted reliable assessment of transmissibility and geographical dispersion, while assessment of clinical severity was less straightforward. Our results highlight the potential value in constructing a minimum dataset with standardized format and definition, and regular updates of patient status. Such an approach could be particularly useful for diseases that spread across multiple countries

Knowledge, attitudes and practices (KAP) relating to avian influenza in urban and rural areas of China

<p>Abstract</p> <p>Background</p> <p>Studies have revealed that visiting poultry markets and direct contact with sick or dead poultry are significant risk factors for H5N1 infection, the practices of which could possibly be influenced by people's knowledge, attitudes and practices (KAPs) associated with avian influenza (AI). To determine the KAPs associated with AI among the Chinese general population, a cross-sectional survey was conducted in China.</p> <p>Methods</p> <p>We used standardized, structured questionnaires distributed in both an urban area (Shenzhen, Guangdong Province; n = 1,826) and a rural area (Xiuning, Anhui Province; n = 2,572) using the probability proportional to size (PPS) sampling technique.</p> <p>Results</p> <p>Approximately three-quarters of participants in both groups requested more information about AI. The preferred source of information for both groups was television. Almost three-quarters of all participants were aware of AI as an infectious disease; the urban group was more aware that it could be transmitted through poultry, that it could be prevented, and was more familiar with the relationship between AI and human infection. The villagers in Xiuning were more concerned than Shenzhen residents about human AI viral infection. Regarding preventative measures, a higher percentage of the urban group used soap for hand washing whereas the rural group preferred water only. Almost half of the participants in both groups had continued to eat poultry after being informed about the disease.</p> <p>Conclusions</p> <p>Our study shows a high degree of awareness of human AI in both urban and rural populations, and could provide scientific support to assist the Chinese government in developing strategies and health-education campaigns to prevent AI infection among the general population.</p

A novel selective chemosensor for Mg²⁺ detection based on quinoline-hydrazone-crown ether

120-126A novel Mg2+-selective chemosensor based on quinoline-hydrazone-crown ether (L1) has been synthesized and characterized by 1H NMR, elemental analysis and ESI-mass spectrometry. The absorption and emission spectra of L1 have been investigated in the presence of alkali and alkaline-earth cations (Li+, Na+, K+, Mg2+, Ca2+, Sr2+ and Ba2+). Mg2+ results in an instant color change of L1 from colorless to yellow in ethanol. Upon binding of Mg2+, a significant fluorescence enhancement is triggered in acetonitrile. Thus, L1 is expected to be used as an interesting Mg2+ sensitive chemosensor. The 1:1 stoichiometry binding mode of L1 with Mg2+ is supported by the Benesi-Hildebrand analysis. The binding constants (lgK) in ethanol and acetonitrile are found to be 4.54 and 3.57 by the goodness of the linear fitting of the Benesi-Hildebrand plot from the results of Ultraviolet–visible (UV-Vis) and fluorescence titrations, respectively

Multiple-level feature-based measure for retargeted image quality

Objective image retargeting quality assessment aims to use computational models to predict the retargeted image quality consistent with subjective perception. In this paper, we propose a multiple-level feature (MLF)-based quality measure to predict the perceptual quality of retargeted images. We first provide an in-depth analysis on the low-level aspect ratio similarity feature, and then propose a mid-level edge group similarity feature, to better address the shape/structure related distortion. Furthermore, a high-level face block similarity feature is designed to deal with sensitive region deformation. The multiple-level features are complementary as they quantify different aspects of quality degradation in the retargeted image, and the MLF measure learned by regression is used to predict the perceptual quality of retargeted images. Extensive experimental results performed on two public benchmark databases demonstrate that the proposed MLF measure achieves higher quality prediction accuracy than the existing relevant state-of-the-art quality measures.NRF (Natl Research Foundation, S’pore)MOE (Min. of Education, S’pore