The use of antibiotics in the livestock and poultry industries has raised significant concern about environmental and health problems. In light of this, accurate knowledge of antibiotic residues in livestock and poultry manure is important for pollution management and strategic decision-making at national level. This study aims to provide a comprehensive report on antibiotic residues in livestock and poultry manure in China using the published data of 3,751 livestock and poultry feces in 29 provincial-level units over the past 20 years. In this study, the overall status of antibiotic residues in livestock and poultry feces was analyzed by mathematical statistics. Besides, the spatio-temporal variation characteristics were analyzed by spatial statistics, and the differences among livestock and poultry species were evaluated by subgroup analysis. The results showed that tetracyclines (TCs), quinolones (QLs), sulfonamides (SAs) and macrolides (MLs) were the highest residues in livestock and poultry manure. The spatial and temporal variation revealed that the overall trend of antibiotic residues decreased gradually, and the spatial distribution was primarily concentrated in the southeast of Hu Line, exhibiting a "northeast-southwest" distribution. The distribution range also decreased slightly, with the residues of tetracyclines (TCs), quinolones (QLs), sulfonamides (SAs) and platyclines (PMs) showing a significant spatial hot spot. The center of gravity of antibiotics residue shifted to the southwest between 2003 and 2021. In comparison to cow and sheep manure, the tetracyclines (TCs), sulfonamides (SAs), and macrolides (MLs) in pig and chicken manure were higher. The results can serve as reference for the control and reduction of antibiotic pollution in livestock and poultry manure, as well as wise utilization of those resources and achieving goals for clean agriculture.
Image quality analysis of nonlinear algorithms is challenging due to numerous dependencies on the imaging system, algorithmic parameters, object, and stimulus. In particular, traditional notions of linearity and local linearity are of limited utility when the system response is dependent on the stimulus itself. In this work, we analyze the performance of nonlinear systems using perturbation response - the difference between the mean output with and without a stimulus, and introduce a new metric to examine variation of the responses in individual images. We applied the analysis to four algorithms with different degrees of nonlinearity for a spherical stimulus of varying contrast. For model-based reconstruction methods [penalized-likelihood (PL) reconstruction with a quadratic penalty and a Huber penalty], perturbation response analysis reaffirmed known trends in terms of object- and location-dependence. For a CNN denoising network, the response exhibits highly nonlinear behavior as the contrast increases - from the stimulus completely disappearing, to appearing at the right contrast but smaller in size, to being fully admitted by the algorithm. Furthermore, the variation metric for PL reconstruction with a Huber penalty and the CNN network reveals high variation at the edge of the stimulus, i.e., perturbation response computed from the mean images is a smoothed version of individual responses due to "jitter" in edges. This behavior suggests that the mean response alone may not be representative of performance in individual images and image quality metrics traditionally defined based on the mean response may be inappropriate for certain nonlinear algorithms. This work demonstrates the potential utility of perturbation response and response variation in the analysis and optimization of nonlinear imaging algorithms.
The use of antibiotics in the livestock and poultry industries has raised significant concern about environmental and health problems. In light of this, accurate knowledge of antibiotic residues in livestock and poultry manure is important for pollution management and strategic decision-making at the national level. This study aims to provide a comprehensive report on antibiotic residues in livestock and poultry manure in China using the published data of 3751 livestock and poultry feces in 29 provincial-level units over the past 20 years. In this study, the overall status of antibiotic residues in livestock and poultry feces was analyzed by mathematical statistics. Moreover, the spatio-temporal variation characteristics were analyzed by spatial statistics, and the differences among livestock and poultry species were evaluated by subgroup analysis. The finding indicated that tetracyclines (TCs), quinolones (QLs), sulfonamides (SAs), and macrolides (MLs) were the most abundant residues in livestock and poultry manure. The spatial and temporal variation revealed that the overall trend of antibiotic residues decreased gradually, and the spatial distribution was primarily concentrated in the southeast of Hu Line, exhibiting a “northeast-southwest” distribution. The distribution range also decreased slightly, with the residues of tetracyclines (TCs), quinolones (QLs), sulfonamides (SAs), and pleuromutilins (PMs) showing a significant spatial hot spot. The center of gravity of antibiotic residue shifted to the southwest between 2003 and 2021. In comparison to cow and sheep manure, the tetracyclines (TCs), sulfonamides (SAs), and macrolides (MLs) in pig and chicken manure were higher. The results can serve as a reference for the control and reduction of antibiotic pollution in livestock and poultry manure, as well as the wise utilization of those resources and achieving goals for clean agriculture.
As PET imaging is accompanied by substantial radiation exposure and cancer risk, reducing radiation dose in PET scans is an important topic. However, low-count PET scans often suffer from high image noise, which can negatively impact image quality and diagnostic performance. Recent advances in deep learning have shown great potential for recovering underlying signal from noisy counterparts. However, neural networks trained on a specific noise level cannot be easily generalized to other noise levels due to different noise amplitude and variances. To obtain optimal denoised results, we may need to train multiple networks using data with different noise levels. But this approach may be infeasible in reality due to limited data availability. Denoising dynamic PET images presents additional challenge due to tracer decay and continuously changing noise levels across dynamic frames. To address these issues, we propose a Unified Noise-aware Network (UNN) that combines multiple sub-networks with varying denoising power to generate optimal denoised results regardless of the input noise levels. Evaluated using large-scale data from two medical centers with different vendors, presented results showed that the UNN can consistently produce promising denoised results regardless of input noise levels, and demonstrate superior performance over networks trained on single noise level data, especially for extremely low-count data.
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