Parkinson’s disease (PD) is a common and complex neurodegenerative disorder with 5 stages in the Hoehn and Yahr scaling. Given the heterogeneity of PD, it is challenging to classify early stages 1 and 2 and detect brain function alterations. Functional magnetic resonance imaging (fMRI) is a promising tool in revealing functional connectivity (FC) differences and developing biomarkers in PD. Some machine learning approaches like support vector machine and logistic regression have been successfully applied in the early diagnosis of PD using fMRI data, which outperform classifiers based on manually selected morphological features. However, the early-stage characterization in FC changes has not been fully investigated. Given the complexity and non-linearity of fMRI data, we propose the use of a long short-term memory (LSTM) network to characterize the early stages of PD. The study included 84 subjects (56 in stage 2 and 28 in stage 1) from the Parkinson’s Progression Markers Initiative (PPMI), the largest-available public PD dataset. Under a repeated 10-fold stratified cross-validation, the LSTM model reached an accuracy of 71.63%, 13.52% higher than the best traditional machine learning method, indicating significantly better robustness and accuracy compared with other machine learning classifiers. We used the learned LSTM model weights to select the top brain regions that contributed to model prediction and performed FC analyses to characterize functional changes with disease stage and motor impairment to gain better insight into the brain mechanisms of PD.
A novel electromagnetic environmental pollution protective coating with high performance was prepared with Ni-Cu-La-B-coated glass fiber and nickel powder as composite fillers. Its conductivity,shielding effectiveness was discussed. The results showed that the coatings with a thickness of 300 μm containing 6 wt% of Ni-Cu-La-B-coated glass fibers had the lowest resistivity of 0.58 Ω·cm and best shielding effectiveness ranging from 51.45 dB to 62.18 dB in 0.3-1 000 MHz frequency band.
Myocardial perfusion imaging using SPECT is widely utilized to diagnose coronary artery diseases, but image quality can be negatively affected in low-dose and few-view acquisition settings. Although various deep learning methods have been introduced to improve image quality from low-dose or few-view SPECT data, previous approaches often fail to generalize across different acquisition settings, limiting their applicability in reality. This work introduced DiffSPECT-3D, a diffusion framework for 3D cardiac SPECT imaging that effectively adapts to different acquisition settings without requiring further network re-training or fine-tuning. Using both image and projection data, a consistency strategy is proposed to ensure that diffusion sampling at each step aligns with the low-dose/few-view projection measurements, the image data, and the scanner geometry, thus enabling generalization to different low-dose/few-view settings. Incorporating anatomical spatial information from CT and total variation constraint, we proposed a 2.5D conditional strategy to allow the DiffSPECT-3D to observe 3D contextual information from the entire image volume, addressing the 3D memory issues in diffusion model. We extensively evaluated the proposed method on 1,325 clinical 99mTc tetrofosmin stress/rest studies from 795 patients. Each study was reconstructed into 5 different low-count and 5 different few-view levels for model evaluations, ranging from 1% to 50% and from 1 view to 9 view, respectively. Validated against cardiac catheterization results and diagnostic comments from nuclear cardiologists, the presented results show the potential to achieve low-dose and few-view SPECT imaging without compromising clinical performance. Additionally, DiffSPECT-3D could be directly applied to full-dose SPECT images to further improve image quality, especially in a low-dose stress-first cardiac SPECT imaging protocol.
Cardiovascular disease (CVD) is the leading cause of death worldwide, and myocardial perfusion imaging using SPECT has been widely used in the diagnosis of CVDs. The GE 530/570c dedicated cardiac SPECT scanners adopt a stationary geometry to simultaneously acquire 19 projections to increase sensitivity and achieve dynamic imaging. However, the limited amount of angular sampling negatively affects image quality. Deep learning methods can be implemented to produce higher-quality images from stationary data. This is essentially a few-view imaging problem. In this work, we propose a novel 3D transformer-based dual-domain network, called TIP-Net, for high-quality 3D cardiac SPECT image reconstructions. Our method aims to first reconstruct 3D cardiac SPECT images directly from projection data without the iterative reconstruction process by proposing a customized projection-to-image domain transformer. Then, given its reconstruction output and the original few-view reconstruction, we further refine the reconstruction using an image-domain reconstruction network. Validated by cardiac catheterization images, diagnostic interpretations from nuclear cardiologists, and defect size quantified by an FDA 510(k)-cleared clinical software, our method produced images with higher cardiac defect contrast on human studies compared with previous baseline methods, potentially enabling high-quality defect visualization using stationary few-view dedicated cardiac SPECT scanners.
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.
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