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IntroductionHigh-resolution whole-heart coronary magnetic resonance angiography (CMRA) often suffers from unreasonably long scan times, rendering imaging acceleration highly desirable. Traditional reconstruction methods used in CMRA rely on either hand-crafted priors or supervised learning models. Although the latter often yield superior reconstruction quality, they require a large amount of training data and memory resources, and may encounter generalization issues when dealing with out-of-distribution datasets.MethodsTo address these challenges, we introduce an unsupervised reconstruction method that combines deep image prior (DIP) with compressed sensing (CS) to accelerate 3D CMRA. This method incorporates a slice-by-slice DIP reconstruction and 3D total variation (TV) regularization, enabling high-quality reconstruction under a significant acceleration while enforcing continuity in the slice direction. We evaluated our method by comparing it to iterative SENSE, CS-TV, CS-wavelet, and other DIP-based variants, using both retrospectively and prospectively undersampled datasets.ResultsThe results demonstrate the superiority of our 3D DIP-CS approach, which improved the reconstruction accuracy relative to the other approaches across both datasets. Ablation studies further reveal the benefits of combining DIP with 3D TV regularization, which leads to significant improvements of image quality over pure DIP-based methods. Evaluation of vessel sharpness and image quality scores shows that DIP-CS improves the quality of reformatted coronary arteries.DiscussionThe proposed method enables scan-specific reconstruction of high-quality 3D CMRA from a five-minute acquisition, without relying on fully-sampled training data or placing a heavy burden on memory resources.

Evaluating the computational complexity is critical for assessing the time-domain anti-jamming performance of GNSS receivers. The multiplier is the core component that contributes to the computational complexity in time-domain anti-jamming. However, current algorithms aimed at reducing the complexity of time-domain anti-jamming typically concentrate on shortening the filter length, which fails to address the high computational complexity introduced by the use of multipliers. This paper introduces a cascaded multiplier-free approach for implementing time-domain anti-jamming in navigation receivers. We propose a numerical power decomposition technique based on optimal Canonical Signed Digit coding and coefficient decomposition. By substituting the multiplier with minimal adder and shift operations, the computational complexity of the anti-jamming filter with a high quantization bit-width can be considerably decreased. An optimization strategy is presented, and the low-complexity multiplier-free technique is applied to the time-domain anti-jamming filter. Compared to the traditional Canonical Signed Digit multiplier-free technique, our method can reduce the components required for a 12-bit quantization anti-interference filter by one adder, 20 shift operations, and five coded word lengths, while maintaining a pseudo-range measurement deviation below 0.27 ns.

Abstract Background To develop a magnetic resonance imaging (MRI)-based radiomics predictive model for the identification of knee osteoarthritis (OA), based on the tibial and femoral subchondral bone, and compare with the trabecular structural parameter-based model. Methods Eighty-eight consecutive knees were scanned with 3T MRI and scored using MRI osteoarthritis Knee Scores (MOAKS), in which 56 knees were diagnosed to have OA. The modality of sagittal three-dimensional balanced fast-field echo sequence (3D BFFE) was used to image the subchondral bone. Four trabecular structural parameters (bone volume fraction [BV/TV], trabecular thickness [Tb.Th], trabecular separation [Tb.Sp], and trabecular number) and 93 radiomics features were extracted from four regions of the lateral and medial aspects of the femur condyle and tibial plateau. Least absolute shrinkage and selection operator (LASSO) was used for feature selection. Machine learning-based support vector machine models were constructed to identify knee OA. The performance of the models was assessed by area under the curve (AUC) of the receiver operator characteristic (ROC). The correlation between radiomics features and trabecular structural parameters was analyzed using Pearson’s correlation coefficient. Results Our radiomics-based classification model achieved the AUC score of 0.961 (95% confidence interval [CI], 0.912–1.000) when distinguishing between normal and knee OA, which was higher than that of the trabecular parameter-based model (AUC, 0.873; 95% CI, 0.788–0.957). The first-order, texture, and Laplacian of Gaussian-based radiomics features correlated positively with Tb.Th and BV/TV, but negatively with Tb.Sp (P

This study explored the effects of dietary protein levels on Litopenaeus vannamei with its intestinal microbiota and transcriptome responses. Previous studies on the effects of dietary protein levels on L. vannamei have focused on growth performance, antioxidant indices, and digestive enzyme activity, but few studies have been conducted at the microbiological and molecular levels. In this study, five isolipid experimental diets with protein levels of 32% (P32), 36% (P36), 40% (P40), 44% (P44), and 48% (P48) were used in an L. vannamei (0.63 ± 0.02 g) feeding trial for 56 days. At the end of the feeding trial, the growth performance, immunity, intestinal health, and transcriptional responses of L. vannamei were determined. This study demonstrated that higher protein levels (P44) led to superior weight gain and growth rates for L. vannamei, with lower feed conversion ratios (FCR) observed in the P48 and P44 groups compared to the P32 and P36 groups (p ≤ 0.05). The P44 and P48 groups also showed a notably higher protein efficiency ratio (PER) compared to others (p ≤ 0.05), and there was no significant difference between them. Upon Vibrio parahaemolyticus infection, the P48 group exhibited a significantly lower survival rate (SR) within 48 h, while during 72 h of white spot syndrome virus (WSSV) infection, the P44 group had a notably higher survival rate than the P32 group (p ≤ 0.05). Digestive enzyme activity and antioxidant levels in L. vannamei initially increased and then decreased as protein levels increased, usually peaking in the P40 or P44 groups. Lower dietary protein levels significantly reduced the relative abundance of beneficial bacteria and increased the relative abundance of pathogenic bacteria in the intestines of L. vannamei. Transcriptome sequencing analysis revealed that most differentially expressed genes (DEGs) were up-regulated and then down-regulated as dietary protein levels increased. Furthermore, KEGG pathway enrichment analysis indicated that several immune and metabolic pathways, including metabolic pathways, glutathione metabolism, cytochrome P450, and lysosome and pancreatic secretion, were significantly enriched. In summary, the optimal feed protein level for L. vannamei shrimp was 40–44%. Inappropriate feed protein levels reduced antioxidant levels and digestive enzyme activity and promoted pathogen settlement, deceasing factors in various metabolic pathways that respond to microorganisms through transcriptional regulation. This could lead to stunted growth in L. vannamei and compromise their immune function.

Abstract Objective This study aimed to develop a predictive model to detect osteoporosis using radiomic features from lumbar spine computed tomography (CT) images. Methods A total of 133 patients were included in this retrospective study, 41 men and 92 women, with a mean age of 65.45 ± 9.82 years (range: 31–94 years); 53 had normal bone mineral density, 32 osteopenia, and 48 osteoporosis. For each patient, the L1–L4 vertebrae on the CT images were automatically segmented using SenseCare and defined as regions of interest (ROIs). In total, 1,197 radiomic features were extracted from these ROIs using PyRadiomics. The most significant features were selected using logistic regression and Pearson correlation coefficient matrices. Using these features, we constructed three linear classification models based on the random forest (RF), support vector machine (SVM), and K-nearest neighbor (KNN) algorithms, respectively. The training and test sets were repeatedly selected using fivefold cross-validation. The model performance was evaluated using the area under the receiver operator characteristic curve (AUC) and confusion matrix. Results The classification model based on RF had the highest performance, with an AUC of 0.994 (95% confidence interval [CI]: 0.979–1.00) for differentiating normal BMD and osteoporosis, 0.866 (95% CI: 0.779–0.954) for osteopenia versus osteoporosis, and 0.940 (95% CI: 0.891–0.989) for normal BMD versus osteopenia. Conclusions The excellent performance of this radiomic model indicates that lumbar spine CT images can effectively be used to identify osteoporosis and as a tool for opportunistic osteoporosis screening.

The global navigation satellite system is a high-precision radio navigation system based on artificial satellites. Among the factors affecting the positioning accuracy of the navigation satellite system, the multipath signal is the most significant and challenging to eliminate. Furthermore, the multipath environment of the receiver is also becoming more complex, and it seriously threatens the measurement accuracy and stability of the receiver. The multipath mitigation technology is also continuously improved and developed in practical application. This paper firstly introduces the concept and characteristics of multipath signals, and summarizes the influence of multipath signals on navigation satellite systems from two aspects of code tracking loop and carrier tracking loop. Furthermore, the existing multipath mitigation technology is summarized in four stages: signal system design, antenna design, baseband signal processing, and navigation data processing. Finally, according to the summary of the existing technology and the new demand, the future development direction of multipath suppression technology is prospected.