Attenuation correction for positron-emission tomography (PET)/magnetic resonance (MR) hybrid imaging systems and dose planning for MR-based radiation therapy remain challenging due to insufficient high-energy photon attenuation information. We present a novel approach that uses the learned nonlinear local descriptors and feature matching to predict pseudo computed tomography (pCT) images from T1-weighted and T2-weighted magnetic resonance imaging (MRI) data. The nonlinear local descriptors are obtained by projecting the linear descriptors into the nonlinear high-dimensional space using an explicit feature map and low-rank approximation with supervised manifold regularization. The nearest neighbors of each local descriptor in the input MR images are searched in a constrained spatial range of the MR images among the training dataset. Then the pCT patches are estimated through k-nearest neighbor regression. The proposed method for pCT prediction is quantitatively analyzed on a dataset consisting of paired brain MRI and CT images from 13 subjects. Our method generates pCT images with a mean absolute error (MAE) of 75.25 ± 18.05 Hounsfield units, a peak signal-to-noise ratio of 30.87 ± 1.15 dB, a relative MAE of 1.56 ± 0.5% in PET attenuation correction, and a dose relative structure volume difference of 0.055 ± 0.107% in D98%, as compared with true CT. The experimental results also show that our method outperforms four state-of-the-art methods.

Given the complicated relationship between the Magnetic Resonance Imaging (MRI) signals and the attenuation values, the attenuation correction in hybrid Positron Emission Tomography (PET)/MRI systems remains a challenging task. Currently, existing methods are either time-consuming or require sufficient samples to train the models. In this work, an efficient approach for predicting pseudo computed tomography (CT) images from T1- and T2-weighted MRI data with limited data is proposed. The proposed approach uses improved neighborhood anchor regression (INAR) as a baseline method to pre-calculate projected matrices to flexibly predict the pseudo CT patches. Techniques, including the augmentation of the MR/CT dataset, learning of the nonlinear descriptors of MR images, hierarchical search for nearest neighbors, data-driven optimization, and multi-regressor ensemble, are adopted to improve the effectiveness of the proposed approach. In total, 22 healthy subjects were enrolled in the study. The pseudo CT images obtained using INAR with multi-regressor ensemble yielded mean absolute error (MAE) of 92.73 $\pm$ 14.86 HU, peak signal-to-noise ratio of 29.77 $\pm$ 1.63 dB, Pearson linear correlation coefficient of 0.82 $\pm$ 0.05, dice similarity coefficient of 0.81 $\pm$ 0.03, and the relative mean absolute error (rMAE) in PET attenuation correction of 1.30 $\pm$ 0.20% compared with true CT images. Moreover, our proposed INAR method, without any refinement strategies, can achieve considerable results with only seven subjects (MAE 106.89 $\pm$ 14.43, rMAE 1.51 $\pm$ 0.21%). The experiments prove the superior performance of the proposed method over the six innovative methods. Moreover, the proposed method can rapidly generate the pseudo CT images that are suitable for PET attenuation correction.

Existing research indicates that different types of meat have varying effects on health and aging, but the specific causal relationships remain unclear. This study aimed to explore the causal relationship between different types of meat intake and aging-related phenotypes. This study employed Mendelian randomization (MR) to select genetic variants associated with meat intake from large genomic databases, ensuring the independence and pleiotropy-free nature of these instrumental variables (IVs), and calculated the F-statistic to evaluate the strength of the IVs. The validity of causal estimates was assessed through sensitivity analyses and various MR methods (MR-Egger, weighted median, inverse-variance weighted (IVW), simple mode, and weighted mode), with the MR-Egger regression intercept used to test for pleiotropy bias and Cochran’s Q test employed to evaluate the heterogeneity of the results. The findings reveal a positive causal relationship between meat consumers and DNA methylation PhenoAge acceleration, suggesting that increased meat intake may accelerate the biological aging process. Specifically, lamb intake is found to have a positive causal effect on mitochondrial DNA copy number, while processed meat consumption shows a negative causal effect on telomere length. No significant causal relationships were observed for other types of meat intake. This study highlights the significant impact that processing and cooking methods have on meat’s role in health and aging, enhancing our understanding of how specific types of meat and their preparation affect the aging process, providing a theoretical basis for dietary strategies aimed at delaying aging and enhancing quality of life.

Background/Objectives: A bidirectional association between inflammatory bowel disease (IBD) and periodontitis has been observed, yet their causal relationship remains unclear. This study aimed to investigate the potential causal links between these two inflammatory conditions through comprehensive genetic and molecular analyses. Methods: We conducted a bidirectional Mendelian randomization (MR) analysis integrated with bioinformatics approaches. The causal relationships were primarily evaluated using inverse variance weighting (IVW), complemented by multiple sensitivity analyses to assess the robustness of the findings. Additionally, we performed differential gene expression analysis using RNA sequencing data to identify co-expressed genes and shared inflammatory mediators between IBD and periodontitis, followed by pathway enrichment analysis. Results: Bidirectional MR analysis revealed significant causal associations between IBD and periodontitis (p-value < 0.05). Sensitivity analyses demonstrated the consistency of these findings, with no evidence of significant heterogeneity or horizontal pleiotropy (p-value > 0.05). Integrated bioinformatics analysis identified key immune regulators, particularly interleukin 1 beta (IL1B) and C-X-C motif chemokine receptor 4 (CXCR4), and inflammatory signaling pathways, including tumor necrosis factor (TNF-α) and interleukin 17 (IL17), as potential molecular mechanisms underlying the bidirectional relationship between these conditions. Conclusions: Our findings provide genetic evidence supporting a bidirectional causal relationship between IBD and periodontitis. Transcriptomic analysis revealed shared pathological mechanisms and identified crucial immune regulatory factors common to both diseases. These insights enhance our understanding of the molecular interplay between IBD and periodontitis, potentially informing new therapeutic strategies for both conditions.

Dynamic facial expression recognition (DFER) is a rapidly developing field that focuses on recognizing facial expressions in video sequences. However, the complex temporal modeling caused by noisy frames, along with the limited training data significantly hinder the further development of DFER. Previous efforts in this domain have been limited as they tackled these issues separately. Inspired by recent advances of pretrained vision-language models (e.g., CLIP), we propose to leverage it to jointly address the two limitations in DFER. Since the raw CLIP model lacks the ability to model temporal relationships and determine the optimal task-related textual prompts, we utilize DFER-specific domain knowledge, including characteristics of temporal correlations and relationships between facial behavior descriptions at different levels, to guide the adaptation of CLIP to DFER. Specifically, we propose enhancements to CLIP's visual encoder through the design of a hierarchical video encoder that captures both short- and long-term temporal correlations in DFER. Meanwhile, we align facial expressions with action units through prior knowledge to construct semantically rich textual prompts, which are further enhanced with visual contents. Furthermore, we introduce a class-aware consistency regularization mechanism that adaptively filters out noisy frames, bolstering the model's robustness against interference. Extensive experiments on three in-the-wild dynamic facial expression datasets demonstrate that our method outperforms the state-of-the-art DFER approaches. The code is available at https://github.com/liliupeng28/DK-CLIP.