LUCMT: Learnable under-sampling and reconstructed network with cross multi-head attention transformer for accelerating MR image reconstruction.

BACKGROUND AND OBJECTIVE: As a widely used technique for Magnetic Resonance Image (MRI) acceleration, compressed sensing MRI involves two main issues: designing an effective sampling strategy and reconstructing the image from significantly under-sampled K-space data. In this paper, an innovative approach is proposed to address these two challenges simultaneously. METHODS: A novel MRI reconstruction method, termed as LUCMT, is implemented by integrating a learnable under-sampling strategy with a reconstruction network based on the Cross Multi-head Attention Transformer. In contrast to conventional static sampling methods, the proposed adaptive sampling scheme is processed optimally by learning the optimal sampling technique, which involves binarizing the sampling pattern by a sigmoid function and computing gradients by backpropagation. And the reconstruction network is designed by using CS-MRI depth unfolding network that incorporates a Cross Multi-head Attention (CMA) module with inertial and gradient descent terms. RESULTS: T1 brain MR images from the FastMRI dataset are used to validate the performance of the proposed method. A series of experiments are conducted to validate the superior performance of our proposed network in terms of quantitative metrics and visual quality. Compared with other state-of-the-art reconstruction methods, LUCMT achieves better reconstruction performances with more accurate details. Specifically, LUCMT achieves PSNR and SSIM results of 41.87/0.9749, 46.64/0.9868, 50.41/0.9924, and 53.51/0.9955 at sampling rates of 10 %, 20 %, 30 %, and 40 %, respectively. CONCLUSIONS: The proposed LUCMT method can provide a promising way for generating optimal under-sampling mask and accelerating MRI reconstruction accurately.

Effect of laser phase noise on the steady-state field-mirror entanglement and ground-state cooling in a Laguerre-Gaussian optorotational system.

Cavity optomechanical systems are considered as one of the best platforms for studying macroscopic quantum phenomena. In this paper, we studied the effect of laser phase noise on the steady-state entanglement between a cavity mode and a rotating mirror in a Laguerre-Gaussian (L-G) optorotational system. We found that the effect of laser phase noise was non-negligible on the field-mirror entanglement especially at a larger input power and a larger angular momentum. We also investigated the influence of laser phase noise on the ground-state cooling of the rotating mirror. In the presence of laser phase noise, the ground-state cooling of the rotating mirror can still be realized within a range of input powers.

Transcriptomic and metabolomic differences between banana varieties which are resistant or susceptible to Fusarium wilt.

Background: Fusarium wilt, caused by Fusarium oxysporum f. sp. cubense race 4 (Foc4), is the most lethal disease of bananas in Asia. Methods: To better understand the defense response of banana to Fusarium wilt, the transcriptome and metabolome profiles of the roots from resistant and susceptible bananas inoculated with Foc4 were compared. Results: After Foc4 inoculation, there were 172 and 1,856 differentially expressed genes (DEGs) in the Foc4-susceptible variety (G1) and Foc4-resistant variety (G9), respectively. In addition, a total of 800 DEGs were identified between G1 and G9, which were mainly involved in the oxidation-reduction process, cell wall organization, phenylpropanoid biosynthesis, and lipid and nitrogen metabolism, especially the DEGs of Macma4_08_g22610, Macma4_11_g19760, and Macma4_03_g06480, encoding non-classical arabinogalactan protein; GDSL-like lipase; and peroxidase. In our study, G9 showed a stronger and earlier response to Foc4 than G1. As the results of metabolomics, lipids, phenylpropanoids and polyketides, organic acids, and derivatives played an important function in response to Fusarium wilt. More importantly, Macma4_11_g19760 might be one of the key genes that gave G9 more resistance to Foc4 by a lowered expression and negative regulation of lipid metabolism. This study illustrated the difference between the transcriptomic and metabolomic profiles of resistant and susceptible bananas. These results improved the current understanding of host-pathogen interactions and will contribute to the breeding of resistant banana plants.