Fast NMR spectroscopy reconstruction with a sliding window based Hankel matrix.

Nuclear Magnetic Resonance (NMR) spectroscopy is one of the most promising analytical chemistry techniques, although it takes a long time to acquire data. Non-uniform sampling (NUS) is an effective way to reduce the sampling time, but faithful reconstruction methods are needed. The low rank Hankel matrix (LRHM) approach uses the low rank constraint to obtain high-quality spectra from NUS signals, but the reconstruction has a considerable time overhead. In this work, we propose a sliding window based low rank Hankel matrix approach to speed up the spectra reconstruction from NUS signals. Using the sliding window to construct a matrix can effectively reduce the size of the Hankel matrix for faster reconstructions. To further decrease the reconstruction time, parallel computation is applied in the proposed approach. The experiments on both synthetic data and realistic data demonstrate that the reconstruction speed of the proposed method is the fastest among compared methods without sacrificing the quality of spectra.

Factors associated with intention to uptake pneumococcal vaccines among Chinese elderly aged 60 years and older during the early stage of COVID-19 pandemic.

Innoculation of pneumococcal vaccines among the elderly is an effective public health policy to prevent pneumococcal diseases and it is widely promoted by many developed countries. The pneumococcal vaccination rate among the elderly in China was only 3.7% in 2019, it grew rapidly during the early stage of the COVID-19 pandemic. The purpose of this cross-sectional study was to investigate the psychological and demographic-economic factors related to the uptake behavior of pneumococcal vaccination among the Chinese elderly by using an integrated model based on the unified theory of acceptance and use of technology (UTAUT), and knowledge, attitudes and practices (KAP). The theoretical model was tested via structural equation modeling (SEM) with data collected from 516 Chinese older adults aged 60 years and older. Our results suggested that knowledge, performance expectancy, effort expectancy, attitude, and trust had a significant correlation with behavioral intention; behavioral intention and trust had a positive correlation with the uptake behavior, gender, and and education level and chronic obstructive pulmonary disease exerted significant moderating effects. To increase the coverage of pneumococcal vaccination among the elderly, it is necessary to provide effective health education by authoritative experts, thereby enhancing their knowledge and positive attitude towardthe vaccination.

A review on deep learning MRI reconstruction without fully sampled k-space.

BACKGROUND: Magnetic resonance imaging (MRI) is an effective auxiliary diagnostic method in clinical medicine, but it has always suffered from the problem of long acquisition time. Compressed sensing and parallel imaging are two common techniques to accelerate MRI reconstruction. Recently, deep learning provides a new direction for MRI, while most of them require a large number of data pairs for training. However, there are many scenarios where fully sampled k-space data cannot be obtained, which will seriously hinder the application of supervised learning. Therefore, deep learning without fully sampled data is indispensable. MAIN TEXT: In this review, we first introduce the forward model of MRI as a classic inverse problem, and briefly discuss the connection of traditional iterative methods to deep learning. Next, we will explain how to train reconstruction network without fully sampled data from the perspective of obtaining prior information. CONCLUSION: Although the reviewed methods are used for MRI reconstruction, they can also be extended to other areas where ground-truth is not available. Furthermore, we may anticipate that the combination of traditional methods and deep learning will produce better reconstruction results.