Unified Multi-Modal Image Synthesis for Missing Modality Imputation

Multi-modal medical images provide complementary soft-tissue characteristics that aid in the screening and diagnosis of diseases. However, limited scanning time, image corruption and various imaging protocols often result in incomplete multi-modal images, thus limiting the usage of multi-modal data for clinical purposes. To address this issue, in this paper, we propose a novel unified multi-modal image synthesis method for missing modality imputation. Our method overall takes a generative adversarial architecture, which aims to synthesize missing modalities from any combination of available ones with a single model. To this end, we specifically design a Commonality- and Discrepancy-Sensitive Encoder for the generator to exploit both modality-invariant and specific information contained in input modalities. The incorporation of both types of information facilitates the generation of images with consistent anatomy and realistic details of the desired distribution. Besides, we propose a Dynamic Feature Unification Module to integrate information from a varying number of available modalities, which enables the network to be robust to random missing modalities. The module performs both hard integration and soft integration, ensuring the effectiveness of feature combination while avoiding information loss. Verified on two public multi-modal magnetic resonance datasets, the proposed method is effective in handling various synthesis tasks and shows superior performance compared to previous methods.Comment: 10 pages, 9 figure

Predominant T-cell epitopes of SARS-CoV-2 restricted by multiple prevalent HLA-B and HLA-C allotypes in Northeast Asia

Since the outbreak of novel coronavirus pneumonia (COVID-19), numerous T-cell epitopes in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) proteome have been reported. However, most of the identified CD8+ T-cell epitopes have been restricted primarily by HLA-A allotypes. The epitopes restricted by HLA-B and HLA-C allotypes are limited. This study focuses on the screening of T-cell epitopes restricted by 13 prevalent HLA-B and 13 prevalent HLA-C allotypes, which cover over 70% and 90% of the Chinese and Northeast Asian populations, respectively. Totally, 67 HLA-B restricted and 53 HLA-C restricted epitopes were validated as immunogenic epitopes with a herd predominance rate by peptide-PBMCs ex vivo coculture experiments using the PBMCs from convalescent Chinese cohort. In addition, 26 transfected cell lines expressing indicated HLA-B or HLA-C allotype were established, and used in the competitive peptide binding assays to define the affinities and cross-restriction of each validated epitope binding to HLA-B or HLA-C allotypes. These data will facilitate the design of T-cell-directed vaccines and SARS-CoV-2-specific T-cell detection tools tailored to the Northeast Asian population. The herd test of functionally validated T-cell epitopes, and the competitive peptide binding assay onto cell line array expressing prevalent HLA allotypes may serve as an additional criterion for selecting T-cell epitopes used in vaccine

Improving the Radon Adsorption Capacity of Activated Carbon by Liquid Nitrogen Modification

Abstract Radon is a naturally occurring radioactive inert gas that poses a significant threat to the human health. Coconut shell activated carbon has been verified to be the best radon adsorbing material, but its radon adsorption capacity still cannot meet the requirement of industrial applications. Activated carbon modification using liquid nitrogen is an effective method for improving the radon adsorption capacity, but it is necessary to determine the conditions for large-scale production. In this study, the influence of environmental temperature, container geometry, and amount of activated carbon and liquid nitrogen on the modification effect are examined. The results show that the activated carbon has the best modification effect when the container is placed in a water bath at 50 °C. The container geometry and activated carbon mass have a minor influence on the modification effect. Further, the radon adsorption capacity is increased by 36% when 6.5 L of liquid nitrogen is added to 1 kg of activated carbon. The characterization results reveal that the chemical structure and elemental content of the activated carbon do not change after modification, but the number of micropores is significantly increased, especially the micropores with a size of 0.5-0.6 nm, which is related to the radon adsorption capacity of the modified activated carbon. Overall, the liquid-nitrogen-based modification is a simple, environment-friendly, and low-cost method to improve the radon adsorption capacity of activated carbon, which can be used in the large-scale production of highly efficient radon adsorbents.</jats:p

Solvent-promoted catalyst-free N-formylation of amines using carbon dioxide under ambient conditions

An unprecedented catalyst-free formylation of amines using CO2 and hydrosilanes was developed.</p