ISSN 0253-2778

CN 34-1054/N

2024 Vol. 54, No. 3

2024-3 Contents
2024, 54(3): 1-2.
2024-3 Abstract
2024, 54(3): 1-2.
Life Sciences
Supporting the CIF file format of proteins in molecular dynamics simulations
Hengyue Wang, Zhiyong Zhang
2024, 54(3): 0301. doi: 10.52396/JUSTC-2023-0148
Molecular dynamics (MD) simulations can capture the dynamic behavior of proteins in full atomic detail and at very fine temporal resolution, so they have become an important tool in the study of protein dynamics. To date, several MD packages are widely used. An MD simulation starts from an initial structure that is generally taken from the Protein Data Bank (PDB). Until 2014, the PDB format was the standard file format for protein structures. However, there are certain intrinsic limitations in the PDB format, such as the storage of structural information in a fixed-width format, which is an issue for very large protein complexes. Therefore, the CIF (crystallographic information framework) format has been proposed, which is characterized by its superior expansibility. To our knowledge, the current mainstream MD packages support only the PDB format but do not support the CIF format directly. In this study, we modified the source code of one of the MD packages, GROMACS, which enables it to support CIF-formatted structure files as input and subsequently generate molecular topology files. This work simplifies the preprocessing of large protein complexes for MD simulations.
IDDNet: a deep interactive dual-domain convolutional neural network with auxiliary modality for fast MRI reconstruction
Yi Cao, Hongwei Du
2024, 54(3): 0302. doi: 10.52396/JUSTC-2023-0169
Reconstructing a complete image accurately from an undersampled k-space matrix is a viable approach for magnetic resonance imaging (MRI) acceleration. In recent years, numerous deep learning (DL)-based methods have been employed to improve MRI reconstruction. Among these methods, the cross-domain method has been proven to be effective. However, existing cross-domain reconstruction algorithms sequentially link the image domain and k-space networks, disregarding the interplay between different domains, consequently leading to a deficiency in reconstruction accuracy. In this work, we propose a deep interactive dual-domain network (IDDNet) with an auxiliary modality for accelerating MRI reconstruction to effectively extract pertinent information from multiple MR domains and modalities. The IDDNet first extracts shallow features from low-resolution target modalities in the image domain to obtain visual representation information. In the following feature processing, a parallel interactive architecture with dual branches is designed to extract deep features from relevant information of dual domains simultaneously to avoid redundant priority priors in sequential links. Furthermore, the model uses additional information from the auxiliary modality to refine the structure and improve the reconstruction accuracy. Numerous experiments at different sampling masks and acceleration rates on the MICCAI BraTS 2019 brain and fastMRI knee datasets show that IDDNet achieves excellent accelerated MRI reconstruction performance.
Association between active and passive smoking and the clinical course of multiple sclerosis and neuromyelitis optica spectrum disorder
Fengling Qu, Qingqing Zhou, Shuo Feng, Rui Li, Chunrong Tao, Wei Hu, Xinfeng Liu
2024, 54(3): 0303. doi: 10.52396/JUSTC-2023-0004
Objective: Active and passive smoking are common environmental risk factors, but there is no definite conclusion about their effects on relapse and disability progression in multiple sclerosis (MS) and neuromyelitis optica spectrum disorder (NMOSD). Methods: This was a retrospective cohort study. Patients were included from four centers. Demographic and clinical data were extracted from the clinical database, while data involving environmental exposures during daily life, relapse, and disability progression were obtained through telephone follow-up interviews. Determinants of relapse were assessed by Cox proportional models, and disability progression was assessed by linear regression. Kaplan‒Meier survival was used to estimate relapse within five years after the first attack. Results: A total of 130 MS patients and 318 NMOSD patients were included in this study, and females accounted for 60% and 79.6%, respectively. MS patients with an active smoking history had a higher risk of relapse, for which the association became borderline significant after accounting for covariates (aHR=1.52, 95% CI=1.00, 2.31; p=0.052). The relapse risk between ever-smokers who smoked more than 10 cigarettes per day and smokers who smoked less than 10 cigarettes per day was not significantly different (aHR=0.96, 95% CI=0.63, 1.47; p=0.859). However, exposure to passive smoking was associated with a reduced risk of MS relapse (aHR=0.75, 95% CI=0.56, 1.00; p=0.044) compared with never-exposed patients. No associations were observed between active smoking/passive smoking and the risk of NMOSD relapse, but patients with a history of smoking were associated with a lower annual progression rate by Expanded Disability Status Scale (EDSS) (aβ=−0.20, 95% CI=−0.38, −0.01; p=0.036) and Multiple Sclerosis Severity Score (MSSS) (aβ=−0.23, 95% CI=−0.44, −0.03; p=0.028). Conclusion: Our research shows that active smoking increases the relapse risk of MS and has a negative impact on disability progression; thus, smoking cessation should be encouraged.
Life Sciences;Engineering & Materials
Highly transparent and strong nanohesive hydrogel patch for tissue adhesion
Qing Luo, Zhao Pan, Yong-Hong Song, Jie-Yu Huang, Hui Fang, Dong-Quan Liu, Liang Dong
2024, 54(3): 0304. doi: 10.52396/JUSTC-2023-0143
This research aimed to design and fabricate a biocompatible dual-layer chitosan hydrogel adhesive patch with exceptional mechanical properties by employing a nanoadhesive strategy to assess its tissue adhesion performance. The design involves physical cross-linking to construct a robust chitosan hydrogel as a backing membrane, followed by in situ photocuring to create the adhesive hydrogel layer, resulting in an integrated chitosan hydrogel adhesive patch. To facilitate adhesion between the hydrogel patch and biological tissue, surface-activated silica nanoparticles serve as interfacial connectors, analogous to nanoglue, promoting binding of the hydrogel to the substrate. Characterization of the patch reveals an adhesive energy of 282 J/m2 to biological tissues in vitro and a burst pressure of 450 mmHg (1 mmHg=0.133 kPa). The patch exhibits outstanding mechanical properties, with a tensile strength of 4.3 MPa, an elongation rate of 65%, and a fracture toughness of 3.82 kJ/m2. Additionally, the nanohesion-based chitosan hydrogel adhesive patch is highly transparent and demonstrates excellent biocompatibility. It holds promise for applications in various biomedical fields, including tissue repair and drug delivery, thereby providing a robust material foundation for advancements in clinical surgery.
Bowley reinsurance with asymmetric information under reinsurer’s default risk
Zhenfeng Zou, Zichao Xia
2024, 54(3): 0305. doi: 10.52396/JUSTC-2022-0111
Bowley reinsurance with asymmetric information means that the insurer and reinsurer are both presented with distortion risk measures but there is asymmetric information on the distortion risk measure of the insurer. Motivated by predecessors research, we study Bowley reinsurance with asymmetric information under the reinsurer’s default risk. We call this solution the Bowley solution under default risk. We provide Bowley solutions under default risk in a closed form under general assumptions. Finally, some numerical examples are provided to illustrate our main conclusions.
Alternative modified Cholesky decomposition of the precision matrix of longitudinal data
Fei Lu, Yuting Zeng
2024, 54(3): 0306. doi: 10.52396/JUSTC-2023-0127
The correlation matrix might be of scientific interest for longitudinal data. However, few studies have focused on both robust estimation of the correlation matrix against model misspecification and robustness to outliers in the data, when the precision matrix possesses a typical structure. In this paper, we propose an alternative modified Cholesky decomposition (AMCD) for the precision matrix of longitudinal data, which results in robust estimation of the correlation matrix against model misspecification of the innovation variances. A joint mean-covariance model with multivariate normal distribution and AMCD is established, the quasi-Fisher scoring algorithm is developed, and the maximum likelihood estimators are proven to be consistent and asymptotically normally distributed. Furthermore, a double-robust joint modeling approach with multivariate Laplace distribution and AMCD is established, and the quasi-Newton algorithm for maximum likelihood estimation is developed. The simulation studies and real data analysis demonstrate the effectiveness of the proposed AMCD method.