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Introduction: Retinal diseases significantly impact patients' quality of life and increase social medical costs. Optical coherence tomography (OCT) offers highresolution imaging for precise detection and monitoring of these conditions. While deep learning techniques have been employed to extract features from OCT images for classification, convolutional neural networks (CNNs) often fail to capture global context due to their focus on local receptive fields. Transformerbased methods, on the other hand, suffer from quadratic complexity when handling long-range dependencies. Methods: To overcome these limitations, we introduce the Multi-Resolution Visual Mamba (MRVM) model, which addresses long-range dependencies with linear computational complexity for OCT image classification. The MRVM model initially employs convolution to extract local features and subsequently utilizes the retinal Mamba to capture global dependencies. By integrating multi-scale global features, the MRVM enhances classification accuracy and overall performance. Additionally, the multi-directional selection mechanism (MSM) within the retinal Mamba improves feature extraction by concentrating on various directions, thereby better capturing complex, orientation-specific retinal patterns. Results: Experimental results demonstrate that the MRVM model excels in differentiating retinal images with various lesions, achieving superior detection accuracy compared to traditional methods, with overall accuracies of 98.98\% and 96.21\% on two public datasets, respectively. Discussion: This approach offers a novel perspective for accurately identifying retinal diseases and could contribute to the development of more robust artificial intelligence algorithms and recognition systems for medical image-assisted diagnosis. [ABSTRACT FROM AUTHOR]

Introduction: The blood oxygen level-dependent (BOLD) signal derived from functional neuroimaging is commonly used in brain network analysis and dementia diagnosis. Missing the BOLD signal may lead to bad performance and misinterpretation of findings when analyzing neurological disease. Few studies have focused on the restoration of brain functional time-series data. Methods: In this paper, a novel U-shaped convolutional transformer GAN (UCT-GAN) model is proposed to restore the missing brain functional time-series data. The proposed model leverages the power of generative adversarial networks (GANs) while incorporating a U-shaped architecture to effectively capture hierarchical features in the restoration process. Besides, the multi-level temporal-correlated attention and the convolutional sampling in the transformer-based generator are devised to capture the global and local temporal features for the missing time series and associate their long-range relationship with the other brain regions. Furthermore, by introducing multi-resolution consistency loss, the proposed model can promote the learning of diverse temporal patterns and maintain consistency across different temporal resolutions, thus effectively restoring complex brain functional dynamics. Results: We theoretically tested our model on the public Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, and our experiments demonstrate that the proposed model outperforms existing methods in terms of both quantitative metrics and qualitative assessments. The model's ability to preserve the underlying topological structure of the brain functional networks during restoration is a particularly notable achievement. Conclusion: Overall, the proposed model offers a promising solution for restoring brain functional time-series and contributes to the advancement of neuroscience research by providing enhanced tools for disease analysis and interpretation. [ABSTRACT FROM AUTHOR]

The cationic polymerizations of isobutylene (IB) initiated by an H2O/AlCl3 system were carried out in a mixture of n‐hexane/methylene dichloride of 60/40 v/v at −50 °C in the presence of various external electron pair donors (EDs), including methyl benzoate (MB), ethyl benzoate (EB), and methyl acrylate (MA). The effects of the concentrations of EDs ([ED]) and polymerization time on monomer conversion and kinetics of IB polymerization were also investigated. The complexes between AlCl3 and the esters were soluble in the polymerization system at −50 °C. The polymers with high molecular weights and relatively narrow molecular weight distributions were obtained in the presence of the EDs. The rate of polymerization decreased with increasing [ED]. The kinetic orders of the EDs were remarkably dependent on their chemical structure, steric hindrance from moieties, and concentration in the polymerization system. The reaction order of MB was determined to be around −1.3 when [MB] ≤ 0.40 mmol/L, whereas it was −12.9 when [MB] ≥ 0.40 mmol/L. The reaction order of EB was determined to be −1.36 when [EB] ≤ 0.41 mmol/L, whereas it was −3.36 when [EB] ≥ 0.41 mmol/L. The reaction order of MA was determined to be −1.85 when [MA] ≤ 0.48 mmol/L, whereas it was −16.7 when [MA] ≥ 0.48 mmol/L. © 2007 Wiley Periodicals, Inc. J Polym Sci Part A: Polym Chem 45: 3053–3061, 2007 [ABSTRACT FROM AUTHOR]

Presents a study which discussed the effect of R[sub1] and R[sub2] on the polymerization of isobutylene. Materials and procedures; Results and discussion; Conclusion.