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Introduction: Mumps is a vaccine-preventable disease caused by the paramyxovirus affecting the salivary gland and may be complicated by orchitis, oophoritis, and encephalitis. This study aims to describe the epidemiology, clinical presentation, and transmission of mumps cases in the Kingdom of Bahrain between 2012 and 2022.

Methodology: A retrospective cross-sectional study was conducted using national surveillance data of confirmed mumps cases, including all age groups and both Bahraini and non-Bahraini nationals, from January 2012 to December 2022.

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Deep learning has become an attractive tool for addressing the limitations of traditional digital image correlation (DIC). However, extending learning-based DIC methods to three-dimensional (3D-DIC) measurements is challenging due to the limited displacement estimation range, which cannot handle the large displacements caused by stereo-matching disparities. Besides, most of the existing learning-based DIC architectures lack prior information to guide displacement estimation, resulting in insufficient accuracy.

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Optical glass selection is an important research object in optical design, which is an important way to aberration correction. However, these methods to our knowledge either do not correct aberration well or consume too much time. To efficiently solve the apochromatic problems in optical design, this paper presents what we believe to be a novel automatic optimization method for discontinuous optical glass based on auto glass selection (AGS).

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The size of the metasurface unit cell increases with the decrease of its center working frequency (). This is not conducive to the integrated design of the metasurface. To address the problem, a miniaturization design method based on genetic algorithm (GA) is proposed.

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VAE-Surv: A novel approach for genetic-based clustering and prognosis prediction in myelodysplastic syndromes.

Comput Methods Programs Biomed

January 2025

Computational Biomedicine Unit, Department of Medical Sciences, University of Torino, Via Santena 19, 10126, Torino, Italy.

Background And Objectives: Several computational pipelines for biomedical data have been proposed to stratify patients and to predict their prognosis through survival analysis. However, these analyses are usually performed independently, without integrating the information derived from each of them. Clustering of survival data is an underexplored problem, and current approaches are limited for biomedical applications, whose data are usually heterogeneous and multimodal, with poor scalability for high-dimensionality.

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