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Background and Objectives: In the literature, relationships between being married and having prediabetes or diabetes are inconsistent. We aimed to investigate whether marriage is a protective or risk factor for prediabetes and to uncover new insights into its impact on prediabetes. Materials and Methods: In this cross-sectional observational study, questionnaires were distributed by email to 1039 staff members who participated in an employee health check from a hospital affiliated with a medical university in Taiwan. Fasting blood glucose and triglyceride (TG) levels were checked and the questionnaires elicited basic demographic characteristics and included the Copenhagen Burnout Inventory and Nordic Musculoskeletal Questionnaire. The chi-square test or Fisher's exact test, logistic regression, and mediation analysis were conducted for statistical analysis. Results: Among the group aged 20–37 years, married (OR = 1.89, 95%CI: 1.08, 3.33), obesity (OR = 2.95, 95%CI: 1.49, 5.83), neck and shoulder pain (OR = 1.31, 95%CI: 1.01, 1.69), and elevated TG levels (OR = 1.01, 95%CI: 1.00, 1.01) were independent risk factors for prediabetes (impaired fasting glucose). For those >38 years old, overweight (OR = 2.08, 95%CI: 1.27, 3.43), obesity (OR = 4.30, 95%CI: 2.38, 7.79), and elevated triglyceride (TG) (OR = 1.003, 95%CI: 1.00, 1.01) were the independent risk factors for impaired fasting glucose. Increased TG levels serve as a mediating factor (Zm = 2.64, p < 0.01) linking marriage to an increased risk of prediabetes for the group aged 20–37 years. Conclusions: TGs play a significant role in the association between marriage and prediabetes among the group aged 20–37 years. Therefore, dietary habits, especially those of young adult couples should be considered. Our findings connect marital status to prediabetes, facilitating advances in diabetes prevention. [ABSTRACT FROM AUTHOR]

Independent component analysis (ICA) has found great promise in magnetic resonance (MR) image analysis. Unfortunately, two key issues have been overlooked and not investigated. One is the lack of MR images to be used to unmix signal sources of interest. Another is the use of random initial projection vectors by ICA, which causes inconsistent results. In order to address the first issue, this paper introduces a band-expansion process (BEP) to generate an additional new set of images from the original MR images via nonlinear functions. These newly generated images are then combined with the original MR images to provide sufficient MR images for ICA analysis. In order to resolve the second issue, a prioritized ICA (PICA) is designed to rank the ICA-generated independent components (ICs) so that MR brain tissue substances can be unmixed and separated by different ICs in a prioritized order. Finally, BEP and PICA are combined to further develop a new ICA-based approach, referred to as PICA-BEP to perform MR image analysis. Index Terms--Band-expansion process (BEP), FastICA, independent component analysis (ICA), magnetic resonance (MR) analysis, over-complete ICA (OC-ICA), prioritized ICA (PICA), prioritized ICA band-expansion process (PICA-BEP).

This paper presents a new spectral signature detection approach to magnetic resonance (MR) image classification. It is called constrained energy minimization (CEM) method, which is derived from the minimum variance distortionless response in passive sensor array processing. It considers a bank of spectral channels as an array of sensors where each spectral channel represents a sensor and object spectral signature in multispectral MR images are viewed as signals impinging upon the array. The strength of the CEM lies on its ability in detection of spectral signatures of interest without knowing image background. The detected spectral signatures are then used for classification. The CEM makes use of a finite impulse response (FIR) filter to linearly constrain a desired object while minimizing interfering effects caused by other unknown signal sources. Unlike most spatial-based classification techniques, the proposed CEM takes advantage of spectral characteristics to achieve object detection and classification. A series of experiments is conducted and compared with the commonly used c-means method for performance evaluation. The results show that the CEM method is a promising and effective spectral technique for MR image classification.