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  • 1
    In: Annals of Nutrition and Metabolism, S. Karger AG, Vol. 76, No. Suppl. 1 ( 2020), p. 43-52
    Abstract: Adults consuming sugar-sweetened beverages (SSBs) are at increased risk of becoming overweight/obese and developing lifestyle-related diseases. Furthermore, a low water intake is associated with increased health risks, such as CKD. These issues are especially pressing in Mexico where SSB intake is high. The present research aimed to describe the attitudes of Mexican adults who are considered high sugar-low water drinkers (HS-LWDs). HS-LWDs were defined as adults aged 18–45 years, drinking at least 2 servings (500 mL) of SSB/day and maximum 3 servings (750 mL) of water/day. The study included 2.858 HS-LWD (58% males) living in the urban area of Mexico City. Data were collected using an online, self-administered questionnaire. Bayesian approach was applied to analyze attitudes in life and towards drinking. Results showed that social aspects, such as sharing with friends and family and self-image, were the dominant attitudes in life. The main reason to choose a beverage was to get sensations, resulting in 2 axes, one was pleasure oriented and one was health oriented. Getting sensations was also a main driver to drink linked to a moment, together with self-image. The Bayesian network analysis demonstrated 5 attitude profiles, based on the most important attitudes defining each profile: mood and pleasure, self-image and body image, sharing and restoring, pleasure and energy, and health and success. This study allowed describing HS-LWD attitudes, in life and towards drinking. It constitutes a first step in understanding this target group’s attitudes and behavior, offering potential recommendations for tailored interventions to promote the adoption of healthier drinking habits.
    Type of Medium: Online Resource
    ISSN: 0250-6807 , 1421-9697
    Language: English
    Publisher: S. Karger AG
    Publication Date: 2020
    detail.hit.zdb_id: 1481977-6
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  • 2
    In: Medical Physics, Wiley, Vol. 44, No. 11 ( 2017-11), p. 5835-5848
    Abstract: The purpose of this study was to investigate the use of a probabilistic quad‐tree graph (hidden Markov tree, HMT ) to provide fast computation, robustness and an interpretational framework for multimodality image processing and to evaluate this framework for single gross tumor target ( GTV ) delineation from both positron emission tomography ( PET ) and computed tomography ( CT ) images. Methods We exploited joint statistical dependencies between hidden states to handle the data stack using multi‐observation, multi‐resolution of HMT and Bayesian inference. This framework was applied to segmentation of lung tumors in PET / CT datasets taking into consideration simultaneously the CT and the PET image information. PET and CT images were considered using either the original voxels intensities, or after wavelet/contourlet enhancement. The Dice similarity coefficient ( DSC ), sensitivity ( SE ), positive predictive value ( PPV ) were used to assess the performance of the proposed approach on one simulated and 15 clinical PET / CT datasets of non‐small cell lung cancer ( NSCLC ) cases. The surrogate of truth was a statistical consensus (obtained with the Simultaneous Truth and Performance Level Estimation algorithm) of three manual delineations performed by experts on fused PET / CT images. The proposed framework was applied to PET ‐only, CT ‐only and PET / CT datasets, and were compared to standard and improved fuzzy c‐means ( FCM ) multimodal implementations. Results A high agreement with the consensus of manual delineations was observed when using both PET and CT images. Contourlet‐based HMT led to the best results with a DSC of 0.92 ± 0.11 compared to 0.89 ± 0.13 and 0.90 ± 0.12 for Intensity‐based HMT and Wavelet‐based HMT , respectively. Considering PET or CT only in the HMT led to much lower accuracy. Standard and improved FCM led to comparatively lower accuracy than HMT , even when considering multimodal implementations. Conclusions We evaluated the accuracy of the proposed HMT ‐based framework for PET / CT image segmentation. The proposed method reached good accuracy, especially with pre‐processing in the contourlet domain.
    Type of Medium: Online Resource
    ISSN: 0094-2405 , 2473-4209
    URL: Issue
    Language: English
    Publisher: Wiley
    Publication Date: 2017
    detail.hit.zdb_id: 1466421-5
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