12 research outputs found

    Summary of the performances estimates of the two developed machine-learning methods.

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    <p>This table summarizes the performances estimates of the two machine-learning methods used in the present study (support vector machine [SVM] and na茂ve Bayes [NB]) developed with the training sample and validated with the validation sample. For each machine-learning method we show: (1) the estimates of the training step using the LOO procedure; and (2) the estimates obtained with the Validation Set subsample. P-values were obtained after 10.000 permutation cycles as described in the Material and Methods section (**p < 0.01; *p < 0.05).</p

    The data analysis workflow used in the present study.

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    <p>OCD, obsessive-compulsive disorder; MRI, magnetic resonance imaging; DTI, diffusion tensor imaging; SNP, single nucleotide polymorphism; CV, cross-validation.</p

    Relationships of Osteoporosis Health Beliefs to Practiced Exercise Behaviors of Women

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    The purpose of this study was to examine the relationship of health beliefs contained in the Health Belief Model to practiced exercise behavior of women. A descriptive correlation design was used with a convenience sample of 201 women. The revised version of the Osteoporosis Health Belief Exercise Scale developed by Kim, Horan, Gendler and Patel (1991b) was used to measure health beliefs related to osteoporosis. The ARIC/Baecke questionnaire of Habitual Physical Activity was used to measure life style physical activity. Health motivation and exercise benefits were found to be positively correlated to exercise behavior. However, susceptibility and exercise barriers were inversely correlated to exercise behavior. Perceived exercise barriers and health motivation explained the greatest variance in exercise behaviors. The Health Belief Model can be used as a guide by nurses to promote health behaviors consistent with research findings

    Tractography reconstruction framework of the optic radiations.

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    <p>(1) Standard preprocessing of the DWIs including Echo Planar Imaging distortion correction, eddy current distortion correction and head motion correction. (2) Distortion correction of the DWI. (3) Quantitative diffusion fractional anisotropy (FA) mapping. (4-5) Subcortical segmentation and cortical parcellation from FS of the 3D-structural image. (6) Registration of the structural images to the corresponding DWI sequence. (7) Seed and target masks. (8) Probabilistic streamline fiber tracking by high order integration over fiber orientation distributions (iFOD2) derived from constrained spherical deconvolution (CSD) with a maximum harmonic order of 8 and use of ACT during tracking. (9) Conversion of the tract file into a track density image. (10) Exclusion mask comprising CSF, whole contralateral hemisphere and ipsilateral GM regions. (11) Final optic radiation reconstruction in track density image and 3D tract file.</p

    Improved Framework for Tractography Reconstruction of the Optic Radiation - Fig 4

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    <p>Streamlines of the reconstructed OR in five patients with multiple sclerosis: (a) Lesion masks is shown in red. (b) Probabilistic streamlines fiber tracking by iFOD2. (c) Probabilistic streamlines fiber tracking by high order integration over fiber orientation distributions (iFOD2) adding the anatomical exclusion criteria (AEC).</p
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