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sas (version 9.1)  (SAS institute)


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    SAS institute sas (version 9.1)
    Sas (Version 9.1), supplied by SAS institute, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/fmri+data+preprocessing/sas+version+9+4/pmc03124852-108-31-17
    Average 90 stars, based on 1 article reviews
    sas (version 9.1) - by Bioz Stars, 2026-09
    90/100 stars

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    other:

    Article Title: High Intensity Interval Training in Pediatric Heart Transplant Recipients: Evaluating a Novel Telemedicine Video Game-Linked Exercise Platform (MedBIKE™)
    Article Snippet: All statistical analyses will be performed by SAS 9.4 or later (SAS Institute Inc., Cary, NC, USA).

    Article Title: Title Pending 17794
    Article Snippet: This study evaluated the effects of dry-aged beef trimmings inclusion on quality and flavor precursors of ground beef patties.. Lean and fat trim were collected from beef loins aged for 28 d using 4 different methods: wet aging (WA), dry aging (DA), dry aging in water-permeable bag (DWA), and UV-light dry aging (UDA).. Trimmings were ground and incorporated with ground fresh beef top rounds and subcutaneous fat (3 d postmortem) to make patties (80% lean and 20% fat) with different formulations: fresh beef and fat (CON), fresh beef and DA fat only (DA-FAT), and mixtures of 50% fresh lean along with 30% aged lean and 20% aged fat from different aging treatments (WA, DA, DWA, and UDA).

    Article Title: Associations of educational attainment and traditional risk factor control with cardiovascular disease
    Article Snippet: Statistical analyses were conducted using SAS 9.4 software, with a significance level of p < 0.05.

    Article Title: Serum vitamin D levels and clinical frailty in older adults from Qatar.
    Article Snippet: All analyses were conducted using SAS version 9.4 (Cary, NC) and R version 4.0.2 (The R Foundation for Statistical Computing, Vienna, Austria).

    Article Title: Human papillomavirus (HPV) related oropharyngeal cancers in Canada: A multicenter retrospective cohort study.
    Article Snippet: Data management, integration and analysis were conducted according to a detailed statistical plan, and statistical analysis was performed using SAS version 9.4.

    Article Title: Atrial Appendage Micrograft Transplantation in Conjunction With Cardiac Surgery-the AAMS2 Randomized Controlled Trial
    Article Snippet: Statistical analyses-Power analysis was carried out using SAS 9.4 TS Level 1M4 software (SAS Institute Inc., Cary, NC, USA), the POWER Procedure Wilcoxon-Mann-Whitney Test with the fixed scenario elements O'Brien-Castelloe approximation method and two-sided statistical evaluation.

    Article Title: Association between cardiac rehabilitation and LDL-levels, adherence to guideline-recommended medication and mortality rate after myocardial infarction
    Article Snippet: All statistical analyses were conducted using SAS, version 9.4 (NC, Cary, USA).

    Article Title: A Randomized Controlled Study on Continuous Monitoring of Cardiac Index Variability to Guide Patients With Perioperative Myocardial Injury to Wean From Mechanical Ventilation
    Article Snippet: Statistical analysis plan: * Use SAS 10.0 statistical software for statistical analysis.



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    Figure 1. The schematic illustration of the main method. (A) The procedures to obtain multiscale FCNs. (i) the <t>fMRI</t> <t>data</t> from one individual is inputted and preprocessed. (ii) The application of the Schaefer’s multiscale atlases to the fMRI data. The black border lines indicate boundaries of ROIs and the colors encode the resting-state network (RSN) to which the ROI belongs. The RSNs include DMN, frontoparietal network (FP), limbic network (LIM), salience network (SAL), attention network (ATT), somatomotor network (SM), and visual network (VIS). (iii) The extraction of ROI-averaged signals. (iv) The construction of multiscale FCNs from individual fMRI data. (B) The architecture for multiscale atlas-based GCN (MAGCN). The multiscale FCNs are extracted via a series of GCNs connected by the APs. The nodal features h are integrated with skip connections and concatenations, based on which individualized diagnosis is generated.
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    Figure 1. The schematic illustration of the main method. (A) The procedures to obtain multiscale FCNs. (i) the <t>fMRI</t> <t>data</t> from one individual is inputted and preprocessed. (ii) The application of the Schaefer’s multiscale atlases to the fMRI data. The black border lines indicate boundaries of ROIs and the colors encode the resting-state network (RSN) to which the ROI belongs. The RSNs include DMN, frontoparietal network (FP), limbic network (LIM), salience network (SAL), attention network (ATT), somatomotor network (SM), and visual network (VIS). (iii) The extraction of ROI-averaged signals. (iv) The construction of multiscale FCNs from individual fMRI data. (B) The architecture for multiscale atlas-based GCN (MAGCN). The multiscale FCNs are extracted via a series of GCNs connected by the APs. The nodal features h are integrated with skip connections and concatenations, based on which individualized diagnosis is generated.
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    Figure 1. The schematic illustration of the main method. (A) The procedures to obtain multiscale FCNs. (i) the fMRI data from one individual is inputted and preprocessed. (ii) The application of the Schaefer’s multiscale atlases to the fMRI data. The black border lines indicate boundaries of ROIs and the colors encode the resting-state network (RSN) to which the ROI belongs. The RSNs include DMN, frontoparietal network (FP), limbic network (LIM), salience network (SAL), attention network (ATT), somatomotor network (SM), and visual network (VIS). (iii) The extraction of ROI-averaged signals. (iv) The construction of multiscale FCNs from individual fMRI data. (B) The architecture for multiscale atlas-based GCN (MAGCN). The multiscale FCNs are extracted via a series of GCNs connected by the APs. The nodal features h are integrated with skip connections and concatenations, based on which individualized diagnosis is generated.

    Journal: Cerebral cortex (New York, N.Y. : 1991)

    Article Title: Multiscale functional connectome abnormality predicts cognitive outcomes in subcortical ischemic vascular disease.

    doi: 10.1093/cercor/bhab507

    Figure Lengend Snippet: Figure 1. The schematic illustration of the main method. (A) The procedures to obtain multiscale FCNs. (i) the fMRI data from one individual is inputted and preprocessed. (ii) The application of the Schaefer’s multiscale atlases to the fMRI data. The black border lines indicate boundaries of ROIs and the colors encode the resting-state network (RSN) to which the ROI belongs. The RSNs include DMN, frontoparietal network (FP), limbic network (LIM), salience network (SAL), attention network (ATT), somatomotor network (SM), and visual network (VIS). (iii) The extraction of ROI-averaged signals. (iv) The construction of multiscale FCNs from individual fMRI data. (B) The architecture for multiscale atlas-based GCN (MAGCN). The multiscale FCNs are extracted via a series of GCNs connected by the APs. The nodal features h are integrated with skip connections and concatenations, based on which individualized diagnosis is generated.

    Article Snippet: The sagittal T1-weighted images covering the whole brain were acquired by a 3Dfast spoiled gradient recalled echo sequence: TR = 5.6 ms, TE = 1.8 ms, matrix = 256 × 256, inversion time = 450 ms, flip angle = 15◦, slice thickness/gap = 1/0 mm, number of slices = 156, gap = 0, and FOV = 256 × 256 mm2. fMRI Data Preprocessing We adopt the standardized pipeline from the public available toolbox Data Processing Assistant for Resting-State fMRI (Yan and Zang 2010) in Matlab (Mathworks.

    Techniques: Extraction, Biomarker Discovery, Generated