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The Honeywell 1902 Wireless barcode scanner is redefining the standard for handheld scanners.1902 offers industry-leading performance and that require the versatility of area-imaging technology plus the freedom of Bluetooth wireless connectivity.
Honeywell 1900GSR-2 DescriptionOverviewThe Honeywell Xenon 1900 is redefining the standard for handheld scanners. Featuring a custom sensor that is optimized for barcode scanning, the Xenon 1900 offers industry-leading performance and reliability for a wide variety of applications that require the versatility of area-imaging technology. Powered by Adaptus Imaging Technology 6.0, the Xenon 1900 delivers superior barcode scanning and digital image capture. Xenon 1900 incorporates a revolutionary decoding architecture that combines Adaptus Imaging Technology 5.5 and Omniplanar's SwiftDecoder software along with a custom sensor, enabling extended depth of field, faster reading, and improved scanning performance on poor quality barcodes.
Methods: Medicated (n=24) and unmedicated (n=21) first episode schizophrenia patients as well as healthy control participants (n=28) were identified from referrals to the UC Davis Early Detection and Preventative Treatment clinic using the Structured Clinical Interview for DSM-IV. Images were obtained on a 1.5-Tesla General Electric scanner and processed using Freesurfer 4.1 (structural analysis) and SPM8 (fMRI analysis). Statistical analyses of structural data were conducted using a vertex-wide threshold of p
Methods: DTI scans were collected via 3T Siemens whole body scanner on 62 control (M age=30.0, 39 males), 58 nicotine (M age=30.2, 33 males) and 49 cannabis (M age=28.3, 33 males) users. Histogram and tractography analyses were performed to assess global and white matter fiber tract-based FA, respectively.
Conclusions: These results demonstrate that increasing the power of a neuroimaging methodology can increase the accuracy of an fMRI diagnostic test. This improved accuracy was found while testing the same participants using the same scanner and acquisition parameters. This provides support to the concept that the difficulty in identifying reliable results at the individual level of analysis for fMRI is more a function of low signal to noise versus individual differences in brain functional circuitry. In addition, the greater the power of the methodology to detect a difference, the more robust the results obtained at the individual level. As fMRI technology moves to making current or future predictions at the level of the individual in various areas of investigation, these results are encouraging. As the power of the technology improves, so can the accuracy of the results obtained. Further work is required to test these improvements in technology in a broader sample of participants and in diverse testing scenarios.
Methods: We recruited 22 subjects with late-life major depression (LLD) and 21 healthy comparison subjects (HC) from the community. All subjects were 60 years of age or older. Serum levels of IL-6, TNF-a, and IL-1b were assayed. Subjects were scanned on a 3T Phillips Achieva MRI scanner. White matter hyperintensities (WMH) were measured using a semi-automated technique. Anterior cingulate cortex (ACC) thickness and hippocampal volumes were measured using Freesurfer.
Methods: Adolescents (ages 13-18 years) with bipolar I disorder (BD, N=24) were recruited by referral to the Stanford University Pediatric Bipolar Disorders Clinic and the surrounding community along with age and gender matched healthy children (HC, N=24) without any psychiatric diagnosis. Diagnoses were determined by the Washington University in St. Louis Kiddie-Schedule for Affective Disorders and Schizophrenia (Geller et al. 1996) with established interrater reliability (kappa>0.9). BD subjects were permitted to continue any pre-existing pharmacological interventions. Subjects were given a standardized monetary incentive delay (MID) computerized task measuring reward processing with an antecedent mood induction, and symptom severity ratings of mania using the Young Mania (YMRS). During the task, participants were cued to anticipate and respond to a rapidly presented target to gain or avoid losing varying amounts of money. Functional magnetic resonance images (fMRI) were acquired with a 3T GE Signa scanner using a standard whole-head coil (General Electric, Milwaukee) and high-resolution, T1-weighted, spoiled GRASS images. A psychophysiological interaction (PPI) analysis, as implemented in SPM8, was used to evaluate functional connectivity between the sACC and the rest of the brain. The deconvolved activation time courses from the seed ROI was multiplied by a block vector representing the contrast of interest, anticipation of gain > nongain. The individual model contained regressors for the interaction, the seed region time course, the original conditions, and motion regressors. The regressors were convolved with the canonical hemodynamic response function (HRF). The generated contrast images were then compared between BD and HC groups at a second-level random effects analysis. Two-sample t-tests were used to identify significant group differences of activation connectivity with the seed region. A significant PPI effect had a cluster-level threshold FWE corrected for multiple comparisons (p 2b1af7f3a8