https://ogma.newcastle.edu.au/vital/access/ /manager/Index ${session.getAttribute("locale")} 5 https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:47742 n = 10) were compared with PTSD participants with minimal co-morbidities (n = 10). The 2D MRS identified statistically significant increases in the total spectral region containing both free substrate fucose and fucosylated glycans of 31% (P = 0.0013), two of multiple fucosylated glycans (Fuc IV and VI) were elevated by 48% (P = 0.002), and 41% (P = 0.02), respectively, imidazole was increased by 12% (P = 0.002), and lipid saturation was increased by 12.5% (P = 0.009). This is the first evidence of fucosylated glycans, reported in animals to be involved in learning and memory, to be affected in humans with PTSD.]]> Wed 25 Jan 2023 15:28:52 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:24236 Wed 16 Nov 2016 14:36:47 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:24868 Wed 11 Apr 2018 16:46:08 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:21275 in vivo brain magnetic resonance spectroscopy biomarkers is investigated with respect to parameter values for two key stages of post-acquisitional processing. Our effort is specifically motivated by the lack of consensus on approaches and parameter values for the two critical operations, water resonance removal, and baseline correction. The different stages of data processing also introduce varying levels of uncertainty and arbitrary selection of parameter values can significantly underutilize the intrinsic differences between two classes of signals. The sensitivity of biomarkers points to the need for a better understanding of how all stages of post-acquisitional processing affect biomarker discovery and ultimately, clinical translation. Our results also highlight the possibility of optimizing biomarker discovery by the careful selection of parameters that best reveal class differences. Using previously reported data and biomarkers, our results demonstrate that small changes in parameter values affect the statistical significance and corresponding effect size of biomarkers. Consequently, it is possible to increase the strength of biomarkers by selecting optimal parameter values in different spectral intervals. Our analyses with a previously reported data set demonstrate an increase in effect sizes for wavelet-based biomarkers of up to 36%, with increases in classification performance of up to 12%.]]> Wed 11 Apr 2018 12:19:42 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:41391 Tue 02 Aug 2022 17:47:20 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:27115 Sat 24 Mar 2018 07:41:35 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:27597 Sat 24 Mar 2018 07:25:17 AEDT ]]>