https://ogma.newcastle.edu.au/vital/access/ /manager/Index en-au 5 https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:13674 Wed 24 Jun 2020 12:51:30 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:43536 Wed 21 Sep 2022 11:53:29 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:44655 Wed 19 Oct 2022 09:49:47 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:38724 Wed 19 Jan 2022 10:18:02 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:23687 Wed 19 Apr 2023 16:42:57 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:39130 Wed 11 May 2022 16:34:33 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:9283 Wed 11 Apr 2018 18:36:27 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:3002 Wed 11 Apr 2018 17:22:49 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:24445 Wed 11 Apr 2018 16:51:11 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:9259 Wed 11 Apr 2018 16:43:02 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:15138 Wed 11 Apr 2018 16:14:13 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:25160 Wed 11 Apr 2018 16:09:03 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:9494 Wed 11 Apr 2018 15:31:19 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:13480 Wed 11 Apr 2018 15:24:54 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:13673 Wed 11 Apr 2018 15:24:43 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:24220 A and −174 G>C) correlate with MS susceptibility, but with varying results. In the present study, we analyzed these polymorphisms, together with an additional polymorphism (−572 G>C) in 279 healthy controls and 509 patients with MS. We found no significant differences between MS patients and healthy controls for the different −597 or −174 IL6 promoter alleles or genotypes. There was a slight reduction in the percentage of individuals with MS who carried a C allele at position −572, although this was not significant after correction for multiple comparisons. Interestingly, however, the −572 C allele showed a significant correlation with the MS severity score, suggesting a possible role in disease progression.]]> Wed 11 Apr 2018 15:18:27 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:27322 Wed 11 Apr 2018 14:52:52 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:4456 Wed 11 Apr 2018 14:49:36 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:9497 Wed 11 Apr 2018 14:36:12 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:9262 Wed 11 Apr 2018 14:21:31 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:16800 Wed 11 Apr 2018 14:17:27 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:9503 Wed 11 Apr 2018 13:47:54 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:30273 MERTK has been established as an MS susceptibility gene and is part of a family of receptor tyrosine kinases known to be involved in the pathogenesis of demyelinating disease. In this study we have refined the association of MERTK with MS risk to independent signals from both common and low frequency variants. One of the associated variants was also found to be linked with increased expression of MERTK in monocytes and higher expression of MERTK was associated with either increased or decreased risk of developing MS, dependent upon HLA-DRB1*15:01 status. This discordant association potentially extended beyond MS susceptibility to alterations in disease course in established MS. This study provides clear evidence that distinct polymorphisms within MERTK are associated with MS susceptibility, one of which has the potential to alter MERTK transcription, which in turn can alter both susceptibility and disease course in MS patients.]]> Wed 11 Apr 2018 13:23:58 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:28310 Wed 11 Apr 2018 12:29:47 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:30004 Wed 11 Apr 2018 12:27:04 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:9568 Wed 11 Apr 2018 11:52:59 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:7087 Wed 11 Apr 2018 11:42:37 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:9263 Wed 11 Apr 2018 11:42:14 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:24365 Wed 11 Apr 2018 11:41:14 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:21737 16 h). Analysis of lncRNA features revealed that intergenic and cis-antisense RNAs are more stable than those derived from introns, as are spliced lncRNAs compared to unspliced (single exon) transcripts. Subcellular localization of lncRNAs indicated widespread trafficking to different cellular locations, with nuclear-localized lncRNAs more likely to be unstable. Surprisingly, one of the least stable lncRNAs is the well-characterized paraspeckle RNA Neat1, suggesting Neat1 instability contributes to the dynamic nature of this subnuclear domain. We have created an online interactive resource (http://stability.matticklab.com) that allows easy navigation of lncRNA and mRNA stability profiles and provides a comprehensive annotation of ∼7200 mouse lncRNAs.]]> Wed 11 Apr 2018 11:06:11 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:16801 Wed 11 Apr 2018 09:24:30 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:28613 Wed 11 Apr 2018 09:21:51 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:30824 Wed 10 Nov 2021 15:04:57 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:45579 Wed 02 Nov 2022 08:23:58 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:25008 Tue 30 Aug 2022 14:27:23 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:54429 Tue 27 Feb 2024 14:00:08 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:34298 Tue 26 Feb 2019 14:15:13 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:49596 Tue 23 May 2023 12:45:20 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:32234 Tue 15 May 2018 14:22:14 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:43055 k-Nearest Neighbour graph (MST-kNN). Cluster-specific consumer behaviours relating to customer engagement are predicted using symbolic regression analysis which, in a commercial setting, would provide the basis for personalized marketing strategies. Five major clusters were found in the dataset of 371 respondents who answered questions from theoretical marketing constructs related to online consumer behaviours. They are labelled as follows: 'Brand Rationalists', 'Passive Socializers', 'Immersers', 'Hedonic Sharers' and 'Active Participators'. For each of these clusters, a linear model of customer engagement was predicted using symbolic regression analysis. These models inform possible personalized marketing strategies after proper segmentation of the customers based on their online consumer behaviour, rather than simple demographic characteristics.]]> Tue 13 Sep 2022 08:35:50 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:45986 Tue 08 Nov 2022 14:40:22 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:45981 Tue 08 Nov 2022 14:33:13 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:42888 Tue 06 Sep 2022 14:18:31 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:35934 Thu 25 Jun 2020 12:19:41 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:36788 Thu 16 Jul 2020 16:04:16 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:43369 Thu 15 Sep 2022 15:46:26 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:53648 Thu 14 Dec 2023 10:29:19 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:14966 Thu 12 Apr 2018 13:35:44 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:41481 Thu 04 Aug 2022 13:53:19 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:2864 Sat 24 Mar 2018 11:11:31 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:13951 Sat 24 Mar 2018 10:41:26 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:31540 Sat 24 Mar 2018 08:44:23 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:7518 Sat 24 Mar 2018 08:38:28 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:8027 Sat 24 Mar 2018 08:36:47 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:7694 Sat 24 Mar 2018 08:33:34 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:2952 Sat 24 Mar 2018 08:31:00 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:1618 Sat 24 Mar 2018 08:30:34 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:2992 Sat 24 Mar 2018 08:30:21 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:12845 T at 3p24.1 (odds ratio [OR], 1.17; p = 1.6 × 10⁻⁸) near EOMES, rs2150702^G in the second intron of MLANA on chromosome 9p24.1 (OR, 1.16; p = 3.3 × 10⁻⁸), and rs6718520^A in an intergenic region on chromosome 2p21, with THADA as the nearest flanking gene (OR, 1.17; p = 3.4 × 10⁻⁸). The 3 new loci do not have a strong cis effect on RNA expression in PBMCs. Ten other susceptibility loci had a suggestive p < 1 × 10⁻⁶, some of these loci have evidence of association in other inflammatory diseases (ie, IL12B, TAGAP, PLEK, and ZMIZ1). Interpretation: We have performed a meta-analysis of GWAS in MS that more than doubles the size of previous gene discovery efforts and highlights 3 novel MS susceptibility loci. These and additional loci with suggestive evidence of association are excellent candidates for further investigations to refine and validate their role in the genetic architecture of MS.]]> Sat 24 Mar 2018 08:17:22 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:17467 Sat 24 Mar 2018 08:04:12 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:18832 Sat 24 Mar 2018 08:03:17 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:18758 Sat 24 Mar 2018 08:02:46 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:20383 Sat 24 Mar 2018 07:58:08 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:20882 -/- mice, a model of hemochromatosis, relative to age- and gender-matched wildtype controls. Classification by functional pathway analysis revealed transcript changes for various genes important in AD. There were decreases of up to 9-fold in transcripts for amyloid-β protein precursor, tau, apolipoprotein E, presenilin 1, and various other γ-secretase components, as well as Notch signaling pathway molecules. This included decreased transcripts for 'hairy and enhancer of split' Hes1 and Hes5, downstream targets of Notch canonical signaling. The reductions in Hes1 and Hes5 transcripts provide evidence that the changes in levels of transcripts for γ-secretase components and Notch signaling genes have functional consequences. The effects appeared relatively specific for AD in that few genes pertaining to other important neurodegenerative diseases, notably Parkinson's disease and Huntington's disease, or to inflammation, oxidative stress, or apoptosis, showed altered transcript levels. The observed effects on AD-related gene transcripts do not appear to be consistent with increased AD risk in HFE hemochromatosis and might, if anything, be predicted to protect against AD to some extent. As Hfe^-/- mice did not have higher brain iron levels than wildtype controls, these studies highlight the need for further research in models of more severe hemochromatosis with brain iron loading.]]> Sat 24 Mar 2018 07:57:56 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:17878 Sat 24 Mar 2018 07:56:13 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:16267 Sat 24 Mar 2018 07:54:14 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:16274 Sat 24 Mar 2018 07:54:14 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:21139 Disclosed, mines disjunctive closed itemsets by exploring the search space in a depth-first, top-down manner. We evaluated the performance of our algorithm to execute such a task using real microarray gene expression data sets publicly available on the Internet. Our experiments revealed under what situations, the characteristics of a data set, our method obtain a good, bad or average performance. We also compared the performance of our method with the state of the art algorithms for finding disjunctive closed patterns and disjunctive minimal generators. We observed that our approach is two orders of magnitude more efficient, both in terms of time and memory.]]> Sat 24 Mar 2018 07:54:05 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:21125 Sat 24 Mar 2018 07:53:55 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:16299 Sat 24 Mar 2018 07:53:23 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:21225 k-nearest neighbor (kNN) structure can provide an efficient solution to this problem. Only a few attempts were found in the literature that focus on solving the problem using the kNNs. This paper briefs the state-of-the-art strategies for the MST problem and a fast and scalable solution combining the classical Borůvka’s MST algorithm and the kNN graph structure. The proposed algorithm is implemented for CUDA enabled GPUs (kNN-Borůvka-GPU), but the basic approach is simple and adaptable to other available architectures. Speed-ups of 30-40 times compared with CPU implementation was observed for several large-scale synthetic and real world data sets.]]> Sat 24 Mar 2018 07:53:01 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:21833 HFE gene. There is evidence from both human and animal studies that HFE gene variants may affect brain function and modify risks of brain disease. To investigate how disruption of HFE influences brain transcript levels, we used microarray and real-time reverse transcription polymerase chain reaction to assess the brain transcriptome in Hfe^−/− mice relative to wildtype AKR controls (age 10 weeks, n ≥ 4/group). The Hfe^−/− mouse brain showed numerous significant changes in transcript levels (p < 0.05) although few of these related to proteins directly involved in iron homeostasis. There were robust changes of at least 2-fold in levels of transcripts for prominent genes relating to transcriptional regulation (FBJ osteosarcoma oncogene Fos, early growth response genes), neurotransmission (glutamate NMDA receptor Grin1, GABA receptor Gabbr1) and synaptic plasticity and memory (calcium/calmodulin-dependent protein kinase IIα Camk2a). As previously reported for dietary iron-supplemented mice, there were altered levels of transcripts for genes linked to neuronal ceroid lipofuscinosis, a disease characterized by excessive lipofuscin deposition. Labile iron is known to enhance lipofuscin generation which may accelerate brain aging. The findings provide evidence that iron loading disorders can considerably perturb levels of transcripts for genes essential for normal brain function and may help explain some of the neurologic signs and symptoms reported in hemochromatosis patients.]]> Sat 24 Mar 2018 07:52:17 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:18492 Sat 24 Mar 2018 07:51:23 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:18528 Sat 24 Mar 2018 07:50:20 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:6628 Sat 24 Mar 2018 07:46:19 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:6631 Sat 24 Mar 2018 07:46:19 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:6195 Sat 24 Mar 2018 07:44:39 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:26041 Sat 24 Mar 2018 07:26:27 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:25518 Sat 24 Mar 2018 07:26:01 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:25354 Sat 24 Mar 2018 07:24:42 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:3851 Sat 24 Mar 2018 07:19:57 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:22915 (alpha,beta)-k-Feature Set, and a ranking-based feature selection method, based on the use of CM1-scores. We employ two publicly available real-world datasets (one with training and test variants). The classification accuracies have been evaluated using a total of 49 methods from an open source data mining and machine learning package WEKA.]]> Sat 24 Mar 2018 07:16:28 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:23003 Sat 24 Mar 2018 07:15:42 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:25211 Sat 24 Mar 2018 07:14:02 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:24348 Sat 24 Mar 2018 07:10:17 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:50785 Sat 05 Aug 2023 11:23:08 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:53421 Mon 27 Nov 2023 11:33:01 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:38588 th century for which measurements were tabulated, and a governing functional relationship was postulated. Using leave-one-out cross-validation, in training our method ranks first in 350 out of the 352 datasets. Only six machine learning algorithms ranked first in at least one of the 352 datasets on testing; our approach ranked first 192 times, i.e. more all of the other algorithms combined. The results favourably speak about the robustness of our methodology. We conclude that the use of analytic continued fractions in regression deserves further study and we also advocate that Schaffer's data collection should also be included in the repertoire of datasets to test the performance of machine learning and regression algorithms.]]> Mon 20 Nov 2023 14:40:26 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:16196 Mon 20 Nov 2023 14:25:33 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:16197 Mon 20 Nov 2023 14:22:58 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:44565 Mon 17 Oct 2022 11:09:31 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:43528 Mon 17 Apr 2023 14:53:59 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:46269 Mon 14 Nov 2022 14:59:50 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:40403 Mon 11 Jul 2022 13:55:35 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:42849 Mon 05 Sep 2022 15:26:27 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:29463 th and 17^th centuries), to analyze and detect clusters of similar plays. Experimental results and comparison with state-of-the-art clustering methods demonstrate the remarkable performance of our new method for identifying high quality clusters which reflect the commonalities in the literary style of the plays.]]> Fri 24 Jul 2020 15:16:02 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:52671 Fri 20 Oct 2023 09:24:32 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:42779 Fri 02 Sep 2022 14:18:56 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:42782 Fri 02 Sep 2022 12:24:36 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:42778 Fri 02 Sep 2022 11:42:18 AEST ]]>