993 resultados para Support matrix


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The First International Workshop on Behavior Change Support Systems attracted a great research interest. The selected papers focused on abstraction, implementation and evaluation of Behavior Change Support Systems. The workshop is an evidence of how researchers from around the globe have their own perspective of behavior change interventions. In this abstract, we have attempted to outline core issues that can enhance persuasiveness of such support systems. Finally, we highlight important research questions relating to the development of effective Behavior Change Support Systems.

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Cell adhesion and extracellular matrix (ECM) molecules play a significant role in neuronal plasticity both during development and in the adult. Plastic changes in which ECM components are implicated may underlie important nervous system functions, such as memory formation and learning. Heparin-binding growthassociated molecule (HB-GAM, also known as pleiotrophin), is an ECM protein involved in neurite outgrowth, axonal guidance and synaptogenesis during perinatal period. In the adult brain HB-GAM expression is restricted to the regions which display pronounced synaptic plasticity (e.g., hippocampal CA3-CA1 areas, cerebral cortex laminae II-IV, olfactory bulb). Expression of HB-GAM is regulated in an activity-dependent manner and is also induced in response to neuronal injury. In this work mutant mice were used to study the in vivo function of HB-GAM and its receptor syndecan-3 in hippocampal synaptic plasticity and in hippocampus-dependent behavioral tasks. Phenotypic analysis of HBGAM null mutants and mice overexpressing HB-GAM revealed that opposite genetic manipulations result in reverse changes in synaptic plasticity as well as behavior in the mutants. Electrophysiological recordings showed that mice lacking HB-GAM have an increased level of long-term potentiation (LTP) in the area CA1 of hippocampus and impaired spatial learning, whereas animals with enhanced level of HB-GAM expression have attenuated LTP, but outperformed their wild-type controls in spatial learning. It was also found that GABA(A) receptor-mediated synaptic transmission is altered in the transgenic mice overexpressing HB-GAM. The results suggest that these animals have accentuated hippocampal GABAergic inhibition, which may contribute to the altered glutamatergic synaptic plasticity. Structural studies of HB-GAM demonstrated that this protein belongs to the thrombospondin type I repeat (TSR) superfamily and contains two β-sheet domains connected by a flexible linker. It was found that didomain structure is necessary for biological activity of HB-GAM and electrophysiological phenotype displayed by the HB-GAM mutants. The individual domains displayed weaker binding to heparan sulfate and failed to promote neurite outgrowth as well as affect hippocampal LTP. Effects of HB-GAM on hippocampal synaptic plasticity are believed to be mediated by one of its (co-)receptor molecules, namely syndecan-3. In support of that, HB-GAM did not attenuate LTP in mice deficient in syndecan-3 as it did in wild-type controls. In addition, syndecan-3 knockout mice displayed electrophysiological and behavioral phenotype similar to that of HB-GAM knockouts (i.e. enhanced LTP and impaired learning in Morris water-maze). Thus HB-GAM and syndecan-3 are important modulators of synaptic plasticity in hippocampus and play a role in regulation of learning-related behavior.

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This thesis increased the researchers understanding of the relationship between operations and maintenance in underground longwall coal mines, using data from a Queensland underground coal mine. The thesis explores various relationships between recorded variables. Issues with human recorded data was uncovered, and results emphasised the significance of variables associated with conveyor operation to explain production.

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A rank-augmnented LU-algorithm is suggested for computing a generalized inverse of a matrix. Initially suitable diagonal corrections are introduced in (the symmetrized form of) the given matrix to facilitate decomposition; a backward-correction scheme then yields a desired generalized inverse.

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Background Ankylosing spondylitis (AS) is an immune-mediated arthritis particularly targeting the spine and pelvis and is characterised by inflammation, osteoproliferation and frequently ankylosis. Current treatments that predominately target inflammatory pathways have disappointing efficacy in slowing disease progression. Thus, a better understanding of the causal association and pathological progression from inflammation to bone formation, particularly whether inflammation directly initiates osteoproliferation, is required. Methods The proteoglycan-induced spondylitis (PGISp) mouse model of AS was used to histopathologically map the progressive axial disease events, assess molecular changes during disease progression and define disease progression using unbiased clustering of semi-quantitative histology. PGISp mice were followed over a 24-week time course. Spinal disease was assessed using a novel semi-quantitative histological scoring system that independently evaluated the breadth of pathological features associated with PGISp axial disease, including inflammation, joint destruction and excessive tissue formation (osteoproliferation). Matrix components were identified using immunohistochemistry. Results Disease initiated with inflammation at the periphery of the intervertebral disc (IVD) adjacent to the longitudinal ligament, reminiscent of enthesitis, and was associated with upregulated tumor necrosis factor and metalloproteinases. After a lag phase, established inflammation was temporospatially associated with destruction of IVDs, cartilage and bone. At later time points, advanced disease was characterised by substantially reduced inflammation, excessive tissue formation and ectopic chondrocyte expansion. These distinct features differentiated affected mice into early, intermediate and advanced disease stages. Excessive tissue formation was observed in vertebral joints only if the IVD was destroyed as a consequence of the early inflammation. Ectopic excessive tissue was predominantly chondroidal with chondrocyte-like cells embedded within collagen type II- and X-rich matrix. This corresponded with upregulation of mRNA for cartilage markers Col2a1, sox9 and Comp. Osteophytes, though infrequent, were more prevalent in later disease. Conclusions The inflammation-driven IVD destruction was shown to be a prerequisite for axial disease progression to osteoproliferation in the PGISp mouse. Osteoproliferation led to vertebral body deformity and fusion but was never seen concurrent with persistent inflammation, suggesting a sequential process. The findings support that early intervention with anti-inflammatory therapies will be needed to limit destructive processes and consequently prevent progression of AS.

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Wound healing is a complex process that requires an interplay between several cell types. Classically, fibroblasts have been viewed as producers of extracellular matrix, but more recently they have been recognized as orchestrators of the healing response, promoting and directing, inflammation and neovascularization processes. Compared to those from healthy tissue, inflammation-associated fibroblasts display a dramatically altered phenotype and have been described as sentinel cells, able to switch to an immunoregulatory profile on cue. However, the activation mechanism still remains largely uncharacterized. Nemosis is a model for stromal fibroblast activation. When normal human primary fibroblasts are deprived of growth support they cluster, forming multicellular spheroids. Clustering results in upregulation of proinflammatory markers such as cyclooxygenase-2 and secretion of prostaglandins, proteinases, cytokines, and growth factors. Fibroblasts in nemosis induce wound healing and tumorigenic responses in many cell types found in inflammatory and tumor microenvironments. This study investigated the effect of nemotic fibroblasts on two components of the vascular system, leukocytes and endothelium, and characterized the inflammation-promoting responses that arose in these cell types. Fibroblasts in nemosis were found to secrete an array of chemotactic cytokines and attract leukocytes, as well as promote their adhesion to the endothelium. Nuclear factor-kB, the master regulator of many inflammatory responses, is activated in nemotic fibroblasts. Nemotic fibroblasts are known to produce large amounts of hepatocyte growth factor, a motogenic and angiogenic factor. Also, as shown in this study, they produce vascular endothelial growth factor. These two factors induced migratory and sprouting responses in endothelial cells, both required for neovascularization. Nemotic fibroblasts also caused a decrease in the expression of adherens and tight junction components on the surface of endothelial cells. The results allow the conclusion that fibroblasts in nemosis share many similarities with inflammation-associated fibroblasts. Both inflammation and stromal fibroblasts are known to be involved in tumorigenesis and tumor progression. Nemosis may be viewed as a model for stromal fibroblast activation, or it may correlate with cell-cell interactions between adjacent fibroblasts in vivo. Nevertheless, due to nemosis-derived production of proinflammatory cytokines and growth factors, fibroblast nemosis may have therapeutic potential as an inducer of controlled tissue repair. Knowledge of stromal fibroblast activation gained through studies of nemosis, could provide new strategies to control unwanted inflammation and tumor progression.

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Factors contributing to academic achievement among recently arrived Chinese adolescents in Australia remains relatively underexplored. Previous studies focused on Asian migrants, including Chinese, but did not distinguish Chinese from other Asian migrants. The current study specifically looks at Chinese migrants who have recently arrived as opposed to Asian migrants. This study aims to explore the role of social support, school belonging, and acculturative stress on academic achievement of recently arrived Chinese adolescents (n = 55). Questionnaires were administered to this sample. The results indicated that school belonging, interestingly, was negatively associated with academic achievement. Perceived social support and acculturative stress were not significantly associated with academic achievement. The findings provide insights into risk and protective factors influencing academic achievement of Chinese migrants. Implications of the findings are discussed.

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Urban sprawl is the outgrowth along the periphery of cities and along highways. Although an accurate definition of urban sprawl may be debated, a consensus is that urban sprawl is characterized by an unplanned and uneven pattern of growth, driven by multitude of processes and leading to inefficient resource utilization. Urbanization in India has never been as rapid as it is in recent times. As one of the fastest growing economies in the world, India faces stiff challenges in managing the urban sprawl, while ensuring effective delivery of basic services in urban areas. The urban areas contribute significantly to the national economy (more than 50% of GDP), while facing critical challenges in accessing basic services and necessary infrastructure, both social and economic. The overall rise in the population of the urban poor or the increase in travel times due to congestion along road networks are indicators of the effectiveness of planning and governance in assessing and catering for this demand. Agencies of governance at all levels: local bodies, state government and federal government, are facing the brunt of this rapid urban growth. It is imperative for planning and governance to facilitate, augment and service the requisite infrastructure over time systematically. Provision of infrastructure and assurance of the delivery of basic services cannot happen overnight and hence planning has to facilitate forecasting and service provision with appropriate financial mechanisms.

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This thesis is an examination of how organisational context variables affect the performance of new product development (NPD) teams. Specifically, the extent to how team empowerment climate and supervisory support for creativity impact NPD team performance. Moreover, this thesis is a step forward in the ongoing development of work role performance theory by examining Griffin et al.'s (2007) work role performance model in the context of NPD teams. This thesis addresses the lack of research exploring work role performance dimensions in NPD teams and the extent to which a team empowerment climate and supervisory support for creativity impact NPDs performance.

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Neurotrophic factors (NTFs) and the extracellular matrix (ECM) are important regulators of axonal growth and neuronal survival in mammalian nervous system. Understanding of the mechanisms of this regulation is crucial for the development of posttraumatic therapies and drug intervention in the injured nervous system. NTFs act as soluble, target-derived extracellular regulatory molecules for a wide range of physiological functions including axonal guidance and the regulation of programmed cell death in the nervous system. The ECM determines cell adhesion and regulates multiple physiological functions via short range cell-matrix interactions. The present work focuses on the mechanisms of the action of NTFs and the ECM on axonal growth and survival of cultured sensory neurons from dorsal root ganglia (DRG). We first examined signaling mechanisms of the action of the glial cell line-derived neurotrophic factor (GDNF) family ligands (GFLs) on axonal growth. GDNF, neurturin (NRTN) and artemin (ART) but not persephin (PSPN) promoted axonal initiation in cultured DRG neurons from young adult mice. This effect required Src family kinase (SFK) activity. In neurons from GFRalpha2-deficient mice, NRTN did not significantly promote axonal initiation. GDNF and NRTN induced extensive lamellipodia formation on neuronal somata and growth cones. This study suggested that GDNF, NRTN and ARTN may serve as stimulators of nerve regeneration under posttraumatic conditions. Consequently we studied the convergence of signaling pathways induced by NTFs and the ECM molecule laminin in the intracellular signaling network that regulates axonal growth. We demonstrated that co-stimulation of DRG neurons with NTFs (GDNF, NRTN or nerve growth factor (NGF)) and laminin leads to axonal growth that requires activation of SFKs. A different, SFK-independent signaling pathway evoked axonal growth on laminin in the absence of the NTFs. In contrast, axonal branching was regulated by SFKs both in the presence and in the absence of NGF. We proposed and experimentally verified a Boolean model of the signaling network triggered by NTFs and laminin. Our results put forward an approach for predictable, Boolean logics-driven pharmacological manipulation of a complex signaling network. Finally we found that N-syndecan, the receptor for the ECM component HB-GAM was required for the survival of neonatal sensory neurons in vitro. We demonstrated massive cell death of cultured DRG neurons from mice deficient in the N-syndecan gene as compared to wild type controls. Importantly, this cell death could not be prevented by NGF the neurotrophin which activates multiple anti-apoptotic cascades in DRG neurons. The survival deficit was observed during first postnatal week. By contrast, DRG neurons from young adult N-syndecan knock-out mice exhibited normal survival. This study identifies a completely new syndecan-dependent type of signaling that regulates cell death in neurons.

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Background and Objective: Oral submucous fibrosis, a disease of collagen disorder, has been attributed to arecoline present in the saliva of betel quid chewers. However, the molecular basis of the action of arecoline in the pathogenesis of oral submucous fibrosis is poorly understood. The basic aim of our study was to elucidate the mechanism underlying the action of arecoline on the expression of genes in oral fibroblasts. Material and Methods: Human keratinocytes (HaCaT cells) and primary human gingival fibroblasts were treated with arecoline in combination with various pathway inhibitors, and the expression of transforming growth factor-beta isoform genes and of collagen isoforms was assessed using reverse transcription polymerase chain reaction analysis. Results: We observed the induction of transforming growth factor-beta2 by arecoline in HaCaT cells and this induction was found to be caused by activation of the M-3 muscarinic acid receptor via the induction of calcium and the protein kinase C pathway. Most importantly, we showed that transforming growth factor-beta2 was significantly overexpressed in oral submucous fibrosis tissues (p = 0.008), with a median of 2.13 (n = 21) compared with 0.75 (n = 18) in normal buccal mucosal tissues. Furthermore, arecoline down-regulated the expression of collagens 1A1 and 3A1 in human primary gingival fibroblasts; however these collagens were induced by arecoline in the presence of spent medium of cultured human keratinocytes. Treatment with a transforming growth factor-beta blocker, transforming growth factor-beta1 latency-associated peptide, reversed this up-regulation of collagen, suggesting a role for profibrotic cytokines, such as transforming growth factor-beta, in the induction of collagens. Conclusion: Taken together, our data highlight the importance of arecoline-induced epithelial changes in the pathogenesis of oral submucous fibrosis.

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A new mixed-matrix membrane based on stabilized phosphotungstic acid (PTA) incorporated to chitosan (CS)-hydroxy ethyl cellulose (HEC) for application in direct methanol fuel cells (DMFCs) is reported. Membranes are characterised using Fourier Transform Spectroscopy (FTIR), Thermo-Gravimetric Analysis (TGA), Scanning Electron Microscopy (SEM) and their mechanical properties are evaluated. The PTA content in the CS-HEC blend and its influence on proton conductivity, water/methanol sorption, and methanol cross-over in the DMFC is studied. The DMFC with 3 wt. % stabilized PTA-CS-HEC mixed-matrix membrane delivers peak power-density of 58 mW/cm(2) at a load current-density of 210 mA/cm(2) with a lower methanol cross-over than that observed for a DMFC operating with a Nafion membrane electrolyte.

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The problem of unsupervised anomaly detection arises in a wide variety of practical applications. While one-class support vector machines have demonstrated their effectiveness as an anomaly detection technique, their ability to model large datasets is limited due to their memory and time complexity for training. To address this issue for supervised learning of kernel machines, there has been growing interest in random projection methods as an alternative to the computationally expensive problems of kernel matrix construction and sup-port vector optimisation. In this paper we leverage the theory of nonlinear random projections and propose the Randomised One-class SVM (R1SVM), which is an efficient and scalable anomaly detection technique that can be trained on large-scale datasets. Our empirical analysis on several real-life and synthetic datasets shows that our randomised 1SVM algorithm achieves comparable or better accuracy to deep auto encoder and traditional kernelised approaches for anomaly detection, while being approximately 100 times faster in training and testing.

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Identifying unusual or anomalous patterns in an underlying dataset is an important but challenging task in many applications. The focus of the unsupervised anomaly detection literature has mostly been on vectorised data. However, many applications are more naturally described using higher-order tensor representations. Approaches that vectorise tensorial data can destroy the structural information encoded in the high-dimensional space, and lead to the problem of the curse of dimensionality. In this paper we present the first unsupervised tensorial anomaly detection method, along with a randomised version of our method. Our anomaly detection method, the One-class Support Tensor Machine (1STM), is a generalisation of conventional one-class Support Vector Machines to higher-order spaces. 1STM preserves the multiway structure of tensor data, while achieving significant improvement in accuracy and efficiency over conventional vectorised methods. We then leverage the theory of nonlinear random projections to propose the Randomised 1STM (R1STM). Our empirical analysis on several real and synthetic datasets shows that our R1STM algorithm delivers comparable or better accuracy to a state-of-the-art deep learning method and traditional kernelised approaches for anomaly detection, while being approximately 100 times faster in training and testing.