883 resultados para Cognitive Function


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Benzodiazepines are widely prescribed to manage sleep disorders, anxiety and muscular tension. While providing short-term relief, continued use induces tolerance and withdrawal, and in older users, increases the risk of falls. However, long-term prescription remains common, and effective interventions are not widely available. This study developed a self-managed cognitive behaviour therapy package for cessation of benzodiazepine use delivered to participants via mail (M-CBT) and trialled its effectiveness as an adjunct to a general practitioner (GP)-managed dose reduction schedule. In the pilot trial, participants were randomly assigned to GP management with immediate or delayed M-CBT. Significant recruitment and engagement problems were experienced, and only three participants were allocated to each condition. After immediate M-CBT, two participants ceased use, while none receiving delayed treatment reduced daily intake by more than 50%. Across the sample, doses at 12 months remained significantly lower than baseline, and qualitative feedback from participants was positive. While M-CBT may have promise, improved engagement of GPs and participants is needed for this approach to substantially impact on community-wide benzodiazepine use.

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Stigmergy is a biological term used when discussing a sub-set of insect swarm-behaviour describing the apparent organisation seen during their activities. Stigmergy describes a communication mechanism based on environment-mediated signals which trigger responses among the insects. This phenomenon is demonstrated in the behavior of ants and their food gathering process when following pheromone trails, where the pheromones are a form of environment-mediated communication. What is interesting with this phenomenon is that highly organized societies are achieved without an apparent management structure. Stigmergy is also observed in human environments, both natural and engineered. It is implicit in the Web where sites provide a virtual environment supporting coordinative contributions. Researchers in varying disciplines appreciate the power of this phenomenon and have studied how to exploit it. As stigmergy becomes more widely researched we see its definition mutate as papers citing original work become referenced themselves. Each paper interprets these works in ways very specific to the research being conducted. Our own research aims to better understand what improves the collaborative function of a Web site when exploiting the phenomenon. However when researching stigmergy to develop our understanding we discover a lack of a standardized and abstract model for the phenomenon. Papers frequently cited the same generic descriptions before becoming intimately focused on formal specifications of an algorithm, or esoteric discussions regarding sub-facets of the topic. None provide a holistic and macro-level view to model and standardize the nomenclature. This paper provides a content analysis of influential literature documenting the numerous theoretical and experimental papers that have focused on stigmergy. We establish that stigmergy is a phenomenon that transcends the insect world and is more than just a metaphor when applied to the human world. We present from our own research our general theory and abstract model of semantics of stigma in stigmergy. We hope our model will clarify the nuances of the phenomenon into a useful road-map, and standardise vocabulary that we witness becoming confused and divergent. Furthermore, this paper documents the analysis on which we base our next paper: Special Theory of Stigmergy: A Design Pattern for Web 2.0 Collaboration.

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An Artificial Neural Network (ANN) is a computational modeling tool which has found extensive acceptance in many disciplines for modeling complex real world problems. An ANN can model problems through learning by example, rather than by fully understanding the detailed characteristics and physics of the system. In the present study, the accuracy and predictive power of an ANN was evaluated in predicting kinetic viscosity of biodiesels over a wide range of temperatures typically encountered in diesel engine operation. In this model, temperature and chemical composition of biodiesel were used as input variables. In order to obtain the necessary data for model development, the chemical composition and temperature dependent fuel properties of ten different types of biodiesels were measured experimentally using laboratory standard testing equipments following internationally recognized testing procedures. The Neural Networks Toolbox of MatLab R2012a software was used to train, validate and simulate the ANN model on a personal computer. The network architecture was optimised following a trial and error method to obtain the best prediction of the kinematic viscosity. The predictive performance of the model was determined by calculating the absolute fraction of variance (R2), root mean squared (RMS) and maximum average error percentage (MAEP) between predicted and experimental results. This study found that ANN is highly accurate in predicting the viscosity of biodiesel and demonstrates the ability of the ANN model to find a meaningful relationship between biodiesel chemical composition and fuel properties at different temperature levels. Therefore the model developed in this study can be a useful tool in accurately predict biodiesel fuel properties instead of undertaking costly and time consuming experimental tests.

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G protein-coupled receptors (GPCRs) are critical for cardiovascular physiology. Cardiac cells express >100 nonchemosensory GPCRs, indicating that important physiological and potential therapeutic targets remain to be discovered. Moreover, there is a growing appreciation that members of the large, distinct taste and odorant GPCR families have specific functions in tissues beyond the oronasal cavity, including in the brain, gastrointestinal tract and respiratory system. To date, these chemosensory GPCRs have not been systematically studied in the heart. We performed RT-qPCR taste receptor screens in rodent and human heart tissues that revealed discrete subsets of type 2 taste receptors (TAS2/Tas2) as well as Tas1r1 and Tas1r3 (comprising the umami receptor) are expressed. These taste GPCRs are present in cultured cardiac myocytes and fibroblasts, and are enriched in myocytes, which we corroborated using in situ hybridization. Tas1r1 gene-targeted mice (Tas1r1Cre/Rosa26tdRFP) strikingly recapitulated these data. In vivo taste receptor expression levels were developmentally regulated in the postnatal period. Intriguingly, several Tas2rs were upregulated in cultured rat myocytes and in mouse heart in vivo following starvation. The discovery of taste GPCRs in the heart opens an exciting new field of cardiac research. We predict that these taste receptors may function as nutrient sensors in the heart.

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A pilot experiment was performed using the WOMBAT powder diffraction instrument at ANSTO in which the first neutron diffraction peak (Q0) was measured for D2O flowing in a 2 mm internal diameter aluminium tube. Measurements of Q0 were made at -9, 4.3, 6.9, 12, 18.2 and 21.5 °C. The D2O was circulated using a siphon with water in the lower reservoir returned to the upper reservoir using a small pump. This enabled stable flow to be maintained for several hours. For example, if the pump flow increased slightly, the upper reservoir level rose, increasing the siphon flow until it matched the return flow. A neutron wavelength of 2.4 Å was used and data integrated over 60 minutes for each temperature. A jet of nitrogen from a liquid N2 Dewar was directed over the aluminium tube to vary water temperature. After collection of the data, the d spacing of the aluminium peaks was used to calculate the temperature of the aluminium within the neutron beam and therefore was considered to be an accurate measure of water temperature within the beam. Sigmaplot version 12.3 was used to fit a Weibull five parameter peak fit to the first neutron diffraction peak. The values of Q0 obtained in this experiment showed an increase with temperature consistent with data in the literature [1] but were consistently higher than published values for bulk D20. For example at 21.5 °C we obtained a value of 2.008 Å-1 for Q0 compared to a literature value of 1.988 Å-1 for bulk D2O at 20 °C, a difference of 1%. Further experiments are required to see if this difference is real or artifactual.

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X-ray diffraction structure functions for water flowing in a 1.5 mm diameter siphon in the temperature range 4 – 63 °C were obtained using a 20 keV beam at the Australian Synchrotron. These functions were compared with structure functions obtained at the Advanced Light Source for a 0.5 mm thick sample of water in the temperature range 1 – 77 °C irradiated with an 11 keV beam. The two sets of structure functions are similar, but there are subtle differences in the shape and relative position of the two functions suggesting a possible differences between the structure of bulk and siphon water. In addition, the first structural peak (Q0) for water in a siphon, showed evidence of a step-wise increase in Q0 with increasing temperature rather than a smoothly varying increase. More experiments are required to investigate this apparent difference.

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Spectrum sensing of multiple primary user channels is a crucial function in cognitive radio networks. In this paper we propose an optimal, sensing resource allocation algorithm for multi-channel cooperative spectrum sensing. The channel target is implemented as an objective and constraint to ensure a pre-determined number of empty channels are detected for secondary user network operations. Based on primary user traffic parameters, we calculate the minimum number of primary user channels that must be sensed to satisfy the channel target. We implement a hybrid sensing structure by grouping secondary user nodes into clusters and assign each cluster to sense a different primary user channels. We then solve the resource allocation problem to find the optimal sensing configuration and node allocation to minimise sensing duration. Simulation results show that the proposed algorithm requires the shortest sensing duration to achieve the channel target compared to existing studies that require long sensing and cannot guarantee the target.

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RC4-Based Hash Function is a new proposed hash function based on RC4 stream cipher for ultra low power devices. In this paper, we analyse the security of the function against collision attack. It is shown that the attacker can find collision and multi-collision messages with complexity only 6 compress function operations and negligible memory with time complexity 2 13. In addition, we show the hashing algorithm can be distinguishable from a truly random sequence with probability close to one.

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The cryptographic hash function literature has numerous hash function definitions and hash function requirements, and many of them disagree. This survey talks about the various definitions, and takes steps towards cleaning up the literature by explaining how the field has evolved and accurately depicting the research aims people have today.

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In the current market, extensive software development is taking place and the software industry is thriving. Major software giants have stated source code theft as a major threat to revenues. By inserting an identity-establishing watermark in the source code, a company can prove it's ownership over the source code. In this paper, we propose a watermarking scheme for C/C++ source codes by exploiting the language restrictions. If a function calls another function, the latter needs to be defined in the code before the former, unless one uses function pre-declarations. We embed the watermark in the code by imposing an ordering on the mutually independent functions by introducing bogus dependency. Removal of dependency by the attacker to erase the watermark requires extensive manual intervention thereby making the attack infeasible. The scheme is also secure against subtractive and additive attacks. Using our watermarking scheme, an n-bit watermark can be embedded in a program having n independent functions. The scheme is implemented on several sample codes and performance changes are analyzed.

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A key derivation function (KDF) is a function that transforms secret non-uniformly random source material together with some public strings into one or more cryptographic keys. These cryptographic keys are used with a cryptographic algorithm for protecting electronic data during both transmission over insecure channels and storage. In this thesis, we propose a new method for constructing a generic stream cipher based key derivation function. We show that our proposed key derivation function based on stream ciphers is secure if the under-lying stream cipher is secure. We simulate instances of this stream cipher based key derivation function using three eStream nalist: Trivium, Sosemanuk and Rabbit. The simulation results show these stream cipher based key derivation functions offer efficiency advantages over the more commonly used key derivation functions based on block ciphers and hash functions.

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This study explored how the social context influences the stress-buffering effects of social support on employee adjustment. It was anticipated that the positive relationship between support from colleagues and employee adjustment would be more marked for those strongly identifying with their work team. Furthermore, as part of a three-way interactive effect, it was predicted that high identification would increase the efficacy of coworker support as a buffer of two role stressors (role overload and role ambiguity). One hundred and 55 employees recruited from first-year psychology courses enrolled at two Australian universities were surveyed. Hierarchical multiple regression analyses revealed that the negative main effect of role ambiguity on job satisfaction was significant for those employees with low levels of team identification, whereas high team identifiers were buffered from the deleterious effect of role ambiguity on job satisfaction. There also was a significant interaction between coworker support and team identification. The positive effect of coworker support on job satisfaction was significant for high team identifiers, whereas coworker support was not a source of satisfaction for those employees with low levels of team identification. A three-way interaction emerged among the focal variables in the prediction of psychological well-being, suggesting that the combined benefits of coworker support and team identification under conditions of high demand may be limited and are more likely to be observed when demands are low.

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OBJECTIVE To investigate the impact of new-onset diabetic ketoacidosis (DKA) during child- hood on brain morphology and function. RESEARCH DESIGN AND METHODS Patients aged 6–18 years with and without DKA at diagnosis were studied at four time points: <48 h, 5 days, 28 days, and 6 months postdiagnosis. Patients under- went magnetic resonance imaging (MRI) and spectroscopy with cognitive assess- ment at each time point. Relationships between clinical characteristics at presentation and MRI and neurologic outcomes were examined using multiple linear regression, repeated-measures, and ANCOVA analyses. RESULTS Thirty-six DKA and 59 non-DKA patients were recruited between 2004 and 2009. With DKA, cerebral white matter showed the greatest alterations with increased total white matter volume and higher mean diffusivity in the frontal, temporal, and parietal white matter. Total white matter volume decreased over the first 6 months. For gray matter in DKA patients, total volume was lower at baseline and increased over 6 months. Lower levels of N-acetylaspartate were noted at base- line in the frontal gray matter and basal ganglia. Mental state scores were lower at baseline and at 5 days. Of note, although changes in total and regional brain volumes over the first 5 days resolved, they were associated with poorer delayed memory recall and poorer sustained and divided attention at 6 months. Age at time of presentation and pH level were predictors of neuroimaging and functional outcomes. CONCLUSIONS DKA at type 1 diabetes diagnosis results in morphologic and functional brain changes. These changes are associated with adverse neurocognitive outcomes in the medium term.

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Inflammation of the spinal cord after traumatic spinal cord injury leads to destruction of healthy tissue. This “secondary degeneration” is more damaging than the initial physical damage and is the major contributor to permanent loss of functions. In our previous study we showed that combined delivery of two growth factors, vascular endothelial growth factor (VEGF) and platelet-derived growth factor (PDGF), significantly reduced secondary degeneration after hemi-section injury of the spinal cord in the rat. Growth factor treatment reduced the size of the lesion cavity at 30d compared to control animals and further reduced the cavity at 90d in treated animals while in control animals the lesion cavity continued to increase in size. Growth factor treatment also reduced astrogliosis and reduced macroglia/macrophage activation around the injury site. Treatment with individual growth factors alone had similar effects to control treatments. The present study investigated whether growth factor treatment would improve locomotor behaviour after spinal contusion injury, a more relevant preclinical model of spinal cord injury. The growth factors were delivered for the first 7d to the injury site via osmotic minipump. Locomotor behaviour was monitored at 1-28d after injury using the BBB score and at 30d using automated gait analysis. Treated animals had BBB scores of 18; Control animals scored 10. Treated animals had significantly reduced lesion cavities and reduced macroglia/macrophage activation around the injury site. We conclude that growth factor treatment preserved spinal cord tissues after contusion injury, thereby allowing functional recovery. This treatment has the potential to significantly reduce the severity of human spinal cord injuries.

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Background There are few theoretically derived questionnaires of physical activity determinants among youth, and the existing questionnaires have not been subjected to tests of factorial validity and invariance, The present study employed confirmatory factor analysis (CFA) to test the factorial validity and invariance of questionnaires designed to be unidimensional measures of attitudes, subjective norms, perceived behavioral control, and self-efficacy about physical activity. Methods Adolescent girls in eighth grade from two cohorts (N = 955 and 1,797) completed the questionnaires at baseline; participants from cohort 1 (N = 845) also completed the questionnaires in ninth grade (i.e., 1-year follow-up). Factorial validity and invariance were tested using CFA with full-information maximum likelihood estimation in AMOS 4.0, Initially, baseline data from cohort 1 were employed to test the fit and, when necessary, to modify the unidimensional models. The models were cross-validated using a multigroup analysis of factorial invariance on baseline data from cohorts 1 and 2, The models then were subjected to a longitudinal analysis of factorial invariance using baseline and follow-up data from cohort i, Results The CFAs supported the fit of unidimensional models to the four questionnaires, and the models were cross-validated, as indicated by evidence of multigroup factorial invariance, The models also possessed evidence of longitudinal factorial invariance. Conclusions Evidence was provided for the factorial validity and the invariance of the questionnaires designed to be unidimensional measures of attitudes, subjective norms, perceived behavioral control, and self-efficacy about physical activity among adolescent girls, (C) 2000 American Health Foundation and academic Press.