995 resultados para Free tree
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Background Overweight and obesity has become a serious public health problem in many parts of the world. Studies suggest that making small changes in daily activity levels such as “breaking-up” sedentary time (i.e., standing) may help mitigate the health risks of sedentary behavior. The aim of the present study was to examine time spent in standing (determined by count threshold), lying, and sitting postures (determined by inclinometer function) via the ActiGraph GT3X among sedentary adults with differing weight status based on body mass index (BMI) categories. Methods Participants included 22 sedentary adults (14 men, 8 women; mean age 26.5 ± 4.1 years). All subjects completed the self-report International Physical Activity Questionnaire to determine time spent sitting over the previous 7 days. Participants were included if they spent seven or more hours sitting per day. Postures were determined with the ActiGraph GT3X inclinometer function. Participants were instructed to wear the accelerometer for 7 consecutive days (24 h a day). BMI was categorized as: 18.5 to <25 kg/m2 as normal, 25 to <30 kg/m2 as overweight, and ≥30 kg/m2 as obese. Results Participants in the normal weight (n = 10) and overweight (n = 6) groups spent significantly more time standing (after adjustment for moderate-to-vigorous intensity physical activity and wear-time) (6.7 h and 7.3 h respectively) and less time sitting (7.1 h and 6.9 h respectively) than those in obese (n = 6) categories (5.5 h and 8.0 h respectively) after adjustment for wear-time (p < 0.001). There were no significant differences in standing and sitting time between normal weight and overweight groups (p = 0.051 and p = 0.670 respectively). Differences were not significant among groups for lying time (p = 0.55). Conclusion This study described postural allocations standing, lying, and sitting among normal weight, overweight, and obese sedentary adults. The results provide additional evidence for the use of increasing standing time in obesity prevention strategies.
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In this paper we analyse the effects of highway traffic flow parameters like vehicle arrival rate and density on the performance of Amplify and Forward (AF) cooperative vehicular networks along a multi-lane highway under free flow state. We derive analytical expressions for connectivity performance and verify them with Monte-Carlo simulations. When AF cooperative relaying is employed together with Maximum Ratio Combining (MRC) at the receivers the average route error rate shows 10-20 fold improvement compared to direct communication. A 4-8 fold increase in maximum number of traversable hops can also be observed at different vehicle densities when AF cooperative communication is used to strengthen communication routes. However the theorical upper bound of maximum number of hops promises higher performance gains.
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Invasion waves of cells play an important role in development, disease and repair. Standard discrete models of such processes typically involve simulating cell motility, cell proliferation and cell-to-cell crowding effects in a lattice-based framework. The continuum-limit description is often given by a reaction–diffusion equation that is related to the Fisher–Kolmogorov equation. One of the limitations of a standard lattice-based approach is that real cells move and proliferate in continuous space and are not restricted to a predefined lattice structure. We present a lattice-free model of cell motility and proliferation, with cell-to-cell crowding effects, and we use the model to replicate invasion wave-type behaviour. The continuum-limit description of the discrete model is a reaction–diffusion equation with a proliferation term that is different from lattice-based models. Comparing lattice based and lattice-free simulations indicates that both models lead to invasion fronts that are similar at the leading edge, where the cell density is low. Conversely, the two models make different predictions in the high density region of the domain, well behind the leading edge. We analyse the continuum-limit description of the lattice based and lattice-free models to show that both give rise to invasion wave type solutions that move with the same speed but have very different shapes. We explore the significance of these differences by calibrating the parameters in the standard Fisher–Kolmogorov equation using data from the lattice-free model. We conclude that estimating parameters using this kind of standard procedure can produce misleading results.
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The Hepatitis C virus (HCV) affects some 150 million people worldwide. However, unlike hepatitis A and B there is no vaccination for HCV and approximately 75% of people exposed to HCV develop chronic hepatitis. In Australia, around 226,700 people live with chronic HCV infection costing the government approximately $252 million per year. Historically, the standard approved/licenced treatment for HCV is pegylated interferon with ribavirin. There are major drawbacks with interferon-based therapy including side effects, long duration of therapy, limited access and affordability. Our previous survey of an at-risk population reported HCV treatment coverage of only 5%. Since April 2013, a new class of interferon-free treatments for chronic HCV is subsidised under the Pharmaceutical Benefits Scheme: boceprevir and telaprevir - estimated to cost the Australian Government in excess of $220 million over five years. Other biologic interferon-free therapeutic agents are scheduled to enter the Australian market. Use of small molecule generic pharmaceuticals has been advocated as a means of public cost savings. However, with the new biologic agents, generics (biosimilars) may not be feasible or straightforward, due to long patent life; marketing exclusivity; and regulatory complexity for these newer products.
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Background Epidemiological studies have shown a reduced incidence of cardiovascular disease in the Mediterranean population attributed to the consumption of dietary olive oil rich in antioxidants. This has lead to increased interest in the antioxidant properties of other phenolic compounds of olive tree products. It has been suggested that olive leaf extract may also have health benefits due to its antioxidant and anti-inflammatory activities. Antioxidants can prevent the effects of oxidative metabolism by scavenging free radicals and decreasing the hyperactivity of platelets associated with the development of occlusive thrombosis. No studies to date have investigated the effects of olive leaf extract on platelet function to our knowledge. Improved understanding of the antioxidant properties of olive leaf extract and its effect on platelet function could lead to improved cardiovascular health. Objective The current study used an olive leaf extract prepared from the Olea europaea L. tree. The aim was to determine if polyphenols in olive leaf extract would reduce platelet activity and, to establish an optimal dose in vitro that would reduce platelet aggregation and ATP release. Design Eleven subjects with normal platelet counts (150–400 x 109/L) were recruited for the current in vitro study. Olive leaf extract was added to citrated whole blood to obtain five concentrations ranging from 5.4 ug/mL to 54.0 ug/mL for a dose response curve. Baseline samples, without olive leaf extract were used as a negative control for each subject. After 2 hours incubation with olive leaf extract samples were analyzed for platelet aggregation and ATP release from platelets stimulated by the addition of collagen. Results Whole blood analysis (n=11) showed a clear dose-dependant reduction in platelet aggregation with the increasing olive leaf extract concentrations (p<0.0001). There was also a similar decrease in ATP release from collagen stimulated platelets (p=0.02). Conclusion In the current study the olive leaf extract obtained from Olea europaea L. inhibited platelet aggregation and ATP release from collagen stimulated platelets in vitro. This study suggests olive leaf extract may prevent occlusive thrombosis by reducing platelet hyperactivity.
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In this paper, a framework for isolating unprecedented faults for an EGR valve system is presented. Using normal behavior data generated by a high fidelity engine simulation, the recently introduced Growing Structure Multiple Model System (GSMMS) is used to construct models of normal behavior for an EGR valve system and its various subsystems. Using the GSMMS models as a foundation, anomalous behavior of the entire system is then detected as statistically significant departures of the most recent modeling residuals from the modeling residuals during normal behavior. By reconnecting anomaly detectors to the constituent subsystems, the anomaly can be isolated without the need for prior training using faulty data. Furthermore, faults that were previously encountered (and modeled) are recognized using the same approach as the anomaly detectors.
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In this paper, a recently introduced model-based method for precedent-free fault detection and isolation (FDI) is modified to deal with multiple input, multiple output (MIMO) systems and is applied to an automotive engine with exhaust gas recirculation (EGR) system. Using normal behavior data generated by a high fidelity engine simulation, the growing structure multiple model system (GSMMS) approach is used to construct dynamic models of normal behavior for the EGR system and its constituent subsystems. Using the GSMMS models as a foundation, anomalous behavior is detected whenever statistically significant departures of the most recent modeling residuals away from the modeling residuals displayed during normal behavior are observed. By reconnecting the anomaly detectors (ADs) to the constituent subsystems, EGR valve, cooler, and valve controller faults are isolated without the need for prior training using data corresponding to particular faulty system behaviors.
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Conservation of free-ranging cheetah (Acinonyx jubatus) populations is multi faceted and needs to be addressed from an ecological, biological and management perspective. There is a wealth of published research, each focusing on a particular aspect of cheetah conservation. Identifying the most important factors, making sense of various (and sometimes contrasting) findings, and taking decisions when little or no empirical data is available, are everyday challenges facing conservationists. Bayesian networks (BN) provide a statistical modeling framework that enables analysis and integration of information addressing different aspects of conservation. There has been an increased interest in the use of BNs to model conservation issues, however the development of more sophisticated BNs, utilizing object-oriented (OO) features, is still at the frontier of ecological research. We describe an integrated, parallel modeling process followed during a BN modeling workshop held in Namibia to combine expert knowledge and data about free-ranging cheetahs. The aim of the workshop was to obtain a more comprehensive view of the current viability of the free-ranging cheetah population in Namibia, and to predict the effect different scenarios may have on the future viability of this free-ranging cheetah population. Furthermore, a complementary aim was to identify influential parameters of the model to more effectively target those parameters having the greatest impact on population viability. The BN was developed by aggregating diverse perspectives from local and independent scientists, agents from the national ministry, conservation agency members and local fieldworkers. This integrated BN approach facilitates OO modeling in a multi-expert context which lends itself to a series of integrated, yet independent, subnetworks describing different scientific and management components. We created three subnetworks in parallel: a biological, ecological and human factors network, which were then combined to create a complete representation of free-ranging cheetah population viability. Such OOBNs have widespread relevance to the effective and targeted conservation management of vulnerable and endangered species.
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Cell-to-cell adhesion is an important aspect of malignant spreading that is often observed in images from the experimental cell biology literature. Since cell-to-cell adhesion plays an important role in controlling the movement of individual malignant cells, it is likely that cell-to-cell adhesion also influences the spatial spreading of populations of such cells. Therefore, it is important for us to develop biologically realistic simulation tools that can mimic the key features of such collective spreading processes to improve our understanding of how cell-to-cell adhesion influences the spreading of cell populations. Previous models of collective cell spreading with adhesion have used lattice-based random walk frameworks which may lead to unrealistic results, since the agents in the random walk simulations always move across an artificial underlying lattice structure. This is particularly problematic in high-density regions where it is clear that agents in the random walk align along the underlying lattice, whereas no such regular alignment is ever observed experimentally. To address these limitations, we present a lattice-free model of collective cell migration that explicitly incorporates crowding and adhesion. We derive a partial differential equation description of the discrete process and show that averaged simulation results compare very well with numerical solutions of the partial differential equation.
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Due to the demand for better and deeper analysis in sports, organizations (both professional teams and broadcasters) are looking to use spatiotemporal data in the form of player tracking information to obtain an advantage over their competitors. However, due to the large volume of data, its unstructured nature, and lack of associated team activity labels (e.g. strategic/tactical), effective and efficient strategies to deal with such data have yet to be deployed. A bottleneck restricting such solutions is the lack of a suitable representation (i.e. ordering of players) which is immune to the potentially infinite number of possible permutations of player orderings, in addition to the high dimensionality of temporal signal (e.g. a game of soccer last for 90 mins). Leveraging a recent method which utilizes a "role-representation", as well as a feature reduction strategy that uses a spatiotemporal bilinear basis model to form a compact spatiotemporal representation. Using this representation, we find the most likely formation patterns of a team associated with match events across nearly 14 hours of continuous player and ball tracking data in soccer. Additionally, we show that we can accurately segment a match into distinct game phases and detect highlights. (i.e. shots, corners, free-kicks, etc) completely automatically using a decision-tree formulation.
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Whole-image descriptors such as GIST have been used successfully for persistent place recognition when combined with temporal filtering or sequential filtering techniques. However, whole-image descriptor localization systems often apply a heuristic rather than a probabilistic approach to place recognition, requiring substantial environmental-specific tuning prior to deployment. In this paper we present a novel online solution that uses statistical approaches to calculate place recognition likelihoods for whole-image descriptors, without requiring either environmental tuning or pre-training. Using a real world benchmark dataset, we show that this method creates distributions appropriate to a specific environment in an online manner. Our method performs comparably to FAB-MAP in raw place recognition performance, and integrates into a state of the art probabilistic mapping system to provide superior performance to whole-image methods that are not based on true probability distributions. The method provides a principled means for combining the powerful change-invariant properties of whole-image descriptors with probabilistic back-end mapping systems without the need for prior training or system tuning.
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Mesoporous titania microspheres composed of nanosheets with exposed active facets were prepared by hydrothermal synthesis in the presence of hexafluorosilicic acid. They exhibited superior catalytic activity in the solvent-free synthesis of azoxybenzene by oxidation of aniline and could be used for 7 cycles with slight loss of activity.
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Many alternative therapies are used as first aid treatment for burns, despite limited evidence supporting their use. In this study, Aloe vera, saliva and a tea tree oil impregnated dressing (Burnaid) were applied as first aid to a porcine deep dermal contact burn, compared to a control of nothing. After burn creation, the treatments were applied for 20 min and the wounds observed at weekly dressing changes for 6 weeks. Results showed that the alternative treatments did significantly decrease subdermal temperature within the skin during the treatment period. However, they did not decrease the microflora or improve re-epithelialisation, scar strength, scar depth or cosmetic appearance of the scar and cannot be recommended for the first aid treatment of partial thickness burns.
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In October 2012, Simone presented her book Architecture for a Free Subjectivity to the University of Michigan, Taubman College of Architecture and Urban Planning. This book explores the architectural significance of Deleuze’s philosophy of subjectivization, and Guattari’s overlooked dialogue on architecture and subjectivity. In doing so, it proposes that subjectivity is no longer the exclusive provenance of human beings, but extends to the architectural, the cinematic, the erotic, and the political. It defines a new position within the literature on Deleuze and architecture, while highlighting the neglected issue of subjectivity in contemporary discussion.
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Genomic sequences are fundamentally text documents, admitting various representations according to need and tokenization. Gene expression depends crucially on binding of enzymes to the DNA sequence at small, poorly conserved binding sites, limiting the utility of standard pattern search. However, one may exploit the regular syntactic structure of the enzyme's component proteins and the corresponding binding sites, framing the problem as one of detecting grammatically correct genomic phrases. In this paper we propose new kernels based on weighted tree structures, traversing the paths within them to capture the features which underpin the task. Experimentally, we and that these kernels provide performance comparable with state of the art approaches for this problem, while offering significant computational advantages over earlier methods. The methods proposed may be applied to a broad range of sequence or tree-structured data in molecular biology and other domains.