957 resultados para Beijing (China)--Maps


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A patient-specific surface model of the proximal femur plays an important role in planning and supporting various computer-assisted surgical procedures including total hip replacement, hip resurfacing, and osteotomy of the proximal femur. The common approach to derive 3D models of the proximal femur is to use imaging techniques such as computed tomography (CT) or magnetic resonance imaging (MRI). However, the high logistic effort, the extra radiation (CT-imaging), and the large quantity of data to be acquired and processed make them less functional. In this paper, we present an integrated approach using a multi-level point distribution model (ML-PDM) to reconstruct a patient-specific model of the proximal femur from intra-operatively available sparse data. Results of experiments performed on dry cadaveric bones using dozens of 3D points are presented, as well as experiments using a limited number of 2D X-ray images, which demonstrate promising accuracy of the present approach.

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A few signaling pathways are driving the growth of hepatocellular carcinoma. Each of these pathways possesses negative regulators. These enzymes, which normally suppress unchecked cell proliferation, are circumvented in the oncogenic process, either the over-activity of oncogenes is sufficient to annihilate the activity of tumor suppressors or tumor suppressors have been rendered ineffective. The loss of several key tumor suppressors has been described in hepatocellular carcinoma. Here, we systematically review the evidence implicating tumor suppressors in the development of hepatocellular carcinoma.

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AIM: To test whether humoral immune reaction against mycobacteria may play a role in anti-Saccharomyces cerevisiae antibodies (ASCA) generation in Crohn's disease (CD) and/or whether it correlates with clinical subtypes. METHODS: The dominant ASCA epitope was detected by Galanthus nivalis lectin (GNL)-binding assay. ASCA and IgG against mycobacterial lysates (M avium, M smegmatis, M chelonae, M bovis BCG, M avium ssp. paratuberculosis (MAP)] or purified lipoarabinomannans (LAM) were detected by ELISA. ASCA and anti-mycobacterial antibodies were affinity purified to assess cross-reactivities. Anti-mycobacterial IgG were induced by BCG-infection of mice. RESULTS: GNL bound to different extents to mycobacterial lysates, abundantly to purified mannose-capped (Man) LAM from M tuberculosis, but not to uncapped LAM from M smegmatis. Fifteen to 45% of CD patients but only 0%-6% of controls were seropositive against different mycobacterial antigens. Anti-mycobacterial IgG correlated with ASCA (r = 0.37-0.64; P = 0.003-P < 0.001). ASCA-positivity and deficiency for mannan-binding lectin synergistically associated with anti-mycobacterial IgG. In some patients, anti-mycobacterial antibodies represent cross-reactive ASCA. Vice-versa, the predominant fraction of ASCA did not cross-react with mycobacteria. Finally, fistulizing disease associated with antibodies against M avium, M smegmatis and MAP (P = 0.024, 0.004 and 0.045, respectively). CONCLUSION: Similar to ASCA, seroreactivity against mycobacteria may define CD patients with complicated disease and a predisposition for immune responses against ubiquitous antigens. While in some patients anti-mycobacterial antibodies strongly cross-react with yeast mannan; these cross-reactive antibodies only represent a minor fraction of total ASCA. Thus, mycobacterial infection unlikely plays a role in ASCA induction.

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According to Bandura (1997) efficacy beliefs are a primary determinant of motivation. Still, very little is known about the processes through which people integrate situational factors to form efficacy beliefs (Myers & Feltz, 2007). The aim of this study was to gain insight into the cognitive construction of subjective group-efficacy beliefs. Only with a sound understanding of those processes is there a sufficient base to derive psychological interventions aimed at group-efficacy beliefs. According to cognitive theories (e.g., Miller, Galanter, & Pribram, 1973) individual group-efficacy beliefs can be seen as the result of a comparison between the demands of a group task and the resources of the performing group. At the center of this comparison are internally represented structures of the group task and plans to perform it. The empirical plausibility of this notion was tested using functional measurement theory (Anderson, 1981). Twenty-three students (M = 23.30 years; SD = 3.39; 35 % females) of the University of Bern repeatedly judged the efficacy of groups in different group tasks. The groups consisted of the subjects and another one to two fictive group members. The latter were manipulated by their value (low, medium, high) in task-relevant abilities. Data obtained from multiple full factorial designs were structured with individuals as second level units and analyzed using mixed linear models. The task-relevant abilities of group members, specified as fixed factors, all had highly significant effects on subjects’ group-efficacy judgments. The effect sizes of the ability factors showed to be dependent on the respective abilities’ importance in a given task. In additive tasks (Steiner, 1972) group resources were integrated in a linear fashion whereas significant interaction between factors was obtained in interdependent tasks. The results also showed that people take into account other group members’ efficacy beliefs when forming their own group-efficacy beliefs. The results support the notion that personal group-efficacy beliefs are obtained by comparing the demands of a task with the performing groups’ resources. Psychological factors such as other team members’ efficacy beliefs are thereby being considered task relevant resources and affect subjective group-efficacy beliefs. This latter finding underlines the adequacy of multidimensional measures. While the validity of collective efficacy measures is usually estimated by how well they predict performances, the results of this study allow for a somewhat internal validity criterion. It is concluded that Information Integration Theory holds potential to further help understand people’s cognitive functioning in sport relevant situations.

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In just a few years cloud computing has become a very popular paradigm and a business success story, with storage being one of the key features. To achieve high data availability, cloud storage services rely on replication. In this context, one major challenge is data consistency. In contrast to traditional approaches that are mostly based on strong consistency, many cloud storage services opt for weaker consistency models in order to achieve better availability and performance. This comes at the cost of a high probability of stale data being read, as the replicas involved in the reads may not always have the most recent write. In this paper, we propose a novel approach, named Harmony, which adaptively tunes the consistency level at run-time according to the application requirements. The key idea behind Harmony is an intelligent estimation model of stale reads, allowing to elastically scale up or down the number of replicas involved in read operations to maintain a low (possibly zero) tolerable fraction of stale reads. As a result, Harmony can meet the desired consistency of the applications while achieving good performance. We have implemented Harmony and performed extensive evaluations with the Cassandra cloud storage on Grid?5000 testbed and on Amazon EC2. The results show that Harmony can achieve good performance without exceeding the tolerated number of stale reads. For instance, in contrast to the static eventual consistency used in Cassandra, Harmony reduces the stale data being read by almost 80% while adding only minimal latency. Meanwhile, it improves the throughput of the system by 45% while maintaining the desired consistency requirements of the applications when compared to the strong consistency model in Cassandra.

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In this paper we propose a new benchmark to drive making decisions in maintenance of computer systems. This benchmark is made from load average sample data. The main goal is to improve reliability and performance of a set of devices or components. In particular, the stability of the system is measured in terms of variability of the load. A forecast of the behavior of this stability is also proposal as part of the reporting benchmark. At the final stage, a more stable system is obtained and its global reliability and performance can be then evaluated by means of appropriate specifications.

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Se describe el comportamiento de los rellenos de pasta de las cámaras primarias de la mina de Aguas Teñidas y se calcula la resistencia que deben tener dichos rellenos para que no se desmoronen las paredes de los mismos que quedan expuestas al extraer las cámaras secundarias.Abstract:This article presents the study carried out at an underground mine to understand the stress distribution in the paste fills and to calculate the stability of the paste walls. The mine is operated using sublevel stopes. Three-dimensional numerical models designed with the FLAC 3D software are used to study the distribution of the vertical stresses in the paste. The numerical models have demonstrated that an arc-like effect is produced in the paste fills of the primary stopes. This effect relieves the vertical stresses and increases the stability of the exposed paste wall fill. Based on the results of the numerical models, in the 30m high secondary stopes, the arc effect starts to be evident only in paste walls with a width/height ratio lower than 0.8. 3-D calculations show that the use of Mitchell, R. J. et al. (1982) formula may be risky when estimating the fill stability in secondary stopes.

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There is remarkable growing concern about the quality control at the time, which has led to the search for methods capable of addressing effectively the reliability analysis as part of the Statistic. Managers, researchers and Engineers must understand that 'statistical thinking' is not just a set of statistical tools. They should start considering 'statistical thinking' from a 'system', which means, developing systems that meet specific statistical tools and other methodologies for an activity. The aim of this article is to encourage them (engineers, researchers and managers) to develop a new way of thinking.

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Connectivity analysis on diffusion MRI data of the whole-brain suffers from distortions caused by the standard echo-planar imaging acquisition strategies. These images show characteristic geometrical deformations and signal destruction that are an important drawback limiting the success of tractography algorithms. Several retrospective correction techniques are readily available. In this work, we use a digital phantom designed for the evaluation of connectivity pipelines. We subject the phantom to a “theoretically correct” and plausible deformation that resembles the artifact under investigation. We correct data back, with three standard methodologies (namely fieldmap-based, reversed encoding-based, and registration- based). Finally, we rank the methods based on their geometrical accuracy, the dropout compensation, and their impact on the resulting connectivity matrices.