491 resultados para "This-n-that"


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Articular cartilage is the load-bearing tissue that consists of proteoglycan macromolecules entrapped between collagen fibrils in a three-dimensional architecture. To date, the drudgery of searching for mathematical models to represent the biomechanics of such a system continues without providing a fitting description of its functional response to load at micro-scale level. We believe that the major complication arose when cartilage was first envisaged as a multiphasic model with distinguishable components and that quantifying those and searching for the laws that govern their interaction is inadequate. To the thesis of this paper, cartilage as a bulk is as much continuum as is the response of its components to the external stimuli. For this reason, we framed the fundamental question as to what would be the mechano-structural functionality of such a system in the total absence of one of its key constituents-proteoglycans. To answer this, hydrated normal and proteoglycan depleted samples were tested under confined compression while finite element models were reproduced, for the first time, based on the structural microarchitecture of the cross-sectional profile of the matrices. These micro-porous in silico models served as virtual transducers to produce an internal noninvasive probing mechanism beyond experimental capabilities to render the matrices micromechanics and several others properties like permeability, orientation etc. The results demonstrated that load transfer was closely related to the microarchitecture of the hyperelastic models that represent solid skeleton stress and fluid response based on the state of the collagen network with and without the swollen proteoglycans. In other words, the stress gradient during deformation was a function of the structural pattern of the network and acted in concert with the position-dependent compositional state of the matrix. This reveals that the interaction between indistinguishable components in real cartilage is superimposed by its microarchitectural state which directly influences macromechanical behavior.

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Bone metastases are severely debilitating and have a significant impact on the quality of life of women with metastatic breast cancer. Treatment options are limited and in order to develop more targeted therapies, improved understanding of the complex mechanisms that lead to bone lesion development are warranted. Interestingly, whilst prostate-derived bone metastases are characterised by mixed or osteoblastic lesions, breast-derived bone metastases are characterised by osteolytic lesions, suggesting unique regulatory patterns. This study aimed to measure the changes in bone formation and bone resorption activity at two time-points (18 and 36 days) during development of the bone lesion following intratibial injection of MDA-MB-231 human breast cancer cells into the left tibiae of Severely Combined Immuno-Deficient (SCID) mice. The contralateral tibia was used as a control. Tibiae were extracted and processed for undecalcified histomorphometric analysis. We provide evidence that the early bone loss observed following exposure to MDA-MB-231 cells was due to a significant reduction in mineral apposition rate, rather than increased levels of bone resorption. This suggests that osteoblast activity was impaired in the presence of breast cancer cells, contrary to previous reports of osteoclast-dependent bone loss. Furthermore mRNA expression of Dickkopf Homolog 1 (DKK-1) and Noggin were confirmed in the MDA-MB-231 cell line, both of which antagonise osteoblast regulatory pathways. The observed bone loss following injection of cancer cells was due to an overall thinning of the trabecular bone struts rather than perforation of the bone tissue matrix (as measured by trabecular width and trabecular separation, respectively), suggesting an opportunity to reverse the cancer-induced bone changes. These novel insights into the mechanisms through which osteolytic bone lesions develop may be important in the development of new treatment strategies for metastatic breast cancer patients.

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Vibrational spectroscopy enables subtle details of the molecular structure of minyulite KAl2(OH,F)(PO4)2⋅4(H2O). Single crystals of a pure phase from a Brazilian pegmatite were used. Minyulite belongs to the orthorhombic crystal system. This indicates that it has three axes of unequal length, yet all are perpendicular to each other. The infrared and Raman spectroscopy were applied to compare the structure of minyulite with wardite. The reason for the comparison is that both are Al containing phosphate minerals. The Raman spectrum of minyulite shows an intense band at 1012 cm−1 assigned to the ν1PO43- symmetric stretching vibrations. A series of low intensity Raman bands at 1047, 1077, 1091 and 1105 cm−1 are assigned to the ν3PO43- antisymmetric stretching modes. The Raman bands at 1136, 1155, 1176 and 1190 cm−1 are assigned to AlOH deformation modes. The infrared band at 1014 cm−1 is ascribed to the PO43- ν1 symmetric stretching vibrational mode. The infrared bands at 1049, 1071, 1091 and 1123 cm−1 are attributed to the PO43- ν3 antisymmetric stretching vibrations. The infrared bands at 1123, 1146 and 1157 cm−1 are attributed to AlOH deformation modes. Raman bands at 575, 592, 606 and 628 cm−1 are assigned to the ν4 out of plane bending modes of the PO43- unit. In the 2600–3800 cm−1 spectral range, Raman bands for minyulite are found at 3661, 3669 and 3692 cm−1 are assigned to AlOH/AlF stretching vibrations. Broad infrared bands are also found at 2904, 3105, 3307, 3453 and 3523 cm−1. Raman bands at 3225, 3324 cm−1 are assigned to water stretching vibrations. A comparison is made with the vibrational spectra of wardite. Raman spectroscopy complimented with infrared spectroscopy has enabled aspects of the structure of minyulite to be ascertained and compared with that of other phosphate minerals.

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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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Over the past decade, vision-based tracking systems have been successfully deployed in professional sports such as tennis and cricket for enhanced broadcast visualizations as well as aiding umpiring decisions. Despite the high-level of accuracy of the tracking systems and the sheer volume of spatiotemporal data they generate, the use of this high quality data for quantitative player performance and prediction has been lacking. In this paper, we present a method which predicts the location of a future shot based on the spatiotemporal parameters of the incoming shots (i.e. shot speed, location, angle and feet location) from such a vision system. Having the ability to accurately predict future short-term events has enormous implications in the area of automatic sports broadcasting in addition to coaching and commentary domains. Using Hawk-Eye data from the 2012 Australian Open Men's draw, we utilize a Dynamic Bayesian Network to model player behaviors and use an online model adaptation method to match the player's behavior to enhance shot predictability. To show the utility of our approach, we analyze the shot predictability of the top 3 players seeds in the tournament (Djokovic, Federer and Nadal) as they played the most amounts of games.

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Efficient and effective feature detection and representation is an important consideration when processing videos, and a large number of applications such as motion analysis, 3D scene understanding, tracking etc. depend on this. Amongst several feature description methods, local features are becoming increasingly popular for representing videos because of their simplicity and efficiency. While they achieve state-of-the-art performance with low computational complexity, their performance is still too limited for real world applications. Furthermore, rapid increases in the uptake of mobile devices has increased the demand for algorithms that can run with reduced memory and computational requirements. In this paper we propose a semi binary based feature detectordescriptor based on the BRISK detector, which can detect and represent videos with significantly reduced computational requirements, while achieving comparable performance to the state of the art spatio-temporal feature descriptors. First, the BRISK feature detector is applied on a frame by frame basis to detect interest points, then the detected key points are compared against consecutive frames for significant motion. Key points with significant motion are encoded with the BRISK descriptor in the spatial domain and Motion Boundary Histogram in the temporal domain. This descriptor is not only lightweight but also has lower memory requirements because of the binary nature of the BRISK descriptor, allowing the possibility of applications using hand held devices.We evaluate the combination of detectordescriptor performance in the context of action classification with a standard, popular bag-of-features with SVM framework. Experiments are carried out on two popular datasets with varying complexity and we demonstrate comparable performance with other descriptors with reduced computational complexity.

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At the highest level of competitive sport, nearly all performances of athletes (both training and competitive) are chronicled using video. Video is then often viewed by expert coaches/analysts who then manually label important performance indicators to gauge performance. Stroke-rate and pacing are important performance measures in swimming, and these are previously digitised manually by a human. This is problematic as annotating large volumes of video can be costly, and time-consuming. Further, since it is difficult to accurately estimate the position of the swimmer at each frame, measures such as stroke rate are generally aggregated over an entire swimming lap. Vision-based techniques which can automatically, objectively and reliably track the swimmer and their location can potentially solve these issues and allow for large-scale analysis of a swimmer across many videos. However, the aquatic environment is challenging due to fluctuations in scene from splashes, reflections and because swimmers are frequently submerged at different points in a race. In this paper, we temporally segment races into distinct and sequential states, and propose a multimodal approach which employs individual detectors tuned to each race state. Our approach allows the swimmer to be located and tracked smoothly in each frame despite a diverse range of constraints. We test our approach on a video dataset compiled at the 2012 Australian Short Course Swimming Championships.

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A new community and communication type of social networks - online dating - are gaining momentum. With many people joining in the dating network, users become overwhelmed by choices for an ideal partner. A solution to this problem is providing users with partners recommendation based on their interests and activities. Traditional recommendation methods ignore the users’ needs and provide recommendations equally to all users. In this paper, we propose a recommendation approach that employs different recommendation strategies to different groups of members. A segmentation method using the Gaussian Mixture Model (GMM) is proposed to customize users’ needs. Then a targeted recommendation strategy is applied to each identified segment. Empirical results show that the proposed approach outperforms several existing recommendation methods.

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The rapid development of the World Wide Web has created massive information leading to the information overload problem. Under this circumstance, personalization techniques have been brought out to help users in finding content which meet their personalized interests or needs out of massively increasing information. User profiling techniques have performed the core role in this research. Traditionally, most user profiling techniques create user representations in a static way. However, changes of user interests may occur with time in real world applications. In this research we develop algorithms for mining user interests by integrating time decay mechanisms into topic-based user interest profiling. Time forgetting functions will be integrated into the calculation of topic interest measurements on in-depth level. The experimental study shows that, considering temporal effects of user interests by integrating time forgetting mechanisms shows better performance of recommendation.

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Most recommender systems attempt to use collaborative filtering, content-based filtering or hybrid approach to recommend items to new users. Collaborative filtering recommends items to new users based on their similar neighbours, and content-based filtering approach tries to recommend items that are similar to new users' profiles. The fundamental issues include how to profile new users, and how to deal with the over-specialization in content-based recommender systems. Indeed, the terms used to describe items can be formed as a concept hierarchy. Therefore, we aim to describe user profiles or information needs by using concepts vectors. This paper presents a new method to acquire user information needs, which allows new users to describe their preferences on a concept hierarchy rather than rating items. It also develops a new ranking function to recommend items to new users based on their information needs. The proposed approach is evaluated on Amazon book datasets. The experimental results demonstrate that the proposed approach can largely improve the effectiveness of recommender systems.

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Different reputation models are used in the web in order to generate reputation values for products using uses' review data. Most of the current reputation models use review ratings and neglect users' textual reviews, because it is more difficult to process. However, we argue that the overall reputation score for an item does not reflect the actual reputation for all of its features. And that's why the use of users' textual reviews is necessary. In our work we introduce a new reputation model that defines a new aggregation method for users' extracted opinions about products' features from users' text. Our model uses features ontology in order to define general features and sub-features of a product. It also reflects the frequencies of positive and negative opinions. We provide a case study to show how our results compare with other reputation models.

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OBJECTIVE The study investigates the knowledge, intentions, and driving behavior of persons prescribed medications that display a warning about driving. It also examines their confidence that they can self-assess possible impairment, as is required by the Australian labeling system. METHOD We surveyed 358 outpatients in an Australian public hospital pharmacy, representing a well-advised group taking a range of medications including those displaying a warning label about driving. A brief telephone follow-up survey was conducted with a subgroup of the participants. RESULTS The sample had a median age of 53.2 years and was 53 percent male. Nearly three quarters (73.2%) had taken a potentially impairing class of medication and more than half (56.1%) had taken more than one such medication in the past 12 months. Knowledge of the potentially impairing effects of medication was relatively high for most items; however, participants underestimated the possibility of increased impairment from exceeding the prescribed dose and at commencing treatment. Participants' responses to the safety implications of taking drugs with the highest level of warning varied. Around two thirds (62.8%) indicated that they would consult a health practitioner for advice and around half would modify their driving in some way. However, one fifth (20.9%) would drive when the traffic was thought to be less heavy and over a third (37.7%) would modify their medication regime so that they could drive. The findings from the follow-up survey of a subsample taking target drugs at the time of the first interview were also of concern. Only just over half (51%) recalled seeing the warning label on their medications and, of this group, three quarters (78%) reported following the warning label advice. These findings indicated that there remains a large proportion of people who either did not notice or did not consider the warning when deciding whether to drive. There was a very high level of confidence in this group that they could determine whether they were personally affected by the medication, which may be a problem from a safety perspective. CONCLUSION This study involved persons who should have had a very high level of knowledge and awareness of medication warning labeling. Even in this group there was a lack of informed response to potential impairment. A review of the Australian warning system and wider dissemination of information on medication treatment effects would be useful. Clarifying the importance of potential risk in the general community context is recommended for consideration and further research.

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OBJECTIVE The aim of the study is to examine the spatiotemporal pattern of Japanese Encephalitis (JE) in mainland China during 2002-2010. Specific objectives of the study were to quantify the temporal variation in incidence of JE cases, to determine if clustering of JE cases exists, to detect high risk spatiotemporal clusters of JE cases and to provide evidence-based preventive suggestions to relevant stakeholders. METHODS Monthly JE cases at the county level in mainland China during 2002-2010 were obtained from the China Information System for Diseases Control and Prevention (CISDCP). For the purpose of the analysis, JE case counts for nine years were aggregated into four temporal periods (2002; 2003-2005; 2006; and 2007-2010). Local Indicators of Spatial Association and spatial scan statistics were performed to detect and evaluate local high risk space-time clusters. RESULTS JE incidence showed a decreasing trend from 2002 to 2005 but peaked in 2006, then fluctuated over the study period. Spatial cluster analysis detected high value clusters, mainly located in Southwestern China. Similarly, we identified a primary spatiotemporal cluster of JE in Southwestern China between July and August, with the geographical range of JE transmission increasing over the past years. CONCLUSION JE in China is geographically clustered and its spatial extent dynamically changed during the last nine years in mainland China. This indicates that risk factors for JE infection are likely to be spatially heterogeneous. The results may assist national and local health authorities in the development/refinement of a better preventive strategy and increase the effectiveness of public health interventions against JE transmission.

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In the past, people with comorbidity have often received inadequate care. The ethical principle of equal access to quality services has important implications for agencies, when combined with knowledge about comorbidity and its management, and about diffusion of innovations across organizations. Comorbidity is common, and often has profound impacts on individuals and families. Tobacco smoking in particular is endemic and affects morbidity, mortality, and functioning. This implies that screening for co-occurring problems should be routine, and that a boutique comorbidity service is impractical. Large numbers mean that universal screening and intervention must be capable of large-scale implementation. Since multiple, closely linked problems are often present, treatments should address these multiple issues, and closely interrelated problems will require well-integrated treatment. Involvement of a single health agency is typically needed. Numbers and severity of problems can blind practitioners and patients to strengths and unaffected areas; these should be assessed and fostered. Better policies and practices for co-occurring disorders will require organizational change. Co-occurring disorders must become core business for organizations and practitioners, so that effective comorbidity practice is rewarded, required skills are present or taught, cues to use the practices are provided, and a culture supporting their application is established.

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Advocacy is integral to the work of many TESOL specialists. For several decades, ACTA and the state TESOL associations, along with other professional associations, and individual teachers, researchers and administrators have all engaged with conversations about EAL/D education in public forums. These advocates have drawn attention to implications of policy developments for EAL/D students; they have proffered alternative forms of curriculum, pedagogy and assessment to better account for the particularity of EAL/D learning pathways; they have argued the necessity of specialist EAL/D teaching. In response to the Australian Language and Literacy Policy of the early 1990s, for example, there was “a frenzy of writing responses… a conference… and attempts to publicise what was going on through the press and television” (Moore, 1995, p. 6). It is in this spirit that this double issue of TESOL in Context has been compiled...