T2 Relaxation time study of the pig renal allografts

A previous study showed the possibility of the dependence of theT ₂ maps measured by Siemens Magnetom MR imager 1.5 T on the viability of the cadaveric kidney. For theT ₂ relaxometry study the pig model was used. Ten pig renal allografts were examined during 68 h after removal. Significant differences were found in the change of the initialT ₂ ⁱⁿⁱ values in the histogram within the first 20 h [₁=T ₂ ⁱⁿⁱ (2)–T ₂ ⁱⁿⁱ (20)] or 8 h [₂=T ₂ ⁱⁿⁱ (2)–T ₂ ⁿⁱ (8)] after removal between the groups of the damaged kidneys (₁=20.2±6.8 ms, ₂=19.4±3.0) and the kidneys without apparent damage (₁=9.4±8.5 ms, ₂=5.4±5.8).     

CancerSpace: A Simulation-Based Game for Improving Cancer-Screening Rates

Serious games are seeing use in a variety of fields, from the military to corporate management, and are finally being employed in healthcare. One of the biggest, most challenging areas is modeling simulations for medical training, particularly for managing chronic illness and providing system-level population-based care. CancerSpace (Cancer: Simulating Practice and Collaborative Education) is an interactive, Web-based learning application in a game format. CancerSpace, developed by the National Cancer Institute and Oak Ridge Institute for Science and Education, aims to facilitate cancer screening and consequently increase cancer-screening rates in federally qualified health centers. Our goal is to promote strategies and research-tested interventions that physicians and other healthcare workers can implement to overcome barriers and make cancer screening more efficient and cost-effective. This article reports on the development efforts required to create an e-learning tool designed for practitioners in community health centers. It concludes that the computer graphics and animation community has an important role in helping healthcare researchers design higher-fidelity educational games and simulations for improving the delivery of chronic care to the millions requiring it.     

Composite kernel learning

The Support Vector Machine is an acknowledged powerful tool for building classifiers, but it lacks flexibility, in the sense that the kernel is chosen prior to learning. Multiple Kernel Learning enables to learn the kernel, from an ensemble of basis kernels, whose combination is optimized in the learning process. Here, we propose Composite Kernel Learning to address the situation where distinct components give rise to a group structure among kernels. Our formulation of the learning problem encompasses several setups, putting more or less emphasis on the group structure. We characterize the convexity of the learning problem, and provide a general wrapper algorithm for computing solutions. Finally, we illustrate the behavior of our method on multi-channel data where groups correspond to channels.     

Canopy height model characteristics derived from airbone laser scanning and its effectiveness in discriminating various tropical moist forest types

Mapping tropical forests to a sufficient level of spatial resolution and structural detail is a prerequisite for their rational management, which however remains a largely unmet challenge. We explore the degree to which a forest canopy height model (CHM) derived from airborne laser scanning (ALS) can discriminate between five forest types of similar height but varying structure or composition. We systematically compare various textural features (Haralick, Fourier transform-based, and wavelet-based features) and various classification procedures (linear discriminant analysis (LDA), random forest(RF), and support vector machine (SVM)) applied to two sizes of sampling units (64 m × 64 m and 32 m × 32 m). Simple height distribution statistics achieve at best 70% classification accuracy in our sample set comprising 120 sampling units of 64 m × 64 m. Using w avelet-based features, this accuracy increases to 79% but drops by 10% with smaller sampling units (32 m × 32 m). Classifier performance depends on the texture feature set used, but SVM and RF tend to perform better than LDA. High discrimination rates between forests types of similar height indicate that the ALS-derived CHM provides information suitable for mapping of tropical forest types. Wavelet-based texture features coupled with a SVM classifier was found to be the most promising combination of methods. Ancillary data derived from laser scans and notably topography could be used jointly for an improved segmentation scheme.     

Monitoring elevation variations in leaf phenology of deciduous broadleaf forests from SPOT/VEGETATION time-series

In mountain forest ecosystems where elevation gradients are prominent, temperature gradient-based phenological variability can be high. However, there are few studies that assess the capability of remote sensing observations to monitor ecosystem phenology along elevation gradients, despite their relevance under climate change. We investigated the potential of medium resolution remotely sensed data to monitor the elevation variations in the seasonal dynamics of a temperate deciduous broadleaf forested ecosystem. Further, we explored the impact of elevation on the onset of spring leafing. This study was based on the analysis of multi-annual time-series of VEGETATION data acquired over the French Pyrenees Mountain Region (FPMR), in conjunction with simultaneous ground-based observations of leaf phenology made for two dominant tree species in the region (oak and beech). The seasonal variations in the perpendicular vegetation index (PVI) were analyzed during a five-year period (2002 to 2006). The five years of data were averaged into a one sole year in order to fill the numerous large spatio-temporal gaps due to cloud and snow presence - frequent in mountains - without altering the temporal resolution. Since a VEGETATION pixel (1 km²) includes several types of land cover, the broadleaf forest-specific seasonal dynamics of PVI was reconstructed pixel-by-pixel using a temporal unmixing method based on a non-parametric statistical approach. The spatial pattern of the seasonal response of PVI was clearly consistent with the relief. Nevertheless the elevational or geographic range of tree species, which differ in their phenology sensitivity to temperature, also has a significant impact on this pattern. The reduction in the growing season length with elevation was clearly observable from the delay in the increase of PVI in spring and from the advance of its decrease in the fall. The elevation variations in leaf flushing timing were estimated from the temporal change in PVI in spring over the study area. They were found to be consistent with those measured in situ (R2 > 0.95). It was deduced that, over FPMR, the mean delay of leaf flushing timing for every 100 m increase in elevation was estimated be approximately 2.3 days. The expected estimation error of satellite-based leaf unfolding date for a given elevation was approximately 2 days. This accuracy can be considered as satisfactory since it would allow us to detect changes in leafing timing of deciduous broadleaf forests with a magnitude equivalent to that due to an elevation variation of 100 m (2.3 days on average), or in other words, to that caused by a variation in the mean annual air temperature of 0.5 °C. Although averaging the VEGETATION data over five years led to a loss of interannual information, it was found to be a robust approach to characterise the elevation variations in spring leafing and its long-term trends.     

Retrieving wheat Green Area Index during the growing season from optical time series measurements based on neural network radiative transfer inversion

Technological advances have opened the possibility to have Earth observation imagery combining both high spatial resolution with high revisit frequency. The monitoring of land surface processes will greatly benefit from this type of data if biophysical variables such as Leaf Area Index (LAI) can be retrieved from it. However, the large amounts of data that will become available call for methods satisfying constraint of (i) portability across different landscapes, (ii) independence from field measurements and (iii) computational efficiency. Currently, the most suitable technique to approach this objective is neural network inversion of radiative transfer. This paper quantifies the performance of such approach for a specific crop (winter wheat) all along the growing season. By re-defining the retrieved biophysical variable as Green Area Index (GAI) and correcting the typical saturation effect that affects the estimation of higher values, this study shows that physically-based retrieval can yield similar results to those obtained by empirical methods such as stepwise linear regression (with an relative RMSE of 24.7% and 22.8% respectively). The research further shows how a trained neural network for a specific sensor can produce a GAI map from imagery acquired from different viewing conditions without a significant loss in accuracy.     

Channel substrate prediction from GIS for habitat estimation in Lake Erie tributaries

The conservation of ecologically and economically important species, as well as the management of invasive species, benefits from the ability to make broad-scale predictions of habitat. In this paper, we revised an existing substrate size model based upon stream power to include variables that are readily-quantifiable in a Geographic Information System (GIS) (i.e. stream slope and drainage area). We found no significant difference between slopes measured in the field using surveying techniques and slopes measured in a GIS using a 10 m digital elevation model and high resolution stream dataset. GIS-derived drainage areas and those measured with hand-delineations were also statistically similar. The revised model can be applied using both GIS and field-derived variables to predict median particle sizes from stream power in northeastern Ohio streams draining to Lake Erie. Integration of such models into a GIS could result in regional estimates of the amount and location of preferred fish habitat, which has important applications in fisheries management. In particular, we provide examples of how the predictive substrate model could improve assessment methodologies for invasive sea lamprey, thereby improving eradication measures, and how we may better understand geographic linkages between walleye spawning and nursery habitats.     

Understanding civic engagement behaviour on Facebook from a social capital theory perspective

This paper applies the social capital theory to construct a model for investigating the factors that influence online civic engagement behaviour on Facebook. While there is promising evidence that people are making concerted efforts to adopt Facebook to address social issues, research on their civic behaviour from a social capital viewpoint in the social media context remains limited. This study introduces new insights into how Facebook is shaping the landscape of civic engagement by examining three dimensions of social capital – social interaction ties (structural), trust (relational), and shared languages and vision (cognitive). The study contends that these dimensions will influence individuals’ online civic engagement behaviour on Facebook. We also argue that social interaction ties can engender trust, and shared languages and vision among its members, and that shared languages and vision can increase trust among Facebook members. Empirical data collected from 1233 Facebook users provide support for the proposed model. The results help in identifying the motivation underlying the online civic engagement behaviour of individuals in a public virtual community. The implications for theory and practice and future research directions are discussed.     

Replaceable Substructures for Efficient Part‐Based Modeling

A popular mode of shape synthesis involves mixing and matching parts from different objects to form a coherent whole. The key challenge is to efficiently synthesize shape variations that are plausible, both locally and globally. A major obstacle is to assemble the objects with local consistency, i.e., all the connections between parts are valid with no dangling open connections. The combinatorial complexity of this problem limits existing methods in geometric and/or topological variations of the synthesized models. In this work, we introduce replaceable substructures as arrangements of parts that can be interchanged while ensuring boundary consistency. The consistency information is extracted from part labels and connections in the original source models. We present a polynomial time algorithm that discovers such substructures by working on a dual of the original shape graph that encodes inter‐part connectivity. We demonstrate the algorithm on a range of test examples producing plausible shape variations, both from a geometric and from a topological viewpoint.     

Indirect adaptive fuzzy control for a class of nonaffine nonlinear systems with unknown control directions

This article presents an indirect adaptive fuzzy control scheme for a class of nonlinear uncertain nonaffine systems with unknown control directions. The nonlinear nonaffine system is first transformed into an affine form by using a Taylor series expansion, and then fuzzy systems are employed to approximate the equivalent affine system’s unknown nonlinearities. By modifying the estimated input control gain and using a novel smooth robust control term, a stable well-defined adaptive controller is proposed. Simulation results are provided to illustrate the efficiency of the proposed scheme.