Patient-Specific Semi-Supervised Learning for Postoperative Brain Tumor Segmentation

In contrast to preoperative brain tumor segmentation, the problem of postoperative brain tumor segmentation has been rarely approached so far. We present a fully-automatic segmentation method using multimodal magnetic resonance image data and patient-specific semi-supervised learning. The idea behind our semi-supervised approach is to effectively fuse information from both pre- and postoperative image data of the same patient to improve segmentation of the postoperative image. We pose image segmentation as a classification problem and solve it by adopting a semi-supervised decision forest. The method is evaluated on a cohort of 10 high-grade glioma patients, with segmentation performance and computation time comparable or superior to a state-of-the-art brain tumor segmentation method. Moreover, our results confirm that the inclusion of preoperative MR images lead to a better performance regarding postoperative brain tumor segmentation.

Interactive Diffraction from Biological Nanostructures

We describe a technique for interactive rendering of diffraction effects produced by biological nanostructures, such as snake skin surface gratings. Our approach uses imagery from atomic force microscopy that accurately captures the geometry of the nanostructures responsible for structural colouration, that is, colouration due to wave interference, in a variety of animals. We develop a rendering technique that constructs bidirectional reflection distribution functions (BRDFs) directly from the measured data and leverages pre-computation to achieve interactive performance. We demonstrate results of our approach using various shapes of the surface grating nanostructures. Finally, we evaluate the accuracy of our pre-computation-based technique and compare to a reference BRDF construction technique.

Observation and applications of single-electron charge signals in the XENON100 experiment

The XENON100 dark matter experiment uses liquid xenon in a time projection chamber (TPC) to measure xenon nuclear recoils resulting from the scattering of dark matter weakly interacting massive particles (WIMPs). In this paper, we report the observation of single-electron charge signals which are not related to WIMP interactions. These signals, which show the excellent sensitivity of the detector to small charge signals, are explained as being due to the photoionization of impurities in the liquid xenon and of the metal components inside the TPC. They are used as a unique calibration source to characterize the detector. We explain how we can infer crucial parameters for the XENON100 experiment: the secondary-scintillation gain, the extraction yield from the liquid to the gas phase and the electron drift velocity.

Recent Advances in Adaptive Sampling and Reconstruction for Monte Carlo Rendering

Monte Carlo integration is firmly established as the basis for most practical realistic image synthesis algorithms because of its flexibility and generality. However, the visual quality of rendered images often suffers from estimator variance, which appears as visually distracting noise. Adaptive sampling and reconstruction algorithms reduce variance by controlling the sampling density and aggregating samples in a reconstruction step, possibly over large image regions. In this paper we survey recent advances in this area. We distinguish between “a priori” methods that analyze the light transport equations and derive sampling rates and reconstruction filters from this analysis, and “a posteriori” methods that apply statistical techniques to sets of samples to drive the adaptive sampling and reconstruction process. They typically estimate the errors of several reconstruction filters, and select the best filter locally to minimize error. We discuss advantages and disadvantages of recent state-of-the-art techniques, and provide visual and quantitative comparisons. Some of these techniques are proving useful in real-world applications, and we aim to provide an overview for practitioners and researchers to assess these approaches. In addition, we discuss directions for potential further improvements.

A STUDY ON THE EPIDEMIOLOGY OF POSITIVE PHYSICAL HEALTH (NHANES I)

HANES 1 detailed sample data were used to operationalize a definition of health in the absence of disease and to describe and compare the characteristics of the normal (healthy) group versus an abnormal (unhealthy) group.^ Parallel screening gave a 3.8 percent prevalence proportion of physical health, with a female:male ratio of 2:1 and younger ages in the healthy group. Statistically significant Mantel-Haenszel gender-age-adjusted odds ratios (MHOR) were estimated among abnormal non-migrants (1.53), skilled workers/unemployed (1.76), annual family incomes of less than $10,000 (1.56), having ever smoked (1.58), and started smoking before 18 years of age (1.58). Significant MHOR were also found for abnormals for health promoting measures: non-iodized salt use (1.94), needed dental care (1.91); and for fair to poor perceived health (4.28), perceiving health problems (2.52), and low energy level (1.68). Significant protective effects for much to moderate recreational exercise (MHOR 0.42) and very active to moderate non-recreational activity (MHOR 0.49) were also obtained. Covariance analysis additive models detected statistically significant higher mean values for abnormals than normals for serum magnesium, hemoglobin, hematocrit, urinary creatinine, and systolic and diastolic blood pressures, and lower values for abnormals than normals for serum iron. No difference was detected for serum cholesterol. Significant non-additive joint effects were found for body mass index.^ The results suggest positive physical health can be measured with cross-sectional survey data. Gender differentials, and associations between ecologic, socioeconomic, hazardous risk factors, health promoting activities and physical health are in general agreement with published findings on studies of morbidity. Longitudinal prospective studies are suggested to establish the direction of the associations and to enhance present knowledge of health and its promoting factors. ^

Dinoflagellate cyst assemblages, oxygen isotope records of foraminifera, and alkenone and Mg/Ca-based SST estimates for the time interval 3.40-3.18 Ma (Late Pliocene) from five North Atlantic and Caribbean sediment cores

The early Late Pliocene (3.6 to ~3.0 million years ago) is the last extended interval in Earth's history when atmospheric CO2 concentrations were comparable to today's and global climate was warmer. Yet a severe global glaciation during marine isotope stage (MIS) M2 interrupted this phase of global warmth ~3.30 million years ago, and is seen as a premature attempt of the climate system to establish an ice-age world. Our geochemical and palynological records from five marine sediment cores along a Caribbean to eastern North Atlantic transect show that increased Pacific-to-Atlantic flow via the Central American Seaway weakened the North Atlantic Current (NAC) and attendant northward heat transport prior to MIS M2. The consequent cooling of the northern high latitude oceans permitted expansion of the Greenland ice sheet during MIS M2, despite near-modern atmospheric CO2 concentrations. Before and after MIS M2, heat transport via the NAC was crucial in maintaining warm climates comparable to those predicted for the end of this century.

Part of the global DOC versus AOU (dissolved organic carbon/apparent oxygen utilization) data compilation, Azores-I

Temperate, transitional and subtropical waters of the remote Azores Front region east of Azores (24-40°N, 22-32°W) were sampled during three cruises conducted under increasing stratification conditions (April 1999, May 1997 and August 1998). Despite the temporal increase of surface temperature (by 5 °C) and stratification (by 2.1 1/min**2), as well as the thermocline shoaling (by ~15 m), dissolved organic carbon (DOC) and nitrogen (DON) in the surface layer were not significantly different for the early spring, late spring and summer periods, with average concentrations of 69±2 µM-C and 5.2±0.4 µM-N, respectively. The surface excess of semi-labile DOC, compared with the baseline DOC concentration in the deep ocean (47±2 µM-C), represents 33% of the bulk DOC concentration and as much as 85% of the TOC (=POC+DOC) excess. When compared with the winter baseline (56±2 µM-C), the seasonal surface DOC excess is 20% of the bulk DOC concentration and 87% of the seasonal TOC excess. These results confirm the major role played by DOC in the carbon cycle of surface waters of the Azores Front region. The total amount of bioreactive DOC transported from the temperate to the subtropical North Atlantic by the Ekman flux between March and December represents only ~15% of the average annual primary production, and ~15% and ~30% of the measured sinking POC flux+vertical DOC eddy diffusion during early spring and summer, respectively. Vertical eddy diffusion is 35% and 2% of the spring and summer sinking POC flux, respectively. On the other hand, DOC only contributes 13% to the local oxidation of organic matter in subsurface waters (between the pycnocline and 500 m) of the study region.

30 years of synoptic observations from Neumayer Station with links to datasets

The analysis of research data plays a key role in data-driven areas of science. Varieties of mixed research data sets exist and scientists aim to derive or validate hypotheses to find undiscovered knowledge. Many analysis techniques identify relations of an entire dataset only. This may level the characteristic behavior of different subgroups in the data. Like automatic subspace clustering, we aim at identifying interesting subgroups and attribute sets. We present a visual-interactive system that supports scientists to explore interesting relations between aggregated bins of multivariate attributes in mixed data sets. The abstraction of data to bins enables the application of statistical dependency tests as the measure of interestingness. An overview matrix view shows all attributes, ranked with respect to the interestingness of bins. Complementary, a node-link view reveals multivariate bin relations by positioning dependent bins close to each other. The system supports information drill-down based on both expert knowledge and algorithmic support. Finally, visual-interactive subset clustering assigns multivariate bin relations to groups. A list-based cluster result representation enables the scientist to communicate multivariate findings at a glance. We demonstrate the applicability of the system with two case studies from the earth observation domain and the prostate cancer research domain. In both cases, the system enabled us to identify the most interesting multivariate bin relations, to validate already published results, and, moreover, to discover unexpected relations.

Using Clouds to Scale Grid Resources: An Economic Model

Infrastructure as a Service clouds are a flexible and fast way to obtain (virtual) resources as demand varies. Grids, on the other hand, are middleware platforms able to combine resources from different administrative domains for task execution. Clouds can be used by grids as providers of devices such as virtual machines, so they only use the resources they need. But this requires grids to be able to decide when to allocate and release those resources. Here we introduce and analyze by simulations an economic mechanism (a) to set resource prices and (b) resolve when to scale resources depending on the users’ demand. This system has a strong emphasis on fairness, so no user hinders the execution of other users’ tasks by getting too many resources. Our simulator is based on the well-known GridSim software for grid simulation, which we expand to simulate infrastructure clouds. The results show how the proposed system can successfully adapt the amount of allocated resources to the demand, while at the same time ensuring that resources are fairly shared among users.