Evaluation of sampling method effects in 3D non-rigid registration

Since the beginning of 3D computer vision problems, the use of techniques to reduce the data to make it treatable preserving the important aspects of the scene has been necessary. Currently, with the new low-cost RGB-D sensors, which provide a stream of color and 3D data of approximately 30 frames per second, this is getting more relevance. Many applications make use of these sensors and need a preprocessing to downsample the data in order to either reduce the processing time or improve the data (e.g., reducing noise or enhancing the important features). In this paper, we present a comparison of different downsampling techniques which are based on different principles. Concretely, five different downsampling methods are included: a bilinear-based method, a normal-based, a color-based, a combination of the normal and color-based samplings, and a growing neural gas (GNG)-based approach. For the comparison, two different models have been used acquired with the Blensor software. Moreover, to evaluate the effect of the downsampling in a real application, a 3D non-rigid registration is performed with the data sampled. From the experimentation we can conclude that depending on the purpose of the application some kernels of the sampling methods can improve drastically the results. Bilinear- and GNG-based methods provide homogeneous point clouds, but color-based and normal-based provide datasets with higher density of points in areas with specific features. In the non-rigid application, if a color-based sampled point cloud is used, it is possible to properly register two datasets for cases where intensity data are relevant in the model and outperform the results if only a homogeneous sampling is used.

Sampling intervals, fluxes, percentages of total flux, organic carbon and lithogen for sediment trap CB9

We combined the analysis of sediment trap data and satellite-derived sea surface chlorophyll to quantify the amount of organic carbon export to the deep sea in the upwelling induced high production area off northwest Africa. In contrast to the generally global or basin-wide adoption of export models, we used a regionally fitted empirical model. Furthermore, the application of our model was restricted to a dynamically defined region of high chlorophyll concentration in order to restrict the model application to an environment of more homogeneous export processes. We developed a correlation-based approximation to estimate the surface source area for a sediment trap deployed from 11 June 1998 to 7 November 1999 at 21.25°N latitude and 20.64°W longitude off Cape Blanc. We also developed a regression model of chlorophyll and export of organic carbon to the 1000 m depth level. Carbon export was calculated for an area of high chlorophyll concentration (>1 mg/m**3) adjacent to the coast on a daily basis. The resulting zone of high chlorophyll concentration was 20,000-800,000 km**2 large and yielded a yearly export of 1.123 to 2.620 Tg organic carbon. The average organic carbon export within the area of high chlorophyll concentration was 20.6 mg/m**2d comparable to 13.3 mg/m**2d as found in the sediment trap results if normalized to the 1000 m level. We found strong interannual variability in export. The period autumn 1998 to summer 1999 was exceeding the mean of the other three comparable periods by a factor of 2.25. We believe that this approach of using more regionally fitted models can be successfully transferred even to different oceanographic regions by selecting appropriate definition criteria like chlorophyll concentration for the definition of an area to which it is applicable.

Sampling times for monitoring tacrolimus in stable adult liver transplant recipients

The aim of this study was to determine the most informative sampling time(s) providing a precise prediction of tacrolimus area under the concentration-time curve (AUC). Fifty-four concentration-time profiles of tacrolimus from 31 adult liver transplant recipients were analyzed. Each profile contained 5 tacrolimus whole-blood concentrations (predose and 1, 2, 4, and 6 or 8 hours postdose), measured using liquid chromatography-tandem mass spectrometry. The concentration at 6 hours was interpolated for each profile, and 54 values of AUC(0-6) were calculated using the trapezoidal rule. The best sampling times were then determined using limited sampling strategies and sensitivity analysis. Linear mixed-effects modeling was performed to estimate regression coefficients of equations incorporating each concentration-time point (C0, C1, C2, C4, interpolated C5, and interpolated C6) as a predictor of AUC(0-6). Predictive performance was evaluated by assessment of the mean error (ME) and root mean square error (RMSE). Limited sampling strategy (LSS) equations with C2, C4, and C5 provided similar results for prediction of AUC(0-6) (R-2 = 0.869, 0.844, and 0.832, respectively). These 3 time points were superior to C0 in the prediction of AUC. The ME was similar for all time points; the RMSE was smallest for C2, C4, and C5. The highest sensitivity index was determined to be 4.9 hours postdose at steady state, suggesting that this time point provides the most information about the AUC(0-12). The results from limited sampling strategies and sensitivity analysis supported the use of a single blood sample at 5 hours postdose as a predictor of both AUC(0-6) and AUC(0-12). A jackknife procedure was used to evaluate the predictive performance of the model, and this demonstrated that collecting a sample at 5 hours after dosing could be considered as the optimal sampling time for predicting AUC(0-6).

Properties of seawater from a Sea-Bird TSG temperature and conductivity sensor mounted on the continuous surface water sampling system during campaign TARA_20091004Z of the Tara Oceans expedition 2009-2013

The Tara Oceans Expedition (2009-2013) sampled the world oceans on board a 36 m long schooner, collecting environmental data and organisms from viruses to planktonic metazoans for later analyses using modern sequencing and state-of-the-art imaging technologies. Tara Oceans Data are particularly suited to study the genetic, morphological and functional diversity of plankton. The present data publication contains measurements from the Continuous Surface Sampling System [CSSS] made during one campaign of the Tara Oceans Expedition. Water was pumped at the front of the vessel from ~2m depth, then de-bubbled and circulated to a Sea-Bird TSG temperature and conductivity sensor. System maintenance (instrument cleaning, flushing) was done approximately once a week and in port between successive legs. All data were stamped with a GPS.

Properties of seawater from a Sea-Bird TSG temperature and conductivity sensor mounted on the continuous surface water sampling system during campaign TARA_20091011Z of the Tara Oceans expedition 2009-2013

The Tara Oceans Expedition (2009-2013) sampled the world oceans on board a 36 m long schooner, collecting environmental data and organisms from viruses to planktonic metazoans for later analyses using modern sequencing and state-of-the-art imaging technologies. Tara Oceans Data are particularly suited to study the genetic, morphological and functional diversity of plankton. The present data publication contains measurements from the Continuous Surface Sampling System [CSSS] made during one campaign of the Tara Oceans Expedition. Water was pumped at the front of the vessel from ~2m depth, then de-bubbled and circulated to a Sea-Bird TSG temperature and conductivity sensor. System maintenance (instrument cleaning, flushing) was done approximately once a week and in port between successive legs. All data were stamped with a GPS.

Properties of seawater from a Sea-Bird TSG temperature and conductivity sensor mounted on the continuous surface water sampling system during campaign TARA_20091025Z of the Tara Oceans expedition 2009-2013

The Tara Oceans Expedition (2009-2013) sampled the world oceans on board a 36 m long schooner, collecting environmental data and organisms from viruses to planktonic metazoans for later analyses using modern sequencing and state-of-the-art imaging technologies. Tara Oceans Data are particularly suited to study the genetic, morphological and functional diversity of plankton. The present data publication contains measurements from the Continuous Surface Sampling System [CSSS] made during one campaign of the Tara Oceans Expedition. Water was pumped at the front of the vessel from ~2m depth, then de-bubbled and circulated to a Sea-Bird TSG temperature and conductivity sensor. System maintenance (instrument cleaning, flushing) was done approximately once a week and in port between successive legs. All data were stamped with a GPS.