Optimizing Models of the North Atlantic Spring Bloom Using Physical, Chemical and Bio-Optical Observations from a Lagrangian Float

The North Atlantic spring bloom is one of the main events that lead to carbon export to the deep ocean and drive oceanic uptake of CO(2) from the atmosphere. Here we use a suite of physical, bio-optical and chemical measurements made during the 2008 spring bloom to optimize and compare three different models of biological carbon export. The observations are from a Lagrangian float that operated south of Iceland from early April to late June, and were calibrated with ship-based measurements. The simplest model is representative of typical NPZD models used for the North Atlantic, while the most complex model explicitly includes diatoms and the formation of fast sinking diatom aggregates and cysts under silicate limitation. We carried out a variational optimization and error analysis for the biological parameters of all three models, and compared their ability to replicate the observations. The observations were sufficient to constrain most phytoplankton-related model parameters to accuracies of better than 15 %. However, the lack of zooplankton observations leads to large uncertainties in model parameters for grazing. The simulated vertical carbon flux at 100 m depth is similar between models and agrees well with available observations, but at 600 m the simulated flux is larger by a factor of 2.5 to 4.5 for the model with diatom aggregation. While none of the models can be formally rejected based on their misfit with the available observations, the model that includes export by diatom aggregation has a statistically significant better fit to the observations and more accurately represents the mechanisms and timing of carbon export based on observations not included in the optimization. Thus models that accurately simulate the upper 100 m do not necessarily accurately simulate export to deeper depths.

Food Volume Computation for Self Dietary Assessment Applications

There is great demand for easily-accessible, user-friendly dietary self-management applications. Yet accurate, fully-automatic estimation of nutritional intake using computer vision methods remains an open research problem. One key element of this problem is the volume estimation, which can be computed from 3D models obtained using multi-view geometry. The paper presents a computational system for volume estimation based on the processing of two meal images. A 3D model of the served meal is reconstructed using the acquired images and the volume is computed from the shape. The algorithm was tested on food models (dummy foods) with known volume and on real served food. Volume accuracy was in the order of 90 %, while the total execution time was below 15 seconds per image pair. The proposed system combines simple and computational affordable methods for 3D reconstruction, remained stable throughout the experiments, operates in near real time, and places minimum constraints on users.

Sparse computation for large-scale binary classification

Well-known data mining algorithms rely on inputs in the form of pairwise similarities between objects. For large datasets it is computationally impossible to perform all pairwise comparisons. We therefore propose a novel approach that uses approximate Principal Component Analysis to efficiently identify groups of similar objects. The effectiveness of the approach is demonstrated in the context of binary classification using the supervised normalized cut as a classifier. For large datasets from the UCI repository, the approach significantly improves run times with minimal loss in accuracy.

SLR-GNSS analysis in the framework of the ITRF2013 computation

Pre-combined SLR-GNSS solutions are studied and the impact of different types of datum definition on the estimated parameters is assessed. It is found that the origin is realized best by using only the SLR core network for defining the geodetic datum and the inclusion of the GNSS core sites degrades the origin. The orientation, however, requires a dense and continuous network, thus, the inclusion of the GNSS core network is absolutely needed.

Evolving biomarkers improve prediction of long-term mortality in patients with stable coronary artery disease: the BIO-VILCAD score.

OBJECTIVE Algorithms to predict the future long-term risk of patients with stable coronary artery disease (CAD) are rare. The VIenna and Ludwigshafen CAD (VILCAD) risk score was one of the first scores specifically tailored for this clinically important patient population. The aim of this study was to refine risk prediction in stable CAD creating a new prediction model encompassing various pathophysiological pathways. Therefore, we assessed the predictive power of 135 novel biomarkers for long-term mortality in patients with stable CAD. DESIGN, SETTING AND SUBJECTS We included 1275 patients with stable CAD from the LUdwigshafen RIsk and Cardiovascular health study with a median follow-up of 9.8 years to investigate whether the predictive power of the VILCAD score could be improved by the addition of novel biomarkers. Additional biomarkers were selected in a bootstrapping procedure based on Cox regression to determine the most informative predictors of mortality. RESULTS The final multivariable model encompassed nine clinical and biochemical markers: age, sex, left ventricular ejection fraction (LVEF), heart rate, N-terminal pro-brain natriuretic peptide, cystatin C, renin, 25OH-vitamin D3 and haemoglobin A1c. The extended VILCAD biomarker score achieved a significantly improved C-statistic (0.78 vs. 0.73; P = 0.035) and net reclassification index (14.9%; P < 0.001) compared to the original VILCAD score. Omitting LVEF, which might not be readily measureable in clinical practice, slightly reduced the accuracy of the new BIO-VILCAD score but still significantly improved risk classification (net reclassification improvement 12.5%; P < 0.001). CONCLUSION The VILCAD biomarker score based on routine parameters complemented by novel biomarkers outperforms previous risk algorithms and allows more accurate classification of patients with stable CAD, enabling physicians to choose more personalized treatment regimens for their patients.

The struggle to diversify rural livelihoods: Bio-enterprise development initiatives and their impacts on agro-/pastoral communities in the drylands of Kenya

This article analyses the impacts of four different bio-enterprise initiatives on agro-pastoral livelihoods and on improved natural resources management (NRM) in the drylands of Kenya. In this way it contributes to an area of rural development that is gaining increasing interest, but still has little empirical evidence. Data were collected through interviews, focus group discussions, informal discussions and the study of reports. One of the key findings of this article is that diversification into enterprises requires cooperation among the stakeholders with their varying experiences in development, NRM and business development. In addition to initial investments, such enterprises need sustained financial, as well as other support like capacity development to survive the market introduction phase. For such enterprises to defend their market niches, the quantity and quality of the product are critical. In addition to support in human, financial, social, physical and natural capital, mentoring is another crucial factor for success.