Nonlinear bias correction for satellite data assimilation using Taylor series polynomials

Output from a high-resolution ensemble data assimilation system is used to assess the ability of an innovative nonlinear bias correction (BC) method that uses a Taylor series polynomial expansion of the observation-minus background departures to remove linear and nonlinear conditional biases from all-sky satellite infrared brightness temperatures. Univariate and multivariate experiments were performed in which the satellite zenith angle and variables sensitive to clouds and water vapor were used as the BC predictors. The results showed that even though the bias of the entire observation departure distribution is equal to zero regardless of the order of the Taylor series expansion, there are often large conditional biases that vary as a nonlinear function of the BC predictor. The linear 1st order term had the largest impact on the entire distribution as measured by reductions in variance; however, large conditional biases often remained in the distribution when plotted as a function of the predictor. These conditional biases were typically reduced to near zero when the nonlinear 2nd and 3rd order terms were used. The univariate results showed that variables sensitive to the cloud top height are effective BC predictors especially when higher order Taylor series terms are used. Comparison of the statistics for clear-sky and cloudy-sky observations revealed that nonlinear departures are more important for cloudy-sky observations as signified by the much larger impact of the 2nd and 3rd order terms on the conditional biases. Together, these results indicate that the nonlinear BC method is able to effectively remove the bias from all-sky infrared observation departures

Monte Carlo Procedure for Protein Design

A new method for sequence optimization in protein models is presented. The approach, which has inherited its basic philosophy from recent work by Deutsch and Kurosky [Phys. Rev. Lett. 76, 323 (1996)] by maximizing conditional probabilities rather than minimizing energy functions, is based upon a novel and very efficient multisequence Monte Carlo scheme. By construction, the method ensures that the designed sequences represent good folders thermodynamically. A bootstrap procedure for the sequence space search is devised making very large chains feasible. The algorithm is successfully explored on the two-dimensional HP model with chain lengths N=16, 18 and 32.Comment: 7 pages LaTeX, 4 Postscript figures; minor change

Low-dose intra-arterial contrast-enhanced MR aortography in patients based on a theoretically derived injection protocol

Multiple intra-arterial contrast agent injections are necessary during MR-guided endovascular interventions. In respect to the approved limits of maximum daily gadolinium dose, a low-dose injection protocol is mandatory. The objective of this study was to derive and apply a low-dose injection protocol for intra-arterial 3D contrast-enhanced MR aortography in patients. Injection rate (Qinj), concentration of injected gadolinium [Gd]inj and aortal blood flow rate (Qblood) were included for the theoretical evaluation of signal intensity (SI) of the arterial lumen. SI simulations were carried out at Qinj=2 versus 4ml/s in the [Gd]inj range between 0-500mM. Qinj and [Gd]inj with SI above the 75% threshold of the maximal SI were regarded as optimal injection parameters. [Gd]inj=50mM and Qinj=4ml/s were considered as optimal and were administered in five patients for 3D MR aortography. All images revealed clear delineation of the abdominal aorta and its major branches. Mean±SD of contrast-to-noise ratios of the abdominal aorta, common iliac and renal artery were 70.2±15.2, 58.6±12.3 and 67.4±12.3. Approximately seven intra-aortal injections would be permissible in patients during MR-guided interventions without exceeding the maximal dose of gadoliniu

Predicting the Next Best View for 3D Mesh Refinement

3D reconstruction is a core task in many applications such as robot navigation or sites inspections. Finding the best poses to capture part of the scene is one of the most challenging topic that goes under the name of Next Best View. Recently, many volumetric methods have been proposed; they choose the Next Best View by reasoning over a 3D voxelized space and by finding which pose minimizes the uncertainty decoded into the voxels. Such methods are effective, but they do not scale well since the underlaying representation requires a huge amount of memory. In this paper we propose a novel mesh-based approach which focuses on the worst reconstructed region of the environment mesh. We define a photo-consistent index to evaluate the 3D mesh accuracy, and an energy function over the worst regions of the mesh which takes into account the mutual parallax with respect to the previous cameras, the angle of incidence of the viewing ray to the surface and the visibility of the region. We test our approach over a well known dataset and achieve state-of-the-art results.Comment: 13 pages, 5 figures, to be published in IAS-1

Kernel reconstruction for delayed neural field equations

Understanding the neural field activity for realistic living systems is a challenging task in contemporary neuroscience. Neural fields have been studied and developed theoretically and numerically with considerable success over the past four decades. However, to make effective use of such models, we need to identify their constituents in practical systems. This includes the determination of model parameters and in particular the reconstruction of the underlying effective connectivity in biological tissues. In this work, we provide an integral equation approach to the reconstruction of the neural connectivity in the case where the neural activity is governed by a delay neural field equation. As preparation, we study the solution of the direct problem based on the Banach fixed point theorem. Then we reformulate the inverse problem into a family of integral equations of the first kind. This equation will be vector valued when several neural activity trajectories are taken as input for the inverse problem. We employ spectral regularization techniques for its stable solution. A sensitivity analysis of the regularized kernel reconstruction with respect to the input signal u is carried out, investigating the Frechet differentiability of the kernel with respect to the signal. Finally, we use numerical examples to show the feasibility of the approach for kernel reconstruction, including numerical sensitivity tests, which show that the integral equation approach is a very stable and promising approach for practical computational neuroscience

Identification of Amino Acid Sequences with Good Folding Properties in an Off-Lattice Model

Folding properties of a two-dimensional toy protein model containing only two amino-acid types, hydrophobic and hydrophilic, respectively, are analyzed. An efficient Monte Carlo procedure is employed to ensure that the ground states are found. The thermodynamic properties are found to be strongly sequence dependent in contrast to the kinetic ones. Hence, criteria for good folders are defined entirely in terms of thermodynamic fluctuations. With these criteria sequence patterns that fold well are isolated. For 300 chains with 20 randomly chosen binary residues approximately 10% meet these criteria. Also, an analysis is performed by means of statistical and artificial neural network methods from which it is concluded that the folding properties can be predicted to a certain degree given the binary numbers characterizing the sequences.Comment: 15 pages, 8 Postscript figures. Minor change

Observation error statistics for Doppler radar radial wind superobservations assimilated into the DWD COSMO-KENDA system

Currently in operational numerical weather prediction (NWP) the density of high-resolution observations, such as Doppler radar radial winds (DRWs), is severely reduced in part to avoid violating the assumption of uncorrelated observation errors. To improve the quantity of observations used and the impact that they have on the forecast requires an accurate specification of the observation uncertainties. Observation uncertainties can be estimated using a simple diagnostic that utilises the statistical averages of observation-minus-background and observation-minus-analysis residuals. We are the first to use a modified form of the diagnostic to estimate spatial correlations for observations used in an operational ensemble data assimilation system. The uncertainties for DRW superobservations assimilated into the Deutscher Wetterdienst convection-permitting NWP model are estimated and compared to previous uncertainty estimates for DRWs. The new results show that most diagnosed standard deviations are smaller than those used in the assimilation, hence it may be feasible assimilate DRWs using reduced error standard deviations. However, some of the estimated standard deviations are considerably larger than those used in the assimilation; these large errors highlight areas where the observation processing system may be improved. The error correlation length scales are larger than the observation separation distance and influenced by both the superobbing procedure and observation operator. This is supported by comparing these results to our previous study using Met Office data. Our results suggest that DRW error correlations may be reduced by improving the superobbing procedure and observation operator; however, any remaining correlations should be accounted for in the assimilation

Combining gemcitabine, oxaliplatin and capecitabine (GEMOXEL) for patients with advanced pancreatic carcinoma (APC): a phase I/II trial

Background: Gemcitabine remains the mainstay of palliative treatment of advanced pancreatic carcinoma (APC). Adding capecitabine or a platinum derivative each significantly prolonged survival in recent meta-analyses. The purpose of this study was to determine dose, safety and preliminary efficacy of a first-line regimen combining all three classes of active cytotoxic drugs in APC. Patients and methods: Chemotherapy-naive patients with locally advanced or metastatic, histologically proven adenocarcinoma of the pancreas were treated with a 21-day regimen of gemcitabine [1000 mg/m2 day (d) 1, d8], escalating doses of oxaliplatin (80-130 mg/m2 d1) and capecitabine (650-800 mg/m2 b.i.d. d1-d14). The recommended dose (RD), determined in the phase I part of the study by interpatient dose escalation in cohorts of three to six patients, was further studied in a two-stage phase II part with the primary end point of response rate by RECIST criteria. Results: Forty-five patients were treated with a total of 203 treatment cycles. Thrombocytopenia and diarrhea were the toxic effects limiting the dose to an RD of gemcitabine 1000 mg/m2 d1, d8; oxaliplatin 130 mg/m2 d1 and capecitabine 650 mg/m2 b.i.d. d1-14. Central independent radiological review showed partial remissions in 41% [95% confidence interval (CI) 26% to 56%] of patients and disease stabilization in 37% (95% CI 22% to 52%) of patients. Conclusion: This triple combination is feasible and, by far, met the predefined efficacy criteria warranting further investigation