An X-ray spectrum estimation method from transmission measurement combined with scatter correction

PurposeConventional x-ray spectrum estimation methods from transmission measurement often lead to inaccurate results when extensive x-ray scatter is present in the measured projection. This study aims to apply the weighted L1-norm scatter correction algorithm in spectrum estimation for reducing residual differences between the estimated and true spectrum.MethodThe scatter correction algorithm is based on a simple radiographic scattering model where the intensity of scattered x-ray is directly estimated from a transmission measurement. Then, the scatter-corrected measurement is used for the spectrum estimation method that consists of deciding the weights of predefined spectra and representing the spectrum as a linear combination of the predefined spectra with the weights. The performances of the estimation method combined with scatter correction are evaluated on both simulated and experimental data.ResultsThe results show that the estimated spectra using the scatter-corrected projection nearly match the true spectra. The normalized-root-mean-square-error and the mean energy difference between the estimated spectra and corresponding true spectra are reduced from 5.8% and 1.33 keV without the scatter correction to 3.2% and 0.73 keV with the scatter correction for both simulation and experimental data, respectively.ConclusionsThe proposed method is more accurate for the acquisition of x-ray spectrum than the estimation method without scatter correction and the spectrum can be successfully estimated even the materials of the filters and their thicknesses are unknown. The proposed method has the potential to be used in several diagnostic x-ray imaging applications.     

SIMEX for correction of dietary exposure effects with Box-Cox transformed data

Modelling dietary data, and especially 24-hr dietary recall (24HDR) data, is a challenge. Ignoring the inherent measurement error (ME) leads to biased effect estimates when the association between an exposure and an outcome is investigated. We propose an adapted simulation extrapolation (SIMEX) algorithm for modelling dietary exposures. For this purpose, we exploit the ME model of the NCI method where we assume the assumption of normally distributed errors of the reported intake on the Box-Cox transformed scale and of unbiased recalls on the original scale. According to the SIMEX algorithm, remeasurements of the observed data with additional ME are generated in order to estimate the association between the level of ME and the resulting effect estimate. Subsequently, this association is extrapolated to the case of zero ME to obtain the corrected estimate. We show that the proposed method fulfils the key property of the SIMEX approach, that is, that the MSE of the generated data will converge to zero if the ME variance converges to zero. Furthermore, the method is applied to real 24HDR data of the I.Family study to correct the effects of salt and alcohol intake on blood pressure. In a simulation study, the method is compared with the NCI method resulting in effect estimates with either smaller MSE or smaller bias in certain situations. In addition, we found our method to be more informative and easier to implement. Therefore, we conclude that the proposed method is useful to promote the dissemination of ME correction methods in nutritional epidemiology.     

Two human valyl-tRNA synthetase-encoding cDNA sequences deposited in GenBank display extensive differences

Five blocks of significant differences exist between two published sequences of the cDNA encoding human valyl-tRNA synthetase (GenBank X59303 and M98326). By comparison with the partial sequence of rat valyl-tRNA synthetase (GenBank M98327) the correct sequence can be deduced for two such blocks. The possible origin of the diversity for the two sequences is discussed.     

Physiological variation in metabolic phenotyping and functional genomic studies: use of orthogonal signal correction and PLS-DA

Metabolic phenotyping, or metabotyping, is increasingly being used as a probe in functional genomics studies. However, such profiling is subject to intrinsic physiological variation found in all animal populations. Using a nuclear magnetic resonance-based metabonomic approach, we show that diurnal variations in metabolism can obscure the interpretation of strain-related metabolic differences in two phenotypically normal mouse strains (C57BL10J and Alpk:ApfCD). To overcome this problem, diurnal-related metabolic variation was removed from these spectral data by application of orthogonal signal correction (OSC), a data filtering method. Interpretation of the removed orthogonal variation indicated that diurnal-related variation had been removed and that the AM samples contained higher levels of creatine, hippurate, trimethylamine, succinate, citrate and 2-oxo-glutarate and lower levels of taurine, trimethylamine-N-oxide, spermine and 3-hydroxy-iso-valerate relative to the PM samples. We propose OSC will have great potential removing confounding variation obscuring subtle changes in metabolism in functional genomic studies and will be of benefit to optimising interpretation of proteomic and genomic datasets.     

Global Positioning System (GPS) location accuracy improvement due to selective availability removal

Global Positioning System (GPS) is an important new technology for spatio-temporal behaviour studies of animals. Differential correction improves location accuracy. Previously, it mostly removed partially the influence of Selective Availability (SA). SA was deactivated in May 2000. The aim of this study was to quantify the influence of SA cancellation on location accuracy of various GPS receivers. We tested the accuracy of locations obtained from non-differential and differential GPS animal collars before and after SA removal. We found a significant improvement in accuracy for both types of GPS collars. However, differential GPS still provides more accurate locations.     

Ab Initio and DFT Study of the Structures,Vibrations and Energetics of the Urea Dimer and Trimer

Stable structures and electronic properties of small urea clusters are investigated with ab initio calculations. We optimized the cluster geometries and calculated the vibrational frequencies with Hartree–Fock (HF), second-order Møller-Plesset perturbation theory (MP2), and Density Functional Theory (DFT) methods using different basis sets. The most stable dimer was found to consist of two nonplanar urea molecules which are connected by two N–-H...O bonds in a common plane, and the most stable trimer has a flat structure of complex and planar C2 form for each urea molecule, like in the crystal. The interaction energies were corrected for the basis set superposition error (BSSE) using the full Boys*ndash;Bernardi counterpoise correction scheme. The stability of different dimer and trimer structures, the features of formation of H-bonds and presented here are compared to the available experimental data.     

Tests for Genetic Differentiation

When testing for genetic differentiation the joint null hypothesis that there is no allele frequency difference at any locus is of interest. Common approaches to test this hypothesis are based on the summation of χ² statistics over loci and on the Bonferroni correction, respectively. Here, we also consider the Simes adjustment and a recently proposed truncated product method (TPM) to combine P‐values. The summation and the TPM (using a relatively large truncation point) are powerful when there are differences in many or all loci. The Simes adjustment, however, is powerful when there are differences regarding one or a few loci only. As a compromise between the different approaches we introduce a combination between the Simes adjustment and the TPM, i.e. the joint null hypothesis is rejected if at least one of the two methods, Simes and TPM, is significant at the α/2‐level. Simulation results indicate that this combination is a robust procedure with high power over the different types of alternatives.