Bias Removal Research Articles

Genomic alterations in matching primary tumors and metastasis in breast cancer.

1549 Background: We examined DNA sequence alterations in matching normal tissues, primary breast cancers and metastatic lesions to study the genomic evolution of breast cancer. We also assessed technical reproducibility. Methods: DNA was extracted from formalin fixed paraffin embedded tissues obtained during autopsies from 3 patients including 12 specimens. We performed paired-end whole exome sequencing with SOliD V4 Genome Analyzer using the Agilent Sure Select All Exon Kit including technical replicates. Nucleotide variants (NV) were detected by Varscan V2.2 and functional impact was predicted by ANNOVAR. Results: After 4 QC steps (duplicate and strand bias removal, base quality and minor allele frequency filters) that removed 98.81% of variants per sample (range 96.50%-99.99%). We observed on average 2648 new NVs per cancer sample (range 10-5541) at a mean coverage of 72 (range 19-283) and mean library size of 48270660 base (range 7764034 base - 60752085 base). Concordance for high quality NVs reached 95% (range 92%-98%) in replicates. Mutation rate in the primary cancer differed from patient to patient (range 10.41per Mb to 18.29 per Mb) with more mutations seen in patients with longer disease history and extensive prior therapy. We also observed more differences in mutations between primary tumors and metastasis than expected by technical nose alone. On average, 234(83.53%) mutations were shared between a primary tumor and a metastasis (range 4 – 947; 62.5%-97.63%) and the number of site-specific mutations ranged from 0 to 251. An index for assessing mutational heterogeneity was greater in primary cancers compared to their metastasis. Mapping of variants with predicted functional impact yielded different targetable anomalies for different tumor sites. Conclusions: Mutation rates differed among patients according to their disease history. Difference of mutation between primary tumor and metastatic lesion were larger than technical noise and the heterogeneity of primary cancers was larger than that of metastasis. Metastasis often differed from the primary tumor in the spectrum of potentially targetable anomalies.

Comment on “Computed Tomography Evaluation of Craniomandibular Articulation in Class II Division 1 Malocclusion and Class I Normal Occlusion Subjects in North Indian Population”

Clear statement of objective, appropriate location of landmarks and removal of subjective bias in measurement is essential in all kinds of research, especially, orthodontics. The research design should be rationalistic, purposeful, and in accordance with the objectives of the study. In this communication, we highlight the errors in research design, measurement, analysis, and inferences drawn with the help of a published article as the primary source to explain these simple but useful points.

Sensor Rotation Bias Removal for Multiple Hypothesis Tracking Applications

A new approach for rotation bias removal in the presence of clutter and missed detections is derived that requires a cooperative target. This problem is related to the camera pose estimation problem, and the solution method is based on the formulation for the well-known probabilistic data association filter but executes in a batch mode to avoid convergence on a local maximum. Command and control installations that use multiple hypothesis trackers can apply this approach to correct azimuth and elevation measurements at the multiple hypothesis tracker input by making use of blue force position data within each sensor field of view.

Evidence-Based Persuasion

The primacy in modern medical ethics of the principle of respect for autonomy has led to the widespread assumption that it is unethical to change someone's beliefs, because doing so would constitute coercion or paternalism. In this Viewpoint we suggest that persuasion is not necessarily paternalistic and is an essential component of modern medical practice. There are at least 3 different types of persuasion. The first is the removal of biases; the second is recommending a particular course of action and providing evidence and reasons in favor of it; and the third is the potential creation of new biases, which could cross the line into unethical manipulation. The first of these is always mandatory, the second is usually permissible but sometimes inappropriate, and the third is normally impermissible but sometimes acceptable in rare cases.

The simulation of European heat waves from an ensemble of regional climate models within the EURO-CORDEX project

The ability of a large ensemble of regional climate models to accurately simulate heat waves at the regional scale of Europe was evaluated. Within the EURO-CORDEX project, several state-of-the art models, including non-hydrostatic meso-scale models, were run for an extended time period (20 years) at high resolution (12 km), over a large domain allowing for the first time the simultaneous representation of atmospheric phenomena over a large range of spatial scales. Eight models were run in this configuration, and thirteen models were run at a classical resolution of 50 km. The models were driven with the same boundary conditions, the ERA-Interim re-analysis, and except for one simulation, no observations were assimilated in the inner domain. Results, which are compared with daily temperature and precipitation observations (ECA&D and E-OBS data sets) show that, even forced by the same re-analysis, the ensemble exhibits a large spread. A preliminary analysis of the sources of spread, using in particular simulations of the same model with different parameterizations, shows that the simulation of hot temperature is primarily sensitive to the convection and the microphysics schemes, which affect incoming energy and the Bowen ratio. Further, most models exhibit an overestimation of summertime temperature extremes in Mediterranean regions and an underestimation over Scandinavia. Even after bias removal, the simulated heat wave events were found to be too persistent, but a higher resolution reduced this deficiency. The amplitude of events as well as the variability beyond the 90th percentile threshold were found to be too strong in almost all simulations and increasing resolution did not generally improve this deficiency. Resolution increase was also shown to induce large-scale 90th percentile warming or cooling for some models, with beneficial or detrimental effects on the overall biases. Even though full causality cannot be established on the basis of this evaluation work, the drivers of such regional differences were shown to be linked to changes in precipitation due to resolution changes, affecting the energy partitioning. Finally, the inter-annual sequence of hot summers over central/southern Europe was found to be fairly well simulated in most experiments despite an overestimation of the number of hot days and of the variability. The accurate simulation of inter-annual variability for a few models is independent of the model bias. This indicates that internal variability of high summer temperatures should not play a major role in controlling inter-annual variability. Despite some improvements, especially along coastlines, the analyses conducted here did not allow us to generally conclude that a higher resolution is clearly beneficial for a correct representation of heat waves by regional climate models. Even though local-scale feedbacks should be better represented at high resolution, combinations of parameterizations have to be improved or adapted accordingly.

Recovering redshift distributions with cross-correlations: pushing the boundaries

Determining accurate redshift distributions for very large samples of objects has become increasingly important in cosmology. We investigate the impact of extending cross-correlation based redshift distribution recovery methods to include small scale clustering information. The major concern in such work is the ability to disentangle the amplitude of the underlying redshift distribution from the influence of evolving galaxy bias. Using multiple simulations covering a variety of galaxy bias evolution scenarios, we demonstrate reliable redshift recoveries using linear clustering assumptions well into the non-linear regime for redshift distributions of narrow redshift width. Including information from intermediate physical scales balances the increased information available from clustering and the residual bias incurred from relaxing of linear constraints. We discuss how breaking a broad sample into tomographic bins can improve estimates of the redshift distribution, and present a simple bias removal technique using clustering information from the spectroscopic sample alone.

Chromosomal architecture changes upon cell differentiation

Materials and methods To analyze chromosomal architecture between cell types, it is crucial to have a consistent way of analyzing Hi-C data and removal of experimental biases. To this end we developed a comprehensive method of Iterative Correction and Eigenvector decomposition (ICE). ICE maps Hi-C reads to the genome, filters mapped reads and obtains a Hi-C map of relative contact probabilities free of experimental biases. It then decomposes the maps into a set of genomic tracks characterizing high-order chromatin organization.

Interpolation in waveform space: Enhancing the accuracy of gravitational waveform families using numerical relativity

Matched filtering for the identification of compact object mergers in gravitational wave antenna data involves the comparison of the data stream to a bank of template gravitational waveforms. Typically the template bank is constructed from phenomenological waveform models, since these can be evaluated for an arbitrary choice of physical parameters. Recently it has been proposed that singular value decomposition (SVD) can be used to reduce the number of templates required for detection. As we show here, another benefit of SVD is its removal of biases from the phenomenological templates along with a corresponding improvement in their ability to represent waveform signals obtained from numerical relativity (NR) simulations. Using these ideas, we present a method that calibrates a reduced SVD basis of phenomenological waveforms against NR waveforms in order to construct a new waveform approximant with improved accuracy and faithfulness compared to the original phenomenological model. The new waveform family is given numerically through the interpolation of the projection coefficients of NR waveforms expanded onto the reduced basis and provides a generalized scheme for enhancing phenomenological models.

A Single Cell Level Based Method for Copy Number Variation Analysis by Low Coverage Massively Parallel Sequencing

Copy number variations (CNVs), a common genomic mutation associated with various diseases, are important in research and clinical applications. Whole genome amplification (WGA) and massively parallel sequencing have been applied to single cell CNVs analysis, which provides new insight for the fields of biology and medicine. However, the WGA-induced bias significantly limits sensitivity and specificity for CNVs detection. Addressing these limitations, we developed a practical bioinformatic methodology for CNVs detection at the single cell level using low coverage massively parallel sequencing. This method consists of GC correction for WGA-induced bias removal, binary segmentation algorithm for locating CNVs breakpoints, and dynamic threshold determination for final signals filtering. Afterwards, we evaluated our method with seven test samples using low coverage sequencing (4∼9.5%). Four single-cell samples from peripheral blood, whose karyotypes were confirmed by whole genome sequencing analysis, were acquired. Three other test samples derived from blastocysts whose karyotypes were confirmed by SNP-array analysis were also recruited. The detection results for CNVs of larger than 1 Mb were highly consistent with confirmed results reaching 99.63% sensitivity and 97.71% specificity at base-pair level. Our study demonstrates the potential to overcome WGA-bias and to detect CNVs (>1 Mb) at the single cell level through low coverage massively parallel sequencing. It highlights the potential for CNVs research on single cells or limited DNA samples and may prove as a promising tool for research and clinical applications, such as pre-implantation genetic diagnosis/screening, fetal nucleated red blood cells research and cancer heterogeneity analysis.

Comparison of CuPc-based organic thin-film transistors made by different dielectric structures

Copper phthalocyanine-based organic thin-film transistors (OTFTs) with gate dielectric made by different combinations of ZrO2 and Al2O3 are fabricated. Experimental results show that as compared to the OTFTs with ZrO2/Al2O3 stacked and Al2O3/ZrO2/Al2O3 sandwiched gate dielectric, the device fabricated with the Al2O3/ZrO2 stacked gate dielectric manifests better electrical properties such as larger on/off ratio, smaller subthreshold slope, and higher carrier mobility. This could be explained by the fact that Al2O3 has good interface properties with CuPc and can act as a barrier layer, which prevents intermixing of materials at the organic/insulator interface and can slow oxygen diffusion through Al-O matrix, thus suppressing interfacial trap density. The gate-bias stress effect on the performance of OTFTs is also investigated. It is found that the threshold voltage shifts toward positive direction with stress time under a negative gate bias voltage. Longer stress times cause more degradation of the subthreshold and on/off ratio, probably due to more defect-state creation in the channel and an increase of interfacial traps and oxide charges in the dielectric during stress. Results also indicate that OTFTs with Al2O3 interlayer between the high-k dielectric and the gate electrode have less degradation in subthreshold and on/off ratio after a 3600-s stress. The involved mechanism lies in that the Al2O3 interlayer at the high-k dielectric/gate electrode interface can effectively block the injection of electrons from the gate electrode into the high-k material during electrical stress and thus less stress-induced interfacial traps and negative oxide charges in the devices. The electrical characteristics of the OTFTs after the removal of gate bias for a period of time are also studied.

감정 음성 인식을 위한 강인한 음성 파라메터

This paper studied the speech parameters less affected by the human emotion for the development of the robust emotional speech recognition system. For this purpose, the effect of emotion on the speech recognition system and robust speech parameters of speech recognition system were studied using speech database containing various emotions. In this study, mel-cepstral coefficient, delta-cepstral coefficient, RASTA mel-cepstral coefficient, root-cepstral coefficient, PLP coefficient and frequency warped mel-cepstral coefficient in the vocal tract length normalization method were used as feature parameters. And CMS (Cepstral Mean Subtraction) and SBR(Signal Bias Removal) method were used as a signal bias removal technique. Experimental results showed that the HMM based speaker independent word recognizer using frequency warped RASTA mel-cepstral coefficient in the vocal tract length normalized method, its derivatives and CMS as a signal bias removal showed the best performance.

Examination of Analysis and Forecast Errors of High-Resolution Assimilation, Bias Removal, and Digital Filter Initialization with an Ensemble Kalman Filter

Abstract Mesoscale atmospheric data assimilation is becoming an integral part of numerical weather prediction. Modern computational resources now allow assimilation and subsequent forecasting experiments ranging from resolutions of tens of kilometers over regional domains to smaller grids that employ storm-scale assimilation. To assess the value of the high-resolution capabilities involved with assimilation and forecasting at different scales, analyses and forecasts must be carefully evaluated to understand 1) whether analysis benefits gained at finer scales persist into the forecast relative to downscaled runs begun from lower-resolution analyses, 2) how the positive analysis effects of bias removal evolve into the forecast, and 3) how digital filter initialization affects analyses and forecasts. This study applies a 36- and 4-km ensemble Kalman filter over 112 assimilation cycles to address these important issues, which could all be relevant to a variety of short-term, high-resolution, real-time forecasting applications. It is found that with regard to surface wind and temperature, analysis improvements gained at higher resolution persist throughout the 12-h forecast window relative to downscaled, high-resolution forecasts begun from analyses on the coarser grid. Aloft, however, no forecast improvements were found with the high-resolution analysis/forecast runs. Surface wind and temperature bias removal, while clearly improving surface analyses, degraded surface forecasts and showed little forecast influence aloft. Digital filter initialization degraded temperature analyses with or without bias removal, degraded wind analyses when bias removal was used, but improved wind analyses when bias removal was absent. No forecast improvements were found with digital filter initialization. The consequences of these results with regard to operational assimilation/forecasting systems on nested grids are discussed.

A Simple Model for Correcting Sodar and Lidar Errors in Complex Terrain

Abstract Ground-based sensing of wind profiles by sodars and lidars is becoming the standard for wind energy and other applications. However, there remain difficulties in complex terrain since the instruments sense wind components in spatially separated volumes, and systematic spatial variations of the wind components can lead to systematic bias in wind estimation. The errors are typically less than 6%, so corrections do not need to be very sophisticated. Analytic potential flow models are developed for the flow over a bell-shaped hill and over an escarpment. These models are then used to find the radial Doppler shift from sampling volumes in typical sodar and lidar beam geometries, thereby allowing spatial variation bias to be removed. Since the models are straightforward, bias removal is readily achieved, and also lends itself to an understanding of the significant parameters affecting wind errors. The bell model is tested against field data from sodars and lidars in both moderately complex and in very complex terrain. It is found that corrected winds are to within approximately 1% of those measured by mast instruments. Much more complex models do not correct wind errors better than these simple models.

Subjective Well-Being and Retirement: Analysis and Policy Recommendations

This study examines the relationship between retirement and subjective well-being (SWB), utilizing international data from sixteen countries in Western Europe and the US. Differences in social security regimes are exploited to estimate the retirement decision such that it is exogenous to individual-level characteristics. Although results from traditional ordinary least squares suggest an ambiguous relationship between retirement and SWB, this is due to comparatively lower SWB among those who choose retirement. The removal of selection bias reveals a large, positive effect that fades over a few years, suggesting a multi-stage adjustment to retirement. Individuals facing formal retirement at age 65 or later experience an increase in SWB that is roughly equivalent in total value to that of individuals facing earlier retirement, and both groups return to trend by age 70. This suggests that raising the formal retirement age, which is widely discussed today by policymakers, is relatively neutral with regard to SWB in the long-term.

Combining CMORPH and Rain Gauges Observations over the Rio Negro Basin

Abstract Several algorithms that combine daily precipitation surface data and satellite Climate Prediction Center Morphing Technique (CMORPH) estimations were implemented and tested for the Rio Negro basin in northeastern Uruguay. Bias removal of satellite data through quantile matching—which requires historical data on nearby rain gauges—produces an unbiased estimate whose skill, as measured by the probability of detection (POD), is better than that obtained from surface observations for distances larger than approximately 50 km, which is twice the network characteristic distance between gauges of 23 km. Adjustment of satellite estimate using spatial interpolation of CMORPH deviations evaluated at nearby points—which requires simultaneous neighboring surface observations—eliminates biases to a large degree. Moreover, it shows higher POD skill than using only surface data for the entire range of distances and daily precipitation thresholds and for both seasons (cold and warm). The skill improvement attained, though, is small when the network density is as high as in the present study. However, these results suggest a promising scenario for the combined use of surface data and satellite retrievals as the latter continues to improve over time, both in resolution—spatial and temporal—and skill.

Increased cancer risk with drug use among patients with diabetes: Are the biased methods the culprit?

Increased cancer risk with drug use among patients with diabetes: Are the biased methods the culprit?

Calibration of satellite measurements of river discharge using a global hydrology model

► Orbital microwave sensors can monitor daily river discharge changes. ► Calibration of remote sensing signal to discharge units is necessary. ► Hydrological modeling is evaluated as a method to calibrate the remote sensing. ► Model-based calibration produces satisfactory results when compared to ground-based information. ► Independent river discharge information can now be obtained by use of model-calibrated remote sensing. Measurements of river discharge and watershed runoff are essential to water resources management, efficient hydropower generation, accurate flood prediction and control, and improved understanding of the global water cycle. Previous work demonstrates that orbital remote sensing can measure river discharge variation in a manner closely analogous to its measurement at ground stations, and using reach flow surface area instead of stage as the discharge estimator. For international measurements, hydrological modeling can, in principle, be used to provide the needed calibration of sensor data to discharge. The present study tests this approach and investigates the accuracy of the results. We analyze six sites within the US where gauging station, satellite measurements, and WBM model results are all available. Knowledge is thereby gained concerning how accurately satellite sensors can measure discharge, if the signal is calibrated only from global modeling results without any ground-based information. The calibration (rating) equations obtained for the remote sensing signal are similar, whether based on gauging station or on model information: r 2 correlation coefficients for least squares fits at one example site (#524; White River, Indiana) are both .66 ( n = 144, comparing monthly daily maxima, minima, and mean, 2003–2006). Space-based 4-day mean discharge values for this site when using the model calibration are accurate to within ±67% on the average ( n = 1824; largest percent errors occur at low discharges), and annual total runoff is accurate to ±9%, 2003–2008. Comparison of gauging station versus modeled discharge commonly indicates a small positive model bias; the observed errors of satellite-observed annual runoff are also positive and could be improved by bias removal from the rating curves. Also, analysis of a large flood event, along the Indus River in 2010, shows that the model does not capture flood wave attenuation by overbank flow, and thus predicts faster flood wave celerity and higher peak discharge than was measured by the remote sensing. The incorporation of overbank processes would improve discharge estimation via modeling, and also facilitate more accurate satellite-based measurement of peak discharge. The analysis shows that existing and planned microwave sensors can usefully characterize global river discharge dynamics, and that water balance model-based rating curves provide acceptable calibration of remote sensing signal to discharge.

Effective bias removal for fringe projection profilometry using the dual-tree complex wavelet transform

When reconstructing the three-dimensional (3D) object height profile using the fringe projection profilometry (FPP) technique, the light intensity reflected from the object surface can yield abruptly changing bias in the captured fringe image, which leads to severe reconstruction error. The traditional approach tries to remove the bias by suppressing the zero spectrum of the fringe image. It is based on the assumption that the aliasing between the frequency spectrum of the bias, which is around the zero frequency, and the frequency spectrum of the fringe is negligible. This, however, is not the case in practice. In this paper, we propose a novel (to our knowledge) technique to eliminate the bias in the fringe image using the dual-tree complex wavelet transform (DT-CWT). The new approach successfully identifies the features of bias, fringe, and noise in the DT-CWT domain, which allows the bias to be effectively extracted from a noisy fringe image. Experimental results show that the proposed algorithm is superior to the traditional methods and facilitates accurate reconstruction of objects' 3D models.

Voltage-dependent low-field resistivity of CaTiO3: Sc ceramics

The electrical conductivity of CaTiO3: Sc ceramics increases with time under a dc-bias voltage of ≤100 V cm−1 and at 300–600 °C. The conductivity increase is reversible on removal of the dc bias. This low-field effect, is independent of electrode materials and atmosphere, and is attributed to ionization of underbonded O2− ions adjacent to acceptor-doped Sc3+ ions on application of a dc bias. Conduction is predominantly p-type and therefore, holes located on oxygen, as O− ions, are the principal charge carriers.

More than maps: the fMRI orientation signal persists after removal of radial bias

More than maps: the fMRI orientation signal persists after removal of radial bias