Accurate Background Research Articles

Technical Note: Improved CT number stability across patient size using dual-energy CT virtual monoenergetic imaging.

The purpose of this study was to evaluate, over a wide range of phantom sizes, CT number stability achieved using two techniques for generating dual-energy computed tomography (DECT) virtual monoenergetic images. Water phantoms ranging in lateral diameter from 15 to 50 cm and containing a CT number test object were scanned on a DSCT scanner using both single-energy (SE) and dual-energy (DE) techniques. The SE tube potentials were 70, 80, 90, 100, 110, 120, 130, 140, and 150 kV; the DE tube potential pairs were 80/140, 70/150Sn, 80/150Sn, 90/150Sn, and 100/150Sn kV (Sn denotes that the 150 kV beam was filtered with a 0.6 mm tin filter). Virtual monoenergetic images at energies ranging from 40 to 140 keV were produced from the DECT data using two algorithms, monoenergetic (mono) and monoenergetic plus (mono+). Particularly in large phantoms, water CT number errors and/or artifacts were observed; thus, datasets with water CT numbers outside ±10 HU or with noticeable artifacts were excluded from the study. CT numbers were measured to determine CT number stability across all phantom sizes. Data exclusions were generally limited to cases when a SE or DE technique with a tube potential of less than 90 kV was used to scan a phantom larger than 30 cm. The 90/150Sn DE technique provided the most accurate water background over the large range of phantom sizes evaluated. Mono and mono+ provided equally improved CT number stability as a function of phantom size compared to SE; the average deviation in CT number was only 1.4% using 40 keV and 1.8% using 70 keV, while SE had an average deviation of 11.8%. The authors' report demonstrates, across all phantom sizes, the improvement in CT number stability achieved with mono and mono+ relative to SE.

低次の誤差関数を用いた外積展開による背景雑音推定

This paper shows a new denoising algorithm based on the outer product expansion with lower norms for the background noise. We have proposed novel source separation methods using the outer product expansion with L1 norm minimization. Effectiveness of outer product expansions for artificial signals and an electromagnetic wave data was represented. However, the denoising performance is decreasing with an increasing of local signals. In this paper, we propose the outer product expansion with lower norms (0.1 ∼ 0.9). Simulation results show that the proposed method produces the accurate background noise reduction.

Robust CFAR Detector Based on Truncated Statistics in Multiple-Target Situations

A new and robust constant false alarm rate (CFAR) detector based on truncated statistics (TSs) is proposed for ship detection in single-look intensity and multilook intensity synthetic aperture radar data. The approach is aimed at high-target-density situations such as busy shipping lines and crowded harbors, where the background statistics are estimated from potentially contaminated sea clutter samples. The CFAR detector uses truncation to exclude possible statistically interfering outliers and TSs to model the remaining background samples. The derived truncated statistic CFAR (TS-CFAR) algorithm does not require prior knowledge of the interfering targets. The TS-CFAR detector provides accurate background clutter modeling, a stable false alarm regulation property, and improved detection performance in high-target-density situations.

Robust Data Detection for the Photon-Counting Free-Space Optical System With Implicit CSI Acquisition and Background Radiation Compensation

Since atmospheric turbulence and pointing errors cause signal intensity fluctuations and the background radiation surrounding the free-space optical (FSO) receiver contributes an undesired noisy component, the receiver requires accurate channel state information (CSI) and background information to adjust the detection threshold. In most previous studies, for CSI acquisition, pilot symbols were employed, which leads to a reduction of spectral and energy efficiency; and an impractical assumption that the background radiation component is perfectly known was made. In this paper, we develop an efficient and robust sequence receiver, which acquires the CSI and the background information implicitly and requires no knowledge about the channel model information. It is robust since it can automatically estimate the CSI and background component and detect the data sequence accordingly. Its decision metric has a simple form and involves no integrals, and thus can be easily evaluated. A Viterbi-type trellis-search algorithm is adopted to improve the search efficiency, and a selective-store strategy is adopted to overcome a potential error floor problem as well as to increase the memory efficiency. To further simplify the receiver, a decision-feedback symbol-by-symbol receiver is proposed as an approximation of the sequence receiver. By simulations and theoretical analysis, we show that the performance of both the sequence receiver and the symbol-by-symbol receiver, approach that of detection with perfect knowledge of the CSI and background radiation, as the length of the window for forming the decision metric increases.

Satellite measurements oversee China’s sulfur dioxide emission reductions from coal-fired power plants

To evaluate the real reductions in sulfur dioxide (SO2) emissions from coal-fired power plants in China, Ozone Monitoring Instrument (OMI) remote sensing SO2 columns were used to inversely model the SO2 emission burdens surrounding 26 isolated power plants before and after the effective operation of their flue gas desulfurization (FGD) facilities. An improved two-dimensional Gaussian fitting method was developed to estimate SO2 burdens under complex background conditions, by using the accurate local background columns and the customized fitting domains for each target source. The OMI-derived SO2 burdens before effective FGD operation were correlated well with the bottom-up emission estimates (R = 0.92), showing the reliability of the OMI-derived SO2 burdens as a linear indicator of the associated source strength. OMI observations indicated that the average lag time period between installation and effective operation of FGD facilities at these 26 power plants was around 2 years, and no FGD facilities have actually operated before the year 2008. The OMI estimated average SO2 removal equivalence (56.0%) was substantially lower than the official report (74.6%) for these 26 power plants. Therefore, it has been concluded that the real reductions of SO2 emissions in China associated with the FGD facilities at coal-fired power plants were considerably diminished in the context of the current weak supervision measures.

The application of high-resolution mass spectrometry-based data-mining tools in tandem to metabolite profiling of a triple drug combination in humans

Patients are usually exposed to multiple drugs, and metabolite profiling of each drug in complex biological matrices is a big challenge. This study presented a new application of an improved high resolution mass spectrometry (HRMS)-based data-mining tools in tandem to fast and comprehensive metabolite identification of combination drugs in human. The model drug combination was metronidazole-pantoprazole-clarithromycin (MET-PAN-CLAR), which is widely used in clinic to treat ulcers caused by Helicobacter pylori. First, mass defect filter (MDF), as a targeted data processing tool, was able to recover all relevant metabolites of MET-PAN-CLAR in human plasma and urine from the full-scan MS dataset when appropriate MDF templates for each drug were defined. Second, the accurate mass-based background subtraction (BS), as an untargeted data-mining tool, worked effectively except for several trace metabolites, which were buried in the remaining background signals. Third, an integrated strategy, i.e., untargeted BS followed by improved MDF, was effective for metabolite identification of MET-PAN-CLAR. Most metabolites except for trace ones were found in the first step of BS-processed datasets, and the results led to the setup of appropriate metabolite MDF template for the subsequent MDF data processing. Trace metabolites were further recovered by MDF, which used both common MDF templates and the novel metabolite-based MDF templates. As a result, a total of 44 metabolites or related components were found for MET-PAN-CLAR in human plasma and urine using the integrated strategy. New metabolic pathways such as N-glucuronidation of PAN and dehydrogenation of CLAR were found. This study demonstrated that the combination of accurate mass-based multiple data-mining techniques in tandem, i.e., untargeted background subtraction followed by targeted mass defect filtering, can be a valuable tool for rapid metabolite profiling of combination drugs in vivo.

Satellite data assimilation of upper-level sounding channels in HWRF with two different model tops

The Advanced Microwave Sounding Unit-A (AMSU-A) onboard the NOAA satellites NOAA-18 and NOAA-19 and the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) MetOp-A, the hyperspectral Atmospheric Infrared Sounder (AIRS) onboard Aqua, the High resolution InfraRed Sounder (HIRS) onboard NOAA-19 and MetOp-A, and the Advanced Technology Microwave Sounder (ATMS) onboard Suomi National Polar-orbiting Partnership (NPP) satellite provide upper-level sounding channels in tropical cyclone environments. Assimilation of these upper-level sounding channels data in the Hurricane Weather Research and Forecasting (HWRF) system with two different model tops is investigated for the tropical storms Debby and Beryl and hurricanes Sandy and Isaac that occurred in 2012. It is shown that the HWRF system with a higher model top allows more upper-level microwave and infrared sounding channels data to be assimilated into HWRF due to a more accurate upper-level background profile. The track and intensity forecasts produced by the HWRF data assimilation and forecast system with a higher model top are more accurate than those with a lower model top.

MADGiC: a model-based approach for identifying driver genes in cancer.

Motivation: Identifying and prioritizing somatic mutations is an important and challenging area of cancer research that can provide new insights into gene function as well as new targets for drug development. Most methods for prioritizing mutations rely primarily on frequency-based criteria, where a gene is identified as having a driver mutation if it is altered in significantly more samples than expected according to a background model. Although useful, frequency-based methods are limited in that all mutations are treated equally. It is well known, however, that some mutations have no functional consequence, while others may have a major deleterious impact. The spatial pattern of mutations within a gene provides further insight into their functional consequence. Properly accounting for these factors improves both the power and accuracy of inference. Also important is an accurate background model.Results: Here, we develop a Model-based Approach for identifying Driver Genes in Cancer (termed MADGiC) that incorporates both frequency and functional impact criteria and accommodates a number of factors to improve the background model. Simulation studies demonstrate advantages of the approach, including a substantial increase in power over competing methods. Further advantages are illustrated in an analysis of ovarian and lung cancer data from The Cancer Genome Atlas (TCGA) project.Availability and implementation: R code to implement this method is available at http://www.biostat.wisc.edu/ kendzior/MADGiC/.Contact: kendzior@biostat.wisc.eduSupplementary information: Supplementary data are available at Bioinformatics online.

Real-time airborne gamma-ray background estimation using NASVD with MLE and radiation transport for calibration

Helicopter-mounted gamma-ray detectors can provide law enforcement officials the means to quickly and accurately detect, identify, and locate radiological threats over a wide geographical area. The ability to accurately distinguish radiological threat-generated gamma-ray signatures from background gamma radiation in real time is essential in order to realize this potential. This problem is non-trivial, especially in urban environments for which the background may change very rapidly during flight. This exacerbates the challenge of estimating background due to the poor counting statistics inherent in real-time airborne gamma-ray spectroscopy measurements. To address this challenge, we have developed a new technique for real-time estimation of background gamma radiation from aerial measurements without the need for human analyst intervention. The method can be calibrated using radiation transport simulations along with data from previous flights over areas for which the isotopic composition need not be known. Over the examined measured and simulated data sets, the method generated accurate background estimates even in the presence of a strong, 60Co source. The potential to track large and abrupt changes in background spectral shape and magnitude was demonstrated. The method can be implemented fairly easily in most modern computing languages and environments.

Least-squares migration in the presence of velocity errors

Seismic migration requires an accurate background velocity model that correctly predicts the kinematics of wave propagation in the true subsurface. Least-squares migration, which seeks the inverse rather than the adjoint of a forward modeling operator, is especially sensitive to errors in this background model. This can result in traveltime differences between predicted and observed data that lead to incoherent and defocused migration images. We have developed an alternative misfit function for use in least-squares migration that measures amplitude differences between predicted and observed data, i.e., differences after correcting for nonzero traveltime shifts between predicted and observed data. We demonstrated on synthetic and field data that, when the background velocity model is incorrect, the use of this misfit function results in better focused migration images. Results suggest that our method best enhances image focusing when differences between predicted and observed data can be explained by traveltime shifts.

Low-complexity background subtraction based on spatial similarity

Robust detection of moving objects from video sequences is an important task in machine vision systems and applications. To detect moving objects, accurate background subtraction is essential. In real environments, due to complex and various background types, background subtraction is a challenging task. In this paper, we propose a pixel-based background subtraction method based on spatial similarity. The main difficulties of background subtraction include various background changes, shadows, and objects similar in color to background areas. In order to address these problems, we first computed the spatial similarity using the structural similarity method (SSIM). Spatial similarity is an effective way of eliminating shadows and detecting objects similar to the background areas. With spatial similarity, we roughly eliminated most background pixels such as shadows and moving background areas, while preserving objects that are similar to the background regions. Finally, the remaining pixels were classified as background pixels and foreground pixels using density estimation. Previous methods based on density estimation required high computational complexity. However, by selecting the minimum number of features and deleting most background pixels, we were able to significantly reduce the level of computational complexity. We compared our method with some existing background modeling methods. The experimental results show that the proposed method produced more accurate and stable results.

"You Could Not Get Any Person to be Trusted Except the State": Poorer Workers' Loss of Faith in Voluntarism in Late 19th Century Britain

This article describes the reasons for a move by poorer workers in late-Victorian Britain away from a belief that voluntary worker-led organizations could provide effective insurance and welfare support, to a greater faith in the State and its services. Relationships within friendly societies—often argued to be key institutions in the development of working-class unity and identity—were less harmonious and trusting in this period than is normally portrayed. Poorer workers in particular developed a distrust of these institutions and especially of how the often better-off working-class leadership treated members. This loss of faith was seen in the great growth of the “dividing” friendly societies as workers abandoned the mainstream clubs, and an increased belief in the potential fairness of State-administered institutions as the resentments felt about the societies overcame working-class suspicions of government provision and control caused by the new Poor Law and its miseries. What has previously been seen as the ideological rejection of State interference by ordinary society members was instead often a response to specific legislative provisions which would have reduced the power of individual members within and against the distrusted clubs. Examining relationships within these hugely important working-class organizations allows us to understand more fully what have often been seen as the confused and inconsistent attitudes of the poorer working-class towards the role of the State, and provides a more accurate historical background to recent arguments about whether welfare provision should be handed back by governments to the management of such voluntarist bodies.

Tracking Vehicles Through Shadows and Occlusions in Wide-Area Aerial Video

We present a new approach for simultaneous tracking and segmentation of multiple targets in low frame rate aerial video. We focus on building an accurate background model that accounts for both global camera motion and moving objects in the scene. We then apply a probabilistic framework for simultaneous tracking and segmentation that incorporates this background model. By using a background model, we are able to track the object through dramatic appearance changes caused by shadows and lighting changes. Furthermore, the incorporation of segmentation into the tracking algorithm reduces the impact of common tracking problems, such as drift and partial occlusion. Results are shown for the Columbus Large Image Format (CLIF) 2007 data set, demonstrating successful tracking under significant occlusions, target appearance changes, and near similar moving objects.

New transverse Zeeman effect method for mercury detection based on common mercury lamp

The accurate background correction can determine the minimum limit of trace mercury measurement in atmosphere by the cold vapor atomic absorption method. This paper studies a new method of mercury detection using the common mercury lamp as sources which correct the background according to the transverse Zeeman effect. The resonance spectral line (253.65 nm) of the meccury lamp generates σ-, σ+, and π linear polarized light in the vertical direction of the magnetic field. This study obtains mercury absorbance of σ-, σ+, and π light in different magnetic field intensity by using ultra-high resolution spectrometer, then gets the minimum field intensity of the method. We discuss the existing possible interference caused by benzene with narrow-band absorption and acetone with broadband absorption under 1.78 T magnetic field intensity. Taking σ- and σ+ as background light, and π as absorption light, we quantify the saturated mercury vapor cell with different lengths. With the accurate background correction, the R value of absorption fitting curve can achieve 0.99. Results indicate that the method can accomplish the job of accurate background correction and can be applied to trace mercury measurement in atmosphere.

An iterative spherical mean value method for background field removal in MRI

The sophisticated harmonic artifact reduction for phase data (SHARP) method has been proposed for the removal of background field in MRI phase data. It relies on the spherical mean value (SMV) property of harmonic functions, and its accuracy depends on the radius of the sphere used for computing the SMV and truncation threshold needed for deconvolution. The goal of this study was to develop an alternative SMV-based background field removal method with reduced dependences on these parameters. The proposed background field removal method, termed iterative SMV (iSMV), consists of applying the SMV operation repeatedly on the field map. It was validated in a phantom and in vivo brain data of five healthy volunteers. The iSMV method demonstrates accurate background field removal in the phantom. Compared with SHARP, the iSMV method shows a significantly reduced dependence on the SMV radius both in phantom and in human data. Because a smaller radius can be chosen, the iSMV method allows retaining a larger part of the region of interest compared with SHARP. The iSMV method is an effective background field removal method with a reduced dependence on method parameters. Magn Reson Med 72:1065-1071, 2014. © 2013 Wiley Periodicals, Inc.

Case-based background modeling: associative background database towards low-cost and high-performance change detection

Background modeling and subtraction is an essential task in video surveillance applications. Many researchers have discussed about an improvement of performance of a background model, and a reduction of memory usage or computational cost. To adapt to background changes, a background model has been enhanced by introducing various information including a spatial consistency, a temporal tendency, etc. with a large memory allocation. Meanwhile, an approach to reduce a memory cost cannot provide better accuracy of a background subtraction. To tackle the trade-off problem, this paper proposes a novel framework named "case-based background modeling". The characteristics of the proposed method are (1) a background model is created, or removed when necessary, (2) case-by-case model sharing by some of the pixels, (3) pixel features are divided into two groups, one for model selection and the other for modeling. These approaches realize a low-cost and high accurate background model. The memory usage and the computational cost could be reduced by half of a traditional method and the accuracy was superior to the method.

An effective method for determining the ingredients of Shuanghuanglian formula in blood samples using high‐resolution LC–MS coupled with background subtraction and a multiple data processing approach

Shuanghuanglian formula (SF) is a combination of Flos lonicerae japonicae, Radix scutellariae, and Fructus forsythiae, commonly used to treat viral or bacterial infections. However, the constituents absorbed into the blood after oral administration of SF are difficult to determine and thus remain unclear. Here, we report the application of an accurate background subtraction and multiple data processing approach (Bs-Mpa) for the comprehensive detection of compounds of SF in vivo. A sensitive and reliable ultra-performance LC coupled with ESI quadrupole TOF MS (UPLC-ESI-Q-TOF-MS) approach coupled with Bs-Mpa, which is implemented in the Strip tool from UPLC to remove nonrelated ion signals from accurate mass LC-MS data, was established to characterize the chemical constituents and rat metabolites of SF. In the loading plot of the principal component analysis, 68 ions of interest were extracted from blood samples, among them, 39 absorbed prototype components of SF and 29 metabolites were identified in vivo. It is concluded that the integrative Bs-Mpa method can be successfully applied for the rapid discovery of multiple components from a traditional Chinese medicine. The above challenge was addressed by using the proposed Bs-Mpa method and it was particularly suitable for applying to the global characterization of the constituents or metabolites in rat blood after oral administration of other well-known formulae.

Pitfalls in prestack inversion of merged seismic surveys

Modern 3D seismic surveys are often of such good quality and 3D interpretation packages so user-friendly that seismic interpretation is no longer exclusively carried out by geophysicists. This ease-of-use has also been extended to more quantitative workflows, such as 3D prestack inversion, putting it in the hands of the “nonexpert” — be it geologist, engineer, or new-hire geophysicist. Indeed, given good quality input seismic data, almost any interpreter who can generate good well ties and define an accurate background model of P-impedance, S-impedance, and density can generate a quality prestack inversion. Two of the authors are new geophysicists who fell into the prestack inversion “pit.” Fortunately, they were able to recognize that something was wrong. We applied prestack inversion to gathers that were carefully reprocessed by a major service company. The problem, however, was not with the processing, but with our lack of understanding of the input legacy data that formed part of a larger “megamerge” survey. Not all of the surveys that were merged had the same offset range. In the migration step, gaps in long offsets of the older surveys were not muted. Migration noise from newer surveys was allowed to fill this space. We share our initial workflow and suspicious results. We also clarify the meaning of “fold” and “offset” for prestack-migrated gathers. In addition to presenting some QC tools useful in analyzing megamerge surveys, we show how, by limiting the offsets used in our prestack inversion, we obtain less aggressive but still useful results.

The CRAPome: a contaminant repository for affinity purification-mass spectrometry data.

Affinity purification coupled with mass spectrometry (AP-MS) is now a widely used approach for the identification of protein-protein interactions. However, for any given protein of interest, determining which of the identified polypeptides represent bona fide interactors versus those that are background contaminants (e.g. proteins that interact with the solid-phase support, affinity reagent or epitope tag) is a challenging task. While the standard approach is to identify nonspecific interactions using one or more negative controls, most small-scale AP-MS studies do not capture a complete, accurate background protein set. Fortunately, negative controls are largely bait-independent. Hence, aggregating negative controls from multiple AP-MS studies can increase coverage and improve the characterization of background associated with a given experimental protocol. Here we present the Contaminant Repository for Affinity Purification (the CRAPome) and describe the use of this resource to score protein-protein interactions. The repository (currently available for Homo sapiens and Saccharomyces cerevisiae) and computational tools are freely available online at www.crapome.org.

Kernel-Based Machine Learning for Background Estimation of NaI Low-Count Gamma-Ray Spectra

Virtually all gamma-ray spectrometry measurements contain background components due to the ubiquitous presence of primordial radionuclides in the Earth's crust and cosmic radiation interactions high in the Earth's atmosphere. In principle, spectral signatures due to radiation source(s) of actual interest can be extracted from the measured gamma-ray spectrum by background subtraction. However, if separate background measurements are unavailable or infeasible, and particularly for measurements exhibiting low signal-to-noise ratio (SNR), background subtraction is nontrivial, and it requires accurate background estimation . An example application of gamma-ray spectroscopy with low SNR is the “source search” scenario, where the position of a source is sought using measurements taken over very short time intervals by a detector in motion. We have developed an algorithm for background estimation in low-count gamma-ray spectra using kernel-based Gaussian processes (GP) taken from the field of machine learning. We have evaluated the performance of our algorithm using a group of three kernels tested against a dataset composed of background spectra measured in an urban environment using a mobile sodium iodide (NaI) detector. We have also simulated datasets containing nonbackground gamma-ray sources in an urban background measured with a NaI detector. The simulated scenarios employed a variety of source-detector distances and different types of source shielding. As a metric of algorithm performance, we calculated correlation coefficients, Theil inequality coefficients, and count difference statistics between estimated and actual backgrounds. We concluded that our method adequately estimates the gamma-ray background, but we also observed a strong dependence of the algorithm's performance on the selected kernel.