Quality Of Raw Data Research Articles

Non-contact small animal fluorescence imaging system for simultaneous multi-directional angular-dependent data acquisition.

We present a novel non-contact small animal fluorescent molecular tomography (FMT) imaging system. At the heart of the system is a new mirror-based imaging head that was designed to provide 360-degree measurement data from an entire animal surface in one step. This imaging head consists of two conical mirrors, which considerably reduce multiple back reflections between the animal and mirror surfaces. These back reflections are common in existing mirror-based imaging heads and tend to degrade the quality of raw measurement data. In addition, the introduction of a novel ray-transfer operator allows for the inclusion of the angular dependent data in the image reconstruction process, which results in higher image resolution. We describe in detail the system design and implementation of the hardware components as well as the transport-theory-based image reconstruction algorithm. Using numerical simulations, measurements on a well-defined phantom and a live animal, we evaluate the system performance and show the advantages of our approach.

Multi-perspective quality control of Illumina exome sequencing data using QC3

Advances in next-generation sequencing (NGS) technologies have greatly improved our ability to detect genomic variants for biomedical research. The advance in NGS technologies has also created significant challenges in bioinformatics. One of the major challenges is the quality control of sequencing data. There has been heavy focus on performing raw data quality control. In order to correctly interpret the quality of the DNA sequencing data, however, proper quality control should be conducted at all stages of DNA sequencing data analysis: raw data, alignment, and variant detection. We designed QC3, a quality control tool aimed at those three major stages of DNA sequencing. QC3 monitors quality control metrics at each stage of NGS data and provides unique and independent evaluations of the data quality from different perspectives. QC3 offers unique features such as detection of batch effect and cross contamination. QC3 and its source code are freely downloadable at https://github.com/slzhao/QC3.

Review of current methods, applications, and data management for the bioinformatics analysis of whole exome sequencing.

The advent of next-generation sequencing technologies has greatly promoted advances in the study of human diseases at the genomic, transcriptomic, and epigenetic levels. Exome sequencing, where the coding region of the genome is captured and sequenced at a deep level, has proven to be a cost-effective method to detect disease-causing variants and discover gene targets. In this review, we outline the general framework of whole exome sequence data analysis. We focus on established bioinformatics tools and applications that support five analytical steps: raw data quality assessment, pre-processing, alignment, post-processing, and variant analysis (detection, annotation, and prioritization). We evaluate the performance of open-source alignment programs and variant calling tools using simulated and benchmark datasets, and highlight the challenges posed by the lack of concordance among variant detection tools. Based on these results, we recommend adopting multiple tools and resources to reduce false positives and increase the sensitivity of variant calling. In addition, we briefly discuss the current status and solutions for big data management, analysis, and summarization in the field of bioinformatics.

Color and Depth Priors in Natural Images

Natural scene statistics have played an increasingly important role in both our understanding of the function and evolution of the human vision system, and in the development of modern image processing applications. Because range (egocentric distance) is arguably the most important thing a visual system must compute (from an evolutionary perspective), the joint statistics between image information (color and luminance) and range information are of particular interest. It seems obvious that where there is a depth discontinuity, there must be a higher probability of a brightness or color discontinuity too. This is true, but the more interesting case is in the other direction--because image information is much more easily computed than range information, the key conditional probabilities are those of finding a range discontinuity given an image discontinuity. Here, the intuition is much weaker; the plethora of shadows and textures in the natural environment imply that many image discontinuities must exist without corresponding changes in range. In this paper, we extend previous work in two ways--we use as our starting point a very high quality data set of coregistered color and range values collected specifically for this purpose, and we evaluate the statistics of perceptually relevant chromatic information in addition to luminance, range, and binocular disparity information. The most fundamental finding is that the probabilities of finding range changes do in fact depend in a useful and systematic way on color and luminance changes; larger range changes are associated with larger image changes. Second, we are able to parametrically model the prior marginal and conditional distributions of luminance, color, range, and (computed) binocular disparity. Finally, we provide a proof of principle that this information is useful by showing that our distribution models improve the performance of a Bayesian stereo algorithm on an independent set of input images. To summarize, we show that there is useful information about range in very low-level luminance and color information. To a system sensitive to this statistical information, it amounts to an additional (and only recently appreciated) depth cue, and one that is trivial to compute from the image data. We are confident that this information is robust, in that we go to great effort and expense to collect very high quality raw data. Finally, we demonstrate the practical utility of these findings by using them to improve the performance of a Bayesian stereo algorithm.

Adjustment method for microarray data generated using two-cycle RNA labeling protocol

BackgroundMicroarray technology is widely utilized for monitoring the expression changes of thousands of genes simultaneously. However, the requirement of relatively large amount of RNA for labeling and hybridization makes it difficult to perform microarray experiments with limited biological materials, thus leads to the development of many methods for preparing and amplifying mRNA. It is addressed that amplification methods usually bring bias, which may strongly hamper the following interpretation of the results. A big challenge is how to correct for the bias before further analysis.ResultsIn this article, we observed the bias in rice gene expression microarray data generated with the Affymetrix one-cycle, two-cycle RNA labeling protocols, followed by validation with Real Time PCR. Based on these data, we proposed a statistical framework to model the processes of mRNA two-cycle linear amplification, and established a linear model for probe level correction. Maximum Likelihood Estimation (MLE) was applied to perform robust estimation of the Retaining Rate for each probe. After bias correction, some known pre-processing methods, such as PDNN, could be combined to finish preprocessing. Then, we evaluated our model and the results suggest that our model can effectively increase the quality of the microarray raw data: (i) Decrease the Coefficient of Variation for PM intensities of probe sets; (ii) Distinguish the microarray samples of five stages for rice stamen development more clearly; (iii) Improve the correlation coefficients among stamen microarray samples. We also discussed the necessity of model adjustment by comparing with another simple adjustment method.ConclusionWe conclude that the adjustment model is necessary and could effectively increase the quality of estimation for gene expression from the microarray raw data.

A Survey of Agent Based Pre-Processing and Knowledge Retrieval

Information retrieval is the major task in present scenario as quantum of data is increasing with a tremendous speed. So, to manage & mine knowledge for different users as per their interest, is the goal of every organization whether it is related to grid computing, business intelligence, distributed databases or any other. To achieve this goal of extracting quality information from large databases, software agents have proved to be a strong pillar. Over the decades, researchers have implemented the concept of multi agents to get the process of data mining done by focusing on its various steps. Among which data pre-processing is found to be the most sensitive and crucial step as the quality of knowledge to be retrieved is totally dependent on the quality of raw data. Many methods or tools are available to pre-process the data in an automated fashion using intelligent (self learning) mobile agents effectively in distributed as well as centralized databases but various quality factors are still to get attention to improve the retrieved knowledge quality. This article will provide a review of the integration of these two emerging fields of software agents and knowledge retrieval process with the focus

Vehicle Reidentification with the Inductive Loop Signature Technology

This study proposed a real-time inductive loop signature based vehicle reidentification approach, RTREID-2M, which improved the previously developed RTREID-2 algorithm on two aspects: (1) Develop a cubic spline data imputation approach to replace the existing linear data imputation approach in order to improve the raw signature data quality; (2) Improve the time window setting for vehicles on High-Occupancy Vehicle (HOV) lanes so that vehicles on HOV lanes traveling with free flow speeds would be considered when generating candidate vehicle sets even during congestion time periods. In addition, a stratified-random sampling method was developed to effectively perform ground-truthing task for evaluating the performance of the proposed RTREID-2M. The evaluation results showed desired performance for vehicle reidentification and travel time estimation under both free-flow and congested flow traffic conditions. The future research will focus on the potential applications and arterial vehicle reidentification utilizing the inductive loop signature technologies.

The current state‐of‐the‐art in active region seismology

AbstractHelioseismology is the study of the variations in the internal structure and properties of the dynamics of the Sun from measurements of its surface oscillations. With the 2010 launch of the Solar Dynamics Observatory (SDO) we are undoubtedly approaching a new dawn for local helioseismology, as the extent and quality of raw surface oscillation data has never been better. However, advances in theory and modelling are still required to fully utilise these data, especially in magnetic active regions and sunspots, where the physics is poorly understood (© 2012 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)

TRTViewer: The ATLAS TRT detector monitoring and diagnosticstool

The transition radiation tracker (TRT) is the outermost of the three sub-systems of the ATLAS inner detector at the Large Hadron Collider (LHC) at CERN. It is designed to combine the drift tube tracker with the transition radiation detector, providing an important contribution to the charged particles precise momentum measurement and particle (mainly electron) identification. The TRT consists of a barrel section at small pseudorapidity (η) and two separate end-cap partitions at large η. The detector performance and its operational conditions were permanently monitored during all commissioning and data-taking stages using various software tools, one of which – TRTViewer – is described in the present paper. The TRTViewer is the dedicated program for monitoring the TRT raw data quality and detector performance at different hardware levels: individual straws, readout chips and electronic boards. The data analysis results can be presented on the event-by-event basis or in the form of color maps representing the operation parameters (efficiencies, timing, occupancy, etc.) according to the real geometrical position of the detector hardware elements. The paper describes the TRTViewer software package as the event displaying tool, raw data processor and histogram and operation parameters presenter, which works with the different sources of input information: raw data files, online monitoring histograms, offline analysis histograms and TRT DAQ Configuration database. The package proved to be one of the main instruments for the fast and effective TRT diagnostics during debugging and operation periods.

Nanoscale measurement of the power spectral density of surface roughness: how to solve a difficult experimental challenge

In this study, we show that the correct determination of surface morphology using scanning force microscopy (SFM) imaging and power spectral density (PSD) analysis of the surface roughness is an extremely demanding task that is easily affected by experimental parameters such as scan speed and feedback parameters. We present examples were the measured topography data is significantly influenced by the feedback response of the SFM system and the PSD curves calculated from this experimental data do not correspond to that of the true topography. Instead, either features are "lost" due to low pass filtering or features are "created" due to oscillation of the feedback loop. In order to overcome these serious problems we show that the interaction signal (error signal) can be used not only to quantitatively control but also to significantly improve the quality of the topography raw data used for the PSD analysis. In particular, the calibrated error signal image can be used in combination with the topography image in order to obtain a correct representation of surface morphology ("true" topographic image). From this "true" topographic image a faithful determination of the PSD of surface morphology is possible. The corresponding PSD curve is not affected by the fine-tuning of feedback parameters, and allows for much faster image acquisition speeds without loss of information in the PSD curve.

A New near Point Denoising Algorithm for Point Cloud

For the mass point cloud, which is generated by large work piece for free surface, the point cloud noise removal is the most important step, after denoising point cloud which quality will directly influence the follow-up point normal vector estimation and curvature estimation. Therefore, this paper presents a simple and efficient algorithm for near-point denoising. Firstly, it using the bounding box for messy point cloud data differentiate space topology structure, then, traverse all points, for each point looking for its K neighborhood, and fitting quadric surface using the K neighbors; finally, using Z value method for calculation of the distance that point to the secondary surface distance. Setting threshold, and if the distance beyond the threshold, then the point that noise points and delete. Experiments show that this algorithm compared with the traditional algorithm, not only improve efficiency, and can be a very good retain the original model data, but also for the follow-up process provides high quality raw data, there is a wide useful in three dimensions scanning, projective measurement reverse design and other fields.

Stereoscopic particle image velocimetry of the flow field in the rotor exit region of a forward-blade centrifugal turbomachine

This article reports the measurements of the complicated flow phenomena in the rotor exit region of a multi-blade centrifugal fan with stereoscopic particle image velocimetry (PIV). The main purpose is to enhance the quality of raw PIV data for future turbulence analyses. The instantaneous and averaged quantities are sensitive to phase-matching of the raw data since the blade-to-blade distance is short. An image processing based procedure is devised that identifies the rotor margin and blade tips location and selects the phase-resolved images. The large measurement area is divided into four fields of view. PIV data post-processing optimizes the relative position of different fields of view and enhances matching between them by detecting the phase-resolved images. The accuracy and reliability of measurements are examined by error and convergency analyses. The results are used to analyse the interactions between jet and wake structures as well as between rotor exit flow and the volute stream near the rotor. Jet/wake structures, non-uniform flow patterns, and high vorticity layers with opposite value in both sides of each blade are novel measured patterns in centrifugal fan which also occur in other centrifugal turbomachines. Such data are a necessary prerequisite for the accurate turbulence modelling of the complicated jet/wake flow and the mixing in the volute.

MSnbase-an R/Bioconductor package for isobaric tagged mass spectrometry data visualization, processing and quantitation

MSnbase is an R/Bioconductor package for the analysis of quantitative proteomics experiments that use isobaric tagging. It provides an exploratory data analysis framework for reproducible research, allowing raw data import, quality control, visualization, data processing and quantitation. MSnbase allows direct integration of quantitative proteomics data with additional facilities for statistical analysis provided by the Bioconductor project. MSnbase is implemented in R (version ≥ 2.13.0) and available at the Bioconductor web site (http://www.bioconductor.org/). Vignettes outlining typical workflows, input/output capabilities and detailing underlying infrastructure are included in the package.

Analysis of quality raw data of second generation sequencers with Quality Assessment Software

BackgroundSecond generation technologies have advantages over Sanger; however, they have resulted in new challenges for the genome construction process, especially because of the small size of the reads, despite the high degree of coverage. Independent of the program chosen for the construction process, DNA sequences are superimposed, based on identity, to extend the reads, generating contigs; mismatches indicate a lack of homology and are not included. This process improves our confidence in the sequences that are generated.FindingsWe developed Quality Assessment Software, with which one can review graphs showing the distribution of quality values from the sequencing reads. This software allow us to adopt more stringent quality standards for sequence data, based on quality-graph analysis and estimated coverage after applying the quality filter, providing acceptable sequence coverage for genome construction from short reads.ConclusionsQuality filtering is a fundamental step in the process of constructing genomes, as it reduces the frequency of incorrect alignments that are caused by measuring errors, which can occur during the construction process due to the size of the reads, provoking misassemblies. Application of quality filters to sequence data, using the software Quality Assessment, along with graphing analyses, provided greater precision in the definition of cutoff parameters, which increased the accuracy of genome construction.

Accounting for uncertainty when mapping species distributions: The need for maps of ignorance

Accurate mapping of species distributions is a fundamental goal of modern biogeography, both for basic and applied purposes. This is commonly done by plotting known species occurrences, expert-drawn range maps or geographical estimations derived from species distribution models. However, all three kinds of maps are implicitly subject to uncertainty, due to the quality and bias of raw distributional data, the process of map building, and the dynamic nature of species distributions themselves. Here we review the main sources of uncertainty suggesting a code of good practices in order to minimize their effects. Specifically, we claim that uncertainty should be always explicitly taken into account and we propose the creation of maps of ignorance to provide information on where the mapped distributions are reliable and where they are uncertain.

Proximity loggers: data handling and classification for quality Control

Proximity loggers are a novel biotelemetry device used for quantifying animal-animal interactions in a non-invasive way. Such data has been used for studying a range of interactions from disease spread among badgers and cattle to quantifying cow-calf interactions. Such quantitative behavioral traits could be used for the purpose of selective breeding in domesticated animals. With the use of real data, from the study of oestrus behavior in cattle populations raised in an extensive grazing system, we have identified poor reciprocal agreement (RA) as a source of variation. To date, RA has not been adequately considered or addressed and can have serious implications for further analyses aimed at correctly quantifying and interpreting social behavior. We provide a database schema for storing the millions of contact records produced by proximity loggers and programming functions, for use in the statistical programming language R, for performing raw data quality control, data import, database queries and classification of RA between proximity logger pairs. Poor RA leads to a lack of confidence in the data recorded by a pair of loggers. At best, substantial noise is added to the data set and at worst can lead to over or under estimation of contacts between pairs of animals. Over successive deployments, the identification and removal of loggers consistently involved in poor RA pairs can improve the level of agreement and confidence in the recorded data. This is a necessity for the accurate estimation of genetic parameters based on proximity logger data.

EDGE3: A web-based solution for management and analysis of Agilent two color microarray experiments

BackgroundThe ability to generate transcriptional data on the scale of entire genomes has been a boon both in the improvement of biological understanding and in the amount of data generated. The latter, the amount of data generated, has implications when it comes to effective storage, analysis and sharing of these data. A number of software tools have been developed to store, analyze, and share microarray data. However, a majority of these tools do not offer all of these features nor do they specifically target the commonly used two color Agilent DNA microarray platform. Thus, the motivating factor for the development of EDGE3 was to incorporate the storage, analysis and sharing of microarray data in a manner that would provide a means for research groups to collaborate on Agilent-based microarray experiments without a large investment in software-related expenditures or extensive training of end-users.ResultsEDGE3 has been developed with two major functions in mind. The first function is to provide a workflow process for the generation of microarray data by a research laboratory or a microarray facility. The second is to store, analyze, and share microarray data in a manner that doesn't require complicated software. To satisfy the first function, EDGE3 has been developed as a means to establish a well defined experimental workflow and information system for microarray generation. To satisfy the second function, the software application utilized as the user interface of EDGE3 is a web browser. Within the web browser, a user is able to access the entire functionality, including, but not limited to, the ability to perform a number of bioinformatics based analyses, collaborate between research groups through a user-based security model, and access to the raw data files and quality control files generated by the software used to extract the signals from an array image.ConclusionHere, we present EDGE3, an open-source, web-based application that allows for the storage, analysis, and controlled sharing of transcription-based microarray data generated on the Agilent DNA platform. In addition, EDGE3 provides a means for managing RNA samples and arrays during the hybridization process. EDGE3 is freely available for download at .

Information costs of reduced-effort habitat monitoring in a butterfly recovery program

Habitat monitoring typically requires a large amount of effort and resources. Project managers are likely to consider cost-cutting options but they may not critically review the information costs of implementing those options. An effort recently began in New York State to monitor critical habitat and restoration progress aimed at recovering the federally endangered Karner blue butterfly (Lycaeides melissa samuelis). Specific strategies were proposed to increase efficiency of fieldwork: (1) estimate larval host plant (wild blue lupine, Lupinus perennis) abundance from cover data, (2) area-based standardization of sample size for nectar sampling, (3) use a minimum cover threshold to trigger nectar species counts, (4) use multiple observers. I quantitatively reviewed these time savers for effects on raw data quality, and for potential effects on interpreting habitat quality as part of recovery criteria. Cover-based models of lupine abundance differed between metapopulation recovery areas, area-based sampling was sufficient to detect over 80% of priority nectar species in most sites, nectar information loss was high due to the minimum cover threshold, and overall results from different observers were similar with one exception. Direct lupine stem counting is recommended over cover estimation, statistical interpolation (rarefaction) and extrapolation (asymptotic estimators) is recommended over area-based nectar sampling, use of minimum cover criteria is strongly discouraged, and field crews need botanical expertise and careful instructions. This example highlights why strategies to save time and money with monitoring fieldwork should be ‘put to the test’.

Quantitative data analysis methods for bead-based DNA hybridization assays using generic flow cytometry platforms

Bead-based assays are in demand for rapid genomic and proteomic assays for both research and clinical purposes. Standard quantitative procedures addressing raw data quality and analysis are required to ensure the data are consistent and reproducible across laboratories independent of flow platform. Quantitative procedures have been introduced spanning raw histogram analysis through to absolute target quantitation. These included models developed to estimate the absolute number of sample molecules bound per bead (Langmuir isotherm), relative quantitative comparisons (two-sided t-tests), and statistical analyses investigating the quality of raw fluorescence data. The absolute target quantitation method revealed a concentration range (below probe saturation) of Cy5-labeled synthetic cytokeratin 19 (K19) RNA of c.a. 1 x 10(4) to 500 x 10(4) molecules/bead, with a binding constant of c.a. 1.6 nM. Raw hybridization frequency histograms were observed to be highly reproducible across 10 triplex assay replicates and only three assay replicates were required to distinguish overlapping peaks representing small sequence mismatches. This study provides a quantitative scheme for determining the absolute target concentration in nucleic acid hybridization reactions and the equilibrium binding constants for individual probe/target pairs. It is envisaged that such studies will form the basis of standard analytical procedures for bead-based cytometry assays to ensure reproducibility in inter- and intra-platform comparisons of data between laboratories.

オープンパス型渦相関法データの品質管理

As the number of monitoring sites of carbon dioxide (CO2) and water vapor fluxes is increasing and long-term measurements of these fluxes are popularized, guidelines for practical procedures of quality control of measured flux data are needed to produce reliable datasets which are suitable for data analysis.Several quality control tests for eddy covariance data have been proposed. To examine whether such tests are suitable for practical use, we applied three types of quality control tests to a two-year dataset obtained by the open-path eddy covariance method at a rice field. Raw data quality control tests detected time series inappropriate for flux calculations such as runs with rainfall or instrumental failure. Steady state tests detected two kinds of non-steady state runs. Monotonic trends were detected by the instationarity test, while intermediate changes of turbulence structure were detected by the nonstationarity test. Integral turbulence characteristic tests based on flux-variance similarity were useful for confirming whether turbulence was well developed or not. Results of these quality control tests, together with additional information on the length of recording time, wind direction relative to the sonic anemometer, and precipitation records were categorized into nine classes and combined for evaluation of the final data quality of the covariances. The final data quality was then evaluated as either discard, high quality, low quality, or normal quality. By visual inspection of the daily courses of CO2 fluxes, we confirmed that anomalies and unexpected covariances had been rightly judged to be discarded in the final data quality classification. Energy imbalance ratios also supported the evaluation of the final data quality as the average of the half-hourly energy imbalance ratios decreased with the increase of final data quality. We concluded that the quality control tests examined and the final data quality evaluation proposed in the present study provide a suitable guide to data quality and can be a useful tool for flux studies.