Comparison Of Multiple Data Sets Research Articles

FGRC-Net: A high-information interactive convolutional neural network for identifying ink spectral information

Modifying some keywords or numbers on documents to change the original intention is illegal. In some litigation cases, especially economic cases, there is often a need to examine the type of ink on documents. This paper proposes a nondestructive and accurate detection method for ink inspection. In this work, a high-information interactive, full group residual convolution network (FGRC-Net) is proposed and combined with spectral information to classify the inks of six different brands of pens. First, FGRC-Net realizes the information interaction among channels to effectively improve the feature extraction ability through multigroup convolution and multipath cascade. Meanwhile, to avoid feature degradation, the residual connection is introduced. Second, the ink spectral information of six pens based on the hyperspectral system is obtained. Finally, in comparing FGRC-Net and other deep learning methods, FGRC-Net shows the best classification performance with 98.38% accuracy, 98.53% precision, and 98.40% recall. The results show that combining FGRC-Net with a hyperspectral system is an effective detection method for handwritten ink classification. It also provides an effective detection method for handwritten document ink inspection. At the same time, the universality and validity of FGRC-Net in processing spectral information are verified by the comparison of multiple datasets.

GUCCI - Guided Cardiac Cohort Investigation of Blood Flow Data.

We present the framework GUCCI (Guided Cardiac Cohort Investigation), which provides a guided visual analytics workflow to analyze cohort-based measured blood flow data in the aorta. In the past, many specialized techniques have been developed for the visual exploration of such data sets for a better understanding of the influence of morphological and hemodynamic conditions on cardiovascular diseases. However, there is a lack of dedicated techniques that allow visual comparison of multiple data sets and defined cohorts, which is essential to characterize pathologies. GUCCI offers visual analytics techniques and novel visualization methods to guide the user through the comparison of predefined cohorts, such as healthy volunteers and patients with a pathologically altered aorta. The combination of overview and glyph-based depictions together with statistical cohort-specific information allows investigating differences and similarities of the time-dependent data. Our framework was evaluated in a qualitative user study with three radiologists specialized in cardiac imaging and two experts in medical blood flow visualization. They were able to discover cohort-specific characteristics, which supports the derivation of standard values as well as the assessment of pathology-related severity and the need for treatment.

Searching for outbursts in the ground-based photometry of 67P/Churyumov–Gerasimenko

ABSTRACT 67P/Churyumov–Gerasimenko is a Jupiter-family comet that was the target of the Rosetta mission, the first mission to successfully orbit and land a probe on a comet. This mission was accompanied by a large ground-based observing campaign. We have developed a pipeline to calibrate and measure photometry of comet 67P during its 2016 perihelion passage, making use of all visible wavelength broad-band imaging collected across a wide range of facilities. The pipeline calibrates the brightness of the comet to a common photometric system (Pan-STARRS 1) using background stars within the field allowing for compilation and comparison of multiple data sets. Results follow the predictions based on previous apparitions: 67P shows no obvious change in activity levels from orbit-to-orbit and coma colours remain constant throughout the apparition. We detected an outburst on 2015 August 22 of ∼0.14 mag. The brightness and estimated mass of this outburst puts it in line with the outbursts directly observed on the nucleus by Rosetta. An in situ outburst was observed at the same time as the one seen from the ground; however, linking these two events directly remains challenging.

The 4D Nucleome Data Portal as a resource for searching and visualizing curated nucleomics data

The 4D Nucleome (4DN) Network aims to elucidate the complex structure and organization of chromosomes in the nucleus and the impact of their disruption in disease biology. We present the 4DN Data Portal (https://data.4dnucleome.org/), a repository for datasets generated in the 4DN network and relevant external datasets. Datasets were generated with a wide range of experiments, including chromosome conformation capture assays such as Hi-C and other innovative sequencing and microscopy-based assays probing chromosome architecture. All together, the 4DN data portal hosts more than 1800 experiment sets and 36000 files. Results of sequencing-based assays from different laboratories are uniformly processed and quality-controlled. The portal interface allows easy browsing, filtering, and bulk downloads, and the integrated HiGlass genome browser allows interactive visualization and comparison of multiple datasets. The 4DN data portal represents a primary resource for chromosome contact and other nuclear architecture data for the scientific community.

DendroScan: an open source tool to conduct comparative statistical tests and dendrogrammatic analyses on particle morphometry

Quantitative shape analysis of juvenile pyroclasts is applied in volcanology to reconstruct the dynamics and styles of eruptions, and to explore the details of tephra transport, dispersal, and emplacement. Morphometric analyses often include comparison of multiple data sets with a set of dimensionless shape parameters. Here we present “DendroScan”, an open source Matlab program that provides the user with all the multivariate statistical methods needed to produce such morphometric comparisons. Serving as a statistical “toolbox”, DendroScan conducts Levene-, t-, and equivalence tests, presenting the results in ad hoc interpretable graphs. Furthermore, it is designed to conduct dendrogrammatic analyses of particle morphometry, a recently developed approach for the inter-comparison of multiple morphometric data sets. DendroScan produces tree diagrams, in which the analysed samples are sorted according to their morphometric dissimilarity, allowing the user to identify, e.g., samples that are statistically equivalent. To demonstrate DendroScan’s potential, ten experimental samples are compared with volcanic ash samples generated by the Havre 2012 deep-sea eruption in the Kermadec arc (New Zealand). We show how, using DendroScan-based results, information on the eruptive mechanism can be inferred, and how the cooling history of the experimental melt is reflected in the dissimilarity of thermally granulated fragments.

RNAthor - fast, accurate normalization, visualization and statistical analysis of RNA probing data resolved by capillary electrophoresis.

RNAs adopt specific structures to perform their functions, which are critical to fundamental cellular processes. For decades, these structures have been determined and modeled with strong support from computational methods. Still, the accuracy of the latter ones depends on the availability of experimental data, for example, chemical probing information that can define pseudo-energy constraints for RNA folding algorithms. At the same time, diverse computational tools have been developed to facilitate analysis and visualization of data from RNA structure probing experiments followed by capillary electrophoresis or next-generation sequencing. RNAthor, a new software tool for the fully automated normalization of SHAPE and DMS probing data resolved by capillary electrophoresis, has recently joined this collection. RNAthor automatically identifies unreliable probing data. It normalizes the reactivity information to a uniform scale and uses it in the RNA secondary structure prediction. Our web server also provides tools for fast and easy RNA probing data visualization and statistical analysis that facilitates the comparison of multiple data sets. RNAthor is freely available at http://rnathor.cs.put.poznan.pl/.

Interannual variability of the summer wind energy over China: A comparison of multiple datasets

AbstractBecause of the increase in the nation's need for wind energy, the impacts of climate change on wind energy have been investigated. In addition to long‐term changes, wind energy also shows robust interannual variations, but little effort has been devoted to understanding the underlying mechanisms. In this study, the impact of El Niño on the summer mean wind power density (WPD) over China is investigated. The abilities of five sets of reanalysis data in measuring the interannual variability of the WPD over China are assessed. Encouraging results are seen for all reanalysis datasets, with the MERRA and ERA‐Interim datasets showing the best performance. The relationship between El Niño and the following summer WPD is identified over China. During El Niño decaying year summers, the WPD over south of the Yangtze River valley increases, whereas the WPD over north of the Yangtze River valley decreases. The WPD changes are dominated by an anomalous anticyclone located in the northwestern Pacific. The anticyclone leads to strong southerly winds in southern China and thereby enhances the WPD. In regions north of the Yangtze River valley, the low surface pressure gradient causes a reduction in wind speed and thereby a weak WPD. Because the year‐by‐year variation in El Niño‐Southern Oscillation (ENSO) is highly predictable, our results shed light on the seasonal prediction of wind power over China.

RiboCAT: a new capillary electrophoresis data analysis tool for nucleic acid probing.

Chemical and enzymatic probing of RNA secondary structure and RNA/protein interactions provides the basis for understanding the functions of structured RNAs. However, the ability to rapidly perform such experiments using capillary electrophoresis has been hampered by relatively labor-intensive data analysis software. While these computationally robust programs have been shown to calculate residue-specific reactivities to a high degree of accuracy, they often require time-consuming manual intervention and lack the ability to be easily modified by users. To alleviate these issues, RiboCAT (Ribonucleic acid capillary-electrophoresis analysis tool) was developed as a user-friendly, Microsoft Excel–based tool that reduces the need for manual intervention, thereby significantly shortening the time required for data analysis. Features of this tool include (i) the use of an Excel platform, (ii) a method of intercapillary signal alignment using internal size standards, (iii) a peak-sharpening algorithm to more accurately identify peaks, and (iv) an open architecture allowing for simple user intervention. Furthermore, a complementary tool, RiboDOG (RiboCAT data output generator) was designed to facilitate the comparison of multiple data sets, highlighting potential inconsistencies and inaccuracies that may have occurred during analysis. Using these new tools, the secondary structure of the HIV-1 5′ untranslated region (5′UTR) was determined using selective 2′-hydroxyl acylation analyzed by primer extension (SHAPE), matching the results of previous work.

Reliable volumetry of the cervical spinal cord in MS patient follow-up data with cord image analyzer (Cordial).

Spinal cord (SC) atrophy is an important contributor to the development of disability in many neurological disorders including multiple sclerosis (MS). To assess the spinal cord atrophy in clinical trials and clinical practice, largely automated methods are needed due to the sheer amount of data. Moreover, using these methods in longitudinal trials requires them to deliver highly reliable measurements, enabling comparisons of multiple data sets of the same subject over time. We present a method for SC volumetry using 3D MRI data providing volume measurements for SC sections of fixed length and location. The segmentation combines a continuous max flow approach with SC surface reconstruction that locates the SC boundary based on image voxel intensities. Two cutting planes perpendicular to the SC centerline are determined based on predefined distances to an anatomical landmark, and the cervical SC volume (CSCV) is then calculated in-between these boundaries. The development of the method focused on its application in MRI follow-up studies; the method provides a high scan-rescan reliability, which was tested on healthy subject data. Scan-rescan reliability coefficients of variation (COV) were below 1%, intra- and interrater COV were even lower (0.1-0.2%). To show the applicability in longitudinal trials, 3-year follow-up data of 48 patients with a progressive course of MS were assessed. In this cohort, CSCV loss was the only significant predictor of disability progression (p=0.02). We are, therefore, confident that our method provides a reliable tool for SC volumetry in longitudinal clinical trials.

Interaction between Esophageal Squamous Cell Carcinoma and Adipose Tissue in Vitro

Esophageal squamous cell carcinoma (ESCC) develops within the squamous epithelial layer and invades the submucosa to the subadventitia that has adipose tissue (AT). AT seems critical to ESCC progression, but the underlying mechanism is unknown. We aimed to address the association between ESCC and AT invitro. ESCC cells were cultured on rat or human subcutaneous AT-embedded or -non-embedded collagen gel. AT promoted the growth of ESCC cells and inhibited their apoptosis. AT promoted the expression of the squamous differentiation marker involucrin in ESCC cells. AT accelerated the expression of invasion-related factors in poorly differentiated ESCC cells only. AT promoted the expression of phosphorylated-insulin-like growth factor-1 receptor in ESCC cells, whereas it inhibited that of the human epidermal growth factor receptor 2. Insulin-like growth factor-1, but not leptin, adiponectin, or resistin, promoted and inhibited the growth and apoptosis of ESCC cells, respectively. In turn, ESCC cells decreased the production of these adipokines in AT and the number of preadipocytes and mesenchymal stem cell-like cells, which developed from AT. These results suggest that i) AT may influence the progression of ESCC with increased growth or invasion and decreased apoptosis through insulin-like growth factor-1/insulin-like growth factor-1 receptor signaling, ii) AT may affect human epidermal growth factor receptor 2-targeted therapy; and iii) the cancer cells may affect adipokine production in AT.

Applying the Analytic Hierarchy Process (AHP) to an FSV-Based Comparison of Multiple Datasets

This paper presents research extending the feature selective validation (FSV) method to quantitatively compare multiple datasets. The analytic hierarchy process is introduced to weight different FSV results when comparison is conducted by comparing multiple numerical results with their referencing counterparts. Only pairwise comparisons are required which simplify the process of comprehensive validation. The efficiency of this approach is demonstrated by the validation of a simplified crosstalk model.

Comparison of multiple datasets with gridded precipitation observations over the Tibetan Plateau

Precipitation is a critical component of the water balance, and hence its variability is critical for cryospheric and climate change in the Tibetan Plateau (TP). Mean annual and seasonal precipitation totals are compared between gridded observations interpolated to a high resolution (0.5° × 0.5°) and multiple reanalysis type-datasets during 1979–2001. The latter include two NCEP reanalyses (NCEP1 and NCEP2), two European Centre for Medium-Range Weather Forecasts (ECMWF) reanalyses (ERA-40 and ERA-Interim), three modern reanalyses [the twentieth century reanalysis (20century), MERRA and CFSR] and three merged analysis datasets (CMAP1, CMAP2 and GPCP). Observations show an increase in mean precipitation from the northwestern to the southeastern (SE) regions of the TP which are divided by an isohyet of 400 mm, and overall trends during the studied period are positive. Compared with observations, most of the datasets (NCEP1, NCEP2, CMAP1, CMAP2, ERA-Interim, ERA-40, GPCP, 20century, MERRA and CFSR) can both broadly capture the spatial distributions and identify temporal patterns and variabilities of mean precipitation. However, most multi-datasets overestimate precipitation especially in the SE where summer convection is dominant. There remain substantial disagreements and large discrepancies in precipitation trends due to differences in assimilation systems between datasets. Taylor diagrams are used to show the correlation coefficients, standard deviation, and root-mean-square difference of precipitation totals between interpolated observations and assimilated values on an annual and seasonal basis. Merged analysis data (CMAP1 and CMAP2) agree with observations more closely than reanalyses. Thus not all datasets are equally biased and choice of dataset is important.

Visualize: a free and open source multifunction tool for proteomics data analysis.

A major challenge in the field of high-throughput proteomics is the conversion of the large volume of experimental data that is generated into biological knowledge. Typically, proteomics experiments involve the combination and comparison of multiple data sets and the analysis and annotation of these combined results. Although there are some commercial applications that provide some of these functions, there is a need for a free, open source, multifunction tool for advanced proteomics data analysis. We have developed the Visualize program that provides users with the abilities to visualize, analyze, and annotate proteomics data; combine data from multiple runs, and quantitate differences between individual runs and combined data sets. Visualize is licensed under GNU GPL and can be downloaded from http://proteomics.mcw.edu/visualize. It is available as compiled client-based executable files for both Windows and Mac OS X platforms as well as PERL source code.

SvdPPCS: an effective singular value decomposition-based method for conserved and divergent co-expression gene module identification

BackgroundComparative analysis of gene expression profiling of multiple biological categories, such as different species of organisms or different kinds of tissue, promises to enhance the fundamental understanding of the universality as well as the specialization of mechanisms and related biological themes. Grouping genes with a similar expression pattern or exhibiting co-expression together is a starting point in understanding and analyzing gene expression data. In recent literature, gene module level analysis is advocated in order to understand biological network design and system behaviors in disease and life processes; however, practical difficulties often lie in the implementation of existing methods.ResultsUsing the singular value decomposition (SVD) technique, we developed a new computational tool, named svdPPCS (SVD-based Pattern Pairing and Chart Splitting), to identify conserved and divergent co-expression modules of two sets of microarray experiments. In the proposed methods, gene modules are identified by splitting the two-way chart coordinated with a pair of left singular vectors factorized from the gene expression matrices of the two biological categories. Importantly, the cutoffs are determined by a data-driven algorithm using the well-defined statistic, SVD-p. The implementation was illustrated on two time series microarray data sets generated from the samples of accessory gland (ACG) and malpighian tubule (MT) tissues of the line W118 of M. drosophila. Two conserved modules and six divergent modules, each of which has a unique characteristic profile across tissue kinds and aging processes, were identified. The number of genes contained in these models ranged from five to a few hundred. Three to over a hundred GO terms were over-represented in individual modules with FDR < 0.1. One divergent module suggested the tissue-specific relationship between the expressions of mitochondrion-related genes and the aging process. This finding, together with others, may be of biological significance. The validity of the proposed SVD-based method was further verified by a simulation study, as well as the comparisons with regression analysis and cubic spline regression analysis plus PAM based clustering.ConclusionssvdPPCS is a novel computational tool for the comparative analysis of transcriptional profiling. It especially fits the comparison of time series data of related organisms or different tissues of the same organism under equivalent or similar experimental conditions. The general scheme can be directly extended to the comparisons of multiple data sets. It also can be applied to the integration of data sets from different platforms and of different sources.

Comparing time series using wavelet-based semblance analysis

Similarity measures are becoming increasingly commonly used in comparison of multiple datasets from various sources. Semblance filtering compares two datasets on the basis of their phase, as a function of frequency. Semblance analysis based on the Fourier transform suffers from problems associated with that transform, in particular its assumption that the frequency content of the data must not change with time (for time-series data) or location (for data measured as a function of position). To overcome these problems, semblance is calculated here using the continuous wavelet transform. When calculated in this way, semblance analysis allows the local phase relationships between the two datasets to be studied as a function of both scale (or wavelength) and time. Semblance analysis is demonstrated on synthetic datasets and on gravity and aeromagnetic data from the Vredefort Dome, South Africa. Matlab source code is available from the IAMG server at www.iamg.org.

Controlling for Doubt and Uncertainty Through Multiple Lines of Evidence: A New Look at the Mesoamerican Nahua Migrations

Nahuatl represents a relatively recent extension of the Uto-Aztecan language family into Mesoamerica. Ethnohistorians have linked Nahuatl's arrival to the historically attested migrations of nomadic people into central Mexico in the last centuries before the Spanish Conquest. Archaeologists have tended to treat migration as an explanation for a change in material culture rather than a social question to be examined theoretically. We approach this migration through the comparison of multiple data sets and conclude that what has previously been treated as a historical event is instead part of a longer term process tying together Mesoamerica's northern periphery with its highland core. While we find that certain themes from migration theory are reflected in this preindustrial migration as well, other variables are unique and bode well for archaeology's ability to address and contribute to theoretical issues relating to migration.

Second-order chemometric standardization for high-speed hyphenated gas chromatography: Analysis of GC/MS and comprehensive GC�GC data

Second-order data such as those provided by hyphenated instruments like gas chromatography with mass spectrometry detection (GC/MS) provide a wealth of information. Multichannel detection combined with multivariate analysis allows compounds that have been incompletely resolved by chromatography to be accurately quantitated. Because of this ability, chromatographic instruments can be operated in a significantly faster, reduced resolution mode. One reason this practice has not become common is that retention time variations complicate comparison of multiple data sets. In this article, an objective retention time standardization technique is presented that substantially improves quantitation. This work demonstrates a key advantage of second-order chemical analysis—that is, the ability to correct for retention time variation found in data from GC/MS analyzers and a relatively new hyphenated gas chromatographic analyzer known as high-speed comprehensive two-dimensional gas chromatography (comprehensive GC×GC). Second-order chromatographic standardization is demonstrated for the GC/MS analysis of methyltert-butyl ether (MTBE) in gasoline. The selectivity of capillary gas chromatography and mass spectrometry, combined with second-order analysis, allows this analysis to be performed in 40 s. Generalized rank annihilation method (GRAM) is used to quantify MTBE. Without standardization, an average retention time variation of 1.3% between standards and samples led to a quantitative precision error of about 20%. Second-order chromatographic standardization reduced this error to <5%. The standardization technique was also tested on comprehensive GC×GC data. In comprehensive GC×GC, the second of two columns repeatedly performed subsecond separations on portions of the effluent from a first column of different chemical selectivity. Comprehensive GC×GC of a white gas mixture modified with several test aromatic compounds was performed. The standardization demonstrated that retention times in comprehensive GC×GC are very repeatable in short column runs. GRAM was used to quantify overlapped ethylbenzene and p-xylene peaks in comprehensive GC×GC data. Because GRAM can be successfully applied to comprehensive GC×GC data, full resolution of all the analytes of interest is not necessary. As a result, comprehensive GC×GC run times can be dramatically shortened, which has significant implications for analyses in which short cycle times are of paramount importance. ©1999 John Wiley & Sons, Inc. J Micro Sep 11: 97–107, 1999

The use of GIST (Geographical Information System Toolkit) as an aid to the geological analysis of satellite imagery and other sources of mappable information: A case study from northern Xinjiang, China

GIST has recently been designed to combine functions of digital image processing and geographical information systems, so that it can support and integrate the main stages of a project requiring both quantitative and qualitative analysis of remotely sensed imagery and other sources of mappable information. It can be used for the manipulation, registration and comparison of multiple data sets, for the analysis of imagery, including a capacity to support visual interpretation at a sophisticated level based on on-screen image interpretation, and for the editing and production of final maps. This paper describes the contributions of the authors to the development of GIST while participating in a programme of collaboration between the Institute of Remote Sensing Applications and Scott Wilson Kirkpatrick & Partners, UK. It examines the development and use of GIST in a case study of regional geological mapping and resource evaluation, based on a Test Area in the Altai Mountains piedmont of northern Xinjiang. The general value of GIST as a means of combining well-established and new techniques of applied geological remote sensing is discussed.

行動科学データ解析のための探索的方法

The number of published studies on Exploratory Data Analysis(EDA)has increased at a considerable rate, during the past two decades. This article provides explanatory and bibliographic informations of this area, with reviews of research trends within and across the following categories of research: description of distributions; re-expression; comparison of multiple data sets; resistant smoothing; wandering schematic plot; resistant line; resistant two-way analysis. Our discussions in the present article proceed mainly by comparing the techniques in EDA with the traditional methods used in general. The bibliographies are by no means exhaustive, but they should be enough in order to understand the general features of EDA.