A framework for spatial normalization and voxelwise analysis of diffusion propagators in multiple MAP-MRI data sets

AbstractWe describe a pipeline for constructing a study-specific template of diffusion propagators measured with mean apparent propagator (MAP) MRI that supports direct voxelwise analysis of differences between propagators across multiple data sets. The pipeline leverages the fact that MAP-MRI is a generalization of diffusion tensor imaging (DTI) and combines simple and robust processing steps from existing tensor-based image registration methods. First, we compute a DTI study template which provides the reference frame and scaling parameters needed to construct a standardized set of MAP-MRI basis functions at each voxel in template space. Next, we transform each subjects diffusion data, including diffusion weighted images (DWIs) and gradient directions, from native to template space using the corresponding tensor-based deformation fields. Finally, we fit MAP coefficients in template space to the transformed DWIs of each subject using the standardized template of MAP basis functions. The consistency of MAP basis functions across all data sets in template space allows us to: 1. compute a template of propagators by directly averaging MAP coefficients and 2. quantify voxelwise differences between co-registered propagators using the angular dissimilarity, or a probability distance metric, such as the Jensen-Shannon Divergence. We illustrate the application of this method by generating a template of MAP propagators for a cohort of healthy volunteers and show a proof-of-principle example of how this pipeline may be used to detect subtle differences between propagators in a single-subject longitudinal clinical data set. The ability to standardize and analyze multiple clinical MAP-MRI data sets could improve assessments in cross-sectional and single-subject longitudinal clinical studies seeking to detect subtle microstructural changes, such as those occurring in mild traumatic brain injury (mTBI), or during the early stages of neurodegenerative diseases, or cancer.

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Multiple Data Set ILI for Mechanical Damage Assessment

Volume 2: Pipeline Integrity Management ◽

10.1115/ipc2012-90244 ◽

2012 ◽

Author(s):

Chris Goller ◽

James Simek ◽

Jed Ludlow

Keyword(s):

Damage Assessment ◽

Public Awareness ◽

Mechanical Damage ◽

Data Sets ◽

Data Set ◽

Multiple Data ◽

Pipe Joints ◽

Multiple Data Sets ◽

Multiple Field ◽

Flux Leakage

The purpose of this paper is to present a non-traditional pipeline mechanical damage ranking system using multiple-data-set in-line inspection (ILI) tools. Mechanical damage continues to be a major factor in reportable incidents for hazardous liquid and gas pipelines. While several ongoing programs seek to limit damage incidents through public awareness, encroachment monitoring, and one-call systems, others have focused efforts on the quantification of mechanical damage severity through modeling, the use of ILI tools, and subsequent feature assessment at locations selected for excavation. Current generation ILI tools capable of acquiring multiple-data-sets in a single survey may provide an improved assessment of the severity of damaged zones using methods developed in earlier research programs as well as currently reported information. For magnetic flux leakage (MFL) type tools, using multiple field levels, varied field directions, and high accuracy deformation sensors enables detection and provides the data necessary for enhanced severity assessments. This paper will provide a review of multiple-data-set ILI results from several pipe joints with simulated mechanical damage locations created mimicing right-of-way encroachment events in addition to field results from ILI surveys using multiple-data-set tools.

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Data Fusion Using a Multi-Sensor Sparse-Based Clustering Algorithm

Remote Sensing ◽

10.3390/rs12234007 ◽

2020 ◽

Vol 12 (23) ◽

pp. 4007

Author(s):

Kasra Rafiezadeh Shahi ◽

Pedram Ghamisi ◽

Behnood Rasti ◽

Robert Jackisch ◽

Paul Scheunders ◽

...

Keyword(s):

Clustering Algorithm ◽

Spatial Information ◽

Clustering Algorithms ◽

Hyperspectral Data ◽

Sensor Data ◽

Data Sets ◽

Data Types ◽

Data Set ◽

Multiple Data Sets ◽

Imaging Sensors

The increasing amount of information acquired by imaging sensors in Earth Sciences results in the availability of a multitude of complementary data (e.g., spectral, spatial, elevation) for monitoring of the Earth’s surface. Many studies were devoted to investigating the usage of multi-sensor data sets in the performance of supervised learning-based approaches at various tasks (i.e., classification and regression) while unsupervised learning-based approaches have received less attention. In this paper, we propose a new approach to fuse multiple data sets from imaging sensors using a multi-sensor sparse-based clustering algorithm (Multi-SSC). A technique for the extraction of spatial features (i.e., morphological profiles (MPs) and invariant attribute profiles (IAPs)) is applied to high spatial-resolution data to derive the spatial and contextual information. This information is then fused with spectrally rich data such as multi- or hyperspectral data. In order to fuse multi-sensor data sets a hierarchical sparse subspace clustering approach is employed. More specifically, a lasso-based binary algorithm is used to fuse the spectral and spatial information prior to automatic clustering. The proposed framework ensures that the generated clustering map is smooth and preserves the spatial structures of the scene. In order to evaluate the generalization capability of the proposed approach, we investigate its performance not only on diverse scenes but also on different sensors and data types. The first two data sets are geological data sets, which consist of hyperspectral and RGB data. The third data set is the well-known benchmark Trento data set, including hyperspectral and LiDAR data. Experimental results indicate that this novel multi-sensor clustering algorithm can provide an accurate clustering map compared to the state-of-the-art sparse subspace-based clustering algorithms.

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Estimating observation and model error variances using multiple data sets

Atmospheric Measurement Techniques ◽

10.5194/amt-11-4239-2018 ◽

2018 ◽

Vol 11 (7) ◽

pp. 4239-4260 ◽

Cited By ~ 8

Author(s):

Richard Anthes ◽

Therese Rieckh

Keyword(s):

Error Variance ◽

Specific Humidity ◽

Data Sets ◽

Data Set ◽

Multiple Data ◽

Gfs Model ◽

Multiple Data Sets ◽

Using Data ◽

The Tropics ◽

Estimated Error

Abstract. In this paper we show how multiple data sets, including observations and models, can be combined using the “three-cornered hat” (3CH) method to estimate vertical profiles of the errors of each system. Using data from 2007, we estimate the error variances of radio occultation (RO), radiosondes, ERA-Interim, and Global Forecast System (GFS) model data sets at four radiosonde locations in the tropics and subtropics. A key assumption is the neglect of error covariances among the different data sets, and we examine the consequences of this assumption on the resulting error estimates. Our results show that different combinations of the four data sets yield similar relative and specific humidity, temperature, and refractivity error variance profiles at the four stations, and these estimates are consistent with previous estimates where available. These results thus indicate that the correlations of the errors among all data sets are small and the 3CH method yields realistic error variance profiles. The estimated error variances of the ERA-Interim data set are smallest, a reasonable result considering the excellent model and data assimilation system and assimilation of high-quality observations. For the four locations studied, RO has smaller error variances than radiosondes, in agreement with previous studies. Part of the larger error variance of the radiosondes is associated with representativeness differences because radiosondes are point measurements, while the other data sets represent horizontal averages over scales of ∼ 100 km.

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Two-stage Linked Component Analysis for Joint Decomposition of Multiple Biologically Related Data Sets

10.1101/2021.03.22.435728 ◽

2021 ◽

Author(s):

By Huan Chen ◽

Brian Caffo ◽

Genevieve Stein-O’Brien ◽

Jinrui Liu ◽

Ben Langmead ◽

...

Keyword(s):

R Package ◽

Component Analysis ◽

Biological Data ◽

Joint Analysis ◽

Data Sets ◽

Biological Processes ◽

Two Stage ◽

Data Set ◽

Multiple Data ◽

Multiple Data Sets

SummaryIntegrative analysis of multiple data sets has the potential of fully leveraging the vast amount of high throughput biological data being generated. In particular such analysis will be powerful in making inference from publicly available collections of genetic, transcriptomic and epigenetic data sets which are designed to study shared biological processes, but which vary in their target measurements, biological variation, unwanted noise, and batch variation. Thus, methods that enable the joint analysis of multiple data sets are needed to gain insights into shared biological processes that would otherwise be hidden by unwanted intra-data set variation. Here, we propose a method called two-stage linked component analysis (2s-LCA) to jointly decompose multiple biologically related experimental data sets with biological and technological relationships that can be structured into the decomposition. The consistency of the proposed method is established and its empirical performance is evaluated via simulation studies. We apply 2s-LCA to jointly analyze four data sets focused on human brain development and identify meaningful patterns of gene expression in human neurogenesis that have shared structure across these data sets. The code to conduct 2s-LCA has been complied into an R package “PJD”, which is available at https://github.com/CHuanSite/PJD.

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Embedding to Reference t-SNE Space Addresses Batch Effects in Single-Cell Classification

10.1101/671404 ◽

2019 ◽

Cited By ~ 2

Author(s):

Pavlin G. Poličar ◽

Martin Stražar ◽

Blaž Zupan

Keyword(s):

Single Cell ◽

Secondary Data ◽

Primary Data ◽

Data Sets ◽

Batch Effects ◽

Data Set ◽

Reduction Techniques ◽

Straightforward Application ◽

Cell Gene Expression ◽

Multiple Data Sets

AbstractDimensionality reduction techniques, such as t-SNE, can construct informative visualizations of high-dimensional data. When working with multiple data sets, a straightforward application of these methods often fails; instead of revealing underlying classes, the resulting visualizations expose data set-specific clusters. To circumvent these batch effects, we propose an embedding procedure that takes a t-SNE visualization constructed on a reference data set and uses it as a scaffold for embedding new data. The new, secondary data is embedded one data-point at the time. This prevents any interactions between instances in the secondary data and implicitly mitigates batch effects. We demonstrate the utility of this approach with an analysis of six recently published single-cell gene expression data sets containing up to tens of thousands of cells and thousands of genes. In these data sets, the batch effects are particularly strong as the data comes from different institutions and was obtained using different experimental protocols. The visualizations constructed by our proposed approach are cleared of batch effects, and the cells from secondary data sets correctly co-cluster with cells from the primary data sharing the same cell type.

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Normative Influences on Adolescents’ Self-Reported Pro-Environmental Behaviors: The Role of Parents and Friends

Environment and Behavior ◽

10.1177/0013916517744591 ◽

2017 ◽

Vol 51 (3) ◽

pp. 288-314 ◽

Cited By ~ 11

Author(s):

Silvia Collado ◽

Henk Staats ◽

Patricia Sancho

Keyword(s):

Environmental Behavior ◽

Cross Sectional Study ◽

Data Sets ◽

Bias Effect ◽

Cross Sectional ◽

Environmental Behaviors ◽

Data Set ◽

Personal Norms ◽

Environmental Actions ◽

Role Of Parents

Pro-environmental behavioral patterns are influenced by relevant others’ actions and expectations. Studies about the intergenerational transmission of environmentalism have demonstrated that parents play a major role in their children’s pro-environmental actions. However, little is known about how other social agents may shape youth’s environmentalism. This cross-sectional study concentrates on the role that parents and peers have in the regulation of 12- to 19-year-olds’ pro-environmental behaviors. We also consider the common response bias effect by examining the associations between parents, peers, and adolescents’ pro-environmentalism in two independent data sets. Data Set 1 ( N = 330) includes adolescents’ perceptions of relevant others’ behaviors. Data Set 2 ( N = 152) includes relevant others’ self-reported pro-environmental behavior. Our results show that parents’ and peers’ descriptive and injunctive norms have a direct effect on adolescents’ pro-environmental behavior and an indirect one, through personal norms. Adolescents seem to be accurate in the perception of their close ones’ environmental actions.

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Stream Flow Measurement: Development of a Relationship between the Float Method and the Current Meter Method

10.5194/egusphere-egu2020-21719 ◽

2020 ◽

Author(s):

Ishmael Kanu

Keyword(s):

Flow Measurement ◽

River Flow ◽

Measurement Development ◽

Data Sets ◽

Cross Sectional ◽

Data Set ◽

Flow Measurements ◽

Float Method ◽

Hydropower Potential ◽

A Current

<p>In diverse developments such as hydropower potential assessment, flood mitigation studies, water supply, irrigation, bridge and culvert hydraulics, the magnitude of stream or river flows is a potential design input. Several methods of flow measurement exist; some basic and some more sophisticated. The sophisticated methods use equipment which, although they provide more accurate and reliable results, are invariably expensive and unaffordable by many institutions that depend greatly on flow records to plan and execute their projects. The need for skilled expertise in the use of these equipment and the associated maintenance problems preclude them from consideration in most projects developed and executed in developing regions such as Africa. For countries or institutions in these regions, there is a need for less expensive, but relatively reliable methods for stream or river flow measurement to be investigated; methods that require no equipment maintenance schemes. One such method is the float method in which the velocity of an object thrown in a river is measured by recording the time taken for the object to traverse a known distance and multiplying the velocity by the cross-sectional area of the river or stream. This method looks simplistic, but when flows obtained from it are correlated with those obtained from the more accurate and conventional methods, reliable results can be obtained. In this study, flow measurements were done at 42 different stream sections using the float method and a more reliable and generally accepted but expensive flow measurement method using a current meter. A statistical relationship was then developed between the flows obtained by the two methods by fitting a linear regression model to the set of data points obtained at the 42 locations on several reaches of selected streams in the western area of Freetown.&#160; The study was conducted on streams with tranquil or laminar flow with flow magnitudes in the range of 0.39 m3/s to 4 m3/s in practically straight reaches with stable banks. The material of the stream beds was laterite soil. Thirty-two data sets were used to develop and calibrate the model and the remaining ten data sets were used to verify the model. The current meter method flows were regressed on the float method flows. For a significance level of 5%, the predicted flows of a current meter, given a float method flow, showed a high level of agreement with the observed current meter flows for the tested data set.&#160;</p>

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Nonlinear Model Identification From Multiple Data Sets Using an Orthogonal Forward Search Algorithm

Journal of Computational and Nonlinear Dynamics ◽

10.1115/1.4023864 ◽

2013 ◽

Vol 8 (4) ◽

Cited By ~ 7

Author(s):

Ping Li ◽

Hua-Liang Wei ◽

Stephen A. Billings ◽

Michael A. Balikhin ◽

Richard Boynton

Keyword(s):

Search Algorithm ◽

Model Identification ◽

Identification Problem ◽

Data Sets ◽

Nonlinear Dynamic Model ◽

Data Set ◽

Multiple Data ◽

Orthogonal Search ◽

Data Points ◽

Multiple Data Sets

A basic assumption on the data used for nonlinear dynamic model identification is that the data points are continuously collected in chronological order. However, there are situations in practice where this assumption does not hold and we end up with an identification problem from multiple data sets. The problem is addressed in this paper and a new cross-validation-based orthogonal search algorithm for NARMAX model identification from multiple data sets is proposed. The algorithm aims at identifying a single model from multiple data sets so as to extend the applicability of the standard method in the cases, such as the data sets for identification are obtained from multiple tests or a series of experiments, or the data set is discontinuous because of missing data points. The proposed method can also be viewed as a way to improve the performance of the standard orthogonal search method for model identification by making full use of all the available data segments in hand. Simulated and real data are used in this paper to illustrate the operation and to demonstrate the effectiveness of the proposed method.

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Impacts of Sample Size and Quality-Adjusted Imputed Prices on Own-Price Elasticities Estimated Using Cross-Sectional Data

Journal of Agricultural and Applied Economics ◽

10.1017/s1074070800021374 ◽

2003 ◽

Vol 35 (2) ◽

pp. 415-421

Author(s):

Matthew C. Stockton

Keyword(s):

Sample Size ◽

Data Sets ◽

Sample Sizes ◽

Food Category ◽

Cross Sectional ◽

Data Set ◽

Food Away From Home ◽

Price Elasticities ◽

Quality Adjustment

Cross-sectional data sets containing expenditure and quantity information are typically used to calculate quality-adjusted imputed prices. Do sample size and quality adjustment of price statistically alter estimates for own-price elasticities? This paper employs a data set pertaining to three food categories—pork, cheese, and food away from home—with four sample sizes for each food category. Twelve sample sizes were used for both adjusted and unadjusted prices to derive elasticities. No statistical differences were found between own-price elasticities among sample sizes. However, elasticities that were based on adjusted price imputations were significantly different from those that were based on unadjusted prices.

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Comparing the performance of linear and nonlinear principal components in the context of high-dimensional genomic data integration

Statistical Applications in Genetics and Molecular Biology ◽

10.1515/sagmb-2016-0066 ◽

2017 ◽

Vol 16 (3) ◽

Author(s):

Shofiqul Islam ◽

Sonia Anand ◽

Jemila Hamid ◽

Lehana Thabane ◽

Joseph Beyene

Keyword(s):

Principal Component Analysis ◽

Data Integration ◽

Principal Components ◽

Mirna Expression ◽

Principal Component ◽

Component Analysis ◽

Kernel Principal Component Analysis ◽

Data Sets ◽

Data Set ◽

Multiple Data Sets

AbstractLinear principal component analysis (PCA) is a widely used approach to reduce the dimension of gene or miRNA expression data sets. This method relies on the linearity assumption, which often fails to capture the patterns and relationships inherent in the data. Thus, a nonlinear approach such as kernel PCA might be optimal. We develop a copula-based simulation algorithm that takes into account the degree of dependence and nonlinearity observed in these data sets. Using this algorithm, we conduct an extensive simulation to compare the performance of linear and kernel principal component analysis methods towards data integration and death classification. We also compare these methods using a real data set with gene and miRNA expression of lung cancer patients. First few kernel principal components show poor performance compared to the linear principal components in this occasion. Reducing dimensions using linear PCA and a logistic regression model for classification seems to be adequate for this purpose. Integrating information from multiple data sets using either of these two approaches leads to an improved classification accuracy for the outcome.

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