scholarly journals Comparison of observation-based and model-based identification of alert concentrations from concentration–expression data

2021 ◽  
Author(s):  
Franziska Kappenberg ◽  
Marianna Grinberg ◽  
Xiaoqi Jiang ◽  
Annette Kopp-Schneider ◽  
Jan G Hengstler ◽  
...  
Keyword(s):  
Critical Level ◽  
Supplementary Information ◽  
Simulation Studies ◽  
Expression Data ◽  
Important Goal ◽  
Parametric Curve ◽  
Model Based ◽  
Effect Level ◽  
Valid Estimate ◽  
The Relationship

Abstract Motivation An important goal of concentration–response studies in toxicology is to determine an ‘alert’ concentration where a critical level of the response variable is exceeded. In a classical observation-based approach, only measured concentrations are considered as potential alert concentrations. Alternatively, a parametric curve is fitted to the data that describes the relationship between concentration and response. For a prespecified effect level, both an absolute estimate of the alert concentration and an estimate of the lowest concentration where the effect level is exceeded significantly are of interest. Results In a simulation study for gene expression data, we compared the observation-based and the model-based approach for both absolute and significant exceedance of the prespecified effect level. Results show that, compared to the observation-based approach, the model-based approach overestimates the true alert concentration less often and more frequently leads to a valid estimate, especially for genes with large variance. Availability and implementation The code used for the simulation studies is available via the GitHub repository: https://github.com/FKappenberg/Paper-IdentificationAlertConcentrations. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Model-based clustering of multi-tissue gene expression data

2019 ◽  
Author(s):  
Pau Erola ◽  
Johan L M Björkegren ◽  
Tom Michoel
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Clustering Algorithm ◽  
Expression Profiles ◽  
Individual Characteristics ◽  
Supplementary Information ◽  
Expression Data ◽  
Model Based ◽  
Tissue Gene Expression ◽  
Lemon Tree

Abstract Motivation Recently, it has become feasible to generate large-scale, multi-tissue gene expression data, where expression profiles are obtained from multiple tissues or organs sampled from dozens to hundreds of individuals. When traditional clustering methods are applied to this type of data, important information is lost, because they either require all tissues to be analyzed independently, ignoring dependencies and similarities between tissues, or to merge tissues in a single, monolithic dataset, ignoring individual characteristics of tissues. Results We developed a Bayesian model-based multi-tissue clustering algorithm, revamp, which can incorporate prior information on physiological tissue similarity, and which results in a set of clusters, each consisting of a core set of genes conserved across tissues as well as differential sets of genes specific to one or more subsets of tissues. Using data from seven vascular and metabolic tissues from over 100 individuals in the STockholm Atherosclerosis Gene Expression (STAGE) study, we demonstrate that multi-tissue clusters inferred by revamp are more enriched for tissue-dependent protein-protein interactions compared to alternative approaches. We further demonstrate that revamp results in easily interpretable multi-tissue gene expression associations to key coronary artery disease processes and clinical phenotypes in the STAGE individuals. Availability and implementation Revamp is implemented in the Lemon-Tree software, available at https://github.com/eb00/lemon-tree Supplementary information Supplementary data are available at Bioinformatics online.

ExperimentSubset: an R package to manage subsets of Bioconductor Experiment objects

2021 ◽  
Author(s):  
Irzam Sarfraz ◽  
Muhammad Asif ◽  
Joshua D Campbell
Keyword(s):  
Single Cell ◽  
R Package ◽  
Poor Quality ◽  
Data Matrix ◽  
Supplementary Information ◽  
Data Provenance ◽  
Rna Seq ◽  
Efficient Management ◽  
The Matrix ◽  
The Relationship

Abstract Motivation R Experiment objects such as the SummarizedExperiment or SingleCellExperiment are data containers for storing one or more matrix-like assays along with associated row and column data. These objects have been used to facilitate the storage and analysis of high-throughput genomic data generated from technologies such as single-cell RNA sequencing. One common computational task in many genomics analysis workflows is to perform subsetting of the data matrix before applying down-stream analytical methods. For example, one may need to subset the columns of the assay matrix to exclude poor-quality samples or subset the rows of the matrix to select the most variable features. Traditionally, a second object is created that contains the desired subset of assay from the original object. However, this approach is inefficient as it requires the creation of an additional object containing a copy of the original assay and leads to challenges with data provenance. Results To overcome these challenges, we developed an R package called ExperimentSubset, which is a data container that implements classes for efficient storage and streamlined retrieval of assays that have been subsetted by rows and/or columns. These classes are able to inherently provide data provenance by maintaining the relationship between the subsetted and parent assays. We demonstrate the utility of this package on a single-cell RNA-seq dataset by storing and retrieving subsets at different stages of the analysis while maintaining a lower memory footprint. Overall, the ExperimentSubset is a flexible container for the efficient management of subsets. Availability and implementation ExperimentSubset package is available at Bioconductor: https://bioconductor.org/packages/ExperimentSubset/ and Github: https://github.com/campbio/ExperimentSubset. Supplementary information Supplementary data are available at Bioinformatics online.

A new underwater positioning model based on average sound speed

2021 ◽  
pp. 1-17
Author(s):  
Yixu Liu ◽  
Xiushan Lu ◽  
Shuqiang Xue ◽  
Shengli Wang
Keyword(s):  
Efficient Algorithm ◽  
Sound Speed ◽  
Incident Angle ◽  
Function Model ◽  
Positioning Accuracy ◽  
Correction Model ◽  
Model Based ◽  
Underwater Positioning ◽  
Speed Function ◽  
The Relationship

Abstract The layout of seafloor datum points is the key to constructing the seafloor geodetic datum network, and a reliable underwater positioning model is the prerequisite for achieving precise deployment of the datum points. The traditional average sound speed positioning model is generally adopted in underwater positioning due to its simple and efficient algorithm, but it is sensitive to incident angle related errors, which lead to unreliable positioning results. Based on the relationship between incident angle and sound speed, the sound speed function model considering the incident angle has been established. Results show that the accuracy of positioning is easily affected by errors related to the incident angle; the new average sound speed correction model based on the incident angle proposed in this paper is used to significantly improve the underwater positioning accuracy.

scholarly journals TADOSS: computational estimation of tandem domain swap stability

2018 ◽  
Vol 35 (14) ◽  
pp. 2507-2508 ◽  
Author(s):  
Aleix Lafita ◽  
Pengfei Tian ◽  
Robert B Best ◽  
Alex Bateman
Keyword(s):  
Large Scale ◽  
Domain Swapping ◽  
Coarse Grained ◽  
Supplementary Information ◽  
Simulation Studies ◽  
Computational Tools ◽  
Domain Swap ◽  
Computational Estimation ◽  
High Propensity ◽  
The Stability

Abstract Summary Proteins with highly similar tandem domains have shown an increased propensity for misfolding and aggregation. Several molecular explanations have been put forward, such as swapping of adjacent domains, but there is a lack of computational tools to systematically analyze them. We present the TAndem DOmain Swap Stability predictor (TADOSS), a method to computationally estimate the stability of tandem domain-swapped conformations from the structures of single domains, based on previous coarse-grained simulation studies. The tool is able to discriminate domains susceptible to domain swapping and to identify structural regions with high propensity to form hinge loops. TADOSS is a scalable method and suitable for large scale analyses. Availability and implementation Source code and documentation are freely available under an MIT license on GitHub at https://github.com/lafita/tadoss. Supplementary information Supplementary data are available at Bioinformatics online.

Flight Simulation for Research

1964 ◽  
Vol 68 (646) ◽  
pp. 645-652 ◽  
Author(s):  
D. H. Perry ◽  
J. M. Naish
Keyword(s):  
Flight Simulation ◽  
Simulation Studies ◽  
Aircraft Control ◽  
Motion Cues ◽  
Stability And Control ◽  
Research Problems ◽  
And Control ◽  
The Relationship ◽  
Aircraft Stability And Control ◽  
Vtol Aircraft

SummarySome of the uses of ground based flight simulation as a research tool to aid the design of new aircraft and their equipment are described. The function of the simulator is to provide a method for investigating humon flying tasks in the laboratory, so that the relationship between the pilot's capabilities and the equipment's characteristics can be systematically studied. The paper is presented in two parts describing recent work on two research simulators at the RAE.Part I deals with the use of simulation for studying aircraft stability and control characteristics. The equipment used at RAE for this work is described, with particular emphasis on methods of presenting to the pilot a simulated view of the outside world, and for reproducing some of the motion cues which he experiences in flight. Experimental evidence of the importance of these simulation cues when making aircraft control assessments is also presented. Several examples of simulation studies into the control of conventional and VTOL aircraft are given, to illustrate the type of research problems in this field which may be tackled and the techniques involved in solving them.

Competing Explanations of Split-ticket Voting in American National Elections

2000 ◽  
Vol 28 (3) ◽  
pp. 379-407 ◽  
Author(s):  
FRANCO MATTEI ◽  
JOHN S. HOWES
Keyword(s):  
Small Group ◽  
National Elections ◽  
Congressional Voting ◽  
Party Polarization ◽  
Model Based ◽  
Alternative Approach ◽  
Candidate Evaluations ◽  
Electoral Expectations ◽  
The Relationship

In this article, the authors propose an extension to Fiorina's balancing model based on voters' electoral expectations and test this extension and several implications of the theory. The authors examine the observed pattern of ticket-splitting and find it less consistent with a balancing perspective than with an alternative approach stressing separation between presidential and congressional voting. They also address the relationship between party polarization and ticket-splitting; their results indicate that the occurrence of split ballots does not increase with polarization. A further test identifies respondents with both the sophistication and the motive to engage in balancing behavior. According to this analysis, balancing considerations influence, at most, the very small group of voters whose sophistication and electoral expectations give them the tools and the incentive to pursue balance with a split ballot. Ticket-splitting appears to result far more from incumbency and cross-pressured voters holding candidate evaluations at odds with their partisan learnings.

scholarly journals Towards functional modelling of relationships between the acoustics and perception of vowels

2005 ◽  
Vol 40 ◽  
pp. 133-144
Author(s):  
Hartmut R. Pfitzinger
Keyword(s):  
Steady State ◽  
Sufficient Accuracy ◽  
Acoustic Properties ◽  
Vowel Quality ◽  
Model Based ◽  
Functional Modelling ◽  
The Relationship ◽  
Vowel Height

This paper summarizes our research efforts in functional modelling of the relationship between the acoustic properties of vowels and perceived vowel quality. Our model is trained on 164 short steady-state stimuli. We measured F1, F2, and additionally F0 since the effect of F0 on perceptual vowel height is evident. 40 phonetically skilled subjects judged vowel quality using the Cardinal Vowel diagram. The main focus is on refining the model and describing its transformation properties between the F1/F2 formant chart and the Cardinal Vowel diagram. An evaluation of the model based on 48 additional vowels showed the generalizability of the model and confirmed that it predicts perceived vowel quality with sufficient accuracy.  

scholarly journals Approaches for Structural Investigations of Binary Data Using Confirmatory Factor Models

2018 ◽  
Vol 7 (6) ◽  
pp. 68
Author(s):  
Karl Schweizer ◽  
Siegbert Reiß ◽  
Stefan Troche
Keyword(s):  
Sample Size ◽  
Binary Data ◽  
Factor Models ◽  
The Other ◽  
Simulation Studies ◽  
Sample Sizes ◽  
Structural Investigations ◽  
Ml Estimation ◽  
Confirmatory Factor ◽  
The Relationship

An investigation of the suitability of threshold-based and threshold-free approaches for structural investigations of binary data is reported. Both approaches implicitly establish a relationship between binary data following the binomial distribution on one hand and continuous random variables assuming a normal distribution on the other hand. In two simulation studies we investigated: whether the fit results confirm the establishment of such a relationship, whether the differences between correct and incorrect models are retained and to what degree the sample size influences the results. Both approaches proved to establish the relationship. Using the threshold-free approach it was achieved by customary ML estimation whereas robust ML estimation was necessary in the threshold-based approach. Discrimination between correct and incorrect models was observed for both approaches. Larger CFI differences were found for the threshold-free approach than for the threshold-based approach. Dependency on sample size characterized the threshold-based approach but not the threshold-free approach. The threshold-based approach tended to perform better in large sample sizes, while the threshold-free approach performed better in smaller sample sizes.

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