scholarly journals Psychological Perturbation Data on Attitudes Towards the Consumption of Meat

2018 ◽  
Vol 6 ◽  
Author(s):  
Ria H. A. Hoekstra ◽  
Jolanda J. Kossakowski ◽  
Han L. J. van der Maas
Keyword(s):  
Perturbation Data

Speech Waveform Perturbation Analysis

1986 ◽  
Vol 29 (1) ◽  
pp. 50-64 ◽  
Author(s):  
Anders G. Askenfelt ◽  
Britta Hammarberg
Keyword(s):  
Relative Frequency ◽  
Clinical Applications ◽  
Rate Of Change ◽  
Frequency Of Occurrence ◽  
Pitch Period ◽  
Acoustic Measures ◽  
Before And After ◽  
Perturbation Data ◽  
Acoustic Measure ◽  
Pathological Voice

The performance of seven acoustic measures of cycle-to-cycle variations (perturbations) in the speech waveform was compared. All measures were calculated automatically and applied on running speech. Three of the measures refer to the frequency of occurrence and severity of waveform perturbations in special selected parts of the speech, identified by means of the rate of change in the fundamental frequency. Three other measures refer to statistical properties of the distribution of the relative frequency differences between adjacent pitch periods. One perturbation measure refers to the percentage of consecutive pitch period differences with alternating signs. The acoustic measures were tested on tape recorded speech samples from 41 voice patients, before and after successful therapy. Scattergrams of acoustic waveform perturbation data versus an average of perceived deviant voice qualities, as rated by voice clinicians, are presented. The perturbation measures were compared with regard to the acoustic-perceptual correlation and their ability to discriminate between normal and pathological voice status. The standard deviation of the distribution of the relative frequency differences was suggested as the most useful acoustic measure of waveform perturbations for clinical applications.

scholarly journals "From Perturbation Data, Regenerate of Data in Matlab"

2011 ◽  
Vol 14 (7) ◽  
pp. 7-10
Author(s):  
Fehreen Hasan ◽  
Niranjan Singh
Keyword(s):  
Perturbation Data

scholarly journals Determination of substorm onset from the geomagnetic D-perturbation data.

1978 ◽  
Vol 30 (3) ◽  
pp. 187-189 ◽  
Author(s):  
B. L. SHIRMAN ◽  
B. A. UNDSENKOV ◽  
V. A. SHAPIRO
Keyword(s):  
Substorm Onset ◽  
Perturbation Data

scholarly journals Inference of differential gene regulatory networks based on gene expression and genetic perturbation data

2019 ◽  
Vol 36 (1) ◽  
pp. 197-204 ◽  
Author(s):  
Xin Zhou ◽  
Xiaodong Cai
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Networks ◽  
Structural Equation ◽  
Regulatory Networks ◽  
Supplementary Information ◽  
Specific Gene ◽  
Joint Inference ◽  
Perturbation Data ◽  
Gene Regulatory ◽  
The Difference

Abstract Motivation Gene regulatory networks (GRNs) of the same organism can be different under different conditions, although the overall network structure may be similar. Understanding the difference in GRNs under different conditions is important to understand condition-specific gene regulation. When gene expression and other relevant data under two different conditions are available, they can be used by an existing network inference algorithm to estimate two GRNs separately, and then to identify the difference between the two GRNs. However, such an approach does not exploit the similarity in two GRNs, and may sacrifice inference accuracy. Results In this paper, we model GRNs with the structural equation model (SEM) that can integrate gene expression and genetic perturbation data, and develop an algorithm named fused sparse SEM (FSSEM), to jointly infer GRNs under two conditions, and then to identify difference of the two GRNs. Computer simulations demonstrate that the FSSEM algorithm outperforms the approaches that estimate two GRNs separately. Analysis of a dataset of lung cancer and another dataset of gastric cancer with FSSEM inferred differential GRNs in cancer versus normal tissues, whose genes with largest network degrees have been reported to be implicated in tumorigenesis. The FSSEM algorithm provides a valuable tool for joint inference of two GRNs and identification of the differential GRN under two conditions. Availability and implementation The R package fssemR implementing the FSSEM algorithm is available at https://github.com/Ivis4ml/fssemR.git. It is also available on CRAN. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Transcriptional signatures of cell-cell interactions are dependent on cellular context

2021 ◽  
Author(s):  
Brendan T Innes ◽  
Gary D Bader
Keyword(s):  
Gene Expression ◽  
Gene Expression Signature ◽  
Cell Interaction ◽  
Cell Interactions ◽  
Receptor Interaction ◽  
Perturbation Data ◽  
Cellular Context ◽  
Transcriptomics Data ◽  
Downstream Gene Expression ◽  
Cell Cell

Cell-cell interactions are often predicted from single-cell transcriptomics data based on observing receptor and corresponding ligand transcripts in cells. These predictions could theoretically be improved by inspecting the transcriptome of the receptor cell for evidence of gene expression changes in response to the ligand. It is commonly expected that a given receptor, in response to ligand activation, will have a characteristic downstream gene expression signature. However, this assumption has not been well tested. We used ligand perturbation data from both the high-throughput Connectivity Map resource and published transcriptomic assays of cell lines and purified cell populations to determine whether ligand signals have unique and generalizable transcriptional signatures across biological conditions. Most of the receptors we analyzed did not have such characteristic gene expression signatures - instead these signatures were highly dependent on cell type. Cell context is thus important when considering transcriptomic evidence of ligand signaling, which makes it challenging to build generalizable ligand-receptor interaction signatures to improve cell-cell interaction predictions.

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