scholarly journals Spectral and topological analysis of the cortical representation of the head position: does hypnotizability matter?

2018 ◽  
Author(s):  
Esther Ibanez-Marcelo ◽  
Lisa Campioni ◽  
Diego Manzoni ◽  
Enrica L Santarcangelo ◽  
Giovanni Petri
Keyword(s):  
Spectral Analysis ◽  
Data Analysis ◽  
Large Scale ◽  
Topological Analysis ◽  
Head Position ◽  
Topological Data Analysis ◽  
Proprioceptive Information ◽  
Head Positions ◽  
First Time ◽  
Topological Data

The aim of the study was to assess the EEG correlates of head positions, which have never been studied in humans, in participants with different psychophysiological characteristics, as encoded by their hypnotizability scores. This choice is motivated by earlier studies suggesting different processing of the vestibular/neck proprioceptive information in subjects with high (highs) and low (lows) hypnotizability scores maintaining their head rotated toward one side (RH). We analysed EEG signals recorded in 20 highs and 19 lows in basal conditions (head forward) and during RH, using spectral analysis, which captures changes localized to specific recording sites, and Topological Data Analysis (TDA), which instead describes large-scale differences in processing and representing sensorimotor information. Spectral analysis revealed significant differences related to the head position for alpha1, beta2, beta3, gamma bands, but not to hypnotizability. TDA instead revealed global hypnotizability-related differences in the strengths of the correlations among recording sites during RH. Significant changes were observed in lows on the left parieto-occipital side and in highs in right fronto-parietal region. Significant differences between the two groups were found in the occipital region, where changes were larger in lows than in highs. The study reports findings of the EEG correlates of the head posture for the first time, indicates that hypnotizability modulates its representation/processing on large-scale and that spectral and topological data analysis provide complementary results.

scholarly journals Topological data analysis and diagnostics of compressible magnetohydrodynamic turbulence

2018 ◽  
Vol 84 (4) ◽  
Author(s):  
I. Makarenko ◽  
P. Bushby ◽  
A. Fletcher ◽  
R. Henderson ◽  
N. Makarenko ◽  
...  
Keyword(s):  
Data Analysis ◽  
Random Fields ◽  
Large Scale ◽  
Betti Numbers ◽  
Mean Field ◽  
Topological Data Analysis ◽  
Topological Measures ◽  
Magnetohydrodynamic Simulations ◽  
Random Flows ◽  
Topological Data

The predictions of mean-field electrodynamics can now be probed using direct numerical simulations of random flows and magnetic fields. When modelling astrophysical magnetohydrodynamics, it is important to verify that such simulations are in agreement with observations. One of the main challenges in this area is to identify robust quantitative measures to compare structures found in simulations with those inferred from astrophysical observations. A similar challenge is to compare quantitatively results from different simulations. Topological data analysis offers a range of techniques, including the Betti numbers and persistence diagrams, that can be used to facilitate such a comparison. After describing these tools, we first apply them to synthetic random fields and demonstrate that, when the data are standardized in a straightforward manner, some topological measures are insensitive to either large-scale trends or the resolution of the data. Focusing upon one particular astrophysical example, we apply topological data analysis to H iobservations of the turbulent interstellar medium (ISM) in the Milky Way and to recent magnetohydrodynamic simulations of the random, strongly compressible ISM. We stress that these topological techniques are generic and could be applied to any complex, multi-dimensional random field.

scholarly journals A COVID-19 Drug Repurposing Strategy through Quantitative Homological Similarities Using a Topological Data Analysis-Based Framework

Pharmaceutics ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 488
Author(s):  
Raul Pérez-Moraga ◽  
Jaume Forés-Martos ◽  
Beatriz Suay-García ◽  
Jean-Louis Duval ◽  
Antonio Falcó ◽  
...  
Keyword(s):  
Data Analysis ◽  
Protein Structures ◽  
Three Dimensional ◽  
Drug Repurposing ◽  
Viral Proteins ◽  
Topological Data Analysis ◽  
Global Pandemic ◽  
Massive Number ◽  
First Time ◽  
Topological Data

Since its emergence in March 2020, the SARS-CoV-2 global pandemic has produced more than 116 million cases and 2.5 million deaths worldwide. Despite the enormous efforts carried out by the scientific community, no effective treatments have been developed to date. We applied a novel computational pipeline aimed to accelerate the process of identifying drug repurposing candidates which allows us to compare three-dimensional protein structures. Its use in conjunction with two in silico validation strategies (molecular docking and transcriptomic analyses) allowed us to identify a set of potential drug repurposing candidates targeting three viral proteins (3CL viral protease, NSP15 endoribonuclease, and NSP12 RNA-dependent RNA polymerase), which included rutin, dexamethasone, and vemurafenib. This is the first time that a topological data analysis (TDA)-based strategy has been used to compare a massive number of protein structures with the final objective of performing drug repurposing to treat SARS-CoV-2 infection.

scholarly journals Topological data analysis of zebrafish patterns

2020 ◽  
Vol 117 (10) ◽  
pp. 5113-5124 ◽  
Author(s):  
Melissa R. McGuirl ◽  
Alexandria Volkening ◽  
Björn Sandstede
Keyword(s):  
Data Analysis ◽  
Pattern Formation ◽  
Large Scale ◽  
Model Organism ◽  
Topological Data Analysis ◽  
Wild Type ◽  
Cell Dynamics ◽  
Global Pattern ◽  
Agent Based ◽  
Topological Data

Self-organized pattern behavior is ubiquitous throughout nature, from fish schooling to collective cell dynamics during organism development. Qualitatively these patterns display impressive consistency, yet variability inevitably exists within pattern-forming systems on both microscopic and macroscopic scales. Quantifying variability and measuring pattern features can inform the underlying agent interactions and allow for predictive analyses. Nevertheless, current methods for analyzing patterns that arise from collective behavior capture only macroscopic features or rely on either manual inspection or smoothing algorithms that lose the underlying agent-based nature of the data. Here we introduce methods based on topological data analysis and interpretable machine learning for quantifying both agent-level features and global pattern attributes on a large scale. Because the zebrafish is a model organism for skin pattern formation, we focus specifically on analyzing its skin patterns as a means of illustrating our approach. Using a recent agent-based model, we simulate thousands of wild-type and mutant zebrafish patterns and apply our methodology to better understand pattern variability in zebrafish. Our methodology is able to quantify the differential impact of stochasticity in cell interactions on wild-type and mutant patterns, and we use our methods to predict stripe and spot statistics as a function of varying cellular communication. Our work provides an approach to automatically quantifying biological patterns and analyzing agent-based dynamics so that we can now answer critical questions in pattern formation at a much larger scale.

scholarly journals Persistence Landscape based Topological Data Analysis for Personalized Arrhythmia Classification

2019 ◽  
Author(s):  
Yan Yan ◽  
Kamen Ivanov ◽  
Jian Cen ◽  
Qiu-Hua Liu ◽  
Lei Wang
Keyword(s):  
Data Analysis ◽  
High Performance ◽  
Topological Analysis ◽  
Point Clouds ◽  
Topological Data Analysis ◽  
Reconstruction Technique ◽  
Biomedical Data ◽  
Training Set ◽  
Set Size ◽  
Topological Data

Data can be illustrated in shapes, and the shapes could provide insight for data modeling and information extraction. Topological data analysis provides an alternative insight in biomedical data analysis and knowledge discovery with the algebra topology tools. In present work, we study the application of topological data analysis for personalized electrocardiographic signal classification toward arrhythmia analysis. Using phase space reconstruction technique, the signal samples are converted into point clouds for topological analysis facility. With topological techniques the persistence landscapes from the point clouds are extracted as features to perform the arrhythmia classification task. We find that the proposed method is robust to the training set size, with only a training set size of 20% percents, the normal heartbeat class are 100% recognized, ventricular beats for 97.13%, supra-ventricular beats for 94.27% and fusion beats for 94.27% within the corresponding experiments. The property of keeping high performance when using smaller training sample proves that the proposed method is especially applicable to personalized analysis. With the present study, we show that the topological data analysis technique could be a useful tool in biomedical signal analysis, and provide powerful ability in personalized analysis.

scholarly journals Identification of Stem Cells from Large Cell Populations with Topological Scoring

2020 ◽  
Author(s):  
Mihaela E. Sardiu ◽  
Box C. Andrew ◽  
Jeff Haug ◽  
Michael P. Washburn
Keyword(s):  
Machine Learning ◽  
Flow Cytometry ◽  
Data Analysis ◽  
Large Scale ◽  
Topological Analysis ◽  
Topological Data Analysis ◽  
Cell Populations ◽  
Hematopoietic Stem ◽  
Flow Cytometry Data ◽  
Genomics And Proteomics

AbstractMachine learning and topological analysis methods are becoming increasingly used on various large-scale omics datasets. Modern high dimensional flow cytometry data sets share many features with other omics datasets like genomics and proteomics. For example, genomics or proteomics datasets can be sparse and have high dimensionality, and flow cytometry datasets can also share these features. This makes flow cytometry data potentially a suitable candidate for employing machine learning and topological scoring strategies, for example, to gain novel insights into patterns within the data. We have previously developed the Topological Score (TopS) and implemented it for the analysis of quantitative protein interaction network datasets. Here we show that the TopS approach for large scale data analysis is applicable to the analysis of a previously described flow cytometry sorted human hematopoietic stem cell dataset. We demonstrate that TopS is capable of effectively sorting this dataset into cell populations and identify rare cell populations. We demonstrate the utility of TopS when coupled with multiple approaches including topological data analysis, X-shift clustering, and t-Distributed Stochastic Neighbor Embedding (t-SNE). Our results suggest that TopS could be effectively used to analyze large scale flow cytometry datasets to find rare cell populations.

scholarly journals tmap: an integrative framework based on topological data analysis for population-scale microbiome stratification and association studies

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Tianhua Liao ◽  
Yuchen Wei ◽  
Mingjing Luo ◽  
Guo-Ping Zhao ◽  
Haokui Zhou
Keyword(s):  
Data Analysis ◽  
Large Scale ◽  
Association Studies ◽  
Topological Data Analysis ◽  
Association Patterns ◽  
Integrative Framework ◽  
Environmental Features ◽  
Analytic Methods ◽  
Population Scale ◽  
Topological Data

AbstractUntangling the complex variations of microbiome associated with large-scale host phenotypes or environment types challenges the currently available analytic methods. Here, we present tmap, an integrative framework based on topological data analysis for population-scale microbiome stratification and association studies. The performance of tmap in detecting nonlinear patterns is validated by different scenarios of simulation, which clearly demonstrate its superiority over the most commonly used methods. Application of tmap to several population-scale microbiomes extensively demonstrates its strength in revealing microbiome-associated host or environmental features and in understanding the systematic interrelations among their association patterns. tmap is available at https://github.com/GPZ-Bioinfo/tmap.

scholarly journals tmap: topological analysis of population-scale microbiome data

2018 ◽  
Author(s):  
Tianhua Liao ◽  
Yuchen Wei ◽  
Mingjing Luo ◽  
Guoping Zhao ◽  
Haokui Zhou
Keyword(s):  
Data Analysis ◽  
Large Scale ◽  
Topological Analysis ◽  
Topological Data Analysis ◽  
Link Type ◽  
Online Documentation ◽  
Microbiome Research ◽  
Population Scale ◽  
Microbiome Data ◽  
Complex Dataset

AbstractPopulation-scale microbiome study poses specific challenges in data analysis, from enterotype analysis, identification of driver species, to microbiome-wide association of host covariates. Application of advanced data mining techniques to high-dimensional complex dataset is expected to meet the rapid advancement in large scale and integrative microbiome research. Here, we present tmap, a topological data analysis framework for population-scale microbiome study. This framework can capture complex shape of large scale microbiome data into a compressive network representation. We also develop network-based statistical analysis for driver species identification and microbiome-wide association analysis. tmap can be used for exploring variations in a population-scale microbiome landscape to study host-microbiome association.Availability and implementationtmap is available at GitHub (https://github.com/GPZ-Bioinfo/tmap), accompanied with online documentation and tutorial (http://tmap.readthedocs.io).Contacthttp://[email protected]

scholarly journals Identification of Relevant Genetic Alterations in Cancer using Topological Data Analysis

2020 ◽  
Author(s):  
Raúl Rabadán ◽  
Yamina Mohamedi ◽  
Udi Rubin ◽  
Tim Chu ◽  
Oliver Elliott ◽  
...  
Keyword(s):  
Data Analysis ◽  
Large Scale ◽  
Molecular Mechanisms ◽  
Fold Increase ◽  
Suppressor Gene ◽  
Genetic Alterations ◽  
Topological Data Analysis ◽  
Integrated Analysis ◽  
Cancer Genes ◽  
Topological Data

AbstractLarge-scale cancer genomic studies enable the systematic identification of mutations that lead to the genesis and progression of tumors, uncovering the underlying molecular mechanisms and potential therapies. While some such mutations are recurrently found in many tumors, many others exist solely within a few samples, precluding detection by conventional recurrence-based statistical approaches. Integrated analysis of somatic mutations and RNA expression data across 12 tumor types reveals that mutations of cancer genes are usually accompanied by substantial changes in expression. We use topological data analysis to leverage this observation and uncover 38 elusive candidate cancer-associated genes, including inactivating mutations of the metalloproteinase ADAMTS12 in lung adenocarcinoma. We show that ADAMTS12−/− mice have a five-fold increase in the susceptibility to develop lung tumors, confirming the role of ADAMTS12 as a tumor suppressor gene. Our results demonstrate that data integration through topological techniques can increase our ability to identify previously unreported cancer-related alterations.

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