Dataset Information

Interactive Sonification Exploring Emergent Behavior Applying Models for Biological Information and Listening.

ABSTRACT: Sonification is an open-ended design task to construct sound informing a listener of data. Understanding application context is critical for shaping design requirements for data translation into sound. Sonification requires methodology to maintain reproducibility when data sources exhibit non-linear properties of self-organization and emergent behavior. This research formalizes interactive sonification in an extensible model to support reproducibility when data exhibits emergent behavior. In the absence of sonification theory, extensibility demonstrates relevant methods across case studies. The interactive sonification framework foregrounds three factors: reproducible system implementation for generating sonification; interactive mechanisms enhancing a listener's multisensory observations; and reproducible data from models that characterize emergent behavior. Supramodal attention research suggests interactive exploration with auditory feedback can generate context for recognizing irregular patterns and transient dynamics. The sonification framework provides circular causality as a signal pathway for modeling a listener interacting with emergent behavior. The extensible sonification model adopts a data acquisition pathway to formalize functional symmetry across three subsystems: Experimental Data Source, Sound Generation, and Guided Exploration. To differentiate time criticality and dimensionality of emerging dynamics, tuning functions are applied between subsystems to maintain scale and symmetry of concurrent processes and temporal dynamics. Tuning functions accommodate sonification design strategies that yield order parameter values to render emerging patterns discoverable as well as rehearsable, to reproduce desired instances for clinical listeners. Case studies are implemented with two computational models, Chua's circuit and Swarm Chemistry social agent simulation, generating data in real-time that exhibits emergent behavior. Heuristic Listening is introduced as an informal model of a listener's clinical attention to data sonification through multisensory interaction in a context of structured inquiry. Three methods are introduced to assess the proposed sonification framework: Listening Scenario classification, data flow Attunement, and Sonification Design Patterns to classify sound control. Case study implementations are assessed against these methods comparing levels of abstraction between experimental data and sound generation. Outcomes demonstrate the framework performance as a reference model for representing experimental implementations, also for identifying common sonification structures having different experimental implementations, identifying common functions implemented in different subsystems, and comparing impact of affordances across multiple implementations of listening scenarios.

SUBMITTER: Choi I

PROVIDER: S-EPMC5934483 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Publications

Interactive Sonification Exploring Emergent Behavior Applying Models for Biological Information and Listening.

Choi Insook I

Frontiers in neuroscience 20180427

Sonification is an open-ended design task to construct sound informing a listener of data. Understanding application context is critical for shaping design requirements for data translation into sound. Sonification requires methodology to maintain reproducibility when data sources exhibit non-linear properties of self-organization and emergent behavior. This research formalizes interactive sonification in an extensible model to support reproducibility when data exhibits emergent behavior. In the ...[more]

PMID: 29755311

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Project description:Purpose: The goals of this study were to establish in vivo models that recapitulated the heterogeneous behavior of osteosarcoma and to develop an approach to distinguish genes expressed from tumor cells apart from those expressed from host cells in next generation sequencing (NGS) data from xenograft tumors. Methods: We used cell lines derived from two spontaneous canine osteosarcoma tumors with distinctly different biological behaviors for heterotypic in vivo modeling. NGS was used to compare the transcriptome profiles of the xenograft tumors to the transcriptome profiles of the parental cell lines and to study the tumor microenvironment of the xenograft tumors. Sequenced reads were trimmed for adaptor sequence, and masked for low-complexity or low-quality sequence, then mapped to a HISAT2 index for mapping which was built from a multi-sequence fasta file containing both the canine (canFam3) and murine (mm10) genomes with the HISAT2 aligner (2.0.2-beta) using default parameters. Rsubread featureCounts (1.4.6) was used to estimate transcript abundance using default parameters. The Bioconductor package ‘edgeR’ (version 3.12.1) was used for differential gene expression analysis and Partek Genomic Suite (version 6.6) was used for clustering and visualization. Qiagen's Ingenuity Pathway Analysis application (version 01-04) and Thomson Reuter's MetaCore software suite were used for pathway and upstream regulator analysis with differentially expressed genes. Results: We document that orthotopic canine osteosarcoma xenografts preserve the biological, molecular, and heterotypic biology observed in the tumors from which they were derived. The data also provide insight into the tumor-host interactions and identify targets that could play a role in treatment strategies. Conclusions: Our study represents the first detailed analysis of xenograft transcriptomes with an approach that allows for accurate examination of tumor-intrinsic properties, as well as organotypic tumor-stromal interaction that may influence tumor progression.

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Dataset Information

Interactive Sonification Exploring Emergent Behavior Applying Models for Biological Information and Listening.

Publications

Interactive Sonification Exploring Emergent Behavior Applying Models for Biological Information and Listening.

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