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Joshua C. Bowden

Researcher at Commonwealth Scientific and Industrial Research Organisation

Publications -  16
Citations -  7114

Joshua C. Bowden is an academic researcher from Commonwealth Scientific and Industrial Research Organisation. The author has contributed to research in topics: Diffusion MRI & Cartilage. The author has an hindex of 8, co-authored 15 publications receiving 5730 citations. Previous affiliations of Joshua C. Bowden include French Institute for Research in Computer Science and Automation & Flinders University.

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De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis

TL;DR: This protocol provides a workflow for genome-independent transcriptome analysis leveraging the Trinity platform and presents Trinity-supported companion utilities for downstream applications, including RSEM for transcript abundance estimation, R/Bioconductor packages for identifying differentially expressed transcripts across samples and approaches to identify protein-coding genes.

De novo transcript sequence reconstruction from RNA-Seq: reference generation and analysis with Trinity

TL;DR: This protocol describes the use of the Trinity platform for de novo transcriptome assembly from RNA-Seq data in non-model organisms and presents Trinity’s supported companion utilities for downstream applications, including RSEM for transcript abundance estimation and R/Bioconductor packages for identifying differentially expressed transcripts across samples.
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Anisotropy of collagen fibre alignment in bovine cartilage: comparison of polarised light microscopy and spatially resolved diffusion-tensor measurements

TL;DR: In this article, the authors compared the alignment angles obtained from DTI and diffusion tensor imaging in bovine articular cartilage using T2-weighted, diffusion-tensor, and PLM images.
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Diffusion tensor imaging of articular cartilage as a measure of tissue microstructure

TL;DR: In this paper, the authors used diffusion tensor MR micro-imaging to observe differences in magnitude and anisotropy of water diffusion between healthy cartilage and cartilage enzymatically degraded to simulate arthritic damage.
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Risk-conscious correction of batch effects: maximising information extraction from high-throughput genomic datasets

TL;DR: Harman’s ability to better remove batch noise, and better preserve biologically meaningful signal simultaneously within a single study, and maintain the user-set trade-off between batch noise rejection and signal preservation across different studies makes it an effective alternative method to deal with batch effects in high-throughput genomic datasets.