University of Sydney

About: University of Sydney is a education organization based out in Sydney, New South Wales, Australia. It is known for research contribution in the topics: Population & Health care. The organization has 61532 authors who have published 187345 publications receiving 6114218 citations. The organization is also known as: Sydney University & USyd.

Comprehensive genomic analysis of rhabdomyosarcoma reveals a landscape of alterations affecting a common genetic axis in fusion-positive and fusion-negative tumors

Abstract: Despite gains in survival, outcomes for patients with metastatic or recurrent rhabdomyosarcoma remain dismal. In a collaboration between the National Cancer Institute, Children's Oncology Group, and Broad Institute, we performed whole-genome, whole-exome, and transcriptome sequencing to characterize the landscape of somatic alterations in 147 tumor/normal pairs. Two genotypes are evident in rhabdomyosarcoma tumors: those characterized by the PAX3 or PAX7 fusion and those that lack these fusions but harbor mutations in key signaling pathways. The overall burden of somatic mutations in rhabdomyosarcoma is relatively low, especially in tumors that harbor a PAX3/7 gene fusion. In addition to previously reported mutations in NRAS, KRAS, HRAS, FGFR4, PIK3CA, and CTNNB1, we found novel recurrent mutations in FBXW7 and BCOR, providing potential new avenues for therapeutic intervention. Furthermore, alteration of the receptor tyrosine kinase/RAS/PIK3CA axis affects 93% of cases, providing a framework for genomics-directed therapies that might improve outcomes for patients with rhabdomyosarcoma. Significance This is the most comprehensive genomic analysis of rhabdomyosarcoma to date. Despite a relatively low mutation rate, multiple genes were recurrently altered, including NRAS, KRAS, HRAS, FGFR4, PIK3CA, CTNNB1, FBXW7, and BCOR. In addition, a majority of rhabdomyosarcoma tumors alter the receptor tyrosine kinase/RAS/PIK3CA axis, providing an opportunity for genomics-guided intervention.

Yangians and Classical Lie Algebras

Abstract: Contents §0. Introduction §1. The Yangian §2. The quantum determinant and the centre of §3. The twisted Yangian §4. The Sklyanin determinant and the centre of §5. The quantum contraction and the quantum Liouville formula for the Yangian §6. The quantum contraction and the quantum Liouville formula for the twisted Yangian §7. The quantum determinant and the Sklyanin determinant of block matrices Bibliography

Improving genetic diagnosis in Mendelian disease with transcriptome sequencing

Abstract: Exome and whole-genome sequencing are becoming increasingly routine approaches in Mendelian disease diagnosis. Despite their success, the current diagnostic rate for genomic analyses across a variety of rare diseases is approximately 25 to 50%. We explore the utility of transcriptome sequencing [RNA sequencing (RNA-seq)] as a complementary diagnostic tool in a cohort of 50 patients with genetically undiagnosed rare muscle disorders. We describe an integrated approach to analyze patient muscle RNA-seq, leveraging an analysis framework focused on the detection of transcript-level changes that are unique to the patient compared to more than 180 control skeletal muscle samples. We demonstrate the power of RNA-seq to validate candidate splice-disrupting mutations and to identify splice-altering variants in both exonic and deep intronic regions, yielding an overall diagnosis rate of 35%. We also report the discovery of a highly recurrent de novo intronic mutation in COL6A1 that results in a dominantly acting splice-gain event, disrupting the critical glycine repeat motif of the triple helical domain. We identify this pathogenic variant in a total of 27 genetically unsolved patients in an external collagen VI–like dystrophy cohort, thus explaining approximately 25% of patients clinically suggestive of having collagen VI dystrophy in whom prior genetic analysis is negative. Overall, this study represents a large systematic application of transcriptome sequencing to rare disease diagnosis and highlights its utility for the detection and interpretation of variants missed by current standard diagnostic approaches.

Decoding, Calibration and Rectification for Lenselet-Based Plenoptic Cameras

Abstract: Plenoptic cameras are gaining attention for their unique light gathering and post-capture processing capabilities. We describe a decoding, calibration and rectification procedure for lenselet-based plenoptic cameras appropriate for a range of computer vision applications. We derive a novel physically based 4D intrinsic matrix relating each recorded pixel to its corresponding ray in 3D space. We further propose a radial distortion model and a practical objective function based on ray reprojection. Our 15-parameter camera model is of much lower dimensionality than camera array models, and more closely represents the physics of lenselet-based cameras. Results include calibration of a commercially available camera using three calibration grid sizes over five datasets. Typical RMS ray reprojection errors are 0.0628, 0.105 and 0.363 mm for 3.61, 7.22 and 35.1 mm calibration grids, respectively. Rectification examples include calibration targets and real-world imagery.