7,090 research outputs found

    Inter-individual variation of the human epigenome & applications

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    Graduate Catalog of Studies, 2023-2024

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    UMSL Bulletin 2023-2024

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    The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp

    Graduate Catalog of Studies, 2023-2024

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    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    UMSL Bulletin 2022-2023

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    The 2022-2023 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1087/thumbnail.jp

    Guided rewriting and constraint satisfaction for parallel GPU code generation

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    Graphics Processing Units (GPUs) are notoriously hard to optimise for manually due to their scheduling and memory hierarchies. What is needed are good automatic code generators and optimisers for such parallel hardware. Functional approaches such as Accelerate, Futhark and LIFT leverage a high-level algorithmic Intermediate Representation (IR) to expose parallelism and abstract the implementation details away from the user. However, producing efficient code for a given accelerator remains challenging. Existing code generators depend on the user input to choose a subset of hard-coded optimizations or automated exploration of implementation search space. The former suffers from the lack of extensibility, while the latter is too costly due to the size of the search space. A hybrid approach is needed, where a space of valid implementations is built automatically and explored with the aid of human expertise. This thesis presents a solution combining user-guided rewriting and automatically generated constraints to produce high-performance code. The first contribution is an automatic tuning technique to find a balance between performance and memory consumption. Leveraging its functional patterns, the LIFT compiler is empowered to infer tuning constraints and limit the search to valid tuning combinations only. Next, the thesis reframes parallelisation as a constraint satisfaction problem. Parallelisation constraints are extracted automatically from the input expression, and a solver is used to identify valid rewriting. The constraints truncate the search space to valid parallel mappings only by capturing the scheduling restrictions of the GPU in the context of a given program. A synchronisation barrier insertion technique is proposed to prevent data races and improve the efficiency of the generated parallel mappings. The final contribution of this thesis is the guided rewriting method, where the user encodes a design space of structural transformations using high-level IR nodes called rewrite points. These strongly typed pragmas express macro rewrites and expose design choices as explorable parameters. The thesis proposes a small set of reusable rewrite points to achieve tiling, cache locality, data reuse and memory optimisation. A comparison with the vendor-provided handwritten kernel ARM Compute Library and the TVM code generator demonstrates the effectiveness of this thesis' contributions. With convolution as a use case, LIFT-generated direct and GEMM-based convolution implementations are shown to perform on par with the state-of-the-art solutions on a mobile GPU. Overall, this thesis demonstrates that a functional IR yields well to user-guided and automatic rewriting for high-performance code generation

    Recombinant spidroins from infinite circRNA translation

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    Spidroins are a diverse family of peptides and the main components of spider silk. They can be used to produce sustainable, lightweight and durable materials for a large variety of medical and engineering applications. Spiders’ territorial behaviour and cannibalism precludes farming them for silk. Recombinant protein synthesis is the most promising way of producing these peptides. However, many approaches have been unsuccessful in obtaining large titres of recombinant spidroins or ones of sufficient molecular weight. The work described here is focused on expressing high molecular weight spidroins from short circular RNA molecules. Mammalian host cells were transfected with designed circular-RNA-producing plasmid vectors. A backsplicing approach was implemented to successfully circularise RNA in a variety of mammalian cell types. This approach could not express any recombinant spidroins based on a variety of qualitative protein assays. Further experiments investigated the reasons behind this. Additionally, due to the diversity of spidroins in a large number of spider lineages, there are potentially many spidroin sequences left to be discovered. A bioinformatic pipeline was developed that accepts transcriptome datasets from RNA sequencing and uses tandem repeat detection and profile HMM annotation to identify novel sequences. This pipeline was specifically designed for the identification of repeat domains in expressed sequences. 21 transcriptomes from 17 different species, encompassing a wide selection of basal and derived spider lineages, were investigated using this pipeline. Six previously undescribed spidroin sequences were discovered. This pipeline was additionally tested in the context of the suckerin protein family. These proteins have recently been investigated for their potential properties in medicine and engineering including adhesion in wet environments. The computational pipeline was able to double the number of suckerins known to date. Further phylogenetic analysis was implemented to expand on the knowledge of suckerins. This pipeline enables the identification of transcripts that may have been overlooked by more mainstream analysis methods such as pairwise homology searches. The spidroins and suckerins discovered by this pipeline may contribute to the large repertoire of potentially useful properties characteristic of this diverse peptide family
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