Publications by authors named "Travis Ostbye"

Background: Manual microscopy remains a widely-used tool for malaria diagnosis and clinical studies, but it has inconsistent quality in the field due to variability in training and field practices. Automated diagnostic systems based on machine learning hold promise to improve quality and reproducibility of field microscopy. The World Health Organization (WHO) has designed a 55-slide set (WHO 55) for their External Competence Assessment of Malaria Microscopists (ECAMM) programme, which can also serve as a valuable benchmark for automated systems.

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Background: Microscopic examination of Giemsa-stained blood films remains a major form of diagnosis in malaria case management, and is a reference standard for research. However, as with other visualization-based diagnoses, accuracy depends on individual technician performance, making standardization difficult and reliability poor. Automated image recognition based on machine-learning, utilizing convolutional neural networks, offers potential to overcome these drawbacks.

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Clustered regularly interspaced short palindromic repeats (CRISPR)-Cas9 technology is an important tool for genome editing because the Cas9 endonuclease can induce targeted DNA double-strand breaks. Targeting of the DNA break is typically controlled by a single-guide RNA (sgRNA), a chimeric RNA containing a structural segment important for Cas9 binding and a 20mer guide sequence that hybridizes to the genomic DNA target. Previous studies have demonstrated that CRISPR-Cas9 technology can be used for efficient, marker-free genome editing in Saccharomyces cerevisiae.

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