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- Genre: Academic theses
While the COVID-19 pandemic continues to evolve, America’s nursing work force continue to work in the most challenging of circumstances. While expected to hold the fort and continue on, deep inside, they bury an unprecedented level of acute stress, anxiety and depression. Peer support groups have been posed as a possible coping behavior. This cross-sectional designed project was developed to assess the worth and feasibility of a virtual peer support group with a focus on healthcare provider wellness during a period of surge of the COVID-19 pandemic. Overwhelmed staff, technology/documentation changes and challenges, competing clinical demands, short-staffing and Zoom fatigue were identified as the limiting factors for this project’s completion within its given timeframe. These findings informed of current barriers, providing a basis for future program development to mitigate the impact of psychological distress among healthcare providers. Evolving literature on this topic supports recommendations for further study and action by individual health care providers, organizations and at the state and national levels.
This can make mutation detection difficult; and while increasing sequencing depth
can often help, sequence-specific errors and other non-random biases cannot be de-
tected by increased depth. The problem of accurate genotyping is exacerbated when
there is not a reference genome or other auxiliary information available.
I explore several methods for sensitively detecting mutations in non-model or-
ganisms using an example Eucalyptus melliodora individual. I use the structure of
the tree to find bounds on its somatic mutation rate and evaluate several algorithms
for variant calling. I find that conventional methods are suitable if the genome of a
close relative can be adapted to the study organism. However, with structured data,
a likelihood framework that is aware of this structure is more accurate. I use the
techniques developed here to evaluate a reference-free variant calling algorithm.
I also use this data to evaluate a k-mer based base quality score recalibrator
(KBBQ), a tool I developed to recalibrate base quality scores attached to sequencing
data. Base quality scores can help detect errors in sequencing reads, but are often
inaccurate. The most popular method for correcting this issue requires a known
set of variant sites, which is unavailable in most cases. I simulate data and show
that errors in this set of variant sites can cause calibration errors. I then show that
KBBQ accurately recalibrates base quality scores while requiring no reference or other
information and performs as well as other methods.
Finally, I use the Eucalyptus data to investigate the impact of quality score calibra-
tion on the quality of output variant calls and show that improved base quality score
calibration increases the sensitivity and reduces the false positive rate of a variant
calling algorithm.