The purpose of this investigation was to identify the critical tasks encountered by correctional officers (COs) on the job and to conduct a comprehensive assessment and characterization of the physical demands of these tasks. These are the first steps in developing a fitness screening test for COs in compliance with recent legislation. The most important, physically demanding, and frequently occurring tasks were identified using Delphi methodology, focus groups, and questionnaire responses from 190 experienced front-line COs. These tasks were structured into emergency response scenarios for which a physical and physiological characterization was conducted to verify their relative physical demands analysis. Oxygen consumption and the forces exerted by COs were quantified while they were responding and then controlling and restraining inmates. The female COs used less force than the male COs did to control and restrain the same inmates (body control = 46 vs. 60 kg, wrist hold = 32 vs. 49 kg, and arm retraction = 37 vs. 47 kg) and did not exert their maximal strength during their control and restraint activities. The mean oxygen consumption of the female and male COs while performing the on-the-job tasks was similar (39.5 vs. 38.5 mL.kg-1.min-1). We concluded that the essential components of a fitness screening protocol for CO applicants are cell search, expeditious response, body control, arm restraint, inmate relocation, and an assessment of aerobic fitness. The criterion performance standards for completing these tasks in a circuit were set at the job performance level of safe and efficient female COs.
Motivation: Selecting a subset of k-mers in a string in a local manner is a common task in bioinformatics tools for speeding up computation. Arguably the most well-known and common method is the minimizer technique, which selects the "lowest-ordered" k-mer in a sliding window. Recently, it has been shown that minimizers are a sub-optimal method for selecting subsets of k-mers when mutations are present. There is however a lack of understanding behind the theory of why certain methods perform well. Results: We first theoretically investigate the conservation metric for k-mer selection methods. We derive an exact expression for calculating the conservation of a k-mer selection method. This turns out to be tractable enough for us to prove closed-form expressions for a variety of methods, including (open and closed) syncmers, (a, b, n)-words, and an upper bound for minimizers. As a demonstration of our results, we modified the minimap2 read aligner to use a more optimal k-mer selection method and demonstrate that there is up to an 8.2% relative increase in number of mapped reads. Availability and supplementary information: Simulations and supplementary methods available at https://github.com/bluenote-1577/local-kmer-selection-results. os-minimap2 is a modified version of minimap2 and available at https://github.com/bluenote-1577/os-minimap2.
Motivation Selecting a subset of k-mers in a string in a local manner is a common task in bioinformatics tools for speeding up computation. Arguably the most well-known and common method is the minimizer technique, which selects the ‘lowest-ordered’ k-mer in a sliding window. Recently, it has been shown that minimizers may be a sub-optimal method for selecting subsets of k-mers when mutations are present. There is, however, a lack of understanding behind the theory of why certain methods perform well. Results We first theoretically investigate the conservation metric for k-mer selection methods. We derive an exact expression for calculating the conservation of a k-mer selection method. This turns out to be tractable enough for us to prove closed-form expressions for a variety of methods, including (open and closed) syncmers, (a, b, n)-words, and an upper bound for minimizers. As a demonstration of our results, we modified the minimap2 read aligner to use a more conserved k-mer selection method and demonstrate that there is up to an 8.2% relative increase in number of mapped reads. However, we found that the k-mers selected by more conserved methods are also more repetitive, leading to a runtime increase during alignment. We give new insight into how one might use new k-mer selection methods as a reparameterization to optimize for speed and alignment quality. Availability and implementation Simulations and supplementary methods are available at https://github.com/bluenote-1577/local-kmer-selection-results. os-minimap2 is a modified version of minimap2 and available at https://github.com/bluenote-1577/os-minimap2. Supplementary information Supplementary data are available at Bioinformatics online.
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