Association between Self-Reported Prior Nights’ Sleep and Single-Task Gait in Healthy Young Adults: An Exploratory Study Using Machine Learning

Failure to obtain 7-9 hours of sleep has been associated with decreased gait speed in young adults. While Machine Learning (ML) has been used to identify sleep quality in young adults, there are no current studies that have used ML to identify prior night’s sleep in a sample of young adults. PURPOSE: To use ML to identify prior night’s sleep in healthy young adults using single-task walking gait. METHODS: Participants (n=126, age 24.3±4.0yrs; 65% female) completed a survey on their prior night’s sleep and performed a 2-minute walk around a 6m track. Gait data were collected using inertial sensors. Participants were split into 2 groups (\u3c7hs or \u3e9hs: poor sleepers; 7-9hs: good sleepers) and gait characteristics were used to classify participants into each group using ML models via a 10-fold cross validation. A post-hoc ANCOVA was used to assess gait differences. RESULTS: Using Random Forest Classifiers (RFC), top 9 features were extracted. Classification results suggest a 0.79 correlation between gait parameters and prior night’s sleep. The RFC models had a 65.03% mean classification accuracy rate. Top 0.3% of the models had 100% classification accuracy rate. The top 9 features were primarily characteristics that measured variance between lower limb movements. Post-hoc analyses suggest significantly greater variances between lower limb characteristics. CONCLUSION: Good sleepers had more asymmetrical gait patterns (faster gait speed, less trunk motion). Poor sleepers had trouble maintaining gait speed (increased variance in cadence, larger stride lengths, and less time spent in single leg support time). Although the mechanisms of these gait changes are unknown, these findings provide evidence that gait is different for individuals who not receive 7-9 hours of sleep the night before. As evidenced by the high correlation co-efficient of our classification models, gait may be a good way of identifying prior night’s sleep

Association between Self-Reported Prior Night’s Sleep and Single-Task Gait in Healthy, Young Adults: A Study Using Machine Learning

Failure to obtain the recommended 7–9 h of sleep has been associated with injuries in youth and adults. However, most research on the influence of prior night’s sleep and gait has been conducted on older adults and clinical populations. Therefore, the objective of this study was to identify individuals who experience partial sleep deprivation and/or sleep extension the prior night using single task gait. Participants (n = 123, age 24.3 ± 4.0 years; 65% female) agreed to participate in this study. Self-reported sleep duration of the night prior to testing was collected. Gait data was collected with inertial sensors during a 2 min walk test. Group differences (<7 h and >9 h, poor sleepers; 7–9 h, good sleepers) in gait characteristics were assessed using machine learning and a post-hoc ANCOVA. Results indicated a correlation (r = 0.79) between gait parameters and prior night’s sleep. The most accurate machine learning model was a Random Forest Classifier using the top 9 features, which had a mean accuracy of 65.03%. Our findings suggest that good sleepers had more asymmetrical gait patterns and were better at maintaining gait speed than poor sleepers. Further research with larger subject sizes is needed to develop more accurate machine learning models to identify prior night’s sleep using single-task gait

Improved Activity of a Thermophilic Cellulase, Cel5A, from Thermotoga maritima on Ionic Liquid Pretreated Switchgrass

Ionic liquid pretreatment of biomass has been shown to greatly reduce the recalcitrance of lignocellulosic biomass, resulting in improved sugar yields after enzymatic saccharification. However, even under these improved saccharification conditions the cost of enzymes still represents a significant proportion of the total cost of producing sugars and ultimately fuels from lignocellulosic biomass. Much of the high cost of enzymes is due to the low catalytic efficiency and stability of lignocellulolytic enzymes, especially cellulases, under conditions that include high temperatures and the presence of residual pretreatment chemicals, such as acids, organic solvents, bases, or ionic liquids. Improving the efficiency of the saccharification process on ionic liquid pretreated biomass will facilitate reduced enzyme loading and cost. Thermophilic cellulases have been shown to be stable and active in ionic liquids but their activity is typically at lower levels. Cel5A_Tma, a thermophilic endoglucanase from Thermotoga maritima, is highly active on cellulosic substrates and is stable in ionic liquid environments. Here, our motivation was to engineer mutants of Cel5A_Tma with higher activity on 1-ethyl-3-methylimidazolium acetate ([C(2)mim][OAc]) pretreated biomass. We developed a robotic platform to screen a random mutagenesis library of Cel5A_Tma. Twelve mutants with 25–42% improvement in specific activity on carboxymethyl cellulose and up to 30% improvement on ionic-liquid pretreated switchgrass were successfully isolated and characterized from a library of twenty thousand variants. Interestingly, most of the mutations in the improved variants are located distally to the active site on the protein surface and are not directly involved with substrate binding