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This study aimed to validate the accuracy and prediction performance of machine learning (ML), deep learning (DL), and logistic regression methods in the treatment of medial meniscus posterior root tears (MMPRT). From July 2003 to May 2018, 640 patients diagnosed with MMPRT were included. First, the affecting factors for the surgery were evaluated using statistical analysis. Second, AI technology was introduced using X-ray and MRI. Finally, the accuracy and prediction performance were compared between ML&DL and logistic regression methods. Affecting factors of the logistic regression method corresponded well with the feature importance of the six top-ranked factors in the ML&DL method. There was no significant difference when comparing the accuracy, F1-score, and error rate between ML&DL and logistic regression methods (accuracy = 0.89 and 0.91, F1 score = 0.89 and 0.90, error rate = 0.11 and 0.09; p = 0.114, 0.422, and 0.119, respectively). The area under the curve (AUC) values showed excellent test quality for both ML&DL and logistic regression methods (AUC = 0.97 and 0.94, respectively) in the evaluation of prediction performance (p = 0.289). The affecting factors of the logistic regression method and the influence of the ML&DL method were not significantly different. The accuracy and performance of the ML&DL method in predicting the fate of MMPRT were comparable to those of the logistic regression method. Therefore, this ML&DL algorithm could potentially predict the outcome of the MMRPT in various fields and situations. Furthermore, our method could be efficiently implemented in current clinical practice.

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Modern operational environments can place significant demands on a service member's cognitive resources, increasing the risk of errors or mishaps due to overburden. The ability to monitor cognitive burden and associated performance within operational environments is critical to improving mission readiness. As a key step toward a field-ready system, we developed a simulated marksmanship scenario with an embedded working memory task in an immersive virtual reality environment. As participants performed the marksmanship task, they were instructed to remember numbered targets and recall the sequence of those targets at the end of the trial. Low and high cognitive load conditions were defined as the recall of three- and six-digit strings, respectively. Physiological and behavioral signals recorded included speech, heart rate, breathing rate, and body movement. These features were input into a random forest classifier that significantly discriminated between the low- and high-cognitive load conditions (AUC = 0.94). Behavioral features of gait were the most informative, followed by features of speech. We also showed the capability to predict performance on the digit recall (AUC = 0.71) and marksmanship (AUC = 0.58) tasks. The experimental framework can be leveraged in future studies to quantify the interaction of other types of stressors and their impact on operational cognitive and physical performance.

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Sleepiness is a major contributor to motor vehicle crashes and shift workers are particularly vulnerable. There is currently no validated objective field-based measure of sleep-related impairment prior to driving. Ocular parameters are promising markers of continuous driver alertness in laboratory and track studies, however their ability to determine fitness-to-drive in naturalistic driving is unknown. This study assessed the efficacy of a pre-drive ocular assessment for predicting sleep-related impairment in naturalistic driving, in rotating shift workers. Fifteen healthcare workers drove an instrumented vehicle for 2 weeks, while working a combination of day, evening and night shifts. The vehicle monitored lane departures and behavioural microsleeps (blinks >500 ms) during the drive. Immediately prior to driving, ocular parameters were assessed with a 4-min test. Lane departures and behavioural microsleeps occurred on 17.5 % and 10 % of drives that had pre-drive assessments, respectively. Pre-drive blink duration significantly predicted behavioural microsleeps and showed promise for predicting lane departures (AUC = 0.79 and 0.74). Pre-drive percentage of time with eyes closed had high accuracy for predicting lane departures and behavioural microsleeps (AUC = 0.73 and 0.96), although was not statistically significant. Pre-drive psychomotor vigilance task variables were not statistically significant predictors of lane departures. Self-reported sleep-related and hazardous driving events were significantly predicted by mean blink duration (AUC = 0.65 and 0.69). Measurement of ocular parameters pre-drive predict drowsy driving during naturalistic driving, demonstrating potential for fitness-to-drive assessment in operational environments.

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Global seawater temperatures are increasing and becoming more variable, with consequences for all marine animals including those in food production systems. In several countries around the world,arming of Atlantic salmon (Salmo salar) occurs towards the upper end of the thermal tolerance window for this species, and marked effects on salmon production during summers have been experienced but never empirically investigated. This project tracked the effects of an extreme summer heatwave on two different cohorts of fish stocked into farm cages either during early winter (EW) or late winter (LW). The farm site experienced an unprecedented high water temperature event, with a peak water temperature of 22.9 °C and 117 days above 18 °C. Fish in both EW and LW cohorts experienced a temperature-induced cessation of voluntary feed intake as well as inefficient osmoregulatory, liver and renal function during high temperature periods. Flesh colour declined primarily in the dorsal and ventral regions of the fillet and secondarily along the midline, with over 20% of fish demonstrated a complete loss of flesh colour during the months of March and April. A return to feeding in autumn occurred faster in some fish and caused a marked bimodal size distribution to appear within both the EW and LW cohorts as autumn progressed. However, the LW cohort returned to feeding at seawater temperatures of 20.2 °C, compared with 18.6 °C for the EW cohort. There was a strong positive relationship between fillet colour recovery and residual condition index (RCI). These findings identified alkaline phosphatase as a potential marker to non-destructively track individual fish for signs of recovery after a thermal stress event, and shed light on the physiological consequences of marine heatwaves on fishes. This study also identified that supporting feed intake or promoting a return to feeding may help mitigate the negative impacts of climate warming on cultured Atlantic salmon.

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Drag area (Ad ) is a primary factor determining aerodynamic resistance during level cycling and is therefore a key determinant of level time trial performance. However, Ad has traditionally been difficult to measure. Our purpose was to determine the value of adding field-measured Ad as a correlate of level cycling time trial performance. In the field, 19 male cyclists performed a level (22.1 km) time trial. Separately, field-determined Ad and rolling resistance were calculated for subjects along with projected frontal area assessed directly (AP ) and indirectly (Est AP ). Also, a graded exercise test was performed to determine [Formula: see text] peak, lactate threshold (LT), and economy. [Formula: see text] peak ([Formula: see text]) and power at LT were significantly correlated to power measured during the time trial (r = 0.83 and 0.69, respectively) but were not significantly correlated to performance time (r = - 0.42 and -0.45). The correlation with performance time improved significantly (p < 0.05) when these variables were normalized to Ad . Of note, Ad alone was better correlated to performance time (r = 0.85, p < 0.001) than any combination of non-normalized physiological measure. The best correlate with performance time was field-measured power output during the time trial normalized to Ad (r = - 0.92). AP only accounted for 54% of the variability in Ad . Accordingly, the correlation to performance time was significantly lower using power normalized to AP (r = - 0.75) or Est AP (r = - 0.71). In conclusion, unless normalized to Ad , level time trial performance in the field was not highly correlated to common laboratory measures. Furthermore, our field-measured Ad is easy to determine and was the single best predictor of level time trial performance.

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OBJECTIVE: To develop a probabilistic model for discovering and quantifying determinants of outbreak detection and to use the model to predict detection performance for new outbreaks. MATERIALS AND METHODS: We used an existing software platform to simulate waterborne disease outbreaks of varying duration and magnitude. The simulated data were overlaid on real data from visits to emergency department in Montreal for gastroenteritis. We analyzed the combined data using biosurveillance algorithms, varying their parameters over a wide range. We then applied structure and parameter learning algorithms to the resulting data set to build a Bayesian network model for predicting detection performance as a function of outbreak characteristics and surveillance system parameters. We evaluated the predictions of this model through 5-fold cross-validation. RESULTS: The model predicted performance metrics of commonly used outbreak detection methods with an accuracy greater than 0.80. The model also quantified the influence of different outbreak characteristics and parameters of biosurveillance algorithms on detection performance in practically relevant surveillance scenarios. In addition to identifying characteristics expected a priori to have a strong influence on detection performance, such as the alerting threshold and the peak size of the outbreak, the model suggested an important role for other algorithm features, such as adjustment for weekly patterns. CONCLUSION: We developed a model that accurately predicts how characteristics of disease outbreaks and detection methods will influence on detection. This model can be used to compare the performance of detection methods under different surveillance scenarios, to gain insight into which characteristics of outbreaks and biosurveillance algorithms drive detection performance, and to guide the configuration of surveillance systems.

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We used diffusion modelling to predict vulnerability to decline in psychomotor vigilance task (PVT) performance following a night of total sleep deprivation (SD). A total of 135 healthy young adults (69 women, age = 21.9 ± 1.7 years) participated in several within-subject cross-over design studies that incorporated the PVT. Participants were classified as vulnerable (lower tertile) or non-vulnerable (upper tertile) according to their change in lapse rate [lapse = reaction time (RT) ≥ 500 ms] between the evening before (ESD) and the morning after SD. RT data were fitted using Ratcliff's diffusion model. Although both groups showed significant change in RT during SD, there was no significant group difference in RT during the ESD session. In contrast, during ESD, the mean diffusion drift of vulnerable subjects was significantly lower than for non-vulnerable subjects. Mean drift and non-decision times were both adversely affected by sleep deprivation. Both mean drift and non-decision time showed significant state × vulnerability interaction. Diffusion modelling appears to have promise in predicting vulnerability to vigilance decline induced by a night of total sleep deprivation.

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