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Simple Summary: Diagnosing most cancers at an earlier stage improves outcomes because treatments are more effective and often less invasive. This study looked at the health patterns of adults enrolled in Alberta's Tomorrow Project before they were diagnosed with cancer to identify factors related to cancers that are caught early or late. These identified factors were then tested in a similar group of adults from the British Columbia Generations Project who also subsequently developed cancer, to see how well they could predict stage at diagnosis. The confirmed health patterns can be used to improve cancer prevention programs and activities to find cancer earlier in people who are at most risk of being diagnosed with late-stage cancer. Risk prediction models for cancer stage at diagnosis may identify individuals at higher risk of late-stage cancer diagnoses. Partial proportional odds risk prediction models for cancer stage at diagnosis for males and females were developed using data from Alberta's Tomorrow Project (ATP). Prediction models were validated on the British Columbia Generations Project (BCGP) cohort using discrimination and calibration measures. Among ATP males, older age at diagnosis was associated with an earlier stage at diagnosis, while full- or part-time employment, prostate-specific antigen testing, and former/current smoking were associated with a later stage at diagnosis. Among ATP females, mammogram and sigmoidoscopy or colonoscopy were associated with an earlier stage at diagnosis, while older age at diagnosis, number of pregnancies, and hysterectomy were associated with a later stage at diagnosis. On external validation, discrimination results were poor for both males and females while calibration results indicated that the models did not over- or under-fit to derivation data or over- or under-predict risk. Multiple factors associated with cancer stage at diagnosis were identified among ATP participants. While the prediction model calibration was acceptable, discrimination was poor when applied to BCGP data. Updating our models with additional predictors may help improve predictive performance. [ABSTRACT FROM AUTHOR]

Approximately 3.5% of deliveries in Canada result in potentially preventable neonatal readmission, often times due to preventable morbidities. With complexities in hospital discharge planning, health care providers may benefit in identifying infants at risk of readmission for additional monitoring. To develop and validate models for predicting 7-day neonatal readmission following vaginal or cesarean births. All liveborn term singleton infants without congenital anomalies in the province of Alberta who were not admitted to the NICU were identified using perinatal and hospitalization databases. A temporal split-sample was used for model development (2012–2014, vaginal n = 63,378; cesarean n = 21,225) and external validation (2014–2015, vaginal n = 21,583, cesarean n = 7,477). Multivariable logistic regression models using backward stepwise selection were used to identify predictors of 7-day readmission. We evaluated predictors of maternal age, Apgar score, length-of-stay, birthweight, gestational age, parity, residence, and sex. Hosmer-Lemeshow test and c-statistics were used to estimate calibration and discrimination. The rate of readmission was 3.3% (95% CI 3.1%, 3.4%) and 2.1% (95% CI 1.9%, 2.3%) following vaginal and cesarean births in the development dataset. Prediction model following vaginal birth, excluding predictors of length-of-stay and birthweight, had sub-optimal performance in development (c-statistics 0.69) and validation data (c-statistics 0.68). Prediction model following cesarean birth, excluding predictors of maternal age, birthweight, and residence, had sub-optimal performance in development (c-statistics 0.62) and validation data (c-statistics 0.64). Readmission was observed in 7.9% (95% CI 7.1%, 8.8%) and 4.9% (95% CI 3.9%, 6.1%) of infants of vaginal and cesarean births, respectively, in the top quintile for the risk of 7-day readmission. Using routinely collected administrative data, we developed and validated prediction models for neonatal readmission following vaginal and cesarean births. Presently the model is sub-optimal for use in risk assessment and planning at discharge, however, additional information may improve the predictive performance. [ABSTRACT FROM AUTHOR]

Abstract: In the recent years, by rapid growth of wind power generation in addition to its high penetration in power systems, the wind power prediction has been known as an important research issue. Wind power has a complicated dynamic for modeling and prediction. In this paper, different hybrid prediction models based on neural networks trained by various optimization approaches are examined to forecast the wind power time series from Alberta, Canada. At first, time series analysis is performed based on recurrence plots and correlation analysis to select the proper input sets for the forecasting models. Next, a comparative study is carried out among neural networks trained by imperialist competitive algorithm (ICA), genetic algorithm (GA), and particle swarm optimization approach. The simulation results are representative of the out-performance of ICA in tuning the neural network for wind power forecasting. [Copyright &y& Elsevier]

Abstract: The vast Athabasca Oil Sands of Alberta, Canada has an estimated resource of more than 1.7 trillion barrels of bitumen in-place, the majority of which is hosted in the Lower Cretaceous McMurray Formation. Despite its economical significance the depositional environment of the formation, and particularly the middle part that is the primary reservoir in most areas, is still a matter of considerable debate. These strata of interest are widely known to comprise fluvial and estuarine point bar deposits that were subject to varying degrees of marine influence. The orientation of point bar strata from the formation is tabulated and the majority is observed to dip paleo-basinward, to the north. This observation has proven difficult to explain by geologists attempting to build predictive models for the formation over the last two decades. However, the basinward-dipping point bar strata can be attributed to widespread down-valley translation of point bars in confined north-south oriented valleys, which have previously been delineated in the region. Differentiating the deposits of lateral point bar migration and down-valley translation in the rock record is not possible at the facies scale, and thus translated point bars have not been previously recognized or interpreted in the rock record, despite their prevalence in numerous modern fluvial valley systems. Their identification in the McMurray Formation has important implications for the delineation and development of Canada’s economically significant oil sands resources. [Copyright &y& Elsevier]