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Since the beginning of the global pandemic of Coronavirus (SARS-COV-2), there has been many studies devoted to predicting the COVID-19 related deaths/hospitalizations. The aim of our work is to (1) explore the lagged dependence between the time series of case counts and the time series of death counts; and (2) utilize such a relationship for prediction. The proposed approach can also be applied to other infectious diseases or wherever dynamics in lagged dependence are of primary interest. Different from the previous studies, we focus on time-varying coefficient models to account for the evolution of the coronavirus. Using two different types of time-varying coefficient models, local polynomial regression models and piecewise linear regression models, we analyze the province-level data in Canada as well as country-level data using cumulative counts. We use out-of-sample prediction to evaluate the model performance. Based on our data analyses, both time-varying coefficient modeling strategies work well. Local polynomial regression models generally work better than piecewise linear regression models, especially when the pattern of the relationship between the two time series of counts gets more complicated (e.g., more segments are needed to portray the pattern). Our proposed methods can be easily and quickly implemented via existing R packages. [ABSTRACT FROM AUTHOR]

Wind power has been utilized well in power systems, so steady and successful wind speed forecasting is crucial to security management power grid market economy. To date, most researchers have often discounted the interpretability of prediction models, leading to obscure forecasts. This study puts forward a unique forecasting methodology that incorporates notable decomposition techniques, multifactor interpretable forecasting models, and optimization algorithms. In the proposed model, variational mode decomposition is employed to break down the raw wind speed sequence into a set of intrinsic mode functions. Adaptive differential evolution is then used for optimizing several parameters of temporal fusion transformers (TFT) to achieve satisfactory forecasting performance. TFT is a new attention-based deep learning model that puts together high-performance multi-horizon prediction and interpretable insights into temporal dynamics. Empirical studies using eight real-world 1-h wind speed data sets in Albert, Canada, and Five Points, USA demonstrate that the system using the proposed model outperforms those employing other comparable models in nearly all performance metrics. Examples of TFT's interpretable outputs are the importance ranking of the decomposed wind speed sub-sequences and meteorological data and attention analysis of different step lengths. The findings signify substantial progress for wind speed prediction and aid policymakers. • Propose a novel interpretable wind speed prediction methodology named VMD-ADE-TFT. • Impacts of influencing factors are analyzed by TFT's interpretability capability. • VMD-ADE-TFT is used to achieve better forecasting accuracy for eight cases. [ABSTRACT FROM AUTHOR]

Climate change has led to governments mandating greater energy efficiency in building codes throughout Canada and the US. Increased energy efficiency requires a high level of insulation of the building envelopes, which may introduce unintended durability concerns. To investigate potential durability issues, six types of high thermal resistance (High RSI) wall assemblies were tested at a building envelope test facility. This study compares the hygrothermal performance of conventional 38 × 140 mm framing (i.e. baseline wall) with three types of deep cavity walls and three types of exterior insulated walls. The wall assemblies tested have an average installed thermal resistance of RSI 6.8 and they were installed at both north and south-facing orientations of the building envelope test facility to observe their relative performance. The hygrothermal conditions of test walls were monitored under three consecutive periods: baseline period without air leakage in fall/winter time, air leakage period with air injection in winter/spring time, and drying period without air leakage in spring time. The hygrothermal performance in terms of moisture content profile of OSB sheathing and mold growth risk at the interior surface of OSB sheathing was analysed with respect to wall types and orientation. The exterior insulated walls had lower moisture content than deep cavity walls and the baseline wall throughout the whole monitoring period, and there was no condensation or mold growth risk under simulated air leakage. Although the deep cavity walls had higher moisture content levels than the baseline wall, they were less responsive to air leakage than the baseline wall due to the hygroscopic characteristics of cellulose fiber insulation installed in the stud cavity. The deep cavity walls had higher risk of moisture problems than the baseline wall and the exterior insulated walls. • Hygrothermal performance of highly insulated wood framed walls was investigated through field measurement. • Effect of air leakage was investigated by injecting indoor air into wall assemblies. • Exterior insulated walls were less responsive to air leakage than deep cavity walls. • OSB sheathing in deep cavity walls had higher moisture content than that in exterior insulated walls. • Deep cavity walls have greater mold growth risks than exterior insulated walls. [ABSTRACT FROM AUTHOR]

Liposomes are considered one of the most successful drug delivery systems (DDS) given their established utility and success in the clinic. In the past 40–50 years, Canadian scientists have made ground-breaking discoveries, many of which were successfully translated to the clinic, leading to the formation of biotech companies, the creation of research tools, such as the Lipex Extruder and the NanoAssemblr™, as well as contributing significantly to the development of pharmaceutical products, such as Abelcet®, MyoCet®, Marqibo®, Vyxeos®, and Onpattro™, which are making positive impacts on patients' health. This review highlights the Canadian contribution to the development of these and other important liposomal technologies that have touched patients. In this review, we try to address the question of what drives innovation: Is it the individual, the teams, the funding, and/or an entrepreneurial spirit that leads to success? From this perspective, it is possible to define how innovation will translate to meaningful commercial ventures and products with impact in the future. We begin with a brief history followed by descriptions of drug delivery technologies influenced by Canadian researchers. We will discuss recent advances in liposomal technologies, including the Metaplex technology from the author's lab. The latter exemplifies how a nanotechnology platform can be designed based on multidisciplinary groups with expertise in coordination chemistry, nanomedicines, disease, and business to create new therapeutics that can effect better outcomes in patient populations. We conclude that the team is central to the effort; arguing if the team is entrepreneurial and well positioned, the funds needed will be found, but likely not solely in Canada. [ABSTRACT FROM AUTHOR]

Environmental factors have a significant impact on the transmission of infectious diseases. Existing results show that the novel coronavirus can persist outside the host. We propose a susceptible-exposed-presymptomatic-infectious-asymptomatic-recovered-susceptible (SEPIARS) model with a vaccination compartment and indirect incidence to explore the effect of environmental conditions, temperature and humidity, on the transmission of the SARS-CoV-2 virus. Using climate data and daily confirmed cases data in two Canadian cities with different atmospheric conditions, we evaluate the mortality rates of the SARS-CoV-2 virus and further estimate the transmission rates by the inverse method, respectively. The numerical results show that high temperature or humidity can be helpful in mitigating the spread of COVID-19 during the warm summer months. Our findings verify that nonpharmaceutical interventions are less effective if the virus can persist for a long time on surfaces. Based on climate data, we can forecast the transmission rate and the infection cases up to four weeks in the future by a generalized boosting machine learning model., Competing Interests: Declaration of competing interest The authors declare that they have no conflict of interest concerning the publication of this manuscript., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Background: The Teriflunomide Multiple Sclerosis Oral (TEMSO) trial, a randomized, double-blind, placebo-controlled phase III study, demonstrated that teriflunomide significantly reduced annualized relapse rate (ARR), disease progression and magnetic resonance imaging (MRI) activity, with a favorable safety profile in relapsing multiple sclerosis (RMS) patients., Objective: The purpose of this study was to report the effects of teriflunomide on ARR and disability progression in pre-specified subgroups., Methods: RMS patients (n=1088) were randomized to placebo or teriflunomide, 7 mg or 14 mg, once daily, for 108 weeks. Subgroup analyses were performed for ARR and disability progression by baseline demographics (gender, race, age), disease characteristics (Expanded Disability Status Scale (EDSS) strata, relapse history, multiple sclerosis (MS) subtype), MRI parameters (gadolinium-enhancing lesions, total lesion volume) and prior use of MS drugs. A generalized estimating equation method and Cox regression model were used to assess consistency of the treatment effect across subgroups, utilizing a treatment-by-subgroup interaction test for each factor separately., Results: Reductions in ARR and disability progression were consistent across subgroups in favor of teriflunomide, with no treatment-by-subgroup interaction test reaching statistical significance., Conclusion: The positive effects of teriflunomide were demonstrated consistently across subgroups in TEMSO.