To document the extent to which AEs, resulting from intervention studies targeting muscle-strengthening training (MST) in youth, are reported by researchers.

Five authors of fiction-criticism – Paul Savoie, Angela Cozea, Thomas Ayouti, Alexander Christie, and Nicholas Hauck – explore motifs – urban life, travel, seascapes, illness – developed in a creative-writing piece by Catherine Parayre. The book illustrations show artworks from a series titled Pseudo-fiction by Canadian artist Shawn Serfas and Catherine Parayre. Editor: Nicholas Hauck. In French.

Sprinting is multifactorial and dependent on a variety of kinematic, kinetic, and neuromuscular features. A key objective in sprinting is covering a set amount of distance in the shortest amount of time. To achieve this, sprinters are required to coordinate their entire body to achieve a fast sprint velocity. This suggests that a whole-body kinematic and neuromuscular coordinative strategy exists which is associated with improved sprint performance. The purpose of this study was to leverage inertial measurement units (IMUs) and wireless surface electromyography (sEMG) to find coordinative strategies associated with peak over-ground sprint velocity using machine learning. We recruited 40 healthy university age sprint-based athletes from a variety of athletic backgrounds. IMU and sEMG data were used as inputs into a principal components analysis (PCA) to observe major modes of variation (i.e., PC scores). PC scores were then used as inputs into a stepwise multivariate linear regression model to derive associations of each mode of variation with peak sprint velocity. Both the kinematic (R2 = 0.795) and sEMG data (R2 = 0.586) produced significant multivariate linear regression models. The PCs that were selected as inputs into the multivariate linear regression model were reconstructed using multi-component reconstruction to produce a representation of the whole-body movement pattern and changes in the sEMG waveform associated with faster sprint velocities. The findings of this work suggest that distinct features are associated with faster sprint velocity. These include the timing of the contralateral arm and leg swing, stance leg kinematics, dynamic trunk extension at toe-off, asymmetry between the right and left swing side leg and a phase shift feature of the posterior chain musculature. These results demonstrate the utility of data-driven frameworks in identifying different coordinative features that are associated with a movement outcome. Using our framework, coaches and biomechanists can make decisions based on objective movement information, which can ultimately improve an athlete’s performance.

Whether scales reduce cutaneous evaporative water loss in lepidosaur reptiles (Superorder Lepidosauria) such as lizards and snakes has been a contentious issue for nearly half a century. Furthermore, while many studies have looked at whether dehydration affects thermal preference in lepidosaurs, far fewer have examined whether normally hydrated lepidosaurs can assess their instantaneous rate of evaporative water loss and adjust their thermal preference to compensate in an adaptive manner. We tested both of these hypotheses using three captive-bred phenotypes of bearded dragon (Pogona vitticeps) sourced from the pet trade: ‘Wild Types’ with normal scalation, ‘Leatherbacks’ exhibiting scales of reduced prominence, and scaleless bearded dragons referred to as ‘Silkbacks’. Silkbacks on average lost water evaporatively at about twice the rate that Wild Types did. Leatherbacks on average were closer in their rates of evaporative water loss to Silkbacks than they were to Wild Types. Additionally, very small (at most ~1°C) differences in thermal preference existed between the three phenotypes that were not statistically significant. This suggests a lack of plasticity in thermal preference in response to an increase in rate of evaporative water loss, and may be reflective of a thermal ‘strategy’ as employed by thermoregulating bearded dragons that prioritises immediate thermal benefits over the threat of future dehydration. The results of this study bolster an often-discounted hypothesis regarding the present adaptive function of scales and have implications for the applied fields of animal welfare and conservation.

The accurate modeling of epidemics on social contact networks is difficult due to the variation between different epidemics and the large number of parameters inherent to the problem. To reduce complexity, evolutionary computation is used to create a generative representation of the epidemic model. Previous gains from the use of local, verses global, operators are further explored to better balance exploration and exploitation of the genetic algorithm. A typical parameter study is conducted to test this new local operator and the new method of point packing is utilized as a proof of concept to perform a better search of the parameter space. All experiments from both approaches are tested against nine epidemic profiles. The point-packing driven parameter search demonstrates that the algorithm parameters interact substantially and in a non-linear fashion, and also shows that the good parameter settings are problem specific.

Alzheimer's disease (AD) is an irreversible, progressive neurological disorder that causes memory and thinking skill loss. Many different methods and algorithms have been applied to extract patterns from neuroimaging data in order to distinguish different stages of Alzheimer's disease (AD). However, the similarity of the brain patterns in older adults and in different stages makes the classification of different stages a challenge for researchers. In this paper, convolutional neuronal network architecture AlexNet was applied to fMRI datasets to classify different stages of the disease. We classified five different stages of Alzheimer's using a deep learning algorithm. The method successfully classified normal healthy control (NC), significant memory concern (SMC), early mild cognitive impair (EMCI), late cognitive mild impair (LMCI), and Alzheimer's disease (AD). The model was implemented using GPU high performance computing. Before applying any classification, the fMRI data were strictly preprocessed. Then, low to high level features were extracted and learned using the AlexNet model. Our experiments show significant improvement in classification. The average accuracy of the model was 97.63%. We then tested our model on test datasets to evaluate the accuracy of the model per class, obtaining an accuracy of 94.97% for AD, 95.64% for EMCI, 95.89% for LMCI, 98.34% for NC, and 94.55% for SMC.

Creating a representation capable of generating personal contact networks that are most likely to exhibit specific epidemic behavior is difficult due to the inherit volatility of an epidemic and the numerous parameters accompanying the problem. To surpass these hurdles, evolutionary algorithms are used to create a generative solution which generates personal contact networks, modeling human populations, to satisfy the epidemic duration and epidemic profile matching problems. This representation is entitled the Local THADS-N representation. Two new operators are added to the original THADS-N system, and tested with a traditional parameter sweep and a parameter selection method known as point packing on nine epidemic profiles. Additionally, a new epidemic model is implemented in order to allow for lost immunity within a population thus increasing the length of an epidemic.

The Summit Series, a best-on-best hockey tournament played between the former-Soviet and Canadian men’s teams, occupies an outsized role in the Canadian sporting imagination. The September He Remembers: Josef Kompalla and the 1972 Summit Series reframes this famous Series in sport history through the story, experiences, and memories of Josef Kompalla, a German referee who played a key role in the events as one of the few esteemed officials selected to officiate during the Series. Given Kompalla’s important place in the history of the Summit Series, one of the most mythologized and celebrated tournaments in Canadian sport history, it should come as a surprise that his perspective on the events has not been previously documented. In this book, Bolz seeks to rectify this longstanding oversight through intrepid archival creation and analysis, as well as previously-untold stories from Kompalla himself. Although Kompalla’s role in the Summit Series has been largely omitted or obfuscated for the past half century, The September He Remembers is finally the first step in correcting a major historical oversight and adding a new chapter in the Summit Series’ growing legacy.

Someone dies. Three friends meet; one travels from the West, another comes back to Canada, the third one welcomes them in his garden.

Standard cell culture is routinely performed at supraphysiological oxygen levels (~18% O2). Conversely, O2 levels in most mammalian tissues range from 1–6% (physioxia). Such hyperoxic conditions in cell culture can alter reactive oxygen species (ROS) production, metabolism, mitochondrial networks, and response to drugs and hormones. The aim of this study was to investigate the transcriptional response to different O2 levels and determine whether it is similar across cell lines, or cell line-specific. Using RNA-seq, we performed differential gene expression and functional enrichment analyses in four human cancer cell lines, LNCaP, Huh-7, PC-3, and SH-SY5Y cultured at either 5% or 18% O2 for 14 days. We found that O2 levels affected transcript abundance of thousands of genes, with the affected genes having little overlap between cell lines. Functional enrichment analysis also revealed different processes and pathways being affected by O2 in each cell line. Interestingly, most of the top differentially expressed genes are involved in cancer biology, which highlights the importance of O2 levels in cancer cell research. Further, we observed several hypoxia-inducible factor (HIF) targets, HIF-2α targets particularly, upregulated at 5% O2, consistent with a role for HIFs in physioxia. O2 levels also differentially induced the transcription of mitochondria-encoded genes in most cell lines. Finally, by comparing our transcriptomic data from LNCaP and PC-3 with datasets from the Prostate Cancer Transcriptome Atlas, a correlation between genes upregulated at 5% O2 in LNCaP cells and the in vivo prostate cancer transcriptome was found. We conclude that the transcriptional response to O2 over the range from 5–18% is robust and highly cell-type specific. This latter finding indicates that the effects of O2 levels are difficult to predict and thus highlights the importance of regulating O2 in cell culture.