26734 research outputs found
Sort by
The legality of weight discrimination in Canada: an environmental scan of case law and the limits of Canadian legislation
No full textAbstract Weight stigma negatively impacts people with higher weights across the lifespan as well as social contexts and can lead to weight discrimination. As weight is not a protected identity in Canadian human rights legislation, it is important to better understand how weight discrimination is being argued in Canada’s legal system. The purpose of this environmental scan was to examine and describe Canadian case law and scholarly articles pertaining to the argumentation of weight discrimination in Canada. A three-step search process was taken to identify relevant cases and articles that included; (1) Boolean keyword searches in HeinOnline, WestLaw, and LexisPlus; (2) citation searching within all results that met inclusion criteria; and (3) a keyword search in CanLII. These searches yielded a total of 33 documents that were included for analysis, including 8 scholarly articles and 25 cases. Scholarly articles highlighted consistent criticisms of existing human rights protections for higher-weight people in Canada, mostly pertaining to Limitations of disability protections. Of the 25 cases included, 16 were unsuccessful and 9 were successful, with most cases related to employment (n = 19). Our findings point to significant gaps in Canada’s legal system for identifying and correcting instances of weight discrimination. Current Canadian disability protections are inadequate for those who experience weight discrimination, especially those who do not experience disability due to their weight. Our results highlight that weight ought to be a bona fide human rights issue, independent from disability protections
Error analysis of Tight Probability Bounds for Hausdorff Random Variables in Cancer Radiation Therapy Planning
No full textIntensity-Modulated Radiation Therapy (IMRT) plays a key role in cancer treatment by enabling precise delivery of high radiation doses to tumours while reducing exposure to nearby healthy organs. Inverse planning for IMRT is commonly evaluated through Dose–Volume Histograms (DVHs), which summarize how dose is distributed across tissues. In this thesis, the DVH is interpreted as a probability distribution, and moment-based optimization methods are studied as a way to approximate clinical DVH constraints. Since only a finite number of statistical moments can be enforced, the true DVH cannot be captured exactly. A central focus of this work is to analyze how tight these moment-based approximations are and to quantify the potential error they introduce. We examine formulations that bound the DVH under moment constraints, connect the analysis to the classical problem of moments, and investigate how incorporating patient-specific geometry through the dose deposition matrix affects the results. We also explore additional strategies for better controlling high-dose regions of organs at risk, comparing mean-tail dose and Conditional Value-at-Risk (CVaR) as two complementary approaches. Preliminary numerical experiments demonstrate how these formulations perform and highlight the trade-offs between tractability and conservatism. The findings contribute to a clearer understanding of the reliability of moment-based DVH approximations in the context of radiation therapy planning
What Goes Where In Calgary? A Garbage Classification System Based on Images and Natural Language
No full textDisposing of garbage using the correct trash bin is important because it maximizes recycling and is good for the environment. However, this is a challenging task for individuals without proper knowledge or training to dispose of garbage properly. Artificial Intelligence methods, deep learning in special, can be leveraged in this task. Most current deep learning systems assume that all the necessary information for garbage classification is contained in images. We hypothesize that combining images with natural language descriptions of the objects provided by the individual trying to dispose of the piece of garbage can add contextual information that may not be present in the image and vice-versa, and by combining these two sources of information, images and text, it is possible to achieve better garbage classification results when performing classification using either image- or text-only information. This thesis propose (1) a novel public benchmark dataset, which includes 20,000 images of garbage with corresponding text descriptions and class labels; (2) a multimodal garbage classification model based on what we call "Reverse Cross Attention" (RCA), which explores the complementarity of information between image and text. Our proposed model achieved improved results compared to unimodal models based solely on images or text and state-of-the-art multimodal models. Our work demonstrates that the proposed model outperforms the best unimodal results by an average of 2% across all metrics when combining text and image information using the RCA mechanism
Foundations in Open Educational Resources (OER) Workshop
No full textAdams, S. (2025, August 27). Foundations in Open Educational Resources (OER) Workshop [PowerPoint Slides]. Libraries and Cultural Resources, University of Calgary. CC BY-SA 4.0.Libraries and Cultural Resources Block Week workshop description: This workshop will provide attendees with an understanding about what OER are, approaches for integrating them into teaching and learning practices, and ways for accessing and assessing OER resources and tools. The workshop will consist of key learnings, helpful resources, and activities to equip participants with the skills they need to achieve their goals for teaching with OER. At the end of this workshop, learners will be able to: (1)Define open educational resources (OER), copyright, and open licenses; (2) Understand the implications copyright and open licensing have on the adoption, adaptation, and creation of OER; (3) Describe the key considerations for assessing and adopting an OER; and (4) Identify ways of incorporating OER into their teaching and learning practices
Revisiting the link between oropharyngeal dysphagia and cancer in autoimmune myositis: a descriptive study
No full textAbstract Objectives To explore the link between moderate to severe oropharyngeal dysphagia and cancer in autoimmune myositis (AIM) other than inclusion body myositis (IBM). Methods The medical records of patients with AIM seen in rheumatology in two university hospitals from January 2000 to December 2022 were retrospectively reviewed. Using an updated AIM subclassification, patients were classified by expert opinion as pure dermatomyositis (DM), immune-mediated necrotizing myopathy (IMNM), scleromyositis, lupomyositis, anti-MDA-5 syndrome, antisynthetase syndrome (ASyS) or polymyositis syndrome. Objective oropharyngeal dysphagia at myositis diagnosis was defined by an abnormal videofluoroscopic swallowing study and/or the need for percutaneous gastrojejunostomy. The presence of cancer within 3 years of myositis diagnosis was recorded. Results Pure DM accounted for 50% (n = 20/40) of the cases of objective oropharyngeal dysphagia, while anti-MDA-5 syndrome and ASyS together represented 8% (n = 3/40). Cancers occurred predominantly in pure DM (n = 27/33), and rarely in scleromyositis (n = 1/53), anti-MDA-5 syndrome and ASyS (n = 0/62). Among patients 50 years of age or older with pure DM (n = 50), cancer was present in 40% (n = 4/10) of patients with no muscle weakness, 45% (n = 10/22) in those with proximal weakness alone, 44% (n = 4/9) in those with moderate dysphagia and 100% of those with severe dysphagia (n = 9/9) (p = 0.02 by two-sided Fisher’s exact test). Conclusion Recognizing scleromyositis, anti-MDA-5 and ASyS as distinct from pure DM improves risk stratification for cancer screening. In patients ≥ 50 years with pure DM, severe oropharyngeal dysphagia is strongly associated with cancer, suggesting a paraneoplastic myopathy with an ineffective anticancer immune response.Key messages 1 Objective oropharyngeal dysphagia is linked to cancer mostly in pure DM. 2 No cancer was seen in other AIM presenting with a DM rash such as scleromyositis, anti-MDA-5 syndrome and ASyS. 3 Differentiating pure DM from other AIM improves risk stratification for cancer screening
Endovascular treatment of delayed cerebral ischemia after aneurysmal subarachnoid hemorrhage – an international survey
No full textAbstract Background Delayed cerebral ischemia (DCI) is a major cause of morbidity after aneurysmal subarachnoid hemorrhage (SAH). Endovascular treatment (ET) has emerged as a rescue strategy, but its optimal timing, indication, and modality remain unclear. This study assessed international ET practices, focusing on treatment variability and clinical decision-making. Methods A 25-question survey was developed with input from specialists in interventional neuroradiology, neurosurgery, neurology, and neurocritical care. It was disseminated via professional societies to physicians involved in bedside decisions. Respondents reviewed clinical scenarios representing common DCI presentations, including proximal/distal vasospasm and conscious/unconscious patients. Descriptive analysis was performed. Results 179 respondents from 38 countries participated; 76.5% reported ET availability at their institution. The most common strategy was single or repeated intra-arterial spasmolysis (76.5%), followed by continuous intra-arterial vasodilator infusion (23.0%). In unconscious patients, 50% applied spasmolysis as first-line treatment. For refractory proximal vasospasm, a stepwise approach was preferred, starting with intra-arterial pharmacologic spasmolysis, then angioplasty. While angioplasty was widely used, 66.5% considered it riskier than spasmolysis. Conclusion This survey highlights marked variability in ET practices for DCI. Intra-arterial spasmolysis is the predominant strategy, with alternative approaches like continuous infusion and angioplasty also in use. These findings underscore the need for randomized trials to define optimal ET strategies and inform evidence-based protocols for DCI following SAH
Public concerns about human metapneumovirus: insights from Google search trends, X social networks, and web news mining to enhance public health communication
No full textAbstract The respiratory virus known as human metapneumovirus (hMPV) is linked to seasonal outbreaks and primarily affects elderly people and young children. Infodemiology, which uses digital data sources, including social media, online news, and search trends, is a useful substitute for monitoring public concerns and risk perceptions because surveillance gaps and underreporting impede public health interventions despite their clinical value. To assess public search interest, we analyzed global search behavior between June 1, 2024, and June 1, 2025, and examined over 1.3 million tweets collected during the peak outbreak period from January to March 2025. Our findings show a sharp rise in public interest following official reports of HMPV outbreak in China, with simultaneous search peaks across both hemispheres regardless of season. Search activity expanded to 177 countries and revealed sustained interest in Australia, Thailand, the United Kingdom, and the United States. Regional differences in terminology and platform usage were also observed, with non-English-speaking countries favoring the abbreviation “HMPV” and English-speaking regions more often using the full term. Additionally, discrepancies between search activity and social media engagement in some countries point to distinct patterns of public information-seeking behavior. These results underscore the importance of adapting health communication strategies to local language norms and preferred digital platforms. They also highlight the need for real-time monitoring and proactive responses to misinformation. Together, search and social media data offer a valuable lens for understanding public sentiment and improving the reach, accuracy, and impact of global outbreak communication
Do ψ-ontic models capture the reality of quantum states? A measure-theoretic response.
No full textOntological models of quantum theory seek to realize quantum theory as a statistical theory over underlying degrees of freedom, whose measurement statistics are described by classical probability theory. Harrigan and Spekkens (2010) proposed a distinction between ontological models in which pure quantum states represent states of reality (ψ-ontic) or knowledge about reality (ψ-epistemic) depending on whether their associated probability distributions overlap, subsequently formalized into measure-theoretic form by Leifer (2014). In this thesis, I examine whether this ψ-ontic/epistemic distinction truly captures the intended difference between ontic and epistemic conceptions of pure states. I consider some mathematical alternatives relating to different notions of non-overlap between probability measures and compare them. I discuss the implications of these results for quantum ontology theorems – theorems that rule out ψ-epistemic readings of pure states under certain assumptions about the ontological model – such as the Pusey-Barrett-Rudolph Theorem (2012)
Influence of dietary components on the gut microbiota of middle-aged adults: the gut-Mediterranean connection
No full textAbstract Background A plant-focused, healthy dietary pattern, such as the Mediterranean diet enriched with dietary fiber, polyphenols, and polyunsaturated fats, is well known to positively influence the gut microbiota. Conversely, a processed diet high in saturated fats and sugars negatively impacts gut diversity, potentially leading to weight gain, insulin resistance, and chronic, low-grade inflammation. Despite this understanding, the mechanisms by which the Mediterranean diet impacts the gut microbiota and its associated health benefits remain unclear. Methods This retrospective, observational study explored the relationships between Mediterranean dietary components—vegetables, fruits and nuts, legumes, whole grains, fish, meat, dairy, alcohol, saturated and unsaturated fats—and the gut microbiota in middle-aged adults enrolled in Alberta’s Tomorrow Project, Canada. Diet was recorded using the Canadian Dietary History Questionnaire (CDHQ-II) and participants were classified into four quartiles based on a modified Mediterranean Diet Score. Blood and fecal samples were collected for metabolomics and 16S rRNA sequencing, respectively. Results Findings revealed that higher adherence to the Mediterranean diet was associated with increased alpha diversity and a greater abundance of beneficial fiber-degrading bacteria, including Prevotella, Parabacteroides, Clostridium XIVb, Coprobacter, and Turicibacter. Furthermore, participants who consumed more Mediterranean diet components exhibited higher concentrations of serum microbial metabolites including p-hydroxy hippuric acid and indole-acetaldehyde. Conclusions Results demonstrate a pivotal role of the gut microbiota, via its metabolites in harnessing the health benefits of the Mediterranean diet, highlighting its potential to promote metabolic health and prevent chronic disease
Developing a Statistical Risk Assessment and Grid Prediction Tool for Power System Reliability
No full textModern electricity grids face mounting reliability pressures as renewable generation expands, demand patterns shift, and extreme weather intensifies. This project developed a Grid Risk Assessment Tool to identify early warning signs of instability using historical datasets from ISOs and RTOs (AESO, ERCOT, and CAISO). A structured MySQL database consolidated system frequency, pricing, generation mix, intertie flows, and operator-declared events, from which predictive features such as ramp rates, reserve adequacy, renewable penetration, and frequency deviations were engineered. Logistic Regression, Random Forest, and XGBoost models were tested and evaluated using ROC-AUC, precision, and recall. Random Forest achieved both the strongest contextual performance and the best real-time performance. Feature analysis highlighted system electricity prices, renewable share, intertie support, and ramping activity as key drivers of instability. The findings demonstrate that predictive modeling can provide actionable early warning signals, supporting operators and policymakers in strengthening grid resilience and planning