Phosphatidic Acid (PA) is a natural phospholipid compound derived from lecithin which is commonly found in egg yolk, grains, fish, soybeans, peanuts and yeast. It has been suggested that PA is involved in several intracellular processes associated with muscle hypertrophy. Specifically, PA has been reported to activate protein synthesis through the mammalian target of rapamycin (mTOR) signaling pathway and thereby may enhance the anabolic effects of resistance training. To our knowledge, no one has examined the effect of PA supplementation in humans while undergoing a progressive resistance training program. To examine the effect of PA supplementation on lean soft tissue mass (LM) and strength after 8 weeks of resistance training. Fourteen resistance-trained men (mean ± SD; age 22.7 ± 3.3 yrs; height: 1.78 ± 0.10m; weight: 89.3 ± 16.3 kg) volunteered to participate in this randomized, double-blind, placebocontrolled, repeated measures study. The participants were assigned to a PA group (750mg/day; Mediator®, ChemiNutra, MN, n=7) or placebo group (PL; rice flower; n=7), delivered in capsule form that was identical in size, shape and color. Participants were tested for 1RM strength in the bench press (BP) and squat (SQ) exercise. LM was measured using dual-energy X-ray absorptiometry. After base line testing, the participants began supplementing PA or PLfor 8 weeks during a progressive resistance training program intended for muscular hypertrophy. Data was analyzed using magnitude-based inferences on mean changes for BP, SQ and LM. Furthermore, the magnitudes of the interrelationships between changes in total training volume and LM were interpreted using Pearson correlation coefficients, which had uncertainty (90% confidence limits) of approximately +0.25. iv In the PA group, the relationship between changes in training volume and LM was large(r=0.69, +0.27; 90%CL), however, in the PL group the relationship was small (r=0.21, +0.44; 90%CL). PA supplementation was determined to be likely beneficial at improving SQ and LM over PL by 26% and 64%, respectively. The strong relationship between changes in total training volume and LM in the PA group suggest that greater training volume most likely lead to the greater changes in LM, however, no such relationship was found with PL group. For the BP data, the PA group resulted in a 42% greater increase in strength over PL, although the effect was considered unclear. While more research is needed to elucidate mechanism of action; the current findings suggest that in experienced resistance trained men supplementing 750mg PA per day for 8 weeks may likely benefit greater changes in muscle mass and strength compared with resistance training only.

Background  Maximum voluntary bite force (BF) and hand grip strength (HS) serve as muscle strength markers from the jaw motor system and hand motor system.   Aim  To investigate i) differences in maximum BF and HS between dominant and non-dominant sides, ii) differences between repeated tests in the same session and iii) correlation between BF and HS.     Methods Fifteen resistance trained adults (n=6 women, mean age 24 (SD 1.04) years and n=9 men, mean age 27 (SD 4.06) years) were tested with electronic BF and HS devices, with three repeated tests per side. Paired sample T-test was used to detect differences in BF and HS between sides and whether there was a difference between repeated tests. Pearson test was used to determine correlation between BF and HS. P-value <0.05 was considered statistically significant.    Results HS showed differences between dominant and non-dominant sides in three tests (T1 P=<0.0001, T2 P=0.0002 and T3 P=0.0011). BF showed differences between repeated tests in the same session for T1-T2 (P=0.007), T1-T3 (P<0.0001) and T2-T3 (P=0.028) on dominant side and between T1-T2 (P=0.014), T1-T3 (P=0.010) on non-dominant side. Correlation between BF and HS showed r=0.41 for merged data (dominant + non-dominant side) (P=0.02).   Conclusions  In the context of resistance trained adults, the findings showed that BF did not alter between sides while HS did, with higher force production for the dominant hand. Repeated tests showed differences between tests for BF, but not for HS. A weak to moderate correlation could be observed when comparing BF to HS.

No description available.

In the last decade, the potential of β-glucan, a fungal cell wall component, to induce epigenetic and functional modification of innate immune cells, signified as trained innate immunity (TII) has been demonstrated in several pre-clinical and clinical studies. Parenteral administration of β-glucan has resulted in centrally induced TII in the bone marrow/circulating monocytes. Such trained innate immune cells play a critical role in protection against secondary infections. However, there are now indications that inducing local long-lasting immunity at mucosal barrier tissues such as the lung is warranted for protective immunity against respiratory pathogens. Currently, it remains unclear whether respiratory mucosal administration of β-glucan will induce long-lasting resident-memory macrophages and TII and if so, what are the underlying mechanisms of development and maintenance of memory macrophages at respiratory mucosa. To address this, and kinetics of immune responses in the lung were studied. Profound changes in airway macrophage (AM) pools were observed starting from 3 days post-exposure, which was associated with monocyte recruitment, and this was followed by a series of phenotypic shifts in AMs. The altered AM phenotype profile persisted for up to 8 weeks post-exposure. Importantly, β-glucan-trained AMs demonstrated heightened MHC II expression, enhanced responses to secondary stimulation and improved capacity to perform bacterial phagocytosis. Furthermore, mice with, β-glucan-trained AMs displayed higher rates of survival and improved bacterial control, in the lung and periphery, following a lethal S. pneumoniae infection. Our findings together indicate that a single intranasal delivery of β-glucan is able to train AMs. Further work into epigenetics, metabolism, and the contribution of AMs in protection is needed. / Thesis / Master of Health Sciences (MSc) / The immune system has been classically divided into two major compartments known as the innate and adaptive immune system. For decades, the predominant consensus amongst the field was that only the adaptive immune system can form memory against any pathogens encountered. It has been well established that plants and invertebrates only possess an innate immune system and still show boosted responses and enhanced protection against previously encountered as well as new pathogens. Recently, such capacity for innate immune memory has also been demonstrated in humans and pre-clinical animal models. Innate immune memory provides non-specific, broad- spectrum protection whereas adaptive memory is specific to a singular pathogen. Inducing broad-spectrum protection can be crucial for the future of human medicine. Activation of both adaptive and innate immune arms could prove to be extremely beneficial in vaccination strategies. Through the use of a pre-clinical model, we have found that administering β-glucan, a component of fungal cell wall, directly into the lung significantly alters the phenotype and functionality of lung immune cells, and also provides enhanced protection against a heterologous infection.

<p>This study examined the challenges female-professional immigrants from East Africa face within the Canadian workforce. The analysis of their experiences helps us understand the employment challenges professional immigrants may face upon settlement in Canada. The main goal of the study was to explore the experiences of East African (Kenyan, Ugandan and Tanzanian) immigrant-female registered nurses in navigating the Canadian labour market. The evidence for the study was collected through interviewing five East African nurses. Although there is research that focuses on labour market experiences of women of colour, few researchers have specifically focused on African immigrant women’s connection with the Canadian labour force. The study particularly focuses on strategies nurses used to cope with the job search barriers encountered, the challenges they faced with the College of Nurses of Ontario with regard to the evaluation of their international-nursing credentials, and their job expectations before and after arriving in Canada. Their experience was examined through gender, race, and place of origin lenses.</p> <p>The study highlights the need for future longitudinal studies exploring East African nurses’ experience with integration to their profession within the Canadian workforce. The analysis of the results emphasizes that the Canadian government in conjunction with the regulatory bodies need to be more transparent in relation to internationally trained nurses so that they do not feel they are being wasted in Canada. This, in turn, will address the existing barriers and consequential negative impacts such as health conditions, tensions, and discrepancies outlined within the study, as well as encourage changes to Canadian immigration practices and policies</p> / Master of Social Work (MSW)

Pulmonary tuberculosis (TB) caused by Mycobacterium tuberculosis (M.tb) is the leading cause of infectious disease-related death worldwide. The critical role of adaptive immunity in anti-TB host defence has been firmly established; thus, current efforts in developing novel vaccination strategies against TB are primarily focused on generating protective adaptive immunity at the infection site, the lungs. Innate immunity has not been a target for vaccination strategies against TB due to the belief that innate immune cells cannot exhibit memory-like characteristics which are known to be central to the long-lasting immunity created by vaccines. Also, the importance of innate immunity in anti-TB immunity has been overlooked. However, over 25% of individuals that are heavily exposed to M.tb clear infection without any detectable conventional T cell immune responses, suggesting a crucial role for innate immune cells in bacterial clearance. Interestingly, the early protection in these individuals is associated with their Bacillus Calmette-Guerin (BCG) vaccination status. Epidemiological studies have shown that BCG is capable of providing protection against numerous infections unrelated to TB in an innate-immune dependent manner. Such observations suggest that the innate immune system exhibits memory-like characteristics, capable of remembering the exposure to the vaccine and thereby responding in an augmented manner to future systemic infections. Nonetheless, it still remains unknown whether parenteral BCG immunization modulates the innate immune cells in the lung and airways, and if so, what role the trained innate immune cells play in early protection against pulmonary TB. Using a subcutaneous BCG immunization and pulmonary TB challenge murine model, we show that early protection against M.tb is independent of adaptive responses in the BCG immunized host. Our data suggest that enhanced early protection is mediated by the BCG-trained memory alveolar macrophages that we have shown to be functionally, phenotypically, metabolically, and transcriptionally altered following immunization. These novel findings suggest a significant anti-TB immune role for the innate immune memory established in the lung following parenteral BCG immunization and have important implications for the development of novel vaccination strategies against TB. / Thesis / Master of Science (MSc) / Pulmonary tuberculosis (TB) is a disease of the lung and is now one of the leading causes of human mortality worldwide. For more than eight decades, parenterally administered Bacillus Calmette–Guérin (BCG) vaccine has been globally used as the only approved vaccine against TB. Recently, it has also been observed that BCG vaccination provides protection against other diseases unrelated to TB and reduces childhood mortality in many developing countries where it is routinely administered to children shortly after birth. The mechanisms underlying the off-target protective effects of BCG vaccine remains largely under-investigated. In this project, we investigated how BCG vaccination enhances the immune system responses against TB and other unrelated infectious diseases. A better understanding of how the BCG vaccination modulates our immune system will provide us with the knowledge that will be useful in the development of more effective vaccination strategies against infectious diseases.

The advent of social media has revolutionized human communication, significantly improving individuals' lives. It makes people closer to each other, provides access to enormous real-time information, and eases marketing and business. Despite its uncountable benefits, however, we must consider some of its negative implications such as online harassment and cyberbullying. Cyberbullying is becoming a serious, large-scale problem damaging people's online lives. This phenomenon is creating a need for automated, data-driven techniques for analyzing and detecting such behaviors. In this research, we aim to address the computational challenges associated with harassment-based cyberbullying detection in social media by developing machine-learning framework that only requires weak supervision. We propose a general framework that trains an ensemble of two learners in which each learner looks at the problem from a different perspective. One learner identifies bullying incidents by examining the language content in the message; another learner considers the social structure to discover bullying. Each learner is using different body of information, and the individual learner co-train one another to come to an agreement about the bullying concept. The models estimate whether each social interaction is bullying by optimizing an objective function that maximizes the consistency between these detectors. We first developed a model we referred to as participant-vocabulary consistency, which is an ensemble of two linear language-based and user-based models. The model is trained by providing a set of seed key-phrases that are indicative of bullying language. The results were promising, demonstrating its effectiveness and usefulness in recovering known bullying words, recognizing new bullying words, and discovering users involved in cyberbullying. We have extended this co-trained ensemble approach with two complementary goals: (1) using nonlinear embeddings as model families, (2) building a fair language-based detector. For the first goal, we incorporated the efficacy of distributed representations of words and nodes such as deep, nonlinear models. We represent words and users as low-dimensional vectors of real numbers as the input to language-based and user-based classifiers, respectively. The models are trained by optimizing an objective function that balances a co-training loss with a weak-supervision loss. Our experiments on Twitter, Ask.fm, and Instagram data show that deep ensembles outperform non-deep methods for weakly supervised harassment detection. For the second goal, we geared this research toward a very important topic in any online automated harassment detection: fairness against particular targeted groups including race, gender, religion, and sexual orientations. Our goal is to decrease the sensitivity of models to language describing particular social groups. We encourage the learning algorithm to avoid discrimination in the predictions by adding an unfairness penalty term to the objective function. We quantitatively and qualitatively evaluate the effectiveness of our proposed general framework on synthetic data and data from Twitter using post-hoc, crowdsourced annotation. In summary, this dissertation introduces a weakly supervised machine learning framework for harassment-based cyberbullying detection using both messages and user roles in social media. / Doctor of Philosophy / Social media has become an inevitable part of individuals social and business lives. Its benefits, however, come with various negative consequences such as online harassment, cyberbullying, hate speech, and online trolling especially among the younger population. According to the American Academy of Child and Adolescent Psychiatry,1 victims of bullying can suffer interference to social and emotional development and even be drawn to extreme behavior such as attempted suicide. Any widespread bullying enabled by technology represents a serious social health threat. In this research, we develop automated, data-driven methods for harassment-based cyberbullying detection. The availability of tools such as these can enable technologies that reduce the harm and toxicity created by these detrimental behaviors. Our general framework is based on consistency of two detectors that co-train one another. One learner identifies bullying incidents by examining the language content in the message; another learner considers social structure to discover bullying. When designing the general framework, we address three tasks: First, we use machine learning with weak supervision, which significantly alleviates the need for human experts to perform tedious data annotation. Second, we incorporate the efficacy of distributed representations of words and nodes such as deep, nonlinear models in the framework to improve the predictive power of models. Finally, we decrease the sensitivity of the framework to language describing particular social groups including race, gender, religion, and sexual orientation. This research represents important steps toward improving technological capability for automatic cyberbullying detection.

<p dir="ltr">Software Package Registries are an integral part of the Software Supply Chain, acting as collaborative platforms that unite contributors, users, and packages, and streamline package management processes. Much of the engineering work around reusing packages from these platforms deals with the issue of synthesis, combining multiple packages into a new package or downstream project. Recently, researchers have examined registries that specialize in providing Pre-Trained Models (PTMs), to explore the nuances of the PTM Supply Chain. These works suggest that the main engineering challenge of PTM reuse is not synthesis but selection. However, these findings have been primarily qualitative and lacking quantitative evidence of the observed differences. I therefore evaluate the following hypothesis:</p><p dir="ltr"><i>The prioritization of selection over synthesis in Pre-Trained Model reuse means that the evolution and reuse of Pre-Trained Models differs compared to traditional software. </i><i>The evolution of models will be more linear, and the reuse of models will be more centralized.</i></p>

Les ß-glucanes (BG) sont les polysaccharides les plus abondants de la paroi de Saccharomyces cerevisiae. Depuis des millénaires, ils sont utilisés pour leurs propriétés immunostimulantes et leurs potentiels thérapeutiques. L'objectif de ce travail était de caractériser la réponse immunitaire induite par les BG et de comprendre leurs modes d'action sur les macrophages murins en contexte infectieux. Nous avons montré que (i) les extraits de paroi enrichis en BG n'induisent qu'une faible production de cytokines par les macrophages contrairement aux extraits bruts, (ii) la réponse inflammatoire médiée par les extraits bruts résulte de la signalisation des TLRs et non de Dectin-1 et (iii) les BG stimulent la synthèse tardive de GM-CSF via Dectin-1. En conditions infectieuses, les BG enrichis confèrent une forte signature inflammatoire aux macrophages prétraités conduisant à l'amplification de la production cytokinique, à la synthèse de ROS et l'optimisation de la clairance bactérienne. En conclusion, cette étude souligne les enjeux de l'utilisation des BG enrichis comme adjuvants dans l'amélioration de la résistance des individus aux infections. / ß-glucans (BG) are the most abundant polysaccharides of the Saccharomyces cerevisiae cell wall. For decades, they have been extensively used because of their immuno-modulatory properties and their potential therapeutic effects. The aim of this study was to characterize the immune response induced by BG and to understand their mechanisms of action on murine macrophages occurring upon bacterial infections. We demonstrated that (i) BG-enriched extracts trigger low amounts of cytokine production in contrast with crude products, (ii) the immune response mediated by crude extracts results from TLRs and not from Dectin-1 signaling and (iii) BG-enriched compounds stimulate the late and strong induction of GM-CSF in a Dectin-1-dependent manner. Upon bacteria exposure, BG-enriched extracts confer a strong inflammatory to pretreated macrophages leading to synergistic increase of cytokine release, ROS production and better clearance of pathogens. Altogether, our findings emphasize the relevancy of using BG-enriched extracts for the design of novel adjuvant formulations contributing to individuals' resistance to infections.

I dagens affärsmiljö strävar många organisationer efter att automatisera processen för att hämta information från fakturor. Målet är att göra hanteringen av stora mängder fakturor mer effektiv. Trots detta möter man utmaningar på grund av den varierande strukturen hos fakturor. Placeringen och formatet för information kan variera betydligt mellan olika fakturor, vilket skapar komplexitet och hinder vid automatiserad utvinning av fakturainformation. Dessa utmaningar kan påverka noggrannheten och effektiviteten i processen. Förmågan att navigera genom dessa utmaningar blir därmed avgörande för att framgångsrikt implementera automatiserade system för hantering av fakturor. Detta arbete utforskar fyra olika textextraktions metoder som använder optisk teckenigenkänning, bildbehandling, vanlig textextraktion och textbearbetning, följt av en jämförelse mellan de naturliga språkbehandlingsmodellerna GPT- 3.5 (Generative Pre-trained Transformer) och GPT-4 för parameterextraktion av fakturor. Dessa modeller testades på sin förmåga att extrahera åtta specifika fält i PDF-dokument, sedan jämfördes deras resultat. Resultatet presenteras med valideringsmetoden ”Micro F1-poäng” en skala mellan 0 till 1, där 1 är en perfekt extraktion. Metoden som använde GPT-4 visade sig vara mest framgångsrik, som gav ett resultat på 0.98 och felfri extraktion i sex av åtta fält när den testades på 19 PDF-dokument. GPT 3.5 kom på andraplats och visade lovande resultat i fyra av de åtta fält, men presterade inte lika bra i de återstående fält, vilket resulterade i ett Micro F1-poäng på 0.71. På grund av det begränsade datamängden kunde GPT 3.5 inte uppnå sin fulla potential, eftersom finjustering och validering kräver större datamängder. Likaså behöver GPT-4 valideras med ett mer omfattande dataset för att kunna dra slutsatser om modellernas faktiska prestanda. Ytterligare forskning är nödvändig för att fastställa GPT-modellernas kapacitet med dessa förbättringar. / In today’s business environment, many organizations aim to automate the process of extracting information from invoices with the goal of making the management of large volumes of invoices more efficient. However, challenges arise due to the varied structure of invoices. The placement and format of information can significantly differ between different invoices, creating complexity and obstacles in the automated extraction of invoice information. These challenges can impact the accuracy and efficiency of the process, making the ability to navigate through them crucial for the successful implementation of automated systems for invoice management. This work explores four different text extraction methods that use optical character recognition, image processing, plain text extraction, and text processing, followed by a comparison between the natural language processing models GPT-3.5 (Generative Pre-trained Transformer) and GPT-4 for parameter extraction of invoices. These models were tested on their ability to extract eight specific fields in PDF documents, after which their results were compared. The results are presented using the ”Micro F1-Score” validation method, a scale from 0 to 1, where 1 represents perfect extraction. The method that used GPT-4 proved to be the most successful, yielding a result of 0.98 and error-free extraction in six out of eight fields when tested on 19 PDF documents. GPT-3.5 came in second place and showed promising results in four of the eight fields but did not perform as well in the remaining fields, resulting in a Micro F1-Score of 0.71. Due to the limited amount of data, GPT-3.5 could not reach its full potential, as fine-tuning and validation require larger datasets. Similarly, GPT-4 needs validation with a more comprehensive dataset to draw conclusions about the models’ actual performance. Further research is necessary to determine the capacities of GPT models with these improvements.