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Diet and Exercise Interventions in Patients with Pancreatic Cancer: Effect on Health-related Quality of Life
Get PDFPancreatic cancer is a deadly disease with few curative treatment options. Therefore, preserving health-related quality of life (HRQoL) is significant. Factors that may negatively affect HRQoL include malnutrition and muscle wasting, which are prevalent in patients with pancreatic cancer. However, little is known regarding the role of diet and exercise interventions in maintaining HRQoL. A scoping review of the literature was performed to identify what is already known, and the research gaps that remain, surrounding diet and exercise interventions in patients with pancreatic cancer (Chapter 3). Studies (n=62) were heterogenous in the types of interventions investigated and the main outcomes studied. Seven research gaps were identified to guide the design of future studies. In response to the research gaps presented, Chapters 4 and 5 explored HRQoL outcomes in patients undergoing multimodal prehabilitation (prehab) prior to hepato-pancreato-biliary (HPB) surgery. Prehab included a diet, exercise and relaxation intervention that began 4 weeks prior to surgery. A control group (rehab) was observed for the 4-week preoperative period and began the same program right after surgery; both groups were followed for 8 weeks postoperatively. Chapter 4 explored associations between HRQoL and nutritional status, physical strength/function, muscle mass and cancer symptoms in the subset of patients awaiting pancreatic resection. There were strong, negative relationships between cancer symptoms (r=-0.832) and malnutrition (r=-0.697, p<0.001) at baseline, but not with physical strength/function or body composition. There was no difference in HRQoL outcomes between prehab or rehab over the study period. However, both groups achieved baseline HRQoL levels at 8-weeks postoperatively, quicker than the expected 3 to 6 months. Chapter 5 outlined the effectiveness of dietary counselling to meet protein recommendations and relationships between nutritional status and HRQoL in patients awaiting HPB surgery. In the preoperative period prehab, but not rehab, significantly increased protein intake (+0.3+/-0.1 g/kg, p<0.001). Additionally, nutritional status was negatively associated with HRQoL only in those who did not experience a minimally important HRQoL improvement (B:-2.83, p<0.001). This dissertation demonstrates a positive effect on HRQoL from diet and exercise interventions in patients awaiting pancreatic resection. Future research directions arising from this study are explored in Chapter 6
Latent Spaces for Antimicrobial Peptide Design
Get PDFCurrent antibacterial treatments cannot overcome the growing resistance of bacteria to antibiotic drugs, and novel treatment methods are required. One option is the development of new antimicrobial peptides (AMPs), to which bacterial resistance build-up is comparatively slow. Deep generative models have emerged as a powerful method for generating novel therapeutic candidates from existing datasets; however, there has been less research focused on evaluating the search spaces associated with these generators. In this research I employ five deep learning model architectures for de novo generation of antimicrobial peptide sequences and assess the properties of their associated latent spaces. I train a RNN, RNN with attention, WAE, AAE and Transformer model and compare their abilities to construct desirable latent spaces in 32, 64, and 128 dimensions. I assess reconstruction accuracy, generative capability, and model interpretability and demonstrate that while most models are able to create a partitioning in their latent spaces into regions of low and high AMP sampling probability, they do so in different manners and by appealing to different underlying physicochemical properties. In this way I demonstrate several benchmarks that must be considered for such models and suggest that for optimization of search space properties, an ensemble methodology is most appropriate for design of new AMPs. I design an AMP discovery pipeline and present candidate sequences and properties from three models that achieved high benchmark scores. Overall, by tuning models and their accompanying latent spaces, targeted sampling of anti-microbial peptides with ideal characteristics is achievable
Time and Phonology: Precedence-Based Representations
Get PDFA major factor hindering the establishment of a successful neuroscience of phonology centers around the biological viability of a given phonological framework. The ultimate aim of this project is to find potential alignments between linguistics and neuroscience. In this vein, the main topic of the thesis rests upon establishing the minimal complexity requirements for a phonological representation that is biologically plausible, cognitively sound, and empirically motivated. Heeding Minimalist proposals (Chomsky, 1995) that encourage efficiency in computation and economy in representation, I embark on an in-depth exploration of the parameters of cognition that are necessary and sufficient in a phonological representation while discounting the processes and parameters that can be said to be “domain-general”. To that end, I take seriously Ernst Po ̈ppel’s (2004) exhortation to consider the role of temporal events like linear order and precedence in the study of cognitive systems like phonology by surveying the literature on time perception. The conclusions support a separation of order from phonological representations, extending the scope of substance-freeness (Hale and Reiss, 2000) by characterizing order as substance. Such an approach can contribute to thoroughly defining the object of study and offer insight that narrows the search space for potential bridges
Networked
No full textNetworked is a serialized radio drama following Gale, a woman in her thirties, as she uncovers the trail of her partner Annabelle’s disappearance. Annabelle’s data is brought to life through the haphazard efforts of Factory’s virtual archivist, a rogue chatbot named B. Together, Gale and B travel up and down the tower of Factory’s extensive archives, each floor shifting into a chapter of Annabelle’s story via dangerous, untested technology. Parallel to Annabelle’s journey, Gale uncovers a sinister motive behind Factory’s relentless data collection and a scheme that undermines not only her relationship, but the nature of all relationships in technologically inundated societies. Gale’s search for Annabelle, despite being fantastical in its unfolding, relies on the concrete processes of data collection, and aims to demystify data brokerage by exploring the ways in which our lives are identified, quantified, and monetized through our relationship with the digital world. Alongside the notable benefits of allowing our technologies to know us better, Networked aims to uncover the disadvantages—both current and speculative—of unregulated data collection in relation to the nebulous themes of privacy, freedom, and control
A Flexible Heuristic Closed-Loop Algorithm for QoS Assurance in 5G End-to-End Network Slices
Get PDF5G networks present new possibilities in communication technology, but they also create challenges in network management due to the incorporation of new complex concepts such as network slicing and virtual network functions (VNF). These challenges make it difficult for network operators to manually ensure that all quality of service (QoS) requirements are met across all network slices while also monitoring resource and energy consumption. To address this, automated network assurance solutions are required. Although machine learning (ML) and deep learning (DL) techniques have shown potential in this field, they come with difficulties such as acquiring real-world labeled training datasets and guaranteeing the quality of ML/DL pipelines during production.
The thesis proposes a time-driven closed-loop algorithm with proactive components that are differentiated by slice type, key performance indicator (KPI) type, and current resource consumption levels to maintain QoS for 5G end-to-end (E2E) network slices. Through simulations, we show that the proposed closed-loop algorithm is effective in resolving KPI violations and reducing network and compute resource usage, as well as allowing for flexible tradeoffs between QoS guarantees and resource consumption for each slice type via slice-specific parameter adjustments. Our solution not only enables network providers to better negotiate with customers, but also has the potential to generate training data for future ML/DL approaches
The effects of reproducibility & replicability of Parkinson's disease progression prediction using machine learning
Get PDFMachine Learning (ML) techniques are growing in popularity for analyzing T1-weighted magnetic resonance imaging (MRI) as it is a promising source of Parkinson’s disease (PD) biomarkers. However, there is growing concern within the scientific community regarding the reproducibility of research findings. This reproducibility crisis suggests that a significant proportion of studies may not be reliable which impacts the validity of results in a preclinical setting.
The objective of this paper is to reproduce and replicate the findings of a study by (Shu et al., 2020) that uses ML techniques to predict the progression of PD using conventional MRI and radiomic biomarkers in whole-brain white matter. We aim to assess the reproducibility
and replicability of (Shu et al., 2020)’s predictive capabilities using open-source tools. We used the Parkinson’s Progression Markers Initiative (PPMI) dataset, the same dataset used by (Shu et al., 2020) and similar analyses to assess the reproducibility of the findings. While we attempted to follow the methods outlined in (Shu et al., 2020) as closely as possible, some details were unclear and we made educated guesses. We introduced variations in the methodological methods, including different cohorts, feature sets, ML algorithms, and evaluation techniques, to assess the replicability of the findings. Our study could not reproduce nor replicate the predictive capabilities of (Shu et al., 2020). The lack of reproducibility and replicability in this paper highlights the importance of adopting open science practices to ensure that proposed biomarkers are robust
Why are you not burned out yet? The role of psychological needs satisfaction and appraisal in the Job Demands-Resources Model
Get PDFWhile studies have found that greater job demands leads to greater burnout, there have been some inconsistent findings in the effects of demands. Building on Job Demands-Resources research and the challenge-hindrance framework, we investigate how (a) appraisals and (b) psychological needs satisfaction affect the relationship between job demands and burnout/engagement. A time-lagged online survey was conducted with 160 full-time employees across diverse locations and occupations (51.9% women, mean age was 30.0 years old, SD 7.14). The data was analyzed using multiple regression analysis. We found that some hindrance and challenge appraisals of job demands moderated the relationship between job demands (cognitive and emotional) and burnout/engagement, and satisfied psychological needs (relatedness and autonomy) moderated the relationship between job demands and engagement. This study extends existing research by investigating job demand appraisals and psychological needs as moderators of the job demands-well-being relationship and by partially explaining job demands’ relationship with positive and negative well-being outcomes
Understanding Women Middle Managers’ Realities in Québec’s Health and Social Services System
Get PDFThis research captures the lived experiences of eight women middle managers in Québec’s Health and Social Service System (HSSS) and makes recommendations for changes that will support them in their multiple roles, while fostering their psychological well-being. A qualitative inquiry research approach was used to study how women middle managers navigate their professional and private roles, in a public organization where women comprise the majority of the employees and middle managers. Additionally, it explores the opportunities and barriers that these women face in a context where family-friendly policies such as parental leave, affordable daycare, and pay equity have existed for over two decades.
All study participants were white French-Canadian mothers, except one who was a mother-to-be. The research revealed that they felt overextended and that the superwoman syndrome is still alive and well, reinforced by unrealistic organizational and societal expectations grounded in the gendered segregation of both paid and unpaid work. Although these women were highly educated, they still followed the societal norms concerning their domestic, caregiver, and emotional roles, trying to adapt and “do it all” at the expense of their own health and psychological well-being.
The implications of this study show that persistent gender segregation in paid and unpaid work spheres leaves women and mothers squeezed for time which negatively impacts their well-being. This study calls for changes in the organizational culture and a move away from a work-centric focus to a focus on human capital. It also has important implications for governments as their policies and practices contribute to work environments which are maladapted to women’s and especially mothers’ realities. If true progress is to be made, society must value caregiving and develop policies and practices which allow people to thrive in all spheres of their lives: self, family, and work. We have to stop trying to fix women and rather focus on system change and transformation
DESIGN OF A LINE FIELD OPTICAL COHERENCE TOMOGRAPHY FOR IMAGING APPLICATIONS
Get PDFEndoscopic imaging, an essential subfield of biomedical imaging, is the focus of the present study. While X-Ray, Ultrasound, and MRI have been used widely, Optical coherence tomography (OCT) has recently received much interest owing to its potential use in non-destructive tissue imaging and industrial component testing. OCT is a tomographic technique that produces cross-sectional images of objects with a resolution of 2 to 10 µm.
Recently, Liverpool university developed a Line Field OCT system to improve scanning speed while reducing scanning distortion errors and motion abnormalities. However, contemporary OCT employ refractive optics for scanning and traditional spectrometers for data analysis. The fundamental shortcoming of refractive optics is chromatic aberration, particularly in OCT, where a broadband light source is utilized. Also, traditional spectrometers have nonlinearity in k-space, which reduces the signal sensitivity.
This thesis aims to design a reflective optics-based line scan (LS-OCT) with cylindrical optics where 2D cross-sectional imaging data can be obtained without requiring a mechanical scanner. Chromatic aberration is eliminated with the use of reflective optics. Further, a novel linear k-space spectrometer has been developed as a part of this thesis to reduce the signal sensitivity drop-off. The scanner and spectrometers design include an analytical study with MATLAB and optical modeling with ZEMAX.
The design is optimized for wavelength range of 830 ± 100 nm. The scanning system is designed to provide a scan range of 2 2 2 mm, and the designed scanner is 30% smaller than a similar design in literature, while providing higher image quality within the scan range. Multiple linear k-space spectrometers are designed and analyzed as part of this work. The optimization is performed to maximize linearity and image quality while keeping the size of the spectrometer minimum.
Finally, on the data analysis aspect of the thesis, texture identification approach based on the Deep Recurrent Neural Network (DRNN) model is presented. For this purpose, different geometrical defects are 3D printed and imaged with an OCT. From the images, training is performed for defect identification. The performance of the various training approaches with different datasets for texture recognition is assessed, a two-layer LSTM network obtained the best outcome (97 % accuracy)
The Theological Aesthetics of the English Reformation: The Development of English National Heritage and Cultural Identity
Get PDFThe cultural and historical heritage of the United Kingdom is arguably one of the richest and most famed national identities sociologically speaking. The aesthetic qualities and values of England are well ingrained within the nation’s consequent theological history. An analysis of English heritage and culture, particularly literary culture, is not complete without taking into account the complexities and specific nature of the English theological identity and Church-State relations. The English Reformation of the 16th century not only created a distinctive British culture, but more importantly cemented the nation as leaders in literary and rhetorical spheres. Particularly, William Shakespeare’s contributions to the canon of English Literature were widely based upon, and somewhat constrained to, England’s theological state during the Renaissance and the Early Modern Period. Importantly, while Henry VIII technically spearheaded and created the Church of England, his daughter Elizabeth I should be given the true recognition for definitively situating England within the world’s elite in literature, aesthetics, and the creation of a distinct national identity. Therefore, it is paramount to address the importance and influence of the six wives of Henry VIII, and Queen Elizabeth, in creating and perpetuating the aesthetic practices and identity of Early Modern England. Through isolating the significant figures, texts, and aesthetic theories of the English Reformation, one can certify that English culture, heritage, and national identity are a distinct entity that warrant study and analysis outside of the purely imperial lens