2,697 research outputs found
Identifying alterations in adipose tissue-derived islet GPCR peptide ligand mRNAs in obesity: implications for islet function
In addition to acting as an energy reservoir, white adipose tissue is a vital endocrine organ involved in the modulation of cellular function and the maintenance of metabolic homeostasis through the synthesis and secretion of peptides, known as adipokines. It is known that some of these secretory peptides play important regulatory roles in glycaemic control by acting directly on islet β-cells or on insulin-sensitive tissues. Excess adiposity causes alterations in the circulating levels of some adipokines which, depending on their mode of action, can have pro-inflammatory, pro-diabetic or anti-inflammatory, anti-diabetic properties. Some adipokines that are known to act at β-cells have actions that are transduced by binding to G protein- coupled receptors (GPCRs). This large family of receptors represents ~35% of all current drug targets for the treatment of a wide range of diseases, including type 2 diabetes (T2D). Islets express ~300 GPCRs, yet only one islet GPCR is currently directly targeted for T2D treatment. This deficit represents a therapeutic gap that could be filled by the identification of adipose tissue-derived islet GPCR peptide ligands that increase insulin secretion and overall β-cell function. Thus, by defining their mechanisms of action, there is potential for the development of new pharmacotherapies for T2D. Therefore, this thesis describes experiments which aimed to compare the expression profiles of adipose tissue-derived islet GPCR peptide ligand mRNAs under lean and obese conditions, and to characterise the functional effects of a selected candidate of interest on islet cells. Visceral fat depots were retrieved from high-fat diet-induced and genetically obese mouse models, and from human participants. Fat pads were either processed as whole tissue, or mature adipocyte cells were separated from the stromal vascular fraction (SVF) which contains several other cell populations, including preadipocytes and macrophages. The expression levels of 155 islet GPCR peptide ligand mRNAs in whole adipose tissue or in isolated mature adipocytes were quantified using optimised RNA extraction and reverse transcription-quantitative polymerase chain reaction (RT-qPCR) protocols. Comparisons between lean and obese states in mice models and humans revealed significant modifications in the expression levels of several adipokine mRNAs. As expected, mRNAs encoding the positive control genes, Lep and AdipoQ were quantifiable, with the expression of Lep mRNA increasing and that of AdipoQ mRNA decreasing in obesity. Expression of Ccl4 mRNA, encoding chemokine (C-C motif) ligand 4, was significantly upregulated in whole adipose tissue across all models of obesity compared to their lean counterparts. This coincided with elevated circulating Ccl4 peptide levels. This increase was not replicated in isolated mature adipocytes, indicating that the source of upregulated Ccl4 expression in obesity was the SVF of adipose tissue. Based on this significant increase in Ccl4 mRNA expression within visceral fat and its undetermined effects on β-cell function, Ccl4 was selected for further investigation in MIN6 β-cells and mouse islets. PRESTO-Tango β-arrestin reporter assays were performed to determine which GPCRs were activated by exogenous Ccl4. Experiments using HTLA cells expressing a protease-tagged β- arrestin and transfected with GPCR plasmids of interest indicated that 100ng/mL Ccl4 significantly activated Cxcr1 and Cxcr5, but it was not an agonist at the previously identified Ccl4-target GPCRs Ccr1, Ccr2, Ccr5, Ccr9 and Ackr2. RNA extraction and RT-qPCR experiments using MIN6 β-cells and primary islets from lean mice revealed the expression of Cxcr5 mRNA in mouse islets, but it was absent in MIN6 β-cells. The remaining putative Ccl4 receptors (Ccr1, Ccr2, Ccr5, Ccr9, Cxcr1 and Ackr2) were either absent or present at trace levels in mouse islets and MIN6 β-cells. Recombinant mouse Ccl4 protein was used for functional experiments at concentrations of 5, 10, 50 and 100ng/mL, based on previous reports of biological activities at these concentrations. Trypan blue exclusion testing was initially performed to assess the effect of exogenous Ccl4 on MIN6 β-cell viability and these experiments indicated that all concentrations (5-100ng/mL) were well-tolerated. Since β-cells have a low basal rate of apoptosis, cell death was induced by exposure to the saturated free fatty acid, palmitate, or by a cocktail of pro-inflammatory cytokines (interleukin-1β, tumour necrosis factor-α and interferon-γ). In MIN6 β-cells, Ccl4 demonstrated concentration-dependent protective effects against palmitate-induced and cytokine-induced apoptosis. Conversely, while palmitate and cytokines also increased apoptosis of mouse islets, Ccl4 did not protect islets from either inducer. Quantification of bromodeoxyuridine (BrdU) incorporation into β-cell DNA indicated that Ccl4 caused a concentration-dependent reduction in proliferation of MIN6 β-cells in response to 10% fetal bovine serum (FBS). In contrast, immunohistochemical quantification of Ki67-positive mouse islet β-cells showed no differences in β-cell proliferation between control- and Ccl4-treated islets. Whilst the number of β-cells and δ-cells were unaffected, α- cells were significantly depleted by Ccl4 treatment. Exogenous Ccl4 had no effect on nutrient- stimulated insulin secretion from both MIN6 β-cells and primary mouse islets. The 3T3-L1 preadipocyte cell line was used to assess potential Ccl4-mediated paracrine and/or autocrine signalling within adipose tissue. Ccl4 did not alter the mRNA expression of Pparγ, a master regulator of adipocyte differentiation, but did significantly downregulate the mRNA expression of the crucial adipogenic gene, adiponectin. Oil Red O staining and Western blotting were performed to assess lipid accumulation, and insulin and lipolytic signalling, respectively, and these experiments indicated that the observed Ccl4-induced decrease in adiponectin expression failed to correlate with any changes in adipocyte function. In summary, these data demonstrated anti-apoptotic and anti-proliferative actions of the adipokine, Ccl4, on MIN6 β-cells that were not replicated in mouse islets. The absence of any anti-apoptotic, insulin secretory and/or pro-proliferative effects of Ccl4 in islet β-cells suggests that it is unlikely to play a role in regulating β-cell function via crosstalk between adipose tissue and islets. The divergent functional effects highlight that whilst MIN6 cells are a useful primary β-cell surrogate for some studies, primary islets should always be used to confirm physiological relevance. On the other hand, significant α-cell depletion following Ccl4 treatment suggests a cell-specific function within the islets. Furthermore, Ccl4 impaired adiponectin mRNA expression in adipocytes, although, how adipocyte function is affected as a result requires further investigation. Collectively, these data have contributed increased understanding of the role of obesity in modifying the expression of adipose tissue-derived islet GPCR peptide ligands
Analytical validation of innovative magneto-inertial outcomes: a controlled environment study.
peer reviewe
On the Utility of Representation Learning Algorithms for Myoelectric Interfacing
Electrical activity produced by muscles during voluntary movement is a reflection of the firing patterns of relevant motor neurons and, by extension, the latent motor intent driving the movement. Once transduced via electromyography (EMG) and converted into digital form, this activity can be processed to provide an estimate of the original motor intent and is as such a feasible basis for non-invasive efferent neural interfacing. EMG-based motor intent decoding has so far received the most attention in the field of upper-limb prosthetics, where alternative means of interfacing are scarce and the utility of better control apparent. Whereas myoelectric prostheses have been available since the 1960s, available EMG control interfaces still lag behind the mechanical capabilities of the artificial limbs they are intended to steer—a gap at least partially due to limitations in current methods for translating EMG into appropriate motion commands. As the relationship between EMG signals and concurrent effector kinematics is highly non-linear and apparently stochastic, finding ways to accurately extract and combine relevant information from across electrode sites is still an active area of inquiry.This dissertation comprises an introduction and eight papers that explore issues afflicting the status quo of myoelectric decoding and possible solutions, all related through their use of learning algorithms and deep Artificial Neural Network (ANN) models. Paper I presents a Convolutional Neural Network (CNN) for multi-label movement decoding of high-density surface EMG (HD-sEMG) signals. Inspired by the successful use of CNNs in Paper I and the work of others, Paper II presents a method for automatic design of CNN architectures for use in myocontrol. Paper III introduces an ANN architecture with an appertaining training framework from which simultaneous and proportional control emerges. Paper Iv introduce a dataset of HD-sEMG signals for use with learning algorithms. Paper v applies a Recurrent Neural Network (RNN) model to decode finger forces from intramuscular EMG. Paper vI introduces a Transformer model for myoelectric interfacing that do not need additional training data to function with previously unseen users. Paper vII compares the performance of a Long Short-Term Memory (LSTM) network to that of classical pattern recognition algorithms. Lastly, paper vIII describes a framework for synthesizing EMG from multi-articulate gestures intended to reduce training burden
An investigation of entorhinal spatial representations in self-localisation behaviours
Spatial-modulated cells of the medial entorhinal cortex (MEC) and neighbouring cortices are thought to provide the neural substrate for self-localisation behaviours. These cells include grid cells of the MEC which are thought to compute path integration operations to update self-location estimates. In order to read this grid code, downstream cells are thought to reconstruct a positional estimate as a simple rate-coded representation of space.
Here, I show the coding scheme of grid cell and putative readout cells recorded from mice performing a virtual reality (VR) linear location task which engaged mice in both beaconing and path integration behaviours. I found grid cells can encode two unique coding schemes on the linear track, namely a position code which reflects periodic grid fields anchored to salient features of the track and a distance code which reflects periodic grid fields without this anchoring. Grid cells were found to switch between these coding schemes within sessions. When grid cells were encoding position, mice performed better at trials that required path integration but not on trials that required beaconing. This result provides the first mechanistic evidence linking grid cell activity to path integration-dependent behaviour.
Putative readout cells were found in the form of ramp cells which fire proportionally as a function of location in defined regions of the linear track. This ramping activity was found to be primarily explained by track position rather than other kinematic variables like speed and acceleration. These representations were found to be maintained across both trial types and outcomes indicating they likely result from recall of the track structure.
Together, these results support the functional importance of grid and ramp cells for self-localisation behaviours. Future investigations will look into the coherence between these two neural populations, which may together form a complete neural system for coding and decoding self-location in the brain
Integrating Edge Computing and Software Defined Networking in Internet of Things: A Systematic Review
The Internet of Things (IoT) has transformed our interaction with the world by connecting devices, sensors, and systems to the Internet, enabling real-time monitoring, control, and automation in various applications such as smart cities, healthcare, transportation, homes, and grids. However, challenges related to latency, privacy, and bandwidth have arisen due to the massive influx of data generated by IoT devices and the limitations of traditional cloud-based architectures. Moreover, network management, interoperability, security, and scalability issues have emerged due to the rapid growth and heterogeneous nature of IoT devices. To overcome such problems, researchers proposed a new architecture called Software Defined Networking for Edge Computing in the Internet of Things (SDN-EC-IoT), which combines Edge Computing for the Internet of Things (EC-IoT) and Software Defined Internet of Things (SDIoT). Although researchers have studied EC-IoT and SDIoT as individual architectures, they have not yet addressed the combination of both, creating a significant gap in our understanding of SDN-EC-IoT. This paper aims to fill this gap by presenting a comprehensive review of how the SDN-EC-IoT paradigm can solve IoT challenges. To achieve this goal, this study conducted a literature review covering 74 articles published between 2019 and 2023. Finally, this paper identifies future research directions for SDN-EC-IoT, including the development of interoperability platforms, scalable architectures, low latency and Quality of Service (QoS) guarantees, efficient handling of big data, enhanced security and privacy, optimized energy consumption, resource-aware task offloading, and incorporation of machine learnin
Towards a Digital Capability Maturity Framework for Tertiary Institutions
Background: The Digital Capability (DC) of an Institution is the extent to which the institution's culture, policies, and infrastructure enable and support digital practices (Killen et al., 2017), and maturity is the continuous improvement of those capabilities. As technology continues to evolve, it is likely to give rise to constant changes in teaching and learning, potentially disrupting Tertiary Education Institutions (TEIs) and making existing organisational models less effective. An institution’s ability to adapt to continuously changing technology depends on the change in culture and leadership decisions within the individual institutions. Change without structure leads to inefficiencies, evident across the Nigerian TEI landscape. These inefficiencies can be attributed mainly to a lack of clarity and agreement on a development structure.
Objectives: This research aims to design a structure with a pathway to maturity, to support the continuous improvement of DC in TEIs in Nigeria and consequently improve the success of digital education programmes.
Methods: I started by conducting a Systematic Literature Review (SLR) investigating the body of knowledge on DC, its composition, the relationship between its elements and their respective impact on the Maturity of TEIs. Findings from the review led me to investigate further the key roles instrumental in developing Digital Capability Maturity in Tertiary Institutions (DCMiTI).
The results of these investigations formed the initial ideas and constructs upon which the proposed structure was built. I then explored a combination of quantitative and qualitative methods to substantiate the initial constructs and gain a deeper understanding of the relationships between elements/sub-elements. Next, I used triangulation as a vehicle to expand the validity of the findings by replicating the methods in a case study of TEIs in Nigeria. Finally, after using the validated constructs and knowledge base to propose a structure based on CMMI concepts, I conducted an expert panel workshop to test the model’s validity.
Results: I consolidated the body of knowledge from the SLR into a universal classification of 10 elements, each comprising sub-elements. I also went on to propose a classification for DCMiTI. The elements/sub-elements in the classification indicate the success factors for digital maturity, which were also found to positively impact the ability to design, deploy and sustain digital education. These findings were confirmed in a UK University and triangulated in a case study of Northwest Nigeria. The case study confirmed the literature findings on the status of DCMiTI in Nigeria and provided sufficient evidence to suggest that a maturity structure would be a well-suited solution to supporting DCM in the region. I thus scoped, designed, and populated a domain-specific framework for DCMiTI, configured to support the educational landscape in Northwest Nigeria.
Conclusion: The proposed DCMiTI framework enables TEIs to assess their maturity level across the various capability elements and reports on DCM as a whole. It provides guidance on the criteria that must be satisfied to achieve higher levels of digital maturity. The framework received expert validation, as domain experts agreed that the proposed Framework was well applicable to developing DCMiTI and would be a valuable tool to support TEIs in delivering successful digital education. Recommendations were made to engage in further iterations of testing by deploying the proposed framework for use in TEI to confirm the extent of its generalisability and acceptability
Cognitive Decay And Memory Recall During Long Duration Spaceflight
This dissertation aims to advance the efficacy of Long-Duration Space Flight (LDSF) pre-flight and in-flight training programs, acknowledging existing knowledge gaps in NASA\u27s methodologies. The research\u27s objective is to optimize the cognitive workload of LDSF crew members, enhance their neurocognitive functionality, and provide more meaningful work experiences, particularly for Mars missions.The study addresses identified shortcomings in current training and learning strategies and simulation-based training systems, focusing on areas requiring quantitative measures for astronaut proficiency and training effectiveness assessment. The project centers on understanding cognitive decay and memory loss under LDSF-related stressors, seeking to establish when such cognitive decline exceeds acceptable performance levels throughout mission phases. The research acknowledges the limitations of creating a near-orbit environment due to resource constraints and the need to develop engaging tasks for test subjects. Nevertheless, it underscores the potential impact on future space mission training and other high-risk professions. The study further explores astronaut training complexities, the challenges encountered in LDSF missions, and the cognitive processes involved in such demanding environments. The research employs various cognitive and memory testing events, integrating neuroimaging techniques to understand cognition\u27s neural mechanisms and memory. It also explores Rasmussen\u27s S-R-K behaviors and Brain Network Theory’s (BNT) potential for measuring forgetting, cognition, and predicting training needs. The multidisciplinary approach of the study reinforces the importance of integrating insights from cognitive psychology, behavior analysis, and brain connectivity research. Research experiments were conducted at the University of North Dakota\u27s Integrated Lunar Mars Analog Habitat (ILMAH), gathering data from selected subjects via cognitive neuroscience tools and Electroencephalography (EEG) recordings to evaluate neurocognitive performance. The data analysis aimed to assess brain network activations during mentally demanding activities and compare EEG power spectra across various frequencies, latencies, and scalp locations. Despite facing certain challenges, including inadequacies of the current adapter boards leading to analysis failure, the study provides crucial lessons for future research endeavors. It highlights the need for swift adaptation, continual process refinement, and innovative solutions, like the redesign of adapter boards for high radio frequency noise environments, for the collection of high-quality EEG data. In conclusion, while the research did not reveal statistically significant differences between the experimental and control groups, it furnished valuable insights and underscored the need to optimize astronaut performance, well-being, and mission success. The study contributes to the ongoing evolution of training methodologies, with implications for future space exploration endeavors
Towards trustworthy computing on untrustworthy hardware
Historically, hardware was thought to be inherently secure and trusted due to its
obscurity and the isolated nature of its design and manufacturing. In the last two
decades, however, hardware trust and security have emerged as pressing issues.
Modern day hardware is surrounded by threats manifested mainly in undesired
modifications by untrusted parties in its supply chain, unauthorized and pirated
selling, injected faults, and system and microarchitectural level attacks. These threats,
if realized, are expected to push hardware to abnormal and unexpected behaviour
causing real-life damage and significantly undermining our trust in the electronic and
computing systems we use in our daily lives and in safety critical applications. A
large number of detective and preventive countermeasures have been proposed in
literature. It is a fact, however, that our knowledge of potential consequences to
real-life threats to hardware trust is lacking given the limited number of real-life
reports and the plethora of ways in which hardware trust could be undermined. With
this in mind, run-time monitoring of hardware combined with active mitigation of
attacks, referred to as trustworthy computing on untrustworthy hardware, is proposed
as the last line of defence. This last line of defence allows us to face the issue of live
hardware mistrust rather than turning a blind eye to it or being helpless once it occurs.
This thesis proposes three different frameworks towards trustworthy computing
on untrustworthy hardware. The presented frameworks are adaptable to different
applications, independent of the design of the monitored elements, based on
autonomous security elements, and are computationally lightweight. The first
framework is concerned with explicit violations and breaches of trust at run-time,
with an untrustworthy on-chip communication interconnect presented as a potential
offender. The framework is based on the guiding principles of component guarding,
data tagging, and event verification. The second framework targets hardware elements
with inherently variable and unpredictable operational latency and proposes a
machine-learning based characterization of these latencies to infer undesired latency
extensions or denial of service attacks. The framework is implemented on a DDR3
DRAM after showing its vulnerability to obscured latency extension attacks. The
third framework studies the possibility of the deployment of untrustworthy hardware
elements in the analog front end, and the consequent integrity issues that might arise
at the analog-digital boundary of system on chips. The framework uses machine
learning methods and the unique temporal and arithmetic features of signals at this
boundary to monitor their integrity and assess their trust level
University of Windsor Graduate Calendar 2023 Spring
https://scholar.uwindsor.ca/universitywindsorgraduatecalendars/1027/thumbnail.jp
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