Now showing items 1-20 of 570

    • Relationships between sales of legal medical cannabis and alcohol in Canada

      Armstrong, Michael J. (Elsevier BV, 2022-11)
      The extent to which legalizing cannabis use might lead to increased or decreased alcohol use has important implications for public health, economic growth, and government policy. This study analyzed Canada’s monthly per capita sales of alcohol and legal medical cannabis using fixed effect panel data linear regressions. The data covered seven Canadian regions from January 2011 to September 2018, and controlled for changing levels of retail activity, alcohol prices, tertiary education, unemployment, and impaired driving penalties. The analysis estimated that each dollar of legal medical cannabis sold was associated with an average alcohol sales decrease of roughly $0.74 to $0.84. This suggests that medical cannabis was an economic substitute for alcohol in Canada, and that the country’s 2017-2018 alcohol sales were roughly 1.8% lower than they would have been without legal medical cannabis. The results therefore indirectly imply that reduced alcohol consumption might have partly offset cannabis legalization’s health and economic impacts.
    • Culture of Cancer Cells at Physiological Oxygen Levels Affects Gene Expression in a Cell-Type Specific Manner

      Alva, Ricardo; Moradi, Fereshteh; Liang, Ping; Stuart, Jeffrey A. (MDPI, 2022)
      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.
    • Data to accompany manuscript: Thermoconforming rays of the star-nosed mole

      Tattersall, Glenn; Campbell, Kevin (2022-11-18)
      The star-nosed mole (Condylura cristata) is well known for its unique star-like rostrum (‘star’) which is formed by 22 nasal appendages highly specialised for tactile sensation. As a northerly distributed insectivorous mammal occupying both aquatic and terrestrial habitats, this sensory appendage is regularly exposed to cold water and thermally conductive soil, leading us to ask whether the surface temperature, a proxy for blood flow to the star, conforms to the local ambient temperature to conserve body heat. Alternatively, given the high functioning and sensory nature of the star, we posited it was possible that the rays may be kept continually warm when foraging, with augmented peripheral blood flow serving metabolic needs of this tactile sensory organ. To test these ideas, we remotely monitored the surface temperatures of the star and other uninsulated appendages in response to changes in local water or ground temperature in wild-caught star-nosed moles briefly studied in captive situation. While the tail responded to increasing heat load through vasodilation, the surface temperature of the star consistently thermoconformed, varying passively in surface temperature, suggesting little evidence for thermoregulatory vasomotion. This thermoconforming response may have evolved as a compensatory response related to the high costs of heat dissipation to water or soil in this actively foraging insectivore.
    • Children's participation as neo-liberal governance?

      Raby, Rebecca (Routledge, 2014)
      Children's participation initiatives have been increasingly introduced within various institutional jurisdictions around the world, partly in response to Article 12 of the United Nations Convention on the Rights of the Child. Such initiatives have been critically evaluated from a number of different angles. This article engages with an avenue of critique which argues that children's participatory initiatives resonate with a neoliberal economic and political context that prioritises middle class, western individualism and ultimately fosters children's deeper subjugation through self-governance. Respecting these as legitimate concerns, this article draws on two counter-positions to argue that while children's participation can certainly be conceptualised and practised in ways that reflect neo-liberal, individualised self-governance, it does not necessarily do so. To make this argument I engage, on the one hand, with Foucault's work on the care of the self, and on the other, with more collective approaches to participation.
    • Pandemic: A Graph Evolution Story

      Dube, Michael; Houghten, Sheridan; Ashlock, Daniel (IEEE, 2019-07)
      The Graph Evolution Tool (GET)was built to generate personal contact networks representing who can infect whom within a community. The tool is expanded in order to permit an infection scheme which divides the community into different districts, thus permitting within-district and between-district infections. The evolutionary algorithm comprising GET is expanded upon to simulate communities which include 512 individuals in up to eight districts, initially infecting one person in one district and spreading through a community. The overall goal is to generate communities that will maximize the length of an epidemic. The problem associated with adequately exploring the numerous parameters accompanying evolutionary algorithms is addressed using a point packing and insight from previous work. The Susceptible-Infected-Removed (SIR)model of infection was chosen as it provides a sufficient balance of simplicity and complexity for the problem.
    • Using Genetic Programming to Investigate a Novel Model of Resting Energy Expenditure for Bariatric Surgery Patients

      Hughes, James Alexander; Reid, Ryan E. R.; Houghten, Sheridan; Andersen, Ross E. (IEEE, 2020-10-27)
      Traditionally, models developed to estimate resting energy expenditure (REE) in the bariatric population have been limited to linear modelling based on data from `normal' or `overweight' individuals - not `obese'. This type of modelling can be restrictive and yield functions which poorly estimate this important physiological outcome.Linear and nonlinear models of REE for individuals after bariatric surgery are developed with linear regression and symbolic regression via genetic programming. Features not traditionally used in REE modelling were also incorporated and analyzed and genetic programming's intrinsic feature selection was used as a measure of feature importance.A collection of effective new linear and nonlinear models were generated. The linear models generated outperformed the nonlinear on testing data, although the nonlinear models fit the training data better. Ultimately, the newly developed linear models showed an improvement over existing models and the feature importance analysis suggested that the typically used features (age, weight, and height) were the most important.
    • Deep Learning for the Prediction of Stock Market Trends

      Fazeli, Arvand; Houghten, Sheridan (IEEE, 2019-12)
      In this study, deep learning will be used to test the predictability of stock trends. Stock markets are known to be volatile, prices fluctuate, and there are many complicated financial indicators involved. Various data including news or financial indicators can be used to predict stock prices. In this study, the focus will be on using past stock prices and using technical indicators to increase the performance of the results. The goal of this study is to measure the accuracy of predictions and evaluate the results. Historical data is gathered for Apple, Microsoft, Google and Intel stocks. A prediction model is created by using past data and technical indicators were used as features in the model. The experiments were performed by using long short-term memory networks. Different approaches and techniques were tested to boost the performance of the results. To prove the usability of the final model in the real world and measure the profitability of results backtesting was performed. The final results show that while it is not possible to predict the exact price of a stock in the future to gain profitable results, deep learning can be used to predict the trend of stock markets to generate buy and sell signals.
    • Compression of Biological Networks using a Genetic Algorithm with Localized Merge

      Houghten, Sheridan; Romualdo, Angelo; Collins, Tyler K.; Brown, Joseph Alexander (IEEE, 2019-07)
      Network graphs appear in a number of important biological data problems, recording information relating to protein-protein interactions, gene regulation, transcription regulation and much more. These graphs are of such a significant size that they are impossible for a human to understand. Furthermore, the ever-expanding quantity of such information means that there are storage issues. To help address these issues, it is common for applications to compress nodes to form supernodes of similarly connected components. In previous graph compression studies it was noted that such supernodes often contain points from disparate parts of the graph. This study aims to correct this flaw by only allowing merges to occur within a local neighbourhood rather than across the entire graph. This restriction was found to not only produce more meaningful compressions, but also to reduce the overall distortion created by the compression for two out of three biological networks studied.
    • Descriptive Symbolic Models of Gaits from Parkinson's Disease Patients

      Hughes, James Alexander; Houghten, Sheridan; Brown, Joseph Alexander (IEEE, 2019-07)
      Parkinson's disease (PD) is a degenerative disorder of the central nervous system that has many debilitating symptoms which affect the patient's motor system and can cause significant changes in their gait. By using genetic programming, we aim to develop descriptive symbolic nonlinear models of PD patient gait from time series data recorded from pressure sensors under subjects' feet. When compared to popular types of linear regression (OLS and LASSO), the nonlinear models fit their data better and generalize to unseen data significantly better. It was found that models developed for healthy control subjects generalized to other control subjects well, however the models trained on subjects with PD did not generalize well to other PD patients, which complicates the issue of being able to detect the progression of the disease. It is suspected that health care professionals can have difficulty classifying PD due to a lack of accurate data from patient reports; having individually trained models for active monitoring of patients would help in effectively diagnosing PD.
    • Cryptanalysis of RSA: Integer Prime Factorization Using Genetic Algorithms

      Rutkowski, Emilia; Houghten, Sheridan (IEEE, 2020-07)
      In recent years, researchers have been exploring alternative methods to solving Integer Prime Factorization, the decomposition of an integer into its prime factors. This has direct application to cryptanalysis of RSA, as one means of breaking such a cryptosystem requires factorization of a large number that is the product of two prime numbers. This paper applies three different genetic algorithms to solve this issue, utilizing mathematical knowledge concerning distribution of primes to improve the algorithms. The best of the three genetic algorithms has a chromosome that represents m in the equation prime = 6 m ± 1, and is able to factor a number of up to 22 decimal digits. This is a significantly larger number than the largest factored by comparable methods in earlier work. This leads to the conclusion that approaches such as genetic algorithms are a promising avenue of research into the problem of integer factorization.
    • Evolutionary Graph Compression and Diffusion Methods for City Discovery in Role Playing Games

      Brown, Joseph Alexander; Ashlock, Daniel; Houghten, Sheridan; Romualdo, Angelo (IEEE, 2020-07)
      Cities, while exciting in their visualization and permitting several layouts, do not take into account the placement of crucial characters which might be part of the narrative. Narrative graphs, a connected graph of all potential and existing relations within a game, can enable an ability to find a Nonplayer Character (NPC) who is likely to live nearby, under the assumption that those who interact most frequently are also close in distance. We examine the use of an evolutionary graph compression method and a method using simulated diffusion to cluster features based on relational information about players to generate relationally intimate groups. This clustering can be used to generate information about the game world and cities to inform PCG as to how the connectivity of these areas is, and should be, arranged. The algorithms are validated as being human competitive.
    • Gait Model Analysis of Parkinson’s Disease Patients under Cognitive Load

      Hughes, James Alexander; Houghten, Sheridan; Brown, Joseph Alexander (IEEE, 2020-07)
      Parkinson's disease is a neurodegenerative disease that affects close to 10 million with various symptoms including tremors and changes in gait. Observing differences or changes in an individual's manifestations of gait may provide a mechanism to identify Parkinson's disease and understand specific changes. In this study, timeseries data from both Control subjects and Parkinson's disease patients was modelled with symbolic regression and extreme gradient boosting. Model effectiveness was analyzed along with the differences in the models between modelling strategies, between Control subjects and Parkinson's disease patients, and between normal walking and walking while under a cognitive load. Both modelling strategies were found to effective. The symbolic regression models were more easily interpreted, while extreme gradient boosting had higher overall accuracy. Interpretation of the models identified certain characteristics that distinguished Control subjects from Parkinson's disease patients and normal walking conditions from walking while under a cognitive load.
    • Extracting Information from Weighted Contact Networks via Genetic Algorithms

      Rutkowski, Emilia; Houghten, Sheridan; Brown, Joseph Alexander (IEEE, 2020-10-27)
      Epidemic contact tracing examines the movement of infection through a population based upon links in a contact network, and weighted networks represent the potential of transfer of the contagion. Graph compression reduces the size of a network by merging groups of nodes into supernodes. This study considers the use of genetic algorithms to select the nodes to be merged, grouping together highly connected sections of the graphs. Examined is a dataset that is extracted from contacts that occurred during several days of the "Infectious: Stay Away" event. The incorporation of weights, to indicate the strength of interactions between individuals, is an important contribution of this work. The demonstrated outcomes are that by including weighted information on the edges, there is more effective detection of highly interacting subgroups when compared to the unweighted version of graphs. These methods not only compress the networks with a low rate of distortion, but also the identification of supernodes in the networks allows for better targeting of interventions by public health upon individuals in such groups. This is crucial because when one member becomes infected, all members of the group are exposed to the contagion.
    • Evolving the Curve

      Dube, Michael; Houghten, Sheridan; Ashlock, Daniel; Hughes, James Alexander (IEEE, 2020-10-27)
      Evolutionary algorithms are used to generate personal contact networks, modelling human populations, that are most likely to match a given epidemic profile. The Susceptible-Infected-Removed (SIR) model is used and also expanded upon to allow for an extended period of infection, termed the SIIR model. The networks generated for each of these models are thoroughly evaluated for their ability to match nine different epidemic profiles. The addition of the SIIR model showed that the model of infection has an impact on the networks generated. For the SIR and SIIR models, these differences were relatively minor in most cases.
    • Effective Side Effect Machines for Decoding

      Banik, Sharnendu; Houghten, Sheridan (IEEE, 2020-10-27)
      The development of general edit metric decoders is a challenging problem, especially with the inclusion of additional biological restrictions that can occur when using error correcting codes in biological applications. Side effect machines (SEMs), an extension of finite state machines, can provide efficient decoding algorithms for such edit metric codes.Several codes of varying lengths are used to study the effectiveness of evolutionary programming (EP) as a general approach for finding SEMs for edit metric decoding. Direct and fuzzy classification methods are compared while also changing some of the EP settings to observe how decoding accuracy is affected. Regardless of code length, the best results are found using the fuzzy classification methods. For codes of length 10, a maximum accuracy of up to 99.4% is achieved for distance 1 whereas distance 2 and 3 achieve up to 97.1% and 85.9%, respectively. The accuracy suffers for longer codes, as the maximum accuracies achieved by codes of length 14 were 92.4%, 85.7% and 69.2% for distance 1, 2, and 3 respectively. Additionally, the SEMs are examined for potential bloat by comparing the number of reachable states against the total number of states. Bloat is seen more in larger machines than it is in smaller machines. Furthermore, the results are analyzed to find potential trends and relationships among the parameters, with the most consistent trend being that, when allowed, the longer codes generally show a propensity for larger machines.
    • Proteomes Are of Proteoforms: Embracing the Complexity

      Carbonara, Katrina; Andonovski, Martin; Coorssen, Jens R. (MDPI AG, 2021-08-31)
      Proteomes are complex—much more so than genomes or transcriptomes. Thus, simplifying their analysis does not simplify the issue. Proteomes are of proteoforms, not canonical proteins. While having a catalogue of amino acid sequences provides invaluable information, this is the Proteome-lite. To dissect biological mechanisms and identify critical biomarkers/drug targets, we must assess the myriad of proteoforms that arise at any point before, after, and between translation and transcription (e.g., isoforms, splice variants, and post-translational modifications [PTM]), as well as newly defined species. There are numerous analytical methods currently used to address proteome depth and here we critically evaluate these in terms of the current ‘state-of-the-field’. We thus discuss both pros and cons of available approaches and where improvements or refinements are needed to quantitatively characterize proteomes. To enable a next-generation approach, we suggest that advances lie in transdisciplinarity via integration of current proteomic methods to yield a unified discipline that capitalizes on the strongest qualities of each. Such a necessary (if not revolutionary) shift cannot be accomplished by a continued primary focus on proteo-genomics/-transcriptomics. We must embrace the complexity. Yes, these are the hard questions, and this will not be easy…but where is the fun in easy?
    • Profit versus Quality: The Enigma of Scientific Wellness

      Carbonara, Katrina; MacNeil, Adam J.; O'Leary, Deborah D.; Coorssen, Jens R. (Journal of Personalized Medicine, 2022-01-03)
      The “best of both worlds” is not often the case when it comes to implementing new health models, particularly in community settings. It is often a struggle between choosing or balancing between two components: depth of research or financial profit. This has become even more apparent with the recent shift to move away from a traditionally reactive model of medicine toward a predictive/preventative one. This has given rise to many new concepts and approaches with a variety of often overlapping aims. The purpose of this perspective is to highlight the pros and cons of the numerous ventures already implementing new concepts, to varying degrees, in community settings of quite differing scales—some successful and some falling short. Scientific wellness is a complex, multifaceted concept that requires integrated experimental/analytical designs that demand both high-quality research/healthcare and significant funding. We currently see the more likely long-term success of those ventures in which any profit is largely reinvested into research efforts and health/healthspan is the primary focus.
    • Post-CEO retirement appointments and financial accounting—Evidence from CEO turnovers

      Ho, Nam; Pacharn, Parunchana; Brown, Kareen (Wiley Online Library, 2021-11-16)
      Prior research has shown that when boards seek to appoint CEOs as outside directors, the director labor market rewards CEOs’ accounting performance. This study examines whether the external labor market’s assessment of the accounting performance is moderated by CEOs’ past exercise of financial reporting discretion in the form of accruals and real earnings management and financial statement readability. Our results show a positive association between post-CEO board opportunities and within-GAAP accruals management as well as to more readable financial statements. Earnings restatements are associated with fewer board positions and director pay. However, the director labor market appears to punish R&D expenditure above the industry median, suggesting that boards view overinvestment as a risky avenue for growth. Finally, the results suggest that for CEOs with planned retirement, the director labor market provides some mitigating effect on the horizon problem.
    • Modelling of Vaccination Strategies for Epidemics using Evolutionary Computation

      Dube, Michael; Houghten, Sheridan; Ashlock, Daniel (IEEE, 2020-07)
      Personal contact networks that represent social interactions can be used to identify who can infect whom during the spread of an epidemic. The structure of a personal contact network has great impact upon both epidemic duration and the total number of infected individuals. A vaccine, with varying degrees of success, can reduce both the length and spread of an epidemic, but in the case of a limited supply of vaccine a vaccination strategy must be chosen, and this has a significant effect on epidemic behaviour.In this study we consider four different vaccination strategies and compare their effects upon epidemic duration and spread. These are random vaccination, high degree vaccination, ring vaccination, and the base case of no vaccination. All vaccinations are applied as the epidemic progresses, as opposed to in advance. The strategies are initially applied to static personal contact networks that are known ahead of time. They are then applied to personal contact networks that are evolved as the vaccination strategy is applied. When any form of vaccination is applied, all strategies reduce both duration and spread of the epidemic. When applied to a static network, random vaccination performs poorly in terms of reducing epidemic duration in comparison to strategies that take into account connectivity of the network. However, it performs surprisingly well when applied on the evolved networks, possibly because the evolutionary algorithm is unable to take advantage of a fixed strategy.
    • Genetic programming for improved cryptanalysis of elliptic curve cryptosystems

      Ribaric, Tim; Houghten, Sheridan (IEEE, 2017-06)
      Public-key cryptography is a fundamental compo- nent of modern electronic communication that can be constructed with many different mathematical processes. Presently, cryp- tosystems based on elliptic curves are becoming popular due to strong cryptographic strength per small key size. At the heart of these schemes is the intractability of the elliptic curve discrete logarithm problem (ECDLP). Pollard’s Rho algorithm is a well known method for solving the ECDLP and thereby breaking ciphers based on elliptic curves. It has the same time complexity as other known methods but is advantageous due to smaller memory requirements. This paper considers how to speed up the Rho process by modifying a key component: the iterating function, which is the part of the algorithm responsible for determining what point is considered next when looking for a collision. It is replaced with an alternative that is found through an evolutionary process. This alternative consistently and significantly decreases the number of iterations required by Pollard’s Rho Algorithm to successfully find a solution to the ECDLP.