Browsing by Author "Franczak, Brian C."
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Item Comparative analysis of chemical similarity methods for modular natural products with a hypothetical structure enumeration algorithm(2017) Skinnider, Michael A.; Dejong, Chris A.; Franczak, Brian C.; McNicholas, Paul D.; Magarvey, Nathan A.Natural products represent a prominent source of pharmaceutically and industrially important agents. Calculating the chemical similarity of two molecules is a central task in cheminformatics, with applications at multiple stages of the drug discovery pipeline. Quantifying the similarity of natural products is a particularly important problem, as the biological activities of these molecules have been extensively optimized by natural selection. The large and structurally complex scaffolds of natural products distinguish their physical and chemical properties from those of synthetic compounds. However, no analysis of the performance of existing methods for molecular similarity calculation specific to natural products has been reported to date. Here, we present LEMONS, an algorithm for the enumeration of hypothetical modular natural product structures. We leverage this algorithm to conduct a comparative analysis of molecular similarity methods within the unique chemical space occupied by modular natural products using controlled synthetic data, and comprehensively investigate the impact of diverse biosynthetic parameters on similarity search. We additionally investigate a recently described algorithm for natural product retrobiosynthesis and alignment, and find that when rule-based retrobiosynthesis can be applied, this approach outperforms conventional two-dimensional fingerprints, suggesting it may represent a valuable approach for the targeted exploration of natural product chemical space and microbial genome mining. Our open-source algorithm is an extensible method of enumerating hypothetical natural product structures with diverse potential applications in bioinformatics.Item Deciphering the immunomodulatory capacity of oncolytic vaccinia virus to enhance the immune response to breast cancer(2020) Umer, Brittany A.; Noyce, Ryan S.; Franczak, Brian C.; Shenouda, Mira M.; Kelly, Rees G.; Favis, Nicole A.; Desaulniers, Megan; Baldwin, Troy A.; Hitt, Mary M.; Evans, David H.Vaccinia virus (VACV) is a double-stranded DNA virus that devotes a large portion of its 200 kbp genome to suppressing and manipulating the immune response of its host. Here, we investigated how targeted removal of immunomodulatory genes from the VACV genome impacted immune cells in the tumor microenvironment with the intention of improving the therapeutic efficacy of VACV in breast cancer. We performed a head-to-head comparison of six mutant oncolytic VACVs, each harboring deletions in genes that modulate different cellular pathways, such as nucleotide metabolism, apoptosis, inflammation, and chemokine and interferon signaling. We found that even minor changes to the VACV genome can impact the immune cell compartment in the tumor microenvironment. Viral genome modifications had the capacity to alter lymphocytic and myeloid cell compositions in tumors and spleens, PD-1 expression, and the percentages of virus-targeted and tumor-targeted CD8+ T cells. We observed that while some gene deletions improved responses in the nonimmunogenic 4T1 tumor model, very little therapeutic improvement was seen in the immunogenic HER2/neu TuBo model with the various genome modifications. We observed that the most promising candidate genes for deletion were those that interfere with interferon signaling. Collectively, this research helped focus attention on the pathways that modulate the immune response in the context of VACV oncolytic virotherapy. They also suggest that the greatest benefits to be obtained with these treatments may not always be seen in “hot tumors.”Item Effects of ocean acidification on dopamine-mediated behavioral responses of a coral reef damselfish(2023) Hamilton, Trevor; Tresguerres, Martin; Kwan, Garfield T.; Szaszkiewicz, Joshua; Franczak, Brian C.; Cyronak, Tyler; Andersson, Andreas J.; Kline, David I.We investigated whether CO2-induced ocean acidification (OA) affects dopamine receptor-dependent behavior in bicolor damselfish (Stegastes partitus). Damselfish were kept in aquaria receiving flow through control (pH ~ 8.03; pCO2 ~ 384 μatm) or OA (pH ~ 7.64; CO2 ~ 1100 μatm) seawater at a rate of 1 L min−1. Despite this relatively fast flow rate, fish respiration further acidified the seawater in both control (pH ~7.88; pCO2 ~ 595 μatm) and OA (pH ~7.55; pCO2 ~ 1450 μatm) fish-holding aquaria. After five days of exposure, damselfish locomotion, boldness, anxiety, and aggression were assessed using a battery of behavioral tests using automated video analysis. Two days later, these tests were repeated following application of the dopamine D1 receptor agonist SKF 38393. OA-exposure induced ceiling anxiety levels that were significantly higher than in control damselfish, and SKF 38393 increased anxiety in control damselfish to a level not significantly different than that of OA-exposed damselfish. Additionally, SKF 38393 decreased locomotion and increased boldness in control damselfish but had no effect in OA-exposed damselfish, suggesting an alteration in activity of dopaminergic pathways that regulate behavior under OA conditions. These results indicate that changes in dopamine D1 receptor function affects fish behavior during exposure to OA. However, subsequent measurements of seawater sampled using syringes during the daytime (~3–4 pm local time) from crevasses in coral reef colonies, which are used as shelter by damselfish, revealed an average pH of 7.73 ± 0.03 and pCO2 of 925.8 ± 62.2 μatm; levels which are comparable to Representative Concentration Pathway (RCP) 8.5 predicted end-of-century mean OA levels in the open ocean. Further studies considering the immediate environmental conditions experienced by fish as well as individual variability and effect size are required to understand potential implications of the observed OA-induced behavioral effects on damselfish fitness in the wild.Item Estimated discharge of microplastics via urban stormwater during individual rain events(2023) Ross, Matthew S.; Loutan, Alyssa; Groeneveld, Tianna M.; Molenaar, Danielle; Kroetch, Kimberly; Bujaczek, Taylor; Kolter, Sheldon; Moon, Sarah; Huynh, Alan; Khayam, Rosita; Franczak, Brian C.Urban stormwater runoff is an important pathway for the introduction of microplastics and other anthropogenic pollutants into aquatic environments. Highly variable concentrations of microplastics have been reported globally in runoff, but knowledge of key factors within urban environments contributing to this variability remains limited. Furthermore, few studies to date have quantitatively assessed the release of microplastics to receiving waters via runoff. The objectives of this study were to assess the influence of different catchment characteristics on the type and amount of microplastics in runoff and to provide an estimate of the quantity of microplastics discharged during rain events. Stormwater samples were collected during both dry periods (baseflow) and rain events from 15 locations throughout the city of Calgary, Canada’s fourth largest city.Item Estimated discharge of microplastics via urban stormwater during individual rain events(2023) Ross, Matthew S.; Loutan, Alyssa; Groeneveld, Tianna M.; Molenaar, Danielle; Kroetch, Kimberly; Bujaczek, Taylor; Kolter, Sheldon; Moon, Sarah; Franczak, Brian C.Urban stormwater runoff is an important pathway for the introduction of microplastics and other anthropogenic pollutants into aquatic environments. Highly variable concentrations of microplastics have been reported globally in runoff, but knowledge of key factors within urban environments contributing to this variability remains limited. Furthermore, few studies to date have quantitatively assessed the release of microplastics to receiving waters via runoff. The objectives of this study were to assess the influence of different catchment characteristics on the type and amount of microplastics in runoff and to provide an estimate of the quantity of microplastics discharged during rain events. Stormwater samples were collected during both dry periods (baseflow) and rain events from 15 locations throughout the city of Calgary, Canada’s fourth largest city. These catchments ranged in size and contained different types of predominant land use. Microplastics were found in all samples, with total concentrations ranging from 0.7 to 200.4 pcs/L (mean = 31.9 pcs/L). Fibers were the most prevalent morphology identified (47.7 ± 33.0%), and the greatest percentage of microplastics were found in the 125–250 µm size range (26.6 ± 22.9%) followed by the 37–125 µm size range (24.0 ± 22.3%). Particles were predominantly black (33.5 ± 33.8%), transparent (22.6 ± 31.3%), or blue (16.0 ± 21.6%). Total concentrations, dominant morphologies, and size distributions of microplastics differed between rain events and baseflow, with smaller particles and higher concentrations being found during rain events. Concentrations did not differ significantly amongst catchments with different land use types, but concentrations were positively correlated with maximum runoff flow rate, catchment size, and the percentage of impervious surface area within a catchment. Combining microplastic concentrations with hydrograph data collected during rain events, we estimated that individual outfalls discharged between 1.9 million to 9.6 billion microplastics to receiving waters per rain event. These results provide further evidence that urban stormwater runoff is a significant pathway for the introduction of microplastics into aquatic environments and suggests that mitigation strategies for microplastic pollution should focus on larger urbanized catchments.Item Examining behavioural test sensitivity and locomotor proxies of anxiety-like behaviour in zebrafish(2023) Johnson, Andrea; Loh, Erica; Slessor, Jordan; Verbitsky, Ryan; Franczak, Brian C.; Schalomon, Melike; Hamilton, TrevorThis study assessed the sensitivity of four anxiety-like behaviour paradigms in zebrafish: the novel tank dive test, shoaling test, light/dark test, and the less common shoal with novel object test. A second goal was to measure the extent to which the main effect measures are related to locomotor behaviours to determine whether swimming velocity and freezing (immobility) are indicative of anxiety-like behaviour. Using the well-established anxiolytic, chlordiazepoxide, we found the novel tank dive to be most sensitive followed by the shoaling test. The light/dark test and shoaling plus novel object test were the least sensitive. A principal component analysis and a correlational analysis also showed the locomotor variables, velocity and immobility, did not predict the anxiety-like behaviours across all behaviour tests.Item Handling missing data in consumer hedonic tests arising from direct scaling(2016) Franczak, Brian C.; Castura, John C.; Browne, Ryan P.; Findlay, Christopher J.; McNicholas, Paul D.In sensory evaluation, it may be necessary to design experiments that yield incomplete data sets. As such, sensory scientists will need to utilize statistical methods capable of handling data sets with missing values. This article demonstrates the advantages of a model-based imputation procedure that simultaneously accounts for heterogeneity while imputing. We compare this model-based approach to the current state-of-the-art imputation procedures using two real data sets that arose from central location tests. These data sets contain missing values by design. In addition, these data sets have two data sets nested within each of them. We use these nested data sets to validate the results. Compared to the considered state-of-the-art imputation procedures, we find evidence that the model-based approach is able to recover the group structure and key characteristics of the data sets when a high percentage of the data are missing.Item A mixture of coalesced generalized hyperbolic distributions(2019) Tortora, Cristina; Franczak, Brian C.; Browne, Ryan P.; McNicholas, Paul D.A mixture of multiple scaled generalized hyperbolic distributions (MMSGHDs) is introduced. Then, a coalesced generalized hyperbolic distribution (CGHD) is developed by joining a generalized hyperbolic distribution with a multiple scaled generalized hyperbolic distribution. After detailing the development of the MMSGHDs, which arises via implementation of a multi-dimensional weight function, the density of the mixture of CGHDs is developed. A parameter estimation scheme is developed using the ever-expanding class of MM algorithms and the Bayesian information criterion is used for model selection. The issue of cluster convexity is examined and a special case of the MMSGHDs is developed that is guaranteed to have convex clusters. These approaches are illustrated and compared using simulated and real data. The identifiability of the MMSGHDs and the mixture of CGHDs are discussed in an appendix.Item Model-based clustering, classification, and discriminant analysis using the generalized hyperbolic distribution: MixGHD R package(2021) Tortora, Cristina; Browne, Ryan P.; ElSherbiny, Aisha; Franczak, Brian C.; McNicholas, Paul D.The MixGHD package for R performs model-based clustering, classification, and discriminant analysis using the generalized hyperbolic distribution (GHD). This approach is suitable for data that can be considered a realization of a (multivariate) continuous random variable. The GHD has the advantage of being flexible due to skewness, concentration, and index parameters; as such, clustering methods that use this distribution are capable of estimating clusters characterized by different shapes. The package provides five different models all based on the GHD, an efficient routine for discriminant analysis, and a function to measure cluster agreement. This paper is split into three parts: the first is devoted to the formulation of each method, extending them for classification and discriminant analysis applications, the second focuses on the algorithms, and the third shows the use of the package on real datasets. Software: GPL General Public License version 2 or version 3 or a GPL-compatible license.Item Opposing effects of acute and repeated nicotine exposure on boldness in zebrafish(2020) Dean, Rachel; Duperreault, Erika; Newton, Dustin; Krook, Jeffrey T.; Ingraham, Erica; Gallup, Joshua; Franczak, Brian C.; Hamilton, TrevorNicotine is an addictive compound that activates neuronal nicotinic acetylcholine receptors (nAChRs) and causes behavioural effects that vary with dose, schedule of administration, and animal model. In zebrafish (Danio rerio), acute doses of nicotine have been consistently found to have anxiolytic properties, whereas, chronic exposure elicits anxiogenic effects. To date, however, studies on repeated nicotine administration and the effects of nicotine withdrawal have not been well explored using this model. In this study, we administered nicotine with three different dosing regimens: 1. Single exposures of a “high” dose (25, 50, 100, or 400 mg/L) for 3 minutes. 2. Single exposures to a “low” dose (2.5, 5, or 20 mg/L) for one hour. 3. Repeated one-hour exposure to a “low” dose (2.5, 5, or 20 mg/L) for 21 days. The novel object approach test was used to examine boldness based on the tendency of the fish to explore a novel object. Acutely, nicotine significantly increased the time spent approaching the object with both three-minute and one hour durations of exposure, indicating increased boldness. Conversely, after repeated nicotine exposure for 21 days, fish spent less time approaching the object suggesting a decrease in boldness. Distance moved was unaffected one hour after repeated nicotine exposure, yet decreased after a two-day withdrawal period. Our work suggests that nicotine can have opposing effects on boldness that vary based on dosage and schedule of exposure.Item Subspace clustering with the multivariate-t distribution(2018) Pesevski, Angelina; Franczak, Brian C.; McNicholas, Paul D.Clustering procedures suitable for the analysis of very high-dimensional data are needed for many modern data sets. One approach, called high-dimensional data clustering (HDDC), uses a family of Gaussian mixture models for clustering. HDDC is based on the idea that high-dimensional data usually exists in lower-dimensional subspaces; as such, an intrinsic dimension for each sub-population of the observed data can be estimated and cluster analysis can be performed in this lower-dimensional subspace. As a result, only a fraction of the total number of parameters needs to be estimated. This family of models has gained attention due to its superior classification performance compared to other families of mixture models; however, it still suffers from the usual limitations of Gaussian mixture model-based approaches, e.g., these models are sensitive to outlying or spurious points. In this paper, a robust analog of the HDDC approach is proposed. This approach, which extends the HDDC procedure to the multivariate-t distribution, encompasses 28 models that rectify the aforementioned shortcoming of the HDDC procedure. Our tHDDC procedure is compared to the HDDC procedure using both simulated and real data sets, which includes an image reconstruction problem that arose from satellite imagery of the surface of Mars.Item Variable selection for clustering and classification of data with missing values(2024) O'Connell, Brynn; Franczak, Brian C.This poster presentation embarks on a comprehensive exploration of explicit variable selection procedures in model-based classification, where classification aims to assign labels to unlabelled observations. Delving into existing methodologies, we will dissect the intricacies of variable selection, setting the stage for an extensive examination of an approach aimed at minimizing within-group variance while maximizing between-group variance, known as Variable Selection for Clustering and Classification (VSCC). With a focus on enhancing classification accuracy and interpretability, we will unveil the details of VSCC, elucidating its significance in model-based classification frameworks. Furthermore, we will investigate how this approach performs when applied to simulated and real data sets with missing values. Through meticulous evaluation and analysis, we will scrutinize the performance and robustness of the variable selection approach in handling the challenges posed by incomplete data. Our findings will be synthesized into a comprehensive discussion, shedding light on the implications of the results and offering valuable insights for future research directions and refinements in variable selection methodologies within model-based classification.Item Vision of conspecifics decreases the effectiveness of ethanol on zebrafish behaviour(2021) Dean, Rachel; Hurst Radke, Nicole; Velupillai, Nirudika; Franczak, Brian C.; Hamilton, TrevorAquatic organisms in pharmacology and toxicology research are often exposed to compounds in isolation prior to physiological or behavioural testing. Recent evidence suggests that the presence of conspecifics during a stressful event can modulate behavioural outcomes (called ‘social buffering’) when testing occurs within the same context. It is unknown, however, whether the social environment during exposure interacts with the efficacy of anxiety-altering substances when subsequently tested in the absence of conspecifics. In this study, zebrafish were individually exposed to habitat water or ethanol (1.0% vol/vol) while untreated conspecifics were visually present or absent during dosing. Using the novel object approach test, a validated test of boldness and anxiety-like behaviour, we observed significantly greater effects of ethanol in isolated fish, compared to fish with a view of conspecifics during dosing. These results were not explained by altered locomotion during exposure, which might otherwise increase drug uptake. This highlights the need to consider the social environment during exposure when conducting and interpreting behavioural research involving drug or toxicant exposure.