Abstract Candida albicans is a commensal fungal pathogen that grows in yeast and hyphal forms in the human gut. C. albicans causes mucosal and cutaneous diseases that can result in significant mortality following systematic infections and it also exhibits drug resistance. Zebrafish have been an excellent model to investigate C. albicans infections because of their transparency and the availability of many transgenic lines. However, there is a limitation in using zebrafish as a model because the fish embryos cannot survive at 37°C therefore it is not suitable for studying Candida infections at physiological relevant human body temperature. In this thesis, the normal embryonic development of Arabian killifish (A. dispar) is investigated, revealing that embryogenesis was divided into 32 stages based on diagnostic patterns of development. A. dispar can also found to tolerate a wide range of temperatures and salinities. This suggests that A. dispar could be developed as a novel model to investigate host-pathogen interactions. The tolerance of A. dispar to high temperatures may in part be attributable to brown pigment cells with a highly fluorescent character that may have developed to allow the fish to adapt to live within extreme environmental conditions with strong sunlight and a wide range of temperatures (Chapter 3). In terms of Candida infections, this study examined A. dispar as a model to test C. albicans pathogenicity. The survival of A. dispar embryos following Candida infection showed a dose dependent relationship. We also found that A. dispar can survive longer than zebrafish after infection. Furthermore, C. albicans cells were observed to undergo a transition from yeast to hyphae at 37°C. An investigation of the ability of mutant strains of C. albicans with defects in cell wall mannosylation revealed a significant impact on virulence, host mortality, and the fishes’ immune response. The present study found that although the deletion of O- and N-mannan from the cell wall of C. albicans, affected fungal burden (attenuation), and the survival of the infected embryos per se was significantly decreased in the infections of the mutant strains compared to the WT. This data confirms the importance of the mannosylation state of the cell wall in triggering an immune recognition event (Chapter 4). A. dispar is also shown to be suitable for studying the effectiveness of 3 | P a g e antifungals. Fluconazole treatment of infected embryos and eggs promoted greater rates of survival at high doses, alongside a significant reduction of C. albicans CFUs (Chapter 4). When looking at the Candida-host interaction, we directly observed phagocytosed yeast cells within macrophages. Various detection methods were used to follow macrophages and neutrophils including Western blotting, immunostaining and histological staining (Sudan black and FITC-tyramide) allowing the monitoring of the time course of the immune cells. A biphasic response of macrophages was detected by L-plastin Western blotting, suggesting activation of two different type of macrophage: activated macrophage (M1) and alternative macrophage (M2). We also assayed reactive oxygen species (ROS) within infected embryos using a fluorescent probe (H2DCFDA), revealing the accumulation of the fluorescent probe at the sites of infection. Quantitative and qualitative analyses of the oxidative and immune response using the H2DCFDA and qPCR were also accomplished within A. dispar embryos after infection with both the WT and mutant strains of Candida albicans (WT, pmr1∆, mnt1-mnt2∆, and och1∆). The results confirmed that the mutant strains did not activate a host oxidative stress response nor immune cell accumulation when compared to WT, suggesting that the immune response is less activated against these mutants. Finally, a new transgenic line of A. dispar fish was developed using Betaactin-DsR-LoxP-GFP. The new transgenic A. dispar is suggested to be an ideal model for real time observation of host-pathogen interactions and for investigation of molecular functions of the immune response. Overall these results improve our understanding of the use of a new transparent fish model to study fungal pathogenesis and demonstrates the potential advantages of using this species in future studies of bacterial, fungal and viral pathogens at a physiologically relevant temperature for human infection. Such a model could lead us to investigate in more depth the key interactions between pathogens and their host and permit the screening and development of new antifungal therapies (that might target the pathogens directly or target the host immune system). View full metadata
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This study aimed at identifying how children express the emerging coronavirus in general and according to their age groups (4-13 years) by analyzing 91 of their drawings published online, using the descriptive content analytical approach. The results showed that children's artistic expression of the virus came according to the concepts and ideas they carried about the virus for the age groups of (4-7 years) and (7-9 years), while it came according to visual perception for age groups (9-11 years), and from (11-13) years. Also, most children were aware about the presence of the virus and its widespread around the world, but (99%) of them do not realize the seriousness of the virus. It was confirmed that between (25-34%) of children were su
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This paper presents a new design of a nonlinear multi-input multi-output PID neural controller of the active brake steering force and the active front steering angle for a 2-DOF vehicle model based on modified Elman recurrent neural. The goal of this work is to achieve the stability and to improve the vehicle dynamic’s performance through achieving the desired yaw rate and reducing the lateral velocity of the vehicle in a minimum time period for preventing the vehicle from slipping out the road curvature by using two active control actions: the front steering angle and the brake steering force. Bacterial forging optimization algorithm is used to adjust the parameters weights of the proposed controller. Simulation resul
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Monaural source separation is a challenging issue due to the fact that there is only a single channel available; however, there is an unlimited range of possible solutions. In this paper, a monaural source separation model based hybrid deep learning model, which consists of convolution neural network (CNN), dense neural network (DNN) and recurrent neural network (RNN), will be presented. A trial and error method will be used to optimize the number of layers in the proposed model. Moreover, the effects of the learning rate, optimization algorithms, and the number of epochs on the separation performance will be explored. Our model was evaluated using the MIR-1K dataset for singing voice separation. Moreover, the proposed approach achi
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The Mauddud reservoir, Khabaz oil field which is considered one of the main carbonate reservoirs in the north of Iraq. Recognizing carbonate reservoirs represents challenges to engineers because reservoirs almost tend to be tight and overall heterogeneous. The current study concerns with geological modeling of the reservoir is an oil-bearing with the original gas cap. The geological model is establishing for the reservoir by identifying the facies and evaluating the petrophysical properties of this complex reservoir, and calculate the amount of hydrocarbon. When completed the processing of data by IP interactive petrophysics software, and the permeability of a reservoir was calculated using the concept of hydraulic units then, there
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In present work the effort has been put in finding the most suitable color model for the application of information hiding in color images. We test the most commonly used color models; RGB, YIQ, YUV, YCbCr1 and YCbCr2. The same procedures of embedding, detection and evaluation were applied to find which color model is most appropriate for information hiding. The new in this work, we take into consideration the value of errors that generated during transformations among color models. The results show YUV and YIQ color models are the best for information hiding in color images.
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Spatial Autoregressive Model (SAR) is one of the modeling frameworks that indicates a spatial dependence in the response variable. SAR model has a weakness, which is represented by the unknown variance of the residuals. Therefore, an alternative model has used titled Spatial Autoregressive Quantile Regression (SARQR) model That which is obtained by combining SAR and Quantile Regression (QR) models, is a regression method with the approach of dividing the data into particular quantiles that are likely to have different estimate values. This alternative model addresses the variance issues in SAR models. Additionally, the SARQR model not only resolves the issue of spatial variance but also serves as a solution for dealing with non-normal data
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