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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Recently Genetic Algorithms (GAs) have frequently been used for optimizing the solution of estimation problems. One of the main advantages of using these techniques is that they require no knowledge or gradient information about the response surface. The poor behavior of genetic algorithms in some problems, sometimes attributed to design operators, has led to the development of other types of algorithms. One such class of these algorithms is compact Genetic Algorithm (cGA), it dramatically reduces the number of bits reqyuired to store the poulation and has a faster convergence speed. In this paper compact Genetic Algorithm is used to optimize the maximum likelihood estimator of the first order moving avergae model MA(1). Simulation results
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A new Azo‐Schiff base ligand L was prepared by reaction of m‐hydroxy benzoic acid with (Schiff base B) of 3‐[2‐(1H–indol‐3‐yl)‐ethylimino]‐1.5‐dimethyl‐2‐phenyl‐2,3‐dihydro‐1H‐pyrazol‐4‐ylamine. This synthesized ligand was used for complexation with different metal ions like Ni(II), Co(II), Pd(II) and Pt(IV) by using a molar ratio of ligand: metal as 1:1. Resulted compounds were characterized by NMR (1H and 13C), UV–vis spectroscopy, TGA, FT‐IR, MS, elemental analysis, magnetic moment and molar conductivity studies. The activation thermodynamic parameters, such as ΔE*, ΔH*, ΔS*, ΔG*and
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Theresearch took the spatial autoregressive model: SAR and spatial error model: SEM in an attempt to provide a practical evident that proves the importance of spatial analysis, with a particular focus on the importance of using regression models spatial andthat includes all of them spatial dependence, which we can test its presence or not by using Moran test. While ignoring this dependency may lead to the loss of important information about the phenomenon under research is reflected in the end on the strength of the statistical estimation power, as these models are the link between the usual regression models with time-series models. Spatial analysis had
... Show MoreBackground: Accurate measurement of a patient’s height and weight is an essential part of diagnosis and therapy, but there is some controversy as to how to calculate the height and weight of patients with disabilities. Objective: This study aims to use anthropometric measurements (arm span, length of leg, chest circumference, and waist circumference) to find a model (alternatives) that can allow the calculation of the height and the body weight of patients with disabilities. Additionally, a model for the prediction of weight and height measurements of patients with disabilities was established. Method: Four hander patients aged 20-80 years were enrolled in this study and divided into two groups, 210 (52.5%) male and 190 (47.5%) fe
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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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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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