The purpose of this study is to investigate the biostimulation effect of 532 nm CW laser on the metabolism of Saccharomyces cerevisiae yeast. Cells were irradiated by 532 nm Nd:YAG laser using 0.153 W/cm2 power density at 30, 45, 60,180 and 300 seconds exposure times in their respective orders. Intrafluorescence parameters were measured by detection the autofluorescence intensity, proliferation rate and Imaging the fluorescent mitochondria using confocal laser scanning microscope. The results showed that the 30 and 45 second exposure times seem to have stimulated changes in the cells that led to increase proliferation, viability and mitochondrial activity. Autofluorescence of cells increased after 45 and 60 seconds exposure time. After 300 seconds there seems to be very noticeable decrease in proliferation, viability and autofluorescence. Confocal microscopy images showed that here is a correlation between fluorescence intensity using mitochondrial probes and proliferation rates of cells.
Introduction
Saccharomyces cerevisiae is one of the most important fungi in the history of the world. This yeast is responsible for the production of alcoholic beverages and bread and a source of protein and was used in biotechnology and genetics as a host for the genes of other organisms (Madigan, et al. 2006). Low energy laser irradiation of which output power is in the range of mW modulates various biological effects and has been shown to have positive effect on living organisms both in vitro and in vivo. However, the true effect of low energy laser on cell proliferation is sill controversial, because of conflicting reports on the effects of visible laser light on the cells in culture (Antonio, et al., 2002).
There are many evidences that the most intracellular autofluorescence
The development of wireless sensor networks (WSNs) in the underwater environment leads to underwater WSN (UWSN). It has severe impact over the research field due to its extensive and real-time applications. However effective execution of underwater WSNs undergoes several problems. The main concern in the UWSN is sensor nodes’ energy depletion issue. Energy saving and maintaining quality of service (QoS) becomes highly essential for UWASN because of necessity of QoS application and confined sensor nodes (SNs). To overcome this problem, numerous prevailing methods like adaptive data forwarding techniques, QoS-based congestion control approaches, and various methods have been devised with maximum throughput and minimum network lifesp
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Desalination is a process where fresh water produces from high salinity solutions, many ways used for this purpose and one of the most important processes is membrane distillation (MD). Direct contact membrane distillation (DCMD) can be considered as the most prominent type from MD types according to ease of design and modus operandi. This work studies the efficiency of using DCMD operation for desalination brine with different concentration (1.75, 3.5, 5 wt. % NaCl). Frame and plate cell was used with flat sheet PTFE hydrophobic type membrane. The study proves that MD is an effective process for desalination brines with feed temperature less than 60˚C especially for feed with low TDS. 37˚C, 47˚C, and 57˚C was feed t
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Researchers are increasingly using multimodal biometrics to strengthen the security of biometric applications. In this study, a strong multimodal human identification model was developed to address the growing problem of spoofing attacks in biometric security systems. Through the use of metaheuristic optimization methods, such as the Genetic Algorithm(GA), Ant Colony Optimization(ACO), and Particle Swarm Optimization (PSO) for feature selection, this unique model incorporates three biometric modalities: face, iris, and fingerprint. Image pre-processing, feature extraction, critical image feature selection, and multibiometric recognition are the four main steps in the workflow of the system. To determine its performance, the model wa
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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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Background: Febrile convulsions are the most frequent type of seizures in children under 6 years of age. Significant percentage of these children will later suffer from recurrence of febrile convulsion.Objectives: To identify the main risk factors for recurrent febrile convulsions in children.Methods: we carried out a case control study involving 89 children those who experienced first attack of febrile convulsions and 92 children with recurrent attack of febrile convulsions. The study was conducted in Central Children Teaching Hospital, Baghdad during the period 2006- 2007. Results: Compared to children with first attack of febrile convulsion, children with recurrent seizures were younger at onset (4- 12m) (67% vs. 44%), mainly male (70
... Show MoreVisual analytics becomes an important approach for discovering patterns in big data. As visualization struggles from high dimensionality of data, issues like concept hierarchy on each dimension add more difficulty and make visualization a prohibitive task. Data cube offers multi-perspective aggregated views of large data sets and has important applications in business and many other areas. It has high dimensionality, concept hierarchy, vast number of cells, and comes with special exploration operations such as roll-up, drill-down, slicing and dicing. All these issues make data cubes very difficult to visually explore. Most existing approaches visualize a data cube in 2D space and require preprocessing steps. In this paper, we propose a visu
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