Neural stem cells (NSCs) are progenitor cells which have the ability to self‑renewal and potential for differentiating into neurons, oligodendrocytes, and astrocytes. The in vitro isolation, culturing, identification, cryopreservation were investigated to produce neural stem cells in culture as successful sources for further studies before using it for clinical trials. In this study, mouse bone marrow was the source of neural stem cells. The results of morphological study and immunocytochemistry of isolated cells showed that NSCs can be produced successfully and maintaining their self‑renewal and successfully forming neurosphere for multiple passages. The spheres preserved their morphology in culture and cryopreserved to be a ready source for use in experiments as a model for neurological disorders.
The seed propagation is the predominant method of Echinacea propagation, which has been criticized for its time-consuming control over the separation factor and the uncertainty of pathogen-free plants produced by this method. The technology of tissue culture has provided multiple opportunities for the production of secondary metabolites continuously without being restricted to a specific season, due to the possibility of controlling the environmental conditions and the components of the nutrient medium needed by the plant. This study was conducted to investigate the effects of salicylic acid as elicitor and tyrosine as precursor on propagation and some secondary compounds production in coneflower in vitro. The result showed the superiori
... Show MoreBackground: One effective second-generation triptan for migraine attacks is sumatriptan. Following oral use, it has a 40% restricted bioavailability because of the first-pass metabolism. Aim: To develop the best intranasal Solusomes formula as a substitute that delivers into the brain directly, improving its bioavailability, and removing the first-pass outcome was the aim of this effort. Methodology: We developed solute formulations based on the Box-Behnken design and subsequently produced them via thin-film hydration. The quality by design technique was used to establish a correlation between the formulation parameters (Soluplus® and phosphatidylcholine (PC) concentrations) and signif¬icant quality powers (entrapment efficiency (
... Show MoreWith the continuous progress of image retrieval technology, the speed of searching for the required image from a large amount of image data has become an important issue. Convolutional neural networks (CNNs) have been used in image retrieval. However, many image retrieval systems based on CNNs have poor ability to express image features. Content-based Image Retrieval (CBIR) is a method of finding desired images from image databases. However, CBIR suffers from lower accuracy in retrieving images from large-scale image databases. In this paper, the proposed system is an improvement of the convolutional neural network for greater accuracy and a machine learning tool that can be used for automatic image retrieval. It includes two phases
... Show MoreImage Fusion Using A Convolutional Neural Network
Four photosensitizers were used to test inhibitory effect of Helicobacter pylori bacteria using
low power helium: neon red laser radiation. Biopsies were collected from 176 patients and H. pylori were
isolated, identified and bacterial suspension was prepared. Samples of this suspension were mixed with
various low concentrations of the test sensitizer. The mixture samples were exposed to different laser
radiation doses. The samples were then inoculated and the inhibition zones were studied and compared
with their analogues of control samples. The most effective sensitizer with optimum concentration and
irradiation dose was determined. Statistical analysis of results was performed. The sensitizers' toluidine
blue and
In this paper, we derive and prove the stability bounds of the momentum coefficient µ and the learning rate ? of the back propagation updating rule in Artificial Neural Networks .The theoretical upper bound of learning rate ? is derived and its practical approximation is obtained
The speech recognition system has been widely used by many researchers using different
methods to fulfill a fast and accurate system. Speech signal recognition is a typical
classification problem, which generally includes two main parts: feature extraction and
classification. In this paper, a new approach to achieve speech recognition task is proposed by
using transformation techniques for feature extraction methods; namely, slantlet transform
(SLT), discrete wavelet transforms (DWT) type Daubechies Db1 and Db4. Furthermore, a
modified artificial neural network (ANN) with dynamic time warping (DTW) algorithm is
developed to train a speech recognition system to be used for classification and recognition
purposes. T
Artificial Neural Networks (ANN) is one of the important statistical methods that are widely used in a range of applications in various fields, which simulates the work of the human brain in terms of receiving a signal, processing data in a human cell and sending to the next cell. It is a system consisting of a number of modules (layers) linked together (input, hidden, output). A comparison was made between three types of neural networks (Feed Forward Neural Network (FFNN), Back propagation network (BPL), Recurrent Neural Network (RNN). he study found that the lowest false prediction rate was for the recurrentt network architecture and using the Data on graduate students at the College of Administration and Economics, Univer
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