Search results for: neural progentor cells

Authors: Rasha Ahmadi

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Glioblastoma is one of the most frequent brain malignancies, having a high mortality rate and limited survival in individuals with this malignancy. Despite different treatments and surgery, recurrence of glioblastoma cancer stem cells may arise as a subsequent tumor. For this reason, it is crucial to research the markers associated with glioblastoma stem cells and specifically their microenvironment. In this study, using bioinformatics analysis, we analyzed and nominated genes in the microenvironment pathways of glioblastoma stem cells. In this study, an appropriate database was selected for analysis by referring to the GEO database. This dataset comprised gene expression patterns in stem cells derived from glioblastoma patients. Gene clusters were divided as high and low expression. Enrichment databases such as Enrichr, STRING, and GEPIA were utilized to analyze the data appropriately. Finally, we extracted the potential genes 2700 high-expression and 1100 low-expression genes are implicated in the metabolic pathways of glioblastoma cancer progression. Cellular senescence, MAPK, TNF, hypoxia, zimosterol biosynthesis, and phosphatidylinositol metabolism pathways were substantially expressed and the metabolic pathways were downregulated. After assessing the association between protein networks, MSMP, SOX2, FGD4 ,and CNTNAP3 genes with high expression and DMKN and SBSN genes with low were selected. All of these genes were observed in the survival curve, with a survival of fewer than 10 percent over around 15 months. hsa-mir-192-5p, hsa-mir-129-5p, hsa-mir-215-5p, hsa-mir-335-5p, and hsa-mir-340-5p played key function in glioblastoma cancer stem cells microenviroments. We introduced critical genes through integrated and regular bioinformatics studies by assessing the amount of gene expression profile data that can play an important role in targeting genes involved in the energy and microenvironment of glioblastoma cancer stem cells. Have. This study indicated that hsa-mir-192-5p, and hsa-mir-129-5p are appropriate candidates for this.

Keywords: Glioblastoma, Cancer Stem Cells, Biomarker Discovery, Gene Expression Profiles, Bioinformatics Analysis, Tumor Microenvironment

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Authors: Wassim Guermazi, Saoussan Boukhris, Neila Annabi Trabelsi, Tarek Rebai, Alya Sellami-Kamoun, Habib Ayadi

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This work investigates the protective effects of the microalga Halamphora sp. extract (H. Ext) as a natural product on lead-intoxicated liver and kidney human cells in vitro and in vivo on rats wistar. HepG2 cells line derived from human hepatocellular carcinoma and HEK293 cells line derived from human embryonic kidney were used for the in vitro study. The analysis of the fatty acids methyl esters of the extract was performed by a GC/MS. Four groups of rats, each of which was composed of six animals, were used for the in vivo experiment. The pretreatment of HepG2 and HEK293 cells line with the extract (100 µg mL-1) significantly (p < 0.05) protected against cytotoxicity induced by lead exposure. In vivo, the biochemical parameters in serum, namely malondialdehyde level (MDA), superoxide dismutase (SOD), catalase (CAT) and glutathione peroxidase (GPx) activities, were measured in supernatants of organ homogenates. H. Ext was found to be rich in fatty acids, essentially palmitic and palmitoleic accounting respectively 29.46% and 42.07% of total fatty acids. Both in vitro and in vivo, the co-treatment with H. Ext allowed the protection of the liver and kidney cells structure, as well as the significant preservation of normal antioxidant and biochemical parameters in rats. Halamphora extract rich in fatty acids has been proven to be effective in protection against Pb-induced toxicity.

Keywords: microalga extract, human cells line, fatty acid, lead exposure, oxidative stress, rats

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Authors: Ananya Gupta, Sangeeta Bhaskar

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Mycobacterium indicus pranii (MIP) is a saprophytic mycobacterium. It is a predecessor of M. avium complex (MAC). Whole genome analysis and growth kinetics studies have placed MIP in between pathogenic and non-pathogenic species. It shares significant antigenic repertoire with M. tuberculosis and have unique immunomodulatory properties. MIP provides better protection than BCG against pulmonary tuberculosis in animal models. Immunization with MIP by aerosol route provides significantly higher protection as compared to immunization by subcutaneous (s.c.) route. However, mechanism behind differential protection has not been studied. In this study, using mice model we have evaluated and compared the M.tb specific immune response in lung compartments (airway lumen / lung interstitium) as well as spleen following MIP immunization via nasal (i.n.) and s.c. route. MIP i.n. vaccination resulted in increased seeding of memory T cells (CD4+ and CD8+ T-cells) in the airway lumen. Frequency of CD4+ T cells expressing Th1 migratory marker (CXCR3) and activation marker (CD69) were also high in airway lumen of MIP i.n. group. Significantly high ex vivo secretion of cytokines- IFN-, IL-12, IL-17 and TNF- from cells of airway luminal spaces provides evidence of antigen-specific lung immune response, besides generating systemic immunity comparable to MIP s.c. group. Analysis of T cell response on per cell basis revealed that antigen specific T-cells of MIP i.n. group were functionally superior as higher percentage of these cells simultaneously secreted IFN-gamma, IL-2 and TNF-alpha cytokines as compared to MIP s.c. group. T-cells secreting more than one of the cytokines simultaneously are believed to have robust effector response and crucial for protection, compared with single cytokine secreting T-cells. Adoptive transfer of airway luminal T-cells from MIP i.n. group into trachea of naive B6 mice revealed that MIP induced CD8 T-cells play crucial role in providing long term protection. Thus the study demonstrates that MIP intranasal vaccination induces M.tb specific memory T-cells in the airway lumen that results in an early and robust recall response against M.tb infection.

Keywords: airway lumen, Mycobacterium indicus pranii, Th1 migratory markers, vaccination

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Authors: Hailiang Liu, Yan Ni, Jie Zheng, Xiaoyan Jiang, Min Hong

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Allergic contact dermatitis (ACD) is a commonly clinical type IV allergic skin disease, with the pathological features of infiltration by mononuclear cells and tissue necrosis. Traditional Chinese medicine Radix Saposhnikoviae (RS) is traditionally employed to treat exogenous evils, rubella, itching, rheumatism and tetanus. Meanwhile, it is an important component of the commonly used anti-allergy compound. It’s now widely used as an immuno-modulating agent in mixed herbal decoctions to treat inflammation. However, its mechanism of anti-allergy remains unknown. RS was found to reduce ear thickness, as well as the infiltration of eosinophils. The proliferation of T lymphocytes was inhibited significantly by RS, markedly decreased IFN-γ levels in the supernatant of cells cultured and serum were detected with the treatment of RS. RS significantly decreased the amount of DCs in the mouse lymph nodes, and inhibited the expression of CD4 0 and CD86. Meanwhile, T-bet mRNA expression was down remarkably regulated by RS. These results indicate that RS cures Th1-induced allergic skin inflammation by regulating Th1/Th2 balance with decreasing Th1 differentiation, which might be associated with DCs.

Keywords: allergic contact dermatitis, Radix saposhnikoviae, dendritic cells, T-bet, GATA-3, CD4+ CD25+ Foxp3+ treg cells

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Authors: Danilo López, Johana Hernández, Edwin Rivas

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The use of the Multilayer Perceptron Neural Networks (MLPNN) technique is presented to estimate the future state of use of a licensed channel by primary users (PUs); this will be useful at the spectral decision stage in cognitive radio networks (CRN) to determine approximately in which time instants of future may secondary users (SUs) opportunistically use the spectral bandwidth to send data through the primary wireless network. To validate the results, sequences of occupancy data of channel were generated by simulation. The results show that the prediction percentage is greater than 60% in some of the tests carried out.

Keywords: cognitive radio, neural network, prediction, primary user

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Authors: S. Nickolas, Shobha K.

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The treatment of incomplete data is an important step in the data pre-processing. Missing values creates a noisy environment in all applications and it is an unavoidable problem in big data management and analysis. Numerous techniques likes discarding rows with missing values, mean imputation, expectation maximization, neural networks with evolutionary algorithms or optimized techniques and hot deck imputation have been introduced by researchers for handling missing data. Among these, imputation techniques plays a positive role in filling missing values when it is necessary to use all records in the data and not to discard records with missing values. In this paper we propose a novel artificial neural network based clustering algorithm, Adaptive Resonance Theory-2(ART2) for imputation of missing values in mixed attribute data sets. The process of ART2 can recognize learned models fast and be adapted to new objects rapidly. It carries out model-based clustering by using competitive learning and self-steady mechanism in dynamic environment without supervision. The proposed approach not only imputes the missing values but also provides information about handling the outliers.

Keywords: ART2, data imputation, clustering, missing data, neural network, pre-processing

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Authors: Negar Zaheddoust, Negin Zaheddoust, Abbas Asoudeh-Fard

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Probiotic bacteria have been employed as a novel and less side-effect strategy for anticancer therapy. Since the oral cavity is a host for probiotic and pathogen bacteria to colonize, more investigation is needed to evaluate the effectiveness of this novel adjunctive treatment for oral cancer. We considered Lactobacillus plantarum as a probiotic and Streptococcus mutans as a pathogen bacterium in our study. The aim of this study is to examine the effect of Lactobacillus plantarum and Streptococcus mutans on Casp3, AKT / PTEN, and MAPK signaling pathway, which is involved in apoptosis or survival of oral cancer KB cells. On the other hand, to study the effects of these bacteria on normal cells, we used HUVEC cells. The KB and HUVEC cell lines were co-cultured with Lactobacillus plantarum and Streptococcus mutans isolated from traditional Iranian dairy and dental plaque, respectively. The growth-inhibitory effects of these two bacteria on KB and HUVEC cells were determined by (3-(4, 5-dimethylthiazolyl-2)-2,5diphenyltetrazolium bromide) MTT assay. MTT results demonstrated that the proliferation of KB cells was affected in a time, dose, and strain-dependent manner. In the following, the examination of induced apoptosis or necrosis in co-cultured KB cells with the best IC50 concentration of the Lactobacillus plantarum and Streptococcus mutans will be analyzed by FACS flow cytometry, and the changes in gene expression of Casp3, AKT / PTEN, MAPK genes will be evaluated using real-time polymerase chain reaction.

Keywords: cancer therapy, induced apoptosis, oral cancer, probiotics

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Authors: Guanghua Zhang, Fubao Wang, Weijun Duan

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Convolution neural network (CNN) has attracted more and more attention on recent years. Especially in the field of computer vision and image classification. However, unsupervised learning with CNN has received less attention than supervised learning. In this work, we use a new powerful tool which is deep convolutional generative adversarial networks (DCGANs) to generate images from Sloan Digital Sky Survey. Training by various star and galaxy images, it shows that both the generator and the discriminator are good for unsupervised learning. In this paper, we also took several experiments to choose the best value for hyper-parameters and which could help to stabilize the training process and promise a good quality of the output.

Keywords: convolution neural network, discriminator, generator, unsupervised learning

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Authors: Loredana G. Marcu, David Marcu, Sanda M. Filip

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Advanced head and neck cancers are aggressive tumours, which require aggressive treatment. Treatment efficiency is often hindered by cancer cell repopulation during radiotherapy, which is due to various mechanisms triggered by the loss of tumour cells and involves both stem and differentiated cells. The aim of the current paper is to present in silico simulations of radiotherapy schedules on a virtual head and neck tumour grown with biologically realistic kinetic parameters. Using the linear quadratic formalism of cell survival after radiotherapy, altered fractionation schedules employing various treatment breaks for normal tissue recovery are simulated and repopulation mechanism implemented in order to evaluate the impact of various cancer cell contribution on tumour behaviour during irradiation. The model has shown that the timing of treatment breaks is an important factor influencing tumour control in rapidly proliferating tissues such as squamous cell carcinomas of the head and neck. Furthermore, not only stem cells but also differentiated cells, via the mechanism of abortive division, can contribute to malignant cell repopulation during treatment.

Keywords: radiation, tumour repopulation, squamous cell carcinoma, stem cell

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Authors: Ren Moses

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The process of classifying vehicles on a highway is hereby viewed as a pattern recognition problem in which connectionist techniques such as artificial neural networks (ANN) can be used to assign vehicles to their correct classes and hence to establish optimum axle spacing thresholds. In the United States, vehicles are typically classified into 13 classes using a methodology commonly referred to as “Scheme F”. In this research, the ANN model was developed, trained, and applied to field data of vehicles. The data comprised of three vehicular features—axle spacing, number of axles per vehicle, and overall vehicle weight. The ANN reduced the classification error rate from 9.5 percent to 6.2 percent when compared to an existing classification algorithm that is not ANN-based and which uses two vehicular features for classification, that is, axle spacing and number of axles. The inclusion of overall vehicle weight as a third classification variable further reduced the error rate from 6.2 percent to only 3.0 percent. The promising results from the neural networks were used to set up new thresholds that reduce classification error rate.

Keywords: artificial neural networks, vehicle classification, traffic flow, traffic analysis, and highway opera-tions

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Authors: Khalid Salhi, El Miloud Jaara, Mohammed Talibi Alaoui

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In this paper, we present a neural approach for unsupervised natural color-texture image segmentation, which is based on both Kohonen maps and mathematical morphology, using a combination of the texture and the image color information of the image, namely, the fractal features based on fractal dimension are selected to present the information texture, and the color features presented in RGB color space. These features are then used to train the network Kohonen, which will be represented by the underlying probability density function, the segmentation of this map is made by morphological watershed transformation. The performance of our color-texture segmentation approach is compared first, to color-based methods or texture-based methods only, and then to k-means method.

Keywords: segmentation, color-texture, neural networks, fractal, watershed

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Authors: Swati Tyagi, Syed Abbas

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Fractional-order Hopfield neural networks are generally used to model the information processing among the interacting neurons. To show the constancy of the processed information, it is required to analyze the stability of these systems. In this work, we perform Mittag-Leffler stability for the corresponding Caputo fractional-order bidirectional associative memory (BAM) neural networks with various time-delays. We derive sufficient conditions to ensure the existence and uniqueness of the equilibrium point by using the theory of topological degree theory. By applying the fractional Lyapunov method and Mittag-Leffler functions, we derive sufficient conditions for the global Mittag-Leffler stability, which further imply the global asymptotic stability of the network equilibrium. Finally, we present two suitable examples to show the effectiveness of the obtained results.

Keywords: bidirectional associative memory neural network, existence and uniqueness, fractional-order, Lyapunov function, Mittag-Leffler stability

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Authors: Arefeh Jafarian, Mohammad Taghikani, Saied Abroun, Amir Allahverdi, Masoud Soleimani

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Introduction: The miRNAs have key roles in control of pancreatic islet development and insulin secretion. In this regards, current study investigated the pancreatic differentiation of human bone marrow mesenchymal stem cells (hBM-MSCs) by up-regulation of miR-375 and down-regulation of miR-9 by lentiviruses containing miR-375 and anti-miR-9. Findings: After 21 days of induction, islet-like clusters containing insulin producing cells (IPCs) were confirmed by dithizone (DTZ) staining. The IPCs and β cell specific related genes and proteins were detected using qRT-PCR and immunofluorescence on days 7, 14 and 21 of differentiation. Glucose challenge test was performed at different concentrations of glucose as well as extracellular and intracellular insulin and C-peptide were assayed using ELISA kit. In derived IPCs by miR-375 alone are capable to express insulin and other endocrine specific transcription factors, the cells lack the machinery to respond to glucose. The differentiated hMSCs by miR-375 and anti-miR-9 lentiviruses could secrete insulin and c-peptide in a glucose-regulated manner. Conclusion: It was found that over-expression of miR-375 led to a reduction in levels of Mtpn protein in derived IPCs, while treatment with anti-miR-9 following miR-375 over-expression had synergistic effects on MSCs differentiation and insulin secretion in a glucose-regulated manner. The researchers reported that silencing of miR-9 increased OC-2 protein in IPCs that may contribute to the observed glucose-regulated insulin secretion. These findings highlight miRNAs functions in stem cells differentiation and suggest that they could be used as therapeutic tools for gene-based therapy in diabetes mellitus.

Keywords: diabetes, differentiation, MSCs, insulin producing cells, miR-375, miR-9

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Authors: P. Kooche Baghy, S. Eskandari, E.javanmard

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Neural network has been used as a predictive means of cold rolling force in this dissertation. Thus, imposed average force on rollers as a mere input and five pertaining parameters to its as a outputs are regarded. According to our study, feed-forward multilayer perceptron network has been selected. Besides, Bayesian algorithm based on the feed-forward back propagation method has been selected due to noisy data. Further, 470 out of 585 all tests were used for network learning and others (115 tests) were considered as assessment criteria. Eventually, by 30 times running the MATLAB software, mean error was obtained 3.84 percent as a criteria of network learning. As a consequence, this the mentioned error on par with other approaches such as numerical and empirical methods is acceptable admittedly.

Keywords: artificial neural network, Bayesian, cold rolling, force evaluation

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Authors: Divvela Vishnu Sai Kumar, Deepak Arora, Sheenu Rizvi

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Forecasting a cyclone through satellite images consists of the estimation of the intensity of the cyclone and predicting it before a cyclone comes. This research work can help people to take safety measures before the cyclone comes. The prediction of the intensity of a cyclone is very important to save lives and minimize the damage caused by cyclones. These cyclones are very costliest natural disasters that cause a lot of damage globally due to a lot of hazards. Authors have proposed five different CNN (Convolutional Neural Network) models that estimate the intensity of cyclones through INSAT-3D IR images. There are a lot of techniques that are used to estimate the intensity; the best model proposed by authors estimates intensity with a root mean squared error (RMSE) of 10.02 kts.

Keywords: estimating cyclone intensity, deep learning, convolution neural network, prediction models

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Authors: Cristian Pauna

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Trading the financial markets is a widespread activity today. A large number of investors, companies, public of private funds are buying and selling every day in order to make profit. Algorithmic trading is the prevalent method to make the trade decisions after the electronic trading release. The orders are sent almost instantly by computers using mathematical models. This paper will present a price prediction methodology based on a multi-dimensional neural network. Using the Fisher transform, the neural network will be instructed for a low-latency auto-adaptive process in order to predict the price evolution for the next period of time. The model is designed especially for algorithmic trading and uses the real-time price series. It was found that the characteristics of the Fisher function applied at the nodes scale level can generate reliable trading signals using the neural network methodology. After real time tests it was found that this method can be applied in any timeframe to trade the financial markets. The paper will also include the steps to implement the presented methodology into an automated trading system. Real trading results will be displayed and analyzed in order to qualify the model. As conclusion, the compared results will reveal that the neural network methodology applied together with the Fisher transform at the nodes level can generate a good price prediction and can build reliable trading signals for algorithmic trading.

Keywords: algorithmic trading, automated trading systems, financial markets, high-frequency trading, neural network

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Authors: Mark Berry

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A number of studies have identified several factors involved in the malignant progression of cancer cells. The Primary modulator in driving inflammation to these transformed cells has been identified as the transcription factor known as nuclear factor-κB. This essential regulator of inflammation and the development of cancer, combined with a microenvironment of inflammation and signaling molecules, plays a major role in the malignant progression of cancer, and this progression is the result of the mutagenic predisposition of persistent substances that combat infection at tumor sites and other areas of chronic inflammation. Inflammation-induced tumors, and their inflammatory cells and regulators may be the primary source of metastasis of tumor cells through angiogenesis. Previous research on cytokines and chemokines, including their downstream targets, has been the focus of the cancer/inflammation connection. The identification of the biological mechanisms of other proteins vital to the inflammation cascade and their interactions are crucial to novel and effective therapeutic protocols for the treatment of inflammation-induced cancers. The Ancelim HSRP Protocol is just such a therapeutic intervention.

Keywords: ancelim, cancer, inflammation, tumor

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Authors: Kamila Duś-Szachniewicz, Kinga M. Walaszek, Paweł Skiba, Paweł Kołodziej, Piotr Ziółkowski

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Cell adhesion is of fundamental importance in the cell communication, signaling, and motility, and its dysfunction occurs prevalently during cancer progression. The knowledge of the molecular and cellular processes involved in abnormalities in cancer cells adhesion has greatly increased, and it has been focused mainly on cellular adhesion molecules (CAMs) and tumor microenvironment. Unfortunately, most of the data regarding CAMs expression relates to study on cells maintained in standard oxygen condition of 21%, while the emerging evidence suggests that culturing cells in ambient air is far from physiological. In fact, oxygen in human tissues ranges from 1 to 11%. The aim of this study was to compare the effects of physiological lymph node normoxia (5% O2), and hyperoxia (21% O2) on the expression of cellular adhesion molecules of primary diffuse large B-cell lymphoma cells (DLBCL) isolated from 10 lymphoma patients. Quantitative RT-PCR and immunohistochemistry were used to confirm the differential expression of several CAMs, including ICAM, CD83, CD81, CD44, depending on the level of oxygen. Our findings also suggest that DLBCL cells maintained at ambient O2 (21%) exhibit reduced growth rate and migration ability compared to the cells growing in normoxia conditions. Taking into account all the observations, we emphasize the need to identify the optimal human cell culture conditions mimicking the physiological aspects of tumor growth and differentiation.

Keywords: adhesion molecules, diffuse large B-cell lymphoma, physiological normoxia, quantitative RT-PCR

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Authors: Raquel M. De sousa, Sofiane Labidi, Allan Kardec D. Barros, Alex O. Barradas Filho, Aldalea L. B. Marques

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Several parameters are established in order to measure biodiesel quality. One of them is the iodine value, which is an important parameter that measures the total unsaturation within a mixture of fatty acids. Limitation of unsaturated fatty acids is necessary since warming of a higher quantity of these ones ends in either formation of deposits inside the motor or damage of lubricant. Determination of iodine value by official procedure tends to be very laborious, with high costs and toxicity of the reagents, this study uses an artificial neural network (ANN) in order to predict the iodine value property as an alternative to these problems. The methodology of development of networks used 13 esters of fatty acids in the input with convergence algorithms of backpropagation type were optimized in order to get an architecture of prediction of iodine value. This study allowed us to demonstrate the neural networks’ ability to learn the correlation between biodiesel quality properties, in this case iodine value, and the molecular structures that make it up. The model developed in the study reached a correlation coefficient (R) of 0.99 for both network validation and network simulation, with Levenberg-Maquardt algorithm.

Keywords: artificial neural networks, biodiesel, iodine value, prediction

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Authors: Slimane Ouhmad, Abdellah Halimi

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In this work, we have applied neural networks method MLP type to a database from an array of six sensors for the detection of three toxic gases. As the choice of the number of hidden layers and the weight values has a great influence on the convergence of the learning algorithm, we proposed, in this article, a mathematical formulation to determine the optimal number of hidden layers and good weight values based on the method of back propagation of errors. The results of this modeling have improved discrimination of these gases on the one hand, and optimize the computation time on the other hand, the comparison to other results achieved in this case.

Keywords: MLP Neural Network, back-propagation, number of neurons in the hidden layer, identification, computing time

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Authors: Royan Dawud Aldian, Endah Purwanti, Soegianto Soelistiono

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In this research we have been developed an automatic investigation to classify normal children voice or autistic by using modern computation technology that is computation based on artificial neural network. The superiority of this computation technology is its capability on processing and saving data. In this research, digital voice features are gotten from the coefficient of linear-predictive coding with auto-correlation method and have been transformed in frequency domain using fast fourier transform, which used as input of artificial neural network in back-propagation method so that will make the difference between normal children and autistic automatically. The result of back-propagation method shows that successful classification capability for normal children voice experiment data is 100% whereas, for autistic children voice experiment data is 100%. The success rate using back-propagation classification system for the entire test data is 100%.

Keywords: autism, artificial neural network, backpropagation, linier predictive coding, fast fourier transform

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Authors: Rina Lee, Chung-Hun Oh, Eunjin Lee, Jeongyun Kim

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The Photodynamic therapy (PDT) is a treatment that uses a photosensitizer as a drug to damage and kill cancer cells. After injecting the photosensitizer into the bloodstream, the drug is absorbed by cancer cells selectively. Then the area to be treated is exposed to specific wavelengths of light and the photosensitizer produces a form of oxygen that kills nearby cancer cells. PDT is has an advantage to destroy the tumor with minimized side-effects on normal cells. But, PDT is not a completed method for cancer therapy. Because the mechanism of PDT is quite clear yet and the parameters such as intensity of light and dose of photosensitizer are not optimized for different types of cancers. To optimize these parameters, we suggest a novel microfluidic system to automatically control intensity of light exposure with a personal computer (PC). A polydimethylsiloxane (PDMS) microfluidic chip is composed with (1) a cell culture channels layer where cancer cells were trapped to be tested with various dosed photofrin (1μg/ml used for the test) as the photosensitizer and (2) a color dye layer as a neutral density (ND) filter to reduce intensity of light which exposes the cell culture channels filled with cancer cells. Eight different intensity of light (10%, 20%, …, 100%) are generated through various concentrations of blue dye filling the ND filter. As a light source, a light emitting diode (LED) with 635nm wavelength was placed above the developed PDMS microfluidic chip. The total time for light exposure was 30 minutes and HeLa and PC3 cell lines of cancer cells were tested. The cell viability of cells was evaluated with a Live/Dead assay kit (L-3224, Invitrogen, USA). The stronger intensity of light exposed, the lower viability of the cell was observed, and vice versa. Therefore, this system was demonstrated through investigating the PDT against cancer cell to optimize the parameters as critical light intensity and dose of photosensitizer. Our results suggest that the system can be used for optimizing the combinational parameters of light intensity and photosensitizer dose against diverse cancer cell types.

Keywords: photodynamic therapy, photofrin, high throughput screening, hela

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Authors: Thierry Pauporté

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Wide bandgap n-type oxide layers (TiO2, SnO2, ZnO etc.) play key roles in perovskite solar cells. They act as electron transport layers, and they permit the charge separation. They are also the substrate for the preparation of perovskite in the direct architecture. Therefore, they have a strong influence on the perovskite loading, its crystallinity and they can induce a degradation phenomenon upon annealing. The interface between the oxide and the perovskite is important, and the quality of this heterointerface must be optimized to limit the recombination of charges phenomena and performance losses. One can also play on the oxide and use two oxide contact layers for improving the device stability and durability. These aspects will be developed and illustrated on the basis of recent results obtained at Chimie-ParisTech.

Keywords: oxide, hybrid perovskite, solar cells, impedance

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Authors: Humayun Kabir, Amirul Hasan, Yu Miyaoka, Makiko Yamaguchi, Chisaki Kadota, Kazuaki Takehara

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From field isolates of Newcastle disease virus (NDV) in Japan, one avirulent strain, APMV/northern pintail/Japan/Aomori/2003 (dk-Aomori/03, NDV 261), was selected for its excellent thermostability, and the strain was heat-treated at 56℃ temperatures for 30 min with each passage into Vero cells to maintain thermostability and to adapt Vero cells. After serial 20 passages in Vero cells, it was named NDV Vero20. When growth curves were tested in Vero cells, NDV Vero20 grew well to compare the original NDV261. The HN gene was sequenced, and found motifs that show thermostability. The intracerebral pathogenicity index (ICPI) test score was 0. The thermostability of the virus was confirmed by storing it at different temperatures, including at 37°C. When susceptible chicks were inoculated with NDV Vero20 through eye drops, induced adequate levels of antibody were measured using a serum neutralization test. The results showed that NDV Vero20, a vaccine candidate strain is thermostable, Vero cell adapted, and has immunogenic potential, which would make as an alternative to the traditional embryonated chicken eggs-based vaccine.

Keywords: Newcastle disease virus, thermostability, vaccine, Vero cell adaptability

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Authors: Salina Yahya Saddik

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The present study is designed to demonstrate the Ovarian Surface Epithelial cells (OSE) Estrogen Receptor α (ERα) and Progesterone Receptor (PR) during pregnancy and estrous cycle in rat. Moreover, determination of the levels of plasma progesterone, estradiol, FSH and LH were also made. The levels of plasma progesterone, estradiol, FSH and LH concentrations were determined on days 7 (n=5), 14 (n=5), and 21(n=5) of pregnancy in three groups of rats and during the estrous cycle (n=5) using ELISA kit. Immunohistochemical method for PR and ERα expression was also made on the ovary. During pregnancy, FSH and LH remained low except at term when LH levels began to increase from 16 ng/ml to 47 ng/ml. Progesterone levels significantly exceeded estradiol values in all pregnant rats with a peak value of 202 ng/ml on day 14. Elevated progesterone levels were associated negatively with LH and estradiol levels during pregnancy. The levels of estradiol surged significantly on day 21. Immunohistochemistry of the ovary showed low levels of OSE cells staining positive for ERα expression. ERα positive cells were absent on day 7 and 14 of pregnancy, only day 21 recorded a very low percentage of immunostaining (0.5%) within the nuclei of OSE cells. On the contrary, immunostaining of PR was not observed within the nuclei of OSE cells in all groups of study. In conclusions, these results may suggest that progesterone effect during pregnancy seems to be overriding the positive effect of estrogens on OSE cells. High progesterone levels may have a direct negative effect on gonadotropin production and thereby it might inhibit events leading to both follicular development and OSE proliferation. Understanding the factors affecting OSE proliferation may help elucidating the mechanism(s) of assisted diseases such as ovarian cancer.

Keywords: ovarian surface, pregnancy, gonadotropins, steroids

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Authors: John Pomerat, Aviv Segev

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Recently, deep learning has had many theoretical breakthroughs. For deep learning to be successful in the industry, however, there need to be practical algorithms capable of handling many real-world hiccups preventing the immediate application of a learning algorithm. Although AI promises to revolutionize the healthcare industry, getting access to patient data in order to train learning algorithms has not been easy. One proposed solution to this is data- sharing. In this paper, we propose an alternative protocol, based on multi-party computation, to train deep learning models while maintaining both the privacy and security of training data. We examine three methods of training neural networks in this way: Transfer learning, average ensemble learning, and series network learning. We compare these methods to the equivalent model obtained through data-sharing across two different experiments. Additionally, we address the security concerns of this protocol. While the motivating example is healthcare, our findings regarding multi-party computation of neural network training are purely theoretical and have use-cases outside the domain of healthcare.

Keywords: neural network aggregation, multi-party computation, transfer learning, average ensemble learning

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Authors: Edward Guillén, Jhordany Rodriguez, Rafael Páez

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Anomaly detection techniques have been focused on two main components: data extraction and selection and the second one is the analysis performed over the obtained data. The goal of this paper is to analyze the influence that each of these components has over the system performance by evaluating detection over network scenarios with different setups. The independent variables are as follows: the number of system inputs, the way the inputs are codified and the complexity of the analysis techniques. For the analysis, some approaches of artificial neural networks are implemented with different number of layers. The obtained results show the influence that each of these variables has in the system performance.

Keywords: network intrusion detection, machine learning, artificial neural network, anomaly detection module

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Authors: Angeliki Peponi, Paulo Morgado, Jorge Trindade

Abstract:

Artificial Neural Networks (ANNs) and Geographic Information Systems (GIS) are applied as a robust tool for modeling and forecasting the erosion changes in Costa Caparica, Lisbon, Portugal, for 2021. ANNs present noteworthy advantages compared with other methods used for prediction and decision making in urban coastal areas. Multilayer perceptron type of ANNs was used. Sensitivity analysis was conducted on natural and social forces and dynamic relations in the dune-beach system of the study area. Variations in network’s parameters were performed in order to select the optimum topology of the network. The developed methodology appears fitted to reality; however further steps would make it better suited.

Keywords: artificial neural networks, backpropagation, coastal urban zones, erosion prediction

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Authors: Ginger Egberts, Marianne Schaaphok, Fred Vermolen, Paul van Zuijlen

Abstract:

A few years ago, a promising morphoelastic model was developed for the simulation of contraction formation after burn injuries. Contraction can lead to a serious reduction in physical mobility, like a reduction in the range-of-motion of joints. If this is the case in a healing burn wound, then this is referred to as a contracture that needs medical intervention. The morphoelastic model consists of a set of partial differential equations describing both a chemical part and a mechanical part in dermal wound healing. These equations are solved with the numerical finite element method (FEM). In this method, many calculations are required on each of the chosen elements. In general, the more elements, the more accurate the solution. However, the number of elements increases rapidly if simulations are performed in 2D and 3D. In that case, it not only takes longer before a prediction is available, the computation also becomes more expensive. It is therefore important to investigate alternative possibilities to generate the same results, based on the input parameters only. In this study, a surrogate neural network has been designed to mimic the results of the one-dimensional morphoelastic model. The neural network generates predictions quickly, is easy to implement, and there is freedom in the choice of input and output. Because a neural network requires extensive training and a data set, it is ideal that the one-dimensional FEM code generates output quickly. These feed-forward-type neural network results are very promising. Not only can the network give faster predictions, but it also has a performance of over 99%. It reports on the relative surface area of the wound/scar, the total strain energy density, and the evolutions of the densities of the chemicals and mechanics. It is, therefore, interesting to investigate the applicability of a neural network for the two- and three-dimensional morphoelastic model for contraction after burn injuries.

Keywords: biomechanics, burns, feasibility, feed-forward NN, morphoelasticity, neural network, relative surface area wound

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Authors: Oluwatosin Olofintoye, Josiah Adeyemo, Gbemileke Shomade

Abstract:

The relationship between rainfall and runoff is an important issue in surface water hydrology therefore the understanding and development of accurate rainfall-runoff models and their applications in water resources planning, management and operation are of paramount importance in hydrological studies. This paper reviews some of the previous works on the rainfall-runoff process modeling. The hydrologic applications of conceptual models and artificial neural networks (ANNs) for the precipitation-runoff process modeling were studied. Gradient training methods such as error back-propagation (BP) and evolutionary algorithms (EAs) are discussed in relation to the training of artificial neural networks and it is shown that application of EAs to artificial neural networks training could be an alternative to other training methods. Therefore, further research interest to exploit the abundant expert knowledge in the area of artificial intelligence for the solution of hydrologic and water resources planning and management problems is needed.

Keywords: artificial intelligence, artificial neural networks, evolutionary algorithms, gradient training method, rainfall-runoff model

Procedia PDF Downloads 454