Search results for: neural stem cells

Authors: Adilah Shariff, Nurhidayah Mohamed Noor, Alexander Lau, Muhammad Azwan Mohd Ali

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Biomass such as corn and cassava wastes if left to decay will release significant quantities of greenhouse gases (GHG) including carbon dioxide and methane. The biomass wastes can be converted into biochar via thermochemical process such as slow pyrolysis. This approach can reduce the biomass wastes as well as preserve its carbon content. Biochar has the potential to be used as a carbon sequester and soil amendment. The aim of this study is to investigate the characteristics of the corn cob, cassava stem, and cassava rhizome in order to identify their potential as pyrolysis feedstocks for biochar production. This was achieved by using the proximate and elemental analyses as well as calorific value and lignocellulosic determination. The second objective is to investigate the effect of pyrolysis temperature on the biochar produced. A fixed bed slow pyrolysis reactor was used to pyrolyze the corn cob, cassava stem, and cassava rhizome. The pyrolysis temperatures were varied between 400 °C and 600 °C, while the heating rate and the holding time were fixed at 5 °C/min and 1 hour, respectively. Corn cob, cassava stem, and cassava rhizome were found to be suitable feedstocks for pyrolysis process because they contained a high percentage of volatile matter more than 80 mf wt.%. All the three feedstocks contained low nitrogen and sulphur content less than 1 mf wt.%. Therefore, during the pyrolysis process, the feedstocks give off very low rate of GHG such as nitrogen oxides and sulphur oxides. Independent of the types of biomass, the percentage of biochar yield is inversely proportional to the pyrolysis temperature. The highest biochar yield for each studied temperature is from slow pyrolysis of cassava rhizome as the feedstock contained the highest percentage of ash compared to the other two feedstocks. The percentage of fixed carbon in all the biochars increased as the pyrolysis temperature increased. The increment of pyrolysis temperature from 400 °C to 600 °C increased the fixed carbon of corn cob biochar, cassava stem biochar and cassava rhizome biochar by 26.35%, 10.98%, and 6.20% respectively. Irrespective of the pyrolysis temperature, all the biochars produced were found to contain more than 60 mf wt.% fixed carbon content, much higher than its feedstocks.

Keywords: biochar, biomass, cassava wastes, corn cob, pyrolysis

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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: 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: Sven Borén, Henrik Ny

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Ambitions within the EU for moving towards sustainable transport include major emission reductions for fossil fuel road vehicles, especially for buses, trucks, and cars. The electric driveline seems to be an attractive solution for such development. This study first applied the Framework for Strategic Sustainable Development to compare sustainability effects of today’s fossil fuel vehicles with electric vehicles that have batteries or hydrogen fuel cells. The study then addressed a scenario were electric vehicles might be in majority in Europe by 2050. The methodology called Strategic Lifecycle Assessment was first used, were each life cycle phase was assessed for violations against sustainability principles. This indicates where further analysis could be done in order to quantify the magnitude of each violation, and later to create alternative strategies and actions that lead towards sustainability. A Life Cycle Assessment of combustion engine cars, plug-in hybrid cars, battery electric cars and hydrogen fuel cell cars was then conducted to compare and quantify environmental impacts. The authors found major violations of sustainability principles like use of fossil fuels, which contribute to the increase of emission related impacts such as climate change, acidification, eutrophication, ozone depletion, and particulate matters. Other violations were found, such as use of scarce materials for batteries and fuel cells, and also for most life cycle phases for all vehicles when using fossil fuel vehicles for mining, production and transport. Still, the studied current battery and hydrogen fuel cell cars have less severe violations than fossil fuel cars. The life cycle assessment revealed that fossil fuel cars have overall considerably higher environmental impacts compared to electric cars as long as the latter are powered by renewable electricity. By 2050, there will likely be even more sustainable alternatives than the studied electric vehicles when the EU electricity mix mainly should stem from renewable sources, batteries should be recycled, fuel cells should be a mature technology for use in vehicles (containing no scarce materials), and electric drivelines should have replaced combustion engines in other sectors. An uncertainty for fuel cells in 2050 is whether the production of hydrogen will have had time to switch to renewable resources. If so, that would contribute even more to a sustainable development. Except for being adopted in the GreenCharge roadmap, the authors suggest that the results can contribute to planning in the upcoming decades for a sustainable increase of EVs in Europe, and potentially serve as an inspiration for other smaller or larger regions. Further studies could map the environmental effects in LCA further, and include other road vehicles to get a more precise perception of how much they could affect sustainable development.

Keywords: strategic, electric vehicles, sustainability, LCA

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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: 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: Oseyemi O. Olubomehin, Edith O. Ajaiyeoba, Kio A. Abo, Eleonora D. Goosen

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Diabetes is a major degenerative disease of global concern and it is the third most lethal disease of mankind, accounting for about 3.2 million deaths annually. Lowering postprandial hyperglycemia by inhibition of carbohydrate hydrolyzing enzyme such as alpha-amylase is one of the therapeutic approaches to treat Type 2 Diabetes. Alpha-amylase inhibitors from plants have been found to be effective in managing postprandial hyperglycemia. In continuation of our anti-diabetic activities of this plant, bioassay-guided fractionation and isolation using 0.1-1.0 mg/mL furnished djalonenol, a monoterpene diol with a significant 53.7% α-amylase inhibition (p<0.001) from the stem bark which was comparable to acarbose which gave a 54.9% inhibition. Spectral characterization using Infra-red, Gas Chromatogrphy-Mass spectrometry, 1D and 2D NMR of the isolated compound was done to elucidate the structure of the compound.

Keywords: alpha-amylase inhibitor, hyperglycemia, postprandial, diabetes

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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: C. P. Mallapur, U. K. Hulihalli, D. N. Kambrekar

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Maize (Zea mays L.) an important cereal crop in the world has diversified uses including human consumption, animal feed, and industrial uses. A major constraint in low productivity of maize in India is undoubtedly insect pests particularly two species of stem borers, Chilo partellus (Swinhoe) and Sesamia inferens (Walker). The stem borers cause varying level of yield losses in different agro-climate regions (25.7 to 80.4%) resulting in a huge economic loss to the farmers. Although these pests are rather difficult to manage, efforts have been made to combat the menace by using effective insecticides. However, efforts have been made in the present study to integrate various possible approaches for sustainable management of these borers. Two field experiments were conducted separately during 2016-17 at Main Agricultural Research Station, University of Agricultural Sciences, Dharwad, Karnataka, India. In the first experiment, six treatments were randomized in RBD. The insect eggs at pinhead stage (@ 40 eggs/plant) were stapled to the under surface of leaves covering 15-20 % of plants in each plot after 15 days of sowing. The second experiment was planned with nine treatments replicated thrice. The border crop with NB -21 grass was planted all around the plots in the specific treatments while, cowpea intercrop (@6:1-row proportion) was sown along with the main crop and later, the insecticidal spray with chlorantraniliprole and nimbecidine was taken upon need basis in the specific treatments. The results indicated that the leaf injury and dead heart incidence were relatively more in the treatments T₂ and T₄ wherein, no insect control measures were made after the insect release (58.30 & 40.0 % leaf injury and 33.42 and 25.74% dead heart). On the contrary, these treatments recorded higher stem tunneling (32.4 and 24.8%) and resulted in lower grain yield (17.49 and 26.79 q/ha) compared to 29.04, 32.68, 40.93 and 46.38 q/ha recorded in T₁, T₃, T₅ and T₆ treatments, respectively. A maximum yield loss of 28.89 percent was noticed in T₂ followed by 19.59 percent in T₄ where no sprays were imposed. The data on integrated management trial revealed the lowest stem borer damage (19.28% leaf injury and 1.21% dead heart) in T₅ (seed treatment with thiamethoxam 70FS @ 8ml/kg seed + cow intercrop along with nimbecidine 0.03EC @ 5.0 ml/l and chlorantraniliprole 18.5SC spray @ 0.2 ml/l). The next best treatment was T₆ (ST+ NB-21 borer with nimbecidine and chlorantraniliprole spray) with 21.3 and 1.99 percent leaf injury and dead heart incidence, respectively. These treatments resulted in highest grain yield (77.71 and 75.53 q/ha in T₅ and T₆, respectively) compared to the standard check, T₁ (ST+ chlorantraniliprole spray) wherein, 27.63 percent leaf injury and 3.68 percent dead heart were noticed with 60.14 q/ha grain yield. The stem borers can cause yield loss up to 25-30 percent in maize which can be well tackled by seed treatment with thiamethoxam 70FS @ 8ml/kg seed and sowing the crop along with cowpea as intercrop (6:1 row proportion) or NB-21 grass as border crop followed by application of nimbecidine 0.03EC @ 5.0 ml/l and chlorantraniliprole 18.5SC @ 0.2 ml/l on need basis.

Keywords: Maize stem borers, Chilo partellus, Sesamia inferens, crop loss, integrated management

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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: A. M. T. Jacinto, M.O. Osi

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Tamarind has various medicinal properties among which is its antibacterial property. Its bark contains saponins, alkaloids, sesquiterpenes and tannins. It is rich in phlobapenes which is responsible for antibacterial property. The objective of the study was to determine which bark will produce the highest antibacterial property, develop it into a topical cream and evaluate its quality and characteristics. Powdered barks of Tamarind were extracted by soxhlet method using 70% acetone. Stem bark produced a higher yield than trunk bark (5.85 g vs. 4.73 g). It was found that the trunk bark was more sensitive than stem bark to microorganisms namely Staphylococcus aureus, Corynebacterium minutissimum, and Streptococcus spp. Sensitivity of trunk bark can be attributed to a more developed phytoconstituents. Dermal sensitization test on both sexes of rabbits using the following concentrations: 100%, 40% and 20% of extract showed that Tamarind has no irritating property and therefore safe for formulation into an antibacterial cream. Excipients used for formulation such as methyl paraben, propyl paraben, stearyl alcohol and white petrolatum were compatible with the Tamarind acetone extract through Differential Scanning Calorimetry except sodium lauryl sulfate that exhibited crystallization when subjected at 200˚C. The method of manufacture used in cream is fusion, therefore strict compliance of processing temperature should be observed to prevent polymorphism. Quality control tests of formulated cream based on USP 30 and Philippine Pharmacopeia were satisfactory.

Keywords: antibacterial, differential scanning calorimetry, tannins, dermal sensitization

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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: Aktar-Uz-Zaman, M. Tuhina-Khatun, Mohamed Hanafi Musa

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Three rust diseases: leaf (brown) rust caused by Puccinia triticina Eriks, stripe (yellow) rust caused by Puccinia striiformis West, and stem (black) rust caused by Puccinia graminis f. sp. tritici are economically important diseases of wheat in world wide. Yield loss due to leaf rust is 40% in susceptible cultivars. Yield losses caused by the stem rust pathogens in the mid of 20 century reached 20-30% in Eastern and Central Europe and the most virulent stem rust race Ug99 emerged first in Uganda and after that in Kenya, Ethiopia, Yemen, in the Middle East and South Asia. Yield losses were estimated up to 100%, whereas, up to 80% have been reported in Kenya during 1999. In case of stripe rust, severity level has been recorded 60% - 70% as compared to 100% severity of susceptible check in disease screening nurseries in Kenya. Improvement of resistant varieties or cultivars is the sustainable, economical and environmentally friendly approaches for increasing the global wheat production to suppress the rust diseases. More than 68 leaf rust, 49 stripe rust and 53 stem rust resistance genes have been identified in the global wheat cultivars or varieties using different molecular breeding approaches. Among these, Lr1, Lr9, Lr10, Lr19, Lr21, Lr24, Lr25, Lr28, Lr29, Lr34, Lr35, Lr37, Lr39, Lr47, Lr51, Lr3bg, Lr18, Lr40, Lr46, and Lr50 leaf rust resistance genes have been identified by using molecular, enzymatic and microsatellite markers from African, Asian, European cultivars of hexaploid wheat (Triticum aestivum), durum wheat and diploid wheat species. These genes are located on 20, of the 21 chromosomes of hexaploid wheat. Similarly, Sr1, Sr2, Sr24, and Sr3, Sr31 stem rust resistance genes have been recognized from wheat cultivars of Pakistan, India, Kenya, and Uganda etc. A race of P. striiformis (stripe rust) Yr9, Yr18, and Yr29 was first observed in East Africa, Italy, Pakistan and India wheat cultivars. These stripe rust resistance genes are located on chromosomes 1BL, 4BL, 6AL, 3BS and 6BL in bread wheat cultivars. All these identified resistant genes could be used for notable improvement of susceptible wheat cultivars in the future.

Keywords: hexaploid wheat, resistance genes, rust disease, triticum aestivum

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Authors: Sara Ataei, Molouk Hadjibabaie, Amirhossein Moslehi, Maryam Taghizadeh-Ghehi, Asieh Ashouri, Elham Amini, Kheirollah Gholami, Alireza Hayatshahi, Mohammad Vaezi, Ardeshir Ghavamzadeh

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Acute kidney injury (AKI) is one of the complications of hematopoietic stem cell transplantation and is associated with increased mortality. N-acetylcysteine (NAC) is a thiol compound with antioxidant and vasodilatory properties that has been investigated for the prevention of AKI in several clinical settings. In the present study, we evaluated the effects of intravenous NAC on the prevention of AKI in allogeneic hematopoietic stem cell transplantation patients. A double-blind randomized placebo-controlled trial was conducted, and 80 patients were recruited to receive 100 mg/kg/day NAC or placebo as intermittent intravenous infusion from day -6 to day +15. AKI was determined on the basis of the Risk-Injury-Failure-Loss-Endstage renal disease and AKI Network criteria as the primary outcome. We assessed urine neutrophil gelatinase-associated lipocalin (uNGAL) on days -6, -3, +3, +9, and +15 as the secondary outcome. Moreover, transplant-related outcomes and NAC adverse reactions were evaluated during the study period. Statistical analysis was performed using appropriate parametric and non-parametric methods including Kaplan–Meier for AKI and generalized estimating equation for uNGAL. At the end of the trial, data from 72 patients were analyzed (NAC: 33 patients and placebo: 39 patients). Participants of each group were not different considering baseline characteristics. AKI was observed in 18% of NAC recipients and 15% of placebo group patients, and the occurrence pattern was not significantly different (p = 0.73). Moreover, no significant difference was observed between groups for uNGAL measures (p = 0.10). Transplant-related outcomes were similar for both groups, and all patients had successful engraftment. Three patients did not tolerate NAC because of abdominal pain, shortness of breath and rash with pruritus and were dropped from the intervention group before transplantation. However, the frequency of adverse reactions was not significantly different between groups. In conclusion, our findings could not show any clinical benefits from high-dose NAC particularly for AKI prevention in allogeneic hematopoietic stem cell transplantation patients.

Keywords: acute kidney injury, N-acetylcysteine, hematopoietic stem cell transplantation, urine neutrophil gelatinase-associated lipocalin, randomized controlled trial

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Authors: Salina Yahya Saddik

Abstract:

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

Abstract:

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

Abstract:

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: Anna Pick Kiong Ling, Jia May Chin, Rhun Yian Koh, Ying Pei Wong

Abstract:

Background: Uncontrolled inflammation may cause serious inflammatory diseases if left untreated. Non-steroidal anti-inflammatory drug (NSAIDs) is commonly used to inhibit pro-inflammatory enzymes, thus, reduce inflammation. However, long term administration of NSAIDs leads to various complications. Medicinal plants are getting more attention as it is believed to be more compatible with human body. One of them is a flavonoid-containing medicinal plants, Strobilanthes crispus which has been traditionally claimed to possess anti-inflammatory and antioxidant activities. Nevertheless, its anti-inflammatory activities are yet to be scientifically documented. Objectives: This study aimed to examine the anti-inflammatory activity of S. crispus by investigating its effects on intracellular oxidative stress and prostaglandin E₂ (PGE₂) levels. Materials and Methods: In this study, the Maximum Non-toxic Dose (MNTD) of methanol extract of both leaves and stems of S. crispus was first determined using 3-(4,5-dimethylthiazolyl-2)-2,5-diphenytetrazolium Bromide (MTT) assay. The effects of S. crispus extracts at MNTD and half MNTD (½MNTD) on intracellular ROS as well as PGE₂ levels in 1.0 µg/mL LPS-stimulated RAW 264.7 macrophages were then be measured using DCFH-DA and a competitive enzyme immunoassay kit, respectively. Results: The MNTD of leaf extract was determined as 700µg/mL while for stem was as low as 1.4µg/mL. When LPS-stimulated RAW 264.7 macrophages were subjected to the MNTD of S. crispus leaf extract, both intracellular ROS and PGE₂ levels were significantly reduced. In contrast, stem extract at both MNTD and ½MNTD did not significantly reduce the PGE₂ level, but significantly increased the intracellular ROS level. Conclusion: The methanol leaf extract of S. crispus may possess anti-inflammatory properties as it is able to significantly reduce the intracellular ROS and PGE₂ levels of LPS-stimulated cells. Nevertheless, further studies such as investigating the interleukin, nitric oxide and cytokine tumor necrosis factor-α (TNFα) levels has to be conducted to further confirm the anti-inflammatory properties of S. crispus.

Keywords: anti-inflammatory, natural products, prostaglandin E2, reactive oxygen species

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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

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