Search results for: neural progentor cells

Authors: Forough Ataollahi, Sumit Pramanik, Belinda Pingguan-Murphy, Wan Abu Bakar Bin Wan Abas, Noor Azuan Bin Abu Osman

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Endothelium proliferation is an important process in cardiovascular homeostasis and can be regulated by extracellular environment, as cells can actively sense mechanical environment. In this study, we evaluated endothelial cell proliferation on PDMS/alumina (Al2O3) composites and pure PDMS. The substrates were prepared from pure PDMS and its composites with 5% and 10% Al2O3 at curing temperature 50˚C for 4 h and then characterized by mechanical, structural and morphological analyses. Higher stiffness was found in the composites compared to the pure PDMS substrate. Cell proliferation of the cultured bovine aortic endothelial cells on substrate materials were evaluated via Resazurin assay and 1, 1’-Dioctadecyl-1, 3, 3, 3’, 3’-Tetramethylindocarbocyanine Perchlorate-Acetylated LDL (Dil-Ac-LDL) cell staining, respectively. The results revealed that stiffer substrates promote more endothelial cells proliferation to the less stiff substrates. Therefore, this study firmly hypothesizes that the stiffness elevates endothelial cells proliferation.

Keywords: stiffness, proliferation, bovine aortic endothelial cells, extra cellular matrix, vascular

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Authors: Wenxing Chen, Guangying Chen, Yin Lu, Shile Huang

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Cryptotanshinone (CPT), a fat-soluble tanshinone from Salvia miltiorrhiza Bunge, has been demonstrated to inhibit mTOR pathway, resulting in inhibition of cancer cell proliferation. However, the molecular mechanism how CPT acts on mTOR is unknown. Here, cancer cells expressing rapamycin-resistant mutant mTOR are also sensitive to CPT, while phosphorylation of AMPK and TSC2 was activated, suggesting that CPT inhibition of mTOR maybe due to activating upstream of mTOR, AMPK, but not directly binding to and inhibiting mTOR. Further results indicated that Compound C, inhibitor of AMPK, could partially reversed CPT inhibition effect on cancer cells, and dominant-negative AMPK in cancer cells conferred resistance to CPT inhibition of 4EBP1 and phosphorylation of S6K1, as well as sh-AMPK. Furthermore, compared with MEF cells with AMPK positive, MEF cells with AMPK knock out are less sensitive to CPT by the findings that 4E-BP1 and phosphorylation of S6K1 express comparatively much. Furthermore, downexpression of TSC2 slightly recovered expression of 4EBP1 and phosphorylation of S6K1, while co-immunoprecipitation of TSC2 did not affect expression of TSC1 by CPT. Collectively, the above-mentioned results suggest that CPT inhibited mTOR pathway mostly was due to activation of AMPK-TSC2 pathway rather than specific inhibition of mTOR and then induction of subsequent lethal cellular effect.

Keywords: cryptotanshinone, AMPK, TSC2, mTOR, cancer cells

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Authors: Sharmi Mukherjee, Anindita Chakraborty

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Cellular irradiation incites complex responses including arrest of cell cycle progression. This article accentuates the effects of radiation on cell cycle status of radiation non-targeted cells. Human Hepatoma HepG2 cells were exposed to increasing doses of γ radiations (1, 2, 4, 6 Gy) and their cell culture media was transferred to non-targeted HepG2 cells cultured in other Petri plates. These radiation non-targeted cells cultured in the ICCM (Irradiated cell conditioned media) were the bystander cells on which cell cycle analysis was performed using flow cytometry. An apparent decrease in the distribution of bystander cells at G0/G1 phase was observed with increased radiation doses upto 4 Gy representing a linear relationship. This was accompanied by a gradual increase in cellular distribution at G2/M phase. Interestingly the number of cells in G2/M phase at 1 and 2 Gy irradiation was not significantly different from each other. However, the percentage of G2 phase cells at 4 and 6 Gy doses were significantly higher than 2 Gy dose indicating the IC50 dose to be between 2 and 4 Gy. Cell cycle arrest is an indirect indicator of genotoxic damage in cells. In this study, bystander stress signals through the cell culture media of irradiated cells disseminated the radiation induced DNA damages in the non-targeted cells which resulted in arrest of the cell cycle progression at G2/M phase checkpoint. This implies that actual radiation biological effects represent a penumbra with effects encompassing a larger area than the actual beam. This article highlights the existence of genotoxic damages as bystander effects of γ rays in human Hepatoma cells by cell cycle analysis and opens up avenues for appraisal of bystander stress communications between tumor cells. Contemplation of underlying signaling mechanisms can be manipulated to maximize damaging effects of radiation with minimum dose and thus has therapeutic applications.

Keywords: bystander effect, cell cycle, genotoxic damage, hepatoma

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Authors: Marcela Capcarova, Adriana Kolesarova, Marina Medvedova, Peter Petruska, Alexander V. Sirotkin

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The aim of this study was to determine the activity of superoxide dismutase (SOD), glutathione peroxidase (GPx), total antioxidant status (TAS) and accumulation of Hsp70 in porcine ovarian granulosa cells after deoxynivalenol (DON) and zearalenone (ZEA) exposure in vitro. Porcine ovarian granulosa cells were incubated with DON/ZEA administrations as follows: group A (10/10 ng/mL), group B (100/100 ng/mL), group C (1000/1000 ng/mL), and the control group without any additions for 24h. In this study mycotoxins developed stress reaction of porcine ovarian granulosa cells and increased accumulation of Hsp70 what resulted in increasing activities of SOD and GPx in groups with lower doses of mycotoxins. High dose of DON and ZEA had opposite effect on GPx activity than the lower doses. Slight increase in TAS of porcine granulosa cells was observed after mycotoxins exposure. These results contribute towards the understanding of cellular stress and its response.

Keywords: deoxynivalenol, zearalenone, antioxidants, Hsp70, granulosa cells

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Authors: Fuji Astuti, Helen Onyeaka

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The aim of the study was to investigate the combine effects of cinnamaldehyde (0.25 and 0.45% v/v) on thermal resistance of pathogenic Escherichia coli during low temperature long time (LT-LT) cooking below 60℃. Three different static temperatures (48, 53 and 50℃) were performed, and the number of viable cells was studied. The starting concentrations of cells were 10⁸ CFU/ml. In this experiment, heat treatment efficiency for safe reduction indicated by decimal logarithm reduction of viable recovered cells, which was monitored for heating over 6 hours. Thermal inactivation was measured by means of establishing the death curves between the mean log surviving cells (log₁₀ CFU/ml) and designated time points (minutes) for each temperature test. The findings depicted that addition of cinnamaldehyde exhibited to elevate the thermal sensitivity of E. coli. However, the injured cells found to be well-adapted to all temperature tests after certain time point of cooking, in which they grew to more than 10⁵ CFU/ml.

Keywords: cinnamaldehyde, decimal logarithm reduction, Escherichia coli, LT-LT cooking

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Authors: H. Kiani, S. Moradi, B. Soltani Soulgani, S. Mousavian

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Desalting/dehydration plants (DDP) are often installed in crude oil production units in order to remove water-soluble salts from an oil stream. In order to optimize this process, desalting unit should be modeled. In this research, artificial neural network is used to model efficiency of desalting unit as a function of input parameter. The result of this research shows that the mentioned model has good agreement with experimental data.

Keywords: desalting unit, crude oil, neural networks, simulation, recovery, separation

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Authors: J. K. Adedeji, S. T. Ijatuyi

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The application of neural network using pattern recognition to study the fluid dynamics and predict the groundwater reservoirs properties has been used in this research. The essential of geophysical survey using the manual methods has failed in basement environment, hence the need for an intelligent computing such as predicted from neural network is inevitable. A non-linear neural network with an XOR (exclusive OR) output of 8-bits configuration has been used in this research to predict the nature of groundwater reservoirs and fluid dynamics of a typical basement crystalline rock. The control variables are the apparent resistivity of weathered layer (p₁), fractured layer (p₂), and the depth (h), while the dependent variable is the flow parameter (F=λ). The algorithm that was used in training the neural network is the back-propagation coded in C++ language with 300 epoch runs. The neural network was very intelligent to map out the flow channels and detect how they behave to form viable storage within the strata. The neural network model showed that an important variable g_r (gravitational resistance) can be deduced from the elevation and apparent resistivity p_a. The model results from SPSS showed that the coefficients, a, b and c are statistically significant with reduced standard error at 5%.

Keywords: gravitational resistance, neural network, non-linear, pattern recognition

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Authors: Latef M. Ali, Farah A. Abed, Zheen L. Mohammed

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In this paper, the effect of the Barium fluoride (BaF2) layer on the absorption efficiency of GaAs/InAs nanowire solar cells was investigated using the finite difference time domain (FDTD) method. By inserting the BaF2 as antireflection with the dominant size of 10 nm to fill the space between the shells of wires on the Si (111) substrate. The absorption is significantly improved due to the strong reabsorption of light reflected at the shells and compared with the reference cells. The present simulation leads to a higher absorption efficiency (Qabs) and reaches a value of 97%, and the external quantum efficiencies (EQEs) above 92% are observed. The current density (Jsc) increases by 0.22 mA/cm2 and the open-circuit voltage (Voc) is enhanced by 0.11 mV. it explore the design and optimization of high-efficiency solar cells on low-reflective absorption efficiency of GaAs/InAs using simulation software tool. The changes in the core and shell diameters profoundly affects the generation and recombination process, thus affecting the conversion efficiency of solar cells.

Keywords: nanowire solar cells, absorption efficiency, photovoltaic, band structures, FDTD simulation

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Authors: Hassane Ben Slimane, Benmoussa Dennai, Abderrahman Hemmani, Abderrachid Helmaoui

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During the past few years a great variety of multi-junction solar cells has been developed with the aim of a further increase in efficiency beyond the limits of single junction devices. This paper analyzes the GaAs/CIGS based tandem solar cell performance by AMPS-1D numerical modeling. Various factors which affect the solar cell’s performance are investigated, carefully referring to practical cells, to obtain the optimum parameters for the GaAs and CIGS top and bottom solar cells. Among the factors studied are thickness and band gap energy of dual junction cells.

Keywords: multijunction solar cell, GaAs, CIGS, AMPS-1D

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Authors: Remi Safi, Aline Hamade, Najat Bteich, Jamal El Saghir, Mona Diab Assaf, Marwan El-Sabban, Fadia Najjar

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Estrogen is considered a risk factor for breast cancer since it promotes breast-cell proliferation. The jaesckeanadiol-3-p-hydroxyphenylpropanoate, a hemi-synthetic analogue of the natural phytoestrogen ferutinin (jaesckeanadiol-p-hydroxybenzoate), is designed to be devoid of estrogenic activity. This analogue induces a cytotoxic effect 30 times higher than that of ferutinin towards MCF-7 breast cancer cell line. We compared these two compounds with respect to their effect on proliferation, cell cycle distribution and cancer stem-like cells in the MCF-7 cell line. Treatment with ferutinin (30 μM) and its analogue (1 μM) produced a significant accumulation of cells at the pre G0/G1 cell cycle phase and triggered apoptosis. Importantly, this compound retains its anti-proliferative activity against breast cancer stem/progenitor cells that are naturally insensitive to ferutinin at the same dose. These results position ferutinin analogue as an effective compound inhibiting the proliferation of estrogen-dependent breast cancer cells and consistently targeting their stem-like cells.

Keywords: ferutinin, hemi-synthetic analogue, breast cancer, estrogen, stem/progenitor cells

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Authors: Rosdyana Mangir Irawan Kusuma, Wei-Chun Kao, Ho-Thi Trang, Yu-Yen Ou, Kai-Lung Hua

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Stock market prediction is still a challenging problem because there are many factors that affect the stock market price such as company news and performance, industry performance, investor sentiment, social media sentiment, and economic factors. This work explores the predictability in the stock market using deep convolutional network and candlestick charts. The outcome is utilized to design a decision support framework that can be used by traders to provide suggested indications of future stock price direction. We perform this work using various types of neural networks like convolutional neural network, residual network and visual geometry group network. From stock market historical data, we converted it to candlestick charts. Finally, these candlestick charts will be feed as input for training a convolutional neural network model. This convolutional neural network model will help us to analyze the patterns inside the candlestick chart and predict the future movements of the stock market. The effectiveness of our method is evaluated in stock market prediction with promising results; 92.2% and 92.1 % accuracy for Taiwan and Indonesian stock market dataset respectively.

Keywords: candlestick chart, deep learning, neural network, stock market prediction

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Authors: A. A. Balkhi, G. M. Mir, Javid A. Sheikh

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To cater the demands of increasing traffic with new applications the cellular mobile networks face new changes in deployment in infrastructure for making cellular networks heterogeneous. To reduce overhead processing the densely deployed cells require smart behavior with self-organizing capabilities with high adaptation to the neighborhood. We propose self-organization of unused resources usually excessive unused channels of neighbouring cells with densely populated cells to reduce handover failure rates. The neighboring cells share unused channels after fulfilling some conditional candidature criterion using threshold values so that they are not suffered themselves for starvation of channels in case of any abrupt change in traffic pattern. The cells are classified as ‘red’, ‘yellow’, or ‘green’, as per the available channels in cell which is governed by traffic pattern and thresholds. To combat the deficiency of channels in red cell, migration of unused channels from under-loaded cells, hierarchically from the qualified candidate neighboring cells is explored. The resources are returned back when the congested cell is capable of self-contained traffic management. In either of the cases conditional sharing of resources is executed for enhanced traffic management so that User Equipment (UE) is provided uninterrupted services with high Quality of Service (QoS). The fuzzy logic-based simulation results show that the proposed algorithm is efficiently in coincidence with improved successful handoffs.

Keywords: candidate cell, channel sharing, fuzzy logic, handover, small cells

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Authors: Junichi Hosoi

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Epidermal Langerhans cells reside in upper layer of epidermis and play a role in immune surveillance. The finding of the close association of nerve endings to Langerhans cells triggered the research on systemic regulation of Langerhans cells. They disappear from epidermis after exposure to environmental and internal stimuli and reappear about a week later. Myeloid progenitor cells are assumed to be one of the sources of Langerhans cells. We examined the effects of cortisol on the reappearance of Langerhans cells in vitro. Cord-blood derived CD34-positive cells were cultured in the medium supplemented with stem cell factor/Flt3 ligand/granulocyte macrophage-colony stimulating factor/tumor necrosis factor alpha/bone morphologic protein 7/transforming growth factor beta in the presence or absence of cortisol. Cells were analyzed by flow cytometry for CD1a (cluster differentiation 1a), a marker of Langerhans cells and dermal dendritic cells, and CD39 (cluster differentiation factor 39), extracellular adenosine triphosphatase. Both CD1a-positive cells and CD39-positive cells were decreased by treatment with cortisol (suppression by 35% and 22% compared to no stress hormone, respectively). Differentiated Langerhans cells are attracted to epidermis by chemokines that are secreted from keratinocytes. Epidermal keratinocytes were cultured in the presence or absence of cortisol and analyzed for the expression of CCL2 (C-C motif chemokine ligand 2) and CCL20 (C-C motif chemokine ligand 20), which are typical attractants of Langerhans cells, by quantitative reverse transcriptase polymerase chain reaction. The expression of both chemokines, CCL2 and CCL20, were suppressed by treatment with cortisol (suppression by 38% and 48% compared to no stress hormone, respectively). We examined the possible regulation of the suppression by cortisol with plant extracts. The extracts of Ganoderma lucidum and Iris protected the suppression of the differentiation to CD39-positive cells and also the suppression of the gene expression of LC-chemoattractants. These results suggest that cortisol, which is either systemic or locally produced, blocks the supply of epidermal Langerhans cells at 2 steps, differentiation from the precursor and attraction to epidermis. The suppression is possibly blocked by some plant extracts.

Keywords: Langerhans cell, stress, CD39, chemokine

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Authors: Dina Fatmawati, Sofia Mubarika, Mae Sri Wahyuningsih

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Chemotherapy for breast cancer is largely ineffective, but innovative combinations of chemotherapeutic agents and natural compounds represent a promising strategy. In our previous study, the combination of Doxorubicin (Dox) and ethanolic extract of Dioscorea esculenta tuber ((EED) was found to have a synergistic effect on T47D human breast cancer cell line. In this study, we investigated the apoptotic effect of the combination on T47D human breast cancer cells and normal fibroblasts cell line and its effects on the expression of Caspase-3 and cleaved poly (ADP-Ribose) Polymerase-1 (cPARP-1) protein. T47D cell lines and fibroblasts cells were treated with the combination of Dox and EED. Apoptotic effect of the combination was determined using flow cytrometry assay. Protein expressions were determined by immunocytochemistry staining. The percentage of apoptotic cells were significantly higher in T47D cell lines (75%) than that of in fibroblast cells (23%). The expression of Caspase 3 (84.53%) and cPARP-1 (83.36%) were significantly higher in the cancer cell lines than those of normal cells. These results indicate that the combination of doxorubicin and Dioscorea esculenta is a promising candidate for the treatment of breast cancer cells.

Keywords: Dioscorea esculenta, Doxorubicin, apoptosis, immunocytochemistry, cancer cells

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Authors: Nootchanat Mairuae, Walaiporn Tongjaroenbuangam, Chalisa Louicharoen Cheepsunthorn, Poonlarp Cheepsunthorn

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The aim of this study was to investigate the anti-oxidative effect, the anti-inflammatory effects, and the molecular mechanisms of okra (Abelmoschus esculentus Linn.) on lipopolysaccharide (LPS)-stimulated BV2 microglial cells. The BV2 cells were treated with LPS in the presence or absence of okra. Reactive oxygen species (ROS) and nitric oxide (NO) production were measured using the ROS detection reagent DCF-DA and the Griess reaction, respectively. The phosphorylation levels of nuclear factor-kappa B (NF-kB) p65 was detected by Western blot assay. Treatment of BV2 microglia cells with okra was found to significantly suppress the LPS-induced inflammatory mediator NO as well as ROS compared to untreated cells. The levels of LPS-induced NF-kB p65 phosphorylation were significantly decreased following okra treatment too. These results show that okra exerts anti-oxidative and anti-inflammatory effects in LPS-stimulated BV2 microglial cells by suppressing the NF-κB pathway. This suggests okra might be a valuable agent for treatment of anti-neuroinflammatory diseases mediated by microglial cells.

Keywords: Abelmoschus esculentus Linn, microglia, neuroinflammation, reactive oxygen spicy

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Authors: Salim Ouchtati, Jean Sequeira, Mouldi Bedda

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In this work we present an off line system for the recognition of the Arabic handwritten words of the Algerian departments. The study is based mainly on the evaluation of neural network performances, trained with the gradient back propagation algorithm. The used parameters to form the input vector of the neural network are extracted on the binary images of the handwritten word by several methods: the parameters of distribution, the moments centered of the different projections and the Barr features. It should be noted that these methods are applied on segments gotten after the division of the binary image of the word in six segments. The classification is achieved by a multi layers perceptron. Detailed experiments are carried and satisfactory recognition results are reported.

Keywords: handwritten word recognition, neural networks, image processing, pattern recognition, features extraction

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Authors: Boukari Nassim

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This paper describes the use of odd pair autoregressive coefficients (Yule _Walker and Burg) for the feature extraction of electroencephalogram (EEG) signals. In the classification: the radial basis function neural network neural network (RBFNN) is employed. The RBFNN is described by his architecture and his characteristics: as the RBF is defined by the spread which is modified for improving the results of the classification. Five types of EEG signals are defined for this work: Set A, Set B for normal signals, Set C, Set D for interictal signals, set E for ictal signal (we can found that in Bonn university). In outputs, two classes are given (AC, AD, AE, BC, BD, BE, CE, DE), the best accuracy is calculated at 99% for the combined odd pair autoregressive coefficients. Our method is very effective for the diagnosis of epileptic EEG signals.

Keywords: epilepsy, EEG signals classification, combined odd pair autoregressive coefficients, radial basis function neural network

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Authors: Mohanad Alhabo, Naveed Nawaz

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The ongoing call or data session must be maintained to ensure a good quality of service. This can be accomplished by performing the handover procedure while the user is on the move. However, the dense deployment of small cells in 5G networks is a challenging issue due to the extensive number of handovers. In this paper, a neighbour cell list method is proposed to reduce the number of target small cells and hence minimizing the number of handovers. The neighbour cell list is built by omitting cells that could cause an unnecessary handover and handover failure because of short time of stay of the user in these cells. A multi-attribute decision making technique, simple additive weighting, is then applied to the optimized neighbour cell list. Multi-tier small cells network is considered in this work. The performance of the proposed method is analysed and compared with that of the existing methods. Results disclose that our method has decreased the candidate small cell list, unnecessary handovers, handover failure, and short time of stay cells compared to the competitive method.

Keywords: handover, HetNets, multi-attribute decision making, small cells

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Authors: Deniz Erçelen, Özlem Selcuk Bozkurt

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The course of daily human life calls for the need for memories and remembering the time and place for certain events. Recalling memories takes up a substantial amount of time for an individual. Unfortunately, scientists lack the proper technology to fully understand and observe different brain regions that interact to form or retrieve memories. The hippocampus, a complex brain structure located in the temporal lobe, plays a crucial role in memory. The hippocampus forms memories as well as allows the brain to retrieve them by ensuring that neurons fire together. This process is called “neural synchronization.” Sadly, the hippocampus is known to deteriorate often with age. Proteins and hormones, which repair and protect cells in the brain, typically decline as the age of an individual increase. With the deterioration of the hippocampus, an individual becomes more prone to memory loss. Many memory loss starts off as mild but may evolve into serious medical conditions such as dementia and Alzheimer’s disease. In their quest to fully comprehend how memories work, scientists have created many different kinds of technology that are used to examine the brain and neural pathways. For instance, Magnetic Resonance Imaging - or MRI- is used to collect detailed images of an individual's brain anatomy. In order to monitor and analyze brain functions, a different version of this machine called Functional Magnetic Resonance Imaging - or fMRI- is used. The fMRI is a neuroimaging procedure that is conducted when the target brain regions are active. It measures brain activity by detecting changes in blood flow associated with neural activity. Neurons need more oxygen when they are active. The fMRI measures the change in magnetization between blood which is oxygen-rich and oxygen-poor. This way, there is a detectable difference across brain regions, and scientists can monitor them. Electroencephalography - or EEG - is also a significant way to monitor the human brain. The EEG is more versatile and cost-efficient than an fMRI. An EEG measures electrical activity which has been generated by the numerous cortical layers of the brain. EEG allows scientists to be able to record brain processes that occur after external stimuli. EEGs have a very high temporal resolution. This quality makes it possible to measure synchronized neural activity and almost precisely track the contents of short-term memory. Science has come a long way in monitoring memories using these kinds of devices, which have resulted in the inspections of neurons and neural pathways becoming more intense and detailed.

Keywords: brain, EEG, fMRI, hippocampus, memories, neural pathways, neurons

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Authors: Chuan Yin

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Cancer stem cells (CSCs) represent a subpopulation of cancer cells that possess the characteristics associated with normal stem cells. CD133 is a phenotype of melanoma CSCs responsible for melanoma metastasis and drug resistance. Although adriamycin (ADR) is commonly used drug in melanoma therapy, but it is ineffective in the treatment of melanoma CSCs. In this study, we constructed CD133 antibody conjugated ADR immunoliposomes (ADR-Lip-CD133) to target CD133+ melanoma CSCs. The results showed that the immunoliposomes possessed a small particle size (~150 nm), high drug encapsulation efficiency (~90%). After 72 hr treatment on the WM266-4 melanoma tumorspheres, the IC50 values of the drug formulated in ADR-Lip-CD133, ADR-Lip (ADR liposomes) and ADR are found to be 24.42, 57.13 and 59.98 ng/ml respectively, suggesting that ADR-Lip-CD133 was more effective than ADR-Lip and ADR. Significantly, ADR-Lip-CD133 could almost completely abolish the tumorigenic ability of WM266-4 tumorspheres in vivo, and showed the best therapeutic effect in WM266-4 melanoma xenograft mice. It is noteworthy that ADR-Lip-CD133 could selectively kill CD133+ melanoma CSCs of WM266-4 cells both in vitro and in vivo. ADR-Lip-CD133 represent a potential approach in targeting and killing CD133+ melanoma CSCs.

Keywords: cancer stem cells, melanoma, immunoliposomes, CD133

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Authors: Behzad Niknam, Kourosh Shahriar, Hassan Madani

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This paper demonstrates the applicability of Hybrid Neural Networks that combine with back propagation networks (BPN) and Genetic Algorithms (GAs) for predicting the flame length of tunnel fire A hybrid neural network model has been developed to predict the flame length of tunnel fire based parameters such as Fire Heat Release rate, air velocity, tunnel width, height and cross section area. The network has been trained with experimental data obtained from experimental work. The hybrid neural network model learned the relationship for predicting the flame length in just 3000 training epochs. After successful learning, the model predicted the flame length.

Keywords: tunnel fire, flame length, ANN, genetic algorithm

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Authors: A. Hakimiyan, H. Namazi

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Human behavior is defined as a range of behaviors exhibited by humans who are influenced by different internal or external sources. Human behavior is the subject of much research in different areas of psychology and neuroscience. Despite some advances in studies related to forecasting of human behavior, there are not many researches which consider the effect of the time delay between the presence of stimulus and the related human response. Analysis of EEG signal as a fractal time series is one of the major tools for studying the human behavior. In the other words, the human brain activity is reflected in his EEG signal. Artificial Neural Network has been proved useful in forecasting of different systems’ behavior especially in engineering areas. In this research, a time delay neural network is trained and tested in order to forecast the human EEG signal and subsequently human behavior. This neural network, by introducing a time delay, takes care of the lagging time between the occurrence of the stimulus and the rise of the subsequent action potential. The results of this study are useful not only for the fundamental understanding of human behavior forecasting, but shall be very useful in different areas of brain research such as seizure prediction.

Keywords: human behavior, EEG signal, time delay neural network, prediction, lagging time

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Authors: Khalaf Khatatneh, Nabeel Al-Milli, Amjad Hudaib, Monther Ali Tarawneh

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Software fault prediction identify potential faults in software modules during the development process. In this paper, we present a novel approach for software fault prediction by combining a feedforward neural network with particle swarm optimization (PSO). The PSO algorithm is employed as a feature selection technique to identify the most relevant metrics as inputs to the neural network. Which enhances the quality of feature selection and subsequently improves the performance of the neural network model. Through comprehensive experiments on software fault prediction datasets, the proposed hybrid approach achieves better results, outperforming traditional classification methods. The integration of PSO-based feature selection with the neural network enables the identification of critical metrics that provide more accurate fault prediction. Results shows the effectiveness of the proposed approach and its potential for reducing development costs and effort by detecting faults early in the software development lifecycle. Further research and validation on diverse datasets will help solidify the practical applicability of the new approach in real-world software engineering scenarios.

Keywords: feature selection, neural network, particle swarm optimization, software fault prediction

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Authors: Salvador Garcia Bernal, Chi Zheng, Keqi Zhang, Lei Mao

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Circulating Tumor Cells (CTC) is used to detect tumoral cell metastases using blood samples of patients with cancer (lung, breast, etc.). Using an immunofluorescent method a three channel image (Red, Green, and Blue) are obtained. These set of images usually overpass the 11 x 30 M pixels in size. An aided tool is designed for imaging cell analysis to segmented and identify the tumorous cell based on the three markers signals. Our Method, it is cell-based (area and cell shape) considering each channel information and extracting and making decisions if it is a valid CTC. The system also gives information about number and size of tumor cells found in the sample. We present results in real-life samples achieving acceptable performance in identifying CTCs in short time.

Keywords: Circulating Tumor Cells (CTC), cell analysis, immunofluorescent, medical image analysis

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Authors: Paulo Cesar Pereira Junior, Alexandre Monteiro, Rafael da Luz Ribeiro, Antonio Carlos Sobieranski, Aldo von Wangenheim

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In this paper, we present a comparison between convolutional neural networks and classical computer vision approaches, for the specific precision agriculture problem of weed mapping on sugarcane fields aerial images. A systematic literature review was conducted to find which computer vision methods are being used on this specific problem. The most cited methods were implemented, as well as four models of convolutional neural networks. All implemented approaches were tested using the same dataset, and their results were quantitatively and qualitatively analyzed. The obtained results were compared to a human expert made ground truth for validation. The results indicate that the convolutional neural networks present better precision and generalize better than the classical models.

Keywords: convolutional neural networks, deep learning, digital image processing, precision agriculture, semantic segmentation, unmanned aerial vehicles

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Authors: Mehdi Abbasi, Shadan Navid, Mohammad Pourahmadi, M. Majidi Zolbin

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We have recently reported that melatonin as antioxidant enhances the efficacy of colonization of spermatogonial stem cells (SSCs). Melatonin as an antioxidant plays a vital role in the development of SSCs in vitro. This study aimed to investigate evaluation of sertoli cells and melatonin simultaneously on SSC proliferation following transplantation to testis of adult mouse busulfan-treated azoospermia model. SSCs and sertoli cells were isolated from the testes of three to six-day old male mice.To determine the purity, Flow cytometry technique using PLZF antibody were evaluated. Isolated testicular cells were cultured in αMEM medium in the absence (control group) or presence (experimental group) of sertoli cells and melatonin extract for 2 weeks. We then transplanted SSCs by injection into the azoospermia mice model. Higher viability, proliferation, and Id4, Plzf, expression were observed in the presence of simultaneous sertoli cells and melatonin in vitro. Moreover, immunocytochemistry results showed higher Oct4 expression in this group. Eight weeks after transplantation, injected cells were localized at the base of seminiferous tubules in the recipient testes. The number of spermatogonia and the weight of testis were higher in the experimental group relative to control group. The results of our study suggest that this new protocol can increase the transplantation of these cells can be useful in the treatment of male infertility.

Keywords: colonization, melatonin, spermatogonial stem cell, transplantation

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Authors: Nestor A. Garcia Fragoso, Tetyana Baydyk, Ernst Kussul

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This article contains information from our investigation in the field of voice recognition. For this purpose, we created a voice database that contains different phrases in two languages, English and Spanish, for men and women. As a classifier, the LIRA (Limited Receptive Area) grayscale neural classifier was selected. The LIRA grayscale neural classifier was developed for image recognition tasks and demonstrated good results. Therefore, we decided to develop a recognition system using this classifier for voice recognition. From a specific set of speakers, we can recognize the speaker’s voice. For this purpose, the system uses spectrograms of the voice signals as input to the system, extracts the characteristics and identifies the speaker. The results are described and analyzed in this article. The classifier can be used for speaker identification in security system or smart buildings for different types of intelligent devices.

Keywords: extreme learning, LIRA neural classifier, speaker identification, voice recognition

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Authors: Mst Shapna Akter, Hossain Shahriar

Abstract:

Neural network approaches are machine learning methods used in many domains, such as healthcare and cyber security. Neural networks are mostly known for dealing with image datasets. While training with the images, several fundamental mathematical operations are carried out in the Neural Network. The operation includes a number of algebraic and mathematical functions, including derivative, convolution, and matrix inversion and transposition. Such operations require higher processing power than is typically needed for computer usage. Central Processing Unit (CPU) is not appropriate for a large image size of the dataset as it is built with serial processing. While Graphics Processing Unit (GPU) has parallel processing capabilities and, therefore, has higher speed. This paper uses advanced Neural Network techniques such as VGG16, Resnet50, Densenet, Inceptionv3, Xception, Mobilenet, XGBOOST-VGG16, and our proposed models to compare CPU and GPU resources. A system for classifying autism disease using face images of an autistic and non-autistic child was used to compare performance during testing. We used evaluation matrices such as Accuracy, F1 score, Precision, Recall, and Execution time. It has been observed that GPU runs faster than the CPU in all tests performed. Moreover, the performance of the Neural Network models in terms of accuracy increases on GPU compared to CPU.

Keywords: autism disease, neural network, CPU, GPU, transfer learning

Procedia PDF Downloads 118

Authors: Debajyoti Mukherjee, Arathy B. S., Arpita Sahu, Saranga P. Pogula

Abstract:

A functional model to mimic the functional correctness of a neural network compute accelerator IP is very crucial for design validation. Neural network workloads are based on a Brain Floating Point (BF-16) data type. The major challenge we were facing was the incompatibility of GCC compilers to the BF-16 datatype, which we addressed with a native BF-16 generator integrated into our functional model. Moreover, working with big GEMM (General Matrix Multiplication) or SpMM (Sparse Matrix Multiplication) Work Loads (Dense or Sparse) and debugging the failures related to data integrity is highly painstaking. In this paper, we are addressing the quality challenge of such a complex neural network accelerator design by proposing a functional model-based scoreboard or software model using SystemC. The proposed functional model executes the assembly code based on the ISA of the processor IP, decodes all instructions, and executes as expected to be done by the DUT. The said model would give a lot of visibility and debug capability in the DUT, bringing up micro-steps of execution.

Keywords: ISA, neural network, Brain Float-16, DUT

Procedia PDF Downloads 94

Authors: El Mahdi Barrah, Said Safi, Abdessamad Malaoui

Abstract:

In this paper, we present an algorithm for reconstruction from incomplete 2D scans for tifinagh characters. This algorithm is based on using correlation between the lost block and its neighbors. This system proposed contains three main parts: pre-processing, features extraction and recognition. In the first step, we construct a database of tifinagh characters. In the second step, we will apply “shape analysis algorithm”. In classification part, we will use Neural Network. The simulation results demonstrate that the proposed method give good results.

Keywords: Tifinagh character recognition, neural networks, local cost computation, ANN

Procedia PDF Downloads 334