Search results for: neural stem/precursor cells

Authors: R. Boudjelthia

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The aim of this study is to determine the influence of mix composition of concrete as the content of water and cement, water–binder ratio, and the replacement of fly ash on the durability of self compacting concrete (SCC) by using artificial neural networks (ANNs). To achieve this, an ANNs model is developed to predict the durability of self compacting concrete which is expressed in terms of chloride ions permeability in accordance with ASTM C1202-97 or AASHTO T277. Database gathered from the literature for the training and testing the model. A sensitivity analysis was also conducted using the trained and tested ANN model to investigate the effect of fly ash on the durability of SCC. The results indicate that the developed model is reliable and accurate. the durability of SCC expressed in terms of total charge passed over a 6-h period can be significantly improved by using at least 25% fly ash as replacement of cement. This study show that artificial neural network have strong potentialas a feasible tool for predicting accurately the durability of SCC containing fly ash.

Keywords: artificial neural networks, durability, chloride ions permeability, self compacting concrete

Procedia PDF Downloads 378

Authors: Min-Chien Su, Tzu-Hsuan Hsu, Guan-Xuan Wu, Shyh-Ming Kuo

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Lung cancer is one of the clinically challenging malignant diseases worldwide. For efficient therapeutics in cancer, combination therapy has developed to acquire a better outcome. PLK-1 was one of the major factors affecting cell mitosis in cancer cells, its inhibitor Bi6727 was proven effective in treating several different cancers namely oral cancer, colon cancer and lung cancer. Despite its low toxicity toward normal cells compared to traditional chemotherapy, it is still yet to be evaluated in detail. Livistona Chinensis (LC) is a Chinese herb that used as a traditional prescription to treat lung cancer. Due to the uncertainty of the efficacy of LC, we utilized a water extraction method to extract the Livistona Chinensis and then lyophilized into powder for further study. In this study we investigated the antiproliferation activities of Bi6727 and LC extracts (LCE) on A549 non-small lung cancer cells. The IC50 of Bi6727 and LCE on A549 are 60 nM and 0.8 mg/mL, respectively. The fluorescent staining images shown nucleolus damage in cells treated with Bi6727 and mitochondrial damage after treated with LCE. A549 cells treated with Bi6727 and LCE showed increased expression of Bax, Caspase-3 and Caspase-9 proteins from Western blot assay. LCE also inhibited A549 cells growth keeping cells at G2-M phase from cell cycle assay. Apoptosis assay results showed that LCE induced late apoptosis of A549 cells. JC-1 assay showed that the mitochondria damaged at the LCE concentration of 0.4 mg/mL. In our preliminary anti-proliferation test of combined LCE and Bi-6727 on A549 cells, we found a dramatically decrease in proliferation after treated with LCE first for 24-h and then Bi-6727 for extra 24-h. This was an important finding regarding synergistic anti-proliferation effect of these drugs, However, the usage, the application sequence of LCE and Bi-6727 on A549 cells and their related mechanisms still need to be evaluated. In summary, the drugs exerted anti-proliferation effect on A549 cells independently. We hopefully combine the usage of these two drugs will bring a different and potential outcome in treating lung cancer.

Keywords: anti-proliferation, A549, Livistona Chinensis fruit extracts, PLK-1 inhibitor

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Authors: Jyothi Nagraj, Sudeshna Mukherjee, Rajdeep Chowdhury

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Autophagy has recently been linked with cancer cell survival post drug insult contributing to acquisition of resistance. However, the molecular signaling governing autophagic survival response is poorly explored. In our study, in osteosarcoma (OS) cells cisplatin shock was found to activate both MAPK and autophagy signaling. An activation of JNK and autophagy acted as pro-survival strategy, while ERK1/2 triggered apoptotic signals upon cisplatin stress. An increased sensitivity of the cells to cisplatin was obtained with simultaneous inhibition of both autophagy and JNK pathway. Furthermore, we observed that the autophagic stimulation upon drug stress regulates other developmentally active signaling pathways like the Hippo pathway in OS cells. Cisplatin resistant cells were thereafter developed by repetitive drug exposure followed by clonal selection. Basal levels of autophagy were found to be high in resistant cells to. However, the signaling mechanism leading to autophagic up-regulation and its regulatory effect differed in OS cells upon attaining drug resistance. Our results provide valuable clues to regulatory dynamics of autophagy that can be considered for development of improved therapeutic strategy against resistant type cancers.

Keywords: JNK, autophagy, drug resistance, cancer

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Authors: Celso L. Fernando, Toshio Yoshii, Takahiro Tsubota

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Understanding the causes of a road accident and predicting their occurrence is key to preventing deaths and serious injuries from road accident events. Traditional statistical methods such as the Poisson and the Logistics regressions have been used to find the association of the traffic environmental factors with the accident occurred; recently, an artificial neural network, ANN, a computational technique that learns from historical data to make a more accurate prediction, has emerged. Although the ability to make accurate predictions, the ANN has difficulty dealing with highly unbalanced attribute patterns distribution in the training dataset; in such circumstances, the ANN treats the minority group as noise. However, in the real world data, the minority group is often the group of interest; e.g., in the road traffic accident data, the events of the accident are the group of interest. This study proposes a combination of the k-means with the ANN to improve the predictive ability of the neural network model by alleviating the effect of the unbalanced distribution of the attribute patterns in the training dataset. The results show that the proposed method improves the ability of the neural network to make a prediction on a highly unbalanced distributed attribute patterns dataset; however, on an even distributed attribute patterns dataset, the proposed method performs almost like a standard neural network.

Keywords: accident risks estimation, artificial neural network, deep learning, k-mean, road safety

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Authors: Cristina Núñez, Rufina Bastida, Elena Labisbal, Alejandro Macías, María T. Pereira, José M. Vila

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A highly selective quinoline-based fluorescent sensor L was designed in order to functionalize gold nanoparticles (GNPs@L). The cytotoxicity of compound L and GNPs@L on the MCF-7 breast cancer cells was explored and it was observed that L and GNPs@L compounds induced apoptosis in MCF-7 cancer cells. The cellular uptake of the hybrid system GNPs@L was studied using confocal laser scanning microscopy (CLSM).

Keywords: cytotoxicity, fluorescent probes, nanoparticles, quinoline

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Authors: M. Khouil, N. Saber, M. Mestari

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In this paper, a different architecture of a collision detection neural network (DCNN) is developed. This network, which has been particularly reviewed, has enabled us to solve with a new approach the problem of collision detection between two convex polyhedra in a fixed time (O (1) time). We used two types of neurons, linear and threshold logic, which simplified the actual implementation of all the networks proposed. The study of the collision detection is divided into two sections, the collision between a point and a polyhedron and then the collision between two convex polyhedra. The aim of this research is to determine through the AMAXNET network a mini maximum point in a fixed time, which allows us to detect the presence of a potential collision.

Keywords: collision identification, fixed time, convex polyhedra, neural network, AMAXNET

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Authors: Zan Azma Nasruddin, Irwan Mazlin, Nor Aziah Daud, Fauziah Redzuan, Fariza Hanis Abdul Razak

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This paper focuses on a machine learning that learn the correct pronunciation of Makhraj Huroofs. Usually, people need to find an expert to pronounce the Huroof accurately. In this study, the researchers have developed a system that is able to learn the selected Huroofs which are ha, tsa, zho, and dza using the Convolutional Neural Network. The researchers present the chosen type of the CNN architecture to make the system that is able to learn the data (Huroofs) as quick as possible and produces high accuracy during the prediction. The researchers have experimented the system to measure the accuracy and the cross entropy in the training process.

Keywords: convolutional neural network, Makhraj recognition, speech recognition, signal processing, tensorflow

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Authors: Aliya K. Salahova

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Scientific thinking is a fundamental cognitive skill that plays a crucial role in preparing young minds for an increasingly complex world. This study explores the connection between scientific thinking and the ability to ask questions in children aged 5-9. The research aims to identify and assess how questioning ability serves as an indicator of scientific thinking development in this age group. A longitudinal investigation was conducted over a span of 240 weeks, involving 72 children from diverse backgrounds. The participants were divided into an experimental group, engaging in weekly STEM activities, and a control group with no STEM involvement. The development of scientific thinking was evaluated through a comprehensive assessment of questioning skills, hypothesis formulation, logical reasoning, and problem-solving abilities. The findings reveal a significant correlation between the ability to ask questions and the level of scientific thinking in children aged 5-9. Participants in the experimental group exhibited a remarkable improvement in their questioning ability, which positively influenced their scientific thinking growth. In contrast, the control group, devoid of STEM activities, showed minimal progress in questioning skills and subsequent scientific thinking development. This study highlights the pivotal role of questioning ability as a key indicator of scientific thinking in young children. The results provide valuable insights for educators and researchers, emphasizing the importance of fostering and nurturing questioning skills to enhance scientific thinking capabilities from an early age. The implications of these findings are crucial for designing effective educational interventions to promote scientific curiosity and critical thinking in the next generation of scientific minds.

Keywords: scientific thinking, education, STEM, intervention, psychology, pedagogy, collaborative learning, longitudinal study

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Authors: Konstantinos Perifanos, Eirini Florou, Dionysis Goutsos

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This paper presents and benchmarks a number of end-to-end Deep Learning based models for metaphor detection in Greek. We combine Convolutional Neural Networks and Recurrent Neural Networks with representation learning to bear on the metaphor detection problem for the Greek language. The models presented achieve exceptional accuracy scores, significantly improving the previous state-of-the-art results, which had already achieved accuracy 0.82. Furthermore, no special preprocessing, feature engineering or linguistic knowledge is used in this work. The methods presented achieve accuracy of 0.92 and F-score 0.92 with Convolutional Neural Networks (CNNs) and bidirectional Long Short Term Memory networks (LSTMs). Comparable results of 0.91 accuracy and 0.91 F-score are also achieved with bidirectional Gated Recurrent Units (GRUs) and Convolutional Recurrent Neural Nets (CRNNs). The models are trained and evaluated only on the basis of training tuples, the related sentences and their labels. The outcome is a state-of-the-art collection of metaphor detection models, trained on limited labelled resources, which can be extended to other languages and similar tasks.

Keywords: metaphor detection, deep learning, representation learning, embeddings

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Authors: Sirilak Areerachakul

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Water quality has initiated serious management efforts in many countries. Artificial Neural Network (ANN) models are developed as forecasting tools in predicting water quality trend based on historical data. This study endeavors to automatically classify water quality. The water quality classes are evaluated using 6 factor indices. These factors are pH value (pH), Dissolved Oxygen (DO), Biochemical Oxygen Demand (BOD), Nitrate Nitrogen (NO3N), Ammonia Nitrogen (NH3N) and Total Coliform (T-Coliform). The methodology involves applying data mining techniques using multilayer perceptron (MLP) neural network models. The data consisted of 11 sites of Saen Saep canal in Bangkok, Thailand. The data is obtained from the Department of Drainage and Sewerage Bangkok Metropolitan Administration during 2007-2011. The results of multilayer perceptron neural network exhibit a high accuracy multilayer perception rate at 94.23% in classifying the water quality of Saen Saep canal in Bangkok. Subsequently, this encouraging result could be combined with GIS data improves the classification accuracy significantly.

Keywords: artificial neural network, geographic information system, water quality, computer science

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Authors: Akwu A. Nneka, Naidoo, Yougasphree

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A significantly high number of microbes exist in higher plants; these microbes include bacteria, fungi, and actinomycetes. There are reports on the benefits of endophytic fungi and their products of metabolism to the host plant and man, consequently, it is expedient to explore the changes that could arise as a result of manipulating their growth media. Grewia lasiocarpa E. Mey. ex Harv. (Malvaceae) is an indigenous Southern African plant, that belongs to a genus with known medicinal properties. Three media were used to culture the endophytic fungi viz., Potato Dextrose Agar (PDA), Malt Extract Agar (MEA), and Bacteriological Agar (BA) were used singly, and supplemented with three dilutions of the leaves and stem bark extracts. The manipulated growth media composition had a significant effect on the diversity of the isolated fungal populations. Several endophytic fungi were isolated; their distribution and diversity revealed a significant relatedness with the manipulated media. The media supplemented with the plant extracts was observed to give a significant increase in the growth rate and yield of the endophytes. To the best of our knowledge, this is the first study describing the endophytic fungi present in the leaves and stem bark of G. lasiocarpa E. Mey. ex Harv.

Keywords: Grewia lasiocarpa, plant-based extracts, endophytic fungi, Malvaceae

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Authors: A. B. A. Ahmed, D. I. Amar, R. M. Taha

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This research aims to investigate callus induction, somatic embryogenesis and indirect plant regeneration of Crassula ovata (Mill.) Druce – the famous ornamental plant. Experiment no.1: Callus induction was obtained from leaf and stem explants on Murashige and Skoog (MS) medium supplemented with various plant growth regulators (PGRs). Effects of different PGRs, plant regeneration and subsequent plantlet conversion were also assessed. Indirect plant regeneration was achieved from the callus of stem explants by the addition of 1.5 mg/L Kinetin (KN) alone. Best shoot induction was achieved (6.5 shoots/per explant) after 60 days. For successful rooting, regenerated plantlets were sub-cultured on the same MS media supplemented with 1.5 mg/L KN alone. The rooted plantlets were acclimatized and the survival rate was 90%. Experiment no.2: Results revealed that 0.5 mg/L 2,4-D alone and in combination with 1.0 mg/L 6-Benzyladenine (BA) gave 89.8% callus from the stem explants as compared to leaf explants. Callus proliferation and somatic embryo formation were also evaluated by ‘Double Staining Method’ and different stages of somatic embryogenesis were revealed by scanning electron microscope. Full Strength MS medium produced the highest number (49.6%) of cotyledonary stage somatic embryos (SEs). Mature cotyledonary stage SEs developed into plantlets after 12 weeks of culture. Well-rooted plantlets were successfully acclimatized at the survival rate of 85%. Indirectly regenerated plants did not show any detectable variation in morphological and growth characteristics when compared with the donor plant.

Keywords: callus induction, indirect plant regeneration, double staining, somatic embryogenesis, Crassula ovata

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Authors: Venkat Garigapati

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Cell-based therapies are limited due to underlying host immune system activity. Microencapsulation of living cells to overcome this issue has some serious drawbacks, such as limitations of nutrient and oxygen diffusion, which pose a threat to the function and longevity of cells. The conformal coating could overcome the issues which are generally involved in traditional microencapsulation. Some of the theoretical advantages of conformal coating include superior nutrient and oxygen supply to cells, prolonged lifespan, improved drug-secreting cell functionality and an opportunity to load high cell doses in small volumes. Despite several advantages to the conformal coating, there are no suitable methods available to apply to living cells. The ultra-thin conformal coating was achieved utilizing click-reactive methacryloyloxyethyl phosphorylcholine (MPC) polymers, which are capable of specifically reacting one polymer to another at neutral pH in the aqueous isotonic system at the desired temperature suitable for living cells without the need of deleterious initiators. ARPE-19 (Adult Retinal Pigment Epithelial cell line-19) cell-spheroids and rat pancreatic islets were used in the formulation studies. The in vitro studies of coated ARPE-19 cell-spheroids and rat islets indicate that the coat was intact; cells were viable and functioning. The in vitro study results revealed that the conformal coating technology seems promising and in vivo studies are being planned.

Keywords: cells, hydrogel, conformal coating, microencapsulation, insulin

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Authors: M. Heydari Vini

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There is a complicated relation between effective input parameters of cold rolling and output rolling force and exit thickness of strips.in many mathematical models, the effect of some rolling parameters have been ignored and the outputs have not a desirable accuracy. In the other hand, there is a special relation among input thickness of strips,the width of the strips,rolling speeds,mandrill tensions and the required exit thickness of strips with rolling force and the real exit thickness of the rolled strip. First of all, in this paper the effective parameters of cold rolling process modeled using an artificial neural network according to the optimum network achieved by using a written program in MATLAB,it has been shown that the prediction of rolling stand parameters with different properties and new dimensions attained from prior rolled strips by an artificial neural network is applicable.

Keywords: cold rolling, artificial neural networks, rolling force, real rolled thickness of strips

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Authors: Damian A. Mooney, Michael T. P. Mc Cann, J. M. Don MacElroy, Olli Antson, Denis P. Dowling

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Atmospheric pressure plasma liquid deposition (APPLD) is a novel technology used for the deposition of thin films via the injection of a reactive liquid precursor into a high-energy discharge plasma at ambient pressure. In this work, APPLD, utilising a TEOS precursor, was employed to produce asymmetric membranes consisting of a thin (100 nm) layer of deposited silica on a microporous silica support in order to assess their suitability for high temperature gas separation applications. He and N₂ gas permeability measurements were made for each of the fabricated membranes and a maximum ideal He/N₂ selectivity of 66 was observed at room temperature. He, N₂ and CO2 gas permeances were also measured at the elevated temperature of 673K and ideal He/N₂ and CO₂/N₂ selectivities of 300 and 7.4, respectively, were observed. The results suggest that this plasma-based deposition technique can be a viable method for the manufacture of membranes for the efficient separation of high temperature, post-combustion gases, including that of CO₂/N₂ where the constituent gases differ in size by fractions of an Ångstrom.

Keywords: asymmetric membrane, CO₂ separation, high temperature, plasma deposition, thin films

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Authors: G. Vasanthi

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The purpose of the study was to analyse the red blood cells and white blood cells during three different menstrual phases between sedentary and sports women. To achieve this purpose, fifteen female sedentary post graduate students (M.A., M.Sc.) and fifteen students of Master of Physical Education and Sports (M.P.Ed.) women who regularly involved in vigouous sports training and participated in sports competition on different games were selected by adopting random sampling method. All the students were hostelers and their age group was between 20 to 22 years. The blood sample were collected during the mid-period of the three different phases to calculate the red blood cells and white blood cells. The data collected were treated statistically by using analysis of variance. The results reveal that the RBC and WBC is found to be significant between sedentary and sports women during the three different menstrual phases.

Keywords: RBC, WBC, menstrual, proliferative, secretary, sedentary women, sports women

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Authors: Yu-Chen Hu

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Gene editing by CRISPR and gene regulation by microRNA or CRISPR activation have dramatically changed the way to manipulate cellular gene expression and cell fate. In recent years, various gene editing and gene manipulation technologies have been applied to control stem cell differentiation to enhance tissue regeneration. This research will focus on how to develop CRISPR, CRISPR activation (CRISPRa), CRISPR inhibition (CRISPRi), as well as bi-directional CRISPR-AI gene regulation technologies to control cell differentiation and bone regeneration. Moreover, in this study, CRISPR/Cas13d-mediated RNA editng for miRNA editing and bone regeneration will be discussed.

Keywords: gene therapy, bone regeneration, stem cell, CRISPR, gene regulation

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Authors: Vidhula Ahire, Amit Kumar, K. P. Mishra, Gauri Kulkarni

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Herbal polyphenols have gained significance because of their increasing promise in prevention and treatment of cancer. Therefore, development of a dietary compound as an effective radiosensitizer and a radioprotector is highly warranted for cervical cancer patients undergoing therapy. This study describes the cytotoxic effects of the flavonoid, ellagic acid (EA) when administered either alone or in combination with gamma radiation on cervical cancer HeLa cells in vitro. Apoptotic index and proliferation were measured by using trypan blue assay. Reproductive cell death was analyzed by clonogenic assay. Propidium iodide staining for flowcytometry was performed to analyze cell cycle modulation. Nuclear and mitochondrial changes were studied with specific dyes. DNA repair kinetics was analyzed by immunofluorescence assay. Evaluation and comparison of EA effects were performed with other clinically used breast cancer drugs. When tumor cells were exposed to 2 and 4 Gy of irradiation in presence of EA (10 μM), it yielded a synergistic cytotoxic effect on cervical cancer cells whereas in NIH3T3 cells it reversed the injury caused by irradiation and abetted in the regaining of normal healthy cells. At 24h ~25foci/cell was observed and 2.6 fold decrease in the mitochondrial membrane potential. Up to 40% cell were arrested in the G1 phase and 20-36% cells exhibited apoptosis. Our results demonstrate the role of increased apoptosis and cell cycle modulation in the mechanism of EA mediated radiosensitization of cervical cancer cells and thus advocating EA as an adjuvant for preclinical trials in cancer chemo- radiotherapy.

Keywords: cervical cancer, ellagic acid, sensitization, radiation therapy

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Authors: Panagiotis Karadimos, Leonidas Anthopoulos

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Predicting the actual cost and duration in construction projects concern a continuous and existing problem for the construction sector. This paper addresses this problem with modern methods and data available from past public construction projects. 39 bridge projects, constructed in Greece, with a similar type of available data were examined. Considering each project’s attributes with the actual cost and the actual duration, correlation analysis is performed and the most appropriate predictive project variables are defined. Additionally, the most efficient subgroup of variables is selected with the use of the WEKA application, through its attribute selection function. The selected variables are used as input neurons for neural network models through correlation analysis. For constructing neural network models, the application FANN Tool is used. The optimum neural network model, for predicting the actual cost, produced a mean squared error with a value of 3.84886e-05 and it was based on the budgeted cost and the quantity of deck concrete. The optimum neural network model, for predicting the actual duration, produced a mean squared error with a value of 5.89463e-05 and it also was based on the budgeted cost and the amount of deck concrete.

Keywords: actual cost and duration, attribute selection, bridge construction, neural networks, predicting models, FANN TOOL, WEKA

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Authors: Kais Manai

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The main part of this work focuses on the study of pion/muon separation at low energy using a nuclear Emulsion Cloud Chamber (ECC) made of lead and nuclear emulsion films. The work consists of two parts: particle reconstruction algorithm and a Neural Network that assigns to each reconstructed particle the probability to be a muon or a pion. The pion/muon separation algorithm has been optimized by using a detailed Monte Carlo simulation of the ECC and tested on real data. The algorithm allows to achieve a 60% muon identification efficiency with a pion misidentification smaller than 3%.

Keywords: nuclear emulsion, particle identification, tracking, neural network

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Authors: Milind Patil

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Present paper is a discussion on Hindi-Marathi Morphological Analysis and generating rules for Machine Translation on the basis of Machine Readable Dictionary (MRD). This used Transformative Generative Grammar (TGG) rules to design the MRD. As per TGG rules, the suffix of a particular root word is based on its Tense, Aspect, Modality and Voice. That's why the suffix is very important for the word meanings (or root meanings). The Hindi and Marathi Language both have relation with Indo-Aryan language family. Both have been derived from Sanskrit language and their script is 'Devnagari'. But there are lots of differences in terms of semantics and grammatical level too. In Marathi, there are three genders, but in Hindi only two (Masculine and Feminine), the Natural gender is absent in Hindi. Likewise other grammatical categories also differ in their level of use. For MRD the suffixes (or Morpheme) are of particular root word for GNP (Gender, Number and Person) are based on its natural phenomena. A particular Suffix and Morphine change as per the need of person, number and gender. The design of MRD also based on this format. In first, Person, Number, Gender and Tense are key points than root words and suffix of particular Person, Number Gender (PNG). After that the inferences are drawn on the basis of rules that is (V.stem) (Pre.T/Past.T) (x) + (Aux-Pre.T) (x) → (V.Stem.) + (SP.TM) (X).

Keywords: MRD, TGG, stem, morph, morpheme, suffix, PNG, TAM&V, root

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Authors: A. Ejah Umraeni Salam, M. Tola, M. Selintung, F. Maricar

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Clean water is an essential and fundamental human need. Therefore, its supply must be assured by maintaining the quality, quantity and water pressure. However the fact is, on its distribution system, leakage happens and becomes a common world issue. One of the technical causes of the leakage is a leaking pipe. The purpose of the research is how to use the Radial Basis Function Neural (RBFNN) model to detect the location and the magnitude of the pipeline leakage rapidly and efficiently. In this study the RBFNN are trained and tested on data from EPANET hydraulic modeling system. Method of Radial Basis Function Neural Network is proved capable to detect location and magnitude of pipeline leakage with of the accuracy of the prediction results based on the value of RMSE (Root Meant Square Error), comparison prediction and actual measurement approaches 0.000049 for the whole pipeline system.

Keywords: radial basis function neural network, leakage pipeline, EPANET, RMSE

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Authors: Khader S. Al-Aidmat, Venus W. Samawi

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Blurred vision in the misty atmosphere is essential problem which needs to be resolved. To solve this problem, we developed a technique to restore foggy degraded image from its original version using Back-propagation neural network (BP-NN). The suggested technique is based on mapping between foggy scene and its corresponding original scene. Seven different approaches are suggested based on type of features used in image restoration. Features are extracted from spatial and spatial-frequency domain (using DCT). Each of these approaches comes with its own BP-NN architecture depending on type and number of used features. The weight matrix resulted from training each BP-NN represents a fog filter. The performance of these filters are evaluated empirically (using PSNR), and perceptually. By comparing the performance of these filters, the effective features that suits BP-NN technique for restoring foggy images is recognized. This system proved its effectiveness and success in restoring moderate foggy images.

Keywords: artificial neural network, discrete cosine transform, feed forward neural network, foggy image restoration

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Authors: Nidal H. Abu-Zahra, Mahmoud Algazzar

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In this research, n-dodecylthiol was added to P3HT/PC70BM polymer solar cells to improve the crystallinity of P3HT and enhance the phase separation of P3HT/PC70BM. The improved crystallinity of P3HT/PC70BM doped with 0-5% by volume of n-dodecylthiol resulted in improving the power conversion efficiency of polymer solar cells by 33%. In addition, thermal annealing of the P3HT/PC70MB/n-dodecylthiolcompound showed further improvement in crystallinity with n-dodecylthiol concentration up to 2%. The highest power conversion efficiency of 3.21% was achieved with polymer crystallites size L of 11.2nm, after annealing at 150°C for 30 minutes under a vacuum atmosphere. The smaller crystallite size suggests a shorter path of the charge carriers between P3HT backbones, which could be beneficial to getting a higher short circuit current in the devices made with the additive.

Keywords: n-dodecylthiol, congugated PSC, P3HT/PCBM, polymer solar cells

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Authors: Soheil Zorofchian Moghadamtousi, Habsah Abdul Kadir, Mohammadjavad Paydar, Elham Rouhollahi, Hamed Karimian

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The present study was designed to evaluate the molecular mechanisms of Annona muricata leaves ethyl acetate extract (AMEAE) against lung cancer A549 cells. Cell viability analysis revealed the selective cytotoxic effect of AMEAE towards A549 cells. Treatment of A549 cells with AMEAE significantly elevated the reactive oxygen species formation, followed by attenuation of mitochondrial membrane potential via upregulation of Bax and downregulation of Bcl-2, accompanied by cytochrome c release to the cytosol. The released cytochrome c triggered the activation of caspase-9 followed by caspase-3. In addition, AMEAE-induced apoptosis was accompanied by cell cycle arrest at G1 phase. Our data showed for the first time that AMEAE inhibited the proliferation of A549 cells, leading to cell cycle arrest and programmed cell death through activation of the mitochondrial-mediated signaling pathway.

Keywords: Annona muricata, lung cancer, apoptosis, mitochondria

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Authors: Jeonghun Nam, Seonggil Kim, Hyunjoo Choi, Chae Seung Lim

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Quantification and analysis of rare cells are challenging in clinical applications and cell biology due to its extremely small number in blood. In this work, we propose a viscoelastic microfluidic device for continuous cell concentration without sheath flows. Due to the viscoelastic effect on suspending cells, cells with the blockage ratio higher than 0.1 could be tightly focused at the center of the microchannel. The blockage ratio was defined as the particle diameter divided by the channel width. Finally, cells were concentrated through the center outlet and the additional suspending medium was removed to the side outlets. Since viscoelastic focusing is insensitive to the flow rate higher than 10 μl/min, the non-powered hand pump sprayer could be used with no accurate control of the flow rate, which is suitable for clinical settings in resource-limited developing countries. Using multiple concentration processes, high-throughput concentration of white blood cells in lysed blood sample was achieved by ~ 300-fold.

Keywords: cell concentration, high-throughput, non-powered, viscoelastic fluid

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Authors: Arthur D. Wiedemann, Christopher Fuller, Kyle A. Pascioni

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With the introduction of electric motors, small unmanned aerial vehicle designers have to consider trade-offs between acoustic noise and thrust generated. Currently, there are few low-computational tools available for predicting acoustic noise emitted by a propeller into the far-field. Artificial neural networks offer a highly non-linear and adaptive model for predicting isolated and interactive tonal noise. But neural networks require large data sets, exceeding practical considerations in modeling experimental results. A methodology known as physics guided machine learning has been applied in this study to reduce the required data set to train the network. After building and evaluating several neural networks, the best model is investigated to determine how the network successfully predicts the acoustic waveform. Lastly, a post-network transfer function is developed to remove discontinuity from the predicted waveform. Overall, methodologies from physics guided machine learning show a notable improvement in prediction performance, but additional loss functions are necessary for constructing predictive networks on small datasets.

Keywords: aeroacoustics, machine learning, propeller, rotor, neural network, physics guided machine learning

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Authors: Jamshaid Ul Rahman, Faiza Makhdoom, Dianchen Lu

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Activation functions play a decisive role in determining the capacity of Deep Neural Networks (DNNs) as they enable neural networks to capture inherent nonlinearities present in data fed to them. The prior research on activation functions primarily focused on the utility of monotonic or non-oscillatory functions, until Growing Cosine Unit (GCU) broke the taboo for a number of applications. In this paper, a Convolutional Neural Network (CNN) model named as ASU-CNN is proposed which utilizes recently designed activation function ASU across its layers. The effect of this non-monotonic and oscillatory function is inspected through feature map visualizations from different convolutional layers. The optimization of proposed network is offered by Adam with a fine-tuned adjustment of learning rate. The network achieved promising results on both training and testing data for the classification of CIFAR-10. The experimental results affirm the computational feasibility and efficacy of the proposed model for performing tasks related to the field of computer vision.

Keywords: amplifying sine unit, activation function, convolutional neural networks, oscillatory activation, image classification, CIFAR-10

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Authors: Pierre-Louis Lucas, Corinne Loutelier-Bourhis, Narimane Mati-Baouche, Philippe Chan Tchi-Song, Patrice Lerouge, Elodie Mathieu-Rivet, Muriel Bardor

Abstract:

N-glycosylation is a post-translational modification taking place in the Endoplasmic Reticulum and the Golgi apparatus where defined glycan features are added on protein in a very specific sequence Asn-X-Thr/Ser/Cys were X can be any amino acid except proline. Because it is well-established that those N-glycans play a critical role in protein biological activity, protein half-life and that a different N-glycan structure may induce an immune response, they are very important in Biopharmaceuticals which are mainly glycoproteins bearing N-glycans. From now, most of the biopharmaceuticals are produced by mammalian cells like Chinese Hamster Ovary cells (CHO) for their N-glycosylation similar to the human, but due to the high production costs, several other species are investigated as the possible alternative system. In this purpose, the green microalgae Chlamydomonas reinhardtii was investigated as the potential production system for Biopharmaceuticals. This choice was influenced by the facts that C. reinhardtii is a well-study microalgae which is growing fast with a lot of molecular biology tools available. This organism is also producing N-glycan on its endogenous proteins. However, the analysis of the N-glycan structure of this microalgae has revealed some differences as compared to the human. Rather than in Human where the glycans are processed by key enzymes called N-acetylglucosaminyltransferase I and II (GnTI and GnTII) adding GlcNAc residue to form a GlcNAc₂Man₃GlcNAc₂ core N-glycan, C. reinhardtii lacks those two enzymes and possess a GnTI independent glycosylation pathway. Moreover, some enzymes like xylosyltransferases and methyltransferases not present in human are supposed to act on the glycans of C. reinhardtii. Furthermore, the recent structural study by mass spectrometry shows that the N-glycosylation precursor supposed to be conserved in almost all eukaryotic cells results in a linear Man₅GlcNAc₂ rather than a branched one in C. reinhardtii. In this work, we will discuss the new released MS information upon C. reinhardtii N-glycan structure and their impact on our attempt to modify the glycan in a Human manner. Two strategies will be discussed. The first one consisted in the study of Xylosyltransferase insertional mutants from the CLIP library in order to remove xyloses from the N-glycans. The second will go further in the humanization by transforming the microalgae with the exogenous gene from Toxoplasma gondii having an activity similar to GnTI and GnTII with the aim to synthesize GlcNAc₂Man₃GlcNAc₂ in C. reinhardtii.

Keywords: Chlamydomonas reinhardtii, N-glycosylation, glycosyltransferase, mass spectrometry, humanization

Procedia PDF Downloads 177

Authors: Gökçe Erdemir, İlhan Yaylım, Serap Erdem-Kuruca, Musa Mutlu Can

Abstract:

It has been determined that the main reason for the death of cancer disease is caused by metastases rather than the primary tumor. The cells that leave the primary tumor and enter the circulation and cause metastasis in the secondary organs are called "circulating tumor cells" (CTCs). The presence and number of circulating tumor cells has been associated with poor prognosis in many major types of cancer, including breast, prostate, and colorectal cancer. It is thought that knowledge of circulating tumor cells, which are seen as the main cause of cancer-related deaths due to metastasis, plays a key role in the diagnosis and treatment of cancer. The fact that tissue biopsies used in cancer diagnosis and follow-up are an invasive method and are insufficient in understanding the risk of metastasis and the progression of the disease have led to new searches. Liquid biopsy tests performed with a small amount of blood sample taken from the patient for the detection of CTCs are easy and reliable, as well as allowing more than one sample to be taken over time to follow the prognosis. However, since these cells are found in very small amounts in the blood, it is very difficult to capture them and specially designed analytical techniques and devices are required. Methods based on the biological and physical properties of the cells are used to capture these cells in the blood. Early diagnosis is very important in following the prognosis of tumors of epithelial origin such as breast, lung, colon and prostate. Molecules such as EpCAM, vimentin, and cytokeratins are expressed on the surface of cells that pass into the circulation from very few primary tumors and reach secondary organs from the circulation, and are used in the diagnosis of cancer in the early stage. For example, increased EpCAM expression in breast and prostate cancer has been associated with prognosis. These molecules can be determined in some blood or body fluids to be taken from patients. However, more sensitive methods are required to be able to determine when they are at a low level according to the course of the disease. The aim is to detect these molecules found in very few cancer cells with the help of sensitive, fast-sensing biosensors, first in breast cancer cells reproduced in vitro and then in blood samples taken from breast cancer patients. In this way, cancer cells can be diagnosed early and easily and effectively treated.

Keywords: electrochemical biosensors, breast cancer, circulating tumor cells, EpCAM, Vimentin, Cytokeratins

Procedia PDF Downloads 261