Search results for: neural progentor cells

Authors: Samar Rabah

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Background: Incense woods are special kind of trees called Agarwood, which characterized by good smelling odors and many medical benefits. Incense smoke is heavily used in Saudi Arabia although comprehensive studies of its effects on health are limited. The present study demonstrated lung ultrastructure changes of mice after exposure and cessation to Incense smoke. Eighty mice are divided equally into four groups, three groups are exposed to different concentrations of Incense smoke (2, 4 and 6 gm) for three months, while the fourth group is control one. At the end of each month, lungs of five animals from each group are gathered, while the last five animals from each group are kept for another 60 days without exposure to the Incense smoke to allow for recovery. Results: Transmission electron microscope investigations of all exposed groups showed hypertrophy and hyperplasia in Clara Cells and some an enlargement of the macrophage to the point that it fills a large part of the alveolar lumen. Scanning electron microscope marks presence of mucus materials attached to the epithelial bronchioles. After prevention of exposure to the Incense smoke for 60 days, necrosis and degeneration in some cells of epithelial bronchioles, fibrosis of peribronchial, thickening in alveolar walls and aggregation of lymphoid cells were demonstrated. Conclusion: Based on the above findings and other related studies (not published), we conclude that exposure to Incense smoke causes harmful effects due to sever changes in pulmonary ultrastructure, such effects do not disappear even when Incense smoke inhalation was stopped. Therefore, we recommend that Incense smoke should use only in open places to reduce its harms.

Keywords: Incense smoke, lungs, ultrastructure of lungs, Agarwood

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Authors: Vijay Pal Singh, Yogesh Chandra Kotiyal

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Structures are a combination of various load carrying members which transfer the loads to the foundation from the superstructure safely. At the design stage, the loading of the structure is defined and appropriate material choices are made based upon their properties, mainly related to strength. The strength of materials kept on reducing with time because of many factors like environmental exposure and deformation caused by unpredictable external loads. Hence, to predict the strength of materials used in structures, various techniques are used. Among these techniques, Non-Destructive Techniques (NDT) are the one that can be used to predict the strength without damaging the structure. In the present study, the compressive strength of concrete has been predicted using Artificial Neural Network (ANN). The predicted strength was compared with the experimentally obtained actual compressive strength of concrete and equations were developed for different models. A good co-relation has been obtained between the predicted strength by these models and experimental values. Further, the co-relation has been developed using two NDT techniques for prediction of strength by regression analysis. It was found that the percentage error has been reduced between the predicted strength by using combined techniques in place of single techniques.

Keywords: rebound, ultra-sonic pulse, penetration, ANN, NDT, regression

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Authors: Mecheri Zeid Belmecheri, Maamar Ahfir, Izzet Kale

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Automatic cardiac auscultation is still a subject of research in order to establish an objective diagnosis. Recorded heart sounds as Phonocardiogram signals (PCG) can be used for automatic segmentation into components that have clinical meanings. These are the first sound, S1, the second sound, S2, and the systolic and diastolic components, respectively. In this paper, an automatic method is proposed for the robust segmentation of heart sounds. This method is based on calculating an intermediate sawtooth-shaped signal from the length variation of the recorded Phonocardiogram (PCG) signal in the time domain and, using its positive derivative function that is a binary signal in training a Recurrent Neural Network (RNN). Results obtained in the context of a large database of recorded PCGs with their simultaneously recorded ElectroCardioGrams (ECGs) from different patients in clinical settings, including normal and abnormal subjects, show a segmentation testing performance average of 76 % sensitivity and 94 % specificity.

Keywords: heart sounds, PCG segmentation, event detection, recurrent neural networks, PCG curve length

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Authors: Min-Ah Woo, Min-Cheol Lim, Hyun-Joo Chang, Sung-Wook Choi

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We developed a paper-based colorimetric sensor utilizing magnetic nanoparticles conjugated with aptamers (MNP-Apts) against E. coli O157:H7. The MNP-Apts were applied to a test sample solution containing the target cells, and the solution was simply dropped onto PVDF (polyvinylidene difluoride) membrane. The membrane moves the sample radially to form the sample spots of different compounds as concentric rings, thus the MNP-Apts on the membrane enabled specific recognition of the target cells through a color ring generation by MNP-promoted colorimetric reaction of TMB (3,3',5,5'-tetramethylbenzidine) and H₂O₂. This method could be applied to rapidly and visually detect various bacterial pathogens in less than 1 h without cell culturing.

Keywords: aptamer, colorimetric sensor, E. coli O157:H7, magnetic nanoparticle, polyvinylidene difluoride

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Authors: Ashit Syngle, Nidhi Garg, Pawan Krishan

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Background: Endothelial Progenitor Cells (EPCs) are depleted and contribute to increased cardiovascular (CV) risk in rheumatoid arthritis (RA). Statins exert a protective effect in CAD partly by promoting EPC mobilization. This vasculoprotective effect of statin has not yet been investigated in RA. We aimed to investigate the effect of rosuvastatin on EPCs in RA. Methods: 50 RA patients were randomized to receive 6 months of treatment with rosuvastatin (10 mg/day, n=25) and placebo (n=25) as an adjunct to existing stable antirheumatic drugs. EPCs (CD34+/CD133+) were quantified by Flow Cytometry. Inflammatory measures included DAS28, CRP and ESR were measured at baseline and after treatment. Lipids and pro-inflammatory cytokines (TNF-α, IL-6, and IL-1) were estimated at baseline and after treatment. Results: At baseline, inflammatory measures and pro-inflammatory cytokines were elevated and EPCs depleted among both groups. At baseline, EPCs inversely correlated with DAS28 and TNF-α in both groups. EPCs increased significantly (p < 0.01) after treatment with rosuvastatin but did not show significant change with placebo. Rosuvastatin exerted positive effect on lipid spectrum: lowering total cholesterol, LDL, non HDL and elevation of HDL as compared with placebo. At 6 months, DAS28, ESR, CRP, TNF-α and IL-6 improved significantly in rosuvastatin group. Significant negative correlation was observed between EPCs and DAS28, CRP, TNF-α, and IL-6 after treatment with rosuvastatin. Conclusion: First study to show that rosuvastatin improves inflammation and EPC biology in RA possibly through its anti-inflammatory and lipid lowering effect. This beneficial effect of rosuvastatin may provide a novel strategy to prevent cardiovascular events in RA.

Keywords: RA, Endothelial Progenitor Cells, rosuvastatin, cytokines

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Authors: Syed Sadique Anwer Askari, Mukul Kumar Das

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Zinc Oxide (ZnO) solar cells have drawn great attention due to the enhanced efficiency and low-cost fabrication process. In this study, ZnO thin film is used as the active layer, hole blocking layer, antireflection coating (ARC) as well as transparent conductive oxide. To improve the conductivity of ZnO, top layer of ZnO is doped with aluminum, for top contact. Intrinsic carrier concentration of silicon substrate plays an important role in enhancing the power conversion efficiency (PCE) of ZnO/Si solar cell. With the increase of intrinsic carrier concentration PCE decreased due to increase in dark current in solar cell. At 80nm ZnO and 160µm Silicon substrate thickness, power conversion efficiency of 26.45% and 21.64% is achieved with intrinsic carrier concentration of 1x109/cm3, 1.4x1010/cm3 respectively.

Keywords: hetero-junction solar cell, solar cell, substrate intrinsic carrier concentration, ZnO/Si

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Authors: Annika Stechemesser, Rachel Pounds, Emma Lucas, Chris Dawson, Julia Lipecki, Pavle Vrljicak, Jan Brosens, Sean Kehoe, Jason Yap, Lawrence Young, Sascha Ott

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Advances in technology make it now possible to retrieve the genetic information of thousands of single cancerous cells. One of the key challenges in single cell analysis of cancerous tissue is to determine the number of different cell types and their characteristic genes within the sample to better understand the tumors and their reaction to different treatments. For this analysis to be possible, it is crucial to filter out background noise as it can severely blur the downstream analysis and give misleading results. In-depth analysis of the state-of-the-art filtering methods for single cell data showed that they do, in some cases, not separate noisy and normal cells sufficiently. We introduced an algorithm that filters and clusters single cell data simultaneously without relying on certain genes or thresholds chosen by eye. It detects communities in a Shared Nearest Neighbor similarity network, which captures the similarities and dissimilarities of the cells by optimizing the modularity and then identifies and removes vertices with a weak clustering belonging. This strategy is based on the fact that noisy data instances are very likely to be similar to true cell types but do not match any of these wells. Once the clustering is complete, we apply a set of evaluation metrics on the cluster level and accept or reject clusters based on the outcome. The performance of our algorithm was tested on three datasets and led to convincing results. We were able to replicate the results on a Peripheral Blood Mononuclear Cells dataset. Furthermore, we applied the algorithm to two samples of ovarian cancer from the same patient before and after chemotherapy. Comparing the standard approach to our algorithm, we found a hidden cell type in the ovarian postchemotherapy data with interesting marker genes that are potentially relevant for medical research.

Keywords: cancer research, graph theory, machine learning, single cell analysis

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Authors: Amal Khalifa, Nicolas Vana Santos

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Steganography has been known for centuries as an efficient approach for covert communication. Due to its popularity and ease of access, image steganography has attracted researchers to find secure techniques for hiding information within an innocent looking cover image. In this research, we propose a novel deep-learning approach to digital image steganography. The proposed method, DeepWaveletFusion, uses convolutional neural networks (CNN) to hide a secret image into a cover image of the same size. Two CNNs are trained back-to-back to merge the Discrete Wavelet Transform (DWT) of both colored images and eventually be able to blindly extract the hidden image. Based on two different image similarity metrics, a weighted gain function is used to guide the learning process and maximize the quality of the retrieved secret image and yet maintaining acceptable imperceptibility. Experimental results verified the high recoverability of DeepWaveletFusion which outperformed similar deep-learning-based methods.

Keywords: deep learning, steganography, image, discrete wavelet transform, fusion

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Authors: Anoop T. R., Otman Basir, Robert F. Hess, Eileen E. Birch, Brooke A. Koritala, Reed M. Jost, Becky Luu, David Stager, Ben Thompson

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Strabismus refers to a misalignment of the eyes. Early detection and treatment of strabismus in childhood can prevent the development of permanent vision loss due to abnormal development of visual brain areas. We developed a two-stage method for strabismus detection and classification based on photographs of the face. The first stage detects the presence or absence of strabismus, and the second stage classifies the type of strabismus. The first stage comprises face detection using Haar cascade, facial landmark estimation, face alignment, aligned face landmark detection, segmentation of the eye region, and detection of strabismus using VGG 16 convolution neural networks. Face alignment transforms the face to a canonical pose to ensure consistency in subsequent analysis. Using facial landmarks, the eye region is segmented from the aligned face and fed into a VGG 16 CNN model, which has been trained to classify strabismus. The CNN determines whether strabismus is present and classifies the type of strabismus (exotropia, esotropia, and vertical deviation). If stage 1 detects strabismus, the eye region image is fed into stage 2, which starts with the estimation of pupil center coordinates using mask R-CNN deep neural networks. Then, the distance between the pupil coordinates and eye landmarks is calculated along with the angle that the pupil coordinates make with the horizontal and vertical axis. The distance and angle information is used to characterize the degree and direction of the strabismic eye misalignment. This model was tested on 100 clinically labeled images of children with (n = 50) and without (n = 50) strabismus. The True Positive Rate (TPR) and False Positive Rate (FPR) of the first stage were 94% and 6% respectively. The classification stage has produced a TPR of 94.73%, 94.44%, and 100% for esotropia, exotropia, and vertical deviations, respectively. This method also had an FPR of 5.26%, 5.55%, and 0% for esotropia, exotropia, and vertical deviation, respectively. The addition of one more feature related to the location of corneal light reflections may reduce the FPR, which was primarily due to children with pseudo-strabismus (the appearance of strabismus due to a wide nasal bridge or skin folds on the nasal side of the eyes).

Keywords: strabismus, deep neural networks, face detection, facial landmarks, face alignment, segmentation, VGG 16, mask R-CNN, pupil coordinates, angle deviation, horizontal and vertical deviation

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Authors: Marinna Madrid, Nabiha Saklayen, Marinus Huber, Nicolas Vogel, Christos Boutopoulos, Michel Meunier, Eric Mazur

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Delivering material into cells is important for a diverse range of biological applications, including gene therapy, cellular engineering and imaging. We present a plasmonic substrate for delivering membrane-impermeable material into cells at high throughput and high efficiency while maintaining cell viability. The substrate fabrication is based on an affordable and fast colloidal self-assembly process. When illuminated with a femtosecond laser, the light interacts with the electrons at the surface of the metal substrate, creating localized surface plasmons that form bubbles via energy dissipation in the surrounding medium. These bubbles come into close contact with the cell membrane to form transient pores and enable entry of membrane-impermeable material via diffusion. We use fluorescence microscopy and flow cytometry to verify delivery of membrane-impermeable material into HeLa CCL-2 cells. We show delivery efficiency and cell viability data for a range of membrane-impermeable cargo, including dyes and biologically relevant material such as siRNA. We estimate the effective pore size by determining delivery efficiency for hard fluorescent spheres with diameters ranging from 20 nm to 2 um. To provide insight to the cell poration mechanism, we relate the poration data to pump-probe measurements of micro- and nano-bubble formation on the plasmonic substrate. Finally, we investigate substrate stability and reusability by using scanning electron microscopy (SEM) to inspect for damage on the substrate after laser treatment. SEM images show no visible damage. Our findings indicate that self-assembled plasmonic substrates are an affordable tool for high-throughput, high-efficiency delivery of material into mammalian cells.

Keywords: femtosecond laser, intracellular delivery, plasmonic, self-assembly

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Authors: Li Hui, Riyadh Hindi

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In the United States, bridge construction often employs overhang brackets to support the deck overhang, the weight of fresh concrete, and loads from construction equipment. This approach, however, can lead to significant torsional moments on the exterior girders, potentially causing excessive girder rotation. Such rotations can result in various safety and maintenance issues, including thinning of the deck, reduced concrete cover, and cracking during service. Traditionally, these issues are addressed by installing temporary lateral bracing systems and conducting comprehensive torsional analysis through detailed finite element analysis for the construction of bridge deck overhang. However, this process is often intricate and time-intensive, with the spacing between temporary lateral bracing systems usually relying on the field engineers’ expertise. In this study, a deep neural network model is introduced to limit exterior girder rotation during bridge deck construction. The model predicts the optimal spacing between temporary bracing systems. To train this model, over 10,000 finite element models were generated in SAP2000, incorporating varying parameters such as girder dimensions, span length, and types and spacing of lateral bracing systems. The findings demonstrate that the deep neural network provides an effective and efficient alternative for limiting the exterior girder rotation for bridge deck construction. By reducing dependence on extensive finite element analyses, this approach stands out as a significant advancement in improving safety and maintenance effectiveness in the construction of bridge decks.

Keywords: bridge deck construction, exterior girder rotation, deep learning, finite element analysis

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Authors: Ming-Jen Huang, Chun-Fang Huang, Chiching Wei

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With the recent advance of the deep neural network, we observe new applications of NLP (natural language processing) and CV (computer vision) powered by deep neural networks for processing business documents. However, creating a real-world document processing system needs to integrate several NLP and CV tasks, rather than treating them separately. There is a need to have a unified approach for processing documents containing textual and graphical elements with rich formats, diverse layout arrangement, and distinct semantics. In this paper, a framework that fulfills this unified approach is presented. The framework includes a representation model definition for holding the information generated by various tasks and specifications defining the coordination between these tasks. The framework is a blueprint for building a system that can process documents with rich formats, styles, and multiple types of elements. The flexible and lightweight design of the framework can help build a system for diverse business scenarios, such as contract monitoring and reviewing.

Keywords: document processing, framework, formal definition, machine learning

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Authors: Eun Jung Sohn, Hwan Tae Park

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Autophagy is a cellular response to stress or environment on cell survival. Here, we investigated the role of ectopic expression of miR 200c-3p in autophagy. Ectopic expression of miR 200c-3p increased the expression of IRE1alpha, ATF6 and CHOP by western blot and RT-qPCR. Furthermore, the level of microRNA 200c-3p was enhanced by treatment of TG or overexpression of GRP 78. Also, ectopic expression of miR200c-3p increased the LC3 II expression by western blot and RT-qPCR. Also, we found that western blot assay showed that miR200c-3p inhibitor was blocked the starvation–induced LC3II levels. Furthermore, starvation stress increased the level of miR200c-3p in different kinetics. Ectopic expression of miR200c-3p attenuated LC3II expression in IRE1 siRNA transfected PC3 cells. Here, we first demonstrate that miR200c-3p regulates autophagy via ER stress pathway.

Keywords: Autophagy, ER stress, LC3II, miR200c-3p

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Authors: Maxime Moreau, Silvère Baron, Jean-Marc Lobaccaro, Karine Charlet, Sébastien Menecier, Frédéric Perisse

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Cold Atmospheric Plasma (CAP) is an ionized gas generated at atmospheric pressure with the temperature of heavy particles (molecules, ions, atoms) close to the room temperature. Recent studies have shown that both in-vitro and in-vivo plasma exposition to many cancer cell lines are efficient to induce the apoptotic way of cell death. In some other works, normal cell lines seem to be less impacted by plasma than cancer cell lines. This is called selectivity of plasma. It is highly likely that the generated RNOS (Reactive Nitrogen Oxygen Species) in the plasma jet, but also in the medium, play a key-role in this selectivity. In this study, two CAP devices will be compared to electrical power, chemical species composition and their efficiency to kill cancer cells. A particular focus on the action of hydrogen peroxide will be made. The experiments will take place as described next for both devices: electrical and spectroscopic characterization for different voltages, plasma treatment of normal and cancer cells to compare the CAP efficiency between cell lines and to show that death is induced by an oxidative stress. To enlighten the importance of hydrogen peroxide, an inhibitor of H2O2 will be added in cell culture medium before treatment and a comparison will be made between the results of cell viability in this case and those from a simple plasma exposition. Besides, H2O2 production will be measured by only treating medium with plasma. Cell lines will also be exposed to different concentrations of hydrogen peroxide in order to characterize the cytotoxic threshold for cells and to make a comparison with the quantity of H2O2 produced by CAP devices. Finally, the activity of catalase for different cell lines will be quantified. This enzyme is an important antioxidant agent against hydrogen peroxide. A correlation between cells response to plasma exposition and this activity could be a strong argument in favor of the predominant role of H2O2 to explain the selectivity of plasma cancer treatment by cold atmospheric plasma.

Keywords: cold atmospheric plasma, hydrogen peroxide, prostate cancer, selectivity

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Authors: P. Behera, K. K. Singh, D. K. Saini, M. De

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Implementation of 2D materials in the detection of bio analytes is highly advantageous in the field of sensing because of its high surface to volume ratio. We have designed our sensor array with different cationic two-dimensional MoS₂, where surface modification was achieved by cationic thiol ligands with different functionality. Green fluorescent protein (GFP) was chosen as signal transducers for its biocompatibility and anionic nature, which can bind to the cationic MoS₂ surface easily, followed by fluorescence quenching. The addition of bio-analyte to the sensor can decomplex the cationic MoS₂ and GFP conjugates, followed by the regeneration of GFP fluorescence. The fluorescence response pattern belongs to various analytes collected and transformed to linear discriminant analysis (LDA) for classification. At first, 15 different proteins having wide range of molecular weight and isoelectric points were successfully discriminated at 50 nM with detection limit of 1 nM. The sensor system was also executed in biofluids such as serum, where 10 different proteins at 2.5 μM were well separated. After successful discrimination of protein analytes, the sensor array was implemented for bacteria sensing. Six different bacteria were successfully classified at OD = 0.05 with a detection limit corresponding to OD = 0.005. The optimized sensor array was able to classify uropathogens from non-uropathogens in urine medium. Further, the technique was applied for discrimination of bacteria possessing resistance to different types and amounts of drugs. We found out the mechanism of sensing through optical and electrodynamic studies, which indicates the interaction between bacteria with the sensor system was mainly due to electrostatic force of interactions, but the separation of native bacteria from their drug resistant variant was due to Van der Waals forces. There are two ways bacteria can be detected, i.e., through bacterial cells and lysates. The bacterial lysates contain intracellular information and also safe to analysis as it does not contain live cells. Lysates of different drug resistant bacteria were patterned effectively from the native strain. From unknown sample analysis, we found that discrimination of bacterial cells is more sensitive than that of lysates. But the analyst can prefer bacterial lysates over live cells for safer analysis.

Keywords: array-based sensing, drug resistant bacteria, linear discriminant analysis, two-dimensional MoS₂

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Authors: Piyangkun Kukutapan, Siridech Boonsang

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The article proposes maximum power point tracking without mechanical sensor using Multilayer Perceptron Neural Network (MLPNN). The aim of article is to reduce the cost and complexity but still retain efficiency. The experimental is that duty cycle is generated maximum power, if it has suitable qualification. The measured data from DC generator, voltage (V), current (I), power (P), turnover rate of power (dP), and turnover rate of voltage (dV) are used as input for MLPNN model. The output of this model is duty cycle for driving the converter. The experiment implemented using Arduino Uno board. This diagram is compared to MPPT using MLPNN and P&O control (Perturbation and Observation control). The experimental results show that the proposed MLPNN based approach is more efficiency than P&O algorithm for this application.

Keywords: maximum power point tracking, multilayer perceptron netural network, optimal duty cycle, DC generator

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Authors: Maha M.Saber, Eitedal Daoud, Moetazza M. Alshafei, Lobna M. Abd El-Latif

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Children in the third world consumes many food products colored red like sweets and soft drink without knowing its effect on health or the type of color used in these products Erythrosine (ER,FD & C Red No.3) is one of the most common coloring agent used in these products and in coloring cherry in compotes. The possible adverse effect of erythrosine ER on the thyroid gland function is investigated in albino rats. Forty-five adult male albino rats were divided to three groups two groups will receive ER orally in doses 68 and I36mg/kg respectively. Third group will receive distilled water for three months Sections of thyroid glands were examined for histopathological, morphometric analysis and MIB-I Ki67 (proliferative marker). Serum concentration of triiodothyronine (T3), Thyroxin (T4) and thyrotrophin (TSH) were determined, results showed histological changes in the two treatment groups versus control group in the group with 68mg/kg dose show vaculation of the cytoplasm of follicular cells and pleomorphism of their nuclei. While the other treated group {136mg /kg} showed congestion of blood vessels, hyperplasia of the interstitial cells and increased multilayer of the follicular cells. Highly significant increase in the mean area of the thyroid follicles in both treated groups compared to control group.Erythrosine treated groups showed a very highly significant decrease (P < 0.001) in serum concentration of T3 and T 4 while TSH showed a very highly significant increase versus control.

Keywords: erythrosine, thyroid, morphometrics, proliferative marker

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Authors: Somesh Kumar Sharma, Sunanad Gupta

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The contribution of aviation to society and economy is undisputedly significant. The aviation industry drives economic and social progress by contributing prominently to tourism, commerce and improved quality of life. Identifying the amount of fuel consumed by an aircraft while moving in both airspace and ground networks is critical to air transport economics. Aviation fuel is a major operating cost parameter of the aviation industry and at the same time it is prone to various constraints. This article aims to develop a model for fuel consumption of aviation product. The paper tailors the information for the fuel consumption optimization in terms of information development, information evaluation and information refinement. The information is evaluated and refined using statistical package R and Factor Analysis which is further validated with neural networking. The study explores three primary dimensions which are finally summarized into 23 influencing variables in contrast to 96 variables available in literature. The 23 variables explored in this study should be considered as highly influencing variables for fuel consumption which will contribute significantly towards fuel optimization.

Keywords: fuel consumption, civil aviation industry, neural networking, optimization

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Authors: Fernanda M. Assef, Maria Teresinha A. Steiner

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The aim of this paper is to compare and discuss better classifier algorithm options for credit risk assessment by applying different Machine Learning techniques. Using records from a Brazilian financial institution, this study uses a database of 5,432 companies that are clients of the bank, where 2,600 clients are classified as non-defaulters, 1,551 are classified as defaulters and 1,281 are temporarily defaulters, meaning that the clients are overdue on their payments for up 180 days. For each case, a total of 15 attributes was considered for a one-against-all assessment using four different techniques: Artificial Neural Networks Multilayer Perceptron (ANN-MLP), Artificial Neural Networks Radial Basis Functions (ANN-RBF), Logistic Regression (LR) and finally Support Vector Machines (SVM). For each method, different parameters were analyzed in order to obtain different results when the best of each technique was compared. Initially the data were coded in thermometer code (numerical attributes) or dummy coding (for nominal attributes). The methods were then evaluated for each parameter and the best result of each technique was compared in terms of accuracy, false positives, false negatives, true positives and true negatives. This comparison showed that the best method, in terms of accuracy, was ANN-RBF (79.20% for non-defaulter classification, 97.74% for defaulters and 75.37% for the temporarily defaulter classification). However, the best accuracy does not always represent the best technique. For instance, on the classification of temporarily defaulters, this technique, in terms of false positives, was surpassed by SVM, which had the lowest rate (0.07%) of false positive classifications. All these intrinsic details are discussed considering the results found, and an overview of what was presented is shown in the conclusion of this study.

Keywords: artificial neural networks (ANNs), classifier algorithms, credit risk assessment, logistic regression, machine Learning, support vector machines

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Authors: Souhir Ben Amor, Heni Boubaker, Lotfi Belkacem

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This aims of this paper is to forecast the electricity spot prices. First, we focus on modeling the conditional mean of the series so we adopt a generalized fractional -factor Gegenbauer process (k-factor GARMA). Secondly, the residual from the -factor GARMA model has used as a proxy for the conditional variance; these residuals were predicted using two different approaches. In the first approach, a local linear wavelet neural network model (LLWNN) has developed to predict the conditional variance using the Back Propagation learning algorithms. In the second approach, the Gegenbauer generalized autoregressive conditional heteroscedasticity process (G-GARCH) has adopted, and the parameters of the k-factor GARMA-G-GARCH model has estimated using the wavelet methodology based on the discrete wavelet packet transform (DWPT) approach. The empirical results have shown that the k-factor GARMA-G-GARCH model outperform the hybrid k-factor GARMA-LLWNN model, and find it is more appropriate for forecasts.

Keywords: electricity price, k-factor GARMA, LLWNN, G-GARCH, forecasting

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Authors: Chaomeng Liu, Li Li, Xiao Wang, Li Ren, Qinge Zhang

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Late-life depression (LLD) is a prevalent mental disorder among the elderly, frequently accompanied by significant cognitive decline, and has emerged as a worldwide public health concern. Microglia, astrocytes, and peripheral immune cells play pivotal roles in regulating inflammatory responses within the central nervous system (CNS) across diverse cerebral disorders. This review commences with the clinical research findings and accentuates the recent advancements pertaining to microglia and astrocytes in the neuroinflammation process of LLD. The reciprocal communication network between the CNS and immune system is of paramount importance in the pathogenesis of depression and cognitive decline. Stress-induced downregulation of tight and gap junction proteins in the brain results in increased blood-brain barrier permeability and impaired astrocyte function. Concurrently, activated microglia release inflammatory mediators, initiating the kynurenine metabolic pathway and exacerbating the quinolinic acid/kynurenic acid imbalance. Moreover, the balance between Th17 and Treg cells is implicated in the preservation of immune homeostasis within the cerebral milieu of individuals suffering from LLD. The ultimate objective of this review is to present future strategies for the management and treatment of LLD, informed by the most recent advancements in research, with the aim of averting or postponing the onset of AD.

Keywords: neuroinflammation, late-life depression, microglia, astrocytes, central nervous system, blood-brain barrier, Kynurenine pathway

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Authors: Hyun-Jong Choi, Minjun Kwak, Doo-Won Seo, Sang-Kuk Woo, Sun-Dong Kim

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Solid Oxide Cell(SOC) systems can contribute to the transition to the hydrogen society by utilized as a power and hydrogen generator by the electrochemical reaction with high efficiency at high operation temperature (>750 ℃). La1-xSrxCo1-yFeyO3, which is an air electrode, is occurred stability degradations due to reaction and delamination with yittria stabilized zirconia(YSZ) electrolyte in a water electrolysis mode. To complement this phenomenon SOCs need gadolinium doped ceria(GDC) buffer layer between electrolyte and air electrode. However, GDC buffer layer requires a high sintering temperature and it causes a reaction with YSZ electrolyte. This study carried out low temperature sintering of GDC layer by applying Cu-oxide as a sintering aid. The effect of a copper additive as a sintering aid to lower the sintering temperature for the construction of solid oxide fuel cells (SOFCs) was investigated. GDC buffer layer with 0.25-10 mol% CuO sintering aid was prepared by reacting GDC power and copper nitrate solution followed by heating at 600 ℃. The sintering of CuO-added GDC powder was optimized by investigating linear shrinkage, microstructure, grain size, ionic conductivity, and activation energy of CuO-GDC electrolytes at temperatures ranging from 1100 to 1400 ℃. The sintering temperature of the CuO-GDC electrolyte decreases from 1400 ℃ to 1100 ℃ by adding the CuO sintering aid. The ionic conductivity of the CuO-GDC electrolyte shows a maximum value at 0.5 mol% of CuO. However, the addition of CuO has no significant effects on the activation energy of GDC electrolyte. GDC-LSCF layers were co-sintering at 1050 and 1100 ℃ and button cell tests were carried out at 750 ℃.

Keywords: Co-Sintering, GDC-LSCF, Sintering Aid, solid Oxide Cells

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Authors: I. Alhajri, G. Zahedi, R. Alazmi, A. Akbari

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In this paper, an Artificial Neural Network (ANN) was developed to predict Asphaltene Precipitation (AP) during the injection of carbon dioxide into crude oil reservoirs. In this study, the experimental data from six different oil fields were collected. Seventy percent of the data was used to develop the ANN model, and different ANN architectures were examined. A network with the Trainlm training algorithm was found to be the best network to estimate the AP. To check the validity of the proposed model, the model was used to predict the AP for the thirty percent of the data that was unevaluated. The Mean Square Error (MSE) of the prediction was 0.0018, which confirms the excellent prediction capability of the proposed model. In the second part of this study, the ANN model predictions were compared with modified Hirschberg model predictions. The ANN was found to provide more accurate estimates compared to the modified Hirschberg model. Finally, the proposed model was employed to examine the effect of different operating parameters during gas injection on the AP. It was found that the AP is mostly sensitive to the reservoir temperature. Furthermore, the carbon dioxide concentration in liquid phase increases the AP.

Keywords: artificial neural network, asphaltene, CO2 injection, Hirschberg model, oil reservoirs

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Authors: Tesfaye Fenta Boka, Niu Zhendong

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Recommender systems play a crucial role in connecting individuals with the items they require, as is particularly evident in the rapid growth of online grocery shopping platforms. These systems predominantly rely on user-centered recommendations, where items are suggested based on individual preferences, garnering considerable attention and adoption. However, our focus lies on the item-centered recommendation task within the grocery shopping context. In the reverse next item period recommendation (RNPR) task, we are presented with a specific item and challenged to identify potential users who are likely to consume it in the upcoming period. Despite the ever-expanding inventory of products on online grocery platforms, the cold start item problem persists, posing a substantial hurdle in delivering personalized and accurate recommendations for new or niche grocery items. To address this challenge, we propose a Graph Neural Network (GNN)-based approach. By capitalizing on the inherent relationships among grocery items and leveraging users' historical interactions, our model aims to provide reliable and context-aware recommendations for cold-start items. This integration of GNN technology holds the promise of enhancing recommendation accuracy and catering to users' individual preferences. This research contributes to the advancement of personalized recommendations in the online grocery shopping domain. By harnessing the potential of GNNs and exploring item-centered recommendation strategies, we aim to improve the overall shopping experience and satisfaction of users on these platforms.

Keywords: recommender systems, cold start item recommendations, online grocery shopping platforms, graph neural networks

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Authors: Tefera Worku Mekonnen, Hiseh Chih Tsai

Abstract:

Enhancement of drug efficacy is essential in cancer treatment. The immune stimulator ovalbumin (Ova)-coated citric acid (AC-)-stabilized iron oxide nanoparticles (AC-IO-Ova NPs) and enhanced permeability and retention (EPR) based tumor targeted 4.5 (4.5G) poly(amidoamine) dendrimer-cisplatin nanocomplex (4.5GDP-Cis-pt NC) were used for enhanced anticancer efficiency. The formations of 4.5GDP-Cis-pt NC, AC-IO, and AC-IO-Ova NPs have been examined by FTIR, X-ray diffraction, Raman, and X-ray photoelectron spectroscopy. The conjugation of cisplatin (Cis-pt) with 4.5GDP was confirmed using carbon NMR. The tumor-specific 4.5GDP-Cis-pt NC provided ~45% and 28% cumulative cisplatin release in 72 h at pH 6.5 and 7.4, respectively. A significant immune response with high TNF-α and IL-6 cytokine secretion was confirmed when the co-incubation of AC-IO-Ova with RAW 264.7 or HaCaT cells. AC-IO-Ova NP was biocompatible in different cell lines, even at a high concentration (200 µg mL−1). In contrast, AC-IO-Ova NPs mixed with 4.5GDP-Cis-pt NC (Cis-pt at 15 µg mL−1) significantly increased the cytotoxicity against the cancer cells, which is dose-dependent on the concentration of AC-IO-Ova NPs. The increased anticancer effects may be attributed to the generation of reactive oxygen species (ROS). Moreover, the efficiency of anticancer cells may be further assisted by induction of an innate immune response via M1 macrophage polarization due to the presence of AC-IO-Ova NPs. We provide a better synergestic chemoimmunotherapeutic strategy to enhance the efficiency of anticancer of cisplatin via chemotherapeutic agent 4.5GDP-Cis-pt NC and induction of proinflammatory cytokines to stimulate innate immunity through AC-IO-Ova NPs against tumors.

Keywords: cisplatin-release, iron oxide, ovalbumin, poly(amidoamine) dendrimer

Procedia PDF Downloads 145

Authors: Seul Ki Min, Gwang Heum Yoon, Jung Hyun Joo, Hwa Sung Shin

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Cellular membrane distortion is known as a factor to change intracellular signaling. However, progress of relevant studies is difficult because there are no facilities that can control membrane distortion finely. In this study, we developed microfluidic device which can inflict mechanical stress on cell membrane of Chlamydomonas reinhardtii using regular height of the channels. And cellular physiological changes were analyzed from cells cultured in the device. Excessive calcium ion influx through into cytoplasm was induced from mechanical stress. The results revealed that compressed cells had up-regulated Mat3 mRNA which regulates cell size and cell cycle from a prolonged G1 phase. Additionally, TAG used for the production of biodiesel was raised rapidly from 4 h after compression. Taken together, membrane distortion can be considered as an attractive inducer for biofuel production.

Keywords: mechanical stress, membrane distortion, Chlamydomonas reinhardtii, deflagellation, cell cycle, lipid metabolism

Procedia PDF Downloads 375

Authors: Serap Pektas

Abstract:

Ataxia telangiectasia mutated (ATM) is a serine-threonine kinase, which is the major regulator of the DNA damage response. ATM is activated upon the formation of DNA double-strand breaks (DSBs) in the cells. ATM phosphorylates the proteins involved in apoptotic responses, cell cycle checkpoint control, DNA repair, etc. Tumor protein p53, known as p53 is one of these proteins that phosphorylated by ATM. Phosphorylation of p53 at Ser15 residue leads to p53 stabilization in the cells. Often enzymes activity is affected by hydrogen ion concentration (pH). In order to find the optimal pH range for ATM activity, steady-state kinetic assays were performed at acidic and basic pH ranges. Ser15 phosphorylation of p53 is determined by using ELISA. The results indicated that the phosphorylation rate was better at basic pH range compared with the acidic pH range. This could be due to enzyme stability, or enzyme-substrate interaction is pH dependent.

Keywords: ataxia telangiectasia mutated, DNA double strand breaks, DNA repair, tumor protein p53

Procedia PDF Downloads 134

Authors: Abbas Ahmadi, Bijan Raie, Mohammad Reza Neyshabouri, Mohammad Ali Ghorbani, Farrokh Asadzadeh

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In recent years, vast areas of Urmia Lake in Dasht-e-Tabriz has dried up leading to saline sediments exposure on the surface lake coastal areas being highly susceptible to wind erosion. This study was conducted to investigate wind erosion and its relevance to soil physicochemical properties and also modeling of wind erodibility (WE) using artificial intelligence techniques. For this purpose, 96 soil samples were collected from 0-5 cm depth in 414000 hectares using stratified random sampling method. To measure the WE, all samples (<8 mm) were exposed to 5 different wind velocities (9.5, 11, 12.5, 14.1 and 15 m s-1 at the height of 20 cm) in wind tunnel and its relationship with soil physicochemical properties was evaluated. According to the results, WE varied within the range of 76.69-9.98 (g m-2 min-1)/(m s-1) with a mean of 10.21 and coefficient of variation of 94.5% showing a relatively high variation in the studied area. WE was significantly (P<0.01) affected by soil physical properties, including mean weight diameter, erodible fraction (secondary particles smaller than 0.85 mm) and percentage of the secondary particle size classes 2-4.75, 1.7-2 and 0.1-0.25 mm. Results showed that the mean weight diameter, erodible fraction and percentage of size class 0.1-0.25 mm demonstrated stronger relationship with WE (coefficients of determination were 0.69, 0.67 and 0.68, respectively). This study also compared efficiency of multiple linear regression (MLR), gene expression programming (GEP), artificial neural network (MLP), artificial neural network based on genetic algorithm (MLP-GA) and artificial neural network based on whale optimization algorithm (MLP-WOA) in predicting of soil wind erodibility in Dasht-e-Tabriz. Among 32 measured soil variable, percentages of fine sand, size classes of 1.7-2.0 and 0.1-0.25 mm (secondary particles) and organic carbon were selected as the model inputs by step-wise regression. Findings showed MLP-WOA as the most powerful artificial intelligence techniques (R2=0.87, NSE=0.87, ME=0.11 and RMSE=2.9) to predict soil wind erodibility in the study area; followed by MLP-GA, MLP, GEP and MLR and the difference between these methods were significant according to the MGN test. Based on the above finding MLP-WOA may be used as a promising method to predict soil wind erodibility in the study area.

Keywords: wind erosion, erodible fraction, gene expression programming, artificial neural network

Procedia PDF Downloads 71

Authors: Vipul M. Patel, Hemantkumar B. Mehta

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Technological innovations in electronic world demand novel, compact, simple in design, less costly and effective heat transfer devices. Closed Loop Pulsating Heat Pipe (CLPHP) is a passive phase change heat transfer device and has potential to transfer heat quickly and efficiently from source to sink. Thermal performance of a CLPHP is governed by various parameters such as number of U-turns, orientations, input heat, working fluids and filling ratio. The present paper is an attempt to predict the thermal performance of a CLPHP using Artificial Neural Network (ANN). Filling ratio and heat input are considered as input parameters while thermal resistance is set as target parameter. Types of neural networks considered in the present paper are radial basis, generalized regression, linear layer, cascade forward back propagation, feed forward back propagation; feed forward distributed time delay, layer recurrent and Elman back propagation. Linear, logistic sigmoid, tangent sigmoid and Radial Basis Gaussian Function are used as transfer functions. Prediction accuracy is measured based on the experimental data reported by the researchers in open literature as a function of Mean Absolute Relative Deviation (MARD). The prediction of a generalized regression ANN model with spread constant of 4.8 is found in agreement with the experimental data for MARD in the range of ±1.81%.

Keywords: ANN models, CLPHP, filling ratio, generalized regression, spread constant

Procedia PDF Downloads 292

Authors: Mohanapriya Subramanian, V. Raj

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Direct methanol fuel cells (DMFCs) are considered to be one of the most promising candidates for portable and stationary applications in the view of their advantages such as high energy density, easy manipulation, high efficiency and they operate with liquid fuel which could be used without requiring any fuel-processing units. Electrolyte membrane of DMFC plays a key role as a proton conductor as well as a separator between electrodes. Increasing concern over environmental protection, biopolymers gain tremendous interest owing to their eco-friendly bio-degradable nature. Pectin is a natural anionic polysaccharide which plays an essential part in regulating mechanical behavior of plant cell wall and it is extracted from outer cells of most of the plants. The aim of this study is to develop and demonstrate pectin based polymer composite membranes as methanol impermeable polymer electrolyte membranes for DMFCs. Pectin based nanocomposites membranes are prepared by solution-casting technique wherein pectin is blended with chitosan followed by the addition of optimal amount of sulphonic acid modified Titanium dioxide nanoparticle (S-TiO2). Nanocomposite membranes are characterized by Fourier Transform-Infra Red spectroscopy, Scanning electron microscopy, and Energy dispersive spectroscopy analyses. Proton conductivity and methanol permeability are determined into order to evaluate their suitability for DMFC application. Pectin-chitosan blends endow with a flexible polymeric network which is appropriate to disperse rigid S-TiO2 nanoparticles. Resulting nanocomposite membranes possess adequate thermo-mechanical stabilities as well as high charge-density per unit volume. Pectin-chitosan natural polymeric nanocomposite comprising optimal S-TiO2 exhibits good electrochemical selectivity and therefore desirable for DMFC application.

Keywords: biopolymers, fuel cells, nanocomposite, methanol crossover

Procedia PDF Downloads 136