Search results for: neural cellular automata

Authors: Avatharam Ganivada, Krishna Shah

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The Mask R-CNN is recently introduced by the team of Facebook AI Research (FAIR), which is mainly concerned with instance segmentation in images. Here, the Mask R-CNN is based on ResNet and feature pyramid network (FPN), where a single dropout method is employed. This paper provides a modified Mask R-CNN by adding multiple dropout methods into the Mask R-CNN. The proposed model has also utilized the concepts of Resnet and FPN to extract stage-wise network feature maps, wherein a top-down network path having lateral connections is used to obtain semantically strong features. The proposed model produces three outputs for each object in the image: class label, bounding box coordinates, and object mask. The performance of the proposed network is evaluated in the segmentation of every instance in images using COCO and cityscape datasets. The proposed model achieves better performance than the state-of-the-networks for the datasets.

Keywords: instance segmentation, object detection, convolutional neural networks, deep learning, computer vision

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Authors: Olusola Olalekan Elekofehinti, Isaac Gbadura Adanlawo, Joao Batista Teixeira Rocha

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We evaluated the potential neuroprotective effect of Phyllantus nuriri against Fe2+ and SNP induced oxidative stress in mitochondria of rats brain. Cellular viability was assessed by MTT reduction, reactive oxygen species (ROS) generation was measured using the probe 2,7-dichlorofluorescein diacetate (DCFH-DA). Glutathione content was measured using dithionitrobenzoic acid (DTNB). Fe2+ (10µM) and SNP (5µM) significantly decreased mitochondrial activity, assessed by MTT reduction assay, in a dose-dependent manner, this occurred in parallel with increased glutathione oxidation, ROS production and lipid peroxidation end-products (thiobarbituric acid reactive substances, TBARS). The co-incubation with methanolic extract of Phyllantus nuriri (10-100 µg/ml) reduced the disruption of mitochondrial activity, gluthathione oxidation, ROS production as well as the increase in TBARS levels caused by both Fe2+ and SNP in a dose dependent manner. HPLC analysis of the extract revealed the presence of gallic acid (20.54±0.01), caffeic acid (7.93±0.02), rutin (25.31±0.05), quercetin (31.28±0.03) and kaemferol (14.36±0.01). This result suggests that these phytochemicals account for the protective actions of Phyllantus nuriri against Fe2+ and SNP -induced oxidative stress. Our results show that Phyllantus nuriri consist important bioactive molecules in the search for an improved therapy against the deleterious effects of Fe2+, an intrinsic producer of reactive oxygen species (ROS), that leads to neuronal oxidative stress and neurodegeneration.

Keywords: Phyllantus niruri, neuroprotection, oxidative stress, mitochondria, synaptosome

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Authors: Omid Shahmirzadi, Adam Lugowski, Kenneth Younge

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Automatic measurement of semantic text similarity is an important task in natural language processing. In this paper, we evaluate the performance of different vector space models to perform this task. We address the real-world problem of modeling patent-to-patent similarity and compare TFIDF (and related extensions), topic models (e.g., latent semantic indexing), and neural models (e.g., paragraph vectors). Contrary to expectations, the added computational cost of text embedding methods is justified only when: 1) the target text is condensed; and 2) the similarity comparison is trivial. Otherwise, TFIDF performs surprisingly well in other cases: in particular for longer and more technical texts or for making finer-grained distinctions between nearest neighbors. Unexpectedly, extensions to the TFIDF method, such as adding noun phrases or calculating term weights incrementally, were not helpful in our context.

Keywords: big data, patent, text embedding, text similarity, vector space model

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Authors: Moumita Chanda, Md. Fazlul Karim Patwary

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Smoking is one of the most popular recreational drug use behaviors, and it contributes to birth defects, COPD, heart attacks, and erectile dysfunction. To completely eradicate this disease, it is imperative that it be identified and treated. Numerous smoking cessation programs have been created, and they demonstrate how beneficial it may be to help someone stop smoking at the ideal time. A tomography meter is an effective smoking detector. Other wearables, such as RF-based proximity sensors worn on the collar and wrist to detect when the hand is close to the mouth, have been proposed in the past, but they are not impervious to deceptive variables. In this study, we create a machine that can discriminate between smokers and non-smokers in real-time with high sensitivity and specificity by watching and collecting the human lung and analyzing the X-ray data using machine learning. If it has the highest accuracy, this machine could be utilized in a hospital, in the selection of candidates for the army or police, or in university entrance.

Keywords: CNN, smoker detection, non-smoker detection, OpenCV, artificial Intelligence, X-ray Image detection

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Authors: Mateen A. Khan, Elizabeth J. Theil, Dixie J. Goss

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Cellular iron homeostasis is accomplished by the coordinated regulated expression of iron uptake, storage, and export. Iron regulate the translation of ferritin and mitochondrial aconitase iron responsive element (IRE)-mRNA by interaction with an iron regulatory protein (IRPs). Iron increases protein biosynthesis encoded in iron responsive element. The noncoding structure IRE-mRNA, approximately 30-nt, folds into a stem loop to control synthesis of proteins in iron trafficking, cell cycling, and nervous system function. Fluorescence anisotropy measurements showed the presence of one binding site on IRP1 for ferritin and mitochondrial aconitase IRE-mRNA. Scatchard analysis revealed the binding affinity (Kₐ) and average binding sites (n) for ferritin and mitochondrial aconitase IRE-mRNA were 68.7 x 10⁶ M⁻¹ and 9.2 x 10⁶ M⁻¹, respectively. In order to understand the relative importance of equilibrium and stability, we further report the contribution of electrostatic interactions in the overall binding of two IRE-mRNA with IRP1. The fluorescence quenching of IRP1 protein was measured at different ionic strengths. The binding affinity of IRE-mRNA to IRP1 decreases with increasing ionic strength, but the number of binding sites was independent of ionic strength. Such results indicate a differential contribution of electrostatics to the interaction of IRE-mRNA with IRP1, possibly related to helix bending or stem interactions and an overall conformational change. Selective destabilization of ferritin and mitochondrial aconitase RNA/protein complexes as reported here explain in part the quantitative differences in signal response to iron in vivo and indicate possible new regulatory interactions.

Keywords: IRE-mRNA, IRP1, binding, ionic strength

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Authors: Devendra Kr Tayal, Vrinda Gupta, Aastha Bansal, Khushi Singh, Sristi Sharma, Hunny Gaur

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In today's world, suicide is a serious problem. In order to save lives, early suicide attempt detection and prevention should be addressed. A good number of at-risk people utilize social media platforms to talk about their issues or find knowledge on related chores. Twitter and Reddit are two of the most common platforms that are used for expressing oneself. Extensive research has already been done in this field. Through supervised classification techniques like Nave Bayes, Bernoulli Nave Bayes, and Multiple Layer Perceptron on a Reddit dataset, we demonstrate the early recognition of suicidal ideation. We also performed comparative analysis on these approaches and used accuracy, recall score, F1 score, and precision score for analysis.

Keywords: machine learning, suicide ideation detection, supervised classification, natural language processing

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Authors: Filiz Ersoz, Taner Ersoz, Erkin Guler

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This paper addresses the issues and technique for textile industry using data mining techniques. Data mining has been applied to the stitching of garments products that were obtained from a textile company. Data mining techniques were applied to the data obtained from the CHAID algorithm, CART algorithm, Regression Analysis and, Artificial Neural Networks. Classification technique based analyses were used while data mining and decision model about the production per person and variables affecting about production were found by this method. In the study, the results show that as the daily working time increases, the production per person also decreases. In addition, the relationship between total daily working and production per person shows a negative result and the production per person show the highest and negative relationship.

Keywords: data mining, textile production, decision trees, classification

Procedia PDF Downloads 331

Authors: A. P. Mikhailovich, V. V. Fomin, E. M. Agapitov, V. E. Rogachev, E. A. Kostousova, E. S. Perekhodova

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Woody vegetation among the upper limit of its habitat is a sensitive indicator of biota reaction to regional climate changes. Quantitative assessment of temporal and spatial changes in the distribution of trees and plant biocenoses calls for the development of new modeling approaches based upon selected data from measurements on the ground level and ultra-resolution aerial photography. Statistical models were developed for the study area located in the Polar Urals. These models allow obtaining probabilistic estimates for placing Siberian Larch trees into one of the three age intervals, namely 1-10, 11-40 and over 40 years, based on the Weilbull distribution of the maximum horizontal crown projection. Authors developed the distribution map for larch trees with crown diameters exceeding twenty centimeters by deciphering aerial photographs made by a UAV from an altitude equal to fifty meters. The total number of larches was equal to 88608, forming the following distribution row across the abovementioned intervals: 16980, 51740, and 19889 trees. The results demonstrate that two processes can be observed in the course of recent decades: first is the intensive forestation of previously barren or lightly wooded fragments of the study area located within the patches of wood, woodlands, and sparse stand, and second, expansion into mountain tundra. The current expansion of the Siberian Larch in the region replaced the depopulation process that occurred in the course of the Little Ice Age from the late 13ᵗʰ to the end of the 20ᵗʰ century. Using data from field measurements of Siberian larch specimen biometric parameters (including height, diameter at root collar and at 1.3 meters, and maximum projection of the crown in two orthogonal directions) and data on tree ages obtained at nine circular test sites, authors developed a model for artificial neural network including two layers with three and two neurons, respectively. The model allows quantitative assessment of a specimen's age based on height and maximum crone projection values. Tree height and crown diameters can be quantitatively assessed using data from aerial photographs and lidar scans. The resulting model can be used to assess the age of all Siberian larch trees. The proposed approach, after validation, can be applied to assessing the age of other tree species growing near the upper tree boundaries in other mountainous regions. This research was collaboratively funded by the Russian Ministry for Science and Education (project No. FEUG-2023-0002) and Russian Science Foundation (project No. 24-24-00235) in the field of data modeling on the basis of artificial neural network.

Keywords: treeline, dynamic, climate, modeling

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Authors: Jossitt Williams Vargas Cruz, Aura Jazmín Pérez Ríos

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The variations of solar dynamics have become a relevant topic of study due to the effects of climate changes generated on the earth. One of the most disconcerting aspects is the variability that the sun has on the climate is the role played by sunspots (extra-atmospheric variable) in the modulation of the Cosmic Rays CR (extra-atmospheric variable). CRs influence the earth's climate by affecting cloud formation (atmospheric variable), and solar cycle influence is associated with the presence of solar storms, and the magnetic activity is greater, resulting in less CR entering the earth's atmosphere. The different methods of climate prediction in Colombia do not take into account the extra-atmospheric variables. Therefore, correlations between atmospheric and extra-atmospheric variables were studied in order to implement a Python code based on neural networks to make the prediction of the extra-atmospheric variable with the highest correlation.

Keywords: correlations, cosmic rays, sun, sunspots and variations.

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Authors: Anteneh Berhane Yeyi, Tefera Belachew

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Any woman who could become pregnant is at risk of having a baby with neural tube defects (NTDs). A spontaneous aborted women with immediately preceding pregnancy may have an increased risk of develop NTDs. Ethiopia has one of the highest rates of micronutrient deficiencies, including folate and iron deficiency. Currently, in Ethiopia, NTDs is emerged as a public health concern. Even if Ethiopia, has implement different strategies for reducing maternal and neonatal mortality and morbidity, there is no room in the health care system and lack of integration for preventing the risk of NTDs for those women who aborted spontaneously and women who discontinue long acting contraception to become pregnant. The purpose of this study was to evaluate the effect of preconception picture-based nutrition education on knowledge and adherence to iron-folic acid supplement (IFAS) intake to reduce the risk of developing neural tube defects (NTDs) and iron deficiency anemia (IDA) among women who had a planned to pregnancy in Ethiopia, a country with a high burden of NTDs. Methodology: This study was conducted in Eastern Ethiopia. A double blinded parallel randomized controlled trial design was employed among women in the age group of 18-45 years who requested to interrupt modern contraceptive who have an intention to be pregnant and women with spontaneous abortion who refused to take a contraceptive. The interventional arm (n=122) received a preconception picture-based nutrition education with iron-folic acid supplement, and the control arm (n=122) received only preconception IFAS. In this study male partners were participated. Result: After three months of intervention the proportion of adherence to IFAS was 23% (n=56). With regard to adherence within the groups, 42.6% (n=52) in the intervention group and 3.3% (n=4) in the control group and the intervention group were significantly higher than in control group. In the intervention group the proportion of adherence to IFAS intake among participants increased by 40.1% and there were statistically difference (P<0.0001). The difference in difference between the two groups of adherence to IFAS intake was 37.6% and there were a statistical significance (P<0.0001). Level of knowledge between the groups did differ before and after intervention (P= 0.87 Vs P<0.0001). The overall the mean change in knowledge Mean (+SE) between group was 0.9 (+3.04 SE) and there were significant differences between two groups (P<0.001). Conclusion: In general this intervention is effective toward adherence to IFAS and a critical milestone to improve maternal health and reduce the neonate mortality due to NTDs and other advert effect of pregnancy and birth outcomes. This intervention is very short, simple, and cost effective and has potential for adaptation, feasible development to large-scale implementation in the existing health care system. Furthermore, this type of interventional approach has the potential to reduce the country's ANC program dropout rates and increase male partner’s participation on reproductive health.

Keywords: NTDs, IFAS, WRA, Ethiopia

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Authors: Shima Mahmoudi, Babak Pourakbari, Setareh Mamishi, Mostafa Teymuri, Majid Marjani

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Although cytokine analysis has greatly contributed to the understanding of tuberculosis (TB) pathogenesis, data on cytokine profiles that might distinguish progression from latency of TB infection are scarce. Since PE/PPE proteins are known to induce strong humoral and cellular immune responses, the aim of this study was to evaluate the diagnostic potential of interleukin-2 (IL-2) as biomarker after specific antigen stimulation with PE35 and PPE68 for the discrimination of active and latent tuberculosis infection (LTBI). The production of IL-2 was measured in the antigen-stimulated whole-blood supernatants following stimulation with recombinant PE35 and PPE68. All the patients with active TB and LTBI had positive QuantiFERON-TB Gold in Tube test. The level of IL-2 following stimulation with recombinant PE35 and PPE68 were significantly higher in LTBI group than in patients with active TB infection or control group. The discrimination performance (assessed by the area under ROC curve) for IL-2 following stimulation with recombinant PE35 and PPE68 between LTBI and patients with active TB were 0.837 (95%CI: 0.72-0.97) and 0.75 (95%CI: 0.63-0.89), respectively. Applying the 12.4 pg/mL cut-off for IL-2 induced by PE35 in the present study population resulted in sensitivity of 78%, specificity of 78%, PPV of 78% and NPV of 100%. In addition, a sensitivity of 81%, specificity of 70%, PPV of 67% and 87% of NPV was reported based on the 4.4 pg/mL cut-off for IL-2 induced by PPE68. In conclusion, peptides of the antigen PE35 and PPE68, absent from commonly used BCG strains, stimulated strong IL-2- positive T cell responses in patients with LTBI. This study confirms IL-2 induced by PE35 and PPE68 as a sensitive and specific biomarker and highlights IL-2 as new promising adjunct markers for discriminating of LTBI and Active TB infection.

Keywords: IL-2, PE35, PPE68, tuberculosis

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Authors: Chiagoziem A. Otuechere, Ebenezer O. Farombi

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The protective effect of dichloromethane: methanol extract of Pterocarpus mildbraedii (PME), a widely consumed Nigerian leafy vegetable, on the toxicity of propanil was investigated in male rats. Animals were distributed into eight groups of five each. Group 1 served as control and received normal saline while rats in groups 2, 3, and 4 received 100 mg/kg, 200 mg/kg, and 400 mg/kg extract doses respectively. Group 5 rats were orally administered 200 mg/kg propanil while groups 6, 7, and 8 rats were given propanil plus extract. Oral administration of propanil elicited a 14.8%, 5%, 122%, and 78% increase in the activity of serum enzymes; alanine aminotransferase (AST), alanine aminotransferase(ALT), Alkaline phoshatase (ALP) and Gamma glutamyl transferase (ﻻGT). There were also increase in Lactate dehydrogenase (LDH) activity, direct bilirubin and lipid peroxidation levels. Furthermore, PME significantly attenuated the marked hepatic oxidative damage that accompanied propanil treatment. The extract significantly decreased LDH activity and bilirubin levels following propanil treatment. Furthermore, propanil-induced alterations in the activities of antioxidant enzymes: Superoxide dismutase (SOD), catalase (CAT) and glutathione s-transferase (GST) in these rats were modulated by the extract. The percentage DPPH Radical Scavenging Activity of the extract was determined as 55% and compared to those of Gallic acid (49%). Hepatic histology examination further confirmed the damage to the liver as it revealed severe periportal cellular infiltration of the hepatocytes. These biochemical and morphological alterations were attenuated in rats pre-treated with 100 mg/kg and 200 mg/kg doses of the extract. These results suggest that PME possesses protective effect against propanil-induced hepatotoxicity.

Keywords: antioxidant, hepatoprotection, Pterocarpus mildbraedii, propanil

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Authors: Nasrin Takzaree, Gholamreza Hassanzadeh, Abbas Hadjiakhoondi, Mohammadreza Rouini

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Introduction: Today herbal medicine are extensively used for various therapeutic reasons. Whereas Achillea millefolium L. comprises different chemical compounds it is used in classic and modern medicine for different purposes. Concerning the family planning as a principle matter, the idea of using specific herbal medicine is of great importance. Purpose: To investigate the effects of Achillea millefolium L. extract on fertility power and spermatogenesis process in male mature Wistar rats and the anti-fertility effects of this extract in male genital system. Material and methods: In this study 32 male mature Wistar rats were randomly divided in to 4 experimental groups. 1st experimental group included 8 rats receiving Achillea millefolium extract at the dose of 200 mg/kg intraperitoneally. Second and third groups received the extract the same at the doses of 400 and 800 mg/kg respectively. 4th group was considered as control group in which the parenteral distilled water was administered. after 20 days, rats were sacrificed and the spermatogenesis process was histologically examined. Results: In experimental groups receiving high doses of extract comparing with control group, thickness in seminiferous tubules basal membrane, decrease in germinal epithelium cells, congestion in testicular tissue, disarrangement in germinal epithelium cells as well as decrease in cellular condense were observed (p<0.001). Conclusion: Findings suggest that alcoholic extract of Achillea millefolium at high concentrations lead to the structural alterations and changes in spermatogenesis in testicular tissue.

Keywords: spermatogenesis, alcoholic extract of Achillea millefolium L., testis, Wistar rat

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Authors: Jingpeng Gao, Xinrui Mao, Zhibin Deng

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Aiming at the low recognition rate on the composite signal modulation in low signal to noise ratio (SNR), this paper proposes a modulation recognition algorithm based on ICanny-CNN. Firstly, the radar signal is transformed into the time-frequency image by Choi-Williams Distribution (CWD). Secondly, we propose an image processing algorithm using the Guided Filter and the threshold selection method, which is combined with the hole filling and the mask operation. Finally, the shallow convolutional neural network (CNN) is combined with the idea of the depth-wise convolution (Dw Conv) and the point-wise convolution (Pw Conv). The proposed CNN is designed to complete image classification and realize modulation recognition of radar signal. The simulation results show that the proposed algorithm can reach 90.83% at 0dB and 71.52% at -8dB. Therefore, the proposed algorithm has a good classification and anti-noise performance in radar signal modulation recognition and other fields.

Keywords: modulation recognition, image processing, composite signal, improved Canny algorithm

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Authors: Pegah Eshraghi, Zahra Sadat Zomorodian, Mohammad Tahsildoost

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Over the past decade, energy consumption in educational buildings has steadily increased. The purpose of this research is to provide a method to quickly predict the energy consumption of buildings using separate evaluation of zones and decomposing the building to eliminate the complexity of geometry at the early design stage. To produce this framework, machine learning algorithms such as Support vector regression (SVR) and Artificial neural network (ANN) are used to predict energy consumption and thermal comfort metrics in a school as a case. The database consists of more than 55000 samples in three climates of Iran. Cross-validation evaluation and unseen data have been used for validation. In a specific label, cooling energy, it can be said the accuracy of prediction is at least 84% and 89% in SVR and ANN, respectively. The results show that the SVR performed much better than the ANN.

Keywords: early stage of design, energy, thermal comfort, validation, machine learning

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Authors: Dylan Santos De Pinho, Nabil Ouerhani

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Energy consumption of machine-tools is becoming critical for machine-tool builders and end-users because of economic, ecological and legislation-related reasons. Many machine-tool builders are seeking for solutions that allow the reduction of energy consumption of machine-tools while preserving the same productivity rate and the same quality of machined parts. In this paper, we present the first results of a project conducted jointly by academic and industrial partners to reduce the energy consumption of a Swiss-Type lathe. We employ genetic algorithms to find optimal machining parameters – the set of parameters that lead to the best trade-off between energy consumption, part quality and tool lifetime. Three main machining process parameters are considered in our optimization technique, namely depth of cut, spindle rotation speed and material feed rate. These machining process parameters have been identified as the most influential ones in the configuration of the Swiss-type machining process. A state-of-the-art multi-objective genetic algorithm has been used. The algorithm combines three fitness functions, which are objective functions that permit to evaluate a set of parameters against the three objectives: energy consumption, quality of the machined parts, and tool lifetime. In this paper, we focus on the investigation of the fitness function related to energy consumption. Four different energy consumption related fitness functions have been investigated and compared. The first fitness function refers to the Kienzle cutting force model. The second fitness function uses the Material Removal Rate (RMM) as an indicator of energy consumption. The two other fitness functions are non-deterministic, learning-based functions. One fitness function uses a simple Neural Network to learn the relation between the process parameters and the energy consumption from experimental data. Another fitness function uses Lasso regression to determine the same relation. The goal is, then, to find out which fitness functions predict best the energy consumption of a Swiss-Type machining process for the given set of machining process parameters. Once determined, these functions may be used for optimization purposes – determine the optimal machining process parameters leading to minimum energy consumption. The performance of the four fitness functions has been evaluated. The Tornos DT13 Swiss-Type Lathe has been used to carry out the experiments. A mechanical part including various Swiss-Type machining operations has been selected for the experiments. The evaluation process starts with generating a set of CNC (Computer Numerical Control) programs for machining the part at hand. Each CNC program considers a different set of machining process parameters. During the machining process, the power consumption of the spindle is measured. All collected data are assigned to the appropriate CNC program and thus to the set of machining process parameters. The evaluation approach consists in calculating the correlation between the normalized measured power consumption and the normalized power consumption prediction for each of the four fitness functions. The evaluation shows that the Lasso and Neural Network fitness functions have the highest correlation coefficient with 97%. The fitness function “Material Removal Rate” (MRR) has a correlation coefficient of 90%, whereas the Kienzle-based fitness function has a correlation coefficient of 80%.

Keywords: adaptive machining, genetic algorithms, smart manufacturing, parameters optimization

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Authors: Amir Mabhout, Toktam Ghafarian, Amirhossein Farzin, Zahra Makki, Sajjad Alizadeh, Amirhossein Ghavi

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The high number of Netflix subscribers makes it attractive for data scientists to extract valuable knowledge from the viewers' behavioural analyses. This paper presents a set of statistical insights into viewers' viewing history. After that, a deep learning model is used to predict the future watching behaviour of the users based on previous watching history within the Netflixlatte data pool. Netflixlatte in an aggregated and anonymized data pool of 320 Netflix viewers with a length 250 000 data points recorded between 2008-2022. We observe insightful correlations between the distribution of viewing time and the COVID-19 pandemic outbreak. The presented deep learning model predicts future movie and TV series viewing habits with an average loss of 0.175.

Keywords: data analysis, deep learning, LSTM neural network, netflix

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Authors: Yair Bleiberg, Michael Werman

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Coordinate-based Multi-Layer Perceptrons (MLPs) are known to have difficulty reconstructing high frequencies of the training data. A common solution to this problem is Positional Encoding (PE), which has become quite popular. However, PE has drawbacks. It has high-frequency artifacts and adds another hyper hyperparameter, just like batch normalization and dropout do. We believe that under certain circumstances, PE is not necessary, and a smarter construction of the network architecture together with a smart training method is sufficient to achieve similar results. In this paper, we show that very simple MLPs can quite easily output a frequency when given input of the half-frequency and quarter-frequency. Using this, we design a network architecture in blocks, where the input to each block is the output of the two previous blocks along with the original input. We call this a Fibonacci Network. By training each block on the corresponding frequencies of the signal, we show that Fibonacci Networks can reconstruct arbitrarily high frequencies.

Keywords: neural networks, positional encoding, high frequency intepolation, fully connected

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Authors: Magdy Al Shourbagi

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Diabetes mellitus is the commonest cause of neuropathy. The common pattern is a distal symmetrical sensory polyneuropathy, associated with autonomic disturbances. Less often, Diabetes mellitus is responsible for a focal or multifocal neuropathy. Common causes for non-healing of diabetic foot are the infection and ischemia. Diabetes mellitus is associated with a defective cellular and humoral immunity. Particularly, decreased phagocytosis, decreased chemotaxis, impaired bacterial killing and abnormal lymphocytic function resulting in a reduced inflammatory reaction and defective wound healing. Hyperbaric oxygen therapy is defined by the Undersea and Hyperbaric Medical Society as a treatment in which a patient intermittently breathes 100% oxygen and the treatment chamber is pressurized to a pressure greater than sea level (1 atmosphere absolute). The pressure increase may be applied in mono-place (single person) or multi-place chambers. Multi-place chambers are pressurized with air, with oxygen given via face mask or endotracheal tube; while mono-place chambers are pressurized with oxygen. Oxygen gas plays an important role in the physiology of wound healing. Hyperbaric oxygen therapy can raise tissue oxygen tensions to levels where wound healing can be expected. HBOT increases the killing ability of leucocytes also it is lethal for certain anaerobic bacteria and inhibits toxin formation in many other anaerobes. Multiple anecdotal reports and studies in HBO therapy in diabetic patients report that HBO can be an effective adjunct therapy in the management of diabetic foot wounds and is associated with better functional outcomes.

Keywords: hyperbari oxygen therapy, diabetic foot, neuropathy, multiplace chambers

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Authors: Micheal Olaolu Arowolo, Muhammad Azam, Fei He, Mihail Popescu, Dong Xu

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As the quantity of biological articles rises, so does the number of biological route figures. Each route figure shows gene names and relationships. Annotating pathway diagrams manually is time-consuming. Advanced image understanding models could speed up curation, but they must be more precise. There is rich information in biological pathway figures. The first step to performing image understanding of these figures is to recognize gene names automatically. Classical optical character recognition methods have been employed for gene name recognition, but they are not optimized for literature mining data. This study devised a method to recognize an image bounding box of gene name as a photo using deep Siamese neural network models to outperform the existing methods using ResNet, DenseNet and Inception architectures, the results obtained about 84% accuracy.

Keywords: biological pathway, gene identification, object detection, Siamese network

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Authors: Rafid A. Al-Khannak, Fawzi M. Al-Naima

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Power load is one of the most important controlling keys which decide power demands and illustrate power usage to shape power market. Hence, power load forecasting is the parameter which facilitates understanding and analyzing all these aspects. In this paper, power load forecasting is solved under MATLAB environment by constructing a neural network for the power load to find an accurate simulated solution with the minimum error. A developed algorithm to achieve load forecasting application with faster technique is the aim for this paper. The algorithm is used to enable MATLAB power application to be implemented by multi machines in the Grid computing system, and to accomplish it within much less time, cost and with high accuracy and quality. Grid Computing, the modern computational distributing technology, has been used to enhance the performance of power applications by utilizing idle and desired Grid contributor(s) by sharing computational power resources.

Keywords: DeskGrid, Grid Server, idle contributor(s), grid computing, load forecasting

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Authors: JaeYoon Lee, Gi-Hoon Yang, JongHan Ha, MyungGu Yeo, SeungHyun Ahn, Hyeongjin Lee, HoJun Jeon, YongBok Kim, Minseong Kim, GeunHyung Kim

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One of the important factors in tissue engineering is to design optimal biomedical scaffolds, which can be governed by topographical surface characteristics, such as size, shape, and direction. Of these properties, we focused on the effects of nano- to micro-sized hierarchical surface. To fabricate the hierarchical surface structure on poly(ε-caprolactone) (PCL) film, we employed a micro-casting technique by pressing the mold and nano-etching technique using a modified plasma process. The micro-sized topography of PCL film was controlled by sizes of the micro structures on lotus leaf. Also, the nano-sized topography and hydrophilicity of PCL film were controlled by a modified plasma process. After the plasma treatment, the hydrophobic property of the PCL film was significantly changed into hydrophilic property, and the nano-sized structure was well developed. The surface properties of the modified PCL film were investigated in terms of initial cell morphology, attachment, and proliferation using osteoblast-like-cells (MG63). In particular, initial cell attachment, proliferation and osteogenic differentiation in the hierarchical structure were enhanced dramatically compared to those of the smooth surface. We believe that these results are because of a synergistic effect between the hierarchical structure and the reactive functional groups due to the plasma process. Based on the results presented here, we propose a new biomimetic surface model that maybe useful for effectively regenerating hard tissues.

Keywords: hierarchical surface, lotus leaf, nano-etching, plasma treatment

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Authors: Khosro Ghazvinian, Touba Khodaeian

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To improve the oxidative stability of meat products, the use of dietary form of antioxidants can extend the shelf life and acceptability of muscle food during exposition or storage condition. As shown, this method is more effective than adding direct preservatives due to uniform incorporation of dietary additives into sub cellular membrane and therefore, they can properly inhibit the oxidative reaction at their localized sites. Furthermore, postmortem addition of antioxidants to meat cannot directly inhibit the oxidation in membrane phospholipids. Therefore, this study was designed to evaluate the effects of feed supplementation with Melissa officinalis leaves on lipid peroxidation of chicken breast fillets during refrigerated storage. In this study, 72 one-day old Ross 308 broilers distributed in four groups with six replicates (3 chickens each) were fed a basal diet (CONT) or basal diet supplemented with 5, 10, and 15 gr/Kg M.officinalis, for 6 weeks. Following slaughter, fillets from breast were stored at 4 °C in the dark for 12 days, and lipid oxidation was assessed on the basis of thiobarbituric acid reactive substances (TBARS) formed. Results showed that incorporation of M.officinalis in broiler diets delayed lipid oxidation in raw breast meat during refrigerated storage comparative with CONT(p<0.05). In this regard, TBARS levels of breast samples containing higher concentrations (10 and 15 gr/Kg) of M. officinalis (625.43 and 504.32 µg/kg MDA equivalents, respectively )were significantly lower than those of control and 5g/kg samples (872.75 and 841.32 µg/kg MDA equivalents, respectively) (p<0.05). Therefore, M. officinalis might be utilized in novel applications as a nutritional supplement or a functional food component.

Keywords: breast fillet, lipid oxidation, Melissa officinalis, TBARS assay

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Authors: Hrishi Rakshit, Pooneh Bagheri Zadeh

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This paper investigates the performance of drone classification methods using conventional DCNN with different hyperparameters, when additional drone audio data is embedded in the dataset for training and further classification. In this paper, first a custom dataset is created using different images of drones from University of South California (USC) datasets and Leeds Beckett university datasets with embedded drone audio signal. The three well-known DCNN architectures namely, Resnet50, Darknet53 and Shufflenet are employed over the created dataset tuning their hyperparameters such as, learning rates, maximum epochs, Mini Batch size with different optimizers. Precision-Recall curves and F1 Scores-Threshold curves are used to evaluate the performance of the named classification algorithms. Experimental results show that Resnet50 has the highest efficiency compared to other DCNN methods.

Keywords: drone classifications, deep convolutional neural network, hyperparameters, drone audio signal

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Authors: Gyati Shilakari Asthana, Abhay Asthana

Abstract:

Purpose: The therapeutic potential of oligonucleotide (ODN) is primarily dependent upon its safe and efficient delivery to specific cells overcoming degradation and maximizing cellular uptake in vivo. The present study is focused to design low molecular weight chitosan nanoconstructs to meet the requirements of safe and effectual delivery of ODNs. LMW-chitosan is a biodegradable, water soluble, biocompatible polymer and is useful as a non-viral vector for gene delivery due to its better stability in water. Methods: LMW chitosan ODN nanoparticles (CHODN NPs) were formulated by self assembled method using various N/P ratios (moles ratio of amine groups of CH to phosphate moieties of ODNs; 0.5:1, 1:1, 3:1, 5:1 and 7:1) of CH to ODN. The developed CHODN NPs were evaluated with respect to gel retardation assay, particle size, zeta potential and cytotoxicity and transfection efficiency. Results: Complete complexation of CH/ODN was achieved at the charge ratio of 0.5:1 or above and CHODN NPs displayed resistance against DNase I. On increasing the N/P ratio of CH/ODN, particle size of the NPs decreased whereas zeta potential (ZV) value increased. No significant toxicity was observed at all CH concentrations. The transfection efficiency was increased on increasing N/P ratio from 1:1 to 3:1, whereas it was decreased with further increment in N/P ratio upto 7:1. Maximum transfection of CHODN NPs with both the cell lines (Raw 267.4 cells and Hela cells) was achieved at N/P ratio of 3:1. The results suggest that transfection efficiency of CHODN NPs is dependent on N/P ratio. Conclusion: Thus the present study states that LMW chitosan nanoparticulate carriers would be acceptable choice to improve transfection efficiency in vitro as well as in vivo delivery of oligonucleotide.

Keywords: LMW-chitosan, chitosan nanoparticles, biocompatibility, cytotoxicity study, transfection efficiency, oligonucleotide

Procedia PDF Downloads 478

Authors: Tian Lan, Yixiang Wang, Wenxin Tai, Yilan Lyu, Zufeng Wu

Abstract:

The monaural speech enhancement task in the time-frequencydomain has a myriad of approaches, with the stacked con-volutional neural network (CNN) demonstrating superiorability in feature extraction and selection. However, usingstacked single convolutions method limits feature represen-tation capability and generalization ability. In order to solvethe aforementioned problem, we propose an attention-basedadaptive convolutional network that integrates the multi-scale convolutional operations into a operation-specific blockvia input dependent attention to adapt to complex auditoryscenes. In addition, we introduce a two-stage progressivelearning method to enlarge the receptive field without a dra-matic increase in computation burden. We conduct a series ofexperiments based on the TIMIT corpus, and the experimen-tal results prove that our proposed model is better than thestate-of-art models on all metrics.

Keywords: speech enhancement, adaptive convolu-tion, progressive learning, time-frequency domain

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Authors: Rezaei Hachesu Peyman, Oliyaee Azadeh, Salahzadeh Zahra, Alizadeh Somayyeh, Safaei Naser

Abstract:

Coronary Artery Disease (CAD) is one major cause of disability in adults and one main cause of death in developed. In this study, data mining techniques including Decision Trees, Artificial neural networks (ANNs), and Support Vector Machine (SVM) analyze CAD data. Data of 4948 patients who had suffered from heart diseases were included in the analysis. CAD is the target variable, and 24 inputs or predictor variables are used for the classification. The performance of these techniques is compared in terms of sensitivity, specificity, and accuracy. The most significant factor influencing CAD is chest pain. Elderly males (age > 53) have a high probability to be diagnosed with CAD. SVM algorithm is the most useful way for evaluation and prediction of CAD patients as compared to non-CAD ones. Application of data mining techniques in analyzing coronary artery diseases is a good method for investigating the existing relationships between variables.

Keywords: classification, coronary artery disease, data-mining, knowledge discovery, extract

Procedia PDF Downloads 638

Authors: Charalampos N. Bompas, Eleni Poulou-Sidiropoulou, Martina Samiotaki, Alexios Vlamis-Gardikas

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Ιn all living organisms, antioxidant defenses are orchestrated by the thioredoxin (Trx) and glutaredoxin (Grx) systems. The Trx system of Escherichia coli (E. coli) is comprised of Trx1 and Trx2, both reduced by thioredoxin reductase (TrxR). The Grx system consists of four Grxs (Grx1, Grx2, Grx3, and Grx4), all reduced by glutathione (GSH) except for Grx4, which is reduced by TrxR. Under normal conditions, the GSH reductase of the Grx system keeps GSH at its reduced state. NADPH+ provides the electrons for all reductions in the Trx and Grx systems. Although the role of the E. coli Trx system is widely known, the function of the Grx system reflects the main property of Grx1, which is the reduction of ribonucleotide reductase Ia (RRIa). E. coli Grx3 (encoded by grxC) may also reduce RRIa in vitro but with slow kinetics. The molecule may account for up to 0.4% of total soluble protein and has been the subject of extensive structural studies. Its biological function, however, remains unknown. Herein, affinity chromatography with monothiol Grx3 serving as bait was used to detect the interactions of Grx3 with other proteins. Different types of interactions were identified (covalent, weak, and strong non-covalent) that suggested novel functions for Grx3. In silico approaches were employed to validate selected interactions. In addition, total protein extracts from the null mutant for grxC and the wild-type strain were compared. The overall findings suggest that Grx3 is involved in various metabolic processes, protein synthesis, and stress responses, expanding the recognized functions of Grx3 beyond the possible reduction of RRIa.

Keywords: escherichia coli, glutaredoxin 3, interactome, thiol-disulfide oxidoreductase

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Authors: Gül Fatma Türker, Hatice Akman

Abstract:

Portable muscle stimulators for real-time applications has first introduced by Liberson in 1961. Now these systems has been advanced. In this study, FES (Functional Electrical Stimulator) and NMES (Neuromuscular Electrostimulator) systems are analyzed through their hardware and their quality of life improvements for foot drop patients. FES and NMES systems are used for people whose leg muscles and leg neural connections are healty but not able to walk properly because of their injured central nervous system like spinal cord injuries. These systems are used to stimulate neurons or muscles by getting information from other movements and programming these stimulations to get natural walk and it is accepted as a rehabilitation method for the correction of drop foot. This systems support person to approach natural form of walking. Foot drop is characterized by steppage gait. It is a gait abnormality. This systems helps to person for plantar and dorse reflection movements which are hard to done for foot drop patients.

Keywords: FES, foot drop, NMES, stimulator

Procedia PDF Downloads 372

Authors: Harish Rajak, Kamlesh Raghuwanshi

Abstract:

Histone deacetylase (HDAC) are promising target for cancer treatment. The panobinostat (Farydak; Novartis; approved by USFDA in 2015) and chidamide (Epidaza; Chipscreen Biosciences; approved by China FDA in 2014) are the novel HDAC inhibitors ratified for the treatment of patients with multiple myeloma and peripheral T cell lymphoma, respectively. On the other hand, two other HDAC inhibitors, Vorinostat (SAHA; approved by USFDA in 2006) and Romidepsin (FK228; approved by USFDA in 2009) are already in market for the treatment of cutaneous T-cell lymphoma. Several hydroxamic acid based HDAC inhibitors i.e., belinostat, givinostat, PCI24781 and JNJ26481585 are in clinical trials. HDAC inhibitors consist of three pharmacophoric features - an aromatic cap group, zinc binding group (ZBG) and a linker chain connecting cap group to ZBG. Herein, we report synthesis, characterization and biological evaluation of HDAC inhibitors possessing substituted isatin moiety as cap group which recognize the surface of active enzyme pocket and thiosemicarbazide moiety incorporated as linker group responsible for connecting cap group to ZBG (hydroxamic acid). Several analogues were found to inhibit HDAC and cellular proliferation of Hela cervical cancer cells with GI50 values in the micro molar range. Some of the compounds exhibited promising results in vitro antiproliferative studies. Attempts were also made to establish the structure activity relationship among synthesized HDAC inhibitors.

Keywords: HDAC inhibitors, hydroxamic acid derivatives, isatin derivatives, antiproliferative activity, docking

Procedia PDF Downloads 291