Search results for: neural cellular automata

Authors: Elias Nemer, Greg Vines

Abstract:

Time-frequency transformation and spectral representations of audio signals are commonly used in various machine learning applications. Training networks on frequency features such as the Mel-Spectrogram or Cochleogram have been proven more effective and convenient than training on-time samples. In practical realizations, these features are created on a different processor and/or pre-computed and stored on disk, requiring additional efforts and making it difficult to experiment with different features. In this paper, we provide a PyTorch framework for creating various spectral features as well as time-frequency transformation and time-domain filter-banks using the built-in trainable conv1d() layer. This allows computing these features on the fly as part of a larger network and enabling easier experimentation with various combinations and parameters. Our work extends the work in the literature developed for that end: First, by adding more of these features and also by allowing the possibility of either starting from initialized kernels or training them from random values. The code is written as a template of classes and scripts that users may integrate into their own PyTorch classes or simply use as is and add more layers for various applications.

Keywords: neural networks Mel-Spectrogram, chromagram, cochleogram, discrete Fourrier transform, PyTorch conv1d()

Procedia PDF Downloads 225

Authors: Esther N. Maina, Anna Skowronska, Sridhar Chaganti, Malcolm A. Taylor, Paul G. Murray, Tatjana Stankovic

Abstract:

Epstein-Barr virus (EBV) has been implicated in the pathogenesis of human tumours of B-cell origin. The demonstration that a proportion of Hodgkin lymphomas and all Burkitt’s lymphomas harbour EBV suggests that the virus contributes to the development of these malignancies. However, the mechanisms of lymphomagenesis remain largely unknown. To determine whether EBV causes DNA damage and alters DNA damage response in cells of EBV-driven lymphoma origin, Germinal Centre (GC) B cells were infected with EBV and DNA damage responses to gamma ionising radiation (IR) assessed at early time points (12hr – 72hr) after infection and prior to establishment of lymphoblastoid (LCL) cell lines. In the presence of EBV, we observed induction of spontaneous DNA DSBs and downregulation of ATM-dependent phosphorylation in response to IR. This downregulation coincided with reduced ability of infected cells to repair IR-induced DNA double-strand breaks, as measured by the kinetics of gamma H2AX, a marker of double-strand breaks, and by the tail moment of the comet assay. Furthermore, we found that alteration of DNA damage responses coincided with the expression of LMP-1 protein. The presence of the EBV virus did not affect the localization of the ATM-dependent DNA repair proteins to sites of damage but instead lead to an increased expression of PP5, a phosphatase that regulates ATM function. The impact of the virus on DNA repair was most prominent 24h after infection, suggesting that this time point is crucial for the viral establishment in B cells. Our results suggest that during an early infection EBV virus dampens crucial cellular responses to DNA double-strand breaks which facilitate successful viral infection, but at the same time might provide the mechanism for tumor development.

Keywords: EBV, ATM, DNA damage, germinal center cells

Procedia PDF Downloads 342

Authors: Haozhe Xiang

Abstract:

With the rise of the Internet of Things era, intelligent products are gradually integrating into people's lives. Pedestrian trajectory prediction has become a key issue, which is crucial for the motion path planning of intelligent agents such as autonomous vehicles, robots, and drones. In the current technological context, deep learning technology is becoming increasingly sophisticated and gradually replacing traditional models. The pedestrian trajectory prediction algorithm combining neural networks and attention mechanisms has significantly improved prediction accuracy. Based on in-depth research on deep learning and pedestrian trajectory prediction algorithms, this article focuses on physical environment modeling and learning of historical trajectory time dependence. At the same time, social interaction between pedestrians and scene interaction between pedestrians and the environment were handled. An improved pedestrian trajectory prediction algorithm is proposed by analyzing the existing model architecture. With the help of these improvements, acceptable predicted trajectories were successfully obtained. Experiments on public datasets have demonstrated the algorithm's effectiveness and achieved acceptable results.

Keywords: deep learning, graph convolutional network, attention mechanism, LSTM

Procedia PDF Downloads 61

Authors: Yinebeb Mezgebu Dagnachew, Hwee Ying Lim, Liao Wupeng, Sheau Yng Lim, Lim Sheng Jie Natalie, Veronique Angeli

Abstract:

Collagen is essential for maintaining lung structure and function, and its remodeling has been associated with respiratory diseases, including chronic obstructive pulmonary disease (COPD). However, the cellular mechanisms driving collagen remodeling and the functional implications of this process in the pathophysiology of pulmonary diseases remain poorly understood. Using a mouse model of Lyve-1 expressing macrophage depletion, we found that the absence of this subpopulation of tissue-resident macrophage led to the preferential deposition of type I collagen fibers around the alveoli and bronchi in the steady state. Further analysis by polarized light microscopy revealed that the collagen fibers accumulating in the lungs depleted of Lyve-1+ macrophages were thicker and crosslinked. A decrease in MMP-9 gene expression and proteolytic activity, together with an increase in Col1a1, Timp-3 and Lox gene expression, accompanied the collagen alterations. Next, we investigated the effect of the collagen remodeling on the pathophysiology of COPD and airway function in mouse lacking Lyve-1+ macrophage exposed chronically to cigarette smoke (CS), a well-established animal model of COPD. We showed that the deposition of collagen protected mouse against the destruction of alveoli (emphysema) and bronchi thickening after CS exposure and prevented loss of airway function. Thus, we demonstrate that interstitial Lyve-1+ macrophages regulate the composition, amount, and architecture of the collagen network in the lungs and that such collagen remodeling functionally impacts the development of COPD. This study further supports the potential of targeting collagen as a promising approach to treating respiratory diseases.

Keywords: lung, extracellular matrix, chronic obstructive pulmonary disease, matrix metalloproteinases, collagen

Procedia PDF Downloads 25

Authors: Imdad Ullah Khan, Yongseok Yoon, Kyeung Uk Choi, Kwang Rae Jo, Namyul Kim, Eunbee Lee, Wan Hee Kim, Oh-Kyeong Kweon

Abstract:

The primary spinal cord injury (SCI) causes mechanical damage to the neurons and blood vessels. It leads to secondary SCI, which activates multiple pathological pathways, which expand neuronal damage at the injury site. It is characterized by vascular disruption, ischemia, excitotoxicity, oxidation, inflammation, and apoptotic cell death. It causes nerve demyelination and disruption of axons, which perpetuate a loss of impulse conduction through the injured spinal cord. It also leads to the production of myelin inhibitory molecules, which with a concomitant formation of an astroglial scar, impede axonal regeneration. The pivotal role regarding the neuronal necrosis is played by oxidation and inflammation. During an early stage of spinal cord injury, there occurs an abundant expression of reactive oxygen species (ROS) due to defective mitochondrial metabolism and abundant migration of phagocytes (macrophages, neutrophils). ROS cause lipid peroxidation of the cell membrane, and cell death. Abundant migration of neutrophils, macrophages, and lymphocytes collectively produce pro-inflammatory cytokines such as tumor necrosis factor-alpha (TNF-α), interleukin-6 (IL-6), interleukin-1beta (IL-1β), matrix metalloproteinase, superoxide dismutase, and myeloperoxidases which synergize neuronal apoptosis. Therefore, it is crucial to control inflammation and oxidation injury to minimize the nerve cell death during secondary spinal cord injury. Therefore, in response to oxidation and inflammation, heme oxygenase-1 (HO-1) is induced by the resident cells to ameliorate the milieu. In the meanwhile, neurotrophic factors are induced to promote neuroregeneration. However, it seems that anti-stress enzyme (HO-1) and neurotrophic factor (BDNF) do not significantly combat the pathological events during secondary spinal cord injury. Therefore, optimum healing can be induced if anti-inflammatory and neurotrophic factors are administered in a higher amount through an exogenous source. During the first experiment, the inflammation and neuroregeneration were selectively targeted. HO-1 expressing MSCs (HO-1 MSCs) and BDNF expressing MSCs (BDNF MSC) were co-transplanted in one group (combination group) of dogs with subacute spinal cord injury to selectively control the expression of inflammatory cytokines by HO-1 and induce neuroregeneration by BDNF. We compared the combination group with the HO-1 MSCs group, BDNF MSCs group, and GFP MSCs group. We found that the combination group showed significant improvement in functional recovery. It showed increased expression of neural markers and growth-associated proteins (GAP-43) than in other groups, which depicts enhanced neuroregeneration/neural sparing due to reduced expression of pro-inflammatory cytokines such as TNF-alpha, IL-6 and COX-2; and increased expression of anti-inflammatory markers such as IL-10 and HO-1. Histopathological study revealed reduced intra-parenchymal fibrosis in the injured spinal cord segment in the combination group than in other groups. Thus it was concluded that selectively targeting the inflammation and neuronal growth with the combined use of HO-1 MSCs and BDNF MSCs more favorably promote healing of the SCI. HO-1 MSCs play a role in controlling the inflammation, which favors the BDNF induced neuroregeneration at the injured spinal cord segment of dogs.

Keywords: HO-1 MSCs, BDNF MSCs, neuroregeneration, inflammation, anti-inflammation, spinal cord injury, dogs

Procedia PDF Downloads 115

Authors: Seyede Sanaz Seyedebrahimi, Shima Rahimi, Shohreh Fakhari, Ali Jalili

Abstract:

Background: CXCR4, as a chemokine receptor, plays well-known roles in various types of cancers. Several studies have been conducted to overcome CXCR4 axis acts in multiple myeloma (MM) pathogenesis and progression. Dexamethasone, a standard treatment for multiple myeloma, has been shown to increase CXCR4 levels in multiple myeloma cell lines. Herein, we focused on the effects of metformin and dexamethasone on CXCR4 at the cellular level and the migration rate of cell lines after exposure to a combination compared to single-agent models. Materials and Method: Multiple myeloma cell lines (U266 and RPMI8226) were cultured with different metformin and dexamethasone concentrations in single-agent and combination models. The simultaneous combination doses were calculated by CompuSyn software. Cell surface and mRNA expression of CXCR4 were determined using flow cytometry and the quantitative reverse transcription-polymerase chain reaction (qRT-PCR) assay, respectively. The Transwell cell migration assay evaluated the migration ability. Results: In concurred with previous studies, our results showed a dexamethasone up-regulation effect on CXCR4 in a dose-dependent manner. Although, the metformin single-agent model could reduce CXCR4 expression of U266 and RPMI8226 in cell surface and mRNA expression level. Moreover, the administration of metformin and dexamethasone simultaneously exerted a higher suppression effect on CXCR4 expression than the metformin single-agent model. The migration rate through the combination model's matrigel membrane was remarkably lower than the metformin and dexamethasone single-agent model. Discussion: According to our findings, the combination of metformin and dexamethasone effectively inhibited dexamethasone-induced CXCR4 expression in multiple myeloma cell lines. As a result, metformin may be counted as an alternative medicine combined with other chemotherapies to combat multiple myeloma. However, more research is required.

Keywords: CXCR4, dexamethasone, metformin, migration, multiple myeloma

Procedia PDF Downloads 147

Authors: Güzi̇n Ti̇rkeş, Özge Teki̇n, Kerem Kurtuluş, Y. Yekta Yurtseven, Murat Baran

Abstract:

A scanning electron microscope (SEM) is a type of electron microscope mainly used in nanoscience and nanotechnology areas. Automatic image recognition and classification are among the general areas of application concerning SEM. In line with these usages, the present paper proposes a deep learning algorithm that classifies SEM images into nine categories by means of an online application to simplify the process. The NFFA-EUROPE - 100% SEM data set, containing approximately 21,000 images, was used to train and test the algorithm at 80% and 20%, respectively. Validation was carried out using a separate data set obtained from the Middle East Technical University (METU) in Turkey. To increase the accuracy in the results, the Inception ResNet-V2 model was used in view of the Fine-Tuning approach. By using a confusion matrix, it was observed that the coated-surface category has a negative effect on the accuracy of the results since it contains other categories in the data set, thereby confusing the model when detecting category-specific patterns. For this reason, the coated-surface category was removed from the train data set, hence increasing accuracy by up to 96.5%.

Keywords: convolutional neural networks, deep learning, image classification, scanning electron microscope

Procedia PDF Downloads 118

Authors: Senda Trabelsi, Sébastien Poncet, Michel Poirier

Abstract:

Ice slurries are considered as a promising phase-changing secondary fluids for air-conditioning, packaging or cooling industrial processes. An experimental study has been here carried out to measure the rheological characteristics of ice slurries. Ice slurries consist in a solid phase (flake ice crystals) and a liquid phase. The later is composed of a mixture of liquid water and an additive being here either (1) Propylene-Glycol (PG) or (2) Ethylene-Glycol (EG) used to lower the freezing point of water. Concentrations of 5%, 14% and 24% of both additives are investigated with ice mass fractions ranging from 5% to 85%. The rheological measurements are carried out using a Discovery HR-2 vane-concentric cylinder with four full-length blades. The experimental results show that the behavior of ice slurries is generally non-Newtonian with shear-thinning or shear-thickening behaviors depending on the experimental conditions. In order to determine the consistency and the flow index, the Herschel-Bulkley model is used to describe the behavior of ice slurries. The present results are finally validated against an experimental database found in the literature and the predictions of an Artificial Neural Network model.

Keywords: ice slurry, propylene-glycol, ethylene-glycol, rheology

Procedia PDF Downloads 255

Authors: Allmin Susaiyah, Aki Härmä, Milan Petković

Abstract:

Automatic generation of insights from data using insight mining systems (IMS) is useful in many applications, such as personal health tracking, patient monitoring, and business process management. Existing IMS face challenges in controlling insight extraction, scaling to large databases, and generalising to unseen domains. In this work, we propose a hybrid approach consisting of rule-based and neural components for generating insights from data while overcoming the aforementioned challenges. Firstly, a rule-based data 2CNL component is used to extract statistically significant insights from data and represent them in a controlled natural language (CNL). Secondly, a BERTSum-based CNL2NL component is used to convert these CNLs into natural language texts. We improve the model using task-specific and domain-specific fine-tuning. Our approach has been evaluated using statistical techniques and standard evaluation metrics. We overcame the aforementioned challenges and observed significant improvement with domain-specific fine-tuning.

Keywords: data mining, insight mining, natural language generation, pre-trained language models

Procedia PDF Downloads 106

Authors: Xin Li, Yan-Rong Guo, Jin-Fang Lin, Yi Feng, Håkan Billig, Ruijin Shao

Abstract:

Background: Young women with polycystic ovary syndrome (PCOS) have a high risk of developing endometrial carcinoma. There is a need for the development of new medical therapies that can reduce the need for surgical intervention so as to preserve the fertility of these patients. The aim of the study was to describe and discuss cases of PCOS and insulin resistance (IR) women with early endometrial carcinoma while being co-treated with Diane-35 and metformin. Methods: Five PCOS-IR women who were scheduled for diagnosis and therapy for early endometrial carcinoma were recruited. The hospital records and endometrial pathology reports were reviewed. All patients were co-treated with Diane-35 and metformin for 6 months to reverse the endometrial carcinoma and preserve their fertility. Before, during, and after treatment, endometrial biopsies and blood samples were obtained and oral glucose tolerance tests were performed. Endometrial pathology was evaluated. Body weight (BW), body mass index (BMI), follicle-stimulating hormone (FSH), luteinizing hormone (LH), total testosterone (TT), sex hormone-binding globulin (SHBG), free androgen index (FAI), insulin area under curve (IAUC), and homeostasis model assessment of insulin resistance (HOMA-IR) were determined. Results: Clinical stage 1a, low grade endometrial carcinoma was confirmed before treatment. After 6 months of co-treatment, all patients showed normal epithelia. No evidence of atypical hyperplasia or endometrial carcinoma was found. Co-treatment resulted in significant decreases in BW, BMI, TT, FAI, IAUC, and HOMA-IR in parallel with a significant increase in SHBG. There were no differences in the FSH and LH levels after co-treatment. Conclusions: Combined treatment with Diane-35 and metformin has the potential to revert the endometrial carcinoma into normal endometrial cells in PCOS-IR women. The cellular and molecular mechanisms behind this effect merit further investigation.

Keywords: PCOS, progesterone resistance, insulin resistance, steroid hormone receptors, endometrial carcinoma

Procedia PDF Downloads 402

Authors: Vikash Yadav, Ashish Arora

Abstract:

Microorganisms have self-defense mechanisms to protect themselves from toxic environments. Phenolic acid decarboxylase(pad) is responsible for the defense against toxicity caused by phenolic acids, converting them into less toxic vinyl derivatives. The transcription of the pad gene is regulated by a negative transcription factor, phenolic acid decarboxylase regulators (PadR), in a substrate-inducible manner. The PadR family members share the conserved DNA-binding features and interact with the operator DNA using a winged helix-turn-helix (wHTH) motif, which contains a three-helix motif and a β-stranded wing. The members of this family function as transcriptional regulators that are involved in various cellular survival processes, such as toxin production, detoxification, multidrug resistance, antibiotic biosynthesis, and carbon catabolism. Rv1176 of Mycobacterium tuberculosis H37Rv has been assigned to the PadR family protein that remains to be structurally and functionally uncharacterized. To reveal the structural mechanism by which Rv1176 could regulates effector-responsive transcription, several experiments were performed, including Electrophoretic Mobility Shift Assay (EMSA) for DNA protein interaction, differential scanning calorimetry (DSC) and Differential Scanning Fluorimetry (DSF) for temperature and ligand-dependent protein stability, Circular Dichroism (CD) spectroscopy for secondary structure analysis. Further, to evaluate the functional role of Rv1176, the intracellular survival of recombinant M. smegmatis was examined in murine macrophage cell line J774A.1 and different stressed conditions like oxidative, pH, and nutritive stress. All these studies demonstrated that Rv1176 could behave as a transcription regulator and its expression in recombinant M. smegmatis increases intracellular survival.

Keywords: EMSA, Mycobacterium tuberculosis, PadR family protein, transcriptional regulator

Procedia PDF Downloads 70

Authors: Murali Aarthy, Sanjeev Kumar Singh

Abstract:

High risk human papillomaviruses are highly associated with the carcinoma of the cervix and the other genital tumors. Cervical cancer develops through the multistep process in which increasingly severe premalignant dysplastic lesions called cervical intraepithelial neoplastic progress to invasive cancer. The oncoprotein E7 of human papillomavirus expressed in the lower epithelial layers drives the cells into S-phase creating an environment conducive for viral genome replication and cell proliferation. The replication of the virus occurs in the terminally differentiating epithelium and requires the activation of cellular DNA replication proteins. To date, no suitable drug molecule is available to treat HPV infection whereas identification of potential drug targets and development of novel anti-HPV chemotherapies with unique mode of actions are expected. Hence, our present study aimed to identify the potential inhibitors analogous to EGCG, a green tea molecule which is considered to be safe to use for mammalian systems. A 3D similarity search on the natural small molecule library from natural product database using EGCG identified 11 potential hits based on their similarity score. The structure based docking strategies were implemented in the potential hits and the key interacting residues of protein with compounds were identified through simulation studies and binding free energy calculations. The conformational changes between the apoprotein and the complex were analyzed with the simulation and the results demonstrated that the dynamical and structural effects observed in the protein were induced by the compounds and indicated the dominance to the oncoprotein. Overall, our study provides the basis for the structural insights of the identified potential hits and EGCG and hence, the analogous compounds identified can be potent inhibitors against the HPV 16 E7 oncoprotein.

Keywords: EGCG, oncoprotein, molecular dynamics simulation, analogues

Procedia PDF Downloads 119

Authors: Wiwat Kittinaraporn, Napat Harnpornchai, Sutja Boonyachut

Abstract:

This paper deals with the application of a novel neural network technique, so-called Support Vector Machine (SVM). The objective of this study is to explore the variable and parameter of forecasting factors in the construction industry to build up forecasting model for construction quantity in Thailand. The scope of the research is to study the non-residential construction quantity in Thailand. There are 44 sets of yearly data available, ranging from 1965 to 2009. The correlation between economic indicators and construction demand with the lag of one year was developed by Apichat Buakla. The selected variables are used to develop SVM models to forecast the non-residential construction quantity in Thailand. The parameters are selected by using ten-fold cross-validation method. The results are indicated in term of Mean Absolute Percentage Error (MAPE). The MAPE value for the non-residential construction quantity predicted by Epsilon-SVR in corporation with Radial Basis Function (RBF) of kernel function type is 5.90. Analysis of the experimental results show that the support vector machine modelling technique can be applied to forecast construction quantity time series which is useful for decision planning and management purpose.

Keywords: forecasting, non-residential, construction, support vector machines

Procedia PDF Downloads 429

Authors: Arya Prakash Padhi, Anupam Chakrabarti, Rajib Chowdhury

Abstract:

Topology or layout optimization often gives better performing economic structures and is very helpful in the conceptual design phase. But traditionally it is being done in finite element-based optimization schemes which, although gives a good result, is very time-consuming especially in 3D structures. Among other alternatives machine learning, especially deep learning-based methods, have a very good potential in resolving this computational issue. Here convolutional neural network (3D-CNN) based variational auto encoder (VAE) is trained using a dataset generated from commercially available topology optimization code ABAQUS Tosca using solid isotropic material with penalization (SIMP) method for compliance minimization. The encoded data in latent space is then fed to a 3D generative adversarial network (3D-GAN) to generate the outcome in 64x64x64 size. Here the network consists of 3D volumetric CNN with rectified linear unit (ReLU) activation in between and sigmoid activation in the end. The proposed network is seen to provide almost optimal results with significantly reduced computational time, as there is no iteration involved.

Keywords: 3D generative adversarial network, deep learning, structural topology optimization, variational auto encoder

Procedia PDF Downloads 167

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

Abstract:

The number of biological papers is growing quickly, which means that the number of biological pathway figures in those papers is also increasing quickly. Each pathway figure shows extensive biological information, like the names of genes and how the genes are related. However, manually annotating pathway figures takes a lot of time and work. Even though using advanced image understanding models could speed up the process of curation, these models still need to be made more accurate. To improve gene name recognition from pathway figures, we applied a Siamese network to map image segments to a library of pictures containing known genes in a similar way to person recognition from photos in many photo applications. We used a triple loss function and a triplet spatial pyramid pooling network by combining the triplet convolution neural network and the spatial pyramid pooling (TSPP-Net). We compared VGG19 and VGG16 as the Siamese network model. VGG16 achieved better performance with an accuracy of 93%, which is much higher than OCR results.

Keywords: biological pathway, image understanding, gene name recognition, object detection, Siamese network, VGG

Procedia PDF Downloads 280

Authors: Gorkem Algan, Ilkay Ulusoy, Saban Gonul, Banu Turgut, Berker Bakbak

Abstract:

Computer vision systems recently made a big leap thanks to deep neural networks. However, these systems require correctly labeled large datasets in order to be trained properly, which is very difficult to obtain for medical applications. Two main reasons for label noise in medical applications are the high complexity of the data and conflicting opinions of experts. Moreover, medical imaging datasets are commonly tiny, which makes each data very important in learning. As a result, if not handled properly, label noise significantly degrades the performance. Therefore, a label-noise-robust learning algorithm that makes use of the meta-learning paradigm is proposed in this article. The proposed solution is tested on retinopathy of prematurity (ROP) dataset with a very high label noise of 68%. Results show that the proposed algorithm significantly improves the classification algorithm's performance in the presence of noisy labels.

Keywords: deep learning, label noise, robust learning, meta-learning, retinopathy of prematurity

Procedia PDF Downloads 152

Authors: Swarna Pundir, Prabuddha Hans

Abstract:

As AI has already stepped into the daily life of human society, one cannot be ignorant about the data it collects and used it to provide a quality of services depending up on the individuals’ choices. It also helps in giving option for making decision Vs choice selection with a calculation based on the history of our search criteria. Over the past decade or so, the way Artificial Intelligence (AI) has impacted society is undoubtedly large.AI has changed the way we shop, the way we entertain and challenge ourselves, the way information is handled, and has automated some sections of our life. We have answered as to what AI is, but not why one may see it as useful. AI is useful because it is capable of learning and predicting outcomes, using Machine Learning (ML) and Deep Learning (DL) with the help of Artificial Neural Networks (ANN). AI can also be a system that can act like humans. One of the major impacts be Joblessness through automation via AI which is seen mostly in manufacturing sectors, especially in the routine manual and blue-collar occupations and those without a college degree. It raises some serious concerns about AI in regards of less employment, ethics in making moral decisions, Individuals privacy, human judgement’s, natural emotions, biased decisions, discrimination. So, the question is if an error occurs who will be responsible, or it will be just waved off as a “Machine Error”, with no one taking the responsibility of any wrongdoing, it is essential to form some rules for using the AI where both machines and humans are involved.

Keywords: AI, ML, DL, ANN

Procedia PDF Downloads 83

Authors: Igor Vishnevskyi

Abstract:

Introduction: The urinary bladder undergoes repeated strain during its working cycle, suggesting the presence of an efficient support system, force transmission, and mechanical amplification. The concept of a "hydrostatic skeleton" (HS) could contribute to our understanding of the functional relationships among bladder constituents. Methods: A multidisciplinary literature review was conducted to identify key features of the HS and to gather evidence supporting its applicability in urinary bladder biomechanics. The collected evidence was synthesized to propose a framework for understanding the potential hydrostatic properties of the urinary bladder based on existing knowledge and HS principles. Results: Our analysis revealed similarities in biomechanical features between living fluid-filled structures and the urinary bladder. These similarities include the geodesic arrangement of fibres, the role of enclosed fluid (urine) in force transmission, prestress as a determinant of stiffness, and the ability to maintain shape integrity during various activities. From a biomechanical perspective, urine may be considered an essential component of the bladder. The hydrostatic skeleton, with its autonomy and flexibility, may provide insights for researchers involved in bladder engineering. Discussion: The concept of a hydrostatic skeleton offers a holistic perspective for understanding bladder function by considering multiple mechanical factors as a single structure with emergent properties. Incorporating viewpoints from various fields on HS can help identify how this concept applies to live fluid-filled structures or organs and reveal its broader relevance to biological systems, both natural and artificial. Conclusion: The hydrostatic skeleton (HS) design principle can be applied to the urinary bladder. Understanding the bladder as a structure with HS can be instrumental in biomechanical modelling and engineering. Further research is required to fully elucidate the cellular and molecular mechanisms underlying HS in the bladder.

Keywords: hydrostatic skeleton, urinary bladder morphology, shape integrity, prestress, biomechanical modelling

Procedia PDF Downloads 72

Authors: Aliro Villacorta, Laura Rubio, Mohamed Alaraby, Montserrat López Mesas, Victor Fuentes-Cebrian, Oscar H. Moriones, Ricard Marcos, Alba Hernández.

Abstract:

Micro and nano plastics (MNPLs) are emergent environmental pollutants requiring urgent information on their potential risks to human health. One of the problems associated with the evaluation of their undesirable effects is the lack of real samples matching those resulting from the environmental degradation of plastic wastes. To such end, we propose an easy method to obtain polyethylene terephthalate nano plastics from water plastic bottles (PET-NPLs) but, in principle, applicable to any other plastic goods sources. An extensive characterization indicates that the proposed process produces uniform samples of PET-NPLs of around 100 nm, as determined by using a multi-angle and dynamic light scattering methodology. An important point to be highlighted is that to avoid the metal contamination resulting from methods using metal blades/burrs for milling, trituration, or sanding, we propose to use diamond burrs to produce metal-free samples. To visualize the toxicological profile of the produced PET-NPLs, we have evaluated their ability to be internalized by cells, their cytotoxicity, and their ability to induce oxidative stress and induce DNA damage. In this preliminary approach, we have detected their cellular uptake, but without the induction of significant biological effects. Thus, no relevant increases in toxicity, reactive oxygen species (ROS) induction, or DNA damage -as detected with the comet assay- have been observed. The use of real samples, as produced in this study, will generate relevant data in the discussion about the potential health risks associated with MNPLs exposures.

Keywords: nanoplastics, polyethylene terephthalate, physicochemical characterization, cell uptake, cytotoxicity

Procedia PDF Downloads 88

Authors: Rita Emília Szabó, Róbert Polanek, Tünde Tőkés, Zoltán Szabó, Szabolcs Czifrus, Katalin Hideghéty

Abstract:

Purpose: Several feature of zebrafish are making them amenable for investigation on therapeutic approaches such as ionizing radiation. The establishment of zebrafish model for comprehensive radiobiological research stands in the focus of our investigation, comparing the radiation effect curves of neutron and photon irradiation. Our final aim is to develop an appropriate vertebrate model in order to investigate the relative biological effectiveness of laser driven ionizing radiation. Methods and Materials: After careful dosimetry series of viable zebrafish embryos were exposed to a single fraction whole-body neutron-irradiation (1,25; 1,875; 2; 2,5 Gy) at the research reactor of the Technical University of Budapest and to conventional 6 MeV photon beam at 24 hour post-fertilization (hpf). The survival and morphologic abnormalities (pericardial edema, spine curvature) of each embryo were assessed for each experiment at 24-hour intervals from the point of fertilization up to 168 hpf (defining the dose lethal for 50% (LD50)). Results: In the zebrafish embryo model LD50 at 20 Gy dose level was defined and the same lethality were found at 2 Gy dose from the reactor neutron beam resulting RBE of 10. Dose-dependent organ perturbations were detected on macroscopic (shortening of the body length, spine curvature, microcephaly, micro-ophthalmia, micrognathia, pericardial edema, and inhibition of yolk sac resorption) and microscopic (marked cellular changes in skin, cardiac, gastrointestinal system) with the same magnitude of dose difference. Conclusion: In our observations, we found that zebrafish embryo model can be used for investigating the effects of different type of ionizing radiation and this system proved to be highly efficient vertebrate model for preclinical examinations.

Keywords: ionizing radiation, LD50, relative biological effectiveness, zebrafish embryo

Procedia PDF Downloads 300

Authors: Rahma Goussi, Walid Derbali, Arafet Manaa, Simone Cantamessa, Graziella Berta, Chedly Abdelly, Roberto Barbato

Abstract:

Salinity is one of the most important abiotic affecting plant growth and productivity worldwide. Photosynthesis, together with cell growth, is among the primary processes to be affected by salinity. Here, we report the effects of salinity stress on the primary processes of photosynthesis in a model halophyte Thellungiella Salsuginea. Plants were cultivated in hydroponic system with different NaCl concentrations (0, 100, 200 and 400 mM) during 2 weeks. The obtained results showed an obvious change in the photosynthetic efficiency of photosystem I (PSI) and phostosytem II (PSII), related to NaCl concentration supplemented to the medium and the stress duration considered. With moderate salinity (100 and 200 mM NaCl), no significant variation was observed in photosynthetic parameters of PSI and PSII and Chl fluorescence whatever the time of stress application. Also, the photosynthesis apparatus Fo, Fm and Fv fluorescence, as well as Fv/Fm were not affected by salt stress. While a significant decrease was observed on quantum yields Y(I), Y(II) and electron transport rate ETR(I), ETR(II) under high salt treatment (400 mM NaCl) with prolonged period (15 days). This reduction is quantitatively compensated by a corresponding increase of energy dissipation Y(NPQ) and a progressive decrease in Fv/Fm under salt treatment. The intensity of the OJIP fluorescence transient decreased with increase in NaCl concentration, with a major effect observed during prolonged period of salt stress. Ultrastructural analysis with Light Microscopy and Transmission Electron Microscopy of T. salsuginea chloroplasts showed some cellular changes, such as the shape of the mesophyll cells and number of chloroplast/cell only under higher NaCl concentration. Salt-stress caused the swelling of thylakoids in T. Salsuginea mesophyll with more accumulation of starch as compared to control plant.

Keywords: fluorescence, halophyte, photosynthesis, salt stress

Procedia PDF Downloads 368

Authors: Jaspreet Singh, Gurvinder Singh, Prabhsimran Singh, Rajinder Singh, Prithvipal Singh, Karanjeet Singh Kahlon, Ravinder Singh Sawhney

Abstract:

Morphological evaluation of Indian languages is one of the burgeoning fields in the area of Natural Language Processing (NLP). The evaluation of a language is an eminent task in the era of information retrieval and text mining. The extraction and classification of knowledge from text can be exploited for sentiment analysis and morphological evaluation. This study coalesce morphological evaluation and sentiment analysis for the task of classification of farmer suicide cases reported in Punjab state of India. The pre-processing of Punjabi text involves morphological evaluation and normalization of Punjabi word tokens followed by the training of proposed model using deep learning classification on Punjabi language text extracted from online Punjabi news reports. The class-wise accuracies of sentiment prediction for four negatively oriented classes of farmer suicide cases are 93.85%, 88.53%, 83.3%, and 95.45% respectively. The overall accuracy of sentiment classification obtained using proposed framework on 275 Punjabi text documents is found to be 90.29%.

Keywords: deep neural network, farmer suicides, morphological processing, punjabi text, sentiment analysis

Procedia PDF Downloads 314

Authors: Chuanbo Xu, Xinying Li, Gejirifu De, Yunna Wu

Abstract:

Energy Internet (EI) is a new form that deeply integrates the Internet and the entire energy process from production to consumption. The assessment of energy efficient performance is of vital importance for the long-term sustainable development of EI project. Although the newly proposed fuzzy integrated cloud evaluation algorithm considers the randomness of uncertainty, it relies too much on the experience and knowledge of experts. Fortunately, the enrichment of EI data has enabled the utilization of data-driven methods. Therefore, the main purpose of this work is to assess the energy efficient of park-level EI by using a combination of a data-driven method with the fuzzy integrated cloud evaluation algorithm. Firstly, the indicators for the energy efficient are identified through literature review. Secondly, the artificial neural network (ANN)-based data-driven method is employed to cluster the values of indicators. Thirdly, the energy efficient of EI project is calculated through the fuzzy integrated cloud evaluation algorithm. Finally, the applicability of the proposed method is demonstrated by a case study.

Keywords: energy efficient, energy internet, data-driven, fuzzy integrated evaluation, cloud model

Procedia PDF Downloads 195

Authors: Sebastien Relave, Christophe Desrayaud, Aurelien Vilani, Alexey Sova

Abstract:

Laser beam melting (LBM) is an additive manufacturing process that enables complex 3D parts to be designed. This process is now commonly employed for various applications such as chemistry or energy, requiring the use of stainless steel grades. LBM can offer comparable and sometimes superior mechanical properties to those of wrought materials. However, we observed an anisotropic microstructure which results from the process, caused by the very high thermal gradients along the building axis. This microstructure can be harmful depending on the application. For this reason, control and prediction of the microstructure are important to ensure the improvement and reproducibility of the mechanical properties. This study is focused on the 316L SS grade and aims at understanding the solidification and transformation mechanisms during process. Experiments to analyse the nucleation and growth of the microstructure obtained by the LBM process according to several conditions. These samples have been designed on different type of support bulk and lattice. Samples are produced on ProX DMP 200 LBM device. For the two conditions the analysis of microstructures, thanks to SEM and EBSD, revealed a single phase Austenite with preferential crystallite growth along the (100) plane. The microstructure was presented a hierarchical structure consisting columnar grains sizes in the range of 20-100 µm and sub grains structure of size 0.5 μm. These sub-grains were found in different shapes (columnar and cellular). This difference can be explained by a variation of the thermal gradient and cooling rate or element segregation while no sign of element segregation was found at the sub-grain boundaries. A high dislocation concentration was observed at sub-grain boundaries. These sub-grains are separated by very low misorientation walls ( < 2°) this causes a lattice of curvature inside large grain. A discussion is proposed on the occurrence of these microstructures formation, in regard of the LBM process conditions.

Keywords: selective laser melting, stainless steel, microstructure

Procedia PDF Downloads 154

Authors: Meruert Serik, Nassipzhan Duisegaliyeva, Danara Tleumagambetova

Abstract:

The article highlights and describes the characteristic features of real-time face detection in images and videos using machine learning algorithms. Students of educational programs reviewed the research work "6B01511-Computer Science", "7M01511-Computer Science", "7M01525- STEM Education," and "8D01511-Computer Science" of Eurasian National University named after L.N. Gumilyov. As a result, the advantages and disadvantages of Haar Cascade (Haar Cascade OpenCV), HoG SVM (Histogram of Oriented Gradients, Support Vector Machine), and MMOD CNN Dlib (Max-Margin Object Detection, convolutional neural network) detectors used for face detection were determined. Dlib is a general-purpose cross-platform software library written in the programming language C++. It includes detectors used for determining face detection. The Cascade OpenCV algorithm is efficient for fast face detection. The considered work forms the basis for the development of machine learning methods by future computer science teachers.

Keywords: algorithm, artificial intelligence, education, machine learning

Procedia PDF Downloads 68

Authors: Farhad Asadi, Mohammad Javad Mollakazemi, Aref Ghafouri

Abstract:

Recent research in neural networks science and neuroscience for modeling complex time series data and statistical learning has focused mostly on learning from high input space and signals. Local linear models are a strong choice for modeling local nonlinearity in data series. Locally weighted projection regression is a flexible and powerful algorithm for nonlinear approximation in high dimensional signal spaces. In this paper, different learning scenario of one and two dimensional data series with different distributions are investigated for simulation and further noise is inputted to data distribution for making different disordered distribution in time series data and for evaluation of algorithm in locality prediction of nonlinearity. Then, the performance of this algorithm is simulated and also when the distribution of data is high or when the number of data is less the sensitivity of this approach to data distribution and influence of important parameter of local validity in this algorithm with different data distribution is explained.

Keywords: local nonlinear estimation, LWPR algorithm, online training method, locally weighted projection regression method

Procedia PDF Downloads 495

Authors: Mayada Mazher, Ahmed Moustafa, Ahmed Abdellatif

Abstract:

Background: Autophagy is a eukaryotic, highly conserved catabolic process implicated in many pathophysiologies such as wound healing. Autophagy-associated genes serve as a scaffolding platform for signal transduction of macrophage polarization during the inflammatory phase of wound healing and tissue repair process. In the current study, we report a model for the interplay between autophagy-associated genes and macrophages polarization associated genes. Methods: In silico analysis was performed on 249 autophagy-related genes retrieved from the public autophagy database and gene expression data retrieved from Gene Expression Omnibus (GEO); GSE81922 and GSE69607 microarray data macrophages polarization 199 DEGS. An integrated protein-protein interaction network was constructed for autophagy and macrophage gene sets. The gene sets were then used for GO terms pathway enrichment analysis. Common transcription factors for autophagy and macrophages' polarization were identified. Finally, microRNAs enriched in both autophagy and macrophages were predicated. Results: In silico prediction of common transcription factors in DEGs macrophages and autophagy gene sets revealed a new role for the transcription factors, HOMEZ, GABPA, ELK1 and REL, that commonly regulate macrophages associated genes: IL6,IL1M, IL1B, NOS1, SOC3 and autophagy-related genes: Atg12, Rictor, Rb1cc1, Gaparab1, Atg16l1. Conclusions: Autophagy and macrophages' polarization are interdependent cellular processes, and both autophagy-related proteins and macrophages' polarization related proteins coordinate in tissue remodelling via transcription factors and microRNAs regulatory network. The current work highlights a potential new role for transcription factors HOMEZ, GABPA, ELK1 and REL in wound healing.

Keywords: autophagy related proteins, integrated network analysis, macrophages polarization M1 and M2, tissue remodelling

Procedia PDF Downloads 143

Authors: Mohd Farooq Naqshbandi

Abstract:

The four fractions of aqueous extract of Sesame Seeds (Sesamum indicum L.) were studied for invitro DNA fragmentation, cell migration, and cellular apoptosis on SW480 and HTC116 human colorectal cancer cell lines. The seeds of Sesamum indicum were extracted with six solvents, including Methanol, Ethanol, Aqueous, Chloroform, Acetonitrile, and Hexane. The aqueous extract (IC₅₀ value 154 µg/ml) was found to be the most active in terms of cytotoxicity with SW480 human colorectal cancer cell lines. Further fractionation of this aqueous extract on flash chromatography gave four fractions. These four fractions were studied for anticancer and DNA binding studies. Cell viability was assessed by colorimetric assay (MTT). IC₅₀ values for all these four fractions ranged from 137 to 548 µg/mL for the HTC116 cancer cell line and 141 to 402 µg/mL for the SW480 cancer cell line. The four fractions showed good anticancer and DNA binding properties. The DNA binding constants ranged from 10.4 ×10⁴ 5 to 28.7 ×10⁴, showing good interactions with DNA. The DNA binding interactions were due to intercalative and π-π electron forces. The results indicate that aqueous extract fractions of sesame showed inhibition of cell migration of SW480 and HTC116 human colorectal cancer cell lines and induced DNA fragmentation and apoptosis. This was demonstrated by calculating the low wound closure percentage in cells treated with these fractions as compared to the control (80%). Morphological features of nuclei of cells treated with fractions revealed chromatin compression, nuclear shrinkage, and apoptotic body formation, which indicate cell death by apoptosis. The flow cytometer of fraction-treated cells of SW480 and HTC116 human colorectal cancer cell lines revealed death due to apoptosis. The results of the study indicate that aqueous extract of sesame seeds may be used to treat colorectal cancer.

Keywords: Sesamum indicum, cell migration inhibition, apoptosis induction, anticancer activity, colorectal cancer

Procedia PDF Downloads 80

Authors: Leila Maleki, Ahmad Esmali Kooraneh, Hossein Taghi Derakhshi

Abstract:

Neural activity in the human brain starts from the early stages of prenatal development. This activity or signals generated by the brain are electrical in nature and represent not only the brain function but also the status of the whole body. At the present moment, three methods can record functional and physiological changes within the brain with high temporal resolution of neuronal interactions at the network level: the electroencephalogram (EEG), the magnet oencephalogram (MEG), and functional magnetic resonance imaging (fMRI); each of these has advantages and shortcomings. EEG recording with a large number of electrodes is now feasible in clinical practice. Multichannel EEG recorded from the scalp surface provides a very valuable but indirect information about the source distribution. However, deep electrode measurements yield more reliable information about the source locations، Intracranial recordings and scalp EEG are used with the source imaging techniques to determine the locations and strengths of the epileptic activity. As a source localization method, Low Resolution Electro-Magnetic Tomography (LORETA) is solved for the realistic geometry based on both forward methods, the Boundary Element Method (BEM) and the Finite Difference Method (FDM). In this paper, we review The findings EEG- LORETA about epilepsy.

Keywords: epilepsy, EEG, EEG-LORETA

Procedia PDF Downloads 539

Authors: Sudha Summarwar, Sudesh Agarwal, Deepali Lall, Nalini Kataria, Jyotsana Pandey

Abstract:

Assessment of antioxidant status is an effective tool to appraise the presence of oxidative stress. A combination of assays can be used to evaluate the antioxidant status like serum catalase (CAT), superoxide dismutase (SOD) and monoamine oxidase (MAO). In human medicine pregnancy is known to be associated with oxidative stress. Oxidative stress produces harmful effects to the developing foetus. Several metabolic changes occur in the maternal body to meet the demand of energy of developing foetus. Due to these changes susceptibility of maternal body increases to oxidative stress. There is paucity of research work on this aspect in nondescript goats. Therefore, the present study was intended to appraise the oxidative stress in pregnant and non-pregnant non-descript goat. Blood samples were collected for serum separation in otherwise healthy pregnant and non-pregnant nondescript goats. Mean values of serum CAT, SOD and MAO were found on a higher side (p≤0.05) with serum SOD values showing a rise of 2.5 times higher than the control healthy value. Correlations among all the three parameters were found to be highly significant (p≤0.01) especially greatest in youngest group of pregnant animals. Illustration of result enlightened the veracity of bumped up production of free radicals in pregnant animals. Technical savoir-faire of oxidative stress supervision is essential for upholding of health status of foetus. The upshot of present study undoubtedly implied the development of oxidative stress in pregnant goats on the basis of altered antioxidant status. These findings conclude that initially the oxidative stress due to pregnancy is critically combated by the intricate defensive mechanism of natural antioxidant system of the body. It appears that this imbalance between oxidant and antioxidant must be checked in time to prevent cellular damage by regularly appraising the antioxidant status through laboratory methods.

Keywords: antioxidant, oxidative stress, pregnancy, serum catalase

Procedia PDF Downloads 328