Search results for: neural cellular automata

Authors: Natesan Balasubramanian, Palpandi Pounpandi, Venkatraman Thamil Priya, Vellasamy Shanmugaiah, Karubbiah Balakrishnan, Mandayam Anandam Thirunarayan

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Streptococcus agalactiae is commonly known as Group B Streptococcus (GBS) and it is the most common cause of life-threatening bacterial infection. GBS first considered as a veterinary pathogen causing mastitis in cattle later becomes a human pathogen for severe neonatal infections. In this present study, a total of 20 new clinical isolates of S. agalactiae were collected from male (6) and female patient (14) with different age group. The isolates were from Urinary tract infection (UTI), blood, pus and eye ulcer. All the 20 S. agalactiae isolates has clear hemolysis properties on blood agar medium and were identified by serogrouping and MALTI-TOF-MS analysis. Antibiotic susceptibility/resistance test was performed for 20 S. agalactiae isolates, further phenotypic resistance pattern was observed for tetracycline, vancomycin, ampicillin and penicillin. Genotypically we found two antibiotic resistance genes such as Betalactem antibiotic resistance gene (Tem) (70%) and tetracycline resistance gene Tet(O) 15% in our isolates. Six virulence factors encoding genes were performed by PCR in twenty GBS isolates, cfb gene (100%), followed by, cylE(90.47%), lmp(85.7%), bca(71.42%), rib (38%) and low frequency in bac gene (4.76%) were determined. Most of the S. agalactiae isolates produced strong biofilm in the polystyrene surface (hydrophobic), and low-level biofilm formation was found in glass tube (hydrophilic) surface. lytR is secreted protein and localized in bacterial cell wall, extra cellular membrane, and cytoplasm. In silico docking studies were performed for lytR protein with four antibiofilm compounds, including a peptide (PR39) with the docking study showed peptide has strong interaction followed by ellagic acid and interaction length is 2.95, 2.97 and 2.95 A°. In ligand EGCGO10 and O11 two atoms intract with lytR (Leu271), with binding bond affinity length is 3.24 and 3.14. The aminoacid Leu 271 is act as an impartant aminoacid, since ellagic acid and EGCG interact with same aminoacid.

Keywords: antibiotics, biofilms, clinical isolates, S. agalactiae, virulence

Procedia PDF Downloads 96

Authors: Silvia Ximena Barrios, Diego Armando Villamizar Mantilla, Raquel Elvira Ocazionez, , Elena E. Stashenko, María Pilar Vinardell, Jorge Luis Fuentes

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Introduction: Skin overexposure to solar radiation has been a serious public health concern, because of its potential carcinogenicity. Therefore, preventive protection strategies using photoprotective agents are critical to counteract the harmful effect of solar radiation. Plants may be a source of photoprotective compounds that inhibit cellular mutations involved in skin cancer initiation. This work evaluated the photoprotective and antigenotoxic effects against ultraviolet B (UVB) radiation of a mixture of Posoqueria latifolia flower extract and Kaempferol (MixPoKa). Methods: The photoprotective efficacy of MixPoka (Posoqueria latifolia flower extract 250 μg/ml and Kaempferol 349.5 μM) was evaluated using in vitro indices such as sun protection factor SPFᵢₙ ᵥᵢₜᵣₒ and critical wavelength (λc). The MixPoKa photostability (Eff) at human minimal erythema doses (MED), according to the Fitzpatrick skin scale, was also estimated. Cytotoxicity and genotoxicity/antigenotoxicity were studied in MRC5 human fibroblasts using the trypan blue exclusion and Comet assays, respectively. Kinetics of the genetic damage repair post irradiation in the presence and absence of the MixPoka, was also evaluated. Results: The MixPoka -UV absorbance spectrum was high across the spectral bands between 200 and 400 nm. The UVB photoprotection efficacy of MixPoka was high (SPFᵢₙ ᵥᵢₜᵣₒ = 25.70 ± 0.06), showed wide photoprotection spectrum (λc = 380 ± 0), and resulted photostable (Eff = 92.3–100.0%). The MixPoka was neither cytotoxic nor genotoxic in MRC5 human fibroblasts; but presented significant antigenotoxic effect against UVB radiation. Additionally, MixPoka stimulate DNA repair post-irradiation. The potential of this phytochemical mixture as sunscreen ingredients was discussed. Conclusion: MixPoka showed a significant antigenotoxic effect against UVB radiation and stimulated DNA repair after irradiation. MixPoka could be used as an ingredient in a sunscreen cream.

Keywords: flower extract, photoprotection, antigenotoxicity, cytotoxicity, genotoxicit

Procedia PDF Downloads 56

Authors: Amir Andria Gad Shehata

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Minimizing the inventory cost, optimizing the inventory quantities, and increasing system operational availability are the main motivations to enhance forecasting demand of spare parts in a major power utility company in Medina. This paper reports in an effort made to optimize the orders quantities of spare parts by improving the method of forecasting the demand. The study focuses on equipment that has frequent spare parts purchase orders with uncertain demand. The pattern of the demand considers a lumpy pattern which makes conventional forecasting methods less effective. A comparison was made by benchmarking various methods of forecasting based on experts’ criteria to select the most suitable method for the case study. Three actual data sets were used to make the forecast in this case study. Two neural networks (NN) approaches were utilized and compared, namely long short-term memory (LSTM) and multilayer perceptron (MLP). The results as expected, showed that the NN models gave better results than traditional forecasting method (judgmental method). In addition, the LSTM model had a higher predictive accuracy than the MLP model.

Keywords: spare part, spare part inventory, inventory model, optimization, maintenanceneural network, LSTM, MLP, forecasting demand, inventory management

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Authors: Ajish K. Abraham

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Hearing aid is the most fundamental technology employed towards rehabilitation of persons with sensory neural hearing impairment. Hearing in noise is still a matter of major concern for many hearing aid users and thus continues to be a challenging issue for the hearing aid designers. Several techniques are being currently used to enhance the speech at the hearing aid output. Most of these techniques, when implemented, result in reduction of intelligibility of the speech signal. Thus the dissatisfaction of the hearing aid user towards comprehending the desired speech amidst noise is prevailing. Multichannel Wiener Filter is widely implemented in binaural hearing aid technology for noise reduction. In this study, Wiener filter based noise reduction approach is experimented for a single microphone based hearing aid set up. This method checks the status of the input speech signal in each frequency band and then selects the relevant noise reduction procedure. Results showed that the Wiener filter based algorithm is capable of enhancing speech even when the input acoustic signal has a very low Signal to Noise Ratio (SNR). Performance of the algorithm was compared with other similar algorithms on the basis of improvement in intelligibility and SNR of the output, at different SNR levels of the input speech. Wiener filter based algorithm provided significant improvement in SNR and intelligibility compared to other techniques.

Keywords: hearing aid output speech, noise reduction, SNR improvement, Wiener filter, speech enhancement

Procedia PDF Downloads 233

Authors: Ali Berkan Ural

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This paper presents the analysis, evaluation, and pre-diagnosis of early stage breast based diagnostic problems (breast cancer, nodulesorlumps) by Computer Aided Diagnosing (CAD) system from mammogram radiological images. According to the statistics, the time factor is crucial to discover the disease in the patient (especially in women) as possible as early and fast. In the study, a new algorithm is developed using advanced image processing and deep learning method to detect and classify the problem at earlystagewithmoreaccuracy. This system first works with image processing methods (Image acquisition, Noiseremoval, Region Growing Segmentation, Morphological Operations, Breast BorderExtraction, Advanced Segmentation, ObtainingRegion Of Interests (ROIs), etc.) and segments the area of interest of the breast and then analyzes these partly obtained area for cancer detection/lumps in order to diagnosis the disease. After segmentation, with using the Spectrogramimages, 5 different deep learning based methods (specified Convolutional Neural Network (CNN) basedAlexNet, ResNet50, VGG16, DenseNet, Xception) are applied to classify the breast based problems.

Keywords: computer aided diagnosis, breast cancer, region growing, segmentation, deep learning

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Authors: Maria Ezziani, Rabie Zine, Amine Amar, Ilhame Kissani

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This paper introduces a predictive model for urban transportation engineering, which is vital for efficient traffic management. Utilizing comprehensive datasets and advanced statistical techniques, the model accurately forecasts travel times by considering temporal variations and traffic dynamics. Machine learning algorithms, including regression trees and neural networks, are employed to capture sequential dependencies. Results indicate significant improvements in predictive accuracy, particularly during peak hours and holidays, with the incorporation of traffic flow and speed variables. Future enhancements may integrate weather conditions and traffic incidents. The model's applications range from adaptive traffic management systems to route optimization algorithms, facilitating congestion reduction and enhancing journey reliability. Overall, this research extends beyond travel time estimation, offering insights into broader transportation planning and policy-making realms, empowering stakeholders to optimize infrastructure utilization and improve network efficiency.

Keywords: predictive analytics, traffic flow, travel time estimation, urban transportation, machine learning, traffic management

Procedia PDF Downloads 58

Authors: Azzi Mohammed Amin

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Summary: This note aims to demonstrate the impact that comes out of electronic commerce to improve the quality of banking services and to answer the questions raised in the problem; it also aims to find out the methods applied in the banks to improve the quality of banking. And it identified a conceptual framework for electronic commerce and electronic banking. In addition, the inclusion of case study includes the Algerian Popular Credit Bank to measure the impact of electronic commerce on the quality of banking services. Has been focusing on electronic banking services as a field of modern knowledge, including fields characterized by high module in content and content, where banking management concluded that the service and style of electronic submission is the only area to compete and improve their quality. After studying the exploration of some of the banks operating in Algeria, and concluded that the majority relies sites, especially on the Internet, to introduce themselves and their affiliates as well as the definition of customer coverage for traditional and electronic, which are still at the beginning of the road where only some plastic cards, e-Banking, Bank of cellular, ATM and fast transfers. The establishment of an electronic network that requires the use of an effective banking system overall settlement of all economic sectors also requires the Algerian banks to be ready to receive this technology through the modernization of management and modernization of services (expand the use of credit cards, electronic money, and expansion of the Internet). As well as the development of the banking media to contribute to the dissemination of electronic banking culture in the community. Has been reached that the use of the communications revolution has made e-banking services inevitable impose itself in determining the future of banks and development, has also been reached that there is the impact of electronic commerce on the improvement of banking services through the provision of the information base and extensive refresher on-site research and development, and apply strategies Marketing, all of which help banks to increase the performance of its services, despite the presence of some of the risks of the means of providing electronic service and not the nature of the service itself and clear impact also by changing the shape or location of service from traditional to electronic which works to reduce and the costs of providing high-quality service and thus access to the largest segment.

Keywords: e-commerce, e-banking, impact e-commerce, B2C

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Authors: Lianzhong Zhang, Chao Huang

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Sea-land segmentation is a basic step in many tasks such as sea surface monitoring and ship detection. The existing sea-land segmentation algorithms have poor segmentation accuracy, and the parameter adjustments are cumbersome and difficult to meet actual needs. Also, the current sea-land segmentation adopts traditional deep learning models that use Convolutional Neural Networks (CNN). At present, the transformer architecture has achieved great success in the field of natural images, but its application in the field of radar images is less studied. Therefore, this paper proposes a sea-land segmentation method based on the transformer architecture to strengthen edge supervision. It uses a self-attention mechanism with a gating strategy to better learn relative position bias. Meanwhile, an additional edge supervision branch is introduced. The decoder stage allows the feature information of the two branches to interact, thereby improving the edge precision of the sea-land segmentation. Based on the Gaofen-3 satellite image dataset, the experimental results show that the method proposed in this paper can effectively improve the accuracy of sea-land segmentation, especially the accuracy of sea-land edges. The mean IoU (Intersection over Union), edge precision, overall precision, and F1 scores respectively reach 96.36%, 84.54%, 99.74%, and 98.05%, which are superior to those of the mainstream segmentation models and have high practical application values.

Keywords: SAR, sea-land segmentation, deep learning, transformer

Procedia PDF Downloads 149

Authors: Suleiman Garba, Haruna Ishaku

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Information and communication technology (ICT) includes any communication device encompassing: radio, television, cellular phones, computer, satellite systems and so on, as well as the various services and applications associated with them. The significance of ICT cannot be over-emphasized in education. The teaching and learning processes have integrated with the application of ICTs for effectiveness and enhancement of academic performance among the students. Today, as the educational sector is faced with series of changes and reforms, it was noted that the problem of information technology illiteracy was a serious one among the schools’ teachers in the country as it cuts across primary, secondary schools and tertiary institutions. This study investigated the impact of ICT on the academic performance of senior secondary schools students in Gwagwalada Area Council of Federal Capital Territory (FCT), Abuja. A sample of 120 SSS III students was involved in the study. They were selected by using simple random sampling technique. A questionnaire was developed and validated through expert judgement and reliability co-efficient of 0.81 was obtained. It was used to gather relevant data from the respondents. Findings revealed that there was positive impact of ICT on academic performance of senior secondary schools students. The findings indicated the causes of poor academic performance among the students as lack of qualified teachers to teach in schools, peer group influence, and bullying. Significantly, the findings revealed that ICT had a positive impact on students’ academic performance. The null hypotheses were tested using t-test at 0.05 level of significance. It was discovered that there was significant difference between male and female secondary schools’ students' impact of ICT on academic performance in Gwagawalada Area Council of FCT-Abuja. Based on these findings, some recommendations were made which include: adequate funds should be provided towards procurement of ICT resources, relevant textbooks to enhance students’ active participation in learning processes and students should be provided with internet accessibility at inexpensive rate so as to create a platform for accessing useful information in the pursuit of academic excellence.

Keywords: academic performance, impact, information communication technology, schools, students

Procedia PDF Downloads 207

Authors: Ju-Hong Lee, Ching-Wei Liao

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Owing to close antenna spacing between antenna sensors within a compact space, a part of data in one antenna sensor would outflow to other antenna sensors when the antenna sensors in an antenna array operate simultaneously. This phenomenon is called mutual coupling effect (MCE). It has been shown that the performance of antenna array systems can be degraded when the antenna sensors are in close proximity. Especially, in a systems equipped with massive antenna sensors, the degradation of beamforming performance due to the MCE is significantly inevitable. Moreover, it has been shown that even a small angle error between the true direction angle of the desired signal and the steering angle deteriorates the effectiveness of an array beamforming system. However, the true direction vector of the desired signal may not be exactly known in some applications, e.g., the application in land mobile-cellular wireless systems. Therefore, it is worth developing robust techniques to deal with the problem due to the MCE and steering angle error for array beamforming systems. In this paper, we present an efficient technique for performing adaptive beamforming with robust capabilities against the MCE and the steering angle error. Only the data vector received by an antenna array is required by the proposed technique. By using the received array data vector, a correlation matrix is constructed to replace the original correlation matrix associated with the received array data vector. Then, the mutual coupling matrix due to the MCE on the antenna array is estimated through a recursive algorithm. An appropriate estimate of the direction angle of the desired signal can also be obtained during the recursive process. Based on the estimated mutual coupling matrix, the estimated direction angle, and the reconstructed correlation matrix, the proposed technique can effectively cure the performance degradation due to steering angle error and MCE. The novelty of the proposed technique is that the implementation procedure is very simple and the resulting adaptive beamforming performance is satisfactory. Simulation results show that the proposed technique provides much better beamforming performance without requiring complicated complexity as compared with the existing robust techniques.

Keywords: adaptive beamforming, mutual coupling effect, recursive algorithm, steering angle error

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Authors: Yong Zhao, Jian He, Cheng Zhang

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Cardiovascular diseases caused by hypertension are extremely threatening to human health, and early diagnosis of hypertension can save a large number of lives. Traditional hypertension detection methods require special equipment and are difficult to detect continuous blood pressure changes. In this regard, this paper first analyzes the principle of heart rate variability (HRV) and introduces sliding window and power spectral density (PSD) to analyze the time domain features and frequency domain features of HRV, and secondly, designs an HRV-based hypertension prediction network by combining Resnet, attention mechanism, and multilayer perceptron, which extracts the frequency domain through the improved ResNet18 features through a modified ResNet18, its fusion with time-domain features through an attention mechanism, and the auxiliary prediction of hypertension through a multilayer perceptron. Finally, the network was trained and tested using the publicly available SHAREE dataset on PhysioNet, and the test results showed that this network achieved 92.06% prediction accuracy for hypertension and outperformed K Near Neighbor(KNN), Bayes, Logistic, and traditional Convolutional Neural Network(CNN) models in prediction performance.

Keywords: feature extraction, heart rate variability, hypertension, residual networks

Procedia PDF Downloads 78

Authors: F. A. Olutoge, O.R. Olulope, M. O. Odelola

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This study investigates the strength potentials of groundnut husk ash as partial cement replacement in mortar and also develops a predictive model using Artificial Neural Network. Groundnut husks sourced from Ogbomoso, Nigeria, was sun dried, calcined to ash in a furnace at a controlled temperature of 600⁰ C for a period of 6 hours, and sieved through the 75 microns. The ash was subjected to chemical analysis and setting time test. Fine aggregate (sand) for the mortar was sourced from Ado Ekiti, Nigeria. The cement: GHA constituents were blended in ratios 100:0, 95:5, 90:10, 85:15 and 80:20 %. The sum of SiO₂, Al₂O₃, and Fe₂O₃ content in GHA is 26.98%. The compressive strength for mortars PC, GHA5, GHA10, GHA15, and GHA20 ranged from 6.3-10.2 N/mm² at 7days, 7.5-12.3 N/mm² at 14 days, 9.31-13.7 N/mm² at 28 days, 10.4-16.7 N/mm² at 56days and 13.35- 22.3 N/mm² at 90 days respectively, PC, GHA5 and GHA10 had competitive values up to 28 days, but GHA10 gave the highest values at 56 and 90 days while GHA20 had the lowest values at all ages due to dilution effect. Flexural strengths values at 28 days ranged from 1.08 to 1.87 N/mm² and increased to a range of 1.53-4.10 N/mm² at 90 days. The ANN model gave good prediction for compressive strength of the mortars. This study has shown that groundnut husk ash as partial cement replacement improves the strength properties of mortar.

Keywords: compressive strength, groundnut husk ash, mortar, pozzolanic index

Procedia PDF Downloads 141

Authors: Aoxue Yang, Weili Tian, Yonghe Wu, Haikun Liu

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Brain tumor stem cells (BTSCs) are resistant to therapy and give rise to recurrent tumors. These rare and elusive cells are likely to disseminate during cancer progression, and some may enter dormancy, remaining viable but not increasing. The identification of dormant BTSCs is thus necessary to design effective therapies for glioblastoma (GBM) patients. Little progress has been made in therapeutic treatment of glioblastoma in the last decade despite rapid progress in molecular understanding of brain tumors1. Here we show that the stress hormone glucocorticoid is essential for the maintenance of brain tumor stem cells (BTSCs), which are resistant to conventional therapy. The glucocorticoid receptor (GR) regulates metabolic plasticity and chemoresistance of the dormant BTSC via controlling expression of GPD1 (glycerol-3-phosphate dehydrogenase 1), which is an essential regulator of lipid metabolism in BTSCs. Genomic, lipidomic and cellular analysis confirm that GR/GPD1 regulation is essential for BTSCs metabolic plasticity and survival. We further demonstrate that the GR agonist dexamethasone (DEXA), which is commonly used to control edema in glioblastoma, abolishes the effect of chemotherapy drug temozolomide (TMZ) by upregulating GPD1 and thus promoting tumor cell dormancy in vivo, this provides a mechanistic explanation and thus settle the long-standing debate of usage of steroid in brain tumor patient edema control. Pharmacological inhibition of GR/GPD1 pathway disrupts metabolic plasticity of BTSCs and prolong animal survival, which is superior to standard chemotherapy. Patient case study shows that GR antagonist mifepristone blocks tumor progression and leads to symptomatic improvement. This study identifies an important mechanism regulating cancer stem cell dormancy and provides a new opportunity for glioblastoma treatment.

Keywords: cancer stem cell, dormancy, glioblastoma, glycerol-3-phosphate dehydrogenase 1, glucocorticoid receptor, dexamethasone, RNA-sequencing, phosphoglycerides.

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Authors: Niousha Bagheri Khulenjani, Mohammad Saniee Abadeh

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Learning from very big datasets is a significant problem for most present data mining and machine learning algorithms. MicroRNA (miRNA) is one of the important big genomic and non-coding datasets presenting the genome sequences. In this paper, a hybrid method for the classification of the miRNA data is proposed. Due to the variety of cancers and high number of genes, analyzing the miRNA dataset has been a challenging problem for researchers. The number of features corresponding to the number of samples is high and the data suffer from being imbalanced. The feature selection method has been used to select features having more ability to distinguish classes and eliminating obscures features. Afterward, a Convolutional Neural Network (CNN) classifier for classification of cancer types is utilized, which employs a Genetic Algorithm to highlight optimized hyper-parameters of CNN. In order to make the process of classification by CNN faster, Graphics Processing Unit (GPU) is recommended for calculating the mathematic equation in a parallel way. The proposed method is tested on a real-world dataset with 8,129 patients, 29 different types of tumors, and 1,046 miRNA biomarkers, taken from The Cancer Genome Atlas (TCGA) database.

Keywords: cancer classification, feature selection, deep learning, genetic algorithm

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Authors: Luis Fonseca, Filipe Cabral Pinto, Susana Sargento

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The increase of the world population, especially in large urban centers, has resulted in new challenges particularly with the control and optimization of public safety. Thus, in the present work, a solution is proposed for the prediction of criminal occurrences in a city based on historical data of incidents and demographic information. The entire research and implementation will be presented start with the data collection from its original source, the treatment and transformations applied to them, choice and the evaluation and implementation of the Machine Learning model up to the application layer. Classification models will be implemented to predict criminal risk for a given time interval and location. Machine Learning algorithms such as Random Forest, Neural Networks, K-Nearest Neighbors and Logistic Regression will be used to predict occurrences, and their performance will be compared according to the data processing and transformation used. The results show that the use of Machine Learning techniques helps to anticipate criminal occurrences, which contributed to the reinforcement of public security. Finally, the models were implemented on a platform that will provide an API to enable other entities to make requests for predictions in real-time. An application will also be presented where it is possible to show criminal predictions visually.

Keywords: crime prediction, machine learning, public safety, smart city

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Authors: Antonio Montes, Antonio Cozar, Clara Pereyra, Diego Valor, Enrique Martinez de la Ossa

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Tissues and organs can be damaged because of traumatism, congenital illnesses, or cancer and the traditional therapeutic alternatives, such as surgery, cannot usually completely repair the damaged tissues. Tissue engineering allows regeneration of the patient's tissues, reducing the problems caused by the traditional methods. Scaffolds, polymeric structures with interconnected porosity, can be promoted the proliferation and adhesion of the patient’s cells in the damaged area. Furthermore, by means of impregnation of the scaffold with beneficial active substances, tissue regeneration can be induced through a drug delivery process. The objective of the work is the fabrication of a PVA scaffold coated with Gallic Acid and polypyrrole through a one-step foaming and impregnation process using the SSI technique (Supercritical Solvent Impregnation). In this technique, supercritical CO₂ penetrates into the polymer chains producing the plasticization of the polymer. In the depressurization step a CO₂ cellular nucleation and growing to take place to an interconnected porous structure of the polymer. The foaming process using supercritical CO₂ as solvent and expansion agent presents advantages compared to the traditional scaffolds’ fabrication methods, such as the polymer’s high solubility in the solvent or the possibility of carrying out the process at a low temperature, avoiding the inactivation of the active substance. In this sense, the supercritical CO₂ avoids the use of organic solvents and reduces the solvent residues in the final product. Moreover, this process does not require long processing time that could cause the stratification of substance inside the scaffold reducing the therapeutic efficiency of the formulation. An experimental design has been carried out to optimize the SSI technique operating conditions, as well as a study of the morphological characteristics of the scaffold for its use in tissue engineerings, such as porosity, conductivity or the release profiles of the active substance. It has been proved that the obtained scaffolds are partially porous, conductors of electricity and are able to release Gallic Acid in the long term.

Keywords: scaffold, foaming, supercritical, PVA, polypyrrole, gallic acid

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Authors: Nilesh Bandekar, Sumita Dasgupta, Luis Alberto Allcahuaman Huaya, Souvik Manna

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Cryptobiosis is a dormant state where all measurable metabolic activities are at a halt, allowing an organism to survive in extreme conditions like low temperature (cryobiosis), extreme drought (anhydrobiosis), etc. This phenomenon is observed especially in tardigrades that can retain this state for decades depending on the abiotic environmental conditions. On returning to favorable conditions, tardigrades re-attain a metabolically active state. In this study, cyanobacteria as a model organism are being chosen to induce cryptobiosis for its effective preservation over a long period of time. Preserving cyanobacteria using this strategy will have multiple space applications because of its ability to produce oxygen. In addition, research has shown the survivability of this organism in space for a certain period of time. Few species of cyanobacterial residents of the soil such as Microcoleus, are able to survive in extreme drought as well. This work specifically focuses on Synechococcus elongatus, an endolith cyanobacteria with multiple benefits. It has the capability to produce 25% oxygen in water bodies. It utilizes carbon dioxide to produce oxygen via photosynthesis and also uses carbon dioxide as an energy source to form glucose via the Calvin cycle. There is a fair possibility of initiating cryptobiosis in such an organism by inducing certain proteins extracted from tardigrades such as Heat Shock Proteins (Hsp27 and Hsp30c) and/or hydrophilic Late Embryogenesis Abundant proteins (LEA). Existing methods like cryopreservation are difficult to execute in space keeping in mind their cost and heavy instrumentation. Also, extensive freezing may cause cellular damage. Therefore, cryptobiosis-induced cyanobacteria for its transportation from Earth to Mars as a part of future terraforming missions on Mars will save resources and increase the effectiveness of preservation. Finally, Cyanobacteria species like Synechococcus elongatus can also produce oxygen and glucose on Mars in favorable conditions and holds the key to terraforming Mars.

Keywords: cryptobiosis, cyanobacteria, glucose, mars, Synechococcus elongatus, tardigrades

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Authors: Hiroki Tsuneda, Takamasa Suzuki, Hideki Yamamoto, Kimito Kawamura, Eiji Tamura, Katharina Wochner, Roberto Plasenzotti

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Flow property of human blood is one of the important factors on the prevention of the circulatory condition such as a high blood pressure, a diabetes mellitus, and a cardiac infarction. However, the measurement of flow property of human blood, especially blood viscosity, is not so easy, because of their coagulation or aggregation behaviors after taking a sample from blood vessel. In the experiment, some kinds of anticoagulant were added into the human blood to avoid its solidification. Anticoagulant used in the blood test has been chosen for each purpose of blood test, for anticoagulant effect on blood is different mechanism for each. So that, there is a problem that the evaluation of measured blood property with different anticoagulant is so difficult. Therefore, it is so important to make clear the difference of anticoagulant effect on the blood property. In the previous work, a compact-size falling needle rheometer (FNR) has been developed in order to measure the flow property of human blood such as a flow curve, an apparent viscosity. It was found that FNR system can apply to a rheometer or a viscometry for various experimental conditions for not only human blood but also mammalians blood. In this study, the measurements of human blood viscosity with different anticoagulant (EDTA and Heparin) were carried out using newly developed FNR system. The effect of anticoagulant on blood viscosity was also tested by using the standard liquid for each. The accuracy on the viscometry was also tested by using the standard liquid for calibrating materials (JS-10, JS-20) and observed data have satisfactory agreement with reference data around 1.0% at 310K. The flow curve of six males and females with different anticoagulant were measured using FNR. In this experiment, EDTA and Heparin were chosen as anticoagulant for blood. Heparin can inhibit the coagulation of human blood by activating the body of anti-thrombin. To examine the effect of human blood viscosity on anticoagulant, flow curve was measured at high shear rate (＞350s-1), and apparent viscosity of each person were determined with different anticoagulant. The apparent viscosity of human blood with heparin was 2%-9% higher than that with EDTA. However, the difference of blood viscosity for two anticoagulants for same blood was different for each. Further discussion, we need the consideration of effect on other physical property, such as cellular component and plasma component.

Keywords: falling-needle rheometer, human blood, viscosity, anticoagulant

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Authors: Vasudha Devi, Arul Amuthan, K. Narayanan, Praveen KS, Venkata Rao J

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Background: Siddha Medicine is one of the Indian traditional medical systems, in which the cancer disease is mentioned as 'putrunoi' which literally means the disease of growth like termite mound. There are number of formulations available for the treatment of cancer disease. Neeradi muthu vallathymezugu (NMV) and thamira kattu chendooram (TKC) are two drugs commonly prescribed by Siddha physicians. These drugs have been clinically reported to be safe and effective when given orally. Though these formulations are in practice for centuries, no efforts have been made to standardize them and explore their anti-cancer potential systematically. Objective: To compare the cytotoxic activity of NMV and TKC with doxorubicin using cancer cell lines. Materials and methods: For this study, ethanol extract of NMV was taken, whereas TKC was used as such. In vitro cytotoxic activity was evaluated by sulphorhodamine (SRB) assay against human hepatic cancer cells (HepG2), human breast cancer cells (MCF-7) and human cervical cancer cells [KeLa]. Doxorubicin was used as the standard. The SRB assay is based on the ability of cellular proteins to bind with sulphorhodamine-B. The number of live cells in drug treated cell lines directly affects the color formation in the assay, which is estimated calorimetrically by measuring the absorbance at 540 nm to calculate the cytotoxicity (inhibitory concentration - IC50 value) of the drug. Results: The IC50values of NMV, TKC and doxorubicin against HepG2 were 3.08 µg/ml, 20.21 µg/ml and 1.21µg/ml respectively. In MCF-7, it was 11.75 µg/ml, 17.67 µg/ml and 2.8µg/ml. In HeLa, the values were 24.76 µg/ml, 73.35 µg/ml and 1.12µg/ml. Conclusions: The study proves the possible anti-cancer potential of these two formulations. Compared to TKC, NMV showed good cytotoxic effect even at low dose. Human hepatic cancer cells responded well even at very low dose, when compared to other cancer cells. Though, cytotoxic potential of these compounds was found to be less compared to doxorubicin, the isolated lead compound may have the potential to be used as an anticancer drug clinically.

Keywords: Neeradi muthu vallathymezugu (Hydnocarpus laurifolia), thamira kattu chendooram, cytotoxicity, in-vitro, Siddha Medicine

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Authors: Lindani Mbatha, Suvendi Rimer, Mpho Gololo

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Image segmentation is the process of dividing an image into several sections, such as the object's background and the foreground. It is a critical technique in both image-processing tasks and computer vision. Most of the image segmentation algorithms have been developed for gray-scale images and little research and algorithms have been developed for the color images. Most image segmentation algorithms or techniques vary based on the input data and the application. Nearly all of the techniques are not suitable for noisy environments. Most of the work that has been done uses the Markov Random Field (MRF), which involves the computations and is said to be robust to noise. In the past recent years' image segmentation has been brought to tackle problems such as easy processing of an image, interpretation of the contents of an image, and easy analysing of an image. This article reviews and summarizes some of the image segmentation techniques and algorithms that have been developed in the past years. The techniques include neural networks (CNN), edge-based techniques, region growing, clustering, and thresholding techniques and so on. The advantages and disadvantages of medical ultrasound image segmentation techniques are also discussed. The article also addresses the applications and potential future developments that can be done around image segmentation. This review article concludes with the fact that no technique is perfectly suitable for the segmentation of all different types of images, but the use of hybrid techniques yields more accurate and efficient results.

Keywords: clustering-based, convolution-network, edge-based, region-growing

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Authors: Shadia Al-Bahlani, Ruqaya Al-Rashdi, Shadia Al-Sinawi, Maya Al-Bahri

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Background: Breast cancer is the most common cancer in women worldwide. Triple-negative breast cancer (TNBC) is an aggressive type of breast cancer, which is defined by the absence of Estrogen (ER), Progesterone (PR) and Human epidermal growth factor (Her-2) receptors. The calpain system plays an important role in many cellular processes including apoptosis, necrosis, cell signaling and proliferation. The role of clapins in pathogenesis and tumor progression has been studied in certain cancer types; however, its definite role is not yet established in breast cancer especially in the TNBC subtype. Objectives: This study aims to measure calpain-1 expression and correlate this measurement with the proliferating/apoptotic index as well with the prognostic factors in TNBC patients’ tissue. Materials and Methods: Thirty nine paraffin blocks from patients diagnosed with TNBC were used to measure the expression of calpain-1 and Ki-67 (proliferating marker) proteins using immunohistochemistry. Apoptosis was assessed morphological and biochemically using conventional Haematoxylin and Eosin (H&E) staining method and terminal deoxynucleotidyl transferase-mediate dUTP nick and labeling (TUNEL) assay respectively. Data was statistically analyzed using Pearson X2 test of association. Results: Calpain-1 content was visualized in the nucleus of the TNBC cells and its expression varied from low to high among the patients tissue. Calpain expression showed no significant correlation with the proliferating/apoptotic index as well with the clinicopathological variables. Apoptotic counts quantified by H&E staining showed significant association with the apoptotic TUNEL assay, validating both approaches. Conclusion: Although calpain-1 expression showed no significant association with the clinical outcome, its variable level of expression might indicate a hidden role in breast cancer tissue. Larger number of samples and different mode of assessments are needed to fully investigate such role. Exploring the involvement of calpain-1 in cancer progression might help in considering it as a biomarker of breast cancer.

Keywords: breast cancer, calpain, apoptosis, prognosis

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Authors: Sunidhi Syal, Rasangi Tennakoon, Patrick O'Donoghue

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Polyglutamine (polyQ) diseases are a group of diseases related to neurodegeneration caused by repeats of the amino acid glutamine (Q) in the DNA, which translates into an elongated polyQ tract in the protein. The pathological explanation is that the polyQ tract forms cytotoxic aggregates in the neurons, leading to their degeneration. There are no cures or preventative efforts established for these diseases as of today, although the symptoms of these diseases can be relieved. This study specifically focuses on Huntington's disease, which is a type of polyQ disease in which aggregation is caused by the extended cytosine, adenine, guanine (CUG) codon repeats in the huntingtin (HTT) gene, which encodes for the huntingtin protein. Using this principle, we attempted to create six models, which included mutating wildtype tRNA alanine variant tRNA-AGC-8-1 to have glutamine anticodons CUG and UUG so serine is incorporated at glutamine sites in poly Q tracts. In the process, we were successful in obtaining tAla-8-1 CUG mutant clones in the HTTexon1 plasmids with a polyQ tract of 23Q (non-pathogenic model) and 74Q (disease model). These plasmids were transfected into mouse neuroblastoma cells to characterize protein synthesis and aggregation in normal and mistranslating cells and to investigate the effects of glutamines replaced with alanines on the disease phenotype. Notably, we observed no noteworthy differences in mean fluorescence between the CUG mutants for 23Q or 74Q; however, the Triton X-100 assay revealed a significant reduction in insoluble 74Q aggregates. We were unable to create a tAla-8-1 UUG mutant clone, and determining the difference in the effects of the two glutamine anticodons may enrich our understanding of the disease phenotype. In conclusion, by generating structural disruption with the amino acid alanine, it may be possible to find ways to minimize the toxicity of Huntington's disease caused by these polyQ aggregates. Further research is needed to advance knowledge in this field by identifying the cellular and biochemical impact of specific tRNA variants found naturally in human genomes.

Keywords: Huntington's disease, polyQ, tRNA, anticodon, clone, overlap PCR

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Authors: Aakansha Gupta, Neha Vadnere, Tapasvi Soni, M. Anbarsi

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Protein secondary structure prediction is one of the most important goals pursued by bioinformatics and theoretical chemistry; it is highly important in medicine and biotechnology. Some aspects of protein functions and genome analysis can be predicted by secondary structure prediction. This is used to help annotate sequences, classify proteins, identify domains, and recognize functional motifs. In this paper, we represent protein secondary structure as a mathematical model. To extract and predict the protein secondary structure from the primary structure, we require a set of parameters. Any constants appearing in the model are specified by these parameters, which also provide a mechanism for efficient and accurate use of data. To estimate these model parameters there are many algorithms out of which the most popular one is the EM algorithm or called the Expectation Maximization Algorithm. These model parameters are estimated with the use of protein datasets like RS126 by using the Bayesian Probabilistic method (data set being categorical). This paper can then be extended into comparing the efficiency of EM algorithm to the other algorithms for estimating the model parameters, which will in turn lead to an efficient component for the Protein Secondary Structure Prediction. Further this paper provides a scope to use these parameters for predicting secondary structure of proteins using machine learning techniques like neural networks and fuzzy logic. The ultimate objective will be to obtain greater accuracy better than the previously achieved.

Keywords: model parameters, expectation maximization algorithm, protein secondary structure prediction, bioinformatics

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Authors: L. Ebralidze, A. Tsertsvadze, D. Berashvili, A. Bakuridze

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Current cancer therapy strategies are based on surgery, radiotherapy and chemotherapy. The problems associated with chemotherapy are one of the biggest challenges for clinical medicine. These include: low specificity, broad spectrum of side effects, toxicity and development of cellular resistance. Therefore, anti-cance drugs need to be develop urgently. Particularly, in order to increase efficiency of anti-cancer drugs and reduce their side effects, scientists work on formulation of nano-drugs. The objective of this study was to develop composition and technology of vincristine nanoparticles using high-molecular carbohydrates of plant origin. Plant polysacharides, particularly, soy bean seed polysaccharides, flaxseed polysaccharides, citrus pectin, gum arabic, sodium alginate were used as objects. Based on biopharmaceutical research, vincristine containing nanoparticle formulations were prepared. High-energy emulsification and solvent evaporation methods were used for preparation of nanosystems. Polysorbat 80, polysorbat 60, sodium dodecyl sulfate, glycerol, polyvinyl alcohol were used in formulation as emulsifying agent and stabilizer of the system. The ratio of API and polysacharides, also the type of the stabilizing and emulsifying agents are very effective on the particle size of the final product. The influence of preparation technology, type and concentration of stabilizing agents on the properties of nanoparticles were evaluated. For the next stage of research, nanosystems were characterized. Physiochemical characterization of nanoparticles: their size, shape, distribution was performed using Atomic force microscope and Scanning electron microscope. The present study explored the possibility of production of NPs using plant polysaccharides. Optimal ratio of active pharmaceutical ingredient and plant polysacharids, the best stabilizer and emulsifying agent was determined. The average range of nanoparticles size and shape was visualized by SEM.

Keywords: nanoparticles, target delivery, natural high molecule carbohydrates, surfactants

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Authors: Kenichi Yamahara, Makiko Ohshima, Shunsuke Ohnishi, Hidetoshi Tsuda, Akihiko Taguchi, Toshihiro Soma, Hiroyasu Ogawa, Jun Yoshimatsu, Tomoaki Ikeda

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We have previously reported the therapeutic potential of rat fetal membrane(FM)-derived mesenchymal stem cells (MSCs) using various rat models including hindlimb ischemia, autoimmune myocarditis, glomerulonephritis, renal ischemia-reperfusion injury, and myocardial infarction. In this study, 1) we isolated and characterized MSCs from human amnion and chorion; 2) we examined their differences in the expression profile of growth factors and cytokines; and 3) we investigated the therapeutic potential and difference of these MSCs using murine hindlimb ischemia and acute graft-versus-host disease (GVHD) models. Isolated MSCs from both amnion and chorion layers of FM showed similar morphological appearance, multipotency, and cell-surface antigen expression. Conditioned media obtained from amnion- and chorion-derived MSCs inhibited cell death caused by serum starvation or hypoxia in endothelial cells and cardiomyocytes. Amnion and chorion MSCs secreted significant amounts of angiogenic factors including HGF, IGF-1, VEGF, and bFGF, although differences in the cellular expression profile of these soluble factors were observed. Transplantation of human amnion or chorion MSCs significantly increased blood flow and capillary density in a murine hindlimb ischemia model. In addition, compared to human chorion MSCs, human amnion MSCs markedly reduced T-lymphocyte proliferation with the enhanced secretion of PGE2, and improved the pathological situation of a mouse model of GVHD disease. Our results highlight that human amnionand chorion-derived MSCs, which showed differences in their soluble factor secretion and angiogenic/immuno-suppressive function, could be ideal cell sources for regenerative medicine.

Keywords: amnion, chorion, fetal membrane, mesenchymal stem cells

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Authors: Nuri Caglayan, H. Kursat Celik

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There are many factors that influence the health and productivity of the animals in livestock production fields, including temperature, humidity, carbon dioxide (CO2), ammonia (NH3), hydrogen sulfide (H2S), physical activity and particulate matter. High NH3 concentrations reduce feed consumption and cause daily weight gain. At high concentrations, H2S causes respiratory problems and CO2 displace oxygen, which can cause suffocation or asphyxiation. Good air quality in livestock facilities can have an impact on the health and well-being of animals and humans. Air quality assessment basically depends on strictly given limits without taking into account specific local conditions between harmful gases and other meteorological factors. The stated limitations may be eliminated. using controlling systems based on neural networks and fuzzy logic. This paper describes a fuzzy logic based ventilation algorithm, which can calculate different fan speeds under pre-defined boundary conditions, for removing harmful gases from the production environment. In the paper, a fuzzy logic model has been developed based on a Mamedani’s fuzzy method. The model has been built on MATLAB software. As the result, optimum fan speeds under pre-defined boundary conditions have been presented.

Keywords: air quality, fuzzy logic model, livestock housing, fan speed

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Authors: Chanya Inprasit, Yi-Wen Lin

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Motion sickness (MS) is an acute disorder that occurs in healthy persons without considering gender, age or ethnicity worldwide. All signs and symptoms of MS are the results of confliction and mismatch among neural signal inputs. It is known that no singular remedy works for everybody, and electroacupuncture (EA) is one of the popular alternative therapies used for MS. Our study utilized a mouse model in order to exclude any psychological factors of MS and EA. Mice lack an emetic reflex. Therefore pica behavior, which is a normal consumption of non-nutritive substances, was found to measure the response of MS in mice. In the laboratory, Kaolin was used as a non-nutrient food substance instead of natural substances lacking nutritional value such as wood, cloth, charcoal, soil or grass. It was hypothesized that EA treatment could reduce the symptoms of MS through the TRPV1 pathways. The results of pica behavior showed a significantly increased intake of kaolin in the MS group throughout the experiment period. Moreover, the Kaolin intake of the EA group decreased to the average baseline of the control group. There was no recorded difference in the food and water intake of each group. The results indicated an increase of the TRPV1, pERK, pJNK and pmTOR protein levels in the thalamus after MS stimulation, and a significant decrease in the EA group compared with that of the control group. These findings suggest that TRPV1 pathways are associated in MS mechanisms and can be reduced by EA.

Keywords: electroacupuncture, motion sickness, Thalamus, TRPV1

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Authors: Daniel Aberdam

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Haploinsufficiency of PAX6 in humans is the main cause of congenital aniridia, a rare eye disease characterized by reduced visual acuity. Patients have also progressive disorders including cataract, glaucoma and corneal abnormalities making their condition very challenging to manage. Aniridia-related keratopathy (ARK), caused by a combination of factors including limbal stem-cell deficiency, impaired healing response, abnormal differentiation, and infiltration of conjunctival cells onto the corneal surface, affects up to 95% of patients. It usually begins in the first decade of life resulting in recurrent corneal erosions, sub-epithelial fibrosis with corneal decompensation and opacification. Unfortunately, current treatment options for aniridia patients are currently limited. Although animal models partially recapitulate this disease, there is no in vitro cellular model of AKT needed for drug/therapeutic tools screening and validation. We used genome editing (CRISPR/Cas9 technology) to introduce a nonsense mutation found in patients into one allele of the PAX6 gene into limbal stem cells. Resulting mutated clones, expressing half of the amount of PAX6 protein and thus representative of haploinsufficiency were further characterized. Sequencing analysis showed that no off-target mutations were induced. The mutated cells displayed reduced cell proliferation and cell migration but enhanced cell adhesion. Known PAX6 targets expression was also reduced. Remarkably, addition of soluble recombinant PAX6 protein into the culture medium was sufficient to activate endogenous PAX6 gene and, as a consequence, rescue the phenotype. It strongly suggests that our in vitro model recapitulates well the epithelial defect and becomes a powerful tool to identify drugs that could rescue the corneal defect in patients. Furthermore, we demonstrate that the homeotic transcription factor Pax6 is able to be uptake naturally by recipient cells to function into the nucleus.

Keywords: Pax6, crispr/cas9, limbal stem cells, aniridia, gene therapy

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Authors: Jay L. Fu

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Patients with Alzheimer's disease progressively lose their memory and thinking skills and, eventually, the ability to carry out simple daily tasks. The disease is irreversible, but early detection and treatment can slow down the disease progression. In this research, publicly available MRI data and demographic data from 373 MRI imaging sessions were utilized to build models to predict dementia. Various machine learning models, including logistic regression, k-nearest neighbor, support vector machine, random forest, and neural network, were developed. Data were divided into training and testing sets, where training sets were used to build the predictive model, and testing sets were used to assess the accuracy of prediction. Key risk factors were identified, and various models were compared to come forward with the best prediction model. Among these models, the random forest model appeared to be the best model with an accuracy of 90.34%. MMSE, nWBV, and gender were the three most important contributing factors to the detection of Alzheimer’s. Among all the models used, the percent in which at least 4 of the 5 models shared the same diagnosis for a testing input was 90.42%. These machine learning models allow early detection of Alzheimer’s with good accuracy, which ultimately leads to early treatment of these patients.

Keywords: Alzheimer's disease, clinical diagnosis, magnetic resonance imaging, machine learning prediction

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Authors: Soofiyan Atar, Adil Shaikh, Sahil Rajpurkar, Pragnesh Bhalala, Aniket Desai, Irfan Siddavatam

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Deep reinforcement learning (deep RL) algorithms leverage the symbolic power of complex controllers by automating it by mapping sensory inputs to low-level actions. Deep RL eliminates the complex robot dynamics with minimal engineering. Deep RL provides high-risk involvement by directly implementing it in real-world scenarios and also high sensitivity towards hyperparameters. Tuning of hyperparameters on a pneumatic quadruped robot becomes very expensive through trial-and-error learning. This paper presents an automated learning control for a pneumatic quadruped robot using sample efficient deep Q learning, enabling minimal tuning and very few trials to learn the neural network. Long training hours may degrade the pneumatic cylinder due to jerk actions originated through stochastic weights. We applied this method to the pneumatic quadruped robot, which resulted in a hopping gait. In our process, we eliminated the use of a simulator and acquired a stable gait. This approach evolves so that the resultant gait matures more sturdy towards any stochastic changes in the environment. We further show that our algorithm performed very well as compared to programmed gait using robot dynamics.

Keywords: model-based reinforcement learning, gait stability, supervised learning, pneumatic quadruped

Procedia PDF Downloads 293