Search results for: biological molecular networks

Authors: Bousnane Nour El Houda, Chennaf Ali, Yahia Mouloud, Benbia Souhila

Abstract:

Male infertility or the inability of a man to procreate is a major public health problem, where it is a leading cause of marital discord in several countries such Algeria. The objective of this work is to study some biological profiles of infertile men from the city of Batna/Algeria and to identify the causes of infertility in a population of infertile males to improve its management and to establish a good therapeutic strategy through a study that lasted 10 months in the Department of Urology of the University Hospital of Banta and on a population of 140 infertile subjects. For every man, series of assessments was performed to determine the exact causes of infertility. We found 102 cases of primary infertility against 38 cases of secondary infertility; the average age of men was 39.7 years, with a predominance of the age group (46-50 years). 34.29% of subjects had genital infections against 17.14% with varicocele. 132 men presented spermiologiques abnormalities; a asthénospermie (AS) in 27.27% of the cases, astheno-terato spermiea (OATS) 11.36% while Azoospermes showed 5.07%. Genital infections are the main causes of infertility (34.29%) of the cases. The results of spermocytogramme showed a predominance of head abnormalities (41.70%), while the flagellum abnormalities presented 33.83%. The dosage of the seminal plasma carnitine showed no pathological cases, which makes it difficult to know their association with infertility. By against some disturbances Fructose and Zinc have been reported.

Keywords: male infertility, spermogramme, spermocytogramme, biological profils

Procedia PDF Downloads 310

Authors: Sihem Benmimoune, Abdelbaki Lemgharbi, Ahmed Ait Yahia, Abdelkrim Kameli

Abstract:

Our study is based on chemical and phytochemical characterization of Artemisia absinthium L and in vitro tests to demonstrate the biological activities of essential oil and natural extract. A qualitative and quantitative comparison of the essential oil extracted by two extraction procedures was performed by analysis of CG/SM and the yield calculation. The method of hydrodistillation has a chemical composition and provides oil content than the best training water vapor. These oils are composed mainly of thujone followed chamazulene and ρ-cymene. The antimicrobial activity of wormwood oil was tested in vitro by two methods (agar diffusion and microdilution) on four plant pathogenic fungi (Aspergillus sp, Botrytis cinerea, Fusarium culmorum and Helminthosporium sp). The study of the antifungal effect showed that this oil has an inhibitory effect counterpart the microorganisms tested in particular the strain Botrytis cinerea. Otherwise, this activity depends on the nature of the oil and the germ itself. The antioxidant activity in vitro was studied with the DPPH method. The activity test shows that the oil and extract of Artemisia absinthium have a very low antioxidant capacity compared to the antioxidants used as a reference. The extract has a potentially high antiradical power not from its oil. The quantitative determinations of phenolic compounds by the Folin-Ciocalteu revealed that absinthe is low in total polyphenols and tannins.

Keywords: artemisia absinthium, biological activities, essential oil, extraction processes

Procedia PDF Downloads 329

Authors: Kyungae Jo, Eun Young Kim, Byungsoo Shin, Kwang Soon Shin, Hyung Joo Suh

Abstract:

Gamma-aminobutyric acid (GABA) and 5-hydroxytryptophan (5-HTP) are amino acids of digested nutrients or food ingredients and these can possibly be utilized as non-pharmacologic treatment for sleep disorder. We previously investigated the GABA/5-HTP mixture is the principal concept of sleep-promoting and activity-repressing management in nervous system of D. melanogaster. Two experiments in this study were designed to evaluate sleep-promoting effect of GABA/5-HTP mixture, to clarify the possible ratio of sleep-promoting action in the Drosophila invertebrate model system. Behavioral assays were applied to investigate distance traveled, velocity, movement, mobility, turn angle, angular velocity and meander of two amino acids and GABA/5-HTP mixture with caffeine treated flies. In addition, differentially expressed gene (DEG) analyses from next generation sequencing (NGS) were applied to investigate the signaling pathway and functional interaction network of GABA/5-HTP mixture administration. GABA/5-HTP mixture resulted in significant differences between groups related to behavior (p < 0.01) and significantly induced locomotor activity in the awake model (p < 0.05). As a result of the sequencing, the molecular function of various genes has relationship with motor activity and biological regulation. These results showed that GABA/5-HTP mixture administration significantly involved the inhibition of motor behavior. In this regard, we successfully demonstrated that using a GABA/5-HTP mixture modulates locomotor activity to a greater extent than single administration of each amino acid, and that this modulation occurs via the neuronal system, neurotransmitter release cycle and transmission across chemical synapses.

Keywords: sleep, γ-aminobutyric acid, 5-hydroxytryptophan, Drosophila melanogaster

Procedia PDF Downloads 296

Authors: Yemane Tadesse Gebreslassie, Fisseha Guesh Gebremeskel

Abstract:

Nanotechnology has made remarkable advancements in recent years, revolutionizing various scientific fields, industries, and research institutions through the utilization of metal and metal oxide nanoparticles. Among these nanoparticles, copper oxide nanoparticles (CuO NPs) have garnered significant attention due to their versatile properties and wide-range applications, particularly, as effective antimicrobial and anticancer agents. CuO NPs can be synthesized using different methods, including physical, chemical, and biological approaches. However, conventional chemical and physical approaches are expensive, resource-intensive, and involve the use of hazardous chemicals, which can pose risks to human health and the environment. In contrast, biological synthesis provides a sustainable and cost-effective alternative by eliminating chemical pollutants and allowing for the production of CuO NPs of tailored sizes and shapes. This comprehensive review focused on the green synthesis of CuO NPs using various biological resources, such as plants, microorganisms, and other biological derivatives. Current knowledge and recent trends in green synthesis methods for CuO NPs are discussed, with a specific emphasis on their biomedical applications, particularly in combating cancer and microbial infections. This review highlights the significant potential of CuO NPs in addressing these diseases. By capitalizing on the advantages of biological synthesis, such as environmental safety and the ability to customize nanoparticle characteristics, CuO NPs have emerged as promising therapeutic agents for a wide range of conditions. This review presents compelling findings, demonstrating the remarkable achievements of biologically synthesized CuO NPs as therapeutic agents. Their unique properties and mechanisms enable effective combating against cancer cells and various harmful microbial infections. CuO NPs exhibit potent anticancer activity through diverse mechanisms, including induction of apoptosis, inhibition of angiogenesis, and modulation of signaling pathways. Additionally, their antimicrobial activity manifests through various mechanisms, such as disrupting microbial membranes, generating reactive oxygen species, and interfering with microbial enzymes. This review offers valuable insights into the substantial potential of biologically synthesized CuO NPs as an alternative approach for future therapeutic interventions against cancer and microbial infections.

Keywords: biological synthesis, copper oxide nanoparticles, microbial infection, nanotechnology

Procedia PDF Downloads 44

Authors: Charalabos Antonatos, Mariza Panoutsopoulou, Georgios K. Georgakilas, Evangelos Evangelou, Yiannis Vasilopoulos

Abstract:

The extended tissue damage and severe clinical outcomes of autoimmune diseases, accompanied by the high annual costs to the overall health care system, highlight the need for an efficient therapy. Increasing knowledge over the pathophysiology of specific chronic inflammatory diseases, namely Psoriasis (PsO), Inflammatory Bowel Diseases (IBD) consisting of Crohn’s disease (CD) and Ulcerative colitis (UC), and Rheumatoid Arthritis (RA), has provided insights into the underlying mechanisms that lead to the maintenance of the inflammation, such as Tumor Necrosis Factor alpha (TNF-α). Hence, the anti-TNFα biological agents pose as an ideal therapeutic approach. Despite the efficacy of anti-TNFα agents, several clinical trials have shown that 20-40% of patients do not respond to treatment. Nowadays, high-throughput technologies have been recruited in order to elucidate the complex interactions in multifactorial phenotypes, with the most ubiquitous ones referring to transcriptome quantification analyses. In this context, a random effects meta-analysis of available gene expression cDNA microarray datasets was performed between responders and non-responders to anti-TNFα therapy in patients with IBD, PsO, and RA. Publicly available datasets were systematically searched from inception to 10th of November 2020 and selected for further analysis if they assessed the response to anti-TNFα therapy with clinical score indexes from inflamed biopsies. Specifically, 4 IBD (79 responders/72 non-responders), 3 PsO (40 responders/11 non-responders) and 2 RA (16 responders/6 non-responders) datasetswere selected. After the separate pre-processing of each dataset, 4 separate meta-analyses were conducted; three disease-specific and a single combined meta-analysis on the disease-specific results. The MetaVolcano R package (v.1.8.0) was utilized for a random-effects meta-analysis through theRestricted Maximum Likelihood (RELM) method. The top 1% of the most consistently perturbed genes in the included datasets was highlighted through the TopConfects approach while maintaining a 5% False Discovery Rate (FDR). Genes were considered as Differentialy Expressed (DEGs) as those with P ≤ 0.05, |log2(FC)| ≥ log2(1.25) and perturbed in at least 75% of the included datasets. Over-representation analysis was performed using Gene Ontology and Reactome Pathways for both up- and down-regulated genes in all 4 performed meta-analyses. Protein-Protein interaction networks were also incorporated in the subsequentanalyses with STRING v11.5 and Cytoscape v3.9. Disease-specific meta-analyses detected multiple distinct pro-inflammatory and immune-related down-regulated genes for each disease, such asNFKBIA, IL36, and IRAK1, respectively. Pathway analyses revealed unique and shared pathways between each disease, such as Neutrophil Degranulation and Signaling by Interleukins. The combined meta-analysis unveiled 436 DEGs, 86 out of which were up- and 350 down-regulated, confirming the aforementioned shared pathways and genes, as well as uncovering genes that participate in anti-inflammatory pathways, namely IL-10 signaling. The identification of key biological pathways and regulatory elements is imperative for the accurate prediction of the patient’s response to biological drugs. Meta-analysis of such gene expression data could aid the challenging approach to unravel the complex interactions implicated in the response to anti-TNFα therapy in patients with PsO, IBD, and RA, as well as distinguish gene clusters and pathways that are altered through this heterogeneous phenotype.

Keywords: anti-TNFα, autoimmune, meta-analysis, microarrays

Procedia PDF Downloads 162

Authors: Sukanya Devi Ramachandran, Vaishnavi Muralidharan, Kavya Chandrasekaran

Abstract:

Polycaprolactone is polymer belonging to the polyester family that has noticeable characteristics of biodegradability and biocompatibility which is essential for medical applications. Polycaprolactone is produced by the ring opening polymerization of the monomer epsilon-Caprolactone (ε-CL) which is a closed ester, comprising of seven-membered ring. This process is normally catalysed by metallic components such as stannous octoate. It is difficult to remove the catalysts after the reaction, and they are also toxic to the human body. An alternate route of using enzymes as catalysts is being employed to reduce the toxicity. Lipase enzyme is a subclass of esterase that can easily attack the ester bonds of ε-CL. This research paper throws light on the extraction of lipase from germinating sunflower seeds and the activity of the biocatalyst in the polymerization of ε-CL. Germinating Sunflower seeds were crushed with fine sand in phosphate buffer of pH 6.5 into a fine paste which was centrifuged at 5000rpm for 10 minutes. The clear solution of the enzyme was tested for activity at various pH ranging from 5 to 7 and temperature ranging from 40oC to 70oC. The enzyme was active at pH6.0 and at 600C temperature. Polymerization of ε-CL was done using toluene as solvent with the catalysis of lipase enzyme, after which chloroform was added to terminate the reaction and was washed in cold methanol to obtain the polymer. The polymerization was done by varying the time from 72 hours to 6 days and tested for the molecular weight and the conversion of the monomer. The molecular weight obtained at 6 days is comparably higher. This method will be very effective, economical and eco-friendly to produce as the enzyme used can be regenerated as such at the end of the reaction and can be reused. The obtained polymers can be used for drug delivery and other medical applications.

Keywords: lipase, monomer, polycaprolactone, polymerization

Procedia PDF Downloads 281

Authors: Ali Noorian

Abstract:

Crude oil contains various compounds of hydrocarbons but low concentrations of inorganic compounds or metals. Vanadium and Nickel are the most common metals in crude oil. These metals usually exist in solution in the oil and residual fuel oil in the refining process is condensed. Deleterious effects of metals in petroleum have been known for some time. These metals do not only contaminate the product but also cause intoxication and loss of catalyst and corrosion to equipment. In this study, removal of heavy metals and petroleum residues were investigated. These methods include physical, chemical and biological treatment processes. For example, processes such as solvent extraction and hydro-catalytic and catalytic methods are effective and practical methods, but typically often have high costs and cause environmental pollution. Furthermore, biological methods that do not cause environmental pollution have been discussed in recent years, but these methods have not yet been industrialized.

Keywords: removal, metal, heavy oil, nickel, vanadium

Procedia PDF Downloads 357

Authors: Mst Shapna Akter, Hossain Shahriar

Abstract:

Neural network approaches are machine learning methods used in many domains, such as healthcare and cyber security. Neural networks are mostly known for dealing with image datasets. While training with the images, several fundamental mathematical operations are carried out in the Neural Network. The operation includes a number of algebraic and mathematical functions, including derivative, convolution, and matrix inversion and transposition. Such operations require higher processing power than is typically needed for computer usage. Central Processing Unit (CPU) is not appropriate for a large image size of the dataset as it is built with serial processing. While Graphics Processing Unit (GPU) has parallel processing capabilities and, therefore, has higher speed. This paper uses advanced Neural Network techniques such as VGG16, Resnet50, Densenet, Inceptionv3, Xception, Mobilenet, XGBOOST-VGG16, and our proposed models to compare CPU and GPU resources. A system for classifying autism disease using face images of an autistic and non-autistic child was used to compare performance during testing. We used evaluation matrices such as Accuracy, F1 score, Precision, Recall, and Execution time. It has been observed that GPU runs faster than the CPU in all tests performed. Moreover, the performance of the Neural Network models in terms of accuracy increases on GPU compared to CPU.

Keywords: autism disease, neural network, CPU, GPU, transfer learning

Procedia PDF Downloads 98

Authors: Elena Carcano, James Ball

Abstract:

This work details the strategies adopted by the Italian Water Utilities during the distribution of water in emergency conditions which glide from earthquakes and droughts to floods and fires. Several water bureaus located over the national territory have been interviewed, and the collected information has been used in a database of potential interventions to be taken. The work discusses the actions adopted by water utilities. These are generally prioritized in order to minimize the social, temporal, and economic burden that the damaged and nearby areas need to support. Actions are defined relying on the Bayesian Network Approach, which constitutes the hard core of any decision support system. The Bayesian Networks give answers to interventions to real and most likely risky cases. The added value of this research consists in supplying the National Bureau, namely Protezione Civile, in charge of managing havoc and catastrophic situations with a univocal plot outline so as to be able to handle actions uniformly at the expense of different local laws or contradictory customs which squander any recovery conditions, proper technical service, and economic aids. The paper is organized as follows: in section 1, the introduction is stated; section 2 provides a brief discussion of BNNs (Bayesian Networks), section 3 introduces the adopted methodology; and in the last sections, results are presented, and conclusions are drawn.

Keywords: hierarchical process, strategic plan, water emergency conditions, water supply

Procedia PDF Downloads 139

Authors: Alper Taner, Yesim Benal Oztekin, Huseyin Duran

Abstract:

In this study, an Artificial Neural Network (ANN) was developed in order to classify barley varieties. For this purpose, physical properties of barley varieties were determined and ANN techniques were used. The physical properties of 8 barley varieties grown in Turkey, namely thousand kernel weight, geometric mean diameter, sphericity, kernel volume, surface area, bulk density, true density, porosity and colour parameters of grain, were determined and it was found that these properties were statistically significant with respect to varieties. As ANN model, three models, N-l, N-2 and N-3 were constructed. The performances of these models were compared. It was determined that the best-fit model was N-1. In the N-1 model, the structure of the model was designed to be 11 input layers, 2 hidden layers and 1 output layer. Thousand kernel weight, geometric mean diameter, sphericity, kernel volume, surface area, bulk density, true density, porosity and colour parameters of grain were used as input parameter; and varieties as output parameter. R2, Root Mean Square Error and Mean Error for the N-l model were found as 99.99%, 0.00074 and 0.009%, respectively. All results obtained by the N-l model were observed to have been quite consistent with real data. By this model, it would be possible to construct automation systems for classification and cleaning in flourmills.

Keywords: physical properties, artificial neural networks, barley, classification

Procedia PDF Downloads 164

Authors: Malik Muhammad Arslan, Muneeb Ullah, Dai Shihan, Faizan Khan, Xiaodong Yang

Abstract:

Internet Service Providers are driving endless demands for higher bandwidth and data throughput as new services and applications require higher bandwidth. Users want immediate and accurate data delivery. This article focuses on converting old conventional networks into passive optical networks based on time division and wavelength division multiplexing. The main focus of this research is to use a hybrid of time-division multiplexing and wavelength-division multiplexing to improve network efficiency and performance. In this paper, we design an 80 Gbps Passive Optical Network (PON), which meets the need of the Next Generation PON Stage 2 (NGPON2) proposed in this paper. The hybrid of the Time and Wavelength division multiplexing (TWDM) is said to be the best solution for the implementation of NGPON2, according to Full-Service Access Network (FSAN). To co-exist with or replace the current PON technologies, many wavelengths of the TWDM can be implemented simultaneously. By utilizing 8 pairs of wavelengths that are multiplexed and then transmitted over optical fiber for 40 Kms and on the receiving side, they are distributed among 256 users, which shows that the solution is reliable for implementation with an acceptable data rate. From the results, it can be concluded that the overall performance, Quality Factor, and bandwidth of the network are increased, and the Bit Error rate is minimized by the integration of this approach.

Keywords: bit error rate, fiber to the home, passive optical network, time and wavelength division multiplexing

Procedia PDF Downloads 57

Authors: Jasneet Kaur, Swapandeep Singh Chimni

Abstract:

The unfavorable use of metal-based catalysts that are often extortionate and toxic can be overcome by using small organic molecules known as organocatalysts. A variety of small organic molecules, including Brønsted/Lewis bases and acids, based on sulfonic acids, phosphoric acids, amines, phosphines or carbenes, Cinchona alkaloids, have been used as organocatalysts. One of the key reasons for using organocatalysis is their ability to be effectively removed from the final product in comparison to the metallic counterparts, which are exceedingly difficult to remove. The present investigation seeks to explore the catalytic nature of Cinchona alkaloids as an organocatalyst for enantioselective synthesis of 3-substituted-3-aminooxindole, which is known to exhibit a variety of biological activities and pharmacological activities. In this context, an organocatalytic asymmetric route for the synthesis of 3-aminooxindoles via reaction of isatin imine with α-acetoxy-β-ketoesters has been developed. The bifunctional Cinchona derived thiourea catalyzed the reaction of α-acetoxy-β-ketoesters derivatives with isatin imine to afford 3-substituted-aminooxindole derivatives in up to 93% yield, 95% enantiomeric excess and >20:1 diastereomeric ratio. The reaction was performed at room temperature for two hours using 10 mol% of catalyst, in the presence of 4Å molecular sieves in tetrahydrofuran as a solvent at ambient temperature. After the completion of the reaction, the pure product could be easily separated by using column chromatography using hexane and ethyl acetate as solvents. In conclusion, the catalytic potential of Cinchona derived chiral thiourea-tertiary amine catalyst was explored for an organocatalytic enantioselective Mannich reaction of β-ketoester derivatives with various isatin imine derivatives under mild conditions.

Keywords: asymmetric synthesis, aminooxindoles, enantioselective, isatin imine

Procedia PDF Downloads 104

Authors: J. Barilla, M. Lokajíček, H. Pisaková, P. Simr

Abstract:

The final biological effect of ionizing particles may be influenced strongly by some chemical substances present in cells mainly in the case of low-LET radiation. The influence of oxygen may be particularly important because oxygen is always present in living cells. The corresponding processes are then running mainly in the chemical stage of radio biological mechanism. The radical clusters formed by densely ionizing ends of primary or secondary charged particles are mainly responsible for final biological effect. The damage effect depends then on radical concentration at a time when the cluster meets a DNA molecule. It may be strongly influenced by oxygen present in a cell as oxygen may act in different directions: at small concentration of it the interaction with hydrogen radicals prevails while at higher concentrations additional efficient oxygen radicals may be formed. The basic radical concentration in individual clusters diminishes, which is influenced by two parallel processes: chemical reactions and diffusion of corresponding clusters. The given simultaneous evolution may be modeled and analyzed well with the help of Continuous Petri nets. The influence of other substances present in cells during irradiation may be studied, too. Some results concerning the impact of oxygen content will be presented.

Keywords: radiobiological mechanism, chemical phase, DSB formation, Petri nets

Procedia PDF Downloads 299

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

Abstract:

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

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

Procedia PDF Downloads 68

Authors: Yuriy A. Knirel

Abstract:

Enteric bacteria Escherichia coli is the predominant facultative anaerobe of the colonic flora, and some specific serotypes are associated with enteritis, hemorrhagic colitis, and hemolytic uremic syndrome. Shigella spp. are human pathogens that cause diarrhea and bacillary dysentery (shigellosis). They are in effect E. coli with a specific mode of pathogenicity. Strains of Salmonella enterica are responsible for a food-borne infection (salmonellosis), and specific serotypes cause typhoid fever and paratyphoid fever. All these bacteria are closely related in respect to structure and genetics of the lipopolysaccharide, including the O-polysaccharide part (O‑antigen). Being exposed to the bacterial cell surface, the O antigen is subject to intense selection by the host immune system and bacteriophages giving rise to diverse O‑antigen forms and providing the basis for typing of bacteria. The O-antigen forms of many bacteria are unique, but some are structurally and genetically related to others. The sequenced O-antigen gene clusters between conserved galF and gnd genes were analyzed taking into account the O-antigen structures established by us and others for all S. enterica and Shigella and most E. coli O-serogroups. Multiple genetic mechanisms of diversification of the O-antigen forms, such as lateral gene transfer and mutations, were elucidated and are summarized in the present paper. They include acquisition or inactivation of genes for sugar synthesis or transfer or recombination of O-antigen gene clusters or their parts. The data obtained contribute to our understanding of the origins of the O‑antigen diversity, shed light on molecular evolutionary relationships between the O-antigens of enteric bacteria, and open a way for studies of the role of gene polymorphism in pathogenicity.

Keywords: enteric bacteria, O-antigen gene cluster, polysaccharide biosynthesis, polysaccharide structure

Procedia PDF Downloads 130

Authors: Panaya Sudta, Wanchalerm Patanacharoenwong, Prachya Bumrungkun

Abstract:

As With the evolution of smart grids, ensuring the reliability and efficiency of electrical systems in smart cities has become crucial. This paper proposes a distinct approach that combines advanced machine learning techniques to accurately predict electrical failures and address voltage reliability issues. This approach aims to improve the accuracy and efficiency of reliability evaluations in smart cities. The aim of this research is to develop a comprehensive predictive model that accurately predicts electrical failures and voltage reliability in smart cities. This model integrates RFM analysis, K-means clustering, and LSTM networks to achieve this objective. The research utilizes RFM analysis, traditionally used in customer value assessment, to categorize and analyze electrical components based on their failure recency, frequency, and monetary impact. K-means clustering is employed to segment electrical components into distinct groups with similar characteristics and failure patterns. LSTM networks are used to capture the temporal dependencies and patterns in customer data. This integration of RFM, K-means, and LSTM results in a robust predictive tool for electrical failures and voltage reliability. The proposed model has been tested and validated on diverse electrical utility datasets. The results show a significant improvement in prediction accuracy and reliability compared to traditional methods, achieving an accuracy of 92.78% and an F1-score of 0.83. This research contributes to the proactive maintenance and optimization of electrical infrastructures in smart cities. It also enhances overall energy management and sustainability. The integration of advanced machine learning techniques in the predictive model demonstrates the potential for transforming the landscape of electrical system management within smart cities. The research utilizes diverse electrical utility datasets to develop and validate the predictive model. RFM analysis, K-means clustering, and LSTM networks are applied to these datasets to analyze and predict electrical failures and voltage reliability. The research addresses the question of how accurately electrical failures and voltage reliability can be predicted in smart cities. It also investigates the effectiveness of integrating RFM analysis, K-means clustering, and LSTM networks in achieving this goal. The proposed approach presents a distinct, efficient, and effective solution for predicting and mitigating electrical failures and voltage issues in smart cities. It significantly improves prediction accuracy and reliability compared to traditional methods. This advancement contributes to the proactive maintenance and optimization of electrical infrastructures, overall energy management, and sustainability in smart cities.

Keywords: electrical state prediction, smart grids, data-driven method, long short-term memory, RFM, k-means, machine learning

Procedia PDF Downloads 39

Authors: Masoud Rafiee

Abstract:

Study of the effect of vermin compost on yield, and Land equivalent ration (LER) of chickpea-barley mixed cropping under normal dry land condition can be useful in order to increase qualitative and quantitative performance. In this case, two factors include fertilizer (vermicompost biological fertilizer, ammonium phosphate chemical fertilizer, vermicompost + %75 chemical fertilizer) and chickpea + barley mixed cropping (sole chickpea, %75 chickpea: %25 barley, %50 chickpea: %50 barley, %25 chickpea: %75 barley, and sole barley) in RCBD in three replications in two experiments include normal and dry land conditions were studied. Result showed that total LER base on dry matter was affected by environment and mixed cropping interaction and was more than 1 in all mixed cropping treatments. In different mixed cropping rates, wet forage yield decreased by decreasing chickpea ratio as well as increasing barley ratio. Total LER mean in base on forage dry matter in mixed-, chemical-, and vermicompost fertilizer treatments were 1.12, 1.05 and 1.10 in normal condition and 1.15, 1.08 and 1.14 in dry land condition, respectively, represented the important of biological fertilizer in mixed cropping systems.

Keywords: land equivalent ration, biological fertilizer, mixed cropping systems, water stress

Procedia PDF Downloads 292

Authors: Benghezel Hichem

Abstract:

Thyroid dysfunction in hemodialysis subjects is represented mainly by hypothyroidism. The objective of our work is to determine the thyroid profile of our hemodialysis patients and to highlight the prevalence of different thyroid disorders. Methods: This is a retrospective study performed on a mono centric 2 months (February and March 2013) on 42 hemodialysis patients (11 male and 31 female). We made the dosage of thyroid hormones Thyrotropin (TSH) ((free thyroxin ) FT4 and free Triodothyronin ) FT3) by chemiluminescence immunoassay method on cobas 6000 Roche Diagnostics. The results: The prevalence of biological hypothyroidism was 18% (7% with a high TSH isolated and a mean +/- SD 9.44 +/- 6.29, 5% with high TSH, and with low FT4 a mean +/- SD is 8.18 +/- 0.53 for TSH and 9.69 +/- 0.22 for FT4, One patient with a high TSH, and low FT4, FT3. 4% of patients with a low T3 syndrome with a mean +/- SD of 3.93 +/- 0,3 for FT3), we notice that 5% of patients with hyperthyroidism TSH collapsed and mean +/- SD of TSH is 0.017 +/- 0,001. Conclusion: The biological Hypothyroidism is a common endocrine disorder in chronic hemodialysis.

Keywords: hypothyroidism, hemodialysis, thyréostimulin, free thyroxin, triodothyronin

Procedia PDF Downloads 408

Authors: Rohit Semwal, Shivam Arora, Saurav, Sangita Roy

Abstract:

This work proposes a supervised framework with deep convolutional neural networks (CNNs) for vision-based sign language recognition of static gestures. Our approach addresses the acquisition and segmentation of correct inputs for the CNN-based classifier. Microsoft Kinect™ sensor, despite complex environmental conditions, can track hands efficiently. Skin Colour based segmentation is applied on cropped images of hands in different poses, used to depict different sign language gestures. The segmented hand images are used as an input for our classifier. The CNN classifier proposed in the paper is able to classify the input images with a high degree of accuracy. The system was trained and tested on 39 static sign language gestures, including 26 letters of the alphabet and 13 commonly used words. This paper includes a problem definition for building the proposed system, which acts as a sign language translator between deaf/mute and the rest of the society. It is then followed by a focus on reviewing existing knowledge in the area and work done by other researchers. It also describes the working principles behind different components of CNNs in brief. The architecture and system design specifications of the proposed system are discussed in the subsequent sections of the paper to give the reader a clear picture of the system in terms of the capability required. The design then gives the top-level details of how the proposed system meets the requirements.

Keywords: sign language, CNN, HCI, segmentation

Procedia PDF Downloads 137

Authors: Lukas Ded

Abstract:

Studying the tissue structure and histogenesis in the natural, 3D context is challenging but highly beneficial process. Contrary to classical approach of the physical tissue sectioning and subsequent imaging, it enables to study the relationships of individual cellular and histological structures in their native context. Recent developments in the tissue clearing approaches and microscopic volume imaging/data processing enable the application of these methods also in the areas of developmental and reproductive biology. Here, using the CLARITY tissue procedure and 3D confocal volume imaging we optimized the protocol for clearing, staining and imaging of the mice seminiferous tubules isolated from the testes without cardiac perfusion procedure. Our approach enables the high magnification and fine resolution axial imaging of the whole diameter of the seminiferous tubules with possible unlimited lateral length imaging. Hence, the large continuous pieces of the seminiferous tubule can be scanned and digitally reconstructed for the study of the single tubule seminiferous stages using nuclear dyes. Furthermore, the application of the antibodies and various molecular dyes can be used for molecular labeling of individual cellular and subcellular structures and resulting 3D images can highly increase our understanding of the spatiotemporal aspects of the seminiferous tubules development and sperm ultrastructure formation. Finally, our newly developed algorithms for 3D data processing enable the massive parallel processing of the large amount of individual cell and tissue fluorescent signatures and building the robust spermatogenic models under physiological and pathological conditions.

Keywords: CLARITY, spermatogenesis, testis, tissue clearing, volume imaging

Procedia PDF Downloads 128

Authors: Zhou Luchen, Wu Yubing, Burra Venkata Durga Kumar

Abstract:

As the scale of the network becomes larger and more complex than before, the density of user devices is also increasing. The development of Unmanned Aerial Vehicle (UAV) networks is able to collect and transform data in an efficient way by using software-defined networks (SDN) technology. This paper proposed a three-layer distributed and dynamic cluster architecture to manage UAVs by using an AI-based resource allocation calculation algorithm to address the overloading network problem. Through separating services of each UAV, the UAV hierarchical cluster system performs the main function of reducing the network load and transferring user requests, with three sub-tasks including data collection, communication channel organization, and data relaying. In this cluster, a head node and a vice head node UAV are selected considering the Central Processing Unit (CPU), operational (RAM), and permanent (ROM) memory of devices, battery charge, and capacity. The vice head node acts as a backup that stores all the data in the head node. The k-means clustering algorithm is used in order to detect high load regions and form the UAV layered clusters. The whole process of detecting high load areas, forming and selecting UAV clusters, and moving the selected UAV cluster to that area is proposed as offloading traffic algorithm.

Keywords: k-means, resource allocation, SDN, UAV network, unmanned aerial vehicles

Procedia PDF Downloads 93

Authors: Wen-Jay Lee, Kuo-Ning Chiang

Abstract:

The graphene binding with silicon substrate has apparently Schottky barriers property, which can be used in the application of solar cell and light source. Because graphene has only one atom layer, the atomistic structure of graphene binding with the silicon surface plays an important role to affect the properties of graphene. In this work, temperature effect on the morphology of graphene sheet attached on different crystal planes of silicon substrates are investigated by Molecular dynamics (MD) (LAMMPS, developed by Sandia National Laboratories). The results show that the covered graphene sheet would cause the structural deformation of the surface Si atoms of stubtrate. To achieve a stable state in the binding process, the surface Si atoms would adjust their position and fit the honeycomb structure of graphene after the graphene attaches to the Si surface. The height contour of graphene on different plane of silicon surfaces presents different pattern, leading the local residual stress at the interface. Due to the high density of dangling bond on the Si (111)7x7 surface, the surface of Si(111)7x7 is not matching with the graphene so well in contrast with Si(100)2x1and Si(111)2x1. Si(111)7x7 is found that only partial silicon adatoms are rearranged on surface after the attachment when the temperature is lower than 200K, As the temperature gradually increases, the deformation of surface structure becomes significant, as well as the residue stress. With increasing temperature till the 815K, the graphene sheet begins to destroy and mixes with the silicon atoms. For the Si(100)2x1 and Si(111)2x1, the silicon surface structure keep its structural arrangement with a higher temperature. With increasing temperature, the residual stress gradually decrease till a critical temperatures. When the temperature is higher than the critical temperature, the residual stress gradually increases and the structural deformation is found on the surface of the Si substrates.

Keywords: molecular dynamics, graphene, silicon, Schottky barriers, interface

Procedia PDF Downloads 309

Authors: Sara Przetocka, Krzysztof Żak, Grzegorz Dubin, Tadeusz Holak

Abstract:

Toll-like receptors (TLRs) play central role in the innate immune response and inflammation by recognizing pathogen-associated molecular patterns (PAMPs). A fundamental basis of TLR signalling is dependent upon the recruitment and association of adaptor molecules that contain the structurally conserved Toll/interleukin-1 receptor (TIR) domain. MyD88 (myeloid differentiation primary response gene 88) is the universal adaptor for TLRs and cooperates with Mal (MyD88 adapter-like protein, also known as TIRAP) in TLR4 response which is predominantly used in inflammation, host defence and carcinogenesis. Up to date two possible models of MyD88, Mal and TLR4 interactions have been proposed. The aim of our studies is to confirm or abolish presented models and accomplish the full structural characterisation of TIR domains interaction. Using molecular cloning methods we obtained several construct of MyD88 and Mal TIR domain with GST or 6xHis tag. Gel filtration method as well as pull-down analysis confirmed that recombinant TIR domains from MyD88 and Mal are binding in complexes. To examine whether obtained complexes are homo- or heterodimers we carried out cross-linking reaction of TIR domains with BS3 compound combined with mass spectrometry. To investigate which amino acid residues are involved in this interaction the NMR titration experiments were performed. 15N MyD88-TIR solution was complemented with non-labelled Mal-TIR. The results undoubtedly indicate that MyD88-TIR interact with Mal-TIR. Moreover 2D spectra demonstrated that simultaneously Mal-TIR self-dimerization occurs which is necessary to create proper scaffold for Mal-TIR and MyD88-TIR interaction. Final step of this study will be crystallization of MyD88 and Mal TIR domains complex. This crystal structure and characterisation of its interface will have an impact in understanding the TLR signalling pathway and possibly will be used in development of new anti-cancer treatment.

Keywords: cancer, MyD88, TIR domains, Toll-like receptors

Procedia PDF Downloads 279

Authors: Ganesh Nandakumaran, Mehmet Karaata

Abstract:

A wave is a distributed execution, often made up of a broadcast phase followed by a feedback phase, requiring the participation of all the system processes before a particular event called decision is taken. Wave algorithms with one initiator such as the 1-wave algorithm have been shown to be very efficient for broadcasting messages in tree networks. Extensions of this algorithm broadcasting a sequence of waves using a single initiator have been implemented in algorithms such as the m-wave algorithm. However as the network size increases, having a single initiator adversely affects the message delivery times to nodes further away from the initiator. As a remedy, broadcast waves can be allowed to be initiated by multiple initiator nodes distributed across the network to reduce the completion time of broadcasts. These waves initiated by one or more initiator processes form a collection of waves covering the entire network. Solutions to global-snapshots, distributed broadcast and various synchronization problems can be solved efficiently using waves with multiple concurrent initiators. In this paper, we propose the first stabilizing multi-wave sequence algorithm implementing waves started by multiple initiator processes such that every process in the network receives at least one sequence of broadcasts. Due to being stabilizing, the proposed algorithm can withstand transient faults and do not require initialization. We view a fault as a transient fault if it perturbs the configuration of the system but not its program.

Keywords: distributed computing, multi-node broadcast, propagation of information with feedback and cleaning (PFC), stabilization, wave algorithms

Procedia PDF Downloads 490

Authors: Taek Hwan Lee, Moon Ho Do, Lalita Subedi, Young Un Park, Sun Yeou Kim

Abstract:

Natural and artificial toxic substances namely neurotoxins induce the bitter effect in the nervous system termed as neurotoxicity. It can modulate the normal functioning of the nervous system either hyperactivate it or damage homeostasis of neuronal system. Neurotoxins induced toxicity ultimately kills the neuron. The present study investigated the neuroprotective effects of germinated Dolichos lablab on 6-hydroxydopamine (6-OHDA)-induced neurotoxicity using SH-SY5Y neuroblastoma cells. Germination is a process of plant growth from a seed. Sprouting of a seedling from a seed induced many molecular changes in the seed in order to prepare it for further growth. Because of these molecular and chemical changes, the neuroprotective effect of Dolichos lablab is higher in the germinated form than in the normal condition. SH-SY5Y cells were treated with Dolichos lablab extract (50, 100 g/ml) followed by 6-OHDA (25M) induced toxicity. Cell Viability was measured to check the cell survival against 6-OHDA induced toxicity using MTT assay. Dolichos lablab showed a neuroprotective effect against 6-OHDA induced neuronal cell death in neuroblastoma cell at a higher concentration of 100g/ml however the effect is much better even at the lower concentration after germination 50g/ml. Cell survival was increased dramatically after 15 h of germination and increased with time of germination in concentration dependent manner. Trigonelline as a representative compound was validated in germinated Dolichos lablab by HPLC analysis that might enhance the neuroprotective effect of Dolichos lablab. This result suggests that Dolichos lablab possess neuroprotective effect in neuroblastoma cells against 6-OHDA however its activity was more potent in the germinated form.

Keywords: dolichos lablab, germination, neuroprotection, trigonelline

Procedia PDF Downloads 310

Authors: Sara atibi, Azzeddine Atibi, Salim Ahmed, Khadija El Kababi

Abstract:

The concept of chemical bonding is fundamental in chemistry education, ubiquitous in school curricula, and essential to numerous topics in the field. Mastery of this concept enables students to predict and explain the physical and chemical properties of substances. However, chemical bonding is often regarded as one of the most complex concepts for secondary and higher education students to comprehend, due to the underlying complex theory and the use of abstract models. Teachers also encounter significant challenges in conveying this concept effectively. This study aims to identify the difficulties and alternative conceptions faced by Moroccan secondary school students in learning about chemical bonding, as well as the pedagogical strategies employed by teachers to overcome these obstacles. A survey was conducted involving 150 Moroccan secondary school physical science teachers, using a structured questionnaire comprising closed, open-ended, and multiple-choice questions. The results reveal frequent student misconceptions, such as the octet rule, molecular geometry, and molecular polarity. Contributing factors to these misconceptions include the abstract nature of the concepts, the use of models, and teachers' difficulties in explaining certain aspects of chemical bonding. The study proposes improvements for teaching chemical bonding, such as integrating information and communication technologies (ICT), diversifying pedagogical tools, and considering students' pre-existing conceptions. These recommendations aim to assist teachers, curriculum developers, and textbook authors in making chemistry more accessible and in addressing students' misconceptions.

Keywords: chemical bonding, alternative conceptions, chemistry education, pedagogical strategies

Procedia PDF Downloads 7

Authors: Siming Xie

Abstract:

In social networks, the term homophily refers to the tendency of agents with similar characteristics to link with one another and is so robustly observed across many contexts and dimensions. The starting point of my research is the observation that the “type” of agents is not a single exogenous variable. Agents, despite their differences in race, religion, and other hard to alter characteristics, may share interests and engage in activities that cut across those predetermined lines. This research aims to capture the interactions of homophily effects in a model where agents have two-dimension characteristics (i.e., race and personal hobbies such as basketball, which one either likes or dislikes) and with biases in meeting opportunities and in favor of same-type friendships. A novel feature of my model is providing a matching process with biased meeting probability on different dimensions, which could help to understand the structuring process in multidimensional networks without missing layer interdependencies. The main contribution of this study is providing a welfare based matching process for agents with multi-dimensional characteristics. In particular, this research shows that the biases in meeting opportunities on one dimension would lead to the emergence of homophily on the other dimension. The objective of this research is to determine the pattern of homophily in network formations, which will shed light on our understanding of segregation and its remedies. By constructing a two-dimension matching process, this study explores a method to describe agents’ homophilous behavior in a social network with multidimension and construct a game in which the minorities and majorities play different strategies in a society. It also shows that the optimal strategy is determined by the relative group size, where society would suffer more from social segregation if the two racial groups have a similar size. The research also has political implications—cultivating the same characteristics among agents helps diminishing social segregation, but only if the minority group is small enough. This research includes both theoretical models and empirical analysis. Providing the friendship formation model, the author first uses MATLAB to perform iteration calculations, then derives corresponding mathematical proof on previous results, and last shows that the model is consistent with empirical evidence from high school friendships. The anonymous data comes from The National Longitudinal Study of Adolescent Health (Add Health).

Keywords: homophily, multidimension, social networks, friendships

Procedia PDF Downloads 156

Authors: Maria Isabel Garcia-Planas, Maria Victoria Garcia-Camba

Abstract:

Recent advances in cognitive neuroscience have allowed a step forward in perceiving the processes involved in learning from the point of view of the acquisition of new information or the modification of existing mental content. The evoked potentials technique reveals how basic brain processes interact to achieve adequate and flexible behaviours. The objective of this work, using evoked potentials, is to study if it is possible to distinguish if a patient suffers a specific type of learning disorder to decide the possible therapies to follow. The methodology used, is the analysis of the dynamics of different areas of the brain during a cognitive activity to find the relationships between the different areas analyzed in order to better understand the functioning of neural networks. Also, the latest advances in neuroscience have revealed the existence of different brain activity in the learning process that can be highlighted through the use of non-invasive, innocuous, low-cost and easy-access techniques such as, among others, the evoked potentials that can help to detect early possible neuro-developmental difficulties for their subsequent assessment and cure. From the study of the amplitudes and latencies of the evoked potentials, it is possible to detect brain alterations in the learning process specifically in dyscalculia, to achieve specific corrective measures for the application of personalized psycho pedagogical plans that allow obtaining an optimal integral development of the affected people.

Keywords: dyscalculia, neurodevelopment, evoked potentials, Learning disabilities, neural networks

Procedia PDF Downloads 122

Authors: Josephine Hai, Jie Jiang, Karen M. Lyons

Abstract:

Of the musculoskeletal tissues, tendon is least understood in terms of biological development. The current study examines Cysteine-rich angiogenic inducer 61, or CCN1, a member of the CCN family of secreted matricellular proteins that regulate cell behavior via intercellular signaling. Though CCN1 is notable in limiting fibrosis by inducing senescence in fibroblasts, little is known about its role in normal fibrous tissue, where it may be essential to the development of ECM-rich structures like tendons. We found that CCN1 knockout mice (using limb-specific Prx1-Cre) exhibited clubfoot and waddling gaits, a unique phenotype not described in any other mutant to date. Histological analysis showed that the Achilles and patellar tendons, where we previously found high CCN1 expression in adult reporter mice, were thicker and denser in the Prx1-Cre knockouts than in their wildtype littermates. We then hypothesized that CCN1 is required directly in tendon progenitor cells for normal tendon development and generated tendon-specific CCN1 knockout mice using Scx-Cre. We observed similar Achilles/patellar tendon morphology among the Scx-Cre and Prx1-Cre mutants, indicating that the phenotype is a direct result of CCN1’s loss in tendon. To further study phenotype onset and progression, we will histologically characterize these tendons across different developmental time-points. We will also perform RNA-seq and qPCR to analyze tenocyte gene expression and expect fibrotic marker upregulation in the Scx-Cre mutants if CCN1 is required to maintain a normal tendon phenotype. Thus, our study aims to elucidate the molecular mechanisms underlying tendon formation and maintenance. Understanding tendons at the most basic level invites novel approaches to tendon repair.

Keywords: development, matricellular, musculoskeletal, tendon

Procedia PDF Downloads 165

Authors: Vagolu S. Krishna, Shan Zheng, Estharla M. Rekha, Luke W. Guddat, Dharmarajan Sriram

Abstract:

Tuberculosis (TB) remains a major threat to human health. This due to the fact that current drug treatments are less than optimal as well as the rising occurrence of multi drug-resistant and extensively drug-resistant strains of the etiological agent, Mycobacterium tuberculosis (Mt). Given the wide-spread significance of this disease, we have undertaken a design and evaluation program to discover new anti-TB drug leads. Here, our attention is focused on ketol-acid reductoisomerase (KARI), the second enzyme in the branched-chain amino acid biosynthesis pathway. Importantly, this enzyme is present in bacteria but not in humans, making it an attractive proposition for drug discovery. In the present work, we used high-throughput virtual screening to identify seventeen potential inhibitors of KARI using the Birla Institute of Technology and Science in-house database. Compounds were selected based on high docking scores, which were assigned as the result of favourable interactions between the compound and the active site of KARI. The Ki values for two leads, compounds 14 and 16 are 3.71 and 3.06 µM, respectively for Mt KARI. To assess the mode of binding, 100 ns molecular dynamics simulations for these two compounds in association with Mt KARI were performed and showed that the complex was stable with an average RMSD of less than 2.5 Å for all atoms. Compound 16 showed an MIC of 2.06 ± 0.91 µM and a 1.9 fold logarithmic reduction in the growth of Mt in an infected macrophage model. The two compounds exhibited low toxicity against murine macrophage RAW 264.7 cell lines. Thus, both compounds are promising candidates for development as an anti-TB drug leads.

Keywords: ketol-acid reductoisomerase, macrophage, molecular docking and dynamics, tuberculosis

Procedia PDF Downloads 109