Search results for: neural progentor cells

Authors: Colin Schmidt, Adrien Lecossier, Pascal Crubleau, Philippe Blanchard, Simon Richir

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Introduction:The widespread availability of artificial intelligence, exemplified by Generative Pre-trained Transformers (GPT) relying on large language models (LLM), has caused a seismic shift in the realm of knowledge. Everyone now has the capacity to swiftly learn how these models can either serve them well or not. Today, conversational AI like ChatGPT is grounded in neural transformer models, a significant advance in natural language processing facilitated by the emergence of renowned LLMs constructed using neural transformer architecture. Inventiveness of an LLM : OpenAI's GPT-3 stands as a premier LLM, capable of handling a broad spectrum of natural language processing tasks without requiring fine-tuning, reliably producing text that reads as if authored by humans. However, even with an understanding of how LLMs respond to questions asked, there may be lurking behind OpenAI’s seemingly endless responses an inventive model yet to be uncovered. There may be some unforeseen reasoning emerging from the interconnection of neural networks here. Just as a Soviet researcher in the 1940s questioned the existence of Common factors in inventions, enabling an Under standing of how and according to what principles humans create them, it is equally legitimate today to explore whether solutions provided by LLMs to complex problems also share common denominators. Theory of Inventive Problem Solving (TRIZ) : We will revisit some fundamentals of TRIZ and how Genrich ALTSHULLER was inspired by the idea that inventions and innovations are essential means to solve societal problems. It's crucial to note that traditional problem-solving methods often fall short in discovering innovative solutions. The design team is frequently hampered by psychological barriers stemming from confinement within a highly specialized knowledge domain that is difficult to question. We presume ChatGPT Utilizes TRIZ 40. Hence, the objective of this research is to decipher the inventive model of LLMs, particularly that of ChatGPT, through a comparative study. This will enhance the efficiency of sustainable innovation processes and shed light on how the construction of a solution to a complex problem was devised. Description of the Experimental Protocol : To confirm or reject our main hypothesis that is to determine whether ChatGPT uses TRIZ, we will follow a stringent protocol that we will detail, drawing on insights from a panel of two TRIZ experts. Conclusion and Future Directions : In this endeavor, we sought to comprehend how an LLM like GPT addresses complex challenges. Our goal was to analyze the inventive model of responses provided by an LLM, specifically ChatGPT, by comparing it to an existing standard model: TRIZ 40. Of course, problem solving is our main focus in our endeavours.

Keywords: artificial intelligence, Triz, ChatGPT, inventiveness, problem-solving

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Authors: Felipe Osorio Ospina, Maria Adelaida Mejia Arango, Esteban Onésimo Vallejo Agudelo, Victoria Lucía Dávila Osorio, Natalia Vargas Grisales, Lina María Martínez Sanchez, Camilo Andrés Agudelo Vélez, Ángela Maria Londoño García, Isabel Cristina Ortiz Trujillo

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Excessive exposure to ultraviolet radiation, has shown to be a risk factor for photodamage, alteration of the immune mechanisms to recognize malignant cells and cutaneous pro-inflamatorios States and skin cancers. Objective: Identify the time of exposure to ultraviolet radiation for the production of chromosomal damage in human lymphocytes. Methodology: We conducted an in vitro study serial, in which samples were taken from heparinized blood of healthy people, who do not submit exposure to agents that could induce chromosomal alterations. The samples were cultured in RPMI-1640 medium containing 10% fetal bovine serum, penicillin and streptomycin antibiotic. Subsequently, they were grouped and exposed to ultraviolet light for 1 to 20 seconds. At the end of the treatments, cytology samples were prepared, and it was colored with Giemsa (5%). Reading was carried out in an optical microscope and 100 metaphases analysed by treatment for posting chromosomal alterations. Each treatment was conducted at three separate times and each became two replicas. Results: We only presented chromosomal alterations in lymphocytes exposed to UV for a groups 1 to 3 seconds (p<0.05). Conclusions: Exposure to ultraviolet radiation generates visible damage in chromosomes from human lymphocytes observed in light microscopy, the highest rates of injury was observed between two and three seconds, and above this value, the reduction in the number of mitotic cells was evident.

Keywords: ultraviolet rays, lymphocytes, chromosome breakpoints, photodamage

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Authors: Felipe Osorio Ospina, Maria Adelaida Mejia Arango, Esteban Onésimo Vallejo Agudelo, Victoria Lucía Dávila Osorio, Natalia Vargas Grisales, Lina María Martínez Sanchez, Camilo Andrés Agudelo Vélez, Ángela Maria Londoño García, Isabel Cristina Ortiz Trujillo

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Excessive exposure to ultraviolet radiation, has shown to be a risk factor for photodamage, alteration of the immune mechanisms to recognize malignant cells and cutaneous pro-inflamatorios states and skin cancers. Objective: To identify the time of exposure to ultraviolet radiation for the production of chromosomal damage in human lymphocytes. Methodology: We conducted an in vitro study serial, in which samples were taken from the heparinized blood of healthy people, who do not submit exposure to agents that could induce chromosomal alterations. The samples were cultured in RPMI-1640 medium containing 10% fetal bovine serum, penicillin, and streptomycin antibiotic. Subsequently, they were grouped and exposed to ultraviolet light for 1 to 20 seconds. At the end of the treatments, cytology samples were prepared, and it was colored with Giemsa (5%). Reading was carried out in an optical microscope and 100 metaphases analysed by treatment for posting chromosomal alterations. Each treatment was conducted at three separate times and each became two replicas. Results: We only presented chromosomal alterations in lymphocytes exposed to UV for groups 1 to 3 seconds (p < 0.05). Conclusions: Exposure to ultraviolet radiation generates visible damage in chromosomes from human lymphocytes observed in light microscopy, the highest rates of injury was observed between two and three seconds, and above this value, the reduction in the number of mitotic cells was evident.

Keywords: chromosome breakpoints, lymphocytes, photodamage, ultraviolet rays

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Authors: Shreeyam, Ranjan Kumar Sah, Shivangi

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Recognizing and solving handwritten mathematical expressions can be a challenging task, particularly when certain characters are segmented and classified. This project proposes a solution that uses Convolutional Neural Network (CNN) and image processing techniques to accurately solve various types of equations, including arithmetic, quadratic, and trigonometric equations, as well as logical operations like logical AND, OR, NOT, NAND, XOR, and NOR. The proposed solution also provides a graphical solution, allowing users to visualize equations and their solutions. In addition to equation solving, the platform, called CNNCalc, offers a comprehensive learning experience for students. It provides educational content, a quiz platform, and a coding platform for practicing programming skills in different languages like C, Python, and Java. This all-in-one solution makes the learning process engaging and enjoyable for students. The proposed methodology includes horizontal compact projection analysis and survey for segmentation and binarization, as well as connected component analysis and integrated connected component analysis for character classification. The compact projection algorithm compresses the horizontal projections to remove noise and obtain a clearer image, contributing to the accuracy of character segmentation. Experimental results demonstrate the effectiveness of the proposed solution in solving a wide range of mathematical equations. CNNCalc provides a powerful and user-friendly platform for solving equations, learning, and practicing programming skills. With its comprehensive features and accurate results, CNNCalc is poised to revolutionize the way students learn and solve mathematical equations. The platform utilizes a custom-designed Convolutional Neural Network (CNN) with image processing techniques to accurately recognize and classify symbols within handwritten equations. The compact projection algorithm effectively removes noise from horizontal projections, leading to clearer images and improved character segmentation. Experimental results demonstrate the accuracy and effectiveness of the proposed solution in solving a wide range of equations, including arithmetic, quadratic, trigonometric, and logical operations. CNNCalc features a user-friendly interface with a graphical representation of equations being solved, making it an interactive and engaging learning experience for users. The platform also includes tutorials, testing capabilities, and programming features in languages such as C, Python, and Java. Users can track their progress and work towards improving their skills. CNNCalc is poised to revolutionize the way students learn and solve mathematical equations with its comprehensive features and accurate results.

Keywords: AL, ML, hand written equation solver, maths, computer, CNNCalc, convolutional neural networks

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Authors: B. Samuel Raj, Solomon R. D. Jebakumar

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Microbial fuel cells (MFCs) are an emerging and promising method for achieving sustainable energy since they can remove contaminated organic matter and simultaneously generate electricity. Our approach was driven in three different ways like Bacterial fuel cell, Soil Microbial fuel cell (Soil MFC) and Plant Microbial fuel cell (Plant MFC). Bacterial MFC: Sulphate reducing bacteria (SRB) were isolated and identified as the efficient electricigens which is able to produce ±2.5V (689mW/m2) and it has sustainable activity for 120 days. Experimental data with different MFC revealed that high electricity production harvested continuously for 90 days 1.45V (381mW/m2), 1.98V (456mW/m2) respectively. Biofilm formation was confirmed on the surface of the anode by high content screening (HCS) and scanning electron Microscopic analysis (SEM). Soil MFC: Soil MFC was constructed with low cost and standard Mudwatt soil MFC was purchased from keegotech (USA). Vermicompost soil (V1) produce high energy (± 3.5V for ± 400 days) compared to Agricultural soil (A1) (± 2V for ± 150 days). Biofilm formation was confirmed by HCS and SEM analysis. This finding provides a method for extracting energy from organic matter, but also suggests a strategy for promoting the bioremediation of organic contaminants in subsurface environments. Our Soil MFC were able to run successfully a 3.5V fan and three LED continuously for 150 days. Plant MFC: Amaranthus candatus (P1) and Triticum aestivium (P2) were used in Plant MFC to confirm the electricity production from plant associated microbes, four uniform size of Plant MFC were constructed and checked for energy production. P2 produce high energy (± 3.2V for 40 days) with harvesting interval of two times and P1 produces moderate energy without harvesting interval (±1.5V for 24 days). P2 is able run 3.5V fan continuously for 10days whereas P1 needs optimization of growth conditions to produce high energy.

Keywords: microbial fuel cell, biofilm, soil microbial fuel cell, plant microbial fuel cell

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Authors: Ling-Ling E., Hong-Chen Liu, Dong-Sheng Wang, Fang Su, Xia Wu, Zhan-Ping Shi, Yan Lv, Jia-Zhu Wang

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Objective: The objective of the present study is to evaluate the capacity of a tissue-engineered bone complex of recombinant human bone morphogenetic protein 2 (rhBMP-2) mediated dental pulp stem cells (DPSCs) and nano-hydroxyapatite/collagen/poly(L-lactide)(nHAC/PLA) to reconstruct critical-size alveolar bone defects in New Zealand rabbit. Methods: Autologous DPSCs were isolated from rabbit dental pulp tissue and expanded ex vivo to enrich DPSCs numbers, and then their attachment and differentiation capability were evaluated when cultured on the culture plate or nHAC/PLA. The alveolar bone defects were treated with nHAC/PLA, nHAC/PLA+rhBMP-2, nHAC/PLA+DPSCs, nHAC/PLA+DPSCs+rhBMP-2, and autogenous bone (AB) obtained from iliac bone or were left untreated as a control. X-ray and a polychrome sequential fluorescent labeling were performed post-operatively and the animals were sacrificed 12 weeks after operation for histological observation and histomorphometric analysis. Results: Our results showed that DPSCs expressed STRO-1 and vementin, and favoured osteogenesis and adipogenesis in conditioned media. DPSCs attached and spread well, and retained their osteogenic phenotypes on nHAC/PLA. The rhBMP-2 could significantly increase protein content, alkaline phosphatase (ALP) activity/protein, osteocalcin (OCN) content, and mineral formation of DPSCs cultured on nHAC/PLA. The X-ray graph, the fluorescent, histological observation and histomorphometric analysis showed that the nHAC/PLA+DPSCs+rhBMP-2 tissue-engineered bone complex had an earlier mineralization and more bone formation inside the scaffold than nHAC/PLA, nHAC/PLA+rhBMP-2 and nHAC/PLA+DPSCs, or even autologous bone. Implanted DPSCs contribution to new bone were detected through transfected eGFP genes. Conclutions: Our findings indicated that stem cells existed in adult rabbit dental pulp tissue. The rhBMP-2 promoted osteogenic capability of DPSCs as a potential cell source for periodontal bone regeneration. The nHAC/PLA could serve as a good scaffold for autologous DPSCs seeding, proliferation and differentiation. The tissue-engineered bone complex with nHAC/PLA, rhBMP-2, and autologous DPSCs might be a better alternative to autologous bone for the clinical reconstruction of periodontal bone defects.

Keywords: nano-hydroxyapatite/collagen/poly (L-lactide), dental pulp stem cell, recombinant human bone morphogenetic protein, bone tissue engineering, alveolar bone

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Authors: Srinaath Anbu Durai, Wang Zhaoxia

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Twitter sentiment has been used as a predictor to predict price values or trends in both the stock market and housing market. The pioneering works in this stream of research drew upon works in behavioural economics to show that sentiment or emotions impact economic decisions. Latest works in this stream focus on the algorithm used as opposed to the data used. A literature review of works in this stream through the lens of data used shows that there is a paucity of work that considers the impact of sentiments caused due to an external factor on either the stock or the housing market. This is despite an abundance of works in behavioural economics that show that sentiment or emotions caused due to an external factor impact economic decisions. To address this gap, this research studies the impact of Twitter sentiment pertaining to the Covid-19 pandemic on resale Housing Development Board (HDB) apartment prices in Singapore. It leverages SNSCRAPE to collect tweets pertaining to Covid-19 for sentiment analysis, lexicon based tools VADER and TextBlob are used for sentiment analysis, Granger Causality is used to examine the relationship between Covid-19 cases and the sentiment score, and neural networks are leveraged as prediction models. Twitter sentiment pertaining to Covid-19 as a predictor of HDB price in Singapore is studied in comparison with the traditional predictors of housing prices i.e., the structural and neighbourhood characteristics. The results indicate that using Twitter sentiment pertaining to Covid19 leads to better prediction than using only the traditional predictors and performs better as a predictor compared to two of the traditional predictors. Hence, Twitter sentiment pertaining to an external factor should be considered as important as traditional predictors. This paper demonstrates the real world economic applications of sentiment analysis of Twitter data.

Keywords: sentiment analysis, Covid-19, housing price prediction, tweets, social media, Singapore HDB, behavioral economics, neural networks

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Authors: Maria Del Carmen Millan-Linares, Ana Lemus Conejo, Rocio Toscano, Alvaro Villanueva, Francisco Millan, Justo Pedroche, Sergio Montserrat-De La Paz

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GPETAFLR (Glycine-Proline-Glutamine-Threonine-Alanine-Phenylalanine-Leucine-Arginine) is a peptide isolated from Lupinus angustifolius L. protein hydrolysate (LPH). Herein, the effect of this peptide was investigated in two different models of neuroinflammation: in the immortalized murine microglia cell line BV-2 and in a high-fat-diet-induced obesity mouse model. Methods and Results: Effects of GPETAFLR on neuroinflammation were evaluated by RT-qPCR, flow cytometry, and ELISA techniques. In BV-2 microglial cells, Lipopolysaccharides (LPS) enhanced the release of pro-inflammatory cytokines (TNF-α, IL-1β, and IL-6) whereas GPETAFLR decreased pro-inflammatory cytokine levels and increased the release of the anti-inflammatory cytokine IL-10 in BV2 microglial cells. M1 (CCR7 and iNOS) and M2 (Arg-1 and Ym-1) polarization markers results showed how the GPETAFLR octapeptide was able to decrease M1 polarization marker expression and increase the M2 polarization marker expression compared to LPS. Animal model results indicate that GPETAFLR has an immunomodulatory capacity, both decreasing pro-inflammatory cytokine IL-6 and increasing the anti-inflammatory cytokine IL-10 in brain tissue. Polarization markers in the brain tissue were also modulated by GPETAFLR that decreased the pro-inflammatory expression (M1) and increased the anti-inflammatory expression (M2). Conclusion: Our results suggest that GPETAFLR isolated from LPH has significant potential for management of neuroinflammatory conditions and offer benefits derived from the consumption of Lupinus angustifolius L. in the prevention of neuroinflammatory-related diseases.

Keywords: GPETAFLR peptide, BV-2 cell line, neuroinflammation, cytokines, high-fat-diet

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Authors: James Ladzekpo

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Background: The urgent need to identify new pharmacological targets for diabetes treatment and prevention has been amplified by the disease's extensive impact on individuals and healthcare systems. A deeper insight into the biological underpinnings of diabetes is crucial for the creation of therapeutic strategies aimed at these biological processes. Current predictive models based on genetic variations fall short of accurately forecasting diabetes. Objectives: Our study aims to pinpoint key epigenetic factors that predispose individuals to diabetes. These factors will inform the development of an advanced predictive model that estimates diabetes risk from genetic profiles, utilizing state-of-the-art statistical and data mining methods. Methodology: We have implemented a recursive feature elimination with cross-validation using the support vector machine (SVM) approach for refined feature selection. Building on this, we developed six machine learning models, including logistic regression, k-Nearest Neighbors (k-NN), Naive Bayes, Random Forest, Gradient Boosting, and Multilayer Perceptron Neural Network, to evaluate their performance. Findings: The Gradient Boosting Classifier excelled, achieving a median recall of 92.17% and outstanding metrics such as area under the receiver operating characteristics curve (AUC) with a median of 68%, alongside median accuracy and precision scores of 76%. Through our machine learning analysis, we identified 31 genes significantly associated with diabetes traits, highlighting their potential as biomarkers and targets for diabetes management strategies. Conclusion: Particularly noteworthy were the Gradient Boosting Classifier and Multilayer Perceptron Neural Network, which demonstrated potential in diabetes outcome prediction. We recommend future investigations to incorporate larger cohorts and a wider array of predictive variables to enhance the models' predictive capabilities.

Keywords: diabetes, machine learning, prediction, biomarkers

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Authors: F. Ruiz-Navarro, M. Matzner, G. Kobinia

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Background: Cerebral palsy (CP) encompasses the largest group of childhood movement disorders, the patterns and severity varies widely. Today, the management focuses only on a rehabilitation therapy that tries to secure the functions remained and prevents complications. However the treatments are not aimed to cure the disease. Stem cells (SCs) transplant via intrathecal is a new approach to the disease. Method: Our aim was to performed a pilot study under the condition of unproven treatment on clinical practice to assessed the safety and efficacy of Neuron Point-of-care Stem cell Therapy (N-POCST), an ambulatory procedure of autologous bone marrow derived SCs (BM-SCs) harvested from the posterior superior iliac crest undergo an on-site cell separation for intrathecal infusion via lumbar puncture. Results: 82 patients were treated in a period of 28 months, with a follow-up after 6 months. They had a mean age of 6,2 years old and male predominance (65,9%). Our preliminary results show that: A. No patient had any major side effects, B. Only 20% presented mild headache due to LP, C. 53% of the patients had an improvement in spasticity, D. 61% improved the coordination abilities, 23% improved the motor function, 15% improved the speech, 23% reduced the number of convulsive events with the same doses or less doses of anti-convulsive medication and 94% of the patients report a subjective general improvement. Conclusions: These results support previous worldwide publications that described the safety and effectiveness of autologous BM-SCs transplant for patients wit CP.

Keywords: autologous transplant, cerebral palsy, point of care, childhood movement disorders

Procedia PDF Downloads 414

Authors: Hana Al-Baghaoi, Kumar Shiva Gubbiyappa, Mayuren Candasamy, Kiruthiga Perumal Vijayaraman

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Acne is a chronic inflammatory disease of the sebaceous glands characterized by areas of skin with seborrhea, comedones, papules, pustules, nodules, and possibly scarring. Propionibacterium acnes (P. acnes), plays a key role in the pathogenesis of acne. Their colonization and proliferation trigger the host’s inflammatory response leading to the production of pro-inflammatory cytokines such as interleukin-8 (IL-8) and tumour necrosis factor-α (TNF-α). The usage of honey and natural compounds to treat skin ailments has strong support in the current trend of drug discovery. The present study was carried out evaluate antimicrobial and anti-inflammatory potential of Doani Sidr honey and its fractions against P. acnes and to screen madecassoside alone and in combination with fractions of honey. The broth dilution method was used to assess the antibacterial activity. Also, ultra structural changes in cell morphology were studied before and after exposure to Sidr honey using transmission electron microscopy (TEM). The three non-toxic concentrations of the samples were investigated for suppression of cytokines IL 8 and TNF α by testing the cell supernatants in the co-culture of the human peripheral blood mononuclear cells (hPBMCs) heat killed P. acnes using enzyme immunoassay kits (ELISA). Results obtained was evaluated by statistical analysis using Graph Pad Prism 5 software. The Doani Sidr honey and polysaccharide fractions were able to inhibit the growth of P. acnes with a noteworthy minimum inhibitory concentration (MIC) value of 18% (w/v) and 29% (w/v), respectively. The proximity of MIC and MBC values indicates that Doani Sidr honey had bactericidal effect against P. acnes which is confirmed by TEM analysis. TEM images of P. acnes after treatment with Doani Sidr honey showed completely physical membrane damage and lysis of cells; whereas non honey treated cells (control) did not show any damage. In addition, Doani Sidr honey and its fractions significantly inhibited (> 90%) of secretion of pro-inflammatory cytokines like TNF α and IL 8 by hPBMCs pretreated with heat-killed P. acnes. However, no significant inhibition was detected for madecassoside at its highest concentration tested. Our results suggested that Doani Sidr honey possesses both antimicrobial and anti-inflammatory effects against P. acnes and can possibly be used as therapeutic agents for acne. Furthermore, polysaccharide fraction derived from Doani Sidr honey showed potent inhibitory effect toward P. acnes. Hence, we hypothesize that fraction prepared from Sidr honey might be contributing to the antimicrobial and anti-inflammatory activity. Therefore, this polysaccharide fraction of Doani Sidr honey needs to be further explored and characterized for various phytochemicals which are contributing to antimicrobial and anti-inflammatory properties.

Keywords: Doani sidr honey, Propionibacterium acnes, IL-8, TNF alpha

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Authors: Grigoria Athanasaki, Veerarajan Vimala, A. M. Kannan, Louis Cindrella

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Energy usage has been increased throughout the years, leading to severe environmental impacts. Since the majority of the energy is currently produced from fossil fuels, there is a global need for clean energy solutions. Proton Exchange Membrane Fuel Cells (PEMFCs) offer a very promising solution for transportation applications because of their solid configuration and low temperature operations, which allows them to start quickly. One of the main components of PEMFCs is the Gas Diffusion Layer (GDL), which manages water and gas transport and shows direct influence on the fuel cell performance. In this work, a novel dual-layer GDL with gradient porosity was prepared, using polyethylene glycol (PEG) as pore former, to improve the gas diffusion and water management in the system. The microporous layer (MPL) of the fabricated GDL consists of carbon powder PUREBLACK, sodium dodecyl sulfate as a surfactant, 34% wt. PTFE and the gradient porosity was created by applying one layer using 30% wt. PEG on the carbon substrate, followed by a second layer without using any pore former. The total carbon loading of the microporous layer is ~ 3 mg.cm-2. For the assembly of the catalyst layer, Nafion membrane (Ion Power, Nafion Membrane NR211) and Pt/C electrocatalyst (46.1% wt.) were used. The catalyst ink was deposited on the membrane via microspraying technique. The Pt loading is ~ 0.4 mg.cm-2, and the active area is 5 cm2. The sample was ex-situ characterized via wetting angle measurement, Scanning Electron Microscopy (SEM), and Pore Size Distribution (PSD) to evaluate its characteristics. Furthermore, for the performance evaluation in-situ characterization via Fuel Cell Testing using H2/O2 and H2/air as reactants, under 50, 60, 80, and 100% relative humidity (RH), took place. The results were compared to a single layer GDL, fabricated with the same carbon powder and loading as the dual layer GDL, and a commercially available GDL with MPL (AvCarb2120). The findings reveal high hydrophobic properties of the microporous layer of the GDL for both PUREBLACK based samples, while the commercial GDL demonstrates hydrophilic behavior. The dual layer GDL shows high and stable fuel cell performance under all the RH conditions, whereas the single layer manifests a drop in performance at high RH in both oxygen and air, caused by catalyst flooding. The commercial GDL shows very low and unstable performance, possibly because of its hydrophilic character and thinner microporous layer. In conclusion, the dual layer GDL with PEG appears to have improved gas diffusion and water management in the fuel cell system. Due to its increasing porosity from the catalyst layer to the carbon substrate, it allows easier access of the reactant gases from the flow channels to the catalyst layer, and more efficient water removal from the catalyst layer, leading to higher performance and stability.

Keywords: gas diffusion layer, microporous layer, proton exchange membrane fuel cells, relative humidity

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Authors: Cyoung-Huei Huang, Chiu-Li Yeh, Man-Hui Pai, Sung-Ling Yeh

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This study evaluated the effect of different dietary n-6/n-3 polyunsaturated fatty acid (PUFA) ratios on modulating helper T (Th) and regulatory T (Treg) lymphocytes in mice with dextran sulfate sodium (DSS)-induced colitis. There were 3 control and 3 colitis groups in this study. Mice were fed for 24 d with an AIN-93G diet either with soybean oil (S), a mixture of soybean oil and low fish oil content (LF) or high fish oil content (HF). The ratio of n-6/n-3 PUFA in the LF diet was 4:1, and that in the HF diet was 2:1. The control groups drank distilled water while colitis groups provided 2% DSS in drinking water during day 15-19. All mice drank distilled water from day 20-24 for recovery and sacrificed on day 25. The results showed that colitis resulted in higher Th1, Th2, and Th17 and lower Treg percentages in the blood. Also, plasma haptoglobin and proinflammatory chemokines were elevated in colon lavage fluid. Colitic groups with fish oil had lower inflammatory mediators in the plasma and colon lavage fluid. Further, the percentages of Th1, Th2, and Th17 cells in the blood were lower, whereas Treg cell percentages were higher than those in the soybean oil group. The colitis group with n-6/n-3 PUFA ratio 2:1 had more pronounce effects than ratio 4:1. These results suggest that diets with an n-6/n-3 PUFA ratio of 2:1 or 4:1 regulate the Th/Treg balance and attenuate inflammatory mediator production in colitis. Compared to the n-6/n-3 PUFA ratio 4:1, the ratio of 2:1 was more effective in reducing inflammatory reactions in DSS-induced colitis.

Keywords: inflammatory bowel disease, n-3 polyunsaturated fatty acids, helper T lymphocyte, regulatory T lymphocyte

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Authors: Faraz Zarghami, Elham Anari, Nosratollah Zarghami, Yones Pilehvar-Soltanahmadi, Abolfazl Akbarzadeh, Sepideh Jalilzadeh-Tabrizi

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The development of nanotherapy has presented a new method of drug delivery targeted directly to the neoplasmic tissues, to maximize the action with fewer dose requirements. In the past two decades, poly(lactic-co-glycolic acid) (PLGA) has frequently been investigated by many researchers and is a popular polymeric candidate, due to its biocompatibility and biodegradability, exhibition of a wide range of erosion times, tunable mechanical properties, and most notably, because it is a FDA-approved polymer. Chrysin is a natural flavonoid which has been reported to have some significant biological effects on the processes of chemical defense, nitrogen fixation, inflammation, and oxidation. However, the low solubility in water decreases its bioavailability and consequently disrupts the biomedical benefits. Being loaded with PLGA-PEG increases chrysin solubility and drug tolerance, and decreases the discordant effects of the drug. The well-structured chrysin efficiently accumulates in the breast cancer cell line (T47D). In the present study, the structure and chrysin loading were delineated using proton nuclear magnetic resonance (HNMR), Fourier-transform infrared spectroscopy (FT-IR), and scanning electron microscopy (SEM), and the in vitro cytotoxicity of pure and nanochrysin was studied by the MTT assay. Next, the RNA was exploited and the cytotoxic effects of chrysin were studied by real-time PCR. In conclusion, the nanochrysin therapy developed is a novel method that could increase cytotoxicity to cancer cells without damaging the normal cells, and would be promising in breast cancer therapy.

Keywords: MTT assay, chrysin, flavonoids, nanotherapy

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Authors: Alireza Arab, Morteza Elsa, Amirhossein Moghanian

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In this study, experiments were carried out to achieve a promising multifunctional and modified silicate based bioactive glass (BG). The main aim of the study was investigating the effect of lithium (Li) and magnesium (Mg) substitution, on in vitro bioactivity of substituted-58S BG. Moreover, it is noteworthy to state that modified BGs were synthesized in 60SiO₂–(36-x)CaO–4P₂O₅–(x)Li₂O and 60SiO₂–(36-x)CaO–4P₂O₅–(x)MgO (where x = 0, 5, 10 mol.%) quaternary systems, by sol-gel method. Their performance was investigated through different aspects such as biocompatibility, antibacterial activity as well as their effect on alkaline phosphatase (ALP) activity, and proliferation of MC3T3 cells. The antibacterial efficiency was evaluated against methicillin-resistant Staphylococcus aureus bacteria. To do so, CaO was substituted with Li₂O and MgO up to 10 mol % in 58S-BGs and then samples were immersed in simulated body fluid up to 14 days and then, characterized by X-ray diffraction, Fourier transform infrared spectroscopy, inductively coupled plasma atomic emission spectrometry, and scanning electron microscopy. Results indicated that this modification led to a retarding effect on in vitro hydroxyapatite (HA) formation due to the lower supersaturation degree for nucleation of HA compared with 58s-BG. Meanwhile, magnesium revealed further pronounced effect. The 3-(4,5 dimethylthiazol-2-yl)-2,5 diphenyltetrazolium bromide (MTT) and ALP analysis illustrated that substitutions of both Li₂O and MgO, up to 5 mol %, had increasing effect on biocompatibility and stimulating proliferation of the pre-osteoblast MC3T3 cells in comparison to the control specimen. Regarding to bactericidal efficiency, the substitution of either Li or Mg for Ca in the 58s BG composition led to statistically significant difference in antibacterial behaviors of substituted-BGs. Meanwhile, the sample containing 5 mol % CaO/Li₂O substitution (BG-5L) was selected as a multifunctional biomaterial in bone repair/regeneration due to the improved biocompatibility, enhanced ALP activity and antibacterial efficiency among all of the synthesized L-BGs and M-BGs.

Keywords: alkaline, alkaline earth, bioactivity, biomedical applications, sol-gel processes

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Authors: Jinyoung Park, Eun Joo Song

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Introduction: Deubiquitinating enzymes (DUBs) are proteases that cleave ubiquitin or ubiquitin-like modifications on substrates. Deubiquitination could regulate cellular physiology, such as signal transduction, DNA damage and repair, and cell cycle progression. Although more than 100 DUBs are encoded in the human and the importance of DUBs has been realized, the functions of most DUBs are unknown. This study aims to identify the molecular mechanism by which deubiquitinating enzyme USP35 regulates cell cycle progression for the first time. Methods: USP35 RNAi was mainly used to identify the function of USP35 in cell cycle progression. To find substrates of USP35, we analyzed protein-protein interaction using LC-MS. Several biological methods, such as ubiquitination assay, cell synchronization, immunofluorescence, and immunoprecipitation assay were used to investigate the exact mechanism by which USP35 affects successful completion of mitosis. Results: USP35 knockdown caused not only reduction of mitotic cell number but also induction of mitotic cells with abnormal spindle formation. Actually, cell proliferation was decreased by USP35 knockdown. Interestingly, we found that loss of USP35 decreased the stability and expression of Aurora B, a member of chromosomal passenger complex (CPC), and the phosphorylation of its substrate. Indeed, USP35 interacted with Aurora B and deubiquitinated it. In addition, USP35 knockdown induced abnormal localization of Aurora B in mitotic cells. Finally, CDH1-mediated ubiquitination of Aurora B level was rescued by USP35 overexpression, but not inactive form of USP35, USP35 C450A. Discussion: Our findings suggest that USP35 regulates Aurora B-mediated mitotic spindle assembly and G2-M transition by blocking CDH1-induced degradation of Aurora B.

Keywords: USP35, HSP90, Aurora B, cell cycle progression

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Authors: Heichia Wang, Yalan Chao

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Social inequality is a persistent problem. One of the ways to solve this problem is through education. At present, vulnerable groups are often less geographically accessible to educational resources. However, compared with educational resources, communication equipment is easier for vulnerable groups. Now that information and communication technology (ICT) has entered the field of education, today we can accept the convenience that ICT provides in education, and the mobility that it brings makes learning independent of time and place. With mobile learning, teachers and students can start discussions in an online chat room without the limitations of time or place. However, because liquidity learning is quite convenient, people tend to solve problems in short online texts with lack of detailed information in a lack of convenient online environment to express ideas. Therefore, the ICT education environment may cause misunderstanding between teachers and students. Therefore, in order to better understand each other's views between teachers and students, this study aims to clarify the essays of the analysts and classify the students into several types of learning questions to clarify the views of teachers and students. In addition, this study attempts to extend the description of possible omissions in short texts by using external resources prior to classification. In short, by applying a short text classification, this study can point out each student's learning problems and inform the instructor where the main focus of the future course is, thus improving the ICT education environment. In order to achieve the goals, this research uses convolutional neural network (CNN) method to analyze short discussion content between teachers and students in an ICT education environment. Divide students into several main types of learning problem groups to facilitate answering student problems. In addition, this study will further cluster sub-categories of each major learning type to indicate specific problems for each student. Unlike most neural network programs, this study attempts to extend short texts with external resources before classifying them to improve classification performance. In short, by applying the classification of short texts, we can point out the learning problems of each student and inform the instructors where the main focus of future courses will improve the ICT education environment. The data of the empirical process will be used to pre-process the chat records between teachers and students and the course materials. An action system will be set up to compare the most similar parts of the teaching material with each student's chat history to improve future classification performance. Later, the function of short text classification uses CNN to classify rich chat records into several major learning problems based on theory-driven titles. By applying these modules, this research hopes to clarify the main learning problems of students and inform teachers that they should focus on future teaching.

Keywords: ICT education improvement, social equality, short text analysis, convolutional neural network

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Authors: Ercan Karadogan, Fatih Usta

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Auxetic materials have a negative Poisson’s ratio (NPR), which is not often found in nature. They are metamaterials that have potential applications in many engineering fields. Mechanical metamaterials are synthetically designed structures with unusual mechanical properties. These mechanical properties are dependent on the properties of the matrix structure. They have the following special characteristics, i.e., improved shear modulus, increased energy absorption, and intensive fracture toughness. Non-auxetic materials compress transversely when they are stretched. The system naturally is inclined to keep its density constant. The transversal compression increases the density to balance the loss in the longitudinal direction. This study proposes to improve the crushing performance of hybrid auxetic materials. The re-entrant honeycomb structure has been combined with a star honeycomb, an S-shaped unit cell, a double arrowhead, and a structurally hexagonal re-entrant honeycomb by 9 X 9 cells, i.e., the number of cells is 9 in the lateral direction and 9 in the vertical direction. The Finite Element (FE) and experimental methods have been used to determine the compression behavior of the developed hybrid auxetic structures. The FE models have been developed by using Abaqus software. The specimens made of polymer plastic materials have been 3D printed and subjected to compression loading. The results are compared in terms of specific energy absorption and strength. This paper describes the quasi-static crushing behavior of two types of hybrid lattice structures (auxetic + auxetic and auxetic + non-auxetic). The results show that the developed hybrid structures can be useful to control collapse mechanisms and present larger energy absorption compared to conventional re-entrant auxetic structures.

Keywords: auxetic materials, compressive behavior, metamaterials, negative Poisson’s ratio

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

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Tissue engineering scaffolds must be biocompatible and biodegradable, provide adequate mechanical strength and cell attachment site for proliferation and differentiation. Furthermore, the scaffold morphology (such as pore size, porosity and pore interconnectivity) plays an important role. The electrospinning process has been widely used to fabricate micro/nano-sized fibres. Electrospinning allows for the fabrication of non-woven meshes containing micro- to nano-sized fibers providing high surface-to-volume area for cell attachment. Due to its advantageous characteristics, electrospinning is a useful method for skin, cartilage, bone, and nerve regeneration. In this study, we fabricated PCL scaffolds (SP) consisting of spiral-like struts using 3D melt-plotting system and micro/nanofibers using direct electrospinning writing. By altering the conditions of the conventional melt-plotting method, spiral-like struts were generated. Then, micro/nanofibers were deposited selectively. The control scaffold composed of perpendicular PCL struts was fabricated using the conventional melt-plotting method to compare the cellular activities. The effect on the attached cells (osteoblast-like cells (MG63)) was evaluated depending on the bending instability of the struts. The SP scaffolds showed enhanced biological properties such as initial cell attachment, proliferation and osteogenic differentiation. These results suggest that the SP scaffolds has potential as a bioengineered substitute for soft and hard tissue regeneration.

Keywords: cell attachment, electrospinning, mechanical strength, melt-plotting

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Authors: Nicole C. Valdez, Vincent L. Borromeo, Conrad C. Chong, Ahmad F. Mazahery

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Breast cancer is the most prevalent cancer worldwide, where the majority of cases are estrogen-receptor positive and involve 2 receptor proteins. The binding of estrogen to estrogen receptor alpha (ERα) promotes breast cancer growth, while it's binding to estrogen-receptor beta (ERβ) inhibits tumor growth. While natural products have been a promising source of chemotherapeutic agents, the challenge remains in finding a bioactive compound that specifically targets cancer cells, minimizing side effects on normal cells. Flavonoids are natural products that act as phytoestrogens and induce the same response as estrogen. They are able to compete with estrogen for binding to ERα; however, it has a higher binding affinity for ERβ. Their abundance in nature and low toxicity make them a potential candidate for breast cancer treatment. This study aimed to determine which particular flavonoids can specifically recognize ERβ and potentially be used for breast cancer treatment through molecular docking. A total of 206 flavonoids comprised of 97 isoflavones and 109 flavanones were collected from ZINC15, while the 3D structures of ERβ and ERα were obtained from Protein Data Bank. These flavonoid subclasses were chosen as they bind more strongly to ERs due to their chemical structure. The structures of the flavonoid ligands were converted using Open Babel, while the estrogen receptor protein structures were prepared using Autodock MGL Tools. The optimal binding site was found using BIOVIA Discovery Studio Visualizer before docking all flavonoids on both ERβ and ERα through Autodock Vina. Genistein is a flavonoid that exhibits anticancer effects by binding to ERβ, so its binding affinity was used as a baseline. Eriodictyol and 4”,6”-Di-O-Galloylprunin both exceeded genistein’s binding affinity for ERβ and was lower than its binding affinity for ERα. Of the two, eriodictyol was pursued due to its antitumor properties on a lung cancer cell line and on glioma cells. It is able to arrest the cell cycle at the G2/M phase by inhibiting the mTOR/PI3k/Akt cascade and is able to induce apoptosis via the PI3K/Akt/NF-kB pathway. Protein pathway and gene analysis were also conducted using ChEMBL and PANTHER and it was shown that eriodictyol might induce anticancer effects through the ROS1, CA7, KMO, and KDM1A genes which are involved in cell proliferation in breast cancer, non-small cell lung cancer, and other diseases. The high binding affinity of eriodictyol to ERβ, as well as its potential affected genes and antitumor effects, therefore, make it a candidate for the development of new breast cancer treatment. Verification through in vitro experiments such as checking the upregulation and downregulation of genes through qPCR and checking cell cycle arrest using a flow cytometry assay is recommended.

Keywords: breast cancer, estrogen receptor, flavonoid, molecular docking

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Authors: Mohammed Jarrar, Shalini Behl, Nadia Shaheen, Abeer Fatima, Reem Nasab

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Skin aging is a slow multifactorial process influenced by both internal as well as external factors. Ultra-violet radiations (UV), diet, smoking and personal habits are the most common environmental factors that affect skin aging. Fat contents and fibrous proteins as collagen and elastin are core internal structural components. The direct influence of UV on elastin integrity and health is crucial on aging of skin by time. The deposition of abnormal elastic material is a major marker in a photo-aged skin. Searching for compounds that may protect against cutaneous photo-damage is highly valued. Retinoids and Alpha Hydroxy Acids protective and or repairing effects of UV have been endorsed by some researchers. For consolidating a better understanding of anti and protective effects of such anti-aging agents, we evaluated the combinatory effects of various dosages of lactic acid and retinol on the dermal fibroblasts elastin levels exposed to UV. The UV exposed cells showed significant reduction in the elastin levels. A combination of drugs with a higher concentration of lactic acid (30-35 mM) and a lower concentration of retinol (10-15mg/mL) showed to work better in enhancing elastin concentration in UV exposed cells. We assume this enhancement could be the result of increased tropo-elastin gene expression stimulated by retinol and lactic acid probably repaired the UV irradiated damage by enhancing the amount and integrity of the elastin fibers.

Keywords: alpha hydroxy acid, elastin, retinol, ultraviolet radiations

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Authors: Bliss Singhal

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Epilepsy is a prevalent neurological disorder affecting approximately 50 million individuals worldwide and 1.2 million Americans. There exist millions of pediatric patients with intractable epilepsy, a condition in which seizures fail to come under control. The occurrence of seizures can result in physical injury, disorientation, unconsciousness, and additional symptoms that could impede children's ability to participate in everyday tasks. Predicting seizures can help parents and healthcare providers take precautions, prevent risky situations, and mentally prepare children to minimize anxiety and nervousness associated with the uncertainty of a seizure. This research proposes a comprehensive framework to predict seizures in pediatric patients by evaluating machine learning algorithms on unimodal neuroimaging data consisting of electroencephalogram signals. The bandpass filtering and independent component analysis proved to be effective in reducing the noise and artifacts from the dataset. Various machine learning algorithms’ performance is evaluated on important metrics such as accuracy, precision, specificity, sensitivity, F1 score and MCC. The results show that the deep learning algorithms are more successful in predicting seizures than logistic Regression, and k nearest neighbors. The recurrent neural network (RNN) gave the highest precision and F1 Score, long short-term memory (LSTM) outperformed RNN in accuracy and convolutional neural network (CNN) resulted in the highest Specificity. This research has significant implications for healthcare providers in proactively managing seizure occurrence in pediatric patients, potentially transforming clinical practices, and improving pediatric care.

Keywords: intractable epilepsy, seizure, deep learning, prediction, electroencephalogram channels

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Authors: Ian O'Loughlin

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Memory networks aim to integrate some of the recent successes in machine learning with a dynamic memory base that can be updated and deployed in artificial reasoning tasks. These models involve training networks to identify, update, and operate over stored elements in a large memory array in order, for example, to ably perform question and answer tasks parsing real-world and simulated discourses. This family of approaches still faces numerous challenges: the performance of these network models in simulated domains remains considerably better than in open, real-world domains, wide-context cues remain elusive in parsing words and sentences, and even moderately complex sentence structures remain problematic. This innovation, employing an array of stored and updatable ‘memory’ elements over which the system operates as it parses text input and develops responses to questions, is a compelling one for at least two reasons: first, it addresses one of the difficulties that standard machine learning techniques face, by providing a way to store a large bank of facts, offering a way forward for the kinds of long-term reasoning that, for example, recurrent neural networks trained on a corpus have difficulty performing. Second, the addition of a stored long-term memory component in artificial reasoning seems psychologically plausible; human reasoning appears replete with invocations of long-term memory, and the stored but dynamic elements in the arrays of memory networks are deeply reminiscent of the way that human memory is readily and often characterized. However, this apparent psychological plausibility is belied by a recent turn in the study of human memory in cognitive science. In recent years, the very notion that there is a stored element which enables remembering, however dynamic or reconstructive it may be, has come under deep suspicion. In the wake of constructive memory studies, amnesia and impairment studies, and studies of implicit memory—as well as following considerations from the cognitive neuroscience of memory and conceptual analyses from the philosophy of mind and cognitive science—researchers are now rejecting storage and retrieval, even in principle, and instead seeking and developing models of human memory wherein plasticity and dynamics are the rule rather than the exception. In these models, storage is entirely avoided by modeling memory using a recurrent neural network designed to fit a preconceived energy function that attains zero values only for desired memory patterns, so that these patterns are the sole stable equilibrium points in the attractor network. So although the array of long-term memory elements in memory networks seem psychologically appropriate for reasoning systems, they may actually be incurring difficulties that are theoretically analogous to those that older, storage-based models of human memory have demonstrated. The kind of emergent stability found in the attractor network models more closely fits our best understanding of human long-term memory than do the memory network arrays, despite appearances to the contrary.

Keywords: artificial reasoning, human memory, machine learning, neural networks

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Authors: Mahmoud Almasri, Xavier Marjou, Fanny Parzysz

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The demand for services provided by Unmanned Aerial Vehicles (UAVs) is increasing pervasively across several sectors including potential public safety, economic, and delivery services. As the number of applications using UAVs grows rapidly, more and more powerful, quality of service, and power efficient computing units are necessary. Recently, cellular technology draws more attention to connectivity that can ensure reliable and flexible communications services for UAVs. In cellular technology, flying with a high speed and altitude is subject to several key challenges, such as frequent handovers (HOs), high interference levels, connectivity coverage holes, etc. Additional HOs may lead to “ping-pong” between the UAVs and the serving cells resulting in a decrease of the quality of service and energy consumption. In order to optimize the number of HOs, we develop in this paper a Q-learning-based algorithm. While existing works focus on adjusting the number of HOs in a static network topology, we take into account the impact of cells deployment for three different simulation scenarios (Rural, Semi-rural and Urban areas). We also consider the impact of the decision distance, where the drone has the choice to make a switching decision on the number of HOs. Our results show that a Q-learning-based algorithm allows to significantly reduce the average number of HOs compared to a baseline case where the drone always selects the cell with the highest received signal. Moreover, we also propose which hyper-parameters have the largest impact on the number of HOs in the three tested environments, i.e. Rural, Semi-rural, or Urban.

Keywords: drones connectivity, reinforcement learning, handovers optimization, decision distance

Procedia PDF Downloads 108

Authors: Adriana Postal, Silvio C. Sampaio, Marcio A. Villas Boas, Josué P. Castro

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This study deals with the estimation of reference evapotranspiration (ET₀) in an agricultural context, focusing on efficient irrigation management to meet the growing interest in the sustainable management of water resources. Given the importance of water in agriculture and its scarcity in many regions, efficient use of this resource is essential to ensure food security and environmental sustainability. The methodology used involved the application of artificial intelligence techniques, specifically Multilayer Perceptron (MLP) Artificial Neural Networks (ANNs), to predict ET₀ in the state of Paraná, Brazil. The models were trained and validated with meteorological data from the Brazilian National Institute of Meteorology (INMET), together with data obtained from a producer's weather station in the western region of Paraná. Two optimizers (SGD and Adam) and different meteorological variables, such as temperature, humidity, solar radiation, and wind speed, were explored as inputs to the models. Nineteen configurations with different input variables were tested; amidst them, configuration 9, with 8 input variables, was identified as the most efficient of all. Configuration 10, with 4 input variables, was considered the most effective, considering the smallest number of variables. The main conclusions of this study show that MLP ANNs are capable of accurately estimating ET₀, providing a valuable tool for irrigation management in agriculture. Both configurations (9 and 10) showed promising performance in predicting ET₀. The validation of the models with cultivator data underlined the practical relevance of these tools and confirmed their generalization ability for different field conditions. The results of the statistical metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Coefficient of Determination (R²), showed excellent agreement between the model predictions and the observed data, with MAE as low as 0.01 mm/day and 0.03 mm/day, respectively. In addition, the models achieved an R² between 0.99 and 1, indicating a satisfactory fit to the real data. This agreement was also confirmed by the Kolmogorov-Smirnov test, which evaluates the agreement of the predictions with the statistical behavior of the real data and yields values between 0.02 and 0.04 for the producer data. In addition, the results of this study suggest that the developed technique can be applied to other locations by using specific data from these sites to further improve ET₀ predictions and thus contribute to sustainable irrigation management in different agricultural regions. The study has some limitations, such as the use of a single ANN architecture and two optimizers, the validation with data from only one producer, and the possible underestimation of the influence of seasonality and local climate variability. An irrigation management application using the most efficient models from this study is already under development. Future research can explore different ANN architectures and optimization techniques, validate models with data from multiple producers and regions, and investigate the model's response to different seasonal and climatic conditions.

Keywords: agricultural technology, neural networks in agriculture, water efficiency, water use optimization

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Authors: Howaida I. Abd-Alla, Amal Z. Hassan, Maha Soltan, Atef G. Hanna, Mounir M. El-Safty

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Acokanthera oblongifolia (Apocynaceae) is used for treatment of several infection diseases and is a well-known cardiac glycoside-containing plant. The infusion of their leaves is gargled to treat tonsillitis and is used medicinally to treat snakebites. The total cardiac glycosides content in the leaves was determined by referring to gitoxigenin as a reference compound. Two triterpenes, lup-20(29)-en-3β-ol (1) and oleanolic acid (2); two cardenolides, acovenoside A (3) and acobioside A (4) were isolated from the ethyl acetate extract. Their structures were determined on the basis of spectral analysis. Major constituents isolated from this species were evaluated for cytotoxicity against normal lung cell line (Wi38) and antimicrobial activities against Gram-positive (two strains) and Gram-negative bacteria (four strains), yeast-like fungi (two strains) and fungi (five strains). The minimum inhibitory concentration (MIC) of the compounds was determined using broth microdilution method. Their viral inhibitory effects against avian influenza virus type A (AI-H5N1) and Newcastle disease virus (NDV) in specific pathogen free (SPF) embryonated chicken eggs (ECE), chicken embryo fibroblasts (CEF) and Vero cells were evaluated. The cardenolide (3) showed viral inhibitory effects against AI-H5N1 and NDV in SPF ECE. The two cardenolides isolated have shown potent cytotoxicity against Vero cells. Compound (3) showed potent anti-Gram-negative bacteria activity. These results suggested that acovenoside A might be promising for future antiviral and antimicrobial drug design.

Keywords: Acokanthera, AI-H5N1, Cardenolides, NDV, SPF-ECE, VERO, Wi38 , Microbe

Procedia PDF Downloads 178

Authors: Hritwik Ghosh, Irfan Sadiq Rahat, Sachi Nandan Mohanty, J. V. R. Ravindra

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In the rapidly evolving landscape of medical diagnostics, the early detection and accurate classification of skin cancer remain paramount for effective treatment outcomes. This research delves into the transformative potential of Artificial Intelligence (AI), specifically Deep Learning (DL), as a tool for discerning and categorizing various skin conditions. Utilizing a diverse dataset of 3,000 images representing nine distinct skin conditions, we confront the inherent challenge of class imbalance. This imbalance, where conditions like melanomas are over-represented, is addressed by incorporating class weights during the model training phase, ensuring an equitable representation of all conditions in the learning process. Our pioneering approach introduces a hybrid model, amalgamating the strengths of two renowned Convolutional Neural Networks (CNNs), VGG16 and ResNet50. These networks, pre-trained on the ImageNet dataset, are adept at extracting intricate features from images. By synergizing these models, our research aims to capture a holistic set of features, thereby bolstering classification performance. Preliminary findings underscore the hybrid model's superiority over individual models, showcasing its prowess in feature extraction and classification. Moreover, the research emphasizes the significance of rigorous data pre-processing, including image resizing, color normalization, and segmentation, in ensuring data quality and model reliability. In essence, this study illuminates the promising role of AI and DL in revolutionizing skin cancer diagnostics, offering insights into its potential applications in broader medical domains.

Keywords: artificial intelligence, machine learning, deep learning, skin cancer, dermatology, convolutional neural networks, image classification, computer vision, healthcare technology, cancer detection, medical imaging

Procedia PDF Downloads 86

Authors: Masoud Ghaemi, MohammadJafar Hedayati, Faezeh Yousefzadeh, Hoseinali Heydarzadeh

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According to the statistics of the International Committee on Large Dams, internal erosion and piping (scour) are major causes of the destruction of earth-fill dams. If such dams are constructed in narrow valleys, the valley walls will increase the arching of the dam body due to the transfer of vertical and horizontal stresses, so the occurrence of hydraulic fracturing in these embankments is more likely. Roudbar Dam in Lorestan is a clay-core pebble earth-fill dam constructed in a relatively narrow valley in western Iran. Three years after the onset of impoundment, there has been a fall in dam behavior. Evaluation of the dam behavior based on the data recorded on the instruments installed inside the dam body and foundation confirms the occurrence of internal erosion in the lower and adjacent parts of the core on the left support (abutment). The phenomenon of hydraulic fracturing is one of the main causes of the onset of internal erosion in this dam. Accordingly, the main objective of this paper is to evaluate the validity of this hypothesis. To evaluate the validity of this hypothesis, the dam behavior during construction and impoundment has been first simulated with a three-dimensional numerical model. Then, using validated empirical equations, the safety factor of the occurrence of hydraulic fracturing phenomenon upstream of the dam score was calculated. Then, using the artificial neural network, the failure time of the given section was predicted based on the maximum stress trend created. The study results show that steep slopes of valley walls, sudden changes in coefficient, and differences in compressibility properties of dam body materials have caused considerable stress transfer from core to adjacent valley walls, especially at its lower levels. This has resulted in the coefficient of confidence of the occurrence of hydraulic fracturing in each of these areas being close to one in each of the empirical equations used.

Keywords: arching, artificial neural network, FLAC3D, hydraulic fracturing, internal erosion, pore water pressure

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Authors: Anna Rinaldi, Pierfrancesco Dellino

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COVID-19 vaccine hesitancy can be observed at different rates in different countries. In June 2021, 1,068 people were surveyed in France and Italy to inquire about individual potential acceptance, focusing on time preferences in a risk-return framework: having the vaccination today, in a month, and in 3 months; perceived risks of vaccination and COVID-19; and expected benefit of the vaccine. A randomized controlled trial was conducted to understand how everyday stimuli like fact-based news about vaccines impact an audience's acceptance of vaccination. The main experiment involved two groups of participants and two different articles about vaccine-related thrombosis taken from two Italian newspapers. One article used a more abstract description and language, and the other used a more anecdotal description and concrete language; each group read only one of these articles. Two other groups were assigned categorization tasks; one was asked to complete a concrete categorization task, and the other an abstract categorization task. Individual preferences for vaccination were found to be variable and unstable over time, and individual choices of accepting, refusing, or delaying could be affected by the way news is written. In order to understand these dynamic preferences, the present work proposes a new model based on seven categories of human behaviors that were validated by a neural network. A treatment effect was observed: participants who read the articles shifted to vaccine hesitancy categories more than participants assigned to other treatments and control. Furthermore, there was a significant gender effect, showing that the type of language leading to a lower hesitancy rate for men is correlated with a higher hesitancy rate for women and vice versa. This outcome should be taken into consideration for an appropriate gender-based communication campaign aimed at achieving herd immunity. The trial was registered at ClinicalTrials.gov NCT05582564 (17/10/2022).

Keywords: vaccine hesitancy, risk elicitation, neural network, covid19

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Authors: Anthony John Walker, Glen Bright

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The challenge faced by manufactures, when producing custom products, is that each product needs exact components. This can cause work-in-process instability due to component matching constraints imposed on assembly cells. Clearing type flow control policies have been used extensively in mediating server access between multiple arrival processes. Although the stability and performance of clearing policies has been well formulated and studied in the literature, the growth in arrival to departure sequence disorder for each arriving job, across a serving resource, is still an area for further analysis. In this paper, a closed form industrial model has been formulated that characterizes arrival-to-departure sequence disorder through stable manufacturing systems under clearing type flow control policy. Specifically addressed are the effects of sequence disorder imposed on a downstream assembly cell in terms of work-in-process instability induced through component matching constraints. Results from a simulated manufacturing system show that steady state average sequence disorder in parallel upstream processing cells can be balanced in order to decrease downstream assembly system instability. Simulation results also show that the closed form model accurately describes the growth and limiting behavior of average sequence disorder between parts arriving and departing from a manufacturing system flow controlled via clearing policy.

Keywords: assembly system constraint, custom products, discrete sequence disorder, flow control

Procedia PDF Downloads 178