Search results for: artificial immune system

Authors: Djermani Farouk, Sri Rahayu Hijrah Hati, Fenitra Maminirin, Permata Wulandari

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This study investigates the implications of Artificial Intelligence Marketing (AIM) marketing mix (PRD) Product, (PRC) Price, (PRM), Promotion and (PLC) Place on consumption patterns of halal food (CPHF). A quantitative approach was adopted in this study and responses were obtained from 350 Indonesian consumers. Using Partial Least Squares-Structural Equation Modeling (PLS-SEM), the results show that there is a direct support of marketing mix (PRD, PRC, PLC) to AIM and CPHF, while PRM does not play a significant role in CPHF. In addition, the findings reveal that AIM mediates significantly the relationship between PLC, PRC and PRM and CPHF, while AIM indicates no mediation between PRD and CPHF. Indonesian consumer’s exhibit serious concerns with consumption patterns of halal food. it is recommended that managers focus their attention on marketing strategies to predict consumer behavior in terms of consumption patterns of halal food through the integration of AIM.

Keywords: marketing mix, consumption patterns, artificial intelligence marketing, Halal food

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Authors: Salma Loukman, Reda Benmrid, Najat Bouchmaa, Hicham Hboub, Rachid El Fatimy, Rachid Benhida

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In this study, it use integrated transcriptomic datasets from the GEO repository with the purpose of investigating immune dysregulation in COVID-19. Thus, in this context, we decided to be focused on NK cells and CD14+ monocytes gene expression, considering datasets GSE165461 and GSE198256, respectively. Other datasets with PBMCs, lung, olfactory, and sensory epithelium and lymph were used to provide robust validation for our results. This approach gave an integrated view of the immune responses in COVID-19, pointing out a set of potential biomarkers and therapeutic targets with special regard to standards of physiological conditions. IFI27, MKI67, CENPF, MBP, HBA2, TMEM158, THBD, HBA1, LHFPL2, SLA, and AC104564.3 were identified as key genes from our analysis that have critical biological processes related to inflammation, immune regulation, oxidative stress, and metabolic processes. Consequently, such processes are important in understanding the heterogeneous clinical manifestations of COVID-19—from acute to long-term effects now known as 'long COVID'. Subsequent validation with additional datasets consolidated these genes as robust biomarkers with an important role in the diagnosis of COVID-19 and the prediction of its severity. Moreover, their enrichment in key pathophysiological pathways presented them as potential targets for therapeutic intervention.The results provide insight into the molecular dynamics of COVID-19 caused by cells such as NK cells and other monocytes. Thus, this study constitutes a solid basis for targeted diagnostic and therapeutic development and makes relevant contributions to ongoing research efforts toward better management and mitigation of the pandemic.

Keywords: SARS-COV-2, RNA-seq, biomarkers, severity, long COVID-19, bio analysis

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Authors: Ismail Abubakar, Hamid Mehrabi, Reg Morton

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Currently, extensive signal analysis is performed in order to evaluate structural health of turbomachinery blades. This approach is affected by constraints of time and the availability of qualified personnel. Thus, new approaches to blade dynamics identification that provide faster and more accurate results are sought after. Generally, modal analysis is employed in acquiring dynamic properties of a vibrating turbomachinery blade and is widely adopted in condition monitoring of blades. The analysis provides useful information on the different modes of vibration and natural frequencies by exploring different shapes that can be taken up during vibration since all mode shapes have their corresponding natural frequencies. Experimental modal testing and finite element analysis are the traditional methods used to evaluate mode shapes with limited application to real live scenario to facilitate a robust condition monitoring scheme. For a real time mode shape evaluation, rapid evaluation and low computational cost is required and traditional techniques are unsuitable. In this study, artificial neural network is developed to evaluate the mode shape of a lab scale rotating blade assembly by using result from finite element modal analysis as training data. The network performance evaluation shows that artificial neural network (ANN) is capable of mapping the correlation between natural frequencies and mode shapes. This is achieved without the need of extensive signal analysis. The approach offers advantage from the perspective that the network is able to classify mode shapes and can be employed in real time including simplicity in implementation and accuracy of the prediction. The work paves the way for further development of robust condition monitoring system that incorporates real time mode shape evaluation.

Keywords: modal analysis, artificial neural network, mode shape, natural frequencies, pattern recognition

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Authors: Yasaman Sanayei, Alireza Bahiraie

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This paper presents a systematic methodology based on the application of artificial neural networks for sequencing batch reactor (SBR). The SBR is a fill-and-draw biological wastewater technology, which is specially suited for nutrient removal. Employing reactive dye by Sphingomonas paucimobilis bacteria at sequence batch reactor is a novel approach of dye removal. The influent COD, MLVSS, and reaction time were selected as the process inputs and the effluent COD and BOD as the process outputs. The best possible result for the discrete pole parameter was a= 0.44. In orderto adjust the parameters of ANN, the Levenberg-Marquardt (LM) algorithm was employed. The results predicted by the model were compared to the experimental data and showed a high correlation with R2> 0.99 and a low mean absolute error (MAE). The results from this study reveal that the developed model is accurate and efficacious in predicting COD and BOD parameters of the dye-containing wastewater treated by SBR. The proposed modeling approach can be applied to other industrial wastewater treatment systems to predict effluent characteristics. Note that SBR are normally operated with constant predefined duration of the stages, thus, resulting in low efficient operation. Data obtained from the on-line electronic sensors installed in the SBR and from the control quality laboratory analysis have been used to develop the optimal architecture of two different ANN. The results have shown that the developed models can be used as efficient and cost-effective predictive tools for the system analysed.

Keywords: artificial neural network, COD removal, SBR, Sphingomonas paucimobilis

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Authors: Sofia Quaglia, Carmine Di Martino, Brendan Tierney

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Artificial Intelligence (A.I.) constitutes various aspects of modern life, from the Machine Learning algorithms predicting the stocks on Wall streets to the killing of belligerents and innocents alike on the battlefield. Moreover, the end goal is to create autonomous A.I.; this means that the presence of humans in the decision-making process will be absent. The question comes naturally: when an A.I. does something wrong when its behavior is harmful to the community and its actions go against the law, which is to be held responsible? This research’s subject matter in A.I. and Robot Ethics focuses mainly on Robot Rights and its ultimate objective is to answer the questions: (i) What is the function of rights? (ii) Who is a right holder, what is personhood and the requirements needed to be a moral agent (therefore, accountable for responsibility)? (iii) Can an A.I. be a moral agent? (ontological requirements) and finally (iv) if it ought to be one (ethical implications). With the direction to answer this question, this research project was done via a collaboration between the School of Computer Science in the Technical University of Dublin that oversaw the technical aspects of this work, as well as the Department of Philosophy in the University of Milan, who supervised the philosophical framework and argumentation of the project. Firstly, it was found that all rights are positive and based on consensus; they change with time based on circumstances. Their function is to protect the social fabric and avoid dangerous situations. The same goes for the requirements considered necessary to be a moral agent: those are not absolute; in fact, they are constantly redesigned. Hence, the next logical step was to identify what requirements are regarded as fundamental in real-world judicial systems, comparing them to that of ones used in philosophy. Autonomy, free will, intentionality, consciousness and responsibility were identified as the requirements to be considered a moral agent. The work went on to build a symmetrical system between personhood and A.I. to enable the emergence of the ontological differences between the two. Each requirement is introduced, explained in the most relevant theories of contemporary philosophy, and observed in its manifestation in A.I. Finally, after completing the philosophical and technical analysis, conclusions were drawn. As underlined in the research questions, there are two issues regarding the assignment of moral agency to artificial agent: the first being that all the ontological requirements must be present and secondly being present or not, whether an A.I. ought to be considered as an artificial moral agent. From an ontological point of view, it is very hard to prove that an A.I. could be autonomous, free, intentional, conscious, and responsible. The philosophical accounts are often very theoretical and inconclusive, making it difficult to fully detect these requirements on an experimental level of demonstration. However, from an ethical point of view it makes sense to consider some A.I. as artificial moral agents, hence responsible for their own actions. When considering artificial agents as responsible, there can be applied already existing norms in our judicial system such as removing them from society, and re-educating them, in order to re-introduced them to society. This is in line with how the highest profile correctional facilities ought to work. Noticeably, this is a provisional conclusion and research must continue further. Nevertheless, the strength of the presented argument lies in its immediate applicability to real world scenarios. To refer to the aforementioned incidents, involving the murderer of innocents, when this thesis is applied it is possible to hold an A.I. accountable and responsible for its actions. This infers removing it from society by virtue of its un-usability, re-programming it and, only when properly functioning, re-introducing it successfully

Keywords: artificial agency, correctional system, ethics, natural agency, responsibility

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Authors: Niki Hayatgheib, SéGolèNe Calvez, Catherine Fournel, Lionel Pineau, Herve Pouliquen, Emmanuelle Moreau

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Furunculosis caused by infection with Aeromonas salmonicida subsp. salmonicida has been a known disease found principally in salmonid aquaculture. Vaccination has been partly successful in preventing this disease, but outbreaks still occur. The application of functional feed additive found to be a promising yield to improve fish health against diseases. In this study, we tested the efficacy of prebiotics and plant essential oils-enriched diet on immune response and disease resistance in vaccinated and non-vaccinated rainbow trout (Oncorhynchus mykiss) against furunculosis. A total of 600 fish were fed with the basal diet or supplement. On 4th week of feeding, fish were vaccinated with an autovaccine. Following 8 weeks, fish were challenged with Aeromonas salmonicida subsp. salmonicida and mortalities were recorded for 3 weeks. Lysozyme activity and antibody titer in serum were measured in different groups. The results of this study showed that lysozyme and circulatory antibody titer in plasma elevated significantly in vaccinated fish fed with additive. The best growth rate and relative percentage survival (62%) were in fish fed with a supplement, while 15% in control fish. Overall, prebiotics and essential oils association can be considered as a potential component for enhancing vaccine efficacy against furunculosis by increasing the growth performance, immune responses and disease resistance in rainbow trout.

Keywords: aeromonas salmonicida subsp. salmonicida, aquaculture, disease resistance, fish, immune response, prebiotics-essential oils feed additive, rainbow trout, vaccination

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Authors: Dereje Ferede, Solomon Negash

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Artificial Intelligence (AI) is a key enabler and driver to transform and revolutionize the healthcare industries. However, utilizing AI and achieving these benefits is challenging for different sectors in wide-ranging, more difficult for developing economy healthcare. Due to this, real-world clinical execution and implementation of AI have not yet aged. We believe that examining the determinants is key to addressing these challenges. Furthermore, the literature does not yet particularize determinants of AI capabilities and ways of empowering the healthcare ecosystem to develop AI capabilities in a developing economy. Thus, this study aims to position AI as a digital transformation weapon for the healthcare ecosystem by examining AI capability determinants and providing insights on better empowering the healthcare industry to develop AI capabilities. To do so, we base on the technology-organization-environment (TOE) model and will apply a mixed research approach. We will conclude with recommendations based on findings for future practitioners and researchers.

Keywords: artificial intelligence, capability, digital transformation, developing economies, healthcare

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Authors: David Tena-Gago, Jose M. Alcaraz Calero, Qi Wang

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The evolution and integration of automated vehicles have become more and more tangible in recent years. State-of-the-art technological advances in the field of camera-based Artificial Intelligence (AI) and computer vision greatly favor the performance and reliability of the Advanced Driver Assistance System (ADAS), leading to a greater knowledge of vehicular operation and resembling human behavior. However, the exclusive use of this technology still seems insufficient to control vehicular operation at 100%. To reveal the degree of accuracy of the current camera-based automated driving AI modules, this paper studies the structure and behavior of one of the main solutions in a controlled testing environment. The results obtained clearly outline the lack of reliability when using exclusively the AI model in the perception stage, thereby entailing using additional complementary sensors to improve its safety and performance.

Keywords: accuracy assessment, AI-driven mobility, artificial intelligence, automated vehicles

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Authors: Madhusudana S. N., Reeta Mani, Ashwini S. Satishchandra P., Netravati, Udhani V., Fiaz A., Karande S.

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Rabies is an acute encephalitis which is considered 100% fatal despite occasional reports of survivors. However, in recent times more cases of human rabies survivors are being reported. In the last 5 years, there are six laboratories confirmed human rabies survivors in India alone. All cases were children below 15 years and all contracted the disease by dog bites. All of them also had received the full or partial course of rabies vaccination and 4 out of 6 had also received rabies immunoglobulin. All cases were treated in intensive care units in hospitals at Bangalore, Mumbai, Chandigarh, Lucknow and Goa. We report here the results of immunological and virological studies conducted at our laboratory on these patients. The clinical samples that were obtained from these patients were Serum, CSF, nuchal skin biopsy and saliva. Serum and CSF samples were subjected to standard RFFIT for estimation of rabies neutralizing antibodies. Skin biopsy, CSF and saliva were processed by TaqMan real-time PCR for detection of viral RNA. CSF, saliva and skin homogenates were also processed for virus isolation by inoculation of suckling mice. The PBMCs isolated from fresh blood was subjected to ELISPOT assay to determine the type of immune response (Th1/Th2). Both CSF and serum were also investigated for selected cytokines by Luminex assay. The level of antibodies to virus G protein and N protein were determined by ELISA. All survivors had very high titers of RVNA in serum and CSF 100 fold higher than non-survivors and vaccine controls. A five-fold rise in titer could be demonstrated in 4 out of 6 patients. All survivors had a significant increase in antibodies to G protein in both CSF and serum when compared to non-survivors. There was a profound and robust Th1 response in all survivors indicating that interferon gamma could play an important factor in virus clearance. We could isolate viral RNA in only one patient four years after he had developed symptoms. The partial N gene sequencing revealed 99% homology to species I strain prevalent in India. Levels of selected cytokines in CSF and serum did not reveal any difference between survivors and non-survivors. To conclude, survival from rabies is mediated by virus-specific immune responses of the host and clearance of rabies virus from CNS may involve the participation of both Th2 and Th1 immune responses.

Keywords: rabies, rabies treatment, rabies survivors, immune reponse in rabies encephalitis

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Authors: Sidra Islam, Moin Uddin, Mir A. Rouf

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A wide variety of pathological disorders owing to hyperglycemic conditions involves structural rearrangements and condensations of proteins. The implication of methylglyoxal (MG) modified immunoglobulin G (IgG) in the onset and progression of diabetes type 2 (T2DM) is studied in the present study. Using biophysical and biochemical approaches MG was found to perturb the structure of IgG, effect its microenvironment and leads to aggregate formation. Furthermore, MG-IgG was found to be highly immunogenic inducing high titre antibodies in female rabbits. Clinical studies revealed the presence of circulating anti-MG-IgG antibodies as analyzed by direct binding ELISA. The circulating auto antibodies were highly specific for MG-IgG as revealed by inhibition ELISA. Thus it can be concluded that MG is a powerful agent with a high damaging potential. To IgG. It is highly capable of generating immune response that contributes to the immunopathology associated with diabetes. Dicarbonyl adducts may emerge as potential biomarkers for T2DM.

Keywords: immunogenicity, Immunoglobulin G, methylglyoxal, Type 2 Diabetes Mellitus

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Authors: Narges Bahmanyar, Masoud Ghorbani

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Rabies is caused by several species of the genus Lyssavirus in the Rhabdoviridae family. The disease is deadly encephalitis transmitted from warm-blooded animals to humans, and domestic and wild carnivores play the most crucial role in its transmission. The prevalence of rabies in poor areas of developing salinities is constantly posed as a global threat to public health. According to the World Health Organization, approximately 60,000 people die yearly from rabies. Of these, 60% of deaths are related to the Middle East. Although rabies encephalitis is incurable to date, awareness of the disease and the use of vaccines is the best way to combat the disease. Although effective vaccines are available, there is a high cost involved in vaccine production and management to combat rabies. Increasing the prevalence and discovery of new strains of rabies virus requires the need for safe, effective, and as inexpensive vaccines as possible. One of the approaches considered to achieve the quality and quantity expressed through the manufacture of recombinant types of rabies vaccine. Currently, livestock rabies vaccines are used only in inactivated or live attenuated vaccines, the process of inactivation of which pays attention to considerations. The rabies virus contains a negatively polarized single-stranded RNA genome that encodes the five major structural genes (N, P, M, G, L) from '3 to '5 . Rabies virus glycoprotein G, the major antigen, can produce the virus-neutralizing antibody. N-antigen is another candidate for developing recombinant vaccines. However, because it is within the RNP complex of the virus, the possibility of genetic diversity based on different geographical locations is very high. Glycoprotein G is structurally and antigenically more protected than other genes. Protection at the level of its nucleotide sequence is about 90% and at the amino acid level is 96%. Recombinant vaccines, consisting of a pathogenic subunit, contain fragments of the protein or polysaccharide of the pathogen that have been carefully studied to determine which of these molecules elicits a stronger and more effective immune response. These vaccines minimize the risk of side effects by limiting the immune system's access to the pathogen. Such vaccines are relatively inexpensive, easy to produce, and more stable than vaccines containing viruses or whole bacteria. The problem with these vaccines is that the pathogenic subunits may elicit a weak immune response in the body or may be destroyed before they reach the immune cells, which requires nanoparticles to overcome. Suitable for use as an adjuvant. Among these, biodegradable nanoparticles with functional levels are good candidates as adjuvants for the vaccine. In this study, we intend to use beta-glucan nanoparticles as adjuvants. The surface glycoprotein of the rabies virus (G) is responsible for identifying and binding the virus to the target cell. This glycoprotein is the major protein in the structure of the virus and induces an antibody response in the host. In this study, we intend to use rabies virus surface glycoprotein conjugated with beta-glucan nanoparticles to produce vaccines.

Keywords: rabies, vaccines, beta glucan, nanoprticles, adjuvant, recombinant protein

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Authors: Sawsan Eissa, Momen Gharib, Omnia Kabbany

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A flushing numerical study has been performed for intended artificial islands on the Persian Gulf coast in Abu Dhabi, UAE. The island masterplan was tested for flushing using the DELFT 3D hydrodynamic model, and it was found that its residence time exceeds the acceptable PIANC flushing Criteria. Therefore, a number of mitigation measures were applied and tested one by one using the flushing model. Namely, changing the location of the entrance opening, dredging, removing part of the mangrove existing in the near vicinity to create a channel, removing the mangrove altogether, using culverts of different numbers and locations, and pumping at selected points. The pumping option gave the best solution, but it was disregarded due to high capital and running costs. Therefore, it opted for a combination of other solutions, including removing mangroves, introducing culverts, and adjusting island boundaries and types of protection.

Keywords: hydrodynamics, flushing, delft 3d, Persian Gulf, artificial islands.

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Authors: Esshili Awatef, Manitz Marie-Pierre, Eßlinger Manuela, Gerhardt Alexandra, Plümper Jennifer, Wachholz Simone, Friebe Astrid, Juckel Georg

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Maternal immune activation (MIA) resulting from maternal viral infection during pregnancy is a known risk factor for schizophrenia. The neural mechanisms by which maternal infections increase the risk for schizophrenia remain unknown, although the prevailing hypothesis argues that an activation of the maternal immune system induces changes in the maternal-fetal environment that might interact with fetal brain development. It may lead to an activation of fetal microglia inducing long-lasting functional changes of these cells. Based on post-mortem analysis showing an increased number of activated microglial cells in patients with schizophrenia, it can be hypothesized that these cells contribute to disease pathogenesis and may actively be involved in gray matter loss observed in such patients. In the present study, we hypothesize that prenatal treatment with the inflammatory agent Poly(I:C) during embryogenesis at contributes to microglial activation in the offspring, which may, therefore, represent a contributing factor to the pathogenesis of schizophrenia and underlines the need for new pharmacological treatment options. Pregnant rats were treated with intraperitoneal injections a single dose of Poly(I:C) or saline on gestation day 17. Brains of control and Poly(I:C) offspring, were removed and into 20-μm-thick coronal sections were cut by using a Cryostat. Brain slices were fixed and immunostained with ba1 antibody. Subsequently, Iba1-immunoreactivity was detected using a secondary antibody, goat anti-rabbit. The sections were viewed and photographed under microscope. The immunohistochemical analysis revealed increases in microglia cell number in the prefrontal cortex, in offspring of poly(I:C) treated-rats as compared to the controls injected with NaCl. However, no significant differences were observed in microglia activation in the cerebellum among the groups. Prenatal immune challenge with Poly(I:C) was able to induce long-lasting changes in the offspring brains. This lead to a higher activation of microglia cells in the prefrontal cortex, a brain region critical for many higher brain functions, including working memory and cognitive flexibility. which might be implicated in possible changes in cortical neuropil architecture in schizophrenia. Further studies will be needed to clarify the association between microglial cells activation and schizophrenia-related behavioral alterations.

Keywords: Microglia, neuroinflammation, PolyI:C, schizophrenia

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Authors: Ben Khayut, Lina Fabri, Maya Avikhana

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The models of the modern Artificial Narrow Intelligence (ANI) cannot: a) independently and continuously function without of human intelligence, used for retraining and reprogramming the ANI’s models, and b) think, understand, be conscious, cognize, infer, and more in state of Uncertainty, and changes in situations, and environmental objects. To eliminate these shortcomings and build a new generation of Artificial Intelligence systems, the paper proposes a Conception, Model, and Method of Computational Psycholinguistic Cognitive Situational-Fuzzy Self-Controlled Brain and Mind System (CPCSFSCBMSUU) using a neural network as its computational memory, operating under uncertainty, and activating its functions by perception, identification of real objects, fuzzy situational control, forming images of these objects, modeling their psychological, linguistic, cognitive, and neural values of properties and features, the meanings of which are identified, interpreted, generated, and formed taking into account the identified subject area, using the data, information, knowledge, and images, accumulated in the Memory. The functioning of the CPCSFSCBMSUU is carried out by its subsystems of the: fuzzy situational control of all processes, computational perception, identifying of reactions and actions, Psycholinguistic Cognitive Fuzzy Logical Inference, Decision making, Reasoning, Systems Thinking, Planning, Awareness, Consciousness, Cognition, Intuition, Wisdom, analysis and processing of the psycholinguistic, subject, visual, signal, sound and other objects, accumulation and using the data, information and knowledge in the Memory, communication, and interaction with other computing systems, robots and humans in order of solving the joint tasks. To investigate the functional processes of the proposed system, the principles of Situational Control, Fuzzy Logic, Psycholinguistics, Informatics, and modern possibilities of Data Science were applied. The proposed self-controlled System of Brain and Mind is oriented on use as a plug-in in multilingual subject Applications.

Keywords: computational brain, mind, psycholinguistic, system, under uncertainty

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Authors: Marda Vidrianto, Tania Surya Utami

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Electrical Submersible Pump (ESP) is an artificial lift of choice to produce oil on Offshore Java Sea. It is selected based on the production rate capacity and running life expectation. ESP performance in a mature field is highly affected by oil well conditions. The presence of sand, scale, gas, and low influx will create unstable ESP operation hence lowering the run life expectation and system efficiency. This paper reviews the current energy usage and efficiency on every part of the ESP system. The hydraulic and electrical losses, as well as system efficiency for each well, are calculated to identify energy losses and the possibility for improvement. It is shown that high back pressure on the system and low-efficiency pump are the major contributors to energy losses. It was found that optimized production rate and the use of advanced technology on pump and motor unit could improve energy efficiency.

Keywords: advance technology, energy efficiency, ESP, mature field, production rate

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Authors: Rashima Mahajan, Dipali Bansal, Shweta Singh

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Real time non-invasive Brain Computer Interfaces have a significant progressive role in restoring or maintaining a quality life for medically challenged people. This manuscript provides a comprehensive review of emerging research in the field of cognitive/affective computing in context of human neural responses. The perspectives of different emotion assessment modalities like face expressions, speech, text, gestures, and human physiological responses have also been discussed. Focus has been paid to explore the ability of EEG (Electroencephalogram) signals to portray thoughts, feelings, and unspoken words. An automated workflow-based protocol to design an EEG-based real time Brain Computer Interface system for analysis and classification of human emotions elicited by external audio/visual stimuli has been proposed. The front end hardware includes a cost effective and portable Emotive EEG Neuroheadset unit, a personal computer and a set of external stimulators. Primary signal analysis and processing of real time acquired EEG shall be performed using MATLAB based advanced brain mapping toolbox EEGLab/BCILab. This shall be followed by the development of MATLAB based self-defined algorithm to capture and characterize temporal and spectral variations in EEG under emotional stimulations. The extracted hybrid feature set shall be used to classify emotional states using artificial intelligence tools like Artificial Neural Network. The final system would result in an inexpensive, portable and more intuitive Brain Computer Interface in real time scenario to control prosthetic devices by translating different brain states into operative control signals.

Keywords: brain computer interface, electroencephalogram, EEGLab, BCILab, emotive, emotions, interval features, spectral features, artificial neural network, control applications

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Authors: Awan A. Zaima, Tanvieer Ayesha, Mirshahi Shahsoltan, Pocard Marc, Mirshahi Massoud

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Brain-derived neurotrophic factor (BDNF) is described as a factor helping to support the survival of existing neurons by involving the growth and differentiation of new neurons and synapses. Cancer diagnosis impacts the mental health, and in consequences, depression arise eventually hinders recovery and disrupts the quality of life and surviving chances of patients. The focus of this study is to hint upon a prospective biomarker as a promising diagnostic tool for an early indicator/predictor of depression prevalence in cancer patients for better care and treatment options. The study aims to analyze peripheral biomarkers from neuro immune axis (BDNF, IL21 as a NK cell activator) using co-relation approach. Samples were obtained from random non cancer candidates and advanced peritoneum carcinomatosis patients with 25% pseudomyxoma, 21% Colon cancer,19% stomach cancer, 10% ovarian cancer, 8% appendices cancer, and 10% other area of peritoneum cancer patients. Both groups of the study were categorized by gender and age, with a range of 18 to 86 years old. Biomarkers were analyzed in collected plasma by performing multiplex sandwich ELISA system. Data were subjected to statistical analysis for the assessment of the correlation. Our results demonstrate that BNDF and IL 21 down regulated significantly in patient groupas compared to non-cancer candidates (ratio of patients/normalis 2.57 for BNDF and 1.32 for IL21). This preliminary investigation suggested that the neuro immune biomarkers are down regulated in carcinomatosis patients and can be associated with cancer expansion and cancer genesis. Further studies on larger cohort are necessary to validate this hypothesis.

Keywords: biomarkers, depression, peritoneum carcinoma, BNDF, IL21

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Authors: Dua Hişam, Serhat İkizoğlu

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Identifying the problem behind balance disorder is one of the most interesting topics in the medical literature. This study has considerably enhanced the development of artificial intelligence (AI) algorithms applying multiple machine learning (ML) models to sensory data on gait collected from humans to classify between normal people and those suffering from Vestibular System (VS) problems. Although AI is widely utilized as a diagnostic tool in medicine, AI models have not been used to perform feature extraction and identify VS disorders through training on raw data. In this study, three machine learning (ML) models, the Random Forest Classifier (RF), Extreme Gradient Boosting (XGB), and K-Nearest Neighbor (KNN), have been trained to detect VS disorder, and the performance comparison of the algorithms has been made using accuracy, recall, precision, and f1-score. With an accuracy of 95.28 %, Random Forest Classifier (RF) was the most accurate model.

Keywords: vestibular disorder, machine learning, random forest classifier, k-nearest neighbor, extreme gradient boosting

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Authors: Alhaji Modu Bukar, Faez Firdaus Abdullah Jesse, Che Azurahanim Che Abdullah, Mustapha M. Noordin, Mohd-Lila Mohd Azmia

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Orf virus (ORFV) is the causative agent of a proliferative skin lesion known as contagious ecthyma in sheep and goats. Currently used live attenuated vaccines against ORFV infection have been reported to cause severe outbreaks in vaccinated animals. In this study, we investigated the immunogenicity of the B2L and F1L proteins of the virus, which are thought to elicit a protective immune response The 6-week-old 50 female mice were divided into 8 groups: seven experimental groups and one control group. Each animal in the experimental group received an initial immunisation with the nanoparticles or proteins coated with the nanoparticles, followed by two booster immunizations with the same products 14 days apart. Ten days after the last booster inoculation, the mice were either humanely killed or lethally challenged with UPM /HSN-2-ORFV at a dose of 106 TCID50/mL in a volume of 50 μl. The spleen was examined for histopathological changes and quantification of T cells by flow cytometry. On the other hand, the degree of protection of mice from the lethal virus was evaluated by lesion size, weight loss, and histopathological examination of skin and liver. The results showed that mice immunised with rB2L alone, rB2L-Al₂O₃-NPs, rB2L/rF1L, and rB2L/rF1L-Al₂O₃-NPs elicited statistically higher levels of anti-rB2L and/or rF1L-specific IgA/IgG and CD4/CD8 cell immune responses than mice in the control groups (p < 0.01). The vaccine candidate did not exhibit severe skin damage after monitoring histopathology, morbidity, and mortality. Overall, the results suggest that recombinant rB2L and rF1L antigens may be useful universal vaccine candidates against ORFV infections.

Keywords: orf virus, antigen nanoparticles, virus, nanoparticles

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Authors: Shelin Jin, Jin Huang, Shiqin Cao, Qiuzhen Jia, Bo Zhang, Zhenyu Sun

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Stripe rust and leaf rust, caused by Puccinia striiformis f.sp. tritici and Puccinia recondita f.sp. tritici are two of the most damaging diseases of wheat in China. In recent years, leaf rust has migrated to some wheat growing areas previously suitable for stripe rust, resulting in a mixture of the two diseases occurring in the same area and at the same time, and seriously damage wheat production in China, Gansu Province. The most effective method of prevention those two diseases are through the use of resistant cultivars. However, many studies have only carried out of resistance of wheat varieties for a single disease; resistance to both diseases is unknown. In order to definite the resistance composition of wheat varieties to these two diseases, 715 wheat varieties/lines from 8 breeding units in Gansu province were collected to test for the resistance to stripe rust and leaf rust at seedling stage in greenhouse and at adult plant stage in field in 2016-2018, respectively. Spore suspensions with the fresh mixture races of CYR32, CYR33, and CYR34 of Puccinia striiformis f.sp. tritici and mixture races of THTP, THTT, TKTT, and THTS of Puccinia recondita f.sp. tritici were used for inoculation separately. The result shows that only 4.74% of the varieties/lines show comprehensive resistance to strip rust and leaf rust at all growth stages, and there are 34 wheat varieties/lines including Tianxuan 67, 2006-1-4-1-4-2-7-2-3-10, 03-139-1-2-2-1-2-1, Qingnong 21, Lenghan 5, 04-203-1-1-1 and so on. In seedling stage, the frequencies of resistant varieties/lines to wheat strip rust and leaf rust were 56.64% and 30.23%. While the materials were susceptible to these diseases were 43.36% and 69.77%. 71 varieties/lines were resistant to those two diseases, accounted for 9.93%. 10 varieties/lines, accounted for 1.4%, were highly resistant (including immune/near immune) to those two diseases. In adult-plant stage, the frequencies of resistant varieties/lines to wheat strip rust and leaf rust were 76.53% and 36.11%. While the materials were susceptible to these diseases were 23.47% and 63.89%. 137 varieties/lines were resistant to those two diseases, accounted for 19.16%. 59 varieties/lines, accounted for 8.25%, were highly resistant (including immune/near immune) to those two diseases. Overall, the 715 varieties /lines had high resistance to wheat strip rust, but poor resistance to leaf rust. This study found out some resistant materials which had better comprehensive resistance to leaf rust and strip rust, also pointed out the resistance characteristics of 715 varieties/lines to those two diseases at the seedling stage and adult-plant stage, which will be of great guiding significance in wheat resistance breeding and comprehensive control those two diseases in China, Gansu Province in the future.

Keywords: Puccinia striiformis f.sp. tritici, Puccinia recondita f.sp. tritici, resistance of variety, wheat

Procedia PDF Downloads 118

Authors: Jamerson Felipe Pereira Lima, Jeane Cecília Bezerra de Melo

Abstract:

The difficulty and cost related to obtaining of protein tertiary structure information through experimental methods, such as X-ray crystallography or NMR spectroscopy, helped raising the development of computational methods to do so. An approach used in these last is prediction of tridimensional structure based in the residue chain, however, this has been proved an NP-hard problem, due to the complexity of this process, explained by the Levinthal paradox. An alternative solution is the prediction of intermediary structures, such as the secondary structure of the protein. Artificial Intelligence methods, such as Bayesian statistics, artificial neural networks (ANN), support vector machines (SVM), among others, were used to predict protein secondary structure. Due to its good results, artificial neural networks have been used as a standard method to predict protein secondary structure. Recent published methods that use this technique, in general, achieved a Q3 accuracy between 75% and 83%, whereas the theoretical accuracy limit for protein prediction is 88%. Alternatively, to achieve better results, support vector machines prediction methods have been developed. The statistical evaluation of methods that use different AI techniques, such as ANNs and SVMs, for example, is not a trivial problem, since different training sets, validation techniques, as well as other variables can influence the behavior of a prediction method. In this study, we propose a prediction method based on artificial neural networks, which is then compared with a selected SVM method. The chosen SVM protein secondary structure prediction method is the one proposed by Huang in his work Extracting Physico chemical Features to Predict Protein Secondary Structure (2013). The developed ANN method has the same training and testing process that was used by Huang to validate his method, which comprises the use of the CB513 protein data set and three-fold cross-validation, so that the comparative analysis of the results can be made comparing directly the statistical results of each method.

Keywords: artificial neural networks, protein secondary structure, protein structure prediction, support vector machines

Procedia PDF Downloads 621

Authors: Isobel Hambleton, Mark Carrington

Abstract:

Trypanosoma brucei, the causal agent of a range of diseases in humans and livestock, evades the mammalian immune system through a population survival strategy based on the expression of a series of antigenically distinct variant surface glycoproteins (VSGs). RNAi mediated knockdown of the active VSG gene triggers a precytokinesis cell cycle arrest. To determine whether this phenotype is the result of reduced VSG transcript or depleted VSG protein, we used morpholino antisense oligonucleotides to block translation of VSG mRNA. The same precytokinesis cell cycle arrest was observed, suggesting that VSG protein abundance is monitored closely throughout the cell cycle. An inducible expression system has been developed to test various GPI-anchored proteins for their ability to rescue this cell cycle arrest. This system has been used to demonstrate that wild-type VSG expressed from a T7 promoter rescues this phenotype. This indicates that VSG expression from one of the specialised bloodstream expression sites (BES) is not essential for cell division. The same approach has been used to define the minimum essential features of a VSG necessary for function.

Keywords: bloodstream expression site, morpholino, precytokinesis cell cycle arrest, variant surface glycoprotein

Procedia PDF Downloads 150

Authors: Ruqaiya Khalil, Saman Usmani, Zaheer Ul-Haq

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The accurate characterization of ligand binding affinity is indispensable for designing molecules with optimized binding affinity. Computational tools help in many directions to predict quantitative correlations between protein-ligand structure and their binding affinities. Molecular dynamics (MD) simulation is a modern state-of-the-art technique to evaluate the underlying basis of ligand-protein interactions by characterizing dynamic and energetic properties during the event. Autoimmune diseases arise from an abnormal immune response of the body against own tissues. The current regimen for the described condition is limited to immune-modulators having compromised pharmacodynamics and pharmacokinetics profiles. One of the key player mediating immunity and tolerance, thus invoking autoimmunity is Interleukin-2; a cytokine influencing the growth of T cells. Molecular dynamics simulation techniques are applied to seek insight into the inhibitory mechanisms of newly synthesized compounds that manifested immunosuppressant potentials during in silico pipeline. In addition to estimation of free energies associated with ligand binding, MD simulation yielded us a great deal of information about ligand-macromolecule interactions to evaluate the pattern of interactions and the molecular basis of inhibition. The present study is a continuum of our efforts to identify interleukin-2 inhibitors of both natural and synthetic origin. Herein, we report molecular dynamics simulation studies of Interluekin-2 complexed with different antagonists previously reported by our group. The study of protein-ligand dynamics enabled us to gain a better understanding of the contribution of different active site residues in ligand binding. The results of the study will be used as the guide to rationalize the fragment based synthesis of drug-like interleukin-2 inhibitors as immune-modulators.

Keywords: immuno-modulators, MD simulation, protein-ligand interaction, structure-based drug design

Procedia PDF Downloads 262

Authors: Ulas Vural, M. Ergun Okay, E. Mesut Yildiz

Abstract:

Telecommunication service providers demand accurate and precise prediction of customer churn probabilities to increase the effectiveness of their customer relation services. The large amount of customer data owned by the service providers is suitable for analysis by machine learning methods. In this study, expenditure data of customers are analyzed by using an artificial neural network (ANN). The ANN model is applied to the data of customers with different billing duration. The proposed model successfully predicts the churn probabilities at 83% accuracy for only three months expenditure data and the prediction accuracy increases up to 89% when the nine month data is used. The experiments also show that the accuracy of ANN model increases on an extended feature set with information of the changes on the bill amounts.

Keywords: customer relationship management, churn prediction, telecom industry, deep learning, artificial neural networks

Procedia PDF Downloads 145

Authors: Jongtae Rhee, Sangkwon Han, Seungbin Ji, Junhyeong Park, Byeonghun Kim, Taekyung Kim, Byeonghyeon Jeon, Jiwoo Yang

Abstract:

The shared kitchen is a concept that transfers the value of the sharing economy to the kitchen. It is a type of kitchen equipped with cooking facilities that allows multiple companies or chefs to share time and space and use it jointly. These shared kitchens provide economic benefits and convenience, such as reduced investment costs and rent, but also increase the risk of safety management, such as cross-contamination of food ingredients. Therefore, to manage the safety of food ingredients and finished products in a shared kitchen where several entities jointly use the kitchen and handle various types of food ingredients, it is critical to manage followings: the freshness of food ingredients, user hygiene and safety and cross-contamination of cooking equipment and facilities. In this study, it propose a machine learning-based system for hygiene safety and cross-contamination management, which are highly difficult to manage. User clothing management and user access management, which are most relevant to the hygiene and safety of shared kitchens, are solved through machine learning-based methodology, and cutting board usage management, which is most relevant to cross-contamination management, is implemented as an integrated safety management system based on artificial intelligence. First, to prevent cross-contamination of food ingredients, we use images collected through a real-time camera to determine whether the food ingredients match a given cutting board based on a real-time object detection model, YOLO v7. To manage the hygiene of user clothing, we use a camera-based facial recognition model to recognize the user, and real-time object detection model to determine whether a sanitary hat and mask are worn. In addition, to manage access for users qualified to enter the shared kitchen, we utilize machine learning based signature recognition module. By comparing the pairwise distance between the contract signature and the signature at the time of entrance to the shared kitchen, access permission is determined through a pre-trained signature verification model. These machine learning-based safety management tasks are integrated into a single information system, and each result is managed in an integrated database. Through this, users are warned of safety dangers through the tablet PC installed in the shared kitchen, and managers can track the cause of the sanitary and safety accidents. As a result of system integration analysis, real-time safety management services can be continuously provided by artificial intelligence, and machine learning-based methodologies are used for integrated safety management of shared kitchens that allows dynamic contracts among various users. By solving this problem, we were able to secure the feasibility and safety of the shared kitchen business.

Keywords: artificial intelligence, food safety, information system, safety management, shared kitchen

Procedia PDF Downloads 69

Authors: Kyu Jin Kim, Byung Kwan Oh, Hyo Seon Park

Abstract:

The seismic responses-based structural health monitoring system has been performed to reduce seismic damage. The inter-story drift ratio which is the major index of the seismic capacity assessment is employed for estimating the seismic damage of buildings. Meanwhile, seismic response analysis to estimate the structural responses of building demands significantly high computational cost due to increasing number of high-rise and large buildings. To estimate the inter-story drift ratio of buildings from the earthquake efficiently, this paper suggests the estimation method of inter-story drift for buildings using an artificial neural network (ANN). In the method, the radial basis function neural network (RBFNN) is integrated with optimization algorithm to optimize the variable through evolutionary learning that refers to evolutionary radial basis function neural network (ERBFNN). The estimation method estimates the inter-story drift without seismic response analysis when the new earthquakes are subjected to buildings. The effectiveness of the estimation method is verified through a simulation using multi-degree of freedom system.

Keywords: structural health monitoring, inter-story drift ratio, artificial neural network, radial basis function neural network, genetic algorithm

Procedia PDF Downloads 327

Authors: Vladimir Sharapaev

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This paper seeks to address the question of the authorship of copyrighted works created solely by the artificial intelligence or with the use thereof, and proposes possible interpretational or legislative solutions to the problems arising from the plurality of the persons potentially involved in the ultimate creation of the work and division of tasks among such persons. Being based on the commonly accepted assumption that a copyrighted work can only be created by a natural person, the paper does not deal with the issues regarding the creativity of the artificial intelligence per se (or the lack thereof), and instead focuses on the distribution of the intellectual property rights potentially belonging to the creators of the artificial intelligence and/or the creators of the content used for the formation of the copyrighted work. Moreover, the technical development and rapid improvement of the AI-based programmes, which tend to be reaching even greater independence on a human being, give rise to the question whether the initial creators of the artificial intelligence can be entitled to the intellectual property rights to the works created by such AI at all. As the juridical practice of some European courts and legal doctrine tends to incline to the latter opinion, indicating that the works created by the AI may not at all enjoy copyright protection, the questions of authorships appear to be causing great concerns among the investors in the development of the relevant technology. Although the technology companies dispose with further instruments of protection of their investments, the risk of the works in question not being copyrighted caused by the inconsistency of the case law and a certain research gap constitutes a highly important issue. In order to assess the possible interpretations, the author adopted a doctrinal and analytical approach to the research, systematically analysing the European and Czech copyright laws and case law in some EU jurisdictions. This study aims to contribute to greater legal certainty regarding the issues of the authorship of the AI-created works and define possible clues for further research.

Keywords: artificial intelligence, copyright, authorship, copyrighted work, intellectual property

Procedia PDF Downloads 122

Authors: Oyebade K. Oyedotun

Abstract:

The cardiotocogram is a technical recording of the heartbeat rate and uterine contractions of a fetus during pregnancy. During pregnancy, several complications can occur to both the mother and the fetus; hence it is very crucial that medical experts are able to find technical means to check the healthiness of the mother and especially the fetus. It is very important that the fetus develops as expected in stages during the pregnancy period; however, the task of monitoring the health status of the fetus is not that which is easily achieved as the fetus is not wholly physically available to medical experts for inspection. Hence, doctors have to resort to some other tests that can give an indication of the status of the fetus. One of such diagnostic test is to obtain cardiotocograms of the fetus. From the analysis of the cardiotocograms, medical experts can determine the status of the fetus, and therefore necessary medical interventions. Generally, medical experts classify examined cardiotocograms into ‘normal’, ‘suspect’, or ‘pathological’. This work presents an artificial neural network based decision support system which can filter cardiotocograms data, producing the corresponding statuses of the fetuses. The capability of artificial neural network to explore the cardiotocogram data and learn features that distinguish one class from the others has been exploited in this research. In this research, feedforward and radial basis neural networks were trained on a publicly available database to classify the processed cardiotocogram data into one of the three classes: ‘normal’, ‘suspect’, or ‘pathological’. Classification accuracies of 87.8% and 89.2% were achieved during the test phase of the trained network for the feedforward and radial basis neural networks respectively. It is the hope that while the system described in this work may not be a complete replacement for a medical expert in fetus status evaluation, it can significantly reinforce the confidence in medical diagnosis reached by experts.

Keywords: decision support, cardiotocogram, classification, neural networks

Procedia PDF Downloads 332

Authors: Thomas E. Portegys

Abstract:

An animal behavior problem is presented in the form of a nest-building task that involves two cooperating virtual birds, a male and female. The female builds a nest into which she lays an egg. The male's job is to forage in a forest for food for both himself and the female. In addition, the male must fetch stones from a nearby desert for the female to use as nesting material. The task is completed when the nest is built, and an egg is laid in it. A goal-seeking neural network and a recurrent neural network were trained and tested with little success. The goal-seeking network was then enhanced with “place cells”, allowing the birds to spatially navigate the world, building the nest while keeping themselves fed. Place cells are neurons in the hippocampus that map space.

Keywords: artificial animal intelligence, artificial life, goal-seeking neural network, nest-building, place cells, spatial navigation

Procedia PDF Downloads 59

Authors: Netanel Stern

Abstract:

Artificial intelligence (AI) algorithms have emerged as powerful tools in the field of psychiatry, offering new possibilities for enhancing diagnosis and treatment outcomes. This article explores the utilization of AI algorithms in psychiatry, highlighting their potential to revolutionize patient care. Various AI algorithms, including machine learning, natural language processing (NLP), reinforcement learning, clustering, and Bayesian networks, are discussed in detail. Moreover, ethical considerations and future directions for research and implementation are addressed.

Keywords: AI, software engineering, psychiatry, neuroimaging

Procedia PDF Downloads 116