Search results for: specific emitter identification

Authors: Najeeb Hassan, Mahmudur Rahman

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Arial Khan River is one of the main south-eastward outlets of the River Padma. This river maintains a meander channel through its course and is erosional in nature. The specific objective of the research is to study and evaluate the hydrological characteristics in the form of assessing changes of cross-sections, discharge, water level and velocity profile in different stations and to create a hydrodynamic model of the Arial Khan River. Necessary data have been collected from Bangladesh Water Development Board (BWDB) and Center for Environment and Geographic Information Services (CEGIS). Satellite images have been observed from Google earth. In this study, hydrodynamic model of Arial Khan River has been developed using well known steady open channel flow code Hydrologic Engineering Centers and River Analysis System (HEC-RAS) using field surveyed geometric data. Cross-section properties at 22 locations of River Arial Khan for the years 2011, 2013 and 2015 were also analysed. 1-D HEC-RAS model has been developed using the cross sectional data of 2015 and appropriate boundary condition is being used to run the model. This Arial Khan River model is calibrated using the pick discharge of 2015. The applicable value of Mannings roughness coefficient (n) is adjusted through the process of calibration. The value of water level which ties with the observed data to an acceptable accuracy is taken as calibrated model. The 1-D HEC-RAS model then validated by using the pick discharges from 2009-2018. Variation in observed water level in the model and collected water level data is being compared to validate the model. It is observed that due to seasonal variation, discharge of the river changes rapidly and Mannings roughness coefficient (n) also changes due to the vegetation growth along the river banks. This river model may act as a tool to measure flood area in future. By considering the past pick flow discharge, it is strongly recommended to improve the carrying capacity of Arial Khan River to protect the surrounding areas from flash flood.

Keywords: BWDB, CEGIS, HEC-RAS

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Authors: Soledad Carinelli, Iñigo Fernández, José Luis González-Mora, Pedro A. Salazar-Carballo

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Mastitis is the most relevant inflammatory disease in cattle, affecting the animal health and causing important economic losses on dairy farms. This disease takes place in the mammary gland or udder when some opportunistic microorganisms, such as Staphylococcus aureus, Streptococcus agalactiae, Corynebacterium bovis, etc., invade the teat canal. According to the severity of the inflammation, mastitis can be classified as sub-clinical, clinical and chronic. Standard methods for mastitis detection include counts of somatic cells, cell culture, electrical conductivity of the milk, and California test (evaluation of “gel-like” matrix consistency after cell lysed with detergents). However, these assays present some limitations for accurate detection of subclinical mastitis. Currently, haptoglobin, an acute phase protein, has been proposed as novel and effective biomarker for mastitis detection. In this work, an electrochemical biosensor based on polydopamine-modified magnetic nanoparticles (MNPs@pDA) for haptoglobin detection is reported. Thus, MNPs@pDA has been synthesized by our group and functionalized with hemoglobin due to its high affinity to haptoglobin protein. The protein was labeled with specific antibodies modified with alkaline phosphatase enzyme for its electrochemical detection using an electroactive substrate (1-naphthyl phosphate) by differential pulse voltammetry. After the optimization of assay parameters, the haptoglobin determination was evaluated in milk. The strategy presented in this work shows a wide range of detection, achieving a limit of detection of 43 ng/mL. The accuracy of the strategy was determined by recovery assays, being of 84 and 94.5% for two Hp levels around the cut off value. Milk real samples were tested and the prediction capacity of the electrochemical biosensor was compared with a Haptoglobin commercial ELISA kit. The performance of the assay has demonstrated this strategy is an excellent and real alternative as screen method for sub-clinical bovine mastitis detection.

Keywords: bovine mastitis, haptoglobin, electrochemistry, magnetic nanoparticles, polydopamine

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Authors: Muhammad Umar Mushtaq, Muhammad Bilal Khan Niazi, Nouman Ahmad, Dooa Arif

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Apart from organic dyes, heavy metals such as Pb, Ni, Cr, and Cu are present in textile effluent and pose a threat to humans and the environment. Many studies on removing heavy metallic ions from textile wastewater have been conducted in recent decades using metal-organic frameworks (MOFs). In this study new polyether sulfone ultrafiltration membrane, modified with Cu/Co and Cu/Zn-based bimetal-organic frameworks (MOFs), was produced. Phase inversion was used to produce the membrane, and atomic force microscopy (AFM), scanning electron microscopy (SEM) were used to characterize it. The bimetallic MOFs-based membrane structure is complex and can be comprehended using characterization techniques. The bimetallic MOF-based filtration membranes are designed to selectively adsorb specific contaminants while allowing the passage of water molecules, improving the ultrafiltration efficiency. MOFs' adsorption capacity and selectivity are enhanced by functionalizing them with particular chemical groups or incorporating them into composite membranes with other materials, such as polymers. The morphology and performance of the bimetallic MOF-based membrane were investigated regarding pure water flux and metal ion rejection. The advantages of developed bimetallic MOFs based membranes for wastewater treatment include enhanced adsorption capacity because of the presence of two metals in their structure, which provides additional binding sites for contaminants, leading to a higher adsorption capacity and more efficient removal of pollutants from wastewater. Based on the experimental findings, bimetallic MOF-based membranes are more capable of rejecting metal ions from industrial wastewater than conventional membranes that have already been developed. Furthermore, the difficulties associated with operational parameters, including pressure gradients and velocity profiles, are simulated using Ansys Fluent software. The simulation results obtained for the operating parameters are in complete agreement with the experimental results.

Keywords: bimetallic MOFs, heavy metal ions, industrial wastewater treatment, ultrafiltration.

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Authors: Myriam Rousseau, Suzie McKinnon, Mathieu Mireault, Anaïs V. Berthiaume, Marie-Hélène Poulin, Jacinthe Bourassa, Louis-Simon Maltais

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The increasing presence of young people with autism is forcing schools to adapt to this new situation and to offer services that meet the needs of this clientele. However, school staff often feels unqualified to support these students, lacking the preparation, skills and training to meet their needs. Continuing education for these staff is therefore essential to ensure that they can meet the needs of these students. As a result, the Government of Quebec has developed a bilingual (French and English) online training on autism specific to the needs of school staff. Therefore, adequate training for all school staff is likely to provide quality learning opportunities for these students. The research project focuses on the participants' appreciation, contributions, and social validity of the training. More specifically, it aims to: 1) evaluate the knowledge and self-efficacy of the participants, 2) evaluate the social validity and 3) document the evaluation of the ergonomics of the platform hosting the training. The evaluation carried out as part of this descriptive study uses a quantitative method. Data are collected using questionnaires completed online. The analysis of preliminary data reveals that participants' knowledge of autism and their sense of self-efficacy increased significantly. They value the training positively and consider it to be acceptable, appropriate, and suitable. The participants find it important for school staff to take this training. Almost all the items measuring the ergonomics of the platform have averages above 4.57/5. In general, the study shows that the training allows participating of the trainee school staff to improve their knowledge of autism and their sense of self-efficacy with young people with autism. In addition, participants recognize that the training has good social validity and appreciate the online modality. However, these results should be interpreted with caution given the limited number of participants who completed the research project. It is therefore important to continue the research with a larger number of participants to allow an adequate and general representativeness of the social validity, the feeling of competence and the appreciation of the platform.

Keywords: autism, online training, school staff, social validity

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Authors: Daniel Mokatsanyane, Johnny Jansen Van Rensburg

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The integral role oil-crushing companies play in sunflower oil production is often overlooked to offer high-quality oil to refineries and end consumers. Sunflower oil crushing companies in South Africa are exposed to price fluctuations resulting from the local and international markets. Hedging instruments enable these companies to hedge themselves against unexpected prices spikes and to ensure sustained profitability. A crushing company is a necessary middleman, and as such, these companies have exposure to the purchasing and selling sides of sunflower. Sunflower oil crushing companies purchase sunflower seeds from farmers or agricultural companies that provide storage facilities. The purchasing price is determined by the supply and demand of sunflower seed, both national and international. When the price of sunflower seeds in South Africa is high but still below import parity, then the crush margins realised by these companies are reduced or even negative at times. There are three main products made by sunflower oil crushing companies, oil, meal, and shells. Profits are realised from selling three products, namely, sunflower oil, meal and shells. However, when selling sunflower oil to refineries, sunflower oil crushing companies needs to hedge themselves against a reduction in vegetable oil prices. Hedging oil prices is often done via futures and is subject to specific volume commitments before a hedge position can be taken in. Furthermore, South African oil-crushing companies hedge sunflower oil with international, Over-the-counter contracts as South Africa is a price taker of sunflower oil and not a price maker. As such, South Africa provides a fraction of the world’s sunflower oil supply and, therefore, has minimal influence on price changes. The advantage of hedging using futures ensures that the sunflower crushing company will know the profits they will realise, but the downside is that they can no longer benefit from a price increase. Alternative hedging instruments like options might pose a solution to the opportunity cost does not go missing and that profit margins are locked in at the best possible prices for the oil crushing company. This paper aims to investigate the possibility of employing options alongside futures to simulate different scenarios to determine if options can bridge the opportunity cost gap.

Keywords: derivatives, hedging, price risk, sunflower, sunflower oil, South Africa

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Authors: S. K. Kakraliya, R. D. Jat, H. S. Jat, P. C. Sharma, M. L. Jat

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The traditional rice-wheat (RW) system in the IGP of South Asia is tillage, water, energy, and capital intensive. Coupled with more pumping of groundwater over the years to meet the high irrigation water requirement of the RW system has resulted in over-exploitation of groundwater. Replacement of traditional rice with less water crops such as maize under climate-smart agriculture (CSA) based management (tillage, crop establishment and residue management) practices are required to promote sustainable intensification. Furthermore, inefficient nutrient management practices are responsible for low crop yields and nutrient use efficiencies in maize-wheat (MW) system. A 7-year field experiment was conducted in farmer’s participatory strategic research mode at Taraori, Karnal, India to evaluate the effects of tillage and crop establishment (TCE) methods, residue management, mungbean integration, and nutrient management practices on crop yields, water productivity and profitability of MW system. The main plot treatments included four combinations of TCE, residue and mungbean integration [conventional tillage (CT), conventional tillage with mungbean (CT + MB), permanent bed (PB) and permanent bed with MB (PB + MB] with three nutrient management practices [farmer’s fertilizer practice (FFP), recommended dose of fertilizer (RDF) and site-specific nutrient management (SSNM)] using Nutrient Expert® as subplot treatments. System productivity, water use efficiency (WUE) and net returns under PB + MB were significantly increased by 25–30%, 28–31% and 35–40% compared to CT respectively, during seven years of experimentation. The integration of MB in MW system contributed ~25and ~ 28% increases in system productivity and net returns compared with no MB, respectively. SSNM based nutrient management increased the mean (averaged across 7 yrs) system productivity by 12- 15% compared with FFP. The study revealed that CSA based sustainable intensification (PB + MB) and SSNM approach provided opportunities for enhancing crop productivity, WUE and profitability of the MW system in India.

Keywords: Conservation Agriculture, Precision water and nutrient management, Permanent beds, Crop yields

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Authors: Reza Safdari, Goli Arji, Robab Abdolkhani Maryam zahmatkeshan

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Background and purpose: Cardiovascular diseases are among the most common diseases in all societies. The most important step in minimizing myocardial infarction and its complications is to minimize its risk factors. The amount of medical data is increasingly growing. Medical data mining has a great potential for transforming these data into information. Using data mining techniques to generate predictive models for identifying those at risk for reducing the effects of the disease is very helpful. The present study aimed to collect data related to risk factors of heart infarction from patients’ medical record and developed predicting models using data mining algorithm. Methods: The present work was an analytical study conducted on a database containing 350 records. Data were related to patients admitted to Shahid Rajaei specialized cardiovascular hospital, Iran, in 2011. Data were collected using a four-sectioned data collection form. Data analysis was performed using SPSS and Clementine version 12. Seven predictive algorithms and one algorithm-based model for predicting association rules were applied to the data. Accuracy, precision, sensitivity, specificity, as well as positive and negative predictive values were determined and the final model was obtained. Results: five parameters, including hypertension, DLP, tobacco smoking, diabetes, and A+ blood group, were the most critical risk factors of myocardial infarction. Among the models, the neural network model was found to have the highest sensitivity, indicating its ability to successfully diagnose the disease. Conclusion: Risk prediction models have great potentials in facilitating the management of a patient with a specific disease. Therefore, health interventions or change in their life style can be conducted based on these models for improving the health conditions of the individuals at risk.

Keywords: decision trees, neural network, myocardial infarction, Data Mining

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Authors: Leila Rashki Ghaleno, Mohamad Amin Hajari, Leila Montazeri, Abdolhossein Shahverdi, Mojtaba Rezazadeh Valojerdi

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Background: Spermatogonia stem cells (SSCs) exhibit pluripotency, enabling them to undergo differentiation into many cell lineages, including neurons, glia, endothelial cells, and hepatocytes when cultured in vitro. Although the specific mechanisms are not yet fully understood, it has been observed that biopolymer agents, such as hyaluronic acid (HA) and alginate (Alg), have the potential to induce transdifferentiation of SSCs. The current work aimed to examine the process of in vitro spermatogenesis and the conversion of mouse testicular cells into hepatocytes and erythrocyte-like cells utilizing the HA-Alg hydrogel. Method: After being extracted from the testes of a 5-day postpartum mouse (5 DPP), the testicular cells were separated into two enzymatic stages and then put into a composite hydrogel containing 0.5% HA and 1% alginate. On days 14 and 28 of culture, the colonies' growth, the cells' viability, and their histology were assessed. Result: Despite observing significant cell proliferation on day 14 and the development of circular-shaped organoids on day 28, it was noted that the organoids generated in the HA-Alg medium tended to maintain their circular morphology on day 28. Notably, the testicular cells underwent transdifferentiation into cell types resembling erythrocytes and hepatocytes. The hepatocyte-like cells exhibited the presence of glycogen and lipid deposits, indicating their hepatocyte-like characteristics. Interestingly, immunostaining analysis revealed the secretion of albumin and the presence of VEGFR on day 14. However, on day 28, albumin expression was not detected, while the expression of Sox9 (a marker for hepatocytes), Vegf, CD34, and C-kit (markers for erythrocytes) showed increased levels in the gene expression evaluation. Conclusion: The present findings indicated that HA-Alg could be a potent and effective agent for the transdifferentiation of testis cells into erythrocyte and hepatocyte-like cells, as recent studies have confirmed the transformation of SSCs into hepatocyte cells during in vitro culture.

Keywords: 3D culture, mouse testicular cell, hyaluronic acid, liver organoids

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Authors: Umberto Di Staso, Marco Soave, Alessio Giori, Federico Prandi, Raffaele De Amicis

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The increasing availability of information about earth surface elevation (Digital Elevation Models DEM) generated from different sources (remote sensing, Aerial Images, Lidar) poses the question about how to integrate and make available to the most than possible audience this huge amount of data. In order to exploit the potential of 3D elevation representation the quality of data management plays a fundamental role. Due to the high acquisition costs and the huge amount of generated data, highresolution terrain surveys tend to be small or medium sized and available on limited portion of earth. Here comes the need to merge large-scale height maps that typically are made available for free at worldwide level, with very specific high resolute datasets. One the other hand, the third dimension increases the user experience and the data representation quality, unlocking new possibilities in data analysis for civil protection, real estate, urban planning, environment monitoring, etc. The open-source 3D virtual globes, which are trending topics in Geovisual Analytics, aim at improving the visualization of geographical data provided by standard web services or with proprietary formats. Typically, 3D Virtual globes like do not offer an open-source tool that allows the generation of a terrain elevation data structure starting from heterogeneous-resolution terrain datasets. This paper describes a technological solution aimed to set up a so-called “Terrain Builder”. This tool is able to merge heterogeneous-resolution datasets, and to provide a multi-resolution worldwide terrain services fully compatible with CesiumJS and therefore accessible via web using traditional browser without any additional plug-in.

Keywords: Terrain Builder, WebGL, Virtual Globe, CesiumJS, Tiled Map Service, TMS, Height-Map, Regular Grid, Geovisual Analytics, DTM

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Authors: Miao Li, Mengyuan Xu

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As a low-carbon travel mode, bicycling has drawn increasing political interest in the contemporary Chinese urban context, and the public sharing bikes have become the most popular ways of bike usage in China now. This research aims to explore the spatial-temporal relationship between sharing bike usage and different characteristics of the urban street built environment. In the research, street segments were used as the analytic unit of the street built environment defined by street intersections. The sharing bike usage data in the research include a total of 2.64 million samples that are the entire sharing bike distribution data recorded in two days in 2018 within a neighborhood of 185.4 hectares in the city of Wuhan, China. And these data are assigned to the 97 urban street segments in this area based on their geographic location. The built environment variables used in this research are categorized into three sections: 1) street design characteristics, such as street width, street greenery, types of bicycle lanes; 2) condition of other public transportation, such as the availability of metro station; 3) Street function characteristics that are described by the categories and density of the point of interest (POI) along the segments. Spatial Lag Models (SLM) were used in order to reveal the relationships of specific urban streets built environment characteristics and the likelihood of sharing bicycling usage in whole and different periods a day. The results show: 1) there is spatial autocorrelation among sharing bicycling usage of urban streets in case area in general, non-working day, working day and each period of a day, which presents a clustering pattern in the street space; 2) a statistically strong association between bike sharing usage and several different built environment characteristics such as POI density, types of bicycle lanes and street width; 3) the pattern that bike sharing usage is influenced by built environment characteristics depends on the period within a day. These findings could be useful for policymakers and urban designers to better understand the factors affecting bike sharing system and thus propose guidance and strategy for urban street planning and design in order to promote the use of sharing bikes.

Keywords: big data, sharing bike usage, spatial statistics, urban street built environment

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Authors: Nicole Ramlachan, Samuel Mark West

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Lung cancer is the leading cause of cancer-associated deaths in North America, with the vast majority being non-small cell lung cancer (NSCLC), with a five-year survival rate of only 24%. Non-invasive discovery of biomarkers associated with early-diagnosis of NSCLC can enable precision oncology efforts using liquid biopsy-based multiomics profiling of plasma. Although tissue biopsies are currently the gold standard for tumor profiling, this method presents many limitations since these are invasive, risky, and sometimes hard to obtain as well as only giving a limited tumor profile. Blood-based tests provides a less-invasive, more robust approach to interrogate both tumor- and non-tumor-derived signals. We intend to examine 30 stage III-IV NSCLC patients pre-surgery and collect plasma samples.Cell-free DNA (cfDNA) will be extracted from plasma, and next-generation sequencing (NGS) performed. Through the analysis of tumor-specific alterations, including single nucleotide variants (SNVs), insertions, deletions, copy number variations (CNVs), and methylation alterations, we intend to identify tumor-derived DNA—ctDNA among the total pool of cfDNA. This would generate data to be used as an accurate form of cancer genotyping for diagnostic purposes. Using liquid biopsies offer opportunities to improve the surveillance of cancer patients during treatment and would supplement current diagnosis and tumor profiling strategies previously not readily available in Trinidad and Tobago. It would be useful and advantageous to use this in diagnosis and tumour profiling as well as to monitor cancer patients, providing early information regarding disease evolution and treatment efficacy, and reorient treatment strategies in, timethereby improving clinical oncology outcomes.

Keywords: genomics, multiomics, clinical genetics, genotyping, oncology, diagnostics

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Authors: Pedro A. Marques, Francisco Monteiro, Alessandra Zumbo, Alessia Simonini, Miguel A. Mendez

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Cryogenic fuels such as Liquid Hydrogen (LH₂) must be transported and stored at extremely low temperatures. Without expensive active cooling solutions, preventing fuel boil-off over time is impossible. Hence, one must resort to venting systems at the cost of significant energy and fuel mass loss. These losses increase significantly in propellant tanks installed on vehicles, as the presence of external accelerations induces sloshing. Sloshing increases heat and mass transfer rates and leads to significant pressure oscillations, which might further trigger propellant venting. To make LH₂ economically viable, it is essential to minimize these factors by using advanced control techniques. However, these require accurate modelling and a full understanding of the tank's thermodynamics. The present research aims to implement a simple thermodynamic model capable of predicting the state of a cryogenic fuel tank under different operating conditions (i.e., filling, pressurization, fuel extraction, long-term storage, and sloshing). Since this model relies on a set of closure parameters to drive the system's transient response, it must be calibrated using experimental or numerical data. This work focuses on the former approach, wherein the model is calibrated through an experimental campaign carried out on a reduced-scale model of a cryogenic tank. The thermodynamic model of the system is composed of three control volumes: the ullage, the liquid, and the insulating walls. Under this lumped formulation, the governing equations are derived from energy and mass balances in each region, with mass-averaged properties assigned to each of them. The gas-liquid interface is treated as an infinitesimally thin region across which both phases can exchange mass and heat. This results in a coupled system of ordinary differential equations, which must be closed with heat and mass transfer coefficients between each control volume. These parameters are linked to the system evolution via empirical relations derived from different operating regimes of the tank. The derivation of these relations is carried out using an inverse method to find the optimal relations that allow the model to reproduce the available data. This approach extends classic system identification methods beyond linear dynamical systems via a nonlinear optimization step. Thanks to the data-driven assimilation of the closure problem, the resulting model accurately predicts the evolution of the tank's thermodynamics at a negligible computational cost. The lumped model can thus be easily integrated with other submodels to perform complete system simulations in real time. Moreover, by setting the model in a dimensionless form, a scaling analysis allowed us to relate the tested configurations to a representative full-size tank for naval applications. It was thus possible to compare the relative importance of different transport phenomena between the laboratory model and the full-size prototype among the different operating regimes.

Keywords: destratification, hydrogen, modeling, pressure-drop, pressurization, sloshing, thermodynamics

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Authors: Nazeema Ahmed

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This paper reports on the development of a student peer mentoring programme at a higher education institution. The programme is dependent on volunteering senior undergraduate students who are trained to mentor first-year students studying towards an engineering degree. The evaluation of the programme took the form of first-year students completing a self-report paper questionnaire at the onset of a lecture and mentors completing their questionnaire electronically. The evaluation yielded mixed findings. Peer mentoring clearly benefited some students in their adjustment to the institution. Specific mentors’ personal attributes enabled the establishment of successful mentoring relationships, where encouragement, advice and academic assistance was provided. Gains were reciprocal with mentors reporting that the programme contributed towards their personal development. Confidence in the programme was expressed in mentors feeling that it was an initiative worth continuing and first-year students agreeing that it be recommended to future first-year students. This was despite many unfavourable experiences of mentors where their professionalism and commitment to the programme was suspect. It is evident that while mentors began with noble intentions they appear either to lose interest or become overwhelmed with their own workload as the academic year progresses. On the other hand, some mentors reported feeling challenged by the apathy of first-year students who failed to maximise the opportunity available to them. The different attitudes towards mentoring that manifested as a mentoring culture in some departments were particularly pertinent to its successful implementation. The findings point to the key role of academic staff in the mentoring programme who model the mentoring relationship in their interaction with student mentors. While their involvement in the programme may be perceived as a drain on resources in an already demanding academic teaching environment, it is imperative that structural changes be put in place for the programme to be both efficient and sustainable. A pervasive finding concerns the evolving institutional culture of student development in the faculty. Mentors and first-year students alike alluded to the potential of the mentoring programme provided it is seriously endorsed at both the departmental and faculty level. The findings provide a foundation from which to develop the programme further and to begin improving its capacity for maximizing student retention in South African higher education.

Keywords: engineering students, first-year students, peer mentoring

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Authors: Priyanka Sharma, Ranjita Das, Mohan C. Kalita, Debajit Thakur

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Background: Actinomycetes are the richest source of novel bioactive secondary metabolites such as antibiotics, enzymes and other therapeutically useful metabolites with diverse biological activities. The present study aims at the antimicrobial potential and genetic diversity of culturable Actinomycetes isolated from protected forest ecosystems of Assam which includes Kaziranga National Park (26°30˝-26°45˝N and 93°08˝-93°36˝E), Pobitora Wildlife Sanctuary (26º12˝-26º16˝N and 91º58˝-92º05˝E) and Gibbon Wildlife Sanctuary (26˚40˝-26˚45˝N and 94˚20˝-94˚25˝E) which are located in the North-eastern part of India. Northeast India is a part of the Indo-Burma mega biodiversity hotspot and most of the protected forests of this region are still unexplored for the isolation of effective antibiotic-producing Actinomycetes. Thus, there is tremendous possibility that these virgin forests could be a potential storehouse of novel microorganisms, particularly Actinomycetes, exhibiting diverse biological properties. Methodology: Soil samples were collected from different ecological niches of the protected forest ecosystems of Assam and Actinomycetes were isolated by serial dilution spread plate technique using five selective isolation media. Preliminary screening of Actinomycetes for an antimicrobial activity was done by spot inoculation method and the secondary screening by disc diffusion method against several test pathogens, including multidrug resistant Staphylococcus aureus (MRSA). The strains were further screened for the presence of antibiotic synthetic genes such as type I polyketide synthases (PKS-I), type II polyketide synthases (PKS-II) and non-ribosomal peptide synthetases (NRPS) genes. Genetic diversity of the Actinomycetes producing antimicrobial metabolites was analyzed through 16S rDNA-RFLP using Hinf1 restriction endonuclease. Results: Based on the phenotypic characterization, a total of 172 morphologically distinct Actinomycetes were isolated and screened for antimicrobial activity by spot inoculation method on agar medium. Among the strains tested, 102 (59.3%) strains showed activity against Gram-positive bacteria, 98 (56.97%) against Gram-negative bacteria, 92 (53.48%) against Candida albicans MTCC 227 and 130 (75.58%) strains showed activity against at least one of the test pathogens. Twelve Actinomycetes exhibited broad spectrum antimicrobial activity in the secondary screening. The taxonomic identification of these twelve strains by 16S rDNA sequencing revealed that Streptomyces was found to be the predominant genus. The PKS-I, PKS-II and NRPS genes detection indicated diverse bioactive products of these twelve Actinomycetes. Genetic diversity by 16S rDNA-RFLP indicated that Streptomyces was the dominant genus amongst the antimicrobial metabolite producing Actinomycetes. Conclusion: These findings imply that Actinomycetes from the protected forest ecosystems of Assam, India, are a potential source of bioactive secondary metabolites. These areas are as yet poorly studied and represent diverse and largely unscreened ecosystem for the isolation of potent Actinomycetes producing antimicrobial secondary metabolites. Detailed characterization of the bioactive Actinomycetes as well as purification and structure elucidation of the bioactive compounds from the potent Actinomycetes is the subject of ongoing investigation. Thus, to exploit Actinomycetes from such unexplored forest ecosystems is a way to develop bioactive products.

Keywords: Actinomycetes, antimicrobial activity, forest ecosystems, RFLP

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Authors: Seyed Mehdi Ghezi, Hesam Hasanpoor

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This scientific article explores enhancement methods for ensemble learning with the aim of improving the performance of classifiers in the analysis and classification of brain signals. The research approach in this field consists of two main parts, each with its own strengths and weaknesses. The choice of approach depends on the specific research question and available resources. By combining these approaches and leveraging their respective strengths, researchers can enhance the accuracy and reliability of classification results, consequently advancing our understanding of the brain and its functions. The first approach focuses on utilizing machine learning methods to identify the best features among the vast array of features present in brain signals. The selection of features varies depending on the research objective, and different techniques have been employed for this purpose. For instance, the genetic algorithm has been used in some studies to identify the best features, while optimization methods have been utilized in others to identify the most influential features. Additionally, machine learning techniques have been applied to determine the influential electrodes in classification. Ensemble learning plays a crucial role in identifying the best features that contribute to learning, thereby improving the overall results. The second approach concentrates on designing and implementing methods for selecting the best classifier or utilizing meta-classifiers to enhance the final results in ensemble learning. In a different section of the research, a single classifier is used instead of multiple classifiers, employing different sets of features to improve the results. The article provides an in-depth examination of each technique, highlighting their advantages and limitations. By integrating these techniques, researchers can enhance the performance of classifiers in the analysis and classification of brain signals. This advancement in ensemble learning methodologies contributes to a better understanding of the brain and its functions, ultimately leading to improved accuracy and reliability in brain signal analysis and classification.

Keywords: ensemble learning, brain signals, classification, feature selection, machine learning, genetic algorithm, optimization methods, influential features, influential electrodes, meta-classifiers

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Authors: Sakeh N. Mohd, Bahari M. N. Abdul, Abdullah S. N. Akmar

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Oil palm production generates high export earnings to many countries especially in Southeast Asian region. Infection by necrotrophic fungus, Ganoderma boninense on oil palm results in basal stem rot which compromises oil palm production leading to significant economic loss. There are no reliable disease treatments nor promising resistant oil palm variety has been cultivated to eradicate the disease up to date. Thus, understanding molecular mechanisms underlying early interactions of oil palm with Ganoderma boninense may be vital to promote preventive or control measure of the disease. In the present study, four months old oil palm seedlings were infected via artificial inoculation of Ganoderma boninense on rubber wood blocks. Roots of six biological replicates of treated and untreated oil palm seedlings were harvested at 0, 3, 7 and 11 days post inoculation. Next-generation sequencing was performed to generate high-throughput RNA-Seq data and identify differentially expressed genes (DEGs) during early oil palm-Ganoderma boninense interaction. Based on de novo transcriptome assembly, a total of 427,122,605 paired-end clean reads were assembled into 30,654 unigenes. DEGs analysis revealed upregulation of 173 transcription factors on Ganoderma boninense-treated oil palm seedlings. Sixty-one transcription factors were categorized as DEGs according to stringent cut-off values of genes with log2 ratio [Number of treated oil palm seedlings/ Number of untreated oil palm seedlings] ≥ |1.0| (corresponding to 2-fold or more upregulation) and P-value ≤ 0.01. Transcription factors in response to biotic stress will be screened out from abiotic stress using reverse transcriptase polymerase chain reaction. Transcription factors unique to biotic stress will be verified using real-time polymerase chain reaction. The findings will help researchers to pinpoint defense response mechanism specific against Ganoderma boninense.

Keywords: Ganoderma boninense, necrotrophic, next-generation sequencing, transcription factors

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Authors: Ajay Kumar Pandey, Mayank Kumar

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White grub (Coleoptera: Scarabaeidae) is a major destructive pest in western Himalayan region of Uttarakhand. Beetles feed on apple, apricot, plum, walnut etc. during night while, second and third instar grubs feed on live roots of cultivated as well as non-cultivated crops. Collection and identification of scarab beetles through light trap was carried out at Crop Research Centre, Govind Ballab Pant University Pantnagar, Udham Singh Nagar (Uttarakhand) during 2018. Field trials were also conducted in 2018 to evaluate pre and post sown application of different insecticides against the white grub infesting soybean. The insecticides like Carbofuran 3 Granule (G) (750 g a.i./ha), Clothianidin 50 Water Dispersal Granule (WG) (120 g a.i./ha), Fipronil 0.3 G (50 g a.i./ha), Thiamethoxam 25 WG (80 g a.i./ha), Imidacloprid 70 WG (300 g a.i./ha), Chlorantraniliprole 0.4% G(100 g a.i./ha) and mixture of Fipronil 40% and Imidacloprid 40% WG (300 g a.i./ha) were applied at the time of sowing in pre sown experiment while same dosage of insecticides were applied in standing soybean crop during (first fortnight of July). Commutative plant mortality data were recorded after 20, 40, 60 days intervals and compared with untreated control. Total 23 species of white grub beetles recorded on the light trap and Holotrichia serrata Fabricious (Coleoptera: Melolonthinae) was found to be predominant species by recording 20.6% relative abundance out of the total light trap catch (i.e. 1316 beetles) followed by Phyllognathus sp. (14.6% relative abundance). H. rosettae and Heteronychus lioderus occupied third and fourth rank with 11.85% and 9.65% relative abundance, respectively. The emergence of beetles of predominant species started from 15th March, 2018. In April, average light trap catch was 382 white grub beetles, however, peak emergence of most of the white grub species was observed from June to July, 2018 i.e. 336 beetles in June followed by 303 beetles in the July. On the basis of the emergence pattern of white grub beetles, it may be concluded that the Peak Emergence Period (PEP) for the beetles of H. serrata was second fortnight of April for the total period of 15 days. In May, June and July relatively low population of H. serrata was observed. A decreasing trend in light trap catch was observed and went on till September during the study. No single beetle of H. serrata was observed on light trap from September onwards. The cumulative plant mortality data in both the experiments revealed that all the insecticidal treatments were significantly superior in protection-wise (6.49-16.82% cumulative plant mortality) over untreated control where highest plant mortality was 17.28 to 39.65% during study. The mixture of Fipronil 40% and Imidacloprid 40% WG applied at the rate of 300 g a.i. per ha proved to be most effective having lowest plant mortality i.e. 9.29 and 10.94% in pre and post sown crop, followed by Clothianidin 50 WG (120 g a.i. per ha) where the plant mortality was 10.57 and 11.93% in pre and post sown treatments, respectively. Both treatments were found significantly at par among each other. Production-wise, all the insecticidal treatments were found statistically superior (15.00-24.66 q per ha grain yields) over untreated control where the grain yield was 8.25 & 9.13 q per ha. Treatment Fipronil 40% + Imidacloprid 40% WG applied at the rate of 300 g a.i. per ha proved to be most effective and significantly superior over Imidacloprid 70WG applied at the rate of 300 g a.i. per ha.

Keywords: bio efficacy, insecticide, soybean, white grub

Procedia PDF Downloads 132

Authors: Tomasz Balabanski, Anna Biedunkiewicz

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Research on basic bacteriological and physicochemical parameters conducted by state institutions (Provincial Sanitary and Epidemiological Station and District Sanitary and Epidemiological Station) are limited to bathing waters under constant sanitary and epidemiological supervision. Unfortunately, no routine or monitoring tests are carried out for the presence of microfungi. This also applies to beach sand used for recreational purposes. The purpose of the planned own research was to determine the diversity of the mycobiota present on supervised and unsupervised sandy beaches, on the shores of lakes, of municipal baths used for recreation. The research material consisted of microfungi isolated from April to October 2019 from sandy beaches of supervised and unsupervised lakes located within the administrative boundaries of the city of Olsztyn (North-Eastern Poland, Europe). Four lakes, out of the fifteen available (Tyrsko, Kortowskie, Skanda, and Ukiel), whose bathing waters are subjected to routine bacteriological tests, were selected for testing. To compare the diversity of the mycobiota composition on the surface and below the sand mixing layer, samples were taken from two depths (10 cm and 50 cm), using a soil auger. Micro-fungi from sand samples were obtained by surface inoculation on an RBC medium from the 1st dilution (1:10). After incubation at 25°C for 96-144 h, the average number of CFU/dm³ was counted. Morphologically differing yeast colonies were passaged into Sabouraud agar slants with gentamicin and incubated again. For detailed laboratory analyses, culture methods (macro- and micro-cultures) and identification methods recommended in diagnostic mycological laboratories were used. The conducted research allowed obtaining 140 yeast isolates. The total average population ranged from 1.37 × 10⁻² CFU/dm³ before the bathing season (April 2019), 1.64 × 10⁻³ CFU/dm³ in the season (May-September 2019), and 1.60 × 10⁻² CFU/dm³ after the end of the season (October 2019). More microfungi were obtained from the surface layer of sand (100 isolates) than from the deeper layer (40 isolates). Reported microfungi may circulate seasonally between individual elements of the lake ecosystem. From the sand/soil from the catchment area beaches, they can get into bathing waters, stopping periodically on the coastal phyllosphere. The sand of the beaches and the phyllosphere are a kind of filter for the water reservoir. The presence of microfungi with various pathogenicity potential in these places is of major epidemiological importance. Therefore, full monitoring of not only recreational waters but also sandy beaches should be treated as an element of constant control by appropriate supervisory institutions, allowing recreational areas for public use so that the use of these places does not involve the risk of infection. Acknowledgment: 'Development Program of the University of Warmia and Mazury in Olsztyn', POWR.03.05.00-00-Z310/17, co-financed by the European Union under the European Social Fund from the Operational Program Knowledge Education Development. Tomasz Bałabański is a recipient of a scholarship from the Programme Interdisciplinary Doctoral Studies in Biology and Biotechnology (POWR.03.05.00-00-Z310/17), which is funded by the 'European Social Fund'.

Keywords: beach, microfungi, sand, yeasts

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Authors: Brian Tung, Yam Wing Siu, Tsun Se Cheong

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Purpose: This research aims to explore how personal and environmental factors, especially the socio-economic changes and behavioral changes fostered by the COVID-19 outbreak pandemic, affect the financial vulnerability of a specific segment of people in financial distress. Innovative research methodology of machine learning will be applied to data collected from over 300 local individuals in Hong Kong seeking counseling or similar services in recent years. Results: First, machine learning has found that too much exposure to digital services and information on digitized services may lead to adverse effects on respondents’ financial vulnerability. Second, the improvement in financial literacy level provides benefits to the financially vulnerable group, especially those respondents who have started with a lower level. Third, serious addiction to digital technology can lead to worsened debt servicing ability. Machine learning also has found a strong correlation between debt servicing situations and income-seeking behavior as well as spending behavior. In addition, if the vulnerable groups are able to make appropriate investments, they can reduce the probability of incurring financial distress. Finally, being too active in borrowing and repayment can result in a higher likelihood of over-indebtedness. Conclusion: Findings can be employed in formulating a better counseling strategy for professionals. Debt counseling services can be more preventive in nature. For example, according to the findings, with a low level of financial literacy, the respondents are prone to overspending and unable to react properly to the e-marketing promotion messages pop-up from digital services or even falling into financial/investment scams. In addition, people with low levels of financial knowledge will benefit from financial education. Therefore, financial education programs could include tech-savvy matters as special features.

Keywords: personal finance, digitization of the economy, COVID-19 pandemic, addiction to digital technology, financial vulnerability

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Authors: Shivdayal Patel, Suhail Ahmad

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Safety assurance and failure prediction of composite material component of an offshore structure due to low velocity impact is essential for associated risk assessment. It is important to incorporate uncertainties associated with material properties and load due to an impact. Likelihood of this hazard causing a chain of failure events plays an important role in risk assessment. The material properties of composites mostly exhibit a scatter due to their in-homogeneity and anisotropic characteristics, brittleness of the matrix and fiber and manufacturing defects. In fact, the probability of occurrence of such a scenario is due to large uncertainties arising in the system. Probabilistic finite element analysis of composite plates due to low-velocity impact is carried out considering uncertainties of material properties and initial impact velocity. Impact-induced damage of composite plate is a probabilistic phenomenon due to a wide range of uncertainties arising in material and loading behavior. A typical failure crack initiates and propagates further into the interface causing de-lamination between dissimilar plies. Since individual crack in the ply is difficult to track. The progressive damage model is implemented in the FE code by a user-defined material subroutine (VUMAT) to overcome these problems. The limit state function is accordingly established while the stresses in the lamina are such that the limit state function (g(x)>0). The Gaussian process response surface method is presently adopted to determine the probability of failure. A comparative study is also carried out for different combination of impactor masses and velocities. The sensitivity based probabilistic design optimization procedure is investigated to achieve better strength and lighter weight of composite structures. Chain of failure events due to different modes of failure is considered to estimate the consequences of failure scenario. Frequencies of occurrence of specific impact hazards yield the expected risk due to economic loss.

Keywords: composites, damage propagation, low velocity impact, probability of failure, uncertainty modeling

Procedia PDF Downloads 282

Authors: Peiming Li

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This paper presents an innovative blockchain-based approach to enhance the reliability and efficiency of federated learning systems. By integrating a decentralized oracle consensus mechanism into the federated learning framework, we address key challenges of data and model integrity. Our approach utilizes a network of redundant oracles, functioning as independent validators within an epoch-based training system in the federated learning model. In federated learning, data is decentralized, residing on various participants' devices. This scenario often leads to concerns about data integrity and model quality. Our solution employs blockchain technology to establish a transparent and tamper-proof environment, ensuring secure data sharing and aggregation. The decentralized oracles, a concept borrowed from blockchain systems, act as unbiased validators. They assess the contributions of each participant using a Hidden Markov Model (HMM), which is crucial for evaluating the consistency of participant inputs and safeguarding against model poisoning and malicious activities. Our methodology's distinct feature is its epoch-based training. An epoch here refers to a specific training phase where data is updated and assessed for quality and relevance. The redundant oracles work in concert to validate data updates during these epochs, enhancing the system's resilience to security threats and data corruption. The effectiveness of this system was tested using the Mnist dataset, a standard in machine learning for benchmarking. Results demonstrate that our blockchain-oriented federated learning approach significantly boosts system resilience, addressing the common challenges of federated environments. This paper aims to make these advanced concepts accessible, even to those with a limited background in blockchain or federated learning. We provide a foundational understanding of how blockchain technology can revolutionize data integrity in decentralized systems and explain the role of oracles in maintaining model accuracy and reliability.

Keywords: federated learning system, block chain, decentralized oracles, hidden markov model

Procedia PDF Downloads 66

Authors: Sobia Shafqat, Saeed Ahmad Qaisrani

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MicroRNAs play an essential role in the regulation and development of all processes in most eukaryotes because of their prospective part as mediators controlling cell growth and differentiation towards the exact position of RNAs response in plants under biotic and abiotic factors or stressors. In a few cases, Zn is oblivious poisonous for plants due to its heavy metal status. Some other metals are extremely toxic, like Cd, Hg, and Pb, but these elements require in rice for the programming of genes under abiotic stress resembling Zn stress when micro RNAs268 was importantly introduced in rice. The micro RNAs overexpressed in transgenic plants with an accumulation of a large amount of melanin dialdehyde, hydrogen peroxide, and an excessive quantity of Zn in the seedlings stage. Let out results for rice pliability under Zn stress micro RNAs act as negative controllers. But the role of micro RNA268 act as a modulator in different ecological condition. It has been explained clearly with a long understanding of the role of micro RNA268 under stress conditions; pliability and practically showed outcome to increase plant sufferance under Zn stress because micro RNAs is an intervention technique for gene regulation in gene expression. The proposed study was experimented with by using genetic factors of Zn stress and toxicity effect on rice plants done at District Vehari, Pakistan. The trial was performed randomly with three replications in a complete block design (RCBD). These blocks were controlled with different concentrations of genetic factors. By overexpression of micro RNA268 rice, seedling growth was not stopped under Zn deficiency due to the accumulation of a large amount of melanin dialdehyde, hydrogen peroxide, and an excessive quantity of Zn in their seedlings. Results showed that micro RNA268 act as a negative controller under Zn stress. In the end, under stress conditions, micro RNA268 showed the necessary function in the tolerance of rice plants. The directorial work sketch gave out high agronomic applications and yield outcomes in rice with a specific amount of Zn application.

Keywords: micro RNA268, zinc, rice, agronomic approach

Procedia PDF Downloads 62

Authors: Jennifer Hunter, Francisco Ramirez, Neil A. Nedley, Thania Solorio, Christian Freed, Erica Kinjo

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People who have autoimmune disease are often at an increased risk for psychological disorders such as anxiety. Untreated psychological conditions can affect the development of disease and can affect one’s general quality of life. In this study, it was hypothesized that an educational community-based intervention would be useful in decreasing the anxiety levels of participants with autoimmune disease. Programs, 2-hours long each, were held weekly over a period of eight weeks. During every meeting, a 45-minute DVD presentation by a skilled physician was shown, a small group discussion was guided by trained facilitators, and weekly practical assignments were given to each participant. The focus of the program was to educate participants about healthy lifestyle behaviors such as exercise, nutrition, sleep hygiene, helpful thought patterns etc., and to provide a group environment in which each participant was supported. Participants were assessed pre-post program for anxiety using the Depression and Anxiety Assessment Test (registration TX 7-398-022), a validated mental health test based on DSM-5 criteria and demographics. Anxiety scores were classified according to the DSM-5 criteria into 4 categories: none (0-6), mild (7-10), moderate (11-19) or severe (20 or more). Out of the participants who participated in programs conducted in the manner explained above (n=431), the average age was 54.9 (SD 16.6) and 81.9% were female. At baseline, the mean group anxiety level was 9.4 (SD 5.4). Within the baseline group, anxiety levels were as follows: none (21.1%), mild (22.0%), moderate (27.1%) and severe (29.7%). After the program, mean group anxiety decreased to 4.7 (SD 4.0). Post-program anxiety levels were as follows: none (54.8%), mild (27.1%), moderate (12.5%), severe (5.6%). The decrease in overall anxiety levels was significant t(431)=19.3 p<.001, 95% CI [0.815, 1.041]. It was concluded that the eight-week intensive was beneficial in decreasing the anxiety levels of participants. A long-term follow-up study would be beneficial in determining how lasting such improvements are especially since autoimmune diseases are often chronic. Additionally, future studies that utilize a control group would aid in establishing whether the improvements seen are due to the use of this specific lifestyle-educational program.

Keywords: anxiety, auto-immune disease, community-based educational program, lifestyle

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Authors: Lisa Fox, Jill Aylott

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There is a global need to identify ways to engage doctors in non-clinical matters such as medical leadership, service improvement and health system transformation. Using the core principles of Realistic Evaluation (RE), this study examined what works, for doctors of different grades, specialities and experience in an acute NHS Hospital Trust in the UK. Realistic Evaluation is an alternative to more traditional cause and effect evaluation models and seeks to understand the interdependencies of Context, Mechanism and Outcome proposing that Context (C) + Mechanism (M) = Outcome (O). In this study, the context, mechanism and outcome were examined from within individual medical leaders to determine what enables levels of medical engagement in a specific improvement project to reduce hospital inpatient mortality. Five qualitative case studies were undertaken with consultants who had regularly completed mortality reviews over a six month period. The case studies involved semi-structured interviews to test the theory behind the drivers for medical engagement. The interviews were analysed using a theory-driven thematic analysis to identify CMO configurations to explain what works, for whom and in what circumstances. The findings showed that consultants with a longer length of service became more engaged if there were opportunities to be involved in the beginning of an improvement project, with more opportunities to affect the design. Those that are new to a consultant role were more engaged if they felt able to apply any learning directly into their own settings or if they could use it as an opportunity to understand more about the organisation they are working in. This study concludes that RE is a useful methodology for better understanding the complexities of motivation and consultant engagement in a trust wide service improvement project. The study showed that there should be differentiated and bespoke training programmes to maximise each individual doctor’s propensity for medical engagement. The RE identified that there are different ways to ensure that doctors have the right skills to feel confident in service improvement projects.

Keywords: realistic evaluation, medical leadership, medical engagement, patient safety, service improvement

Procedia PDF Downloads 221

Authors: Adeyi Akindele Oluwatosin, Mustapha Kaosarat Keji, Ajisebiola Babafemi Siji, Adeyi Olubisi Esther, Damilohun Samuel Metibemu, Raphael Emuebie Okonji

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Envenoming by Echis ocellatus is potentially life-threatening due to severe hemorrhage, renal failure, and capillary leakage. These effects are attributed to snake venom metalloproteinases (SVMPs). Due to drawbacks in the use of antivenom, natural inhibitors from plants are of interest in studies of new antivenom treatment. Antagonizing effects of bioactive compounds of Moringa oleifera, a known antisnake plant, are yet to be tested against SVMPs of E. ocellatus (SVMP-EO). Ethanol crude extract of M. oleifera was partitioned using n-hexane and ethyl acetate. Each partition was fractionated using column chromatography and tested against SVMP-EO purified through ion-exchange chromatography with EchiTab-PLUS polyvalent anti-venom as control. Phytoconstituents of ethyl acetate fraction were screened against the catalytic site of crystal of BaP1-SVMP, while drug-likeness and ADMET toxicity of compound were equally determined. The molecular weight of isolated SVMP-EO was 43.28 kDa, with a specific activity of 245 U/ml, a percentage yield of 62.83 %, and a purification fold of 0.920. The Vmax and Km values are 2 mg/ml and 38.095 μmol/ml/min, respectively, while the optimal pH and temperature are 6.0 and 40°C, respectively. Polyvalent anti-venom, crude extract, and ethyl acetate fraction of M. oleifera exhibited a complete inhibitory effect against SVMP-EO activity. The inhibitions of the P-1 and P-II metalloprotease’s enzymes by the ethyl acetate fraction are largely due to methanol, 6, 8, 9-trimethyl-4-(2-phenylethyl)-3-oxabicyclo[3.3.1]non-6-en-1-yl)- and paroxypropione, respectively. Both compounds are potential drug candidates with little or no concern of toxicity, as revealed from the in-silico predictions. The inhibitory effects suggest that this compound might be a therapeutic candidate for further exploration for treatment of Ocellatus’ envenoming.

Keywords: Echis ocellatus, Moringa oleifera, anti-venom, metalloproteases, snakebite, molecular docking

Procedia PDF Downloads 152

Authors: Kirsten Wilson, Patrick M. Brauer, Sandra Babic, Diana Golubeva, Jessica Van Eyk, Tinya Wang, Avanti Karkhanis, Tim A. Le Fevre, Andy I. Kokaji, Allen C. Eaves, Sharon A. Louis, , Nooshin Tabatabaei-Zavareh

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Long-lived plasma cells are rare, non-proliferative B cells generated from antibody-secreting cells (ASCs) following an immune response to protect the host against pathogen re-exposure. Despite their therapeutic potential, the lack of in vitro protocols in the field makes it challenging to use B cells as a cellular therapeutic tool. As a result, there is a need to establish robust and reproducible methods for the generation of B cells. To address this, we have developed a culture system for generating B cells from hematopoietic stem and/or progenitor cells (HSPCs) derived from human umbilical cord blood (CB) or pluripotent stem cells (PSCs). HSPCs isolated from CB were cultured using the StemSpan™ B Cell Generation Kit and produced CD19+ B cells at a frequency of 23.2 ± 1.5% and 59.6 ± 2.3%, with a yield of 91 ± 11 and 196 ± 37 CD19+ cells per input CD34+ cell on culture days 28 and 35, respectively (n = 50 - 59). CD19+IgM+ cells were detected at a frequency of 31.2 ± 2.6% and were produced at a yield of 113 ± 26 cells per input CD34+ cell on culture day 35 (n = 50 - 59). The B cell receptor loci of CB-derived B cells were sequenced to confirm V(D)J gene rearrangement. ELISpot analysis revealed that ASCs were generated at a frequency of 570 ± 57 per 10,000 day 35 cells, with an average IgM+ ASC yield of 16 ± 2 cells per input CD34+ cell (n = 33 - 42). PSC-derived HSPCs were generated using the STEMdiff™ Hematopoietic - EB reagents and differentiated to CD10+CD19+ B cells with a frequency of 4 ± 0.8% after 28 days of culture (n = 37, 1 embryonic and 3 induced pluripotent stem cell lines tested). Subsequent culture of PSC-derived HSPCs increased CD19+ frequency and generated ASCs from 1 - 2 iPSC lines. This method is the first report of a serum- and feeder-free system for the generation of B cells from CB and PSCs, enabling further B lineage-specific research for potential future clinical applications.

Keywords: stem cells, B cells, immunology, hematopoiesis, PSC, differentiation

Procedia PDF Downloads 59

Authors: Aishi Kundu, Elhan Raza

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"Necessity is the mother of invention" - Rapid increase in the global human population has directed the agricultural domain toward machine learning. The basic need of human beings is considered to be food which can be satisfied through farming. Farming is one of the major revenue generators for the Indian economy. Agriculture is not only considered a source of employment but also fulfils humans’ basic needs. So, agriculture is considered to be the source of employment and a pillar of the economy in developing countries like India. This paper provides a brief review of the progress made in implementing Machine Learning in the agricultural sector. Accurate predictions are necessary at the right time to boost production and to aid the timely and systematic distribution of agricultural commodities to make their availability in the market faster and more effective. This paper includes a thorough analysis of various machine learning algorithms applied in different aspects of agriculture (crop management, soil management, water management, yield tracking, livestock management, etc.).Due to climate changes, crop production is affected. Machine learning can analyse the changing patterns and come up with a suitable approach to minimize loss and maximize yield. Machine Learning algorithms/ models (regression, support vector machines, bayesian models, artificial neural networks, decision trees, etc.) are used in smart agriculture to analyze and predict specific outcomes which can be vital in increasing the productivity of the Agricultural Food Industry. It is to demonstrate vividly agricultural works under machine learning to sensor data. Machine Learning is the ongoing technology benefitting farmers to improve gains in agriculture and minimize losses. This paper discusses how the irrigation and farming management systems evolve in real-time efficiently. Artificial Intelligence (AI) enabled programs to emerge with rich apprehension for the support of farmers with an immense examination of data.

Keywords: machine Learning, artificial intelligence, crop management, precision farming, smart farming, pre-harvesting, harvesting, post-harvesting

Procedia PDF Downloads 108

Authors: H. B. Grobler, K. Du Plooy, P. Kruger, S. Ellis

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Objectives: The primary objective of the study was to determine the common mental disorders (CMD) identified by professional South African rugby players and its correlation with their mental toughness, as a first step towards developing such a programme within a larger research project. Design: Survey research, within the theoretical perspective of field theory, was conducted, utilising an adaptation of an already existing mental health questionnaire. The aim was to obtain feedback from as many possible professional South African rugby players in order to make certain generalizations and come to conclusions with regard to the current mental health experiences of these rugby players. Methods: Non-randomized sampling was done, linking it with internet research in the form of the online completion of a questionnaire. A sample of 215 rugby players participated and completed the online questionnaire. Permission was obtained to make use of an existing questionnaire, previously used by the specific authors with retired professional rugby players. A section on mental toughness was added. Data were descriptively analysed by means of the SPSS software platform. Results: Results indicated that the most significant problem that the players are experiencing, is a problem with alcohol (47.9%). Other problems that featured are distress (16.3%), sleep disturbances (7%), as well as anxiety and depression (4.2%). 4.7% of the players indicated that they smoke. 3.3% of the players experience themselves as not being mentally tough. A positive correlation between mental toughness and sound sleep (0.262) was found while a negative correlation was found between mental toughness and the following: anxiety/depression (-0.401), anxiety/depression positive (-0.423), distress (-0.259) and common mental disorder problems in general (-0.220). Conclusions: Although the presence of CMD at first glance do not seem significantly high amongst all the players, it must be considered that if one player in a team experiences the presence of CMD, it will have an impact on his mental toughness and most likely on his performance, as well as on the performance of the whole team. It is therefore important to ensure mental health in the whole team, by addressing individual CMD problems. A mental health support programme is therefore needed to be implemented to the benefit of these players within the South African context.

Keywords: common mental disorders, mental toughness, professional athletes, rugby players

Procedia PDF Downloads 221

Authors: Prince S. Campbell, Janine B. Adams, Melusi Thwala, Opeoluwa Oyedele, Paula E. Melariri

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Schistosomiasis is an infectious water-borne disease that holds substantial medical and veterinary importance and is transmitted by Schistosoma flatworms. The transmission and spread of the disease are geographically and temporally confined to water bodies (rivers, lakes, lagoons, dams, etc.) inhabited by its obligate intermediate host snails and human water contact. Human infection with the parasite occurs via skin penetration subsequent to exposure to water infested with schistosome cercariae. Environmental factors play a crucial role in the spread of the disease, as the survival of intermediate host snails is dependent on favourable conditions. These factors include physical and chemical components of water, including pH, salinity, temperature, electrical conductivity, dissolved oxygen, turbidity, water hardness, total dissolved solids, and velocity, as well as biological factors such as predator-prey interactions, competition, food availability, and the presence and density of aquatic vegetation. This study evaluated the physicochemical properties of the water bodies, vegetation type, distribution, and habitat presence of the snail intermediate host. A quantitative cross-sectional research design approach was employed in this study. Eight sampling sites were selected based on their proximity to residential areas. Snails and water physicochemical properties were collected over different seasons for 9 months. A simple dip method was used for surface water samples and measurements were done using multiparameter meters. Snails captured using a 300 µm mesh scoop net and predominant plant species were gathered and transported to experts for identification. Vegetation composition and cover were visually estimated and recorded at each sampling point. Data was analysed using R software (version 4.3.1). A total of 844 freshwater snails were collected, with Physa genera accounting for 95.9% of the snails. Bulinus and Biomphalaria snails, which serve as intermediate hosts for the disease, accounted for (0.9%) and (0.6%) respectively. Indicator macrophytes such as Eicchornia crassipes, Stuckenia pectinate, Typha capensis, and floating macroalgae were found in several water bodies. A negative and weak correlation existed between the number of snails and physicochemical properties such as electrical conductivity (r=-0.240), dissolved oxygen (r=-0.185), hardness (r=-0.210), pH (r=-0.235), salinity (r=-0.242), temperature (r=-0.273), and total dissolved solids (r=-0.236). There was no correlation between the number of snails and turbidity (r=-0.070). Moreover, there was a negative and weak correlation between snails and vegetation coverage (r=-0.127). Findings indicated that snail abundance marginally declined with rising physicochemical concentrations, and the majority of snails were located in regions with less vegetation cover. The reduction in Bulinus and Biomphalaria snail populations may also be attributed to other factors, such as competition among the snails. Snails of the Physa genus were abundant due to their noteworthy resilience in difficult environments. These snails have the potential to function as biological control agents in areas where the disease is endemic, as they outcompete other snails, including schistosomiasis intermediate host snails.

Keywords: intermediate host snails, physicochemical properties, schistosomiasis, vegetation type

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Authors: Yaw Opoku-Damoah, Run Zhang, Hang Thu Ta, Zhi Ping Xu

Abstract:

Gas therapy is still at an early stage of research and development. Even though most gasotransmitters have proven their therapeutic potential, their handling, delivery, and controlled release have been extremely challenging. This research work employs a versatile nanosystem that is capable of delivering a gasotransmitter in the form of a photo-responsive carbon monoxide-releasing molecule (CORM) for targeted cancer therapy. The therapeutic action was mediated by upconversion nanoparticles (UCNPs) designed to transfer bio-friendly low energy near-infrared (NIR) light to ultraviolet (UV) light capable of triggering carbon monoxide (CO) from a water-soluble amphiphilic manganese carbonyl complex CORM incorporated into a carefully designed lipid drug delivery system. Herein, gaseous CO that plays a role as a gasotransmitter with cytotoxic and homeostatic properties was investigated to instigate cellular apoptosis. After successfully synthesizing the drug delivery system, the ability of the system to encapsulate and mediate the sustained release of CO after light excitation was demonstrated. CO fluorescence probe (COFP) was successfully employed to determine the in vitro drug release profile upon NIR light irradiation. The uptake of nanoparticles enhanced by folates and its receptor interaction was also studied for cellular uptake purposes. The anticancer potential of the final lipid nanoparticle Lipid/UCNPs/CORM/FA (LUCF) was also determined by cell viability assay. Intracellular CO release and a subsequent therapeutic action involving ROS production, mitochondrial damage, and CO production was also evaluated. In all, this current project aims to use in vitro studies to determine the potency and efficiency of a NIR-mediated CORM prodrug delivery system.

Keywords: carbon monoxide-releasing molecule, upconversion nanoparticles, site-specific delivery, amphiphilic manganese carbonyl complex, prodrug delivery system.

Procedia PDF Downloads 114