Search results for: artificial cell

Authors: Rima Harche, Abdelkader Mouheb

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The Algiers refinery as all the other refineries always suffers from the problem of stopping of the tubes of heat exchanger. For that a study experimental of this phenomenon was undertaken in site on the cell of heat exchangers E101 (E101 CBA and E101 EDF) intended for the heating of the crude before its fractionation, which are exposed to the problem of the fouling on the side tubes exchangers. It is of tube-calenders type with head floating. Each cell is made up of three heat exchangers, laid out in series.

Keywords: fouling, fluid temperatue , oil, tubular heat exchanger, fouling resistance, modeling, heat transfer coefficient

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Authors: Leo Nnamdi Ozurumba-Dwight

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Single-cell genomic analytical systems have proved to be a platform to isolate bulk cells into selected single cells for genomic, proteomic, and related metabolomic studies. This is enabling systematic investigations of the level of heterogeneity in a diverse and wide pool of cell populations. Single cell technologies, embracing techniques such as high parameter flow cytometry, single-cell sequencing, and high-resolution images are playing vital roles in these investigations on messenger ribonucleic acid (mRNA) molecules and related gene expressions in tracking the nature and course of disease conditions. This entails targeted molecular investigations on unit cells that help us understand cell behavoiur and expressions, which can be examined for their health implications on the health state of patients. One of the vital good sides of single-cell RNA sequencing (scRNA seq) is its probing capacity to detect deranged or abnormal cell populations present within homogenously perceived pooled cells, which would have evaded cursory screening on the pooled cell populations of biological samples obtained as part of diagnostic procedures. Despite conduction of just single-cell transcriptome analysis, scRNAseq now permits comparison of the transcriptome of the individual cells, which can be evaluated for gene expressional patterns that depict areas of heterogeneity with pharmaceutical drug discovery and clinical treatment applications. It is vital to strictly work through the tools of investigations from wet lab to bioinformatics and computational tooled analyses. In the precise steps for scRNAseq, it is critical to do thorough and effective isolation of viable single cells from the tissues of interest using dependable techniques (such as FACS) before proceeding to lysis, as this enhances the appropriate picking of quality mRNA molecules for subsequent sequencing (such as by the use of Polymerase Chain Reaction machine). Interestingly, scRNAseq can be deployed to analyze various types of biological samples such as embryos, nervous systems, tumour cells, stem cells, lymphocytes, and haematopoietic cells. In haematopoietic cells, it can be used to stratify acute myeloid leukemia patterns in patients, sorting them out into cohorts that enable re-modeling of treatment regimens based on stratified presentations. In immunotherapy, it can furnish specialist clinician-immunologist with tools to re-model treatment for each patient, an attribute of precision medicine. Finally, the good predictive attribute of scRNAseq can help reduce the cost of treatment for patients, thus attracting more patients who would have otherwise been discouraged from seeking quality clinical consultation help due to perceived high cost. This is a positive paradigm shift for patients’ attitudes primed towards seeking treatment.

Keywords: immunotherapy, transcriptome, re-modeling, mRNA, scRNA-seq

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Authors: Karl Kingsley

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Many mechanisms are involved in the control of cellular differentiation and growth, which are often dysregulated in many cancers. Many distinct pathways are involved in these mechanisms of control, including deoxyribonuclease (DNA) methyltransferase and histone deacetylase (HDAC) activation that controls both genetic and epigenetic modifications and micro ribonucleic acid (RNA) expression. Less is known about the expression of DNA methyltransferase (DNMT) and HDAC in oral cancers and the effect on microRNA expression. The primary objective of this study was to evaluate the expression of DNMT and HDAC family members in oral cancer and the concomitant expression of cancer-associated microRNAs. Using commercially available oral cancers, including squamous cell carcinoma (SCC)-4, SCC-9, SCC-15, and SCC-25, RNA was extracted and screened for DNMT, HDAC, and microRNA expression using highly-specific primers and quantitative polymerase chain reaction (qPCR). These data revealed low or absent expression of DNMT-1, which is associated with cellular differentiation but increased expression of DNMT-3a and DNMT-3b in all SCC cell lines compared with normal non-cancerous cell controls. In addition, no expression of HDAC1 and HDAC2 expression was found among the normal, non-cancerous cells but was highly expressed in each of the SCC cell lines examined. Differential expression of oncogenic and cancer-associated microRNAs was also observed among the SCC cell lines, including miR-21, miR-133, miR-149, miR-155, miR-365, and miR-720. These findings also appeared to vary according to observed growth rates among these cells. These data may be the first to demonstrate the expression and association between HDAC and DNMT3 family members among oral cancers. In addition, the differential expression of these epigenetic modifiers may be associated with the expression of specific microRNAs in these cancers, which have not previously been observed to the best of the author's knowledge. In addition, some associations and relationships may exist between the expression of these biomarkers and the rates of growth and proliferation, which may suggest that these expression patterns might represent potentially useful biomarkers to determine tumor aggressiveness and other phenotypic behaviors among oral cancers.

Keywords: oral cancer, DNA methyltransferase, histone deacetylase, microRNA

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Authors: Thiago Fabrizio Gavilano Farje, Marcelo Patricio Herrera Malpartida

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This study examines the influence of artificial intelligence (AI) on the work efficiency of administrative employees in the financial sector of Metropolitan Lima, Peru, during the year 2024. Focusing on the relationship between AI implementation and work efficiency, it addresses specific variables such as decision-making, motivation, and employee productivity. To accomplish the analysis between AI and work efficiency within the financial sector of Metropolitan Lima, it is necessary to evaluate how AI optimizes time in administrative tasks, examine how AI impacts the agility of the process of making decisions, and investigate the influence of AI on the satisfaction and motivation of employees. The research adopts a correlational and explanatory approach, designed to establish and understand the connections between AI and work efficiency. A survey design adapted from an OECD study is used, applying questionnaires to a representative sample of administrative workers in the financial sector who incorporate AI into their functions. The target population includes administrative workers in the financial sector of Metropolitan Lima, estimated at 73,097 employees based on data from the Censo Nacional de Empresas y Establecimientos and studies by the BCRP. The sample, selected through simple random sampling, comprises 246 workers.

Keywords: business management, artificial intelligence, decision making, labor efficiency, financial sector

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Authors: Ya L. V., Benjamin Ong Wei Lin, Wanli Niu, Benjamin Seah Chin Tat

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This article introduces a methodology that improves reliability and cyclability of 2nd-life Li-ion battery system repurposed as an energy storage system (ESS). Most of the 2nd-life retired battery systems in the market have module/pack-level state-of-health (SOH) indicator, which is utilized for guiding appropriate depth-of-discharge (DOD) in the application of ESS. Due to the lack of cell-level SOH indication, the different degrading behaviors among various cells cannot be identified upon reaching retired status; in the end, considering end-of-life (EOL) loss and pack-level DOD, the repurposed ESS has to be oversized by > 1.5 times to complement the application requirement of reliability and cyclability. This proposed battery grading algorithm, using non-invasive methodology, is able to detect outlier cells based on historical voltage data and calculate cell-level historical maximum temperature data using semi-analytic methodology. In this way, the individual battery cell in the 2nd-life battery system can be graded in terms of SOH on basis of the historical voltage fluctuation and estimated historical maximum temperature variation. These grades will have corresponding DOD grades in the application of the repurposed ESS to enhance system reliability and cyclability. In all, this introduced battery grading algorithm is non-invasive, compatible with all kinds of retired Li-ion battery systems which lack of cell-level SOH indication, as well as potentially being embedded into battery management software for preventive maintenance and real-time cyclability optimization.

Keywords: battery grading algorithm, 2nd-life repurposing battery system, semi-analytic methodology, reliability and cyclability

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Authors: Lee Jeong Min, Lee Mi Hee, Eo Yang Dam

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Geospatial feature extraction is a very important issue in the remote sensing research. In the meantime, the image classification based on statistical techniques, but, in recent years, data mining and machine learning techniques for automated image processing technology is being applied to remote sensing it has focused on improved results generated possibility. In this study, artificial neural network and decision tree technique is applied to classify the high-resolution satellite images, as compared to the MLC processing result is a statistical technique and an analysis of the pros and cons between each of the techniques.

Keywords: remote sensing, artificial neural network, decision tree, maximum likelihood classification

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Authors: David A. Ullisch, Yantree D. Sankar-Thomas, Stefan Wilke, Thomas Selge, Matthias Pump, Thomas Leibold, Kai Schütte, Gilbert Gorr

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Plants have been considered as a source of natural substances for ages. Secondary metabolites from plants are utilized especially in medical applications but are more and more interesting as cosmetical ingredients and in the field of nutraceuticals. However, supply of compounds from natural harvest can be limited by numerous factors i.e. endangered species, low product content, climate impacts and cost intensive extraction. Especially in the pharmaceutical industry the ability to provide sufficient amounts of product and high quality are additional requirements which in some cases are difficult to fulfill by plant harvest. Whereas in many cases the complexity of secondary metabolites precludes chemical synthesis on a reasonable commercial basis, plant cells contain the biosynthetic pathway – a natural chemical factory – for a given compound. A promising approach for the sustainable production of natural products can be plant cell fermentation (PCF®). A thoroughly accomplished development process comprises the identification of a high producing cell line, optimization of growth and production conditions, the development of a robust and reliable production process and its scale-up. In order to address persistent, long lasting production, development of cryopreservation protocols and generation of working cell banks is another important requirement to be considered. So far the most prominent example using a PCF® process is the production of the anticancer compound paclitaxel. To demonstrate the power of plant suspension cultures here we present three case studies: 1) For more than 17 years Phyton produces paclitaxel at industrial scale i.e. up to 75,000 L in scale. With 60 g/kg dw this fully controlled process which is applied according to GMP results in outstanding high yields. 2) Thapsigargin is another anticancer compound which is currently isolated from seeds of Thapsia garganica. Thapsigargin is a powerful cytotoxin – a SERCA inhibitor – and the precursor for the derivative ADT, the key ingredient of the investigational prodrug Mipsagargin (G-202) which is in several clinical trials. Phyton successfully generated plant cell lines capable to express this compound. Here we present data about the screening for high producing cell lines. 3) The third case study covers ingenol-3-mebutate. This compound is found in the milky sap of the intact plants of the Euphorbiacae family at very low concentrations. Ingenol-3-mebutate is used in Picato® which is approved against actinic keratosis. Generation of cell lines expressing significant amounts of ingenol-3-mebutate is another example underlining the strength of plant cell culture. The authors gratefully acknowledge Inspyr Therapeutics for funding.

Keywords: Ingenol-3-mebutate, plant cell culture, sustainability, thapsigargin

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Authors: Mu-Yen Chen, Min-Hsuan Fan, Chia-Chen Chen, Siang-Yu Jhong

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In recent years, real estate prediction or valuation has been a topic of discussion in many developed countries. Improper hype created by investors leads to fluctuating prices of real estate, affecting many consumers to purchase their own homes. Therefore, scholars from various countries have conducted research in real estate valuation and prediction. With the back-propagation neural network that has been popular in recent years and the orthogonal array in the Taguchi method, this study aimed to find the optimal parameter combination at different levels of orthogonal array after the system presented different parameter combinations, so that the artificial neural network obtained the most accurate results. The experimental results also demonstrated that the method presented in the study had a better result than traditional machine learning. Finally, it also showed that the model proposed in this study had the optimal predictive effect, and could significantly reduce the cost of time in simulation operation. The best predictive results could be found with a fewer number of experiments more efficiently. Thus users could predict a real estate transaction price that is not far from the current actual prices.

Keywords: artificial neural network, Taguchi method, real estate valuation model, investors

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Authors: Ali Reza Tahavvor, Saeed Hosseini, Nazli Jowkar, Afshin Karimzadeh Fard

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Experimental & numeral study of temperature distribution during milling process, is important in milling quality and tools life aspects. In the present study the milling cross-section temperature is determined by using Artificial Neural Networks (ANN) according to the temperature of certain points of the work piece and the points specifications and the milling rotational speed of the blade. In the present work, at first three-dimensional model of the work piece is provided and then by using the Computational Heat Transfer (CHT) simulations, temperature in different nods of the work piece are specified in steady-state conditions. Results obtained from CHT are used for training and testing the ANN approach. Using reverse engineering and setting the desired x, y, z and the milling rotational speed of the blade as input data to the network, the milling surface temperature determined by neural network is presented as output data. The desired points temperature for different milling blade rotational speed are obtained experimentally and by extrapolation method for the milling surface temperature is obtained and a comparison is performed among the soft programming ANN, CHT results and experimental data and it is observed that ANN soft programming code can be used more efficiently to determine the temperature in a milling process.

Keywords: artificial neural networks, milling process, rotational speed, temperature

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Authors: Firas M. Tuaimah

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This paper presents an Artificial Neural Network based approach for short-term load forecasting and exactly for two days ahead. Two seasons have been discussed for Iraqi power system, namely summer and winter; the hourly load demand is the most important input variables for ANN based load forecasting. The recorded daily load profile with a lead time of 1-48 hours for July and December of the year 2012 was obtained from the operation and control center that belongs to the Ministry of Iraqi electricity. The results of the comparison show that the neural network gives a good prediction for the load forecasting and for two days ahead.

Keywords: short-term load forecasting, artificial neural networks, back propagation learning, hourly load demand

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Authors: Jienny Lee, In-Soo Cho, Sang-Ho Cha

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Adult mesenchymal stem cells (MSCs) have been investigated using preclinical approaches for tissue regeneration. Porcine MSCs (pMSCs) are capable of growing and attaching to plastic with a fibroblast-like morphology and then differentiating into bone, adipose, and cartilage tissues in vitro. This study was conducted to investigate the proliferating abilities, differentiation potentials, and multipotency of miniature pig adipose tissue-derived MSCs (mpAD-MSCs) with or without long-term cryopreservation, considering that cryostorage has the potential for use in clinical applications. After confirming the characteristics of the mpAD-MSCs, we examined the effect of long-term cryopreservation (> 2 years) on expression of cell surface markers (CD34, CD90 and CD105), proliferating abilities (cumulative population doubling level, doubling time, colony-forming unit, and MTT assay) and differentiation potentials into mesodermal cell lineages. As a result, the expression of cell surface markers is similar between thawed and fresh mpAD-MSCs. However, long-term cryopreservation significantly lowered the differentiation potentials (adipogenic, chondrogenic, and osteogenic) of mpAD-MSCs. When compared with fresh mpAD-MSCs, thawed mpAD-MSCs exhibited lower expression of mesodermal cell lineage-related genes such as peroxisome proliferator-activated receptor-g2, lipoprotein lipase, collagen Type II alpha 1, osteonectin, and osteocalcin. Interestingly, long-term cryostoraged mpAD-MSCs exhibited significantly higher cell viability than the fresh mpAD-MSCs. Long-term cryopreservation induced a 30% increase in the cell viability of mpAD-MSCs when compared with the fresh mpAD-MSCs at 5 days after thawing. However, long-term cryopreservation significantly lowered expression of stemness markers such as Oct3/4, Sox2, and Nanog. Furthermore, long-term cryopreservation negatively affected expression of senescence-associated genes such as telomerase reverse transcriptase and heat shock protein 90 of mpAD-MSCs when compared with the fresh mpAD-MSCs. The results from this study might be important for the successful application of MSCs in clinical trials after long-term cryopreservation.

Keywords: mesenchymal stem cells, cryopreservation, stemness, senescence

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Authors: Vineeth Siripuram, Abhineet Nigam

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A bioreactor is an engineered system that supports a biologically active environment. Along the years, the advancements in bioreactors have been widely accepted all over the world for varied applications ranging from sewage treatment to tissue cloning. Driven by tissue and organ shortage, tissue engineering has emerged as an alternative to transplantation for the reconstruction of lost or damaged organs. In this study, Computational fluid dynamics (CFD) has been used to model porous medium flow in scaffolds (taken from the literature) with different flow patterns. A detailed analysis of different scaffold geometries and their influence on cell deposition in the perfusion system is been carried out using Computational fluid dynamics (CFD). Considering the fact that, the scaffold should mimic the organs or tissues structures in a three-dimensional manner, certain assumptions were made accordingly. The research on scaffolds has been extensively carried out in different bioreactors. However, there has been less focus on the morphology of the scaffolds and the flow patterns in which the perfusion system is laid upon. The objective of this paper is to employ a computational approach using CFD simulation to determine the optimal morphology and the anisotropic measurements of the various samples of scaffolds. Using predictive computational modelling approach, variables which exert dominant effects on the cell deposition within the scaffold were prioritised and corresponding changes in morphology of scaffold and flow patterns in the perfusion systems are made. A Eulerian approach was carried on in multiple CFD simulations, and it is observed that the morphological and topological changes in the scaffold perfusion system are of great importance in the commercial applications of scaffolds.

Keywords: cell seeding, CFD, flow patterns, modelling, perfusion systems, scaffold

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Authors: Vongsathorn Ngampuak, Yutachai Chookaew, Wipawee Dejtisakdi

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Dunaliella salina has great potential as a system for generating commercially valuable products, including beta-carotene, pharmaceuticals, and biofuels. Our goal is to improve this potential by enhancing growth rate and other properties of D. salina under optimal growth conditions. We used ethyl methane sulfonate (EMS) to generate random mutants in D. salina KU11, a strain classified in Thailand. In a preliminary experiment, we first treated D. salina cells with 0%, 0.8%, 1.0%, 1.2%, 1.44% and 1.66% EMS to generate a killing curve. After that, we randomly picked 30 candidates from approximately 300 isolated survivor colonies from the 1.44% EMS treatment (which permitted 30% survival) as an initial test of the mutant screen. Among the 30 survivor lines, we found that 2 strains (mutant #17 and #24) had significantly improved growth rates and cell number accumulation at stationary phase approximately up to 1.8 and 1.45 fold, respectively, 2 strains (mutant #6 and #23) had significantly decreased growth rates and cell number accumulation at stationary phase approximately down to 1.4 and 1.35 fold, respectively, while 26 of 30 lines had similar growth rates compared with the wild type control. We also analyzed cell size for each strain and found there was no significant difference comparing all mutants with the wild type. In addition, mutant #24 had shown an increase of biomass accumulation approximately 1.65 fold compared with the wild type strain on day 5 that was entering early stationary phase. From these preliminary results, it could be feasible to identify D. salina mutants with significant improved growth rate, cell accumulation and biomass production compared to the wild type for the further study; this makes it possible to improve this microorganism as a platform for biotechnology application.

Keywords: Dunaliella salina, ethyl methyl sulfonate, growth rate, biomass

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Authors: Iulianna Taritsa, Kuldeep Neote, Eric Fossel

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Lysosome-induced immunogenic cell death (LIICD) is a powerful mechanism of targeting cancer cells that kills circulating malignant cells and primes the host’s immune cells against future remission. Current immunotherapies for cancer are limited in preventing recurrence – a gap that can be bridged by training the immune system to recognize cancer neoantigens. Lysosomal leakage can be induced therapeutically to traffic antigens from dying cells to dendritic cells, which can later present those tumorigenic antigens to T cells. Previous research has shown that oxidative agents administered in the tumor microenvironment can initiate LIICD. We generated a fusion protein between an oxidative agent known as xanthine oxidase (XO) and a mini-antibody specific for EGFR/HER2-sensitive breast tumor cells. The anti-EGFR single domain antibody fragment is uniquely sourced from llama, which is functional without the presence of a light chain. These llama micro-antibodies have been shown to be better able to penetrate tissues and have improved physicochemical stability as compared to traditional monoclonal antibodies. We demonstrate that the fusion protein created is stable and can induce early markers of immunogenic cell death in an in vitro human breast cancer cell line (SkBr3). Specifically, we measured overall cell death, as well as surface-expressed calreticulin, extracellular ATP release, and HMGB1 production. These markers are consensus indicators of ICD. Flow cytometry, luminescence assays, and ELISA were used respectively to quantify biomarker levels between treated versus untreated cells. We also included a positive control group of SkBr3 cells dosed with doxorubicin (a known inducer of LIICD) and a negative control dosed with cisplatin (a known inducer of cell death, but not of the immunogenic variety). We looked at each marker at various time points after cancer cells were treated with the XO/antibody fusion protein, doxorubicin, and cisplatin. Upregulated biomarkers after treatment with the fusion protein indicate an immunogenic response. We thus show the potential for this fusion protein to induce an anticancer effect paired with an adaptive immune response against EGFR/HER2+ cells. Our research in human cell lines here provides evidence for the success of the same therapeutic method for patients and serves as the gateway to developing a new treatment approach against breast cancer.

Keywords: apoptosis, breast cancer, immunogenic cell death, lysosome

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Authors: Olfat A. Fadali, Mohamed S. Mahmoud, Omnia H. Abdelraheem, Shimaa G. Mohammed

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The effect of electromagnetic field (EMF) on the removal of edible oil from oil-in-water emulsion by means of electrocoagulation was investigated in rectangular batch electrochemical cell with DC current. Iron (Fe) plate anodes and stainless steel cathodes were employed as electrodes. The effect of different magnetic field intensities (1.9, 3.9 and 5.2 tesla), three different positions of EMF (below, perpendicular and parallel to the electrocoagulation cell), as well as operating time; had been investigated. The application of electromagnetic field (5.2 tesla) raises percentage of oil removal from 72.4% for traditional electrocoagulation to 90.8% after 20 min.

Keywords: electrocoagulation, electromagnetic field, Oil-water emulsion, edible oil

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Authors: Nihan Gurel Ulusan

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It is obvious that electrical energy, which is an inseparable part of modern day’s human and also the most important power source of our age, should be generated on a level that will suffice the nation’s requirements. The electrical energy used for a sustainable architectural design should be reduced as much as possible. Designing the buildings as energy efficient systems which aim at reducing the artificial illumination loads has been a current subject of our times as a result of concepts gaining importance like conscious consumption of energy sources, environment-friendly designs and sustainability. Reducing the consumption of electrical energy regarding the artificial lighting carries great significance, especially in the volumes which are used all day long like the educational buildings. Starting out with such an aim in this paper, the educational buildings are explored in terms of energy efficient lighting. Firstly, illumination techniques, illumination systems, light sources, luminaries, illumination controls and 'efficient energy' usage in lighting are mentioned. In addition, natural and artificial lighting systems used in educational buildings and also the spaces building up these kind buildings are examined in terms of energy efficient lighting. Lastly, the illumination properties of the school sample chosen for this study, Kağıthane Anadolu Lisesi, a typical high school in Istanbul, is observed. Suggestions are made in order to improve the system by evaluating the illumination properties of the classes with the survey carried out with the users.

Keywords: educational buildings, energy efficient, illumination techniques, lighting

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Authors: Sandip Suman

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Mechanical defects (cracks, voids, irregularities) in rocket motor propellant are not new and it is induced due to various reasons, which could be an improper manufacturing process, lot-to-lot variation in chemicals or just the natural aging of the products. These defects are normally identified during the examination of radiographic films by quality inspectors. However, a lot of times, these defects are under or over-classified by human inspectors, which leads to unpredictable performance during lot acceptance tests and significant economic loss. The human eye can only visualize larger cracks and defects in the radiographs, and it is almost impossible to visualize every small defect through the human eye. A different artificial intelligence-based machine vision methodology has been proposed in this work to identify and classify the structural defects in the radiographic films of rocket motors with solid propellant. The proposed methodology can extract the features of defects, characterize them, and make intelligent decisions for acceptance or rejection as per the customer requirements. This will automatize the defect detection process during manufacturing with human-like intelligence. It will also significantly reduce production downtime and help to restore processes in the least possible time. The proposed methodology is highly scalable and can easily be transferred to various products and processes.

Keywords: artificial intelligence, machine vision, defect detection, rocket motor propellant grains

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Authors: Jiaqi Dong, Qing-Hua Qin, Yi Xiao

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Some of the most fundamental challenges of elastic metamaterials (EMMs) optimization can be attributed to the high consumption of computational power resulted from finite element analysis (FEA) simulations that render the optimization process inefficient. Furthermore, due to the inherent mesh dependence of FEA, minuscule geometry features, which often emerge during the later stages of optimization, induce very fine elements, resulting in enormously high time consumption, particularly when repetitive solutions are needed for computing the objective function. In this study, a surrogate modelling algorithm is developed to reduce computational time in structural optimization of EMMs. The surrogate model is constructed based on a multilayer feedforward artificial neural network (ANN) architecture, trained with prepopulated eigenfrequency data prepopulated from FEA simulation and optimized through regime selection with genetic algorithm (GA) to improve its accuracy in predicting the location and width of the primary elastic band gap. With the optimized ANN surrogate at the core, a Nelder-Mead (NM) algorithm is established and its performance inspected in comparison to the FEA solution. The ANNNM model shows remarkable accuracy in predicting the band gap width and a reduction of time consumption by 47%.

Keywords: artificial neural network, machine learning, mechanical metamaterials, Nelder-Mead optimization

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Authors: Jimmy Jiun-Ming Su, Yuan-Min Lin

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Bioprinting has been applied to produce 3D cellular constructs for tissue engineering. Microextrusion printing is the most common used method. However, printing low viscosity bioink is a challenge for this method. Herein, we developed a new 3D printing system to fabricate cell-laden hydrogels via a DLP-based projector. The bioprinter is assembled from affordable equipment including a stepper motor, screw, LED-based DLP projector, open source computer hardware and software. The system can use low viscosity and photo-polymerized bioink to fabricate 3D tissue mimics in a layer-by-layer manner. In this study, we used gelatin methylacrylate (GelMA) as bioink for stem cell encapsulation. In order to reinforce the printed construct, surface modified hydroxyapatite has been added in the bioink. We demonstrated the silanization of hydroxyapatite could improve the crosslinking between the interface of hydroxyapatite and GelMA. The results showed that the incorporation of silanized hydroxyapatite into the bioink had an enhancing effect on the mechanical properties of printed hydrogel, in addition, the hydrogel had low cytotoxicity and promoted the differentiation of embedded human bone marrow stem cells (hBMSCs) and retinal pigment epithelium (RPE) cells. Moreover, this bioprinting system has the ability to generate microchannels inside the engineered tissues to facilitate diffusion of nutrients. We believe this 3D bioprinting system has potential to fabricate various tissues for clinical applications and regenerative medicine in the future.

Keywords: bioprinting, cell encapsulation, digital light processing, GelMA hydrogel

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Authors: Md. M. Hossain, Regan Roat, Jenica Christopherson, Colette Free, Zhiguang Guo

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Long noncoding RNA (lncRNA) mediated post-transcriptional gene regulation, and their epigenetic landscapes have been shown to be involved in many human diseases. However, their regulation in diabetes through governing islet’s β-cell function and survival needs to be elucidated. Due to the technical and ethical constraints, it is difficult to study their role in β-cell function and survival in human under in vivo condition. In this study, humanized mice have been developed through transplanting human pancreatic islet under the kidney capsule of NOD.SCID mice and induced β-cell death leading to diabetes condition to study lncRNA mediated regulation. For this, human islets from 3 donors (3000 IEQ, purity > 80%) were transplanted under the kidney capsule of STZ induced diabetic NOD.scid mice. After at least 2 weeks of normoglycecemia, lymphocytes from diabetic NOD mice were adoptively transferred and islet grafts were collected once blood glucose reached > 200 mg/dl. RNA from human donor islets, islet grafts from humanized mice with either adoptive lymphocyte transfer (ALT) or PBS control (CTL) were ribodepleted; barcoded fragment libraries were constructed and sequenced on the Ion Proton sequencer. lncRNA expression in isolated human islets, islet grafts from humanized mice with and without induced β-cell death and their regulation in human islets function in vitro under glucose challenge, cytokine mediated inflammation and induced apoptotic condition were investigated. Out of 3155 detected lncRNAs, 299 that highly expressed in islets were found to be significantly downregulated and 224 upregulated in ALT compared to CTL. Most of these are found to be collocated within 5 kb upstream and 1 kb downstream of 788 up- and 624 down-regulated mRNAs. Genomic Regions Enrichment of Annotations Analysis revealed deregulated and collocated genes are related to pancreas endocrine development; insulin synthesis, processing, and secretion; pancreatitis and diabetes. Many of them, that found to be located within enhancer domains for islet specific gene activity, are associated to the deregulation of known islet/βcell specific transcription factors and genes that are important for β-cell differentiation, identity, and function. RNA sequencing analysis revealed aberrant lncRNA expression which is associated to the deregulated mRNAs in β-cell function as well as in molecular pathways related to diabetes. A distinct set of candidate lncRNA isoforms were identified as highly enriched and specific to human islets, which are deregulated in human islets from donors with different BMIs and with type 2 diabetes. These RNAs show an interesting regulation in cultured human islets under glucose stimulation and with induced β-cell death by cytokines. Aberrant expression of these lncRNAs was detected in the exosomes from the media of islets cultured with cytokines. Results of this study suggest that the islet specific lncRNAs are deregulated in human islet with β-cell death, hence important in diabetes. These lncRNAs might be important for human β-cell function and survival thus could be used as biomarkers and novel therapeutic targets for diabetes.

Keywords: β-cell, humanized mouse, pancreatic islet, LncRNAs

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Authors: Myoung-Jin Kim, Yeong-Seop Ahn, Hyun-Jee Yang, Hyoung-Kyu Song

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This paper proposes the new cooperative communication scheme for the wireless communication system. When the receiver is located in the edge of coverage, the signal from the transmitter is distorted by the inter-cell interference (ICI) and power reduction by distance. In order to improve communication performance, the proposed scheme adds the relay. By using the relay, the receiver receives the signal from the transmitter and relay at the same time. Therefore, the new cooperative communication scheme obtains diversity gain and is improved by the relay.

Keywords: cooperative communication, diversity gain, OFDM, MIMO

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Authors: Alò Roberta, Bottiglione Francesco, Mantriota Giacomo

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The efficiency of the actuation system of lower limb exoskeletons and of active orthoses is a significant aspect of the design of such devices because it affects their efficacy. F-IVT is an innovative actuation system to power artificial knee joint with energy recovery capabilities. Its key and non-conventional elements are a flywheel, that acts as a mechanical energy storage system, and an Infinitely Variable Transmission (IVT). The design of the F-IVT can be optimized for a certain walking condition, resulting in a heavy reduction of both the electric energy consumption and of the electric peak power. In this work, by means of simulations of level ground walking at different speeds, it is demonstrated how F-IVT is still an advantageous actuator, even when it does not work in nominal conditions.

Keywords: active orthoses, actuators, lower extremity exoskeletons, knee joint

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Authors: Neda Sinaei, Davood Zare, Mehrdad Azin

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Polyhydroxyalkanoates (PHAs) are biocompatible and biodegradable polymers produced by a wide range of bacterial strains. These biopolymers are significantly studied for drug delivery and tissue engineering applications because of their fascinating physicochemical properties. Polyhydroxybutyrate (PHB) scaffold that has been extracted from a novel bacteria using oil wastewater was selected to study. Some physical parameters affecting scaffold properties such as PHB concentration, solvent evaporation speed, and ultrasonic time were investigated. Scanning electron microscopy was used to evaluate the porosity. Afterward, the biocompatibility of PHB scaffold was assessed. Initial results showed the highly porous PHB scaffold structure with a variety of pore sizes. Subsequent results indicated that more unique pore sizes can be obtained by optimizing physical factors. It would be noticed that the morphology of the pore structure was accordingly affected by ultrasonic time. Hence, In vitro cell viability tests on the PHB scaffold using human foreskin fibroblasts revealed strong cell attachment and proliferation supports. Therefore, it can be concluded that the cost-effective PHB scaffold has the potential using as a biomaterial cell adhesion substrate in therapeutic applications.

Keywords: Polyhydroxybutyrate, biocompatible, scaffold, porous, tissue engineering

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Authors: ZhenlinJu, Christopher P. Vellano, RehanAkbani, Yiling Lu, Gordon B. Mills

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The epithelial–mesenchymal transition (EMT) is a process by which epithelial cells acquire mesenchymal characteristics, such as profound disruption of cell-cell junctions, loss of apical-basolateral polarity, and extensive reorganization of the actin cytoskeleton to induce cell motility and invasion. A hallmark of EMT is its capacity to promote metastasis, which is due in part to activation of several transcription factors and subsequent downregulation of E-cadherin. Unfortunately, current approaches have yet to uncover robust protein marker sets that can classify tumors as possessing strong EMT signatures. In this study, we utilize reverse phase protein array (RPPA) data and consensus clustering methods to successfully classify a subset of cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) tumors into an EMT protein signaling group (EMT group). The overall survival (OS) of patients in the EMT group is significantly worse than those in the other Hormone and PI3K/AKT signaling groups. In addition to a shrinkage and selection method for linear regression (LASSO), we applied training/test set and Monte Carlo resampling approaches to identify a set of protein markers that predicts the EMT status of CESC tumors. We fit a logistic model to these protein markers and developed a classifier, which was fixed in the training set and validated in the testing set. The classifier robustly predicted the EMT status of the testing set with an area under the curve (AUC) of 0.975 by Receiver Operating Characteristic (ROC) analysis. This method not only identifies a core set of proteins underlying an EMT signature in cervical cancer patients, but also provides a tool to examine protein predictors that drive molecular subtypes in other diseases.

Keywords: consensus clustering, TCGA CESC, Silhouette, Monte Carlo LASSO

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Authors: Natalia Espinosa, Arthur Amorim, Rudolf Huebner

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Epilepsy is a chronic neural disease and around 50 million people in the world suffer from this disease, however, in many cases, the individual acquires resistance to the medication, which is known as drug-resistant epilepsy, where a detection system is necessary. This paper showed the development of an automatic system for seizure detection based on artificial neural networks (ANN), which are common techniques of machine learning. Discrete Wavelet Transform (DWT) is used for decomposing electroencephalogram (EEG) signal into main brain waves, with these frequency bands is extracted features for training a feedforward neural network with backpropagation, finally made a pattern classification, seizure or non-seizure. Obtaining 95% accuracy in epileptic EEG and 100% in normal EEG.

Keywords: Artificial Neural Network (ANN), Discrete Wavelet Transform (DWT), Epilepsy Detection , Seizure.

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Authors: Thomas Schalow

Abstract:

This paper posits the need to take a cross-cultural approach to communication with non-human cultures and intelligences in order to meet the following three imminent contingencies: communicating with sentient biological intelligences, communicating with extraterrestrial intelligences, and communicating with artificial super-intelligences. The paper begins with a discussion of how intelligence emerges. It disputes some common assumptions we maintain about consciousness, intention, and language. The paper next explores cross-cultural communication among humans, including non-sapiens species. The next argument made is that we need to become much more serious about communicating with the non-human, intelligent life forms that already exist around us here on Earth. There is an urgent need to broaden our definition of communication and reach out to the other sentient life forms that inhabit our world. The paper next examines the science and philosophy behind CETI (communication with extraterrestrial intelligences) and how it has proven useful, even in the absence of contact with alien life. However, CETI’s assumptions and methodology need to be revised and based on the cross-cultural approach to communication proposed in this paper if we are truly serious about finding and communicating with life beyond Earth. The final theme explored in this paper is communication with non-biological super-intelligences using a cross-cultural communication approach. This will present a serious challenge for humanity, as we have never been truly compelled to converse with other species, and our failure to seriously consider such intercourse has left us largely unprepared to deal with communication in a future that will be mediated and controlled by computer algorithms. Fortunately, our experience dealing with other human cultures can provide us with a framework for this communication. The basic assumptions behind intercultural communication can be applied to the many types of communication envisioned in this paper if we are willing to recognize that we are in fact dealing with other cultures when we interact with other species, alien life, and artificial super-intelligence. The ideas considered in this paper will require a new mindset for humanity, but a new disposition will prepare us to face the challenges posed by a future dominated by artificial intelligence.

Keywords: artificial intelligence, CETI, communication, culture, language

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Authors: Neha Bhadauria, Indu Gaur, Shilpi Shilpi, Susmita Goswami, Prabir K. Paul

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The aerial surface of plants colonized by Human Enteric Pathogens ()has been implicated in outbreaks of enteric diseases in humans. Practice of organic farming primarily using animal dung as manure and sewage water for irrigation are the most significant source of enteric pathogens on the surface of leaves, fruits and vegetables. The present work aims to have an insight into the molecular mechanism of interaction of Human Enteric Pathogens or their metabolites with cell wall receptors in plants. Tomato plants grown under aseptic conditions at 12 hours L/D photoperiod, 25±1°C and 75% RH were inoculated individually with S. fonticola and K. pneumonia. The leaves from treated plants were sampled after 24 and 48 hours of incubation. The cell wall and cytoplasmic proteins were extracted and isocratically separated on 1D SDS-PAGE. The sampled leaves were also subjected to formaldehyde treatment prior to isolation of cytoplasmic proteins to study protein-protein interactions induced by Human Enteric Pathogens. Protein bands extracted from the gel were subjected to MALDI-TOF-TOF MS analysis. The foremost interaction of Human Enteric Pathogens on the plant surface was found to be cell wall bound receptors which possibly set ups a wave a critical protein-protein interaction in cytoplasm. The study revealed the expression and suppression of specific cytoplasmic and cell wall-bound proteins, some of them being important components of signaling pathways. The results also demonstrated HEP induced rearrangement of signaling pathways which possibly are crucial for adaptation of these pathogens to plant surface. At the end of the study, it can be concluded that controlling the over-expression or suppression of these specific proteins rearrange the signaling pathway thus reduces the outbreaks of food-borne illness.

Keywords: cytoplasmic protein, cell wall-bound protein, Human Enteric Pathogen (HEP), protein-protein interaction

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Authors: Seyedeh Mozhgan Seyed-Talebi, Weng-Kent Chan, Hsin-Yi Tiffany Chen

Abstract:

Lead-free perovskite photovoltaic (PV) technology employing non-toxic tin halide perovskite absorbers is pivotal for advancing perovskite solar cell (PSC) commercialization. Despite challenges posed by perovskite sensitivity to oxygen and humidity, our study utilizes DFT calculations using VASP and NanoDCAL software and SCAPS-1D simulations to elucidate the charge transport mechanism at the interface of CsSnI₃-FASnI₃ heterojunction. Results reveal how inherent electric fields facilitate efficient carrier transport, reducing recombination losses. We predict optimized power conversion efficiencies (PCEs) and highlight the potential of CsSnI3-FASnI3 heterojunctions for cost-effective and efficient charge transport layer-free (CTLF) photovoltaic devices. Our study provides insights into the future direction of recognizing more efficient, nontoxic heterojunction perovskite devices.

Keywords: charge transport layer free, CsSnI₃-FASnI₃ heterojunction, lead-free perovskite solar cell, tin halide perovskite., Charge transport layer free

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Authors: Marlon Gallo, Eduardo Miguel, Laura Oca, Eneko Gonzalez, Unai Iraola

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The heat generation of an energy storage system is an essential topic when designing a battery pack and its cooling system. Heat generation estimation is used together with thermal models to predict battery temperature in operation and adapt the design of the battery pack and the cooling system to these thermal needs guaranteeing its safety and correct operation. In the present work, a comparison between the use of a heat flux sensor (HFS) for indirect measurement of heat losses in a cell and the widely used and simplified version of Bernardi’s equation for estimation is presented. First, a Li-ion cell is thermally characterized with an HFS to measure the thermal parameters that are used in a first-order lumped thermal model. These parameters are the equivalent thermal capacity and the thermal equivalent resistance of a single Li-ion cell. Static (when no current is flowing through the cell) and dynamic (making current flow through the cell) tests are conducted in which HFS is used to measure heat between the cell and the ambient, so thermal capacity and resistances respectively can be calculated. An experimental platform records current, voltage, ambient temperature, surface temperature, and HFS output voltage. Second, an equivalent circuit model is built in a Matlab-Simulink environment. This allows the comparison between the generated heat predicted by Bernardi’s equation and the HFS measurements. Data post-processing is required to extrapolate the heat generation from the HFS measurements, as the sensor records the heat released to the ambient and not the one generated within the cell. Finally, the cell temperature evolution is estimated with the lumped thermal model (using both HFS and Bernardi’s equation total heat generation) and compared towards experimental temperature data (measured with a T-type thermocouple). At the end of this work, a critical review of the results obtained and the possible mismatch reasons are reported. The results show that indirectly measuring the heat generation with HFS gives a more precise estimation than Bernardi’s simplified equation. On the one hand, when using Bernardi’s simplified equation, estimated heat generation differs from cell temperature measurements during charges at high current rates. Additionally, for low capacity cells where a small change in capacity has a great influence on the terminal voltage, the estimated heat generation shows high dependency on the State of Charge (SoC) estimation, and therefore open circuit voltage calculation (as it is SoC dependent). On the other hand, with indirect measuring the heat generation with HFS, the resulting error is a maximum of 0.28ºC in the temperature prediction, in contrast with 1.38ºC with Bernardi’s simplified equation. This illustrates the limitations of Bernardi’s simplified equation for applications where precise heat monitoring is required. For higher current rates, Bernardi’s equation estimates more heat generation and consequently, a higher predicted temperature. Bernardi´s equation accounts for no losses after cutting the charging or discharging current. However, HFS measurement shows that after cutting the current the cell continues generating heat for some time, increasing the error of Bernardi´s equation.

Keywords: lithium-ion battery, heat flux sensor, heat generation, thermal characterization

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Authors: Nabilah Filzah Mohd Radzuan, Andi Putra, Zalinda Othman, Azuraliza Abu Bakar, Abdul Razak Hamdan

Abstract:

Precipitation forecast is important to avoid natural disaster incident which can cause losses in the involved area. This paper reviews three techniques logistic regression, decision tree, and random forest which are used in making precipitation forecast. These combination techniques through the vector auto-regression (VAR) model help in finding the advantages and strengths of each technique in the forecast process. The data-set contains variables of the rain’s domain. Adaptation of artificial intelligence techniques involved in rain domain enables the forecast process to be easier and systematic for precipitation forecast.

Keywords: logistic regression, decisions tree, random forest, VAR model

Procedia PDF Downloads 429