Search results for: neural stem cells

Authors: Eric Beyegue, Boris Azantza, Judith Laure Ngondi, Julius E. Oben

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Background: Insulin resistance (IR) and oxidative stress are associated with obesity, diabetes mellitus, and other cardio metabolic disorders. The aim of this study was to investigate the effect of Coula edulis extracts (CEE) on insulin resistance and oxidative stress markers in high-fat/high sucrose diet-induced insulin resistance in rats. Materials and Methods: Thirty male rats were divided into 6 groups of 5 rats each fed, received daily oral administration of CE extracts for 8 weeks as follows: Group 1 or negative control group, fed with standard diet (SD); Group 2 fed with high-fat/high sucrose diet (HFHS) only; Group3 fed with HFHS + CEAq 200; Group 4 fed with HFHS + CEAq 400; Group 5 fed with HFHS + CEEt 200; Group 6 fed with HFHS + CEEt 400. At the end of the experiment (8 weeks), animals were sacrificed plasma lipid profile, glucose, insulin, oxidative marker and digestive enzyme activities were measured. The homeostasis model assessment for insulin resistance (HOMA-IR) was determined. Results: Feedings with HFHS significantly (p < 0.01) induced plasma hyperglycaemia, hyperinsulinaemia, increased triglyceride, total cholesterol, and low-density lipoprotein levels, decreased high-density lipoprotein levels, alterations of α amylase, and glucose-6-phosphatase activities, and oxidative stress. Daily oral administration with CEE for eight weeks after insulin resistance induction had a hypolipidaemic action, antioxidative activities and modulated metabolic markers. Ethanolic extract at the higher dose had the best effect on body weight gain and insulin resistance, whereas aqueous extract showed the better activity on hyperlipidemia. Conclusion: These results suggest that CEAq and CEEt at 400mg/kg are promising complementary supplements that can be used to protect better from metabolic disorders associated with HFHS.

Keywords: Coula edulis Baill, high-fat / high sucrose diet, insulin resistance, oxidative stress

Procedia PDF Downloads 274

Authors: Luis Fonseca, Filipe Cabral Pinto, Susana Sargento

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The increase of the world population, especially in large urban centers, has resulted in new challenges particularly with the control and optimization of public safety. Thus, in the present work, a solution is proposed for the prediction of criminal occurrences in a city based on historical data of incidents and demographic information. The entire research and implementation will be presented start with the data collection from its original source, the treatment and transformations applied to them, choice and the evaluation and implementation of the Machine Learning model up to the application layer. Classification models will be implemented to predict criminal risk for a given time interval and location. Machine Learning algorithms such as Random Forest, Neural Networks, K-Nearest Neighbors and Logistic Regression will be used to predict occurrences, and their performance will be compared according to the data processing and transformation used. The results show that the use of Machine Learning techniques helps to anticipate criminal occurrences, which contributed to the reinforcement of public security. Finally, the models were implemented on a platform that will provide an API to enable other entities to make requests for predictions in real-time. An application will also be presented where it is possible to show criminal predictions visually.

Keywords: crime prediction, machine learning, public safety, smart city

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Authors: Sanaa AM Elgayar, Sohair A Eltony, Maha Mahmoud Abd El Rouf

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Background: Retinopathy remains an important complication of diabetes. Aim of work: This work was carried out to evaluate the protective effects of genistein from diabetic retinopathy in rat. Material and Methods: Fifteen adult male albino rats were divided into two groups; Group I: control (n=5) and Group II: streptozotocin induced diabetic group (n=10), which is equally divided into two subgroups; IIa (diabetic vehicle control) and IIb (diabetic genistein-treated). Specimens were taken from the retina 12 weeks post induction, processed and examined using light, immunohistochemical, ultrastructural techniques. Blood samples were assayed for the levels of glucose. Results: In comparison with the diabetic non-treated group, the histological changes in macro and microglial glial cells reactivity and retinal blood capillaries were improved in genistein-treated groups. In addition, GFAP and iNOS expressions in the retina and the blood glucose level were reduced. Conclusion: Genistein ameliorates the histological changes of diabetic retinopathy reaching healing features, which resemble that of a normal retina.

Keywords: diabetic retinopathy, genistein, glia, capillaries.

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Authors: Azyuni Aziz, Syed A. Malik, Shahrul Kadri Ayop, Fatin Hana Naning

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Currently, organic-inorganic hybrid thin films have attracted researchers to explore them, where these thin films can give a lot of benefits. Hybrid thin films are thin films that consist of inorganic and organic materials. Inorganic and organic materials give high efficiency and low manufacturing cost in some applications such as solar cells application, furthermore, organic materials are environment-friendly. In this study, poly (3-hexylthiophene) was choosing as organic material which combined with inorganic nanoparticles, Cadmium Sulfide (CdS) quantum dots. Samples were prepared using new technique, Angle Lifting Deposition (ALD) at different weight percentage. All prepared samples were then characterized by Field Emission Scanning Electron Microscopy (FESEM) with Energy-dispersive X-ray spectroscopy (EDX) and Atomic Force Microscopy (AFM) to study surface of samples and determine their surface roughness. Results show that these inorganic nanoparticles have affected the surface of samples and surface roughness of samples increased due to increasing of weight percentage of CdS in the thin films samples.

Keywords: AFM, CdS, FESEM-EDX, hybrid thin films, P3HT

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Authors: L. Chetibi, D. Hamana, T. O. Busko, M. P. Kulish, S. Achour

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TiO2 nanostructures have attracted much attention due to their optical, dielectric and photocatalytic properties as well as applications including optical coating, photocatalysis and photoelectrochemical solar cells. This work aims to prepare TiO2 nanofibers (NFs) on titanium substrate (Ti) by in situ oxidation of Ti foils in a mixture solution of concentrated H2O2 and NaOH followed by proton exchange and calcinations. Scanning Electron microscopy (SEM) revealed an obvious network of TiO2 nanofibers. The photoluminescence (PL) spectra of these nanostructures revealed a broad intense band in the visible light range with a reduced near edge band emission. The PL bands in the visible region, mainly, results from surface oxygen vacancies and others defects. After irradiation with Ti+ ions (the irradiation energy was E = 140 keV with doses of 1013 ions/cm2), the intensity of the PL spectrum decreased as a consequence of the radiation treatment. The irradiation with Ti+ leads to a reduction of defects and generation of non irradiative defects near to the level of the conduction band as evidenced by the PL results. On the other hand, reducing the surface defects on TiO2 nanostructures may improve photocatalytic and optoelectronic properties of this nanostructure.

Keywords: TiO2, nanofibers, photoluminescence, irradiation

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Authors: Franky Michael Hamonangan Siagian, Affan Maulana, Himawan Tri Bayu Murti Petrus, Panut Mulyono, Widi Astuti

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In this study, the feasibility of the direct electrowinning method was used to produce zero-valent iron from red mud. The bauxite residue sample came from the Tayan mine, Indonesia, which contains high hematite (Fe₂O₃). Before electrolysis, the samples were characterized by various analytical techniques (ICP-AES, SEM, XRD) to determine their chemical composition and mineralogy. The direct electrowinning method of red mud suspended in NaOH was introduced at low temperatures ranging from 30 - 110 °C. Variations of current density, red mud: NaOH ratio and temperature were carried out to determine the optimum operation of the direct electrowinning process. Cathode deposits and residues in electrochemical cells were analyzed using XRD, XRF, and SEM to determine the chemical composition and current recovery. The low-temperature electrolysis current efficiency on Redmud can reach 20% recovery at a current density of 920,945 A/m². The moderate performance of the process was investigated with red mud, which was attributed to the troublesome adsorption of red mud particles on the cathode, making the reduction far less efficient than that with hematite.

Keywords: alumina, red mud, electrochemical reduction, iron production

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Authors: Lawal Alkasim, Clement M. Gonah, Zainab S. Aliyu

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This work is focus on recovering silicon form photovoltaic cells and repurposing it toward the use in glass, ceramics or glass ceramics as it is made up of silicon material. Silicon is the main back-bone and responsible for the thermodynamic properties of glass, ceramics and glass ceramics materials. Antireflection silicon is soluble in hot alkali. Successfully the recovered material composed of silicon and silicon nitride of the A.R, with a small amount of silver, Aluminuim, lead & copper in the sunshine of crystalline/non-crystalline silicon solar cell. Aquaregia is used to remove the silver, Aluminium, lead & copper. The recovered material treated with hot alkali highly concentrated to produce sodium silicate, which is an alkali silicate glass (water glass). This type of glass is produced through chemical process, unlike other glasses that are produced through physical process of melting and non-crystalline solidification. It has showed a property of being alkali silicate glass from its solubility in water and insoluble in alcohol. The XRF analysis shows the presence of sodium silicate.

Keywords: unrecyclable solar PV, crystalline silicon, hot conc. alkali, sodium silicate

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Authors: Ishrat Jahan Saifi, Sheelu Shafiq Siddiqi, M. R. Ajmal

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Glycation of protein is very important as well as a harmful process, which may lead to develop DM in human body. Human Serum Albumin (HSA) is the most abundant protein in blood and it is highly prone to glycation by the reducing sugars. 2-¬deoxy d-¬Ribose (dRib) is a highly reactive reducing sugar which is produced in cells as a product of the enzyme thymidine phosphorylase. It is generated during the degradation of DNA in human body. It may cause glycation in HSA rapidly and is involved in the development of DM. In present study, we did in¬vitro glycation of HSA with different concentrations of 2-¬deoxy d-¬ribose and found that dRib glycated HSA rapidly within 4h incubation at 37◦C. UV¬ Spectroscopy, Fluorescence spectroscopy, Fourier transform infrared spectroscopy (FTIR) and Circular Dichroism (CD) technique have been done to determine the structural changes in HSA upon glycation. Results of this study suggested that dRib is the potential glycating agent and it causes alteration in protein structure and biophysical properties which may lead to development and progression of Diabetes mellitus.

Keywords: 2-deoxy D-ribose, human serum albumin, glycation, diabetes mellitus

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Authors: Rahul Agarwal, Ashutosh Singh

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Colorectal cancer (CRC) also refers as bowel cancer or colon cancer. It involves the development of abnormal growth of cells in colon or rectum part of the body. This work leads to the development of a miRNA database in colorectal cancer. We named this database- miCoRe. This database comprises of all validated colon-rectal cancer miRNAs information from various published literature with an effectual knowledge based information retrieval system. miRNAs have been collected from various published literature reports. MySQL is used for main-framework of miCoRe while the front-end was developed in PHP script. The aim of developing miCoRe is to create a comprehensive central repository of colorectal carcinoma miRNAs with all germane information of miRNAs and their target genes. The current version of miCoRe consists of 238 miRNAs which are known to be implicated in malignancy of CRC. Alongside with miRNA information, miCoRe also contains the information related to the target genes of these miRNA. miCoRe furnishes the information about the mechanism of incidence and progression of the disease, which would further help the researchers to look for colorectal specific miRNAs therapies and CRC specific targeted drug designing. Moreover, it will also help in development of biomarkers for the better and early detection of CRC and will help in better clinical management of the disease.

Keywords: colorectal cancer, database, miCoRe, miRNAs

Procedia PDF Downloads 251

Authors: Maryam Mohammad Hadi, Heather Nesbitt, Hamzah Masood, Hashim Ahmed, Mark Emberton, John Callan, Alexander MacRobert, Anthony McHale, Nikolitsa Nomikou

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Sonodynamic therapy (SDT) utilises ultrasound in combination with sensitizers, such as porphyrins, for the production of cytotoxic reactive oxygen species (ROS) and the confined ablation of tumours. Ultrasound can be applied locally, and the acoustic waves, at frequencies between 0.5-2 MHz, are transmitted efficiently through tissue. SDT does not require highly toxic agents, and the cytotoxic effect only occurs upon ultrasound exposure at the site of the lesion. Therefore, this approach is not associated with adverse side effects. Further highlighting the benefits of SDT, no cancer cell population has shown resistance to therapy-triggered ROS production or their cytotoxic effects. This is particularly important, given the as yet unresolved issues of radiation and chemo-resistance, to the authors’ best knowledge. Another potential future benefit of this approach – considering its non-thermal mechanism of action – is its possible role as an adjuvant to immunotherapy. Substantial pre-clinical studies have demonstrated the efficacy and targeting capability of this therapeutic approach. However, SDT has yet to be fully characterised and appropriately exploited for the treatment of cancer. In this study, a formulation based on multistimulus-responsive sensitizer-containing nanoparticles that can accumulate in advanced prostate tumours and increase the therapeutic efficacy of SDT has been developed. The formulation is based on a polyglutamate-tyrosine (PGATyr) co-polymer carrying hematoporphyrin. The efficacy of SDT in this study was demonstrated using prostate cancer as the translational exemplar. The formulation was designed to respond to the microenvironment of advanced prostate tumours, such as the overexpression of the proteolytic enzymes, cathepsin-B and prostate-specific membrane antigen (PSMA), that can degrade the nanoparticles, reduce their size, improving both diffusions throughout the tumour mass and cellular uptake. The therapeutic modality was initially tested in vitro using LNCaP and PC3 cells as target cell lines. The SDT efficacy was also examined in vivo, using male SCID mice bearing LNCaP subcutaneous tumours. We have demonstrated that the PGATyr co-polymer is digested by cathepsin B and that digestion of the formulation by cathepsin-B, at tumour-mimicking conditions (acidic pH), leads to decreased nanoparticle size and subsequent increased cellular uptake. Sonodynamic treatment, at both normoxic and hypoxic conditions, demonstrated ultrasound-induced cytotoxic effects only for the nanoparticle-treated prostate cancer cells, while the toxicity of the formulation in the absence of ultrasound was minimal. Our in vivo studies in immunodeficient mice, using the hematoporphyrin-containing PGATyr nanoparticles for SDT, showed a 50% decrease in LNCaP tumour volumes within 24h, following IV administration of a single dose. No adverse effects were recorded, and body weight was stable. The results described in this study clearly demonstrate the promise of SDT to revolutionize cancer treatment. It emphasizes the potential of this therapeutic modality as a fist line treatment or in combination treatment for the elimination or downstaging of difficult to treat cancers, such as prostate, pancreatic, and advanced colorectal cancer.

Keywords: sonodynamic therapy, nanoparticles, tumour ablation, ultrasound

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Authors: Damou Mehdi, Nouri Keltoum, Feham Mohammed, Khazini Mohammed, Bouazza Tayb Habibi Chawki

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The Half mode SIW filter is treated by two softwares (HFSS (High Frequency Structure Simulator) and CST (Computer Simulation Technology)). The filter HMSIW has a very simple structure and a very compact size. The simulated results by CST are presented and compared with the results simulated by a high-frequency structure simulator. Good agreement between the simulated CST and simulated results by HFSS is observed. By cascading two of them according to design requirement, a X-band bandpass filter is designed and simulated to meet compact size, low insertion loss, good return loss as well as second harmonic suppression. As an example, we designed the proposed HMSIW filter at X band by HFSS. The filter has a pass-band from 7.3 GHz to 9.8 GHz, and its relative operating fraction bandwidth is 29.5 %. There are one transmission zeros are located at 14.4 GHz.

Keywords: substrate integrated waveguide, filter, HMSIW, defected ground structures (DGS), simulation BPF

Procedia PDF Downloads 558

Authors: Andualem Workie

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In this study, we examined the sinterability and bioactivity of MgO-SiO₂-P₂O₅-CaO-CaF₂ glass compositions created through spray pyrolysis. We evaluated the bioactivity of the materials by immersing them for varying periods of time in simulated bodily fluid (SBF) and found that bioactivity was related to the sintering temperature and soaking time. The material's pH value during immersion in SBF was within the range of 7.4-8.2, which is below 8.5 and improves compatibility and reduces toxicity in biological applications. We used X-ray diffraction and scanning electron microscopy to determine the phase compositions and morphologies of the samples and found that the 1100°C sintered A-W GC sample exhibited the highest bioactivity after soaking in SBF. This sample was dominated by fluorapatite, wollastonite, and whitlockite crystals scattered throughout the glass matrix. The crystallinity (%) of the A-W GC increased as its bioactivity improved, making it more suitable for use in pharmaceutical applications. We also conducted a cytotoxicity test on A-W GC samples sintered at different temperatures and found that the glass-ceramics were non-toxic to MC3T3-E1 cells at all extraction concentrations, except for those sintered at 700°C at concentrations of 250, 200, and 150 mg/ml where cell viability (%) was below the threshold of 70%.

Keywords: apatite wollastonite glass ceramics, bioactivity, calcination, cell viability

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Authors: Lindani Mbatha, Suvendi Rimer, Mpho Gololo

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Image segmentation is the process of dividing an image into several sections, such as the object's background and the foreground. It is a critical technique in both image-processing tasks and computer vision. Most of the image segmentation algorithms have been developed for gray-scale images and little research and algorithms have been developed for the color images. Most image segmentation algorithms or techniques vary based on the input data and the application. Nearly all of the techniques are not suitable for noisy environments. Most of the work that has been done uses the Markov Random Field (MRF), which involves the computations and is said to be robust to noise. In the past recent years' image segmentation has been brought to tackle problems such as easy processing of an image, interpretation of the contents of an image, and easy analysing of an image. This article reviews and summarizes some of the image segmentation techniques and algorithms that have been developed in the past years. The techniques include neural networks (CNN), edge-based techniques, region growing, clustering, and thresholding techniques and so on. The advantages and disadvantages of medical ultrasound image segmentation techniques are also discussed. The article also addresses the applications and potential future developments that can be done around image segmentation. This review article concludes with the fact that no technique is perfectly suitable for the segmentation of all different types of images, but the use of hybrid techniques yields more accurate and efficient results.

Keywords: clustering-based, convolution-network, edge-based, region-growing

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Authors: Anna Vildová, H. Hendrychová, J. Kubeš, L. Tůmová

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The abiotic elicitation is one of the methods for increasing the secondary metabolites production in plant tissue cultures and it seems to be more effective than traditional strategies. This study verified the use of silver nitrate as elicitor to enhance flavonolignans and flavonoid taxifolin production in suspension culture of Sylibum marianum (L.) Gaertn. Silver nitrate in various concentrations (5.887.10-3 mol/L, 5.887.10-4 mol/L, 5.887.10-5 mol/L) was used as elicitor. The content of secondary metabolites in cell suspension cultures was determined by high performance liquid chromatography. The samples were taken after 6, 12, 24, 48, 72 and 168 hours of treatment. The highest content of taxifolin production (2.2 mg.g-1) in cell suspension culture of Silybum marianum (L.) Gaertn. was detected after silver nitrate (5.887.10-4 mol/L) treatment and 72 h application. Flavonolignans such as silybinA, silybin B, silydianin, silychristin, isosilybin A, isosilybin B were not produced by cell suspension culture of S. marianum after elicitor treatment. Our results show that the secondarymetabolites could be released from S. marianum cells into the nutrient medium by changed permeability of cell wall.

Keywords: Silybum marianum (L.) Gaertn., elicitation, silver nitrate, taxifolin

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Authors: Aakansha Gupta, Neha Vadnere, Tapasvi Soni, M. Anbarsi

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Protein secondary structure prediction is one of the most important goals pursued by bioinformatics and theoretical chemistry; it is highly important in medicine and biotechnology. Some aspects of protein functions and genome analysis can be predicted by secondary structure prediction. This is used to help annotate sequences, classify proteins, identify domains, and recognize functional motifs. In this paper, we represent protein secondary structure as a mathematical model. To extract and predict the protein secondary structure from the primary structure, we require a set of parameters. Any constants appearing in the model are specified by these parameters, which also provide a mechanism for efficient and accurate use of data. To estimate these model parameters there are many algorithms out of which the most popular one is the EM algorithm or called the Expectation Maximization Algorithm. These model parameters are estimated with the use of protein datasets like RS126 by using the Bayesian Probabilistic method (data set being categorical). This paper can then be extended into comparing the efficiency of EM algorithm to the other algorithms for estimating the model parameters, which will in turn lead to an efficient component for the Protein Secondary Structure Prediction. Further this paper provides a scope to use these parameters for predicting secondary structure of proteins using machine learning techniques like neural networks and fuzzy logic. The ultimate objective will be to obtain greater accuracy better than the previously achieved.

Keywords: model parameters, expectation maximization algorithm, protein secondary structure prediction, bioinformatics

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Authors: Nuri Caglayan, H. Kursat Celik

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There are many factors that influence the health and productivity of the animals in livestock production fields, including temperature, humidity, carbon dioxide (CO2), ammonia (NH3), hydrogen sulfide (H2S), physical activity and particulate matter. High NH3 concentrations reduce feed consumption and cause daily weight gain. At high concentrations, H2S causes respiratory problems and CO2 displace oxygen, which can cause suffocation or asphyxiation. Good air quality in livestock facilities can have an impact on the health and well-being of animals and humans. Air quality assessment basically depends on strictly given limits without taking into account specific local conditions between harmful gases and other meteorological factors. The stated limitations may be eliminated. using controlling systems based on neural networks and fuzzy logic. This paper describes a fuzzy logic based ventilation algorithm, which can calculate different fan speeds under pre-defined boundary conditions, for removing harmful gases from the production environment. In the paper, a fuzzy logic model has been developed based on a Mamedani’s fuzzy method. The model has been built on MATLAB software. As the result, optimum fan speeds under pre-defined boundary conditions have been presented.

Keywords: air quality, fuzzy logic model, livestock housing, fan speed

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Authors: Chanya Inprasit, Yi-Wen Lin

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Motion sickness (MS) is an acute disorder that occurs in healthy persons without considering gender, age or ethnicity worldwide. All signs and symptoms of MS are the results of confliction and mismatch among neural signal inputs. It is known that no singular remedy works for everybody, and electroacupuncture (EA) is one of the popular alternative therapies used for MS. Our study utilized a mouse model in order to exclude any psychological factors of MS and EA. Mice lack an emetic reflex. Therefore pica behavior, which is a normal consumption of non-nutritive substances, was found to measure the response of MS in mice. In the laboratory, Kaolin was used as a non-nutrient food substance instead of natural substances lacking nutritional value such as wood, cloth, charcoal, soil or grass. It was hypothesized that EA treatment could reduce the symptoms of MS through the TRPV1 pathways. The results of pica behavior showed a significantly increased intake of kaolin in the MS group throughout the experiment period. Moreover, the Kaolin intake of the EA group decreased to the average baseline of the control group. There was no recorded difference in the food and water intake of each group. The results indicated an increase of the TRPV1, pERK, pJNK and pmTOR protein levels in the thalamus after MS stimulation, and a significant decrease in the EA group compared with that of the control group. These findings suggest that TRPV1 pathways are associated in MS mechanisms and can be reduced by EA.

Keywords: electroacupuncture, motion sickness, Thalamus, TRPV1

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Authors: Alessandra Rossodivita, Alexei Tikhomirov, Andrey Trufanov, Nikolay Kinash, Olga Berestneva, Svetlana Nikitina, Fabio Casati, Alessandro Visconti, Tommaso Saporito

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It is well known that world and national societies are exposed to diverse threats: anthropogenic, technological, and natural. Anthropogenic ones are of greater risks and, thus, attract special interest to researchers within wide spectrum of disciplines in efforts to lower the pertinent risks. Some researchers showed by means of multilayered, complex network models how media promotes the prevention of disease spread. To go further, not only are mass-media sources included in scope the paper suggests but also personificated social bots (socbots) linked according to reflexive theory. The novel scope considers information spread over conscious and unconscious agents while counteracting both natural and man-made threats, i.e., infections and terrorist hazards. Contrary to numerous publications on misinformation disseminated by ‘bad’ bots within social networks, this study focuses on ‘good’ bots, which should be mobilized to counter the former ones. These social bots deployed mixture with real social actors that are engaged in concerted actions at spreading, receiving and analyzing information. All the contemporary complex network platforms (multiplexes, interdependent networks, combined stem networks et al.) are comprised to describe and test socbots activities within competing information sharing tools, namely mass-media hubs, social networks, messengers, and e-mail at all phases of disasters. The scope and concomitant techniques present evidence that embedding such socbots into information sharing process crucially change the network topology of actor interactions. The change might improve or impair robustness of social network environment: it depends on who and how controls the socbots. It is demonstrated that the topological approach elucidates techno-social processes within the field and outline the roadmap to a safer world.

Keywords: complex network platform, counterterrorism, information sharing topology, social bots

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Authors: Fatima Melit, Nedjemeddine Bounar

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Polycrystalline samples of Ce1-xMxO2-δ (x=0.1, 0.15, 0.2)(M=Gd, Y) were prepared by solid-state chemical reaction from mixtures of pre-dried oxides powders of CeO2, Gd2O3 and Y2O3 in the appropriate stoichiometric ratio to explore their use as solid electrolytes for intermediate temperature solid oxide fuel cells (IT-SOFCs). Their crystal structures and ionic conductivities were characterised by X-ray powder diffraction (XRD) and AC complex impedance spectroscopy (EIS). The XRD analyses confirm that all the resulting synthesised co-doped cerium oxide powders are single-phase and crystallise in the cubic structure system with the space group Fm3m. On the one hand, the lattice parameter (a ) of the phases increases with increasing Gd content; on the other hand, with increasing Y-substitution rate, the latter decreases. The results of complex impedance conductivity measurements have shown that doping has a remarkable effect on conductivity. The co-doped cerium phases showed significant ionic conductivity values, making these materials excellent candidates for solid oxide electrolytes at intermediate temperatures.

Keywords: electrolyte, Ceria, X-ray diffraction, EIS, SEM, SOFC

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Authors: Jay L. Fu

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Patients with Alzheimer's disease progressively lose their memory and thinking skills and, eventually, the ability to carry out simple daily tasks. The disease is irreversible, but early detection and treatment can slow down the disease progression. In this research, publicly available MRI data and demographic data from 373 MRI imaging sessions were utilized to build models to predict dementia. Various machine learning models, including logistic regression, k-nearest neighbor, support vector machine, random forest, and neural network, were developed. Data were divided into training and testing sets, where training sets were used to build the predictive model, and testing sets were used to assess the accuracy of prediction. Key risk factors were identified, and various models were compared to come forward with the best prediction model. Among these models, the random forest model appeared to be the best model with an accuracy of 90.34%. MMSE, nWBV, and gender were the three most important contributing factors to the detection of Alzheimer’s. Among all the models used, the percent in which at least 4 of the 5 models shared the same diagnosis for a testing input was 90.42%. These machine learning models allow early detection of Alzheimer’s with good accuracy, which ultimately leads to early treatment of these patients.

Keywords: Alzheimer's disease, clinical diagnosis, magnetic resonance imaging, machine learning prediction

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Authors: Irene Carmagnola, Valeria Chiono, Sandra Pacharra, Jochen Salber, Sean McMahon, Chris Lovell, Pooja Basnett, Barbara Lukasiewicz, Ipsita Roy, Xiang Zhang, Gianluca Ciardelli

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Thrombosis and restenosis after stenting procedure can be prevented by promoting fast stent wall endothelialisation. It is well known that surface functionalisation with antifouling molecules combining with extracellular matrix proteins is a promising strategy to design biomimetic surfaces able to promote fast endothelialization. In particular, REDV has gained much attention for the ability to enhance rapid endothelialization due to its specific affinity with endothelial cells (ECs). In this work, a two-step plasma treatment was performed to polymerize a thin layer of acrylic acid, used to subsequently graft PEGylated-REDV and polyethylene glycol (PEG) at different molar ratio with the aim to selectively promote endothelial cell adhesion avoiding platelet activation. PEGylate-REDV was provided by Biomatik and it is formed by 6 PEG monomer repetitions (Chempep Inc.), with an NH2 terminal group. PEG polymers were purchased from Chempep Inc. with two different chain lengths: m-PEG6-NH2 (295.4 Da) with 6 monomer repetitions and m-PEG12-NH2 (559.7 Da) with 12 monomer repetitions. Plasma activation was obtained by operating at 50W power, 5 min of treatment and at an Ar flow rate of 20 sccm. Pure acrylic acid (99%, AAc) vapors were diluted in Ar (flow = 20 sccm) and polymerized by a pulsed plasma discharge applying a discharge RF power of 200 W, a duty cycle of 10% (on time = 10 ms, off time = 90 ms) for 10 min. After plasma treatment, samples were dipped into an 1-(3-dimethylaminopropyl)-3- ethylcarbodiimide (EDC)/N-hydroxysuccinimide (NHS) solution (ratio 4:1, pH 5.5) for 1 h at 4°C and subsequently dipped in PEGylate-REDV and PEGylate-REDV:PEG solutions at different molar ratio (100 μg/mL in PBS) for 20 h at room temperature. Surface modification was characterized through physico-chemical analyses and in vitro cell tests. PEGylated-REDV peptide and PEG were successfully bound to the carboxylic groups that are formed on the polymer surface after plasma reaction. FTIR-ATR spectroscopy, X -ray Photoelectron Spectroscopy (XPS) and contact angle measurement gave a clear indication of the presence of the grafted molecules. The use of PEG as a spacer allowed for an increase in wettability of the surface, and the effect was more evident by increasing the amount of PEG. Endothelial cells adhered and spread well on the surfaces functionalized with the REDV sequence. In conclusion, a selective coating able to promote a new endothelial cell layer on polymeric stent surface was developed. In particular, a thin AAc film was polymerised on the polymeric surface in order to expose –COOH groups, and PEGylate-REDV and PEG were successful grafted on the polymeric substrates. The REDV peptide demonstrated to encourage cell adhesion with a consequent, expected improvement of the hemocompatibility of these polymeric surfaces in vivo. Acknowledgements— This work was funded by the European Commission 7th Framework Programme under grant agreement number 604251- ReBioStent (Reinforced Bioresorbable Biomaterials for Therapeutic Drug Eluting Stents). The authors thank all the ReBioStent partners for their support in this work.

Keywords: endothelialisation, plasma treatment, stent, surface functionalisation

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Authors: Nelson Barrientos, Matthew J. Davies, Marco C. Marques, Darren N. Nesbeth, Gary J. Lye, Nicolas Szita

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Microbioreactor technology is making important advances towards its application in cell culture and bioprocess development. In particular, the technology promises flexible and controllable devices capable to perform parallelised experimentation at low cost. Currently, state of the art methods (e.g. optical sensors) allow the accurate monitoring of the microbioreactor operation. In addition, the laminar flow regime encountered in these devices allows more predictive fluid dynamics modelling, improving the control over the soluble, physical and mechanical environment of the cells. This work describes the development and characterisation of a novel microbioreactor cassette system (microbioreactor volume is 150 μL. The volumetric oxygen transfer coefficient (KLa) and mixing time have been characterised to be between 25 to 113 h-1 and 0.5 and 0.1 s, respectively. In addition, the Residence time distribution (RTD) analysis confirms that the reactor operates at well mixed conditions. Finally, Staphylococcus carnosus TM300 growth is demonstrated via batch culture experiments. Future work consists in expanding the optics of the microbioreactor design to include the monitoring of variables such as fluorescent protein expression, among others.

Keywords: microbioreactor, cell-culture, fermentation, microfluidics

Procedia PDF Downloads 387

Authors: V. Cesaroni, C. Girometta, A. Bernicchia, M. Brusoni, F. Corana, R. M. Baiguera, C. M. Cusaro, M. L. Guglielminetti, B. Mannucci, H. Kawagishi, C. Perini, A. M. Picco, P. Rossi, E. Salerni, E. Savino

Abstract:

Fungi of the genus Hericium contain various compounds with antibacterial activity, cytotoxic effect on cancer cells and bioactive molecules. Some of the active metabolites stimulate the synthesis of the Nerve Growth Factor (NGF). Recently, the effect of dietary supplement based on Hericium erinaceus on recognition memory and on hippocampal mossy fiber-CA3 neurotransmission was published. The aim of this study was to investigate the presence of Hericium species on Italian territory in order to isolate the strains for further studies and applications. The first step was to collect Hericium sporophores in Tuscany: H. alpestre Pers., H. coralloides (Scop.) Pers. and H. erinaceus (Bull.) Pers. were the species present. The strains of H. alpestre (H.a.1), H. coralloides (H.c.1) and H. erinaceus (H.e.1 & H.e.2) have been isolated in pure culture and preserved in the collection of the University of Pavia (MicUNIPV). The DNA sequences obtained from the strains were compared to other sequences found in international databases. Therefore, it was possible to construct a phylogenetic tree that highlights the clear separation in clades of the sequences and the molecular identification of our strains with the species of Hericium considered. The second step was to cultivate indoor and outdoor H. erinaceus in order to obtain as many sporophores as possible for further chemical analysis. All the procedures for H. erinaceus cultivation have been followed. Among the available recipes for indoor H. erinaceus cultivation, it was used a substrate formulation contained 70% oak sawdust, 20% rice bran, 10% wheat straw, 1% CaCO3 and 1% sucrose. The bioactive compounds present in the mycelia and in the sporophores of H. erinaceus were chemically analyzed in collaboration with the Centro Grandi Strumenti of the University of Pavia using high-performance liquid chromatography/electrospray ionization tandem mass spectrometry (HPLC/ESI-MS/MS). The materials to be analyzed were previously freeze-dried and then extracted with an alcoholic procedure. Preliminary chromatographic analysis revealed the presence of potentially bioactive and structurally different secondary metabolites such as polysaccharides, erinacins, ericenones, steroids and other terpenoids. Ericenones C and D (in sporophores) and erinacin A (in mycelium) have been identified by comparison with the respective standards. These molecules are known to have effects on the Central Nervous System (CNS) cells, which is the main objective of our studies. Thanks to the high sensitivity in the detection of bioactive compounds of H. erinaceus, it will be possible to use the To obtain lyophilized mycelium and the respective culture broth, 4 small pieces (about 5 mm2) of the respective H.e.1 or H.c.1 strains, taken from the margin of growing cultures (MEA), were inoculated into 1 liter of 2% ME (malt extract, Biokar Diagnostics). The static liquid cultures were kept at 24 °C in the dark chamber and fungi grew for one month. 10 replicates for each strain have been done. The method proposed as an analytical screening protocol to determine the optimal growth conditions of the fungus and to improve the production chain of H. erinaceus. These results encourage to carry out chemical analyzes also on H. alpestre and H. coralloides in order to evaluate the presence of bioactive compounds in these two species.

Keywords: Hericium species, Hercium erinaceus bioactive compounds, medicinal mushrooms, mushroom cultivation

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Authors: Soofiyan Atar, Adil Shaikh, Sahil Rajpurkar, Pragnesh Bhalala, Aniket Desai, Irfan Siddavatam

Abstract:

Deep reinforcement learning (deep RL) algorithms leverage the symbolic power of complex controllers by automating it by mapping sensory inputs to low-level actions. Deep RL eliminates the complex robot dynamics with minimal engineering. Deep RL provides high-risk involvement by directly implementing it in real-world scenarios and also high sensitivity towards hyperparameters. Tuning of hyperparameters on a pneumatic quadruped robot becomes very expensive through trial-and-error learning. This paper presents an automated learning control for a pneumatic quadruped robot using sample efficient deep Q learning, enabling minimal tuning and very few trials to learn the neural network. Long training hours may degrade the pneumatic cylinder due to jerk actions originated through stochastic weights. We applied this method to the pneumatic quadruped robot, which resulted in a hopping gait. In our process, we eliminated the use of a simulator and acquired a stable gait. This approach evolves so that the resultant gait matures more sturdy towards any stochastic changes in the environment. We further show that our algorithm performed very well as compared to programmed gait using robot dynamics.

Keywords: model-based reinforcement learning, gait stability, supervised learning, pneumatic quadruped

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Authors: Vivek Anand, Rainer Naegele

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Industry 4.0 is a global mega-trend revolutionizing the world of advanced manufacturing, but also bringing up challenges for SMEs. In response, many regional, as well as digital Industry 4.0 Platforms, have been set up to boost the competencies of established enterprises as well as SMEs. The concept of 'Clusters' is a policy tool that aims to be a starting point to establish sustainable and self-supporting structures in industries of a region by identifying competencies and supporting cluster actors with services that match their growth needs. This paper is motivated by the idea that Clusters have the potential to enable firms, particularly SMEs, to accelerate the innovation process and transition to digital technologies. In this research, the efficacy of Industry 4.0 platforms as Cluster ecosystems is evaluated, especially for SMEs. Focusing on the Baden Wurttemberg region in Germany, an action research method is employed to study how SMEs leverage other actors on Industry 4.0 Platforms to further their Industry 4.0 journeys. The aim is to evaluate how such Industry 4.0 platforms stimulate innovation, cooperation and competitiveness. Additionally, the barriers to these platforms fulfilling their promise to serve as capacity building cluster ecosystems for SMEs in a region will also be identified. The findings will be helpful for academicians and policymakers alike, who can leverage a ‘cluster policy’ to enable Industry 4.0 ecosystems in their regions. Furthermore, relevant management and policy implications stem from the analysis. This will also be of interest to the various players in a cluster ecosystem - like SMEs and service providers - who benefit from the cooperation and competition. The paper will improve the understanding of how a dialogue orientation, a bottom-up approach and active integration of all involved cluster actors enhance the potential of Industry 4.0 Platforms. A strong collaborative culture is a key driver of digital transformation and technology adoption across sectors, value chains and supply chains; and will position Industry 4.0 Platforms at the forefront of the industrial renaissance. Motivated by this argument and based on the results of the qualitative research, a roadmap will be proposed to position Industry 4.0 Platforms as effective clusters ecosystems to support Industry 4.0 adoption in a region.

Keywords: cluster policy, digital transformation, industry 4.0, innovation clusters, innovation policy, SMEs and startups

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Authors: H. Akbari Khorami, P. Wild, N. Djilali

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This paper reports on the response of a fiber-optic sensing probe to small concentrations of hydrogen peroxide (H2O2) vapor at room temperature. H2O2 has extensive applications in industrial and medical environments. Conversely, H2O2 can be a health hazard by itself. For example, H2O2 induces cellular damage in human cells and its presence can be used to diagnose illnesses such as asthma and human breast cancer. Hence, development of reliable H2O2 sensor is of vital importance to detect and measure this species. Ferric ferrocyanide, referred to as Prussian blue (PB), was deposited on the tip of a multimode optical fiber through the single source precursor technique and served as an indicator of H2O2 in a spectroscopic manner. Sensing tests were performed in H2O2-H2O vapor mixtures with different concentrations of H2O2. The results of sensing tests show the sensor is able to detect H2O2 concentrations in the range of 50.6 ppm to 229.5 ppm. Furthermore, the sensor response to H2O2 concentrations is linear in a log-log scale with the adjacent R-square of 0.93. This sensing behavior allows us to detect and quantify the concentration of H2O2 in the vapor phase.

Keywords: chemical deposition, fiber-optic sensor, hydrogen peroxide vapor, prussian blue

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Authors: Bandar Alahmadi, Lethia Jackson

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Machine Learning model is used today in many real-life applications. The safety and security of such model is important, so the results of the model are as accurate as possible. One challenge of machine learning model security is the adversarial examples attack. Adversarial examples are designed by the attacker to cause the machine learning model to misclassify the input. We propose a method to generate adversarial examples to attack image classifiers. We are modifying the successfully classified images, so a classifier misclassifies them after the modification. In our method, we do not update the whole image, but instead we detect the important region, modify it, place it back to the original image, and then run it through a classifier. The algorithm modifies the detected region using two methods. First, it will add abstract image matrix on back of the detected image matrix. Then, it will perform a rotation attack to rotate the detected region around its axes, and embed the trace of image in image background. Finally, the attacked region is placed in its original position, from where it was removed, and a smoothing filter is applied to smooth the background with foreground. We test our method in cascade classifier, and the algorithm is efficient, the classifier confident has dropped to almost zero. We also try it in CNN (Convolutional neural network) with higher setting and the algorithm was successfully worked.

Keywords: adversarial examples, attack, computer vision, image processing

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Authors: Hamidreza Ghazvinian, Khosro Ghazvinian, Touba Khodaiean

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The importance of evaporation estimation in water resources and agricultural studies is undeniable. Pan evaporation are used as an indicator to determine the evaporation of lakes and reservoirs around the world due to the ease of interpreting its data. In this research, intelligent models were investigated in estimating pan evaporation on a daily basis. Shahroud and Mayamey were considered as the studied cities. These two cities are located in Semnan province in Iran. The mentioned cities have dry weather conditions that are susceptible to high evaporation potential. Meteorological data of 11 years of synoptic stations of Shahrood and Mayamey cities were used. The intelligent models used in this study are Artificial Neural Network (ANN), Least Squares Support Vector Machine (LSSVM), and M5 tree models. Meteorological parameters of minimum and maximum air temperature (Tmax, Tmin), wind speed (WS), sunshine hours (SH), air pressure (PA), relative humidity (RH) as selected input data and evaporation data from pan (EP) to The output data was considered. 70% of data is used at the education level, and 30 % of the data is used at the test level. Models used with explanation coefficient evaluation (R2) Root of Mean Squares Error (RMSE) and Mean Absolute Error (MAE). The results for the two Shahroud and Mayamey stations showed that the above three models' operations are rather appropriate.

Keywords: pan evaporation, intelligent methods, shahroud, mayamey

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Authors: Yexin Qu

Abstract:

Izafe is a construction typically found in Iranian languages, which is attested already in Old Avestan and Old Persian. The narrow sense of izafe can be described as the linear structure of [NP pt Modifier] with pt as an uninflectable particle or clitic. The history of the Iranian izafe has the following stages: Stage I: Verbless nominal relative clauses, Stage II: Verbless nominal relative clauses with Case Attraction; and Stage III: Narrow sense izafe. Previous works suggest that embedded relative clauses and correlatives in other Indo-European languages might be relevant for the source of the izafe-construction. Stage I, as the precursor of narrow sense izafe, or so-called “prezafe” is not found in branches other than Iranian. Comparable cases have been demonstrated in Vedic, Greek, and some rare cases in Latin. This suggests “prezafe” may date back very early in Indo-European. Izafe-like structures are not attested in branches such as Balto-Slavic and Germanic, but Balto-Slavic definite adjectives and Germanic weak adjectives can be compared to the verbless nominal relative clauses and analyzed as developments of verbless relative clauses parallel to izafe in Indo-Iranian, as are called “parizafe” in this paper. In this paper, the verbless RC is compared with Germanic weak adjectives. The Germanic languages used n-stem derivation to form determined derivatives, which are semantically equivalent to the appositive RC and eventually became weak adjectives. To be more precise, starting from an adjective “X”, the Germanic weak adjective structure is formed as [det X-n], literally “the X”, with the meaning “the X one”, which can be shown to be semantically equivalent to “the one which is X”. In this paper, Stage I suggest that, syntactically, the Germanic verbless relative clauses went through CP to DP relabeling like Iranian, based on the following observations: (1) Germanic relative pronouns (e.g., Gothic saei, Old English se) and determiners (e.g., Gothic sa, Old English se) are both from the *so/to pronominal roots; (2) the semantic equivalence of Germanic weak adjectives and the izafe structure. This may suggest that Germanic may also have had “Prezafe” Stages I and II. In conclusion: “Prezafe” in Stage I may have been a phenomenon of the proto-language, Stage II was the result of independent parallel developments and then each branch had its own strategy.

Keywords: izafe, relative clause, Germanic, Indo-European

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Authors: Melodi Mbuyi Mata, Blessing Olamide Taiwo, Afolabi Ayodele David

Abstract:

The purpose of this research is to create empirical models for assessing the profitability of granite aggregate production in Akure, Ondo state aggregate quarries. In addition, an artificial neural network (ANN) model and multivariate predicting models for granite profitability were developed in the study. A formal survey questionnaire was used to collect data for the study. The data extracted from the case study mine for this study includes granite marketing operations, royalty, production costs, and mine production information. The following methods were used to achieve the goal of this study: descriptive statistics, MATLAB 2017, and SPSS16.0 software in analyzing and modeling the data collected from granite traders in the study areas. The ANN and Multi Variant Regression models' prediction accuracy was compared using a coefficient of determination (R²), Root mean square error (RMSE), and mean square error (MSE). Due to the high prediction error, the model evaluation indices revealed that the ANN model was suitable for predicting generated profit in a typical quarry. More quarries in Nigeria's southwest region and other geopolitical zones should be considered to improve ANN prediction accuracy.

Keywords: national development, granite, profitability assessment, ANN models

Procedia PDF Downloads 75