Search results for: protein stability prediction

Authors: Simona Dostalova, Pavel Kopel, Marketa Vaculovicova, Vojtech Adam, Rene Kizek

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Protein apoferritin seems to be a very promising structure for use as a nanocarrier. It is prepared from intracellular ferritin protein naturally found in most organisms. The role of ferritin proteins is to store and transport ferrous ions. Apoferritin is a hollow protein cage without ferrous ions that can be prepared from ferritin by reduction with thioglycolic acid or dithionite. The structure of apoferritin is composed of 24 protein subunits, creating a sphere with 12 nm in diameter. The inner cavity has a diameter of 8 nm. The drug encapsulation process is based on the response of apoferritin structure to the pH changes of surrounding solution. In low pH, apoferritin is disassembled into individual subunits and its structure is “opened”. It can then be mixed with any desired cytotoxic drug and after adjustment of pH back to neutral the subunits are reconnected again and the drug is encapsulated within the apoferritin particles. Excess drug molecules can be removed by dialysis. The receptors for apoferritin, SCARA5 and TfR1 can be found in the membrane of both healthy and cancer cells. To enhance the specific targeting of apoferritin nanocarrier, it is possible to modify its surface with targeting moieties, such as antibodies. To ensure sterically correct complex, we used a a peptide linker based on a protein G with N-terminus affinity towards Fc region of antibodies. To connect the peptide to the surface of apoferritin, the C-terminus of peptide was made of cysteine with affinity to gold. The surface of apoferritin with encapsulated doxorubicin (ApoDox) was coated either with gold nanoparticles (ApoDox-Nano) or gold (III) chloride hydrate reduced with sodium borohydride (ApoDox-HAu). The applied amount of gold in form of gold (III) chloride hydrate was 10 times higher than in the case of gold nanoparticles. However, after removal of the excess unbound ions by electrophoretic separation, the concentration of gold on the surface of apoferritin was only 6 times higher for ApoDox-HAu in comparison with ApoDox-Nano. Moreover, the reduction with sodium borohydride caused a loss of doxorubicin fluorescent properties (excitation maximum at 480 nm with emission maximum at 600 nm) and thus its biological activity. Fluorescent properties of ApoDox-Nano were similar to the unmodified ApoDox, therefore it was more suited for the intended use. To evaluate the specificity of apoferritin modified with antibodies, we used ELISA-like method with the surface of microtitration plate wells coated by the antigen (goat anti-human IgG antibodies). To these wells, we applied ApoDox without targeting antibodies and ApoDox-Nano modified with targeting antibodies (human IgG antibodies). The amount of unmodified ApoDox on antigen after incubation and subsequent rinsing with water was 5 times lower than in the case of ApoDox-Nano modified with targeting antibodies. The modification of non-gold ApoDox with antibodies caused no change in its targeting properties. It can therefore be concluded that the demonstrated procedure allows us to create nanocarrier with enhanced targeting properties, suitable for nanomedicine.

Keywords: apoferritin, doxorubicin, nanocarrier, targeting antibodies

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Authors: Mirnawati, Gita Ciptaan, Ferawati

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Background and Objective: Palm kernel cake (PKC) is a waste of the palm oil industry. Indonesia, as the largest palm oil producer in the world, produced 45-46% palm kernel cake. Palm kernel cake can potentially be used as animal ration but its utilization for poultry is limited. Thus, fermentation process was done in order to increase the utilization PKC in poultry ration. An experiment was conducted to study the effect between Inoculum Doses with Bacillus subtilis and fermentation time to improve the quality and nutrient content of fermented Palm Kernel Cake. Material and Methods: 1) Palm kernel cake derived from Palm Kernel Processing Manufacture of Andalas Agro Industry in Pasaman, West Sumatra. 2) Bacillus subtilis obtained from The Research Center of Applied Chemistry LIPI, Bogor. 3) Preparations nutrient agar medium (NA) produced by Difoo - Becton Dickinson. 4) Rice bran 5) Aquades and mineral standard. The experiment used completely randomize design (CRD) with 3 x 3 factorial and 3 replications. The first factors were three doses of inoculum Bacillus subtilis: (3%), (5%), and (7%). The second factor was fermentation time: (1) 2 day, (2) 4 day, and (3) 6 day. The parameters were crude protein, crude fiber, nitrogen retention, and crude fiber digestibility of fermented palm kernel cake (FPKC). Results: The result of the study showed that there was significant interaction (P<0.01) between factor A and factor B and each factor A and B also showed significant effect (P<0.01) on crude protein, crude fiber, nitrogen retention, and crude fiber digestibility. Conclusion: From this study, it can be concluded that fermented PKC with 7% doses of Bacillus subtilis and 6 days fermentation time provides the best result as seen from 24.65% crude protein, 17.35% crude fiber, 68.47% nitrogen retention, 53.25% crude fiber digestibility of fermented palm kernel cake (FPKC).

Keywords: fermentation, Bacillus Subtilis, inoculum, palm kernel cake, quality, nutrient

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Authors: Uma Maheshwari V., Rajanikanth Aluvalu, Kumar Gautam

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The COVID-19 disease is a highly contagious viral infection with major worldwide health implications. The global economy suffers as a result of COVID. The spread of this pandemic disease can be slowed if positive patients are found early. COVID-19 disease prediction is beneficial for identifying patients' health problems that are at risk for COVID. Deep learning and machine learning algorithms for COVID prediction using X-rays have the potential to be extremely useful in solving the scarcity of doctors and clinicians in remote places. In this paper, a convolutional neural network (CNN) with deep layers is presented for recognizing COVID-19 patients using real-world datasets. We gathered around 6000 X-ray scan images from various sources and split them into two categories: normal and COVID-impacted. Our model examines chest X-ray images to recognize such patients. Because X-rays are commonly available and affordable, our findings show that X-ray analysis is effective in COVID diagnosis. The predictions performed well, with an average accuracy of 99% on training photographs and 88% on X-ray test images.

Keywords: deep CNN, COVID–19 analysis, feature extraction, feature map, accuracy

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Authors: A. Schander, T. Tessmann, H. Stemmann, S. Strokov, A. Kreiter, W. Lang

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For the chronic application of neural prostheses and other brain-computer interfaces, long-term stable microelectrodes for electrical stimulation are essential. In recent years many developments were done to investigate different appropriate materials for these electrodes. One of these materials is the electrical conductive polymer poly(3,4-ethylenedioxythiophene) (PEDOT), which has lower impedance and higher charge injection capacity compared to noble metals like gold and platinum. However the long-term stability of this polymer is still unclear. Thus this paper reports on the in-vitro evaluation of the long-term stability of PEDOT coated gold microelectrodes. For this purpose a highly flexible electrocorticography (ECoG) electrode array, based on the polymer polyimide, is used. This array consists of circular gold electrodes with a diameter of 560 µm (0.25 mm2). In total 25 electrodes of this array were coated simultaneously with the polymer PEDOT:PSS in a cleanroom environment using a galvanostatic electropolymerization process. After the coating the array is additionally sterilized using a steam sterilization process (121°C, 1 bar, 20.5 min) to simulate autoclaving prior to the implantation of such an electrode array. The long-term measurements were performed in phosphate-buffered saline solution (PBS, pH 7.4) at the constant body temperature of 37°C. For the in-vitro electrical stimulation a one channel bipolar current stimulator is used. The stimulation protocol consists of a bipolar current amplitude of 5 mA (cathodal phase first), a pulse duration of 100 µs per phase, a pulse pause of 50 µs and a frequency of 1 kHz. A PEDOT:PSS coated gold electrode with an area of 1 cm2 serves as the counter electrode. The electrical stimulation is performed continuously with a total amount of 86.4 million bipolar current pulses per day. The condition of the PEDOT coated electrodes is monitored in between with electrical impedance spectroscopy measurements. The results of this study demonstrate that the PEDOT coated electrodes are stable for more than 3.6 billion bipolar current pulses. Also the unstimulated electrodes show currently no degradation after the time period of 5 months. These results indicate an appropriate long-term stability of this electrode coating for chronic recording and electrical stimulation. The long-term measurements are still continuing to investigate the life limit of this electrode coating.

Keywords: chronic recording, electrical stimulation, long-term stability, microelectrodes, PEDOT

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Authors: Isabela Moreira Queiroz

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Slope stability analyses are largely carried out by deterministic methods and evaluated through a single security factor. Although it is known that the geotechnical parameters can present great dispersal, such analyses are considered fixed and known. The probabilistic methods, in turn, incorporate the variability of input key parameters (random variables), resulting in a range of values of safety factors, thus enabling the determination of the probability of failure, which is an essential parameter in the calculation of the risk (probability multiplied by the consequence of the event). Among the probabilistic methods, there are three frequently used methods in geotechnical society: FOSM (First-Order, Second-Moment), Rosenblueth (Point Estimates) and Monte Carlo. This paper presents a comparison between the results from deterministic and probabilistic analyses (FOSM method, Monte Carlo and Rosenblueth) applied to a hypothetical slope. The end was held to evaluate the behavior of the slope and consequent risk analysis, which is used to calculate the risk and analyze their mitigation and control solutions. It can be observed that the results obtained by the three probabilistic methods were quite close. It should be noticed that the calculation of the risk makes it possible to list the priority to the implementation of mitigation measures. Therefore, it is recommended to do a good assessment of the geological-geotechnical model incorporating the uncertainty in viability, design, construction, operation and closure by means of risk management. 

Keywords: probabilistic methods, risk assessment, risk management, slope stability

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Authors: Masih Moore, Saeed Ziaei-Rad

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In this study, the stability characteristics of a bi-stable composite plate with different asymmetric composition are considered. The interest in bi-stable structures comes from their ability that these structures can have two different stable equilibrium configurations to define a discrete set of stable shapes. The structures can easily change the first stable shape to the second one by a simple snap action. The main purpose of the current research is to consider the effect of including resin layers on the stability characteristics of bi-stable laminates. To this end and In order to determine the magnitude of the loads that are responsible for snap through and snap back phenomena between two stable shapes of the laminate, a non-linear finite element method (FEM) is utilized. An experimental investigation was also carried out to study the critical loads that caused snapping between two different stable shapes. Several specimens were manufactured from T300/5208 graphite-epoxy with [0/90]T, [-30/60]T, [-20/70]T asymmetric stacking sequence. In order to create an accurate finite element model, different thickness of resin layers created during the manufacturing process of the laminate was measured and taken into account. The geometry of each lamina and the resin layers was characterized by optical microscopy from different locations of the laminates thickness. The exact thickness of each lamina and the resin layer in all specimens with [0/90]T,[-30/60]T, [-20/70]T stacking sequence were determined by using image processing technique.

Keywords: bi-stable laminates, finite element method, graphite-epoxy plate, snap behavior

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Authors: Seung Hwan Park, Cheng-Sool Park, Jun Seok Kim, Youngji Yoo, Daewoong An, Jun-Geol Baek

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Depending on the big data analysis becomes important, yield prediction using data from the semiconductor process is essential. In general, yield prediction and analysis of the causes of the failure are closely related. The purpose of this study is to analyze pattern affects the final test results using a die map based clustering. Many researches have been conducted using die data from the semiconductor test process. However, analysis has limitation as the test data is less directly related to the final test results. Therefore, this study proposes a framework for analysis through clustering using more detailed data than existing die data. This study consists of three phases. In the first phase, die map is created through fail bit data in each sub-area of die. In the second phase, clustering using map data is performed. And the third stage is to find patterns that affect final test result. Finally, the proposed three steps are applied to actual industrial data and experimental results showed the potential field application.

Keywords: die-map clustering, feature extraction, pattern recognition, semiconductor manufacturing process

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Authors: J. Balaraju, M. Govinda Raj, C. S. N. Murthy

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Every industry is constantly looking for enhancement of its day to day production and productivity. This can be possible only by maintaining the men and machinery at its adequate level. Prediction of performance characteristics plays an important role in performance evaluation of the equipment. Analytical and statistical approaches will take a bit more time to solve complex problems such as performance estimations as compared with software-based approaches. Keeping this in view the present study deals with an Artificial Neural Network (ANN) modelling of a Load-Haul-Dump (LHD) machine to predict the performance characteristics such as reliability, availability and preventive maintenance (PM). A feed-forward-back-propagation ANN technique has been used to model the Levenberg-Marquardt (LM) training algorithm. The performance characteristics were computed using Isograph Reliability Workbench 13.0 software. These computed values were validated using predicted output responses of ANN models. Further, recommendations are given to the industry based on the performed analysis for improvement of equipment performance.

Keywords: load-haul-dump, LHD, artificial neural network, ANN, performance, reliability, availability, preventive maintenance

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Authors: Chukwuma Anya, Mekwa Eme

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The term ‘’FORM’’ in design could be referred to as the combination of various shapes of different sizes and assembling them in appropriate positions to achieve a unique figure of high aesthetic value. A deduction from this definition is that forms contribute immensely to the actualization of aesthetics in a building. When these various shapes and figures are properly assembled, it may give rise to a concept in design. However some architects and other designers either misuse or abuse the use of these shapes, hence resulting to a design imbalance, lack of uniformity and expression. This academic work is designed to educate the public on the proper usage of some regular shapes like circles, rectangles, pentagons, hexagons, triangles etc, to achieve a unique form in design. By the end of this work, one should be able to assemble different shapes to express different emotions of the mind, such as peace, love, confusion, war, and unity. Some elements of design, such as balance, stability, functionality and aesthetics, will also be achieved even as the building maintains its unique form.

Keywords: aesthetics, form, balance, stability

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Authors: Mohamed Aly, Aliaa Rehan Youssef, Emad Sawerees, Mounir Guirgis

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Relevance: Osteoarthritis (OA) is the most common degenerative disease seen in all populations. It causes disability and substantial socioeconomic burden. Evidence supports that exercise are the most effective conservative treatment for patients with OA. Therapists experience and clinical judgment play major role in exercise prescription and scientific evidence for this regard is lacking. The development of clinical prediction rules to identify patients who are most likely benefit from exercise may help solving this dilemma. Purpose: This study investigated whether body mass index and functional ability at baseline can predict patients’ response to a selected exercise program. Approach: Fifty-six patients, aged 35 to 65 years, completed an exercise program consisting of open kinetic chain strengthening and passive stretching exercises. The program was given for 3 sessions per week, 45 minutes per session, for 6 weeks Evaluation: At baseline and post treatment, pain severity was assessed using the numerical pain rating scale, whereas functional ability was being assessed by step test (ST), time up and go test (TUG) and 50 feet time walk test (50 FTW). After completing the program, global rate of change (GROC) score of greater than 4 was used to categorize patients as successful and non-successful. Thirty-eight patients (68%) had successful response to the intervention. Logistic regression showed that BMI and 50 FTW test were the only significant predictors. Based on the results, patients with BMI less than 34.71 kg/m2 and 50 FTW test less than 25.64 sec are 68% to 89% more likely to benefit from the exercise program. Conclusions: Clinicians should consider the described strengthening and flexibility exercise program for patents with BMI less than 34.7 Kg/m2 and 50 FTW faster than 25.6 seconds. The validity of these predictors should be investigated for other exercise.

Keywords: clinical prediction rule, knee osteoarthritis, physical therapy exercises, validity

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Authors: Sukhbir Singh

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This paper describes the modeling of novel solar air collector (NSAC) system by using artificial neural network (ANN) model. The objective of the study is to demonstrate the application of the ANN model to predict the performance of the NSAC with acetamide as a phase change material (PCM) storage. Input data set consist of time, solar intensity and ambient temperature wherever as outlet air temperature of NSAC was considered as output. Experiments were conducted between 9.00 and 24.00 h in June and July 2014 underneath the prevailing atmospheric condition of Kurukshetra (city of the India). After that, experimental results were utilized to train the back propagation neural network (BPNN) to predict the outlet air temperature of NSAC. The results of proposed algorithm show that the BPNN is effective tool for the prediction of responses. The BPNN predicted results are 99% in agreement with the experimental results.

Keywords: Evacuated tube solar air collector, Artificial neural network, Phase change material, solar air collector

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Authors: Sarmiza Stanca, Wolfgang Fritzsche, Christoph Krafft, Jürgen Popp

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Independent of the cell type a thin layer of a few nanometers thickness surrounds the cell interior as the cellular membrane. The transport of ions and molecules through the membrane is achieved in a very precise way by pores. Understanding the process of opening and closing the pores due to an electrochemical gradient across the membrane requires knowledge of the pore constitutive proteins. Recent reports prove the access to the molecular level of the cellular membrane by atomic force microscopy (AFM). This technique also permits an electrochemical study in the immediate vicinity of the tip. Specific molecules can be electrochemically localized in the natural cellular membrane. Our work aims to recognize the protein domains of the pores using an AFM probe as a miniaturized amperometric sensor, and to follow the protein behavior while changing the applied potential. The intensity of the current produced between the surface and the AFM probe is amplified and detected simultaneously with the surface imaging. The AFM probe plays the role of the working electrode and the substrate, a conductive glass on which the cells are grown, represent the counter electrode. For a better control of the electric potential on the probe, a third electrode Ag/AgCl wire is mounted in the circuit as a reference electrode. The working potential is applied between the electrodes with a programmable source and the current intensity in the circuit is recorded with a multimeter. The applied potential considers the overpotential at the electrode surface and the potential drop due to the current flow through the system. The reported method permits a high resolved electrochemical study of the protein domains on the living cell membrane. The amperometric map identifies areas of different current intensities on the pore depending on the applied potential. The reproducibility of this method is limited by the tip shape, the uncontrollable capacitance, which occurs at the apex and a potential local charge separation.

Keywords: AFM, sensor, membrane, pores, proteins

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Authors: H. Mansoori Yarahmadi, A. Aghazadeh, K. Nazeradl

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This study was done to evaluate the effects of feeding canola seed for enrichment of UFA and milk performance of early lactation dairy cows. Twelve multi parous Holstein cows (635.3±18 kg BW and 36±9 DIM) were assigned to 1 of 3 treatments: 1- Control (CON) without canola seed, 2- 7.5% raw canola seed (CUT), and 3- 7.5% Heat-treated canola seed (CHT) of the total ration. Diets contained same crude protein, but varied in net energy. Diets were composed by basis of corn silage and alfalfa. Cows were milked twice daily for 4 wk. The inclusion of canola seed did not alter DM intake, weight gain, or body condition score of cows. Milk fat from CHT cows had greater proportions of UFA and MUFA (P < 0.05). Feeding CUT increased PUFA without significant difference. Milk fat from CHT had a greater proportion of C18 UFA and tended to have a higher proportion of other UFA. FCM milk yields, milk fat and protein percentages and total yield of these components were similar between treatments. Milk urea nitrogen was lower in cows fed CON and CHT. Feeding canola seed to lactating dairy cows resulted in milk fat with higher proportions of healthful fatty acids without adverse affecting milk yield or milk composition.

Keywords: canola seed, fatty acid, dairy cow, milk

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Authors: Jan Henrik Wosnitza

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The logistic regression has developed into a standard approach for estimating conditional probabilities in a wide range of applications including credit risk prediction. The article at hand contributes to the current literature on logistic regression fourfold: First, it is demonstrated that the binary logistic regression automatically meets its model assumptions under very general conditions. This result explains, at least in part, the logistic regression's popularity. Second, the requirement of homoscedasticity in the context of binary logistic regression is theoretically substantiated. The variances among the groups of defaulted and non-defaulted obligors have to be the same across the level of the aggregated default indicators in order to achieve linear logits. Third, this article sheds some light on the question why nonlinear logits might be superior to linear logits in case of a small amount of data. Fourth, an innovative methodology for estimating correlations between obligor-specific log-odds is proposed. In order to crystallize the key ideas, this paper focuses on the example of credit risk prediction. However, the results presented in this paper can easily be transferred to any other field of application.

Keywords: correlation, credit risk estimation, default correlation, homoscedasticity, logistic regression, nonlinear logistic regression

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Authors: Mohammad Reza Dahmardeh Ghaleno, Mohammad Nohtani, Saeedeh Khaledi

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Hydrological models are applied to simulation and prediction floods in watersheds. WetSpa is a distributed, continuous and physically model with daily or hourly time step that explains of precipitation, runoff and evapotranspiration processes for both simple and complex contexts. This model uses a modified rational method for runoff calculation. In this model, runoff is routed along the flow path using Diffusion-Wave Equation which depend on the slope, velocity and flow route characteristics. Garmabrood watershed located in Mazandaran province in Iran and passing over coordinates 53° 10´ 55" to 53° 38´ 20" E and 36° 06´ 45" to 36° 25´ 30"N. The area of the catchment is about 1133 km2 and elevations in the catchment range from 213 to 3136 m at the outlet, with average slope of 25.77 %. Results of the simulations show a good agreement between calculated and measured hydrographs at the outlet of the basin. Drawing upon Nash-Sutcliffe Model Efficiency Coefficient for calibration periodic model estimated daily hydrographs and maximum flow rate with an accuracy up to 61% and 83.17 % respectively.

Keywords: watershed simulation, WetSpa, runoff, flood prediction

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Authors: Roya Khajepour, Alireza B. Novinzadeh

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A major issue with spherical robot is it surface shape, which is not always predictable. This means that given only the dynamic model of the robot, it is not possible to control the robot. Due to the fact that in certain conditions it is not possible to measure surface friction, control methods must be prepared for these conditions. Moreover, although spherical robot never becomes unstable or topples thanks to its special shape, since it moves by rolling it has a non-holonomic constraint at point of contact and therefore it is considered a non-holonomic system. Existence of such a point leads to complexity and non-linearity of robot's kinematic equations and makes the control problem difficult. Due to the non-linear dynamics and presence of uncertainty, the sliding-mode control is employed. The proposed method is based on Lyapunov Theory and guarantees system stability. This controller is insusceptible to external disturbances and un-modeled dynamics.

Keywords: sliding mode, spherical robot, non-holomonic constraint, system stability

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Authors: Silvia Gastaldello, Maria Grillo, Luca Tassoni, Claudio Maran, Stefano Balbo

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Antioxidants are nowadays attractive not only for the countless benefits to the human and animal health, but also for the perspective of use as food preservative instead of synthetic chemical molecules. In this study, the radical scavenging activity of six protein extracts from pulse and oleaginous seeds was evaluated. The selected matrices are Pisum sativum (yellow pea from two different origins), Carthamus tinctorius (safflower), Helianthus annuus (sunflower), Lupinus luteus cv Mister (lupin) and Glycine max (soybean), since they are economically interesting for both human and animal nutrition. The seeds were grinded and proteins extracted from 20mg powder with a specific vegetal-extraction kit. Proteins have been quantified through Bradford protocol and scavenging activity was revealed using DPPH assay, based on radical DPPH (2,2-diphenyl-1-picrylhydrazyl) absorbance decrease in the presence of antioxidants molecules. Different concentrations of the protein extract (1, 5, 10, 50, 100, 500 µg/ml) were mixed with DPPH solution (DPPH 0,004% in ethanol 70% v/v). Ascorbic acid was used as a scavenging activity standard reference, at the same six concentrations of protein extracts, while DPPH solution was used as control. Samples and standard were prepared in triplicate and incubated for 30 minutes in dark at room temperature, the absorbance was read at 517nm (ABS30). Average and standard deviation of absorbance values were calculated for each concentration of samples and standard. Statistical analysis using t-students and p-value were performed to assess the statistical significance of the scavenging activity difference between the samples (or standard) and control (ABSctrl). The percentage of antioxidant activity has been calculated using the formula [(ABSctrl-ABS30)/ABSctrl]*100. The obtained results demonstrate that all matrices showed antioxidant activity. Ascorbic acid, used as standard, exhibits a 96% scavenging activity at the concentration of 500 µg/ml. At the same conditions, sunflower, safflower and yellow peas revealed the highest antioxidant performance among the matrices analyzed, with an activity of 74%, 68% and 70% respectively (p < 0.005). Although lupin and soybean exhibit a lower antioxidant activity compared to the other matrices, they showed a percentage of 46 and 36 respectively. All these data suggest the possibility to use undervalued edible matrices as antioxidants source. However, further studies are necessary to investigate a possible synergic effect of several matrices as well as the impact of industrial processes for a large-scale approach.

Keywords: antioxidants, DPPH assay, natural matrices, vegetal proteins

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Authors: Sarah El Mokadem

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Egypt has been the focus of international media since 2011 revolution and its repercussions. By the end of 2017, President Abdel Fattah El Sisi will have finished his first term of presidency. With an upcoming presidential election, all eyes are returning back to Egypt as there are speculations about whether the current regime will uphold or change points in the constitution determining the years of presidency term and the allowed number or reelections. In this paper, the researcher examines the reports related to Egypt in three international news channels with different ideologies. The research aims to identify the frames used to portray major issues in Egypt like the economic struggle, democracy levels and stability and safety of the country. All available reports from these three channels in 2017 on YouTube were analyzed which is the year before the presidential elections.

Keywords: content analysis, Egypt, image building, news channel ideology

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Authors: Benjaporn Buranrat, Nootchanat Mairuae

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Cratoxylum formosum (Jack) Dyer (CF) has been used for the traditional medicines in South East Asian and Thailand. Normally, northeast Thai vegetables have proven cytotoxic to many cancer cells. Therefore, the present study aims to explore the molecular mechanisms underlying CF-induced cancer cell death and apoptosis on breast and liver cancer cells. The cytotoxicity and antiproliferative effects of CF on the human breast MCF-7 and liver HepG2 cancer cell lines were evaluated using sulforhodamine B assay and colony formation assay. Cell migration assay was measured using wound healing assay. The apoptosis induction mechanisms were investigated through reactive oxygen species formation, caspase 3 activity, and JC-1 activity. Gene expression by real-time PCR and apoptosis related protein levels by Western blot analysis. CF induced MCF-7 and HepG2 cell death by time- and dose-dependent manner. Furthermore, CF had the greater cytotoxic potency on MCF-7 more than HepG2 cells with IC50 values of 85.70+4.52 μM and 219.03±9.96 μM respectively, at 24 h. Treatment with CF also caused a dose-dependent decrease in colony forming ability and cell migration, especially on MCF-7 cells. CF induced ROS formation, increased caspase 3 activities, and decreased the mitochondrial membrane potential, and causing apoptotic body production and DNA fragmentation. CF significantly decreased expression of the cell cycle regulatory protein RAC1 and downstream proteins, cdk6. Additionally, CF enhanced p21 and reduced cyclin D1 protein levels. CF leaf extract induced cell death, apoptosis, antimigration in both of MCF-7 and HepG2 cells. CF could be useful for developing to anticancer drug candidate for breast and liver cancer therapy.

Keywords: cratoxylum formosum (jack) dyer, breast cancer, liver cancer, cell death

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Authors: Misuk Kim, Seokho Kang, Jehyuk Lee, Hyunchang Cho, Sungzoon Cho

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In semiconductor manufacturing, virtual metrology (VM) refers to methods to predict properties of a wafer based on machine parameters and sensor data of the production equipment, without performing the (costly) physical measurement of the wafer properties (Wikipedia). Additional benefits include avoidance of human bias and identification of important factors affecting the quality of the process which allow improving the process quality in the future. It is however rare to find VM applied to other areas of manufacturing. In this work, we propose to use VM to copper clad laminate (CCL) manufacturing. CCL is a core element of a printed circuit board (PCB) which is used in smartphones, tablets, digital cameras, and laptop computers. The manufacturing of CCL consists of three processes: Treating, lay-up, and pressing. Treating, the most important process among the three, puts resin on glass cloth, heat up in a drying oven, then produces prepreg for lay-up process. In this process, three important quality factors are inspected: Treated weight (T/W), Minimum Viscosity (M/V), and Gel Time (G/T). They are manually inspected, incurring heavy cost in terms of time and money, which makes it a good candidate for VM application. We developed prediction models of the three quality factors T/W, M/V, and G/T, respectively, with process variables, raw material, and environment variables. The actual process data was obtained from a CCL manufacturer. A variety of variable selection methods and learning algorithms were employed to find the best prediction model. We obtained prediction models of M/V and G/T with a high enough accuracy. They also provided us with information on “important” predictor variables, some of which the process engineers had been already aware and the rest of which they had not. They were quite excited to find new insights that the model revealed and set out to do further analysis on them to gain process control implications. T/W did not turn out to be possible to predict with a reasonable accuracy with given factors. The very fact indicates that the factors currently monitored may not affect T/W, thus an effort has to be made to find other factors which are not currently monitored in order to understand the process better and improve the quality of it. In conclusion, VM application to CCL’s treating process was quite successful. The newly built quality prediction model allowed one to reduce the cost associated with actual metrology as well as reveal some insights on the factors affecting the important quality factors and on the level of our less than perfect understanding of the treating process.

Keywords: copper clad laminate, predictive modeling, quality control, virtual metrology

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Authors: Iman Taraghi, Sandra Paszkiewicz, Daria Pawlikowska, Anna Szymczyk, Izabela Irska, Rafal Stanik, Amelia Linares, Tiberio A. Ezquerra, Elżbieta Piesowicz

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Currently, the massive use of thermoplastic materials in industrial applications causes huge amounts of polymer waste. The poly (ethylene glycol-co-1,4-cyclohexanedimethanol terephthalate) (PET-G) has been widely used in food packaging and polymer foils. In this research, the PET-G foils have been recycled and reused as a matrix to combine with different types of nanofillers such as carbon nanotubes, graphene nanoplatelets, and nanosized carbon black. The mechanical and electrical properties, as well as thermal stability and thermal conductivity of the PET-G, improved along with the addition of the aforementioned nanofillers and hybrid system of them.

Keywords: polymer hybrid nanocomposites, carbon nanofillers, recycling, physical performance

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Authors: Ammar Alali, Mahmoud Abughaban

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Cost reduction and drilling optimization is the goal of many drilling operators. Historically, stuck pipe incidents were a major segment of non-productive time (NPT) associated costs. Traditionally, stuck pipe problems are part of the operations and solved post-sticking. However, the real key to savings and success is in predicting the stuck pipe incidents and avoiding the conditions leading to its occurrences. Previous attempts in stuck-pipe predictions have neglected the local geology of the problem. The proposed predictive tool utilizes geophysical data processing techniques and Machine Learning (ML) algorithms to predict drilling activities events in real-time using surface drilling data with minimum computational power. The method combines two types of analysis: (1) real-time prediction, and (2) cause analysis. Real-time prediction aggregates the input data, including historical drilling surface data, geological formation tops, and petrophysical data, from wells within the same field. The input data are then flattened per the geological formation and stacked per stuck-pipe incidents. The algorithm uses two physical methods (stacking and flattening) to filter any noise in the signature and create a robust pre-determined pilot that adheres to the local geology. Once the drilling operation starts, the Wellsite Information Transfer Standard Markup Language (WITSML) live surface data are fed into a matrix and aggregated in a similar frequency as the pre-determined signature. Then, the matrix is correlated with the pre-determined stuck-pipe signature for this field, in real-time. The correlation used is a machine learning Correlation-based Feature Selection (CFS) algorithm, which selects relevant features from the class and identifying redundant features. The correlation output is interpreted as a probability curve of stuck pipe incidents prediction in real-time. Once this probability passes a fixed-threshold defined by the user, the other component, cause analysis, alerts the user of the expected incident based on set pre-determined signatures. A set of recommendations will be provided to reduce the associated risk. The validation process involved feeding of historical drilling data as live-stream, mimicking actual drilling conditions, of an onshore oil field. Pre-determined signatures were created for three problematic geological formations in this field prior. Three wells were processed as case studies, and the stuck-pipe incidents were predicted successfully, with an accuracy of 76%. This accuracy of detection could have resulted in around 50% reduction in NPT, equivalent to 9% cost saving in comparison with offset wells. The prediction of stuck pipe problem requires a method to capture geological, geophysical and drilling data, and recognize the indicators of this issue at a field and geological formation level. This paper illustrates the efficiency and the robustness of the proposed cross-disciplinary approach in its ability to produce such signatures and predicting this NPT event.

Keywords: drilling optimization, hazard prediction, machine learning, stuck pipe

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Authors: Subhamita Chakraborty, Shubhabrata Datta, Sujay Kumar Mukherjea, Partha Protim Chattopadhyay

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Manufacturing of multiphase high strength steel in hot strip mill have drawn significant attention due to the possibility of forming low temperature transformation product of austenite under continuous cooling condition. In such endeavor, reliable prediction of temperature profile of hot strip coil is essential in order to accesses the evolution of microstructure at different location of hot strip coil, on the basis of corresponding Continuous Cooling Transformation (CCT) diagram. Temperature distribution profile of the hot strip coil has been determined by using finite volume method (FVM) vis-à-vis finite difference method (FDM). It has been demonstrated that FVM offer greater computational reliability in estimation of contact pressure distribution and hence the temperature distribution for curved and irregular profiles, owing to the flexibility in selection of grid geometry and discrete point position, Moreover, use of finite volume concept allows enforcing the conservation of mass, momentum and energy, leading to enhanced accuracy of prediction.

Keywords: simulation, modeling, thermal analysis, coil cooling, contact pressure, finite volume method

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Authors: M. Meenasaranya, S. Saravanan

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Nonlinear stability analysis is carried out to determine the effect of surface temperature modulation in an infinite horizontal porous layer heated from below. The layer is saturated by an electrically conducting, viscous, incompressible and Newtonian fluid. The Brinkman model is used for momentum equation, and the Boussinesq approximation is invoked. The system is assumed to be bounded by rigid boundaries. The energy theory is implemented to find the global exponential stability region of the considered system. The results are analysed for arbitrary values of modulation frequency and amplitude. The existence of subcritical instability region is confirmed by comparing the obtained result with the known linear result. The vertical magnetic field is found to stabilize the system.

Keywords: Brinkman model, energy method, magnetic field, surface temperature modulation

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Authors: G. Korotcenkov, V. Brinzari, B. K. Cho

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The thermoelectric properties of nano-scaled In₂O₃:Sn films deposited by spray pyrolysis are considered in the present report. It is shown that multicomponent In₂O₃:Sn-based films are promising material for the application in thermoelectric devices. It is established that the increase in the efficiency of thermoelectric conversion at C_Sn~5% occurred due to nano-scaled structure of the films studied and the effect of the grain boundary filtering of the low energy electrons. There are also analyzed the limitations that may appear during such material using in devices developed for the market of thermoelectric generators and refrigerators. Studies showed that the stability of nano-scaled film’s parameters is the main problem which can limit the application of these materials in high temperature thermoelectric converters.

Keywords: energy conversion technologies, thermoelectricity, In2O3-based films, power factor, nanocomposites, stability

Procedia PDF Downloads 232

Authors: M. L. Mangena, N. Mokoena, K. Rashamuse, M. G. Tlou

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Tertiary alcohol ester (TAE) hydrolases are a group of esterases (EC 3.1.1.-) that catalyze the kinetic resolution of TAEs and as a result, they are sought-after for the production of optically pure tertiary alcohols (TAs) which are useful as building blocks for number biologically active compounds. What sets these enzymes apart is, the presence of a GGG(A)X-motif in the active site which appears to be the main reason behind their activity towards the sterically demanding TAEs. The genome of Pseudomonas syringae pv. maculicola (Psm) comprises a multitude of genes that encode esterases. We therefore, hypothesize that some of these genes encode TAE hydrolases. In this study, Psm was screened for TAE hydrolase activity using the linalyl acetate (LA) plate assay and a positive reaction was observed. As a result, the genome of Psm was screened for esterases with a GGG(A)X-motif using the motif search tool and two potential TAE hydrolase genes (PsmEST1 and 2, 1100 and 1000bp, respectively) were identified, PsmEST1 was amplified by PCR and the gene sequenced for confirmation. Analysis of the sequence data with the SingnalP 4.1 server revealed that the protein comprises a signal peptide (22 amino acid residues) on the N-terminus. Primers specific for the gene encoding the mature protein (without the signal peptide) were designed such that they contain NdeI and XhoI restriction sites for directional cloning of the PCR products into pET28a. The gene was expressed in E. coli JM109 (DE3) and the clones screened for TAE hydrolase activity using the LA plate assay. A positive clone was selected, overexpressed and the protein purified using nickel affinity chromatography. The activity of the esterase towards LA was confirmed using thin layer chromatography.

Keywords: hydrolases, tertiary alcohol esters, tertiary alcohols, screening, Pseudomonas syringae pv., maculicola genome, esterase activity, linalyl acetate

Procedia PDF Downloads 355

Authors: Chunxiao Wu, Dongyun Jiang, Tao Jiang, Luxia Xu, Qian Xu, Meng Zhao, Qin Zhu, Zhigang Guo, Jinlan Pan, Suning Chen

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The stability and evolution of human genetics depends on chromosomes (and chromosome-chromosome interactions). We wish to understand the spatial location of chromosomes in dividing cells in order to understand the relationship between chromosome-chromosome interactions and to further investigate the role of chromosomes and their impact on cell biological behavior. In this study, we explored the relative spatial positional relationships of chromosomes [t (9;22) and t (15;17)] in B-ALL cells by using the three-dimensions DNA in situ fluorescent hybridization (3D-FISH) method. The results showed that chromosomes [t (9;22) and t (15;17)] showed relatively stable spatial relationships. The relative stability of the spatial location of chromosomes in dividing cells may be relevant to disease.

Keywords: chromosome, human genetics, chromosome territory, 3D-FISH

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Authors: M.Rüştü Karaman, İsmail İşeri, Kadir Saltalı, A.Reşit Brohi, Ayhan Horuz, Mümin Dizman

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Considerable relations has recently been given to the environmental hazardous caused by agricultural chemicals such as excess fertilizers. In this study, a neural network approach was investigated in the prediction of potential nitrate pollution across different land-use patterns by using a feedforward multilayered computer model of artificial neural network (ANN) with proper training. Periodical concentrations of some anions, especially nitrate (NO3-), and cations were also detected in drainage waters collected from the drain pipes placed in irrigated tomato field, unirrigated wheat field, fallow and pasture lands. The soil samples were collected from the irrigated tomato field and unirrigated wheat field on a grid system with 20 m x 20 m intervals. Site specific nitrate concentrations in the soil samples were measured for ANN based simulation of nitrate leaching potential from the land profiles. In the application of ANN model, a multi layered feedforward was evaluated, and data sets regarding with training, validation and testing containing the measured soil nitrate values were estimated based on spatial variability. As a result of the testing values, while the optimal structures of 2-15-1 was obtained (R2= 0.96, P < 0.01) for unirrigated field, the optimal structures of 2-10-1 was obtained (R2= 0.96, P < 0.01) for irrigated field. The results showed that the ANN model could be successfully used in prediction of the potential leaching levels of nitrate, based on different land use patterns. However, for the most suitable results, the model should be calibrated by training according to different NN structures depending on site specific soil parameters and varied agricultural managements.

Keywords: artificial intelligence, ANN, drainage water, nitrate pollution

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Authors: Jae Woong Sull

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Lipid levels are related to the risk of cardiovascular diseases. Cholesterol ester transfer protein (CETP) gene is one of the candidate genes of cardiovascular diseases. A total of 2,304 persons were chosen from a Hospital (N=4,294) in South Korea. Female subjects with the CG/GG genotype had a 2.03 -fold (p=0.0001) higher risk of having abnormal HDL cholesterol levels (<40 mg/dL) than subjects with the CC genotype. Male subjects with the CG/GG genotype had a 1.34 -fold (p=0.0019) higher risk than subjects with the CC genotype. When analyzed by body mass index, the association with CETP was much stronger in male subjects with BMI>=25.69 (OR=1.55, 95% CI: 1.15-2.07, P=0.0037) than in male lean subjects. When analyzed by family history of diabetes, the association with CETP was much stronger in male subjects with positive family history of low physical activity (OR=4.82, 95% CI: 1.86-12.5, P=0.0012) than in male subjects with negative family history of diabetes. This study clearly demonstrates that genetic variants in CETP influence HDL cholesterol levels in Korean adults.

Keywords: CETP, diabetes, obesity, polymorphisms

Procedia PDF Downloads 143

Authors: Pejman Hosseiniun, Rose Shayeghi, Iman Rahbari, Mohamad Reza Kalhor

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CNFET has emerged as an alternative material to silicon for high performance, high stability and low power SRAM design in recent years. SRAM functions as cache memory in computers and many portable devices. In this paper, a new SRAM cell design based on CNFET technology is proposed. The proposed SRAM cell design for CNFET is compared with SRAM cell designs implemented with the conventional CMOS and FinFET in terms of speed, power consumption, stability, and leakage current. The HSPICE simulation and analysis show that the dynamic power consumption of the proposed 8T CNFET SRAM cell’s is reduced about 48% and the SNM is widened up to 56% compared to the conventional CMOS SRAM structure at the expense of 2% leakage power and 3% write delay increase.

Keywords: SRAM cell, CNFET, low power, HSPICE

Procedia PDF Downloads 414