Search results for: miRNA:mRNA target prediction

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: Mandeep Singh Azad.Dibyendu Chakraborty, Vikas Vohra

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Introduction: Poonch is one of the remotest districts of the Jammu and Kashmir (UT) and situated on international borders. This native poultry population in these areas is quite hardy and thrives well in adverse climatic conditions. Till date, no local breed from this area (Jammu Province) has been characterized thus present study was undertaken with the main objectives of molecular characterization of ChB6 gene in local native chicken of Poonch region located at international borders between India and Pakistan. The chicken B-cell marker (ChB6) gene has been proposed as a candidate gene in regulating B-cell development. Material and Method: RNA was isolated by Blood RNA Purification Kit (HiPura) and Trizol method from whole blood samples. Positive PCR products with size 1110 bp were selected for further purification, sequencing and analysis. The amplified PCR product was sequenced by Sangers dideoxy chain termination method. The obtained sequence of ChB6 gene of Poonchi chicken were compared by MEGAX software. BioEdit software was used to construct phylogenic tree, and Neighbor Joining method was used to infer evolutionary history. In order to compute evolutionary distance Maximum Composite Likelihood method was used. Results: The positively amplified samples of ChB6 genes were then subjected to Sanger sequencing with “Primer Walking. The sequences were then analyzed using MEGA X and BioEdit software. The sequence results were compared with other reported sequence from different breed of chicken and with other species obtained from the NCBI (National Center for Biotechnology Information). ClustalW method using MEGA X software was used for multiple sequence alignment. The sequence results of ChB6 gene of Poonchi chicken was compared with Centrocercus urophasianus, G. gallus mRNA for B6.1 protein, G. gallus mRNA for B6.2, G. gallus mRNA for B6.3, Gallus gallus B6.1, Halichoeres bivittatus, Miniopterus fuliginosus Ferringtonia patagonica, Tympanuchus phasianellus. The genetic distances were 0.2720, 0.0000, 0.0245, 0.0212, 0.0147, 1.6461, 2.2394, 2.0070 and 0.2363 for ChB6 gene of Poonchi chicken sequence with other sequences in the present study respectively. Sequencing results showed variations between different species. It was observed that AT content were higher then GC content for ChB6 gene. The lower AT content suggests less thermostable. It was observed that there was no sequence difference within the Poonchi population for ChB6 gene. The high homology within chicken population indicates the conservation of ChB6 gene. The maximum difference was observed with Miniopterus fuliginosus (Eastern bent-wing bat) followed by Ferringtonia patagonica and Halichoeres bivittatus. Conclusion: Genetic variation is the essential component for genetic improvement. The results of immune related gene Chb6 shows between population genetic variability. Therefore, further association studies of this gene with some prevalent diseases in large population would be helpful to identify disease resistant/ susceptible genotypes in the indigenous chicken population.

Keywords: ChB6, sequencing, ClustalW, genetic distance, poonchi chicken, SNP

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Authors: Steffen Happel

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Non-standard isotopes such as Sc-44/47, Zr-89, and Sn-117mare finding interest is increasing in radiopharmaceutical applications. Methods for the separation of these elements from typical target materials were developed. The methods used in this paper are based on the use of extraction chromatographic resins such as UTEVA, TBP, and DGA resin. Information on the selectivity of the resins (Dw values of selected elements in HCl and HNO3 of varying concentration) will be presented as well as results of the method development such as elution studies, chemical recoveries, and decontamination factors. Developed methods are based on the use of vacuum supported separation allowing for fast and selective separation.

Keywords: elution, extraction chromatography, radiopharmacy, decontamination factors

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Authors: Yijun Shao, Yan Cheng, Rashmee U. Shah, Charlene R. Weir, Bruce E. Bray, Qing Zeng-Treitler

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Deep neural network (DNN) models are being explored in the clinical domain, following the recent success in other domains such as image recognition. For clinical adoption, outcome prediction models require explanation, but due to the multiple non-linear inner transformations, DNN models are viewed by many as a black box. In this study, we developed a deep neural network model for predicting 1-year mortality of patients who underwent major cardio vascular procedures (MCVPs), using temporal image representation of past medical history as input. The dataset was obtained from the electronic medical data warehouse administered by Veteran Affairs Information and Computing Infrastructure (VINCI). We identified 21,355 veterans who had their first MCVP in 2014. Features for prediction included demographics, diagnoses, procedures, medication orders, hospitalizations, and frailty measures extracted from clinical notes. Temporal variables were created based on the patient history data in the 2-year window prior to the index MCVP. A temporal image was created based on these variables for each individual patient. To generate the explanation for the DNN model, we defined a new concept called impact score, based on the presence/value of clinical conditions’ impact on the predicted outcome. Like (log) odds ratio reported by the logistic regression (LR) model, impact scores are continuous variables intended to shed light on the black box model. For comparison, a logistic regression model was fitted on the same dataset. In our cohort, about 6.8% of patients died within one year. The prediction of the DNN model achieved an area under the curve (AUC) of 78.5% while the LR model achieved an AUC of 74.6%. A strong but not perfect correlation was found between the aggregated impact scores and the log odds ratios (Spearman’s rho = 0.74), which helped validate our explanation.

Keywords: deep neural network, temporal data, prediction, frailty, logistic regression model

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Authors: Marimuthu Gurusamy

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In vibration analysis (with accelerometers) of rotating machines with rolling element bearing, the customers are interested to know the failure of the machine well in advance to plan the spare inventory and maintenance. But in real world most of the machines fails before the prediction of vibration analyst or Expert analysis software. Presently the prediction of failure is based on ISO 10816 vibration limits only. But this is not enough to monitor the failure of machines well in advance. Because more than 50% of the machines will fail even the vibration readings are within acceptable zone as per ISO 10816.Hence it requires further detail analysis and different techniques to predict the failure well in advance. In vibration Analysis, the velocity spectrum is used to analyse the root cause of the mechanical problems like unbalance, misalignment and looseness etc. The envelope spectrum are used to analyse the bearing frequency components, hence the failure in inner race, outer race and rolling elements are identified. But so far there is no correlation made between these two concepts. The author used both velocity spectrum and Envelope spectrum to analyse the machine behaviour and bearing condition to correlated the changes in dynamic load (by unbalance, misalignment and looseness etc.) and effect of impact on the bearing. Hence we could able to predict the expected life of the machine and bearings in the rotating equipment (with rolling element bearings). Also we used process parameters like temperature, flow and pressure to correlate with flow induced vibration and load variations, when abnormal vibration occurs due to changes in process parameters. Hence by correlation of velocity spectrum, envelope spectrum and process data with 20 years of experience in vibration analysis, the author could able to predict the rotating Equipment and its component’s deterioration and expected duration for maintenance.

Keywords: vibration analysis, velocity spectrum, envelope spectrum, prediction of deterioration

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

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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

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Authors: Elda Maraj, Shkelqim Kuka

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Coronary heart disease causes many deaths in the world. Unfortunately, this problem will continue to increase in the future. In this paper, a fuzzy logic model to predict coronary heart disease is presented. This model has been developed with seven input variables and one output variable that was implemented for 30 patients in Albania. Here fuzzy logic toolbox of MATLAB is used. Fuzzy model inputs are considered as cholesterol, blood pressure, physical activity, age, BMI, smoking, and diabetes, whereas the output is the disease classification. The fuzzy sets and membership functions are chosen in an appropriate manner. Centroid method is used for defuzzification. The database is taken from University Hospital Center "Mother Teresa" in Tirana, Albania.

Keywords: coronary heart disease, fuzzy logic toolbox, membership function, prediction model

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Authors: Abdullah Al Faruque, Ram Balachandar

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Series of laboratory tests were carried out to study the extent of scour caused by a three-dimensional wall jets exiting from a square cross-section nozzle and into a non-cohesive sand beds. Previous observations have indicated that the effect of the tailwater depth was significant for densimetric Froude number greater than ten. However, the present results indicate that the cut off value could be lower depending on the value of grain size-to-nozzle width ratio. Numbers of equations are drawn out for a better scaling of numerous scour parameters. Also suggested the empirical prediction of scour to predict the scour centre line profile and plan view of scour profile at any particular time.

Keywords: densimetric froude number, jets, nozzle, sand, scour, tailwater, time

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Authors: Emad Alenany, M. Adel El-Baz

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In this paper, the flow of different classes of patients into a hospital is modelled and analyzed by using the queueing network analyzer (QNA) algorithm and discrete event simulation. Input data for QNA are the rate and variability parameters of the arrival and service times in addition to the number of servers in each facility. Patient flows mostly match real flow for a hospital in Egypt. Based on the analysis of the waiting times, two approaches are suggested for improving performance: Separating patients into service groups, and adopting different service policies for sequencing patients through hospital units. The separation of a specific group of patients, with higher performance target, to be served separately from the rest of patients requiring lower performance target, requires the same capacity while improves performance for the selected group of patients with higher target. Besides, it is shown that adopting the shortest processing time and shortest remaining processing time service policies among other tested policies would results in, respectively, 11.47% and 13.75% reduction in average waiting time relative to first come first served policy.

Keywords: queueing network, discrete-event simulation, health applications, SPT

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Authors: Myonghwa Park, Eun Jeong Choi

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Persons with dementia living at home have complex caregiving demands, which can be significant sources of stress for the family caregivers. Thus, the dementia family support program facilitators struggle to provide various health and social services, facing diverse challenges. The purpose of this study was to research the effects of training program for the dementia family support program facilitators on self-efficacy, competence, and target complaints concerning operating their program. We created a training program with systematic contents, which was composed of 10 sessions and we provided the program for the facilitators. The participants were 32 people at 28 community dementia support centers who manage dementia family support programs and they completed quantitative and qualitative self-report questionnaire before and after participating in the training program. For analyzing the data, descriptive statistics were used and with a paired t-test, pretest and posttest scores of self-efficacy, competence, and target complaints were analyzed. We used Statistical Package for the Social Sciences (SPSS) statistics (Version 21) to analyze the data. The average age of the participants was 39.6 years old and the 84.4% of participants were nurses. There were statistically meaningful increases in facilitators’ self-efficacy scores (t = -4.45, p < .001) and competence scores (t = -2.133, p = 0.041) after participating in training program and operating their own dementia family support program. Also, the facilitators’ difficulties in conducting their dementia family support program were decreased which was assessed with target complaints. Especially, the facilitators’ lack of dementia expertise and experience was decreased statistically significantly (t = 3.520, p = 0.002). Findings provided evidence of the benefits of the training program for facilitators to enhance managing dementia family support program by improving the facilitators’ self-efficacy and competence and decreasing their difficulties regarding operating their program.

Keywords: competence, dementia, facilitator, family, self-efficacy, training

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Authors: R. Fardid, R. Coppes

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Introduction: In mediastinal radiotherapy and to a lesser extend also in total-body irradiation (TBI) radiation exposure may lead to development of cardiac diseases. Radiation-induced heart disease is dose-dependent and it is characterized by a loss of cardiac function, associated with progressive heart cells degeneration. We aimed to determine the in-vivo radiation effects on fibronectin, ColaA1, ColaA2, galectin and TGFb1 gene expression levels in left ventricle heart tissues of rats after irradiation. Material and method: Four non-treatment adult Wistar rats as control group (group A) were selected. In group B, 4 adult Wistar rats irradiated to 20 Gy single dose of 150 Mev proton beam locally in heart only. In heart plus lung irradiate group (group C) 4 adult rats was irradiated by 50% of lung laterally plus heart radiation that mentioned in before group. At 8 weeks after radiation animals sacrificed and left ventricle heart dropped in liquid nitrogen for RNA extraction by Absolutely RNA® Miniprep Kit (Stratagen, Cat no. 400800). cDNA was synthesized using M-MLV reverse transcriptase (Life Technologies, Cat no. 28025-013). We used Bio-Rad machine (Bio Rad iQ5 Real Time PCR) for QPCR testing by relative standard curve method. Results: We found that gene expression of fibronectin in group C significantly increased compared to control group, but it was not showed significant change in group B compared to group A. The levels of gene expressions of Cola1 and Cola2 in mRNA did not show any significant changes between normal and radiation groups. Changes of expression of galectin target significantly increased only in group C compared to group A. TGFb1 expressions in group C more than group B showed significant enhancement compared to group A. Conclusion: In summary we can say that 20 Gy of proton exposure of heart tissue may lead to detectable damages in heart cells and may distribute function of them as a component of heart tissue structure in molecular level.

Keywords: gene expression, heart damage, proton irradiation, radiotherapy

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Authors: Liang Zhao, Jili Zhang, Chongquan Zhong

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Energy consumption data, in particular those involving public buildings, are impacted by many factors: the building structure, climate/environmental parameters, construction, system operating condition, and user behavior patterns. Traditional methods for data analysis are insufficient. This paper delves into the data mining technology to determine its application in the analysis of building energy consumption data including energy consumption prediction, fault diagnosis, and optimal operation. Recent literature are reviewed and summarized, the problems faced by data mining technology in the area of energy consumption data analysis are enumerated, and research points for future studies are given.

Keywords: data mining, data analysis, prediction, optimization, building operational performance

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Authors: Hermanto J. M, Mirna Febriani

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Resin acrylic, which is widely used, has the physical properties that can absorb liquids. This can lead to a change in the dimensions of the acrylic resin material. If repeated immersions were done, its strength would be affected. Disinfectant solutions have been widely used to reduce microorganisms both inside and outside the patient's mouth. One of the disinfecting materials that can be used is a clover solution. The purpose of this research is to find the ratio of water absorption of the acrylic resin material of self-cured type, soaked in clover solution for 10 minutes. The results showed that the average value obtained before soaked in clover solution was 0.0692 mg/cm3 and after soaked, in clover solution, the value was 0.090 mg/cm3. The conclusion of this research shows that the values of water sorption of acrylic resin before and after soaked in clover solution is still in ISO standard 1567/2001. Differences in water sorption value of self-cured acrylic resin before and after the immersion are caused by the process of liquid diffusion into the acrylic resin.

Keywords: absorption of fluid, self-cured acrylic resin, soaked, clover solution

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Authors: Omar M. Elmabrouk., Roaa Y. Taha., Najat M. Ebrahim, Sabbreen A. Mohammed

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Power transformers are the most crucial part of power electrical system, distribution and transmission grid. This part is maintained using predictive or condition-based maintenance approach. The diagnosis of power transformer condition is performed based on Dissolved Gas Analysis (DGA). There are five main methods utilized for analyzing these gases. These methods are International Electrotechnical Commission (IEC) gas ratio, Key Gas, Roger gas ratio, Doernenburg, and Duval Triangle. Moreover, due to the importance of the transformers, there is a need for an accurate technique to diagnose and hence predict the transformer condition. The main objective of this technique is to avoid the transformer faults and hence to maintain the power electrical system, distribution and transmission grid. In this paper, the DGA was utilized based on the data collected from the transformer records available in the General Electricity Company of Libya (GECOL) which is located in Benghazi-Libya. The Fuzzy Logic (FL) technique was implemented as a diagnostic approach based on IEC gas ratio method. The FL technique gave better results and approved to be used as an accurate prediction technique for power transformer faults. Also, this technique is approved to be a quite interesting for the readers and the concern researchers in the area of FL mathematics and power transformer.

Keywords: dissolved gas-in-oil analysis, fuzzy logic, power transformer, prediction

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Authors: Cindy Sahera, Sarwono Sutikno

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Rapid technological developments also have negative impacts, namely the increasing criminal cases based on technology or cybercrime. One technique that can be used to conduct cybercrime attacks are phishing email. The issue is whether the user is aware that email can be misused by others so that it can harm the user's own? This research was conducted to measure the susceptibility of selected targets against email abuse. The objectives of this research are measurement of targets’ susceptibility and find vulnerability in email recipient. There are three steps being taken in this research, (1) the information gathering phase, (2) the design phase, and (3) the execution phase. The first step includes the collection of the information necessary to carry out an attack on a target. The next step is to make the design of an attack against a target. The last step is to send phishing emails to the target. The levels of susceptibility are three: level 1, level 2 and level 3. Level 1 indicates a low level of targets’ susceptibility, level 2 indicates the intermediate level of targets’ susceptibility, and level 3 indicates a high level of targets’ susceptibility. The results showed that users who are on level 1 and level 2 more that level 3, which means the user is not too careless. However, it does not mean the user to be safe. There are still vulnerabilities that may occur, such as automatic location detection when opening emails and automatic downloaded malware as user clicks a link in the email.

Keywords: cybercrime, email phishing, susceptibility, vulnerability

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Authors: Prozorkevitch D., Mishurov A., Sokolov K., Karsakov L., Pestrikova L.

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The current body of knowledge about the marine environment and the dynamics of marine ecosystems includes a huge amount of heterogeneous data collected over decades. It generally includes a wide range of hydrological, biological and fishery data. Marine researchers collect these data and analyze how and why the ecosystem changes from past to present. Based on these historical records and linkages between the processes it is possible to predict future changes. Multivariate analysis of trends and their interconnection in the marine ecosystem may be used as an instrument for predicting further ecosystem evolution. A wide range of information about the components of the marine ecosystem for more than 50 years needs to be used to investigate how these arrays can help to predict the future.

Keywords: barents sea ecosystem, abiotic, biotic, data sets, trends, prediction

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Authors: Yasir E. Mohieldeen

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Qatar is one of the most water scarce countries in the World. In 2014, the average per capita rainfall was less than 29 m3/y/ca, while the global average is 6,000 m3/y/ca. However, the per capita water consumption in Qatar is among the highest in the World: more than 500 liters per person per day, whereas the global average is 160 liters per person per day. Since the early 2000s, Qatar has been relying heavily on desalinated water from the Arabian Gulf as the main source of fresh water. In 2009, about 99.9% of the total potable water produced was desalinated. Reliance on desalinated water makes Qatar very vulnerable to water related natural disasters, such as the red-tide phenomenon. Qatar’s strategic water reserve lasts for only 7 days. In case of red-tide outbreak, the country would not be able to desalinate water for days, let alone the months that this disaster would bring about (as it clogs the desalination equipment). The 2008-09 red-tide outbreak, for instance, lasted for more than eight months and forced the closure of desalination plants in the region for weeks. This study aims at identifying favorite conditions for red-tide outbreaks, using satellite data along with in-situ measurements. This identification would allow the prediction of these outbreaks and their hotspots. Prediction and monitoring of outbreaks are crucial to water security in the country, as different measures could be put in place in advance to prevent an outbreak and mitigate its impact if it happened. Red-tide outbreaks are detected using different algorithms for chlorophyll concentration in the Gulf waters. Vegetation indices, such as Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) were used along with Surface Algae Bloom Index (SABI) to detect known outbreaks. MODIS (or Moderate Resolution Imaging Spectroradiometer) bands are used to calculate these indices. A red-tide outbreaks atlas in the Arabian Gulf is being produced. Prediction of red-tide outbreaks ahead of their occurrences would give critical information on possible water-shortage in the country. Detecting known outbreaks in the past few decades and related parameters (e.g. water salinity, water surface temperature, nutrition, sandstorms, … etc) enables the identification of favorite conditions of red-tide outbreak that are key to the prediction of these outbreaks.

Keywords: Arabian Gulf, MODIS, red-tide detection, strategic water reserve, water desalination

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Authors: Dariush Jafari, S. Mostafa Nowee

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In this study a ternary system containing sodium chloride as solute, water as primary solvent and ethanol as the antisolvent was considered to investigate the application of artificial neural network (ANN) in prediction of sodium solubility in the mixture of water as the solvent and ethanol as the antisolvent. The system was previously studied using by Extended UNIQUAC model by the authors of this study. The comparison between the results of the two models shows an excellent agreement between them (R2=0.99), and also approves the capability of ANN to predict the thermodynamic behavior of ternary electrolyte systems which are difficult to model.

Keywords: thermodynamic modeling, ANN, solubility, ternary electrolyte system

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Authors: Yujiang Wu

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As electrical machine (e-machine) power density re-querulents become more stringent in vehicle electrification, mounting a temperature sensor for e-machine stator windings becomes increasingly difficult. This can lead to higher manufacturing costs, complicated harnesses, and reduced reliability. In this paper, we propose a deep-learning method for predicting electric machine winding temperature, which can either replace the sensor entirely or serve as a backup to the existing sensor. We compare the performance of our method, the stateful long short-term memory networks (LSTM) with truncated backpropagation through time (TBTT), with that of linear regression, as well as stateless LSTM with/without residual connection. Our results demonstrate the strength of combining stateful LSTM and TBTT in tackling nonlinear time series prediction problems with long sequence lengths. Additionally, in industrial applications, high-temperature region prediction accuracy is more important because winding temperature sensing is typically used for derating machine power when the temperature is high. To evaluate the performance of our algorithm, we developed a temperature-stratified MSE. We propose a simple but effective data preprocessing trick to improve the high-temperature region prediction accuracy. Our experimental results demonstrate the effectiveness of our proposed method in accurately predicting winding temperature, particularly in high-temperature regions, while also reducing manufacturing costs and improving reliability.

Keywords: deep learning, electrical machine, functional safety, long short-term memory networks (LSTM), thermal management, time series prediction

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Authors: A. J. Regan, F. J. Lidgey, M. Betteridge, P. Georgiou, C. Toumazou, K. Hayatleh, J. R. Dibble

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Our goal is development of an algorithm capable of predicting the directional trend of the Standard and Poor’s 500 index (S&P 500). Extensive research has been published attempting to predict different financial markets using historical data testing on an in-sample and trend basis, with many authors employing excessively complex mathematical techniques. In reviewing and evaluating these in-sample methodologies, it became evident that this approach was unable to achieve sufficiently reliable prediction performance for commercial exploitation. For these reasons, we moved to an out-of-sample strategy based on linear regression analysis of an extensive set of financial data correlated with historical closing prices of the S&P 500. We are pleased to report a directional trend accuracy of greater than 55% for tomorrow (t+1) in predicting the S&P 500.

Keywords: financial algorithm, GPU, S&P 500, stock market prediction

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Authors: Badawia Ibrahim, Iman Hussein, Samar El Sheikh, Fatma Abou Elkasem, Hazem Abo Ismael

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Background: Response of breast carcinoma to neoadjuvant chemotherapy (NAC) varies regarding many factors including hormonal receptor status. Breast cancer is a heterogenous disease with different outcomes, hence a need arises for new markers predicting the outcome of NAC especially for the triple negative group when estrogen, progesterone receptors and Her2/neu are negative. FOXP3 is a promising target with unclear role. Aim: To examine the value of FOXP3 expression in locally advanced triple negative breast cancer tumoral cells as well as tumor infiltrating lymphocytes (TILs) and to elucidate its relation to the extent of NAC response. Material and Methods: Forty five cases of immunohistochemically confirmed to be triple negative breast carcinoma were evaluated for NAC (Doxorubicin, Cyclophosphamide AC x 4 cycles + Paclitaxel x 12 weeks, patients with ejection fraction less than 60% received Taxotere or Cyclophosphamide, Methotrexate, Fluorouracil CMF) response in both tumour and lymph nodes status according to Miller & Payne's and Sataloff's systems. FOXP3 expression in tumor as well as TILs evaluated in the pretherapy biopsies was correlated with NAC response in breast tumor and lymph nodes as well as other clinicopathological factors. Results: Breast tumour cells showed FOXP3 positive cytoplasmic expression in (42%) of cases. High FOXP3 expression percentage was detected in (47%) of cases. High infiltration by FOXP3+TILs was detected in (49%) of cases. Positive FOXP3 expression was associated with negative lymph node metastasis. High FOXP3 expression percentage and high infiltration by FOXP3+TILs were significantly associated with complete therapy response in axillary lymph nodes. High FOXP3 expression in tumour cells was associated with high infiltration by FOXP3+TILs. Conclusion: This result may provide evidence that FOXP3 marker is a good prognostic and predictive marker for triple negative breast cancer (TNBC) indicated for neoadjuvant chemotherapy and can be used for stratifications of TNBC cases indicated for NAC. As well, this study confirmed the fact that the tumour cells and the surrounding microenvironment interact with each other and the tumour microenvironment can influence the treatment outcomes of TNBC.

Keywords: breast cancer, FOXP3 expression, prediction of neoadjuvant chemotherapy effect, triple negative

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Authors: Masoud Hassanzade Novin, Bahloul Salmani

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The process of the translation is not merely the linguistic aspects. It is also considered in the cultural framework of both the source and target text cultures. The translation process and translated texts are confronted the new aspect in 20th century which is considered mostly in the patronage framework and ideological grillwork of the target language. To have these factors scrutinized in the process of the translation both micro-element factors and macro-element factors can be taken into consideration. For the purpose of this study through a qualitative type of research based on critical discourse analysis approach, the case study of the novel “1984” written by George Orwell was chosen as the corpus of the study to have the contrastive analysis by its Persian translated texts. Results of the study revealed some distortions embedded in the target texts which were overshadowed by ideological aspect and patronage network. The outcomes of the manipulated terms were different in various categories which revealed the manipulation aspects in the texts translated.

Keywords: critical discourse analysis, ideology, patronage network, translated texts

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Authors: Emanuela Chiarella, Clelia Nisticò, Nicola Lombardo, Giovanna Lucia Piazzetta, Nadia Lobello, Maria Mesuraca

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Killian’s Antrochoanal Polyp is a benign lesion of the maxillary sinus characterized by unilateral nasal obstruction, pus discharge, and headache. It affects, more commonly children and young adults. Although its etiology still remains unclear, chronic inflammation, autoreactivity, allergies, and viral infections are strongly associated with its formation and development, resulting in nasal tissue remodeling. We aimed to investigate the stem cells components which reside in this pathological tissue. In particular, we adopted a protocol for the isolation and culturing of mesenchymal stem cells from surgical biopsies of three Killian nasal polyp patients (KNP-MSCs) as well as from their healthy nasal tissue (HNT-MSCs) that were used as controls. The immunophenotype profile of HNT-MSCs and KNP-MSCs was more similar, with a marked positivity for CD73, CD90, and CD105 expression, while being negative for CD34 and CD14 haematopoietic genes. Cell proliferation assay showed that KNP-MSCs had a replicative disadvantage compared to HNT-MSCs, as evidenced by the significantly lower number of cells in the S-phase of the cell cycle. KNP-MSCs also took longer to close a wound than HNT-MSCs, indicating a partial epithelial phenotype in which low levels of ICAM-1 mRNA and a significant increase in E-CAD transcript were detectable. Subsequently, the differentiation potential of both MSCs populations was analyzed by inducing osteoblastic or adipocyte differentiation for up to 20 days. KNP-MSCs showed the ability to differentiate into osteoblasts, although ALP activity as well as the number and size of calcium deposits were lower than osteogenic induced-HNT-MSCs. Also, mRNA levels of osteoblastic marker genes (OCN, OPN, OSX, RUNX2) resulted lower compared to control cell population. Instead, the analysis of the adipogenic differentiation potential showed a similar behavior between KNP-MSCs and HNT-MSCs considering that the amount of lipid droplets, the expression of adipocyte-specific genes (FABP4, AdipoQ, PPARγ2, LPL) and the content of triacylglycerols were almost overlapping. Taken together, these results first demonstrated that Killian's nasal polyp is a source of mesenchymal stem cells with self-renewal and multi-differentiative capabilities.

Keywords: Mesenchymal stem cells, adipogenic differentiation, osteogenic differentiation, EMT

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Authors: Jong Won, Park, Sung Hyun, Kim

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The kinetic energy to pass subjected to shock and chisel reciprocating piston hydraulic power supplied by the excavator using for the purpose of crushing the rock, and roads, buildings, etc., hydraulic breakers blow. Impact frequency, efficiency measurement of the impact energy, hydraulic breakers, to demonstrate the ability of hydraulic breaker manufacturers and users to a very important item. And difficult in order to confirm the initial performance degradation in the life of the hydraulic breaker has been thought to be a problem.In this study, we measure the efficiency of hydraulic breaker, Impact energy and Impact frequency, the degradation analysis of research to predict the life.

Keywords: impact energy, impact frequency, hydraulic breaker, life prediction

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Authors: Deepak Raj Bhat, Ryuichi Yatabe

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In this study, a residual-state creep failure model was developed based on the residual-state creep test results of clayey soils. To develop the proposed model, the regression analyses were done by using the R. The model results of the failure time (tf) and critical displacement (δc) were compared with experimental results and found in close agreements to each others. It is expected that the proposed regression model for residual-state creep failure will be more useful for the prediction of displacement of different clayey soils in the future.

Keywords: regression model, residual-state creep failure, displacement prediction, clayey soils

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Authors: Nuwan Kuruwitaarachchi, M. K. M. Peiris, C. N. Madawala, K. M. A. R. Perera, V. U. N Perera

Abstract:

Dealing with businesses with the foreign market always took a special place in a country’s economy. Political and social factors came into play making currency rate changes fluctuate rapidly. Currency rate prediction has become an important factor for larger international businesses since large amounts of money exchanged between countries. This research focuses on comparing the accuracy of mainly three models; Autoregressive Integrated Moving Average (ARIMA), Artificial Neural Networks(ANN) and Support Vector Machines(SVM). series of data import, export, USD currency exchange rate respect to LKR has been selected for training using above mentioned algorithms. After training the data set and comparing each algorithm, it was able to see that prediction in SVM performed better than other models. It was improved more by combining SVM and SVR models together.

Keywords: ARIMA, ANN, FFNN, RMSE, SVM, SVR

Procedia PDF Downloads 185

Authors: Shayan Rezvanigilkolaei, Shayesteh Vefaghnematollahi

Abstract:

Electrical impedance tomography is a non-invasive medical imaging technique suitable for medical applications. This paper describes an electrical impedance tomography device with the ability to navigate a robotic arm to manipulate a target object. The design of the device includes various hardware and software sections to perform medical imaging and control the robotic arm. In its hardware section an image is formed by 16 electrodes which are located around a container. This image is used to navigate a 3DOF robotic arm to reach the exact location of the target object. The data set to form the impedance imaging is obtained by having repeated current injections and voltage measurements between all electrode pairs. After performing the necessary calculations to obtain the impedance, information is transmitted to the computer. This data is fed and then executed in MATLAB which is interfaced with EIDORS (Electrical Impedance Tomography Reconstruction Software) to reconstruct the image based on the acquired data. In the next step, the coordinates of the center of the target object are calculated by image processing toolbox of MATLAB (IPT). Finally, these coordinates are used to calculate the angles of each joint of the robotic arm. The robotic arm moves to the desired tissue with the user command.

Keywords: electrical impedance tomography, EIT, surgeon robot, image processing of electrical impedance tomography

Procedia PDF Downloads 261

Authors: Hassan Baji, Chun-Qing Li, Wei Yang

Abstract:

Water seepage is one of the most common causes of damage in tunnel structures, which can cause direct and indirect e.g. reinforcement corrosion and calcium leaching damages. Estimation of water seepage or inflow is one of the main challenges in probabilistic assessment of tunnels. The methodology proposed in this study is an attempt for mathematically modeling the water seepage in tunnel structures and further predicting its service life. Using the time-dependent reliability, water seepage is formulated as a failure mode, which can be used for prediction of service life. Application of the formulated seepage failure mode to a case study tunnel is presented.

Keywords: water seepage, tunnels, time-dependent reliability, service life

Procedia PDF Downloads 460

Authors: Fitri CaturLestari

Abstract:

According to National Population and Family Planning Board (BKKBN), demographic bonus will occur in 2025-2035, when the number of people within the productive age bracket is higher than the number of elderly people and children. This time will be a gold moment for Indonesia to achieve maximum productivity and prosperity. But it will be a demographic bomb if it isn’t balanced by economic and social aspect considerations. Therefore it is important to make a prediction mapping of all provinces in Indonesia whether in demographic bomb or bonus condition after 100 years Indonesian independence. The purpose of this research were to make the demographic mapping based on the economic and social aspects of the provinces in Indonesia and categorizing them into demographic bomb and bonus condition. The research data are gained from Statistics Indonesia (BPS) as the secondary data. The multiregional component method, regression and quadrant analysis were used to predict the number of people, economic growth, Human Development Index (HDI), and gender equality in education and employment. There were different characteristic of provinces in Indonesia from economic aspect and social aspect. The west Indonesia was already better developed than the east one. The prediction result, many provinces in Indonesia will get demographic bonus but the others will get demographic bomb. It is important to prepare particular strategy to particular provinces with all of their characteristic based on the prediction result so the demographic bomb can be minimalized.

Keywords: demography, economic growth, gender, HDI

Procedia PDF Downloads 314

Authors: Senol Dogan, Gunay Karli

Abstract:

Identification of relevant publications based on a Medline query is time-consuming and error-prone. An all based process has the potential to solve this problem without any manual work. To the best of our knowledge, our study is the first to investigate the ability of machine learning to identify relevant articles accurately. 5 different machine learning algorithms were tested using 23 predictors based on several metadata fields attached to publications. We find that the Boosted model is the best-performing algorithm and its overall accuracy is 96%. In addition, specificity and sensitivity of the algorithm is 97 and 93%, respectively. As a result of the work, we understood that we can apply the same procedure to understand cancer gene expression big data.

Keywords: prediction of publications, machine learning, algorithms, bariatric surgery, comparison of algorithms, boosted, tree, logistic regression, ANN model

Procedia PDF Downloads 192