Search results for: pathogen propagation

Authors: Marwan Khalil Qader, Arshad Mohammad Abdullah

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Antimicrobial resistance is a major cause of significant morbidity and mortality globally. Seven plant extracts (Plantago mediastepposa, Quercusc infectoria, Punic granatum, Thymus lcotschyana, Ginger officeinals, Rhus angustifolia and Cinnamon) were collected from different regions of Kurdistan region of Iraq. These plants’ extracts were dissolved in absolute ethanol and distillate water, after which they were assayed in vitro as an antimicrobial activity against Candida tropicalis, Candida albicanus, Candida dublinensis, Candida krusei and Candida glabrata also against 2 Gram-positive (Bacillus subtilis and Staphylococcus aureus) and 3 Gram-negative bacteria (Escherichia coli, Pseudomonas aeruginosa and Klebsilla pneumonia). The antimicrobial activity was determined in ethanol extracts and distilled water extracts of these plants. The ethanolic extracts of Q. infectoria showed the maximum activity against all species of Candida fungus. The minimum inhibition zone of the Punic granatum ethanol extracts was 0.2 mg/ml for all microorganisms tested. Klebsilla pneumonia was the most sensitive bacterial strain to Quercusc infectoria and Rhus angustifolia ethanol extracts. Among both Gram-positive and Gram-negative bacteria tested with MIC of 0.2 mg/ml, the minimum inhibition zone of Ginger officeinals D. W. extracts was 0.2 mg/mL against Pseudomonas aeruginosa and Klebsilla pneumonia. The most sensitive bacterial strain to Thymus lcotschyana and Plantago mediastepposa D.W. extracts was S. aureus and E. coli.

Keywords: antimicrobial activity, pathogenic bacteria, plant extracts, chemical systems engineering

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Authors: Riznaldi Akbar

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In this study, we compared the performance of the Artificial Neural Network (ANN) model with back-propagation algorithm in correctly predicting in-sample and out-of-sample external debt crisis in Indonesia. We found that exchange rate, foreign reserves, and exports are the major determinants to experiencing external debt crisis. The ANN in-sample performance provides relatively superior results. The ANN model is able to classify correctly crisis of 89.12 per cent with reasonably low false alarms of 7.01 per cent. In out-of-sample, the prediction performance fairly deteriorates compared to their in-sample performances. It could be explained as the ANN model tends to over-fit the data in the in-sample, but it could not fit the out-of-sample very well. The 10-fold cross-validation has been used to improve the out-of-sample prediction accuracy. The results also offer policy implications. The out-of-sample performance could be very sensitive to the size of the samples, as it could yield a higher total misclassification error and lower prediction accuracy. The ANN model could be used to identify past crisis episodes with some accuracy, but predicting crisis outside the estimation sample is much more challenging because of the presence of uncertainty.

Keywords: debt crisis, external debt, artificial neural network, ANN

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Authors: Z. Olczykowski

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The paper presents the impact of work on the electric arc furnace. Arc equipment is one of the largest receivers powered by the power system. Electric arc disturbances arising during melting process occurring in these furnaces are the cause of an abrupt change of the passive power of furnaces. Currents drawn by these devices undergo an abrupt change, which in turn cause voltage fluctuations and light flicker. The quantitative evaluation of the voltage fluctuations is now the basic criterion of assessment of an influence of unquiet receiver on the supplying net. The paper presents the method of determination of range of voltage fluctuations and light flicker at parallel operation of arc devices. The results of measurements of voltage fluctuations and light flicker indicators recorded in power supply networks of steelworks were presented, with different number of parallel arc devices. Measurements of energy quality parameters were aimed at verifying the proposed method in practice. It was also analyzed changes in other parameters of electricity: the content of higher harmonics, asymmetry, voltage dips.

Keywords: power quality, arc furnaces, propagation of voltage fluctuations, disturbances

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Authors: Hao-Hsiang Ku, Ching-Ho Chi

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Big data applications have become an imperative for many fields. Many researchers have been devoted into increasing correct rates and reducing time complexities. Hence, the study designs and proposes an Ontology-based backpropagation neural network classification and reasoning strategy for NoSQL big data applications, which is called ON4NoSQL. ON4NoSQL is responsible for enhancing the performances of classifications in NoSQL and SQL databases to build up mass behavior models. Mass behavior models are made by MapReduce techniques and Hadoop distributed file system based on Hadoop service platform. The reference engine of ON4NoSQL is the ontology-based backpropagation neural network classification and reasoning strategy. Simulation results indicate that ON4NoSQL can efficiently achieve to construct a high performance environment for data storing, searching, and retrieving.

Keywords: Hadoop, NoSQL, ontology, back propagation neural network, high distributed file system

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Authors: Souhir Ben Amor, Heni Boubaker, Lotfi Belkacem

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This aims of this paper is to forecast the electricity spot prices. First, we focus on modeling the conditional mean of the series so we adopt a generalized fractional -factor Gegenbauer process (k-factor GARMA). Secondly, the residual from the -factor GARMA model has used as a proxy for the conditional variance; these residuals were predicted using two different approaches. In the first approach, a local linear wavelet neural network model (LLWNN) has developed to predict the conditional variance using the Back Propagation learning algorithms. In the second approach, the Gegenbauer generalized autoregressive conditional heteroscedasticity process (G-GARCH) has adopted, and the parameters of the k-factor GARMA-G-GARCH model has estimated using the wavelet methodology based on the discrete wavelet packet transform (DWPT) approach. The empirical results have shown that the k-factor GARMA-G-GARCH model outperform the hybrid k-factor GARMA-LLWNN model, and find it is more appropriate for forecasts.

Keywords: electricity price, k-factor GARMA, LLWNN, G-GARCH, forecasting

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Authors: Mohsin Ali Shaikh, Song Weiguo, Muhammad Kashan Surahio, Usman Shahid, Rehmat Karim

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This study investigated a catastrophic arson fire incident that occurred at a textile garment building in Karachi, Pakistan. Unfortunately, a catastrophic event led to the loss of 262 lives and caused 55 severe injuries. The primary objective is to analyze the aspects of the fire incident and understand the causes of arson fire disasters. The study utilized Fire Dynamic Simulation (F.D.S) was employed to simulate fire propagation, visibility, harmful gas concentration, fire temperature, and numerical results. The analysis report has determined the specific circumstances that created the unpleasant incident in the present study. The significance of the current findings lies in their potential to prevent arson fires, improve fire safety measures, and the development of safety plans in building design. The fire dynamic simulation findings can serve as a theoretical basis for the investigation of arson fires and evacuation planning in textile garment buildings.

Keywords: investigation, fire arson incident, textile garment, fire dynamic simulation (FDS)

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Authors: Yi-Hui Lai, Chih-Hsiang Yang, Li-Chung Hsu

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The inflammasome is a protein complex that modulates caspase-1 activity, resulting in proteolytic cleavage of proinflammatory cytokines such as IL-1β and IL-18, into their bioactive forms. It has been shown that the inflammasomes play a crucial role in the clearance of pathogenic infection and tissue repair. However, dysregulated inflammasome activation contributes to a wide range of human diseases such as cancers and auto-inflammatory diseases. Yet, regulation of NLRP3 inflammasome activation remains largely unknown. We discovered a novel DEAD box protein, whose biological function has not been reported, not only negatively regulates NLRP3 inflammasome activation by interfering NLRP3 inflammasome assembly and cellular localization but also mitigate pyroptosis upon pathogen evasion. The DEAD-box protein is the first DEAD-box protein gets involved in modulation of the inflammasome activation. In our study, we found that caspase-1 activation and mature IL-1β production were largely enhanced upon LPS challenge in the DEAD box-containing protein- deleted THP-1 macrophages and bone marrow-derived macrophages (BMDMs). In addition, this DEAD box-containing protein migrates from the nucleus to the cytoplasm upon LPS stimulation, which is required for its inhibitory role in NLRP3 inflammasome activation. The DEAD box-containing protein specifically interacted with the LRR motif of NLRP3 via its DEAD domain. Furthermore, due to the crucial role of the NLRP3 LRR domain in the recruitment of NLRP3 to mitochondria and binding to its adaptor ASC, we found that the interaction of NLRP3 and ASC was downregulated in the presence of the DEAD box-containing protein. In addition to the mechanical study, we also found that this DEAD box protein protects host cells from inflammasome-triggered cell death in response to broad-ranging pathogens such as Candida albicans, Streptococcus pneumoniae, etc., involved in nosocomial infections and severe fever shock. Collectively, our results suggest that this novel DEAD box molecule might be a key therapeutic strategy for various infectious diseases.

Keywords: inflammasome, inflammation, innate immunity, pyroptosis

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Authors: M. Chaichanyut, S. Tungjitkusolmun

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This paper is concerned with microwave (MW) ablation for a liver cancer tissue by using helix antenna. The antenna structure supports the propagation of microwave energy at 2.45 GHz. A 1½ turn spiral catheter-based microwave antenna applicator has been developed. We utilize the three-dimensional finite element method (3D FEM) simulation to analyze where the tissue heat flux, lesion pattern and volume destruction during MW ablation. The configurations of helix antenna where Helix air-core antenna and Helix Dielectric-core antenna. The 3D FEMs solutions were based on Maxwell and bio-heat equations. The simulation protocol was power control (10 W, 300s). Our simulation result, both helix antennas have heat flux occurred around the helix antenna and that can be induced the temperature distribution similar (teardrop). The region where the temperature exceeds 50°C the microwave ablation was successful (i.e. complete destruction). The Helix air-core antenna and Helix Dielectric-core antenna, ablation zone or axial ratios (Widest/length) were respectively 0.82 and 0.85; the complete destructions were respectively 4.18 cm³ and 5.64 cm³.

Keywords: liver cancer, Helix antenna, finite element, microwave ablation

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Authors: Ali Raza, Shafiq Ur Rehman Khan, Raja Sher Afgun Usmani, Asif Raza, Basit Umair

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Detection of Fake news and missing information is gaining popularity, especially after the advancement in social media and online news platforms. Social media platforms are the main and speediest source of fake news propagation, whereas online news websites contribute to fake news dissipation. In this study, we propose a framework to detect fake news using the temporal features of text and consider user feedback to identify whether the news is fake or not. In recent studies, the temporal features in text documents gain valuable consideration from Natural Language Processing and user feedback and only try to classify the textual data as fake or true. This research article indicates the impact of recurring and non-recurring events on fake and true news. We use two models BERT and Bi-LSTM to investigate, and it is concluded from BERT we get better results and 70% of true news are recurring and rest of 30% are non-recurring.

Keywords: natural language processing, fake news detection, machine learning, Bi-LSTM

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Authors: Okafor Joan, Nwodo Uchechukwu

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Klebsiella pneumoniae (K. pneumoniae) is a significant pathogen responsible for opportunistic and nosocomial infection. One of the most significant antibiotic resistance mechanisms in K. pneumoniae isolates is efflux pumps. Our current study identified efflux genes (AcrAB, OqxAB, MacAB, and TolC) and regulatory genes (RamR and RarA) in multidrug-resistant (MDR) K. pneumoniae isolated from hospital effluents and investigated their relationship with antibiotic resistance. The sum of 145 K. pneumoniae isolates was established by PCR and screened for antibiotic susceptibility. PCR detected efflux pump genes, and their link with antibiotic resistance was statistically examined. However, 120 (83%) of the confirmed isolated were multidrug-resistant, with the largest percentage of resistance to ampicillin (88.3%) and the weakest rate of resistance to imipenem (5.5%). Resistance to the other antibiotics ranged from 11% to 76.6%. Molecular distribution tests show that AcrA, AcrB, MacA, oqxB oqxA, TolC, MacB were detected in 96.7%, 85%, 76.7%, 70.8%, 55.8%, 39.1%, and 29.1% respectively. However, 14.3% of the isolates harboured all seven genes screened. Efflux pump system AcrAB (83.2%) was the most commonly detected in K. pneumonia isolated across all the antibiotics class-tested. In addition, the frequencies of RamR and RarA were 46.2% and 31.4%, respectively. AcrAB and OqxAB efflux pump genes were significantly associated with fluoroquinolone, beta-lactam, carbapenem, and tetracycline resistance (p<0.05). The high rate of efflux genes in this study demonstrated that this resistance mechanism is the dominant way in K. pneumoniae isolates. Appropriate treatment must be used to reduce and tackle the burden of resistant Klebsiella pneumonia in hospital wastewater effluents.

Keywords: Klebsiella pneumonia, efflux pumps, regulatory genes, multidrug-resistant, hospital, PCR

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Authors: Sharad Shrivastava, Arun Jalan

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In this study tensile testing was performed on randomly oriented short glass fiber/epoxy resin composite specimens which were prepared using hand lay-up method. Samples were tested over a wide range of strain rate/loading rate from 2mm/min to 40mm/min to see the effect on ultimate tensile strength of the composite. A multi layered 'back propagation artificial neural network of supervised learning type' was used to analyze and predict the tensile properties with strain rate and temperature as given input and output as UTS to predict. Various network structures were designed and investigated with varying parameters and network sizes, and an optimized network structure was proposed to predict the UTS of short glass fiber/epoxy resin composite specimens with reasonably good accuracy.

Keywords: glass fiber composite, mechanical properties, strain rate, artificial neural network

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Authors: Ashkan Zakaryazad, Ekrem Duman

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A typical classification technique ranks the instances in a data set according to the likelihood of belonging to one (positive) class. A credit card (CC) fraud detection model ranks the transactions in terms of probability of being fraud. In fact, this approach is often criticized, because firms do not care about fraud probability but about the profitability or costliness of detecting a fraudulent transaction. The key contribution in this study is to focus on the profit maximization in the model building step. The artificial neural network proposed in this study works based on profit maximization instead of minimizing the error of prediction. Moreover, some studies have shown that the back propagation algorithm, similar to other gradient–based algorithms, usually gets trapped in local optima and swarm-based algorithms are more successful in this respect. In this study, we train our profit maximization ANN using the Migrating Birds optimization (MBO) which is introduced to literature recently.

Keywords: neural network, profit-based neural network, sum of squared errors (SSE), MBO, gradient descent

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Authors: A. L. Qureshi, A. A. Mahessar, A. Baloch

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Flood wave propagation in river channel flow can be enunciated by nonlinear equations of motion for unsteady flow. However, it is difficult to find analytical solution of these complex non-linear equations. Hence, verification of the numerical model should be carried out against field data and numerical predictions. This paper presents the verification of developed finite element model applying for unsteady flow in the open channels. The results of a proposed model indicate a good matching with both Preissmann scheme and HEC-RAS model for a river reach of 29 km at both sites (15 km from upstream and at downstream end) for discharge hydrographs. It also has an agreeable comparison with the Preissemann scheme for the flow depth (stage) hydrographs. The proposed model has also been applying to forecast daily discharges at 400 km downstream from Sukkur barrage, which demonstrates accurate model predictions with observed daily discharges. Hence, this model may be utilized for predicting and issuing flood warnings about flood hazardous in advance.

Keywords: finite element method, Preissmann scheme, HEC-RAS, flood forecasting, Indus river

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Authors: Muhammad Rosli Abdullah, Noor Hasmiza Harun

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A microorganism detection system has a potential to be used with the advancement in a biosensor development. The detection system requires an optical sensing system, microfluidic device and biological reagent. Although, the biosensors are available in the market, a label free and a lab-on-chip approach will promote a flexible solution. As a preliminary study of microorganism detection, three mechanisms such as Total Internal Reflection (TIR), Micro Fluidic Channel (MFC) and magnetic-electric field propagation were study and simulated. The objective are to identify the TIR angle, MFC parabolic flow and the wavelength for the microorganism detection. The simulation result indicates that evanescent wave is achieved when TIR angle > 42°, the corner and centre of a parabolic velocity are 0.02 m/s and 0.06 m/s respectively, and a higher energy distribution of a perfect electromagnetic scattering with dipole resonance radiation occurs at 500 nm. This simulation is beneficial to determine the components of the microorganism detection system that does not rely on classical microbiological, immunological and genetic methods which are laborious, time-consuming procedures and confined to specialized laboratories with expensive instrumentation equipment.

Keywords: microorganism, microfluidic, total internal reflection, lab on chip

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Authors: Olumayowa M. Olowe, Michael D. Asemoloye Odunayo J. Olawuyi, Hilda Vasanthakaalam

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Poor management of maize ear rot caused by fungal infection in Nigeria affected the quantity and quality of maize. This study, therefore, aims at characterizing and controlling Fusarium strains using arbuscular mycorrhizal fungi. Maize ear showing rot symptoms were obtained from some selected farms located at Ilesa East and West using random sampling technique. Isolation of Fusarium pathogen from infected maize grain was done using direct pour plate method on potato dextrose agar (PDA) and was characterized based on morphological and molecular ITS-amplification methods. The reaction of PVASYN8F2, T2LCOMP1STR SYN-W-1, and T2LCOMP4 maize varieties, to the Fusarium ear rot pathogens and biocontrol efficacy of the mycorrhizal fungi were assessed on growth, yield, agronomic parameters and symptoms observed. The strains; olowILH1 and olowILH2 identified as Fusarium napiforme were the most dominant and virulent pathogens associated with the maize. They showed genetic similarity with documented ear rot pathogens on NCBI with accession numbers Fusarium proliferatum KT224027, KT224023, and Fusarium sp AY237110. They both exhibited varying inhibitory effects on the three maize varieties compare to control (uninfected plant) which had better growth characteristics. It was also observed that strain olowILH1 was more virulent than olowILH2. T2LCOMP4 was generally more susceptible to both fungal strains compared to the other two maize (T2LCOMP1STR SYN-W-1 and T2LCOMP4 ). In all, strain olowILH1 was more virulent than olowILH2, and Glomus clarum had higher inhibitory pathogenic effect against Fusarium strains compared to G. deserticola.

Keywords: arbuscular mycorrhizal fungi, disease management, Fusarium strains, identification

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Authors: Kassoul Ilyes

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The field of error correcting codes has been revolutionized by the introduction of iteratively decoded codes. Among these, LDPC codes are now a preferred solution thanks to their remarkable performance and low complexity. The binary version of LDPC codes showed even better performance, although it’s decoding introduced greater complexity. This thesis studies the performance of binary LDPC codes using simplified weighted decisions. Information is transported between a transmitter and a receiver by digital transmission systems, either by propagating over a radio channel or also by using a transmission medium such as the transmission line. The purpose of the transmission system is then to carry the information from the transmitter to the receiver as reliably as possible. These codes have not generated enough interest within the coding theory community. This forgetfulness will last until the introduction of Turbo-codes and the iterative principle. Then it was proposed to adopt Pearl's Belief Propagation (BP) algorithm for decoding these codes. Subsequently, Luby introduced irregular LDPC codes characterized by a parity check matrix. And finally, we study simplifications on binary LDPC codes. Thus, we propose a method to make the exact calculation of the APP simpler. This method leads to simplifying the implementation of the system.

Keywords: LDPC, parity check matrix, 5G, BER, SNR

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Authors: W. Badla

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A numerical investigation has been carried out to examine the behaviour of reinforced concrete slabs to uniform blast loading. The aim of this work is to determine the effects of various parameters on the results. Finite element simulations were performed in the non linear dynamic range using an elasto-plastic damage model. The main parameters considered are: the negative phase of blast loading, time duration, equivalent weight of TNT, distance of the explosive and slab dimensions. Numerical modelling has been performed using ABAQUS/Explicit. The results obtained in terms of displacements and propagation of damage show that the above parameters influence considerably the nonlinear dynamic behaviour of reinforced concrete slabs under uniform blast loading.

Keywords: blast loading, reinforced concrete slabs, elasto-plastic damage model, negative phase, time duration, equivalent weight of TNT, explosive distance, slab dimensions

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Authors: M. M. Hlakola, E. Golovins, D. V. Nicolae

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The field of instrumentation electronics is undergoing an explosive growth, due to its wide range of applications. The proliferation of electrical devices in a close working proximity can negatively influence each other’s performance. The degradation in the performance is due to electromagnetic interference (EMI). This paper investigates the negative effects of electromagnetic interference originating in the General Purpose Interface Bus (GPIB) control-network of the ac-dc transfer measurement system. Remedial measures of reducing measurement errors and failure of range of industrial devices due to EMI have been explored. The ac-dc transfer measurement system was analyzed for the common-mode (CM) EMI effects. Further investigation of coupling path as well as more accurate identification of noise propagation mechanism has been outlined. To prevent the occurrence of common-mode (ground loops) which was identified between the GPIB system control circuit and the measurement circuit, a microcontroller-driven GPIB switching isolator device was designed, prototyped, programmed and validated. This mitigation technique has been explored to reduce EMI effectively.

Keywords: CM, EMI, GPIB, ground loops

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Authors: Rajwanlop Kumpoopong, Sukit Yindeesuk, Pornchai Silarom

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Modeling cracks in concrete is complicated by its strain-softening behavior which requires the use of sophisticated energy criteria of fracture mechanics to assure stable and convergent solutions in the finite-element (FE) analysis particularly for relatively large structures. However, for small-scale structures such as beams and slabs, a simpler approach relies on retaining some shear stiffness in the cracking plane has been adopted in literature to model the strain-softening behavior of concrete under monotonically increased loading. According to the shear retaining approach, each element is assumed to be an isotropic material prior to cracking of concrete. Once an element is cracked, the isotropic element is replaced with an orthotropic element in which the new orthotropic stiffness matrix is formulated with respect to the crack orientation. The shear transfer factor of 0.5 is used in parallel to the crack plane. The shear retaining approach is adopted in this research to model cracks in RC bridge deck with some modifications to take into account the effect of repetitive moving truck wheel loads as they cause fatigue cracking of concrete. First modification is the introduction of fatigue tests of concrete and reinforcing steel and the Palmgren-Miner linear criterion of cumulative damage in the conventional FE analysis. For a certain loading, the number of cycles to failure of each concrete or RC element can be calculated from the fatigue or S-N curves of concrete and reinforcing steel. The elements with the minimum number of cycles to failure are the failed elements. For the elements that do not fail, the damage is accumulated according to Palmgren-Miner linear criterion of cumulative damage. The stiffness of the failed element is modified and the procedure is repeated until the deck slab fails. The total number of load cycles to failure of the deck slab can then be obtained from which the S-N curve of the deck slab can be simulated. Second modification is the modification in shear transfer factor. Moving loading causes continuous rubbing of crack interfaces which greatly reduces shear transfer mechanism. It is therefore conservatively assumed in this study that the analysis is conducted with shear transfer factor of zero for the case of moving loading. A customized FE program has been developed using the MATLAB software to accomodate such modifications. The developed procedure has been validated with the fatigue test of the 1/6.6-scale AASHTO bridge deck under the applications of both fixed-point repetitive loading and moving loading presented in the literature. Results are in good agreement both experimental vs. simulated S-N curves and observed vs. simulated crack patterns. Significant contribution of the developed procedure is a series of S-N relations which can now be simulated at any desired levels of cracking in addition to the experimentally derived S-N relation at the failure of the deck slab. This permits the systematic investigation of crack propagation or deterioration of RC bridge deck which is appeared to be useful information for highway agencies to prolong the life of their bridge decks.

Keywords: bridge deck, cracking, deterioration, fatigue, finite-element, moving truck, reinforced concrete

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Authors: Abdelsabour G. A. Khaled, Ahmed W. A. Abdalla And Sabry Y. M. Mahmoud

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Banana plants showing typical mosaic and yellow stripes on leaves as symptoms were collected from Assiut Governorate in Egypt. The causal agent was identified as Cucumber mosaic virus (CMV) on the basis of symptoms, transmission, serology, transmission electron microscopy and reverse transcription polymerase chain reaction (RT-PCR). Coat protein (CP) gene was amplified using gene specific primers for coat protein (CP), followed by cloning into desired cloning vector for sequencing. In this study the CMV was transmitted into propagation host either by aphid or mechanically. The transmission was confirmed through Direct Antigen Coating Enzyme Linked Immuno Sorbent Assay (DAC-ELISA). Analysis of the 120 deduced amino acid sequence of the coat protein gene revealed that the EG-A strain of CMV shared from 97.50 to 98.33% with those strains belonging to subgroup IA. The cluster analysis grouped the Egyptian isolate with strains Fny and Ri8 belonging sub-group IA. It appears that there occurs a high incidence of CMV infecting banana belonging to IA subgroup in most parts of Egypt.

Keywords: banana, CMV, transmission, CP gene, RT-PCR

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Authors: Sara Przetocka, Krzysztof Żak, Grzegorz Dubin, Tadeusz Holak

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Toll-like receptors (TLRs) play central role in the innate immune response and inflammation by recognizing pathogen-associated molecular patterns (PAMPs). A fundamental basis of TLR signalling is dependent upon the recruitment and association of adaptor molecules that contain the structurally conserved Toll/interleukin-1 receptor (TIR) domain. MyD88 (myeloid differentiation primary response gene 88) is the universal adaptor for TLRs and cooperates with Mal (MyD88 adapter-like protein, also known as TIRAP) in TLR4 response which is predominantly used in inflammation, host defence and carcinogenesis. Up to date two possible models of MyD88, Mal and TLR4 interactions have been proposed. The aim of our studies is to confirm or abolish presented models and accomplish the full structural characterisation of TIR domains interaction. Using molecular cloning methods we obtained several construct of MyD88 and Mal TIR domain with GST or 6xHis tag. Gel filtration method as well as pull-down analysis confirmed that recombinant TIR domains from MyD88 and Mal are binding in complexes. To examine whether obtained complexes are homo- or heterodimers we carried out cross-linking reaction of TIR domains with BS3 compound combined with mass spectrometry. To investigate which amino acid residues are involved in this interaction the NMR titration experiments were performed. 15N MyD88-TIR solution was complemented with non-labelled Mal-TIR. The results undoubtedly indicate that MyD88-TIR interact with Mal-TIR. Moreover 2D spectra demonstrated that simultaneously Mal-TIR self-dimerization occurs which is necessary to create proper scaffold for Mal-TIR and MyD88-TIR interaction. Final step of this study will be crystallization of MyD88 and Mal TIR domains complex. This crystal structure and characterisation of its interface will have an impact in understanding the TLR signalling pathway and possibly will be used in development of new anti-cancer treatment.

Keywords: cancer, MyD88, TIR domains, Toll-like receptors

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Authors: Pampita Chakraborty, Sukumar Mukherjee

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The study was done to determine the microbiological profile and changing pattern of the pathogens causing UTI in the ICU patients. All the patients admitted to the ICU with urinary catheter insertion for more than 48hours were included in the study. Urine samples were collected in a sterile container with aseptic precaution using disposable syringe and was processed as per standards. Antimicrobial susceptibility test was done by Disc Diffusion method as per CLSI guidelines. A total of 100 urine samples were collected from ICU patients, out of which 30% showed significant bacterial growth and 7% showed growth of candida spp. Prevalence of UTI was more in female (73%) than male (27.%). Gram-negative bacilli 26(86.67%) were more common in our study followed by gram-positive cocci 4(13.33%). The most common uropathogens isolated were Escherichia coli 14 (46.67%), followed by Klebsiella spp 7(23.33%), Staphylococcus aureus 4(13.33%), Acinetobacter spp 3(10%), Enterococcus faecalis 1(3.33%) and Pseudomonas aeruginosa 1(3.33%). Most of the Gram-negative bacilli were sensitive to amikacin (80%) and nitrofurantoin (80%), where as all gram-positive organisms were sensitive to Vancomycin. A large number ESBL producers were also observed in this study. The study finding showed that E.coli is the predominant pathogen and has increasing resistance pattern to the commonly used antibiotics. The study proposes that the adherence to antibiotic policy is the key ingredients for successful outcome in ICU patients and also emphasizes that repeated evaluation of microbial characteristics and continuous surveillance of resistant bacteria is required for selection of appropriate antibiotic therapy.

Keywords: antimicrobial sensitivity, intensive care unit, nosocomial infection, urinary tract infection

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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: M. R. Abusrea, Shiyi Jiang, Dingding Chen, Kazuo Arakawa

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Novel wind-lens turbine designs can augment power output. Vacuum-Assisted Resin Transfer Molding (VARTM) is used to form large and complex structures from a Carbon Fiber Reinforced Polymer (CFRP) composite. Typically, wind-lens turbine structures are fabricated in segments, and then bonded to form the final structure. This paper introduces five new adhesive joints, divided into two groups: One is constructed between dry carbon and CFRP fabrics, and the other is constructed with two dry carbon fibers. All joints and CFRP fabrics were made in our laboratory using VARTM manufacturing techniques. Specimens were prepared for tensile testing to measure joint performance. The results showed that the second group of joints achieved a higher tensile strength than the first group. On the other hand, the tensile fracture behavior of the two groups showed the same pattern of crack originating near the joint ends followed by crack propagation until fracture.

Keywords: adhesive joints, CFRP, VARTM, resin transfer molding

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Authors: Dhaladhuli Pranavi, Amirtham Rajagopal

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There are various failure modes in ceramic matrix composites; notable ones are fiber breakage, matrix cracking and fiber matrix debonding. Crack nucleation and propagation in microstructure of such composites requires an understanding of interaction of crack with the multiple inclusion heterogeneous system and interfaces. In order to assess structural integrity, the material parameters especially of the interface that governs the crack growth should be determined. In the present work, a nonlocal phase field approach is proposed to model the crack interface interaction in such composites. Nonlocal approaches help in understanding the complex mechanisms of delamination growth and mitigation and operates at a material length scale. The performance of the proposed formulation is illustrated through representative numerical examples. The model proposed is implemented in the framework of the finite element method. Several parametric studies on interface crack interaction are conducted. The proposed model is easy and simple to implement and works very well in modeling fracture in composite systems.

Keywords: composite, interface, nonlocal, phase field

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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: Chico Horacio Jose Sambo

Abstract:

Recently neural network has gained popularity when come to solve complex nonlinear problems. Permeability is one of fundamental reservoir characteristics system that are anisotropic distributed and non-linear manner. For this reason, permeability prediction from well log data is well suited by using neural networks and other computer-based techniques. The main goal of this paper is to predict reservoir permeability from well logs data by using neural network approach. A multi-layered perceptron trained by back propagation algorithm was used to build the predictive model. The performance of the model on net results was measured by correlation coefficient. The correlation coefficient from testing, training, validation and all data sets was evaluated. The results show that neural network was capable of reproducing permeability with accuracy in all cases, so that the calculated correlation coefficients for training, testing and validation permeability were 0.96273, 0.89991 and 0.87858, respectively. The generalization of the results to other field can be made after examining new data, and a regional study might be possible to study reservoir properties with cheap and very fast constructed models.

Keywords: neural network, permeability, multilayer perceptron, well log

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Authors: Naoki Yamamoto, Hidenobu Ozaki, Taiichiro Ookawa, Youming Liu, Kazunori Okada, Aiping Zheng

Abstract:

Rice sheath blight is one of the destructive fungal diseases in rice. We have thought that rice sheath blight resistance is a polygenic trait. Host-pathogen interactions and secondary metabolites such as lignin and phytoalexins are likely to be involved in defense against R. solani. However, to our knowledge, it is still unknown how sheath blight resistance can be enhanced in rice breeding. To seek for an alternative genetic factor that contribute to sheath blight resistance, we mined relevant allelic variations from rice core collections created in Japan. Based on disease lesion length on detached leaf sheath, we selected 30 varieties of the top tail-end and the bottom tail-end, respectively, from the core collections to perform genome-wide association mapping. Re-sequencing reads for these varieties were used for calling single nucleotide polymorphisms among the 60 varieties to create a SNP panel, which contained 1,137,131 homozygous variant sites after filitering. Association mapping highlighted a locus on the long arm of chromosome 11, which is co-localized with three sheath blight QTLs, qShB11-2-TX, qShB11, and qSBR-11-2. Based on the localization of the trait-associated alleles, we identified an ankyryn repeat-containing protein gene (ANK-M) as an uncharacterized candidate factor for rice sheath blight resistance. Allelic distributions for ANK-M in the whole rice population supported the reliability of trait-allele associations. Gene expression characteristics were checked to evaluiate the functionality of ANK-M. Since an ANK-M homolog (OsPIANK1) in rice seems a basal defense regulator against rice blast and bacterial leaf blight, ANK-M may also play a role in the rice immune system.

Keywords: allele mining, GWAS, QTL, rice sheath blight

Procedia PDF Downloads 72

Authors: Azadeh Bashiri

Abstract:

Introduction: Lack of proper diagnosis and inadequate treatment of asthma leads to physical and financial complications. This study aimed to use data mining techniques and creating a neural network intelligent system for diagnosis of asthma. Methods: The study population is the patients who had visited one of the Lung Clinics in Tehran. Data were analyzed using the SPSS statistical tool and the chi-square Pearson's coefficient was the basis of decision making for data ranking. The considered neural network is trained using back propagation learning technique. Results: According to the analysis performed by means of SPSS to select the top factors, 13 effective factors were selected, in different performances, data was mixed in various forms, so the different models were made for training the data and testing networks and in all different modes, the network was able to predict correctly 100% of all cases. Conclusion: Using data mining methods before the design structure of system, aimed to reduce the data dimension and the optimum choice of the data, will lead to a more accurate system. Therefore, considering the data mining approaches due to the nature of medical data is necessary.

Keywords: asthma, data mining, Artificial Neural Network, intelligent system

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Authors: Joulié Aurélien, Isabelle Desjardins, Elsa Jourdain, Sophie Pradier, Dufour Philippe, Elodie Rousset, Agnès Leblond

Abstract:

Q fever is a worldwide zoonosis caused by the bacterium Coxiella burnetii. It may infect a broad range of host species, including horses. Although the role of horses in C. burnetii infections remains unknown, their use as sentinel species may be interesting to better assess the human risk exposure. Thus, we aimed to assess the C. burnetii horse exposition in a French endemic area by describing the antibody dynamics detected in serum; investigating the pathogen circulation in the horse environment, and exploring potential links with unexplained syndromes. Blood samples were collected in 2015 and 2016 on 338 and 294 horses, respectively and analyzed by ELISA. Ticks collected on horses were identified, and C. burnetii DNA detection was performed by qPCR targeting the IS1111 gene. Blood sample analyses revealed a significant increase of the seroprevalence in horses between both years, from 11% [7.67; 14.43] to 25% [20.06; 29.94]. On 36 seropositive horses in 2015 and 73 in 2016, 5 and four respectively showed clinical signs compatible with a C. burnetii infection (i.e., chronic fever or respiratory disorders, unfitness and unexplained weight loss). DNA was detected in almost 40% of ticks (n=59/148 in 2015 and n=103/305 in 2016) and exceptionally in dust samples (n=2/46 in 2015 and n=1/14 in 2016) every year. The C. burnetti detection in both the serum and the environment of horses confirm their exposure to the bacterium. Therefore, consideration should be given to target a relevant sentinel species to better assess the Q fever surveillance depending on the epidemiological context.

Keywords: ELISA, Q fever, qPCR, syndromic surveillance

Procedia PDF Downloads 262