Production of Antibody Raised Against Lipopolysaccharide (LPS) of Vibrio Cholerae Non-O1

Background: Cholera, an infectious disease caused by Vibrio cholerae, is primarily transmitted by ingestion of contaminated food or water. In severe cases, cholera may lead to severe dehydration, metabolic acidosis, and ultimately, hypovolemic shock and death. Methods: In this study V.cholerae non-O1 was cultured in suitable media. LPS was extracted from the surface of  bacteria by hot phenol-water method and then purified by high-speed centrifugation. For production of specific antibody against LPS, white newzeland rabbits were first immunized by whole cell bacteria and then immunized with highly purified LPS. The titre of the antiserum was determined by ELISA for each serogroup. Results: Results presented in this study indicate that serum anti-LPS antibodies raised against purified LPS of V.cholerae non-O1 can detect V.cholerae non-O1 .Conclusion: This antibody had low cross reactivity with V.cholerae O1, serotype Inaba or Ogawa. So, this antibody can be used for for detection of V. cholerae non-O1

A Framework for Integrated Proactive Maintenance Decision Making and Supplier Selection

Part 6: Intelligent Diagnostics and Maintenance SolutionsInternational audienceThe increasing use of sensors in manufacturing enterprises has led to the need for real-time data-driven information systems capable of processing huge amounts of data in order to provide meaningful insights about the actual and the predicted business performance. We propose a framework for real-time, event-driven proactive supplier selection driven by Condition Based Maintenance (CBM). The proposed framework was tested in a real in automotive lighting equipment scenario

Single Multiplicative Neuron Model Artificial Neural Network with Autoregressive Coefficient for Time Series Modelling

Bas, Eren/0000-0002-0263-8804; Egrioglu, Erol/0000-0003-4301-4149WOS: 000434268000024Single multiplicative neuron model and multilayer perceptron have been commonly used for time series prediction problem. Having a simple structure and features of easily applicable differentiates the single multiplicative neuron model from the multilayer perception. While, multilayer perceptron just as many other artificial neural networks are data-based methods, single multiplicative neuron model has a model structure due to it is composed of a single neuron. Multilayer perceptron can highly compliance with data by changing its architecture, though single multiplicative neuron model, in this respect, is insufficient. In this study, to overcome this problem of single multiplicative neuron model, a new model that its weights and biases are obtained by way of autoregressive equations is proposed. Since the time indexes are considered to determine weights and biases from the autoregressive models, the proposed neural network can be evaluated as a data-based model. To show the performance and capability of the proposed method, various implementations have been executed over some well-known data sets. And the obtained results demonstrate that data-based proposed method has outstanding forecasting performance