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Gregorio Iraola,Martín Hernández,Lucía Calleros,Fernando Paolicchi,Silvia Silveyra,Alejandra Velilla,Luis Carretto,Eliana Rodríguez,Ruben Pérez 대한수의학회 2012 JOURNAL OF VETERINARY SCIENCE Vol.13 No.4
Campylobacter (C.) fetus (epsilonproteobacteria) is an important veterinary pathogen. This species is currently divided into C. fetus subspecies (subsp.) fetus (Cff) and C. fetus subsp. venerealis (Cfv). Cfv is the causative agent of bovine genital Campylobacteriosis, an infectious disease that leads to severe reproductive problems in cattle worldwide. Cff is a more general pathogen that causes reproductive problems mainly in sheep although cattle can also be affected. Here we describe a multiplex PCR method to detect C. fetus and differentiate between subspecies in a single step. The assay was standardized using cultured strains and successfully used to analyze the abomasal liquid of aborted bovine fetuses without any pre-enrichment step. Results of our assay were completely consistent with those of traditional bacteriological diagnostic methods. Furthermore, the multiplex PCR technique we developed may be easily adopted by any molecular diagnostic laboratory as a complementary tool for detecting C. fetus subspecies and obtaining epidemiological information about abortion events in cattle.
Ricardo Martínez-Alvarado,Everardo Efrén Granda-Gutiérrez,Alejandra Hernández-Rodríguez,Rolando Javier Praga-Alejo 한국정밀공학회 2020 International Journal of Precision Engineering and Vol.21 No.10
A pulse classification technique for monitoring the type of discharges in an electrochemical discharge machining (ECDM) process is presented in this research paper. The performance of an ECDM process is affected by many factors which make it hard for control strategies to be formulated for this process. The pulse classifier plays an important role to develop control strategies and later to improve the process. The proposed system uses the current and voltage waveforms measured through the gap as input signals for the classification system. A fuzzy inference system (FIS) is used to categorize both input signals into one of the four proposed pulse types, according to their specific behavior. For the experimental validation, data samples taken during the machining process were recorded to evaluate the performance of the pulse classifier with raw data. Raw data of the gap signals is properly classified based on the proposed FIS.