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Leptin Enhances Porcine Preimplantation Embryo Development in Vitro

Posted in: Ontario Pork, Pork Insight Articles by admin on August 23, 2005 | No Comments

Recent studies have suggested that leptin plays an important role in reproduction. Ob-R is expressed in the murine embryo, and is suggested to play a role in embryo development, although contradictory results have been reported. In the present study, Ob-R expression was observed both at themRNAand protein levels in porcine early embryos.We have also demonstrated that leptin is produced in the porcine oviduct, making it spatially available to interact with its receptor during preimplantation development. When included at 10 ng/ml in embryo culture medium, leptin significantly increased the proportion of cleaved embryos (P < 0.01). At day 7 of in vitro culture, leptin at 10 and 100 ng/ml increased the proportion of embryos reaching the blastocyst stage (P < 0.01). We have previously observed that leptin increases oocyte maturation in vitro, and here we report that inclusion of leptin in both IVM and embryo culture medium further increased blastocyst development (P <0.05), compared to when leptin was included in the embryo culture alone, suggesting leptin has a synergistic role on both oocyte maturation and preimplantation embryo development. The study suggest that leptin plays a positive role in the development of porcine preimplantation embryos. In addition to adding to knowledge on regulation of early embryo development, these results may also have clinical applications. In the pig, the current success rate of IVF and nuclear transfer techniques remains low. The finding that the inclusion of leptin in IVM and embryo culture mediums enhances early development suggests that leptin may aid in the optimization of these techniques, possibly leading to improved success rates.

Pork Farm Odour Modelling Using Multiple-Component Multiple-Factor Analysis and Neural Networks

Posted in: Ontario Pork, Pork Insight Articles by admin on August 20, 2005 | No Comments

It is proposed that the multiple-component neural network model be extended to make use of multiple-component multiple-factor analysis. First, a neural network model and a linear multiple regression model are developed and compared using multiple-component analysis to demonstrate the better modelling technique for pork farm odour. The odour samples were collected using a vacuum box fitted with a pump to draw air into 10 L Tedlar bags. The odour dilution threshold was determined within 48 h of sampling using trained human assessors and a dynamic olfactometer. Approximately 20% of the samples, or 26 data points, were randomly selected for model testing and the remaining 80% of the samples, or 105 data points, were used for model development. The neural network model of the pork farm odour yielded more accurate and precise odour intensity predictions than the linear multiple regression models, indicating that neural networks are the better modelling technique for this application. Subsequently, a multiple-component multiple-factor neural network model was developed and compared with the multiple-component neural network. The multiple-component multiple-factor neural network model generated performance gains, indicating that this approach is relevant to modelling pork farm odour. The multiplecomponent neural network model provided better performance than the corresponding linear multiple regression model. This demonstrated that multiple component farm odour models benefit from the use of contemporary intelligent modelling techniques, specifically neural networks.

Circuit and Noise Analysis of Odorant Gas Sensors in an E-Nose

Posted in: Ontario Pork, Pork Insight Articles by admin on February 28, 2005 | No Comments

In this paper, the relationship between typical circuit structures of gas sensorcircuits and their output noise is analyzed. By using averaged segmenting periodical graphand improved histogram estimation methods, we estimated their noise power spectra andoptimal probability distribution functions (pdf). The results were confirmed through experiment studies.

Comparison of Agar Dilution and E-test for antimicrobial susceptibility testing of Campylobacter coli isolates recovered from 80 Ontario swine farms

Posted in: Ontario Pork, Pork Insight Articles by admin on January 1, 2005 | No Comments

The primary aim of this study was to evaluate the level of agreement of the E-test for in vitro antimicrobial susceptibility testing of Campylobacter coli using the agar dilution technique, which is the approved method. A convenience sample of 80 Ontario swine farms was chosen for this study; each farm was visited from January to June 2004. A total of 233 isolates of C. coli were tested for susceptibility to 10 antimicrobials by agar dilution and the E-test. Performance of the tests was evaluated using 7 quality control strains: Escherichia coli ATCC 25922, Staphylococcus aureus ATCC 29213, Campylobacter jejuni ATCC 33560, and Campylobacter coli ATCC 33559 for the E-test and E. coli ATCC 25922, Pseudomonas aeruginosa ATCC 27853, and C. jejuni ATCC 33560 for the agar dilution test. Weighted Cohen’s kappa and prevalence-adjusted bias-adjusted kappa (PABAK) tests were used for statistical analysis. The E-test and agar dilution test results had a strong agreement when resistance to streptomycin and tetracycline were evaluated (weighted kappa: 0.68 and 0.66, respectively). However, marked disagreement was detected when testing susceptibility to nalidixic acid and ampicillin (0.15 and 0.22, respectively). Almost perfect agreement was detected by PABAK when testing susceptibility to gentamicin (0.99). Agreement was found to be moderate for ciprofloxacin, azithromycin, clindamycin, erythromycin, and chloramphenicol. Although the level of agreement between the E-test and agar dilution depended on the antimicrobial being tested, the E-test always detected a lower proportion of resistant isolates compared to agar dilution.

Single-component Modelling of Pig Farm Odour with Statistical Methods and Neural Networks

Posted in: Ontario Pork, Pork Insight Articles by admin on August 20, 2004 | No Comments

In this paper, a neural network approach to the pork farm odour using single-component analysis with the consideration of other relevant factors, such as measurement location is proposed. Neural network models and statistical models for pork farm odour have been developed and compared for single-component models to determine which method produces superior results. Non-linear statistical and neural network models were developed and compared for single-component pork farm odour analysis using NH3 and H2S as odour indicators. The first set of models considered only a single-odour indicator and no other factors. In this case, the odour intensity predictions from the neural networks were generally better than the predictions produced by the non-linear statistical models. The second set of models considered asingl e-odour indicator (NH3 or H2S) and the odour source (pig building or pig manure storage). In this case, both of the developed neural network models N5 and N6 performed better than their corresponding neural network models (N1–N4) for every performance measure considered in this paper. In general, the use of neural networks to model the pork farm odour yields more accurate and precise odour intensity predictions than the statistical models. The measurement location for the pork farm odour was considered in several model comparisons. The neural network models significantly outperformed the statistical models in this comparison because the statistical models are not able to consider the measurement location. This indicates that measurement location is a relevant factor for modelling pork farm odour. This also demonstrates that factors other than odour components should be considered during modelling.

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