Determination of sulphonamides in pork meat extracts by capillary zone electrophoresis

For 16 sulfonamides the effective mobility was measured as a function of pH and from the effective mobilities detd. in 2 electrolyte systems the pK value and mobility at infinite diln. were calcd. A pH of 7.0 was optimum for the sepn. of both std. mixts. and mixts. of sulfonamides dissolved in pork meat exts. For the detn. of the sulfonamides in pork, only a very simple sample pretreatment, consisting of extn. with MeCN and centrifugation, is suitable as the matrix effects at pH 7.0 do not affect the sepn. Calibration graphs for 5 sulfonamides were constructed, and regression coeffs. of at least 0.999 were obtained. The limit of detection was 2-9 ppm for a pressure injection time of 10 s (injection vol. .apprx.18 nL) by using a Polymicro Technol. capillary 116.45 cm long, with a distance between injection and detection of 109.75 cm and an internal diam. of 50 m

Determination of sulphonamides in pork meat extracts by capillary zone electrophoresis

For 16 sulfonamides the effective mobility was measured as a function of pH and from the effective mobilities detd. in 2 electrolyte systems the pK value and mobility at infinite diln. were calcd. A pH of 7.0 was optimum for the sepn. of both std. mixts. and mixts. of sulfonamides dissolved in pork meat exts. For the detn. of the sulfonamides in pork, only a very simple sample pretreatment, consisting of extn. with MeCN and centrifugation, is suitable as the matrix effects at pH 7.0 do not affect the sepn. Calibration graphs for 5 sulfonamides were constructed, and regression coeffs. of at least 0.999 were obtained. The limit of detection was 2-9 ppm for a pressure injection time of 10 s (injection vol. .apprx.18 nL) by using a Polymicro Technol. capillary 116.45 cm long, with a distance between injection and detection of 109.75 cm and an internal diam. of 50 m

Real-time imputation of missing predictor values improved the application of prediction models in daily practice

Objectives: In clinical practice, many prediction models cannot be used when predictor values are missing. We, therefore, propose and evaluate methods for real-time imputation. Study Design and Setting: We describe (i) mean imputation (where missing values are replaced by the sample mean), (ii) joint modeling imputation (JMI, where we use a multivariate normal approximation to generate patient-specific imputations), and (iii) conditional modeling imputation (CMI, where a multivariable imputation model is derived for each predictor from a population). We compared these methods in a case study evaluating the root mean squared error (RMSE) and coverage of the 95i.e., the proportion of confidence intervals that contain the true predictor value) of imputed predictor values. Results: eRMSE was lowest when adopting JMI or CMI, although imputation of individual predictors did not always lead to substantial improvements as compared to mean imputation. JMI and CMI appeared particularly useful when the values of multiple predictors of the model were missing. Coverage reached the nominal level (i.e., 95 for both CMI and JMI. Conclusion: Multiple imputations using either CMI or JMI is recommended when dealing with missing predictor values in real-time settings. Ó 2021 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons. org/licenses/by/4.0/)

Animal welfare: a social networks perspective

Social network theory provides a useful tool to study complex social relationships in animals. The possibility to look beyond dyadic interactions by considering whole networks of social relationships allows researchers the opportunity to study social groups in more natural ways. As such, network-based analyses provide an informative way to investigate the factors influencing the social environment of group-living animals, and so has direct application to animal welfare. For example, animal groups in captivity are frequently disrupted by separations, reintroductions and/or mixing with unfamiliar individuals and this can lead to social stress and associated aggression. Social network analysis of animal groups can help identify the underlying causes of these socially-derived animal welfare concerns. In this review we discuss how this approach can be applied, and how it could be used to identify potential interventions and solutions in the area of animal welfare