A Quality Risk Management Model Approach for Cell Therapy Manufacturing

International regulatory authorities view risk management as an essential production need for the development of innovative, somatic cell‐based therapies in regenerative medicine. The available risk management guidelines, however, provide little guidance on specific risk analysis approaches and procedures applicable in clinical cell therapy manufacturing. This raises a number of problems. Cell manufacturing is a poorly automated process, prone to operator‐introduced variations, and affected by heterogeneity of the processed organs/tissues and lot‐dependent variability of reagent (e.g., collagenase) efficiency. In this study, the principal challenges faced in a cell‐based product manufacturing context (i.e., high dependence on human intervention and absence of reference standards for acceptable risk levels) are identified and addressed, and a risk management model approach applicable to manufacturing of cells for clinical use is described for the first time. The use of the heuristic and pseudo‐quantitative failure mode and effect analysis/failure mode and critical effect analysis risk analysis technique associated with direct estimation of severity, occurrence, and detection is, in this specific context, as effective as, but more efficient than, the analytic hierarchy process. Moreover, a severity/occurrence matrix and Pareto analysis can be successfully adopted to identify priority failure modes on which to act to mitigate risks. The application of this approach to clinical cell therapy manufacturing in regenerative medicine is also discussed.     

Bayesian inference and model comparison for asymmetric smooth transition heteroskedastic models

Inference, quantile forecasting and model comparison for an asymmetric double smooth transition heteroskedastic model is investigated. A Bayesian framework in employed and an adaptive Markov chain Monte Carlo scheme is designed. A mixture prior is proposed that alleviates the usual identifiability problem as the speed of transition parameter tends to zero, and an informative prior for this parameter is suggested, that allows for reliable inference and a proper posterior, despite the non-integrability of the likelihood function. A formal Bayesian posterior model comparison procedure is employed to compare the proposed model with its two limiting cases: the double threshold GARCH and symmetric ARX GARCH models. The proposed methods are illustrated using both simulated and international stock market return series. Some illustrations of the advantages of an adaptive sampling scheme for these models are also provided. Finally, Bayesian forecasting methods are employed in a Value-at-Risk study of the international return series. The results generally favour the proposed smooth transition model and highlight explosive and smooth nonlinear behaviour in financial markets.     

马克思与海德格尔的形而上学批判

马克思从意识形态批判角度出发,海德格尔从存在论角度出发分别对传统形而上学进行了批判。马克思认为异化的根源在于社会组织方式,并主张通过积极的社会实践克服异化;海德格尔认为异化的根源在于对存在的遗忘,人类的拯救在于对“存在的看护”。     

A Note on Job Tenure and Collective Contracts

Abstract. This paper analyses the relationship between individual tenure and the application of collective contracts at the firm level under the specific institutional settings in Germany. The empirical approach is based on a multilevel model and a linked employer–employee data set for the years 1990, 1995, and 2001. The main result is that elapsed tenure is longer in firms applying collective contracts than in companies with individual wage setting: workers in firms with collective contracts benefit not only from higher wages, but also from higher job stability. Furthermore, we find no significant changes in mean tenure during the 1990s as well as stable differences across wage‐setting regimes.     

ESTIMATION IN RICKER'S TWO-RELEASE METHOD: A BAYESIAN APPROACH

The Ricker's two‐release method is a simplified version of the Jolly‐Seber method, from Seber's Estimation of Animal Abundance (1982) , used to estimate survival rate and abundance in animal populations. This method assumes there is only a single recapture sample and no immigration, emigration or recruitment. In this paper, we propose a Bayesian analysis for this method to estimate the survival rate and the capture probability, employing Markov chain Monte Carlo methods and a latent variable analysis. The performance of the proposed method is illustrated with a simulation study as well as a real data set. The results show that the proposed method provides favourable inference for the survival rate when compared with the modified maximum likelihood method.