Survival of poultry-derived Campylobacter jejuni of multilocus sequence type clonal complexes 21 and 45 under freeze,chill, oxidative,acid and heat stresses

The application of multilocus sequence typing (MLST) for studying Campylobacter jejuni diversity reveals that MLST clonal complex (CC) 21 and CC-45 occupies significant proportion in the diverse population of C. jejuni. These two complexes are ecologically abundant and represent an interesting subpopulation for studying C. jejuni survival under different stress conditions. In the present study we characterize and compare 19 C. jejuni strains assigned to CC-21 and CC-45, isolated from chicken meat, based on laboratory stress models maintained in Muller-Hinton broth. Model conditions were mimicking freeze, chill, oxidative, acid and heat stresses. Results show that survival patterns varied between the strains. C. jejuni strains of CC-21 survived significantly better than C. jejuni strains of CC-45 under heat (P value = 0.022) and chill (P value = 0.001) stress models. On the other hand, C. jejuni strains of CC-45 showed significantly better survival compared to C. jejuni strains of CC-21 in response to oxidative (P value = 0.003) and freeze (P value = 0.021) stress models. C. jejuni strains assigned to the founder ST-45 showed significantly better survival (P value = 0.017) under heat stress model compared to their ancestral sequence types. However, an association between survival fitness and the diversification of a clonal group cannot be demonstrated directly from the obtained results. In conclusion, findings of the present study show that genotypic variations of C. jejuni might play a role in enabling certain lineages to be selected when encountering adverse and stressful environments. In future stress response studies, it is recommended to consider the effect of genotypic diversity among C. jejuni strains as that might bias the experimental findings.     

Loss of viability of Listeria monocytogenes in contaminated processed cheese during storage at 4, 12 and 22 °C

The behaviour of Listeria monocytogenes in a processed cheese product was evaluated over time by inoculating the product with three different L. monocytogenes strains (Scott A, CA and a strain isolated from processed cheese) at three different inoculation levels (ca. 6 × 10⁵, ca. 6 × 10³ and 10² CFU/g of cheese or less) and after storage of the contaminated products at 4, 12 or 22 °C. Growth of L. monocytogenes was not observed in any of the experimental trials (experiments involving different combinations of strain, inoculum level and storage temperature) throughout the storage period. L. monocytogenes populations decreased over time with a rate that was strain- and storage temperature-dependent. Nonetheless, for cheeses that had been inoculated with the higher inoculum and stored at 4 °C viable populations of L. monocytogenes could be detected for up to nine months post-inoculation. The L. monocytogenes survival curves obtained from the different trials were characterised by a post-inoculation phase during which the populations remained essentially unchanged (lag phase) followed by a phase of logarithmic decline. The duration of the lag phase and the rate of inactivation of L. monocytogenes in the different trials were estimated based on data from the linear descending portions of the survival curves. In addition, a non-linear Weibull-type equation was fitted to the data from each survival curve with satisfactory results. The results of the present study emphasize that, according to the definition laid down in the European Union Regulation 1441/2007, the processed cheese product tested in this work should be considered and classified as one that does not support the growth of L. monocytogenes under reasonable foreseeable conditions of distribution and storage. However, post-processing contamination of the product should be austerely avoided as the pathogen can survive in the product for extended periods of time, particularly under refrigerated storage (4 °C).     

Survival of Lactobacillus rhamnosus GG as Influenced by Storage Conditions and Product Matrixes

Abstract: Mortality resulting from diarrhea especially that occurs in children younger than 5 y of age ranks 3rd among all deaths caused by infectious diseases worldwide. Probiotics such as Lactobacillus rhamnosus GG are clinically shown to effectively reduce the incidence of diarrhea in children. A food substrate is one of the major factors regulating the colonization of microorganisms in human gastrointestinal tracts. Peanut butter is a nutritious, low‐moisture food that could be a carrier for probiotics. In this study, we observed the influence of storage conditions and product matrixes on the survival of L. rhamnosus GG. Cells of L. rhamnosus GG were inoculated into full fat or reduced fat peanut butter at 10⁷ CFU/g. Inoculated peanut butter was stored at 4, 25, or 37 °C for 48 wk. Samples were drawn periodically to determine the populations of L. rhamnosus GG. Results showed that there was no significant decrease in the viable counts of L. rhamnosus GG in products stored 4 °C. The survivability of L. rhamnosus GG decreased with increasing storage temperature and time. Product matrixes did not significantly affect the survival of L. rhamnosus GG except at 37 °C. Populations of L. rhamnosus GG were preserved at >6 logs in products stored at 4 °C for 48 wk and at 25 °C for 23 to 27 wk. At 37 °C, the 6‐log level could not be maintained for even 6 wk. The results suggest that peanut butter stored at 4 and 25 °C could serve as vehicles to deliver probiotics. Practical Application: Lactobacillus rhamnosus GG is clinically shown to effectively reduce the incidence of diarrhea in children. A food substrate is one of the major factors regulating the colonization of microorganisms in human gastrointestinal tracts. Peanut butter is a nutritious, low‐moisture food that could be a carrier for probiotics. In this study, we demonstrated the influence of storage conditions and product matrixes on the survival of L. rhamnosus GG. Results suggest that peanut butters could be effective delivery systems for probiotics and that probitic peanut butter could be used as one of the strategies to control diarrhea and malnutrition in developing countries.     

Analysis of technology-related innovation characteristics affecting the survival period of SMEs: Focused on the manufacturing industry of Korea

The technology-related innovation characteristics that affect the survival period of SMEs were analyzed, focusing on the manufacturing industry of Korea. In this study, the survival is measured in two ways: payments that are more than 3 months overdue and default. First, it was confirmed that the better the overall technology grade (T-grade), the longer the survival period in overdue and default. Second, it was confirmed that there was a positive relationship between owner capability, management capability, profit prospect and the survival, whereas R&D capability (R&D-related infrastructure and manpower, incurring ongoing costs) and the survival had a negative relationship. In the case of R&D capability, from the point of view of optimization, it can be interpreted that excessive investment and capability compared to the size of the company hinders the survival of the company. On the other hand, considering that the technology-related innovation characteristic, which has a positive relationship with the survival, is an item (owner capability, management capability and profit prospect) that evaluates both technology and business feasibility. Therefore third, it was concluded that the survival period of SMEs should be accompanied by business feasibility, not mere technological excellence.     

回归分析系统的可靠性理论

根据回归分析基本理论及测量平差理论，讨论了观测误差与观测值残差的关系，指明了残差协因素矩阵及多余观测分量在可靠性理论研究中的特殊作用。并在单个一维备选假设下，将可靠性理论的研究方法引入回归分析系统。提出了回归计算系统的内部可靠性及外部可靠性的概念，以及衡量它们的量化尺度。为在大坝资料中分析中剔除粗差，合理筛选观测数据，提供了理论依据。析     

Neural network models for breast cancer prognosis

Estimating the risk of relapse for breast cancer patients is necessary, since it affects the choice of treatment. This problem involves analysing data of times to relapse of patients and relating them to prognostic variables. Some of the times to relapse will usually be censored.We investigate various ways of using neural network models to extend traditional statistical models in this situation. Such models are better able to model both non-linear effects of prognostic factors and interactions between them, than linear logistic or Cox regression models. With the dataset used in our study, however, the prediction of the risk of relapse is not significantly improved when using a neural network model. Predicting the risk that a patient will relapse within three years, say, is possible from this data, but not when any relapse will happen.     

Kendall distributions and level sets in bivariate exchangeable survival models

For a given bivariate survival function , we study the relations between the set of the level curves of and the Kendall distribution. Then we characterize the survival models simultaneously admitting a specified Kendall distribution and a specified set of level curves. Attention will be restricted to exchangeable survival models.     

Tuning into the digital channel: evaluating business model characteristics for Internet firm survival

More than 5,000 Internet firms have failed since the beginning of 2000. One common perception is that the downturn in the economy drove many firms out of business. But then, why have some firms survived? In this research, we provide an empirical analysis by examining how the business model characteristics of an Internet firm affect its survival. We analyze a panel data set of 130 public Internet firms using two different techniques: non-parametric survival analysis, and the semiparametric Cox proportional hazards model. We characterize the survival rates throughout the lifetimes of the public Internet firms in our sample. Our results reveal that smaller firms that facilitate customer-provider interactions, are transaction brokers, and that rely on advertising as their primary source of revenue sources have had a lower likelihood of bankruptcy or failure. In addition, the detrimental effects on failing to serve as interaction platforms for individuals and businesses, and a larger firm size diminish over time as Internet firms mature, and the weaker ones are forced out of the marketplace. Our research also points out important dimensions of an Internet firm’s business model that affect its survival.     

Variance Estimation in Censored Quantile Regression via Induced Smoothing

Statistical inference in censored quantile regression is challenging, partly due to the unsmoothness of the quantile score function. A new procedure is developed to estimate the variance of the Bang and Tsiatis inverse-censoring-probability weighted estimator for censored quantile regression by employing the idea of induced smoothing. The proposed variance estimator is shown to be asymptotically consistent. In addition, a numerical study suggests that the proposed procedure performs well in finite samples, and it is computationally more efficient than the commonly used bootstrap method.     

Coordinate ascent for penalized semiparametric regression on high-dimensional panel count data

This paper explores a fast algorithm to select relevant predictors for the response process with panel count data. Based on the lasso penalized pseudo-objective function derived from an estimating equation, the coordinate ascent accelerates the estimation of regression coefficients. The coordinate ascent algorithm is capable of selecting relevant predictors for underdetermined problems where the number of predictors far exceeds the number of cases. It relies on a tuning constant that can be chosen by generalized cross-validation. Our tests on simulated and real data demonstrate the virtue of penalized regression in model building and prediction for panel count data in ultrahigh-dimensional settings.