Spontaneous Abortions and Shift Work in a Cohort of Nurses in Norway

Background: An association between spontaneous abortions and shift work has been suggested, but present research results are conflicting. The aim of the study is to evaluate the relationship between spontaneous abortions among nurses, shift schedules, and nights worked. Methods: This is a longitudinal study where we identified 914 females from a cohort of nurses in Norway who had worked the same type of shift schedule 2008-2010; either permanent day shift, three-shift rotation or permanent night shift. Information on age, work and life-style factors, as well as spontaneous abortions during lifetime and the past three years (2008-2010) was obtained by annual questionnaires. Results: A higher prevalence of experienced spontaneous abortions before study start (2008) was found among nurses working permanent night shift compared to other nurses. In a linear regression analysis, a risk of 1.3 was found for experienced spontaneous abortions before study start among permanent night shift nurses, with day shift as reference, when adjusting for age, smoking, caffeine and job strain, but the finding was not statistical significant (95 per cent confidence interval 0.8-2.1). Permanent night shift workers had a risk of 1.5 experiencing spontaneous abortions in 2008-2010 compared to day shift nurses, although not statistical significant (95 per cent confidence interval 0.7-3.5). The number of night shifts the past three years was not associated with experiencing spontaneous abortions 2008-2010, but associated with a reduced risk of experiencing spontaneous abortions during lifetime. The results must be interpreted in the light of a possible selection bias; both selections into the occupation of nursing and into the different shift types of the more healthy persons may have occurred in this population. Conclusion: No significant increased risk of spontaneous abortion among permanent night shift nurses compared to day-time nurses was found in this study, and no association was found between spontaneous abortions and the number of worked night shifts.

Feature selection by multi-objective optimisation: Application to network anomaly detection by hierarchical self-organising maps

Feature selection is an important and active issue in clustering and classification problems. By choosing an adequate feature subset, a dataset dimensionality reduction is allowed, thus contributing to decreasing the classification computational complexity, and to improving the classifier performance by avoiding redundant or irrelevant features. Although feature selection can be formally defined as an optimisation problem with only one objective, that is, the classification accuracy obtained by using the selected feature subset, in recent years, some multi-objective approaches to this problem have been proposed. These either select features that not only improve the classification accuracy, but also the generalisation capability in case of supervised classifiers, or counterbalance the bias toward lower or higher numbers of features that present some methods used to validate the clustering/classification in case of unsupervised classifiers. The main contribution of this paper is a multi-objective approach for feature selection and its application to an unsupervised clustering procedure based on Growing Hierarchical Self-Organising Maps (GHSOMs) that includes a new method for unit labelling and efficient determination of the winning unit. In the network anomaly detection problem here considered, this multi-objective approach makes it possible not only to differentiate between normal and anomalous traffic but also among different anomalies. The efficiency of our proposals has been evaluated by using the well-known DARPA/NSL-KDD datasets that contain extracted features and labelled attacks from around 2 million connections. The selected feature sets computed in our experiments provide detection rates up to 99.8% with normal traffic and up to 99.6% with anomalous traffic, as well as accuracy values up to 99.12%.