Background: Asbestos has been shown to cause chromosomal damage and DNA aberrations. Exposure to asbestos causes many lung diseases e.g. asbestosis, malignant mesothelioma, and lung cancer, but the disease-related processes are still largely unknown. We exposed the human cell lines A549, Beas-2B and Met5A to crocidolite asbestos and determined time-dependent gene expression profiles by using Affymetrix arrays. The hybridization data was analyzed by using an algorithm specifically designed for clustering of short time series expression data. A canonical correlation analysis was applied to identify correlations between the cell lines, and a Gene Ontology analysis method for the identification of enriched, differentially expressed biological processes.

The Eemian (the Last Interglacial; ca. 129–116 thousand years ago) presents a testbed for assessing environmental responses and climate feedbacks under warmer-than-present boundary conditions. However, climate syntheses for the Eemian remain hampered by lack of data from the high-latitude land areas, masking the climate response and feedbacks in the Arctic. Here we present a high-resolution (sub-centennial) record of Eemian palaeoclimate from northern Finland, with multi-model reconstructions for July and January air temperature. In contrast with the mid-latitudes of Europe, our data show decoupled seasonal trends with falling July and rising January temperatures over the Eemian, due to orbital and oceanic forcings. This leads to an oceanic Late-Eemian climate, consistent with an earlier hypothesis of glacial inception in Europe. The interglacial is further intersected by two strong cooling and drying events. These abrupt events parallel shifts in marine proxy data, linked to disturbances in the North Atlantic oceanic circulation regime.

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We test several quantitative algorithms as palaeoclimate reconstruction tools for North American and European fossil pollen data, using both classical methods and newer machine-learning approaches based on regression tree ensembles and artificial neural networks. We focus on the reconstruction of secondary climate variables (here, January temperature and annual water balance), as their comparatively small ecological influence compared to the primary variable (July temperature) presents special challenges to palaeo-reconstructions. We test the pollen–climate models using a novel and comprehensive cross-validation approach, running a series of h-block cross-validations using h values of 100–1500 km. Our study illustrates major benefits of this variable h-block cross-validation scheme, as the effect of spatial autocorrelation is minimized, while the cross-validations with increasing h values can reveal instabilities in the calibration model and approximate challenges faced in palaeo-reconstructions with poor modern analogues. We achieve well-performing calibration models for both primary and secondary climate variables, with boosted regression trees providing the overall most robust performance, while the palaeoclimate reconstructions from fossil datasets show major independent features for the primary and secondary variables. Our results suggest that with careful variable selection and consideration of ecological processes, robust reconstruction of both primary and secondary climate variables is possible.