From learning taxonomies to phylogenetic learning: Integration of 16S rRNA gene data into FAME-based bacterial classification

<p>Abstract</p> <p>Background</p> <p>Machine learning techniques have shown to improve bacterial species classification based on fatty acid methyl ester (FAME) data. Nonetheless, FAME analysis has a limited resolution for discrimination of bacteria at the species level. In this paper, we approach the species classification problem from a taxonomic point of view. Such a taxonomy or tree is typically obtained by applying clustering algorithms on FAME data or on 16S rRNA gene data. The knowledge gained from the tree can then be used to evaluate FAME-based classifiers, resulting in a novel framework for bacterial species classification.</p> <p>Results</p> <p>In view of learning in a taxonomic framework, we consider two types of trees. First, a FAME tree is constructed with a supervised divisive clustering algorithm. Subsequently, based on 16S rRNA gene sequence analysis, phylogenetic trees are inferred by the NJ and UPGMA methods. In this second approach, the species classification problem is based on the combination of two different types of data. Herein, 16S rRNA gene sequence data is used for phylogenetic tree inference and the corresponding binary tree splits are learned based on FAME data. We call this learning approach 'phylogenetic learning'. Supervised Random Forest models are developed to train the classification tasks in a stratified cross-validation setting. In this way, better classification results are obtained for species that are typically hard to distinguish by a single or flat multi-class classification model.</p> <p>Conclusions</p> <p>FAME-based bacterial species classification is successfully evaluated in a taxonomic framework. Although the proposed approach does not improve the overall accuracy compared to flat multi-class classification, it has some distinct advantages. First, it has better capabilities for distinguishing species on which flat multi-class classification fails. Secondly, the hierarchical classification structure allows to easily evaluate and visualize the resolution of FAME data for the discrimination of bacterial species. Summarized, by phylogenetic learning we are able to situate and evaluate FAME-based bacterial species classification in a more informative context.</p

Periglacial landscape evolution and environmental changes of Arctic lowland areas for the last 60,000 years (Western Laptev Sea coast, Cape Mamontov Klyk)

Non-glaciated Arctic lowlands in north-east Siberia were subjected to extensive landscape and environmental changes during the Late Quaternary. Coastal cliffs along the Arctic shelf seas expose terrestrial archives containing numerous palaeoenvironmental indicators (e.g., pollen, plant macro-fossils and mammal fossils) preserved in the permafrost. The presented sedimentological (grain size, magnetic susceptibility and biogeochemical parameters), cryolithological, geochronological (radiocarbon, accelerator mass spectrometry and infrared-stimulated luminescence), heavy mineral and palaeoecological records from Cape Mamontov Klyk record the environmental dynamics of an Arctic shelf lowland east of the Taymyr Peninsula, and thus, near the eastern edge of the Eurasian ice sheet, over the last 60 Ky. This region is also considered to be the westernmost part of Beringia, the non-glaciated landmass that lay between the Eurasian and the Laurentian ice caps during the Late Pleistocene. Several units and subunits of sand deposits, peat–sand alternations, ice-rich palaeocryosol sequences (Ice Complex) and peaty fillings of thermokarst depressions and valleys were presented. The recorded proxy data sets reflect cold stadial climate conditions between 60 and 50 Kya, moderate inderstadial conditions between 50 and 25 Kya and cold stadial conditions from 25 to 15 Kya. The Late Pleistocene to Holocene transition, including the Allerød warm period, the early to middle Holocene thermal optimum and the late Holocene cooling, are also recorded. Three phases of landscape dynamic (fluvial/alluvial, irregular slope run-off and thermokarst) were presented in a schematic model, and were subsequently correlated with the supraregional environmental history between the Early Weichselian and the Holocene

Freshwater ostracodes in Quaternary permafrost deposits from the Siberian Arctic

Ostracode analysis was carried out on samples from ice-rich permafrost deposits obtained on the BykovskyPeninsula (Laptev Sea).A composite pro &#64257;le was investigated that covers most of a 38-m thick permafrostsequence and corresponds to the last ca.60 kyr of the Late Quaternary.The ostracode assemblages aresimilar to those known from European Quaternary lake deposits during cold stages.The ostracode habitatswere small,shallow,cold,oligotrophic pools located in low centred ice wedge polygons or in small ther-mokarst depressions.In total,15 taxa,representing 7 genera,were identi &#64257;ed from 65 samples.The studiedsection is subdivided into six ostracode zones that correspond to Late Quaternary climatic and environ-mental stadial-interstadial variations established by other paleoenvironmental proxies:(1)cold and dryZyrianian stadial (58 53 kyr BP);(2)warm and dry Karginian interstadial (48 34 kyr BP);(3)transitionfrom the Karginian interstadial to the cold and dry Sartanian stadial (34 21 kyr BP);(4)transition fromthe Sartanian stadial to the warm and dry Late Pleistocene period,the Allerød (21 14 kyr BP);(5)transition from the Allerød to the warm and wet Middle Holocene (14 7 kyr BP);and (6)cool and wetLate Holocene (ca.3 kyr BP).The abundance and diversity of the ostracodes will be used as an additionalbioindicator for paleoenvironmental reconstructions of the Siberian Arctic