Reconstructions of deltaic environments from Holocene palynological records in the Volga delta, northern Caspian Sea

This article was made available through open access by the Brunel Open Access Publishing Fund.New palynological and ostracod data are presented from the Holocene Volga delta, obtained from short cores and surface samples collected in the Damchik region, near Astrakhan, Russian Federation in the northern Caspian Sea. Four phases of delta deposition are recognized and constrained by accelerated mass spectrometry (AMS) radiocarbon ages. Palynological records show that erosive channels, dunes (Baer hills) and inter-dune lakes were present during the period 11,500–8900 cal. BP at the time of the Mangyshlak Caspian lowstand. The period 8900–3770 cal. BP was characterized regionally by extensive steppe vegetation, with forest present at times with warmer, more humid climates, and with halophytic and xerophytic vegetation present at times of drought. The period 3770–2080 cal. BP was a time of active delta deposition, with forest or woodland close to the delta, indicating relatively warm and humid climates and variable Caspian Sea levels. From 2080 cal. BP to the present-day, aquatic pollen is frequent in highstand intervals and herbaceous pollen and fungal hyphae frequent in lowstand intervals. Soils and incised valley sediments are associated with the regional Derbent regression and may be time-equivalent with the ‘Medieval Warm Period’. Fungal spores are an indicator of erosional or aeolian processes, whereas fungal hyphae are associated with soil formation. Freshwater algae, ostracods and dinocysts indicate mainly freshwater conditions during the Holocene with minor brackish influences. Dinocysts present include Spiniferites cruciformis, Caspidinium rugosum, Impagidinium caspienense and Pterocysta cruciformis, the latter a new record for the Caspian Sea. The Holocene Volga delta is a partial analogue for the much larger oil and gas bearing Mio-Pliocene palaeo-Volga delta.Funding for the data collection and field work was provided from the following sources: 1 – IGCP-UNESCO 2003–2008 (Project 481 CASPAGE, Dating Caspian Sea Level Change); 2 – NWO, Netherlands Science Foundation and RFFI, Russian Science Foundation 2005–2008 (Programme: ‘VHR Seismic Stratigraphy and Paleoecology of the Holocene Volga Delta’); and 3 – BP Exploration (Caspian Sea) Sea Ltd. (Azeri-Chirag-Gunashli) 2005–2008 (‘Unravelling the Small-Scale Stratigraphy and Sediment Dynamics of the Modern Volga Delta Using VHR Marine Geophysics’). The palynological work was funded jointly by BP Exploration (Caspian Sea) Ltd., Delft University of Technology and KrA Stratigraphic Ltd. Ostracod analyses were funded by StrataData Ltd. and funding for two additional radiocarbon dates provided by Deltares

Multiview classification and dimensionality reduction of scalp and intracranial EEG data through tensor factorisation

Electroencephalography (EEG) signals arise as a mixture of various neural processes that occur in different spatial, frequency and temporal locations. In classification paradigms, algorithms are developed that can distinguish between these processes. In this work, we apply tensor factorisation to a set of EEG data from a group of epileptic patients and factorise the data into three modes; space, time and frequency with each mode containing a number of components or signatures. We train separate classifiers on various feature sets corresponding to complementary combinations of those modes and components and test the classification accuracy of each set. The relative influence on the classification accuracy of the respective spatial, temporal or frequency signatures can then be analysed and useful interpretations can be made. Additionaly, we show that through tensor factorisation we can perform dimensionality reduction by evaluating the classification performance with regards to the number mode components and by rejecting components with insignificant contribution to the classification accuracy

Multiway modeling and analysis in stem cell systems biology

<p>Abstract</p> <p>Background</p> <p>Systems biology refers to multidisciplinary approaches designed to uncover emergent properties of biological systems. Stem cells are an attractive target for this analysis, due to their broad therapeutic potential. A central theme of systems biology is the use of computational modeling to reconstruct complex systems from a wealth of reductionist, molecular data (e.g., gene/protein expression, signal transduction activity, metabolic activity, etc.). A number of deterministic, probabilistic, and statistical learning models are used to understand sophisticated cellular behaviors such as protein expression during cellular differentiation and the activity of signaling networks. However, many of these models are bimodal i.e., they only consider row-column relationships. In contrast, multiway modeling techniques (also known as tensor models) can analyze multimodal data, which capture much more information about complex behaviors such as cell differentiation. In particular, tensors can be very powerful tools for modeling the dynamic activity of biological networks over time. Here, we review the application of systems biology to stem cells and illustrate application of tensor analysis to model collagen-induced osteogenic differentiation of human mesenchymal stem cells.</p> <p>Results</p> <p>We applied Tucker1, Tucker3, and Parallel Factor Analysis (PARAFAC) models to identify protein/gene expression patterns during extracellular matrix-induced osteogenic differentiation of human mesenchymal stem cells. In one case, we organized our data into a tensor of type protein/gene locus link × gene ontology category × osteogenic stimulant, and found that our cells expressed two distinct, stimulus-dependent sets of functionally related genes as they underwent osteogenic differentiation. In a second case, we organized DNA microarray data in a three-way tensor of gene IDs × osteogenic stimulus × replicates, and found that application of tensile strain to a collagen I substrate accelerated the osteogenic differentiation induced by a static collagen I substrate.</p> <p>Conclusion</p> <p>Our results suggest gene- and protein-level models whereby stem cells undergo transdifferentiation to osteoblasts, and lay the foundation for mechanistic, hypothesis-driven studies. Our analysis methods are applicable to a wide range of stem cell differentiation models.</p