Molecular dynamics simulation of plane poiseuille flow in nanochannels

This paper was presented at the 2nd Micro and Nano Flows Conference (MNF2009), which was held at Brunel University, West London, UK. The conference was organised by Brunel University and supported by the Institution of Mechanical Engineers, IPEM, the Italian Union of Thermofluid dynamics, the Process Intensification Network, HEXAG - the Heat Exchange Action Group and the Institute of Mathematics and its Applications.This paper presents new techniques and results of simulating microflows in plane channels by the molecular dynamics (MD) method. Mass forces and thermostat are not used in these techniques. The flows are simulated by both hard-sphere molecules and molecules with the Lennard-Jones intermolecular potential. Flow at a given fluid flow rate is implemented. In this case, the initial shock profile is transformed to a parabolic type profile. However, unlike in ordinary Poiseuille flows, a slip effect is recorded on the channel walls. It is shown that, in a nanochannel, a linear pressure gradient occurs. Fluid structuring is studied. The effects of fluid density, accommodation coefficients, and channel dimensions on flow properties are investigated.This work was supported in part by the Russian Foundation for Basic Researches (grant No. 07-08-00164) and by the grant of the President of the Russian Federation for Support of Leading Scientific Schools (project no. NSh-454.2008.1)

Viscosity and thermal conductivity of nanofluids

This paper was presented at the 2nd Micro and Nano Flows Conference (MNF2009), which was held at Brunel University, West London, UK. The conference was organised by Brunel University and supported by the Institution of Mechanical Engineers, IPEM, the Italian Union of Thermofluid dynamics, the Process Intensification Network, HEXAG - the Heat Exchange Action Group and the Institute of Mathematics and its Applications.The purpose of the present work was to study the momentum and energy transport processes in nanofluids by the molecular dynamics (MD) method. The MD-simulation results were compared with known formulae and experimental data. Unlike for suspensions with Brownian and other macroscopic particles, the viscosity and thermal conductivity of nanofluids were found to depend not only on the volume fraction of nanoparticles but also on the mass and radius of nanoparticles. The possible mechanisms of the nanoparticle effect on the transport coefficients of nanofluids are discussed.This work was supported in part by the RFBR grant No. 07-08-00164)

Self-diffusion and viscosity coefficient of fluids in nanochannels

This paper was presented at the 3rd Micro and Nano Flows Conference (MNF2011), which was held at the Makedonia Palace Hotel, Thessaloniki in Greece. The conference was organised by Brunel University and supported by the Italian Union of Thermofluiddynamics, Aristotle University of Thessaloniki, University of Thessaly, IPEM, the Process Intensification Network, the Institution of Mechanical Engineers, the Heat Transfer Society, HEXAG - the Heat Exchange Action Group, and the Energy Institute.Fluid viscosity and molecular diffusion in nanochannels were studied by molecular dynamics simulation. Transport processes in a plane channel, a channel of rectangular cross-section, and in porous media were investigated. The channel height was varied from 2 to 50 nm. The interaction between molecules was simulated using the hard sphere (HS) and the Lennard-Jones (LJ) intermolecular potentials. The porous matrix was modeled by cubic packing of spheres of the same radius, and the packing density and the grain size were varied. The dependence of the transport coefficients on the fluid density and channel characteristics (channel height, channel aspect ratio, porosity of the porous medium, accommodation coefficients, etc.) was investigated.Russian Foundation for Basic Research (Grant No. 10-01-00074) and the Federal Special Program “Scientific and scientific-pedagogical personnel of innovative Russia in 2009-2013” (projects No. P230 and No. 14.740.11.0579, No. 14.740.11.0103)

An intracellular pH gradient in the anammox bacterium Kuenenia stuttgartiensis as evaluated by 31P NMR

The cytoplasm of anaerobic ammonium oxidizing (anammox) bacteria consists of three compartments separated by membranes. It has been suggested that a proton motive force may be generated over the membrane of the innermost compartment, the “anammoxosome”. 31P nuclear magnetic resonance (NMR) spectroscopy was employed to investigate intracellular pH differences in the anammox bacterium Kuenenia stuttgartiensis. With in vivo NMR, spectra were recorded of active, highly concentrated suspensions of K. stuttgartiensis in a wide-bore NMR tube. At different external pH values, two stable and distinct phosphate peaks were apparent in the recorded spectra. These peaks were equivalent with pH values of 7.3 and 6.3 and suggested the presence of a proton motive force over an intracytoplasmic membrane in K. stuttgartiensis. This study provides for the second time—after discovery of acidocalcisome-like compartments in Agrobacterium tumefaciens—evidence for an intracytoplasmic pH gradient in a chemotrophic prokaryotic cell

Spike pattern recognition by supervised classification in low dimensional embedding space

© The Author(s) 2016. This article is published with open access at Springerlink.com under the terms of the Creative Commons Attribution License 4.0, (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.Epileptiform discharges in interictal electroencephalography (EEG) form the mainstay of epilepsy diagnosis and localization of seizure onset. Visual analysis is rater-dependent and time consuming, especially for long-term recordings, while computerized methods can provide efficiency in reviewing long EEG recordings. This paper presents a machine learning approach for automated detection of epileptiform discharges (spikes). The proposed method first detects spike patterns by calculating similarity to a coarse shape model of a spike waveform and then refines the results by identifying subtle differences between actual spikes and false detections. Pattern classification is performed using support vector machines in a low dimensional space on which the original waveforms are embedded by locality preserving projections. The automatic detection results are compared to experts’ manual annotations (101 spikes) on a whole-night sleep EEG recording. The high sensitivity (97 %) and the low false positive rate (0.1 min−1), calculated by intra-patient cross-validation, highlight the potential of the method for automated interictal EEG assessment.Peer reviewedFinal Published versio

Clustering gene expression data with a penalized graph-based metric

<p>Abstract</p> <p>Background</p> <p>The search for cluster structure in microarray datasets is a base problem for the so-called "-omic sciences". A difficult problem in clustering is how to handle data with a manifold structure, i.e. data that is not shaped in the form of compact clouds of points, forming arbitrary shapes or paths embedded in a high-dimensional space, as could be the case of some gene expression datasets.</p> <p>Results</p> <p>In this work we introduce the Penalized k-Nearest-Neighbor-Graph (PKNNG) based metric, a new tool for evaluating distances in such cases. The new metric can be used in combination with most clustering algorithms. The PKNNG metric is based on a two-step procedure: first it constructs the k-Nearest-Neighbor-Graph of the dataset of interest using a low k-value and then it adds edges with a highly penalized weight for connecting the subgraphs produced by the first step. We discuss several possible schemes for connecting the different sub-graphs as well as penalization functions. We show clustering results on several public gene expression datasets and simulated artificial problems to evaluate the behavior of the new metric.</p> <p>Conclusions</p> <p>In all cases the PKNNG metric shows promising clustering results. The use of the PKNNG metric can improve the performance of commonly used pairwise-distance based clustering methods, to the level of more advanced algorithms. A great advantage of the new procedure is that researchers do not need to learn a new method, they can simply compute distances with the PKNNG metric and then, for example, use hierarchical clustering to produce an accurate and highly interpretable dendrogram of their high-dimensional data.</p

Mechanisms of decadal variability in the Labrador Sea and the wider North Atlantic in a high-resolution climate model

A necessary step before assessing the performance of decadal predictions is the evaluation of the processes that bring memory to the climate system, both in climate models and observations. These mechanisms are particularly relevant in the North Atlantic, where the ocean circulation, related to both the Subpolar Gyre and the Meridional Overturning Circulation (AMOC), is thought to be important for driving significant heat content anomalies. Recently, a rapid decline in observed densities in the deep Labrador Sea has pointed to an ongoing slowdown of the AMOC strength taking place since the mid 90s, a decline also hinted by in-situ observations from the RAPID array. This study explores the use of Labrador Sea densities as a precursor of the ocean circulation changes, by analysing a 300-year long simulation with the state-of-the-art coupled model HadGEM3-GC2. The major drivers of Labrador Sea density variability are investigated, and are characterised by three major contributions. First, the integrated effect of local surface heat fluxes, mainly driven by year-to-year changes in the North Atlantic Oscillation, which accounts for 62% of the total variance. Additionally, two multidecadal-to-centennial contributions from the Greenland-Scotland Ridge outflows are quantified; the first associated with freshwater exports via the East Greenland Current, and the second with density changes in the Denmark Strait Overflow. Finally, evidence is shown that decadal trends in Labrador Sea densities are followed by important atmospheric impacts. In particular, a negative winter NAO response appears to follow the positive Labrador Sea density trends, and provides a phase reversal mechanism

Coquillettidia (Culicidae, Diptera) mosquitoes are natural vectors of avian malaria in Africa

<p>Abstract</p> <p>Background</p> <p>The mosquito vectors of <it>Plasmodium </it>spp. have largely been overlooked in studies of ecology and evolution of avian malaria and other vertebrates in wildlife.</p> <p>Methods</p> <p><it>Plasmodium </it>DNA from wild-caught <it>Coquillettidia </it>spp. collected from lowland forests in Cameroon was isolated and sequenced using nested PCR. Female <it>Coquillettidia aurites </it>were also dissected and salivary glands were isolated and microscopically examined for the presence of sporozoites.</p> <p>Results</p> <p>In total, 33% (85/256) of mosquito pools tested positive for avian <it>Plasmodium </it>spp., harbouring at least eight distinct parasite lineages. Sporozoites of <it>Plasmodium </it>spp. were recorded in salivary glands of <it>C. aurites </it>supporting the PCR data that the parasites complete development in these mosquitoes. Results suggest <it>C. aurites</it>, <it>Coquillettidia pseudoconopas </it>and <it>Coquillettidia metallica </it>as new and important vectors of avian malaria in Africa. All parasite lineages recovered clustered with parasites formerly identified from several bird species and suggest the vectors capability of infecting birds from different families.</p> <p>Conclusion</p> <p>Identifying the major vectors of avian <it>Plasmodium </it>spp. will assist in understanding the epizootiology of avian malaria, including differences in this disease distribution between pristine and disturbed landscapes.</p