Dissipative Scaling Functions in Navier-Stokes Turbulence: Experimental Tests

A recent theoretical development in the understanding of the small-scale structure of Navier-Stokes turbulence has been the proposition that the scales $\eta_n(R)$ that separate inertial from viscous behavior of many-point correlation functions depend on the order $n$ and on the typical separations $R$ of points in the correlation. This is a proposal of fundamental significance in itself but it also has implications for the inertial range scaling behaviour of various correlation functions. This dependence has never been observed directly in laboratory experiments. In order to observe it, turbulence data which both display a well-developed scaling range with clean scaling behaviour and are well-resolved in the small scales to well within the viscous range is required. We have analysed the data of the experiments performed in the laboratory of P. Tabeling of Navier-Stokes turbulence in a helium cell with counter-rotating disks, and find that this data satisfies these criteria. We have been able to find strong evidence for the existence of the predicted scaling of the viscous scale.Comment: PRL, submitted, REVTeX, 4 pages, 4 figures, included. Online (HTML) and PS versions of this and related papers available at http://lvov.weizmann.ac.il/onlinelist.htm

The influence of superhydrophobic surfaces on near-wall turbulence

Superhydrophobic surfaces are able to entrap gas pockets in-between surface roughness elements when submerged in water. These entrapped gas pockets give these surfaces the potential to reduce drag due to the overlying flow being able to locally slip over the gas pockets, resulting in a mean slip at the surface. This thesis investigates the different effects that slip and the texturing of the surface have on turbulence over superhydrophobic surfaces. It is shown that, after filtering out the texture-induced flow, the background, overlying turbulence experiences the surface as a homogeneous slip boundary condition. For texture sizes, expressed in wall units, up to $L^+ \lesssim 20$ the only effect of the surface texture on the overlying flow is through this surface slip. The direct effect of slip does not modify the dynamics of the overlying turbulence, which remains canonical and smooth-wall-like. In these cases the flow is governed by the difference between two virtual origins, the virtual origin of the mean flow and the virtual origin experienced by the overlying turbulence. Streamwise slip deepens the virtual origin of the mean flow, while spanwise slip acts to deepen the virtual origin perceived by the overlying turbulence. The drag reduction is then proportional to the difference between the two virtual origins, reminiscent of drag reduction using riblets. The validity of slip-length models to represent textured superhydrophobic surfaces can resultantly be extended up to $L^+ \lesssim 20$. However, for $L^+ \gtrsim 25$ a non-linear interaction with the texture-coherent flow alters the dynamics of the background turbulence, with a reduction in coherence of large streamwise lengthscales. This non-linear interaction causes an increase in Reynolds stress up to $y^+ \lesssim 25$, and decreases the obtained drag reduction compared to that predicted from homogeneous slip-length models.Funding from Engineering and Physical Sciences Research Counci

Hepatocyte generation from pancreatic acinar cell lines

PhD ThesisThe transdifferentiation of pancreatic acinar cells towards hepatocytes is an event that occurs in vitro and in vivo in rodents. The B-13 cell line is a model for studying this phenomenon in vitro; it readily transdifferentiates into hepatocyte-like cells in response to glucocorticoids such as dexamethasone (DEX). The transdifferentiation event is dependent on a transient suppression of Wnt signalling followed by induction of Serine/threonine-protein kinase 1(SGK1) via interactions with the glucocorticoid receptor. This thesis has aimed to further explore pancreatic to hepatic transdifferentiation, using the B-13 cell as a model and also investigated the phenomenon in human cells. As hepatic stellate cells are involved in liver regeneration and may support the progenitor niche in liver, coculture experiments were conducted to assess their effects on B-13 transdifferentiation. Transdifferentiation was enhanced in cocultures and found to be dependent on cell-cell interaction that resulted in further suppression of the Wnt signalling pathway by myofibroblasts. B-13 transdifferentiation was shown to be able to take place in vivo for the first time; cells were found to engraft only into the liver and pancreas of NOD/SCID mice. Interestingly, only cells within the liver environment showed expression of hepatocyte-specific genes. B-13 cells were also cultured in 3D bioreactor devices where they transdifferentiated into functional hepatocyte-like cells with gene expression at levels comparable to primary rat hepatocytes. Elucidating the mechanisms involved during B-13 transdifferentiation will support the isolation of an equivalent human pancreatic cell. Studies with a human cell line and primary exocrine cells demonstrated that glucocorticoids also induce hepatocyte-gene expression, and thus the generation/isolation of a human equivalent to the B-13 is a realistic goal.ITTP MRC studentshi

Implications of single-neuron gain scaling for information transmission in networks

Summary: 

Many neural systems are equipped with mechanisms to efficiently encode sensory information. To represent natural stimuli with time-varying statistical properties, neural systems should adjust their gain to the inputs' statistical distribution. Such matching of dynamic range to input statistics has been shown to maximize the information transmitted by the output spike trains (Brenner et al., 2000, Fairhall et al., 2001). Gain scaling has not only been observed as a system response property, but also in single neurons in developing somatosensory cortex stimulated with currents of different amplitude (Mease et al., 2010). While gain scaling holds for cortical neurons at the end of the first post-natal week, at birth these neurons lack this property. The observed improvement in gain scaling coincides with the disappearance of spontaneous waves of activity in cortex (Conheim et al., 2010).

We studied how single-neuron gain scaling affects the dynamics of signal transmission in networks, using the developing cortex as a model. In a one-layer feedforward network, we showed that the absence of gain control made the network relatively insensitive to uncorrelated local input fluctuations. As a result, these neurons selectively and synchronously responded to large slowly-varying correlated input--the slow build up of synaptic noise generated in pacemaker circuits which most likely triggers waves. Neurons in gain scaling networks were more sensitive to the small-scale input fluctuations, and responded asynchronously to the slow envelope. Thus, gain scaling both increases information in individual neurons about private inputs and allows the population average to encode the slow fluctuations in the input. Paradoxically, the synchronous firing that corresponds to wave propagation is associated with low information transfer. We therefore suggest that the emergence of gain scaling may help the system to increase information transmission on multiple timescales as sensory stimuli become important later in development. 

Methods:

Networks with one and two layers consisting of hundreds of model neurons were constructed. The ability of single neurons to gain scale was controlled by changing the ratio of sodium to potassium conductances in Hodgkin-Huxley neurons (Mainen et al., 1995). The response of single layer networks was studied with ramp-like stimuli with slopes that varied over several hundreds of milliseconds. Fast fluctuations were superimposed on this slowly-varying mean. Then the response to these networks was tested with continuous stimuli. Gain scaling networks captured the slow fluctuations in the inputs, while non-scaling networks simply thresholded the input. Quantifying information transmission confirmed that gain scaling neurons transmit more information about the stimulus. With the two-layer networks we simulated a cortical network where waves could spontaneously emerge, propagate and degrade, based on the gain scaling properties of the neurons in the network