999 resultados para Hydrodynamic interactions


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The interactions between granulocyte-colony stimulating factor (G-CSF) and dextran sulfate/kappa-carrageenan oligosaccharide were studied by capillary zone electrophoresis. Dextran sulfate could strongly interact with G-CSF and the complex was detected. The binding constant and stoichiometry were determined to be 1.2x10(6) (mol/L)(-1) and 3:1, respectively. However, the interaction between K-carrageenan oligosaccharide and G-CSF was not found.

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Molecularly imprinted polymer, exhibiting considerable enantioselectivity for L-mandelic acid, was prepared using metal coordination-chelation interaction. By evaluating the recognition characteristics in the chromatographic mode, the recognition interactions were proposed: specific and nonspecific metal coordination-chelation interaction and hydrophobic interaction were responsible for substrate binding on metal-complexing imprinted polymer; while the selective recognition only came from specific metal coordination-chelation interaction and specific hydrophobic interaction.

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Aqueous solutions of amphiphilic polymers usually comprise of inter- and intramolecular associations of hydrophobic groups often leading to a formation of a rheologically significant reversible network at low concentrations that can be identified using techniques such as static light scattering and rheometry. However, in most studies published till date comparing water soluble polymers with their respective amphiphilic derivatives, it has been very difficult to distinguish between the effects of molecular mass versus hydrophobic associations on hydrodynamic (intrinsic viscosity [g]) and thermodynamic parameters (second virial coefficient A2), owing to the differences between their degrees of polymerization. This study focuses on the dilute and semi-dilute solutions of hydroxyethyl cellulose (HEC) and its amphiphilic derivatives (hmHEC) of the same molecular mass, along with other samples having a different molecular mass using capillary viscometry, rheometry and static light scattering. The weight average molecular masses (MW) and their distributions for the nonassociative HEC were determined using size exclusion chromatography. Various empirical approaches developed by past authors to determine [g] from dilute solution viscometry data have been discussed. hmHEC with a sufficiently high degree of hydrophobic modification was found to be forming a rheologically significant network in dilute solutions at very low concentrations as opposed to the hmHEC with a much lower degree of hydrophobic modification which also enveloped the hydrophobic groups inside the supramolecular cluster as shown by their [g] and A2. The ratio A2MW/[g], which takes into account hydrodynamic as well as thermodynamic parameters, was observed to be less for associative polymers compared to that of the non-associative polymers.

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Gough, John, (2004) 'Quantum Flows as Markovian Limit of Emission, Absorption and Scattering Interactions', Communications in Mathematical Physics 254 pp.498-512 RAE2008

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Poolton, Nigel; Towlson, B.M.; Hamilton, B.; Evans, D.A., (2006) 'Synchrotron-laser interactions in hexagonal boron nitride: an examination of charge trapping dynamics at the boron K-edge', New Journal of Physics 8 pp.76 RAE2008

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This is an author-created, un-copyedited version of an article accepted for publication in Acta Physica Polonica A. The Version of Record is available online at http://przyrbwn.icm.edu.pl/APP/PDF/118/a118z2p31.pdf

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This paper describes a neural model of speech acquisition and production that accounts for a wide range of acoustic, kinematic, and neuroimaging data concerning the control of speech movements. The model is a neural network whose components correspond to regions of the cerebral cortex and cerebellum, including premotor, motor, auditory, and somatosensory cortical areas. Computer simulations of the model verify its ability to account for compensation to lip and jaw perturbations during speech. Specific anatomical locations of the model's components are estimated, and these estimates are used to simulate fMRI experiments of simple syllable production with and without jaw perturbations.

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How do visual form and motion processes cooperate to compute object motion when each process separately is insufficient? A 3D FORMOTION model specifies how 3D boundary representations, which separate figures from backgrounds within cortical area V2, capture motion signals at the appropriate depths in MT; how motion signals in MT disambiguate boundaries in V2 via MT-to-Vl-to-V2 feedback; how sparse feature tracking signals are amplified; and how a spatially anisotropic motion grouping process propagates across perceptual space via MT-MST feedback to integrate feature-tracking and ambiguous motion signals to determine a global object motion percept. Simulated data include: the degree of motion coherence of rotating shapes observed through apertures, the coherent vs. element motion percepts separated in depth during the chopsticks illusion, and the rigid vs. non-rigid appearance of rotating ellipses.

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This article applies a recent theory of 3-D biological vision, called FACADE Theory, to explain several percepts which Kanizsa pioneered. These include 3-D pop-out of an occluding form in front of an occluded form, leading to completion and recognition of the occluded form; 3-D transparent and opaque percepts of Kanizsa squares, with and without Varin wedges; and interactions between percepts of illusory contours, brightness, and depth in response to 2-D Kanizsa images. These explanations clarify how a partially occluded object representation can be completed for purposes of object recognition, without the completed part of the representation necessarily being seen. The theory traces these percepts to neural mechanisms that compensate for measurement uncertainty and complementarity at individual cortical processing stages by using parallel and hierarchical interactions among several cortical processing stages. These interactions are modelled by a Boundary Contour System (BCS) that generates emergent boundary segmentations and a complementary Feature Contour System (FCS) that fills-in surface representations of brightness, color, and depth. The BCS and FCS interact reciprocally with an Object Recognition System (ORS) that binds BCS boundary and FCS surface representations into attentive object representations. The BCS models the parvocellular LGN→Interblob→Interstripe→V4 cortical processing stream, the FCS models the parvocellular LGN→Blob→Thin Stripe→V4 cortical processing stream, and the ORS models inferotemporal cortex.