Driven diffusive systems with mutually interactive Langmuir kinetics

We investigate the simple one-dimensional driven model, the totally asymmetric exclusion process, coupled to mutually interactive Langmuir kinetics. This model is motivated by recent studies on clustering of motor proteins on microtubules. In the proposed model, the attachment and detachment rates of a particle are modified depending upon the occupancy of neighbouring sites. We first obtain continuum mean-field equations and in certain limiting cases obtain analytic solutions. We show how mutual interactions increase (decrease) the effects of boundaries on the phase behavior of the model. We perform Monte Carlo simulations and demonstrate that our analytical approximations are in good agreement with the numerics over a wide range of model parameters. We present phase diagrams over a selective range of parameters.Comment: 9 pages, 8 Figure

Normal stresses in semiflexible polymer hydrogels

Biopolymer gels such as fibrin and collagen networks are known to develop tensile axial stress when subject to torsion. This negative normal stress is opposite to the classical Poynting effect observed for most elastic solids including synthetic polymer gels, where torsion provokes a positive normal stress. As recently shown, this anomalous behavior in fibrin gels depends on the open, porous network structure of biopolymer gels, which facilitates interstitial fluid flow during shear and can be described by a phenomenological two-fluid model with viscous coupling between network and solvent. Here we extend this model and develop a microscopic model for the individual diagonal components of the stress tensor that determine the axial response of semi-flexible polymer hydrogels. This microscopic model predicts that the magnitude of these stress components depends inversely on the characteristic strain for the onset of nonlinear shear stress, which we confirm experimentally by shear rheometry on fibrin gels. Moreover, our model predicts a transient behavior of the normal stress, which is in excellent agreement with the full time-dependent normal stress we measure.Comment: 12 pages, 8 figure

Effect of dietary wood betony, Stachys lavandulifolia extract on growth performance, haematological and biochemical parameters of common carp, Cyprinus carpio

A 6 week study was conducted to assess the effects of wood betony (WB), Stachys lavandulifolia extract on growth performance, hematological and biochemical parameters of common carp, Cyprinus carpio. Different levels of the WB extract (0, 2, 4 and 8 % weight per weight, W/W, 0WB, 2WB, 4WB and 8WB) in the diet were used. The results showed that final weight and weight gain were significantly improved by WB (p0.05). There were no significant differences in hemoglobin, hematocrit, mean erythrocytes of hemoglobin, mean erythrocyte volume, mean hemoglobin erythrocyte concentration and white blood cell (WBC) counts (p>0.05), while, red blood cells (RBC) counts showed significant declining trend by increasing the level of the plant extract from control to 8WB (p<0.05). Significant elevation in the levels of total protein, albumin and globulin and albumin/globulin ratio by increasing WB concentration in the diet were observed (p<0.05). Diet enriched by WB could decrease serum level of triglycerides and cholesterol in comparison with the control (p<0.05). Based on the results of this study, it could be concluded that feeding common carp with WB can improve growth and some immunity characteristics as well as lipid metabolism

Strain-driven criticality underlies nonlinear mechanics of fibrous networks

Networks with only central force interactions are floppy when their average connectivity is below an isostatic threshold. Although such networks are mechanically unstable, they can become rigid when strained. It was recently shown that the transition from floppy to rigid states as a function of simple shear strain is continuous, with hallmark signatures of criticality [Sharma et al., Nature Phys. 12, 584 (2016)]. The nonlinear mechanical response of collagen networks was shown to be quantitatively described within the framework of such mechanical critical phenomenon. Here, we provide a more quantitative characterization of critical behavior in subisostatic networks. Using finite-size scaling we demonstrate the divergence of strain fluctuations in the network at well-defined critical strain. We show that the characteristic strain corresponding to the onset of strain stiffening is distinct from but related to this critical strain in a way that depends on critical exponents. We confirm this prediction experimentally for collagen networks. Moreover, we find that the apparent critical exponents are largely independent of the spatial dimensionality. With subisostaticity as the only required condition, strain-driven criticality is expected to be a general feature of biologically relevant fibrous networks

Concept Store #2: Possible, Probable and Preferable Futures

Possible, Probable and Preferable Futures Launched in 2008 and running to three volumes Concept Store reflects on Arnolfini’s programme, as well as wider concerns providing a discursive space for commissioned texts, artists’ contributions, interviews and experiment. Issue 2 includes contributions by Heiremans & Vermeir, Francesc Ruiz, Graham Gussin, Herman Chong, Tommy Stockel, Marjolin Dijkman, Neil Cummings & Marysia Lewandowsa, Dieter Roelstraate, Max Gane, Bifo, Richard Grussin, Will Holder, Mark von Schlegell, Laura Oldfield Ford, Liu Ding, Geoff Cox, Nav Haq, Tom Trevor, Metahaven, Kianoosh vahebi and Cher Potter Published under a copyleft licenc

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