SBML Reaction Finder: Retrieve and extract specific reactions from the BioModels database

Summary: The SBML Reaction Finder (SRF) application leverages the deep semantic annotations in the BioModels database to provide efficient retrieval and extraction of individual reactions from SBML models. We hope that the SRF will be useful to quantitative modelers who seek to accelerate their modeling efforts by reusing previously published representations of specific chemical reactions.

Availability and Implementation: The SRF is open source, coded in Java, and distributed under the Mozilla Pubic License Version 1.1. Windows, Macintosh and Linux distributions are available for download at 
http://sourceforge.net/projects/sbmlrxnfinder.
&#xa

Analogy between turbulence and quantum gravity: beyond Kolmogorov's 1941 theory

Simple arguments based on the general properties of quantum fluctuations have been recently shown to imply that quantum fluctuations of spacetime obey the same scaling laws of the velocity fluctuations in a homogeneous incompressible turbulent flow, as described by Kolmogorov 1941 (K41) scaling theory. Less noted, however, is the fact that this analogy rules out the possibility of a fractal quantum spacetime, in contradiction with growing evidence in quantum gravity research. In this Note, we show that the notion of a fractal quantum spacetime can be restored by extending the analogy between turbulence and quantum gravity beyond the realm of K41 theory. In particular, it is shown that compatibility of a fractal quantum-space time with the recent Horava-Lifshitz scenario for quantum gravity, implies singular quantum wavefunctions. Finally, we propose an operational procedure, based on Extended Self-Similarity techniques, to inspect the (multi)-scaling properties of quantum gravitational fluctuations.Comment: Sliglty modified version of the article about to appear in IJMP

Performances of infrared emitters applied to the porous thin materials drying

Drying of solids is one of the oldest and most common unit operations found in diverse processes. In this paper the drying of hygroscopic textile materials is discussed. The authors have previously investigated the drying kinetic of different fabrics dried by a hot air jet. In this paper a comparison between the convective and electric IR drying is made. In particular two fabrics with fibers which show a different hygroscopic behaviour are analysed: wool and cellulose/cotton. Unlike the convective drying, IR drying is weakly affected by the radiation properties and by the hygroscopic behaviour of the two fabrics. This is likely due to a better diffusion of the heat flux, which is constant over the entire drying surface in the case of IR heating, and produces unexpected results on the nondimensional kinetic parameter (characteristic curve). Wool shows a complete different characteristic curve if dried with IR or with convective flow. The better performances have been reached with MW emitter, but it has been observed that this advantage decreases with the distance of the source from the surface to be dried

Closed Loop Pulsating Heat Pipes: Ground and Microgravity experiments

non

Models of polymer solutions in electrified jets and solution blowing

Fluid flows hosting electrical phenomena make the subject of a fascinating and highly interdisciplinary scientific field. In recent years, the extraordinary success of electrospinning and solution blowing technologies for the generation of polymer nanofibers has motivated vibrant research aiming at rationalizing the behavior of viscoelastic jets under applied electric fields or other stretching fields including gas streams. Theoretical models unveiled many original aspects in the underpinning physics of polymer solutions in jets, and provided useful information to improve experimental platforms. This article reviews advances in the theoretical description and numerical simulation of polymer solution jets in electrospinning and solution blowing. Instability phenomena of electrical and hydrodynamic origin are highlighted, which play a crucial role in the relevant flow physics. Specifications leading to accurate and computationally viable models are formulated. Electrohydrodynamic modeling, theories for the jet bending instability, recent advances in Lagrangian approaches to describe the jet flow, including strategies for dynamic refinement of simulations, and effects of strong elongational flow on polymer networks are reviewed. Finally, the current challenges and future perspectives of the field are outlined and discussed, including the task of correlating the physics of the jet flows with the properties of realized materials, as well as the development of multiscale techniques for modelling viscoelastic jets.Comment: 135 pages, 42 figure

Michaelis-Menten dynamics in protein subnetworks

To understand the behaviour of complex systems it is often necessary to use models that describe the dynamics of subnetworks. It has previously been established using projection methods that such subnetwork dynamics generically involves memory of the past, and that the memory functions can be calculated explicitly for biochemical reaction networks made up of unary and binary reactions. However, many established network models involve also Michaelis-Menten kinetics, to describe e.g. enzymatic reactions. We show that the projection approach to subnetwork dynamics can be extended to such networks, thus significantly broadening its range of applicability. To derive the extension we construct a larger network that represents enzymes and enzyme complexes explicitly, obtain the projected equations, and finally take the limit of fast enzyme reactions that gives back Michaelis-Menten kinetics. The crucial point is that this limit can be taken in closed form. The outcome is a simple procedure that allows one to obtain a description of subnetwork dynamics, including memory functions, starting directly from any given network of unary, binary and Michaelis-Menten reactions. Numerical tests show that this closed form enzyme elimination gives a much more accurate description of the subnetwork dynamics than the simpler method that represents enzymes explicitly, and is also more efficient computationally

Are objective measures of sleep and sedentary behaviours related to low back pain flares?

Final peer-reviewed manuscript[Abstract] Risk factors for low back pain (LBP) flares have been considered about self-reported measures. This case–crossover study aimed to investigate whether (1) objective measures of physical activity and sleep were associated with the risk of experiencing LBP flares and (2) these associations differed for flares defined as pain 2 or more points greater than average pain over the period using an 11-point Numerical rating scale (0-no pain and 10-worst pain imaginable) (pain-defined flare: PDF) and flares identified by participants according to a broader definition that considered emotions or coping (self-reported flare [SRF]). We included 126 participants who had experienced LBP for >3 months. Physical activity and sleep were monitored for 28 days using wearable sensors. Occurrence of flares (PDF or SRF) was assessed daily using a smartphone application. Data on exposure to risk factors 1, 2, and 3 days preceding PDF or SRF were compared with nonflare control periods. Conditional logistic regression determined association between each factor and flares. Data show that day-to-day variation in physical activity and in-bed time are associated with the risk of LBP flares, but associations differ depending on how flare is defined. Longer in-bed time increased the risk of PDF but not SRF. Although physical activity was not associated with the risk of PDF, greater sedentary behaviour increased the risk of SRF and being more physically active decreased the risk for SRF. These results highlight the potential role of targeting sleep and physical activity in interventions to prevent LBP flares and indicate that risk factors differ depending on how LBP flares are defined.Centre of Research Excellence (Australia); APP1091302Centre of Research Excellence (Australia); APP1079078National Health and Medical Research Council (NHMRC) of Australia; PH—APP1102905National Health and Medical Research Council (NHMRC) of Australia; MF—APP114359