Major air pollutants and risk of COPD exacerbations: a systematic review and meta-analysis

published_or_final_versio

The liquid-glass-jamming transition in disordered ionic nanoemulsions

In quenched disordered out-of-equilibrium many-body colloidal systems, there are important distinctions between the glass transition, which is related to the onset of nonergodicity and loss of low-frequency relaxations caused by crowding, and the jamming transition, which is related to the dramatic increase in elasticity of the system caused by the deformation of constituent objects. For softer repulsive interaction potentials, these two transitions become increasingly smeared together, so measuring a clear distinction between where the glass ends and where jamming begins becomes very difficult or even impossible. Here, we investigate droplet dynamics in concentrated silicone oil-in-water nanoemulsions using light scattering. For zero or low NaCl electrolyte concentrations, interfacial repulsions are soft and longer in range, this transition sets in at lower concentrations, and the glass and the jamming regimes are smeared. However, at higher electrolyte concentrations the interactions are stiffer, and the characteristics of the glass-jamming transition resemble more closely the situation of disordered elastic spheres having sharp interfaces, so the glass and jamming regimes can be distinguished more clearly

Evaluating the Viscoelastic Properties of Tissue from Laser Speckle Fluctuations

Most pathological conditions such as atherosclerosis, cancer, neurodegenerative, and orthopedic disorders are accompanied with alterations in tissue viscoelasticity. Laser Speckle Rheology (LSR) is a novel optical technology that provides the invaluable potential for mechanical assessment of tissue in situ. In LSR, the specimen is illuminated with coherent light and the time constant of speckle fluctuations, τ, is measured using a high speed camera. Prior work indicates that τ is closely correlated with tissue microstructure and composition. Here, we investigate the relationship between LSR measurements of τ and sample mechanical properties defined by the viscoelastic modulus, G*. Phantoms and tissue samples over a broad range of viscoelastic properties are evaluated using LSR and conventional mechanical testing. Results demonstrate a strong correlation between τ and |G*| for both phantom (r = 0.79, p <0.0001) and tissue (r = 0.88, p<0.0001) specimens, establishing the unique capability of LSR in characterizing tissue viscoelasticity

Measurement of Viscosity in Liquids Using Reflection Coefficient: Phase Difference Method

Measurement of viscosity of fluids is a critical parameter in determining the state of the fluid (ie. edible products), and the state of the forming solid (ie. molten metals and glasses). Experiments to measure viscosity using ultrasound, have been carried out since as early as 1951 [1]. Ultrasound has potentially offered a non-invasive, in-line method of property and process monitoring [2,3]. Early research has demonstrated that viscosity measurement can be accomplished by ultrasound using different linear and nonlinear techniques [4]. This paper is devoted to furthering the technique called shear reflectance method [5]

Mean first-passage times of non-Markovian random walkers in confinement

The first-passage time (FPT), defined as the time a random walker takes to reach a target point in a confining domain, is a key quantity in the theory of stochastic processes. Its importance comes from its crucial role to quantify the efficiency of processes as varied as diffusion-limited reactions, target search processes or spreading of diseases. Most methods to determine the FPT properties in confined domains have been limited to Markovian (memoryless) processes. However, as soon as the random walker interacts with its environment, memory effects can not be neglected. Examples of non Markovian dynamics include single-file diffusion in narrow channels or the motion of a tracer particle either attached to a polymeric chain or diffusing in simple or complex fluids such as nematics \cite{turiv2013effect}, dense soft colloids or viscoelastic solution. Here, we introduce an analytical approach to calculate, in the limit of a large confining volume, the mean FPT of a Gaussian non-Markovian random walker to a target point. The non-Markovian features of the dynamics are encompassed by determining the statistical properties of the trajectory of the random walker in the future of the first-passage event, which are shown to govern the FPT kinetics.This analysis is applicable to a broad range of stochastic processes, possibly correlated at long-times. Our theoretical predictions are confirmed by numerical simulations for several examples of non-Markovian processes including the emblematic case of the Fractional Brownian Motion in one or higher dimensions. These results show, on the basis of Gaussian processes, the importance of memory effects in first-passage statistics of non-Markovian random walkers in confinement.Comment: Submitted version. Supplementary Information can be found on the Nature website : http://www.nature.com/nature/journal/v534/n7607/full/nature18272.htm

Global parameter identification of stochastic reaction networks from single trajectories

We consider the problem of inferring the unknown parameters of a stochastic biochemical network model from a single measured time-course of the concentration of some of the involved species. Such measurements are available, e.g., from live-cell fluorescence microscopy in image-based systems biology. In addition, fluctuation time-courses from, e.g., fluorescence correlation spectroscopy provide additional information about the system dynamics that can be used to more robustly infer parameters than when considering only mean concentrations. Estimating model parameters from a single experimental trajectory enables single-cell measurements and quantification of cell--cell variability. We propose a novel combination of an adaptive Monte Carlo sampler, called Gaussian Adaptation, and efficient exact stochastic simulation algorithms that allows parameter identification from single stochastic trajectories. We benchmark the proposed method on a linear and a non-linear reaction network at steady state and during transient phases. In addition, we demonstrate that the present method also provides an ellipsoidal volume estimate of the viable part of parameter space and is able to estimate the physical volume of the compartment in which the observed reactions take place.Comment: Article in print as a book chapter in Springer's "Advances in Systems Biology

Biological measurement beyond the quantum limit

Quantum noise places a fundamental limit on the per photon sensitivity attainable in optical measurements. This limit is of particular importance in biological measurements, where the optical power must be constrained to avoid damage to the specimen. By using non-classically correlated light, we demonstrated that the quantum limit can be surpassed in biological measurements. Quantum enhanced microrheology was performed within yeast cells by tracking naturally occurring lipid granules with sensitivity 2.4 dB beyond the quantum noise limit. The viscoelastic properties of the cytoplasm could thereby be determined with a 64% improved measurement rate. This demonstration paves the way to apply quantum resources broadly in a biological context

Generating MHV super-vertices in light-cone gauge

We constructe the $\mathcal{N}=1$ SYM lagrangian in light-cone gauge using chiral superfields instead of the standard vector superfield approach and derive the MHV lagrangian. The canonical transformations of the gauge field and gaugino fields are summarised by the transformation condition of chiral superfields. We show that $\mathcal{N}=1$ MHV super-vertices can be described by a formula similar to that of the $\mathcal{N}=4$ MHV super-amplitude. In the discussions we briefly remark on how to derive Nair's formula for $\mathcal{N}=4$ SYM theory directly from light-cone lagrangian.Comment: 25 pages, 7 figures, JHEP3 style; v2: references added, some typos corrected; Clarification on the condition used to remove one Grassmann variabl

Resolving the Role of Actoymyosin Contractility in Cell Microrheology

Einstein's original description of Brownian motion established a direct relationship between thermally-excited random forces and the transport properties of a submicron particle in a viscous liquid. Recent work based on reconstituted actin filament networks suggests that nonthermal forces driven by the motor protein myosin II can induce large non-equilibrium fluctuations that dominate the motion of particles in cytoskeletal networks. Here, using high-resolution particle tracking, we find that thermal forces, not myosin-induced fluctuating forces, drive the motion of submicron particles embedded in the cytoskeleton of living cells. These results resolve the roles of myosin II and contractile actomyosin structures in the motion of nanoparticles lodged in the cytoplasm, reveal the biphasic mechanical architecture of adherent cells—stiff contractile stress fibers interdigitating in a network at the cell cortex and a soft actin meshwork in the body of the cell, validate the method of particle tracking-microrheology, and reconcile seemingly disparate atomic force microscopy (AFM) and particle-tracking microrheology measurements of living cells