arXiv subjects

C. Jayaprakash

Publications and source records attributed to C. Jayaprakash.

13 recordsLinked to original sources

arXiv2019

Broadband EPR Spectroscopy in Diverse Field Conditions Using Optically Detected Nitrogen-Vacancy Centers in Diamond

Paramagnetic magnetic resonance, a powerful technique for characterizing and identifying chemical targets, is increasingly used for imaging; however, low spin polarization at room temperature and moderate magnetic fields poses challenges for detecting small numbers of spins. In this work, we use fluorescence from nitrogen-vacancy (NV) centers in diamond to detect the electron paramagnetic resonance (EPR) spectrum of optically inactive target spins under various conditions of field magnitude and orientation. The protocol requires neither direct microwave manipulation of the NV spins nor spectral overlap between NV and target spin resonances, thus enabling broadband detection. This unexpected non-resonant coupling is attributable to a two-phonon process that relaxes NV spins proximate to the fluctuating dipole moment of the target spin, suggesting that the sensitivity is determined by the dipole-dipole coupling strength. This approach holds promise for sensitive EPR detection, particularly in settings where control over the diamond-crystal orientation is difficult. This is notably the case for biological sensing applications, where nanodiamonds are being pursued for their bright, stable fluorescence and biocompatibility.

cond-mat.mes-hall↗

arXiv2014

Mixed Poisson distributions in exact solutions of stochastic auto-regulation models

In this paper we study the interplay between stochastic gene expression and system design using simple stochastic models of auto-activation and auto-inhibition. Using the Poisson Representation, a technique whose particular usefulness in the context of non-linear gene regulation models we elucidate, we find exact results for these feedback models in the steady state. Further, we exploit this representation to analyze the parameter spaces of each model, determine which dimensionless combinations of rates are the shape determinants for each distribution, and thus demarcate where in the parameter-space qualitatively different behaviors arise. These behaviors include power-law tailed distributions, bimodal distributions and sub-Poisson distributions. We also show how these distribution shapes change when the strength of the feedback is tuned. Using our results, we reexamine how well the auto-inhibition and auto-activation models serve their conventionally assumed roles as paradigms for noise suppression and noise exploitation, respectively.

q-bio.QM↗

arXiv2014

Host-to-host variation of ecological interactions in polymicrobial infections

Host-to-host variability with respect to interactions between microorganisms and multicellular hosts are commonly observed in infection and in homeostasis. However, the majority of mechanistic models used in analyzing host-microorganism relationships, as well as most of the ecological theories proposed to explain co-evolution of host and microbes, are based on averages across a host population. By assuming that observed variations are random and independent, these models overlook the role of inter-host differences. Here we analyze mechanisms underlying host-to-host variations, using the well-characterized experimental infection model of polymicrobial otitis media (OM) in chinchillas, in combination with population dynamic models and a Maximum Entropy (MaxEnt) based inference scheme. We find that the nature of the interactions among bacterial species critically regulates host-to-host variations of these interactions. Surprisingly, seemingly unrelated phenomena, such as the efficiency of individual bacterial species in utilizing nutrients for growth and the microbe-specific host immune response, can become interdependent in a host population. The latter finding suggests a potential mechanism that could lead to selection of specific strains of bacterial species during the coevolution of the host immune response and the bacterial species.

q-bio.PE↗

arXiv2014

Observation of Pure Spin Transport in a Diamond Spin Wire

Spin transport electronics - spintronics - focuses on utilizing electron spin as a state variable for quantum and classical information processing and storage. Some insulating materials, such as diamond, offer defect centers whose associated spins are well-isolated from their environment giving them long coherence times; however, spin interactions are important for transport, entanglement, and read-out. Here, we report direct measurement of pure spin transport - free of any charge motion - within a nanoscale quasi 1D 'spin wire', and find a spin diffusion length ~ 700 nm. We exploit the statistical fluctuations of a small number of spins ($\sqrt{N}$ < 100 net spins) which are in thermal equilibrium and have no imposed polarization gradient. The spin transport proceeds by means of magnetic dipole interactions that induce flip-flop transitions, a mechanism that can enable highly efficient, even reversible, pure spin currents. To further study the dynamics within the spin wire, we implement a magnetic resonance protocol that improves spatial resolution and provides nanoscale spectroscopic information which confirms the observed spin transport. This spectroscopic tool opens a potential route for spatially encoding spin information in long-lived nuclear spin states. Our measurements probe intrinsic spin dynamics at the nanometre scale, providing detailed insight needed for practical devices which seek to control spin.

cond-mat.mes-hall↗

arXiv2007

Transcriptional pulsing and consequent stochasticity in gene expression

Transcriptional pulsing has been observed in both prokaryotes and eukaryotes and plays a crucial role in cell to cell variability of protein and mRNA numbers. The issue is how the time constants associated with episodes of transcriptional bursting impact cellular mRNA and protein distributions and reciprocally, to what extent experimentally observed distributions can be attributed to transcriptional pulsing. We address these questions by investigating the exact time-dependent solution of the Master equation for a transcriptional pulsing model of mRNA distributions. We find a plethora of results: we show that, among others, bimodal and long-tailed (power law) distributions occur in the steady state as the rate constants are varied over biologically significant time scales. Since steady state distributions may not be reached experimentally we present results for the time evolution of the distributions. Because cellular behavior is essentially determined by proteins, we investigate the effect of the different mRNA distributions on the corresponding protein distributions. We delineate the regimes of rate constants for which the protein distribution mimics the mRNA distribution and those for which the protein distribution deviates significantly from the mRNA distribution.

q-bio.QM↗

arXiv2006

Conditional Probability as a Measure of Volatility Clustering in Financial Time Series

In the past few decades considerable effort has been expended in characterizing and modeling financial time series. A number of stylized facts have been identified, and volatility clustering or the tendency toward persistence has emerged as the central feature. In this paper we propose an appropriately defined conditional probability as a new measure of volatility clustering. We test this measure by applying it to different stock market data, and we uncover a rich temporal structure in volatility fluctuations described very well by a scaling relation. The scale factor used in the scaling provides a direct measure of volatility clustering; such a measure may be used for developing techniques for option pricing, risk management, and economic forecasting. In addition, we present a stochastic volatility model that can display many of the salient features exhibited by volatilities of empirical financial time series, including the behavior of conditional probabilities that we have deduced.

physics.soc-ph↗

arXiv2005

NF-kappa B oscillations and cell-to-cell variability

Oscillations of the transcription factor NF-kappa B have been observed experimentally both for cell populations by Hoffmann et al. (2002) and for single cells by Nelson et al. (2004). The latter experiments show significant cell-to-cell variability. We use the (stochastic) Gillespie algorithm to study the set of reactions proposed by Hoffmann et al. and variants thereof. The amounts of cellular NF-kappa B and activated kinase IKK are treated as external parameters. We show that intrinsic fluctuations are small in a model with strong transcription, while they are large for weak transcription. Extrinsic noise can be significant: fluctuations in NF-kappa B affect mainly the amplitude of oscillations, whereas fluctuations in activated IKK affect both their amplitude and period. The latter results are in qualitative agreement with the observations of Nelson et al.

q-bio.QM↗

arXiv2005

Log-Poisson Statistics and Extended Self-Similarity in Driven Dissipative Systems

The Bak-Chen-Tang forest fire model was proposed as a toy model of turbulent systems, where energy (in the form of trees) is injected uniformly and globally, but is dissipated (burns) locally. We review our previous results on the model and present our new results on the statistics of the higher-order moments for the spatial distribution of fires. We show numerically that the spatial distribution of dissipation can be described by Log-Poisson statistics which leads to extended self-similarity (ESS). Similar behavior is also found in models based on directed percolation; this suggests that the concept of Log-Poisson statistics of (appropriately normalized) variables can be used to describe scaling not only in turbulence but also in a wide range of driven dissipative systems.

cond-mat.stat-mech↗

arXiv2002

An exact derivation of the dissipation rate correlation exponent μin fully-developed turbulence

We derive for the Navier-Stokes equation an exact equation satisfied by the dissipation rate correlation function. In the equal time limit, in the inertial range, for the homogeneous, isotropic state of fully-developed turbulence, we show that the correlation is characterized by two exponents, one of which depends on the sixth-order static structure function exponent and the other involves the behavior of the fourth-order dynamical structure function. This provides a direct derivation of the intermittency exponent of the dissipation-rate correlation.

nlin.CD↗

arXiv2000

Aspects of the stochastic Burgers equation and their connection with turbulence

We present results for the 1 dimensional stochastically forced Burgers equation when the spatial range of the forcing varies. As the range of forcing moves from small scales to large scales, the system goes from a chaotic, structureless state to a structured state dominated by shocks. This transition takes place through an intermediate region where the system exhibits rich multifractal behavior. This is mainly the region of interest to us. We only mention in passing the hydrodynamic limit of forcing confined to large scales, where much work has taken place since that of Polyakov. In order to make the general framework clear, we give an introduction to aspects of isotropic, homogeneous turbulence, a description of Kolmogorov scaling, and, with the help of a simple model, an introduction to the language of multifractality which is used to discuss intermittency corrections to scaling. We continue with a general discussion of the Burgers equation and forcing, and some aspects of three dimensional turbulence where - because of the mathematical analogy between equations derived from the Navier-Stokes and Burgers equations - one can gain insight from the study of the simpler stochastic Burgers equation. These aspects concern the connection of dissipation rate intermittency exponents with those characterizing the structure functions of the velocity field, and the dynamical behavior, characterized by different time constants, of velocity structure functions. We also show how the exponents characterizing the multifractal behavior of velocity structure functions in the above mentioned transition region can effectively be calculated in the case of the stochastic Burgers equation.

nlin.CD↗

arXiv1999

Intermittency in Turbulence

We derive from the Navier-Stokes equation an exact equation satisfied by the dissipation rate correlation function. We exploit its mathematical similarity to the corresponding equation derived from the 1-dimensional stochastic Burgers equation to determine the intermittency exponents in terms of the exponents characterizing the behavior of the dynamical velocity structure functions. We discuss the role of sweeping and Galilean invariance in determining the intermittency exponents

chao-dyn↗

arXiv1997

Kondo Effect in Fermi Systems with a Gap: A Renormalization Group Study

We present the results of a Wilson Renormalization Group study of the single-impurity Kondo and Anderson models in a system with a gap in the conduction electron spectrum. The behavior of the impurity susceptibility and the zero-frequency response function, $T< >$ are discussed in the cases with and without particle-hole symmetry. In addition, for the asymmetric Anderson model the correlation functions, $<\vec S \cdot\vec σ(0)>$,$ $, and $ $ are computed.

cond-mat.str-el↗

arXiv1993

Non-Gaussian Distributions in Extended Dynamical Systems

We propose a novel mechanism for the origin of non-Gaussian tails in the probability distribution functions (PDFs) of local variables in nonlinear, diffusive, dynamical systems including passive scalars advected by chaotic velocity fields. Intermittent fluctuations on appropriate time scales in the amplitude of the (chaotic) noise can lead to exponential tails. We provide numerical evidence for such behavior in deterministic, discrete-time passive scalar models. Different possibilities for PDFs are also outlined.

cond-mat↗