Search results for "entropy"
showing 10 items of 496 documents
Enthalpy/entropy compensation effects from cavity desolvation underpin broad ligand binding selectivity for rat odorant binding protein 3
2014
Evolution has produced proteins with exquisite ligand binding specificity, and manipulating this effect has been the basis for much of modern rational drug design. However, there are general classes of proteins with broader ligand selectivity linked to function, the origin of which is poorly understood. The odorant binding proteins (OBPs) sequester volatile molecules for transportation to the olfactory receptors. Rat OBP3, which we characterize by X-ray crystallography and NMR, binds a homologous series of aliphatic gamma-lactones within its aromatic-rich hydrophobic pocket with remarkably little variation in affinity but extensive enthalpy/entropy compensation effects. We show that the bin…
Entropy–enthalpy compensation at the single protein level: pH sensing in the bacterial channel OmpF
2014
The pH sensing mechanism of the OmpF channel operates via ligand modification: increasing acidity induces the replacement of cations with protons in critical binding sites decreasing the channel conductance. Aside from the change in enthalpy associated with the binding, there is also a change in the microscopic arrangements of ligands, receptors and the surrounding solvent. We show that the pH-modulation of the single channel conduction involves small free energy changes because large enthalpic and entropic contributions change in opposite ways, demonstrating an approximate enthalpy–entropy compensation for different salts and concentrations. We wish to acknowledge the support from the Span…
Transport properties of 2F = F2 in a temperature gradient as studied by molecular dynamics simulations
2007
International audience; We calculate transport properties of a reacting mixture of F and F2 from results of nonequilibrium molecular dynamics simulations. The reaction investigated is controlled by thermal diffusion and is close to local chemical equilibrium. The simulations show that a formulation of the transport problem in terms of classical non-equilibrium thermodynamics theory is sound. The chemical reaction has a large effect on the magnitude and temperature dependence of the thermal conductivity and the interdiffusion coefficient. The increase in the thermal conductivity in the presence of the chemical reaction, can be understood as a response to an imposed temperature gradient, whic…
Information entropy-based classification of triterpenoids and steroids from Ganoderma
2015
Abstract A set of 71 triterpenoid and steroid compounds from Ganoderma were periodically classified using a procedure based on information entropy with artificial intelligence. Six features were used in hierarchical order to classify the triterpenoids and steroids structurally. The phytochemicals belonging to the same group in the periodic table present similar antioxidant activity, and those compounds belonging to the same period exhibit maximum resemblance. The periodic classification is related to the experimental bioactivity and antioxidant potency data that are available in the literature: a steroid with a three-ketone group conjugated with two carbon–carbon double bonds in the right s…
Seaborgium's complex studies
2015
Christoph E. Dullmann reflects on the excitement, and implications, of probing the reactivity of heavy element seaborgium.
Efficient Computation of Multiscale Entropy over Short Biomedical Time Series Based on Linear State-Space Models
2017
The most common approach to assess the dynamical complexity of a time series across multiple temporal scales makes use of the multiscale entropy (MSE) and refined MSE (RMSE) measures. In spite of their popularity, MSE and RMSE lack an analytical framework allowing their calculation for known dynamic processes and cannot be reliably computed over short time series. To overcome these limitations, we propose a method to assess RMSE for autoregressive (AR) stochastic processes. The method makes use of linear state-space (SS) models to provide the multiscale parametric representation of an AR process observed at different time scales and exploits the SS parameters to quantify analytically the co…
Studying the evolution of neural activation patterns during training of feed-forward ReLU networks
2021
The ability of deep neural networks to form powerful emergent representations of complex statistical patterns in data is as remarkable as imperfectly understood. For deep ReLU networks, these are encoded in the mixed discrete–continuous structure of linear weight matrices and non-linear binary activations. Our article develops a new technique for instrumenting such networks to efficiently record activation statistics, such as information content (entropy) and similarity of patterns, in real-world training runs. We then study the evolution of activation patterns during training for networks of different architecture using different training and initialization strategies. As a result, we see …
MuTE: a new matlab toolbox for estimating the multivariate transfer entropy in physiological variability series
2014
We present a new time series analysis toolbox, developed in Matlab, for the estimation of the Transfer entropy (TE) between time series taken from a multivariate dataset. The main feature of the toolbox is its fully multivariate implementation, that is made possible by the design of an approach for the non-uniform embedding (NUE) of the observed time series. The toolbox is equipped with parametric (linear) and non-parametric (based on binning or nearest neighbors) entropy estimators. All these estimators, implemented using the NUE approach in comparison with the classical approach based on uniform embedding, are tested on RR interval, systolic pressure and respiration variability series mea…
Information Dynamics Analysis: A new approach based on Sparse Identification of Linear Parametric Models*
2020
The framework of information dynamics allows to quantify different aspects of the statistical structure of multivariate processes reflecting the temporal dynamics of a complex network. The information transfer from one process to another can be quantified through Transfer Entropy, and under the assumption of joint Gaussian variables it is strictly related to the concept of Granger Causality (GC). According to the most recent developments in the field, the computation of GC entails representing the processes through a Vector Autoregressive (VAR) model and a state space (SS) model typically identified by means of the Ordinary Least Squares (OLS). In this work, we propose a new identification …
MuTE: a MATLAB toolbox to compare established and novel estimators of the multivariate transfer entropy.
2014
A challenge for physiologists and neuroscientists is to map information transfer between components of the systems that they study at different scales, in order to derive important knowledge on structure and function from the analysis of the recorded dynamics. The components of physiological networks often interact in a nonlinear way and through mechanisms which are in general not completely known. It is then safer that the method of choice for analyzing these interactions does not rely on any model or assumption on the nature of the data and their interactions. Transfer entropy has emerged as a powerful tool to quantify directed dynamical interactions. In this paper we compare different ap…