Second virial coefficient of alpha-crystallin

Light scattering studies were performed on bovine alpha-crystallin measuring the scattering intensities as a function of scattering angle, concentration, and temperature. The data yielded the molecular weight, radius of gyration, and second virial coefficient of alpha-crystallin at different temperatures. The second virial coefficient increased with increasing temperature. Both the enthalpy and entropy of solution of alpha-crystallin are positive. The Flory theta temperature was found to be 271 K.     

Entropy as a factor in the binding of γ-aminobutyric acid and nipecotic acid to the γ-aminobutyric acid transport system

Nipecotic acid is one of the most potent competitive inhibitors and alternative substrates for the high-affinity -aminobutyric acid transport system in neurons, but the structural basis of this potency is unclear. Because -aminobutyrate is a highly flexible molecule in solution, it would be expected to lose rotational entropy upon binding to the transport system, a change which does not favor binding. Nipecotic acid, in contrast, is a much less flexible molecule, and one would expect the loss of conformational entropy upon binding to be smaller thus favoring the binding of nipecotic acid over -aminobutyric acid. To investigate this possibility, the thermodynamic parameters, G°, H°, and S°, were determined for the binding of -aminobutyrate and nipecotic acid to the high affinity GABA transport system in synaptosomes. In keeping with expectations, the apparent entropy change for nipecotic acid binding (112±13 J·K^–1) was more favorable than the apparent entropy change for -aminobutyric acid binding (61.3±6.6 J·K^–1). The results suggest that restricted conformation per se is an important contributory factor to the affinity of nipecotic acid for the high-affinity transport system for -aminobutyric acid.This work was conducted when both authors were at the Department of Chemistry, University of Maryland, College Park.Special issue dedicated to Dr. Elling Kvamme.     

细胞寒害的热力学熵理论(Ⅱ)──超熵产生作为寒害稳定性判据的理论研究

本文从物质和能量交换的角度,运用非平衡态热力学超熵产生理论,分析了寒害定态的稳定性,并建立了超熵产生判据．理论分析所得的结论与实验结果基本相符．     

Insights into the local residual entropy of proteins provided by NMR relaxation.

A simple model is used to illustrate the relationship between the dynamics measured by NMR relaxation methods and the local residual entropy of proteins. The expected local dynamic behavior of well-packed extended amino acid side chains are described by employing a one-dimensional vibrator that encapsulates both the spatial and temporal character of the motion. This model is then related to entropy and to the generalized order parameter of the popular "model-free" treatment often used in the analysis of NMR relaxation data. Simulations indicate that order parameters observed for the methyl symmetry axes in, for example, human ubiquitin correspond to significant local entropies. These observations have obvious significance for the issue of the physical basis of protein structure, dynamics, and stability.     

Information-theoretical entropy as a measure of sequence variability.

We propose the use of the information-theoretical entrophy, S = -sigman pi log2 pi, as a measure of variability at a given position in a set of aligned sequences. pi stands for the fraction of times the i-th type appears at a position. For protein sequences, the sum has up to 20 terms, for nucleotide sequences, up to 4 terms, and for codon sequences, up to 61 terms. We compare S and Vs, a related measure, in detail with Vk, the traditional measure of immunoglobulin sequence variability, both in the abstract and as applied to the immunoglobulins. We conclude that S has desirable mathematical properties that Vk lacks and has intuitive and statistical meanings that accord well with the notion of variability. We find that Vk and the S-based measures are highly correlated for the immunoglobulins. We show by analysis of sequence data and by means of a mathematical model that this correlation is due to a strong tendency for the frequency of occurrence of amino acid types at a given position to be log-linear. It is not known whether the immunoglobulins are typical or atypical of protein families in this regard, nor is the origin of the observed rank-frequency distribution obvious, although we discuss several possible etiologies.     

An Informational Approach to the Analysis of the Low-Frequency Neuronal Impulse Activity in the Rostral Hypothalamus

Statistical analysis of the low-frequency (1.0 sec^-1 and lower) neuronal impulse activity (IA) meets a few fundamental difficulties. Among them, the most significant is the small number of measurements (interspike intervals) recorded within an acceptable analysis epoch. In our study, we examined the possibility of using the normalized (by its maximum value) informational entropy (Hn) for estimation of the significance of changes in the IA generated by low-frequency neurons of the rostral hypothalamus after electrical stimulation of the prefrontal cortex. We compared the efficiencies of using the U-test (Kolmogorov–Mann–Whitney) and Hn estimate for the analysis of the same samples of neuronal responses. The results allow us to conclude that Hn is a significantly more acceptable estimate for detection of stimulation-induced modifications of the IA generated by low-frequency neurons, as compared with the U-test. The direction of shifts in the Hn value makes it possible to estimate the pattern of neuronal response. This value reflects the state of the neuron and correlates with the type of neuronal responses.     

Deep Learning Using Havrda-Charvat Entropy for Classification of Pulmonary Optical Endomicroscopy

1) ObjectivePulmonary optical endomicroscopy (POE) is an imaging technology in real time. It allows to examine pulmonary alveoli at a microscopic level. Acquired in clinical settings, a POE image sequence can have as much as 25% of the sequence being uninformative frames (i.e. pure-noise and motion artifacts). For future data analysis, these uninformative frames must be first removed from the sequence. Therefore, the objective of our work is to develop an automatic detection method of uninformative images in endomicroscopy images.2) Material and methodsWe propose to take the detection problem as a classification one. Considering advantages of deep learning methods, a classifier based on CNN (Convolutional Neural Network) is designed with a new loss function based on Havrda-Charvat entropy which is a parametrical generalization of the Shannon entropy. We propose to use this formula to get a better hold on all sorts of data since it provides a model more stable than the Shannon entropy.3) ResultsOur method is tested on one POE dataset including 3895 distinct images and is showing better results than using Shannon entropy and behaves better with regard to the problem of overfitting. We obtain 70% of accuracy with Shannon entropy versus 77 to 79% with Havrda-Charvat.4) ConclusionWe can conclude that Havrda-Charvat entropy is better suited for restricted and or noisy datasets due to its generalized nature. It is also more suitable for classification in endomicroscopy datasets.     

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

We demonstrate methods for the detection of architectural distortion in prior mammograms of interval-cancer cases based on analysis of the orientation of breast tissue patterns in mammograms. We hypothesize that architectural distortion modifies the normal orientation of breast tissue patterns in mammographic images before the formation of masses or tumors. In the initial steps of our methods, the oriented structures in a given mammogram are analyzed using Gabor filters and phase portraits to detect node-like sites of radiating or intersecting tissue patterns. Each detected site is then characterized using the node value, fractal dimension, and a measure of angular dispersion specifically designed to represent spiculating patterns associated with architectural distortion.Our methods were tested with a database of 106 prior mammograms of 56 interval-cancer cases and 52 mammograms of 13 normal cases using the features developed for the characterization of architectural distortion, pattern classification via quadratic discriminant analysis, and validation with the leave-one-patient out procedure. According to the results of free-response receiver operating characteristic analysis, our methods have demonstrated the capability to detect architectural distortion in prior mammograms, taken 15 months (on the average) before clinical diagnosis of breast cancer, with a sensitivity of 80% at about five false positives per patient.