Modelling of chromatin morphologies in breast cancer cells undergoing apoptosis using generalized Cauchy field.

Chromatin morphologies in human breast cancer cells treated with an anti-cancer agent are analyzed at their early stage of programmed cell death or apoptosis. The gray-level images of nuclear chromatin are modelled as random fields. We used two-dimensional isotropic generalized Cauchy field to characterize local self-similarity and global long-range dependence behaviors in the image spatial data. Generalized Cauchy field allows the description of fractal behavior inferred from fractal dimension and the long-range dependence inferred from correlation exponent to be carried out independently. We demonstrated the usefulness of locally self-similar random fields with long-range dependence for modelling chromatin condensation

Reaction-diffusion kinetics on lattice at the microscopic scale

Lattice-based stochastic simulators are commonly used to study biological reaction-diffusion processes. Some of these schemes that are based on the reaction-diffusion master equation (RDME), can simulate for extended spatial and temporal scales but cannot directly account for the microscopic effects in the cell such as volume exclusion and diffusion-influenced reactions. Nonetheless, schemes based on the high-resolution microscopic lattice method (MLM) can directly simulate these effects by representing each finite-sized molecule explicitly as a random walker on fine lattice voxels. The theory and consistency of MLM in simulating diffusion-influenced reactions have not been clarified in detail. Here, we examine MLM in solving diffusion-influenced reactions in 3D space by employing the Spatiocyte simulation scheme. Applying the random walk theory, we construct the general theoretical framework underlying the method and obtain analytical expressions for the total rebinding probability and the effective reaction rate. By matching Collins-Kimball and lattice-based rate constants, we obtained the exact expressions to determine the reaction acceptance probability and voxel size. We found that the size of voxel should be about 2% larger than the molecule. MLM is validated by numerical simulations, showing good agreement with the off-lattice particle-based method, eGFRD. MLM run time is more than an order of magnitude faster than eGFRD when diffusing macromolecules with typical concentrations in the cell. MLM also showed good agreements with eGFRD and mean-field models in case studies of two basic motifs of intracellular signaling, the protein production-degradation process and the dual phosphorylation cycle. Moreover, when a reaction compartment is populated with volume-excluding obstacles, MLM captures the non-classical reaction kinetics caused by anomalous diffusion of reacting molecules

Classification and identification of frog sound based on entropy approach

A new classification method for animal sound identification using entropy-based approach is proposed. Entropy is a measure of information contents or complexity for a sequence of a signal. This study introduces three definitions of entropy - Shannon entropy, Rènyi entropy and Tsallis entropy, which are used in this paper as the features of extraction for the purpose of the pattern recognition of animal sounds. Sound samples from nine Microhylidae frogs are first segmented into syllables. Then, the features of each syllable are extracted using Shannon entropy, Rènyi entropy and Tsallis entropy. The nonparametric k-th nearest neighbours (k-NN) classifier is then used for frog sound identification system. The result shows that the entropy-based animal sound identification system has successfully identified most of the frog species

Entropy approach for bioacoustics signal analysis of repetitive notes

This paper discusses a method for analyzing animal sound by using information theory namely the Shannon entropy. The basic principle of the theory is first presented, together with the spectral centroid. The well known spectral centroid is used as a reference for result comparison. To assess the performance and suitability of the proposed method, sound from 15 Australian frog species are used as test samples. The sound from these animal species are first segmented into syllables. The Shannon entropy value of the syllables are then determined, and compared to spectral centroid value. It is found that the syllables of the frogs sound have entropy that is different from one to another even from the same species. On the other hand, from the spectral centroid analysis, it is found that almost all of the syllables of frogs sound (from the same species) have a unique frequency value which implies that their sounds are highly consistent with repetitive note

Information Theoretic Approach Based on Entropy for Classification of Bioacoustics Signals

A new hybrid method for automated frog sound identification by incorporating entropy and spectral centroid concept is proposed. Entropy has important physical implications as the amount of “disorder” of a system. This study explores the use of various definitions ofentropies such as the Shannon entropy, Kolmogorov‐Rényi entropy and Tsallis entropy as measure of information contents or complexity for the purpose of the pattern recognitionof bioacoustics signal. Each of these definitions of entropies characterizes different aspects of the signal. The entropies are combined with other standard pattern recognition tools such as the Fourier spectral analysis to form a hybrid spectral‐entropic classification scheme. The efficiency of the system is tested using a database of sound syllables are obtained from a number of species of Microhylidae frogs. Nonparametric k‐NN classifier is used to recognize the frog species based on the spectral‐entropic features. The result showed that the k‐NN classifier based on the selected features is able to identify the species of the frogs with relativity good accuracy compared to features relying on spectral contents alone. The robustness of the developed system is also tested for different noise levels

Investigation on the possibility of using entropy approach for classification and identification on frog species

Animal species identification based on their sound has received attentions from researchers. This is to establish fast and efficient identification method. Identification of frogs have been one of the examples where research activities have shown some progress. Mel Frequency Cepstrum Coefficient (MFCC) and Linear Predictive Coding (LPC), coupled with k-th Nearest Neighbor (k-NN) or Support Vector Machines (SVM) have been the favorate approachs used by researchers. Quite recently, a new classification and identification method of sound using entropy-based approach for species identification of Australian frogs was proposed. Shannon, Rènyi and Tsallis entropy were used as features of extraction for the purpose of pattern recognition. Result shows that the full entropy-based animal sound identification system has successfully identified most of the frog species used in this study. The overall classification accuracy is as high as 91% with two failures from nine samples at 70% and 40%, respectively. A comparative analysis highlights the advantages of full entropy approach over conventional frequency spectral and hybrid methods. This is shown especially in the running time of a computer that required to complete the species identifications process. The result presented in this paper indicates that full entropy-based method can be used for faster frog species identification

Characterization of exchange rate regimes based on scaling and correlation properties of volatility for ASEAN-5 countries

Foreign currency exchange rate policies of ASEAN member countries have undergone tremendous changes following the 1997 Asian financial crisis. In this paper, we study the fractal and long-memory characteristics in the volatility of five ASEAN founding members' exchange rates with respect to US dollar. The impact of exchange rate policies implemented by the ASEAN-5 countries on the currency fluctuations during pre-, mid- and post-crisis are briefly discussed. The time series considered are daily price returns, absolute returns and aggregated absolute returns, each partitioned into three segments based on the crisis regimes. These time series are then modeled using fractional Gaussian noise, fractionally integrated ARFIMA (0, d, 0) and generalized Cauchy process. The first two stationary models provide the description of long-range dependence through Hurst and fractional differencing parameter, respectively. Meanwhile, the generalized Cauchy process offers independent estimation of fractal dimension and long memory exponent. In comparison, among the three models we found that the generalized Cauchy process showed greater sensitivity to transition of exchange rate regimes that were implemented by ASEAN-5 countries. (c) 2006 Elsevier B.V. All rights reserved

Energy, Vibration And Sound Research Group (e-VIBS) School Of Science And Technology Universiti Malaysia Sabah : Bioacoustics Signal Modeling Using Time-Frequency Distribution

Biodiversity is one of the major studies in bio-conservation, which enable to evaluate the quality of ecosystem in a specific area, especially for protected area. In order to monitor the quality of the ecosystem structure, a long term rapid diversity assessment is needed. In term of that, bioacoustics has been introduced as a beneficial method for local species richness estimation. However, this method is still in the infancy state and many improvements are needed for more practical purposes. This research is carried out to develop new bioacoustics species identification method with the improvement in the identification accuracy. The method which developed in this research is based on entropy principles, and implements on Fourier transform (FT) and wavelet transform (WT) of bioacoustics signal. Several entropy principles including Shannon, Renyi and Tsallis, are investigated which representing measurement of richness of the information contents and complexity of a bioacoustics signal. To evaluate the new identification system, nine frog species from Microhylidae family was selected as test samples. Ten syllables were segmented from each frog sounds and characteristic of each syllables was extracted with the corresponding features which carried out in this research. All of the test samples were then sent into the k-nearest neighbor (k-NN) classifier for classification purpose. The k-­NN classifier compared the test samples with the training data set in order to recognize and identify the frog species. To establish a base trial data, the widely used spectral centroid (SC) and wavelet centroid (WC) were used as reference. The SC and WC of the syllables for each species were determined. It is found that, in terms of the average of classification accuracy for all test samples, the SC method has shown slightly better compared to the WC method. The classification results in average were 88.89% for the SC features and 86.67% for the WC features. The entropy alone, if implemented on raw bioacoustics signal shows reduction rather than improvement in the identification accuracy compared to the reference (SC and WC). It is found for example that the average identification accuracy for Shannon entropy (SE), Renyi entropy (RE) and Tsallis entropy (TE) were 76.67%, 75.56% and 83.33%, respectively. Due to the poor classification results of the entropy alone approaches, alternative methods were proposed in this work called wavelet entropy (WE). WE is a combination of interdisciplinary concepts between wavelet transform and entropy. In order to archive this, the entropies (SE, RE and TE) were extracted from three types of wavelet transforms, namely continuous wavelet transform (CWT), discrete wavelet transform (DWT) and wavelet packet decomposition (WPD), of a bioacoustics. signal. In this work, two possible ways to extract the entropy from CWT were introduced, which were wavelet scale entropy (WSE) and wavelet time entropy (WTE). Entropy that extracted from DWT and WPD were called as discrete wavelet entropy (DWE) and wavelet packet entropy (WPE), respectively. The species identification results based on these WE features which extracted from the bioacoustics signals were then examined and compared. In term of SE approach, WPE has given the best classification result compared with others (WSE, WTE and DWE), which was over 98% of accuracy. However, WSE was the best method for the RE approach with the accuracy of 92%. Based on TE approach, WPE has shown the best result with the classification accuracy of 100%. In term of that, this research work has proven that the WPE is the best method in the TE approach for species identification on bioacoustics signals. In conclusion, this work has successfully developed the species identification system based on bioacoustics signals by using the concept of WE. By comparing to the reference methods (conventional or classical methods), this study has proven that the performance of the bioacoustics species identification system can be improved by using the entropy approach with association of WT. Since the bioacoustics species identification system that proposed in this study is based on entropy approach, the computer algorithms is much easier (less complex) compared to the conventional methods, particularly based on spectrogram and sonogram. The proposed method can reduce the energy and time consumptions in terms of data processing