The information capacity of the genetic code: Is the natural code optimal?

We envision the molecular evolution process as an information transfer process and provide a quantitative measure for information preservation in terms of the channel capacity according to the channel coding theorem of Shannon. We calculate Information capacities of DNA on the nucleotide (for non-coding DNA) and the amino acid (for coding DNA) level using various substitution models. We extend our results on coding DNA to a discussion about the optimality of the natural codon-amino acid code. We provide the results of an adaptive search algorithm in the code domain and demonstrate the existence of a large number of genetic codes with higher information capacity. Our results support the hypothesis of an ancient extension from a 2-nucleotide codon to the current 3-nucleotide codon code to encode the various amino acids

Modelling SAR with a Generalisation of the Rayleigh Distribution

Synthetic aperture radar (SAR) imagery has found important applications since its introduction, due to its clear advantage over optical satellite imagery, being operable in various weather conditions. However, due to the physics of radar imaging process, sar images contain unwanted artefacts in the form of a granular look which is called speckle. the assumptions of the classical SAR image generation model lead to the convention that the real and imaginary parts of the received wave follow a Gaussian law, which in turn means that the amplitude of the wave has a Rayleigh distribution- . However, some experimental data show impulsive characteristics which correspond to underlying heavy-tailed distributions, clearly non-rayleigh. some alternative distributions have been suggested such as weibull and log-normal distributions, however, in most of the cases these models are empirical, not derived with the consideration of underlying physical conditions and therefore are case specific. In this report, relaxing some of the assumptions leading to the classical rayleigh model and using the recent results in the literature on $\alpha$-stable distributions, we develop a generalised (heavy-tailed) version of the rayleigh model based on the assumption that the real and the imaginary parts of the received signal follows an isotropic $\alpha$-stable law which is suggested by a generalised form of the central limit theorem. we also derive novel methods for the estimation of the heavy-tailed rayleigh distribution parameter- s based on negative fractional-order statistics for model fitting. our experimental results show that the heavy-tailed rayleigh model can describe a wide range of data which could not be described by the classical rayleigh model

Using generic order moments for separation of dependent sources with linear conditional expectations

In this work, we approach the blind separation of dependent sources based only on a set of their linear mixtures. We prove that, when the sources have a pairwise dependence characterized by the linear conditional expectation (LCE) law, we are able to separate them by maximizing or minimizing a Generic Order Moment (GOM) of their mixture. This general measure includes the higher order as well as the fractional moment cases. Our results, not only confirm some of the existing results for the independent sources case but also they allow us to explore new objective functions for Dependent Component Analysis. A set of examples illustrating the consequences of our theory is presented. Also, a comparison of our GOM based algorithm, the classical FASTICA and a very recently proposed algorithm for dependent sources, the Bounded Component Analysis (BCA) algorithm, is shown.Fil: Caiafa, César Federico. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Instituto Argentino de Radioastronomía. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto Argentino de Radioastronomía; ArgentinaFil: Kuruoglu, Ercan E.. Istituto di Scienza e Tecnologie dell’Informazione; Italia. Consiglio Nazionale delle Ricerche; Italia21ª European Signal Processing ConferenceMarrakechMarruecosEuropean Signal Processing Society (EURASIP

Evidence of a cancer type-specific distribution for consecutive somatic mutation distances

Specific molecular mechanisms may affect the pattern of mutation in particular regions, and therefore leaving a footprint or signature in the DNA of their activity. The common approach to identify these signatures is studying the frequency of substitutions. However, such an analysis ignores the important spatial information, which is important with regards to the mutation occurrence statistics. In this work, we propose that the study of the distribution of distances between consecutive mutations along the DNA molecule can provide information about the types of somatic mutational processes. In particular, we have found that specific cancer types show a power-law in interoccurrence distances, instead of the expected exponential distribution dictated with the Poisson assumption commonly made in the literature. Cancer genomes exhibiting power-law interoccurrence distances were enriched in cancer types where the main mutational process is described to be the activity of the APOBEC protein family, which produces a particular pattern of mutations called Kataegis. Therefore, the observation of a power-law in interoccurence distances could be used to identify cancer genomes with Kataegis

A Generalized Gaussian Extension to the Rician Distribution for SAR Image Modeling

In this paper, we present a novel statistical model, $\textit{the generalized-Gaussian-Rician}$ (GG-Rician) distribution, for the characterization of synthetic aperture radar (SAR) images. Since accurate statistical models lead to better results in applications such as target tracking, classification, or despeckling, characterizing SAR images of various scenes including urban, sea surface, or agricultural, is essential. The proposed statistical model is based on the Rician distribution to model the amplitude of a complex SAR signal, the in-phase and quadrature components of which are assumed to be generalized-Gaussian distributed. The proposed amplitude GG-Rician model is further extended to cover the intensity SAR signals. In the experimental analysis, the GG-Rician model is investigated for amplitude and intensity SAR images of various frequency bands and scenes in comparison to state-of-the-art statistical models that include $\mathcal{K}$, Weibull, Gamma, and Lognormal. In order to decide on the most suitable model, statistical significance analysis via Kullback-Leibler divergence and Kolmogorov-Smirnov statistics are performed. The results demonstrate the superior performance and flexibility of the proposed model for all frequency bands and scenes and its applicability on both amplitude and intensity SAR images.Comment: 20 Pages, 9 figures, 8 table

Modelling SAR with a Generalisation of the Rayleigh Distribution

Synthetic aperture radar (SAR) imagery has found important applications since its introduction, due to its clear advantage over optical satellite imagery, being operable in various weather conditions. However, due to the physics of radar imaging process, sar images contain unwanted artefacts in the form of a granular look which is called speckle. the assumptions of the classical SAR image generation model lead to the convention that the real and imaginary parts of the received wave follow a Gaussian law, which in turn means that the amplitude of the wave has a Rayleigh distribution- . However, some experimental data show impulsive characteristics which correspond to underlying heavy-tailed distributions, clearly non-rayleigh. some alternative distributions have been suggested such as weibull and log-normal distributions, however, in most of the cases these models are empirical, not derived with the consideration of underlying physical conditions and therefore are case specific. In this report, relaxing some of the assumptions leading to the classical rayleigh model and using the recent results in the literature on $\alpha$-stable distributions, we develop a generalised (heavy-tailed) version of the rayleigh model based on the assumption that the real and the imaginary parts of the received signal follows an isotropic $\alpha$-stable law which is suggested by a generalised form of the central limit theorem. we also derive novel methods for the estimation of the heavy-tailed rayleigh distribution parameter- s based on negative fractional-order statistics for model fitting. our experimental results show that the heavy-tailed rayleigh model can describe a wide range of data which could not be described by the classical rayleigh model