Satellite image classification and segmentation using non-additive entropy

Here we compare the Boltzmann-Gibbs-Shannon (standard) with the Tsallis entropy on the pattern recognition and segmentation of coloured images obtained by satellites, via "Google Earth". By segmentation we mean split an image to locate regions of interest. Here, we discriminate and define an image partition classes according to a training basis. This training basis consists of three pattern classes: aquatic, urban and vegetation regions. Our numerical experiments demonstrate that the Tsallis entropy, used as a feature vector composed of distinct entropic indexes $q$ outperforms the standard entropy. There are several applications of our proposed methodology, once satellite images can be used to monitor migration form rural to urban regions, agricultural activities, oil spreading on the ocean etc.Comment: 4 pages, 5 figures, ICMSquare 201

The Presence of GC-C in Extracellular Vesicles Secreted by Colorectal Cancer Cells

Background: Guanylyl Cyclase C (GC-C) is a membrane-bound protein found on intestinal epithelial cells involved in the activation of CFTR. This protein has previously been involved in the development of colorectal cancer. Extracellular vesicles (EVs) are bilayered vesicles of varying size (30 to 1,000 + nm in diameter) that believed to be secreted by all cells in the human body. In the past decade, EVs have garnered attention due to their impact in the field of oncology, where they have been shown to potentially serve as biomarkers for various cancers. In this study, we looked at the EVs secreted by GC-C+ and GC-C- cell lines. We expected GC-C to be present on the EVs secreted by GC-C+ cell lines and that this finding may intake a role for GC-C at tissues distal to the intestinal epithelial cells. Methods: GC-C+ cells lines (T84 and CT26-hGCC) and GC-C- cell lines (SW480 and CT26-WT) were cultured and their media was harvested, then ultracentrifuged to extract the EVs from the media. These EVs were then checked for the presence and absence of various markers (GC-C, Calnexin, TSG101) via Western Blot. Exosome size was assessed via NTA to further provide evidence for the identity of these EVs. Results: Western blot confirmed the presence of TSG101 in both EV types samples, as well as the presence of GC-C in EVs derived from GC-C+ cell lines, but not from GC-C- cell lines. Calnexin was found to be absent in EV samples, excluding the possibility of lysate contamination. NTA analysis confirmed the correct size for the exosomes in sample. Discussion: This study assessed the contents of EVs secreted by colorectal cancer cell lines. Our findings indicate the presence of GC-C on exosomes and microvesicles. Further studies will need to be conducted in order to assess the function of these GC-C+ EVs in the setting of colorectal cancer

Complex network classification using partially self-avoiding deterministic walks

Complex networks have attracted increasing interest from various fields of science. It has been demonstrated that each complex network model presents specific topological structures which characterize its connectivity and dynamics. Complex network classification rely on the use of representative measurements that model topological structures. Although there are a large number of measurements, most of them are correlated. To overcome this limitation, this paper presents a new measurement for complex network classification based on partially self-avoiding walks. We validate the measurement on a data set composed by 40.000 complex networks of four well-known models. Our results indicate that the proposed measurement improves correct classification of networks compared to the traditional ones

Fast, parallel and secure cryptography algorithm using Lorenz's attractor

A novel cryptography method based on the Lorenz's attractor chaotic system is presented. The proposed algorithm is secure and fast, making it practical for general use. We introduce the chaotic operation mode, which provides an interaction among the password, message and a chaotic system. It ensures that the algorithm yields a secure codification, even if the nature of the chaotic system is known. The algorithm has been implemented in two versions: one sequential and slow and the other, parallel and fast. Our algorithm assures the integrity of the ciphertext (we know if it has been altered, which is not assured by traditional algorithms) and consequently its authenticity. Numerical experiments are presented, discussed and show the behavior of the method in terms of security and performance. The fast version of the algorithm has a performance comparable to AES, a popular cryptography program used commercially nowadays, but it is more secure, which makes it immediately suitable for general purpose cryptography applications. An internet page has been set up, which enables the readers to test the algorithm and also to try to break into the cipher in

An efficient algorithm to generate large random uncorrelated Euclidean distances: the random link model

A disordered medium is often constructed by $N$ points independently and identically distributed in a $d$-dimensional hyperspace. Characteristics related to the statistics of this system is known as the random point problem. As $d \to \infty$, the distances between two points become independent random variables, leading to its mean field description: the random link model. While the numerical treatment of large random point problems pose no major difficulty, the same is not true for large random link systems due to Euclidean restrictions. Exploring the deterministic nature of the congruential pseudo-random number generators, we present techniques which allow the consideration of models with memory consumption of order O(N), instead of $O(N^2)$ in a naive implementation but with the same time dependence $O(N^2)$.Comment: 8 pages, 2 figures and 1 tabl

Distance statistics in random media: high dimension and/or high neighborhood order cases

Consider an unlimited homogeneous medium disturbed by points generated via Poisson process. The neighborhood of a point plays an important role in spatial statistics problems. Here, we obtain analytically the distance statistics to $k$th nearest neighbor in a $d$-dimensional media. Next, we focus our attention in high dimensionality and high neighborhood order limits. High dimensionality makes distance distribution behavior as a delta sequence, with mean value equal to Cerf's conjecture. Distance statistics in high neighborhood order converges to a Gaussian distribution. The general distance statistics can be applied to detect departures from Poissonian point distribution hypotheses as proposed by Thompson and generalized here.Comment: 5 pages and 2 figure