Performing edge detection by difference of Gaussians using q-Gaussian kernels

In image processing, edge detection is a valuable tool to perform the extraction of features from an image. This detection reduces the amount of information to be processed, since the redundant information (considered less relevant) can be unconsidered. The technique of edge detection consists of determining the points of a digital image whose intensity changes sharply. This changes are due to the discontinuities of the orientation on a surface for example. A well known method of edge detection is the Difference of Gaussians (DoG). The method consists of subtracting two Gaussians, where a kernel has a standard deviation smaller than the previous one. The convolution between the subtraction of kernels and the input image results in the edge detection of this image. This paper introduces a method of extracting edges using DoG with kernels based on the q-Gaussian probability distribution, derived from the q-statistic proposed by Constantino Tsallis. To demonstrate the method's potential, we compare the introduced method with the traditional DoG using Gaussians kernels. The results showed that the proposed method can extract edges with more accurate details.Comment: 5 pages, 5 figures, IC-MSQUARE 201

PEGylated cationic liposome - DNA nanoparticle assembly in cell culture media: pathway effects and clues to enhanced control and transfection efficiency optimization

XII Spanish-Portuguese Conference on Controlled Drug DeliveryCationic Liposome – DNA nanoparticles constitute a promising approach for safe and efficient delivery of genes for therapeutic applications. In order to be used in vivo, these particles can be coated with an inert and hydrophilic polymer, such as polyethylene-glycol (PEG), which improves blood circulation time by providing steric stabilization against removal by the immune system. In this work we study the influence of the initial salt concentration, which controls the electrostatic atraction between cationic liposomes and anionic DNA, on the structure of PEGylated CL–DNA nanoparticles.info:eu-repo/semantics/publishedVersio

Effect of wood aging on wine mineral composition and 87Sr/86Sr isotopic ratio

The evolution of mineral composition and wine strontium isotopic ratio 87Sr/86Sr (Sr IR) during wood aging were investigated. A red wine was aged in stainless steel tanks with French oak staves (Quercus sessiliflora Salisb.), with three industrial scale replicates. Sampling was carried out after 30, 60, and 90 days of aging, and the wines were evaluated in terms of general analysis, phenolic composition, total polysaccharides, multielement composition, and Sr IR. Li, Be, Mg, Al, Sc, Ti, V, Mn, Co, Ni, Cu, Zn, Ga, Ge, As, Rb, Sr, Y, Zr, Mo, Sb, Cs, Ba, Pr, Nd, Sm, Eu, Dy, Ho, Er, Yb, Lu, Tl, and Pb elements and 87Sr/86Sr were determined by quadrupole inductively coupled plasma mass spectrometry (Q-ICP-MS) and Na, K, Ca, and Fe by flame atomic absorption spectrometry (FAAS). Two-way ANOVA was applied to assess wood aging and time effect on Sr IR and mineral composition. Wood aging resulted in significantly higher concentrations of Mg, V, Co, Ni, and Sr. At the end of the aging period, wine exhibited statistically identical Sr IR compared to control. Study suggests that wood aging does not affect 87Sr/86Sr, not precluding the use of this parameter for wine traceability purposesinfo:eu-repo/semantics/publishedVersio

Dialectical polyptych: an interactive movie

Most of the known video games developed by big software companies usually establish an approach to the cinematic language in an attempt to create a perfect combination of narrative, visual technique and interaction. Unlike most video games, interactive film narratives normally involve an interruption in time whenever the spectator has to make choices. “Dialectical Polyptych” is an interactive movie included in a project called “Characters looking for a spect-actor”, which aims to give the spectator on-the-fly control over film editing, thus exploiting the role of the spectator as an active subject in the presented narrative. This paper presents a system based on a 3D sensor for tracking the spectator's movements and positions, which allows seamless real-timeinteractivity with the movie. Different positions of the body prompt a change in the angle or shot within each narrative, and hand swipes allow the spectator to alternate between the two parallel narratives, both producing a complementary narrative.info:eu-repo/semantics/publishedVersio

Taxonomic Partition Suggests a High Degree of Coevolution Between Termites and Their Termitophiles

Termites have a tight interaction with their social parasitic Corotocini beetles. This association is thought to be mainly host-specific, despite some host-switch events. By analyzing the taxonomic partition between species and genera of Corotocini, we propose the hypothesis that the main driver of the diversity of these termitophiles is coevolution

Structural MRI texture analysis for detecting Alzheimer's disease

Purpose:: Alzheimer’s disease (AD) has the highest worldwide prevalence of all neurodegenerative disorders, no cure, and low ratios of diagnosis accuracy at its early stage where treatments have some effect and can give some years of life quality to patients. This work aims to develop an automatic method to detect AD in 3 different stages, namely, control (CN), mild-cognitive impairment (MCI), and AD itself, using structural magnetic resonance imaging (sMRI). Methods:: A set of co-occurrence matrix and texture statistical measures (contrast, correlation, energy, homogeneity, entropy, variance, and standard deviation) were extracted from a two-level discrete wavelet transform decomposition of sMRI images. The discriminant capacity of the measures was analyzed and the most discriminant ones were selected to be used as features for feeding classical machine learning (cML) algorithms and a convolution neural network (CNN). Results:: The cML algorithms achieved the following classification accuracies: 93.3% for AD vs CN, 87.7% for AD vs MCI, 88.2% for CN vs MCI, and 75.3% for All vs All. The CNN achieved the following classification accuracies: 82.2% for AD vs CN, 75.4% for AD vs MCI, 83.8% for CN vs MCI, and 64% for All vs All. Conclusion:: In the evaluated cases, cML provided higher discrimination results than CNN. For the All vs All comparison, the proposedmethod surpasses by 4% the discrimination accuracy of the state-of-the-art methods that use structural MRI.info:eu-repo/semantics/publishedVersio

Machine learning models for the prediction of diffusivities in supercritical CO2 systems

The molecular diffusion coefficient is fundamental to estimate dispersion coefficients, convective mass transfer coefficients, etc. Since experimental diffusion data is scarce, there is significant demand for accurate models capable of providing reliable diffusion coefficient estimations. In this work we applied machine learning algorithms to develop predictive models to estimate diffusivities of solutes in supercritical carbon dioxide. A database of experimental data containing 13 properties for 174 binary systems totaling 4917 data points was used in the training of the models. Five machine learning algorithms were evaluated and the results were compared with three commonly used classic models. The best results were found using the Gradient Boosted algorithm which showed an average absolute relative deviation (AARD) of 2.58 % (pure prediction). This model has five parameters: temperature, density, solute molar mass, solute critical pressure and solute acentric factor. For the same dataset, the classic Wilke-Chang equation showed AARD of 12.41 %. The developed model is provided as command line program.publishe