Multi-circle detection on images inspired by collective animal behavior

Hough transform (HT) has been the most common method for circle detection that delivers robustness but adversely demands considerable computational efforts and large memory requirements. As an alternative to HT-based techniques, the problem of shape recognition has also been handled through optimization methods. In particular, extracting multiple circle primitives falls into the category of multi-modal optimization as each circle represents an optimum which must be detected within the feasible solution space. However, since all optimization-based circle detectors focus on finding only a single optimal solution, they need to be applied several times in order to extract all the primitives which results on time-consuming algorithms. This paper presents an algorithm for automatic detection of multiple circular shapes that considers the overall process as a multi-modal optimization problem. In the detection, the approach employs an evolutionary algorithm based on the way in which the animals behave collectively. In such an algorithm, searcher agents emulate a group of animals which interact to each other using simple biological rules. These rules are modeled as evolutionary operators. Such operators are applied to each agent considering that the complete group maintains a memory which stores the optimal solutions seen so-far by applying a competition principle. The detector uses a combination of three non-collinear edge points as parameters to determine circle candidates (possible solutions). A matching function determines if such circle candidates are actually present in the image. Guided by the values of such matching functions, the set of encoded candidate circles are evolved through the evolutionary algorithm so that the best candidate (global optimum) can be fitted into an actual circle within the edge-only image. Subsequently, an analysis of the incorporated memory is executed in order to identify potential local optima which represent other circles. Experimental results over several complex synthetic and natural images have validated the efficiency of the proposed technique regarding accuracy, speed and robustness.     

A dynamic,ensemble learning approach to forecast dengue fever epidemic years in Brazil using weather and population susceptibility cycles

Transmission of dengue fever depends on a complex interplay of human, climate and mosquito dynamics, which often change in time and space. It is well known that its disease dynamics are highly influenced by multiple factors including population susceptibility to infection as well as by microclimates: small-area climatic conditions which create environments favourable for the breeding and survival of mosquitoes. Here, we present a novel machine learning dengue forecasting approach, which, dynamically in time and space, identifies local patterns in weather and population susceptibility to make epidemic predictions at the city level in Brazil, months ahead of the occurrence of disease outbreaks. Weather-based predictions are improved when information on population susceptibility is incorporated, indicating that immunity is an important predictor neglected by most dengue forecast models. Given the generalizability of our methodology to any location or input data, it may prove valuable for public health decision-making aimed at mitigating the effects of seasonal dengue outbreaks in locations globally.     

Genetic adaptive coding optimization applied to fractal image compression

This paper presents a fractal image compression method that utilized a genetic optimization algorithm for optimal domain block selection. The technique successfully addresses the problem of finding an optimal domain block pool for a given range partition, one of the most important issues in fractal image compression. This technique utilizes a genetic optimization algorithm that starts with a source image and generates both a random range partition and a random set of candidate domain blocks for each element of the range partition. Each member of the candidate domain block set was mapped to the corresponding element of the range partition. It was subsequently tested via a quantitative objective function, ranked using a linear fitness scheme and modified, as required, using crossover and mutation operators. This evolutionary process converged to produce an optimal iterated function system (IFS) representation of the source image within a few generations in a robust and efficient manner. © 1999 John Wiley & Sons, Inc. Int J Imaging Syst Technol 10, 369–378, 1999     

Adaptive passivity-based control of average dc-to-dc power converter models

An adaptive feedback regulation scheme is proposed for the stabilization of average models of dc-to-dc power converters exhibiting unknown but constant resistive loads. The scheme is based on a dynamical feedback policy which suitably modifies the total energy of the closed-loop system while inducing appropriate damping injections on the desired stabilization error dynamics. The performance of the proposed adaptative regulators is tested through computer simulations including stochastic perturbation inputs. © 1998 John Wiley & Sons, Ltd.     

The effects of grape must fermentation conditions on volatile alcohols and esters formed by Saccharomyces cerevisiae

Changes in aroma compounds synthesised from grape must during fermentation carried out by Saccharomyces cerevisiae, in semi-aerobic, anaerobic, short aeration conditions and after adding ergosterol and oleic acid to the must were studied. The biosynthesis of these aroma compounds was strongly dependent on the fermentation conditions and on the growth of the yeast. Ethanol, isoamyl alcohols, isobutyl alcohol, phenethyl alcohol and isoamyl, butyl and hexyl acetates were produced in greater concentrations in semiaerobic conditions, mainly during cellular growth. 1-Butanol and 1-pentanol were produced in greater levels in anaerobic conditions, when cellular growth was lower. Ergosterol and oleic acid added to the musts generally increased the levels of the aroma compounds in wine compared to those obtained in anaerobic conditions. © 1997 SCI.     

Connexin46 Expression Enhances Cancer Stem Cell and Epithelial-to-Mesenchymal Transition Characteristics of Human Breast Cancer MCF-7 Cells

Connexins (Cxs) are a family of proteins that form two different types of ion channels: hemichannels and gap junction channels. These channels participate in cellular communication, enabling them to share information and act as a synchronized syncytium. This cellular communication has been considered a strong tumor suppressor, but it is now recognized that some type of Cxs can be pro-tumorigenic. For example, Cx46 expression is increased in human breast cancer samples and correlates with cancer stem cell (CSC) characteristics in human glioma. Thus, we explored whether Cx46 and glioma cells, can set up CSC and epithelial-to-mesenchymal transition (EMT) properties in a breast cancer cell line. To this end, we transfected MCF-7 cells with Cx46 attached to a green fluorescent protein (Cx46GFP), and we determined how its expression orchestrates both the gene-expression and functional changes associated with CSC and EMT. We observed that Cx46GFP increased Sox2, Nanog, and OCT4 mRNA levels associated with a high capacity to form monoclonal colonies and tumorspheres. Similarly, Cx46GFP increased the mRNA levels of n-cadherin, Vimentin, Snail and Zeb1 to a higher migratory and invasive capacity. Furthermore, Cx46GFP transfected in MCF-7 cells induced the release of higher amounts of VEGF, which promoted angiogenesis in HUVEC cells. We demonstrated for the first time that Cx46 modulates CSC and EMT properties in breast cancer cells and thus could be relevant in the design of future cancer therapies.     

An Upgrade on the Surveillance System of SARS-CoV-2: Deployment of New Methods for Genetic Inspection

SARS-CoV-2 variants surveillance is a worldwide task that has been approached with techniques such as Next Generation Sequencing (NGS); however, this technology is not widely available in developing countries because of the lack of equipment and limited funding in science. An option is to deploy a RT-qPCR screening test which aids in the analysis of a higher number of samples, in a shorter time and at a lower cost. In this study, variants present in samples positive for SARS-CoV-2 were identified with a RT-qPCR mutation screening kit and were later confirmed by NGS. A sample with an abnormal result was found with the screening test, suggesting the simultaneous presence of two viral populations with different mutations. The DRAGEN Lineage analysis identified the Delta variant, but there was no information about the other three mutations previously detected. When the sequenced data was deeply analyzed, there were reads with differential mutation patterns, that could be identified and classified in terms of relative abundance, whereas only the dominant population was reported by DRAGEN software. Since most of the software developed to analyze SARS-CoV-2 sequences was aimed at obtaining the consensus sequence quickly, the information about viral populations within a sample is scarce. Here, we present a faster and deeper SARS-CoV-2 surveillance method, from RT-qPCR screening to NGS analysis.     

Structural analysis of oxyfluoride borate glass and BaF2 crystallization from phase separation

Oxyfluoride glass and glass-ceramics are being studied due to the possibility of their utilization in many applications, such as thermoluminescent dosimeters, optical fibers, fluorescent concentrators, and temperature sensors. In order to improve the properties of glass, they are often crystallized. For this crystallization to be effective, knowledge and control of the structure of the starting material is required. Much work was done on silicate glass; however, a great interest exists in obtaining these kinds of materials in other matrices such as germanates, phosphates, and borates. Here, we present a structural study of some oxyfluoride borate glass with different concentrations of PbF₂ by means of Nuclear Magnetic Resonance (NMR), Raman, and Fourier Transform Infrared (FTIR) spectroscopies. We also analyzed glass-crystallization using Differential Scanning Calorimetry (DSC). The crystallization study was complemented with the use of Transmission Electron Microscopy (TEM) and Selected Area Electron Diffraction (SAED), for the purpose of identifying possible mechanisms of crystallization. Our results indicate, from the structural point of view, that the glass present groups with one or two BO₄ species, such as diborate or pentaborate. The small variation in the BO₄ fraction with the increase in PbF₂ concentration would be an indication of the formation of F⁻BO_3/2 species. Oxyfluoride borate glass containing BaO as a cation modifier and 30% PbF₂ represents a good starting point for obtaining glass-ceramics with a narrow size distribution of BaF₂ nanocrystals. We also propose that the possible mechanism for oxyfluoride borate glass crystallization started with a separation of phases.