Multithreshold Segmentation by Using an Algorithm Based on the Behavior of Locust Swarms

As an alternative to classical techniques, the problem of image segmentation has also been handled through evolutionary methods. Recently, several algorithms based on evolutionary principles have been successfully applied to image segmentation with interesting performances. However, most of them maintain two important limitations: (1) they frequently obtain suboptimal results (misclassifications) as a consequence of an inappropriate balance between exploration and exploitation in their search strategies; (2) the number of classes is fixed and known in advance. This paper presents an algorithm for the automatic selection of pixel classes for image segmentation. The proposed method combines a novel evolutionary method with the definition of a new objective function that appropriately evaluates the segmentation quality with respect to the number of classes. The new evolutionary algorithm, called Locust Search (LS), is based on the behavior of swarms of locusts. Different to the most of existent evolutionary algorithms, it explicitly avoids the concentration of individuals in the best positions, avoiding critical flaws such as the premature convergence to suboptimal solutions and the limited exploration-exploitation balance. Experimental tests over several benchmark functions and images validate the efficiency of the proposed technique with regard to accuracy and robustness

Multithreshold Segmentation Based on Artificial Immune Systems

Bio-inspired computing has lately demonstrated its usefulness with remarkable contributions to shape detection, optimization, and classification in pattern recognition. Similarly, multithreshold selection has become a critical step for image analysis and computer vision sparking considerable efforts to design an optimal multi-threshold estimator. This paper presents an algorithm for multi-threshold segmentation which is based on the artificial immune systems(AIS) technique, also known as theclonal selection algorithm (CSA). It follows the clonal selection principle (CSP) from the human immune system which basically generates a response according to the relationship between antigens (Ag), that is, patterns to be recognized and antibodies (Ab), that is, possible solutions. In our approach, the 1D histogram of one image is approximated through a Gaussian mixture model whose parameters are calculated through CSA. Each Gaussian function represents a pixel class and therefore a thresholding point. Unlike the expectation-maximization (EM) algorithm, the CSA-based method shows a fast convergence and a low sensitivity to initial conditions. Remarkably, it also improves complex time-consuming computations commonly required by gradient-based methods. Experimental evidence demonstrates a successful automatic multi-threshold selection based on CSA, comparing its performance to the aforementioned well-known algorithms

Los probióticos y sus metabolitos en la acuicultura. Una Revisión

Background: Currently, aquaculture produces half of the fish consumed in the world. In Mexico, this activity must tend towards sustainability, favoring that the means of production and the products obtained increase their quality and quantity, diversify and reduce their environmental impact. Objective: Analyze the information regarding the use of probiotics in aquaculture and its current development perspective in Mexico. Methods: The available literature on probiotics in aquacultural processes was compiled, with emphasis on the evaluation of positive effects in production, safety, food safety and sustainability. Results: The information analyzed allows establishing that the resistance of pathogenic microorganisms to antibiotics has become a problem in this activity when it is desired to prevent or treat diseases in cultivated species. In aquaculture, probiotics have shown great benefits, such as stimulating the immune response, increasing the survival of larvae, appetite and disease resistance, improving growth, yield and production and significantly reducing the production of polluting waste. The most commonly used probiotics are lactic acid bacteria and their metabolites such as bacteriocins, however, other genera of bacteria are also used, such as Bacillus and Streptomyces, as well as microalgae and yeasts. Conclusions: In Mexico the research and use of probiotics in aquaculture production processes must be reinforced, since they represent a great social, economic and ecological-environmental potential and the sectors involved must pay special attention to this, given the successful results obtained in other regions of the world.Antecedentes: Actualmente la acuicultura produce la mitad del pescado que se consume en el mundo. En México esta actividad debe de tender a la sustentabilidad, propiciando que los medios de producción y los productos obtenidos incrementen su calidad y cantidad, se diversifiquen y disminuyan su impacto ambiental. Objetivos: Analizar la información referente al uso de probióticos en la acuicultura y su perspectiva actual de desarrollo en México. Métodos: Se compiló la literatura disponible sobre probióticos en procesos acuaculturales, con énfasis en la evaluación de los efectos positivos en la producción, inocuidad, seguridad alimentaria y sustentabilidad. Resultados: La información analizada permite establecer que, la resistencia de los microorganismos patógenos a antibióticos se ha vuelto un problema en esta actividad cuando se desea prevenir o tratar enfermedades en las especies cultivadas. En la acuicultura, los probióticos han demostrado tener grandes beneficios, como estimular la respuesta inmune, incrementar la sobrevivencia de las larvas, el apetito y la resistencia a enfermedades, mejorar el crecimiento, rendimiento y producción y reducir significativamente la producción de residuos contaminantes. Los probióticos más utilizados son las bacterias ácido lácticas y sus metabolitos como las bacteriocinas, sin embargo, también se utilizan otros géneros de bacterias como: Bacillus y Streptomyces, además de microalgas y levaduras. Conclusiones. En México, la investigación y uso de probióticos en procesos de producción acuícola debe reforzarse, ya que representan un gran potencial social, económico y ecológico-ambiental y los sectores involucrados deben de poner especial atención al respecto, dados los resultados exitosos obtenidos en otras regiones del mundo

White Blood Cell Segmentation by Circle Detection Using Electromagnetism-Like Optimization

Medical imaging is a relevant field of application of image processing algorithms. In particular, the analysis of white blood cell (WBC) images has engaged researchers from fields of medicine and computer vision alike. Since WBCs can be approximated by a quasicircular form, a circular detector algorithm may be successfully applied. This paper presents an algorithm for the automatic detection of white blood cells embedded into complicated and cluttered smear images that considers the complete process as a circle detection problem. The approach is based on a nature-inspired technique called the electromagnetism-like optimization (EMO) algorithm which is a heuristic method that follows electromagnetism principles for solving complex optimization problems. The proposed approach uses an objective function which measures the resemblance of a candidate circle to an actual WBC. Guided by the values of such objective function, the set of encoded candidate circles are evolved by using EMO, so that they can fit into the actual blood cells contained in the edge map of the image. Experimental results from blood cell images with a varying range of complexity are included to validate the efficiency of the proposed technique regarding detection, robustness, and stability

A Multiobjective Approach to Homography Estimation

In several machine vision problems, a relevant issue is the estimation of homographies between two different perspectives that hold an extensive set of abnormal data. A method to find such estimation is the random sampling consensus (RANSAC); in this, the goal is to maximize the number of matching points given a permissible error (Pe), according to a candidate model. However, those objectives are in conflict: a low Pe value increases the accuracy of the model but degrades its generalization ability that refers to the number of matching points that tolerate noisy data, whereas a high Pe value improves the noise tolerance of the model but adversely drives the process to false detections. This work considers the estimation process as a multiobjective optimization problem that seeks to maximize the number of matching points whereas Pe is simultaneously minimized. In order to solve the multiobjective formulation, two different evolutionary algorithms have been explored: the Nondominated Sorting Genetic Algorithm II (NSGA-II) and the Nondominated Sorting Differential Evolution (NSDE). Results considering acknowledged quality measures among original and transformed images over a well-known image benchmark show superior performance of the proposal than Random Sample Consensus algorithm

Identification of parameters in photovoltaic models through a runge kutta optimizer

Recently, the resources of renewable energy have been in intensive use due to their environmental and technical merits. The identification of unknown parameters in photovoltaic (PV) models is one of the main issues in simulation and modeling of renewable energy sources. Due to the random behavior of weather, the change in output current from a PV model is nonlinear. In this regard, a new optimization algorithm called Runge–Kutta optimizer (RUN) is applied for estimating the parameters of three PV models. The RUN algorithm is applied for the R.T.C France solar cell, as a case study. Moreover, the root mean square error (RMSE) between the calculated and measured current is used as the objective function for identifying solar cell parameters. The proposed RUN algorithm is superior compared with the Hunger Games Search (HGS) algorithm, the Chameleon Swarm Algorithm (CSA), the Tunicate Swarm Algorithm (TSA), Harris Hawk’s Optimization (HHO), the Sine–Cosine Algorithm (SCA) and the Grey Wolf Optimization (GWO) algorithm. Three solar cell models—single diode, double diode and triple diode solar cell models (SDSCM, DDSCM and TDSCM)—are applied to check the performance of the RUN algorithm to extract the parameters. the best RMSE from the RUN algorithm is 0.00098624, 0.00098717 and 0.000989133 for SDSCM, DDSCM and TDSCM, respectively

Aportaciones al diseño de actividades educativas con Realidad Aumentada

Es un libro enfocado a la enseñanza, aplicando tecnología.Este es un libro dirigido a profesores, formadores, investigadores y estudiantes en general interesados en el proceso enseñanza aprendizaje utilizando tecnología, en particular con una de las tecnologías emergentes de mayor auge, la Realidad Aumentada (RA). El objetivo de la obra es lograr una publicación que reúna diversas propuestas del uso de la realidad aumentada en diferentes ámbitos, que sirvan de reflexión para una orientación didáctica y para generar una línea de investigación. Por lo que encontraran propuestas de investigación acerca de la recursividad de los sistemas para abordar el desarrollo de aplicaciones con realidad aumentada, la aplicación de la realidad aumentada con fines turísticos y propuestas sobre el uso de la realidad aumentada en la formación profesional del diseñador industrial, experiencias en asignaturas de graficación y multimedios, y un acercamiento al concepto de límite de función real mediante una aplicación con realidad aumentada. Estas importantes reflexiones alrededor del diseño y el uso de la realidad aumentada nos muestran en cierto sentido un estado del arte, dado que diversifican las aplicaciones, además son un punto de partida para diversas investigaciones educativas que los lectores podrán realizar, esperamos una amplia acogida de este esfuerzo de divulgación científica.La publicación del libro estuvo financiada por la Universidad Autónoma del Estado de México, con el apoyo de la Secretaría de Educación Pública, por intermediación del Programa de Fortalecimiento a la Calidad Educativa (PFCE) 2019

Efficacy of β-lactam/β-lactamase inhibitors to treat extended-spectrum beta-lactamase-producing Enterobacterales bacteremia secondary to urinary tract infection in kidney transplant recipients (INCREMENT-SOT Project)

REIPI/INCREMENT-SOT Group.[Background] Whether active therapy with β-lactam/β-lactamase inhibitors (BLBLI) is as affective as carbapenems for extended-spectrum β-lactamase-producing Enterobacterales (ESBL-E) bloodstream infection (BSI) secondary to urinary tract infection (UTI) in kidney transplant recipients (KTRs) remains unclear.[Methods] We retrospectively evaluated 306 KTR admitted to 30 centers from January 2014 to October 2016. Therapeutic failure (lack of cure or clinical improvement and/or death from any cause) at days 7 and 30 from ESBL-E BSI onset was the primary and secondary study outcomes, respectively.[Results] Therapeutic failure at days 7 and 30 occurred in 8.2% (25/306) and 13.4% (41/306) of patients. Hospital-acquired BSI (adjusted OR [aOR]: 4.10; 95% confidence interval [CI]: 1.50-11.20) and Pitt score (aOR: 1.47; 95% CI: 1.21-1.77) were independently associated with therapeutic failure at day 7. Age-adjusted Charlson Index (aOR: 1.25; 95% CI: 1.05-1.48), Pitt score (aOR: 1.72; 95% CI: 1.35-2.17), and lymphocyte count ≤500 cells/μL at presentation (aOR: 3.16; 95% CI: 1.42-7.06) predicted therapeutic failure at day 30. Carbapenem monotherapy (68.6%, primarily meropenem) was the most frequent active therapy, followed by BLBLI monotherapy (10.8%, mostly piperacillin-tazobactam). Propensity score (PS)-adjusted models revealed no significant impact of the choice of active therapy (carbapenem-containing vs any other regimen, BLBLI- vs carbapenem-based monotherapy) within the first 72 hours on any of the study outcomes.[Conclusions] Our data suggest that active therapy based on BLBLI may be as effective as carbapenem-containing regimens for ESBL-E BSI secondary to UTI in the specific population of KTR. Potential residual confounding and unpowered sample size cannot be excluded (ClinicalTrials.gov identifier: NCT02852902).This work was supported by: (1) Plan Nacional de I+D+i 2013-2016 and Instituto de Salud Carlos III (ISCIII), Subdirección General de Redes y Centros de Investigación Cooperativa, Ministerio de Ciencia, Innovación y Universidades, Spanish Network for Research in Infectious Diseases [RD16/0016/0001, RD16/0016/0002, REIPI RD16/0016/0008; RD16/0016/00010], co-financed by European Development Regional Fund “A way to achieve Europe”, Operative Program Intelligent Growth 2014-2020; (2) European Society of Clinical Microbiology and Infectious diseases Study Group for Infections in Compromised Hosts (ESGICH, grant to J.M.A.); (3) Sociedad Andaluza de Trasplante de Órgano Sólido (SATOT, grant to L.M.M.); (4) Research project PI16/01631 integrated into the Plan Estatal de I+D+I 2013-2016 and co-financed by the ISCIII-Subdirección General de Evaluación y Fomento de la Investigación and the Fondo Europeo de Desarrollo Regional (FEDER); (5) M.F.R. holds a research contract “Miguel Servet” (CP 18/00073) from ISCIII, Ministerio de Ciencia, Innovación y Universidades. The work was also supported by the following European Society of Clinical Microbiology and Infectious diseases (ESCMID) study groups: Infections in Compromised Hosts (ESGICH), Bloodstream Infections and Sepsis (ESGBIS) and Antimicrobial Resistance Surveillance (ESGARS).Peer reviewe

Detection of SARS-CoV-2 variants in hospital wastewater in Peru, 2022

Objetivo. Identificar la presencia del virus SARS-CoV-2 en aguas residuales de hospitales en Perú. Materiales y métodos. Se recolectaron muestras de agua en los efluentes de nueve hospitales del Perú durante marzo y septiembre de 2022 y se realizó la identificación de SARS-CoV-2 mediante secuenciación Illumina. Las asignaciones de variantes, linajes y clados se llevaron a cabo con las herramientas Illumina y Nextclado. Verificamos si las variantes de SARS-CoV-2 obtenida de las aguas residuales fueron similares a las reportadas por el Instituto Nacional de Salud del Perú procedentes de pacientes durante el mismo período y región. Resultados. Dieciocho de las 20 muestras de aguas residuales hospitalarias (90%) proporcionaron secuencias con la calidad suficiente para ser clasificadas como variante Ómicron según la clasificación de la OMS. Entre ellos, seis (30%) fueron asignados por Nextclade a los clados 21K linaje BA.1.1 (n=1), 21 L linaje BA.2 (n=2) y 22B linajes BA.5.1 (n=2) y BA.5.5 (n=1). Conclusiones. Se encontraron variantes del SARSCoV- 2 en muestras de aguas residuales hospitalarias y que fueron similares a las reportadas por el sistema de vigilancia en pacientes durante las mismas semanas y áreas geográficas. El monitoreo de aguas residuales podría proporcionar información sobre la variación ambiental y temporal de virus como el SARS-CoV-2.Este trabajo contó con el apoyo de la Escuela de Medicina de la Unión Universidad Peruana y GenLab del Perú S.A.C e Illumina Inc. bajo la convocatoria GenLab 2021