Programa de Educación Física, Recreación y Deporte en el Proyecto «Caminemos Por La Vida» : Physical Education, Recreation and Sports Program in the "Caminemos Por La Vida" (Let's Walk for Life) Project.

El programa de Licenciatura en Educación Física, Recreación y Deporte ha venido liderando este proyecto de Proyección Social «Caminemos por la Vida» abordando una serie de problemáticas que se han detectado en el campo de estudio de la comunidad, especialmente la de los adultos mayores. Así, constituye un epicentro de desarrollo cultural y cimiento de una sociedad más saludable física y mentalmente. A partir de los diferentes segmentos poblacionales que conforman la sociedad y que son los escenarios en donde hace presencia la Universidad Surcolombiana, desde una perspectiva de Ocio Humanista, (Cuenca, 2000), para vitalizar las capacidades del adulto mayor de manera autentica y creativa, por medio de acciones de mejoramiento tales como el deporte, la salud, el ocio, el arte y, en general, las vivencias y experiencias recreativas, educativas o culturales consideradas acciones trascendentes para la preservación y el desarrollo de la salud del ser humano y el mejoramiento de la calidad de vida. &nbsp

Anotación Automática de Imágenes Médicas Usando la Representación de Bolsa de Características

La anotación automática de imágenes médicas se ha convertido en un proceso necesario para la gestión, búsqueda y exploración de las crecientes bases de datos médicas para apoyo al diagnóstico y análisis de imágenes en investigación biomédica. La anotación automática consiste en asignar conceptos de alto nivel a imágenes a partir de las características visuales de bajo nivel. Para esto se busca tener una representación de la imagen que caracterice el contenido visual de ésta y un modelo de aprendizaje entrenado con ejemplos de imágenes anotadas. Este trabajo propone explorar la Bolsa de Características (BdC) para la representación de las imágenes de histología y los Métodos de Kernel (MK) como modelos de aprendizaje de máquina para la anotación automática. Adicionalmente se exploró una metodología de análisis de colecciones de imágenes para encontrar patrones visuales y sus relaciones con los conceptos semánticos usando Análisis de Información Mutua, Selección de Características con Máxima-Relevancia y Mínima-Redundancia (mRMR) y Análisis de Biclustering. La metodología propuesta fue evaluada en dos bases de datos de imágenes, una con imá- genes anotadas con los cuatro tejidos fundamentales y otra con imágenes de tipo de cáncer de piel conocido como carcinoma basocelular. Los resultados en análisis de imágenes revelan que es posible encontrar patrones implícitos en colecciones de imágenes a partir de la representación BdC seleccionan- do las palabras visuales relevantes de la colección y asociándolas a conceptos semánticos mientras que el análisis de biclustering permitió encontrar algunos grupos de imágenes similares que comparten palabras visuales asociadas al tipo de tinción o conceptos. En anotación automática se evaluaron distintas configuraciones del enfoque BdC. Los mejores resultados obtenidos presentan una Precisión de 91 % y un Recall de 88 % en las imágenes de histología, y una Precisión de 59 % y un Recall de 23 % en las imágenes de histopatología. La configuración de la metodología BdC con los mejores resultados en ambas colecciones fue obtenida usando las palabras visuales basadas en DCT con un diccionario de tamaño 1,000 con un kernel Gaussiano. / Abstract. The automatic annotation of medical images has become a necessary process for managing, searching and exploration of growing medical image databases for diagnostic support and image analysis in biomedical research. The automatic annotation is to assign high-level concepts to images from the low-level visual features. For this, is needed to have a image representation that characterizes its visual content and a learning model trained with examples of annotated images. This paper aims to explore the Bag of Features (BOF) for the representation of histology images and Kernel Methods (KM) as models of machine learning for automatic annotation. Additionally, we explored a methodology for image collection analysis in order to _nd visual patterns and their relationships with semantic concepts using Mutual Information Analysis, Features Selection with Max-Relevance and Min- Redundancy (mRMR) and Biclustering Analysis. The proposed methodology was evaluated in two image databases, the _rst have images annotated with the four fundamental tissues, and the second have images of a type of skin cancer known as Basal-cell carcinoma. The image analysis results show that it is possible to _nd implicit patterns in image collections from the BOF representation. This by selecting the relevant visual words in the collection and associating them with semantic concepts, whereas biclustering analysis allowed to _nd groups of similar images that share visual words associated with the type of stain or concepts. The Automatic annotation was evaluated in di_erent settings of BOF approach. The best results have a Precision of 91% and Recall of 88% in the histology images, and a Precision of 59% and Recall of 23% in histopathology images. The con_guration of BOF methodology with the best results in both datasets was obtained using the DCT-based visual words in a dictionary size of 1; 000 with a Gaussian kernel.Maestrí

Los beneficios que brindan las experiencias de ocio de las cajas de compensación familiar en Colombia —Estudio de caso de la Caja de Compensación Familiar Comfamiliar en el Huila—

The present write show the possibles benefits that can write the members and the community in overall of thefamily’s boxes of compensation, through of leisure experiences that those organisations offer. In this sense, the reflexioncarry out in the framework of Colombians CCF and focus in the study case of the family’s compensation boxescomfamilair on the Huila. For these, pick up initially the leisure contemporary perspective in its conception of thehumdan experience and then deepen in the framework of CCF of Colombian surrounding and the case of comfamiliaron the Huila. Finally to close mode they analyse and argue the well-being relationship ( such as personal as social) andthe leisures experiences that are proportioned by CCF. Any way, this article turn around about the deepeing of theleisure study like contemporary phenomenon with implications in the well-being of the society, starting of proposalstrategies by private agents as the CCF in Colombian territory.El presente escrito expone los posibles beneficios que pueden recibir los afiliados y la comunidad en general de las Cajas de Compensación Familiar (CCF), a través de las experiencias de ocio que dichas entidades les ofrecen. En este sentido, la reflexión se realiza en el marco de las CCF colombianas y se enfoca en el caso de estudio de la Caja de Compensación Familiar Comfamiliar en el Huila. Para ello, se recoge inicialmente la perspectiva contemporánea del ocio en su concepción de experiencia humana y luego se ahonda en el marco de las CCF del entorno colombiano y el caso de Comfamiliar en el Huila. Finalmente, a modo de cierre se analizan y argumentan las relaciones de bienestar (tanto personal como social) y las experiencias de ocio que son proporcionadas por las CCF. De esta manera, este artículo gira en torno a la profundización del estudio del ocio como fenómeno contemporáneo con implicaciones en el bienestar de la sociedad, a partir de las estrategias propuestas por agentes privados, como las CCF en el territorio colombiano

Data-driven Representation Learning from Histopathology Image Databases to Support Digital Pathology Analysis

Cancer research is a major public health priority in the world due to its high incidence, diversity and mortality. Despite great advances in this area during recent decades, the high incidence and lack of specialists have proven that one of the major challenges is to achieve early diagnosis. Improved early diagnosis, especially in developing countries, plays a crucial role in timely treatment and patient survival. Recent advances in scanner technology for the digitization of pathology slides and the growth of global initiatives to build databases for cancer research have enabled the emergence of digital pathology as a new approach to support pathology workflows. This has led to the development of many computational methods for automatic histopathology image analysis, which in turn has raised new computational challenges due to the high visual variability of histopathology slides, the difficulty in assessing the effectiveness of methods (considering the lack of annotated data from different pathologists and institutions), and the need of interpretable, efficient and feasible methods for practical use. On the other hand, machine learning techniques have focused on exploiting large databases to automatically extract and induce information and knowledge, in the form of patterns and rules, that allow to connect low-level content with its high-level meaning. Several approaches have emerged as opposed to traditional schemes based on handcrafted features for data representation, which nowadays are known as representation learning. The objective of this thesis is the exploration, development and validation of precise, interpretable and efficient computational machine learning methods for automatic representation learning from histopathology image databases to support diagnosis tasks of different types of cancer. The validation of the proposed methods during the thesis development allowed to corroborate their capability in several histopathology image analysis tasks of different types of cancer. These methods achieve good results in terms of accuracy, robustness, reproducibility, interpretability and feasibility suggesting their potential practical application towards translational and personalized medicine.Resumen. La investigación en cáncer es una de las principales prioridades de salud pública en el mundo debido a su alta incidencia, diversidad y mortalidad. A pesar de los grandes avances en el área en las últimas décadas, la alta incidencia y la falta de especialistas ha llevado a que una de las principales problemáticas sea lograr su detección temprana, en especial en países en vías de desarrollo, como quiera a que de ello depende las posibilidades de un tratamiento oportuno y las oportunidades de supervivencia de los pacientes. Los recientes avances en tecnología de escáneres para digitalización de láminas de patología y el crecimiento de iniciativas mundiales para la construcción de bases de datos para la investigación en cáncer, han permitido el surgimiento de la patología digital como un nuevo enfoque para soportar los flujos de trabajo en patología. Esto ha llevado al desarrollo de una gran variedad de métodos computacionales para el análisis automático de imágenes de histopatología, lo cual ha planteado nuevos desafíos computacionales debido a la alta variabilidad visual de las láminas de histopatología; la dificultad para evaluar la efectividad de los métodos por la falta de datos de diferentes instituciones que cuenten con anotaciones por parte de los patólogos, y la necesidad de métodos interpretables, eficientes y factibles para su uso práctico. Por otro lado, el aprendizaje de máquina se ha enfocado en explotar las grandes bases de datos para extraer e inducir de manera automática información y conocimiento, en forma de patrones y reglas, que permita conectar el contenido de bajo nivel con su significado. Diferentes técnicas han surgido en contraposición a los esquemas tradicionales basados en diseño manual de la representación de los datos, en lo que se conoce como aprendizaje de la representación. El propósito de esta tesis fue la exploración, desarrollo y validación de métodos computacionales de aprendizaje de máquina precisos, interpretables y eficientes a partir de bases de datos de imágenes de histopatología para el aprendizaje automático de la representación en tareas de apoyo al diagnóstico de distintos tipos de cáncer. La validación de los distintos métodos propuestos durante el desarrollo de la tesis permitieron corroborar la capacidad de cada uno de ellos en distintivas tareas de análisis de imágenes de histopatología, en diferentes tipos de cáncer, con buenos resultados en términos de exactitud, robustez, reproducibilidad, interpretabilidad y factibilidad, lo cual sugiere su potencial aplicación práctica hacia la medicina traslacional y personalizada.Doctorad

Una mirada a los conceptos de ocio, tiempo libre y recreación de los estudiantes, docentes y administrativos de la facultad de ciencias exactas y naturales de la Universidad Surcolombiana

The results of an exploratory quantitative investigation are presented where a structured survey with multiple-choice questions was used as an information collection tool. The population under study consisted of 45 students of the applied Maths program and 45 students of the Physics program, 60 teachers of applied Maths and 33 Physics, as well as 7 administrative staff belonging to the Faculty of Exact and Natural Sciences of the Universidad Surcolombiana. It was also concluded that a high percentage of the population is not sufficiently clear about the concepts of leisure, recreation and free time, besides, they are not fully aware of the benefits that these activities generate in the life of the human being.Se presentan los resultados de una investigación cuantitativa de corte exploratorio donde se utilizó como herramienta de recolección de información una encuesta estructurada con preguntas de opción múltiple, la población objeto de estudio estuvo conformada por 45 estudiantes del programa de matemáticas aplicadas y 45 estudiantes del programa de física, 60 docentes de matemáticas aplicadas y 33 de física así como 7 administrativos pertenecientes a la facultad de ciencias exactas y naturales de la universidad Surcolombiana. Se concluyó que un alto porcentaje de la población no tiene claridad frente a los conceptos de Ocio, Recreación y Tiempo Libre. Además, no son conscientes de los beneficios que estas actividades generan en la vida del ser humano

Representación de imágenes de histopatología utilizada en tareas de análisis automático: estado del arte

This paper presents a review of the state-of-the-art in histopathology image representation used in automatic image analysis tasks. Automatic analysis of histopathology images is important for building computer-assisted diagnosis tools, automatic image enhancing systems and virtual microscopy systems, among other applications. Histopathology images have a rich mix of visual patterns with particularities that make them difficult to analyze. The paper discusses these particularities, the acquisition process and the challenges found when doing automatic analysis. Second an overview of recent works and methods addressed to deal with visual content representation in different automatic image analysis tasks is presented. Third an overview of applications of image representation methods in several medical domains and tasks is presented. Finally, the paper concludes with current trends of automatic analysis of histopathology images like digital pathology

QUOTIENT: Two-Party Secure Neural Network Training and Prediction

Recently, there has been a wealth of effort devoted to the design of secure protocols for machine learning tasks. Much of this is aimed at enabling secure prediction from highly-accurate Deep Neural Networks (DNNs). However, as DNNs are trained on data, a key question is how such models can be also trained securely. The few prior works on secure DNN training have focused either on designing custom protocols for existing training algorithms, or on developing tailored training algorithms and then applying generic secure protocols. In this work, we investigate the advantages of designing training algorithms alongside a novel secure protocol, incorporating optimizations on both fronts. We present QUOTIENT, a new method for discretized training of DNNs, along with a customized secure two-party protocol for it. QUOTIENT incorporates key components of state-of-the-art DNN training such as layer normalization and adaptive gradient methods, and improves upon the state-of-the-art in DNN training in two-party computation. Compared to prior work, we obtain an improvement of 50X in WAN time and 6% in absolute accuracy

Assessment of algorithms for mitosis detection in breast cancer histopathology images

The proliferative activity of breast tumors, which is routinely estimated by counting of mitotic figures in hematoxylin and eosin stained histology sections, is considered to be one of the most important prognostic markers. However, mitosis counting is laborious, subjective and may suffer from low inter-observer agreement. With the wider acceptance of whole slide images in pathology labs, automatic image analysis has been proposed as a potential solution for these issues. In this paper, the results from the Assessment of Mitosis Detection Algorithms 2013 (AMIDA13) challenge are described. The challenge was based on a data set consisting of 12 training and 11 testing subjects, with more than one thousand annotated mitotic figures by multiple observers. Short descriptions and results from the evaluation of eleven methods are presented. The top performing method has an error rate that is comparable to the inter-observer agreement among pathologists

Global variation in diabetes diagnosis and prevalence based on fasting glucose and hemoglobin A1c

Fasting plasma glucose (FPG) and hemoglobin A1c (HbA1c) are both used to diagnose diabetes, but these measurements can identify different people as having diabetes. We used data from 117 population-based studies and quantified, in different world regions, the prevalence of diagnosed diabetes, and whether those who were previously undiagnosed and detected as having diabetes in survey screening, had elevated FPG, HbA1c or both. We developed prediction equations for estimating the probability that a person without previously diagnosed diabetes, and at a specific level of FPG, had elevated HbA1c, and vice versa. The age-standardized proportion of diabetes that was previously undiagnosed and detected in survey screening ranged from 30% in the high-income western region to 66% in south Asia. Among those with screen-detected diabetes with either test, the age-standardized proportion who had elevated levels of both FPG and HbA1c was 29-39% across regions; the remainder had discordant elevation of FPG or HbA1c. In most low- and middle-income regions, isolated elevated HbA1c was more common than isolated elevated FPG. In these regions, the use of FPG alone may delay diabetes diagnosis and underestimate diabetes prevalence. Our prediction equations help allocate finite resources for measuring HbA1c to reduce the global shortfall in diabetes diagnosis and surveillance

Worldwide trends in underweight and obesity from 1990 to 2022: a pooled analysis of 3663 population-representative studies with 222 million children, adolescents, and adults

Background Underweight and obesity are associated with adverse health outcomes throughout the life course. We estimated the individual and combined prevalence of underweight or thinness and obesity, and their changes, from 1990 to 2022 for adults and school-aged children and adolescents in 200 countries and territories. Methods We used data from 3663 population-based studies with 222 million participants that measured height and weight in representative samples of the general population. We used a Bayesian hierarchical model to estimate trends in the prevalence of different BMI categories, separately for adults (age ≥20 years) and school-aged children and adolescents (age 5–19 years), from 1990 to 2022 for 200 countries and territories. For adults, we report the individual and combined prevalence of underweight (BMI <18·5 kg/m2) and obesity (BMI ≥30 kg/m2). For schoolaged children and adolescents, we report thinness (BMI <2 SD below the median of the WHO growth reference) and obesity (BMI >2 SD above the median). Findings From 1990 to 2022, the combined prevalence of underweight and obesity in adults decreased in 11 countries (6%) for women and 17 (9%) for men with a posterior probability of at least 0·80 that the observed changes were true decreases. The combined prevalence increased in 162 countries (81%) for women and 140 countries (70%) for men with a posterior probability of at least 0·80. In 2022, the combined prevalence of underweight and obesity was highest in island nations in the Caribbean and Polynesia and Micronesia, and countries in the Middle East and north Africa. Obesity prevalence was higher than underweight with posterior probability of at least 0·80 in 177 countries (89%) for women and 145 (73%) for men in 2022, whereas the converse was true in 16 countries (8%) for women, and 39 (20%) for men. From 1990 to 2022, the combined prevalence of thinness and obesity decreased among girls in five countries (3%) and among boys in 15 countries (8%) with a posterior probability of at least 0·80, and increased among girls in 140 countries (70%) and boys in 137 countries (69%) with a posterior probability of at least 0·80. The countries with highest combined prevalence of thinness and obesity in school-aged children and adolescents in 2022 were in Polynesia and Micronesia and the Caribbean for both sexes, and Chile and Qatar for boys. Combined prevalence was also high in some countries in south Asia, such as India and Pakistan, where thinness remained prevalent despite having declined. In 2022, obesity in school-aged children and adolescents was more prevalent than thinness with a posterior probability of at least 0·80 among girls in 133 countries (67%) and boys in 125 countries (63%), whereas the converse was true in 35 countries (18%) and 42 countries (21%), respectively. In almost all countries for both adults and school-aged children and adolescents, the increases in double burden were driven by increases in obesity, and decreases in double burden by declining underweight or thinness. Interpretation The combined burden of underweight and obesity has increased in most countries, driven by an increase in obesity, while underweight and thinness remain prevalent in south Asia and parts of Africa. A healthy nutrition transition that enhances access to nutritious foods is needed to address the remaining burden of underweight while curbing and reversing the increase in obesit