On adaptive kernel intensity estimation on linear networks

In the analysis of spatial point patterns on linear networks, a critical statistical objective is estimating the first-order intensity function, representing the expected number of points within specific subsets of the network. Typically, non-parametric approaches employing heating kernels are used for this estimation. However, a significant challenge arises in selecting appropriate bandwidths before conducting the estimation. We study an intensity estimation mechanism that overcomes this limitation using adaptive estimators, where bandwidths adapt to the data points in the pattern. While adaptive estimators have been explored in other contexts, their application in linear networks remains underexplored. We investigate the adaptive intensity estimator within the linear network context and extend a partitioning technique based on bandwidth quantiles to expedite the estimation process significantly. Through simulations, we demonstrate the efficacy of this technique, showing that the partition estimator closely approximates the direct estimator while drastically reducing computation time. As a practical application, we employ our method to estimate the intensity of traffic accidents in a neighbourhood in Medellin, Colombia, showcasing its real-world relevance and efficiency.Comment: 19 pages, 7 figure

Non-Parametric Analysis of Spatial and Spatio-Temporal Point Patterns

The analysis of spatial and spatio-temporal point patterns is becoming increasingly necessary, given the rapid emergence of geographically and temporally indexed data in a wide range of fields. Non-parametric point pattern methods are a highly adaptable approach to answering questions about the real-world using complex data in the form of collections of points. Several methodological advances have been introduced in the last few years. This paper examines the current methodology, including the most recent developments in estimation and computation, and shows how various R packages can be combined to run a set of non-parametric point pattern analyses in a guided and intuitive way. An example of non-specific gastrointestinal disease reports in Hampshire, UK, from 2001 to 2003 is used to illustrate the methods, procedures and interpretations

Efectos de la liberacion de la fascia toraxica en 11 estudiantes de 4º ano medio del colegio Valentin Letelier de Linares

45 p.El sistema fascial del organismo es una ininterrumpida red que, de diferentes modos, controla todos los componentes de nuestro cuerpo. A nivel muscular cualquier alteración de la fascia afecta en el correcto funcionamiento de esta unidad. Desde el punto de vista de la respiración, si existe una restricción miofascial en los músculos respiratorios, esta se verá reflejada en la menor capacidad de contracción de estos músculos. Objetivo: El propósito de este estudio fue determinar si existen o no cambios en los volúmenes pulmonares y diámetro transversal de tórax en inspiración máxima al aplicar técnicas kinésicas de liberación miofascial en pacientes asintomáticos respiratorios, en 11 pacientes sanos entre 17 – 20 años de cuarto medio del colegio Valentín Letelier de la cuidad de Linares. Metodología: Para ello se obtuvo una muestra de 11 estudiantes a los cuales se les realizó una espirometría y medición del diámetro transversal antes y después de aplicar dos técnicas de liberación miofascial en el tórax: Inducción de la pared torácica anterior y Plano Transverso diafragmático. Resultados: De los resultados obtenidos tenemos que en relación a la espirometría el VEF1 y la CVF no presentaron variación, a diferencia del PEF, en donde el 54% aumentó 45 lts/min, el 9% se mantuvo sin variación y el 36% disminuyo 21,25 lts/min. En la medición del diámetro trasversal el 100% de los estudiantes aumentó dicho diámetro con un promedio de 1,15 cm, independiente del sexo. Independiente a los cambios obtenidos el análisis Mann Whitney no arrojó diferencias significativas (U= 51; p= 0,5508) entre los estudiantes pre y post-técnica en el Diámetro Transversal. Este mismo análisis también no arrojó diferencias significativas (U= 50,5; p= 0,5301) en la Espirométrica pre y post-tratamiento. Conclusión: El análisis Mann Whitney no arrojó diferencias significativas en la evaluación espirométrica pre y post-tratamiento Por lo tanto se rechaza h1 se acepta h0

Spatio-temporal modeling of infectious diseases by integrating compartment and point process models

Infectious disease modeling plays an important role in understanding disease spreading dynamics and can be used for prevention and control. The well-known SIR (Susceptible, Infected, and Recovered) compartment model and spatial and spatio-temporal statistical models are common choices for studying problems of this kind. This paper proposes a spatio-temporal modeling framework to characterize infectious disease dynamics by integrating the SIR compartment and log-Gaussian Cox process (LGCP) models. The method’s performance is assessed via simulation using a combination of real and synthetic data for a region in São Paulo, Brazil. We also apply our modeling approach to analyze COVID-19 dynamics in Cali, Colombia. The results show that our modified LGCP model, which takes advantage of information obtained from the previous SIR modeling step, leads to a better forecasting performance than equivalent models that do not do that. Finally, the proposed method also allows the incorporation of age-stratified contact information, which provides valuable decision-making insights

Analysing spatial point patterns in digital pathology: immune cells in high-grade serous ovarian carcinomas

Multiplex immunofluorescence (mIF) imaging technology facilitates the study of the tumour microenvironment in cancer patients. Due to the capabilities of this emerging bioimaging technique, it is possible to statistically analyse, for example, the co-varying location and functions of multiple different types of immune cells. Complex spatial relationships between different immune cells have been shown to correlate with patient outcomes and may reveal new pathways for targeted immunotherapy treatments. This tutorial reviews methods and procedures relating to spatial point patterns for complex data analysis. We consider tissue cells as a realisation of a spatial point process for each patient. We focus on proper functional descriptors for each observation and techniques that allow us to obtain information about inter-patient variation. Ovarian cancer is the deadliest gynaecological malignancy and can resist chemotherapy treatment effective in cancers. We use a dataset of high-grade serous ovarian cancer samples from 51 patients. We examine the immune cell composition (T cells, B cells, macrophages) within tumours and additional information such as cell classification (tumour or stroma) and other patient clinical characteristics. Our analyses, supported by reproducible software, apply to other digital pathology datasets

Spatial and spatio-temporal methods for public health surveillance

Public health surveillance provides information to identify public health problems and respond appropriately when they occur. This information is crucial to prevent and control a variety of health conditions such as infectious diseases, chronic diseases, injuries, or health-related behaviors. Quality surveillance is needed to understand the true health status of the population and to guide the use of limited public health resources. Under inadequate surveillance systems, leaders are grossly misinformed and may lose opportunities for the application of early prevention and control measures. In these situations, it is possible the resurgence of previously eradicated diseases or the uncontrolled global spread of diseases as in the case of HIV/AIDS. Surveillance involves four main integrated activities: the collection of health data, the analyses and interpretation of these data, and the timely dissemination of the results to those responsible to respond to a population's health needs. Surveillance systems capture spatial, temporal and person characteristics on health outcomes. Incidence and mortality rates quantify the size of the health problem in a given population and provide the basis for initiating disease control measures and evaluating their effectiveness. Temporal trends and demographic and ethnic group comparisons can provide important clues as to disease etiology. The increased availability of geographically georeferenced health and population data, and the development of geographic information systems (GIS) and software for geocoding addresses, have facilitated the ascent of the investigations of spatial and spatio-temporal variations of disease. There is a wide range of spatial and spatio-temporal methods that can be applied as a surveillance tool including disease mapping, clustering, and geographic correlation studies. Many of these methods may be used for detecting unusual geographic or temporal variation in disease risk, highlighting areas at apparently high risk, early detection of epidemics, detecting significant disease clusters in space and time, assessing the risk in relation to a putative source, and identifying factors associated with the spatial distribution of disease. Unfortunately, naive use of the statistical methods can be highly misleading. Therefore, a thorough understanding of potential problems such as changes in case definitions and completeness issues are critical to the analysis of the data and interpretation of the findings. Over the past few decades, surveillance has undergone considerable development. Certain activities have contributed to the advance of public health surveillance. These include technological innovations such as real-time on-line monitoring and advances in GIS, the development of new statistical methods and computational tools to apply them, and more effective use of electronic media and other tools of communications that facilitate dissemination of surveillance information for public health practice. Also, public health surveillance has changed in response to new public health concerns, such as bioterrorist events and relatively new diseases and epidemics, such as severe acute respiratory syndrome (SARS). As public health needs change and new tools and increased computational capacity of computers become available, statistical methods for disease surveillance must continue to evolve to improve the quality of the analyses, and the interpretation and display of the results in the most useful form and appropriate time-frame to meet the interests of policymakers and stakeholders. The aim of this thesis is to propose new techniques for helping public health surveillance practice. In particular, we focus in spatial and spatio-temporal methods that can help deal with missing data (Chapter 4), model the correlated heterogeneity in disease mapping (Chapter 5), detect spatial and spatio-temporal clusters (Chapter 6), and elucidate spatial variations in temporal trends (Chapter 7)

Graduate! un repositorio de objetos de aprendizaje

Graduate! es un repositorio académico de Objetos de Aprendizaje (OA), abierto, que ha sido creado para archivar, preservar y distribuir fundamentalmente OA y todo tipo de documento digital: producción científica de Investigación y Desarrollo (I+D), tesis, revistas digitales todos ellos en variados formatos. Ha sido desarrollado en base a DSpace, como parte de una tesina de grado en el marco de un proyecto de investigación de la Universidad Nacional de la Patagonia San Juan Bosco. En este artículo se describe algunos aspectos del diseño e implementación.Eje: Workshop Tecnología informática aplicada en educación (WTIAE)Red de Universidades con Carreras en Informática (RedUNCI

NGC 6302: The Tempestuous Life of a Butterfly

NGC 6302 (The ''Butterfly Nebula'') is an extremely energetic bipolar nebula whose central star is among the most massive, hottest, and presumably rapidly evolving of all central stars of planetary nebulae. Our proper-motion study of NGC 6302, based on excellent HST WFC3 images spanning 11 yr, has uncovered at least four different pairs of expanding internal lobes that were ejected at various times over the past two millennia at speeds ranging from 10 to 600 km s^-1. In addition, we find a pair of off-axis flows in constant motion at 760 +/- 100 km s^-1 within which bright [Fe II] feathers are conspicuous. Combining our results with those previously published, we find that the ensemble of flows has an ionized mass > 0.1 M_sun. The kinetic energy of the ensemble, 10^46 - 10^48 ergs, lies at the upper end of gravity-powered processes such as stellar mergers or mass accretion and is too large to be explained by stellar radiation pressure or convective ejections. The structure and dynamics of the Butterfly Nebula suggests that its central engine has had a remarkable history, and the highly unusual patterns of growth within its wings challenge our current understanding of late stellar mass ejection.Comment: 17 pages, 3 figures, 1 tabl