Descubriendo Patrones Craneofaciales Usando Datos Cefalométricos Multivariados para la Toma de Decisiones en Ortodoncia

Indexación: Web of Science; Scielo.The aim was to find craniofacial morphology patterns in a multivariate cephalometric database using a clustering technique. Cephalometric analysis was performed in a sample of 100 teleradiographs collected from Chilean orthodontic patients. Thirty cephalometric measurements were taken from commonly used analysis. The computed variables were used to perform a clustering analysis with the k-means algorithm to identify patterns of craniofacial morphology. The J48 decision tree was used to analyze each cluster, and the ANOVA test to determine the statistical differences between the clusters. Four clusters were found that had significant differences (P<0.001) in 24 of the 30 variables studied, suggesting that they represent different patterns of craniofacial form. Using the decision tree, 8 of the 30 variables appeared to be relevant for describing the clusters. The clustering analysis is effective in identifying different craniofacial patterns based on a multivariate database. The distinct clusters appear to be caused by differences in the compensation process of the facial structure responding to a genetically determined cranial and mandible form. The proposed method can be applied to several databases, creating specific classifications for each one of them. KEY WORDS: Craniofacial patterns; Morphological patterns; Clustering technique; Orthodontics.RESUMEN: El objetivo fue encontrar patrones morfológicos craneofaciales, a partir de una base de datos cefalométricos multivariada, utilizando una técnica de clustering. Se realizó un análisis cefalométrico a una muestra de 100 telerradiografías pertenecientes a pacientes chilenos de ortodoncia. Treinta medidas cefalométricas obtenidas de los análisis más utilizados fueron registradas. Las variables computadas se utilizaron para realizar un análisis de clustering con el algoritmo k-medias, para identificar patrones de morfología craneofacial. El árbol de decisión J48 se utilizó para analizar cada cluster, y test de ANOVA para determinar diferencias estadísticamente significativas entre los clusters. Se encontraron cuatro clusters con diferencia estadísticamente significativas (p<0,001) en 24 de las 30 variables estudiadas, lo que sugiere que efectivamente corresponden a diferentes patrones craneofaciales. Utilizando el árbol de decisión, se pudo determinar que 8 de las 30 variables resultaron ser relevantes en la definición de los clusters. El análisis de clustering es efectivo en identificar patrones morfológicos craneofaciales usando una base de datos multivariada. Los distintos cluster encontrados, aparentemente se formarían a partir de diferencias en el proceso de compensación de la estructura facial, en respuesta a la forma mandibular genéticamente determinada. El método propuesto puede ser aplicado a múltiples bases de datos, creando clasificaciones específicas para cada una de ellas. PALABRAS CLAVE: Patrones craneofaciales; Patrones morfológicos; Técnica de clustering; Ortodoncia.http://ref.scielo.org/qdkkz

Characterising the Didactic-Stochastic Knowledge of Future Mathematics Teachers: The Case of Chile

Background: Stochastics’ teacher education has become an important research topic since mathematics teachers are usually responsible for the teaching of stochastics (statistics and probability) in schools. However, the emergence of new theoretical approaches has resulted in the identification of a problem: organizing and describing the necessary professional knowledge to teach stochastics. Objectives: The aim of this research is to characterize the didactic-stochastic knowledge needed for pre-service mathematics teachers, considering Chile as a case study. Design: Following a qualitative perspective, through a content-analysis. Setting and Participants: The Pedagogical and Disciplinary Standards for Mathematics Teaching Programs. Data collection and analysis: The national guidelines on the stochastic education of pre-service’ teachers were analysed to collect text fragments describing professional knowledge expected in pre-service teachers. Results: We obtained a set of 37 indicators organized according to the Didactic-Mathematical Knowledge Model, which includes disciplinary aspects (stochastic content), the knowledge of students and their learning (cognitive content) and interests (affective content), instruction processes (interactional and mediational content) and their link with the educational context and other knowledge areas (ecological content). Conclusions: We hope that the identified indicators become a useful tool to organize and evaluate stochastic education programs for pre- and in-service teachers. Moreover, we highlight the replicability of the method uses and the possible adaptation of results to other educational contexts

A synopsis of the frogs of the genus Eleutherodactylus from the Sierra Nevada de Santa Marta, Colombia

http://deepblue.lib.umich.edu/bitstream/2027.42/57147/1/OP711.pd

Background studies and shielding effects for the TPC detector of the CAST experiment

Sunset solar axions traversing the intense magnetic field of the CERN Axion Solar Telescope (CAST) experiment may be detected in a Time Projection Chamber (TPC) detector, as X-rays signals. These signals could be masked, however, by the inhomogeneous background of materials in the experimental site. A detailed analysis, based on the detector characteristics, the background radiation at the CAST site, simulations and experimental results, has allowed us to design a shielding which reduces the background level by a factor of ~4 compared to the detector without shielding, depending on its position, in the energy range between 1 and 10 keV. Moreover, this shielding has improved the homogeneity of background measured by the TPC.Comment: 14 pages, 5 figures, accepted in New Journal of Physic

The CAST Time Projection Chamber

One of the three X-ray detectors of the CAST experiment searching for solar axions is a Time Projection Chamber (TPC) with a multi-wire proportional counter (MWPC) as a readout structure. Its design has been optimized to provide high sensitivity to the detection of the low intensity X-ray signal expected in the CAST experiment. A low hardware threshold of 0.8 keV is safely set during normal data taking periods, and the overall efficiency for the detection of photons coming from conversion of solar axions is 62 %. Shielding has been installed around the detector, lowering the background level to 4.10 x 10^-5 counts/cm^2/s/keV between 1 and 10 keV. During phase I of the CAST experiment the TPC has provided robust and stable operation, thus contributing with a competitive result to the overall CAST limit on axion-photon coupling and mass.Comment: 19 pages, 11 figures and images, submitted to New Journal of Physic