Search results for "FUNCTIONAL"
showing 10 items of 4822 documents
FUNCTIONAL NONRETENTIVE FECAL SOILING AND STRESSFUL LIFE EVENTS
2019
Introduction: Functional non-retentive fecal soiling (FNRFS), or encopresis without constipation, is a common problem in pediatric age. FNRFS is associated with high levels of distress for both children and parents and with emotional disorders in about 30%-50% of affected children. This study aimed to evaluate stressors on a sample of children with FNRFS comparing to a group of typical devolpment children (TDC). Methods:154 subjects participated in the study: 56 FNRFS children (37 males; mean age 10.87 years ± 1.68); 98 TDC (65 males; mean age 11.3 years ± 1.85). All participants were evaluated for the presence of stressful events (LCU) using the Coddington Life Events Scales (CLES). Result…
Neuropeptides’ Hypothalamic Regulation of Sleep Control in Children Affected by Functional Non-Retentive Fecal Incontinence
2020
Functional non-retentive fecal incontinence (FNRFI) is a common problem in pediatric age. FNRFI is defined as unintended loss of stool in a 4-year-old or older child after organic causes have been excluded. FNRFI tends to affects up to 3% of children older than 4 years, with males being affected more frequently than females. Clinically, children affected by FNRFI have normal intestinal movements and stool consistency. Literature data show that children with fecal incontinence have increased levels of separation anxiety, specific phobias, general anxiety, attention-deficit/hyperactivity disorder (ADHD), and oppositional defiant disorder. In terms of possible relationship between incontinence…
Episodic memories: how do the hippocampus and the entorhinal ring attractors cooperate to create them?
2020
AbstractThe brain is capable of registering a constellation of events, encountered only once, as an episodic memory that can last for a lifetime. As evidenced by the clinical case of the patient HM, memories preserving their episodic nature still depend on the hippocampal formation, several years after being created, while semantic memories are thought to reside in neocortical areas. The neurobiological substrate of one-time learning and life-long storing in the brain, that must exist at the cellular and circuit level, is still undiscovered. The breakthrough is delayed by the fact that studies jointly investigating the rodent hippocampus and entorhinal cortex are mostly targeted at understa…
Functional Data Analysis in NTCP Modeling: A New Method to Explore the Radiation Dose-Volume Effects
2014
Purpose/Objective(s) To describe a novel method to explore radiation dose-volume effects. Functional data analysis is used to investigate the information contained in differential dose-volume histograms. The method is applied to the normal tissue complication probability modeling of rectal bleeding (RB) for patients irradiated in the prostatic bed by 3-dimensional conformal radiation therapy. Methods and Materials Kernel density estimation was used to estimate the individual probability density functions from each of the 141 rectum differential dose-volume histograms. Functional principal component analysis was performed on the estimated probability density functions to explore the variatio…
Comparing Spatial and Spatio-temporal FPCA to Impute Large Continuous Gaps in Space
2018
Multivariate spatio-temporal data analysis methods usually assume fairly complete data, while a number of gaps often occur along time or in space. In air quality data long gaps may be due to instrument malfunctions; moreover, not all the pollutants of interest are measured in all the monitoring stations of a network. In literature, many statistical methods have been proposed for imputing short sequences of missing values, but most of them are not valid when the fraction of missing values is high. Furthermore, the limitation of the methods commonly used consists in exploiting temporal only, or spatial only, correlation of the data. The objective of this paper is to provide an approach based …
Functional Principal Components Analysis with Survey Data
2008
This work aims at performing Functional Principal Components Analysis (FPCA) with Horvitz-Thompson estimators when the observations are curves collected with survey sampling techniques. FPCA relies on estimations of the eigenelements of the covariance operator which can be seen as nonlinear functionals. Adapting to our functional context the linearization technique based on the influence function developed by Deville (1999), we prove that these estimators are asymptotically design unbiased and convergent. Under mild assumptions, asymptotic variances are derived for the FPCA’ estimators and convergent estimators of them are proposed. Our approach is illustrated with a simulation study and we…
A functional approach to monitor and recognize patterns of daily traffic profiles
2014
Functional Data Analysis (FDA) is a collection of statistical techniques for the analysis of information on curves or functions. This paper presents a new methodology for analyzing the daily traffic flow profiles based on the employment of FDA. A daily traffic profile corresponds to a single datum rather than a large set of traffic counts. This insight provides ideal information for strategic decision-making regarding road expansion, control, and other long-term decisions. Using Functional Principal Component Analysis the data are projected into a low dimensional space: the space of the first functional principal components. Each curve is represented by their vector of scores on this basis.…
Functional Data Analysis and Mixed Effect Models
2004
Panel studies in econometrics as well as longitudinal studies in biomedical applications provide data from a sample of individual units where each unit is observed repeatedly over time (age, etc.). In this context, mixed effect models are often applied to analyze the behavior of a response variable in dependence of a number of covariates. In some important applications it is necessary to assume that individual effects vary over time (age, etc.).
Measuring Dissimilarity Between Curves by Means of Their Granulometric Size Distributions
2008
The choice of a dissimilarity measure between curves is a key point for clustering functional data. Functions are usually pointwise compared and, in many situations, this approach is not appropriate. Mathematical Morphology provides us with a toolbox to overcome this problem. We propose some dissimilarity measures based on morphological granulometries and their performance is evaluated on some functional datasets.
Comparing air quality indices aggregated by pollutant
2011
In this paper a new aggregate Air Quality Index (AQI) useful for describing the global air pollution situation for a given area is proposed. The index, unlike most of currently used AQIs, takes into account the combined effects of all the considered pollutants to human health. Its good performance, tested by means of a simulation plan, is confirmed by a comparison with two other indices proposed in the literature, one of which is based on the Relative Risk of daily mortality, considering an application to real data.