Search results for "regression"
showing 10 items of 2619 documents
Neurosurgical resident training in Czech Republic
2018
Introduction: Resident training is essential to be able and offer high-quality medical care. Neurosurgical training in its traditional form is currently challenged by law-enforced working hour restrictions and general re-structuring within Europe. We aimed to evaluate the current situation of resident training in the Czech Republic. Methods: An electronic survey was sent to European neurosurgical trainees between 06/2014 and 03/2015. The responses of Czech trainees were compared to those of trainees from other European countries. Logistic regression analysis was used to assess the effect size of the relationship between a trainee being from Czech Republic and the outcomes (e.g. satisfaction…
The autoradiographic test for unscheduled DNA synthesis: a sensitive assay for the detection of DNA repair in the HepG2 cell line
2004
International audience; We assessed the DNA-repair capacity of HepG2 cells, which were derived from a human hepatoma, by the unscheduled DNA synthesis assay, using the autoradiography protocol (UDS-AR). We evaluated DNA repair following exposure to direct mutagens (4-nitroquinoline-N-oxide (4-NQO), methyl methanesulfonate (MMS)), to mutagens requiring metabolic activation (benzo[a]pyrene (B[a]P), 2-acetylaminofluorene (2-AAF), N-dimethylnitrosoamine (NDMA)) or to structurally related non-mutagens such as pyrene and 4-acetylaminofluorene (4-AAF). All positive compounds tested induced UDS in HepG2 cells. With 4-NQO and MMS, a concentration-dependent increase in net nuclear grains per cell was…
An AFLP clock for the absolute dating of shallow-time evolutionary history based on the intraspecific divergence of southwestern European alpine plan…
2009
The dating of recent events in the history of organisms needs divergence rates based on molecular fingerprint markers. Here, we used amplified fragment length polymorphisms (AFLPs) of three distantly related alpine plant species co-occurring in the Spanish Sierra Nevada, the Pyrenees and the southwestern Alps/Massif Central to establish divergence rates. Within each of these species (Gentiana alpina, Kernera saxatilis and Silene rupestris), we found that the degree of AFLP divergence (D(N72)) between mountain phylogroups was significantly correlated with their time of divergence (as inferred from palaeoclimatic/palynological data), indicating constant AFLP divergence rates. As these rates d…
Machine learning at the interface of structural health monitoring and non-destructive evaluation
2020
While both non-destructive evaluation (NDE) and structural health monitoring (SHM) share the objective of damage detection and identification in structures, they are distinct in many respects. This paper will discuss the differences and commonalities and consider ultrasonic/guided-wave inspection as a technology at the interface of the two methodologies. It will discuss how data-based/machine learning analysis provides a powerful approach to ultrasonic NDE/SHM in terms of the available algorithms, and more generally, how different techniques can accommodate the very substantial quantities of data that are provided by modern monitoring campaigns. Several machine learning methods will be illu…
Data Augmentation Approach in Bayesian Modelling of Presence-only Data
2011
Abstract Ecologists are interested in prediction of potential distribution of species in suitable areas, essential for planning conservation and management strategies. Unfortunately, often the only available information in such studies is the true presence of the species at few locations of the study area and the associated environmental covariates over the entire area, referred as presence-only data. We propose a Bayesian approach to estimate logistic linear regressions adapted to presence-only data through the introduction of a random approximation of the correction factor in the adjusted logistic model that allows us to overcome the need to know a priori the prevalence of the species.
What is the best fitting function? Evaluation of lactate curves with common methods from the literature
2015
Using the lactate threshold for training prescription is the gold-standard, although there are several open questions. One open question is: What is the best fitting method for the load-lactate data points? This investigation re-analyses over 3500 lactate diagnostic datasets in swimming. Our evaluation software examines six different fitting methods with two different minimization criteria (RMSE and SE). Optimization of parameters of the functions is put in excecution with gradient descent. From a mathematical point of view, the double phase model, which consists of two linear regression lines, shows the least errors (RMSE min 0.254 ± 0.172; SE min 0.311 ± 0.210). However, this method canno…
Regression diagnostics applied in kinetic data processing: Outlier recognition and robust weighting procedures
2010
An efficient protocol, based on advanced statistical diagnostics and robust fitting techniques applied to the least-squares processing of kinetic data of chemical reactions, is presented and discussed. The procedure, which is aimed at obtaining highly accurate estimation of the fitting parameters, consists of the identification of the outliers that remarkably impair the fitting by means of the so-called “leverage analysis” and some related diagnostics. This approach allows the elimination of the actually aberrant observations from the data set and/or their robust weighting to inhibit the negative effects induced on the data fitting, with consequent reduction of the bias introduced into the …
Integration of high and low resolution NDVI data for monitoring vegetation in Mediterranean environments
1998
Abstract The integration of the useful features of high and low spatial and temporal resolution satellite data is a major issue in remote sensing studies. The current work presents the development and testing of a procedure based on classification and regression analysis techniques for generating an NDVI data set with the spatial resolution of Landsat TM images and the temporal resolution of NOAA AVHRR maximum-value composites. The procedure begins with a classification of the high resolution TM data which yields land use references. These are degraded to low spatial resolution in order to produce abundance images comparable with the AVHRR data. Linear regressions are then applied between t…
The Analysis of Auxological Data by Means of Nonlinear Multivariate Growth Curves
1999
In this paper we treat the problem to analyse a data set constituted by multivariate growth curves for different subjects; thus in this context we deal with 3-way data tables. Nevertheless, it is not possible using factorial techniques proposed to deal with 3-way data matrices, because the observations are generally not equally spaced; moreover a multilevel approach founded on polynomial models is not suitable to deal with intrinsic nonlinear models. We propose a non-factorial technique to analyse auxological data sets using an intrinsic nonlinear multivariate growth model with autocorrelated errors. The application to a real data set of growing children gave easily interpretable results.
Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods
2019
An unprecedented spectroscopic data stream will soon become available with forthcoming Earth-observing satellite missions equipped with imaging spectroradiometers. This data stream will open up a vast array of opportunities to quantify a diversity of biochemical and structural vegetation properties. The processing requirements for such large data streams require reliable retrieval techniques enabling the spatiotemporally explicit quantification of biophysical variables. With the aim of preparing for this new era of Earth observation, this review summarizes the state-of-the-art retrieval methods that have been applied in experimental imaging spectroscopy studies inferring all kinds of vegeta…