Search results for "cluster [track data analysis]"
showing 10 items of 1171 documents
Effectiveness of training in guideline-oriented biopsychosocial management of low-back pain in occupational health services – a cluster randomized co…
2021
Objective This study aimed to investigate the effectiveness of brief training in the guideline-oriented biopsychosocial management of low-back pain (LBP) in occupational health services using a cluster-randomized design. A small sample of physiotherapists and physicians from the intervention units (N=12) were given three- to seven-day training focusing on the biopsychosocial management of LBP, while professionals in the control units (N=15) received no such training. Methods Eligible patients with LBP, with or without radicular pain, aged 18-65, were invited to participate. A web-based questionnaire was sent to all recruited patients at baseline, three months and one year. The primary outco…
A computational study of some electric and magnetic properties of gaseous BF3 and BCl3
2005
We present the results of an extended computational study of the electric and magnetic properties connected to Cotton-Mouton birefringences, on the trifluoro- and trichloroborides in the gas phase. The electric dipole polarizabilities, magnetizabilities, quadrupole moments, and higher-order hypersusceptibilities—expressed as quadratic and cubic frequency-dependent response functions—are computed within Hartree-Fock, density-functional, and coupled-cluster response theories employing singly and doubly augmented correlation-consistent basis sets and London orbitals in the magnetic property calculations. The results, which illustrate the capability of time-dependent density-functional theory f…
Association between WHO cut-offs for childhood overweight and obesity and cardiometabolic risk
2012
AbstractObjectiveTo examine the association between cardiovascular risk and childhood overweight and obesity using the BMI cut-offs recommended by the WHO.DesignChildren were classified as normal weight, overweight and obese according to the WHO BMI-for-age reference. Blood pressure, lipids, glucose, insulin, homeostasis model assessment–insulin resistance (HOMA-IR) and uric acid levels were compared across BMI groups. ANOVA and tests of linearity were used to assess overall mean differences across groups. Crude and adjusted odds ratios were calculated for adverse plasma levels of biochemical variables.SettingPaediatric care centres.SubjectsChildren (n149) aged 8–18 years.ResultsAbout 37 %,…
Phenotyping of type 2 diabetes mellitus at onset on the basis of fasting incretin tone: Results of a two-step cluster analysis.
2015
Aims/Introduction According to some authors, in type 2 diabetes there is a reduced postprandial action of glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic polypeptide (GIP). However, little is known about the role of fasting incretins in glucose homeostasis. Our aim was to evaluate, through a two-step cluster analysis, the possibility of phenotyping patients with type 2 diabetes at onset on the basis of fasting GLP-1, GIP and ghrelin. Materials and Methods A total of 96 patients with type 2 diabetes within 6 months of onset (mean age 62.40 ± 6.36 years) were cross-sectionally studied. Clinical, anthropometric and metabolic parameters were evaluated. At fasting the follow…
Processing of rock core microtomography images: Using seven different machine learning algorithms
2016
The abilities of machine learning algorithms to process X-ray microtomographic rock images were determined. The study focused on the use of unsupervised, supervised, and ensemble clustering techniques, to segment X-ray computer microtomography rock images and to estimate the pore spaces and pore size diameters in the rocks. The unsupervised k-means technique gave the fastest processing time and the supervised least squares support vector machine technique gave the slowest processing time. Multiphase assemblages of solid phases (minerals and finely grained minerals) and the pore phase were found on visual inspection of the images. In general, the accuracy in terms of porosity values and pore…
Improving clustering of Web bot and human sessions by applying Principal Component Analysis
2019
View references (18) The paper addresses the problem of modeling Web sessions of bots and legitimate users (humans) as feature vectors for their use at the input of classification models. So far many different features to discriminate bots’ and humans’ navigational patterns have been considered in session models but very few studies were devoted to feature selection and dimensionality reduction in the context of bot detection. We propose applying Principal Component Analysis (PCA) to develop improved session models based on predictor variables being efficient discriminants of Web bots. The proposed models are used in session clustering, whose performance is evaluated in terms of the purity …
Class discovery from semi-structured EEG data for affective computing and personalisation
2017
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link. Many approaches to recognising emotions from metrical data such as EEG signals rely on identifying a very small number of classes and to train a classifier. The interpretation of these classes varies from a single emotion such as stress [24] to features of emotional model such as valence-arousal [4]. There are two major issues here. First classification approach limits the analysis of the data within the selected classes and is also highly dependent on training data/cycles, all of which limits generalisation. Second issue is that it does not exp…
Fast dendrogram-based OTU clustering using sequence embedding
2014
Biodiversity assessment is an important step in a metagenomic processing pipeline. The biodiversity of a microbial metagenome is often estimated by grouping its 16S rRNA reads into operational taxonomic units or OTUs. These metagenomic datasets are typically large and hence require effective yet accurate computational methods for processing.In this paper, we introduce a new hierarchical clustering method called CRiSPy-Embed which aims to produce high-quality clustering results at a low computational cost. We tackle two computational issues of the current OTU hierarchical clustering approach: (1) the compute-intensive sequence alignment operation for building the distance matrix and (2) the …
A Fuzzy Logic C-Means Clustering Algorithm to Enhance Microcalcifications Clusters in Digital Mammograms
2011
The detection of microcalcifications is a hard task, since they are quite small and often poorly contrasted against the background of images. The Computer Aided Detection (CAD) systems could be very useful for breast cancer control. In this paper, we report a method to enhance microcalcifications cluster in digital mammograms. A Fuzzy Logic clustering algorithm with a set of features is used for clustering microcalcifications. The method described was tested on simulated clusters of microcalcifications, so that the location of the cluster within the breast and the exact number of microcalcifications is known.
CRiSPy-CUDA: Computing Species Richness in 16S rRNA Pyrosequencing Datasets with CUDA
2011
Pyrosequencing technologies are frequently used for sequencing the 16S rRNA marker gene for metagenomic studies of microbial communities. Computing a pairwise genetic distance matrix from the produced reads is an important but highly time consuming task. In this paper, we present a parallelized tool (called CRiSPy) for scalable pairwise genetic distance matrix computation and clustering that is based on the processing pipeline of the popular ESPRIT software package. To achieve high computational efficiency, we have designed massively parallel CUDA algorithms for pairwise k-mer distance and pairwise genetic distance computation. We have also implemented a memory-efficient sparse matrix clust…