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See detailMultivariate analysis of a fine-scale breeding bird atlas using a geographical information system and partial canonical correspondence analysis: Environmental and spatial effects
Titeux, N.; Dufrêne, Marc ULg; Jacob, J.-P. et al

in Journal of Biogeography (2004), 31(11), 1841-1856

Aim: To assess the relative roles of environment and space in driving bird species distribution and to identify relevant drivers of bird assemblage composition, in the case of a fine-scale bird atlas data ... [more ▼]

Aim: To assess the relative roles of environment and space in driving bird species distribution and to identify relevant drivers of bird assemblage composition, in the case of a fine-scale bird atlas data set. Location: The study was carried out in southern Belgium using grid cells of 1 x 1 km, based on the distribution maps of the Oiseaux nicheurs de Famenne: Atlas de Lesse et Lomme which contains abundance for 103 bird species. Methods: Species found in < 10% or > 90% of the atlas cells were omitted from the bird data set for the analysis. Each cell was characterized by 59 landscape metrics, quantifying its composition and spatial patterns, using a Geographical Information System. Partial canonical correspondence analysis was used to partition the variance of bird species matrix into independent components: (a) 'pure' environmental variation, (b) spatially-structured environmental variation, (c) 'pure' spatial variation and (d) unexplained, non-spatial variation. Results: The variance partitioning method shows that the selected landscape metrics explain 27.5% of the variation, whilst 'pure' spatial and spatially-structured environmental variables explain only a weak percentage of the variation in the bird species matrix (2.5% and 4%, respectively). Avian community composition is primarily related to the degree of urbanization and the amount and composition of forested and open areas. These variables explain more than half of the variation for three species and over one-third of the variation for 12 species. Main conclusions: The results seem to indicate that the majority of explained variation in species assemblages is attributable to local environmental factors. At such a fine spatial resolution, however, the method does not seem to be appropriated for detecting and extracting the spatial variation of assemblages. Consequently, the large amount of unexplained variation is probably because of missing spatial structures and 'noise' in species abundance data. Furthermore, it is possible that other relevant environmental factors, that were not taken into account in this study and which may operate at different spatial scales, can drive bird assemblage structure. As a large proportion of ecological variation can be shared by environment and space, the applied partitioning method was found to be useful when analysing multispecific atlas data, but it needs improvement to factor out all-scale spatial components of this variation (the source of 'false correlation') and to bring out the 'pure' environmental variation for ecological interpretation. [less ▲]

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See detailMultivariate analysis of biochemical data in acute myocardial infarction
Heusghem, C.; Chapelle, Jean-Paul ULg; Albert, Adelin ULg et al

Poster (1978)

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See detailMultivariate analysis of cognitive profiles in Alzheimer's disease
Bastin, Christine ULg; Leclercq, Yves ULg; Collette, Fabienne ULg et al

in Proceedings of the 8th bi-annual Meeting of the Belgian Society for Neuroscience (2009)

The neuropsychological profiles of patients with early Alzheimer’s disease (AD) appear to be heterogeneous. In this study, we examined whether this heterogeneity corresponds to the existence of ... [more ▼]

The neuropsychological profiles of patients with early Alzheimer’s disease (AD) appear to be heterogeneous. In this study, we examined whether this heterogeneity corresponds to the existence of cognitively distinct subtypes of AD or rather to impairments along a continuum of performances in different cognitive domains. A large group of 187 AD patients recruited in the European project NEST-DD performed a neuropsychological battery. A factor analysis of cognitive performance identified three factors, which respectively reflected attentional/instrumental function, declarative memory and executive function. Three clustering methods were applied on the factor scores in order to explore the existence of separate groups. The clustering methods indicated that cognitive profiles among the patients were sufficiently variable to identify clusters, but there was continuity between clusters rather than clear-cut subtypes. Moreover, clusters corresponded to various combinations of relatively impaired and preserved functions, suggesting multidimensional distribution within a large population of patients. Finally, clusters of cognitive profiles were characterized by different levels of metabolism in brain regions commonly (but variably) involved or relatively preserved in AD. [less ▲]

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See detailMultivariate and multidimensional analysis
Van Steen, Kristel ULg; Molenberghs, G.

in Wilson (Ed.) Biometrics (2003)

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See detailThe multivariate coefficient of variation for comparing serum protein electrophoresis techniques in External Quality Assessment schemes
Zhang, Lixin ULg; Albarède, Stéphanie; Dumont, Gilles et al

in Accreditation and Quality Assurance (2010)

External Quality Assessment (EQA) schemes are national or transnational programmes designed to control the analytical performance of clinical laboratories and to maintain inter-laboratory variability ... [more ▼]

External Quality Assessment (EQA) schemes are national or transnational programmes designed to control the analytical performance of clinical laboratories and to maintain inter-laboratory variability within acceptable limits. In such EQA programmes, participants are usually grouped by the type of assay technique/equipment they use. The coefficient of variation (CV) is a simple tool for comparing the inter-laboratory reproducibility of such techniques: the lower the CV, the better the analytical performance. Serum protein electrophoresis, a laboratory test profile consisting of five fractions (albumin, α1, α2, β and γ globulins) summing up to 100% of total proteins, can also be assayed in different ways depending on the media or the analytical principle. We propose a multivariate coefficient of variation for comparing the performance of electrophoretic techniques in EQA, thus extending the univariate CV concept. First, the compositional nature of electrophoretic data requires a one-to-one transformation from the 5-dimensional to the 4-dimensional space. Next, robust estimations of the mean and the covariance matrix are needed to avoid the effect of outliers. The new approach is illustrated on electrophoretic datasets from the French and Belgian national EQA programmes. [less ▲]

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See detailMultivariate coefficients of variation: comparison and influence functions
Aerts, Stéphanie ULg; Haesbroeck, Gentiane ULg; Ruwet, Christel ULg

E-print/Working paper (2014)

In the univariate setting, coefficients of variation are well known and used to compare the variability of populations characterized by variables expressed in different units or having really different ... [more ▼]

In the univariate setting, coefficients of variation are well known and used to compare the variability of populations characterized by variables expressed in different units or having really different means. When dealing with more than one variable, the use of such a relative dispersion measure is much less common even though several generalizations of the coefficient of variation to the multivariate setting have been introduced in the literature. In this paper, the lack of robustness of the sample versions of the multivariate CV's is illustrated by means of influence functions and a robust counterpart based on the Minimum Covariance Determinant (MCD) estimator is advocated. Then, focusing on two of the considered multivariate CV's, a diagnostic tool based on their influence functions is derived and its efficiency in detecting observations having an unduly large effect on variability is illustrated on a real-life data set. The influence functions are also used to compute asymptotic variances under elliptical distributions, yielding approximate confidence intervals. Finally, simulations are conducted in order to compare the performance of the classical and robust multivariate CV's in a finite sample setting. As expected, when the data are normally distributed, the classical estimator performs better than the robust counterpart based on the MCD estimator, while the reverse is true when the data are contaminated. [less ▲]

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See detailThe multivariate Dale model and genetic associations
Van Steen, Kristel ULg; Molenberghs, G.; Tahri, N.

in American Journal of Human Genetics (2002), 71

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See detailMultivariate discrimination of sands using elongation and abrasivity indices
Pirard, Eric ULg; Vergara, Nicolas

in Proceedings PARTEC (9th European Symposium on Particle Characterization) (2004)

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See detailMultivariate extensions of the coefficient of variation with applications
Zhang, Lixin ULg

Conference (2010, March)

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See detailMultivariate optimization approach for the separation of water-soluble vitamins and related compounds by capillary electrophoresis.
Fotsing, Lucas; Boulanger, Bruno ULg; Chiap, Patrice ULg et al

in Biomedical Chromatography : BMC (2000), 14(1), 10-1

Multivariate optimization approach for the separation of water-soluble vitamins and related compounds by capillary electrophoresis.

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See detailMultivariate pattern analysis: brain decoding
Schrouff, Jessica ULg; Phillips, Christophe ULg

in Schnakers, Caroline; Laureys, Steven (Eds.) Coma and altered states of consciousness (2012)

Two of the most fundamental questions in the field of neurosciences are how information is represented in different brain structures, and how this information evolves over time. Various tools, such as ... [more ▼]

Two of the most fundamental questions in the field of neurosciences are how information is represented in different brain structures, and how this information evolves over time. Various tools, such as Magnetic Resonance (MRI) and Positron Emission Tomography (PET) have been developed over the last few decades to record brain activity and investigate these questions. In particular, functional MRI (fMRI) tracks changes of the Blood Oxygenation Level-Dependent (BOLD) signal, which is a good indicator of brain activity, with a spatial resolution of a few cubic millimeters and a typical temporal resolution in the order of 1 or 2 seconds. Until recently, the methods used to analyze such data focused on characterizing the individual relationship between a cognitive or perceptual state and each image voxel, i.e. following a massively univariate approach. A well-known univariate technique is Statistical Parametric Mapping (SPM) . SPM relies on the General Linear Model to detect which voxels show a statistically significant response to the (combination of) experimental conditions of interest. However, there are limitations on what can be learned about the representation of information by examining voxels in a univariate fashion. For instance, spatially distributed sets of voxels considered as non-significant by a SPM analysis of one experimental condition might still carry information about the presence or absence of that condition. Furthermore, classic voxel-based analytic techniques are agnostic of any a priori information, for example disease-specific information. They are also mainly designed to perform group-wise comparisons and would therefore be unsuitable to evaluate the state of the disease of each individual. On the other hand, Multi-Voxel Pattern Analyses (MVPA) allow an increased sensitivity to detect the presence of a particular mental representation. These multivariate methods, also known as brain decoding or mind reading, attempt to link a particular cognitive, behavioral or perceptual state to specific patterns of voxels’ activity. Application of these methods made it possible to decode the category of a seen object or the orientation of a stripped pattern seen by the subject from the brain activation of the imaged subject. Advances in pattern-classification algorithms also allowed the decoding of less-controlled conditions such as memory retrieval tasks. Advanced mathematical tools are still under development to allow the classification of more complicated experimental data sets, such as examining the content of mind wandering or detecting the state of consciousness of a patient showing no response to a command. [less ▲]

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See detailMultivariate pattern interpretation using PRoNTo
Schrouff, Jessica ULg; Rosa, Maria; Rondina, Jane et al

Poster (2013, June)

Recently, machine learning models have been applied to neuroimaging data, allowing to make predictions about a variable of interest based on the pattern of activation or anatomy over a set of voxels. In ... [more ▼]

Recently, machine learning models have been applied to neuroimaging data, allowing to make predictions about a variable of interest based on the pattern of activation or anatomy over a set of voxels. In addition, they might lead to an increased sensitivity to detect the presence of a particular mental representation compared to univariate methods such as the General Linear Model (GLM). Application of these methods made it possible to decode the category of a seen object or the orientation of a striped pattern seen by the subject. They also allowed classification of patients and healthy controls and could therefore be used as a diagnostic tool due to their ability to predict the class of an unseen sample. The main disadvantage of multivariate machine learning models is that local inference with respect to the brain neuroanatomy is complex: although linear models generate weights for each voxel, the model predictions are based on the whole pattern and therefore one cannot threshold the weights to make regional statistical inferences as in univariate analysis. In the present work, we developed a methodology based on a labelled anatomical template (e.g. AAL or Brodmann) to display a smoothed version of the model weights and compute a ranking of the regions according their contribution to the predictive model. This work is distributed in PRoNTo (Pattern Recognition for Neuroimaging Toolbox), a user-friendly toolbox, making machine learning models available to every neuroscientist. [less ▲]

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See detailMultivariate reconstruction of missing data in sea surface temperature, chlorophyll, and wind satellite fields
Alvera Azcarate, Aïda ULg; Barth, Alexander ULg; Beckers, Jean-Marie ULg et al

in Journal of Geophysical Research. Oceans (2007), 112(C3), 03008

An empirical orthogonal function–based technique called Data Interpolating Empirical Orthogonal Functions (DINEOF) is used in a multivariate approach to reconstruct missing data. Sea surface temperature ... [more ▼]

An empirical orthogonal function–based technique called Data Interpolating Empirical Orthogonal Functions (DINEOF) is used in a multivariate approach to reconstruct missing data. Sea surface temperature (SST), chlorophyll a concentration, and QuikSCAT winds are used to assess the benefit of a multivariate reconstruction. In particular, the combination of SST plus chlorophyll, SST plus lagged SST plus chlorophyll, and SST plus lagged winds have been studied. To assess the quality of the reconstructions, the reconstructed SST and winds have been compared to in situ data. The combination of SST plus chlorophyll, as well as SST plus lagged SST plus chlorophyll, significantly improves the results obtained by the reconstruction of SST alone. All the experiments correctly represent the SST, and an upwelling/downwelling event in the West Florida Shelf reproduced by the reconstructed data is studied. [less ▲]

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See detailMultivariate statistics for wildlife and ecology research
Bogaert, Jan ULg

in Acta Biotheoretica (2001), 49(2), 141-143

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See detailMultivariate statistics to understand the geochemical processes induced by groundwater pollution Multi-scale applying
Gesels, Julie ULg; Joniau, Claire; Batlle-Aguilar, Jordi et al

Conference (2013, June 07)

Different hydrogeochemical approaches (classical diagrams, spatial distribution maps, geochemical equations and multivariate statistics) are combined to obtain a global understanding of the ... [more ▼]

Different hydrogeochemical approaches (classical diagrams, spatial distribution maps, geochemical equations and multivariate statistics) are combined to obtain a global understanding of the hydrogeochemical processus at regional and at local scale. [less ▲]

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See detailMultivessel coronary revascularization in patients with and without diabetes mellitus: 3-year follow-up of the ARTS-II (Arterial Revascularization Therapies Study-Part II) trial.
Daemen, Joost; Kuck, Karl Heinz; Macaya, Carlos et al

in Journal of the American College of Cardiology (2008), 52(24), 1957-67

OBJECTIVES: The purpose of this study was to assess the 3-year outcome of coronary artery bypass graft surgery (CABG) and percutaneous coronary intervention (PCI) using sirolimus-eluting stents (SES) in ... [more ▼]

OBJECTIVES: The purpose of this study was to assess the 3-year outcome of coronary artery bypass graft surgery (CABG) and percutaneous coronary intervention (PCI) using sirolimus-eluting stents (SES) in patients who had multivessel coronary artery disease with and without diabetes mellitus. BACKGROUND: The optimal method of revascularization in diabetic patients remains in dispute. METHODS: The ARTS-II (Arterial Revascularization Therapies Study-Part II) trial is a single-arm study (n = 607) that included 159 diabetic patients treated with SES whose 3-year clinical outcome was compared with that of the historical diabetic and nondiabetic arms of the randomized ARTS-I trial (n = 1,205, including 96 diabetic patients in the CABG arm and 112 in the PCI arm). RESULTS: At 3 years, among nondiabetic patients, the incidence of the primary composite of death, CVA, myocardial infarction (MI), and repeat revascularization (major adverse cardiac and cerebrovascular events [MACCE]), was significantly lower in ARTS-II than in ARTS-I PCI (adjusted odds ratio [OR]: 0.41; 95% confidence interval [CI]: 0.26 to 0.64) and similar to ARTS-I CABG. The ARTS-II patients were at significantly lower risk for death, CVA, and MI as compared with both the ARTS-I PCI (adjusted OR: 0.55; 95% CI: 0.34 to 0.91) and ARTS-I CABG patients (adjusted OR: 0.56; 95% CI: 0.35 to 0.92). Among diabetic patients, the incidence of MACCE in ARTS-II was similar to that of both PCI and CABG in ARTS-I. Conversely, the incidence of death, CVA, and MI was significantly lower in ARTS-II than in ARTS-I PCI (adjusted OR: 0.67; 95% CI: 0.27 to 1.65) and was similar to that of ARTS-I CABG. CONCLUSIONS: At 3 years, PCI using SES for patients with multivessel coronary artery disease seems to be safer and more efficacious than PCI using bare-metal stents, irrespective of the diabetic status of the patient. Hence, PCI using SES appears to be a valuable alternative to CABG for both diabetic and nondiabetic patients. [less ▲]

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