Search results for "Poster"
showing 10 items of 679 documents
Supplementary material 5 from: Van Cann J, Virgilio M, Jordaens K, De Meyer M (2015) Wing morphometrics as a possible tool for the diagnosis of the C…
2015
Preliminary methodological experiment: unconstrained ordination of wing landmarks: Explanation note: Principal component analysis (PCA) showing morphometric differences in wing landmarks of 14 Ceratitis rosa specimens across sexes (M, F), wings (LW: left wing, RW: right wing), repeated images of the same wing (1, 2), repeated measures of the same image (A, B).
Supplementary material 14 from: Van Cann J, Virgilio M, Jordaens K, De Meyer M (2015) Wing morphometrics as a possible tool for the diagnosis of the …
2015
Morphometric differences across morphospecies (wing band areas): Explanation note: PERMANOVA and a posteriori comparisons (t-statistic) testing differences in multivariate patterns of wing band areas among morphospecies (Ceratitis anonae, Ceratitis fasciventris, Ceratitis rosa). d.f.: degrees of freedom; MS: mean square estimates; F: pseudo-F. Probability of Monte Carlo simulations: n.s.: not significant a P<0.05; ***: P<0.001, **: P<0.01; *: P<0.05 (after False Discovery Rate Correction for repeated a posteriori comparisons).
Supplementary material 3 from: Van Cann J, Virgilio M, Jordaens K, De Meyer M (2015) Wing morphometrics as a possible tool for the diagnosis of the C…
2015
Wing landmarks and wing band areas: Explanation note: List of wing landmarks and wing band areas considered in this study.
Supplementary material 1 from: Van Cann J, Virgilio M, Jordaens K, De Meyer M (2015) Wing morphometrics as a possible tool for the diagnosis of the C…
2015
Morphometric data: Explanation note: Specimen list and details and raw morphometric data (for both wing landmarks and wing band areas).
Supplementary material 6 from: Van Cann J, Virgilio M, Jordaens K, De Meyer M (2015) Wing morphometrics as a possible tool for the diagnosis of the C…
2015
Preliminary methodological experiment: unconstrained ordination of wing band areas: Explanation note: Principal component analysis (PCA) showing morphometric differences in wing band areas of 14 Ceratitis rosa specimens across sexes, wings (LW: left wing, RW: right wing), repeated images of the same wing (1, 2), repeated measures of the same image (A, B).
Supplementary material 10 from: Van Cann J, Virgilio M, Jordaens K, De Meyer M (2015) Wing morphometrics as a possible tool for the diagnosis of the …
2015
Constrained ordination of wing landmarks: Explanation note: Discriminant analysis of principal coordinates (DAPC) maximising morphometric differences in wing landmarks between males and females (a) Ceratitis anonae, Ceratitis fasciventris and Ceratitis rosa and (b) genotypic clusters A, F1, F2, R1, R2.
Supplementary material 7 from: Van Cann J, Virgilio M, Jordaens K, De Meyer M (2015) Wing morphometrics as a possible tool for the diagnosis of the C…
2015
Unconstrained ordination of wing landmarks across sexes of each morphospecies: Explanation note: Principal component analysis (PCA) showing morphometric differences in wing landmarks between sexes of each morphospecies (Ceratitis anonae, Ceratitis fasciventris, Ceratitis rosa). (all 227 specimens included).
Discrete cortical representations and their stability in the presence of synaptic turnover
2015
Population imaging in mouse auditory cortex revealed clustering of neural responses to brief complex sounds: the activity of a local population typically falls close to one out of a small number of observed states [1]. These clusters appear to group sets of auditory stimuli into a discrete set of activity patterns and could thereby form the basis for representations of sound categories. However, to be useful for the brain, such representations should be robust against fluctuations in the underlying circuitry, which are significant even in the absences of any explicit learning paradigm [2]. Here we introduce a novel firing rate based circuit model of mouse auditory cortex to study the emerge…
Bayesian Hierarchical Models for Random Routes in Finite Populations
1996
In many practical situations involving sampling from finite populations, it is not possible (or it is prohibitely expensive) to access, or to even produce, a listing of all of the units in the population. In these situations, inferences can not be based on random samples from the population. Random routes are widely used procedures to collect data in absence of well defined sampling frames, and they usually have either been improperly analyzed as random samples, or entirely ignored as useless. We present here a Bayesian analysis of random routes that incorporates the information provided but carefully takes into account the non- randomness in the selection of the units.