Boldness Predicts Social Status in Zebrafish (Danio rerio)

This study explored if boldness could be used to predict social status. First, boldness was assessed by monitoring individual zebrafish behaviour in (1) an unfamiliar barren environment with no shelter (open field), (2) the same environment when a roof was introduced as a shelter, and (3) when the roof was removed and an unfamiliar object (Lego® brick) was introduced. Next, after a resting period of minimum one week, social status of the fish was determined in a dyadic contest and dominant/subordinate individuals were determined as the winner/loser of two consecutive contests. Multivariate data analyses showed that males were bolder than females and that the behaviours expressed by the fish during the boldness tests could be used to predict which fish would later become dominant and subordinate in the ensuing dyadic contest. We conclude that bold behaviour is positively correlated to dominance in zebrafish and that boldness is not solely a consequence of social dominance

O2-PLS, a two-block (X-Y) latent variable regression (LVR) method with an integral OSC filter

The O2-PLS method is derived from the basic partial least squares projections to latent structures (PLS) prediction approach. The importance of the covariation matrix (YX) is pointed out in relation to both the prediction model and the structured noise in both X and Y. Structured noise in X (or Y) is defined as the systematic variation of X (or Y) not linearly correlated with Y (or X). Examples in spectroscopy include baseline, drift and scatter effects. If structured noise is present in X, the existing latent variable regression (LVR) methods, e.g. PLS, will have weakened score-loading correspondence beyond the first component. This negatively affects the interpretation of model parameters such as scores and loadings. The O2-PLS method models and predicts both X and Y and has an integral orthogonal signal correction (OSC) filter that separates the structured noise in X and Y from their joint X-Y covariation used in the prediction model. This leads to a minimal number of predictive components with full score-loading correspondence and also an opportunity to interpret the structured noise. In both a real and a simulated example, O2-PLS and PLS gave very similar predictions of Y. However, the interpretation of the prediction models was clearly improved with O2-PLS, because structured noise was present. In the NIR example, O2-PLS revealed a strong water peak and baseline offset in the structured noise components. In the simulated example the O2-PLS plot of observed versus predicted Y-scores (u vs U) showed good predictions. The corresponding loading vectors provided good interpretation of the covarying analytes in X and Y