Connexin genes in the mouse and human genome

Gap junctions serve for direct intercellular communication by docking of two hemichannels in adjacent cells thereby forming conduits between the cytoplasmic compartments of adjacent cells. Connexin genes code for subunit proteins of gap junction channels and are members of large gene families in mammals. So far, 17 connexin (Cx) genes have been described and characterized in the murine genome. For most of them, orthologues in the human genome have been found (see White and Paul 1999; Manthey et al. 1999; Teubner et al. 2001; Söhl et al. 2001). We have recently performed searches for connexin genes in murine and human gene libraries available at EMBL/Heidelberg, NCBI and the Celera company that have increased the number of identified connexins to 19 in mouse and 20 in humans. For one mouse connexin gene and two human connexin genes we did not find orthologues in the other genome. Here we present a short overview on distinct connexin genes which we found in the mouse and human genome and which may include all members of this gene family, if no further connexin gene will be discovered in the remaining non-sequenced parts (about 1-5%) of the genomes.

Diagnosing synaesthesia with online colour pickers: Maximising sensitivity and specificity

The most commonly used method for formally assessing grapheme-colour synaesthesia (i.e., experiencing colours in response to letter and/or number stimuli) involves selecting colours from a large colour palette on several occasions and measuring consistency of the colours selected. However, the ability to diagnose synaesthesia using this method depends on several factors that have not been directly contrasted. These include the type of colour space used (e.g., RGB, HSV, CIELUV, CIELAB) and different measures of consistency (e.g., city block and Euclidean distance in colour space). This study aims to find the most reliable way of diagnosing grapheme-colour synaesthesia based on maximising sensitivity (i.e., ability of a test to identify true synaesthetes) and specificity (i.e., ability of a test to identify true non-synaesthetes). We show, applying ROC (Receiver Operating Characteristics) to binary classification of a large sample of self-declared synaesthetes and non-synaesthetes, that the consistency criterion (i.e., cut-off value) for diagnosing synaesthesia is considerably higher than the current standard in the field. We also show that methods based on perceptual CIELUV and CIELAB colour models (rather than RGB and HSV colour representations) and Euclidean distances offer an even greater sensitivity and specificity than most currently used measures. Together, these findings offer improved heuristics for the behavioural assessment of grapheme-colour synaesthesia.