In conclusion, the Seeplex Meningitis ACE Detection kit showed high sensitivity and specificity for the 12 most common bacterial and viral agents causing acute meningitis. A high detection rate was observed in the CSF samples obtained from patients clinically diagnosed with acute meningitis. This kit may enable the rapid identification of pathogens in patients with acute meningitis. ColorPhylo: A Color Code to Accurately Display Taxonomic Classifications Color may be very useful to visualise complex data. As far as taxonomy is concerned, color may help observing various species’ characteristics in correlation with classification. However, choosing the number of subclasses to display is often a complex task: on the one hand, assigning a limited number of colors to taxa of interest hides the structure imbedded in the subtrees of the taxonomy; on the other hand, differentiating a high number of taxa by giving them specific colors, without considering the underlying taxonomy, may lead to unreadable results since relationships between displayed taxa would not be supported by the color code. In the present paper, an automatic color coding scheme is proposed to visualise the levels of taxonomic relationships displayed as overlay on any kind of data plot. To achieve this goal, a dimensionality reduction method allows displaying taxonomic “distances” onto a Euclidean two-dimensional space. The resulting map is projected onto a 2D color space (the Hue, Saturation, Brightness colorimetric space with brightness set to 1). Proximity in the taxonomic classification corresponds to proximity on the map and is therefore materialised by color proximity. As a result, each species is related to a color code showing its position in the taxonomic tree. The so called ColorPhylo displays taxonomic relationships intuitively and can be combined with any biological result. A Matlab version of ColorPhylo is available at http://sy.lespi.free.fr/ColorPhylo-homepage.html. Meanwhile, an ad-hoc distance in case of taxonomy with unknown edge lengths is proposed. Many datasets are "naturally" structured as hierarchical classifications. In particular, the Darwin's evolution theory ensures that the relationships between species can be expressed within a tree (named "phylogenic tree"/"taxonomic tree"). However, the exploration of large trees (and graphs) is not easy. 1 Visualising taxonomy together with other pieces of information (obtained from various biological analyses for example) is often extremely instructive; some examples can be found in. [2] [3] [4] In those cases, a level of granularity of the tree is usually chosen and a color is assigned to each subclass thus defined. 4 Several drawbacks can be easily identified. The granularity level is obviously subject to arbitrariness. Moreover, proximity relationships between subclasses are ignored as well as their subdivisions.