In MS lesions, the pathology is heterogeneous and the effect on the oligodendrocyte lineage population varies dramatically (49) . Oligodendrocyte density is severely compromised in demyelinated lesions in some patients with MS. Further studies are imperative to optimize regenerative responses from immature oligodendrocyte lineage cell populations that have been observed in some MS lesions (9, 28) . Targeting inhibitory signals that are upregulated in lesions should be a viable strategy for therapeutics to promote differentiation as needed near lesions. FGF2 expression has been reported in reactive astrocytes of acute and chronic MS plaques (50) . In addition, in cultures from adult human brain white matter, FGF2 inhibited the differentiation of preoligodendrocytes into oligodendrocytes (51) . Strategies to promote remyelination by attenuating FGF2 inhibition of OP differentiation may take advantage of reagents to modulate FGF2 signaling, which are currently being developed for angiogenesis and cancer treatments. In our in vivo analysis, absence of FGF2 was not detrimental in the normal adult or throughout the disease process. Therefore, inhibition of FGF2 signaling may be acceptable across demyelinating disease stages, which would improve treatment feasibility in diseases such as MS that have an unstable disease course. Treatments to promote remyelination should be a valuable complement to strategies that abrogate ongoing causes of demyelination such as immunomodulatory therapies. Automated identification of multiple micro-organisms from resequencing DNA microarrays There is an increasing recognition that detailed nucleic acid sequence information will be useful and even required in the diagnosis, treatment and surveillance of many significant pathogens. Because generating detailed information about pathogens leads to significantly larger amounts of data, it is necessary to develop automated analysis methods to reduce analysis time and to standardize identification criteria. This is especially important for multiple pathogen assays designed to reduce assay time and costs. In this paper, we present a successful algorithm for detecting pathogens and reporting the maximum level of detail possible using multi-pathogen resequencing microarrays. The algorithm filters the sequence of base calls from the microarray and finds entries in genetic databases that most closely match. Taxonomic databases are then used to relate these entries to each other so that the microorganism can be identified. Although developed using a resequencing microarray, the approach is applicable to any assay method that produces base call sequence information. The success and continued development of this approach means that a non-expert can now perform unassisted analysis of the results obtained from partial sequence data. For both surveillance and diagnostic applications, fine-scale pathogen identification and near-neighbor discrimination is important; therefore, an assay that monitors at this very specific level is desirable for many types of samples such as clinical and environmental (1) (2) (3) . To successfully use any method based on DNA or RNA detection, these assays must be coupled with large databases of nucleic acid sequence information for assay design to ensure that the desired information is provided and for the interpretation of raw data. Several well-established techniques use PCR to amplify individual target pieces of sequenced genomes to provide detection of organisms (4) . These methods can roughly be divided into approaches that target individual short sequence lengths or probes (<40 bp) and methods that examine longer probes. The advantage of using short probes is that when the uniqueness of the probe has been assured and unique primers are also selected, this method gives good specificity. This approach is capable of providing fine-scale identification of several genetically close organisms by selecting a sufficient number of probes. However, this can rapidly lead to a very large number of total probes being required to detect all organisms of interest. In addition these selected probes, which in the initial selection process were determined to be unique, are often later found to be less specific as more organisms are sequenced or are less specific under conditions that differ from the original conditions. This is particularly a problem for organisms belonging to a family with a high mutation rate and also for pathogens that have relatively few neighboring pathogens sequenced. In addition, PCR approaches focused on short unique probes are not capable of detecting the presence of new significant mutations nor can they easily resolve base sequence details. Approaches that use longer individual probes avoid many of these issues at the cost of being less specific. This issue means most of these approaches are not suitable for providing the information desired, providing impetus to this work.