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Evidence of (non-)overrepresentation of left-handed athletes has often been derived from cross-sectional data (Aggleton & Wood, 1990; Baker and Schorer, 2013; Grouios et al., 2000; Raymond et al., 1996; Wood and Aggleton, 1989) or from aggregated longitudinal data as a factor without accounting for time (Grondin, Guiard, Ivry and Koren, 1999; Holtzen, 2000; McLean and Ciurczak 1982). Both approaches may have some limitations because left frequencies can be unstable over time and information on variations in the distribution of lateral preferences is lost when data are combined, particularly over a long period of time. Bioinformatics tools that include the integration of data at multiple levels, such as GO overrepresentation analysis, signaling pathway analysis, and various forms of biochemical experimental data underlying protein networks are becoming increasingly important for bridging the gap between gene expression datasets and selection of the most promising genes for functional studies in animal models. New computational tools are needed to discover patterns in gene expression datasets that indicate the presence of functionally linked interesting gene clusters and/or signaling pathways. However, the number of genes indicated with these bioinformatics approaches is usually still far too large to be taken into account in extensive in vivo functional studies. Animal models to study the effect of a molecular target on the regenerative response after spinal cord or peripheral nerve injury are very complex and generally require longitudinal functional follow-up of several months. Therefore, it is very important to further determine which molecules are potentially most important for in vivo studies. Recent advances in high-throughput, high-grade microscopy and gene disruption techniques, including RNA interference or lentiviral vector-mediated overexpression, now allow the simultaneous study of large amounts of target genes in many functional dimensions. The combined application of these techniques in vitro can thus be used to identify genes that function in a particular cellular process. This so-called “cellomics” approach leads to sets of target genes that are functionally linked to a biological parameter of interest, such as neuronal survival or neurite growth (Fig. 37.3).
Two recent studies have used celcelmia to investigate the involvement of hundreds of genes originally identified by microarray in the neurite growth process (MacGillavry et al., 2009; Moore et al., 2009). The positive results resulting from these automated high-bioassay screens have provided important new information on the neuronal intrinsic transcriptional regulation of axon growth and regeneration. We first summarize the results of these studies and conclude this chapter with a discussion of future in vivo strategies to identify the most promising new targets for neuronal repair. The RCAP (1996) and Rudin (2007), among others, identify culture clash as the fourth factor contributing to overrepresentation. According to Rudin, the overrepresentation of minority students and English language learners in special schools has been a persistent problem since the 1960s (DeValenzuela et al., 2006). Data presented in the 2004 IDEA report to Congress (United States Department of Education, 2006) compared the percentage of school-age children belonging to certain ethnic or racial groups with that of all IDEA students. Trends suggest an overrepresentation of Blacks in the categories of emotional disorder and intellectual disability; for Hispanics with specific learning disabilities and hearing impairments; for people in Asia and the Pacific Islands with hearing loss and autism; and for Native Americans/Alaskans with specific learning disabilities and developmental delays. To determine a more successful course for the present and future, it is necessary to fully understand how deficit thinking has influenced past educational practices for historically underserved students. These efforts could lead to a better understanding of the patterns of behaviour and thinking that have contributed and continue to contribute to maintaining over-representation, poor academic achievement, and limited post-secondary education opportunities for these learners (Obiakor & Ford, 2002; Trent and Artiles, 2007). Understanding this history, if recognized, appreciated, and integrated into current and future educational reforms, can also help create a more effective school environment for these students.