Practice and the dynamics of handwriting performance: Evidence for a shift of motor programming load

ABSTRACT. Sixteen adult subjects served in an experiment in which the writing of six unfamiliar graphemes was practiced. To investigate the learning process, we analyzed the absolute and relative changes of movement time of the first three consecutive segments as a function of practice. The results showed that movement time of all three segments decreased. This decrease was significantly less in the first segment than it was in the second and third segment, however. We interpret these effects of practice, from an information-processing viewpoint, as follows: (a) Initially separate response segments become integrated in more comprehensive response chunks, and (b) the preparation of later segments of the grapheme is realized more and more during the real-time execution of the initial segment. The results further revealed that these learning effects were more pronounced in graphemes composed of familiar segments than in graphemes that contained unfamiliar segments. Finally, it turned out that similarity between initial and final segments hindered the writing speed of the first segment; the effect of similarity was independent of the above-mentioned effects of practice. The latter effect is interpreted as confirming evidence for the view that the preparation of later segments of a grapheme is reflected by changes of movement time of the first segments of a grapheme

Characterization of the proneural gene regulatory network during mouse telencephalon development

BACKGROUND: The proneural proteins Mash1 and Ngn2 are key cell autonomous regulators of neurogenesis in the mammalian central nervous system, yet little is known about the molecular pathways regulated by these transcription factors. RESULTS: Here we identify the downstream effectors of proneural genes in the telencephalon using a genomic approach to analyze the transcriptome of mice that are either lacking or overexpressing proneural genes. Novel targets of Ngn2 and/or Mash1 were identified, such as members of the Notch and Wnt pathways, and proteins involved in adhesion and signal transduction. Next, we searched the non-coding sequence surrounding the predicted proneural downstream effector genes for evolutionarily conserved transcription factor binding sites associated with newly defined consensus binding sites for Ngn2 and Mash1. This allowed us to identify potential novel co-factors and co-regulators for proneural proteins, including Creb, Tcf/Lef, Pou-domain containing transcription factors, Sox9, and Mef2a. Finally, a gene regulatory network was delineated using a novel Bayesian-based algorithm that can incorporate information from diverse datasets. CONCLUSION: Together, these data shed light on the molecular pathways regulated by proneural genes and demonstrate that the integration of experimentation with bioinformatics can guide both hypothesis testing and hypothesis generation

Study of flash evaporated Culn1-x GaxTe2 (x=0, 0.5 and 1) thin films

International audienceCuIn1 − xGaxTe2 thin films with x=0, 0.5 and 1, have been prepared by flash evaporation technique. These semiconducting layers present a chalcopyrite structure. The optical measurements have been carried out in the wavelength range 200–3000 nm. The linear dependence of the lattice parameters as a function of Ga content obeying Vegard's law was observed. The films have high absorption coefficients (4•104 cm−1) and optical band gaps ranging from 1.06 eV for CuInTe2 to 1.21 eV for CuGaTe2. The fundamental transition energies of the CuIn1−xGaxTe2 thin films can be fitted by a parabolic equation namely Eg1(x)=1.06+0.237x−0.082x2. The second transition energies of the CuInTe2 and CuGaTe2 films were estimated to be: Eg2=1.21 eV and Eg2=1.39 eV respectively. This variation of the energy gap with x has allowed the achievement of absorber layers with large gaps

Characterization of the proneural gene regulatory network during mouse telencephalon development

Abstract Background The proneural proteins Mash1 and Ngn2 are key cell autonomous regulators of neurogenesis in the mammalian central nervous system, yet little is known about the molecular pathways regulated by these transcription factors. Results Here we identify the downstream effectors of proneural genes in the telencephalon using a genomic approach to analyze the transcriptome of mice that are either lacking or overexpressing proneural genes. Novel targets of Ngn2 and/or Mash1 were identified, such as members of the Notch and Wnt pathways, and proteins involved in adhesion and signal transduction. Next, we searched the non-coding sequence surrounding the predicted proneural downstream effector genes for evolutionarily conserved transcription factor binding sites associated with newly defined consensus binding sites for Ngn2 and Mash1. This allowed us to identify potential novel co-factors and co-regulators for proneural proteins, including Creb, Tcf/Lef, Pou-domain containing transcription factors, Sox9, and Mef2a. Finally, a gene regulatory network was delineated using a novel Bayesian-based algorithm that can incorporate information from diverse datasets. Conclusion Together, these data shed light on the molecular pathways regulated by proneural genes and demonstrate that the integration of experimentation with bioinformatics can guide both hypothesis testing and hypothesis generation.</p

Characterization of the proneural gene regulatory network during mouse telencephalon development-3

34 Ngn2 and Mash1 target genes (list of target genes analyzed in Additional file ). Other TFBSs were identified within 30 bp of the putative Ngn2 or Mash1 binding site. (b) TRANSFAC TFBSs that co-occurred with Ngn2 binding sites versus Mash1 binding sites surrounding Ngn2 target genes versus Mash 1 and common target genes, respectively. A total of 58 Ngn2 sites and 56 Mash1 binding sites were analyzed. A * denotes significantly enriched TFBSs in the sequence surrounding Ngn2 sites versus Mash1 sites (Fisher's exact two-sided test with ≤ 0.05). (c) Comparison of microarray gene expression values of potential Ngn2 and Mash1 co-factors in wild-type dorsal and ventral telencephalon tissues where column three presents results using a 1.5-fold change cut-off value for categorizing gene expression as either preferentially dorsal, ventral, or both (common).<p><b>Copyright information:</b></p><p>Taken from "Characterization of the proneural gene regulatory network during mouse telencephalon development"</p><p>http://www.biomedcentral.com/1741-7007/6/15</p><p>BMC Biology 2008;6():15-15.</p><p>Published online 31 Mar 2008</p><p>PMCID:PMC2330019.</p><p></p

Characterization of the proneural gene regulatory network during mouse telencephalon development-4

Ct the optimal network structure based on the LOF and GOF microarray datasets, evolutionarily conserved TFBS data, prior literature-based knowledge, and spatial and time-specific expression patterns. To highlight the key regulators, the nodes representing genes predicted to be the parent of at least nine other genes are largest in size (Sox9, Mef2a, Elavl4 and Pou6f1), whereas those that are predicted to regulate at least five other genes are medium in size (Ngn2, Centg3, Tef, Tcf4, Wnt7b, Pou2f1, Yy1, Dll1, E2f1, Arx, and Creb). See Additional file for matrix of connectivity and Methods for a more detailed description of algorithm.<p><b>Copyright information:</b></p><p>Taken from "Characterization of the proneural gene regulatory network during mouse telencephalon development"</p><p>http://www.biomedcentral.com/1741-7007/6/15</p><p>BMC Biology 2008;6():15-15.</p><p>Published online 31 Mar 2008</p><p>PMCID:PMC2330019.</p><p></p

Characterization of the proneural gene regulatory network during mouse telencephalon development-5

connections that are significant based on the current microarray dataset are shown in red. Significant relationships were determined through analysis of the distribution of the strength of linkage parameter (β) after 500,000 MCMC simulations (Additional file ). If more than 95% of the simulations have values above zero they are considered significant. A * denotes connections that were significant, but as inhibition.<p><b>Copyright information:</b></p><p>Taken from "Characterization of the proneural gene regulatory network during mouse telencephalon development"</p><p>http://www.biomedcentral.com/1741-7007/6/15</p><p>BMC Biology 2008;6():15-15.</p><p>Published online 31 Mar 2008</p><p>PMCID:PMC2330019.</p><p></p