Multimodal design : the semiotic resources of children's graphic representation

In asking how children's graphic representation can be understood as multimodal design, I argue that meaning-making is a complex process of semiotic interweaving. My definition of graphic representation for this thesis embraces the full range of marks made on any graphic surface. Multimodal design is the socioculturally shaped process of transformation where existing semiotic (meaning-making) resources are chosen, shaped and combined according to the individual's interest and his or her perception of the particular representational or communicational need. I propose that graphic representation might be thought about as multimodal compounds (co-present writing and image) and multimodal composites (an integration of the modes that make up the self-contained entities of writing and image). I explore how texts can be understood multimodally by examining what the semiotic resources of children's graphic representation are, how they carry meaning and how they interrelate. Through in-depth analysis of writing and drawing both discretely and appearing together in the same graphic text, I analyse paper-based and electronic texts produced at home and school for different purposes. I take my interpretations of the signs children have made and my theorization always to be hypotheses. Language-as-writing and drawing-as-image offer potentialities for different ways of making meaning but common and particularized semiotic modes such as presentation, layout and punctuation operate across graphic representation. These modes work together in a semiotic partnership. I suggest that semiotic principles across modes of communication including and going beyond the graphic might include criteriality, connectivity and salience. This implies the notion of a multimodal disposition. The multimodality of children's graphic representational design has implications for pedagogy, curriculum policy, professional development and the research community.

Additional file 1: of A combined linkage, microarray and exome analysis suggests MAP3K11 as a candidate gene for left ventricular hypertrophy

Table S1. Coding variants under the linkage peaks for LVH proxy measurements. Table S2. Selected damaging variants in the coding regions contained in the linkage regions. Table S3. SKAT and burden tests for genes of interest. Table S4. Results of linkage analyses before (LOD1) and after (LOD2) regression on GWAS SNPs under the linkage peaks. Table S5. Descriptive statistics of the Rotterdam study population. Table S6. Replications results in the Rotterdam Study. Figure S1. Venn diagram showing the overlap between the different ERF genotyping experiments. Figure S2. Pedigrees segregating rs138968470. (DOCX 119 kb

Forest plot of the effect of F_ROHLD on height.

<p>Results of a meta-analysis of the association between F_ROHLD and height are shown for twenty-one population samples. The model was adjusted for age and sex in all samples. Additionally, it was adjusted for genomic kinship in samples with pairs of related individuals (CROATIA-Korčula, CROATIA-Split, CROATIA-Vis, ERF, FINRISK, HBCS, H2000, INGI-CARL, INGI-FVG, INGI-VB, MICROS, NFBC1966, NSPHS, ORCADES and YFS). The plot shows estimated effect sizes (solid squares) for each population, with 95% confidence intervals (horizontal lines). Each sample estimate is weighted by the inverse of the squared standard error of the regression coefficient, so that the smaller the standard error of the study, the greater the contribution it makes to the pooled regression coefficient. The area of the solid squares is proportional to the weighting given to each study in the meta-analysis. Effect sizes in z-score units (with 95% confidence intervals) are: CROATIA-Korčula = −0.02 (−0.09, 0.04); CROATIA-Split = −0.06 (−0.1, −0.002); CROATIA-Vis = −0.07 (−0.1, −0.01); EGCUT = −0.09 (−0.04, 0.2); ERF = −0.08 (−0.1, −0.05); FINRISK = −0.1 (−0.2, −0.07); HBCS = −0.04 (−0.2, 0.1); H2000 = −0.2 (−0.5, 0.04); INGI-CARL = 0.02 (−0.03, 0.07); INGI-FVG = −0.0001 (−0.08, 0.08); INGI-VB = 0.005 (−0.03, 0.04); LBC1921 = −0.1 (−0.3, 0.04); LBC1936 = 0.2 (−0.1, 0.4); MICROS = −0.06 (−0.08, −0.05); NFBC1966 = −0.1 (−0.2, −0.1); NSPHS = −0.07 (−0.07, −0.06); ORCADES = −0.04 (−0.08, 0.001); QIMR = −0.07 (−0.5, 0.3); RS = −0.02 (−0.1, 0.08); SOCCS = −0.05 (−0.4, 0.3); YFS = −0.3 (−1.2, 0.7).</p

Sample details.

1<p>All data were analysed using Illumina SNP arrays. 300 refers to the Illumina HumanHap 300 panel, 370 to the Illumina HumanHap 370 Duo/Quad panels, 610 to the Illumina Human 610 Quad panel and 670 to the Illumina Human 670 Quad panel. In order to harmonise the data, the analysis was conducted using only those SNPs present in the HumanHap 300 panel.</p>2<p>Population-based studies.</p>3<p>Population-based studies in isolated populations.</p>4<p>Birth cohort studies.</p>5<p>Case control studies.</p

Meta-analysis of the association between height and genome-wide homozygosity, adjusted for age and sex only.

<p>Meta-analysis of the association between height and genome-wide homozygosity, adjusted for age and sex only.</p

Three alternative measures of mean homozygosity, with 95% confidence intervals, by population sample.

<p>(A) shows mean F_ROH by population sample. F_ROH is defined as the percentage of the genotyped autosomal genome in ROH measuring at least 1.5 Mb. Mean values of F_ROH per population (with 95% confidence intervals) are: CROATIA-Korčula = 1.27 (1.18, 1.36); CROATIA-Split = 0.65 (0.59, 0.71); CROATIA-Vis = 0.94 (0.87,1.01); EGCUT = 0.56 (0.54, 0.58); ERF = 1.12 (1.04, 1.20); FINRISK = 0.79 (0.77, 0.82); HBCS = 0.63 (0.60, 0.65); H2000 = 0.84 (0.82, 0.86); INGI-CARL = 0.78 (0.65, 0.91); INGI-FVG = 1.49 (1.40, 1.58); INGI-VB = 0.76 (0.71, 0.81); LBC1921 = 0.30 (0.25, 0.35); LBC1936 = 0.26 (0.24, 0.28); MICROS = 0.93 (0.87, 0.99); NFBC1966 = 1.02 (1.00, 1.04); NSPHS = 2.83 (2.64, 3.02); ORCADES = 0.81 (0.75, 0.87); QIMR = 0.22 (0.21, 0.23); RS = 0.29 (0.28, 0.30); SOCCS = 0.30 (0.28, 0.32); YFS = 0.81 (0.79, 0.83). (B) shows mean F_ROHLD by population sample. F_ROHLD is defined as the percentage of the genotyped autosomal genome in ROH measuring at least 1.0 Mb, derived from a panel of independent SNPs. Mean values of F_ROHLD per population (with 95% confidence intervals) are: CROATIA-Korčula = 0.67 (0.61, 0.73); CROATIA-Split = 0.13 (0.11, 0.15); CROATIA-Vis = 0.48 (0.43, 0.53); EGCUT = 0.10 (0.09, 0.10); ERF = 0.53 (0.48, 0.58); FINRISK = 0.21 (0.20, 0.23); HBCS = 0.13 (0.11, 0.14); H2000 = 0.23 (0.22, 0.24); INGI-CARL = 0.44 (0.34, 0.54); INGI-FVG = 0.93 (0.86, 0.99); INGI-VB = 0.41 (037, 0.45); LBC1921 = 0.05 (0.02, 0.09); LBC1936 = 0.02 (0.01, 0.03); MICROS = 0.47 (0.43, 0.51); NFBC1966 = 0.32 (0.31, 0.33); NSPHS = 1.17 (1.07, 1.27); ORCADES = 0.35 (0.31, 0.39); QIMR = 0.013 (0.011, 0.015); RS = 0.04 (0.01, 0.07); SOCCS = 0.03 (0.02, 0.04); YFS = 0.20 (0.19, 0.21). (C) shows mean F_hom by population sample. F_hom is defined as the percentage of genotyped autosomal SNPs that are homozygous. Mean values of F_hom per population (with 95% confidence intervals) are: CROATIA-Korčula = 65.47 (65.43, 65.51); CROATIA-Split = 65.28 (65.25, 65.31); CROATIA-Vis = 65.61 (65.58, 65.64); EGCUT = 65.69 (65.68, 65.70); ERF = 65.32 (65.29, 65.35); FINRISK = 65.25 (65.23, 65.27); HBCS = 65.13 (65.12, 65.14); H2000 = 65.24 (65.23, 65.25); INGI-CARL = 65.20 (65.14, 65.26); INGI-FVG = 65.53 (65.49, 65.57); INGI-VB = 65.18 (65.16, 65.20); LBC1921 = 65.00 (64.97, 65.03); LBC1936 = 65.00 (64.99, 65.01); MICROS = 65.26 (65.23, 65.29); NFBC1966 = 65.27 (65.26, 65.28); NSPHS = 66.09 (66.01, 66.17); ORCADES = 65.37 (65.34, 65.40); QIMR = 64.75 (64.74, 64.76); RS = 65.00 (64.99, 65.01); SOCCS = 64.97 (64.95, 64.99); YFS = 65.26 (65.25, 65.27).</p

Meta-analysis assessing potential confounding of SES variables on the association between F_ROHLD and height.

<p>SES variables are educational attainment (EA) and occupational status (OS).</p