DGGE analyses of <i>mglA</i> gene fragments performed using DNA samples extracted from pure myxobacteria cultures (A) and soil samples (B).

<p>A lane, 1, <i>Archangium gephyra</i> DSM 2261; lane 2, <i>Corallococcus macrospores</i> DSM 14697; lane 3, <i>Corallococcus coralloides</i> DSM 52499; lane 4, <i>Myxococcus fulvus</i> DSM 16525; lane 5, <i>Myxococcus virescens</i> DSM 2260; lane 6, <i>Cystobacter minus</i> DSM 14751; lane 7, <i>Stigmatella erecta</i> DSM 16858; lane 8, <i>Byssovorax cruenta</i> DSM 14533; B, Bands (1∼28) that were excised for sequence analysis are numbered.</p

sj-docx-1-aum-10.1177_03128962221142407 – Supplemental material for Should online reviews include pictures? The impact of fit between product type and online review presentation format

Supplemental material, sj-docx-1-aum-10.1177_03128962221142407 for Should online reviews include pictures? The impact of fit between product type and online review presentation format by Qing Yao, Yixuan Han and Defeng Yang in Australian Journal of Management</p

Plots of betweenness.

The mean betweenness values for different types of shareholders in the largest connected component of shareholder networks, (a) for Turkey and (b) for the Netherlands. The red dots are the real data and the box plots for the results obtained from 100 degree preserving null models. We note that most betweenness values for Turkey and Netherlands are significantly different from the randomised networks, some types are lower and some types are higher. That means that there is significant network structure on larger scales and the properties are not just controlled by the degree.</p

The average closeness indices for different types of shareholders in the LCC of Turkish shareholder network.

In Fig (a) we show the results for each shareholder with the red dots for the original data while the box plots are for the randomised data. In (b) each point shows the average ‘Farness’ (the inverse of closeness) of one shareholder type against log(N/k), where k is the average degree of nodes of that type. The higher blue points are for the original data, the lower orange points are for the randomised network. The lines in (b) are for a linear fit to the points. The slope of this fit to the original data is 0.71, 0.26 for the randomised network and the theoretical value in a random branching model is 0.24.</p

Plots of degree distributions.

The degree distributions P(k) (the frequency of nodes with degree k) against degree k edges on a log-log scale for shareholder networks where the holdings are in (a) Turkish companies, (b) Dutch companies. The red dots are the raw data, the green crosses represent the same data in logarithmic bins, and the blue lines are the best linear fits (P(k) ∼ k−γ) to ranges of k values where we see approximately linear behaviour. The slope of the blue lines, −γ, is 2.6 and 2.7 for Turkey and the Netherlands respectively. A summary of the general statistics of these shareholder networks can be found in Table 2.</p

Sequence similarity of excised DNA fragments.

<p>a Bands B1 to B28 are the same bands as 1 to 28 in the denaturing gradient gel (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0108877#pone-0108877-g002" target="_blank">Fig. 2B</a>).</p><p>Sequence similarity of excised DNA fragments.</p

Violin plots of the degree.

Violin plots of the degree of the most common types of shareholders for the largest connected component of shareholder network of (a) Turkish and (b) Dutch companies. There are too few shareholders for other types of investor. This figure breaks down degree distributions into different types of shareholders. We note that in Turkey, the large degrees are contributed by the banks and insurance while in Netherlands, banks’ average degree is higher than the other types of shareholders. It means Netherlands’ banks co-invested a lot with other shareholders.</p

The number of communities found with a given fraction of one type of shareholder.

The communities are found with Infomap in the shareholder network for Turkey and Netherlands. On the left we have the fraction of Family shareholders in different communities while on the right we have the fraction of Corporate shareholders in each community. The figures in first row includes community of all sizes. The fat-tailed distribution means this is dominated by the large number of small communities, and these are almost always of a single type of shareholder, hence the peak at 1.0. The second row shows the same analysis done when we exclude small communities which have three or less nodes (CS = community size). Similar analysis for the Louvain community detection method is given in the S1 Appendix.</p

The average closeness indices for different types of shareholders in the LCC of Dutch shareholder network.

In Fig (a) we show the results for each shareholder with the red dots for the original data while the box plots are for the randomised data. In (b) each point shows the average ‘Farness’ (the inverse of closeness) of one shareholder type against log(N/k), where k is the average degree of nodes of that type. The higher blue points are for the original data, the lower orange points are for the randomised network. The lines in (b) are for a linear fit to the points. The slope of this fit to the original data is 0.34, 0.16 for the randomised network and the theoretical value in a random branching model is 0.17.</p

The number of components increases as the number of nodes removed.

(a) Turkish and (b) Dutch companies. Nodes of one shareholder type are chosen at random and removed one by one from the largest connected component of the shareholder network. Results shown here are averaged over 100 realisations. Blue represents Bank shareholders being removed, green represents Corporate and red represents Families. The larger scale plots display the regions in the dashed boxes of the smaller scale plots to more clearly reveal the behaviour for small numbers of node removals. Note in particular the different role of banks (blue) and corporates (green) in Turkey and Netherlands. The small and big plots share the same axis labels.</p