Hierarchical multiple regression predicting self-reported safety compliance (n = 99).

<p><i>Note</i>: ^*p<.05, ^**p<.01.</p

Means, standard deviation, and Zero-order correlations of all variables (n = 99).

<p><i>Note:</i>^*p<.05, ^**p<.01.</p

Moderating effects of inhibition on the influences of controlled and automatic cognitions on safety behaviors.

<p>Panel A shows the moderating effect of inhibition on the influence of safety compromise on safety compliance. Panel B shows the moderating effect of inhibition on the influence of automatic association on safety compliance. Panel C shows the moderating effect of inhibition on the influence of self-reported safety attitude on safety participation. Panel D shows the moderating effect of inhibition on the influence of automatic association on safety participation.</p

Hierarchical multiple regression predicting self-reported safety participation (n = 99).

<p><i>Note</i>: ^*p<.05, ^**p<.01.</p

Etudes sur le terrain et en laboratoire des causes de la mortalite ovine dans les hauts plateaux ethiopiens en 1986-1987

Reports the major specific causes of sheep mortality in the Debre Berhan area between July 1986 and June 1987. Includes data on seasonal crude death rates, causes of mortality, frequency and age at death

MOESM2 of A robust vegetation index for remotely assessing chlorophyll content of dorsiventral leaves across several species in different seasons

Additional file 2: Figure S1. Contour maps for R2 between LCC and the MDATT index with the combination of 位1 and 位3 derived from the adaxial leaf reflectance for each plant species (the dots represent the wavebands combination with highest R2. Two combinations have the same highest R2 value because 位2 and 位3 are exchangeable in the MDATT index. a, Chinese elm; b, Virginia creeper; c, torch tree; d, Manchurian lilac; e, grapevine; f, Narrow-leaved oleaster; g, white poplar)

MOESM6 of A robust vegetation index for remotely assessing chlorophyll content of dorsiventral leaves across several species in different seasons

Additional file 6: Figure S5. Contour maps for R2 between LCC and the MDATT index with the combination of Îť2 and Îť3 derived from the adaxial and abaxial leaf reflectance for each plant species (the dots and letters represent the same items as in Figure S1)

MOESM3 of A robust vegetation index for remotely assessing chlorophyll content of dorsiventral leaves across several species in different seasons

Additional file 3: Figure S2. Contour maps for R2 between LCC and the MDATT index with the combination of Îť2 and Îť3 derived from the adaxial leaf reflectance for each plant species (the dots and letters represent the same as items in Figure S1)

MOESM5 of A robust vegetation index for remotely assessing chlorophyll content of dorsiventral leaves across several species in different seasons

Additional file 5: Figure S4. Contour maps for R2 between LCC and the MDATT index with the combination of Îť2 and Îť3 derived from the abaxial leaf reflectance for each plant species (the dots and letters represent the same items as in Figure S1)

Representative specular microscopy images of endothelial cells before and after cataract surgery.

<p>(<b>A</b>) Prior to surgery, the endothelial cells were relatively uniform in size and shape. (<b>B</b>) Due to corneal edema on Day 1 after surgery, the posterior surface of cornea was uneven, and it was difficult to capture clear images for all of the endothelial cells. The entire endothelial layer was not at the same focus plane, which indicates DEC swelling. A similar finding was also evident in UHR-OCT images (refer to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0080986#pone-0080986-g001" target="_blank">Figure 1C</a>). Nevertheless, the loss of endothelial cells and the increased size of the cells were evident when compared to the pre-operative endothelium. (<b>C</b>) The larger cells and decrease in cell density were still evident on Day 7 after surgery. Bars = 50 μm.</p