Appendix A. Map of the study sites, E. S. George Reserve, Michigan, USA.

Map of the study sites, E. S. George Reserve, Michigan, USA

Model selection analysis for the constrained distribution-abundance relationship^*.

<p>* Linear, nonlinear, and various segmented regression models were compared employing AIC_c. For example, sloping/horizontal refers to a linear fit of a sloping line to the data up to the breakpoint and then a horizontal line after the breakpoint.</p

Comparison of number of ponds occupied by <i>Pseudacris triseriata</i> versus those suitable across years.

<p>Open symbols and dashed line are ponds occupied and closed symbols and solid line are number of ponds deemed suitable habitat. Ponds estimated as suitable were 1) open-canopy ponds that dried the previous fall, 2) open-canopy ponds that did not dry the previous fall, and 3) closed-canopy ponds, respectively, weighted by the frequency of occupancy in the 2001 to 2008 period, and summed (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0097387#s2" target="_blank">Methods</a> for details).</p

Intraspecific <i>Pseudacris triseriata</i> distribution-abundance relationships for the west and east sides of the ESGR.

<p>Relationships and thresholds between them determined by segmented regression analysis (see text for details).</p

Rodionov regime shift analysis of pond occupancy for the chorus frog^*.

<p>*Regime shift analysis for pond occupancy (0.05 level, expected regime length of 4 years). RSI is the regime shift index <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0097387#pone.0097387-Rodionov1" target="_blank">[44]</a>, and mean and length are mean number of ponds occupied in a regime and the length of the regime. If a regime shift is detected at a pre-determined α-level and expected regime cut-off length, mean values of the old and new regimes differ statistically at least at the given level (actual significance level is calculated for shifts with a number of preceding and following years, e.g., 2000–2001 occupancy boundary differed at the 1.4E-08 level, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0097387#pone.0097387-Rodionov1" target="_blank">[44]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0097387#pone.0097387-Rodionov2" target="_blank">[45]</a>).</p

Model selection analyses for distribution-abundance relationships for the chorus frog^*.

<p>*Analyses for the West and East sides of the E. S. George Reserve. Model designations as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0097387#pone-0097387-t001" target="_blank">Table 1</a> (in the case where two sloping/sloping models are included this indicated the lines were of different orientation to each other).</p

Interspecific distribution-abundance relationship for thirteen species of ESGR amphibians.

<p>Data fit to the relationship, y = a(1−b^x), R² = 0.73, F_1,11 = 30.4, p = 0.0002. Species represented are: <i>Hyla versicolor</i> (Hve), <i>Pseudacris crucifer</i> (Pcr), <i>P. triseriata</i> (Ptr), <i>Rana catesbeiana</i> (Rca), <i>R. clamitans</i> (Rcl), <i>R. pipiens</i> (Rpi), <i>R. sylvatica</i> (Rsy), <i>Ambystoma laterale</i> (Ala), <i>A. maculatum</i> (Ama), <i>A. tigrinum</i> (Ati), and <i>Notophthalmus viridescens</i> (Nvi).</p

Regional population size (histograms) and number of occupied ponds (line) across years for <i>Pseudacris triseriata</i>.

<p>Regional population size was the sum across all ponds of larval population sizes (i.e., average density in a pond x pond surface area corrected for drying on each sample date).</p

Constrained interspecific distribution-abundance relationship for the ESGR amphibians.

<p>The dependent variable is the average fraction of potentially habitable ponds occupied fit to the relationship, y = a(1−b^x), R² = 0.70, F_1,11 = 28.6, p = 0.0002. Species designations as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0097387#pone-0097387-g001" target="_blank">Figure 1</a>.</p

Design and Optimization of Selective Protein Kinase C θ (PKCθ) Inhibitors for the Treatment of Autoimmune Diseases

Protein kinase C θ (PKCθ) has a central role in T cell activation and survival; however, the dependency of T cell responses to the inhibition of this enzyme appears to be dictated by the nature of the antigen and by the inflammatory environment. Studies in PKCθ-deficient mice have demonstrated that while antiviral responses are PKCθ-independent, T cell responses associated with autoimmune diseases are PKCθ-dependent. Thus, potent and selective inhibition of PKCθ is expected to block autoimmune T cell responses without compromising antiviral immunity. Herein, we describe the development of potent and selective PKCθ inhibitors, which show exceptional potency in cells and in vivo. By use of a structure based rational design approach, a 1000-fold improvement in potency and 76-fold improvement in selectivity over closely related PKC isoforms such as PKCδ were obtained from the initial HTS hit, together with a big improvement in lipophilic efficiency (LiPE)