Proportion of mammal species identified to group level (light grey) or identified to species level (dark grey) against respondent gender, respondent age, and species abundance (error bars = standard error).

<p>Note that for the statistical modeling respondent age was treated as a continuous variable.</p

Summary of the selection process for the model for mammal identification.

<p>We modeled the frequency of identifications at a particular level using a Poisson (link = log) GLMM, with village and respondent nested within village included as random effects (not shown). We investigated the effect of respondent gender, respondent age, and species abundance (extant or extirpated) and their interactive effects on the ability of people to correctly name species at two levels (overall (group+species level) and specific (species vs group levels)). Full model details are given in the online supplementary material <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0086598#pone.0086598.s003" target="_blank">Table S3</a>. Starting with the null model, we added and subtracted parameters by hand and assessed the impact of a factor by comparing AIC values. K = number of model parameters. ΔAIC_c = difference between AIC_c of the top ranked model and current model.</p

Summary of the parameter coefficients for the best model for mammal identification.

<p>We modeled the frequency of identifications at a particular level using a Poisson (link = log) GLMM, with village and respondent nested within village included as random effects (not shown). We investigated the effect of respondent gender, respondent age, and species abundance (extant vs extirpated) and their interactive effects on the ability of people to correctly names species at two levels (overall (group + species levels) and specific (species vs groups levels)). The interactive terms were removed during model simplification. Although models including the interactive terms were roughly equivalent (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0086598#pone-0086598-t003" target="_blank">Table 3</a>), none of the coefficients for the interactive terms were significant. Full model details are given in the online supplementary material <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0086598#pone.0086598.s003" target="_blank">Table S3</a>.</p

Proportion of bird species identified to group level (light grey) or identified to species level (dark grey) against respondent gender, respondent age, and species abundance (error bars = standard error).

<p>Note that for the statistical modeling respondent age was treated as a continuous variable.</p

Summary of the selection process for the model for bird identification.

*<p><i>We could not examine the gender:abundance interaction because of complications with the Hauck-Donner effect.</i></p><p>We modeled the frequency of identifications at a particular level using a Poisson (link = log) GLMM, with village and respondent nested within village included as random effects (not shown). We investigated the effect of respondent gender, respondent age, and species abundance (common, rare, or extirpated) and their interactive effects* on the ability of people to correctly name species at two levels (overall (group+species level) and specific (species vs group level)). Full model details are given in the online supplementary material <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0086598#pone.0086598.s002" target="_blank">Table S2</a>. Starting with the null model, we added and subtracted parameters by hand and assessed the impact of a factor by comparing AIC values. K = number of model parameters. ΔAIC_c = difference between AIC_c of the top ranked model and current model.</p

Protective Effects of Dexrazoxane against Doxorubicin-Induced Cardiotoxicity: A Metabolomic Study

<div><p>Cardioprotection of dexrazoxane (DZR) against doxorubicin (DOX)-induced cardiotoxicity is contentious and the indicator is controversial. A pairwise comparative metabolomics approach was used to delineate the potential metabolic processes in the present study. Ninety-six BALB/c mice were randomly divided into two supergroups: tumor and control groups. Each supergroup was divided into control, DOX, DZR, and DOX plus DZR treatment groups. DOX treatment resulted in a steady increase in 5-hydroxylysine, 2-hydroxybutyrate, 2-oxoglutarate, 3-hydroxybutyrate, and decrease in glucose, glutamate, cysteine, acetone, methionine, asparate, isoleucine, and glycylproline.DZR treatment led to increase in lactate, 3-hydroxybutyrate, glutamate, alanine, and decrease in glucose, trimethylamine N-oxide and carnosine levels. These metabolites represent potential biomarkers for early prediction of cardiotoxicity of DOX and the cardioprotective evaluation of DZR.</p></div

Two-paired PLS-DA score plot and S-plot revealed DOX-induced metabolic perturbations.

<p>The comparative analysis of DOX_C and cancer control groups reveal altered metabolite levels following DOX treatment of tumor-bearing mice (<b>A</b>), while the comparative analysis of DOX_N and normal control groups reveal distinct metabolic effects of DOX in normal animals (<b>B</b>).</p

Effects of systemic administration of the non-selective eNOS inhibitor, L-NAME, on renal perfusion pressure (MAP), renal blood flow (RBF), renal vascular resistance (RVR), glomerular filtration rate (GFR), and sodium (UV_Na+) and potassium (U_K+V) excretion rate in wild type (RGS2+/+, n = 4) and RGS2 knockout (RGS2-/-, n = 5) mice.

<p>Values are mean ± SE. ^{#, ##}<i>P</i> < 0.05, 0.01 vs. corresponding baseline; *^,**P < 0.05, 0.01 vs. RGS2+/+ mice. BW, body weight.</p

Baseline glomerular filtration rate (A, B), mean arterial pressure (MAP; C), renal blood flow (RBF; D), renal vascular resistance (RVR; E), and filtration fraction (F) of wild type (RGS2+/+), RGS2 heterozygous (RGS2+/-), and RGS2 knockout (RGS2-/-) mice.

<p>Values are mean ± SE. *^,**<i>P</i> < 0.05, 0.01 vs. RGS2+/+; ^#<i>P</i> < 0.05 vs. RGS2+/-. BW, body weight.</p

Overall profiling of the eight groups and abnormal metabolism in cancer.

<p>Score plot of the eight groups (A) shows treatment differences. The summary of pairwise metabolomics analysis of the model represents the cumulative R2X, R2Y and Q2 levels (<b>B</b>). The score plot and S-plot of the pairwise analysis of cancer and control groups reveal altered metabolites including creatine, UDP-glucose, VLDL/LDL, glycerol, TMAO, taurine, carnosine, lactate, acetone, glutamate, and aspartate (<b>C</b>).</p