Heat map and clustering based on taxon composition and abundance.

<p>(A) total microbiota, (B) active microbiota. Colors in the figure depict the percentage range of sequences assigned to main taxa (abundance >1% in at least one sample).</p

Canonical Correspondence Analysis (CCA) of the patients A, B, C and D in the follow-up study.

<p>(A) total microbiota, (B) active microbiota, (C) genes and (D) gene taxonomy. The antimicrobial effect is represented as a vector with two levels (bactericidal and bacteriostatic). The mode of AB action is represented as a vector with three levels (cell envelop synthesis inhibitor, cell replication inhibitor and protein synthesis inhibitor).</p

Main features of the follow-up study.

<p>AB, antibiotic; D, during the treatment.</p

Functional profiles.

<p>Main roles and sub-roles that change significantly during treatment and their associated p-values (p-value < 0.05). The upward arrow indicates those categories that were more abundant during treatment and the downward arrow those that were less abundant. NS, not significant.</p>*<p>Biosynthesis of cofactors, prosthetic groups, and carriers.</p>**<p>Biosynthesis and degradation of surface polysaccharides and lipopolysaccharides.</p

Microbiota composition of patients A, B, C, and D.

<p>(A) total microbiota (16S rRNA gene) (B) active microbiota (16S rRNA transcripts). The mode of action for each AB used is indicated.</p

Resistance gene profiles.

<p>(A) The dashed lines represent the relative abundance of the total number of resistance genes for patients A, B, C, and D. (B) Relative abundance of the resistance genes throughout AB treatment for patients A, B, and C. The symbol "*" highlights the resistance gene profiles which coincide with the antibiotic administered to patients C, A and B, respectively.</p

Comparison of the major phyla of the gut microbiota before and after smoking cessation.

<p>(A) Phyla Composition. The results for the intervention group (I) and the control groups (non-smoking  = N; smoking  = S) are given for samples taken one week prior to smoking cessation (t1) as well as four weeks (t2) and eight weeks (t3) thereafter. Whereas the intervention group revealed a significant increase in fractions of <i>Firmicutes</i> and <i>Actinobacteria</i> and a decrease in fractions of <i>Proteobacteria</i> and <i>Bacteroidetes</i>, the microbiota of the control groups remained rather stable. The phyla <i>Tenericutes</i>, <i>Verrucomicrobia</i>, <i>Synergistetes</i>, <i>Fusobacteria</i>, <i>Deinococcus-Thermus</i>, <i>TM7</i>, <i>Acidobacteria</i> and <i>OD1</i> are summarized under “other”. (B) Heat Map. The result of paired Student's t-test is shown on the phylum level with a color coded heat map. Significance levels are shown in different colors (shades of red, significant shifts in bacteria composition; shades of yellow, green and blue, non-significant shifts) and are indicated by the exact significance values within the colored squares of the graph. The major changes in the microbiota in the intervention group were observed between the time points before (t1) and after (t2, t3) smoking cessation. In contrast no significant changes were detected in the control groups and – with the exception of <i>Bacteroidetes</i> – after smoking cessation between t2 and t3 in the intervention group (an extended version of the heat map including all identified genera is shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0059260#pone.0059260.s005" target="_blank">Figure S5</a>, n/a =  not applicable).</p

UniFrac distance between samples and rarefaction curve of phylogenetic diversity.

<p>(A) Unweighted UniFrac distance. The higher a UniFrac distance value between two samples the more different the bacterial composition. The highest distance values were determined for subjects undergoing smoking cessation between t1 and t2 as well as between t1 and t3. All other distance values were substantially smaller (error bars indicate SEM; *: p<0.05; ns: not significant). (B) Rarefaction curves. These curves express the accumulation of phylogenetic richness that would be obtained with continuous sampling effort and hence minimize potential differences that would be a result of the variable number of sequences obtained per sample. For the control groups the three sampling time points were combined in a single curve, while for the intervention group separated curves for t1, t2 and t3 were depicted to visualize the increased phlyogenetic diversity (PD) in the samples 4 weeks after smoking cessation (I2) compared to I1 and I3 (additional indices of α-diversity are depicted in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0059260#pone.0059260.s009" target="_blank">Figure S9</a>).</p

A Challenge to the Reigning Theory of the Just War

Troubled times often gives rise to great art that reflects those troubles. So too with political theory. The greatest work of twentieth century political theory, John Rawls's A theory of justice, was inspired in various respects by extreme social and economic inequality, racialized slavery and racial segregation in the United States. Arguably the most influential work of political theory since Rawls—Michael Walzer's Just and unjust wars—a sustained and historically informed reflection on the morality of interstate armed conflict—was written in the midst of the Vietnam War. It should be no surprise, then, that the bellicose period of the past 20 years should give rise to a robust new literature in political theory on the morality of armed conflict. It has been of uneven quality, and to some extent episodic, responding to particular challenges—the increased prevalence of asymmetric warfare and the permissibility of preventive or preemptive war—that have arisen as a result of specific events. In the past decade, however, a group of philosophers has begun to pose more fundamental questions about the reigning theory of the morality of armed conflict warfare—just war theory—as formulated by Walzer and others. Jeff McMahan's concise, inventive and tightly argued work Killing in war is without doubt the most important of these challenges to the reigning theory of the just war. This review article discusses McMahan's work, some of the critical attention it has received, and its potential implications for practice

Phylogeny-based Principal Component Analysis.

<p>Bacterial communities of the three different treatment groups were clustered using PCA and the unweighted UniFrac distance matrix as an input (weighted PCA is shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0059260#pone.0059260.s006" target="_blank">Figure S6</a>). With PCA, a multivariate statistical analyses, axes that reflect the largest part of sample variation are identified (Percentage values at the axes reflect the level of variation explained by each principal coordinate; the first axis indicates the largest fraction of difference). Separation of the different sample collectives in 3 dimensions is visualized. A separation of the samples from the intervention group (I), that is most predominant 4 weeks after smoking cessation, was revealed. In contrast, the samples from the non-smoking (N) and smoking (S) control groups clustered together closely, thus reflecting their overall similar microbial composition.</p