Cumulative distributions of the survival rates.

<p>The red, green and blue lines represent values below (<i>f</i> = 0.950), at (<i>f</i> = 0.994), and above (<i>f</i> = 0.9999) the critical point, respectively. The values of survival rates are distributed most widely at the critical point.</p

Schematic figures of the percolation process of a complete graph.

<p>In our simulation, an initial state (<i>f</i> = 0) is chosen as an ER-graph with a link density <i>p</i> between <i>p</i> = <i>p</i>_<i>c</i> and 1, and we consider removal process of links toward <i>p</i> = 0(<i>f</i> = 1).</p

Size dependence of the probability that the largest cluster has a loop for the three cases shown in Fig 4.

<p>The dotted lines indicate the corresponding critical points.</p

正常マウスにおける軸索HCN電流へ麻酔薬が及ぼす影響

Objective: The objective was to study the in vivo effects of anesthetic agents on peripheral nerve excitability. Methods: Normal male mice were anesthetized by either isoflurane inhalation or a combination of medetomidine, midazolam, and butorphanol intraperitoneal injection (‘‘triple agents’’). Immediately after induction, the tail sensory nerve action potential was recorded and its excitability was monitored. Results: Under both anesthetic protocols, there was an interval excitability change by long hyperpolarizing currents. There was greater threshold reduction approximately 30 min post induction, in comparison to immediately post induction. Other excitability parameters were stable over time. Modeling suggested interval suppression of internodal H conductance or leak current. Conclusions: Anesthetic agents affected responses to long hyperpolarizing currents. Significance: Axonal excitability during intraoperative monitoring may be affected by anesthetic agents. Interpretation of interval excitability changes under anesthesia requires caution, especially with long hyperpolarizing currents

Distinct interacting core taxa in co-occurrence networks enable discrimination of polymicrobial oral diseases with similar symptoms

Polymicrobial diseases, which can be life threatening, are caused by the presence and interactions of multiple microbes. Peri-implantitis and periodontitis are representative polymicrobial diseases that show similar clinical symptoms. To establish a means of differentiating between them, we compared microbial species and functional genes in situ by performing metatranscriptomic analyses of peri-implantitis and periodontitis samples obtained from the same subjects (n = 12 each). Although the two diseases differed in terms of 16S rRNA-based taxonomic profiles, they showed similarities with respect to functional genes and taxonomic and virulence factor mRNA profiles. The latter—defined as microbial virulence types—differed from those of healthy periodontal sites. We also showed that networks based on co-occurrence relationships of taxonomic mRNA abundance (co-occurrence networks) were dissimilar between the two diseases. Remarkably, these networks consisted mainly of taxa with a high relative mRNA-to-rRNA ratio, with some showing significant co-occurrence defined as interacting core taxa, highlighting differences between the two groups. Thus, peri-implantitis and periodontitis have shared as well as distinct microbiological characteristics. Our findings provide insight into microbial interactions in polymicrobial diseases with unknown etiologies