Data_Sheet_1_Size and Shape Directed Novel Green Synthesis of Plasmonic Nanoparticles Using Bacterial Metabolites and Their Anticancer Effects.PDF

The growing need for developing new synthesis methods of plasmonic nanoparticles (PNPs) stems from their various applications in nanotechnology. As a result, a variety of protocols have been developed for the synthesis of PNPs of different shapes, sizes, and compositions. Though widely practiced, the chemical synthesis of PNPs demands stringent control over the experimental conditions, often employs environmentally hazardous chemicals for surface stabilization, and is frequently energy-intensive. Additionally, chemically obtained PNPs require subsequent surface engineering steps for various optoelectronic and biomedicine applications to minimize the toxic effects and render them useful for targeted drug delivery, sensing, and imaging. Considering the pressing need to develop environmentally-friendly technology solutions, “greener” methods of nanoparticle synthesis are gaining importance. Here, we report on the biological synthesis of plasmonic nanoparticles using bacterial metabolites. A peptide-based siderophore pyoverdine and a blue-green pigment pyocyanin obtained from a marine strain of Pseudomonas aeruginosa rapidly produced plasmonic nanoparticles of gold and silver in an aqueous environment. The morphology of plasmonic nanoparticles could be modulated by tuning the concentration of these metabolites and the reaction time. The exposure of pyoverdine to chloroauric acid resulted in anisotropic gold nanoparticles. On the other hand, pyocyanin produced a highly monodispersed population of gold nanoparticles and anisotropic silver nanoparticles. Biologically obtained gold and silver nanoparticles retained pyoverdine and pyocyanin on the nanoparticle surface and were stable for an extended period of time. The biologically obtained gold and silver plasmonic nanoparticles displayed potent anticancer activities against metastatic lung cancer cells. Biogenic nanoparticles were rapidly internalized by cancer cells in high quantity to affect the cellular organization, and karyoplasmic ratio, indicating the potential of these nanoparticles for cancer nanomedicine.</p

Supplemental Materials from Integrating DNA Methylation and Hydroxymethylation Data with the Mint Pipeline

Supplementary Tables 1-3 and Supplementary Figures 1-5</p

Interactive visualization of discipline network, available on the Scholarometer website (scholarometer.indiana.edu/explore.html).

<p>Interactive visualization of discipline network, available on the Scholarometer website (scholarometer.indiana.edu/explore.html).</p

Entropy contours of a model in which tags are drawn from the overall distribution of votes.

<p>When a tag is selected it receives a vote, bringing its total number of votes to . There are tags with at least one vote. We plot the area in which the average change in entropy . The colors represent the magnitude of the decrease in entropy, . Our heuristic threshold , also plotted, tries to capture the number of votes that results in the largest decrease in entropy, making a tag reliable.</p

Distribution of JCR categories for top 100 authors based on

<p><b> (left) and </b><b> (right) selected from a balanced sample of 3000 authors.</b> The -index leads to a more balanced representation of diverse fields.</p

Connections between Scholarometer and other Linked Open Data sources.

<p>Links are labeled with the correspondence relationships between resources. This diagram is a portion of the cloud diagram by Richard Cyganiak and Anja Jentzsch (lod-cloud.net). As in the original cloud diagram, the color of a node represents the theme of the data set and its size reflects the number of triples.</p

Networks of similar authors, available on the Scholarometer website (scholarometer.indiana.edu/explore.html).

<p>In this example scenario, the user is looking for potential members of an interdisciplinary panel on complex networks. Starting from a known physicist (“A L Barabási”) and navigating through “A Vespignani” and “F Menczer,” the user identifies “J Klienberg,” a computer scientist who studies networks.</p

Top disciplines based on and values, defined in the text as averages across all authors tagged with discipline .

<p>We considered disciplines with at least 20 authors (as of April 2012).</p

Top: Number of authors tagged with 20 most common disciplines over time.

<p>Note that the sets of authors in these disciplines may overlap, as authors are often tagged with multiple disciplines. Therefore the total number of unique authors in these 20 disciplines is actually lower than shown here. Bottom: Relative size of top 20 disciplines based on the number of tagged authors.</p

Temporal growth in numbers of authors, disciplines, and queries received by the Scholarometer system.

<p>Temporal growth in numbers of authors, disciplines, and queries received by the Scholarometer system.</p