Cellulose Nanofiber Biotemplated Palladium Composite Aerogels.

Noble metal aerogels offer a wide range of catalytic applications due to their high surface area and tunable porosity. Control over monolith shape, pore size, and nanofiber diameter is desired in order to optimize electronic conductivity and mechanical integrity for device applications. However, common aerogel synthesis techniques such as solvent mediated aggregation, linker molecules, sol⁻gel, hydrothermal, and carbothermal reduction are limited when using noble metal salts. Here, we present the synthesis of palladium aerogels using carboxymethyl cellulose nanofiber (CNF) biotemplates that provide control over aerogel shape, pore size, and conductivity. Biotemplate hydrogels were formed via covalent cross linking using 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide hydrochloride (EDC) with a diamine linker between carboxymethylated cellulose nanofibers. Biotemplate CNF hydrogels were equilibrated in precursor palladium salt solutions, reduced with sodium borohydride, and rinsed with water followed by ethanol dehydration, and supercritical drying to produce freestanding aerogels. Scanning electron microscopy indicated three-dimensional nanowire structures, and X-ray diffractometry confirmed palladium and palladium hydride phases. Gas adsorption, impedance spectroscopy, and cyclic voltammetry were correlated to determine aerogel surface area. These self-supporting CNF-palladium aerogels demonstrate a simple synthesis scheme to control porosity, electrical conductivity, and mechanical robustness for catalytic, sensing, and energy applications

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Similar pathogen targets in Arabidopsis thaliana and homo sapiens protein networks.

We study the behavior of pathogens on host protein networks for humans and Arabidopsis - noting striking similarities. Specifically, we preform [Formula: see text]-shell decomposition analysis on these networks - which groups the proteins into various "shells" based on network structure. We observe that shells with a higher average degree are more highly targeted (with a power-law relationship) and that highly targeted nodes lie in shells closer to the inner-core of the network. Additionally, we also note that the inner core of the network is significantly under-targeted. We show that these core proteins may have a role in intra-cellular communication and hypothesize that they are less attacked to ensure survival of the host. This may explain why certain high-degree proteins are not significantly attacked

Betweenness centrality () vs. number of pathogen interactions () for the nodes in shell of the <i>Arabidopsis</i> protein interaction network.

<p>Betweenness centrality () vs. number of pathogen interactions () for the nodes in shell of the <i>Arabidopsis</i> protein interaction network.</p

Minimum number of pathogen interactions for a given node vs. fraction of EHF neighbors for that node.

<p>Minimum number of pathogen interactions for a given node vs. fraction of EHF neighbors for that node.</p

Average degree () vs. average number pathogen interactions () per node in a shell (log-log scale) with power-law fits.

<p>The core of each network is circled. (A) Human protein interaction network (B) <i>Arabidopsis</i> protein interaction network.</p

Normalized minimum centrality measure (the centrality measures depicted here are degree, betweenness, and shell number) of nodes targeted by at least a certain number of pathogen interactions.

<p>(A) Human protein interaction network. (B) <i>Arabidopsis</i> protein interaction network. Betweenness is defined in the <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0045154#s4" target="_blank">Materials and Methods</a>.</p

Shell depth vs. information centrality:

<p>(A) human protein interaction network, (B) <i>Arabidopsis</i> protein interaction network. Information centrality is defined in the <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0045154#s4" target="_blank">Materials and Methods</a>.</p

Total number of proteins vs. number of EHF proteins in a shell.

<p>Total number of proteins vs. number of EHF proteins in a shell.</p

Average node degree vs. average EHF node degree in a shell.

<p>Average node degree vs. average EHF node degree in a shell.</p