Active and dormant microbial biomass pools in microbial physiology models (modified from Fig. 2 in Lennon & Jones, 2011).

<p>Active and dormant microbial biomass pools in microbial physiology models (modified from Fig. 2 in Lennon & Jones, 2011).</p

Steady state active fraction (<i>r^ss</i>) and substrate saturation level () as a function of <i>α</i> and <i>β</i>; <i>α</i> = <i>m_R</i>/(<i>μ_G</i>+<i>m_R</i>), <i>μ_G</i> and <i>m_R</i> (h⁻¹) are maximum specific growth rate and specific maintenance rate for active microbial biomass, respectivly; <i>β</i> denotes the ratio of dormant specific maintenance rate to <i>m_R</i>.

<p>Steady state active fraction (<i>r^ss</i>) and substrate saturation level () as a function of <i>α</i> and <i>β</i>; <i>α</i>  =  <i>m_R</i>/(<i>μ_G</i>+<i>m_R</i>), <i>μ_G</i> and <i>m_R</i> (h⁻¹) are maximum specific growth rate and specific maintenance rate for active microbial biomass, respectivly; <i>β</i> denotes the ratio of dormant specific maintenance rate to <i>m_R</i>.</p

MEND model simulations against the respiration rates due to added ¹⁴C-labeled glucose in Colores et al. [<b>13</b>].

<p>(a) Fitting of the respiration rates in the exponentially-increasing phase using Equation 14, ‘Obs’ and ‘Sim’ denote observed and simulated data, respectively. (b) Fitting of the respiration rates in both exponentially-increasing and non-exponentially-increasing phases using Equation 12. (c) Simulated substrate (<i>S</i>), total live microbial biomass (<i>B</i>), active fraction (<i>r</i>) and substrate saturation level () based on <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0089252#pone.0089252.e027" target="_blank">Equation 12</a>.</p

MEND model simulations against the experimental dataset used by Stolpovsky et al. (2011).

<p>(a) total live biomass, active and dormant biomass, and active fraction; (b) observed and simulated substrate concentration and prescribed O₂ concentration. There are three manipulations on the substrate and oxygen: (1) at time 0, the substrate (3 mg/L) and O₂ (0.025 mM) are added to the system; (2) after 12 h, the same amount of substrate is injected; (3) at 24 h, additional O₂ (0.04 mM) is injected to the system. The observed concentrations of substrate and total biomass are hourly data interpolated from the original observations in Stolpovsky et al. (2011). We scaled the substrate concentrations (with units of mM in original data) to match the magnitude of biomass concentration in units of mg/L.</p

Lichen-Associated Bacterial 16S Sequences & Analysis Files

The archive contains 5 major elements: [Clon16SFinal.fasta - A fasta-formatted file containing all cloned sequences from the order Rhizobiales generated as part of this study] [FIGS1.pdf - An image of a phylogeny of all cloned sequences from the order Rhizobiales generated as part of this study (the methodology for generating the figure is outlined in the associated publication)] [HodkinsonLichen16S_454_raw_data_archive - A directory containing archived fna + qual files of raw sequence data with associated oligos files (used for processing with Mothur 1.15)] [454_clean - A directory containing the cleaned sequence data set (in the form of a .fasta and a .groups file) after quality checks performed using Mothur 1.15] [Supporting_Information - A directory containing all of the supplementary files referenced in the text of the associated publication along with instructions for using the various scripts written for this study, all found within a system of descriptively-titled sub-directories

MEND model parameter values used for simulation of the experiment described in Fig. 3 of Stolpovsky et al. (2011).

<p>*Medians and 95% confidence intervals of the fitted values from 100 optimization runs, i.e., 100 different random seeds are used for the stochastic optimization algorithm.</p

U(VI) Bioreduction with Emulsified Vegetable Oil as the Electron Donor – Model Application to a Field Test

We amended a shallow fast-flowing uranium (U) contaminated aquifer with emulsified vegetable oil (EVO) and subsequently monitored the biogeochemical responses for over a year. Using a biogeochemical model developed in a companion article (Tang et al., <i>Environ. Sci. Technol.</i> <b>2013</b>, doi: 10.1021/es304641b) based on microcosm tests, we simulated geochemical and microbial dynamics in the field test during and after the 2-h EVO injection. When the lab-determined parameters were applied in the field-scale simulation, the estimated rate coefficient for EVO hydrolysis in the field was about 1 order of magnitude greater than that in the microcosms. Model results suggested that precipitation of long-chain fatty acids, produced from EVO hydrolysis, with Ca in the aquifer created a secondary long-term electron donor source. The model predicted substantial accumulation of denitrifying and sulfate-reducing bacteria, and U­(IV) precipitates. The accumulation was greatest near the injection wells and along the lateral boundaries of the treatment zone where electron donors mixed with electron acceptors in the groundwater. While electron acceptors such as sulfate were generally considered to compete with U­(VI) for electrons, this work highlighted their role in providing electron acceptors for microorganisms to degrade complex substrates thereby enhancing U­(VI) reduction and immobilization

16S_host_denoised_by_sampleID.fasta

fastq file with all denoised 16S sequences produced in this stud

ITS1_host_denoised_by_SampleID.fasta.tar

All denoised ITS1 sequences produced in this study in fasta format and by Sample I

Glomeromycota_454_Host_All

Nexus file including 54 representative OTU sequences from this experiment and reference sequence