追加1. 胃切除術後の鼻腔ゾンデ早期抜去について(シンポジウム 中山式切除術をめぐって,第473回千葉医学会例会,第4回佐藤外科例会)

Additional file 4. Proteomic data of strains wild-type, AG553, AG553, AG601 and LL1210 of Clostridium thermocellum

MOESM2 of Clostridium thermocellum DSM 1313 transcriptional responses to redox perturbation

Additional file 2. Calculated differential expression and adjusted p values for genes showing significant (adjusted p value < 0.05) differential expression during at least one timepoint of either methyl viologen or hydrogen peroxide exposure

MOESM3 of Clostridium thermocellum DSM 1313 transcriptional responses to redox perturbation

Additional file 3. (A) Adjusted OD600 of batch cultures grown at various initial hydrogen peroxide concentrations. Cultures were grown in MTC media containing 1.1 g/L cellobiose; (B) Chemostat OD600 and measured redox potential before, during and after hydrogen peroxide addition; (C) Detailed view of boxed region indicated in panel (B)

MOESM1 of Clostridium thermocellum DSM 1313 transcriptional responses to redox perturbation

Additional file 1. Batch fermentation performance under methyl viologen and hydrogen peroxide initial loadings

Dynamic control of NFV forwarding graphs with end-to-end deadline constraints

There is a strong industrial drive to use cloud computing technologies and concepts for providing timing sensitive services in the networking domain since it would provide the means to share the physical resources among multiple users and thus increase the elasticity and reduce the costs. In this work, we develop a mathematical model for user-stateless virtual network functions forming a forwarding graph. The model captures uncertainties of the performance of these virtual resources as well as the time-overhead needed to instantiate them. The model is used to derive a service controller for horizontal scaling of the virtual resources as well as an admission controller that guarantees that packets exiting the forwarding graph meet their end-to-end deadline. The Automatic Service and Admission Controller (AutoSAC) developed in this work uses feedback and feedforward making it robust against uncertainties of the underlying infrastructure. Also, it has a fast reaction time to changes in the input

Genomic Evaluation of <i>Thermoanaerobacter</i> spp. for the Construction of Designer Co-Cultures to Improve Lignocellulosic Biofuel Production

<div><p>The microbial production of ethanol from lignocellulosic biomass is a multi-component process that involves biomass hydrolysis, carbohydrate transport and utilization, and finally, the production of ethanol. Strains of the genus <i>Thermoanaerobacter</i> have been studied for decades due to their innate abilities to produce comparatively high ethanol yields from hemicellulose constituent sugars. However, their inability to hydrolyze cellulose, limits their usefulness in lignocellulosic biofuel production. As such, co-culturing <i>Thermoanaerobacter</i> spp. with cellulolytic organisms is a plausible approach to improving lignocellulose conversion efficiencies and yields of biofuels. To evaluate native lignocellulosic ethanol production capacities relative to competing fermentative end-products, comparative genomic analysis of 11 sequenced <i>Thermoanaerobacter</i> strains, including a <i>de novo</i> genome, <i>Thermoanaerobacter thermohydrosulfuricus</i> WC1, was conducted. Analysis was specifically focused on the genomic potential for each strain to address all aspects of ethanol production mentioned through a consolidated bioprocessing approach. Whole genome functional annotation analysis identified three distinct clades within the genus. The genomes of Clade 1 strains encode the fewest extracellular carbohydrate active enzymes and also show the least diversity in terms of lignocellulose relevant carbohydrate utilization pathways. However, these same strains reportedly are capable of directing a higher proportion of their total carbon flux towards ethanol, rather than non-biofuel end-products, than other <i>Thermoanaerobacter</i> strains. Strains in Clade 2 show the greatest diversity in terms of lignocellulose hydrolysis and utilization, but proportionately produce more non-ethanol end-products than Clade 1 strains. Strains in Clade 3, in which <i>T. thermohydrosulfuricus</i> WC1 is included, show mid-range potential for lignocellulose hydrolysis and utilization, but also exhibit extensive divergence from both Clade 1 and Clade 2 strains in terms of cellular energetics. The potential implications regarding strain selection and suitability for industrial ethanol production through a consolidated bioprocessing co-culturing approach are examined throughout the manuscript.</p> </div

Annotated hydrogenases within sequenced <i>Thermoanaerobacter</i> spp.

<p>Annotated hydrogenases within sequenced <i>Thermoanaerobacter</i> spp.</p

Phylogenetic analysis of all annotated alcohol dehydrogenase genes within sequenced <i>Thermoanaerobacter</i> strains.

<p>All included sequences belong to COG1063, COG1454 or COG1979. Tmath_0755 was excluded from analysis as the annotated sequence appears to be a CDS fragment. Sequences in bold correspond to the GenBank accession numbers for functionally characterized sequences from <i>T. ethanolicus</i> JW200 <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0059362#pone.0059362-Pei1" target="_blank">[93]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0059362#pone.0059362-Holt1" target="_blank">[95]</a>. Tree construction was as described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0059362#s2" target="_blank">Materials and Methods</a>. Bootstrapping support values are indicated by their respective nodes.</p

Schematic representation of key physiological processes pertinent to lignocellulosic ethanol production in a CBP system.

<p>Physiological processes are identified as the text in blue boxes.</p

Selected¹ transporters associated with carbohydrate import identified within sequenced <i>Thermoanaerobacter</i> spp.

<p>Symbols denote the presence (+) or absence (−) of a particular annotated transporter within each genome. Substrate specificity is inferred based upon KO annotated specificity of the substrate binding protein (ABC transporters) or the membrane linked EIIC components (PTS transporters).</p>1<p>Transport systems presented are limited to complexes showing co-localization of all genes needed to form a functional complex. Complexes lacking annotation of a single component are not included. Redundancy in transport systems exists, but is not identified.</p