dataesr/scientific-paragraphs-categorization huggingface dataset:
Atopobium minutum (X67148) Atopobium fossor (L34620) Bifidobacterium bifidum (U25952)  Matrix-assisted laser-desorption/ionization timeof-flight (MALDI-TOF) MS protein analysis was carried out as previously described  [55]  using a Microflex spectrometer (Bruker Daltonics, Leipzig, Germany). Twelve distinct deposits were done for strain GD5 T from twelve isolated colonies. The twelve GD5 T spectra were imported into the MALDI BioTyper software (version 2. 0, Bruker) and analyzed by standard pattern matching (with default parameter settings) against the main spectra of 4,706 bacteria, which were used as reference data, in the BioTyper database. For strain GD5 T , no significant score was obtained, thus suggesting that our isolate was not a member of a known species. We added the spectrum from strain GD5 T to our database (Figure  4 , Figure  5 ).
Genome project history The organism was selected for sequencing on the basis of its phylogenetic position and 16S rDNA similarity to E. massiliensis and other members of the family Coriobacteriaceae and is part of a study of the human digestive flora aiming at isolating all bacterial species within human feces  [1] [2] [3]. It was the 2 nd genome of an Enorma species and the first genome of E. timonensis sp. nov. The GenBank accession number is CAPF00000000 and consists of 105 contigs. Table  3  shows the project information and its association with MIGS version 2. 0 compliance  [48].
Genome annotation Open Reading Frames (ORFs) were predicted using Prodigal  [56]  with default parameters. However, the predicted ORFs were excluded if they spanned a sequencing gap region. The predicted bacterial protein sequences were searched against the GenBank  [57]  and Clusters of Orthologous Groups (COG) databases using BLASTP. The tRNAs and rRNAs were predicted using the tRNAScanSE  [58]  and RNAmmer [59] tools, respectively. Lipoprotein signal peptides and numbers of transmembrane helices were predicted using SignalP  [60]  and TMHMM  [61] , respectively. ORFans were identified if their BLASTP E-value was lower than 1e-03 for alignment length greater than 80 amino acids. If alignment lengths were smaller than 80 amino acids, we used an E-value of 1e-05. Such parameter thresholds have already been used in previous works to define ORFans.
Artemis  [62]  and DNA Plotter  [63]  were used for data management and visualization of genomic features, respectively. The Mauve alignment tool (version 2. 3. 1) was used for multiple genomic sequence alignment  [64]. To estimate the mean level of nucleotide sequence similarity at the genome level between E. timonensis and five other members of the family Coriobacteriaceae (Table  6 ), we used the Average Genomic Identity Of gene Sequences (AGIOS) home-made software. Briefly, this software combines the Proteinortho software  [65]  for detecting orthologous proteins between genomes compared two by two, then retrieves the corresponding genes and determines the mean percentage of nucleotide sequence identity among orthologous ORFs using the Needleman-Wunsch global alignment algorithm. Enorma timonensis strain GD5 T was compared to E.
The 16S rDNA and genome sequences are deposited in GenBank under accession numbers JX424767 and CAPF00000000, respectively. The G+C content of the genome is 65. 8%. The habitat of the organism is the human digestive tract. The type strain GD5 T (= CSUR P900 = DSM 26111) was isolated from the fecal flora of a 53-year old French patient hospitalized in an intensive care unit. This strain has been found in Marseille, France.
The ir from our automated 6-template fit to our sample of 967 HerMES-Lockman galaxies. 24µm 70% completeness limit is ∼300µJy in the Lockman field (90% at 400µJy). The SWIRE Photometric Redshift Catalogue contains over 1 million galaxies covering 49 sq deg of sky, including 7. 53 sq deg in Lockman, which had been surveyed at 3. 6-160µm by Spitzer and at ugrizJHK in ground-based surveys. Photometric redshifts were determined from ugrizJHK and 3. 6, 4. 5µm photometry (with up to 13 photometric bands available in the Lockman area) and the 5. 8-160µm data were fitted with infrared templates. These allowed prediction of fluxes at submilllimetre wavelengths. If more than one SWIRE source is associated with a HerMES SCAT source, the source with the highest predicted 450µm flux from the SWIRE Photometric Redshift Catalogue is selected.
3. 1 Problematic sources with χ 2 ir > 5 First we show the SEDs of problematic sources with χ 2 ir > 5 in the automated 6-template fit, for sources with redshifts determined from 10 optical and near infrared photometric bands (Figs  5 6 7 8 ). Most have log(S500/S24)> 2 and z < 1. For over half the sources the introduction of the cold cirrus template (ψ=0. 1) solves the problem of the poor fit. In many cases none of our existing templates can match the observed SEDs. Since it is implausible to postulate emission from cold dust with a luminosity exceeding that of the illuminating starlight, the association of the submillimetre emission with the chosen SWIRE galaxy must be incorrect. A possible explanation is provided by galaxy lensing. In several cases we have indicated fits to the submillimetre data with an Arp220 starburst model at a higher redshift than that of the SWIRE galaxy.
We also incorporated photometry from the PACS data  (Poglitsch et al. 2010). The Lockman-SWIRE field was observed by HerMES (AOT Set no. 34,  Oliver et al. 2012 ) using the SPIRE/PACS parallel mode. Maps were reduced using the Unimap software  (Piazzo et al 2012). Photometry was estimated by constructing a "beam" from stacking the PACS map at the location of all 967 sources and fitting this beam.
The Herschel galaxies lie at the upper end of the dust-mass distribution seen for all SWIRE galaxies, shown as a yellow distribution. Much deeper submillimetre surveys would be needed to sample the full range of dust-masses in galaxies. Our results are not directly comparable with most other studies of the SEDs of SPIRE sources. Hwang et al (2010) ,  Symeonidis et al (2013)  and  Magnelli et al (2014)  fit SEDs with a modified blackbody. Conclusions about correlation of 'dust temperature' with infrared luminosity or redshift correspond in our approach to variations in the proportions of different components. The modified blackbody approach gives no insight into whether the dust has high or low optical depth, or whether the galaxy is in a starburst or quiescent phase. Magdis et al (2013)  have studied a sample of 330 galaxies with S(24) > 5 mJy in the SWIRE areas for which Spitzer-IRS data are available.
SPIRE has been developed by a consortium of institutes led by Cardiff Univ. (UK) and including Univ. Lethbridge (Canada); NAOC (China); CEA, LAM (France); IFSI, Univ. Padua (Italy); IAC (Spain); Stockholm Observatory (Sweden); Imperial College London, RAL, UCL-MSSL, UKATC, Univ. Sussex (UK); Caltech, JPL, NHSC, Univ. Colorado (USA). This development has been supported by national funding agencies: CSA (Canada); NAOC (China); CEA, CNES, CNRS (France); ASI (Italy); MCINN (Spain); SNSB (Sweden); STFC (UK); and NASA (USA). The Dark Cosmology Centre (JW) is funded by the Danish National Research Foundation. The data presented in this paper are available through the Herschel Database in Marseille HeDaM 3. SJO acknowledges support from the Science and Technology Facilities Council (grant numbers ST/L000652/1) and from the European Commission Research Executive Agency REA (Grant Agreement Number 607254).
Results of intercomparison measurements between the two mobile laboratories represent the adopted data quality assurance procedures. Most of the compared measurement devices show sufficient agreement for combined data analysis. For the removal of data contaminated by local pollution emissions a video tape analysis method was applied. Analysis tools like positive matrix factorization and peak integration by key analysis applied to high-resolution time-of-flight aerosol mass spectrometer data are used for in-depth data analysis of the organic particulate matter. Several examples, including a combination of MoLa and MOSQUITA measurements on a cross section through the Paris emission plume, are provided to demonstrate how such mobile measurements can be used to investigate the emissions of a megacity.
, 2007; Ulbrich et al. , 2009)  and peak integration by key analysis (PIKA) (ToF-AMS Analysis Software Homepage, 2013) a large amount of information can be obtained about aerosol chemical composition  (Zhang et al. , 2005; Canagaratna et al. , 2007; Sun et al. , 2010; Zhang et al. , 2011). The measurement of carbon dioxide, ozone and nitrogen oxides together with meteorological parameters is needed for numerical simulation of the urban atmospheric chemistry (e. g. Fenger, 1999; Akimoto, 2003; Crutzen, 2004; Gurjar and Lelieveld, 2005). The purpose of this paper is to discuss the general applicability of the developed and deployed measurement and analysis strategies for urban emission investigations using the MEGAPOLI data base as example. In Sect. 2 we provide an overview of the MEGAPOLI field campaigns and describe the two mobile laboratories including specification and intercomparison of the on-board instruments, as well as different measurement strategies.
The recorded trace gases are O 3 , NO, NO 2 , CO, CO 2 and H 2 O. Meteorological parameters including wind direction, pressure, temperature, relative humidity and global radiation as well as GPS information and webcam videos complete the MOSQUITA data set. Table  1. Summary of measurement devices -including information about measured variables, size range, time resolution and detection limit -installed in the mobile laboratory MoLa (Max Planck Institute for Chemistry) during the summer and winter MEGAPOLI field campaigns. AMS detection limits (using the method described in  Drewnick et al. , 2009)  were calculated for summer (S) and winter field campaign (W) separately, because the AMS detection limit can change over time.
Intercomparison of corresponding measurement devices on MoLa and MOSQUITA Intercomparison measurements were carried out several times during both field campaigns. Not only MoLa and MOSQUITA were compared, but also both mobile laboratories with the other stationary ground-based measurement sites and the research aircraft. A detailed description of the results of these other intercomparison exercises can be found in  Freutel et al. (2013)  and  Crippa et al. (2013). For the two mobile laboratories the instruments (see Tables 1 and 2) measuring the following parameters were compared in this study: CO mixing ratio was not compared, because due to issues with the MoLa instrument all measurements were below detection limit during both campaigns and thus were not used for further analysis. NO and NO 2 data are not available for MOSQUITA during the intercomparisons due to instrumental issues.
Accounting for the different size ranges the agreement of total number and temporal variations is satisfying and approximate comparability of the two measurements is given if no extreme concentrations of nucleation mode particles are present. Non-refractory chemical particle components: for the intercomparison of measured non-refractory particle chemical components the time series of particulate organics, nitrate, sulfate, ammonium and chloride were studied. While for both HR-ToF-AMS data sets the same data processing routines were applied (m/z calibration, baseline correction, instrument background measurements and additional calibration parameters) different values for the collection efficiency are used during the summer campaign. For the MoLa data set a standard collection efficiency of 0.
5  (Matthew et al. , 2008)  is applied during both campaigns. For the MOSQUITA data set the same value was used in winter; however, a value of 1. 0 was applied for the summer data set. This unusual collection efficiency is justified on the one hand by long-term experiences with the MOSQUITA instrument, reflecting instrumental features of this HR-ToF-AMS. On the other hand including all intercomparison measurements (not only AMS intercomparisons, but also AMS comparison with additional PM 1 measurements) carried out during both campaigns it seems to be appropriate to apply different collection efficiencies for the MOSQUITA summer and winter AMS data. A second difference between the data sets is the mode of operation of the two HR-ToF-AMSs. The MoLa instrument switched between 10 s in the mass spectrum mode and 10 s in the particle time-of-flight mode, both in V-mode (medium resolution but high sensitivity) only.
The MOSQUITA instrument applied shorter mode switching times, and also measured in V-mode only. During the summer campaign the AMS switched between 3 s in mass spectrum mode and 2 s in particle time-offlight mode. In winter it switched between 5 s in mass spectrum mode and 5 s in particle time-of-flight mode. Due to the shorter measurement time a higher temporal resolution (but also larger uncertainty) of the AMS data were achieved compared to the MoLa instrument. In Table  3  the correlation parameters (slope and regression coefficient R 2 ) of linear fits of the MoLa versus MOSQUITA data for particulate organic, sulfate, nitrate and ammonium of the three intercomparison times are listed, as well as average ratios of mass concentrations of MoLa to MOSQUITA data and mean concentration values of both instruments.
In winter ambient concentrations were well above the detection limits of both instruments; however, especially for ammonium the R 2 value (0. 39) is still low. Calibration errors of one or both instruments can be the reason for the observed differences in absolute mass concentrations (indicated by the slope and the MoLa to MOSQUITA AMS averaged mass concentrations ratio) and discrepancies in the temporal behaviour of the AMS time series (indicated by R 2 ) could be explained by local emissions, which were detected only by one of the AMSs. Nevertheless, general temporal trends are represented similarly by both instruments during all three intercomparison periods. The overall uncertainty of AMS data is, including all uncertainties of operation mode, instrumental differences, different inlet setups, calibrations and analysis variations, about 30 %  (Canagaratna et al.
Peak integration by key analysis PIKA (Peak Integration by Key Analysis; ToF-AMS Analysis Software Homepage, 2013) is another advanced analysis software tool for the speciation and quantification of HR-ToF-AMS data. PIKA is based on SQUIRREL (SeQUential Igor data RetRiEvaL; ToF-AMS Analysis Software Homepage, 2013), the standard software for basic analysis of AMS data, e. g. for application of calibration parameters and to obtain chemically resolved mass concentration time series or particle size distributions. In newer versions PIKA includes APES (Analytical Procedure for Elemental Separation; ToF-AMS Analysis Software Homepage, 2013), a software tool for the separation of high resolution AMS signals into their elemental components. In this study PIKA was mainly applied for the retrieval of the O/C ratio of the organic aerosol which is a marker for the oxidation state and therewith the age of the aerosol.
They also showed that the plume emissions were visible in many measurement parameters as expected due to the various emission sources in the Paris metropolitan area. The first example demonstrated the difference between long-range transported and local or regional pollution. Fresh pollution showed rapid concentration changes while aged pollution varied on longer temporal and spatial scales. The second example consisted of a combination of MoLa and MOSQUITA data sets for information about the spatial structure of the Paris emission plume. A clear reduction of the O/C ratio of the organic aerosol indicated the emission plume, with the lowest O/C values in the lateral core of the plume. The spatial extent of the emission plume -in this example up to about 60 km from the city border -could be seen in example three, an axial measurement trip carried out during the summer campaign.
Proteomics -based clinical studies represent promising resources for the discovery of novel biomarkers or for unraveling molecular mechanisms underlying particular diseases. Here, we present a discovery study of hepatocellular carcinoma developed on nonfibrotic liver (nfHCC) that combines complementary quantitative iTRAQ-based proteomics and phosphoproteomics approaches. Using both approaches, we compared a set of 24 samples (18 nfHCC versus six nontumor liver tissue). We identified 43 proteins (67 peptides) differentially expressed and 32 peptides differentially phosphorylated between the experimental groups. The functional analysis of the two data sets pointed toward the deregulation of a protein homeostasis (proteostasis) network including the up-regulation of the Endoplasmic Reticulum (ER) resident HSPA5, HSP90B1, PDIA6, and P4HB and of the cytosolic HSPA1B, HSP90AA1, HSPA9, UBC, CNDP2, TXN, and VCP as well as the increased phosphorylation of the ER resident calnexin at Ser583.
Antibody-based validation approaches (immunohistochemistry, immunoblot, Alphascreen ® , and AMMP ® ) on independent nfHCC tumor sets (up to 77 samples) confirmed these observations, thereby indicating a common mechanism occurring in nfHCC tumors. Based on these results we propose that adaptation to proteostasis imbalance in nfHCC tumors might confer selective advantages to those tumors. As such, this model could provide an additional therapeutic opportunity for those tumors arising on normal liver by targeting the tumor proteostasis network. Data are available via ProteomeXchange with identifier PXD001253.
Proteomics data were produced with nanoLC U3000 system (Dionex, France) coupled with a LTQ Orbitrap XL (Thermo Scientific, France). Chemicals for proteomic and HPLC were purchased from Sigma Aldrich, France. ITRAQ reagent 8-plex kit was provided from SCIEX, France. Antibodies to Calnexin were described previously  (9). Antiphospho (Ser583)calnexin antibody was from Novus Biologicals (Cambridge, UK), anti-p97/VCP mouse monoclonal antibodies were from Abcam (Paris, France) and anti-p97/VCP rabbit polyclonal antibodies were from Proteintech (Chicago, IL). Anti-KDEL mouse monoclonal antibodies were from Abnova (Tapei city, Tawain). Sample Preparation for Proteomics Analyses-Fifty to 100 mg of liver tissues were homogenized at 4 °C in 0. 5 ml of buffer containing 0. 1 M triethylammonium bicarbonate (TEAB) pH 9.
Ionization potential of the LTQ Orbitrap XL nanospray source was 1. 8 kV and acquisition method was a FT-MS scan (60,000 FWHM @ m/z 400) followed by ion trap MS/MS and FT MS/MS (7500 FWHM @ m/z 400) of the three most intense precursor ions (see supplemental Material for the detailed settings). Data processing was performed with Proteome Discoverer 1. 3 (Thermo Scientific). Query parameters were set as follows: precursor and fragment tolerance respectively 3 ppm and 0. 7 Da, full tryptic peptides, 1 miss-cleavage, static modification on C (ϩ57. 0215), N-terminal and lysine (304. 2054), and dynamic modification on S, T, Y (ϩ79. 9663), N (ϩ0. 984), and M, P (ϩ15. 9949) (see Supplementary Material for the detailed parameters). The human sequences were extracted from Swiss-Prot complete genome databases.
Result filters were applied as: peptide confidence ϭ high (automatic adjustment of XCorr for False Discovery Rate (FDR) Ͻ1%). Further data manipulations were performed using Microsoft Excel to eliminate redundancy of peptides sequences. ITRAQ channels were extracted and processed using Proteome dis-   (11). For each peptide identified in the 4 iTRAQ 8-plex series (18 nfHCC versus six nontumor tissues), the 18 tumors tissues were compared with the six nontumors tissues with Wilcoxon Rank Sum Test and significance was based on False Discovery Rate (Benjamini-Hochberg). The resulting significant peptides were clustered with complete linkage clustering using Euclidian distance and ordering optimization. Networks were built using the String database  (12)  and annotated using the Medusa program.
Kinase-substrate interaction networks were built using the Networkin program  (13, 14). Immunoblot and Immunohistochemistry-Immunoblotting was performed as previously described  (9). Briefly, tissue extracts were resolved by SDS-PAGE and transferred onto PVDF membranes. Membranes were blocked using PBS, 0. 1% Tween 20 for 30 min at room temperature followed by incubation with the primary antibody overnight at 4 °C and blots were revealed using chemoluminescence. Immunohistochemistry was performed on nfHCC tissue sections as previously described by our group on other types of tumors  (15). Following pretreatment at pH 6 and peroxidase blocking, slides were respectively incubated with anti-VCP (1/200), anti-p-calnexin (1/700), or anti-calnexin (1/700) for 30 min. Slides were then washed, treated with the Envision FLEX/HRP system for 20 min and revealed with the EnVision FLEX-DAB chromogen (Dako (Glostrup, Denmark)) and with Mayer's Hematoxylin (Lille's Modification) Histological Staining Ragent (Dako 53309) for 3 min and distilled water for 5 min.
Alphascreen and AMMP Analyses-Alphascreen assays were performed on tissue extracts suspended in SureFire lysis buffer, solubilized and clarified by centrifugation. These extracts were then depleted from IgG (by using proteinA/G-bound magnetic beads) and from biotin (by using streptavin-bound magnetic beads). These extracts were analyzed using the ERK1 and phospho-ERK1 SureFire kits as recommended by the manufacturer. TPER buffer (Pierce-Thermo Scientific, France) supplemented with a protease and phosphatase inhibitor mixture and clarified by centrifugation. These lysates were then assayed on the AMMP reader for phospho-AKT. Statistical Analyses-Data are presented as mean Ϯ S. E. of at least three separate experiments if not specified otherwise and compared with multiple t test corrected using one-way analysis of variance (ANOVA) followed by Dunnett's multiple comparison tests for experiments on mammalian cells.
RESULTS In an attempt to better characterize the molecular signaling events occurring in nfHCC, we designed an experimental strategy relying on the use of differential quantitative proteomics in human tumors (Fig. 1 ). First, a proteomics sample set was built and comprised 18 nfHCC tumors and six nontumor tissues. Protein extracts were generated and trypsin digested as indicated in the Methods section. Peptides were either directly, or after phosphopeptide enrichment via TiO 2 chromatography, labeled with iTRAQ-8plex reagents. For the unpurified peptides (Total fraction) and the peptides purified from TiO 2 (TiO 2 fraction), the 24 tissues were analyzed in four series of iTRAQ. In each series, iTRAQ 113 to 118 were used for labeling individual samples and iTRAQ 119 and 121 were used to label a mixture of nontumors as a common reference for the different series, thus allowing comparison of data between series.
Total tumor or nontumor tissue extracts were trypsin digested and either directly labeled with iTRAQ-8plex or purified using TiO 2 chromatography and labeled prior to mass spectrometry analysis. Phospho and -proteomics data were then qualitatively, quantitatively and functionally analyzed. In the last part of the experimental scheme, data were experimentally validated for (1) kinase activation (left), and (2) proteostasis modulation (right). tained 10 mg tryptic peptides from 100 mg fresh tissue and around 10 g of peptides from TiO 2 chromatography, corre- sponding to 0. 1% of the total peptides. The two following fractions were labeled using iTRAQ: 1) the "total fraction" corresponded to unpurified peptides, and 2) the "TiO 2 fraction" corresponded to peptides eluted from the TiO 2 column.