dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments We thank  Shai Meiri , anonymous referees and the editors for comments on previous manuscript versions. This work was financially supported by the  Villum Kann Rasmussen Foundation  (grant  VKR09b-141  to J. -C. S. ) and the  Danish Council for Independent Research | Natural Sciences  (starting independent researcher grant  11-106163  to W. D. K. and individual postdoc grant  10-093843  to C. F. ). B. S. was supported by  MADALGO -Center for Massive Data Algorithmics  and the  Aarhus University Research Foundation  via the  Center for Interdisciplinary Geospatial Informatics Research (CIGIR). C. S. was supported by the  15. Juni Fonden  (grant to J. -C. S. ). K. T. 's PhD project was financially supported by the  Faculty of Science and Technology, Aarhus University. J. -C. S.
In addition, several groups compiled large-sample data sets with the goal of facilitating collective efforts. The pioneering inter-comparison studies sponsored by the  WMO (1975 WMO ( , 1986 WMO ( , 1992) )  involved several teams and data sets. The Model Parameter Experiment promoted by NOAA (MOPEX, www. nws. noaa. gov/oh/mopex/; see  Schaake et al. , 2001; Chahinian et al. , 2006; Duan et al. , 2006; Schaake and Duan, 2006, etc. )  engaged several groups in the study of a sample of 438 catchments from the USA and 40 in France. Most recently, the Distributed Model Intercomparison Projects (DMIP-I and DMIP-II,  Smith et al. , 2004 Smith et al. , , 2012;; www. nws. noaa. gov/oh/hrl/dmip/) engaged several groups in a comparative assessment of spatially distributed hydrological models (and associated parameter estimation strategies), using comprehensive data sets from a large number of US catchments.
Acknowledgements. This paper was partially developed in the context of the  Panta Rhei research initiative of the International Association of Hydrological Sciences. We wish to express our appreciation to the Editor,  Murugesu Sivapalan , and the several reviewers,  Bethanna Jackson ,  Hillary McMillan ,  Dingbao Wang ,  Evan Coopersmith ,  Sopan Patil ,  Manfred Ostrowski  and  Thorsten Wagener , plus one additional anonymous reviewer, for their detailed and constructive comments. Their many remarks and suggestions helped to streamline and improve the presentation of our opinion paper. The first author acknowledges partial support by a grant from the  Co-operative Research Programme (Trade and Agriculture) of the Organization for European Co-Operation and Development (OECD) , and from the  Australian Research Council.
gewex. org/ Freely Global data sets available relevant to water and energy budgets GRDC Global runoff Global Runoff Data http://www. bafg. de/GRDC/EN/Home/homepagenode. html Available data Centre with a nominal cost USGS Hydrology USGS www. usgs. gov/? Freely relevant US available data sets (e. g. , HCDN/GAUGES data sets) E-OBS Pan-EU European Climate http://eca. knmi. nl/ Freely temperature Assessment & Dataset available and (ECA & D) precipitation ORNL Several useful Oak Ridge National http://daac. ornl. gov/index. shtml Mostly freely DAAC regional to Laboratory Distributed available global data sets Active Archive Center relevant to (ORNL DAAC) water and energy budgets None Global-scale Van der Ent and Savenije.
8 (Roche-454 Life Sciences). Contigs obtained were combined together by Opera software v1. 2 (12) combined to GapFiller v1. 10 (13) to reduce the set. Some manual refinements using CLC Genomics v7 software (CLC bio, Aarhus, Denmark) improved the genome. Finally, the draft genome of M. farcinogenes was found to consist of 5 scaffolds of 63 contigs containing 6,062,162 bp and an estimated size including gaps of 6,139,893 bp. The GϩC content of this genome is 65. 73%. Noncoding genes and miscellaneous features were predicted using RNAmmer  (14) , ARAGORN  (15) , Rfam  (16) , PFAM  (17) , and Infernal  (18). Coding DNA sequences (CDSs) were predicted using Prodigal  (19)  and functional annotation was achieved using BLASTϩ  (20)  and HMMER3  (21)  against the UniProtKB database  (22). The genome was shown to encode at least 76 predicted RNAs including 3 rRNAs in a single operon, 57 tRNAs, 1 transfermessenger RNA, and 15 miscellaneous RNAs.
A total of 5,816 genes yielded a coding capacity of 5,610,858 bp (coding percentage: 91. 3%) and included 749 (12. 87%) genes encoding putative proteins, 1,023 (17. 59%) genes assigned as hypothetical proteins, and 5,766 genes matching a least one sequence in the Clusters of Orthologous Groups (COG) database  (23, 24)  with BLASTP default parameters. Nucleotide sequence accession numbers. The M. farcinogenes NCTC 10955 (ϭ DSM 43637 T ) strain genome sequence has been deposited at DDBJ/EMBL/GenBank under the accession no. HG964481 to HG964485. The whole-genome shotgun master numbers are CCAY010000001 to CCAY010000063.
A prototype of CUORE, Cuoricino, took data at Gran Sasso National Laboratories in the years 2003-2008. Besides demonstrating the feasibility of a large mass bolometric detector, Cuoricino set the most stringent half-life limit for the neutrinoless double-beta decay of 130 Te: > 2. 8 × 10 24 year at 90 % C. L. , with a corresponding upper bound on the neutrino Majorana mass in the range (0. 30-0. 71) eV  [5]. Cuoricino was also a precious tool for the understanding of the key problematics to be solved for the construction of CUORE. An intermediate step has preceded the start of CUORE: its first tower, named CUORE-0, is being operated in the former Cuoricino cryostat and is now acquiring data. CUORE-0 served as a test of the new CUORE assembly line, as well as a high-statistics check of the improvements implemented to reduce the background sources and to improve the bolometric response of the detectors.
CUORE-0 The final step before the start of the CUORE experiment is CUORE-0, a single CUORE-like tower that is now acquiring data in the former Cuoricino cryostat. CUORE-0 consists of 52 CUORE crystals with a total TeO 2 mass of 39 kg. It has been assembled accordingly to the same stringent protocols defined for CUORE. CUORE-0 represents an opportunity to evaluate the bolometric performances of a CUORE-like detector apparatus in a familiar cryostat, and it will be the first large-scale empirical test of the extensive background-reduction measures undertaken.
Acknowledgments The  CUORE Collaboration thanks the Directors and Staff of the Laboratori Nazionali del Gran Sasso  and the technical staff of our Laboratories. This work was supported by the  Istituto Nazionale di Fisica Nucleare (INFN) ; the Director,  Office of Science, of the U. S. Department of Energy  under Contract Nos. DE-AC02-05CH11231  and  DE-AC52-07NA27344 ; the  DOE Office of Nuclear Physics  under Contract Nos. DE-FG02-08ER41551  and  DEFG03-00ER41138 ; the  National Science Foundation  under Grant Nos. NSF-PHY-0605119 ,  NSF-PHY-0500337 ,  NSF-PHY-0855314 , and  NSF-PHY-0902171 ; the  Alfred P. Sloan Foundation ; and the  University of Wisconsin Foundation.
Systems biology approaches aim to provide this type of information. For instance, metabolic networks can be converted to constraintbased models (CBMs), which allow metabolic fluxes to be predicted on a genome-wide scale (6)  (7). As described by  Feist and Palsson (8) , the construction of such genome-scale models can be divided into four key steps: 1-generation of high-throughput datasets (omics data), 2-genome-wide network reconstruction, 3-conversion of network reconstruction into a CBM, and 4-application of computational models for metabolic engineering. For quantitative data on protein levels, enzyme-linked immuno-sorbent assay (ELISA)  (9)   (10)  has long been considered the gold standard, because of its high sensitivity and throughput; although limitations include labor intensiveness and the high cost of assay development  (11).
Bovine serum albumin (BSA) standard reference material 927d was obtained from the National Institute of Standards and Technology (NIST, Gaithersburg, MD). Urea, ammonium bicarbonate, DTT, and iodoacetamide were purchased from Sigma. Sep-Pack tC18 1cc vacuum cartridges were supplied by Waters (Manchester, England). Construction of Expression Strains-Genes coding for the proteins of interest were PCR-amplified from MG1655 gDNA using forward and reverse primers designed with both Vector NTI® and Geneious® software. List of primers is provided in supplemental Data File S1. PCR products were purified using nucleospin® extract II kit and digested with the restriction enzymes indicated in supplemental Data File S1. Fragments were then ligated into pPAL7, a fusion vector FIG. 1. Overview of the central carbon metabolism network in Escherichia coli.
6 buffer for sonication. Lysates were centrifuged 10 min at 12,000 g and supernatant was filtered through 0. 22 m membranes. Protein Purification Conditions and Verification of 15 N isotope Incorporation-Proteins were purified using 5 ml Profinity eXact car-tridges (Bio-Rad®) according to the supplier protocol. The duration of incubation in sodium fluoride (100 mM, pH 7. 6) was adapted for each protein to optimize tag cleavage. For the proteins expressed here, between 2 h at room temperature and 12 h at 4 °C were necessary. Purified proteins were either stored lyophilized or in 25 mM NH 4 HC0 3 10% glycerol (v/v) at Ϫ80 °C. 15 N incorporation into recombinant proteins was determined on peptides after digestion of 15 N labeled proteins, as described below. LC-MS/MS analysis was performed in enhanced resolution data acquisition mode on a 4000 QTRAP instrument (ABSciex, Foster City, CA), as described below.
Simulated isotopic distributions with varying 15 N incorporation were generated for selected 15 N tryptic peptides using Isopro® software (https://sites. google. com/site/isoproms/ home). The experimental isotope incorporation rate was determined by comparing simulated and experimental isotopic distributions using a least-squares regression method (supplemental Data File S2). Amino Acid Analysis (AAA) for Standard Titration-Standards were calibrated using the AAA-MS (Amino Acid Analysis coupled to Mass Spectrometry) approach described by Louwagie et al. (26). Briefly, the protein standard (1 g, previously titrated by UV absorbance at 280 nm) was mixed with 1 g of NIST bovine serum albumin standard. Then microwave-assisted acidic hydrolysis was performed using the Discover protein hydrolysis apparatus (CEM, Matthews, NC).
Data were acquired in positive mode with the ion spray voltage at 5500 V, curtain gas 15 (arbitrary units), interface heater temperature 350 °C. Collision exit, entrance and declustering potential were set to 27, 12, and 50 volts, respectively. For SRM assays, collision energy was calculated using a linear equation based on the manufacturer's recommendations: CE (volts) ϭ 0. 44 ϫ m/z ϩ 4 for doubly charged precursors CE (volts) ϭ 0. 5 ϫ m/z ϩ 5 for triply charged precursors During SRM assay for method development, the mass spectrometer was operated in SRM mode, with a dwell time of 20 ms and a maximum of 100 transitions. After method validation, the instrument was operated using scheduled SRM mode (retention time windows: 60 s; target scan time: 2. 5 s). A 30 s base width was estimated, and twelve points were acquired per chromatographic peak.
Design of SRM Assays and MS/MS Identifications-A set of twenty-two proteins was selected from among proteins involved in the central metabolic pathways in E. coli (Fig. 1 ). Each purified protein standard, in its labeled and unlabeled forms, was reduced, alkylated and digested, according to the protocol described above. LC-MS/MS analysis was performed on a 4000 QTRAP mass spectrometer. Peak lists (mgf files) were generated using Analyst version 1. 5. 1 software (AB SCIEX). MS/MS spectra were assigned to peptides using a sequence database search strategy. Mascot (version 2. 4). was used as the search engine. We generated a home-made Ecoli_K12 protein sequence data bank (12548 sequences) from UniprotKB (http:// www. uniprot. org, release July 28, 2011) to retrieve proteins with 83333 Escherichia coli taxonomy (strain K12) (http://ebi3.
We therefore chose five amino acids to determine the concentration of the standards produced, these were: alanine, isoleucine/leucine, phenylalanine, proline, and valine. Results are expressed as the mean concentration for these five amino acids. Each standard was assayed four times in technical replicates. The coefficients of variation (CVs) calculated for these assays were between 4 and 12% (supplemental Data File S5). For each standard, between 2 and 5 milligrams of protein were obtained per 100 ml culture. We therefore set up a pipeline to produce large amounts of labeled protein standards while limiting substrate costs. Standards were stored at Ϫ80 °C prior to further analyses.
Designing a Scheduled SRM Assay for Key Enzymes in E. coli Central Carbon Metabolism-To set up the SRM assay for key enzymes involved in E. coli CCM, a list of peptides and fragment ions to be monitored must first be carefully defined to ensure the specificity of analyses. Skyline® software  (27)  was recently developed to automate this step. This software requires two inputs: protein sequences and spectral libraries for the target proteins. LC-MS/MS analyses were performed after LysC and trypsin digestion of light and heavy proteins, and MS/MS data were used to create the Skyline® library. SRM methods were automatically generated, with three proteotypic peptides per protein, except Eda, which is a small protein producing only one detectable peptide. Three transitions were monitored per peptide.
All the peptides selected were proteotypic peptides, i. e. of unique sequence in the E. coli database and with good ionization properties  (32). The final list for the SRM assay consisted of 720 SRM transitions (supplemental Data File S6). To monitor these 720 SRM transitions, scheduled SRM analyses  (22)  were performed. Parameters were carefully tuned to optimize the limit of detection for all transitions. It was necessary to find a trade-off between the number of transitions to be monitored, peak resolution and sensitivity of the analysis. We chose to analyze samples on a micro-LC system with a flow rate at 50 l/min. This configuration was chosen because it results in highly reproducible retention times (ϳ10 s shift) allowing a retention time window of 60 s to be used with confidence. Target scan time was set to 2.
(http://www. fda. gov/downloads/Drugs/GuidanceCompliance RegulatoryInformation/Guidances/UCM070107. pdf). A preparation workflow was applied, including LysC digestion in denaturing conditions (2 M urea), followed by trypsin digestion. No prefractionation steps were applied. SRM analyses were performed to determine the heavy/light ratio and thus estimate the amount of light protein present in samples (supplemental Data File S7). Estimated light protein amounts were plotted against the quantities added. All titration points were performed as full-process triplicates. This experiment thus allows us to determine three key parameters of the assay: 1. accuracy of the method, which is indicated by the slope of the curve; 2. precision of the method, which is indicated by the error bars corresponding to the CVs for technical replicates; 3.
Absolute quantification was obtained for the 22 enzymes. The least abundant protein, PpsA, was quantified at 491 copies per cell, although the most abundant, GapA, was measured at 21,119 copies per cell. The biological CVs for these concentrations were 1. 8% and 4. 7%, respectively (Table IV), whereas technical CVs never exceeded 7%. For absolute quantification of the wild-type E. coli (MG1655) strain, we scanned 720 transitions during a single 60 min LC-SRM analysis (30 min effective gradient) (supplemental Data File S8). The results obtained for the wild-type strain confirmed that this workflow is robust, efficient and has a high multiplexing potential, while generating accurate, high-throughput data. In light of these results, the pipeline was used to measure protein concentrations in two strains of biotechnological interest, both genetically engineered to produce high levels of NADPH (NA23 and NA176).
5). We measured the relative metabolic fluxes and enzyme concentrations, and calculated effective catalytic rates for 19 out of the 21 enzymes in all three strains growing on minimal medium with glucose (PpsA and Glk were not considered, as there was no flux data available for the corresponding reactions). The results show that, for some reactions, the meta- bolic flux differences are mainly because of variations in enzyme concentrations, whereas for others they are the result of changes in the catalytic activity of the enzymes (Table  V  and supplemental Data File S9).
coli  (54). These data have been used to estimate the parameter values in a simplified model of the metabolic network, revealing among other things a number of critical issues for model identification in genome-scale kinetic models, such as theoretical and practical limits on the identifiability of parameter values  (55). A limitation of the present work is related to the fact that isoenzymes were not individually considered when analyzing effective catalytic rates. As a consequence, the effective rates of some enzymes might possibly be incorrectly calculated as fluxes might result from the action of several enzymes. However, in prokaryote cells, like E. coli, isoenzymes are rare because a given activity is more commonly controlled by a single protein. Among the 22 chosen enzymes, only the transketolase activity involved two isoenzymes, TktA and TktB, of which only TktB was measured.
Acknowledgements-We thank  Dr Christine Carapito  and  Dr Rainer Figge  for scientific discussions. Thanks to EDyP team, to  Sylvie Kieffer-Jaquinod  for assistance with AAA-MS analyses, to  Mathilde Louwagie  for assistance with AAA sample preparation, to the METabolic EXplorer team for training and technical assistance when producing the protein standards. We are also grateful to  Dr Maighread Gallagher-Gambarelli  for editorial assistance. * This project was supported by the  Ͻ ϽAssociation Nationale de la Recherche et de la TechnologieϾ Ͼ, METabolic EXplorer (Saint-Beauzire, France) , the  Investissement d'Avenir Bioinformatics program  ( RESET  project,  ANR-11-BINF-0005 ) and the  Investissement d'Avenir Infrastructures Nationales en Biologie et Santeprogram  ( ProFI  project,  ANR-10-INBS-08 ).
(Left) The arrows indicate time of addition for oligomycin, FCCP, and rotenone/antimycin A. (Right) Uncoupled, maximal, and basal respiration in scramble and KLF11 knockdown cells. Error bars represent SEM (n = 3). (*) P < 0. 05 as indicated. (F) Rosiglitazonetreated hMADS adipocytes (day 15) were subjected to KLF11 adenovirus (AdKLF11) or empty adenoviral control (AdEV), and expression was evaluated 24 h after transduction. (Top) Overexpression of KLF11 was evaluated by Western blot. (Bottom) Log 2 fold change in gene expression in AdKLF11-treated cells compared with AdEV-treated cells for a subset of brite-selective genes as well as a control group of genes (as defined in A,B). The red lines represent the medians of the data sets. Significance was determined by Student's t-test. occupies sites in the vicinity of brite-selective genes regulated by KLF11.
Human subjects Human subcutaneous abdominal fat biopsies from a double-blind, placebo-controlled, crossover study involving T2D subjects treated with placebo/rosiglitazone for 90 d were used to analyze KLF11 expression. A total of 18 subjects from whom a complete physiological data set is available was included. Details on subjects and procedures were as described previously  (Tan et al. 2005). Significance was determined using a Wilcoxon matched pairs signed-ranks test. Additional information is available in the Supplemental Material.
Error bars represent SEM (n = 3). (*) P < 0. 05 as indicated. (F) Rosiglitazonetreated hMADS adipocytes (day 15) were subjected to KLF11 adenovirus (AdKLF11) or empty adenoviral control (AdEV), and expression was evaluated 24 h after transduction. (Top) Overexpression of KLF11 was evaluated by Western blot. (Bottom) Log 2 fold change in gene expression in AdKLF11-treated cells compared with AdEV-treated cells for a subset of brite-selective genes as well as a control group of genes (as defined in A,B). The red lines represent the medians of the data sets. Significance was determined by Student's t-test.
Acknowledgments We thank  Rasmus Siersbaek  and  Niels Mahler Kristiansen  as well as other members of the  Mandrup laboratory  for experimental support and fruitful discussions. Furthermore, we are grateful to  Christian Dani  ( Universit e de Nice Sophia-Antipolis ) for providing hMADS cells, and  Per Sauerberg  ( Novo Nordisk A/S ) for the kind gift of rosiglitazone. Work in the Mandrup laboratory was supported by grants from the  VILLUM Foundation  to the  VILLUM Center for Bioanalytical Sciences at University of Southern Denmark , the  Lundbeck Foundation , the  Novo Nordisk Foundation , and the  Danish Independent Research Council/ Natural Sciences. Work in the Amri laboratory  was supported by grants from the  EU   FP7  project  DIABAT  ( HEALTH-F2-2011-278373 ), and the  Centre National de la Recherche Scientifique (CNRS).
© 2014 Rath et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons. org/licenses/by/4. 0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons. org/publicdomain/zero/1. 0/) applies to the data made available in this article, unless otherwise stated.
6 ). The genome of Malassezia species has been sequenced (49) and is available on the NCBI website (ncbi. nlm. nih. gov). Genes involved in cell wall biosynthesis have been identified by blast analysis (supplemental Table  2 ). The high amount of chitin and chitosan are in agreement with the presence of six chitin synthase and four chitin deacetylase genes in the M. restricta genome. Similarly, the absence of detection of ␣-(1,3)-glucan in the cell wall of M. restricta is related to the absence of ␣-glucan synthase gene. One FKS gene encoding the putative catalytic subunit (FKS) of the ␤-(1,3)-glucan synthase and only two KRE genes among the five involved in the ␤-(1,6)-glucan synthesis are present in the genomes of Malassezia species. The presence of Crh proteins as a putative transglycosidases involved in chitin-␤-glucan cross-linking is in agreement with the structural data from the M.
The 16S rDNA sequence (GenBank accession number JX424767) of E. timonensis strain GD5 T exhibited the highest similarity (95. 0%) with its phylogenetically closest published species, Enorma massiliensis (Figure  1 ). By comparison with the type species of genera from the family Coriobacteriaceae, E. timonensis exhibited a 16S rDNA sequence similarity ranging from 84 to 95%. This value was lower than the 98. 7% 16S rDNA gene sequence threshold recommended by Stackebrandt and Ebers to delineate a new species without carrying out DNA-DNA hybridization  [8]. Growth at different temperatures (25, 30, 37, 45°C) was tested. No growth was observed at 25°C or 30°C. Growth occurred at both 37 and 45°C, but optimal growth was observed at 37°C after 48 hours of incubation. Colonies were translucent grey and approximately 0.