dataesr/scientific-paragraphs-categorization huggingface dataset:
Reactions were included in the GEM depending on evidence from previously published models and databases (Supplementary Table  S2 ) or on the availability of specific experimental evidence, for example, enzyme assay or protein identification, for the occurrence of the corresponding reaction in adipocytes. Reaction directionality in the model was treated as in the original resource and the directionality was only changed if it was necessary to perform successful simulations of known biological functions of adipocytes. iAdipocytes1809 contains 6160 reactions and 4550 metabolites in 8 different compartments (2497 unique metabolites) (Supplementary Table  S3 ) and it is available in the Systems Biology Mark-up Language (SBML) format at http://www. metabolicatlas. org. There are 1809 genes in the model and 80% of the reactions are associated with one or more genes.
In iAdipocytes1809, individual metabolites rather than generic pool metabolites for 59 FAs (Supplementary Table  S4 ) have been used and this allowed us to incorporate measured concentrations of different FAs in human plasma and adipocytes into the model. Adipocyte-specific metabolome data from the Human Metabolome Database (HMDB)  (Wishart et al, 2009)  and a comprehensive database for lipid biology, Lipidomics Gateway  (Harkewicz and Dennis, 2010) , were used in the reconstruction process. To ensure standardization, each metabolite in the GEM was assigned at least one of the following: HMDB ids, Lipidomics Gateway ids, KEGG ids  (Kanehisa et al, 2010) , Chemical Entities of Biological Interest (ChEBI)  (Degtyarenko et al, 2008) , or International Chemical Identifiers (InChI). In the model, new genes were assigned to the reactions in iAdipocytes1809 using EC numbers from UniProt  (Apweiler et al, 2011)  and the Lipid Map proteome database  (Cotter et al, 2006).
The reconstruction of iAdipocytes1809 involved a comprehensive review of lipid metabolism and gene products related to the lipid metabolism in publicly available databases were closely examined for their existence in adipocytes. In all, 1235 protein encoding genes with proteome evidence in adipocytes and 244 genes in iAB586 were included in iAdipocytes1809 (Figure  3C ). Another 137 genes were included in the model due to positive evidence for the presence of these proteins from the transcriptome data. These 137 genes were significantly higher expressed both in male and in female obese subjects compared with lean subjects, and are hence relevant for the analysis of gene expression data from the SOS Sib Pair Study. Another 193 genes and their associated reactions were included in the model due to connectivity constraints and known function of adipocytes and these genes are mainly associated with transport across membranes.
Model validation and formation of lipid droplets Due to the large size of GEMs (typically thousands of metabolic reactions), proper validation is essential to ensure HMR provides gene-transcript protein reaction (GTPR) links and GTPR represents functional relationships between genes/proteins and the corresponding reactions that they catalyze or control. Genes are first mapped to their transcripts, accounting for alternative splicing, the transcripts are mapped to the proteins and the proteins are then mapped to reactions based on the current knowledge of their effects on the reactions. In HMR, 75% of the reactions have GTPR and this allow us to incorporate proteome and transcriptome evidence in the adipocyte-specific network. The resulting gaps in the adipocyte-specific network were identified using the RAVEN Toolbox and filled manually using publicly available resources like KEGG, HumanCyc and Human Metabolic Atlas.
This was tested by making sure that the model could not generate high-energy compounds from low-energy compounds (such as ATP and water from ADP and phosphate or any organic compound from CO 2 and water). A third issue is that reactions may be dead-ends, meaning that they cannot carry flux, either because the model is not able to produce/take up some of the substrates or because it is not able to consume/excrete some of the products. Dead-end reactions tend to propagate as one unconnected reaction can give rise to many more. Therefore, many GEMs contain a large proportion of dead-end reactions. Considerable efforts were spent in making sure that the number of dead-end reactions was kept to a minimum. Furthermore, with the simulation ready iAdipocytes1809, the production of all metabolites in the model was checked with minimum input to the model (Supplementary Dataset 4) to ensure the connectivity.
Lastly, even a well-connected, thermodynamically correct and balanced model may not be able to perform all relevant metabolic functions, or it may be able to perform functions that it should not do (such as synthesis of essential amino acids or FAs). The model was therefore validated for 250 known metabolic functions of adipocytes, adapted from the definitions provided in connection with setting up HepatoNet1 (Gille et al, 2010; Supplementary Dataset 5). All validation steps and simulations were performed using the RAVEN Toolbox  (Agren et al, 2013). Formation of LDs (Figure  4 ) is included in iAdipocytes1809. LDs protect the cell from the lipotoxic effects of unesterified lipids, but they are also associated with the development of metabolic diseases, such as obesity, type 2 diabetes, atherosclerosis and liver steatosis that are characterized by the extensive accumulation of LDs.
TCA cycle LpL LpL Malonyl-CoA large composite metabolite in our model. LDs have polar surfaces that contain a variety of proteins, PLs and sterols and have a non-polar core that contain CEs, DAGs and TAGs  (Gross et al, 2011; Supplementary   were incorporated into the model to predict the formation of LDs. Based on measurements of the uptake of glucose and TAG and the release of NEFAs over a 24-h period (Figure  5A  and 5B), we simulated the change in LD size (Figure  5C ; Supplementary Dataset 9a). We found from our simulations that lean subjects have large dynamic changes in LD formation compared with obese subjects, which is in agreement with experimental data  (Arner et al, 2011). Furthermore, we predicted a lower acetyl-CoA production in obese subjects, as shown in Figure  5D  (Supplementary Dataset 9b).
Discussion We evaluated the presence/absence of 14 077 proteins in adipocytes using human antibodies and presented a highquality, simulation ready and functional GEM for adipocytes, iAdipocytes1809, based on proteome, metabolome and transcriptome data. iAdipocytes1809 was used to analyze clinically observed transcriptome and fluxome data to understand the mechanistic changes in adipocyte metabolism in response to obesity. Gene expression and associated metabolites in obese and lean subjects were studied using a systems biology approach, which allowed us to understand how the transcriptome data in SAT represent different metabolic functions in obesity. An increased understanding of metabolic pathways altered in complex diseases may contribute to the identification of novel therapeutic targets  (Cheong et al, 2012)  and here potential therapeutic targets for obesity were discovered through Reporter Metabolite analysis and random sampling of flux states.
Information from databases, for example, Uniprot, ENSEMBL, and published data from PubMed were used as guides to establish if immunohistochemical results represent expected protein expression of intended target protein. In addition, other validation strategies were used to ensure antibody validity, for example, protein arrays, western blot and immunofluorescence experiments. When applicable, paired antibodies raised against separate, non-overlapping epitopes on the same target protein were used for extended validation  (Uhlen et al, 2010). IHC-stained tissues were scanned and digitalized at Â 20 magnification. Annotation of high-resolution images was manually performed by certified pathologists. Relative expression was indicated with four different color codes ranging from strong (red), moderate (orange), weak (yellow) and no expression (white)  (Kampf et al, 2004).
Transcriptome data for SAT Gene expression in SAT of subjects involved in SOS Sib Pair Study which includes nuclear families with BMI-discordant sibling pairs (BMI difference X10 kg/m 2 ) was measured by microarray. The study group consisted of 304 subjects (209 female and 95 male) divided into three different groups according to their BMI: lean (18. 5oBMI o25), overweight (25p BMIo30) and obese (30pBMI) (Supplementary Table  S1 ). Total RNA was prepared as previously described  (Carlsson et al, 2009)  and gene expression in human SAT was measured using Affymetrix U133 Plus 2. 0 microarrays (Affymetrix). Probe hybridization intensity values were summarized using to Probe Logarithmic Intensity Error (PLIER) and the robust quantile method was applied to get normalized expression values. The normalization of the microarrays was carried out using the Expression Console software from Affymetrix and the quality assessment was carried out using R Statistical and Computing language and the Bioconductor software  (Gentleman et al, 2004).
Figure3Illustration of the reconstruction process for iAdipocytes1809. (A) Human Metabolic Reaction (HMR) database was updated through the use of literature and previously published GEMs like HepatoNet1, Recon1 and EHMN and the Reactome database, with focus on the extensive lipid metabolism in adipocytes. HMR provides gene-transcript protein reaction (GTPR) links and GTPR represents functional relationships between genes/proteins and the corresponding reactions that they catalyze or control. Genes are first mapped to their transcripts, accounting for alternative splicing, the transcripts are mapped to the proteins and the proteins are then mapped to reactions based on the current knowledge of their effects on the reactions. In HMR, 75% of the reactions have GTPR and this allow us to incorporate proteome and transcriptome evidence in the adipocyte-specific network.
The resulting gaps in the adipocyte-specific network were identified using the RAVEN Toolbox and filled manually using publicly available resources like KEGG, HumanCyc and Human Metabolic Atlas. Based on this, the comprehensive functional genome-scale metabolic model (GEM) for adipocytes, iAdipocytes1809, was reconstructed. (B) The Venn diagram illustrating the comparison of genes associated with reactions in the HMR and previously published literature-based models such as Recon 1, EHMN and HepatoNet 1. HMR includes all of the genes and associated reactions in previous published models. (C) Protein encoding genes and associated reactions with those genes included in iAdipocytes1809 based on proteome (1235 genes) and transcriptome (137 genes) data and evidence from previously published small-scale adipocytes model, iAB586 (244 genes).
Acknowledgements We acknowledge  Prof Fredrik Karpe  and  Dr Barbara A. Fielding ,  Oxford Centre for Diabetes, Endocrinology and Metabolism  for providing adipose tissue uptake rates for lean and obese subjects. This work was supported by grants from the  Knut and Alice Wallenberg Foundation , the  Chalmers Foundation , the  Wellcome Trust  ( GR079534 ) and the  Swedish Research Council  ( K2010-55X-11285-13 ).
Data availability The annotation of the presence or absence of protein targets in adipocytes together with the high-resolution images is publically available through the HPA (http://www. proteinatlas. org). GEM for adipocytes, iAdipocytes1809 and HMR database is publically available in the Systems Biology Mark-up Language (SBML) format at Human Metabolic Atlas (http://www. metabolicatlas. org). Gene expression from SAT of subjects from the SOS Sib Pair Study is publically available in Gene Expression Omnibus (GEO) database with the accession number GSE27916 http://www. ncbi. nlm. nih. gov/geo/ query/acc. cgi?token=phadhqigowykote&acc=GSE27916.
Definitions 1. 2. 1 Jet algorithms In this paper, three jet algorithms are studied: the anti-k t algorithm  [37] , the Cambridge-Aachen (C/A) algorithm  [38, 39] , and the k t algorithm  [40] [41] [42]. These are implemented within the framework of the FastJet software  [43, 44]. They represent the most widely used infrared and collinear-safe jet algorithms available for hadron-hadron collider physics today. Furthermore, in the case of the k t and C/A algorithms, the clustering history of the algorithm -that is, the ordering and structure of the pair-wise subjet recombinations made during jet reconstruction -provides spatial and kinematic information about the substructure of that jet. The anti-k t algorithm provides jets that are defined primarily by the highest-p T constituent, yielding stable, circular jets.
It is therefore possible to validate the JES and JMS, and also to estimate directly the pile-up energy contribution to jets. This approach was used extensively in the measurement of the jet mass and substructure properties of jets in the 2010 data  [20]  where pile-up was significantly less important and the statistical reach of the measurement was smaller than with the full integrated luminosity of 4. 7 fb -1 for the 2011 dataset. The relative uncertainty is determined using the ratio of the transverse momentum of the calorimeter jet, p jet T , to that of the track-jet, p track jet T. The same procedure is repeated for the jet mass, m jet , by using the track-jet mass, m track jet. The ratios are defined as r p T track jet = p jet T p track jet T , r m track jet = m jet m track jet , (3. 1) where the matching between calorimeter and track-jets is performed using a matching criterion of ∆R < 0.
5 ± 1. 2)% for anti-k t jets after trimming. The uncertainties are statistical, as extracted from the fitting procedure. It is also possible to repeat the fitting procedure after dividing the data and Monte Carlo simulation samples into bins of |η|. This allows a test of any potential |η| dependence in a range outside that covered by track-jet techniques. The result of these fits can be seen in figure  13. The results extracted in individual bins show no statistically significant deviation from the average. Furthermore, systematic uncertainties are evaluated for this technique. The Monte Carlo modelling is assessed by replacing the t t and single-top samples with alternative samples as described in section 2. 2. The impact of mismodelling of the jet resolution is assessed by applying additional smearing.
This is done because the uncertainty of the track-jet p T tends to be small compared to the calorimeter-jet p T in the kinematic regime considered here. Tracks are matched to calorimeter-jets using ghost-track association. The jets are required to be within |η| < 2. 1 to ensure coverage of the associated tracks by the tracking detector. [GeV] The largest deviation from unity is seen at low p T and is 4% with a statistical uncertainty of 1%. The statistical uncertainty-weighted average double ratio is indicated by the horizontal line. Similar results are obtained when varying the jet radius parameter between 0. 2 and 0. 5 and for higher pile-up conditions (evaluated using a subset of the data characterized by 13 < µ < 15). A jet energy uncertainty of 3. 5% is assigned. The imperfect knowledge of the material distribution in the tracking detector constitutes the dominating systematic uncertainty.
67 found previously seems to have the best stability. Studies from 2010  [20]  demonstrate that this reduction in the sensitivity to pile-up is due primarily to the filtering step in the algorithm as opposed to the jet selection itself. 5 igure  19  presents the pile-up dependence of the mean leading-p jet T jet mass, m jet 1 , in data compared to the three simulations. Here, only the range 600 GeV ≤ p jet T < 800 GeV for ungroomed and trimmed anti-k t jets is shown for brevity, but similar conclusions apply in all p jet T ranges. The comparison is made using the full 2011 dataset. PYTHIA, HERWIG++, and POWHEG+PYTHIA all model the data fairly accurately, with a slight 5%-10% discrepancy appearing in the predictions from PYTHIA and HERWIG++ for the trimmed jets. Most importantly, the impact of pile-up is very well modelled, with the slope of the dependence of m jet 1 on N PV in data agreeing within 3% with the POWHEG+PYTHIA prediction for both the ungroomed and trimmed jets.
Conclusions It has been demonstrated experimentally in this paper that jet grooming algorithms can improve the identification of Lorentz-boosted physics objects that decay to jets, as well as increase sensitivity to several new physics processes. The performance of large-R jets is improved overall, and the dependence on pile-up and the underlying event is reduced. Jet mass calibrations have been derived in simulation for various large-R jet algorithms, subjets, as well as for jets with grooming applied. These have been validated for boosted W bosons in t t events and using calorimeter-jet versus track-jet double ratios. Uncertainties on the jet energy scale and the jet mass scale have been provided over a wide range of large-R jet momentum. The mass distributions observed in data for large-R jets in the inclusive jet sample, before and after grooming, are well reproduced by the ATLAS simulation, especially using the POWHEG NLO generator.
The substructure variables presented here also show good agreement between data and simulation, typically within 5% for key observables for modern NLO plus parton shower Monte Carlo programs such as POWHEG+PYTHIA, as well as for the LO MC program HERWIG++. The parameters of trimmed, pruned and mass-drop filtered jet algorithms have been optimized for searches and precision Standard Model measurements using various performance measures. Among the configurations tested here, the trimming algorithm exhibits better performance than the pruning algorithm, with superior mass resolution and reduced dependence on pile-up. In particular, the anti-k t algorithm with R = 1. 0 and trimming parameters f cut = 0. 05 and R sub = 0. 3 is recommended for boosted top physics analyses, where a minimum p T requirement of 200 GeV is typical.
The HEPTopTagger has been demonstrated to be a robust and versatile tool to reconstruct hadronically-decaying top quarks in the presence of the underlying event and pile-up, using jet grooming and substructure techniques. A comparison to data shows that the algorithm is well modelled by the simulations. The HEPTopTagger performance (efficiency, rejection, mass resolution) can be optimized for a given analysis by varying the algorithm parameters. From the studies presented here, groomed jets and substructure variables are ready to be used in further ATLAS physics analyses. These techniques will become extremely beneficial tools in upcoming searches for boosted physics objects in supersymmetric and exotic models, measurements of boosted Higgs topologies, and in detailed precision Standard Model measurements of QCD and electroweak processes with jets and boosted hadronic objects.
Acknowledgments We thank  CERN  for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowledge the support of  ANPCyT , Argentina; YerPhI, Armenia; ARC, Australia; BMWF and FWF, Austria; ANAS, Azerbaijan; SSTC, Belarus;  CNPq  and  FAPESP, Brazil ;  NSERC ,  NRC  and  CFI ,  Canada ; CERN; CONICYT, Chile; CAS,  MOST  and  NSFC , China; COLCIENCIAS, Colombia; MSMT CR, MPO CR and VSC CR, Czech Republic; DNRF,  DNSRC  and  Lundbeck Foundation , Denmark; EPLANET,  ERC  and  NSRF ,  European Union ;  IN2P3-CNRS ,  CEA-DSM/IRFU , France; GNSF, Georgia; BMBF,  DFG ,  HGF ,  MPG  and  AvH Foundation, Germany ;  GSRT  and  NSRF, Greece ;  ISF, MINERVA, GIF, DIP and Benoziyo Center, Israel ;  INFN, Italy ;  MEXT  and  JSPS, Japan ; CNRST, Morocco; FOM and  NWO, Netherlands ;  BRF  and  RCN, Norway ;  MNiSW , Poland; GRICES and FCT, Portugal; MERYS (MECTS), Romania; MES of Russia and ROSATOM, Russian Federation; JINR; MSTD, Serbia; MSSR, Slovakia; ARRS and MIZ Š, Slovenia; DST/NRF, South Africa; MICINN, Spain;  SRC and Wallenberg Foundation, Sweden ;  SER ,  SNSF  and  Cantons of Bern and Geneva, Switzerland ;  NSC, Taiwan ;  TAEK, Turkey ;  STFC , the  Royal Society and Leverhulme Trust ,  United Kingdom ;  DOE  and  NSF ,  United States of America.
Acknowledgements We thank the  Detection and Attribution Liaisons  to IPCC AR5 WGII Chapter 18,  Chris Field , and  Mike Mastrandrea  for many helpful discussions. We are also grateful to  Yuka Estrada  and  Monalisa Chatterjee  for assistance in graphic design. While the chapter authors are all members of the AR5 Chapter 18 author team, all opinions, findings, and errors are those of the authors alone. Many of the discussions distilled here were conducted during and around Lead Author Meetings of the IPCC assessment process, supported by various national governments. DS was supported by the  Regional and Global Climate Modeling Program  and the  Earth System Modeling Program of the Office of Biological and Environmental Research in the Department of Energy Office of Science  under contract number  DE-AC02-05CH11231.
Construction of promoter-exchange donor vectors A pUC19-based transfer vector was constructed to insert the selectable pif1 and pif 2 genomic region (nt 31,228 to 36,075) in the Sf NIC-B genome  [11]  (accession number HM595733), that would subsequently be used to insert alternative p10 or egt promoters from Spodoptera exigua NPV (SeMNPV), into the SfMNPV pif 2-pif1 intergenic region by homologous recombination (Fig. 1A ). The primers used for the constructions are listed in Table  S1. First, a plasmid was constructed that contains the left and right flanking regions of pif 2 and pif1 from the SfMNPV genome. The left genomic-flanking region  (1,006 bp; 31,228-32,233)  of the donor cassette, amplified from Sf NIC-B DNA using the Sfarif1. 1/ Sfpif2. 4 primer sets, contained the full arif1 ORF and partial downstream sf 32 ORF of unknown function.
Data acquisition and analysis were handled by Sequence Detector Version 2. 2. 2. software (Applied Biosystems). Sf NIC-B DNA was amplified in the pif1 region by conventional PCR using the qSfBpif1. F and qSf Bpif1. R primer set. The resulting product was electrophoresed in 1% agarose, excised and purified using a DNA purification kit (Macherey-Nagel, Duren, Germany). Purified DNA was cloned into the pGEM-T Easy Vector (Promega, Madison, WI, USA), and its identity was checked by PCR and restriction endonuclease analysis with PstI. Volumes of 1 ml of plasmid DNA dilutions (10 21 to 10 28 ng/ml) were used as internal standards for each qPCR reaction. The number of target gene copies was calculated based on the DNA concentration and the molecular mass of the genome. Relative expression results at each time post-infection were subjected to analysis of variance (ANOVA) followed by Bonferroni means separation tests in SPSS ver.
Probit regression analysis of virus induced mortality in Spodoptera frugiperda second instars inoculated with (I. ) Wild-type SfMNPV (SfNIC-WT) and purified variant SfNIC-B occlusion bodies (OBs). (II. ) OBs comprising co-occluded mixtures of SfNIC-Begt and SfNIC-C defector genotype in the proportions indicated. (III. ) OBs comprising co-occluded mixtures of SfNIC-Bp10 and SfNIC-C defector genotype in the proportions indicated. Probit regressions were fitted using the PoloPlus program  [25]. A test for non-parallelism was not significant (x 2 = 6. 66, d. f. = 11, P = 0. 826). Lines were fitted with a common slope of 1. 02260. 093 (S. E). Relative potencies were calculated as the ratio of effective concentrations relative to the SfNIC-B virus that was assigned a nominal potency of 1. 0. doi:10.