dataesr/scientific-paragraphs-categorization huggingface dataset:
The basic data on gas flaring report atmospheric losses during petroleum production and processing, have large uncertainty and do not distinguish between gas that is flared as CO 2 or vented as CH 4. Fugitive emissions of CH 4 from the so-called upstream sector (coal mining and natural gas distribution, for examples) are not included in the accounts of CO 2 emissions except to the extent that they are captured in the UN energy data and counted as gas "flared or lost". The published CDIAC dataset has 250 countries and regions included. This expanded list includes countries that no longer exist, such as the USSR or East Pakistan. Estimates of CO 2 emissions show that the global total of emissions is not equal to the sum of emissions from all countries. This is largely attributable to emissions that occur in international territory, in particular the combustion of fuels used in international shipping and aviation (bunker fuels), where the emissions are included in the global totals but are not attributed to individual countries.
Due to availability of the input data, detailed estimates are made for the years  1997, 2001, 2004, and 2007  (using the methodology of  Peters et al. , 2011b)  using economic and trade data from the Global Trade and Analysis Project version 8. 1 (GTAP;  Narayanan et al. , 2013). The results cover 57 sectors and 134 countries and regions. The results are extended into an annual time-series from 1990 to the latest year of the fossil-fuel emissions or GDP data (2011 in this budget), using GDP data by expenditure in current USD (from the UN National Accounts main Aggregrates database; UN, 2013c) and time series of trade data from GTAP (based on the methodology in  Peters et al. , 2011b). The consumption-based emission inventories in this carbon budget incorporate several improvements over previous versions (Le  Quéré et al.
, 2013; Peters et al. , 2011b Peters et al. , , 2012b))  1990-2011 are based on an updated version of the GTAP database  (Narayanan et al. , 2013). We estimate the sector level CO 2 emissions using our own calculations based on the GTAP data and methodology, include flaring and cement emissions from CDIAC, and then scale the national totals to match the CDIAC estimates from the most recent carbon budget. We do not include international transportation in our estimates of na- We do not provide an uncertainty estimate for these emissions, but based on model comparisons and sensitivity analysis, they are unlikely to be larger than for the territorial emission estimates  (Peters et al. , 2012a). Uncertainty is expected to increase for more detailed results, and to decrease with aggregation  (Peters et al.
The revised estimate produces higher emissions during 1850-1950 compared to  Houghton et al. (2012)  due to the revision of historical deforestation in Australia and New Zealand based on  Bradshaw (2012). The primary land cover change and biomass data for the bookkeeping model analysis is the Forest Resource Assessment of the FAO published at intervals of five years (FAO, 2010), which is based on countries' self-reporting of statistics on forest cover change and management partially combined with satellite data in more recent assessments (Table  3)  (FAOSTAT, 2010). The LUC data set is non-spatial and aggregated by regions. The carbon stocks on land (biomass and soils), and their response functions subsequent to LUC, are based on averages per land cover type, per biome and per region. Similar results were obtained using forest biomass carbon density based on satellite data  (Baccini et al.
Fire-based method LUC associated CO 2 emissions calculated from satellite-based fire activity in deforested areas (van der Werf et al. , 2010) provide information that is complementary to the bookkeeping approach. They do not provide a direct estimate of E LUC as they do not include non-combustion processes such as respiration, wood harvest, wood products or forest regrowth, or legacy emissions such as decomposition from on-ground debris or soils are missed by this method. They do however provide insight on the year-to-year variations in E LUC that result from the interactions between climate and human activity (e. g. there is more burning and clearing of forests in dry years) that are not as well represented by other methods. The "deforestation fire emissions" assume an important role of fire in removing biomass in the deforestation process, and thus can be used to infer direct CO 2 emissions from deforestation using satellite-derived data on fire activity in regions with active deforestation.
Comparing coincident CO emissions and their atmospheric fate with satellite-derived CO concentrations allows for some validation of this approach (e. g. van der Werf et al. , 2008). Results from the fire-based method are available from 1997 to 2011 only. Le  Quéré et al. (2013)  used annual estimates from the Global Fire Emissions Database (GFED3; available from http://www. globalfiredata. org) to quantify the mean emissions over five years with the satellite-based method to distribute these emissions annually up to 2011. It thus assumed that all land management activities apart from deforestation do not vary significantly on a year-to-year basis.
DGVMs represent processes of vegetation establishment, growth, mortality and decomposition associated with natural cycles and include the vegetation and soil response to increasing atmospheric CO 2 levels, to climate variability and change, in addition to atmospheric N deposition in the presence of nitrogen limitation (in four models; Table  4 ). The DGVMs are independent from the other budget terms except for their use of atmospheric CO 2 concentration to calculate the fertilization effect of CO 2 on primary production. The DGVMs used a consistent land-use change dataset  (Hurtt et al. , 2011) , which provided annual, half-degree, fractional data on cropland, pasture, primary vegetation and secondary vegetation, as well as all underlying transitions between land-use states, including wood harvest and shifting cultivation.
This dataset used the HYDE  (Klein Goldewijk et al. , 2011)  spatially gridded maps of cropland, pasture, and ice/water fractions of each grid cell as an input. The HYDE data is based on annual FAO statistics of change in agricultural area  (FAOSTAT, 2010). For the year 2012, the HYDE dataset was extrapolated from 2011, based on the trend in agricultural area over the previous 5 yr. The HYDE dataset is independent from the data set used in the bookkeeping method  (Houghton, 2003 and updates) , which is based on forest area change statistics  (FAO, 2010). Although the land-use change datasets indicate whether land-use changes occur on forested or non-forested land, the changes in agricultural areas are then implemented differently within each model (for instance, an increased cropland fraction in a grid cell can either be at the expense of grassland, or forest, the latter resulting in deforestation; land cover fractions of the non-agricultural land differ between models).
The annual growth rate is estimated from atmospheric CO rate in units of ppm yr -1 is converted to fluxes by multiplying by a factor of 2. 120 GtC per ppm  (Joos et al. , 2013)  for comparison with the other components. The uncertainty around the annual growth rate based on the multiple stations dataset ranges between 0. 11 and 0. 72 GtC yr -1 , with a mean of 0. 60 GtC yr -1 for 1959-1980 and 0. 19 GtC yr -1 for 1980-2012, when a larger set of stations were available. It is based on the number of available stations, and thus takes into account both the measurement errors and data gaps at each station. This uncertainty is larger than the uncertainty of ±0. 1 GtC yr -1 reported for decadal mean growth rate by the IPCC because errors in annual growth rate are strongly anti-correlated in consecutive years leading to smaller errors for longer time scales.
Conclusions The estimation of global CO 2 emissions and sinks is a major effort by the carbon cycle research community that requires a combination of measurements and compilation of statistical estimates and results from models. The delivery of an annual CO 2 budget serves two purposes. First, there is a large demand for up-to-date information on the state of the anthropogenic perturbation of the climate system and its underpinning causes. A broad stakeholder community relies on the datasets associated with the annual CO 2 budget including scientists, policy makers, businesses, journalists, and the broader society increasingly engaged in adapting to and mitigating human-driven Figures climate change. Second, over the last decade we have seen unprecedented changes in the human and biophysical environments (e.
g. increase in the growth of fossil-fuel emissions, ocean temperatures, and strength of the land sink), which call for more frequent assessments of the state of the Planet, and by implication a better understanding of the future evolution of the carbon cycle, and the requirements for climate change mitigation and adaptation. Both the ocean and the land surface presently remove a large fraction of anthropogenic emissions. Any significant change in the function of carbon sinks is of great importance to climate policymaking, as they affect the excess carbon dioxide remaining in the atmosphere and therefore the compatible emissions for any climate stabilization target. Better constraints of carbon cycle models against contemporary datasets raises the capacity for the models to become more accurate at future projections.
This all requires more frequent, robust, and transparent datasets and methods that can be scrutinized and replicated. After eight annual releases from the GCP, the effort is growing and the traceability of the methods has become increasingly complex. Here, we have documented in detail the datasets and methods used to compile the annual updates of the global carbon budget, explained the rationale for the choices made, the limitations of the information, and finally highlighted need for additional information where gaps exist. This paper via "living data" will help to keep track of new budget updates. The evo-.
Data access The data presented here is made available in the belief that their wide dissemination will lead to greater understanding and new scientific insights of how the carbon cycle works, how humans are altering it, and how we can mitigate the resulting human-driven climate change. The free availability of these data (doi:10. 3334/CDIAC/GCP_2013_v1. 1) does not constitute permission for publication of the data. For research projects, if the data are essential to the work, or if an important result or conclusion depends on the data, co-authorship may need to be considered. Full contact details and information on how to cite the data are given at the top of each page in the accompanying database. The accompanying database includes an Excel file organised in the following spreadsheets: 1.
For the budget, we reduce the list to 219 countries by reallocating emissions to the currently defined territories. This involved both aggregation and disaggregation, and does not change global emissions. Examples of aggregation include merging East and West Germany to the currently defined Germany. Examples of disaggregation include reallocating the emissions from former USSR to the resulting independent countries. For disaggregation, we use the emission shares when the current territory first appeared. Most recent two years, 2011 and 2012, the BP statistics are more aggregated, but we retain the detail in CDIAC by applying the same growth rates to individual countries in CDIAC as in the aggregated regions in the BP dataset. Discussion Paper | Discussion Paper | Discussion Paper |.
tional totals, but include them in the global total. The time-series of trade data provided by GTAP covers the period 1995-2009 and our methodology uses the trade shares of this dataset. For the period 1990-1994 we assume the trade shares of 1995, while in 2010 and 2011 we assume the trade shares of 2008 since 2009 was heavily affected by the global financial crisis. We identified errors in the trade shares of Taiwan in 2008 and 2009, so the trade shares for 2008-2010 are based on the 2007 trade shares.
the difference of simulations with and without LUC, which cause a bias compared to the bookkeeping estimates that makes decadal uncertainty estimates perfectly correlated (Gasser and Ciais, 2013); (3) common and uncertain land cover change input data which cause a bias, though if a different input dataset is used each decade, decadal fluxes from DGVMs may be partly decorrelated; (4) model structural errors (e. g. errors in biomass stocks), which cause bias that correlate decadal estimates. In addition, errors arising from uncertain DGVM parameter values would be random but they are not accounted for in this study, since no DGVM provided an ensemble of runs with perturbed parameters. Prior to 1959, the uncertainty in E LUC is taken as ±33 %, which is the ratio of uncertainty to mean from the 1960s (Table.
Acknowledgements. We thank all people and institutions that provided data used in this carbon budget,  P. Cadule ,  J. C. Enright ,  Ghattas , and  G. Hurtt  for support to the model simulations,  F. Joos  and  S. Khatiwala  for providing historical data, and  M. Heimann  and  G. McKinley  for their reviews and suggestions. The observations and modelling analysis were possible thanks to the generous support from the author's institutions and funding from multiple agencies around the world. The  UK Natural Environment Research Council (NERC)  provided funding to  C. Le Quéré  and the GCP though their International Opportunities Fund specifically to support this publication (project  NE/103002X/1 ). R. J. Andres  and  T. A. Boden  were supported by  US Department of Energy, Office of Science, Biological and Environmental Research (BER)  programs and preformed at  Oak Ridge National Laboratory (ORNL)  under  US Department of Energy  contract  DE-AC05-00OR22725.
G. P. Peters  and  R. M. Andrews  were supported by the  Norwegian Research Council  ( 221355 ). A. Arneth ,  A. Omar ,  C. Le Quéré ,  J. Schwinger ,  P. Ciais ,  P. Friedlingstein ,  P. Regnier ,  S. Sitch  and  S. Zaehle  were supported by the  EU   FP7  for funding through projects  GEOCarbon  ( 283080 ),  COMBINE  ( 226520 ),  CARBOCHANGE  ( 264879 ),  EM-BRACE  ( GA282672 ), and  LUC4C  ( GA603542 ). A. Harper was supported by the  NERC Joint Weather and Climate Research Programme. A. K. Jain  was supported by the  US National Science Foundation  ( NSF AGS 12-43071 ) and the  US Department of Energy, Office of Science and BER programs  ( DOE DE-SC0006706 ). B. D. Stocker was supported by the  Swiss National Science Foundation. A. Wiltshire was supported by the  Joint UK DECC/Defra Met Office Hadley Centre Climate Programme  ( GA01101 ).
When candidate cell cycle genes reported to be CREB targets were examined by qRT-PCR of total RNA isolated from parallel organoid cultures, no evidence of changes in CyclinD1, CyclinA1, Erg1 38 or Bcl-2 39 expression were observed (data not shown). CREB is elevated in intestinal adenomas and adenocarcinoma cells. As CREB loss leads to reduced proliferation in the GI and CREB has been shown to activate PCNA expression  36  and impart oncogenic properties in some tissues,  35, 40  we investigated CREB expression in tumor tissue. Intestinal adenomas from the Apc min/ þ mouse were examined by IHC (Figures  6a  and d ) to show concordant   c  and d ) When mice were exposed to whole-body irradiation and intestines processed, 5 days later it was apparent that the small but significant difference in proliferation observed in unirradiated mice in the SI was not exacerbated with radiation treatment.
This was important for both groups, as Tamoxifen influences transcription. 41, 42 This strategy was also considered to most closely parallel that used to characterize the GI morphology, to examine the GI cells most likely to express pCREB and to capture unanticipated gene expression changes. This unbiased approach confirmed our previous data regarding no changes in the anticipated cell cycle genes (Supplementary Data File). However, several other genes showed significant changes that were confirmed by qRT-PCR, some of which had previously been suggested to be CREB target genes. Most notable was the lower expression of pcna, and of course creb itself. Among the genes showing the differential expression were phosphatases, Ppm1b and PTEN, tyrosine kinase Yes1 and cytotoxic chemotherapy drug transporter ABCC2/MRP2.
RABEP1 Hs00388983_m1 GCTGCTGTTGTGCACGTAAA doi:10. 1371/journal. pone. 0061103. t001 Then, for high density microarrays, total RNA (100 ng) were amplified and biotin-labeled by in vitro transcription (IVT) with a MessageAmp TM II-Bacteria RNA Amplification Kit (Ambion/ Applied BiosystemH). Before amplification, spikes of synthetic mRNA at different concentrations were added to all samples; these positive controls were used to ascertain the quality of the process. Ten micrograms of biotinylated cRNA was fragmented and hybridized onto CodelinkH Whole Mouse Genome Bioarrays (GE/Amersham) for 18 hours at 37uC at 300 rpm in an incubator. The slides were washed and stained with streptavidin-cy5 (GE Healthcare) according to the manufacturer's protocol. The slides were scanned using a GenepixH 4000B scanner (Axon, Union City, USA) and GenepixH software, with the laser set at 635 mm, the laser power at 100%, and the photomultiplier tube voltage at 60%.
The scanned image files were analyzed using CodeLinkH expression software, version 4. 2 (GE Healthcare), which produces both a raw and normalized hybridization signal for each spot on the array. The relative intensity of the raw hybridization signal on arrays varies in different experiments. CodeLinkH software was therefore used to normalize the raw hybridization signal on each array to the median of the array (median intensity is 1 after normalization) for better cross-array comparison. The threshold of detection was calculated using the normalized signal intensity of the 100 negative control samples in the array; spots with signal intensities below this threshold are referred to as ''absent''. The complete set of raw and normalized data is available at the GEO database under accession number GSE33968.
Distribution of normalized data was checked with box plots and correlation plots. For each mRNA, the threshold cycle (Ct) was calculated with the RQ Manager 1. 2 software (Applied BiosystemsH). Data manipulations were done using R scripts and automated Excel sheets. A cutoff at 35 Ct was applied to discard late Ct values. For each TLDA, quality controls were performed on the raw data by checking internal controls and using box plot and scatter plot diagrams. Samples with any kind of problems were discarded so they would not introduce bias during the global normalization procedures. The following formula was used to correct Ct values of every card: ½Ct Normalized ~Ct Ã ½mean of medians =½median of the card Through this approach, the new median value shared by all samples can be considered as a sort of perfect ''virtual housekeeping gene''.
Therefore the standard DDCt method can be used to determine the relative quantities as follows: DCt~½Ct Normalized {½New shared median For the DDCt calculation, as described previously  [25] , it was more relevant for the statistical analyses to use the mean of all DCt obtained across samples for each mRNA, instead of using the DCt of a reference sample: DDCt~DCt{mean½DCt across samples mRNA RQ~2 {(DDCt) The same methodology was used for miRNA analysis. For statistical analysis, normalized relative quantities data were directly input into the TIBCO SpotfireH DecisionSite for Functional Genomics analysis software. Data of interest were then used as variables in a two dimensional principal component analysis (PCA) performed with R 2. 9. 0 package to demonstrate their capabilities to distinguish the different cell lines.
Assays are summarized in table  1. Data normalization: A cut-off of 37 was applied to discard late Ct values. As the recommended 18S normalization gene was found very variable in our experiments (standard deviation of 5. 5 Ct), normalization was performed using the ETV6 gene (standard deviation of 1. 5 Ct) shown as stable on cell lines' experiments. We calculated the mean Ct for ETV6 on all our experiments and used this value to determine tumor-specific correcting factors F, with i genes and j patients: F i,j ~1= N | P N j~1 Ct ETV 6,j Ct ETV 6,j Ct values were then corrected for each tumor and DCt values were calculated as follows: Norm(Ct i,j )~Ct i,j |F i,j DCt i,j ~Norm(Ct i,j ){ 1 N | X N j~1 Ct ETV 6,j For miRNA analysis, 20 patients diagnosed with GIST were included in this study. Groups were formed regarding to the kind of mutations harbored, regardless to any other variable.
High Density Arrays (CodelinkH) The gene expression profiles of the cell lines were first performed to a high density. The aim of this analysis was to identify the most relevant genes that characterized the different ckit cell lines and then to validate them by TLDA analysis. From the complete set of raw (GEO database GSE33968), we selected genes differentially expressed between WT and hemizygous samples grown in the SCF+ condition, whose fold change was superior or equal to 2 and whose expression for at least one of the sample was greater than 1. 3 (commonly admitted threshold for low density arrays). We then selected 83 and 126 genes more, that were differentially expressed between D6 and D54 cell lines (fold change $2; gene amplification. 1. 3), in SCF+ or SCF2 condition, respectively. Finally we obtained a list of 235 genes of which 94 were available for TaqmanH low density array (AppliedH).
Measures Stimuli consisted of four neutral words: "robe" (dress), "pont" (bridge), train, and studio; four words semantically related to the notion of alcohol: "alcool" (alcohol), Ricard, "vin" (wine), and whisky; and four color names: "rouge" (red), "jaune" (yellow), "bleu" (blue), and "vert" (green). Stimuli were individually presented in lowercase letters in Courier New font, 72-point bold, centered on the screen. On average, a word covered a visual angle of 3° broad and height 0. 9°. There were three different blocks: one for the neutral words, one for the words for colors, and one for words related to alcohol. The color words were always presented in an incongruent way (eg, "red" appeared only in green, blue, or yellow). In addition, an analysis of the objective frequencies of the words from the French lexical database Lexique 3  21  showed no significant difference between alcohol-related words with the exception of the word Ricard, which was not contained in this base, and neutral words (t[5] = -0.
Statistical analyses The average response time given by item for each block of correct answers was analyzed. Responses less than 300 ms and more than 1500 ms were excluded from analyses (less than 1% of data). The tests were first made on the words semantically related to alcohol, compared with neutral words, then on the color words compared with neutral words, using SPSS 19 (IBM, Armonk, NY, USA). A Shapiro-Wilk test on the two principal variables (Alcohol Stroop interference and Classical Stroop interference) indicated that these two variables were normally distributed (P = 0. 37 for Alcohol Stroop interference and P = 0. 097 for classical Stroop interference). Two types of analysis were undertaken, with a repeatedmeasures plan of 2 × 3 with type of stimulus (color words or alcohol-related words vs neutral words) and the status of the participant (nonabusers vs admitted patient vs discharged patients).
Experimental Crystal data (i) x þ 1 2 ; Ày þ 3 2 ; z þ 1 2 ; (ii) Àx þ 1; Ày þ 2; Àz þ 1; (iii) Àx þ 1 2 ; y À 1 2 ; Àz þ 1 2. Data collection: APEX2  (Bruker, 2008) ; cell refinement: SAINT  (Bruker, 2008) ; data reduction: SAINT; program(s) used to solve structure: SHELXS97  (Sheldrick, 2008) ; program(s) used to refine structure: SHELXL97  (Sheldrick, 2008) ; molecular graphics: ORTEP-3 for Windows  (Farrugia, 2012)  and Mercury  (Macrae et al. , 2008) ; software used to prepare material for publication: PLATON  (Spek, 2009)  and PARST  (Nardelli, 1995).