dataesr/scientific-paragraphs-categorization huggingface dataset:
Phylogenomic analyses. Phylome reconstruction. Phylomes, the entire collection of phylogenetic trees for each gene in a genome, were reconstructed for each of five Penicillium species (P. chrysogenum, P. oxalicum, P. expansum, P. italicum, and P. digitatum). Nineteen other Pezizomycotina species were included for the phylome reconstruction. The phylomes were reconstructed using an automatic approach that mimics the steps taken to reconstruct a single phylogenetic tree: homology search, multiple sequence alignment, and finally, phylogenetic reconstruction, essentially as described before (Huerta Cepas and Gabaldón 2011). Briefly, for each protein-coding gene encoded in a Penicillium genome, we perform a Smith-Waterman search, using the protein sequence as a query, against the database formed by 24 proteomes.
A neighbor-joining tree was reconstructed as implemented in BIONJ, and then, the likelihood of this topology was computed, allowing branch-length optimization, using seven different models (JTT, LG, WAG, Blosum62, MtREV, VT, and Dayhoff), as implemented in PhyML v3. 0  (Guindon et al. 2010) ; the two models best fitting the data following the AIC criterion were used to derive ML trees. Four rate categories were used and invariant positions were inferred from the data. Branch support was computed using an approximate likelihood ratio test based on a chi-square distribution. The five phylomes have been deposited in phylomeDB  (Huerta-Cepas et al. 2014)  with the phyID numbers 279 to 283. Eight additional phylomes were reconstructed with a closer focus on Penicillium species. In this case, the eight Penicillium species included were supplemented with four Aspergillus species and two Talaromyces species.
These phylomes are stored in phylomeDB with phyID numbers 357 to 364. Species tree reconstruction. Data from the P. expansum phylome was used to reconstruct the species tree. We selected 844 single-copy genes that were present in the 24 species contained in the phylome. A similarity search of these 844 single-copy genes was then performed against the two additional Penicillium genomes that were not included in the large phylome (P. roqueforti and P. camemberti). Only the 524 proteins that had one single hit in each of these species were used to reconstruct the species tree. The final alignment contained 340,352 positions. RAxML v7. 2. 6  (Stamatakis et al. 2012 ) was used to reconstruct the species tree using the PROTGAMMALG model. Bootstraps were run using RAxML. All the nodes in the tree had a bootstrap value of 100.
Quantification of relative gene expression by RT-qPCR. DNase treatment and first-strand cDNA were conducted with the "Maxima H Minus First Strand cDNA synthesis kit with dsDNase" (Thermo Scientific) using 2 g of total RNA. RT-qPCR assays were conducted following MIQE guidelines  (Bustin et al. 2009 ). Gene-specific primer sets were designed for gene expression analysis with Primer3Plus  (Untergasser et al. 2012). RT-qPCR reactions were performed in a Light-Cycler480 System (Roche) using SYBR Green to monitor cDNA amplifications. Cycling conditions were 5 min at 95°C, followed by 45 cycles of 10 s at 95°C, 5 s at 58°C, and 10 s at 72°C. For each primer pair and each sample, the PCR efficiency (E) and the quantification cycle (Cq) were assessed using LinRegPCR software version 2014. 2  (Ruijter et al.
2009). Amplicon specificity was examined by analysis of the melting curve. Relative gene expression for a gene of interest (GOI) with respect to the reference gene (REF) was calculated using the modified equation E GOI ^(-CqGOI) /E REF ^ (-CqREF) from  Pfaffl (2001). Three independent biological replicates with at least two technical replicates were performed for each sample. Dilution series were made for a number of genes, using genomic DNA to confirm that recorded Cq values from the experiments were within the linear dynamic range and that efficiencies were consistent with the values calculated using the LinRegPCR program (data not shown). The Cq value for the reference normalization factor (REF) was calculated by taking the geometric mean of the three P. expansum reference genes: the ribosomal 28S RNA  (Barad et al.
Results were expressed as micrograms of patulin per plate. Chromatography was carried out with a Waters HPLC system equipped with a 600 quaternary pump and a 996-photodiode array detector set at 276 nm. Data were analyzed with the Empower software (Waters). Patulin was separated using a Luna C18 reverse column (250 × 4. 6 mm, 5 m; Phenomenex) coupled to a Bondapak C18 guard column (10 m) and using a mobile phase consisting of a mixture of acidic water (pH 4. 0, with acetic acid) and acetonitrile (96:4, vol/vol). The flow rate was 1 ml/min and the injection volume was 20 l. Patulin was identified in the samples by comparing the retention time and UV-Vis spectra with those of an authentic standard.
ACKNOWLEDGMENTS The technical assistance of  A. Izquierdo  ( IATA-CSIC, Valencia, Spain ) is gratefully acknowledged. We are grateful to  H. Himmelbauer  and the  CRG sequencing core facility  for their technical assistance. Work at L. González-Candelas ' lab was partially supported by the  Spanish Ministry of Economy and Innovation (Research Grants   AGL-2008-04828-C03-02  and  AGL2011-30519-C03-01 ) and by the  Generalitat Valenciana, Spain  ( PROMETEO/2010/010  and  PROMETEOII/2014/027 ). A. -R. Ballester is grateful to  CSIC  and the  European Social Fund  for her postdoctoral contract  JAE-Doc. C. Selma-Lázaro  is recipient of a  FPI Predoctoral fellowship  from the  Spanish MICINN  ( BES-2012-054595 ). The T. Gabaldón group research is funded, in part, by a grant from the  Spanish ministry of Economy and Competitiveness  ( BIO2012-37161 ), a Grant from the  Qatar National Research Fund  grant ( NPRP 5-298-3-086 ), and a grant from the  European Research Council  under the European Union's  Seventh Framework Programme  (FP/2007-2013)/ ERC  (Grant Agreement n.
Methods This study was a cross-sectional cost-effectiveness analysis of delivering measles RI and SIA for the year 2011 in the health district of Natitingou. Data were collected retrospectively and the analysis has only considered oneyear time horizon. All the 18 health care centers delivering immunization services were included. Data were analyzed by Microsoft Excel software. RI is a component of the minimum package of activities provided in health facilities in Benin. RI services were provided in the 18 health facilities through two strategies: fixed and outreach strategy. Fixed strategy was planned for children living within less than 5 km from the health center and outreach strategy was planned for children living beyond 5 km from the facility. Health workers planned two fixed strategies sessions and 1 outreach strategy session per week.
All health care centers had the same configuration: one dispensary and one maternity ward. Staff consisted of 4 persons: a nurse, a midwife and two caregivers. All staffs were equitably involved in immunization activities. Vaccines were supplied monthly by the district team from the regional storehouse to three secondary storehouses of the district. Health workers were responsible for transporting vaccines from the district storehouses to their respective health facilities by motorcycle. Health facilities were equipped with cold chain materials (refrigerators, cold boxes, vaccines carriers) and motorcycles. Data on the number of vaccinated children were collected on tally sheets at each immunization session. A synthesis was made at the end of the month by the head of the health center before transmission to district team.
Measuring costs Costs taken into account included recurrent and capital costs. Both economic and financial costs were considered. Some costs were specific to measles immunization and others were shared with others activities. Data for SIA specific operational costs came from the campaign financial reports and micro planning tools. Shared costs for measles RI activities were supposed to represent 9 % of RI delivery total costs; as measles vaccine was one of the 11 antigens administrated in Benin RI schedule. RI recurrent costs were analyzed using ingredients approach to costing. Data on vaccines used, consumables and management tools used were empirically obtained at health facilities level from vaccine usage reports, tally sheets, vaccine management databases. Costs for personnel, transport, supervision, trainings, fuel for cold chain, electricity and maintenance were obtained from interviews, questionnaires, direct surface measurement of buildings and exploitation of documents (administrative and financial reports, log books), both at health facilities and district administration level.
Calculating effectiveness Four effectiveness indicators were estimated: MCV doses used and vaccinated children relate to productive efficiency while the number of measles cases averted and disability adjusted life years averted refer to allocative efficiency. The primary effectiveness indicator selected was disability adjusted life years (DALYs) averted. It is a widely used indicator that allows easy comparison with a 'no vaccination' strategy and with others public health interventions. MCV doses used represents the amount of vaccines used per strategy. This was empirically obtained from district vaccine management tools (vaccine usage reports, tally sheets, vaccine management databases). Vaccinated children corresponds to the total of targeted children that received a shot, having in mind that for RI the target population is defined by children under 1 year whereas the target population for SIA extends to children under 5 years.
As vaccine efficacy and measles CFR are two key parameters influencing the number of measles cases averted, we performed a sensitivity analysis for these two variables. A measles post-outbreak survey conducted in Democratic Republic of Congo shows that measles vaccine efficacy could fall to 60 %  [15]. Higher measles CFRs are observed during outbreaks, among under 5 years old children, in cases with complications and among children living in rural areas  [16]. A review of 25 community-based studies conducted in India shows that measles CFRs vary between 0. 00 and 31. 25 %  [11]. As vaccination coverage affects both RI and SIA effectiveness, a sensitivity analysis was performed for this variable. For RI, coverage variation used was 50-98 %, based on extreme levels achieved in the past years after analysis of the health district immunization databases.
Acknowledgements This work received a support from the  Priority Solidarity Fund of French Ministry of Foreign Affairs  through the  Agence de Médecine Préventive (AMP). Valuable contributions were provided by  Marjorie Nicol ,  Delphine Adénot  and  Aristide Aplogan  on an earlier version of this paper. Helpful comments on measuring costs were obtained from  Franck Bété ,  Marcel Kounnou  and  Jean-Bernard Le Gargasson. Thanks also to  Maïmouna Sacca Bawa  and  Karl Dossou  for their participation in data's collection. While the support and contribution of the mentioned institutions and persons is appreciated, the authors are fully responsible for the content and view in this paper.
Accurate assessment of anthropogenic carbon dioxide (CO 2 ) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere is important to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe data sets and a methodology to quantify all major components of the global carbon budget, including their uncertainties, based on the combination of a range of data, algorithms, statistics, and model estimates and their interpretation by a broad scientific community. We discuss changes compared to previous estimates as well as consistency within and among components, alongside methodology and data limitations. CO 2 emissions from fossil fuels and industry (E FF ) are based on energy statistics and cement production data, while emissions from land-use change (E LUC ), mainly deforestation, are based on combined evidence from land-cover-change data, fire activity associated with deforestation, and models.
The global atmospheric CO 2 concentration is measured directly and its rate of growth (G ATM ) is computed from the annual changes in concentration. The mean ocean CO 2 sink (S OCEAN ) is based on observations from the 1990s, while the annual anomalies and trends are estimated with ocean models. The variability in S OCEAN is evaluated with data products based on surveys of ocean CO 2 measurements. The global residual terrestrial CO 2 sink (S LAND ) is estimated by the difference of the other terms of the global carbon budget and compared to results of independent dynamic global vegetation models forced by observed climate, CO 2 , and land-cover change (some including nitrogen-carbon interactions). We compare the mean land and ocean fluxes and their variability to estimates from three atmospheric inverse methods for three broad latitude bands.
, 1976). To take into account the difference of mean CO 2 between the NOAA/ESRL and the Scripps station networks used here, the Scripps surface average (from two stations) was harmonised to match the NOAA/ESRL surface average (from multiple stations) by adding the mean difference of 0. 542 ppm, calculated here from overlapping data during 1980-2012. The mean seasonal cycle is also shown from 1980. The global carbon budget presented here refers to the mean, variations, and trends in the perturbation of CO 2 in the atmosphere, referenced to the beginning of the industrial era. It quantifies the input of CO 2 to the atmosphere by emissions from human activities, the growth of CO 2 in the atmosphere, and the resulting changes in the storage of carbon in the land and ocean reservoirs in response to increasing atmospheric CO 2 levels, climate, and variability, and other anthropogenic and natural changes (Fig.
664 multiplied by the value in units of GtC. This paper provides a detailed description of the data sets and methodology used to compute the global carbon budget estimates for the period pre-industrial (1750) to 2014 and in more detail for the period 1959 to 2014. We also provide decadal averages starting in 1960 and including the last decade  (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014) , results for the year 2014, and a projection of E FF for year 2015. Finally we provide cumulative emissions from fossil fuels and land-use change since year 1750, the pre-industrial period, and since year 1870, the reference year for the cumulative carbon estimate used by the IPCC (AR5) based on the availability of global temperature data  (Stocker et al. , 2013). This paper is intended to be updated every year using the format of "living data" to keep a record of budget versions and the changes in new data, revision of data, and changes in methodology that lead to changes in estimates of the carbon budget.
Methods Multiple organisations and research groups around the world generated the original measurements and data used to complete the global carbon budget. The effort presented here is thus mainly one of synthesis, where results from individual groups are collated, analysed, and evaluated for consistency. We facilitate access to original data with the understanding that primary data sets will be referenced in future work (see Table  2  for how to cite the data sets). Descriptions of the measurements, models, and methodologies follow below and in-depth descriptions of each component are described elsewhere (e. g. Andres et al. , 2012; Houghton et al. , 2012). This is the tenth version of the "global carbon budget" (see Introduction for details) and the fourth revised version of the "global carbon budget living data update".
Emissions from fossil fuels and industry and their uncertainty The calculation of global and national CO 2 emissions from fossil fuels, including gas flaring and cement production (E FF ), relies primarily on energy consumption data, specifically data on hydrocarbon fuels, collated and archived by several organisations  (Andres et al. , 2012). These include the Carbon Dioxide Information Analysis Center (CDIAC), the International Energy Agency (IEA), the United Nations (UN), the United States Department of Energy (DoE) Energy Information Administration (EIA), and more recently also the Planbureau voor de Leefomgeving (PBL) Netherlands Environmental Assessment Agency. Where available, we use national emissions estimated by the countries themselves and reported to the UNFCCC for the period 1990-2012 (42 countries).
Most data on energy consumption and fuel quality (carbon content and heat content) are available at the country level (UN, 2014a). In general, CO 2 emissions for equivalent primary energy consumption are about 30 % higher for coal compared to oil, and 70 % higher for coal compared to natural gas  (Marland et al. , 2007). All estimated fossil fuel emissions are based on the mass flows of carbon and assume that the fossil carbon emitted as CO or CH 4 will soon be oxidised to CO 2 in the atmosphere and can be accounted for with CO 2 emissions (see Sect. 2. 7). Our emissions totals for the UNFCCC-reporting countries were recorded as in the UNFCCC submissions, which have a slightly larger system boundary than CDIAC. Additional emissions come from carbonates other than in cement manufacture, and thus UNFCCC totals will be slightly higher than CDIAC totals in general, although there are multiple sources for differences.
g. coal mining and natural gas distribution) 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 data set includes 250 countries and regions. This expanded list includes countries that no longer exist, such as the USSR and East Pakistan. For the carbon budget, we reduce the list to 216 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.
The disaggregated estimates should be treated with care when examining countries' emissions trends prior to their disaggregation. For the most recent years, 2012-2014, the BP statistics are more aggregated, but we retain the detail of CDIAC by applying the growth rates of each aggregated region in the BP data set to its constituent individual countries in CDIAC. 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. In practice, the emissions from international bunker fuels are calculated based on where the fuels were loaded, but they are not included with national emissions estimates.
, 2011a). Due to availability of the input data, detailed estimates are made for the years  1997, 2001, 2004, 2007, and 2011  (using the methodology of  Peters et al. , 2011b)  using economic and trade data from the Global Trade and Analysis Project version 9 (GTAP;  Narayanan et al. , 2015). The results cover 57 sectors and 140 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 (2013 in this budget), using GDP data by expenditure in current exchange rate of US dollars (USD; from the UN National Accounts Main Aggregates Database; UN, 2014c) and time series of trade data from GTAP (based on the methodology in  Peters et al. , 2011b). 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 (excluding bunker fuels) to match the CDIAC estimates from the most recent carbon budget.
Emissions projections Energy statistics from BP are normally available around June for the previous year. To gain insight into emission trends for the current year (2015), we provide an assessment of global emissions for E FF by combining individual assessments of emissions for China and the USA (the two biggest emitting countries), as well as the rest of the world. We specifically estimate emissions in China because the evidence suggests a departure from the long-term trends in the carbon intensity of the economy used in emissions projections in previous global carbon budgets (e. g. Le  Quéré et al. , 2015) , resulting from significant drops in industrial production against continued growth in economic output. This departure could be temporary  (Jackson et al. , 2015). Our 2015 estimate for China uses (1) apparent consumption of coal for January to August estimated using production data from the National Bureau of Statistics (2015b), imports and exports of coal from China Customs Statistics (General Administration of Customs of the People's Republic of  China, 2015a, b) , and from partial data on stock changes from industry sources  (China Coal Industry Association, 2015; China Coal Resource, 2015) ; (2) apparent consumption of oil and gas for January to June from the National Energy Administration (2015); and (3) production of cement reported for January to August (National Bureau of Statistics of China, 2015b).
This effect is discussed in the Results section. For the USA, we use the forecast of the US Energy Information Administration (EIA) "Short-term energy outlook" (October 2015) for emissions from fossil fuels. This is based on an energy forecasting model which is revised monthly, and takes into account heating-degree days, household ex-penditures by fuel type, energy markets, policies, and other effects. We combine this with our estimate of emissions from cement production using the monthly US cement data from USGS for January-July, assuming changes in cement production over the first 7 months apply throughout the year. We estimate an uncertainty range using the revisions of historical October forecasts made by the EIA 1 year later. These revisions were less than 2 % during 2009-2014 (when a forecast was done), except for 2011, when it was -4.
Tropical deforestation is the largest and most variable single contributor to E LUC. Fire emissions associated with deforestation and tropical peat burning are based on the Global Fire Emissions Database (GFED4; accessed October 2015) described in van der Werf et al. (  2010 ) but with updated burned area  (Giglio et al. , 2013)  as well as burned area from relatively small fires that are detected by satellite as thermal anomalies but not mapped by the burned area approach  (Randerson et al. , 2012). The burned area information is used as input data in a modified version of the satellite-driven Carnegie-Ames-Stanford Approach (CASA) biogeochemical model to estimate carbon emissions associated with fires, keeping track of what fraction of fire emissions was due to deforestation (see  van der Werf et al.