dataesr/scientific-paragraphs-categorization huggingface dataset:
(2012) , but are set proportionally to the total food crop demand in each region. -Costs of industrial inputs (fertilisers, pesticides, mineral enrichments) for the pro-   et al. , 2012, Eqs. 34 and 35). Mineral NPK fertilisers represent the most widespread industrial inputs used by agriculture. They do not cover all chemical inputs reported in GTAP but their costs can now be associated with quantities (tonnes of fertilisers) unlike with GTAP. Moreover, absolute values of IC χ are not essential since the model deals with quantities and relative prices, the latter being used only in the trade module of Nexus Land-Use. -New actual and potential yields were computed to take into account multicropping. Using a dataset of harvested areas of each CFT on each grid cell (A. Bondeau, personal communication, 2013) , we increased actual and potential yields proportionally to the number of harvests per year.
As a result, the share of dynamic crops in global food crop calorie production amounts to 87 % while it was 75 % in the previous version (the cultivated area of dynamic crops is still 51 % of total cropland area, see Appendix A3). 3 Historical data for model evaluation  The over the period  1961 -2006 (Sect. 5, Fig. 1  summarises datasets used). Finally, the robustness of the evaluation to input data and parameters is tested. The model is forced backwards in line with historical values. A first input data is the evolution of forest area that sets up a "top-down" constraint on the total area of cropland and pasture. Changes in forest area are simply prescribed from  Ramankutty and Foley (1999). The evolution of other drivers of the extensification/intensification of agriculture over the past 45 yr are: food consumption in caloric units based on Agribiom  (Dorin, 2011)  We also present estimates of trade in food crops and ruminant products from Agribiom (Sect.
Food production and trade in calories As described in  Souty et al. (2012) , the global database Agribiom  (Dorin, 2011)   country-level supply-utilisation accounts in tonnes. Using additional data, such as human population, Agribiom also estimates food availability (kcal per capita) hereafter called "diets. " Over 1961 Over -2006, it , it  shows that average availabilities in plant food increased along with those in animal food (fish and sea food excluded) (Fig. 2 ) but with large regional differences, and a special case, the Former Soviet Union (FSU). The increase in food availability is particularly remarkable in China, India, Africa and the Rest of Asia, since these regions also witnessed a large increase in population. The following Agribiom estimates were used to force or calibrate Nexus Land-Use over 1961-2006: human population; diets in plant, ruminant and monogastric products (availabilities in kcal cap -1 ); other consumptions of edible plant or animal products (in kcal yr -1 ): seed, other non-food uses (mainly lubricant and energy) and waste (between farm production and household provision); trade of plant, ruminant and monogastric products (import and export in kcal yr -1 ).
Fertiliser price and consumption Fertilisers are important inputs to increase agricultural production, especially when agricultural land is limited. The FAO (2012) statistical database provides historical annual country fertiliser consumptions in tonnes for various mineral (inorganic) fertilisers, as well as their aggregates in nitrogen (N), phosphate (P 2 O 5 ) and potash (K 2 O) equivalents. Figure  3  2006, the regional average consumption ranged between 250 kg N ha -1 yr -1 in China and 10 kg N ha -1 yr -1 in the Former Soviet Union, where it fell by  78 % between 1988 and 1994 (-22 % in Europe). Phosphate and potash consumption patterns showed no clear regional trends. The fertiliser price is a determining parameter as it drives the land-fertiliser substitution, and thus strongly influences the intensification level calculated by the model.
Very little data is available to estimate historical changes in FC tot , which leads to several assumptions before computing the regional evolution index of FC tot. Firstly, due to a lack of data on other intermediary consumption like business services and on-farm energy consumption, it is assumed that this cost item evolves as labour and capital. Labour and capital costs in the intensive system are estimated by subtracting the land rent per hectare (λ) from the added value per hectare of the intensive system (VA). Evolution of the cropland area is taken from  Ramankutty and Foley (1999). The values of both VA and λ in 2001 for dynamic crops are taken from the GTAP (  2006 ) database. Since no difference is made between intensive and extensive livestock farming in the GTAP database, we selected only the GTAP categories corresponding to dynamic crops  (Souty et al.
Livestock production model As described in Souty et al. (  2012 ) Sect. 3. 3, feed requirements for animal production is calculated using the  Bouwman et al. (2005)  dataset. This dataset considers monogastrics (non-grazing animals), ruminants in the extensive system fed by grazing only and ruminants in the intensive system fed by grass, food crops, residues and fodder, animal products and product scavenging. For each category,  Bouwman et al. (2005)  gives a feed conversion factor defined as the calories of feed needed to produce one calorie of animal food (β), and feed composition factors defined as the share of each specific feed product in total feed needs (φ). Bouwman et al. (2005)  only report values of β and φ for 1970, 2000 and 2030. Hence, in retrospective simulation, we assumed a linear evolution of these factors between these three dates.
Grassland yield In the Nexus Land-Use model, areas of pastures in intensive and extensive systems (the latter including extensive and residual pastures) are calculated using a "consumed grass yield" for each system in each region (ρ   (2008)  datasets. In this section, we compare these top-down estimations of consumed grass yields to the independent grassland yield estimation from the process-based ecosystem model ORCHIDEE  (Krinner et al. , 2005). The ORCHIDEE model was run on a 0. 5 • × 0. 5 • grid without nitrogen limitation using a C3/C4 map based on  Collatz et al. (1998) , see Fig. 11. In each region, average above and below ground Net Primary Productivity (NPP in g C day -1 m -2 ) over the 1995-2004 period were calculated by distinguishing extensive pastures from intensive and residual ones (the latter cannot be separated because they belong to the same land classes).
Fertiliser subsidies have been progressively suppressed since the 80's/90's. Therefore, we can expect these errors to be of less importance in future simulations. The sharp overestimation of cropland area in the 1970s in China can also be explained by inconsistencies between pasture area evolution from Ramankutty and Foley (1999) and the consumed grass yield of intensive pasture from  Bouwman et al. (2005)  which are based on different land-use datasets. To assess the accuracy of the endogenously calculated intermediary consumption, it was compared to nitrogen fertiliser consumption from the FAO (2012) (Fig. 14 ). Overall, the model tends to underestimate the consumption of fertiliser, which is consistent with yield underestimation and cropland area overestimation. Considering the large variability in trade, simulated net imports of food crops fits rather well with the Agribiom data for most of the regions over 1980-2006 (Fig.
Sensitivity to input data Data on agriculture and land-use are surrounded by large uncertainties. According to Young, "data on land-use are among the least reliable of international statistics" due to unreported harvested areas, degraded croplands, underestimation of land occupied by settlement, uncultivated patches, footpaths and ditches  (Young, 1999 (Young, , 2000)). "For example, the US data on croplands used by FAOSTAT includes idled cropland, which includes land under the Conservation Reserve Program that amounts to roughly 9 % of the total cropland area, and is often idled for longer than 5 yr  (Lubowski et al. , 2006 )" (Ramankutty et al. , 2008). Land-use datasets and potential yield estimations vary greatly according to various studies. As previously shown, this also concerns fertiliser price, which may dramatically decrease or not during the 1960s according to the computing methodology followed.
These discrepancies on input data may thus impact model error. To assess the effect of input data on model outputs, we run in this section a sensitivity analysis to the following input data: -Two indexes of fertiliser price: p To ensure a consistent comparison between results, R min max ratios have been recalibrated for each yield and land-use datasets (Table  6 ). Results of the sensitivity analysis are shown in Fig. 17. The overestimation of the cropland area in 1960 is significantly increased when using p FAO χ (+18 % against +7. 5 %) as it yields much higher values than p WB χ during this preriod (Fig. 5 ). At the world level however, input data have little impact on model performance. CV(RMSE) with LPJmL/Ramankutty/Poulter/p FAO χ. At the regional level, the impact of input data on performance of the model can be significant, especially in the OECD Pacific, the Middle-East and Africa.
Changes from Nexus Land-Use version 1. 0 A1 Intensive pasture areas Intensive pasture areas of the old and the new  Bouwman et al. (2005)  dataset are shown in Table  5. Symmetric changes appear in residual pasture areas since they are defined as the difference between pastures from Ramankutty et al. (  2008 ) located at the right of the limit land class (high yield land classes) and intensive pasture areas from  Bouwman et al. (Souty et al. , 2012 , p. 1308-09). In addition, consumed grass yields on intensive and extensive pastures are adjusted so that total grass consumed in each system and in each region remains unchanged.
A2 Fodder crops in global supply/demand balance To account for fodder crop included in the LPJmL model (dynamic category), the demand for feed to produce ruminant products  (Souty et al. , 2012, Eq. 4)   where, Q LPJmL fodder (t = 2001) is the fodder crop production included in LPJmL at the base year in each region in Mkcal of grain equivalent from  Monfreda et al. (2008)  and D fodder r,int (t = 2001) is the demand for residues and fodder for ruminant animals at the base year. In this way, part of the demand for fodder crops is moved to the demand for food crop feeding ruminant animals D fc r,int. During the simulation, this part is a constant share of the demand for residues and fodder by ruminant animals. This is not needed when using the Licker et al. (  2010 ) yield dataset which does not include fodder crops.
fc r,int , increased for all regions except Brazil where it decreased from 15 % in 1970 to 2 % in 2000. The largest increase of φ fc r,int took place over China and the Rest of Asia, where it climbed from 2 % in 1970 to 10 % in 2000, and over the Middle East (from 7 % to 29 %). The Bouwman et al. (2005) dataset was also used to infer trends in the yield of intensive and extensive grasslands (see Sect. 4. 4). Discussion Paper | Discussion Paper | Discussion Paper | 4 Results: calibration and evaluation of specific model components In the following, the performances of the Nexus Land-Use model are assessed using model-data root-mean-square errors (RMSE) and the coefficients of variation of RMSE (CV(RMSE).
Two land-use datasets: the original land-use dataset of Nexus Land-Use version 1. 0 -comprising cropland and pasture maps from Ramankutty et al. (2008) and a forest map from Poulter et al. (2011) -and the land-use dataset from Erb et al. (2007). -Two potential yields datasets: LPJmL and Licker et al. (2010) (see Appendix B). Total cropland area according to Erb et al. (2007) is 1522 Mha, which is roughly equal to the Ramankutty et al. (2008) estimation (1495 Mha). However, the spatial distributions are quite different, with a mismatch of 400 Mha between the two datasets. Erb et al. (2007) reports 4685 Mha of pasture compared to 2711 Mha in Ramankutty et al. (2008). This discrepancy is explained by methodological differences. The Ramankutty et al. (2008) pasture distribution is explicitely estimated by combining agricultural inventory data with two satellite-derived products, while Erb et al.
(2007) deduce pasture distribution by using a subtractive approach. The Ramankutty et al. (2008) pasture map is almost entirely included in the Erb et al. (2007) one. Forest area from Erb et al. (2007) is 3495 Mha compared to 5064 Mha in Poulter et al. (2011) and is almost completly included in it. Potential yields from Licker et al. (2010) are calculated using a statistical approach based on actual regional data (while LPJmL uses a process-based approach), based on the global crop dataset from Monfreda et al. (2008) in conjunction with globally gridded climate datasets. The mean potential yield at the global scale from Licker et al. (2010) is 17 % lower than in LPJmL. Spatial distributions of actual and potential yields as well as histograms of the land area classes of potential yield are displayed in Appendix BDiscussion Paper | Discussion Paper | Discussion Paper |.
Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper |Table 6. Calibrated ρ min j /ρ max j ratios using LPJmL or Licker et al. (2010) potential yield datasets and land-use datasets of Ramankutty et al. (2008) (for croplands and pastures) and Poulter et al. (2011) (for forests) or Erb et al. (2007.
Acknowledgements. We gratefully acknowledge financial support from the  GIS   Climat Environnement Société ACACCYA project. Authors thanks  Alberte Bondeau ,  Lex Bouwman  and  Rachel Licker  for providing data. Nicolas Viovy  for the ORCHIDEE simulation and  Aude Valade  for her help on English language. The publication of this article is financed by  CNRS-INSU.
- Two land-use datasets: the original land-use dataset of Nexus Land-Use version 1. 0 -comprising cropland and pasture maps from Ramankutty et al. (2008) and a forest map from Poulter et al. (2011) -and the land-use dataset from Erb et al. (2007). -Two potential yields datasets: LPJmL and Licker et al. (2010) (see Appendix B). Total cropland area according to Erb et al. (2007) is 1522 Mha, which is roughly equal to the Ramankutty et al. (2008) estimation (1495 Mha). However, the spatial distributions are quite different, with a mismatch of 400 Mha between the two datasets. Erb et al. (2007) reports 4685 Mha of pasture compared to 2711 Mha in Ramankutty et al. (2008). This discrepancy is explained by methodological differences. The Ramankutty et al. (2008) pasture distribution is explicitely estimated by combining agricultural inventory data with two satellite-derived products, while Erb et al.
Acknowledgments These proceedings from the 2011 Joint Session of the  International Society of Vascular Health  ( East Region ) and Ukrainian Society of Cardiology meeting are published on behalf of the Kiev writing group. As president of the  International Society of Vascular Health I ,  James Cameron  thanks the organizers of the Congress, especially  Professor Sirenko , and all participants. We wish to acknowledge  David Oldfield  for his help in text editing and  Catherine Dognon  for her invaluable work as meeting coordinator.
the common November-December occupation, and another in February 2009 (Table  1 ). The Drake Passage hydrographic programme is a joint effort between the National Oceanography Centre in Southampton and the British Antarctic Survey in Cambridge, U. K. Most section occupations were conducted using the RRS James Clark Ross. Each occupation typically consists of 30 full-depth conductivity-temperature-depth (CTD) stations between Burdwood Bank and Elephant Island in the eastern Drake Passage, with a typical horizontal resolution of 30 nautical miles. In early cruises, the data were collected using a Neil Brown Instrument MkIIIc CTD, whereas the later cruises were undertaken using a SeaBird 911 plus CTD. A description of the CTD calibration and measurement errors is given in the Appendix. Characteristic distributions of potential temperature and salinity are shown in Fig.
All the data used were collected during the same Antarctic late spring / summer season, thus reducing the possibility of any seasonal aliasing of the trend. Moreover, the two occupations in the 2008/2009 season exhibit little difference in the AABW salinity despite having been conducted three months apart (Fig. 4a ), suggesting negligible intra-seasonal variability in the AABW properties. No systematic change is observed in the thickness of the AABW layer examined (Fig. 4b ), indicating that there is no evidence here for a significant alteration in the rate of AABW export from the Weddell gyre. The same analysis was done for the LCDW salinity maximum (γ n = 28. 00 -28. 11 kg m -3 -Fig. 4c, d ) in order to investigate any inter-cruise biases. The calculated freshening trend is not statistically significant at the 90% significance level (p = 0.
Acknowledgments. We thank the National Environmental Research Council for financial support of the Antarctic Deep Rates of Export project (NE/E01366X/1). The SR1b section is a joint effort between the National Oceanography Centre, Southampton (NOCS) and the British Antarctic Survey (BAS) in Cambridge, U. K. Researchers and students from other institutions have also made invaluable contributions to the cruises. The measurements could not have been achieved without the willing cooperation and support of the planning and operations groups at NOCS and BAS. We are grateful to two anonymous reviewers for useful comments, which contributed to improving the quality of the paper. Finally, we gratefully acknowledge the outstanding contribution of the Masters, officers and crew of RRS James Clark Ross and RRS James Cook.
Twelve water samples covering the whole water column were typically taken at each station. Conductivity/salinity was measured for each water sample using a Guildline Autosal 8400B salinometer. The salinometer was standardized using IAPSO Standard Seawater -SSW (Table 1 for details on the different batch used). The differences between the CTD salinity and bottle data were calculated and, if necessary, offsets were applied to minimize the residuals between the CTD and bottle conductivites (In 94/95, due to issue with the conductivity cell, extra care was taken and the CTD-bottle differences were examined on a station by station basis so that the mean difference between the bottle and CTD salinities is -1×10 -4 ). Several duplicates were taken during each cruise to monitor the performance of the salinometer and the differences between duplicates were found to be of the order 1 × 10 -4.
Data analysis SSR marker alleles were scored for presence (1) or absence (0) of the band for each accession. The approximate allele was determined by comparison of its mobility across the gel with the molecular size marker GeneRuler TM 50 bp DNA Ladder (Biolabs). The total number of alleles and the average number of alleles per locus between pairs of genotypes were then calculated. The total number of alleles was calculated as the sum of all alleles detected for all loci. The average number of alleles per locus was estimated by the total number of alleles divided by the total number of loci. (Equation  1 ), where K = number of loci and ni = number of alleles detected per locus. k n = (1/K) ∑ ni i =1 [Equation 1] Polymorphic information content (PIC) For the measurement of each marker locus polymorphism, the polymorphic information content (PIC) was calculated according to Equation  2 , where pi, is the frequency of the i th allele detected over all accessions and N represents the total number of alleles observed at the locus  (Nei, 1973).
Genetic similarity and cluster analysis Genetic distances between individuals were calculated using the similarity coefficient based on presence or absence of alleles. Genetic analysis was performed using the DARwin 5. 0 program  (Pierrer and Jacquemoud-Collet, 2006). A dissimilarity matrix was calculated using Sokal and Sneath 2 coefficient (Equation  3 ). 2(b + c) Dij =---------------- a + 2 (b + c) [Equation 3] Where "i" and "j" are the indices of the two genotypes compared, "a" is the number of cases over all loci where an allele is present in both genotypes, and "b" and "c" are the number of cases where an allele is present in one genotype and absent in the other. A bootstrap analysis was conducted using 1000 replicates with DARwin software, with 60% minimal proportion of valid data required for each unit pair.
Moisan-Thiery et al. (2005)  found an average of 6. 6 alleles per locus for 286 potato cultivars in France with five SSR loci, allowing the creation of a database comprising the allele composition of every cultivar. Mathias et al. (2007)  found that 21 SSR markers showed up scorable products, with allele numbers ranging between 2 and 17. Ghislain et al. (2009b)  reported a range from 2 to 21 for 742 potato landraces with 51 SSR markers. Moreover,  Marchezi et al. (2010)  detected that the number of alleles per locus varied from 1 to 27, with a total of 127 alleles, for 14 potato cultivars in Brazil, while  Martinez et al. (2010)  found a total of 90 alleles with 16 markers, with markers ranging between three and nine alleles, among 16 native potatoes from the district of Anco, Ayacucho, in Peru, using microsatellite markers.
GLUCOSE INTOLERANCE IN RenTg MICE To assess glucose tolerance in the RenTg mice, an intra-peritoneal GTT was administered. Baseline fasting glucose levels were comparable between WT, RenTgMK +/-and RenTgMK +/+ mice (81. 17 ± 15. 68, 89. 80 ± 7. 16, and 93. 25 ± 10. 28 mg/dl, respectively). Heterozygous mice maintained significantly higher levels of glycemia compared to WT within 60 min and remained elevated throughout the GTT (Figure  1D ). These differences were observed as early as 10 weeks of age (data not shown) and became more pronounced with age by 20 weeks. Glucose intolerance in male RenTgMK mice was also evident from a comparison of the glucose AUC (Figure  1E ). The AUC values for both heterozygous and homozygous mice were higher (P < 0. 05) than that of WT mice implying greater glucose intolerance in the transgenics.
ACKNOWLEDGMENTS The authors thank  Dr. Oliver Smithies  for generously providing us with the RenTg mice we used to perform this study and  Dr. K. Carron  and  J. Hagaman  for providing help with animal breeding and genotyping. This work was supported by a  USDA NIFA-NRI  Grant  2005-35200-15224  and an  AHA Grant in Aid (Greater Southeast  affiliate  0755626B ). The authors would like to thank  Jeffrey Morris  and  Taryn Stewart  for their technical assistance.
Isl genome binding is dependent on programming partners Efficient and rapid programming of ESC differentiation into phenotypically distinct neurons by modules that differ only in one transcription factor provides an ideal system in which to study whether individual transcription factors act independently or engage in synergistic interactions. If the individual factors are recruited to DNA independently, replacing Lhx3 with Phox2a in the programming module should not affect the DNA binding preference of Isl1. To test this independent model, we performed chromatin immunoprecipitationsequencing (ChIP-seq) analyses of Isl1 in iNIL and iNIP cells 48 h after Dox induction. Inducible Isl1 was not epitope tagged and we optimized ChIP using a pool of monoclonal antibodies to Isl1. As these antibodies cross-react with both Isl1 and the closely related Isl2, we refer to the data as Isl ChIP-seq.