dataesr/scientific-paragraphs-categorization huggingface dataset:
2 ha, and their mean yield was 118 t•ha -1. The farm located in the southern part of the island (hereafter referred to as FS) had a mean annual rainfall of 940 mm. The 25 fields used in this study were located at altitudes between 170 m and 480 m, and 15 fields were located lower than 300 m and irrigated. Five different sugarcane cultivars were used: R570, R577, R579, R582 and R584. The mean area of the studied fields was 13. 1 ha, and the mean yield was 94 t•ha -1. Although the yields observed in these well-managed farms were higher than the 76 t•ha -1 island average, the farms were selected because (i) the farmers were able to provide agronomic data (cultivar, yield and harvest date) for each field and (ii) both farmers used unique agricultural practices for all of their respective fields, which simplified the error analysis of the methods.
Climatic and Agronomic Data The climatic and agronomic data were collected over three growing cycles  (2010, 2011 and 2012). The climatic data were acquired at daily a time step, and the rainfall, potential evapotranspiration, global radiation and mean temperature data were collected at two climatic and five rainfall stations located close to the studied fields (Figure  1 ). The soil characteristics were extracted from the Reunion soil map  [21]. The cropping practices data were obtained from the farmers' databases and included observed yield and harvest dates for each studied field and irrigation schedules. The observed yield was computed from the weighing of the harvested stalk fresh biomass of each field for the three study years.
MOSICAS Sugarcane Crop Model MOSICAS is a semi-empirical sugarcane crop model  [8] , and it was used to simulate the daily growth of a uniform first ratoon sugarcane field planted with the cultivar R570 as a function of the climatic inputs and specific soil, cultivar and cropping practice parameters. This model relies on a water balance module and growth module (Figure  3 ). The water balance module was adapted by  [33]  from the CERES Maize model  [34] , and it computed a water satisfaction index based on the water inputs and outputs. The growth module was based on a big leaf model  [35] , and it computed the daily accumulated biomass production (dry and fresh) based on the water satisfaction index, global radiation and temperature and radiation use efficiency. Similar to the Kumar-Monteith model, the fAPAR is a major variable in the estimation of the biomass production.
Model's Relevance We tested the ability of three remote sensing-based methods to estimate the yield of sugarcane at the field scale under a wide range of climatic conditions and cropping practices. The accuracy of methods in estimating and providing early forecasts of sugarcane yield were evaluated for two contrasting regions of Reunion Island. The results showed that the integrated NDVI empirical model provided the best yield estimation with an RMSE of 10. 4 t•ha -1 , whereas the conventionally used MOSICAS-RAW method estimated the yield with an RMSE of 15. 3 t•ha -1. An analysis of variance showed that the cropping year (3 years in the data set) had no effect on the linear regressions between the simulated and observed yields. Our dataset did not include the exceptional climatic years (i.
The Eco-gene model and case studies The Eco-gene model has been used to elucidate genetic processes and the consequences of logging and forest fragmentation in the long term  (Sebben et al. , 2008). In this model, data on genetic structure, gene flow and the reproductive biology of Amazonian timber species before and after logging were integrated with data on growth, regeneration and ecology under different scenarios and intensities of logging. The expectation was that these results would help to guide and create new criteria for sustainable logging in the region. Seven species with contrasting ecological and reproductive characteristics were selected for incorporation in the model. The species fit into three ecological groups (pioneer, climax of fast growth/light demanding and climax of slow growth/shade tolerant categories) and have different reproductive systems (dioecious, monoecious, hermaphrodite), with different pollinators and seed dispersers.
Eco-gene simulation results: A quick overview The Eco-gene simulation model was developed to study silvicultural impacts on temperate forests  (Degen et al. , 1996)  and then adapted to be applied in tropical forest management  (Degen et al. 2003 , Degen et al. , 2004 , Kanashiro et al. 2002b). Considerable effort was taken to collect the information needed to run the model, and below we present some of the results. Sebben et al. (2008)  provided results for the four species, B. guianensis, H. courbaril, M. huberi and S. globulifera, with the model parameterized using empirical data from field studies in FLONA. Included data were genotypes at microsatellite loci, demography, ecology and growth for each species. Several scenarios, combining two different cutting diameters (45 and 60 cm dbh) and two different cutting cycles (30 and 65 years) as used in Brazil and French Guiana were tested.
Logging scenarios were applied for six cutting cycles, and final genetic and demographic data were compared to baseline data from corresponding control scenarios. At the end of the simulated period the basal area was strongly reduced under all conditions in B. guianensis, H. courbaril and M. huberi. Symphonia globulifera, however, was able to recover its basal area following logging in two scenarios. Based on these results, a Minimum Cutting Diameter (MCD) of 60 cm diameter at breast height was recommended. Simulations studies for D. odorata and J. copaia were undertaken by  Vinson et al. (2013) , which confirmed the importance in modelling of considering population density, growth patterns and breeding systems. Results in terms of basal area recovery were consistent with concerns stated by  van Gardingen et al.
e. collections were not performed during rainfall, when the temperature was under 10°C or when the wind speed was above 5 m/s. Weather data were recorded on a daily base in the direct vicinity of both crush pens using two wireless weather stations with a data logger (TFA Dostmann, Wertheim-Reicholzheim, Germany). During the entire study period, no other livestock grazed in the immediate surroundings (>300 m) of the study site.
Statistical analysis All statistical analyses were performed using the IBM SPSS Statistics 20. 0 program. The analyses aimed to compare the differences in the four body regions selected for the collections by direct aspiration, and to check whether there were statistically significant differences in the number of midges collected from cattle and sheep. Furthermore, with this analysis the possibility of an influence of the different animals used every day in the results was tested. A mixed ANOVA model for repeated measurements was used in the analysis, where cattle and sheep, which were different every day to minimize individual differences in host attractiveness, were considered as random effects. The four different body regions (head, back, belly/flanks and legs) and the two groups of animals (cattle and sheep) were considered as fixed effects in study B and study C respectively.
Acknowledgements This study was funded by the  Alfonso Martín Escudero Foundation (Madrid, Spain) , which provided the postdoctoral grant for the execution of this study. We would like to thank  Bettina Boehme ,  Susanne Helm ,  Claudia Meier  and  Antje Hoppenheit  for their assistance in the collection of midges. We also acknowledge  Juergen Mai  and the animal caretakers from  LVAT (Lehr-und Versuchsanstalt für Tierzucht und Tierhaltung  e. V. ) for providing the animals and their assistance in animal handling. We appreciate the support of  Ekaterina Edelstein  ( Economics Department, Freie Universität Berlin, Germany ) and  Juan Pablo Gutierrez  ( Animal Production Department, Complutense University, Madrid, Spain ) with the statistical analyses. Claire Garros  ( CIRAD, France ) is thanked for her support and clarifications with the species terminology.
The accessions considered cover the primary and secondary distribution ranges of the two A genome species (Figure  S1 ). Most wild T. m. boeoticum samples were from primary habitats in the central-eastern parts of the Fertile Crescent. Sixty eight samples of domesticated einkorn (T. m. monococcum), mainly from Europe, and nine T. m. aegilopoides from the Balkans were included. For T. urartu, 91 accessions were collected from the western part and 74 from the central-eastern part of the Fertile Crescent; the five accessions from Armenia may represent cases of colonization of segetal habitats. Passport data of T. urartu accessions  (Valkoun et al. 1998)  support the sampling in primary habitats.
DNA extraction and amplified fragment-length polymorphism (AFLP) analysis DNA extraction from 7-d-old seedlings followed a modified CTAB procedure  (Murray and Thompson 1980). AFLP analysis was as in  Vos et al. (1995) , using the AFLP combinations E36-M36, E37-M40, E42M32, E42-M33, E42-M38, E40-M40, and E40-M38. For each wheat line, the presence and absence of amplified fragments was scored, considering only unambiguous electrophoretic readings. From the AFLP matrix, pair-wise genetic distances among accessions were computed as in  Jaccard (1908). Principal coordinates analysis was performed on genetic distance data. Phylogenetic analyses were based on the computer package NTSYS pc V. 2. 1  (Rohlf 2000).
urartu accession ID388. The F 1 plants were self-fertilized and the five derived F 2 plants backcrossed to L118 to obtain 71 lines. In addition, 48 lines were derived from backcrosses to the F 1 without a step of self-fertilization. The lines were subsequently backcrossed four times to L118. The offspring were fingerprinted using a panel of 155 microsatellites mapped in the ID69 • ID49 segregating population. Plant lines bearing nonredundant chromosome segments of T. urartu were selected. The panel of codominant markers used in fingerprinting was based on microsatellite markers mapping to the A genome of hexaploid wheat. The markers were tested on T. m ssp. monococcum L118 and ID69, T. urartu ID388, and T. monococcum ssp. boeoticum ID49 and subsequently mapped on the ID49 • ID69 segregating population  (Taenzler et al.
2005). In our case, the existence and level of DNA interchange among A m and A u chromosomes was implemented based on the following steps. Step 1. In the B53 and B54 AFLP databases, amplified fragments were assigned specifically to T. monococcum when absent in T. urartu, and the reverse. Step 2. The database was queried for AFLP fragments assigned to linkage groups in the T. monococcum map of  Taenzler et al. (2002). Groups of 1 to 12 A m or A u fragments present in populations B53 and B54 and mapping to the same linkage group interval were interpreted as chromosomal regions homozygous in B53 and B54, derived either from T. monococcum or from T. urartu. AFLP fragments present in T. urartu, identical in size with A m fragments segregating in the T. monococcum crosses of  Taenzler et al. (2002) , were considered homeologous, and thus anchored in the  Taenzler et al.
(2002)  backbone map to the corresponding linkage regions. Step 3. Fragments segregating in B53 and B54 were analyzed by the software MAPMAKER/EXP Version 3. 0  (Lincoln et al. 1993). A large number of independent groups of fragments linked through null or reduced recombination values was observed (linkage subgroups). Subgroups were chosen with at least a polymorphism mapping to a specific linkage region of the map of  Taenzler et al. (2002). Within subgroups including both A m and A u fragments, two cases were distinguished. Repulsion between linked A m and A u fragments: in this case two clusters of A m and A u fragments were anchored to the same syntenic region of the backbone map. The region was, accordingly, considered heterozygous for T. monococcum and T. urartu DNA. Coupling between A m and A u fragments: this finding was interpreted as if in one of the two gametes that generated the two original hybrid seeds, a crossing-over event in the F 1 plants created a T.
The modes and means of the two distributions were different: B53 had a mode class of 30-34. 9 g and an average weight of 31. 4 g per 1000 seeds, whereas B54 displayed a mode of 15-19. 9 g and an average weight of 19. 4 g per 1000 seeds. The data support the conclusion that, although T. monococcum and T. urartu crosses produce mostly sterile F 1 plants, it is still possible to recover a high degree of fertility in rare hybrid-derived progenies and to develop introgression lines.
ACKNOWLEDGMENTS Most of the activities described in this paper were supported by projects " From seeds  to pasta" and " Genomica e genetica di frumento monococco a supporto di nutrizione e salute ," funded by Milan municipality and AGER consortium, respectively. Special acknowledgments for their help go to  Delphine Boyer  and  Dr. Valeria Rizzi. The introgression lines generated are available upon request, and a few seeds per line will be distributed.
, 1999). Different primer sets were tested for each gene (data not shown) until a primer pair was identified (Table  1 ) satisfying the following criteria: 1. No PCR product was amplified from 50 ng genomic DNA, using the same PCR conditions designed for the real time RT-PCR assays, 2. In the absence of PCR product, no primer dimers were formed, and 3. The efficiency of the amplification, as determined by the standard curve method, was similar for all genes, and approached 1 (not shown). The ability of the selected primer pairs to detect domain specific expression was tested in real time RT-PCR experiments using as template total RNA extracted from the upper (Top in Figure  1B ) or lower (Bottom in Figure  1B ) halves of WT kernels collected at 10 DAP. As shown in Figure  1B , the upper part of the kernels does not contain transfer cells and very little embryo surrounding region, whilst both the upper and lower halves should contain roughly equivalent amounts of aleurone and starchy endosperm tissues.
, 2006). The availability and variety of markers greatly facilitates the isolation and study of new mutant lines, although their development and design may be cumbersome  (Guitton et al. , 2004). It is not unrealistic to expect that markers based on genes with developmentally regulated expression in a given tissue or cell type are somehow involved in determining its identity  (Li and Wurtzel, 1998; Kubo et al. , 2005) ; thus, the growing information about gene expression timing and localization collected in the bibliography and public databases allows to design directed transcriptomic screens to study particular processes or tissues, even when these are not easily accessible. This work has a dual purpose: we have designed and tested a molecular kit for the screening of a maize seed mutant collection and the dissection of mutant phenotypes affecting seed development, based on a set of genes previously identified as specific of particular kernel compartments.
All primary antibodies were from Cell Signaling Technology (Danvers, MA), except for TLR4, that was from Santa Cruz Biotechnology Inc. (Santa Cruz, CA). Preliminary experiments verified the complete cross reactivity of the human antibodies with the analyzed pig proteins (data not shown). Proteins were detected with horseradish peroxidaseconjugated secondary antibodies (Cell Signaling Technology) and enhanced chemiluminescence reagent (ECL kit LiteAblot Extend, Euroclone), followed by analysis of chemiluminescence with the charge-coupled device camera detection system Las4000 Image Quant (GE Health Care Europe GmbH, Milan). Relative levels of TLR4, MyD88, IRAK-4, Tollip and IRAK-M were normalized to a-tubulin, whereas the phosphorylated proteins were normalized to their corresponding unphosphorylated forms.
ACKNOWLeDGMeNTS This material is based on work supported by the US National Science Foundation under grants #0321678, #0638541, #0822284 and #1026200 to Y. Y. , C. A. M. , S. R. and R. A. W. and the Bud Antle Endowed Chair of Excellence in Agriculture and Life Sciences to R. A. W. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the US National Science Foundation. We thank coauthor J. Carney, author of Black Rice, for her inspiration and encouragement in our attempt to convey the deep-rooted and far-reaching history of African rice farming through the O. glaberrima genome sequence. We thank Z. Lu from Arizona Statistical Consulting Laboratory for statistical advice and J. Ross-Ibarra for comments.
ONLINE METHODS O. glaberrima genome sequencing. A total of 3,485 MTP BAC clones were selected from the O. glaberrima BAC-based physical map. Of those, 166 BACs representing Chr3S were individually Sanger sequenced, and the remaining BACs were divided into 115 chromosome-specific BAC pools (28-30 chromosomally ordered BACs per pool). All MTP BACs were re-end sequenced and compared to the O. glaberrima BAC-end data set to validate the clone identity. About 3-5 µg of pooled BAC DNA was used to construct each Roche/454 Titanium library with multiplex identifiers (MID) using the manufacturer's protocol, followed by Bioanalyzer and fluorometry analyses to assess library quality and quantity, respectively. Clonal amplification of individual libraries was performed by emulsion-based clonal amplification (emPCR amplification), and a group of five to six MID-tagged DNA beads was pooled and loaded onto one picotiter plate (PTP) and sequenced on a Roche/454 GS FLX instrument using Titanium chemistry following the manufacturer's protocols.
sativa ssp. japonica reference genome using Nucmer, and the scaffolds were grouped into either the mapped or unmapped category. All mapped scaffolds were ordered and oriented as per the O. sativa ssp. japonica chromosomes. (vi) Incorporation of Sanger sequences: as a final step, the existing Sanger BAC sequences from the short arm of chromosome 3 and from chromosomes 6, 8, 11 and 12 were spliced into the existing 454 assembly. The 454 scaffolds corresponding to Sanger sequences were identified by BLASTN, and the 454 scaffolds were replaced by the Sanger sequences. Protein-coding and tRNA gene predictions. Gene models were determined using a combination approach of a consensus prediction from ab initio gene predictions (Fgenesh++ 54 , ProtoMap 55 and GeneID  13  ) and an optimal spliced alignment from protein and transcript homology searches using GenomeThreader  56  with a splice site model for rice.
Homologous protein evidence mappings were derived from annotations of the following finished plant genome projects: O. sativa (version TIGR6. 1 (ref. 57) and RAP2 (ref. 58)), Sorghum bicolor (high confidence set of version 1. 4)  59  , Arabidopsis thaliana (version TAIR9)  60  and Brachypodium (version 1. 2)  61. Optimal spliced alignments of TIGR transcript assemblies  62  , comprising several monocotyledonous species (Zea mays, Saccharum officinale, O. sativa, Hordeum vulgare, Triticum aestivum and Brachypodium distachyon) as well as assembled RNA-Seq reads of O. glaberrima, were used for gene predictions based on homology and/or experimental evidence. Additionally, Markov models and position-specific scoring matrices were applied to estimate the coding potential for the models without protein homology and to evaluate splice site and start and stop codons, respectively.
Strand-specific RNA-Seq libraries of the different species were constructed using the protocol described previously  63  and sequenced on one lane of an Illumina HiSeq 2000 instrument. Raw pair-end reads were de novo assembled using Trinity r2012-05-18 (ref. 64). The assembled sequences were aligned to O. sativa ssp. japonica RefSeq using Bowtie V2. 1. 0 (ref. 65). The results of the alignment were visualized and annotated using the Integrative Genomics Viewer (IGV) V2. 3 (ref. 66). Repeat annotation. Known repeats were annotated using RepeatMasker with Rebase TE  67  , TIGR/MSU repeat  57  and in-house Oryza TE databases. De novo repeats were identified with RepeatScout  68. RepeatMasker and LTR-FINDER  69  were used to identify complete LTR-RTs by examination of target site duplications (TSDs), primer binding sites, polypurinic tracts and element boundaries.
HiSeq libraries were constructed on a SPRIworks System I (Beckman Coulter, USA) with custom MID adaptors as described in Huang et al. (2009)   75  and sequenced on an Illumina HiSeq 2000 instrument using 2 × 100-bp cycle sequencing chemistry. For O. barthii accessions sequenced with 5× coverage, 20 individuals were sequenced in one lane of the HiSeq 2000 instrument. For other accessions, one individual was sequenced in one lane of the GAIIx instrument, or five individuals were sequenced in one lane of the HiSeq 2000 instrument. SNP detection. Sequence reads from O. glaberrima and O. barthii accessions were separated according to the 3-bp barcode added in the adaptor using custom Perl scripts. Burrows-Wheeler Aligner (BWA) V0. 5. 9 (ref. 76) was used to map raw pair-end reads onto the O. glaberrima CG14 genome assembly.
History of O. glaberrima domestication. Several methods were applied to identify the population structure of O. barthii. First, the SNPs of the O. barthii population were loaded into the program ADMIXTURE  23  to perform maximum likelihood clustering analysis. The result from ADMIXTURE was plotted using an R script. Second, a NJ tree of the O. barthii population was constructed for all SNP positions using the program Phylip with 1,000 bootstrap steps. Third, PCA was performed to assess the population structure of O. barthii using SMARTPCA  79. The result of PCA was plotted using an R script. Finally, 10,000 SNP positions were randomly selected from the SNP set using a PERL script. The SNPs were input into the program PhyML 3. 0 (ref. 80) to construct a maximum likelihood tree using the HKY85 model with 100 bootstrap steps.
In total, 8,374,445 SNPs were used to investigate the genetic relationship of O. glaberrima and O. barthii. Genetic differentiation (F ST ) between O. glaberrima and each of the O. barthii subpopulations was calculated using VCFtools V0. 1. 12 (ref. 81). The NJ tree of the O. glaberrima and O. barthii accessions was constructed for all SNP positions using the program Phylip with 1,000 bootstrap steps. The country of origin for each O. barthii accession was obtained from ARC and IRRI-GRC. The proportions of O. barthii accessions from different subgroups were calculated for each country. The proportions of O. barthii accessions of different types were also calculated for each country.
07 (ref. 82). The distance that the LD decays to half of the highest value was calculated, and then plotted, using an R script. Identification of regions under artificial selection. The reduction of genetic diversity (π w /π c ) was calculated for nonoverlapping 100-kb windows across the genome using VCFtools V0. 1. 12. The windows that were in the 2. 5% right tail of the distribution (π w /π c > 5. 8) were selected as candidate regions. The distribution of π w /π c along the chromosome was plotted using an R script. SweeD  83  was used for detecting complete selective sweeps in the O. glaberrima genome with default settings. A total of 4,436,289 SNPs were input into the SweeD program after filtering some SNPs according to the program's requirements. This program implements the composite likelihood ratio statistic  30  , which identifies regions with significant deviations from the neutral SFS and performs better with genome-wide SNP data than SweepFinder  30  does.