dataesr/scientific-paragraphs-categorization huggingface dataset:
1) amino acid sequence was used as a query in a BLASTP search of the Brachypodium proteome (http://www. gramene/org/; http://mips. helmholtz muenchen. de/plant/brachypodium/). The predicted protein Bradi2g36910 was found to have the highest sequence homology to OsPMT, sharing 63% amino acid sequence identity and 75% similarity. Recently published phylogenetic trees that contained both Brachypodium and rice BAHD acyltransferases showed that Bradi2g36910 is the most closely related Brachypodium protein to OsPMT  (Bartley et al. , 2013; Molinari et al. , 2013). As the Bradi2g36910 coding sequence was only partially supported by published expressed sequence tag sequences  (Vogel et al. , 2006) , we generated and sequenced Brad-i2g36910 cDNA clones (GenBank accession no. HG421450), from Brachypodium inbred line Bd21-3 stem tissue transcripts, finding the open reading frame (ORF) sequence to be 100% identical to the annotated gene model Bradi2g36910.
1. Quantitative reverse transcription polymerase chain reaction (qRT-PCR) analysis was performed on transcripts that had been isolated from various plant tissues, and revealed that Bradi2g36910 (hereafter referred to as BdPMT) was expressed in all organs tested (spikelet, stem internodes, leaves, and root), with highest expression levels in the spikelet (Figure  2a ). Co-expression analysis using PlaNet (http://aranet. mpimp-golm. mpg. de/index. html), a web-based platform that predicts transcriptomic co-expression networks based on temporal and spatial gene expression data  (Mutwil et al. , 2011; Harrington et al. , 2012) , revealed that BdPMT expression clustered with those of other predicted lignin biosynthetic genes including Phenylalanine Ammonia Lyase (PAL; Bradi3g49250 and Brad-i3g49260), Cinnamoyl-CoA Reductase (CCR; Bradi3g36887), and Caffeic acid O-Methyltransferase (COMT; Brad-i3g16530) (Table  S1 ).
Such data are in line with the reduced levels in these materials determined by the lignin-specific DFRC method. The data from both analysis methods therefore compellingly demonstrate that the PMT activity is associated solely with p-coumaroylation of lignin and not arabinoxylan. To more fully explore how PMT influences lignin biosynthetic fluxes and cell wall composition, we generated transgenic plants that overexpressed BdPMT in the form of an ENHANCED YELLOW FLUORESCENT PROTEIN (EYFP): BdPMT fusion protein, with expression driven by the maize UBIQUITIN promoter and intron; these lines are hereafter referred to as BdPMT OX. qRT-PCR analysis of T 2 generation plants from two independent transformation events (initially chosen as having high expression based on EYFP fluorescence and western blot analysis.
com/). Gene expression data were computed using the CLC Genomics Workbench version 6. 5. Reads were trimmed and filtered on quality with the Trim Sequences algorithm (Limit: 0. 05, Maximum ambiguities: 2). RPKM was generated by aligning expressed sequence tags (ESTs) to Gene annotations using the RNA-Seq Analysis algorithm for annotated sequence. (Parameters: Similarity 0. 8; Length fraction 0. 9). Genome sequence and annotations were downloaded from the National Center for Biotechnology Information (NCBI; http://www. ncbi. nlm. nih. gov/bioproject/ PRJNA74771). Genome sequence, annotation, and expression data were stored in an Oracle relational database and displayed using a custom web application.
ACKNOWLEDGEMENTS We thank  Fr ed eric Leg  ee for performing the Klason lignin analyses,  Cliff Foster  for performing thioacidolysis analyses,  Hoon Kim  for his help with gel-NMR methods,  Nick Thrower  for help processing RNA-Seq datasets, and  Stephen Lutgen ,  Heather Welch , and  Michael Krzyskowski  for prepping tissue samples. We thank  Marek Mutwil  and  Staffan Persson  for providing access to  AraNet Brachypodium  co-expression tools before their publication. This work was supported by the  Department of Energy's Great Lakes Bioenergy Research Center (Department of Energy, Biological and Environmental Research, Office of Science  grant no. DE-FC02-07ER64494 ).
TAp63-dependent regulation of metabolism represents a prognostic factor in cancer We next evaluated the clinical relevance in human cancers of TAp63-dependent regulation of cellular metabolism. By using a bioinformatic approach we implemented a series of coexpression analysis with patient survival estimation in human breast cancer datasets. Assessment of the impact on prognosis of correlation between p63 and metabolic enzymes would suggest a clinical relevance of this molecular axis in human cancers. First we used a big dataset from METABRIC (Molecular Taxonomy of Breast Cancer International Consortium). METABRIC dataset is a collection of over 2,000 clinically annotated primary fresh-frozen breast cancer specimens subjected to copy number and gene expression profiling  [42]. The survival annotation of the patients was used to select two cohorts of patients.
5B ). G6PD is the rate-liming step of PPP, converting glucose-6-phosphate to 6-phosphate-gluconolactone. The PPP is a major glucose metabolic pathway required for cellular demands of anabolism and antioxidant defense. PPP indeed has emerged among the major metabolic pathways involved in malignancies. Hence we decided to investigate deeper whether the TAp63-dependent regulation of this pathways showed a relevant function for cancer prognosis. We selected an additional human breast cancer dataset and we split the samples in two cohorts. The first cohort included all the samples to maximize positive correlation between p63 and the metabolic enzyme, while in the second cohort all the other samples were included. This clusterization divided the dataset in two biological groups: "Correlation" and "No Correlation".
Then, we computed survival estimation for the two cohorts, thus estimating the prognostic ability of the gene correlation. This algorithm was applied to the clinical datasets to assess p63 correlation with the major enzymes of PPP (Glucose-6-phosphate dehydrogenase, G6PD and 6-phosphogluconate dehydrogenase, PGD). As shown correlation factors between p63 and metabolic enzymes of interest in the two cohorts "good prognosis" and "bad prognosis". In dark green positive correlation, in red negative correlation. (C-D) Positive p63/G6PD and p63/PDG correlations ("Correlation" subgroups, in green) represent a negative prognostic factor for survival of breast cancer patients. Breast cancer datasets (GSE3494) was split in two cohorts 'p63/ metabolic enzyme Interaction' and 'p63/metabolic enzyme NO Interaction' on the basis of existence or not of their correlation.
Samples were vortexed for 30minutes at 4°C and thus centrifuged at 16000g for 15min at 4°C. Supernatants were collected for subsequent analyses. MS analyses were performed in polarity switch mode on an Orbitrap Exactive (Thermo Scientific) in line with an Accela autosampler and an Accela 600 pump (Thermo Scientific). Column hardware consisted of a Sequant ZIC-pHILIC column (2. 1x150mm, 5µm) coupled to a Sequant ZIC-pHILIC guard column (2. 1x20mm, 5 µm) (Merck). Flow rate was 100 ml min -1 , buffers consisted of acetonitrile (ACN) for A, and 20mM (NH 4 ) 2 CO 3 , 0. 1% NH 4 OH in ddH 2 O for B. Gradient ran from 80% to 40% ACN in 20min, followed by a wash at 20% ACN and re-equilibration at 80% ACN. Metabolites were identified and quantified using LCquan software (Thermo Scientific). Metabolites were positively identified on the basis of exact mass within 5 p.
p. m. , further validated by concordance with standard retention times and plotted as the peak area for each metabolite. Isotopomer distributions were determined on the basis of the expected reactions involving the investigated metabolite and by looking the actual isotopomeric distribution of mass spectra chromatograms. Results were graphed with Graphpad Prism 5. 01 (Graphpad Software Inc) as means + SD. Statistical analyses were performed with the same software, as a result of paired t-test or two-way ANOVA among the results obtained from doxycycline supplemented (dox+) and non-supplemented (dox-) cells at the steady state or in a time-dependent fashion (independent variables being doxycycline supplementation and time-course assays).
Cell cycle Analysis Quantification of sub-G1 population and cell cycle phase was performed by FACS analysis of propidium iodide-staining nuclei, carried out in a FACScan flow cytometer (Becton Dickinson, Heidelberg, Germany) using the CELLQuest software system. For sub-G1 analysis, cells were grown and detached from plates via trypsinization, then fixed and stained with propidium iodide (PI, 50mg/ml) for 30 min. For cell-cycle analysis BrdU (Invitrogen) was added to live cells for 100min and detected as described previously  [27]. Flow cytometry was performed on a FACSCalibur cytometer. For PI exclusion, PI was added to cell culture medium (1 mg/ml) for 5min. Non-adherent and adherent cells were collected and analysed by Flow cytometry on a FACSCalibur cytometer.
Bioinformatics analyses For correlation analysis between p63 and metabolic enzymes in top and low 10% patients from Metabric dataset, we computed Pearson correlation coefficient between p63 expression profile and profiles of genes from the pathway for each subset of patients ("good" and "poor" prognosis). The p-values were computed using Monte-Carlo simulation procedure. The null hypothesis that was tested for each genes pair assumed correlation of expression between p63 and a gene from the pathways (next referred to as a gene P) in a cohort of selected samples to be equal to the correlation in the whole set of samples. To estimate the p-value, a random cohort of samples has been sampled 1000 times and correlation between p63 and a gene P has been computed. Therefore, we deduced a distribution (of size 1000) of correlation between p63 and a gene P in a randomly selected cohort of ~200 samples.
ACKNOWLEDGEMENTS ADA and LZ are supported by funds from the  Italian National Blood Centre  ( Centro Nazionale Sangue -CNS -Istituto Superiore Sanita` -Rome, Italy ). ADA was supported by student mobility and post-doctoral funds from the  Interuniversity Consortium for Biotechnologies (CIB). The work has been supported in part by the  Cancer Research-UK , by the  Medical Research Council, UK ; grants  AIRC  ( 2011-IG11955 ), AIRC 5xmille (MCO # 9979 ), Telethon Grant  GGPO9133 ,  Min. Salute  (Ric oncol  26/07 ) and IDI-IRCCS (RF08 c. 15, RF07 c. 57) to GM. CRB is financially supported by a  Rubicon Fellowship  from the  Netherlands Organisation for Scientific Research.
Specific primers for these targets genes and Ribosomal Protein L13A (RPL13A), as the reference gene, were constructed using the Universal Probe Library Assay Design Center (\. rocheappliedscience. com) and their sequences were searched against Gen-Bank sequences with the BLAST program to ensure the specificity of primers. Real-time PCR was carried out using a LightCycler 480 apparatus (Roche, Meylan, France). Reactions were set up using the following final concentrations: 0. 5 mM of sense and antisens primers, 1X 480 SYBER Green master mix and 4 ml of 1/20 diluted cDNA. Cycling conditions were as follows: denaturation (95uC for 5 min), amplification and quantitation (95uC for 10 s, 60uC for 10 s and 72uC for 15 s) repeated 40 times, a melting curve program (65-95uC with a ramp rate of 2. 2uC/s) and a cooling step to 4uC.
In addition to the no-reverse transcription and no-template controls, an independently inter run calibrator (IRC) was included in each RT-PCR assay. This IRC was obtained from blast-cells. In each assay, an aliquot of the IRC cDNA was 20 times diluted and was submitted to the qPCR protocol as the unknown samples. PCR efficiencies for each quantified target and reference were calculated using known serial dilutions of each specific cDNA. Data were analysed using the LightCyclerH480 Software release 1. 5. 0. Each specific target transcription level was normalized to the geometric mean of the transcription levels of the reference gene, using the Advanced Relative Quantification of the LightCyclerH480 Software. Efficiency was controlled for each specific gene amplification. For each sample, the results are expressed in concentration ratio (target gene mRNA level/reference gene mRNA level).
Our work has revealed a higher level of endemism than previously recognized. Most (90%) Australian sphingids are endemic to the continent (45%) or to Australia, the Pacific Islands and the Papuan and Wallacean regions (45%). Only seven species (10%) have ranges that extend beyond this major biogeographical boundary toward SE Asia and other regions of the Old World. Main Conclusions: This study has established that overlooked cryptic diversity and inaccurate species delineation produced significant misconceptions concerning diversity and distribution patterns in a group of insects that is considered well known taxonomically. Because DNA barcoding represents a straightforward way to test taxonomic boundaries, its implementation can improve the accuracy of primary diversity data in biogeography and conservation studies.
Particularly comprehensive efforts have been directed toward Australian Lepidoptera. Much of this information is available in an indexed public reference library of DNA barcode sequences  [16, 17]  (database accessible in BOLD at www. boldsystems. org) and via the current taxonomic system for the complete Australian fauna  [18]  (see also http://www. lepbarcoding. org/australia). The order Lepidoptera includes about 130 families whose diversity varies dramatically -from a single to more than 24,000 species  [19]. Because the current count of approximately 160,000 described species is thought to reflect less than one-third of the total  [20] , diversity estimates for some families, especially smallerbodied taxa, are very uncertain. However, other families, such as the Sphingidae, have attracted so much taxonomic interest that present estimates of diversity are thought to be comprehensive except for 'frontier' regions.
Data Analysis A nearest Neighbour analysis was conducted with BOLD using either a dataset restricted to sphingid records from Australia or the full database for world sphingids, including all COI barcode sequences longer than 500 bp. The latter database includes about 23 K sequences, 1. 8 K species and subspecies (as of September 12 th , 2013). BOLD was also used for sequence alignment and calculation of genetic divergences and Neighbour Joining (NJ) trees using a K2P distance model  [33]  after alignment. These trees were imported in iTOL  [34]  to exploit its capacity for the visualization of large trees. In two cases where DNA barcode analysis suggested potential cases of overlooked diversity, we also Table  1. Sampling coverage and genetic distances for the Sphingidae of Australia. generated ''geo-phylogenies'' using GenGIS 2.
2. 0  [35]  to better visualize the geographical structure of genetic variation. MEGA 5. 2. 2  [36]  was also used to produce character-based trees using the maximum parsimony (MP) criterion (all settings at default, with the complete deletion option for missing data) in cases of speciescomplexes where genetic distances are shallow and require a more rigorous method for reconstructing relationships between terminals. The topology of these DNA barcode ''gene trees'' was compared with the ''species tree'' inferred from current taxonomy based on morphology. Attention was directed toward an examination of the incidence of reciprocal monophyly rather than relationships between species. The division of a species into two or more clusters of specimens was considered a potential indicator of cryptic diversity (candidate species) when the species included two or more monophyletic clusters with more than 2% divergence  [37, 38]  in their DNA barcode sequences.
Data Accessibility (BOLD, GenBank) Data on specimen vouchers, sequence data including trace files and registered primer pairs are available on BOLD within the datasets SPH01AUS (dx. doi. org/10. 5883/DATASET-SPH01AUS) for Australian records, and SPH02AUS (dx. doi. org/10. 5883/DS-SPH02AUS) for conspecifics and most closely related taxa outside Australia. All sequences were also deposited in GenBank for COI DNA barcodes and for 28S rDNA sequences (see Tables  S1-S3 ).
The 599 conspecific records include 112 consubspecific records and 140 hetero-subspecific records for 18 polytypic Australian species, and 347 records for 26 monotypic Australian species or species with no available record for distinct subspecies. The results of the analysis of this combined dataset are given in Fig. 3  (see Fig. S5  for details). This analysis revealed an additional very deep split in Eupanacra splendens and ten more cases of deep (. 2%) intraspecific divergences involving Agrius convolvuli, Cephonodes picus, Daphnis placida, Gnathothlibus eras, Hippotion brennus, Macroglossum corythus, M. tenebrosa, Nephele hespera, Psilogramma menephron and Theretra nessus (Table  1 , Fig. 3 ). The two DNA barcode clusters in Agrius convolvuli show deep divergence (Dmax = 5. 37%), and clear geographical segregation (Fig.
Numbers refer to species numbers as listed in Table  1. An interactive and fully explorable version of the tree is available at http://itol. embl. de/shared/rodroug. (See also Fig. S1. ). doi:10. 1371/journal. pone. 0101108. g002 Figure  3. DNA barcode variation in the geographically extended dataset. Neighbour Joining tree based on K2P distances for the 1054 Australian sphingid records analysed in Fig. 2  augmented by 735 records for conspecifics, co-subspecifics and closely related species from outside Australia. In the centre of the tree, the inner (red) and outer (green) histograms represent the maximum intraspecific distance (dG) and the distance to the nearest heterospecific Neighbour on BOLD (Dsp). The species with dG. 2% and Dsp,2% are highlighted in the tree with red and green colour ranges, respectively; those species are also listed with numbers following species numbers in Table  1.
An interactive and fully explorable and searchable version of this tree can be accessed at http://itol. embl. de/shared/rodroug. (See also Fig. S5. ). doi:10. 1371/journal. pone. 0101108. g003 (Fig. S2 ). Finally, both M. corythus and P. menephron represent species complexes requiring a comprehensive revision to establish the validity of the many available names; there are nine valid subspecies names  [21]  for the former, while we found that the sequenced holotypes of 10 species within genus Psilogramma fall within our set of DNA barcode clusters forming the P. menephron complex (Fig. S5 ). In contrast to these cases of deep sequence divergence within species, the extended dataset revealed eight cases where Australian taxa showed less than 2% divergence (Table  S5 ) from non-Australian specimens assigned to a different species (Fig.
Extent of the Wallacean shortfall Knowledge of species distributions is fundamental to the design of sound conservation strategies and gaps in such information have been termed the ''Wallacean shortfall'' by biogeographers and conservation biologists  [6]. Besides the scarcity of distributional data for most species, incorrect or incomplete delineation of taxonomic units will, in most cases, lead to incorrect information on species distributions, impeding efforts to understand the historical and contemporaneous processes underlying the occurrence patterns of species or subspecies  [6, 53]. This study has revealed a significant mismatch between past understanding of the taxonomic diversity of Australian sphingids and that revealed through molecular analysis, especially when results are placed in a broader biogeographical context.
The recent description of two Australian species each of Psilogramma and Hopliocnema  [23, 41, 42] , of one new species of Coenotes  [44] , and the separation by Vaglia & Haxaire  [54]  of the Papuan A. miskinoides from its Australian counterpart, A. miskini, were all motivated by DNA barcode data coupled subsequently with morphological analysis. These updates raise the number of endemic Australian species and subspecies from 26 to 34 (Fig. 4 , Table  S4 ). Further analysis of N. hespera may add another endemic species, while the possible synonymy of subspecies of T. indistincta may change its status from one polytypic species with an endemic Australian subspecies to a monotypic species distributed over Australia and the Malesian region. Our results additionally revealed that seven species with broad ranges (namely Cephonodes picus, Macroglossum corythus, Theretra nessus, Daphnis placida, Nephele hespera, Agrius convolvuli, and Psilogramma menephron; see Fig.
Acknowledgments This work was enabled by the generosity of the many collaborators involved in the DNA barcoding campaign on Lepidoptera. We are especially grateful to  Ron Brechlin ,  Graeme Cocks ,  Ted Edwards ,  Ulf Eitschberger ,  Yves Estradel ,  Egbert Friedrich ,  Marianne Horak ,  David Lane ,  John La Salle ,  Ian McMillan ,  Tomas Melichar ,  Andrew Mitchell ,  Max Moulds ,  Jim Tuttle , and  Thierry Vaglia. We also thank colleagues at the  Canadian Centre for DNA Barcoding.
Accordingly green and blue virtual water flows were calculated as the product of commodity flows and the green and blue virtual water content of the respective crop in the source grid cell. When eventually all demands in deficit cells within a country were met from the nearest national surpluses, the remaining surplus cells were identified as export cells for international trade. Green and blue virtual water exports were determined as weighted averages of crop virtual water contents from these surplus cells, according to their respective contribution to the country's total international export. Bi-national commodity trade data for the period 1998- national crop production, the exports related to this difference were deleted from the trade flow database and flows redirected from producing to importing countries.
Back Close Full requirements in the source regions, we also used national indicators such as malnutrition and stunting of children to address food insecurity and water-related opportunity costs of export production. Undernourished population was taken from the FAO-STAT hunger database -http://www. fao. org/hunger/en/ (last access: 27 January 2013), and nutritional stunting from the WHO database on child growth and malnutritionhttp://www. who. int/nutgrowthdb/en/ (last access: 27 January 2013).
So in the case of soy from Brazil, water stress in the main export production region is higher than national average, in the case of cocoa and coffee from Ivory Coast, Ghana and Nigeria water stress (and food insecurity) in the main export production region is lower than national average. Initial comparison of our global top-down identification of export production regions with bottom-up census data shows relatively good agreement in the case of soy production in Brazil (Fig. 4a ), and for cocoa production in Nigeria (Fig. 4b ), but significant mismatch in Ivory Coast and Ghana (not shown here). Expert consultations confirmed that result, pointing out weaknesses of global crop datasets (here  Monfreda et al. , 2008)   some countries, where sub-national crop production patterns are not sufficiently captured.
We have carried out micro-analytical analysis on such organized data focusing on endogamy and exogamy divided by each generations and we have assessed consanguinity within the sample. We have started from marriage certifications in which, in addition to the names and the age of the partners, the original places, the occupation of the husband and possibly both parents' occupations were recorded. Wives' occupations were rarely reported. Results have finally been explained by cultural and social aspects which were typical of similar populations.