dataesr/scientific-paragraphs-categorization huggingface dataset:
The HIPPO program is supported by the  National Science Foundation (NSF) , and its operation is managed by the  Earth Observing Laboratory (EOL)  of the  National Center for Atmospheric Research (NCAR). We also thank the  Canadian Space Agency (CSA) , which provides most of the funding support for ACE. We are grateful to the  HALOE team  for publishing their data for scientific use. This research was supported in part by the  Environment Research and Technology Development Fund  ( 2A-1102 ) of the  Ministry of the Environment, Japan. TCCON measurements from  Pasadena ,  Lamont ,  Park Falls  and Darwin are funded by  NASA  grant  NNX14AI60G  and  NASA   Orbiting Carbon Observatory Program. We are grateful to the  DOE ARM program  for technical support of TCCON in Lamont and Darwin and to  Jeff Ayers  for technical support of the TCCON measurements in Park Falls.
All 11 populations were tested simultaneously constituting an experimental block of 176 boxes. We then replicated the previous experimental block six times, for a total of 1056 test boxes (see Table  S1  for details). Females laid eggs in 1042 of the 1056 test boxes (98. 7%). We discarded test boxes without eggs (1. 3%) from the analysis. In total, 108,098 eggs were counted on the oviposition substrates during the test phases. All tests were performed in a temperaturecontrolled chamber set at 21 °C. All datasets are available from the Dryad Digital Repository: http://dx. doi. org/10. 5061/dryad. d8d01.
Data analysis In this experimental protocol, oviposition performance was measured as the total number of eggs laid on the correct medium (banana for flies conditioned to avoid strawberry, and strawberry for flies conditioned to avoid banana). Thus, oviposition performance depends on both fecundity and learning ability. We used the proportion of eggs laid in the "correct" substrate to compare learning ability between populations from different ranges. We also measured the fecundity of females from different ranges as the total number of eggs laid through the experiment on any substrate. Number of observations was both large and only slightly unbalanced (Table  S1 ). For all variables, we performed a generalized linear mixed model (GLMM) analysis with Laplace approximation  (Bolker et al. 2009 ), using the lme4 package  (Bates et al.
Results Does invasive D. subobscura lay more eggs on the correct medium than native individuals? Our results showed that invasive populations laid significantly more eggs on the correct medium over the course of the experiment than did the native females (Fig. 2 ; Adjusted means [lower; upper 95% confidence interval (CI)]: n correct eggs (native) = 39. 44 [36. 66; 42. 20] , n correct eggs (invasive) = 71. 69 [66. 78; 76. 77] ; LRT: v 2 1 = 9. 884, P = 0. 002). Significantly more eggs were laid on the correct medium during the second test phase ("test" effect: n correct eggs (test 1) = 44. 16 [40. 93; 47. 69] , n correct eggs (test2) = 80. 83 [75. 54; 85. 93] ; LRT: v 2 1 = 10. 02, P = 0. 002), as expected from its longer duration (12 h vs. 8 h). When using a time "adjusted" dataset with a second test phase of 8 h (assuming a linear relationship between time and egg-laying), similar number eggs were laid in the correct medium during both phases (adjusted "test" effect: n correct eggs (test 1) = 42.
Acknowledgments This work was supported by the  European Research Council  under the European Community's  Seventh Framework Programme  ( FP7/2007-2013)/ERC Grant  agreement no. 209540  to F. Mery. L. E. Castañeda was supported by  Fondo Nacional de Desarrollo Cient ıfico y Tecnol ogico FONDECYT   1140066  (Chile). M. Pascual is a member of the research group  2014SGR-336  of the  Generalitat de Catalunya  and supported by project  CTM2013-48163. We are grateful to the  INRA center of Gotheron  who allow us to collect insects in their orchard. J. Foucaud would like to thank  Andr ee Cubier  and  Clotilde Charlut  for their continuous support.
We examine the evolution of market potential and its role in driving economic growth over the long twentieth century. Theoretically, we exploit a structural gravity model to derive a closed-form solution for a widely-used measure of market potential. We are thus able to express market potential as a function of directly observable and easily estimated variables. Empirically, we collect a large dataset on aggregate and bilateral trade flows as well as output for 51 countries. We find that market potential exhibits an upward trend across all regions of the world from the early 1930s and that this trend significantly deviates from the evolution of world GDP. Finally, using exogenous variation in traderelated distances to world markets, we demonstrate a significant causal role of market potential in driving global income growth over this period.
Our goal, then, comes in assessing the relationship between globalization and growth in the long run by: (1) developing a theoretically-derived measure of market potential appropriate for historical use rather than relying on narratives derived from "data-as-given" time-series such as aggregate exports, allowing us to relate globalization and growth in a more disciplined fashion; (2) collecting a new dataset on aggregate exports, bilateral trade, and GDP for 51 countries; (3) constructing our proposed measure of "structural market potential", as well as charting and decomposing its evolution through time; and (4) exploiting exogenous variation in trade-related distances to world markets in order to determine the causal role of market potential in driving global income growth over this period.
The dataset We collected a large annual dataset for 33 countries over the period from 1910 to 2010 which is comprised of aggregate exports and GDP and for an additional 18 countries from 1950 to 2010. We chose to begin data collection in 1910 in order to maximize the cross-section of countries at our disposal. This data includes newly collected trade observations, in particular, for the periods spanning the World Wars. We provide details on our sources in Appendix III while.
Constructing market potential We construct our preferred measure of market potential as given in equation (  12 ). This approach does not require estimation of the entire term as we simply insert the data directly into the right-hand-side expression. Thus, the data on income yi and global income y W are readily available. We construct domestic trade as the difference between income and total exports, xii = yixi, where xi denotes total exports. As income is measured as GDP and is cast in value-added terms, it is in principle not consistent with exports as a gross-value measure. However, as a robustness check, we are able to use total gross manufacturing production instead of GDP for the years from 1980 to 2006. This leaves our main results materially the same, a point we discuss in fuller detail in Appendix II.
g. , Canada). As Feyrer notes, "[such] heterogeneity can be used to generate a geography based instrument for trade that varies over time. " In a similar vein, Feyrer (2009a) exploits the shock to the global economy embodied by the closure of the Suez Canal from 1967 to 1975. While many trade routes remained unaffected, some did not and found the distances separating markets increasing significantly. For instance, Feyrer reports that India nearly led the pack with a 30. 6% increase in its trade-weighted distance to foreign markets while a country like Canada only experienced a 0. 2% increase. Using this exogenous variation in distance over time, Feyrer goes on to separately estimate the effect of distance on trade and the effect of trade on income. Here, we combine both approaches. In particular, we use the great circle distances from the CEPII GeoDist database (see Appendix III) to represent distances on air routes to Japan, the United Kingdom, and the United States, critical nodes of the world economy throughout the long twentieth century.
See the text above for details. In addition, we investigate bilateral trade cost asymmetries empirically by taking into account, as suggested by  Waugh (2010) , that bilateral trade costs from poor to rich countries might be higher than in the opposite direction. Using the TradeProd dataset (see Appendix II), we introduce a "distance gap" between rich and poor countries assuming (hypothetically) that bilateral distance is higher by 20% from poor to rich countries and simulating trade flows accordingly. We define rich countries as OECD members in the year 2000. We then recompute market and supplier access measures through the Redding-Venables method but find hardly any difference quantitatively. over time, we estimate a slope coefficient of 0. 90 (with a standard error of 0. 007). However, for later years in the sample the slope tends to be statistically indistinguishable from 1.
Overall, we conclude that market access and supplier access are reasonably tightly linked in a way suggested by theory. It is possible that inferior data quality in earlier years renders the relationship less robust. Furthermore, we study a different, more comprehensive dataset to corroborate the relationship between the measures of  Redding and Venables (2004)  and the measure proposed in our paper. More specifically, we work with the TradeProd dataset that is available for download from CEPII for the years from 1980 to 2006. As we are mainly interested in cross-sectional comparisons, we choose a single year, the year 2000. This dataset has two main advantages. First, it comprises many more countries than our historical dataset. In fact, we can work with 165 countries reporting bilateral trade flows.
The dataset contains 25,928 observations out of a maximum possible of 165*165 = 27,225 observations (where the remaining observations are missing). About 30% of the sample (8,131 observations) are zero trade flows, which is standard for an aggregate dataset of this size. In comparison, our historical sample with 33 countries has only 644 observations per cross-section. Second, the TradeProd dataset does not rely on value-added GDP data. Instead, we can use gross production data that we aggregate for all manufacturing industries with matching trade flows. Most importantly, domestic trade flows ("internal flows") are the value of gross production minus the value of gross total exports. We follow the procedure outlined in section 3. 2 to construct our market potential measure based on the structural gravity model and equation (  12 ).
That is, we estimate a PPML gravity regression with logarithmic distance and a contiguity dummy as the trade cost components. The estimated coefficients are -0. 93 and 1. 49, respectively (both significant at the 1% level) and therefore similar to the coefficients we obtain using our historical sample. We are able to obtain market potential measures for 92 countries (for other countries, data on domestic production and domestic trade flows are missing). We also construct the market and supplier access measures based on the Redding-Venables method. This results in market and supplier access measures for 165 countries. We note that these measures do not depend on GDP or production data since they are constructed based on exporter and importer fixed effects which absorb any country-specific variables.
The correlation is now 0. 67. This compares to a slightly lower correlation of 0. 54 for the exact same set of countries in the year 2000 in our original dataset. Therefore, we conclude that as in Figure  A2 , the two measures are reasonably well-related. Furthermore, it appears that for the directly comparable set of 28 countries, the relationship is actually stronger when we use the TradeProd dataset. In addition, we use the more comprehensive TradeProd dataset to study the relationship between market access and supplier access in the  Redding and Venables (2004)  model. In Figure  A5  (analogous to Figure  A3 ), we plot these two measures for the largest possible sample (the panel on the left-hand-side) and the sample that most closely corresponds to our historical dataset (the panel on the right-hand-side with 32 countries due to missing data for Belgium).
Poly(A) mRNA isolation, cDNA synthesis, library generation, indexing, cluster generation and RNA-Seq analyses by Illumina HiSeq 2000 RNA sequencing of 100 bp paired-end reads was carried out by LGC Genomics (Berlin, Germany), using commercial services. The raw Illumina 100-bp pair-end sequences were deposited in the NCBI Sequence Read Archive (SRA). The reads were quality trimmed using Trimmomatic version 0. 32  [37] , and surveyed for the presence of rRNA contamination using homology searches against rRNA databases  [38]. Alignment against the Vitis vinifera reference genome  [39]  was then performed with the software Tophat2 version 2. 0. 12  [40]  set with the parameters -D 15 -R 2 -L 22 -i S,1,1. 15 and end-to-end mode. Quantification and normalization of gene expression values by Fragments Per Kilobase Of Exon Per Million Fragments Mapped (FPKM) was calculated by Cufflinks version 2.
2. 1  [41]. Differential expression calculations were handled by DESeq2 version 1. 4. 5  [42]  considering estimation of size factors, a false discovery rate (FDR) of 0. 05 and a -1. 5 ≥ log2 fold-change ≥ 1. 5, using the raw read counts. EuKaryotic Orthologous Groups (KOG)  [43] , Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG)  [44]  functional annotations were based on sequence homologies against public databases. Rap-search2  [45]  with an e-value cut-off of 10 -5 was used to search against Arabidopsis thaliana sequences in the KOG database and non-redundant ("nr") peptide database (ftp:// ftp. ncbi. nlm. nih. gov/blast/db/ downloaded at November 26, 2013, including all "nr" GenBank CDS translations + PDB + SwissProt + PIR + PRF). To GO and KEGG annotations, the output was submitted to an in-house developed script -Rapsearch2XML (https://github.
To obtain measurements of the correlation between RNA-seq and qRT-PCR data, linear regression and determination coefficient (R 2 ) were determined between the two methods obtained log 2 foldchanges for the same eight genes. To further investigate the robustness of our RNA-seq dataset, similarity of expression profiles between the three biological replicates was determined by Pearson correlation coefficient (PCC) analyses with R 3. 1. 2 software using natural logarithm (ln)-transformed read counts for the differentially expressed genes (DEG) as input.
, Massachusetts, USA) operated at unit mass resolving power with electron impact ionization and a 50-750 atomic mass unit scan range. Metabolites were identified by automated comparison of the ion features in the experimental samples to a reference library of chemical standard entries that included retention time, molecular weight (m/z), preferred adducts, and in-source fragments as well as associated MS/MS2 spectra (Additional file 3) and curated by visual inspection for quality control using a software developed at Metabolon Inc  [53]. Raw area counts for each biochemical compound were rescaled by dividing each sample's value by the median value for the specific biochemical. Welch's two-sample t-tests were then used to determine whether or not each metabolite had significantly increased or decreased in abundance using Array Studio software (Omicsoft) and Microsoft Excel® spreadsheets.
Exploratory analysis of transcriptome and metabolome profile Data regarding transcript and metabolite quantification was natural logarithm (ln) -transformed for adjustment to normal distribution and verified by histogram plotting, using the R software before and after the transformation. Principal Coordinate Analysis (PCoA) was conducted based on the pair-wise correlation matrix using the NTsys-PC 2. 20e software  [55]. The DCENTER module was used to transform the symmetric matrix to scalar product and EIGEN for eigenvalues decomposition to identify orthogonal components of the original matrix modules. The minimum-spanning tree was calculated allowing the visualization of the distances between operational units. R software was used for Orthogonal Signal Correction Partial Least Squares Discriminant Analysis (O-PLS-DA) and heatmap construction with associated hierarchical clustering.
The OPLS-DA analysis of the differential expressed genes plotted by the KOG categories showed an overlap of gene functional categories (Additional file 7A). A positive significant correlation was found between the log2 fold-changes from qRT-PCR and RNA-Seq transcriptomic datasets, confirming the reproducibility of RNA-Seq data (Additional file 2). In agreement, the robustness of the generated RNA-Seq dataset was further revealed by a high correlation of the transcriptome profiles among three biological replicates per treatment (Additional file 8).
Acknowledgments This work was supported by the  Portuguese Fundação para a Ciência e a Tecnologia (FCT)  through project " VitiShade :  PTDC/AGR-GPL/116923/2010 " and the PhD grant  SFRH/BD/69076/2010  to SD, and by  ProDeR Action4. 1:PROdUVA  23921/2/3/4. We acknowledge  Dr Danny Alexander  and his team at  Metabolon Inc. (Durham, USA)  for metabolomic analysis, and  Dr Martin Heine  and his team at  LGC Genomics (Berlin, Germany)  for transcriptome sequencing.
Availability of supporting data The data sets supporting the results of this article have been submitted to the Sequence Read Archive at NCBI (http://www. ncbi. nlm. nih. gov/sra) under accession numbers [SRA:SRX964421, SRX964430, SRX966723, SRX966735, SRX966740, SRX966742, SRX966755, SRX1008101, SRX1008174, SRX1008177, SRX1008181, SRX1008185, SRX1008211, SRX1008213, SRX1008214, SRX1008217, SRX1010114, SRX1010115].
86 5. 73 Stem-specific protein TSJT1-like D7TYY3 Other VIT_02s0033g00830 5. 75 GTPase Rab11/YPT3, predicted F6I079 Intracellular traff. , secretion, vesic. transp. VIT_00s0586g00030 3. 91 5. 80 Stem-specific protein TSJT1-like , predicted D7UE87 Other Gene code identification, fold-change, annotation, UniProtKB accession number and KOG functional category. Data were obtained from 3 independent biological replicates.
, 1998; Yang et al. , 2008; Tsuwamoto et al. , 2008; Pighin et al. , 2004; To et al. , 2012). In the last years, many studies based on the analysis of large datasets have been carried on in order to acquire a deeper understanding of the cellular processes taking place during seed and embryo development  (Belmonte et al. , 2013; Wickramasuriya and Dunwell, 2015; Palovaara et al. , 2013). Although these studies provide resources to support research in seed biology, much remains to be learned through classical genetic and physiology approaches, especially when 17% of Arabidopsis genes are still classified as having unknown function (The Arabidopsis Information Resource [http://www. arabidopsis. org], March 2016). Until recently, small genes and their protein products remain largely unstudied.
3, F-H ). Thus, from genetic analyses, mapping data, and the rescue of the mutant phenotypes, we conclude that At5g01075 corresponds to the TWS1 gene. TWS1 Encodes a Novel Protein AT5G01075 is a small gene that consists of two exons and an 83-bp intron (Fig. 3A ). It encodes an 81-amino-acid peptide, which contains at its N terminus a stretch of highly hydrophobic amino acids (Fig. 3B ) likely to act as a signal peptide  (von Heijne, 1985). Indeed, TWS1 is predicted to enter the secretory pathway by protein subcellular localization prediction softwares such as SignalP (www. cbs. dtu. dk/services/SignalP/). The rest of the protein is predicted to be soluble and lacks any known retention signals, thus the Protein Subcellular Localization Prediction Tool (WoLF PSORT, www. genscript. com/wolf-psort.
DISCUSSION In this study, we present the isolation and characterization of TWS1, a single copy gene in the Arabidopsis genome. Database searches revealed homologs in all flowering plants and in the basal angiosperm Amborella trichopoda. This observation suggests that TWS1 fulfills a function proper to seed plants, although we cannot exclude a rapid and divergent evolution from its putative ancestral form that impairs its identification in more basal plants. Indeed, SP evolutionary studies suggest that they tend to evolve more rapidly and therefore are less conserved than long proteins  (Guigo et al. , 2003; Wei et al. , 2005; Windsor and Mitchell-Olds, 2006). Storage compound accumulation in tws1 mutant seeds is affected with a 17% reduction in fatty acid content followed by an increase in starch, Suc, and storage protein accumulation (Fig.
ACKNOWLEDGMENTS We thank  Dr. Karl-Josef Dietz  for the Vma21::CFP marker line, and  V. Tanty ,  J. Porret , and  J. Kronenberger  for their technical help in the initial characterization of the mutants supported by  Genoplante programme ,  Gerd Jürgens ' laboratory for proATML1::NLS:3xGFP marker line. We thank the  "Observatoire du Végétal"  for plant culture and access to imaging facility and assistance.
In this study, we first addressed the question of the genetic basis of the egg-spots. We then went onto use comparative transcriptomics across species carrying eggspots and blotches to shed light on the origin of this novel trait. Specifically, we identified a total of 1229 genes that were differentially expressed (DE) between egg-spot and non-egg-spot fin tissues in the haplochromine cichlid Astatotilapia burtoni. These genes are evolving at a higher rate than average making this a valuable dataset to study the emergence and rapid diversification of this trait. For a subset of 46 DE genes we measured expression levels in three other species: the egg-spot bearing haplochromine Cynotilapia pulpican, carrying egg-spots on a different region of the anal fin than A. burtoni, and two blotch-bearing species, the basal haplochromine Pseudocrenilabrus philander and the ectodine Callochromis macrops.
burtoni  [29]. In total we identified 1229 genes that were DE between the two types of tissues, with 620 genes being over-expressed in the egg-spot tissue, whilst 609 were under-expressed (Table  1 ). The DE transcripts, their identification using tBLASTx and BLASTx searches (against the NCBI non-redundant database  [30] ), together with the respective expression levels, are provided in Additional file 1. A first inspection of those DE genes between egg-spot and non-egg-spot tissue revealed that our experiment retrieved many genes with a known function in pigment formation and patterning in different model organisms including paired box 7 (pax7), endothelin receptor b1 (ednrb1), microphthalmia-associated transcription factor a (mitfa), Agouti signaling protein 1 (asip1), sex determining region Y box 10 (sox10) and anaplastic lymphoma receptor tyrosine kinase (alk)  [31] , suggesting that our strategy is a valid approach to identify candidate genes for egg-spot morphogenesis.
Functional annotation of the DE genes The reference A. burtoni transcriptome was annotated by performing a BLASTx search against NCBI's Danio rerio protein database  [30]. From the 1229 DE genes, 58. 6 % (720) had significant BLAST hits against the database (annotated datasets can be found in Additional file 2), while 41. 4 % (509) of the DE contigs were nonidentified. From the 720 contigs with a BLAST hit we could functionally annotate 495 using BLAST2GO  [32]. We further described the Gene Ontology (GO) term composition for egg-spot over-expression and egg-spot under-expression in comparison to the reference transcriptome GO representation (Fig. 2 ). Overall, the GO terms representation was similar between the two tissues. However, there were several GO terms for "Molecular function" and "Cellular component" that differed significantly between the two data-sets, suggesting, as expected, that the two tissues are functionally different (Fig.