dataesr/scientific-paragraphs-categorization huggingface dataset:
Intuitively, trade costs are higher when countries tend to trade more with themselves than they do with each other, i. e. as the ratio increases. As the ratio falls and countries trade more internationally than domestically, international trade costs must be falling relative to domestic trade costs. Because trade costs are derived from a ratio with trade flows in the denominator, country pairs that do not trade at all record infinite trade costs. Such observations are treated as missing in our dataset. provides a useful summary indicator of the level of trade costs between countries i and j. Importantly, it is a "top down" measure, in the sense that it uses theory to infer trade costs from the observed pattern of trade and production across countries. Unlike the "bottom up" measures referred to above, it includes all factors that contribute to the standard definition of iceberg trade costs in trade models, namely anything that drives a wedge between the producer price in the exporting country and the consumer price in the importing country.
Data Treatment This section describes the main sources used in construction of our trade costs dataset, covering production and export data. We also outline the main treatments applied to the raw data in order to construct the final dataset. After assembling all components, our dataset covers up to 178 countries for the period 1995-2010. In sectoral terms, we cover total trade, as well as distinguishing between trade in agricultural products and trade in manufactured goods. As noted above, trade costs in this paper are measured using the following formula  (Novy, 2012) : ( ) ( ) ( ) ( ) To implement it in practice, we need data on the value of bilateral trade in each direction  ( and ) , and data on intra-national trade in each country  ( and ). The former data are readily available from standard sources, but the latter are more difficult to obtain.
Importantly, since the models behind the trade costs formula do not allow for input-output relationships among sectors, intra-national trade needs to be measured as gross shipments, not value added (which subtracts intermediate inputs). Our approach, discussed in more detail below, is to use national accounts data and to proxy intra-national trade by total production less total exports. To deal with missing observations, we use linear interpolation to calculate trade costs for countrysector-year combinations where the dataset contains empty values.
International Trade Data Our bilateral trade data are sourced from the Comtrade database, accessed via the World Bank's WITS server. We use reported export data rather than import (mirror) data because it is important for the consistency of our trade costs measures that trade values be measured at FOB, not CIF, prices. The original data are reported in the 1988/1992 Harmonized System classification scheme, and we convert them to the ISIC Rev. 3 classification using a concordance built into WITS. Total trade represents the total of agriculture and manufactured goods exports, whereas agriculture represents the total of ISIC sectors A and B and manufactured products cover ISIC sector D. These definitions correspond to the relevant sectoral definitions in the national accounts. Activities such as mining are therefore excluded from our analysis.
All trade data are expressed in value terms in nominal US dollars, so no further conversions are necessary. The main issue that arises in our trade data is in relation to re-exports. To apply the Novy (2012) formula for trade costs, we need each country's "true" (i. e. , net) exports. Our dataset is therefore based on Comtrade's reported net exports for each country pair, but we are aware that not all countries properly account for re-exports in the original data. In 2012, for example, only 15% of country pairs reported bilateral re-exports for total trade. Many of these instances of missing observations in fact represent zeros, but it is not always the case. For three countries where reexports are known to be large but unreported in Comtrade-Singapore, Belgium, and Luxembourg-we make a further adjustment using data from other sources.
Gross Output and Value Added Data The most challenging part of this exercise from a data point of view is obtaining information on gross domestic shipments, i. e. production made and sold within each country. Our starting point is the United Nations National Accounts Database. That source provides total output on a gross shipments basis disaggregated by ISIC sector for up to 124 countries. We use these data whenever available, converting them to US dollars using the nominal exchange rate applied by the World Development Indicators to convert GDP from local currency into US dollars. When gross output data are unavailable, we take an alternative approach. We obtain data on value added by ISIC aggregate-agriculture and manufactures-from the World Development Indicators, in US dollars. Where value added data are missing from the World Development Indicators, we fill them in using the UN National Accounts Database, converting from local currency to US dollars in the same way as above.
Value added data cannot be directly used in the calculation of trade costs because they net out intermediate goods and therefore tend to understate the level of production. We therefore apply a scaling up factor equal to the average sectoral ratio between value added and gross output for those countries where both sets of data are available. The average ratios we find in the data are 1. 82 (agriculture) and 3. 42 (manufacturing). Multiplying these ratios by the value added data allows us to produce estimated gross output data for the remaining countries in our dataset. In all cases, we compute total gross output as the sum of manufactured goods and agriculture. The final stage in the treatment of these data is to calculate the value of domestic shipments, i. e. intra-national trade. To do this, we follow the existing literature in taking the gross output dataactual and estimated-and subtracting the total value of exports to the rest of the world from the Comtrade data, to give the amount of total production that was both made and consumed domestically.
These results can therefore be considered as relating to extra-regional trade costs. We can also use the dataset to obtain information on intra-regional trade costs, i. e. the costs facing particular groups of countries as they trade among each other as opposed to with the rest of the world. Intra-regional trade costs are important from a development perspective, as they are linked to the idea of South-South trade, which has become a more important force in the world economy in the wake of recovery from the global financial crisis of  2008 -2009 (Hanson, 2011)). Tables  1  and 2  present trade costs matrices summarizing both intra-and inter-regional performance in manufacturing and agriculture respectively. Countries are divided up by World Bank income group. As for the preceding analysis, we focus on stable groups over time so as to avoid composition effects.
The second point to emerge from Table  1  is that for upper middle income countries, it is often less costly to trade with high income countries than it is to trade among themselves. Since many of the most important emerging markets are in this income group, our results reinforce the importance of liberalizing tariff and non-tariff measures, and improving trade facilitation in those markets. Interestingly, this dynamic is not true for lower-middle income and low-income countries, where within-group trade costs are lower than between-group trade costs. One explanation for this result could be that we observe relatively little trade among low-income countries in the dataset, which means that the sample for calculating average costs is biased. In particular, it is biased away from distant low-income country pairs, which tend not to trade at all.
Decomposition of Trade Costs In addition to providing descriptive statistics showing the pattern of trade costs across countries and through time, our dataset can also be used to examine the factors that contribute to the levels of trade costs observed around the world. We follow Chen and Novy (2011) in using a regression approach to analyze the determinants of bilateral trade costs. We include a wide range of variables familiar from the gravity model literature, covering both policy and "natural" factors. As in  Chen and Novy (2011) , we focus on factors that are primarily sources of international, as opposed to domestic, trade costs. Since one of the variables of interest-the World Bank's Air Connectivity Index  (Arvis and Shepherd, 2011) -is only available for a single year, we perform a pure cross-sectional regression.
A final piece of evidence highlighting the importance of non-tariff measures is that our observable trade cost proxies only account for around 50% to 60% of the observed variation in trade costs. The rest is due to unobservables, including non-tariff measures. Although it is impossible due to lack of data to quantify the proportion of the unexplained variance that is due to non-tariff measures and the proportion that is statistical noise, it is likely that non-tariff measures play a substantial role. Table  7  presents results for agricultural products. Results are identical to those for manufactured goods, except that some coefficients are statistically insignificant. This is the case for common colonizer, tariffs, membership of an RTA, and the Air Connectivity Index. Otherwise, all variables have coefficients with the expected signs and magnitudes.
Conclusion and Policy Implications In this paper, we have used newly collected data on international trade and production to infer estimates of trade costs for up to 178 countries over the period 1995-2010. Our estimates distinguish between trade in manufactured goods and trade in agricultural products. In both cases, we find that the absolute levels of trade costs are significant in ad valorem equivalent terms: at least 100% in manufactured goods, and in excess of 200% for agriculture. Our results suggest that although the international economy has integrated considerably in recent decades, there remain potentially large unexploited gains to be reaped from further reducing the wedge between export and import prices. From a development point of view, our results are significant because they show that trade costs fall disproportionately on developing countries.
evansi STIB805 assembly  [29]. We then used an in-house built prolog system to combine the de novo gene predictions and reference-based gene predictions. The genes that are unique to RATT predictions were automatically included in the final set. Genes that are unique to AutoMagi were compared with the NCBI non-redundant (NR) database (accessed 23 February 2011) using 'BLASTp' (default criteria: EXPECT = 210, WORD SIZE = 23, MATRIX_NAME = BLOSUM62, GAP COST = Existence:11 Extension:1) algorithm. All of the 'BLASTp' results were reviewed manually, and genes meeting the following two criteria were retained: (a) an E-value of 5e-6 or lower to the matched NR sequence; (b) coding strand identical to nearest neighbors on either side (i. e. on same poly-cistronic unit). This manual curation removed 165 putative CDS from making into the finalized TevSTIB8805ra.
The gene calls that overlapped between RATT and AutoMagi were subdivided into the following 4 groups. 1) Identical: overlapping exactly, 2) AM_subsetof_RATT: AutoMagi prediction is entirely contained within RATT prediction, 3) RATT_-subsetof_AM: RATT prediction is entirely contained within AutoMagi prediction 4) StaggeredOverlap: both predictions overlap in a staggered fashion. In the first two cases (Identical & AM_subsetof_RATT), AutoMagi gene calls were ignored and RATT models were retained. In the third case (RATT_subseto-f_AM) coordinates from AutoMagi were combined with annotation information from the RATT model. Genes in the fourth category (Staggered) were subjected to a thorough manual review process. The review process involved (i) 'BLASTp' search against NCBI's NR database to identify coordinates for queries and subject; (ii) a ClustalW multiple sequence alignment of both candidate genes with their potential homolog(s) from T.
Phylogenetic analyses The dihydrolipoamide dehydrogenase (LipDH) CDS (Tb927. 11. 16730) was PCR-amplified from total parasite DNA using primers 59-ATA AAG CTT ATG TTC CGT CGC TGC-39 (forward) and 59-ATA AGA TCT TTA GAA GTT GAT TGT TTT GG-39 (reverse) and Phusion polymerase (New England Biolabs, NEB). In cases where direct sequencing of the amplicon revealed heterozygosity, sequence information for individual alleles was obtained after cloning. After removal of the primer sequences, LipDH CDSs were aligned using ClustalX  [37]. Phylogenies were reconstructed using the program MrBayes  [38]  via the Topali platform  [39] , implementing the GTR substitution model and a discrete gamma rate distribution model with four rate categories (to account for rate heterogeneity among sites) as the most appropriate nucleotide substitution model.
1. 21  [41]. Subnetworks were created with 95% confidence limit and then unconnected subnetworks. 10 mutations apart were connected by relaxing the confidence limit. To verify that the haplotypes containing T. equiperdum isolates were on phylogenetically distinct branches, we estimated a phylogenetic tree using the Bayesian approach implemented in MrBayes v. 3. 2  [42]. PartitionFinder v. 1. 0. 1  [43]  determined the Hasegawa, Kishino and Yano nucleotide substitution  [44]  with invariant sites (HKY+I) without partitioning by codon positions as the most appropriate model for the MrBayes analysis. Microsatellite genotyping was carried out exactly as described previously  [40]. Briefly, isolates were typed for eight microsatellite markers  [45]. Principal component analysis (PCA) was performed in R using the package adegenet, as described  [40].
evansi genome, while AutoMagi predicted genes de novo using the consensus of three gene prediction algorithms (genescan, testcode, codonusage)  [28]. A total of 10,110 CDS were identified, with 9368 CDS annotated as T. brucei orthologs by RATT and subsequent manual inspection (Table  1 , columns C, E and F; S1 Table ), and 742 CDS uniquely predicted by AutoMagi (Table  1 , column D; S2 Table ). Thus, 92. 7% of the identified T. evansi CDS have an identified ortholog in T. brucei, while 7. 3% were uniquely detected by de novo gene prediction. Analysis of the 742 AutoMagi CDS by BLAST searching of the GenBank nonredundant database revealed at least one hit (E-value #5e-6) for each of these CDS to a gene from a Trypanosoma species (T. b. gambiense 568 CDS; T. b. brucei 168 CDS; T. equiperdum, T.
36 of the 202 CDS that did not get a hit against Tb927 had a BLASTx hit in the NCBI non-redundant database: 23 CDS matched hypothetical Trypanosoma spp. and 9 appeared to be contaminants from other -mostly bacterial -organisms. The remaining 4 BLAST hits were for T. vivax RNA-dependent DNA polymerase and T. evansi VSGs (2 hits) and diminazeneresistance-associated protein. Because Tb927 was created using a traditional sequencing approach and only covers the 11 large chromosomes  [22] , sequences found in the de novo assembly of T. evansi deep sequencing reads might not be unique to this strain or species, but rather reflect a difference in sequencing methodology or stem from intermediate-sized or mini-chromosomes. To address this, Illumina reads from sequencing T. brucei TREU 927/4 that also did not match Tb927 were de novo assembled into Tb927dn.
Column G shows Tb927 CDS for which no TevSTIB805ra CDS was identified. Column H shows the numbers of Tb927 CDS that had annotation uniquely transferred to a TevSTIB805ra CDS on a different chromosome, i. e. no annotation transfer of these Tb927 CDS was made to the same TevSTIB805ra chromosome. See Tables in Supporting Information for additional information. doi:10. 1371/journal. pntd. 0003404. t001 lytic factor resistance-like proteins, 1 is a putative transporter, and 10 are annotated as putative (or pseudogene) UDP-Gal or UDP-GlcNAc-dependent glycosyltransferase. Glycosyltransferases in T. brucei are frequently found in subtelomeric regions that are difficult to assemble due to repetitive sequences, and are known to be highly variable among trypanosome strains  [33, 47]. Of the 117 TevSTIB805dn_sub CDS for which no match was identified in Tb927, 18 had a BLASTx hit in the NCBI non-redundant database: 9 CDS matched hypothetical genes from various Trypanosoma species and 1 matched a diminazene resistanceassociated protein that was previously suggested to convey diminazene aceturate (Berenil) resistance to certain strains of T.
evansi  [48]. Thus, gene prediction found very few CDS in de novo T. evansi assemblies that are not present in T. brucei, and analysis of these CDS revealed differences reminiscent of those observed among strains of the same species. When T. evansi STIB805 de novo contigs were filtered for minimum coverage of at least 5x, the number of genes without obvious homologs in Tb927 was reduced considerably (Table  2 ). To test the potential absence of these genes in T. b. brucei more rigorously, short read data sets for strains TREU 927/4 and Lister 427 were searched for matches to the 22 candidates from TevSTIB805dn and the 6 candidates from TevSTIB805dn_sub. Only seven CDS candidates remained where the sequence was either entirely absent from both T. b. brucei datasets, or did not contain an undisrupted ORF (S2 Table, highlighted).
RoTat 1. 2 Consistent with earlier reports  [64]  that have suggested RoTat 1. 2 (NCBI accession AF317914) as a predominant and diagnostic T. evansi VSG, we identified a closely related ORF in STIB805. The first 1250 bp of a 1431-bp ORF on a 3. 9-kb de novo contig are nearly identical to the published RoTat1. 2 sequence, while the 200 bp at the 39 end are nearly identical to Tb927. 8. 240, annotated as 'VSG, degenerate' in TriTrypDB (S11 Fig. ). Thus, the gene in STIB805 had most likely obtained a different Cterminal region through recombination, an important mechanism in VSG evolution  [35]. We did not identify any close homologs to the 59 region of RoTat1. 2 in the publicly available T. b. brucei TREU 927/4, T. b. brucei Lister 427 or T. b. gambiense DAL972 datasets.
Such activities include the glycine cleavage complex  [65] , the alternative oxidase  [66]  and ubiquinone biosynthesis  [67] , fatty acid metabolism  [68, 69] , the F 1 -ATP synthase and ADP/ATP carrier  [20] , iron Fig. 7. Evaluation of the genetic differentiation between isolates of T. evansi and T. equiperdum and subspecies of T. brucei using principal component analysis (PCA) of microsat- ellite data. PCA was performed in R using the package adegenet. Within subspecies of T. brucei, the differentiation between temporally and geographically cohesive subgroups was estimated using DEST and calculated with the program smogd. Points representing individual genotypes are connected by a line to the centroid of an ellipse, which circumscribes a region encompassing 95% of the variance observed within six trypanosome subgroups identified: major T.
evansi STIB805 are probably more a reflection of frequent differences in gene content within the subgenus Trypanozoon, rather than events specific to T. evansi. It is unknown if this variability is of any biological significance. A search for ORFs on contigs assembled de novo from T. evansi STIB805 Illumina reads identified 200 candidates for T. evansispecific genes (S1 Data File). These candidates lacked close homologs in other trypanosome species based on the absence of BLASTp hits (E-value cut-off 0. 001) in Tb927 or in the NCBI database (Table  2  and S2 Table). Seven of these candidates came from contigs with at least 5x minimal coverage and mapping of T. b. brucei TREU 927/4 or Lister 427 Illumina reads to these ORFs resulted in no or only incomplete coverage. The size of these ORFs is generally small (306-501 bp) and the surrounding contig regions either lack homology to Tb927 chromosomes entirely or match to repetitive regions such as VSG arrays.
Acknowledgments The authors would like to thank  Wendy Gibson  ( University of Bristol, Bristol, UK ) for sharing parasite DNA and helpful comments on the manuscript,  Ana Jansen  ( FIOCRUZ, Rio de Janeiro, Brazil ) for sharing parasite DNA,  David Martin  ( University of Dundee, Dundee, UK ) for helpful discussions, and the  Wellcome Trust Sanger Institute (Hinxton, UK)  and  George Cross  ( Rockefeller University, New York, USA ) for providing Illumina reads for T. b. brucei strains  TREU 927/4  and  Lister 427  to the research community.
This work was supported by  US National Institutes of Health  (www. nih. gov)grants  AI014102  (to KS) and  AI69057  (to AS),  UK Medical Research Council  (www. mrc. ac. uk) grant  G0600129  (to AS),  EU  grant (ec. europa. eu)FP7/2007-2013 (no. 316304 ),  Bioglobe  grant  CZ. 1. 07/2. 3. 00/30. 0032 ,and the  Praemium Academiae award  (to JL, who is a Fellow of the  Canadian Institute for Advanced Research) , and  National Science Foundation of China  (www. nsfc. gov. cn)grants # 30670275  and # 31272305  (to ZRL). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Data are in S2 Table. 2 cut-off: BLASTp E-value #0. 001. 3 cut-off: BLASTx E-value #0. 001 in NCBI non-redundant protein database. 4filtered for contigs with $5x minimum coverage. 51,951,783 reads from T. evansi STIB805 that matched neither the Tb927 reference, nor a de novo assembly of T. brucei TREU 927/4 Illumina reads, were used for a de novo assembly. 1,416,068 reads could be assembled into 2390 contigs $500 bp with a combined length of 3,239,882 bp (N50 = 1174). doi:10. 1371/journal. pntd. 0003404. t002.
Basic information is summarized on their biology, ecology, economic relevance, and estimated number of undescribed species worldwide. Little less than 30,000 species are listed from Europe. The Coleoptera 2 section of the Fauna Europaea database (Archostemata, Myxophaga, Adephaga and Polyphaga excl. the series Elateriformia, Scarabaeiformia, Staphyliniformia and the superfamily Curculionoidea) encompasses 80 families (according to the previously accepted family-level systematic framework) and approximately 13,000 species. Tabulations included a complete list of the families dealt with, the number of species in each, the names of all involved specialists, and, when possible, an estimate of the gaps in terms of total number of species at an European level. A list of some recent useful references is appended.
Introduction In 1998 the European Commission published the European Community Biodiversity Strategy, providing a framework for the development of Community policies and instruments to comply with the Convention on Biological Diversity. The Strategy recognises the current incomplete state of knowledge at all levels concerning biodiversity, which is a constraint on the successful implementation of the Convention. Fauna Europaea contributes to this Strategy by supporting one of the main themes: to identify and catalogue the components of European biodiversity into a database to serve as a basic tool for science and conservation policies. In regard to biodiversity in Europe, science and policies depend on the knowledge of its components. Biodiversity assessments, monitoring changes, sustainable exploitation of biodiversity, and much legislative work depend upon a validated overview of taxonomic biodiversity, in which Fauna Europaea plays a major role, providing a web-based information infrastructure with an index of scientific names (including important synonyms) of all living European land and freshwater animals, their geographical distribution at country level and some additional optional information (like references and species annotations).