dataesr/scientific-paragraphs-categorization huggingface dataset:
Just as importantly, this is matched by the individual-level patterns: using data from the American National Election Studies, we find that individuals who live farther from the state capital are less informed and display less interest in state politics. In contrast, and quite tellingly, distance to the state capital does not affect the level of interest in politics in general. Whether these patterns are due to media coverage responding to the demands of a given audience, or due to circulation and the degree of individual information responding to the level of coverage -or most likely a combination -there is in any case a connection between isolated capitals and lower levels of media coverage, and of citizens' interest in and knowledge of state politics. When it comes to electoral accountability, we find evidence that people who live in counties that are closer to the state capital are more likely to turn out in state elections, controlling for county demographics and state fixed effects.
This is first of all because state officials are only a fraction of those who are implicated in corruption at the level of state politics. 4 Because of that, looking only at the convictions of state officials would systematically and substantially underestimate the extent of state-level corruption. In addition, the restricted measure is very noisy in its own right. First of all, since there are relatively few state-level officials compared to other levels of government, the share of state officials in aggregate corruption convictions is relatively small -typically about 10% overall, as compiled in the PIS Report. Second, the TRACfed data do not add up to the total of convictions reported in that Report, with an average number of 30% left unaccounted for. 5 The resulting numbers are thus quite small, and correspondingly noisy.
We will also use the restricted measure, however, in order to check robustness. We have compiled yearly data for each individual state and (fiscal) year between 1986 and 2011, and averaged them over the entire available period, again to deal with the yearly fluctuations that are even noisier in light of the smaller number of convictions. We also normalize the data, using the number of state government employees (as of 1980). As it turns out, its correlation with the standard, unrestricted measure is substantial (just under 0. 60). Finally, for the sake of additional robustness, we will also look at an altogether different approach to measuring corruption. Following Saiz and Simonsohn (2011), we build a measure from an online search (performed in 2009) for the term "corruption" close to the name of each state, using the Exalead search tool.
8. For the sake of completeness, we can also consider the coarser measures of concentration of population relative to the capital. We look at the share of population living in the state capital (as of 2010), and a dummy variable for whether the capital is the largest city in the state. Both of these variables are negatively correlated with AvgLogDistance not and AvgLogDistance adj , as one would expect. The raw pairwise correlation hovers around -0. 3 in all cases, significant at the 5% level. We use these measures in regression specifications otherwise identical to those in Columns (  1 ) and (  2 ), using the "Exalead" measure in light of the time period for which we have the population data at the city level. As it turns out, we see a statistically significant negative coefficient in Columns (  7 ) and (  8 ), consistent with the baseline results.
This information on the spatial distribution of each newspaper's total circulation in a given state lets us measure the concentration of its readership around the state capital: we define ReaderConcent as 1 -AvgLogDistance with respect to the capital, with the average now taken with respect to newspaper circulation. The number of newspapers with ABC data available is considerably smaller than what NewsLibrary. com covers, so we end up with a total of 436 newspapers in our sample. Also, we leave aside the circulation of a newspaper outside of its home state, since we are focusing on coverage of home-state politics. For newspapers that have substantial circulation outside their home state, this would tend to overestimate the degree of concentration of their circulation around the home-state capital -a source of measurement error that should be kept in mind when interpreting the results below.
based on similar ANES data. The basic empirical question then is whether it is the case that people who live closer to the state capital city would be more likely to turn out in state-level elections. One alternative to tackle this question would be to look at individual-level survey data on turnout and match it to data on where individuals are located. However, survey data on turnout is notoriously unreliable (e. g. Burden 2000). We thus choose to look at actual turnout data at the county level, once again attributing for simplicity the county's population to its centroid, and computing the distance between each county's centroid and the state capital. We use data from the election cycle between 1997 and 2000, so that we can cover all states and use more comfortably the demographic variables from the 2000 Census.
Isolated Capital Cities and the Provision of Public Goods Finally, since the evidence suggests that an isolated capital city is associated with lower levels of accountability and more widespread corruption, it is worth asking whether this pattern would also be reflected in a diminished provision of public goods. It is certainly plausible, and would be of particular concern, if that were indeed the case. In order to investigate this possibility, we collect data from the US Statistical Abstract (2012 edition) on the pattern of expenditures by US states, and also on outcomes that might be related to the provision of public goods. Most of state government expenditures that might be directly ascribed to public good provision fall under four categories: "Education," "Public Welfare," "Health," and "Hospitals.
This measure corresponds to what Campante and Do (2010)  call the "Gravity-based" CISC (G-CISC). More formally, let Ω ⊂ R 2 be a convex set that can contain (the representation of) all states, px denote the distribution of individuals in a given state, defined over Ω, and z φx be the distance between a point x ∈ Ω and the state capital, which we fix at a point φ ∈ Ω. We then have: GCISC = Ω (1 -α log z φx )dpx, where α > 0 is a normalization parameter to be adjusted so that the measure is contained between 0 and 1. A description of the index as we actually compute it in practice, given the data we have, can be found in the Online Data Appendix.
The main challenge is to keep the deposition velocity as constant as possible. But in the 5 axis configuration, several problems in relation with the tool velocity can be observed. Singular points are one of those problems. Several authors mention that Computer Numerical Control (CNC) data can generate unexpected movements of the rotary axis. This phenomenon is due to the Inverse Kinematic Transformation, which generates singular positions during the calculation of the machine coordinates  [11]. These singularities lead to large movements of the rotary axes through short displacements along the tool path. As a consequence, a significant drop in the tool velocity can be noted. As illustrated in the drawing on fig. 1 , we can identify geometric errors as a source of instability. Indeed, the presence of chordal errors can lead to excessive variation of the tool orientation between two successive points of the trajectory.
Another sensor "L-sensor" was used in order to monitor the incident laser power (the system was not calibrated and thus, only the variations of the laser power level could be tracked). An acquisition module with LABVIEW software from National Instruments was used to collect these signals at a 5 kHz sample frequency and the collected data were analyzed with the Wavemetrics Igor Pro software. Information of the axis movements is obtained directly from the monitoring functions of the CNC with a frequency of 166 Hz. The geometric characterization of the deposits is made by using a focus variation microscope Infinitefocus from Alicona. This device provides the dimensions of the cord with a resolution of 4. 2 microns in the XY plane and less than 1 micron in the Z direction. All of the tests were performed with gas atomized AISI 316L powder (with 45-90μm grain size) clad on AISI 316L 3mm thickness plates.
Calculation of 5 axis tool path The 5 axis trajectory is generated with a specific software developed in collaboration with DELCAM PLC. This software, named POWERCLAD®, enables the user to process a large diversity of parts with several manufacturing strategies. The orientation between the nozzle and the workpiece is defined depending on the former tool path. The tool vector is thus calculated for each point of the trajectory (n) and oriented towards the previous layer (n-1) (Fig;  7 ). In this study, a circular path has been chosen. As a matter of fact, the tool moves along a circular curve, and each layer is shifted from one another, in order to create a 5 axis movement (Fig. 8 ). This configuration was chosen in order to create singularities. Indeed, when the tool orientation approaches the vertical to the B&C rotating table (see Fig.
To investigate the genetic potential for osmo-response in this clade, we have sequenced high-quality draft genomes for 59 species of haloarchaea isolated from 20 countries across six continents and environments ranging from fermented fish sauce to Permian age salt deposits (see Table  S1 ). Combined with 21 previously sequenced haloarchaeal genomes, this dataset provides a rich opportunity for insight into osmoadaptation. Here we present an analysis highlighting adaptations to high salt at the gene and protein levels as well as analysis of ion transport capabilities and transcriptional machinery likely to play a role in mediating responses to changing osmotic conditions. This genomic dataset will also be of use to the broader genomic and archaeal research communities. Although the archaea play major roles in global element cycling and ecosystem stability, this domain has been understudied.
Our sequencing project nearly quintuples the number of available genomes for the haloarchaea and increases by ,30% the number of sequenced archaea. We demonstrate several utilities of this dataset for defining community structure in metagenomic studies, improving automated genome annotation, and inferring the timing of evolutionary events along the haloarchaeal tree. To facilitate large-scale use of this dataset, we have made sequence and annotation information available through an SQL database as well as the NCBI genome repository (for accession numbers, see Table  S2 ). We provide gene calls and annotations derived using two independent automated annotation pipelinesthe Rapid Annotation using Subsystems Technology (RAST) server  [13]  and NCBI's Prokaryotic Genome Annotation Pipeline (PGAAP)  [14].
Lastly, we challenge a recent high-profile proposal regarding the evolutionary origins of the haloarchaea by showing that inclusion of additional genomes significantly reduces support for a proposed large-scale horizontal gene transfer into the ancestral haloarchaeon from the bacterial domain. This result highlights the power of our dataset for making evolutionary inferences, a feature which will make it useful to the broader evolutionary community. We distribute our genomic dataset through a user-friendly graphical interface. deposited in the NCBI genomic database along with annotations derived from the Prokaryotic Genome Annotation Pipeline (PGAAP)  [14]. This annotation pipeline called between 2,945 and 4,645 putative protein coding regions, depending on the species. A large fraction of proteins (,41%) were annotated as hypothetical or of unknown function, likely a consequence of low experimental coverage of the archaeal domain.
Breadth of the haloarchaeal pangenome To estimate the fraction of haloarchaeal phylogenetic diversity represented by this set of 80 haloarchaea, we performed rarefaction analysis, plotting the number of unique protein families against the number of randomly drawn genomes (Figure  3 ). Accurately grouping proteins into families is a nontrivial problem that has sparked the development of a large number of protein clustering algorithms  [23]. As there is no experimental data for the vast majority of haloarchaeal proteins, clustering must rely on computational sequence similarity metrics. We therefore selected three methods to define protein families and generated rarefaction curves for each. The methods were as follows: (1) COG orthology groups  [24] , (2) in-house homology clusters defined using the clustering algorithm TRIBE-MCL  [16]  (see Materials and Methods, Dataset S2, Figures  S1  & S2 )), and (3) TRIBE-MCL defined homology clusters excluding those with only a single member (singletons).
As the COG database is limited to proteins with putative orthologs in at least three major phylogenetic groups, and only two archaeal lineages are included in the genomic dataset used to build the COGs (Euryarchaeota and Crenarchaeota), protein families unique to the archaea are not present in this database. Thus, only protein families that existed prior to the eukaryotic-archaeal split or were subsequently exchanged by horizontal gene transfer between eukaryotes or bacteria and archaea are included. As such, the rarefaction curve of COG proteins in the haloarchaea saturates very quickly, with only 2,172 COGs being present in the haloarchaea, and only 17 genomes being required to discover 90% of these families. In contrast, using the TRIBE-MCL derived homology clusters, which do not exclude proteins specific to the archaea, the number of unique protein families in the sequenced set of haloarchaeal genomes was 17,223.
The haloarchaea did not originate via mass-acquisition of 1,000 bacterial genes by a methanogen A recent paper analyzing 10 haloarchaeal genomes posited that the evolution of the haloarchaeal phenotype was the result of a single mass horizontal transfer of ,1,000 bacterial genes into an ancestral archaeal methanogen  [25]. This conclusion was based on 1) the finding that a large number of gene trees (1,089), built from a set of protein families with both archaeal and bacterial members (1,479), placed haloarchaeal genes in a monophyletic group with bacterial rather than archaeal homologs and 2) diverse phylogenetic evidence supporting the Methanomicrobia as sister group to the Haloarchaea  [26] [27] [28]  (see also Dataset S3). Further supporting this hypothesis, several of these transferred genes were associated with functions required for the proposed physiological transformation of an obligately anaerobic, autotrophic methanogen to a heterotrophic, facultatively aerobic haloarchaeon  [25].
Accuracy of inferred evolutionary events is strongly influenced by the selection of representative species on which the inference is based. We tested whether the conclusions made by Nelson-Sathi, et al. [25]  from an analysis of 10 haloarchaeal genomes were robust to a reanalysis against our more phylogenetically diverse dataset. In our analysis we also observed a large amount of horizontal gene transfer from the bacteria, however, we found that this exchange of genetic material was not limited to a single acquisition event at the haloarchaeal root, but rather occurred in many different transfer events throughout haloarchaeal evolution. For each of the protein families in the original set reported by Nelson-Sathi, et al. [25] , we added homologs from 65 additional haloarchaeal genomes and rebuilt gene trees using the same software tools and parameters (obtained via correspondence with the authors).
This interpretation is further supported by the fact that the putatively transferred genes do not appear to have been transferred from a common bacterial phylum, as indicated by the phylogenetic affiliation of the most closely related bacterial homolog for each protein family (see Table  S2  in  [25] ). The simplest explanation for the difference in findings between the original study and our reanalysis is rooted in the nature of the two genomic datasets investigated. Due to the limited number of genomes available at the time, Nelson-Sathi, et al. worked under the assumption that the 10 haloarchaeal genomes they sampled reasonably represented haloarchaeal diversity. This lead to the identification of genes as bacterial transfers to the last common ancestor of the haloarchaea on the basis of as few as two haloarchaeal homologs.
Based on the distribution of genomes used (two from Clade 1, two from Clade 2, and six from Clade 3, see Figure  2 ), a gene present in only two haloarchaea will often represent a transfer to a single clade, rather than to the haloarchaeal root. By contrast, our genomic dataset, representing a more even phylogenetic sampling of the haloarchaea, revealed multiple, clade specific transfer events indicative of a complicated history of gene transfer between the haloarchaea and bacteria. This analysis highlights the value of large, phylogenetically informed genomic data sets for increasing the accuracy with which we can make evolutionary inferences, and reveals the dangers in making wide-reaching evolutionary claims based on limited genomic data.
The core genome includes 10 protein families predicted to be involved in stress response, including both cold shock and heat shock members as well as stress response proteins with no predicted specific function. Previous studies of haloarchaea have Multi-marker concatenated phylogeny of the 80 genomes included in this study and other haloarchaeal genomes gathered from IMG. The unrooted tree was built from a concatenated alignment of 40 PhyEco markers using PHYML. Branch support values for top-level branches have been removed for ease of visualization. The full tree file can be accessed through Dataset S19. Grey boxes represent haloarchaeal clades as described in  [19] , expanded as described in text. Tree roots in the Methanocella (not shown). doi:10. 1371/journal. pgen. 1004784. g002 Figure  3.
Rarefaction analysis of sampled haloarchaeal protein space. A rarefaction curve of protein diversity was created for the 80 haloarchaea included in this study using three alternative methods to define protein families: COG number (green), TRIBE-MCL clusters removing singletons (red), and TRIBE-MCL clusters without removing singletons (blue). The COG database is expected to represent an underestimate of the true diversity. Similarly, using TRIBE-MCL clusters with all singleton genes excluded underestimates true diversity. The true diversity of the set is likely located between the blue and red curves. doi:10. 1371/journal. pgen. 1004784. g003 shown upregulation of heat and cold shock genes in response to salinity changes, indicating that these chaperones may play a wider role in mediating stress responses than previously believed  [32, 33].
Due to recent interest in haloarchaeal use of compatible solutes (as part of a broader osmoadaptation strategy which also includes potassium accumulation)  [39] [40] [41] , we analyzed the phylogenetic distribution of many genes involved in compatible solute transport and biosynthesis (Dataset S4). This analysis reveals prevalent uptake mechanisms for the compatible solutes glycine betaine, ectoine, and proline (Figure  4C ), commonly used as osmoprotectants by facultative halophiles. Chemotaxis towards, and accumulation of the trimethylammonium compounds glycine betaine, carnitine, and choline, has been demonstrated in a model haloarchaeaon  [39]. We find that, although putative homologs to the binding and transducer proteins for this system (CosB and CosT) are widely distributed, only 10 species possess both members (Figure  S5 ).
The ninth species (Halococcus saccharolyticus) appears to utilize the flavin adenine dinucleotide-bound choline dehydrogenase BetA rather than the type III alcohol dehydrogenase GbsB  [58]  for initial choline oxidation. We also investigated uptake mechanisms for the biologically important magnesium and phosphate ions (Figure  4D  & E ). For phosphate accumulation, all 80 sequenced haloarchaea encode two members of the PHO4 superfamily (PF01384), annotated as an anion permease (TRIBE-MCL cluster 105; Tribe105), and a sodium dependent phosphate transporter (Tribe63). This second annotation potentially enables active phosphate uptake at the expense of the sodium gradient in hypersaline environments. As discussed above in the context of potassium and sodium transport, ATP-dependent phosphate uptake appears not to be favored, with only 22 homologs of ABC-type phosphate transporters encoded in this genome set.
Histograms of predicted isoelectric points of haloarchaeal proteomes revealed an asymmetric bimodal distribution, with a dramatically larger major mode around pH 4. 5, a minor mode around pH 10. 0, and a consistent overall shape across haloarchaeal species (Figure  5  and Data Dryad package  [67] ). Corroborating previous work, this major mode was shifted towards lower pI values compared to non-haloarchaea  [68]. Certain haloarchaeal proteins were not acidified, including many ribosomal subunits, membrane proteins, and DNA-binding proteins (Table  S5 ). Potential reasons for non-acidification include 1) shielding from the hypersaline cytoplasm (eg. internal ribosome subunits, membrane proteins), and 2) presence of a selective force against acidification (eg. DNA-binding proteins). Structural Each of these four species is a representative from the four most populous genera in the set, which collectively contain 66% of the organisms in the study.
For all sequenced haloarchaea, the pI histograms exhibited a bimodal distribution with a major mode at about 4. 5 and much smaller minor mode around 10. For all isoelectric point plots see Data Dryad package  [67]. doi:10. 1371/journal. pgen. 1004784. g005 Figure  6. Ribosomal subunit isoelectric points. Structural mappings of the large and small ribosomal subunits, showing protein monomers with predicted low (red) and high (blue) pI. Subunits are oriented according to (A) complex formation, (B) cytoplasmic view of each subunit, and (C) internal view of each subunit. Subunits exposed to the cytoplasm tend to be acidified (red subunits, visible in B), while subunits buried within the ribosome tend to have an alkaline isoelectric point (blue subunits, visible in C). Structural models used were those for Thermus thermophilus (1FKA)  [97]  and Haloarcula marismortui (1QVG)  [96].
Some regions of the genome are also expected to have low local G+C content due to selective pressure for maintaining a higher A+T percentage (eg. origin of replication sites). In addition, we have been neutral as to the direction of divergence from genome-wide G+C average, in order to allow detection of regions of unusually high as well as unusually low G+C (Figure  7 , Figure  S8 , Datasets S6 & S7). We found these regions to be highly enriched in protein families involved in DNA metabolism and transcriptional regulation, transmembrane transport (and other membrane proteins), and horizontal transfer of genetic information. Specifically, of the seventy-nine functionally annotated protein families with at least five members which were enriched at least eight-fold in the divergent G+C regions, thirty-six (45%) were annotated with DNA/RNA-binding capabilities, eleven (14%) were associated with horizontal gene transfer mechanisms, and seven (9%) were associated with the cell membrane or cell surface (Table  S6 ).