dataesr/scientific-paragraphs-categorization huggingface dataset:
Structural analyses have shed light on the XPO1mediated export molecular mechanism. Crystal structures of XPO1 bound to various NES-cargos and ternary complexes of RanGTP/XPO1/cargos are available in the Protein Data Bank  [25]. Human XPO1 is a 120-kDa protein containing 21 consecutive tandem HEAT repeats (H1-H21). Each HEAT repeat is formed by two antiparallel α-helices A and B connected by loops of varying lengths. The A helices align to form an outer convex surface whereas the B helices form an inner concave surface (Fig. 2 )  [26, 27]. The N-terminal HEAT and the Cterminal HEAT are in close proximity, and XPO1 adopts a ring or toroidal shape. The A helices of H11 and H12 form a hydrophobic groove that constitutes the NESbinding site. The so-called leucine-rich NES motif, which is present in XPO1 cargo proteins, is a short peptide sequence of 10 ordered amino acid residues as follows: Φ1X2-3Φ2X2-3-Φ3XΦ4, where Φ is either isoleucine, leucine, methionine, phenylalanine, or valine, and X is any other amino acids  [28].
XPO1 functions: nuclear export and mitosis control As mentioned previously, NES sequences are diverse but conform to the consensus sequence Φ1X2-3Φ2X2-3-Φ3XΦ4. NESs were first described in the Rev protein of HIV and the cyclic AMP-dependent protein kinase inhibitor α (PKIα)  [34, 35]. Currently, NESs have been identified in more than 200 proteins with various functions, such as cell cycle regulators, transcription factors, ribonucleoprotein complexes, translation factors, and viral proteins. NESs are compiled in the following comprehensive database: NESdb©  [36, 37]. Among the XPO1 cargos, several proteins such as TSPs (p53, p73, FOXO1), growth regulators (RB, p21, CDC25, cyclin B1/ D1), and anti-apoptotic molecules (nucleophosmin, survivin) control essential physiological cellular processes.
Competition for uptake between NH 3 and organic gases considerably slows down the approach to thermodynamic equilibrium  (Silvern et al. , 2017). Based on the findings of this work, modelers should pay particular attention to incorporating dust and sea salt and treating NH 3 wet deposition to improve nitrate simulation. Further evaluation using satellite measurements, such as NH 3 products from IASI and TES, is desired and will be conducted. Such evaluation requires global three-dimensional high-frequency model data. Potential future study also includes estimation of nitrate forcing for climate change. Data availability. All model data can be downloaded from the AeroCom database by contacting Michael  Schulz (michael. schulz@met. no). All measurement data are publicly available.
Dickson at Scripps Institution of Oceanography, USA) was used for calibration and to check the accuracy of the analysis. The integrated nutrient drawdown in the upper 50 m for the bloom period was estimated from salinity-normalized (34. 33) nDIC and -nNO 3 (nitrate) for all stations and converted to carbon using the measured POC/PON ratio of 5. 7 ± 1. 3. The complete N-ICE2015 water column biogeochemical dataset has been published in the Norwegian Polar Data Centre  38. Sediment traps. Ice-tethered sediment traps (KC Denmark) were deployed four times at 5, 25, 50 and 100 m depth during the bloom period. Deployment time varied between 36 and 72 h, but was usually close to 48 h. Before deployment, each trap cylinder was filled with a saturated NaCl solution to reduce microbial activity and thus increase the retention of organic matter.
The EM31 and HEM measurements use the same principle. The height above the bottom of the ice is derived from the strength of electromagnetic induction in the conductive sea water under the ice. For the HEM measurements, the height of the instrument above the surface of the ice or snow is determined with a laser altimeter included in the HEM instrument. The EM31 conductivity values were calibrated with drill-hole measurements and post processed to derive total thickness of ice and snow. Snow thickness was measured with a GPS snow probe (Magnaprobe, Snow-Hydro, Fairbanks, AK, USA)  46. When used together, these two instruments give the spatial distribution of both the total thickness of the ice and snow (from EM31) and the snow depth (from Magnaprobe). For direct comparison of the values, and to subtract' the snow from the EM31 data, we re-sampled the EM31 data on the Magnaprobe track and applied a Gaussian filter to the EM31 data.
The EM31 and Magnaprobe datasets were median-sampled on a 5 m regular grid. Snow depth was subtracted from the EM31 values to derive sea-ice thickness. This work is licensed under a Creative Commons Attribution 4. 0 International License. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons. org/licenses/by/4. 0/ © The Author(s) 2017.
Acknowledgements We are indebted to the captains and crew of RV Lance. This study was supported by the  Centre for Ice, Climate and Ecosystems (ICE)  at the  Norwegian Polar Institute , the  Ministry of Climate and Environment, Norway , the  Research Council of Norway  (projects Boom or Bust no. 244646 , STASIS no. 221961 , CORESAT no. 222681 , CIRFA no. 237906  and  AMOS  CeO no. 223254 ), and the  Ministry of Foreign Affairs, Norway  (project  ID Arctic ), the  ICE-ARC program of the European Union 7 th Framework Program  (grant number  603887 ), the  Polish-Norwegian Research Program  operated by the  National Centre for Research and Development  under the Norwegian Financial Mechanism 2009-2014 in the frame of Project Contract Pol-Nor/197511/40/2013,  CDOM-HEAT , and the  Ocean Acidification Flagship program  within the  FRAM-High North Research Centre for Climate and the Environment, Norway.
© The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4. 0 International License (http://creativecommons. org/licenses/by/4. 0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons. org/publicdomain/zero/1. 0/) applies to the data made available in this article, unless otherwise stated.
9 percent-resulted in a 3. 06 percent increase in the margin of victory of the center-right (CR) coalition in the post-pardon national elections of 2008 relative to the pre-pardon elections of 2006. We also investigate the underlying mechanism linking public policies and electoral behavior (voters' information and beliefs). First, we assemble an original dataset of local news stories that refer to acts of crime committed by former inmates' and make explicit reference to the fact that they were released because of the pardon. Second, we match local news to the municipalities to create a municipal-level measure of exposure to the effects of the pardon. Consistent with the idea that ITR is a driver of information on the local effects of the policy, we show that newspapers were more likely to report news on recidivism by pardoned individuals in municipalities where their ITR was higher.
5   The paper is structured as follows. Section 2 provides background information regarding the 2006 Italian Collective Pardon Bill and its political relevance in the 2008 elections. Section 3 presents the data. Section 4 discusses the empirical strategy. Section 5 reports the effects of the policy on the recidivism of pardoned individuals at the municipal level and presents the main results on voters' electoral response to the effects of the Collective Pardon Bill. Section 6 presents evidence shedding light on the mechanism behind the main results. Section 7 discusses different potential interpretations of the empirical results. Section 8 concludes. The online Appendix presents a retrospective voting model providing a theoretical framework for the empirical analysis. In addition, it describes in details the database on crime-related news and contains additional tables and figures which are also discussed in the main text.
Data The empirical analysis uses data from several different datasets. The first dataset provides information on the characteristics of the prisoners released following the 2006 Collective Pardon Bill. The data contains information on the municipality where each prisoner reside, the length of his residual sentence at the time of release, the length of his original sentence and the type of crime committed. The data contains information on the entire population of individuals pardoned by the bill. However, while the information on the municipality of residence of each Italian prisoner released is informative of his official residence, this is not the case for non-Italian prisoners. That is, the information on the residency of foreign prisoners is not a reliable proxy of their place of residence.
Hence, we exclude non-Italian individuals from the sample. Accordingly, to reduce measurement error, we focus only on municipalities where there is at least one Italian pardoned inmates resident in the municipality. As a result, the final dataset on pardoned inmates is composed by 12,355 Italian individuals resident in 2,256 municipalities. That is, around one-third of the municipalities in Italy had at least one Italian pardoned individual resident in the municipality. The summary statistics are reported in Table  2A  where we average-out the data on the characteristics of pardoned individuals at the municipality level. Figure  5  illustrates the geographical distribution of the (standardized) average ITR of pardoned individuals at the municipal level. This figure shows a substantial level of variation in the ITR that is not correlated with any regional pattern (for example higher in the south or in the north or in any particular region).
In the following analysis, we show that conditional on the average original sentence the variation in the ITR is orthogonal to observable city characteristics. Notice that, if municipalities were to present a very large number of resident pardoned inmates, we would not have enough variation in our key variable. However, this is not the case. The 90 percent of municipalities have less than 9 pardoned individuals resident in the municipality, the 70 percent less than four and the 50 percent less than two. 12  We then complement this dataset using electoral data on the 2006 and 2008 parliamentary elections by the Italian Minister of Internal Affairs, for all municipalities (even the ones with no pardoned Italian prisoners). This dataset reports information on the votes to political parties in the 2006 and 2008 elections.
In order to analyze municipal-level variations in voters' information on the effects of the collective pardon, we extrapolated data-from the Factiva database-regarding news on criminal acts involving pardoned individuals between August 1, 2006 (i. e. , when the pardon became effective) and March 30, 2008 (i. e. , the 2008 elections). We then matched news with municipalities to create a measure of municipal-level exposure of voters to the effects of the pardon. The online Appendix provides detailed information on the construction of this dataset. In addition, we use survey data from the Italian National Elections Study Survey (ITANES) in order to gather information on voters' issue priority and on their evaluation of the incumbent CL government's policies on crime. We also use survey data from the IPSOS Polimetro in order to obtain additional information on voters' issue priority (both nationally and in their local area of residence) and on voters' overall evaluation of the main CL and CR parties.
Heterogeneity in Voters' Response In this section, we present another important piece of evidence that is consistent with an accountability mechanism. As discussed in Section 2, the CL coalition was perceived as being responsible for proposing and designing the pardon bill. However, some MPs of the CR coalition also voted in favor of the bills. 26  We exploit this circumstance, as such CR MPs might have also been held accountable by voters for the realized effects of the policy. If an accountability mechanism is at work, the voters' response to the effects of the policy should favor less the CR coalition in electoral districts where the percentage of CR candidate who voted for the pardon was higher. 27  In order to test this hypothesis, we gathered data from the Italian Ministry of Internal Affairs regarding the identity of all CR candidates in each electoral district for the 2008 elections.
Additional Evidence: Information and Beliefs We now provide additional evidence to unmask the link between the idiosyncratic component in the effects of the policy (the ITR of pardoned individuals) with voters' observed behavior in the 2008 elections. In particular, we show that a higher average ITR translated into both a higher probability of bein exposed to crime news involving pardoned individuals at the municipal level and a more negative evaluation of the incumbent CL government. Voters' information. In order to assess the effects of the incentive to recidivate on the information available to voters' about the effects of the pardon, we focus on news about pardoned former inmates re-committing a criminal act. As explained in the online Appendix, these news storiesextrapolated from the Factiva database-contain words related to crime categories included in the pardon (i.
We find evidence in line with Table  9  from another dataset (IPSOS surveys). We first assess whether the incentive to recommit a crime is associated with an increase of the relevance of crime from the voters' perspective, both in Italy and in the local area where they live. From columns (1) and (  2 ) of Table  10  that reports the results, we find again that the incentive to recommit a crime does not increase the relevance that voters attach to crime. Next, we are able to investigate whether the negative perception of the CL incumbent in dealing with crime (Table  9 ) is also associated with a more negative evaluation of the CL in general. 36  Table  10  shows that when the ITR is higher, voters are also more likely to negatively evaluate the main CL party (the Partito Democratico). 37.
e. , voters should respond to the observed effects of public policies). The results presented in the paper are consistent with this mechanism. In the on-  36  The IPSOS database contains a series of repeated cross-sections spanning from April 2008 to December 2008. We consider all individuals interviewed within this period and control for date of the interview fixed effects. The frequency with which the question on the relevance of the crime issue was asked is lower than the one regarding the valuation of the main CL party. This explains the different number of observations in Columns (  1 ) and (  2 ) with respect to Columns (3) and (4). 37 Table  9  and 10 report the marginal effects from a Probit model. In Tables  A15  and A16  in the online Appendix we report the marginal effects from ordinary least squares.
, 2015). However, while this mechanism is consistent with the baseline results, it does not seem to square with three other pieces of evidence. First, as shown by Table  7 , the gain that the CR coalition obtained in municipalities where the (negative) effects of the pardon were more relevant was lower in districts where more CR candidates voted in favor of the pardon. Hence, the relevance of the crime issue induced by the collective pardon did not translated in an overall higher support for the CR. Rather, this higher support was conditional on the past stance of CR candidates with respect to such a policy. In addition, the evidence from the two survey datasets analyzed in Section 6 does not seem to imply any impact of the ITR on the probability of voters perceiving crime to be the most important issue either in Italy as a whole (Table  9  and Table 10 ) or in their municipality of residence (Table  10 ).
outstanding research assistance. Francesco Sobbrio  is grateful to  Nando Pagnoncelli  and  IPSOS  for allowing access to the data of the Polimetro 2008. Roberto Galbiati  acknowledges the support of the LIEPP through a public grant overseen by the  French National Research Agency (ANR)  as part of the  Investissements d'Avenir program LIEPP  (reference:  ANR-11-LABX0091 ). The usual disclaimers apply.
The final dataset consisted of 6,527 unrelated European ancestry samples: 2,434 individuals diagnosed with TS and 4,093 unselected controls. Genome-wide detection of CNVs was performed using the consensus of two widely used Hidden Markov Model (HMM)-based methods (STAR Methods). Additionally, we used a locusspecific, intensity-based clustering method to generate CNV genotypes in all samples across 11 common HapMap3 loci for sensitivity analysis (Figure  S2 ; Table  S2 ; STAR Methods). Using the proportion of concordant HMM-based calls at these loci as a sensitivity measure, we confirmed the absence of any bias in CNV detection between cases and controls across all loci (p = 0. 54, Fisher's Exact test) and between individuals (p = 0. 15, Welch's t test; see Table  S3 ). Post-call cleaning was performed and CNVs were annotated for gene content and frequency (STAR Methods).
CNVs were considered "genic" if they overlapped any exon of a known protein-coding RefSeq transcript. Frequencies were defined based on a 50% overlap with other CNVs as described elsewhere (CNV and Schizophrenia Working Groups of the Psychiatric Genomics Consortium, 2017); "singletons" denote CNVs with a frequency of one across the entire dataset. We filtered calls for rare (frequency < 1% or < 65 events) CNVs ≥ 30 kb in length and spanning at least ten probes. Finally, using a heuristically derived series of in silico validation metrics, we removed aberrant CNV calls due to mosaicism and misclassified rare events (Figure  S3 ; STAR Methods). In total, we resolved 9,375 rare CNVs (Table  S4 ).
Genotyping Samples selected for this study were all genotyped on the Illumina OmniExpress Exome v1. 1 (Illumina, San Diego, CA, USA) in three separate batches (TS1-3), while samples from the CC, CNP, GPC, and WTCCC2 sets were genotyped on the Illumina OmniExpress 12v1. 0 (Illumina, San Diego, CA, USA). A summary of the datasets, genotyping centers, and arrays used is provided in Table  S1A. The OmniExpress Exome and OmniExpress arrays are identical except for the presence of exome-focused content on the former and additional intensity-only markers on the latter. We have observed that exome-specific assays in general exhibit a much higher variance overall in their Log-R Ratios (LRR) values. Therefore, in order to avoid detection biases due to this differential variance and to unequal probe density, only the SNP assays with common identifiers for all array versions across all datasets were used for quality control (QC) and CNV detection, a total of 689,077 markers.
To ensure the generation of the most reliable SNP calls, intensity measures, and B-allele frequencies (BAF), a custom cluster file was generated for each dataset separately and for each genotype batch when such information was available. Since the performance of Illumina's proprietary normalization and cluster generation process improves with the number of samples, we processed all of the raw intensity data available, regardless of clinical phenotype. An initial round of QC was carried out using Beeline v1. 0 (Illumina, San Diego, CA, USA) to determine baseline call rates and LRR standard deviation (LRR_SD) for each sample using the canonical cluster file ( *. egt) provided by the manufacturer for each array version. Any sample with a call rate < 0. 98 or an LRR_SD > 0. 30 was deemed a failed assay and removed (Pre-cluster QC, Table  S1B ).
Ancestry Estimation Following the exclusion of all clinical non-TS samples from external studies, genotype data for the remaining samples was combined with data from publicly available continental HapMap samples of CEU, YRI, and CHB/JPT ancestry genotyped on the OmniExpress array (Illumina, San Diego, CA, USA). Available European (EU) samples from the 1000 Genomes Project (http://www. internationalgenome. org/) genotyped on the Omni platform were also included to establish an appropriate calibration threshold for EU ancestry designation. We thinned our dataset randomly to 19,024 LD-independent markers for ancestry inference using fastStructure  (Raj et al. , 2014)  with k = 3. Samples were excluded if they contained > 0. 0985 non-EU ancestry (Figure  S1 ). A final round of ancestry exclusion was performed by removing all samples outside of the median ± 3SD on the first three ancestry principal components (PCs).
CNV Calling We created GC wave-adjusted LRR intensity files for all samples using PennCNV's genomic_wave. pl script, and employed two widely used HMM-based CNV calling algorithms, PennCNV v2011-05-03  (Wang et al. , 2007) , and QuantiSNP v2. 0  (Colella et al. , 2007)  to initially detect structural variants in our dataset. For PennCNV, we generated a custom population B-allele frequency file for each dataset separately before calling CNVs using emission probabilities defined in the file hhall. hmm. QuantiSNP calls were generated from the GC-adjusted intensity files using the configuration file levels-hd. dat. A Perl script was used to merge concordant calls generated by both algorithms. CNVs were merged by taking the intersection of overlapping calls of the same CNV type (deletion or duplication).
Additionally, adjacent CNV calls were merged if they were spanned by a CNV called by the other HMM algorithm. As HMMs have been shown to artificially break up large CNVs, we also merged CNV segments in the final concordant callset if they were of the same copy number and the number of intervening markers between them was less than 20% of the total of both segments combined using the PennCNV's clean_cnv. pl script. We repeated this joining process iteratively until no more merging of segments occurred. Scripts and utility files used to generate CNV calls are available on bitbucket (https://bitbucket. org/ucla_coppolalab/tscnv).
Intensity Sample QC The PennCNV calling algorithm generates a number of array intensity-based metrics with regard to CNV assay quality. Intensity-based QC was conducted based on the distribution of all available assays and subsequently combined with the results from the SNP-based QC. To remove samples with data unsuited for CNV detection, we used empirically defined thresholds across several different metrics: 1. Waviness factor (WF) -measures the waviness in intensity values, a known artifact caused by improper DNA concentration that can lead to spurious calls. 2. Log-R ratio standard deviation (LRR_SD) -a measure of the overall variance in intensity. 3. B-allele frequency drift (BAF_DRIFT) -summary of the deviation of BAF from expected values. Thresholds for WF and LRR_SD were determined separately for each dataset by both manual examination of QC metrics and/or taking the mean +3x SD to determine outlying samples.
CNV-load Sample QC Although SNP and intensity-based QC removed most failed assays, we performed a final round of sample QC, removing eight additional samples with excessively high CNV load based on the total number of CNV calls (> 45) or total CNV length (> 10Mb). These thresholds were determined empirically by visual inspection of distributions across all datasets combined. Our final dataset after QC consisted 6,527 samples: 2,434 TS cases and 4,093 controls.