dataesr/scientific-paragraphs-categorization huggingface dataset:
Prototype Implementation We have implemented GSL Ref in an interactive prototype available online at: https://pleiad. cl/gradual-security/. The implementation, realized in Scala, supports all of GSL Ref plus let-bindings. Given a source program, it either shows the result of the elaboration to GSL ε Ref , or reports a static type error. If the source program is well-typed, the evidence-augmented term can be explored interactively, either collapsing or expanding premises of its well-typedness, including evidences. The user can then reduce the term step by step, similarly to PLT Redex's trace facility. At each step, the full typing derivation of the term can again be explored. The reduction shows how evidences are combined by consistent subtyping transitivity, eventually ending up in a value or a runtime security error.
Faceted execution was recently extended to support dynamic information flow with exceptions, declassification and clearance  [Austin et al. 2017 ]. It would be interesting to explore whether basing GSL Ref on faceted execution might yield a gradual security language that fully respects the dynamic gradual guarantee, by avoiding the extra runtime check in assignments. Stefan et al. [2017]  present a dynamic information-flow control system called LIO. Contrary to most approaches to dynamic information flow, LIO does not modify the underlying language runtime semantics, being implemented as a Haskell library. LIO supports both mutable references and exceptions. Exceptions are used to recover from security monitor failures, preserving both confidentiality and integrity. The possibility of securely recovering from runtime security exceptions is an interesting perspective to study in the context of gradual security typing.
Buiras et al. [2015]  propose Hybrid LIO (HLIO), a flexible monadic information-flow control library for Haskell. HLIO is not gradual in the sense that it does not include an unknown security label; instead, HLIO provides a primitive to explicitly and selectively defer label-ordering checks to runtime. Their approach to defer static typing constraints to runtime can even be exploited to postpone type checks beyond security label constraints, opening the door to hybrid type checking in Haskell. In contrast, as a gradual security language, GSL Ref supports a notion of unknown security information and implicitly mediates the interactions between static and dynamic security checking. Gradual security typing. Most directly related to our proposal is prior work on gradual security typing, which combines static and dynamic checking with the express intent of supporting a smooth migration between both checking disciplines by introducing a dynamic (i.
Data The data used in this study come from a distributed data collection exercise aimed at creating a harmonized cross-country micro-aggregated database sourced from firm-level data collected in national business registers. 6 For example the data sources for France and the U. S. are "Fichier Complet Unifié de SUSE" (FICUS) and Census Bureau's Business Dynamics Statistics (BDS) and Longitudinal Business Database (LBD) respectively, which are both built on administrative data with a quasi-universal coverage. These are the typical data used for studies using firm size such as Garicano et al. (2016) and.
A C C E P T E D M A N U S C R I P T Haltiwanger et al. (2013). The high representativeness of the underlying data sources and the large country coverage are two of the key features that make our dataset unique and particularly suitable for the present investigation. These data are produced within the DynEmp project led by the OECD, with the support of national delegates and national experts of member and non-member economies. The DynEmp project builds upon the distributed micro-data methodology proposed by Bartelsman et al. (2004) for analysing and comparing harmonized firm demographics across countries. 7 ta produced by the DynEmp routine include the "annual flow datasets" and the "transition matrices". The "flow datasets" contain annual statistics on gross job flows, such as gross job creation and gross job destruction and on several other statistical indicators of unit-level employment growth, such as mean, median, and standard deviation.
9 The DynEmp database currently includes 20 countries, namely Australia, Austria, Belgium, Brazil, Costa Rica, Denmark, Finland, France, Hungary, Italy, Japan, Luxembourg, the Netherlands, Norway, New Zealand, Portugal, Spain, Sweden, Turkey, the United Kingdom and the United States and covers firms in manufacturing and non-financial business services. 10 Data from most countries cover the 2001-2011 period. A detailed coverage table is provided in Appendix A (see Table  A2 ).
, 2007), we define cell-level employment growth volatility σ j c,t as the weighted average of firm-level volatilities of firms i in cell c, computed over a time window of length j σ j c,t = i∈c,t w j i,t σ j i,t , (3) where weights w j i,t are average employment shares of firms i over the period between t and t + j and the cell c is micro-aggregated according to age classes, size classes and macro-sectors, as previously discussed (c = a, s, m). The focus of the analysis is on firms surviving until time t + j as for these units a complete window to calculate growth volatility is available. At the same level of aggregation, for each detailed cell, the DynEmp database provides information on average size S c,t , as the average of initial size (measured in terms of employment at time t) of all firms in the cell, defined as 12 S c,t = i∈c,t S i N c,t , (4) where N c is the number of firms in cell c.
A C C E P T E D M A N U S C R I P T for the authors, i. e. France. The data source for France is FICUS (Fichier Complet Unifié de SUSE), which is constructed from administrative (fiscal) data with almost universal coverage. 15 As confirmed in Garicano et al. (2016) this is the most appropriate database to study the firm size distribution in France. In line with what we have done in the previous section, we define S i,t as firm i's size in term of employees at time t and σ j i,t as firm-level growth volatility built over a j-years time window. We focus on manufacturing firms and we pool together observations for 2001 and 2004. Even with these precautions the two datasets are not directly comparable. Indeed while average cell volatility (as defined in Equation  3 ) includes into the computation firms with zero volatility, this is not the case when we estimate the relation on individual data where these zero volatility firms are dropped by the log transformation.
A C C E P T E D M A N U S C R I P T Notes: temporal coverage by country of the database used for the analysis. Years for which annual flow data are available are colored. Analysis based on flow data excludes the first available year, since most job flows statistics require two consecutive periods to be computed. For Costa Rica no transition matrix is available due to the limited time extension of the source data. For Japan data refer to the manufacturing sector only. Gray boxes correspond to years that have been excluded from the analysis due to data issues. Data for some countries are still preliminary. Source: OECD DynEmp v. 2 database.
,  Scuola Superiore Sant'Anna and Paris School of Economics. Regarding the United Kingdom, this work contains (CASD), which is part of the  "Investissements d'Avenir" program  (reference:  ANR-10-EQPX-17  ) and supported by a public grant overseen by the  French National Research Agency. Angelo Secchi  acknowledges financial support from the  European Union   Horizon 2020 research and innovation programme  under grant agreement No. 649186  (ISIGrowth).
Methodological notes and cleaning In this subsection we provide further details on the methodology that we apply to create the database used for estimation, including cleaning details. Starting from the aggregate Transition Matrix provided by the DynEmp database, we exclude the cells corresponding to all macro sectors, to avoid double counting. We further exclude cells where employment growth volatility is missing. The strictness of the blanking procedures -aimed at avoiding primary or secondary disclosure -applied on the microaggregated data differs from country to country. These differences (that involve a limited number of cells including few units, generally between 5 and 10) may influence cross-country comparability of the estimates. Countries that implement blanking are Australia, Denmark, Spain, Finland, France, Japan, Luxembourg, the Netherlands, New Zealand, Turkey, the United Kingdom and the United States.
for a review of the literature originating from the pioneering work by Gibrat (1931). 2 Conceptually, cross-sectional variance (or standard deviation) is a measure of between-firm dispersion of growth rates at a given time while volatility is a measure of within-firm variation of growth rates over time (rolling window). The two concepts are, however, very related. Empirically Davis et al. (2007) , using Compustat data show that, while capturing different aspects of business dynamics, the two measures track each other well. Using a different data source Calvino et al. (2016)  provides further support to the existence of a positive correlation between volatility and dispersion. 3 Similarly Capasso and Cefis (2012) discuss the effects of the existence of natural and/or exogenously imposed thresholds in firm size distributions on estimations of the relation between firm size and the variance of firm growth rates.
Second, we use TWAS to investigate genes previously reported as susceptibility genes for prostate cancer identified by eQTL-based analyses. We find a significant overlap with 56 out of 102 previously reported genes assayed in our study also significant in TWAS. Third, we use a novel Bayesian prioritization approach to compute credible sets of genes and prioritize 109 genes that explain at least 90% of the posterior density for association signal at TWAS risk regions. One notable example, IRX4, had 97% posterior probability to explain the association signal at its region with the remaining 3% explained by 9 neighboring genes. Overall, our findings highlight the power of integrating gene expression data with GWAS and provide testable hypotheses for future functional validation of prostate cancer risk.
Results Overview of methods. To identify genes associated with PrCa risk, we performed a TWAS using 48 gene expression panels measured in 45 tissues  22, [30] [31] [32] [33] [34] [35] [36]  integrated with summary data from the OncoArray PrCa GWAS of 142,392 individuals of European ancestry (81,318/61,074 cases/controls; Methods)  29. We performed the summary-based TWAS approach as described in ref. 25  using the FUSION software (Methods). Briefly, this approach uses reference linkage disequilibrium (LD) and reference gene expression panels with GWAS summary statistics to estimate the association between the cis-genetic component of gene expression, or alternative splicing events, and PrCa risk  25. First, for each panel, FUSION estimated the heritability of steadystate gene and alternative splicing expression levels explained by SNPs local to each gene (i.
e. , 1 Mb flanking window) using the mixed-linear model (see Methods). Genes with nominally significant (P < 0. 05) estimates of SNP-heritability (cis-h 2 g ), are then put forward for training predictive models. Genes with nonsignificant estimates of heritability are pruned, as they are unlikely to be accurately predicted. Next, FUSION fits predictive linear models (e. g. , Elastic Net, LASSO, GBLUP  37  , and BSLMM  38  ) for every gene using local SNPs. The model with the best cross-validation prediction accuracy (significant out-ofsample R 2 ; nominal P < 0. 05) was used for prediction into the GWAS cohort. This was repeated for all expression datasets, resulting in 109,170 tissue-specific models spanning 15,383 unique genes using total expression and 4990 using alternatively spliced introns for a combined 16,389 unique genes.
2 tissue-specific models associated with PrCa per reference expression panel (Supplementary Data 1). In 1 Mb regions with at least 1 transcriptome-wide significant gene, we observed 10. 6 tissue-specific associated models on average, and 2. 6 associated genes on average, indicating that further refinement of association signal at TWAS risk loci is necessary. To quantify the overlap between non-HLA, autosomal risk loci in the OncoArray PrCa GWAS and our TWAS results, we partitioned GWAS summary data into 1 Mb regions and observed 131 harboring at least one genome-wide significant SNP. Of these, 127/131 overlapped at least one gene model in our data and 68/ 131 overlapped at least one transcriptome-wide significant gene (Supplementary Figure  7 ). Associated genes were the closest gene to the top GWAS SNP 20% of the time when using 26,292 RefSeq genes.
TWAS genes are indicative of association and do not necessarily reflect causality (e. g. , due to co-regulation at the same region). To prioritize genes at regions with multiple TWAS signals (Fig. 2 ), we used a Bayesian formulation to estimate 90%-credible gene sets (Methods). We found 109 unique genes across 84 nonoverlapping 1 Mb regions comprising our 90% credible sets (Supplementary Data 6, 7). In all, 68/84 credible sets contained either a single gene or the same gene in multiple tissues. The average number of unique genes per credible set was 1. 29 (median 1). We observed that 28/109 prioritized genes were previously reported in eQTL analyses  5, [14] [15] [16] [17] [18] [19] [20]  , which supports the hypothesis that TWAS followed by Bayesian prioritization refines associations to relevant disease genes.
Methods OncoArray GWAS summary statistics. Genome-wide association summary statistics for the OncoArray PrCa study were obtained from ref. 29. Summary statistics were computed using a fixed-effect meta-analysis for 142,392 total samples of European ancestry from the OncoArray (81,318/61,074 cases/controls), UK stage 1 (1854/1894) and UK stage 2 (3706/3884), CaPS 1 (474/482) and CaPS 2 (1458/512), BPC3 (2068/3011), NCI PEGASUS (4600/2941) and iCOGS (20,219/  20,440). The initial summary data contained association statistics for 19,726,430 variants. We filtered out summary statistics for SNPs with MAF <0. 01 and any SNPs with ambiguous alternative alleles (e. g. , A → T; C → G; or vice-versa). Finally, we kept only SNPs with rsIDs defined by dbSNP144. Our QC pipeline resulted in association statistics at 10,516,237 SNPs for downstream TWAS analyses.
Association between total expression of MLPH and PrCa risk was transcriptome-wide significant in normal and tumor prostate tissue results as aggregated in refs. 14, 15. Across these studies, overlap of eQTLs and PrCa risk loci are computed by one of two possible methods. The first method tests known PrCa risk SNPs for association with expression levels of nearby genes/ transcripts. The second method takes a two-step approach. First, genes nearby PrCa risk loci are tested for harboring eQTLs at some significance level. Next, genes with identified eQTL SNPs are tested to be in LD with known PrCa risk variants at some level (e. g. , r 2 > 0. 5). Reference gene expression data and predictive models of expression. We downloaded the FUSION software (see URLs) along with its prepackaged weights for gene expression data.
FUSION is an R package that implements the TWAS scheme described in ref. 25. Weights for gene expression measured using RNA sequencing data were obtained from the CommonMind Consortium  30  (dorsolateral prefrontal cortex, n = 452), the Genotype-Tissue Expression Project  22  (GTEx; 44 tissues; n = 449), the Metabolic Syndrome in Men study  32, 33  (adipose, n = 563), and The Cancer Genome Atlas (TCGA; prostate adenocarcinoma, n = 483)  39. Expression microarray data were obtained from the Netherlands Twins Registry  35  (NTR; blood, n = 1247), and the Young Finns Study  31, 34  (YFS; blood, n = 1264). All non-TCGA expression panel individuals were PrCa controls. Detailed description of quality control procedures on measured gene expression and genotype information for all non-TCGA reference panels are described in refs.
25, 27. TCGA genotype, gene expression, and exon-junction data for 525 samples were downloaded using the Broad GDAC FireHose version 2016_1_28 (see URLs). Genotypes were imputed to the Haplotype Reference Consortium  51  and restricted to well-imputed (INFO > 0. 9) HapMap3 52 sites. Genes (exon junctions) missing in more than half of samples were removed. RPKM and log-adjusted gene expression levels were estimated in a generalized linear model controlling for three gene expression PCs. The estimated logabundances were quantile-normalized and inverse-normal rank-normalized. We estimated alternatively spliced introns using the software MapSplice version 2 (see URLs). A total of 482 samples passed quality control procedures in both genotype and gene expression data. We note that batch effects from measurement biases (e.
g. , RNA-degradation) should be uncorrelated with SNPs local to a gene body definition, and therefore not impact prediction accuracy. By maximizing the sample size, predictive power when using cis-SNPs should increase and be largely unbiased. This is evidenced by the fact that models are largely stable across and within TCGA PRAD datasets (Supplementary Table  1 ). We filtered genes that did not exhibit cis-genetic regulation at current samples sizes by keeping only genes with nominally significant (P < 0. 05) estimates of cis-SNP heritability (cis-h 2 g ), which resulted in 117,459 total tissue-gene pairs from 17,023 unique genes. We refrain from reporting genes from the HLA region due to complicated LD patterns. To train predictive models, FUSION defines gene expression for n samples (y GE ) as a linear function of p SNPs (X) in a 1 Mb region flaking the gene as y GE ¼ Cβ þ Xw GE þ ϵ; where w GE are the p SNP weights, Cβ are covariates (e.
g. , sex, age, genotype principal components, genotyping platform, and PEER factors) and their effects, and ϵ is random environmental noise. FUSION estimated weights for expression of a gene in a tissue using multiple penalized linear models. Generally, FUSION optimizes for ŵGE β " # ¼ arg min w GE ;β ky GE À Xw GE À Cβk 2 2 þ f w GE ð Þ; where f(w GE ) is a parameterized penalty function specific to each model (e. g. , GBLUP  37  , LASSO, the Elastic Net). The exception to this optimization criterion is the Bayesian sparse linear mixed model (i. e. , BSLMM)  38  which fits the posterior mean for w GE using MCMC in the GEMMA v 0. 94 software (see URLs) to obtain weights. To determine which model has the best prediction accuracy for a given gene-tissue pair, FUSION computes out-of-sample R 2 by performing fivefold crossvalidation for each model.
In practice this is not possible due to predictive models not including the same set of SNPs due to QC and technological differences in the original studies. Therefore, as a proxy we Genomes  53  and compute R 2 across shared genes for pairs of tissues (Supplementary Note 1). 0 Transcriptome-wide association study using GWAS summary statistics. FUSION estimates the strength of association between predicted expression of a gene and PrCa (z TWAS ) as function of the vector of GWAS summary Z-scores at a given cis locus z GWAS (i. e. , vector of SNP association Wald statistics) and the LDadjusted weights vector learned from the gene expression data w GE as z TWAS ¼ w ′ GE z GWAS ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi varðw ′ GE z GWAS Þ p ¼ w ′ GE z GWAS ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi w ′ GE Vw GE p ; where V is a correlation matrix across SNPs at the locus (i.
e. |z TWAS , perm |≥|z TWAS |) have been observed such that the empirical null cannot be rejected at a specified level. We define novel risk regions as a flanking region around a transcriptome-wide significant gene (splicing event; P TWAS < 4. 58 × 10 -7 ; two-tailed Z-test) that does not harbor a genome-wide significant SNP (P GWAS < 5 × 10 -8 ; two-tailed Z-test). We consider 2 Mb windows by default (i. e. TSS ± 1 Mb) and show that the results are robust to the choice of window size (Supplementary Data 3). GWAS analyses conditional on predicted expression. To assess the extent of residual association of SNP with PrCa risk after accounting for predicted gene expression levels, FUSION estimates conditional SNP association scores using GWAS summary statistics. Namely, define V as LD for SNPs in the region, V GE as the correlation between predicted expression levels, and C as the correlation between SNPs and predicted expression.
Under the assumption of a single causal gene at a risk region, we sampled TWAS Z-scores for 1000 independent regions. We then performed Bayesian prioritization at each region and computed ρ-credible sets for various levels of ρ while counting the proportion of causal genes identified across all simulations. Pathway analyses. To determine which pathways may be enriched with genes identified from our Bayesian prioritization approach, we used the R package GOseq  57  which internally links gene identifiers to GO terms (GO db: 2017-09-02). We categorized all 16,389 genes into prioritized/not-prioritized and ran the analysis using custom R scripts linking GOseq. GOseq obtains P-values for overrepresented genes using the Wallenius approximation to the non-central hypergeometric distribution. We limited analysis to Gene Ontology Biological Pathways (GO:BP).
Acknowledgements We wish to thank all GWAS study groups contributing to the meta-analysis dataset from which the transcriptome-wide association analyses were conducted:  BPC3  (Breast and  Prostate Cancer Cohort Consortium) ;  CAPS (Cancer of the Prostate in Sweden) ;  PEGASUS (Prostate Cancer Genome-wide Association Study of Uncommon Susceptibility Loci );  APCB BioResource (Australian Prostate Cancer BioResource) ; and The  PRACTICAL (Prostate Cancer Association Group  to  Investigate Cancer-Associated Alterations  in the  Genome) Consortium. This work was supported by  NIH  grants  R01-HG009120 ,  R01-HG006399 , and  U01-CA194393. This work was supported by the  Canadian Institutes of Health Research, European Commission's Seventh Framework Programme  grant agreement  n°223175  ( HEALTH-F2-2009-223175 ),  Cancer Research UK  Grants  C5047/A7357 ,  C1287/A10118 ,  C1287/A16563 ,  C5047/A3354 ,  C5047/ A10692 ,  C16913/A6135 , and The  National Institute of Health (NIH) Cancer Post-Cancer GWAS  initiative grant: No.
Genotyping of the OncoArray was funded by the  US National Institutes of Health (NIH)  [ U19   CA 148537 for ELucidating Loci Involved in Prostate cancer SuscEptibility (ELLIPSE)  project and  X01HG007492  to the  Center for Inherited Disease Research (CIDR)  under contract number  HHSN268201200008I ]. Additional analytic support was provided by  NIH   NCI U01 CA188392. Funding for the iCOGS infrastructure came from: the  European Community 's  Seventh Framework Programme  under grant agreement  n°223175  ( HEALTH-F2-2009-223175 ) (COGS),  Cancer Research UK  ( C1287/A10118 ,  C1287/A 10710 ,  C12292/A11174 ,  C1281/A12014 ,  C5047/A8384 ,  C5047/A15007 ,  C5047/A10692 , and  C8197/A16565 ), the  National Institutes of Health  ( CA128978 ) and  Post-Cancer GWAS initiative  ( 1U19 CA148537 ,  1U19.
Data availability Complete TWAS and fine-mapping results are available at http://github. com/bogdanlab/ prca_twas/. OncoArray PrCa GWAS summary data used in this study are available at http://practical. icr. ac. uk/blog/. Relevant TCGA data are available from Broad Firehouse at http://gdac. broadinstitute. org. FUSION software, weights/models, and reference LD are available at http://gusevlab. org/projects/fusion/.