dataesr/scientific-paragraphs-categorization huggingface dataset:
g. min. for minute), and shortenings, which are idiosyncratic. In the normalisation process, abbreviations were not expanded to their full form in either of the languages, while shortenings were kept in Slovene, and expanded in Croatian and Serbian. This is one of the very few differences in the guidelines, introduced due to the different needs related to the future use of the datasets in various different projects. In addition, abbreviations were assigned a dedicated PoS tag (see Section 4. 2. 1), while tags assigned to shortenings depended on what PoS classes they were normalised to (e. g. msm stands for mislim -I think, and was tagged as a verb).
DATA ANALYSIS In this section we present the results of the analyses conducted on the normalised Slovene, Croatian, and Serbian Twitter datasets. Given that our normalisation guidelines were largely based on descriptive categories that are difficult to identify automatically (e. g. , phonetic transcription or incorrect spelling), the analyses had to be adjusted to look at more readily identifiable criteria. We therefore decided to focus on transformations, i. e. character-level modifications that took place in non-standard language use compared to the standard. Note that this is the opposite from the normalisation process described in Section 3, where standard language forms were assigned to nonstandard ones. For instance, in Section 3 we gave an example of the Croatian Ikavian verb form živin, which was normalised to the standard živim (I live); in the analyses presented in the remainder of the paper we treat this as a transformation of the standard živim into non-standard živin through character replacement.
In addition, Slovene and Croatian are characterised by frequent transformations of other verbs through the shortening of the infinitive, e. g. , gledat for gledati -watch, which is highly atypical of Serbian. Slovene adverbs are mostly shortened (e. g. , tako -so frequently shortened to tko), but other kinds of transformations occur too. An interesting case is zdaj -now, which is transformed in three different ways in the dataset: zdej, zdj and zj. The transformations of interjections are mostly due to repeated vowels or syllables (e. g. , hahahaha). Here, the differences across the languages are in all probability caused by minor differences in the application of the normalisation guidelines (e. g. , despite the shared instructions, ahaha was normalised to haha in Croatian and Serbian, but left as ahaha in Slovene).
Lemma analysis The lemmas that underwent most transformations in each of the three datasets are shown in Table  2 , where for each lemma we report the overall percentage of the transformed forms this lemma covers (% total), on which the lemma ranking is based, as well as the percentage of all forms of that lemma that were transformed (% lemma). We again disregard transformations due to diacritic omissions. There is a high overlap among the lemmas on the lists of all three languages, with some variation in rank. The overall most frequently transformed forms come from the auxiliary verb biti (be), first-ranked in Slovene and Serbian, and second-ranked in Croatian. The full stop, ranked first in Croatian, does not make it to the Slovene list, and is ranked 17th in Serbian. Function words and interjections follow.
Overall, the most frequent transformation type is character dropping, followed by replacements, while insertions are the least frequent manifestation of the non-standard language used on Twitter. We also performed log-likelihood tests on the data relative to the distribution of transformation types (without diacritics), confirming that insertions are the type that differs most between languages (LL=1723. 79). Deletions occupy the second position (LL=400. 71), while replacements reach the highest level of similarity in Slovene, Croatian and Serbian (LL=40. 52). The raw frequencies that the LL values are based on are shown in Table  A3  in the Appendix. The next step in the analysis is to look at the most frequent specific transformations in each of the studied languages (again disregarding diacritic omissions).
g. the wide presence of short infinitives in some dialects, see  Stevanović 1986 ). On a more sociolinguistic side, more shortenings seem to be used in non-standard Serbian than in non-standard Croatian (no data is available for Slovene, as its shortenings were not normalised). The exact reasons for this are yet to be established, given that the communicative and practical constraints are shared. One possible technical explanation is that shortenings are used in Serbian in order to gain the space that Croatian frees through single-vowel droppings. Another hypothesis is that Serbian twitterese is more "playful," and that its users (who might belong to a different demographic than those in Croatia or Slovenia) use language in a particularly creative way. On the other hand, more regional and dialectal forms are used in Slovene and Croatian twitterese than the Serbian version, which could perhaps be traced back to differences in the official language policies of the three countries, and in how much different dialects are used and how they are viewed.
The overall picture thus seems to be one of a (socio-)linguistic non-standardness continuum going from Slovene to Serbian. What is particularly interesting is that Croatian patterns with Slovene in several respects when it comes to the non-standard language, despite the standard language of Croatian being overall much closer to Serbian, linguistically and historically. These conclusions should of course be tested in a more controlled manner in future work, and while some of the results that lead us to them might have been affected by minor discrepancies in the normalisation guidelines for the three languages, the tendencies seem robust enough to provide motivation for further studies. In sum, given the relative scarcity of large-scale empirical data on Slovene, Croatian and Serbian CMC, the analyses reported in this work are intended to provide a valuable first insight into the nature of deviations from their norms, and to serve as a starting point for more focused studies of the linguistic phenomena at hand.
Acknowledgements The research described in this paper was funded by the  Slovenian Research Agency  national basic research project  J6-6842  Resources, Tools and Methods for the  Research of Nonstandard Internet Slovene , the  Swiss National Science Foundation  (through ReLDI -Regional Linguistic Data Initiative, an institutional partnership among the  Universities of Zurich, Belgrade and Zagreb , within the  Scientific Co-operation between Eastern Europe and Switzerland programme ; project No. 160501 , 2015No. 160501, -2017)), and the  Ministry of Education of the Republic of Serbia  (through the national research project  Standard Serbian Language: Syntactic, Semantic and Pragmatic Explorations ; project No. 178004 , 2011 project No. 178004 , -2016)). We are also grateful to our annotators, who made the analyses reported in this paper possible, and to the reviewers for their valuable comments.
After a first screening among papers included in the final list (systematic search+hand search) based on title and abstract, full text evaluation was conducted to select EBM guidelines. A general assessment of the CPGs was performed based on the Appraisal of Guidelines for Research & Evaluation II instrument  9  not for formal appraisal but only to inform discussion. The final goal was to perform a broad assessment of current guidelines on SS, with input of all HCPs, that would serve as basis to define unmet needs. For this matter, a discussion group was set for the evaluation of the existing CPGs and identification of unmet needs. 1. 10. Systematic database search identified 742 records that together with 35 records retrieved through manual search led to a total of 777 references for screening.
Acknowledgements Thanks to all the members of the Steering Committee of the  ERN ReCONNET  for the huge commitment during this work. A special thank goes to all the members of the  ERN ReCONNET team  for providing support during all the phases of the Work Package 3. Contributors VCR, RT, CAS, AV, XM: substantial contributions to the conception and design of the work, the acquisition, analysis and interpretation of data; drafting the work and revising it critically for important intellectual content; final approval of the version to be published; agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. TA, CB,  J-EG , HG, LM, MO, CP, MP, MT, TN, JMV, PV: substantial contributions to the analysis and interpretation of data; final approval of the version to be published; agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.
Acknowledgements I am most grateful to  Dr. Wilson R. Lourenço  ( Muséum national d'Histoire naturelle, Paris, France ) for his comments and review of the manuscript and to three anonymous referees for useful comments and suggestions. I also want to express my sincere gratitude to  Mr. Johan Chevalier  ( Reserve Naturelle de l'Amana, Awala-Yalimapo, French Guiana, France ) for providing the collected specimens of the new species described in this work and for permission to use some of his photos. Thank you also to the  Muséum national d'Histoire naturelle (Paris, France) ,  Ms. Elise-Anne Leguin  ( MNHN, Paris, France ),  Mr. Anthony Thillien  ( Le Mans, France ),  Mr. Giorgio Molisani  ( Bürglen, Switzerland ), and  Elsevier Masson SAS  ( Paris, France ) for permission to use some of their photos.
| Fieldwork and color measurement During 2002-2013 inclusive, we collected longitudinal data on male mating success and multiple sexual traits from five study sites in Central Finland (peat bogs with high visibility, ca. 62°15′N; 25°00′E). Data on morphological traits were collected annually in January-March by catching birds from winter flocks with oat-baited walk-in traps [for details, see  (Kervinen, Alatalo, Lebigre, Siitari, & Soulsbury, 2012; Lebigre, Alatalo, Kilpimaa, Staszewski, & Siitari, 2012) ]. Each captured individual was classified either as a yearling or as an adult based on plumage characteristics. Birds were individually ringed for future identification with an aluminum tarsus ring carrying a unique serial number and three colored tarsus rings. All captured birds were blood sampled (<2 ml, maximum <0.
Moreover, methylation probabilities at most of the sites were not significantly correlated with each other (Table  S3 ). Finally, we tested for a relationship between blue chroma and the percentage of DNA methylation in adulthood. We focused only on birds that were at least two years old during the molt after sampling, as sexual traits are not fully expressed in younger males. For this analysis, we analyzed all of the CpG sites within the AgRP gene simultaneously within a single model and individual identity was again included as a random effect. All of the models were run using the lme4 package  (Bates, Maechler, Bolker, & Walker, 2015)  in R version (R Core Team 2014) and none of the models had variance inflation factors (VIF) above two.
| DNA methylation We sampled 94 males a total of 170 times, at ages varying one to six years old (Table  S3 ). Putative transcription binding site names were lifted over from the JASPAR database (http://jaspar. genereg. net/; Mathelier et al. , 2016). 1 Sox3, 2 RHOXF1, 3 FoxD2, 4 NKX2-8 CpG site methylation was generally uncorrelated among different CpG sites (8/10 correlation coefficients were below 0. 30) and the strength of correlation declined with increasing physical distance between dyads of CpG sites (Table  S3 ).
Methods The Stroke Genetics Network The SiGN was established with the aim of performing the largest genome-wide association study (GWAS) of ischemic stroke to date. The study design has been previously described  12  (e-Methods). Briefly, subjects in SiGN were classified into stroke subtypes using the Causative Classification System (CCS), which subtypes cases through an automated, webbased system that accounts for clinical data, test results, and imaging information. 13, 14 Within the CCS, there are 2 subcategories: CCS causative, which does not allow for competing subtypes in a single sample, and CCS phenotypic, which does. In addition, ;74% of samples were subtyped using the Trial of ORG 10172 in Acute Stroke Treatment (TOAST) subtyping system. 15 After quality control, the SiGN data set comprised 16,851 ischemic stroke cases and 32,473 stroke-free controls (e-Methods and table e-1, links.
Standard protocol approvals, registrations, and patient consents All cohorts included in the SiGN data set received approval from the cohort-specific ethical standards committee. Cohorts received written informed consent from all patients or guardians of patients for participation in the study, where applicable. Details on sample collection have been previously described. 12 entifying AF cases and controls We defined AF in SiGN on the basis of 5 variables available in the CCS phenotyping system: (1) AF, (2) paroxysmal AF, (3) atrial flutter, (4) sick sinus syndrome, and (5) atrial thrombus. This definition yielded 3,190 AF cases for analysis. We also defined a strict case set based on "AF" only (N = 1,751 cases) for sensitivity analyses (e-Methods and figure e-1, links. lww. com/NXG/A123).
Genetic correlation between AF and ischemic stroke subtypes We used summary-level data from a previous AFGen Consortium meta-analysis of AF  5  to calculate a z-score for each SNP in that GWAS. In addition, we calculated a z-score for each SNP from our SiGN GWAS of each stroke subtype and AF. As a null comparator, we downloaded SNP z-scores from a GWAS of educational attainment  17  available through LDHub (ldsc. broadinstitute. org/, accessed November 1, 2017). We calculated Pearson r between z-scores from 2 traits to evaluate correlation (e-Methods and figure e-2, links. lww. com/NXG/A123).
Constructing an AF polygenic risk score (PRS) To construct an AF PRS, we used SNPs from a previously derived AF PRS (e-Methods, links. lww. com/NXG/A123). 18 riefly, the PRS was derived from an AF GWAS of 17,931 cases and 115,142 controls. 5 This PRS comprised 1,168 SNPs with p < 1 × 10 -4 and LD pruned at an r 2 threshold of 0. 5. 18 Of these 1,168 SNPs, we identified 934 SNPs in the SiGN data set with imputation info >0. 8 and MAF >1%. We used these 934 SNPs to construct the AF PRS in the SiGN data set. Additional details on the PRS construction can be found in the e-Methods.
Results We began by testing our ability to rediscover known AF genetic associations in the SiGN data set, assembled to study the genetics of ischemic stroke. We ran a genome-wide association study (GWAS) in SiGN using 3,190 cases, with AF or paroxysmal AF, as well as other diagnoses suggestive of underlying AF  19, 20  (e-Methods, table  1  and table e-1, links. lww. com/NXG/A123), and 28,026 controls (figure e-1). We found the top associated SNPs to be highly concordant with a previous GWAS of AF performed by the AFGen Consortium (table e-2). Adjusting the GWAS for age did not substantially change our findings (r = 0. 83 between SNP effects from the age-unadjusted and age-adjusted GWAS). By extending our analysis beyond these top associations, we next assessed whether patients with stroke with AF have a similar overall genetic predisposition to the arrhythmia as seen in the independent AFGen GWAS.
In addition, we assessed the overlap between genetic predisposition to AF and each stroke subtype, allowing for the known phenotypic concordance between cardioembolic stroke and AF (89. 5% of cardioembolic stroke cases in SiGN also have AF, table e-1, links. lww. com/NXG/A123). We performed a series of GWAS in the SiGN data for AF and each of the stroke subtypes using BOLT-LMM  16  (e-Methods) and calculated the z-score (beta/standard error) of each SNP in each phenotype. We then used summary-level results available from the previous (independent) GWAS of AF 5 (from AFGen) and calculated the z-score for each SNP in that data set. Continuing this analysis across the other stroke subtypes (large artery atherosclerosis, small artery occlusion, and undetermined stroke; figure 1, B, C, E, F), we found near-zero correlation between AFGen and either large artery atherosclerosis or small artery occlusion (figure  1 , B and E), indicating no genetic overlap between the phenotypes.
However, the correlation between AF and the undetermined stroke subtypes (a highly heterogeneous subset of cases  21, 22  that cannot be classified with standard subtyping systems  13, 15  ) increased steadily as we partitioned the AFGen data by z-score (all undetermined vs AFGen r = 0. 04 for AFGen SNPs with | z| > 1 and r = 0. 16 for AFGen SNPs with |z| > 3. 5; figure 1, C and F, and table e-3, links. lww. com/NXG/A123), indicating that genome wide, there is residual genetic correlation between AF and the undetermined stroke categories, some of which could represent causal AF stroke mechanisms in that subgroup. As an additional null comparator, we performed correlations between the AFGen results with z-scores derived from the latest GWAS of educational attainment  17  and found that correlation remained at approximately zero regardless of the z-score threshold used (figure  1 , A and D, and table e-3).
5%-23. 0%; figures e4-e6 and table e-4). Up to this point, our results indicated that AF in ischemic stroke is genetically similar to that discovered in previous genetic studies of AF alone and that the bulk of the genetic risk of cardioembolic stroke seems attributable to AF genetic risk factors. Next, we sought to explicitly test what proportion of cardioembolic stroke risk could be explained by AF loci, independent of known clinical risk factors for AF. First, we identified SNPs from an AF PRS independently derived from the AFGen GWAS 5 (e-Methods, links. lww. com/NXG/A123). Of the 1,168 SNPs used to generate this pre-established PRS, we identified 934 in the SiGN data set with imputation quality >0. 8 and MAF >1%. We computed the PRS per individual (e-Methods), weighting the imputed dosage of each risk allele by the effect of the SNP (i.
47 × 10 -44 , table e-6). None of the other stroke subtypes were significantly associated with the AF PRS (all p > 0. 013, figure  2A  and figure e-7). Because AF status was missing for most controls in the SiGN data set, we performed sensitivity analyses using only the 3,861 controls confirmed as having no AF. Although reducing the set of controls to this refined group did not substantially change the results for the primary stroke subtypes, we found that the AF PRS was modestly associated (p < 5 × 10 -3 , after adjusting for 5 subtypes and 2 control groups) with the overall undetermined subtype (OR per 1 SD = 1. 07 [95% CI: 1. 02-1. 13], p = 4. 15 × 10 -3 ) (figure  2B  and table e-5, links. lww. com/NXG/A123). Further examination of the 2 mutually exclusive subgroups of the undetermined group revealed that the PRS associated significantly with the incomplete/unclassified categorization (OR per 1 SD = 1.
Loss of LKB1 in the kidney results in alterations of tubular architecture, cysts, and chronic inflammation with progressive scarring leading to renal failure, reminiscent of a renal ciliopathy phenotype. LKB1 interacts with ciliopathy proteins, and a functional interaction exists between LKB1 and NPHP1 To obtain insights into what might explain the phenotype of LKB1 deletion in the kidney, we performed a proteomic screen in protein lysates from renal medulla, aiming to identify endogenous interactors of LKB1 that might illuminate its function. The LKB1 precipitates obtained from these lysates were highly enriched for the known LKB1 interactor STRADa and 30 other proteins  (Fig 2A and Datasets EV1 and EV2). Of these, 12 have previously been implicated in renal physiology or development (Dataset EV1).
We did not find consistent signals demonstrating dysregulation of AMPK or mTORC1 signaling in the tubules of Lkb1 DTub kidneys  (Appendix Fig S2) , suggesting that alternative signaling events triggered by LKB1 inactivation are involved in the renal pathology. To approach this issue in an unbiased fashion, we performed microarray analysis in Lkb1 mutant kidneys at an early time point, when there was only little damage to the parenchyma  (Fig 1F; 5 weeks). We compared these data with RNAseq analyses  performed in MDCK cells where Lkb1 was depleted by shRNAinduced knockdown  (Boehlke et al, 2010b; Figs 3A and EV3A and B, and Dataset EV3). This approach allowed us to discard regulated transcripts from the kidney that came from outside renal epithelia. At the same time, it enabled us to look at transcripts that we might have discarded, if we had assumed that they originate in non-epithelial cells, for instance immune cells.
Morphological analyses were performed using Medical Image Processing and Visualization (MIPAV) software (National Institutes of Health). To determine the total kidney volume, the perimeter of the kidney, which could be clearly distinguished from the surrounded tissue, was delineated manually on each slice image. The total kidney volume was calculated from sets of contiguous images by summing up the products of area measurements and slice thickness using the volume of interest tool in MIPAV as previously described  (Grahammer et al, 2014).
Morphological analysis Mouse kidneys were fixed in 4% paraformaldehyde and embedded in paraffin, and 4-lm sections were stained with periodic acid-Schiff (PAS) or Picrosirius Red. PAS-stained sections were imaged using an AxioPlan2 (Zeiss) equipped with a Plan Neofluar 20×/0. 30 NA objective and an AxioCam camera (Zeiss). PAS-stained full-size images were recorded using an AxioObserver Z1 (Zeiss) equipped with an EC Plan Neofluar 2. 5×/0. 085 NA objective and an AxioCam camera (Zeiss) or using a whole slide scanner, Nanozoomer 2. 0 (Hamamatsu), equipped with a 20×/0. 75 NA objective coupled to NDPview software (Hamamatsu). Picrosirius Red-stained sections were imaged using an AxioImager Z1 (Zeiss) equipped with a Plan Apochromat 40×/0. 95 NA objective and an AxioCam camera (Zeiss). Image recording was performed using AxioVision software (Zeiss) or ZenBlue Software (Zeiss).
Plasmids Tagged constructs for CD2AP, GFP, NPHP1, NPHP2, NPHP3, NPHP4, and NPHP8 were previously described  (Hoff et al, 2013). Tagged LKB1 and STRADa constructs were kindly provided by Andrea Thiele from Hubrecht Lab and Clevers Lab, respectively. STRADb was cloned from Clone 3549243 (BioScience). FLAG. MO25a and FLAG. MO25b were cloned from canine cDNA into pcDNA6 in-frame with N-terminal FLAG tag. FLAG. PKD1 construct was kindly provided by Michael Caplan  (Grimm et al, 2003). Flag. LKB1 for rescue experiment was cloned from human cDNA into pLXSN in-frame with Flag tag. YFP. LKB1, STRADa. YFP, and STRADb. YFP were cloned from human cDNA into pLXSN in-frame with N-terminal (LKB1) or C-terminal (STRAD constructs) YFP variant. The primers used for cloning are listed in Appendix Table  S1. Cell culture, transfections, co-immunoprecipitation, and transgenic cell lines Human embryonic kidney (HEK 293T) cells (ATCC Promocell; CRL-11268) are listed in the database of commonly misidentified cell lines maintained by ICLAC and NCBI Biosample and were authenticated by Interspecies Determination (Isoenzyme Analysis and STR Analysis) by the providing company.
Immunofluorescence Four-micrometer sections of paraffin-embedded kidneys, or cells seeded on glass coverslip coated with polylysine (Sigma) were submitted to antigen retrieval and avidin/biotin blocking or cell permeabilization. The sections or coverslips were incubated with primary antibody and subsequently with the Alexa Fluor-conjugated secondary antibodies or rhodamine-labeled Dolichos Biflorus Agglutinin (DBA, Vector Laboratories, RL-1032, 1:100). Images were recorded using an Axiovert 200 M (Zeiss) and AxioVision software (Zeiss). Confocal imaging was performed on an LSM 510 Duo microscope (ZEISS) using ZenBlack Software (Zeiss).