dataesr/scientific-paragraphs-categorization huggingface dataset:
In that case, it is advised to look at the mitochondrial haplogroup for instance, and determine whether the assumed demography of the endogenous sample jibes with the determine haplogroup. However, assumptions about the demography of the endogenous sample can often be misleading. • As of mapDamage2. 0, mapDamage will generate a table of per-position mismatches for each sequence in the reference genome. For poorly assembled genomes, this may result in exceedingly large tables and even program failure, and using the --mergereference-sequences option is recommended. This will record all per-position mismatches in a single table.
• It is also possible that the iterative function of schmutzi does not converge. In such a case, it is possible that the algorithm oscillates between two almost equally likely models. It is recommended to use endoCaller with a low estimate of present-day human contamination and rerun endoCaller again with a higher one. Both predictions should be compared for divergent sites and phylogenetic information should be used to determine which consensus appears as the most likely. • When using endoCaller, the program can predict the contaminant (using optionsingle) and the iterative procedure can use this prediction as a record in the database of putative contaminants (usually specified via [path to schmutzi]/eurasian/freqs/). This is normally done by default by schmutzi. pl. The final present-day human contamination obtained using default parameters might differ from the one obtained without the prediction of the contaminant (see option --notusepredC).
Acknowledgment We would like to thank  Fernando Racimo  for comments and suggestions and  José Victor Moreno Mayar  and  Thorfinn Sand Korneliussen  for their insights into the contamination method using the X chromosome. This work was supported by the  Danish Council for Independent Research, Natural Sciences  (Grant  4002-00152B ); the  Danish National Research Foundation  (Grant  DNRF94 );  Initiative d'Excellence Chaires d'attractivité ,  Université de Toulouse (OURASI) ; the  Villum Fonden  miGENEPI research project;, and; the  European Research Council  ( ERC-CoG-2015-681605 ).
g. ,  Hyönä et al. , 2003; Radach and Kennedy, 2004). Finally, as we collected data from 1,493 persons being at least 60 years or older, we also expect to shed more light on the development of written language processing during later and late adulthood in German given the sparsity of available data, especially when it comes to sentence and text reading, i. e. , reading in an ecologically valid setting. To meet our objectives, in Study 1, we describe the sentence characteristics for all 77 sentences included in the SLS-Berlin as well as the characteristics of the norm sample. We report the distribution of the SLS-Berlin scores for the entire sample and the dependencies of the score from the sample characteristics. Additionally, we present how sentence characteristics relate to the response times of all sentences.
Norm Sample We collected data from 2,174 native German speaking adults with age ranging from 16 to 88 years. Twenty-three participants had to be excluded as they obtained negative SLS-Berlin scores (i. e. , they classified more sentences incorrectly than correctly within 3 min reading time). Three additional participants were removed from all further analyses due to technical difficulties while recording their data. In all, the norm sample consists of data from 2,148 adult participants (see Table  4  for sample characteristics). As most research in adult reading is conducted with either younger (ages: 20 to 35) and/or older readers (60 years and older), we focused on these age groups while collecting norm data. However, to provide a norm sample which covers a wide range of the adult life span, we also present data for ages 36 to 59 (see Supplementary   = 3.
Procedure Test sessions took place at the FU Berlin and at the Charité Campus, Berlin between the years 2013 and 2017. The 1,884 subjects tested at the Charité Campus were part of the Berlin Aging Study II cohort (BASE-II:  Bertram et al. , 2014; Gerstorf et al. , 2016)  and participated in groups of up to six individuals. At the FU Berlin, data from 264 subjects were collected in individual test sessions. At both locations, additional tests were conducted, however, the SLS-Berlin was always the first part of the experimental session and identical in its procedure. Contrary to the children's version of the SLS, the SLS-Berlin was run on a computer while using Objective-C (MPIB) and Python 2. 7 (FU). Ten training sentences and the 77 test sentences were shown individually one after another at the center of a computer screen (left aligned and single spaced) in 32 pt Helvetica using black font on white background.
Data Analysis Analyses of the SLS-Berlin scores and the response times were conducted with R version 3. 4. 3 (R Core Team, 2017). All analyses were performed for the whole norm sample as well as for the two subsamples. In a first step, we assessed the consistency of the test by calculating the split-half reliability. For this purpose, the 77 sentences of the SLS-Berlin were split into two parts with the number of correct and incorrect sentences being roughly the same (part 1 18 correct and 21 incorrect, part 2: 18 correct and 20 incorrect). We also controlled for mean Sentence IDs (part 1: 39. 5; part 2: 38. 4; cf. Figure  1 , lower section) given their predictive power for the other item characteristics. We then obtained the number of correctly answered sentences within 1. 5 min per subject to calculate the SLS-Berlin scores for both parts of the test and correlated the results using the Spearman-Brown prophecy formula  (Brown, 1910; Spearman, 1910).
In a second step, we analyzed the relationship between the SLS-Berlin score and sample characteristics (i. e. , Gender, Age, and Education; see Table  5 ) as well as assessed the influence of the above described sentence characteristics on the response times for the sentences (see Tables  6 7 8 ). To do so, we used only correctly classified sentences (data loss of 4. 73%). Outliers based on response times were excluded by estimating a linear mixed-effects model with random intercepts for participants and items using the lme4 package (version 1. 1-15;  Bates et al. , 2015). Response times with standardized residuals larger than ±3 were removed from the data set (data loss of 1. 48%). To assess the model fit of the regression analyses and to test the accuracy of the predictions, we randomly assigned 70% of the sample data to a training group.
Discussion We collected data from > 2,000 participants including a group of younger (below 60 years of age) and a group of older adults (above 60). The split-half reliabilities for the entire norm sample as well as for the two subsamples indicated a high consistency of the SLS-Berlin for all investigated age groups. Contrary to the children's version of the SLS, which reports slightly higher reading scores for girls than for boys (cf. Wimmer and Mayringer, 2014) , the SLS-Berlin is independent of gender. This is well in line with current results about gender differences in cognitive components of adult reading comprehension. As pointed out by  Hannon (2014) , quantitative gender differences are mostly marginal, especially in comparison to the more important differences in the predictive powers of specific cognitive components of reading comprehension.
5 • of visual angle. All tasks were controlled with Experiment Builder software (RS Research, version 1. 10. 1630). Font type and size was the same in all tasks. The sentences within the SLS-Berlin and the two expository texts were written in Arial, font size 20, double spaced and left aligned. The procedure for the SLS-Berlin followed the routine described for the norm sample. Due to the font size, long sentences, especially from the second part of the test, were presented on three lines. After finishing the SLS-Berlin, participants took a rest in which the experimenter described the text reading task. Participants were instructed to silently read each text at their own speed. They were also informed about a short memory test administered directly after reading. Each text presentation started with the calibration described above followed by a fixation cross presented for 500 ms to the left of the first word of the text.
CONCLUSION The very high Spearman-Brown corrected reliability (r = 0. 95) and the results from Study 2 demonstrate that the SLS-Berlin is a valuable screening tool for the systematic exploration of individual differences in reading proficiency. The test differentiates very well between poor and good readers. The huge number and extensive age range of the norm sample with an emphasis on age groups above 60 ensures the test to be used for a variety of important (developmental) research questions. Further developments are planned to establish a parallel test version as well as increase the relatively small amount of norm data for the age groups between 40 and 60 years of age. However, we think, the SLS-Berlin's short duration and easy administration makes it a highly potential tool for the investigation of reading abilities in not only underresearched populations such as older adults and individuals with brain lesions but also in developing and proficient readers.
ACKNOWLEDGMENTS We would like to thank  Prof. Dr. Ulman Lindenberger  and the recruiting office of the  Max Planck Institute for Human Development, Berlin , for recruiting the subjects and collecting the data. We are grateful to the student assistants of both, the  Department of Experimental and Neurocognitive Psychology at the Freie Universität Berlin  as well as the  Max Planck Institute for Human Development, Berlin  for their hours of tireless work.
DATA AVAILABILITY The raw data supporting the conclusions of this manuscript will be made available by the authors, without undue reservation, to any qualified researcher. The norm data for the SLS-Berlin are part of the Supplementary Table  2. Both, the SLS-Berlin (available for all standard platforms such as Linux, macOS, and Windows as it is written in PsychoPy3;  Peirce and MacAskill, 2019)  as well as continuously updated norms are available at: https://www. ewi-psy. fu-berlin. de/einrichtungen/ arbeitsbereiche/allgpsy/Download/index. html.
m. u. The GC oven was held for 3 min at an initial temperature of 40 °C and was subsequently heated to 280 °C at 5 °C/min. The oven was then held at 280 °C for 1 min. After each injection, a blank analysis was carried out to verify potential residual bleeding of the column and leftovers from the previous experiments. Peak integration and alignment were performed in Xcalibur software using a baseline window of 200, area to noise factor of 100 and a peak noise factor value of 10. The products were identified using the NIST library and quantified by normalising the ion current peak areas with the total area of all the compounds. The methodology of the process is pictorially represented in Fig. 3.
Principal Component Analysis Principal component analysis (PCA) of the quantified peak areas was performed using MATLAB (R2014b) software. The methodology is similar to the one described by Toraman et al. [14]. Briefly, PCA analysis provides a statistical analysis of the variation of the selectivity differences between transgenic and control lines, and hence, indicating the effect of genetic modification on the pyrolysis vapours. The first principal component accounts for as much of the variability in the data as possible (PC1), and each succeeding element accounts for the remaining variability (PC2, PC3, etc. ). This procedure was repeated until ideally the total variance obtained in the original data set was explained and the resulting PCs formed a new basis  [30] [31] [32]. Variations in transgenic and wild-type biomass and their pyrolysis products were studied with the help of score and loading plots, as explained by Toraman et al.
R. T (min) Compound To extract a correlation in the data set six PCA models were developed, corresponding to each of the poplar types. At first, the number of principal components that need to be retained for each kind of transgenic and wild-type poplar pair were determined with the help of scree plots. The scree plot shows the relative contribution of the principal components to the total variance in the data set for either nonpretreated or pretreated samples. Principal components are included to reach a total sum of at least 70% of the original variation. Later, loading and scoring plots are created to gain insights on the significant contributions, either positive or negative, of the variables, i. e. , detected compounds (shown in Table  3 ) towards the principal essential components.
Comparison of CCoAOMT Down-Regulation to Tbr Wild-Type The dataset for the CCoAOMT model consists of Tbr wild-type and three CCoAOMT down-regulated lines (Li02, Li03, and Li04). The scree plot in Supporting Information, corresponding to non-pretreated CCoAOMT and Tbr samples, indicates the proportion of variance described by the first three principal components as 47. 91%, 14. 19% and 12. 78% of the total variance. While for the case of pretreated CCoAOMT and Tbr control samples, the first three principal components account for 30. 36%, 25. 48% and 16. 54% of the total variance (Fig. 8a ). From the score plot (Fig. S2 ) between principal components PC1 and PC2 three clusters were formed, one corresponding to Tbr control line and Li02 together and the remaining two correspond to the data points of Li03 and Li04 (p b.
Acknowledgements The authors thank  Lennart Hoengenaert  from  VIB  and  Diana Vargas  from LCT for their assistance in thioacidolysis experiments. The research leading to these results has received funding from the  European Research Council  under the European Union's  Seventh Framework Programme  ( FP7/2007-2013)/ERC grant agreement n°290793  and the " Long-Term Structural Methusalem Funding  by the  Flemish Government ". The  SBO proposal "Bioleum"  supported by the  Institute for Promotion of Innovation through Science and Technology in Flanders (IWT)  is also acknowledged. The GBFOR experimental unit as well as  Kevin Ader  from  Genobois platform  ( INRA Val de Loire ) are warmly acknowledged for their involvement in the setting and maintenance of the GM poplar field trial, as well as the harvesting and pre-processing of the wood samples.
Study Design The FIRE AND ICE Redo study was an observational, retrospective evaluation of patients who underwent a repeat ablation during the FIRE AND ICE trial to compare arrhythmia recurrence, number and anatomic pattern of reconnected PVs, and index PVI lesion durability in patients originally randomized to cryoballoon or RFC ablation. Of the 16 sites that participated in FIRE AND ICE, 13 sites in Europe (listed below) participated in this trial. The trial was approved by each site's ethics committee/institutional review board, registered, and conducted in accordance with the Declaration of Helsinki. Data to fulfill the predefined objectives about the index procedure, patient characteristics following the index, and the repeat ablation procedures were collected. An independent end point review committee (blinded to randomization) reviewed and adjudicated all repeat ablation study data.
Patient Assessment Before Repeat Ablation Baseline patient characteristics collected at the time of enrollment in FIRE AND ICE were compared between reablation cohorts. Data from the index ablation as well as the type of arrhythmia recurrence before the repeat ablation were collected. Documented arrhythmia recurrence before the reablation was categorized as paroxysmal or persistent AF, atrial tachycardia, and typical/atypical atrial flutter. Repeat ablation procedures were performed at the same hospital center as the index ablation. Both PV and non-PV locations were evaluated for arrhythmic potential at the time of the reablation procedure. PVI was confirmed via bidirectional entrance and exit block. Information about the number and location of gaps in the PV lesions captured via a focal catheter or 3-dimensional electroanatomical mapping were documented as available.
Continuous variables are presented as mean±SD or median with interquartile range, as appropriate. Categorical variables are presented as absolute and relative frequencies. There were no adjustments for multiple testing, and a P<0. 05 was considered statistically significant. All analyses were conducted using SAS software, version 9. 4 (SAS Institute, Cary, NC) and R statistical package, version 3. 2. 2 (https://www. r-project. org).
In this context, DNAm signatures provide a novel functional classification method with significant potential to improve the output of the genome diagnostics. Nicolaides-Baraitser syndrome (NCBRS; [MIM# 601358]), is a rare (prevalence < 1/1,000,000) epigene disorder characterized by coarse facial features, sparse hair, seizures, microcephaly, small stature, prominent interphalangeal joints, and ID. NCBRS was first reported as a distinct clinical entity in 1993 by Paola Nicolaides and Michael Baraitser, and soon after was identified in other patients  [14] [15] [16]. NBCRS is caused by pathogenic missense variants in SMARCA2 (SWI/SNF Related, Matrix Associated, Actin Dependent Regulator of Chromatin, Subfamily A, Member 2; GenBank NG_032162. 2)  [17] , the core catalytic subunit of the mammalian BAF (BRG1-or HBRM-associated factors) chromatin remodeling complex, also known as the SWI/SNF complex.
NCBRS-SMARCA2 DNAm signature We defined a DNAm signature of differentially methylated sites in whole-blood DNA of NCBRS cases with pathogenic SMARCA2 variants (n = 8) compared with age-and sex-matched control samples (n = 23). We termed this an NCBRS-SMARCA2-specific DNAm signature since it was derived specifically on SMARCA2 pathogenic variant samples within the ATPase/helicase domain with an NCBRS clinical diagnosis. We used the cell-type proportion estimation tool in minfi based on Illumina EPIC array data from FACS sorted blood cells  [32]. This indicated a significant decrease in CD4+ T cells and a significant increase in monocytes in the signature cases (Additional file 2: Table  S3 ). Since these cell types are highly correlated, we used only estimated monocyte proportion in the regression model as it was the more significantly different.
The classification is implemented in R. To test specificity, EPIC array data from 94 additional neurotypical controls were scored and classified. To test sensitivity, publically available EPIC array data from NCBRS cases with different variants  [34]  (GSE116992) were scored and classified. All were classified correctly, demonstrating 100% sensitivity and specificity of the signature. Publicly available sorted blood cell type data  [32]  (GSE110554) were also scored and classified.
DNAm validation by sodium bisulfite pyrosequencing An independent analysis of DNAm was performed for NCBRS-SMARCA2 signature cases (n = 8) and a subset of matched controls (n = 8) using sodium bisulfite pyrosequencing. Controls 2, 4, 10, 13, 14, 17, 18, and 24 were used, as they mostly closely matched the age and sex of the NCBRS cases. These assays were designed using QIAGEN Assay Design Software v1. 0. 6 to target-specific CpGs identified by the microarray experiment (Additional file 1: Table  S5 ). Pyrosequencing was done using the PyroMark Q24 system and Pyrosequencing Gold Reagents (QIAGEN).
Variant classification is independent of blood cell type composition and age We assessed our samples for possible differences in blood cell proportions using our DNAm data. We found a significant (p < 0. 001, Students t-test) reduction in predicted CD4+ T cell proportion in the NCBRS cases used to derive the signature versus signature controls and a significant increase (p < 0. 05, Students t-test) in predicted monocyte proportion (Additional file 1: Table  S3 ). Therefore, we accounted for monocyte cell proportion in our regression model. To further ensure that signature classification was not affected by cell-type proportion, we used the NCBRS-SMARCA2 score to classify DNAm data from sorted cell populations; all cell types were assigned negative scores paralleling whole-blood controls more than NCBRS cases (Additional file 1: Figure  S2 ).
Since the NCBRS-SMARCA2 signature was derived using cases and controls greater than 3 years of age, we classified four additional whole-blood samples from one-year-old controls; all were assigned negative scores classifying as benign (Additional file 1: Figure  S3 ). We also scored DNAm data for three technical replicates that were run using the same DNA sample in a different batch of reagents and microarrays: two cases used to derive the signature (SMARCA2_1 SMARCA2_2) and one test variant (SMARCA2_4). All three demonstrated highly similar scores differing by less than 1% from their replicate samples (Additional file 2: Table  S6 ; Additional file 1: Figure  S3 ).
These DNAm data challenge clinical diagnosis of SMARCA2_12 as NCBRS. Further, they call into question the issue of definitive criteria for clinical diagnosis of NCBRS. They also raise the question of whether variants outside the ATPase/helicase domain cause NCBRS or an NCBRS-like phenotype. The patient with the bromo domain variant is reasonably defined as related to but not NCBRS based on phenotypic data; however, in light of her atypical phenotype and partial DNAm signature profile, the appropriateness of the clinical classification of NCBRS for SMARCA2_12 is not as obvious. GO analysis on the CpG sites comprise the NCBRS-SMARCA2 signature identified genes related to NCBRS pathophysiology. Many genes and processes involved in brain function/development were identified, relevant to the high frequency and degree of ID observed in NCBRS.
We propose that the NCBRS-SMARCA2 DNAm signature can be used to aid clinical diagnosis and quantify the overlap of patients with ambiguous phenotypes with typical NCBRS. Because of the added complexity of partial DNAm profiles, implementation of our DNAm signature score as a clinical test would require moving from a binary classification system to a scoring system. At this time, one intermediate sample is not sufficient to develop such a system. Again, scoring additional patients with variants distal to the SMARCA2 ATPase/helicase domain using our approach will be necessary. The DNAm data we present suggest a genotypeepigenotype-phenotype correlation for SMARCA2 variants in NCBRS. Pathogenic variants within the ATPase/ helicase domain lead to a specific DNAm signature associated with classic NCBRS clinical features.
Acknowledgments We would like to thank all of the patients and families for their participation in our research study and the physicians, genetic counselors, and clinical staff for their assistance with recruitment. We would also like to thank  Sean Rampersad ,  Youliang Lou ,  Chunhua Zhao , and  Khadine Wiltshire  for their invaluable contributions to this work. Thank you as well to  Dr. Greg Hanna  for contributing blood DNA samples from neurotypical control individuals who had undergone cognitive/behavioral assessments. We would also like to thank  Dr. Jean-Louis Mandel  for facilitating collaboration with  Dr. Amelie Piton.
Funding This work was supported by the  Canadian Institutes of Health Research (CIHR) , the  Ontario Brain Institute-POND study  and the  Canadian Centre for Computational Genomics  ( C3G ). CIHR provided funds for salary (E. C. D. ) and DNAm microarray and pyrosequencing experiments. POND provided support for salary (M. S. ) and recruitment of controls. Bioinformatics analyses were supported in part by  C3G , part of the  Genome Technology Platform (GTP) , funded by  Genome Canada  through  Genome Quebec  and  Ontario Genomics ; and  Genome Canada  through  Ontario Genomics. These funding agencies did not play any role in the design, analysis or interpretation of the study or in the writing of the manuscript.