dataesr/scientific-paragraphs-categorization huggingface dataset:
Supplementary Material B: Experimental Instructions Instructions and Questionnaire (translated from German) Explanation: Please note that these instructions are precise transcripts of the screens shown to the participants in the online experiment. We use square brackets [] to indicate the alternative wording  [LABEL vs. NOLABEL]  and parameters  (HIGH vs. LOW)  used in the different treatments. The term city district (set in italics) stands for one of the four different municipal districts  Bahnstadt, Feudenheim, Neuenheim, and Schwetzingerstadt/Oststadt, depending on  where the respective participant lives. In the following, we show the wording for the LOW-NOLABEL as the benchmark case and as described above, we also show the deviations in the other treatments by using square brackets. -Screen 1-.
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In contrast, r 2 with SNPs from the other subgenome or randomly selected from either subgenome was substantially lower and did not show any decay within 10 KB, particularly in the Middle Eastern and European samples. High LD and gradual decay within, and not between, subgenomes suggested phasing was largely effective in separating the two subgenomes and preserving signals of their demographic history. The genomic data analyzed in this study were phased using the computational phasing of reads mapped to the C. rubella reference (HapCUT method) and was validated with mapping the same reads to the recent assembly of C. bursa-pastoris  [37]  (HomeoRoq method, see Material and methods). The final alignment between the alternatively phased dataset comprised 800 K SNPs. This alignment was relatively small because mapping to two subgenomes resulted in 2x smaller coverage and subsequent genotype calling, filtering, phasing, and alignment cumulatively further reduced this size (S4 The other 17% still showed a split between Cbp Cg and Cbp Co for the majority of samples, but some single samples were not consistent with the mutual monophyly of the two subgenomes.
This could be either due to incomplete lineage sorting or potential local phasing errors. Nevertheless, the major clustering into two subgenomes and three populations was largely maintained in the 17% of trees (S7 Fig). In all trees, the same samples phased with two alternative methods (HapCUT and HomeoRoq) clustered together for most of the European samples. The Middle Eastern cluster showed slightly less consistent clustering, and the Asian samples showed clustering into two groups according to the phasing method. The cause of these discrepancies is difficult to define because errors are possible in both datasets. To localize the problematic regions, we also compared the phased dataset analyzed in this study (HapCUT phased) with the assembly sequences of C. bursa-pastoris  [37]. This comparison covered almost all positions of the dataset analyzed in this study (10.
In order to control for possible biases due to the unequal genetic distance between the different genomes and C. rubella, we used both C. rubella and A. thaliana SIFT4G annotation databases. Because we were interested in the number of deleterious mutations that accumulated after speciation of C. bursa-pastoris, we polarized the mutations of all three species with the reconstructed ancestral sequences of the common ancestors (see Material and methods). Regardless of the SIFT4G database (C. rubella or A. thaliana), the proportion of deleterious nonsynonymous sites among derived mutations was always significantly higher in C. orientalis and the Cbp Co subgenomes than in C. grandiflora and the Cbp Cg subgenomes (Fig 5, S9 and S10 Tables). Within C. bursa-pastoris, the proportion of deleterious mutations depended on the population considered with the highest value in the ASI population and the smallest in EUR.
It is also noteworthy that the proportion of deleterious nonsynonymous sites of the Cbp Co subgenome in EUR and ME was significantly smaller than that of C. orientalis suggesting that a higher effective population size in the Cbp Co subgenome than in its ancestor led to more efficient purifying selection in these two populations. On the other hand, the proportion of deleterious nonsynonymous sites in the ASI Cbp Co subgenome was larger than in C. orientalis, but this difference was only significant for the A. thaliana database. The Cbp Cg subgenome also had a significantly higher proportion of deleterious sites in ASI than in EUR and ME in all comparisons. In conclusion, the proportion of deleterious sites in the two subgenomes of extant C. bursa-pastoris still reflected the differences between the parental species and the efficacy of purifying selection in the different C.
bursa-pastoris in a hierarchical Bayesian model that integrates information from both RNA and DNA data  [45]. Overall, in agreement with Douglas et al. [10] , one subgenome did not dominate the other in the 24 accessions considered together, though a few genes demonstrated a slight expression shift toward the Cbp Cg subgenome. On average, we assessed HSE in 13,589 genes per accession (range 12,808-15,340) and 18% of them showed significant HSE (posterior probability of HSE > 0. 99). The expression ratios between subgenomes (defined here as Cbp Co / Total) across all assayed genes in the DNA data were close to equal (mean = 0. 495). Thus, there was no strong mapping bias. We found some HSE variation among populations. The mean expression ratios for all genes were 0. 494, 0. 489, and 0. 489 in the ASI, EUR, and ME accessions, respectively, and these mean ratios for genes with significant HSE were 0.
The differences between populations were further enhanced by post-speciation hybridization of C. bursa-pastoris with local parental lineages. Below, we discuss these global and local effects in more detail and their consequences for the history of the species. But before this, a few words need to be said on the reliability of the phasing approach that was chosen here. Our results are based on the computationally phased (HapCUT) genomic data that was generated from the reads mapped to the C. rubella reference genome-the only reference genome available for the Capsella genus at the time of this study. This approach was possible due to the strict disomic inheritance of C. bursa-pastoris  [36, 37]. Disomic inheritance and reproduction by selfing resulted in almost no variation within subgenomes and we therefore were able to treat our data as diploid.
Furthermore, we phased the same data using an alternative approach (HomeoRoq) and mapping our reads to the assembly of C. bursa-pastoris  [37]. The two alternatively phased datasets were largely consistent in the separation between the subgenomes, with a perfect clustering between alternatively phased samples (HapCUT and HomeoRoq) in EUR and a clustering more influenced by phasing in ASI samples. However, even in that case, ASI samples still were more related to each other than to any other cluster and larger-scale grouping remained the same. Given that the assembly of C. bursa-pastoris we used for this validation was done with short reads and that the classification of the scaffolds between homeologues was based only on the coding part and allowed up to 25% of discrepancy between parental species  [37] , the congruence of the results is encouraging.
Sequence data We obtained the whole genome sequences of 31 accessions of C. bursa-pastoris and the transcriptomes of 24 of these accessions. Transcriptome data used in this study was generated previously from seedling growth in the same growth chamber  [35]. Whole genome DNA data consisted of 10 accessions sequenced previously  [10]  and 21 accessions sequenced in this study. New DNA samples were sequenced using the same technology as the downloaded ones (100-bp paired-end reads, Illumina HiSeq 2000 platform, SciLife, Stockholm, Sweden). The mean genomic coverage of C. bursa-pastoris samples was 47x. We also used the previously generated genomic data of 10 C. orientalis and 13 C. grandiflora samples  [10]. For the analysis requiring an out-group, we used the whole genome assembly of Neslia paniculata  [70].
Similarly, we merged the phased SNPs dataset with non-polymorphic sites from the whole genome data to keep the level of heterozygosity as in the unphased data. To ensure that there was no sign of a general bias towards the Cbp Cg subgenome, which is less divergent from the reference genome than Cbp Co , we checked that there was no strong bias towards the reference allele in the VCF file. The bias was only *4% and it should not affect alleles calling in heterozygous sites even at the lowest coverage. We also surveyed the level of heterozygosity and coverage along the genome and showed that there was no regional dropout of Cbp Co haplotypes (S20 Fig). In addition, the accuracy of phasing was assessed with the linkage disequilibrium (LD) variation between and within the subgenomes. From each subgenome, we randomly sampled 1000 SNPs on each chromosome (the principal SNPs) and calculated r 2 between them and the next 1000 SNPs.
These SNPs could originate from the same genome as the principal SNPs or from the opposite subgenome. We only considered SNP pairs where at least 5 samples had genotypes for both SNPs. As a baseline, we also calculated LD in the same way for a genome containing a random mixture of SNPs from both subgenomes. LD gradually decayed with increasing distance between SNPs within homeologues but not between them or when SNPs from both subgenomes were randomly mixed (S3D Fig), further indicating that there was no major phasing error in our data. To verify our phasing procedure with an alternative phasing method, we utilized the Home-oRoq pipeline  [77]  that was successfully used to phase transcriptomic and genomic data in allopolyploid Arabidopsis kamchatica  [78, 79]. While HapCUT relies on the read-back phasing of the reads mapped to one reference, HomeoRoq classifies the reads mapped to two references corresponding to the subgenomes.
As the two references, we used the recently published assembly of C. bursa-pastoris  [37]. We independently mapped our reads to the each assembled subgenome. Then we classified reads with the HomeoRoq to keep only unique and common reads, and to remove unclassified reads from SAM files. These SAM files were processed the same way as described above to obtain SNPs. Next, we aligned the main dataset with the HomeoRoq phased data, according to the alignment between the C. rubella reference and the C. bursa-pastoris assembly. The latter was performed by aligning each subgenome of C. bursapastoris to C. rubella with the LASTZ program  [80]  following the procedure described in  [10]. Finally, we analyzed the clustering of the samples phased with the two different methods using a sliding window phylogenetic analysis as described below.
Reconstruction of the ancestral sequences Several analyses presented in this paper required polarized sequence data. The most common approach to polarizing the alleles is to use an outgroup. However, the alignment of Capsella species and N. paniculata, the nearest outgroup with a whole genome sequence available, resulted in substantial reduction of the dataset due to missing data. To overcome this drawback, as well as to track mutations' origin on the phylogenetic branches, we reconstructed ancestral sequences for major phylogenetic splits. The reconstruction was performed on the tree that was assumed to represent a true history of the Capsella species (S22 Fig) using the empirical Bayes joint reconstruction method implemented in PAML v4. 6  [81].
Population differentiation To assess the degree of differentiation among populations for the two subgenomes, we estimated absolute divergence (Dxy) and nucleotide diversity (π) of the phased genomes using a sliding window approach. The estimates were calculated on non-overlapping 100 Kb windows using the EggLib Python module  [82]. The p-values for the difference in mean values were estimated using 10,000 bootstrap resamples from 100 Kb windows.
Phylogenomic analyses We reconstructed a whole genome phylogeny to explore the relationship between the phased subgenomes of the three populations of C. bursa-pastoris as well as its parental species. To investigate the local phylogenetic relationships along the genome, we also conducted a sliding window phylogenetic analysis using non-overlapping 100 Kb windows. In both analyses, phylogenetic trees were reconstructed using the neighbor-joining algorithm and absolute genetic distance in R package ape  [85]. Additionally, a whole genome phylogenetic tree was also reconstructed using the maximum-likelihood approach with the GTRGAMMA model and 100 bootstrap replicates in RAxML v8. 2. 4  [86]  (S23 Fig). The trees from the sliding window analysis were described by counting the frequency of monophyly of different groups with the Newick Utilities  [87].
Tests for admixture To evaluate the presence of admixture between the parental species and C. bursa-pastoris, we calculated the ABBA-BABA based statistics, D, an estimate of departure from incomplete lineage sorting, and f, an estimate of admixture proportion  [41, 42]. These statistics and their significance, which was estimated with a 1Mb block jackknife method, were calculated from population allele frequencies with scripts from Martin et al. [89]. We also used HAPMIX  [90]  to infer haplotype blocks of admixture with the diploid C. grandiflora, C. rubella, and C. orientalis into the three populations of C. bursa-pastoris for each phased subgenome. We removed sites with more than 20% missing data for each population. The remaining missing data was imputed for the parental populations used in each analysis.
Genetic load estimation To identify differences in genetic load between populations of C. bursa-pastoris (as well as to assess the effect of selfing on accumulation of deleterious mutations), we classified mutations into tolerated and deleterious ones using SIFT4G  [44]. We built the SIFT4G Capsella rubella reference partition database and used it to annotate our SNPs dataset. Then we analyzed the frequencies of tolerated and deleterious mutations. We also verified this analysis by using A. thaliana SIFT4G database and annotating C. bursa-pastoris according to the alignment between the two species. This verification was performed to make sure that the observed results were not due to a reference bias because C. rubella is closer to C. grandiflora than to C. orientalis. To get only the annotation of the mutations that occurred after speciation of C.
00, QD < 2. 00, FS > 30. 000 were filtered out. Calls with coverage of fewer than 10 reads were also excluded. Alleles counting was carried out using ASEReadCounter from GATK. Homeolog-specific expression was assessed within the statistical framework developed by Skelly et al. [45]. This framework uses a Markov chain Monte Carlo (MCMC) method for parameter estimation and incorporates information from both RNA and DNA data to exclude highly biased SNPs and calibrate for the noise in read counts due to statistical sampling and technical variability. First, we used DNA data to identify and remove SNPs that strongly deviated from the 0. 5 mapping ratio. Second, we estimated the variation in allele counts using unbiased SNPs in the DNA data. Next, we fitted an RNA model using parameter estimated from DNA data in the previous step.
Data access DNA sequence data generated for 21 accessions of C. bursa-pastoris is submitted to the NCBI database under the sequence read archive number SRP126886. Previously generated DNA sequence data for 10 accessions of C. bursa-pastoris, 10 accessions C. orientalis and 13 accessions C. grandiflora is available in the NCBI (SRP050328, SRP041585, SRP044121). RNA-Seq data is also available in the NCBI (SRA320558). Both phased and unphased SNPs, phylogenetic trees, reconstructed ancestral sequences, estimates of π and Dxy with sliding window approach, results of PSMC and SMC++, SIFT4G annotations, CLR estimates of sweepFinder2, TWISST and HAPMIX outputs, homeologue-specific gene expression values are deposited to the Open Science Framework Repository (DOI: 10. 17605/OSF. IO/5VC34)  [96].
Acknowledgments We thank  Clement Lafon-Placette  and  Mohammad Foteh Ali  for help with crosses,  Marion Orsucci  and  Adrian Platts  for help with validating the phasing,  Pascal Milesi  and  Ludovic Dutoit  for discussion of the results. We are especially grateful to  Luis Leal  for detailed feedback on the manuscript. Most of the analyses were carried out at  Uppsala Multidisciplinary Center for Advanced Computational Science (UPPMAX)  under the project  b2013191.
92, 0. 75, 0. 46, 0. 76, 0. 61, 0. 65, respectively). To assess whether a potential benefit of tirasemtiv was masked by poor tolerability, two separate analyses were performed on more restrictive data sets. The PPS included participants who  completed !20 weeks of study medication and contributed outcome measures at the 24-week time point, when the primary outcome measure was assessed. It eliminated participants who had stopped study medication for >4 weeks and includes no imputations for missing data. However, participants who reduced their dose were still included within their target dose group. In this data set, the point estimate of effect was greater than for the FAS; for the 250-mg target dosage group in the PPS, percent predicted SVC declined by 10. 0 percentage points, compared  with 13.
088% per day or 16. 9% over 24 weeks and 0. 090% per day for the phase II trial of tirasemtiv. The slower progression rate in the current study may have contributed to the finding of no significant treatment effect. However, as was noted in the phase 2 study (BENEFIT ALS), SVC was the measure that seemed to show the largest signal among all outcome measures, confirming that the choice in primary outcome measure was justified. Thus, although this phase III study failed to show a meaningful effect of tirasemtiv on SVC, subanalyses suggest that tirasemtiv had a biological effect on SVC, such that fast skeletal muscle troponin activation remains worthy of further study. A next-generation FSTA (reldesemtiv), lacking central nervous system effects and having the potential to produce a larger pharmacodynamic effect  (16) , has been reported to show promising effects in spinal muscular atrophy  (17)  and is being tested in a large phase II trial in ALS patients (NCT03160898).
The two domains are covalently linked on a single polypeptide chain by a flexible linker sequence. The X-ray structure of this nonmutated bidomain A3_A3 protein (PDB ID 6FT5) showed that the linker did not affect the global structure of the monomer and functional studies indicated that the linker reinforced the closed form of the bidomain protein. 5 Taking advantage of the nonrepeated sequence of the first domain, mutations could selectively be introduced in this domain. In order to compare the activities of the resulting biohybrids with the previously described dimer's activities, the same positions as in the dimer were chosen for cysteine mutation (F119 and Y26). The corresponding synthetic genes obtained by site directed mutagenesis were sub-cloned into a modified expression vector, which resulted in plasmids encoding the two bidomain variants (A3_A3′)F119C and (A3_A3′)Y26C.
The complete functionalization was confirmed by Ellman's assay, with a complete loss of thiol reactivity (data not shown). MALDI-ToF MS analysis was first used to characterize the biohybrids (Figure  2 ). Peaks at 42 564 ± 5 and 42 706 ± 5 Da were observed respectively for (A3_A3′)Y26C-Phen and (A3_A3′)Y26C-Terpy that were in agreement with the covalent anchoring of one phenanthroline and one terpyridine per protein (calculated mass 42 565 and 42 702 Da, respectively) (Figure  2A ). Peaks at 42 585 ± 5 and 42 720 ± 5 Da were also observed in the mass spectra of (A3_A3′)-F119C-Phen and (A3_A3′)F119C-Terpy, respectively (Figure  2B ), that were in agreement with the covalent anchoring of one phenanthroline and one terpyridine ligand per protein (calculated mass 42 581 and 42 718 Da, respectively).
Solutions containing sample proteins were injected and elution profiles were recorded by following the absorbance at 280 nm. Molecular Modeling. The geometry of the Trojan horse models (cofactor, substrate bound cofactor, and product models) were optimized with Gaussian 09 (Gaussian 09, revision D. 01. Gaussian, Inc. : Wallingford, CT, 2010) using the wave function B3LYP-D3  22  and the SMD continuum model for water  23  (see Supporting Information for further details). The dimeric bio-assembly of the X-ray structure of the A3_A3 bidomain protein (PDB ID 6FT5) was used to model the different biohybrid variants (A3_A3′)F119C and (A3_A3′)Y26C by mutating the relevant side chains. The positions of the bidomain A3_A3′ protein were numbered starting from residue number 3, as the first two residues of the sequence were lacking in the crystal structure.
The numbers of the mutated positions were thus in accordance with those described previously for the A3 homodimer. 3 All the structural handling performed at the beginning of the study was carried out by means of UCSF Chimera embedded software. 24 ovalent protein-ligand docking assays were performed with the program GOLD (version 5. 2)  25  using the GoldScore scoring function  26  and considering the bond between the cofactor and the sulfur of the mutated cysteine as the covalent anchoring. An evaluation sphere of 20 Å radius centered on the sulfur atom of the mutated cysteines was used for each simulation. In all the calculations, the Trojan horses and the linker were treated as flexible structures. The solutions were analyzed by means of GaudiView GUI extension for Chimera 27 taking into account three main criteria, the maximum value of the GoldScore Fitness (F max ), the position in the global energy ranking, and the population of the solution clusters.
Data sources and selection We obtained all Cochrane Systematic Reviews published between March 2011 and September 2014, which were described in a previous work. ( 5 ) For each review, we considered only the first metaanalysis reported (ie, the first comparison and first outcome of the review). We included only metaanalyses of RCTs (ie, analyses not including observational studies) with a binary outcome, and having an estimated pooled treatment effect in the review. If the pooled treatment effect was estimated at a subgroup level only (without an overall estimate), we retained the first subgroup only. In addition, we excluded meta-analyses if the sum of the sample sizes of the included studies was less than 30 and if no patient or all patients of all studies had an event. For each meta-analysis selected, we extracted for each trial included the number of events and number of patients in each group of patients.