dataesr/scientific-paragraphs-categorization huggingface dataset:
Specifically, 4SU was used to induce crosslinking and RNase I conditions were adjusted in PTBP1-iCLIP2, and the 3′ dephosphorylation step was omitted in PTBP1-iCLIP3. These are compared to the published PTBP1 CLIP  [13] , eCLIP  [6]  and irCLIP data  [7]. Finally, we also compare the PTBP1 data to U2AF2 CLIP  [14]  and iCLIP  [15]. It was proposed that presence of non-coinciding cDNAstarts might indicate that some of these cDNAs have read through the crosslink site during reverse transcription  [8]. It has been shown previously that such readthrough cDNAs often contain deletions, which are introduced into cDNAs at the crosslink site during reverse transcription  [4, 16]. We compared the proportion of cDNAs with deletions in the different eIF4A3 datasets. Since the rate of sequencing errors rises with increasing cDNA length, we only examined cDNAs shorter than 40 nt for this purpose.
Strikingly, a bimodal distribution of deletions is apparent in all datasets, with one peak of deletions close to the cDNA-starts (5. 8th nt) and the second close to the cDNA-centres  (22. 27th nt, Fig. 2a ). Thus, the deletions present in iCLIP show the same features as in CLIP and likely inform on the presence of readthrough cDNAs. Importantly, the proportion of deletions is lower by a factor of 5 or more in all eIF4A3 iCLIP experiments compared to CLIP, indicating that readthrough cDNAs represent a minor proportion of iCLIP data. We used sequence motifs as a second feature that can serve as an identifier of crosslink sites. We defined these Fig. 1  An overview of methods and experiments. a A simplified schematic of the iCLIP protocol  [17]. Before, cells or tissues are irradiated with UV light, which creates covalent bonds between proteins and RNAs that are in direct contact (step 1).
We examined this alternative model in more detail, since the prevalence of readthrough cDNAs in iCLIP did not appear sufficient to account for the noncoinciding cDNA-starts. We used the tool developed by the previous study (iCLIPro) to examine the prevalence of non-coinciding cDNA-starts in the PTBP1-iCLIP1 dataset. This tool compares the cDNA-start positions of shorter and longer cDNAs and displays overlapping starts by enrichment at position 0, while non-coinciding starts are enriched at other positions. As seen previously  [8] , we find that the PTBP1-iCLIP1 library contains non-coinciding cDNA-starts (Fig. 3a ). To understand if the prevalence of non-coinciding cDNA-starts depends on the length of binding sites, we first identified regions on RNAs where cDNA-starts are significantly clustered, and we refer to these as 'crosslink clusters' (see 'Methods' for more detail).
4a ) than PTBP1-iCLIP2 (Fig. 4c ). Strikingly, the cDNA-ends of all length categories precisely overlap at the position of cDNA-end peaks in PTBP1-iCLIP1 (Fig. 4b ), while they are enriched over a broader region downstream of the cDNA-end peaks in PTBP1-iCLIP2 (Fig. 4d ). Indeed, this tight constraint of cDNAends in PTBP1-iCLIP1 reveals three distinct peaks of cDNA-starts for each category of cDNA lengths (Fig. 4b ), while these peaks are less prominent in the PTBP1-iCLIP2 library, in which the fold change for cDNA-end constraint is decreased by half (Fig. 4b, d  inner box plot). We (See figure on previous page. ) Fig. 3  Proportion of non-coinciding cDNA-starts differs between PTBP1 iCLIP experiments. a Heatmap for PTBP1-iCLIP1 generated using the previously developed software iCLIPro  [8]  to show the relative positioning of cDNA-starts of shorter iCLIP cDNAs (17-39 nt) compared to cDNA-starts of long cDNAs (longer than 39 nt).
b As in (a), but for cDNAs of PTBP1-iCLIP1 that overlap with 5-30 nt long crosslink clusters. c As in (a), but for cDNAs of PTBP1-iCLIP1 that overlap with >30 nt long crosslink clusters. d As in (a), but for PTBP1-iCLIP2. e As in (a), but for cDNAs of PTBP1-iCLIP2 that overlap with 5-30 nt long crosslink clusters. f As in (a), but for cDNAs of PTBP1-iCLIP2 that overlap with >30 nt long crosslink clusters conclude that the presence of non-coinciding cDNAstarts is reduced in PTBP1-iCLIP2 due to the decreased constraints at cDNA-ends. PTBP1 binding sites can be assigned correctly despite non-coinciding cDNA-starts Analysis of the PTBP1-iCLIP1 demonstrated that the non-coinciding cDNA-starts are most enriched within long crosslink clusters. Thus, we speculated that analysis of long binding sites might detect non-coinciding cDNA-starts also in those iCLIP datasets previously analysed by the iCLIPro tool, even if they had not been detected by iCLIPro.
This probably reflects the deposition of eIF4A3 on the spliced exon intermediate, as would be expected based on previous biochemical studies  [22] [23] [24]. The nucleotide composition at cDNA-ends also differs between eIF4A3-iCLIP1 and eIF4A3-iCLIP2 (Additional file 1: Figure  S4E, F ). These differences suggest that RNA was fragmented by different mechanisms in eIF4A3-iCLIP1 and eIF4A3-iCLIP2, but this remains to be fully understood. Both eIF4A3-iCLIP2 and eIF4A3-iCLIP3 have a strong enrichment of adenosine at the position following the cDNA-end peak, indicating a potential for RNase Iindependent fragmentation (Additional file 1: Figure  S4G ). However, the published eIF4A3-CLIP data used RNase T1 to fragment the RNA  [11] , which prefers to cut after the guanosine, in agreement with a guanosine enrichment at the position preceding the cDNA-end peaks (Additional file 1: Figure  S4H ).
In sum, it is important to ensure that RNase I is the primary source of RNA fragmentation, that 3′ dephosphorylation of RNA fragments is efficient and that the cDNA library has a broad range of cDNA sizes. We show that use of cDNA-starts is appropriate to assign protein-RNA crosslink sites with iCLIP. Interestingly, we find that the number of assigned crosslink clusters can vary greatly between the different datasets in a manner that does not necessarily correlate with the number of unique cDNAs that are present in the library (Figs. 1b, 5e ). These differences might reflect variable amounts of co-purified non-specific RNAs in the different experiments, which could result from the use of different antibodies and purification conditions. To draw more solid conclusions, direct comparisons between multiple methods will need to be done for a larger number of diverse RBPs.
Mapping of iCLIP sequence data To map iCLIP sequence data for PTBP1 and all RBPs other than eIF4A3, we used the UCSC hg19/GRCh37 genome assembly and the Bowtie2 version 2. 1 alignment software with default settings. More than 81% (1,642,850 of 2,007,824) and 85% (8,585,142 out of 9,634,025) of all cDNAs from the published and newly generated iCLIP data, respectively, mapped uniquely to a single genomic position. To map the eIF4A3 iCLIP data, we compiled a set of the longest mRNA sequence available for each multi-exon gene from BioMart Ensembl Genes 79. We mapped to these mRNAs with the Bowtie2 version 2. 1 alignment software, allowing two mismatches. More than 50%  (11,935,475 of 23,040,243)  of cDNAs from all eIF4A3 iCLIP datasets mapped to a unique mRNA position. The first 9 nt of the sequenced iCLIP read correspond to the barcode, which contains the experimental identifier that allows to separate experimental replicates, and the UMIs, which allow to avoid artefacts caused by variable PCR amplification of different cDNAs (Fig.
Definition of crosslink clusters The crosslink clusters were identified by False Discovery Rate peak finding algorithm from iCount (https:// github. com/tomazc/iCount), by assessing the enrichment of cDNA-starts at specific sites compared to shuffled data as described previously  [30] , with the following additional details. At each cDNA-start, the counts of all cDNAs containing their cDNA-start at a maximum spacing of 15 nt were summed up (or at 3 nt spacing for Fig. 5e ). Then crosslink clusters were defined by using the cDNA-starts with counts that passed the false discovery rate < 0. 05 significance threshold (determined by comparing the count distribution to shuffled data). Neighbouring clusters that were less than 21 nt apart (or 3 nt apart for Fig. 5e ) were then merged into single clusters.
All of these pentamers are enriched in pyrimidines, in agreement with the known preference of PTBP1 for UC-rich binding motifs  [31]. Visualisation of cDNA distributions with the density graphs (used in Figs. 4a-d  All normalisations were performed in R (version 3. 1. 0) together with the 'ggplot2' and the 'smoother' package for the final graphical output. For the analysis of eIF4A3 iCLIP, each density graph (RNA map) shows a distribution of cDNA-starts and cDNA-ends relative to positions of exon-exon junctions or end peaks in mRNAs. To avoid any border effects, we examined only exon-exon junctions within coding regions, excluding the first or the last junction. The number of cDNAs starting or ending at each position on the graph was normalised by the number of all cDNAs mapped to representative mRNAs, the mRNA length and the number of examined exonexon junction positions, as described below: RNAmap n ½ ¼ ððcDNAs n ½ =sum cDNAs ð Þ Þ Ã length mRNAs ð Þ =count exon exon junctions ð Þ where [n] stands for a specific position on the density graph.
We thus obtained 'Max-Count-normalised cDNA counts' at each position (which were between 0 and 1). For drawing RNA maps, we wished to examine the enrichment of cDNA counts within binding sites compared to nearby regions outside of binding sites. We therefore calculated the average 'MaxCount-normalised cDNA counts' at each position across the evaluated binding sites and divided this count at each position by the average 'MaxCount-normalised cDNA counts' in the region 50-100 nt downstream of the binding site, as described in the formula below: To draw the graph, we then used the Gaussian method with a 10-nt smoothing window. The empirical cumulative distribution (Fig. 4a, c ) were generated in R with stat_ecdf function from ggplot2 package by using frequency of raw cDNA-start counts for each length category in region 25 nt upstream and downstream from cDNA start peak.
Analysis of cDNA transitions Density of C-to-T transitions across cDNAs was performed by using the samtools software with the following parameters: samtools calmd -u -u genomic. fasta input_BAM > BAM_with_transitions. This pipeline replaces BAM format mapped cDNA sequences with transitions relative to genomic reference. In the next step of the following pipeline we used a custom python script (available on github repository) that returns a density array of C-to-T transitions for cDNAs that are shorter than 40 nt. For the final visualisation of density graphs, we used the same approach as for all other density figures without additional normalisations.
For this purpose, karyotypes of Reichardia species were characterised using molecular cytogenetic techniques: (1) flow cytometry for DNA quantity and GC% assessment; (2) fluorochrome banding for distribution of GC-rich DNA and neutral heterochromatin; (3) FISH for establishing a physical map of 35S (18S-5. 8S-26S) and 5S rRNA genes. In addition, pollen grain size variation relative to basic chromosome number and genome size was measured. The data obtained were analysed in the frame of new molecular phylogenetic evidence.
0 (PE Biosystems, Foster City, California, U. S. A. ). Nucleotide sequencing was carried out at the Centres Cientı ´fics i Tecnològics, University of Barcelona on an ABI PRISM 3700 DNA analyser (PE Biosystems, Foster City, California, U. S. A. ). DNA sequences were edited with Chromas 1. 56 (Technelysium PTy, Tewantin, Queensland, Australia) and aligned visually. The sequences were deposited in GenBank (see the Appendix for the accession numbers). The sequence alignment is available from the corresponding author.
Ancestral character states reconstructions The Phytools package of R  [63]  was used to perform ancestral state reconstructions, using the consensus tree resulting from Bayesian analysis reduced to the set of ingroup taxa. The ancestral 2C-values were reconstructed under maximum likelihood with the fastAnc and contMap commands, and ancestral chromosome numbers were inferred with the re-rooting method. Alternatively, ancestral GS were also reconstructed using maximum parsimony for continuous traits in Mesquite v. 3. 04 software  [64].
Molecular phylogeny, ancestral character states reconstruction and trait correlation The phylogenetic tree resulting from the Bayesian analysis of the ITS dataset is presented in Fig 3. It shows the taxa sharing a common chromosome number clustered in well supported clades, with the clade containing the species with x = 9 recovered as sister to the two other clades of x = 8 and x = 7. This topology is compatible with decreasing dysploidy, which was confirmed by the reconstruction of ancestral chromosome numbers (Fig  3 ). Since ancestral genome size values inferred using Bayesian and parsimony reconstruction methods were similar, we presented here only the results obtained with Bayesian method (Fig  3 ). From the ancestral state reconstructions, it is noticeable that chromosome number and genome size apparently evolved in parallel.
21 revious reports of RSV-related mortality in young children described studies done in one centre, region, or country. None of them were large enough to draw robust conclusions on the clinical and socioeconomic profile of children who die with community-acquired RSV infection globally. To gain insight into the clinical characteristics of RSV-related mortality in young children, we initiated the RSV Global Online Mortality Database (RSV GOLD) study with the aim to gather available retrospective data for fatal community-acquired RSV infections across the world.
Study design and patients RSV GOLD is a global study that retrospectively analysed individual data for children aged 0-59 months who died with community-acquired RSV infection between Jan 1, 1995, and Oct 31, 2015. We identified research groups through a comprehensive literature search on PubMed (for articles published up to Feb 3, 2015) using the key terms "RSV", "respiratory syncytial virus", or "respiratory syncytial viral" combined with "mortality", "fatality", "death", "died", "deaths", or "CFR". We limited the search to papers written in English with the abstract and full text available. We invited authors of scientific, peer-reviewed papers reporting RSV-related mortality in children to collaborate. Additionally, we obtained unpublished individual patient data from researchers and clinicians who we identified via research networks.
Procedures We collected data using an online questionnaire designed by a group of investigators (NMS, LJB, HN, FPP, BDG, and DEG). The questionnaire aimed to collect information about clinical and socioeconomic charac teristics of children with RSV-related mortality. Characteristics of interest were based on risk factors for RSV-related and pneumonia-related mortality as described in the literature and on expert opinion (appendix). We carefully validated patient information by direct communication with participating researchers for data entry errors, missing variables, and conflicting data. We deemed age at death within 0-59 months, laboratoryconfirmed RSV diagnosis, and community-acquired RSV infection minimum essential data for inclusion. After assessment of data quality, we excluded children if.
Research in context Evidence before this study Respiratory syncytial virus (RSV) infection is a leading cause of global acute lower respiratory tract infection in young children. It was associated with 48 000-74 500 in-hospital deaths in children aged younger than 5 years in 2015, with 99% of these deaths occurring in developing countries. However, individual patient data for RSV-related deaths are scarce. We searched PubMed for articles published in English up to July 7, 2017, using search terms related to RSV infection, pneumonia, and childhood mortality. We found case series of RSV-related child deaths that reported 35 cases or fewer.
Children with comorbidities had at least one underlying disease, such as congenital heart disease, a genetic or chromosomal disorder, HIV infection, or active tuberculosis. Healthy term children were born without comorbidities at 37 weeks' gestational age or later and healthy preterm children were born without comorbidities at earlier than 37 weeks' gestational age. When data for comorbidities or prematurity were not recorded, we assumed that the children were healthy term. We categorised countries as high income, upper middle income, lower middle income, or low income on the basis of the World Bank classifications for 2016. 22 e considered Hong Kong and Taiwan high-income countries in line with the World Bank classification. The composite variable neurological disease consisted of neurodevelopmental, neuromuscular, and other neurological disease, such as epilepsy or encephalopathy.
Statistical analysis We calculated descriptive statistics for all variables. We provide frequencies and proportions for categorical variables. We report the summary results for continuous variables as medians with IQRs. We analysed data by World Bank income regions. Additionally, we analysed data separately for children with comorbidities, healthy term children, and healthy preterm children. Using WHO child growth standards,  23  we calculated Z scores for weight for age, height for age, and weight for height. We did not correct weight for age for gestational age. To rule out bias, we did sensitivity analyses excluding cases with missing data for prematurity or comorbidities to test our assumption that children with missing data were healthy term. Similarly, we analysed to what extent our results were sensitive to the contribution of a large number of cases from Argentina and Kenya by doing analysis without these countries.
We analysed the prevalence of comorbidities in our study (table) and distinguished clinical characteristics of children with and without comorbidities (appendix). Data for comorbidities were missing for some children. Available data showed that comorbidities were present in at least 183 (51%) children. Congenital heart disease was the most frequent comorbid condition identified. The proportion of children with comorbidities was similar during sensitivity analyses when children with missing data (for comorbidities and gestational age) were excluded, except that the proportion with comorbidities in low-income or lower middle-income countries was more than doubled (appendix). Specific information about the presence of comorbidities and prematurity was available in 243 (68%) children. The proportion of missing information about comorbidities and prematurity was similar for children aged younger than 6 months at the time of death (54 [30%] of 178) and older children (61 [34%] of 180).
We analysed how the presence of comorbidities affected age at death (figure  3 ). Most healthy term and healthy preterm children were aged younger than 6 months at the time of RSV-related death (appendix). The proportion of children aged younger than 6 months at the time of death was even larger when only children with complete data for comorbidity and gestational age status were included than when all children were included (appendix). Age at death of children with comorbidities from high-income countries was higher than that of those from low-income or middle-income countries. We analysed the proportion of children with low weight for age. More than half of all children had a weight for age of less than -2 SDs (table). In highincome countries, this finding was mainly explained by a large proportion of preterm children and children with comorbidities.
Table: Clinical characteristics and risk factors in RSV-related child deaths is the first descriptive study of global RSV-related mortality using individual case records. We studied clinical characteristics of children who died with RSV in hospitals across the world. We supplemented data from previously published cases of RSV-related in-hospital death with unpublished data by contacting individual investigators, research groups, and networks. Complete clinical information was generally available for children from high-income countries, but clinical data from lowincome and middle-income countries were often incomplete. For the first time, we showed that a substantial proportion of in-hospital RSV-related deaths in young children occurred in those with severe comorbidities. In low-income and middle-income countries, most children who died with RSV infection were aged younger than 6 months.
28 Age at death could influence the effectiveness of perinatal immunisation strategies, including passive immunisation by maternal vaccination during pregnancy and administration of RSV-specific long-half-life monoclonal antibody at birth. Our age distribution data suggest that maternal vaccination will be more effective for healthy term children than for children with comorbidities because antibody concentrations decrease after birth over time. Our data suggest that children with comorbidities will need additional preventive interventions, such as extended passive immunisation or infant vaccination to optimise prevention of RSV-related mortality. A key strength of this study is that we obtained most previously published cases of RSV-related mortality in children and supplemented this data with data sourced through existing networks, leading to a sample size large enough to draw robust conclusions and do some sensitivity analyses.
Statistics The one-way analysis of variance (ANOVA) with Tukey's post hoc test was performed. The belongingness to the three groups: depressed, manic, and control subjects made the independent variable. The dependent variables included selected components of mentalization and cognitive functions as well as clinical factors. The r-Pearson Test was used to determine any correlation between the variables. The calculations were done using the IBM SPSS 21. 0. The level of statistical significance was determined at p < 0. 05.
2016)  and between DLPFC and WCST  (Boschin et al. 2016). This may indicate that a DLPFC abnormality in bipolar patients plays a role for both mentalization deficits and cognitive disorders. Such an abnormality and the resulting disturbances are less intense in BD patients than in schizophrenia  (Bora and Pantelis 2016)  as was confirmed in our study on WCST performance  (Rybakowski et al. 2006). Although the discussion above mainly concerned the prefrontal cortex it should be indicated that other areas of the brain may be also important for mentalization processes. According to a meta-analysis of  Molenberghs et al. (2016) , besides the prefrontal cortex, the second most important brain structure for mentalization is the temporoparietal junction. However, there is no data on the involvement of the temporoparietal junction in such cognitive tests as TMT and WCST.
A monodisperse hydrodynamic size distribution was observed with an average diameter of 279 ± 11 nm, at concentrations of 10 μg/mL and 73 μM in CS-C and N6L, respectively (Figure  1A ). When higher concentrations of CS-C (1000 μg/mL) were used, relatively large and polymodal size distributions were observed, with average diameters at 228 ± 25 and 973 ± 187 nm for the two major populations (Figure  1B ). Confirming the data presented in Figure  1A , we observed by transmission electronic microscopy (TEM) that nanoparticles obtained by mixing a solution of 10 μg/ml CS-C and 73 μM N6L have a particles diameter of about 100 nm (Figure  1C ). We next studied the stability of these N6L-polyplexes over 24 hours periods by measuring the change in particle hydrodynamic size. As shown in Table  1 , while N6L-polyplexes obtained with 1000 μg/ mL CS-C remained relatively stable over a period of 4 hours, a significant size increase of N6L-polyplexes obtained with 10 μg/mL CS-C could be observed 8 hours after the polyplex formation, reaching a diameter of 381 ± 88 nm.