dataesr/scientific-paragraphs-categorization huggingface dataset:
We believe these changes refl ect improvements in our methods, but they also show how some causes of death, even though they result in lower absolute levels of mortality, can be profoundly aff ected by the redistribution of large garbage codes such as heart failure or sepsis. The sensitivity of our fi ndings to how major garbage codes, such as heart failure, sepsis, or ICD-X59 (exposure to unspecifi ed factor), are redistributed emphasises the importance of more systematic research on garbage code practices and improvements in primary data collection to avoid deaths being certifi ed to garbage codes. We used CODEm to model all causes for which suffi cient numbers of deaths are observed in vital registration, sample registration, or verbal autopsy datasets. For these 167 causes, we conducted rigorous out-of-sample validation exercises and documented prediction error and UI coverage.
For all-cause mortality, we include uncertainty due to the following: sampling error in the underlying data; non-sampling error associated with particular child or adult mortality data types; HIV/AIDS crude death rate; and reference age pattern of mortality used in the GBD model life table. We do not, however, include uncertainty in the measurement of income per capita or educational attainment, and we do not include uncertainty from the choice of our model specifi cation for the fi rst stage of the spatiotemporal Gaussian process regression analysis. For causes of death, we capture sampling uncertainty, model specifi cation uncertainty through the creation of CODEm ensembles, and non-sampling error from subnational or non-representative datasets. We do not, however, capture uncertainty due to garbage code redistribution, uncertainty in the covariates used in the models, or uncertainty due to cause of death assignment in verbal autopsy data.
Cause-years are defi ned as the number of years for each cause for which data are available. GBD=Global Burden of Disease. ICD=International Classifi cation of Diseases. BTL=basic tabulation list. VA=verbal autopsy. VR=vital registration. ATG=Antigua and Barbuda. VCT=Saint Vincent and the Grenadines. LCA=Saint Lucia. TTO=Trinidad and Tobago. TLS=Timor-Leste. FSM=Federated States of Micronesia.
In the development of the GBD cause of death database, we have been able to make only limited use of INDEPTH verbal autopsy data collected in multiple sites. Some INDEPTH members like Matlab routinely release physician-certifi ed verbal autopsy data in full detail whereas others have published periodic results in journal articles. In 2015, 22 INDEPTH sites published INTERVA model predictions of individual causes of death for the period 1992 to 2012. 153 No primary data from the verbal autopsy interviews have been released to date. Unfortunately, INTERVA model predictions are highly inaccurate. The only validation study comparing INTERVA to an objectively defi ned cause of death standard at the individual level found it accurately assigned the cause of death in 23•8% of adult deaths, 30•3% of child deaths, and 19•4% of neonatal deaths; at the population level, estimated cause-specifi c mortality fractions were not better than random guessing.
Males in France, 2011 Females in France, 2011 Figure 5: Age-specifi c mortality estimation with GBD life table method versus observed data excluded from the model Country-specifi c examples for (A) males and (B) females in France, 2011. The red line shows the GBD 2015 life table system estimates of age-specifi c mortality rates from birth through age 75 years in log space, compared with observed age-specifi c mortality (blue line). Year 2011 selected for illustration purposes. GBD=Global Burden of Disease.
Differences between Aluexon gene abundance across groups were tested by Kruskal-Wallis Rank Sum test within each RNA fraction (cytoplasmic RNA, p-value = 0. 01819; nuclear RNA, p-value = 0. 3646). Indicated pairwise comparisons were tested with a two-sided Wilcoxon Rank Sum test. ** indicates p-value < 0. 01. (C) RNA expression levels of 12 Alu-exon genes identified from the cytoplasmic and/or nuclear RNAseq datasets. Loss of transcripts from Alu-exon genes in hnRNPC-depleted cells was generally restricted to cytoplasmic RNA. Gene expression was quantified by quantitative RT-PCR from HR1 cells transfected with siRNAs against hnRNPC (siC #1 and #2) or controls ('no siRNA': mock transfection; ctrl: unspecific control oligonucleotide), error bars represent standard deviation of the mean (s. d.
m. ; n = 3 or 4). decrease in expression after hnRNPC depletion, while genes with annotated Alu-exons did not (Figure  1B ). Moreover, cryptic Alu-exons were associated with decreased expression only in cytoplasmic but not nuclear RNA. We used qPCR to validate the cytoplasmic loss of expression for nine out of nine well-expressed Alu-exon genes (Figure  1C ). We also validated the expression of three Alu-exon genes for which we did not detect hnRNPC-mediated changes in our RNAseq data (Figure  1C ), presumably due to low inclusion levels (NUP160, TNPO3) or positioning of the Alu-exon in the 5' UTR (PP2R1B). Finally, we used semi-quantitative RT-PCR to monitor the relative abundance of the transcript isoforms containing the Alu-exon (Alu-exon transcripts). For two out of four genes (AGL, TIMM23), the Alu-exon transcripts were less abundant in the cytoplasm than in the nucleus (Figure  1 -figure supplement 3), and NUP133 showed more complex splicing patterns (see below).
Notably, co-depletion with UPF1 restores expression of Alu-exon genes caused by loss of hnRNPC (Figure  2A ). This strongly suggests that NMD is involved in the cytoplasmic loss of Alu-exon transcripts and that hnRNPC-mediated splicing repression and NMD act together to repress Alu-exon expression. Indeed, changes in exon abundance analysed by DEXSeq  (Anders et al. , 2012)  showed that depletion of either hnRNPC or UPF1 alone resulted in increased Alu-exon abundance and that co-depletion of both factors showed a synergistic effect (Figure  2B ). The Alu-exons most strongly repressed by hnRNPC and UPF1 caused the most pronounced decrease in gene expression upon hnRNPC depletion, verifying a quantitative relationship between exon inclusion and reduced transcript abundance (Spearman rank correlation, p-value < 2.
Intron-retaining transcripts are refractory to NMD and accumulate in the cytoplasm Intrigued by the observation that many Alu-exons are NMD-refractory, we examined the splicing of Alu-exons in more detail. Using a modified DEXSeq approach to monitor intron events, we found retention of introns upstream or downstream of an Alu-exon for ~10% of Alu-exons in at least one of the tested conditions, including 40 cases of significant retention of both introns (adjusted p-value < 0. 01; Figure  3 -figure supplement 1A). All transcripts also include the Alu-exon sequence and are therefore referred to as 'intron-retaining Alu transcripts'. The Alu-exons in these intron-retaining Alu transcripts have weaker 5' and 3' splice sites (Figure  3 -figure supplement 1B), indicating that intron retention might be a by-product of inefficient Alu exonisation.
Materials and methods Cell culture and siRNA transfection HR1 cells  (Ewings et al. , 2007)  were a gift from Simon Cook (Babraham Institute, Cambridge) and are a stable derivative from HEK293 cells (ATCC Cat# CRL-1573, RRID:CVCL_0045). HR1 cells were maintained in DMEM with 10% FBS and 400 mg/ml G418 at 37 ˚C with 5% CO 2 injection, and rou- tinely passaged twice a week. Cells were regularly cultured for 3 days in antibiotic-free medium and tested for mycoplasma using either the LookOut Mycoplasma PCR Detection Kit or the MycoAlert Mycoplasma Detection Kit (Lonza). To deliver siRNAs, Lipofectamin RNAiMax (Life Technologies, now Thermo Fisher Scientific, Waltham, MA, USA) was used according to manufacturer's recommendations. HR1 cells were reversetransfected with 5 nM siRNA targeting HNRNPC mRNA (HSS179304 and HSS179305) or UPF1 mRNA (HSS109172) as well as a siRNA negative control with medium GC content (Invitrogen, Cat.
Quantitative and semi-quantitative RT-PCRs Reverse transcription was done with 500 ng of RNA using RevertAid enzyme (Fermentas, now Thermo Fisher Scientific) according to manufacturer's recommendations. The reverse transcription was primed with equal parts of random N6 and N15 oligonucleotides (Sigma) at 100 mM final concentration. For quantitative RT-PCR, the amplification of the cDNA of interest was normalised by the delta-delta Ct method to the geometrical mean of house keeping genes measured in parallel in all assays (EIF4G and SDH). All quantitative RT-PCR assays were done in technical replicates and averaged before data analysis. For semi-quantitative PCR, we ran 35 cycles of amplification with the primers indicated in each figure, and quantified the abundance of each product using Qiaxcel (Qiagen, Hilden, Germany) gel electrophoresis.
All libraries were sequenced on Illumina HiSeq2 machines in a single-end manner with a read length of 50 nt (unstranded) or 100 nt (stranded). Before mapping the reads, we removed adapter sequences by using the FASTX tool kit version 0. 7, discarded reads shorter than 35 nt and in case of the stranded RNAseq data, trimmed all reads to 75 nt length. RNAseq reads were mapped to UCSC hg19/GRCh37 genome assembly using TopHat v2. 0. 5, RRID:SCR_013035  (Kim et al. , 2013) , allowing up to two mismatches. RNAseq data files of rRNA depleted cytoplasmic and nuclear RNA from cells depleted of hnRNPC combined with a time-course of RAF1 kinase induction are deposited on EBI ArrayExpress under the accession number E-MTAB-4009. RNAseq data files of polyA-selected RNA from cells depleted of hnRNPC and UPF1 are deposited under E-MTAB-4008.
De novo identification of cryptic exons and Alu-exons In order to predict exons from our RNAseq data, we ran Cufflinks (version 0. 9. 3, -min-isoform-fraction 0,  Trapnell et al. , 2012 , RRID:SCR_013307) on the collapsed reads from all samples of our stranded RNAseq data and then extracted the exons of all predicted transcripts. Cufflinks predicted a total of 387,009 exons. All exons with one or both splice sites residing within an antisense Alu element (as annotated by RepeatMasker,  Smit et al. , 1996 Smit et al. , -2010) )  were assigned as Alu-exons. In order to minimise noise, we kept only exons that were predicted as part of multi-exon transcripts and were supported by at least one junction-spanning read. To ensure the best possible annotation of Alu-exons, we included exons which were previously identified in HeLa cells following the same annotation strategy  (Zarnack et al.
Analysis of differential gene expression and differential exon inclusion Analyses of differential gene expression were performed using DESeq and DESeq2, RRID:SCR_ 000154,  (Anders and Huber, 2010; Love et al. , 2014)  with gene coordinates based on ENSEMBL annotation (version 72). To combine the results from both siRNAs targeting HNRNPC, we used a conditional thresholding approach, calling expression changes as significant if they had an adjusted p-value < 0. 01 in at least one of the two hnRNPC depletion conditions and an adjusted p-value < 0. 05 in the other. Differential splicing was determined using DEXSeq, RRID:SCR_012823  (Anders et al. , 2012). The two hnRNPC depletion conditions were integrated by conditional thresholding as described above. To monitor the differential abundance of introns due to intron events, we extracted intron coordinates based on ENSEMBL exon annotations (version 72): For all Alu-exons in the RNAseq dataset, we identified the annotated upstream and downstream exons and extracted the intervening introns.
Analysis of frame-shifts and premature stop codons in regulated exons We selected the longest annotated transcript for each protein-coding gene from UCSC annotation including CDS coordinates and identified the position of the exon-of-interest within the transcript using the intersect function from bedtools (v2. 22. 1). Next, we determined the length of exonic sequence from the annotated start codon to the start of the exon-of-interest to determine the frame of the exon and predicted stop codons within that frame. If an exon did not contain a PTC in frame, we calculated if its length is a multiple of 3, and annotated exons as 'PTC'-or as 'potential frameshift'. For PTC+ exons, we calculated the distance of the PTC to the next exon-exon junction. To ensure sufficient accuracy of our frame prediction, we only used exons for which both junctions were supported by junction-spanning reads in our RNAseq datasets.
This annotation covered 274,082 exons and 209,640 introns, including 6975 introns flanking an Alu-exon. Again, we run DEXseq to identify candidate introns with significant retention. Since this second approach is highly inflated by introns, all analysis on intron-retaining Alu transcripts refers to the first approach with exception of our efforts to estimate how frequently other introns are retained in comparison to introns flanking Alu-exons (Figure  3 -figure supplement 1C and D).
Analysis of differential exon inclusion in human tissues To analyse differential inclusion of Alu-exons across human tissues, we retrieved data on mapped junctions from the V6p release of the GTEx consortium (http://www. gtexportal. org/home/, Ref GTEx Consortium, 2015). We identified junction-spanning reads to Alu-exons in a 5 nt grace window around the splice sites of all Alu-exons and used those to identify the 5' and 3' splice site of the upstream and downstream exon. We then calculated percent exon inclusion (PSI) as the ratio of inclusion junction reads (average of up+downstream junctions) to total junction reads (average of up +downstream junctions + skipping junctions), and inclusion within each tissue as average of all samples. To ensure sequencing depth and gene expression were sufficient to calculate exon inclusion, we only used exons with at least 200 reads across the 8555 samples (average of up+downstream junctions or skipping junctions).
Analysis of publicly available datasets The Illumina BodyMap 2. 0 data were downloaded from ArrayExpress (E-MTAB-513). Before mapping the reads, we removed adapter sequences by using FASTX-Toolkit version 0. 7. RNAseq reads were mapped to the UCSC hg19/GRCh37 genome assembly using TopHat v2. 0. 5  (Kim et al. , 2013) , allowing up to two mismatches. We used the paired-end sequencing data; however, single-end sequencing data gave comparable results (data not shown). To analyse the expression of Alu-exon containing genes, we focused on protein-coding genes and assigned the Alu-exons as follows: The longest annotated transcript was retrieved from ENSEMBL v72 to define the gene coordinates that were then used to assign the 'cryptic Alu-exons' (not annotated in UCSC as described above) as well as the 'alternative' or 'constitutive Alu-exon' based on overlapping coordinates.
Statistics and software Customised scripts written to characterise Alu-exons (such as PTC prediction, inclusion rate, U-tract lengths, liftover of Alu-exons to other species) are deposited at https://github. com/jernejule/Aluexonisation. All statistical analyses were performed in the R software environment  (version 2. 15. 3/3. 1. 3) or in GraphPad PRISM6. Whenever referred to in the text, replicates stands for biological replicates, defined as samples collected independently of one another in separated experiments. All experiments were done with biological replicates as indicated in Materials and methods and Figure legends, with the exception of the initial RNAseq experiment from cytoplasmic and nuclear RNA from cells depleted of hnRNPC combined with a time-course of RAF1 kinase induction, where a block design was used (see above).
Supplemental dataset List of exons with sufficient coverage for DEXSeq analysis in our RNAseq data. The supplementary dataset is the output of the DEXSeq analysis for RNAseq data set E-MTAB-6008. The table includes the position of each exon (hg19), Ensembl transcript and exon ID, base mean across conditions, and statistical testing scores by DEXSeq. Exon_class indicates if the exon is an Alu-exon, a cryptic exon, or not. UCSCoverlap and type indicate if the exon is annotated by UCSC, and if it is a constitutive or alternative exon. If the exon is not annotated in UCSC, it's called a cryptic exon. Stand_alone exons are not predicted by Cufflinks to overlap with another exon. The table also includes the number of junction-spanning reads confirming either splice site. In addition, we predicted premature stop codons in protein-coding genes; for PTC+ exons, it includes the distance between the PTC and downstream exon-exon junction, the following (second) exon-exon junction, and the number of downstream exons.
The last two columns show the ID after merging all discovered Alu-exons with UCSC-annotated Alu-exons which can be cross-referenced with the source data table of Figure  5 , and the grouping of evolutionary groups shown in Figure  5. The table is available from the Dryad data repository (accession doi:10. 5061/dryad. 7h81d). interpretation of data; ZW, generated polysome profiles, Acquisition of data; KZ, Analysis and interpretation of data, Drafting or revising the article; JK, Conception and design, Analysis and interpretation of data; JU, Conception and design, Analysis and interpretation of data, Drafting or revising the article.
All data are presented as 95% confidence intervals of the median estimated by bootstrapping. Figure 5-figure supplement 1 presents the complete distribution of U-tract lengths summarised in Figure 5A and B, as well as the data on the evolutionary youngest Alu-exons specific to the hominoidae lineage. DOI: 10. 7554/eLife. 19545. 018 The following source data and figure supplement are available for figure 5: Source data 1. List of Alu-exons across our datasets and UCSC annotation, including cross-species annotation. DOI: 10. 7554/eLife. 19545. 019 Figure 5 continued on next page.
Acknowledgements The authors thank all members of the  Ule laboratory  for assistance and discussion. We thank the  Genomics Facility Team of the CRUK Cambridge Cancer Institute  and the  Genomics Unit at the Institute of Molecular Biology ,  Mainz  for processing samples for high-throughput sequencing, especially  Chung-Ting Han. We are grateful to  Sarah K Jurmeister  and  Volker Bo  ¨hm for input on this manuscript and critical comments. The HR1 cell line was a gift from  Simon Cook  ( Babraham Institute, Cambridge ). This work was supported by the  European Research Council  (grants  206726-CLIP  and  617837-Translate  to JU), and a  Boehringer Ingelheim Fond PhD fellowship  (to JA). KZ was supported by the  LOEWE program Ubiquitin Networks (Ub-Net) of the State of Hesse (Germany).
Unlike previous network visualizations, ContactTrees highlight how relationships form and change based upon interactions among actors, as well as how relationships and networks vary by contact attributes. Based on a botanical tree metaphor, the design is easy to construct and the resulting tree-like visualization can display many properties at both tie and contact levels, thus recapturing a key ingredient missing from conventional techniques of network visualization. We demonstrate ContactTrees using data sets consisting of up to three waves of 3-month contact diaries over the 2004-2012 period, and discuss how this design can be applied to other types of datasets.
The design is based on a botanical tree metaphor. The main idea is to use the features of a tree (the structure of its branches, leaves, fruits, colors, etc. ) to map the properties of social interactions. This design fits well for visualizing many properties of egocentric networks. We present an application, using specific datasets, to highlight some trends and hypotheses. The datasets contain up to three waves of 3-month contact diaries that were collected between 2004 and 2012  [11]  (i. e. , lists of one-on-one contacts for three periods of 3 months each). Our first contribution to social network visualization lies in the local perspective. While most social network visualizations adopt a global approach, we take an egocentric approach to highlight selected aspects of social interactions.
The specific mapping can be selected according to the intended tasks. Another strength of our design lies in the botanical metaphor used in such a context. In addition to producing attractive representations, our design is extensible, because it is relatively easy to add new glyphs showing other aspects of the dataset. With these strengths, ContactTrees should appeal to social-science researchers who wish to take advantage of visualization tools that readily help them pinpoint critical features embedded within the multilevel data in social networks.
Indeed, deeper and more complex decompositions in visualization may or may not help reveal ties and contacts more clearly, or allow end users to apply their own data to the design as easily. In our design, each small branch represents a tie, while leaves and fruits symbolize attributes of contacts. Each leaf/fruit also holds four features on which we can map a property: color, size, position along the branch, and the side of the branch from which it is growing (see Fig  1b). Further considering the properties one can map on a ContactTree, we see first that there are two boolean ones (side of the trunk, side of the main branches) and two ordinal ones (position along the trunk, position along the main branches) for grouping the ties. Then for each tie, we have two numeric properties (leaves and fruits).
Example of Mapping Many properties of ties and contacts can be mapped using the tree features described above. The example of mapping presented here is based on contact diaries, a major approach of data collection in the social network literature  [9, 11, 51]. A diary normally contains inclusive information about persons with whom the diary keeper has contact during a given period of time, their relationships with the diary keeper, and the situations of each one-on-one contact. Some diaries are more specific about certain properties. For example, contact situations might include how much the diary keeper likes a contacted person, or how the diary keeper feels after each unique contact. In this paper we use an actual dataset of contact diaries. Blue labels in Fig  1b  show an example of mapping.
An Interface for Visualizing ContactTrees A potential issue with our visualization lies in the burden of remembering visual encodings. This problem can be alleviated somewhat by implementing an interface that includes a legend. Using such a legend, social scientists who have tried our system have been able to interpret the ContactTrees quickly. Which mapping to choose highly depends on the user's purpose or the task to be performed. We provide a reconfigurable interface where the user can select mappings and explore a dataset on his own, finding the best perspective for conducting his tasks or comparing mappings if needed. Our prototype has been developed as a plugin of the framework Tulip (http://tulip. labri. fr/ TulipDrupal/)  [52]. This framework includes a powerful data model and rendering system as well as many interaction techniques useful for our purposes.
As an example, the darkness of leaves in Fig 1b  stands  for how positively the person feels about a contact, and their size indicates the contact's duration. Lines are finally replaced by Bézier curves going through points uniformly distributed along them. Botanical tree-like colors are used. A gradient of brown on the trunk and branches shows the age range of the ties, highlighting the asymmetry between the right and left sides of the trunk. These design choices make our visualization look more like a botanical tree (see Fig  1a ). Step 4 (optional): Adding More Features A beneficial aspect of our visualization is its capacity for evolution. One can easily define new features that fulfill the botanical metaphor to highlight some additional aspects of a dataset. An example of this property is shown in Fig 1a.