dataesr/scientific-paragraphs-categorization huggingface dataset:
Merging of the datasets is problematic as, for example, German data include water depths lower than zero to represent basement flooding, in contrast to the other 2 data sources, where water levels are always above the terrain. Transformation of flow velocity from German data and computation of return periods for fluvial and pluvial events are further sources of potential errors. Usable floor space area represents the whole building rather than individual households, though in a minority of cases, it most likely refers to particular households of multifamily buildings only. Yet, inconsistencies and uncertainties are always present in flood loss datasets, so efforts to collect bigger and improved data should continue internationally also to increase the amount of available data points. Modelled flood losses from the 2010 coastal flood were underestimated for 2 French departments that were most affected.
This is possibly to a large degree due to limitations in the hazard and exposure data. Firstly, more than half of the households which submitted insurance claims for flood losses were not identified with the available data. Partially, it is caused by inaccuracies of the hazard data, where many affected locations were not Fig. 8  Comparison between average modelled and reported losses during the 2013 event in Saxony, for different damage models and exposure valuations shown as inundated. Additionally, the OpenStreetMap building dataset does not capture many inundated residencies as 1510 houses were demolished in the aftermath of the 2010 flood by the French government  (Lumbroso and Vinet 2011). Those buildings represented the most severely affected households, hence pushing down the average modelled losses compared to observations.
Also, the velocity node was not used as the information was not available for the case study. Finally, the potentially significant effect of saltwater intrusion was not included in the model, as such information was not available for the fluvial and pluvial event the damage model was based on. For the 2013 Saxony case study, the uncertainty of the results mostly stems from hazard data (lack of velocity data or coverage over the whole affected area) and the problem of uniformizing the reference units (buildings vs individual households) for both observed and modelled losses. The results of the BN model were contrasted with those from other models only for the case study. Comparing all models for the 11 flood events in the study would not be a fair comparison, because only some models were trained with the datasets included here, and none of them with all of them together.
Future outlook The model could be further developed by expanding the input data with more flood events. Post-disaster surveys were carried out, e. g. in France  (Poussin et al. 2015)  and the UK (Defra/Environment Agency 2004). Microdata from insurers could also be used, as, for example, in case of the 2008 and 2010 coastal floods in France  (André et al. 2013). The main obstacles in using these datasets are obtaining them together with geolocation, while at the same time protecting the sensitive nature of the data containing responses of household occupants. Also, each individual dataset contains different variables and/or their definitions, requiring more effort to homogenize the data. The model will be tested further in the framework of the EIT Climate-KIC Demonstrator project "SaferPLACES".
RMSE error of the damage model was on average 7. 8 and 13. 5 percentage points. Errors for the 11 events included in the dataset were largely proportional to the variability of data. Overall losses from events were overestimated for the 1993 Meuse river flood for both buildings and contents loss, and for pluvial events for losses to buildings. The observed loss from the German and Italian events was mostly within the uncertainty range of the model. The BN-based flood damage model was further applied to the 2010 coastal flood in France, as coastal events were not included in the sample used to build the model. It was also compared with alternative flood models for this case study. Some of the other models achieved better performance than our model, but the results are sensitive to exposure estimates.
Acknowledgements This work was supported by  Climate-KIC  through project " SAFERPLACES-Improved  assessment of pluvial, fluvial and coastal flood hazards and risks in European cities as a mean to build safer and resilient communities", Task  ID TC2018B_4. 7. 3-SAFERPL_P430-1A KAVA2 4. 7. 3. Further funding was received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement no. 730381. The authors would like to thank  Fabio Brill ,  Stefan Lüdtke ,  Viktor Rözer ,  Nivedita Sairam  and  Max Steinhausen  (from  GFZ ) for technical support with the German flood data. We thank  Katrin Nissen  and  Nico Becker  (from  FU Berlin ) for their help with German rainfall data. The surveys to collect the fluvial damage data in Germany were supported by the  German Research Network Natural Disasters (German Ministry of Education and Research (BMBF) , no.
In the Mtf2 null, Jarid2 null, and WT EBs, the cells pass through largely genotype-specific clusters with early differentiation intermediates (respectively, cluster 12, 5, and 17), after which they tend to converge into mixed genotype differentiated clusters (Fig. 1c, d , Supplemental Fig. 2a ). To assist a systematic analysis of differentiation potential in relation to the loss of PRC2 subunits, we annotated each of the 22 clusters in our dataset using Anatomy Ontology  32  and used this in combination with matches to cell types in the recently published Mouse Cell Atlas 33 ("Methods"; Supplemental Fig. 3a, b ). We then grouped cell clusters into the germ layers for downstream analyses (Supplemental Fig. 3c ). The EBs produce a rich diversity of cell lineages of all three germ layers. For example, cluster 4 consisted of cells resembling the lateral mesoderm lineage (e.
5, with observations of severe anemia and growth retardation  [48] [49] [50]. By contrast, Jarid2 null mice exhibited early embryonic lethality (as early as E10. 5)  [51] [52] [53]. To study the mechanisms underlying these disparities between the loss of PRC2 subunits, we adopted the use of embryoid bodies and directed differentiation systems for better control of early differentiation intermediates and signaling paradigms to dissect the roles of PRC2. 1 and PRC2. 2. Importantly, single-cell RNA sequencing also allows for untangling the heterogeneity of differentiation, uncovering transient developmental events which are difficult to assess in early embryos  54. For example, we were able to capture aspects of cardiac progenitor development in our dataset (Supplemental Fig. 2a ), which could be applicable to the EB field and the formation of gastruloids  55, 56.
In vitro transcription (Ambion MEGA-Script) was then carried out overnight at 16 °C, with the lid set at 70 °C. An exonuclease digestion step was performed thereafter for 20 min at 37 °C, followed by fragmentation of the RNA samples. After a beads cleanup, the samples were subjected to library RT and amplification to tag the RNA molecules with specific and unique sample indexes (Illumina), followed by a final beads cleanup (1:0. 8, reaction mix: beads) and the sample cDNA libraries were eluted with DNAse free water. Libraries were quantified using by qPCR and sequenced on the NextSeq 500 (Illumina) for 25 million reads per plate. Reads filtering, processing, and downstream analyses pipeline Single-cell RNA. Raw reads were mapped and aligned to the mouse genome GRCm38/mm10 database using the Bowtie2 66 alignment tool.
Aligned reads were indexed and the final count table was derived using HTseq 67. R package 'Scater'  68  was used for sample filtering and quality check, to remove dropouts (cells with <5 reads) and cells with too few recovered genes (<500). The data was then analyzed using R package 'Seurat' 69 (v3) for batch, read counts and gene counts normalization. In summary, after filtering, we captured the transcriptomes of 1196 WT cells, 1254 Mtf2 null cells, 1196 Jarid2 null cells, and 811 Eed null cells. After filtering, technical confounders such as total number of counts and features were also normalized using the 'LogNormalize' function with a scale factor of 10,000. Next, feature selection was performed using the 'vst' method in Seurat to identify the top 2000 most hypervariable genes (HVGs).
Then, the dataset was batch corrected using the linear regression model in built in Seurat to regress out unwanted technical effects of libraries (batches) via scaling. Thereafter, the dataset was subjected to linear dimensional reduction using a principal component analysis of the top 15 principal components (PCs), determined by an elbow plot of the PCs. The cells are then clustered using a shared nearest neighbor (SNN) modularity optimization-based clustering algorithm at a resolution of 2. 5 and the 22 different clusters were projected onto a 2-D UMAP for data visualization. Time course trajectory analyses were performed using the package 'Monocle v2'  70. The clusters information from Seurat were imported into Monocle v2 for analyses and the top 2000 HVG detected in Seurat were used to order the cells during pseudotime analyses.
All cells were used in combination for the trajectory analyses and no ground states were set for the pseudotime analysis. Actual time point information and cluster information were overlaid onto the trajectory plots. Cluster identities were defined by Anatomy ontology  71  and correlated with Mouse Cell Atlas 33 (MCA) cluster data. The single-cell data can be downloaded and viewed in a user interface via the following repository: https://github. com/chethloh/ PRC2_singlecelldata. Bulk RNA. Paired-end Illumina 75-bp sequencing files were mapped to the mouse genome GRCm38/mm10 database using the Bowtie2 66 alignment tool. Reads were quantified using Salmon  72  and the count tables were analyzed using DEseq2 73 (version 1. 18. 1), using Wald statistics (Log2 fold change >1, padj value <0. 001) for pairwise comparison and likelihood ratio test statistics (FDR < 0.
1. 0). GimmeMaelstrom 77 (v 0. 14. 0) was used for Fig. 5c  to identify the transcription factor motifs that are influencing RNA expression dynamics by scanning motifs associated with promoters (+/-0. 5 kb from TSS) of a list of differentially regulated genes for both Mtf2 and Jarid2 null cells against their own wild-types at the undifferentiated stage. By default GimmeMaelstrom uses a non-redundant, clustered database of known vertebrate motifs: gimme. vertebrate. v5. 0. These motifs come from CIS-BP (http://cisbp. ccbr. utoronto. ca/) and other sources such as JASPAR, IMAGE, HOMER, and Swiss Regulon. DNA shape analysis was performed using the DNAshape package  78. For Fig. 5b , the peaks that were selected for their respective RPKM value were peaks that are overlapping with peaks in a +/-0.
Data availability The data that support this study are available from the corresponding author upon reasonable request. The single-cell RNAseq and bulk RNAseq fastq and count matrices have been deposited in the GEO repository under accession code GSE154572. ChIP-seq reads, coverage as genome browser tracks, and peak files have been deposited in the GEO repository under accession code GSE94300. The single-cell data can be downloaded and viewed in a user interface via the following repository: https://github. com/veenstralab/ chetloh_ncomm_prc2_singlecell_2021. Source data are provided with this paper.
Identification of patients bearing germline AGO2 mutations. During trio whole-exome sequencing of a cohort of 50 children affected by developmental disturbances and neurological manifestations of unknown etiology  9  , we identified a patient bearing a de novo missense mutation p. L192P in AGO2 (NM_012154. 5). Based on the ExAC and gnomAD sequencing data, AGO2 is one of the most missense-intolerant genes in the human genome, ranked 15 and 30 respectively. Its Z scores of 7. 696 (ExAC dataset) and 6. 058 (gnomAD dataset) are far higher than the average Z score for genes involved in developmental disorders  [10] [11] [12]. Only two AGO2 non-synonymous alterations (p. G88V and p. E186K) with an allele frequency of more than 0. 0001 are deposited in ExAC and gnomAD datasets. Moreover, the p. L192P variant was not present in publicly available datasets (dbSNP, ExAC and gnomAD), had a high in silico pathogenic prediction score (CADD_Phred of 27.
Eleven missense mutations, one in-frame deletion (Fig. 1a ) and a 235. 3-kb deletion involving the first three exons occurred de novo (Fig. 1b ). In addition, one of the missense mutations (p. T357M) was transmitted from a similarly affected mother. Five mutations (p. L192P, p. G201V, p. T357M, p. M364T, p. C751Y) were recurrent. None of the mutations were present in publicly available datasets, and similarly to p. L192P, all missense mutations display high in silico pathogenic prediction scores (mean CADD_Phred of 29. 1) and change highly conserved residues (Supplementary Data 1; Supplementary Fig. 1 ). Thus, the genetic data, especially the recurrence of mutations, already provide strong evidence for their pathogenicity. Fig. 1  Location of identified AGO2 germline mutations. a Domain structure of AGO2 and position of the single amino acid mutations using the structure of human AGO2 in complex with a miRNA and a target RNA  22.
n = 8 biologically independent experiments; data are means +/-SD; ****p < 0. 0001; one-way ANOVA, followed by Dunnett's multiple comparisons test. Source data are provided as a Source Data file. holo RISC) or with a mismatch at position 8 of the guide (v; g2-8, holo RISC). The underlying structures and pdb codes are listed in Supplementary Fig. 12. This large number of simulations (  12 AGO2 variants in five different states) was chosen as an initial screening approach to identify possible alterations in mutant forms of AGO2; however, this large number also precluded detailed quantitative measurements. Among the simulated complexes, the mutations appeared not to uniformly affect the population distributions on this conformational mode. Principal component analysis of apo-AGO2 trajectories suggested that only two mutations, p.
2f ). This is consistent with the fact that so far only a single homozygous, non-synonymous AGO2 variant carrier (bearing the p. G88V) is documented in the gnomAD dataset. Notably, one of the mutations identified here, p. G733R, stands out from the others as it fails in almost every functional assay. We assume that the substitution of the rather bulky and positively charged Arg for the small Gly will cause structural perturbations and local unfolding in the PIWI domain, thus leading to a complete loss of function. In this respect, we predict it to be functionally similar to the large chromosomal deletion observed in patient 21. The deletion encompasses part of the AGO2 gene and is likely to lead to haploinsufficiency. It does not only lead to loss of the first 3 AGO2 exons, but also the last 23 exons of PTK2.
To our knowledge, no mutation in PTK2 has so far been connected to any human disorder. Several individuals bearing heterozygous loss-of-function mutations in PTK2 have been documented in the gnomAD dataset. Nonetheless, since no additional individuals with similar AGO2 loss-of-function mutations, (either a gross deletion, nonsense or frameshift mutation), are known currently, we cannot deduce to which extent the additional PTK2 deletion might be contributory. It is worth noting that the clinical presentation of this individual somewhat resembles children bearing a deletion that encompasses both AGO1 and AGO3  14. Currently, the pathomechanism of the p. G573S mutation remains somewhat unclear. It is conceivable that the change from glycine to serine, due to the difference in the physico-chemical properties of these residues, may cause some local structural changes and alter the interface between PIWI and MID domains.
Methods Research subjects. Written informed consent for all subjects was obtained in accordance with protocols approved by the respective ethics committees of the institutions involved in this study (approval number by the Ethics Committee of the Hamburg Chamber of Physicians: PV 3802). The authors affirm that the research participants and their legal representatives, and in the case of minors the parents or legal representatives of the human research participants provided informed consent for publication of the images in Supplementary Fig. 2. Genetic analyses. Some of the investigators presenting affected individuals in this study were connected through GeneMatcher, a web-based tool for researchers and clinicians working on identical genes  15. Whole-exome sequencing (WES) or trio whole-exome sequencing (trio-WES) experiments, data annotation and interpretation were performed in nine different centers with slightly different procedures.
Trio-WES in families of cases 4, 6, 7, 8, 12, 13, and 16 was performed at the Radboud University Medical Center in Nijmegen, the Netherlands  46. Exome capture was performed with the Agilent SureSelect Human All Exon v5 enrichment kit (Agilent Technologies). Whole-exome sequencing was performed on the Illumina HiSeq platform (BGI, Copenhagen, Denmark) and the Illumina Nova-Seq6000 (NSW Health Pathology Randwick Genomics, Sydney, Australia). Data were analysed with BWA (read alignment,) and GATK (variant calling) software packages. Variants were annotated using an in-house developed pipeline. Prioritization of variants was done by an in-house designed 'variant interface' and manual curation. Trio WES in families of cases 11, 15, 19, and 20 and quad-WES in a family of cases 17 and 18 were performed on exon targets isolated by capture using the SureSelect Human All Exon V4 (50 Mb) or the IDT xGen Exome Research Panel v1.
1. 18), PINDEL (v 0. 2. 4t), and ExomeDepth (v1. 0. 0)  48. Trio-WES in family of case 14 was performed using SureSelect Human All Exon 50 Mb kit (Agilent Technologies, Santa Clara, CA) on a HiSeq2500 system (Illumina, San Diego, CA, USA). In-house developed scripts were applied to detect protein changes, affected splice sites and overlaps to known variations, with filtering against dbSNP build 138, the 1000 Genomes Project data build November 2014 and ExAC Browser (status from August 2019)  49. DNA of case 9 and his unaffected father was sequenced SureSelect Clinical Research Exome V2 (Agilent Technologies, Santa Clara, CA) on a HiSeq2500 system (Illumina, San Diego, CA, USA). The raw data were analyzed using the Care4Rare analysis pipeline  50. DNA of case 1 and his parents were extracted from peripheral blood using the Gentra Puregene Blood Kit (Gentra Systems Inc.
Variants were identified using haplotype caller within GATK and Freebayes. The intersection of the two variant callers were annotated with SnpEff and loaded into a database using the GEMINI framework. Annotations included predicted functional effect (e. g. , splice-site, nonsense, missense), protein position, known clinical associations (OMIM, CLINVAR), mouse phenotypes (MGI), conservation score (PhastCons, GERP), and effects protein function (PolyPhen), CADD scores, and population allele frequencies (Exome Variant Server and Exome Aggregation Consortium data). Trio-Wes in family of case 5 was performed on MGISEQ-2000 platform (BGI-Wuhan, Wuhan, China). DNA extraction as well as quality controls were performed using standard methods. Half a microgram of genomic DNA was randomly fragmented by Covaris.
Using Agencourt AMPure XP-Medium kit fragments of 150-250 bp were selected. The fragments were then subjected to end-repair, 3′ adenylation and adaptors ligation. After PCR amplification and purification, hybridization using BGI Hybridization and Wash kits were used. After a second PCR and recovering step, the double stranded PCR products were heat-denatured and circularized by the splint oligo sequence. The single-strand circle DNA (ssCir DNA) were formatted as the final library. Library was qualified by Qubit ssDNA kit. The library was amplified to make DNA nanoball which have more than 300 copies of one molecular. The DNBs were loaded into the patterned nanoarray and pair-end 100 bases reads were generated in the way of sequenced by combinatorial Probe-Anchor Synthesis (cPAS). The raw data were then transferred to Limbus (Rostock, Germany) and bioinformatic secondary and tertiary analyses were performed using the standard algorithms of GATK.
Targeted quantification of AGO2 phosphorylation by SRM (Selected Reaction Monitoring). Phosphorylation levels of AGO2 variants were quantified by obtaining selected AGO2 phosphopeptides as well as their non-phosphorylated counterparts as stable isotope-labeled and quantified spike-in standards from JPT (Innovative Peptide solutions, Berlin) f  30. The following 13 C 15 N-labeled peptides: SASFNTDPYVR and SApSFNTDPYVR for detection of phospho-S387; YHLVDKEHDSAEGSHTSGQSNGR and YHLVDKEHDpSAEGSHTSGQSNGR for detection of pS824; and YHLVDKEHDpSAEGpSHTpSGQpSNGR for detection of the pS824/pS828/pS831/pS834 cluster, were used to set up a SRM method on a hybrid triple quadrupole/linear ion trap instrument (QTRAP4500, SCIEX). A spectral library built from DDA (data-dependent analysis) runs of the heavy peptides was built and imported into the open source software Skyline (MacCoss lab software, Seattle, USA).
In Skyline, then a targeted method was built according to the occurrence of precursor charge states +2, +3, +4 during several DDA runs. After manual inspection of MS2 spectra at least 3 transitions were selected for each peptide. The resulting transition list was imported into the instrument software (Analyst 1. 6. 1) and the following parameters were set: Q1 and Q3 at unit resolution (0. 7 m/z half-maximum peak width), dwell time 20 ms, cycle time < 3 s. After annotating peptide retention times from the initial SRM run and setting the following parameters: cycle time: 2 s, retention time window: 5 min, a scheduled SRM method was created in Skyline. Sample preparation of overexpressed and immunoprecipitated Flag/HA-tagged Ago2 variants was performed as follows: after separation on SDS-PAGE AGO2 bands were excised, washed with 50 mM NH 4 HCO 3 , 50 mM NH 4 HCO3/acetonitrile (3/1), 50 mM NH4HCO3/acetonitrile (1/1) and lyophilized.
Acknowledgements We thank all family members for their participation and collaboration,  Hans-Hinrich Hönck  ( Institute for Human Genetics, UKE Hamburg ) for technical assistance, and  UKE microscopic imaging facility (umif)  for providing assistance with confocal microscopes. This work was funded in part by  Werner Otto Stiftung  (to D. L and H-J. K),  Deutsche Forschungsgemeinschaft  ( LE4223/1-1  to D. L. ;  Kr1321/8-2  to H-J. K;  KI 488/7-2  to S. K. ;  SFB960/B3 ;  DFG/ANR 2064/7-1  to G. M. ), postdoctoral fellowships from  Forschungsförderungsfonds Medizin at UKE Hamburg  (to F. H. N. and V. M. ), the  DAAD  fellowship (to A. K. ), the  Clinician Scientist Program Medizinische Fakultät der Universität Leipzig  (to D. L. D. ), the  Dietmar-Hopp-Stiftung  (to S. S,  23011236 ), the  National Institute of Neurological Disorders and Stroke (NINDS)  under award number  K08NS092898 ,  Jordan's Guardian Angels , the  Brotman Baty Institute for Precision Medicine  (to G.