dataesr/scientific-paragraphs-categorization huggingface dataset:
Pauses were identified using previous methodology  (Ishimura et al. , 2014 ) using a 0. 5 reads/codon in all samples to threshold transcripts to analyze. Correlation between gene pauses and gene expression was calculated by taking the average pause scores of transcripts associated with a gene for transcripts with ≥ 0. 5 reads/codon across the B6J. Gtpbp1 -/- samples. The sum of these pause scores across all replicates was then correlated with the sleuth beta values (in the B6J. Gtpbp1 -/-vs. B6J comparison) for genes that passed the reads/codon thresholding in the ribosome profiling datasets. (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint this version posted September 4, 2020. ; https://doi.
org/10. 1101/2020. 09. 03. 281675 doi: bioRxiv preprint 44 Read counts for translational efficiency (TE) analysis were quantified using featureCounts  (Liao et al. , 2014)  with footprints overlapping CDS features and RNA read pairs overlapping gene exon features. Histone mRNAs were removed from the analysis and TE was quantified and tested for using riborex version 2. 3. 4 using the DESeq2 engine  (Li et al. , 2017). TE analysis of AGA A-site filtered datasets was performed by identifying AGA codons in the transcriptome and transferring the coordinates to the genome using ensembldb v. 2. 6. 8  (Rainer et al. , 2019). Then reads with AGAs in the A-site (±1 codon based on riboWaltz A-site offset for each read length) were removed and the above TE analysis was performed. Sets of mouse transcripts with known TOP motifs were identified in  (Yamashita et al.
Gene ontology (GO) and pathway analysis RNA-sequencing data were analyzed using Ingenuity Pathway Analysis (QIAGEN Inc. , https://www. qiagenbioinformatics. com/products/ingenuity-pathway-analysis). Gene Ontology (GO) pathway analysis was performed using the DAVID bioinformatics web server (http://david. abcc. ncifcrf. gov) by uploading the gene lists from our ribosome profiling analysis (AGA stalling transcripts, z ≥ 10, stalls detected in any biological replicates). The functional annotation chart and clustering analysis modules were utilized for gene-term enrichment analysis, and terms with a Benjamini-Hochberg adjusted p-value ≤ 0. 05 were considered enriched.
Acknowledgements We thank  T. Jucius , and  A. Kano  for technical assistance,  Drs. Satori Senju  and  Yasuharu Mishimura  for providing Gtpbp1 -/-mice, the  IGM Genomics Center  at the  University of California San Diego (UCSD)  for support with the preparation of RNA sequencing libraries and sequencing, and the  UCSD School of Medicine Microcopy Core  for providing access to microscopy equipment (Grant  P30 NS047101 ). This work was supported by  NIH   NS094637  (SLA). SLA is an investigator of the  Howard Hughes Medical Institute.
Known astrometric errors in DR12Q were corrected before coordinates were matched. The DR7Q and DR12Q quasars were added to the superset after the winnowing algorithm outlined in §3. 2 was applied, 1 https://github. com/bradlyke/dr16q 2 https://data. sdss. org/datamodel/files/BOSS SPECTRO REDUX/RUN2D/spAll. html as any objects appearing in DR7Q and DR12Q had previously been visually confirmed to be confident quasars. For quasars included in both DR7Q and DR12Q, only the DR7Q observation was propagated to this catalog superset. The superset contains 1,440,615 observations of quasars, stars, and galaxies that were all targeted as quasars (or appeared in previous quasar catalogs).
6%  (8,581 spectra). Finally, objects were removed from the superset where the ZWARNING 3 field had one or more of the following flags set: UNPLUGGED, SKY, LITTLE COVERAGE, BAD TARGET, or NODATA. To characterize the accuracy of our classification scheme, the SDSS-III/SEQUELS data set (see §5. 1 of  Myers et al. 2015)  was used as "truth" to calculate the sample completeness via, Comp = N pq + N vq N tq , (1) where N pq is the number of quasars correctly classified as QSO by our schema, N vq is the number of quasars identified during visual inspections, and N tq is the total number of quasars in the catalog. We also calculated the sample contamination via, Contam = N nq N tq + N nq , (2) where N nq is the number of spectra incorrectly classified as quasar spectra and N tq is as in Eqn. 1. We calculated a completeness of 99.
4. These data are recorded in DR16Q in the columns IS QSO 10K for classification, Z 10K for the redshift, and Z CONF 10K for the redshift confidence in the case of quasars. Eqn. 1 3 www. sdss. org/dr16/algorithms/bitmasks/#ZWARNING was applied to this set, producing a completeness of 99. 8%. Some of the random subsample of 10,000 eBOSS spectra with a low signal-to-noise ratio were classified as quasars by the DR14Q visual inspection team but were reclassified as non-quasars by the DR16Q team. Applying Eqn. 2 and using DR14Q classifications for such cases produced an estimated contamination of 0. 3%. Using DR16Q classifications for such cases produced an estimated contamination of 1. 3%.
ACKNOWLEDGEMENTS BWL, JNM, and ADM were supported by the  U. S. Department of Energy, Office of Science, Office of High Energy Physics , under Award Number  DE-SC0019022. ANH is supported by The  University of Wyoming Science Initiative Wyoming Research Scholars Program  and was funded by  Wyoming NASA Space Grant Consortium ,  NASA  Grant # NNX15AI08H. Funding for the  Sloan Digital Sky Survey IV  has been provided by the  Alfred P. Sloan Foundation , the  U. S. Department of Energy Office of Science , and the Participating Institutions. SDSS-IV acknowledges support and resources from the  Center for High-Performance Computing at the University of Utah. The SDSS web site is www. sdss. org. In addition, this research relied on resources provided to the eBOSS Collaboration by the  National Energy Research Scientific Computing Center (NERSC).
Further, a case study of a 2010 coastal flood in France is used to test the BN model's performance for a type of flood not included in the survey dataset. Overall, the BN model achieved better results than any of 10 alternative damage models for reproducing average losses for the 2010 flood. An additional case study of a 2013 fluvial flood has also shown good performance of the model. The study shows that data from many flood events can be combined to derive most important factors driving flood losses across regions and time, and that resulting damage models could be applied in an open data framework.
They also enable computing loss-frequency curves for whole portfolios, regions or countries  (Schwierz et al. 2010) , i. e. the probability that a loss of given magnitude would occur in a broader geographical area rather than a single location. Further, they are increasingly available for reuse, e. g. from Oasis Loss Modelling Framework (2020). The differences between damage models translate into very different predictions of flood losses  (Apel et al. 2009; Merz et al. 2010; Bubeck et al. 2011; Jongman et al. 2012; Cammerer et al. 2013; Carisi et al. 2018). At the same time, uncertainty related to hazard intensity was found less important than uncertainty related to exposure or vulnerability  (Apel et al. 2009; de Moel et al. 2011; Rojas et al. 2013; Metin et al. 2018). Some limited attempts of an integrated approach were made, such as combining data from multiple events within a country  (Merz et al.
3 Preferably, such a damage model would also be probabilistic to quantify uncertainty and be implementable entirely using openly available datasets. This paper aims at advancing the current methodologies of vulnerability estimation in flood risk assessments by tackling the above-mentioned goals. The approach presented here involves a building-level probabilistic damage model (Sect. 2. 2) created through incorporation of flood loss data from river and pluvial floods in Germany, the Netherlands and Italy from a period of over 20 years (Sect. 2. 1). It is validated (Sect. 2. 3) not only for the 11 events in the sample (Sect. 3. 1), but also for a dedicated case study of a coastal flood in France (Sect. 3. 2) and further confirmed with an additional case study of a fluvial flood in Germany (Sect.
Data collection and processing The flood damage model is based on data collected from 11 flood events that have occurred in Germany, the Netherlands and Italy between 1993 and 2014. For each flood, a postdisaster household survey was carried out, supplemented by hazard and exposure information from various other sources. Since these floods and related survey datasets have been described before, we will refer to the appropriate publications for details, while providing only the most relevant information herein. A summary of the events is provided in Table  1  together with the information on the extent of impacts and post-disaster surveying efforts. The location of all collected data points (individual surveyed households) is presented in Fig. 1.
Flood events and post-disaster surveys German floods represent the largest share of events in the dataset. Six fluvial events include floods caused by summer heavy rainfall in 2002  (Engel 2004; Ulbrich et al. 2003)   (Schröter et al. 2015) ; and by snowmelt in 2011  (Axer et al. 2012). The remaining pluvial flood events affected many locations in Germany, but the post-disaster surveys were carried out only in particular cities. The impact of the 2005 flood  (Rözer et al. 2016)  was surveyed in the towns of Hersbruck (Bavaria) and Lohmar (North Rhine-Westphalia), 2010 flood  (Rözer et al. 2016)  in Osnabrück (Lower Saxony) and 2014 flood in Münster and Greven, both in North Rhine-Westphalia  (Spekkers et al. 2017). Randomly selected households affected by all nine German floods were interviewed by a professional surveying company.
This led to high river discharge in bordering Dutch province of Limburg and extensive flooding along a long stretch of the river Meuse  (Wind et al. 1999 ). After the event, the national government compensated the flood damages, and therefore, experts were sent to collect information on every affected household. The resulting dataset was amended by  Wagenaar et al. (2017 Wagenaar et al. ( , 2018) )  with cadastral data and a hydrodynamic simulation. This modified dataset is used in this study. The final event included in the study occurred in Italy in January 2014 and was caused by a structural dike failure along the Secchia river after a period of heavy rainfall  (Orlandini et al. 2015). After the disaster, local authorities conducted surveys for the purpose of flood loss compensation. This dataset was then amended by  Carisi et al.
Merging and processing data from flood events The datasets from the described events were merged and then amended to increase consistency between the various sources. Also, data for variables not recorded in certain surveys were added from external sources, along with new variables. Variables considered in the study for inclusion in the flood damage model (Sect. 2. 2. 2) are listed in Table  2. It is worth noting that our study focuses on those variables that are available and consistent across all 3 case studies. Furthermore, only continuous variables (as opposed to discrete ones) are considered here as the statistical method used in the study requires specifically continuous variables. In practice, only continuous variables are available for all surveys except for building type. Examples of omitted variables include building age and presence of basement (not available for Italy); number of floors, use of precautionary and emergency measures, household characteristics or contamination of floodwater (only obtainable for Germany, though not for all areas); or various topographical indices such as distance from flood source, which is not applicable to pluvial floods.
The return period was computed firstly by obtaining hourly precipitation data in 1 km resolution from the RADOLAN dataset. This dataset is generated by the German weather service by combining precipitation radar and rain gauges  (Deutscher Wetterdienst, 2018). A total of 13. 5 years of data  (mid-2005-end-2018)  was gathered. At each of the 4 affected areas-Hersbruck 2005, Lohmar 2005, Osnabrück 2010, Münster 2014-the RADOLAN grid cell with the highest total precipitation during each event was selected as a basis of calculating intensity-duration-frequency (IDF).
The values for the Italian dataset were retrieved from compensation claims submitted to the government. The actual amount of compensation paid was also available for the 2014 Secchia flood, and however, it was usually much lower than the claims largely due to limited amount of money made available by the government  (Carisi et al. 2018 ). We therefore relied on the value of claims despite possible overestimation of losses. The relative losses were calculated by dividing the absolute losses by the estimated value of the buildings and contents, description of which follows below. It should be noted that both the damage data and exposure estimates discussed below explicitly exclude private vehicles. Exposure variables are related to the size and value of residential buildings and their contents.
This refers to, where possible, the entire affected building and not only to the household surveyed. Usable floor space area of dwellings was recorded in the surveys, except for the 1993 flood, which was added from the Dutch cadastre  (Wagenaar et al. 2017). The gross (replacement) value of building and household contents is a product of floor space area of the whole building and mean value of the building or contents per m 2. The datasets differ in methods used to derive the mean value per m 2. For Germany, we use the estimates included in the source database of the surveys-HOWAS21  (Kellermann et al. 2020) , that were computed according to a methodology described by  Thieken et al. 2005. The methodology regarding building value per m 2 is based on insurance industry guidelines (Dietz 1999) and distinguishes various characteristics of the buildings (number of storeys, basement size, roof type) recorded in the original survey data.
The valuation of household contents is based on their mean insurance values per household and differentiated spatially using data on postal code-level purchasing power  (Thieken et al. 2005). For the Netherlands, building value was computed with a uniform value per m 2 due to lack of more detailed valuation data accessible for this country. They are used here as provided by  Wagenaar et al. (2017). However, in the original Dutch dataset the value of contents was assumed the same in each household irrespective of their size and hence had to be replaced with a better estimate. Consequently, the value of contents was calculated by multiplying the floor space area by standardized contents value per m 2 based on the methodology described in  Paprotny et al. (2020a). The original study covered only years 2000-2017, and hence, 1993 values were calculated using data listed in Supplementary Table  S1.
Two economic variables at the regional level defined by the Nomenclature of Territorial Units for Statistics (NUTS) were collected to better express local exposure and vulnerability. Gross domestic product (GDP) and net disposable income of households per capita were obtained at NUTS level 3 or 2, depending on availability per variable. The data were collected for the year of each event, which was accessible for all floods in Germany and Italy from Eurostat (2020). In case of the Netherlands, the relevant regional data were only available until 1995 from Statistics Netherlands (2019), and therefore, the 1995 values were extrapolated back to 1993 using national growth rate of GDP and household income per capita. Both variables in all case studies were transformed to real 2015 prices using, respectively, the GDP deflator and the deflator for final consumption expenditure of households, which is a major subcomponent of the household disposable income account.
Households were geolocated in case of the German and Italian surveys on the basis of their street addresses, while in the original Dutch dataset, the households were identified only by six-digit postcode. Wagenaar et al. (2017)  located them and extracted building characteristics from the cadastre on the basis of comparing modelled and surveyed water depths, taking the building among all in a postcode area that had the smallest difference in water depth between the datasets. This procedure could cause errors in calculating exposure and, consequently, relative losses. However,  Wagenaar et al. (2017)  consider potential errors to be limited as the very detailed Dutch postcodes typically refer only to a few buildings, usually of similar characteristics. Also, while the usable floor space area and other variables should refer to the whole building, it was found that at the German and Dutch datasets do not always consistently record damages and exposure for buildings with multiple households.
This method was originally introduced by  Kurowicka and Cooke (2006). Compared to other possible methods, nonparametric BNs have several advantages, as they: • are probabilistic rather than deterministic, thus providing uncertainty bounds of the predictions (in contrast to multivariate regressions); • utilize continuous variables without assuming any marginal distribution, the need for discretizing or normalizing the data, which can significantly alter the results; • can be quantified with data that have partially missing values (not possible in discrete BNs or, e. g. random forests), and also with relatively small datasets; • can be applied, after quantification, in situations where whole variables for conditionalizing the BN are not available; • are graphical and, in contrast to machine learning methods, could be easily presented in their entirety; • the model's quality depends solely on the data and its structure, as there are no tuning parameters, which are numerous in machine learning methods.
The final Bayesian network correlation matrix is shown in Supplementary Figure  S4. Given many gaps in the German data and the size of the Dutch dataset, the analysis was mainly done with a reduced sample, in which the 2 datasets have similar number of records. For that purpose, all available German and Italian data were combined with a random sample of 40% of the Dutch data. This achieved a more balanced and representative sample with 7692 records (5091 with complete information for all variables). It should be noted that as the actual number of records used at each step is the intersection of availability of given two variables, it varies depending on the pair of variables considered. Influence of the sample choice is discussed in the results (Sect. 3. 1). The procedure resulted in a model that adopts five explanatory variables (Fig.
Validation case study: 2010 coastal flood in France The post-disaster surveys collected for this study did not include any instance of coastal inundation, which has different hazard characteristics. Therefore, we collected additional data to recreate residential losses during the 2010 coastal flood in France, which was triggered by the extra-tropical storm Xynthia. Strong winds caused widespread damage in France and other countries. Sixty-five deaths were recorded, including 47 in France, of which 41 died in the coastal flood in Vendée and Charente-Maritime departments  (Kolen et al. 2013; Vinet et al. 2012). The inundation on the 28 February 2010 resulted from a storm surge (up to 1. 6 m) in phase with a high spring tide and waves. 195 km of flood defences was breached or damaged and inundation depths reached up to 2.
In total, 4831 residential buildings were identified as flooded and included in the analysis. In Saxony in 2013, the average number of dwellings per residential building was 2. 79 (Statistisches Bundesamt 2020), and hence, the buildings represent approximately 13,478 households, close to the number actually affected. The estimated exposure in Germany in 2013 per m 2 was 2002 euro for structure and 386 for contents  (Paprotny et al. 2020a, b, c ). An alternative exposure computation, which we use for comparison of the results, was again taken from JRC  (Huizinga et al. 2017). It indicates replacement cost per m 2 being 2296 euro for structures and 1148 for contents. Finally, data for the damage model were collected. Information on velocity was not available as for France. The return period was spatially variable and amounted from 12 to 457 years.
RF-FLEMOps utilizes ensembles of regression trees, a method known as random forests  (Merz et al. 2013). Due to the inflexibility of the method to missing data, we had to adapt the RF-FLEMOps model to operate with four variables only (water depth, return period, floor space and building/ contents value) instead of the original 13. This was done by using the original dataset and data-mining algorithm, but rerun with only four variables. BN-FLEMOps is a discrete Bayesian network-based model with seven variables predicting either building or contents loss  (Wagenaar et al. 2018 ). As noted earlier, a BN-based model can work also with missing data, and hence, BN-FLEMOps was applied without modifications to all study areas. Alternative exposure estimates were obtained from  Huizinga et al. (2017).
Using the symmetric mean absolute percentage error (SMAPE) reveals that the worst performance of the model was recorded for the German pluvial floods of 2005 and 2010 as well as the fluvial flood of 2011. However, the size of the error is further determined by the number of data points for each individual flood. German river floods of 2002, 2010 and 2013 are much more heavily represented in the  dataset, and hence, they have better R 2 and SMAPE values, and lower bias, compared to other German events. In effect, the total loss for the surveyed households is more accurately represented, though the 2005 fluvial flood had the lowest error in modelling total contents loss (Table  3  and Fig. 6 ). Also, the German pluvial flood of 2014 has more data points available and as a consequence is more accurately modelled than the 2005 and 2010 events.
Uncertainties and limitations The Bayesian network-based flood damage model includes uncertainty related to the methodology and input data. Methodologically, using continuous variables only may exclude important discrete variables used in other flood models, such as building type and quality, water contamination, use of precautionary or emergency measures and social characteristics of the household occupants. On the other hand, the availability of such data, both across the datasets and in case studies such as the 2010 coastal flood, is limited. The only  discrete variable obtainable for all datasets and the case study is building type, which is related to the usable floor space area used in the model. The use of nonparametric marginal distributions has the benefit of avoiding making assumptions about the distribution shape or discretizing the data.
However, the limitation of the method is the assumption of a Gaussian copula as the dependency model, which does not account for any possible tail dependencies. Improvements to nonparametric BNs would be needed to include more flexible dependency structures. The heterogeneous input datasets are a large source of uncertainty. The German survey datasets rely almost entirely on the respondents' recollections of the flood event, which could be inaccurate in describing the hazard component (especially flow velocity). In all cases, the data could be uncertain due to the time elapsed between the flood and surveytaking activities (1-2 years). The Dutch and Italian surveys were taken shortly after the flood, but also rely on the output of hydraulic models to extract flood hazard data. In the Dutch dataset, the limited accuracy of geolocation information further compounds the possibility of errors related to the flood hazard component.