dataesr/scientific-paragraphs-categorization huggingface dataset:
Repeat attempts (Supplemental Table  3 ) were annotated with whether the Cas9 type (mRNA vs. protein), amount of Cas9, delivery of reagents (injection versus electroporation), or gRNA locations changed between sequential attempts. All filtering and annotation of the data was performed in Python3. 8. 5 using packages numPy1. 2. 1 58 and pandas1. 2. 2 59. Genes for each attempt were annotated (Supplemental Table  4 ) with their viability (as annotated at the IMPC -viable or homozygous lethal), human orthologs and cell essentiality of human orthologous genes 20 , embryonic expression (GEO GSE11224) 22 , length, GC content, number of CpG sites, and CpG percentage (Supplemental Methods Table  2 ). The human orthologs' probability of being loss-of-function intolerant (pLI) and observed / expected (oe) mutation rate was retrieved from gnomAD  23.
All statistical analyses were performed using the R statistical programing software  64  , along with the packages ggplot2 65 for figures, tidyverse 66 for data manipulations and effects  67, 68  for effect plots. The general linear models of biological variables were fit using the glm function in the R 3. 6. 2 native stats package (https://rdocumentation. org/packages/stats/versions/3. 6. 2) using the factors in Supplemental Methods Table  4  and with founder rate as the dependent variable. All code can be found at https://github. com/The-Centre-for-Phenogenomics/IMPC-Cas9-Production. CC-BY-NC 4. 0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. association of each variable with the success of the attempt to generate founders.
We thank all technical personnel at the IMPC production centres for their contributions. H. E. , E. F. A. , M. G. , L. G. L. , and L. M. J. N. were supported by  Ontario Genomics  and  Genome Canada   OGI-051 ,  OGI-090 ,  OGI-137  and the  Canada Foundation for Innovation. S. A. , M. T. , and A. Y. were supported by the  National BioResource Project of Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan. M-C. B. and Y. H. were supported by the  Université de Strasbourg , the  CNRS , the  INSERM  and the ' Investissements d'avenir' programs  ( ANR-10-IDEX-0002-02 ,  ANR-10-LABX-0030-INRT  and  ANR-10-INBS-07 PHENOMIN ). A. C. , G. F. C. , M. M. , L. T. , and S. W. were supported by the  Medical Research Council   IMPC Strategic Award  ( 53658 ). F. C. , A. G. , and F.
Results Study characteristics. From the 28 invited countries, 18 countries agreed to participate and shipped around 250 samples for both targeted age cohorts of 40-49 and 50-59 years to the RIVM. The characteristics of the sample collections like collection period (2015-2018) and locations are summarised in Table  1. The sex distribution of the samples was obtained from 16 countries. To avoid a selection bias, all samples (N = 1644) from the two age groups collected in a national serosurveillance study in the Netherlands were included. UK data comprised samples from England only. Altogether, the number of subjects included was 10,302. Antibody prevalence for pertussis. The total percentages of sera per country with a level for IgG-PT ≥ 100 IU/mL varied between 0. 0% (Finland) and 9. 7% (Norway) with 13/18 countries showing a level between 2.
The LLOQ for Dt and TT was 0. 001 IU/mL. Statistical analysis. The following information was collected: country, laboratory and location of sample collection, date of collection, sample size, age group, sex and type of collection. The final database included approximately 31,000 IgG results. Antibody levels below the LLOQ were replaced by LLOQ/2. The GMCs with 95% CI were estimated by linear regression modelling of the log-antibody levels, where age group, sex and country, up to their three-way interaction, were used as explanatory variables. Similarly, the seroprevalences with 95% CI were estimated by binomial logistic regression modelling. The IgG-PT cut-off of 100 IU/mL is indicated to have 78% sensitivity and 98% specificity  14. The corresponding seroprevalence estimates were adjusted accordingly using this formula: p true = (p est + Sp -1)/(Se + Sp -1).
Acknowledgements We would like to thank from all participating countries the laboratories and personnel who contributed to the collection of the serum samples for this study, in particular  Karin Baier  (Austria),  Amber Litzroth  (Belgium),  Sophie Guillot ,  Sabine Trombert-Paolantani ,  Véronique Jacomo  (France),  Paraskevi C. Fragkou ,  Theano Georgakopoulou  (Greece),  Erzsébet Rusvai  (Hungary),  Suzanne Cotter  (Ireland),  Jurijs Perevoscikovs  (Latvia),  Gaby Smits ,  Mark Jonker  (the Netherlands),  Cristina Oprea  (Romania),  Teodora Aktas ,  Teghesti Tecleab ,  Tiia Lepp  (Sweden) and  Ezra Linley  ( United Kingdom ). The funder of the study had a role in the study design, data interpretation and writing of the manuscript, but no role in data collection or data analysis. The corresponding authors had full access to all data from the study, and final responsibility for the decision to submit for publication was by consensus of all co-authors.
The study was undertaken as part of the framework contract " Coordination of activities for laboratory surveillance of whooping cough in Member States/EEA countries" (EUPert-LabNet)  [ ECDC/2015/009 ] coordinated by the  National Institute for Public Health  and the Environment (RIVM, the  Netherlands)  and the  University of Turku (Finland)  and funded by  ECDC. Part of this research has been conducted using the  Danish National Biobank resource , supported by the  Novo Nordisk Foundation , grant number  2010-11-12  and  2009-07-28. The Dutch sample collection was commissioned and funded by the  Ministry of Health, Welfare and Sport (VWS)  of the Netherlands.
Abstract. Stream temperature appears to be increasing globally, but its rate remains poorly constrained due to a paucity of long-term data and difficulty in parsing effects of hydroclimate and landscape variability. Here, we address these issues using the physically based thermal model T-NET (Temperature-NETwork) coupled with the EROS semi-distributed hydrological model to reconstruct past daily stream temperature and streamflow at the scale of the entire Loire River basin in France (10 5 km 2 with 52 278 reaches). Stream temperature increased for almost all reaches in all seasons (mean = +0. 38 • C decade-1) over the 1963-2019 period. Increases were greatest in spring and summer, with a median increase of + 0. 38 • C (range = +0. 11 to +0. 76 • C) and +0. 44 • C (+0. 08 to +1. 02 • C) per decade, respectively.
Method and data We assessed how T w responded to climate change over the past 57 years in the Loire River basin in four steps. First, we applied physically based Q and T w models for the 1963-2019 period. Second, we assessed both hydrological and thermal model performance by comparing simulated seasonal and annual Q and T w to data from observation stations. Third, we assessed Q and T w long-term trends. Fourth, we analysed how hydroclimatic changes and landscape features could explain reconstructed trends in T w. We performed all analyses on both seasonal and annual bases, where winter is December-February (DJF), spring is March-May (MAM), summer is June-August (JJA), and autumn is September-November (SON). Analyses were also performed by HER (see Fig. 1  and Sect. 2) to look at the large-scale influence of landscape characteristics.
Modelling daily Q and T w We used two models to calculate T w in the Loire River basin according to the method developed by  Beaufort et al. (2016). The first model is the EROS semi-distributed hydrologic model, which estimates daily Q at sub-basin outlets. The second model is the fully distributed, mechanistic temperature model called Temperature-NETwork (T-NET;  Beaufort et al. , 2016; Loicq et al. , 2018)  that uses Q from EROS and meteorological reanalysis data from Safran atmospheric reanalysis data  (Quintana-Segui et al. , 2008; Vidal et al. , 2010)  to estimate T w at each reach in the Loire River basin. These models are briefly described below and are detailed in  Thiéry (1988) ,  Thiéry and Moutzopoulos (1995), and Thiéry (2018)  for EROS and  Beaufort et al. (2016)  and  Loicq et al.
EROS hydrological model principles and input data The EROS semi-distributed hydrological model simulates daily Q at the outlet of 368 sub-basins (ranges between 40 and 1600 km 2 ; mean drainage area = 300 km 2 ), designed to be as homogeneous as possible with respect to land use and geology. On each of these sub-basins, the water balance is modelled by a lumped model using three reservoirs (see Fig. S2  in the Supplement) and a routing function for propagation across sub-basins  (Thiéry, 1988; Thiéry and Moutzopoulos, 1995; Thiéry, 2018). Water abstractions are not considered in EROS, and it produces natural Q. This hydrological model has already been used in several other studies, including climate change impact studies  (Ducharne et al. , 2011; Habets et al. , 2013; Bustillo et al. , 2014).
T-NET thermal model principles and input data The T-NET thermal model is a fully mechanistic, 1D model that simulates hourly T wi,j at distance, i, along reach, j , by solving the local heat budget. The heat budget of each reach includes six fluxes (W m -2 ): net solar radiation, atmospheric longwave radiation, longwave radiation emitted from the surface water, evaporative heat flux, convective heat flux, and groundwater flux. Briefly, the model calculates the longitudinal change in T w at time t (dT w / dx) for steady-state conditions in order to achieve thermal equilibrium (i. e. H i,j = 0, where H is a heat flux) while accounting for confluence mixing. Detailed information about the T-NET model principles and calculation of the six heat fluxes at the water-air and water-stream bed interfaces and thermal propagation was provided in  Beaufort et al.
(2016)  and  Loicq et al. (2018). The hydrographic network of the model over the Loire basin consists of 52 278 reaches delimited either by confluences of two streams or a headwater source (i. e. firstorder reaches)  (Beaufort et al. , 2016; Loicq et al. , 2018). The mean reach length is 1. 7 km, and 74 % of the reaches have a Strahler order lower than 3 (see Fig. S3 ). To compute the six heat fluxes and the water travel time for each reach, the following input data were used. S5 ), but only the ones with long-term data (in red) were used to assess the T-NET thermal model accuracy for long-term trends. Complete information on T w stations with long-term continuous data is provided in Table  1. The numbers in red in the middle panel correspond to the ID of long-term T w stations in Table  1.
Right panel: altitude (IGN, 2011) and hydrometric stations (extracted from the French national Banque Hydro database: http://www. hydro. eaufrance. fr/, last access: 12 July 2021). All hydrometric stations were used for calibrating the EROS hydrological model (see Table  S2 ), but only stations on the French Reference Hydrometric Network (RHN) with long-term data (in red) were used to assess the EROS hydrological model performance on long-term trends (see Table  S4 ). -Meteorological variables: hourly T a ( • C), specific humidity (g kg -1 ), wind velocity (m s -1 ), shortwave radiation (W m -2 ), and longwave radiation (W m -2 ) were provided by the 8 km gridded Safran atmospheric reanalysis  (Vidal et al. , 2010). All reaches within a grid cell were assigned the values of the meteorological variables in that grid cell.
For reaches flowing through more than one grid cell, meteorological variables were weighted by the relative length of the reach within each grid cell. -Riparian vegetation shading: riparian vegetation is one of the major regulators of shortwave radiation. In the current study, patches of wooded area provided by the BD TOPO ® (IGN, 2008) database were used as a proxy of vegetation. The vegetation species and length of each wooded patch in a buffer of 10 m were extracted for both the right and left river banks  (van Looy and Tormos, 2013). The vegetation density (vc) was then calculated as the ratio of patch length to reach length for both the right and left river banks. In the case of multiple wooded patches on any side of a river bank, the average vegetation density of the patches was considered.
Calibration and assessment of the EROS hydrological model For calibration, 352 hydrometric stations with observed Q data (see Table  S2 ) extracted from the French national Banque Hydro database (http://www. hydro. eaufrance. fr/, last access: 12 July 2021) were used (Fig. 1 , right). Stations along the main Loire (14 stations) and Allier (11 stations) rivers were influenced by operations of four large dams for hydropower, flood control, and low-flow management, notably through summer releases (see Table  S3 ). Time series at these stations were first naturalized by EDF (Électricité de France) based on variations in water storage in reservoirs due to operations based on target storage curves (Naussac, Villerest) and optimization of the hydropower energy generation for the electric grid needs (Grangent, Montpezat).
(2013)  with long-term continuous high-quality data over the 1968-2019 period, especially for low flows (see Table  S4 ). These stations are shown with red points in Fig. 1 , right. Seasonal and annual relative biases, together with Nash-Sutcliffe efficiency on Q, ln(Q), and √ Q were computed on both 352 calibration (over the 1975-2018 period) and 44 RHN stations (over the 1968-2019 period) to provide an overview of the performance of the EROS model. Moreover, to assess EROS model ability to capture long-term trends, decadal trends (in percent per decade) over the 1963-2019 period on seasonal and annual averages from the EROS simulation were compared with corresponding observed trends at each of the 44 RHN stations with long-term continuous daily data. H. Seyedhashemi et al. :  Stream temperature increases faster than air temperature.
T w data at these 67 stations were provided by different organizations: EDF, OFB (Office français de la biodiversité), DREAL (Direction régionale de l'environnement, de l'aménagement et du logement), and FD (Fédération Nationale de la Pêche) (see Tables  1  and S5 ). T w data provided by OFB can be downloaded from http://www. naiades. eaufrance. fr/ (last access: 1 August 2020). The rest of the T w data are not archived for public use. Seasonal and annual absolute biases and root mean square error (RMSE) were assessed at these 67 stations. Note that, unlike the EROS hydrological model, the T-NET thermal model does not have any free parameter, and hence it did not require calibration. Long-term continuous data were available at 14 of the 67 near-natural stations, including 9 stations with 8-13 years of data and 5 stations with 20-40 years of data.
Acknowledgements. The SAFRAN database was provided by the  French national meteorological service (Météo-France). We are grateful to  Electricité de France (French Electricity, EDF)  for providing long-term data on observed stream temperature and naturalized streamflows. We also thank  André Chandesris  for his assistance in the incorporation of vegetation cover into the T-NET thermal model. We also thank  Rohini Kumar , the editor of Hydrology and Earth  System Sciences , and  Adrien Michel  and two anonymous referees for reviewing this article. Financial support. This work was realized in the course of a doctoral project at the  University of Tours , funded by the  European Regional Development Fund (Fonds Européen de développement Régional-FEDER) ,  POI FEDER Loire  (grant no. 2017-EX001784 ), Le plan Loire grandeur nature,  AELB  ( Agence de l'eau Loire-Bretagne) ,  INRAE  (l' Institut national de recherche pour l'agriculture, l'alimentation et l'environnement) , and  EDF  (Hynes team).
In many continental databases representing rivers across Africa, information on rivers characteristics (length, width, intermittent or perennial) is often incomplete, and small streams are largely underrepresented. Fortunately, the use of topographic information from digital elevation models (DEMs) provides an opportunity to have more detailed information on rivers, particularly small streams in broad areas. A common approach to extract streams from DEM is to consider as a stream all DEM cells that drain at least a certain upstream surface, commonly known as the minimum contributing area (A min ). This parameter (A min ) is generally defined uniformly over large areas and this independently of the very variable climate and landscape conditions so that the generated streams have a rather uniform spatial distribution.
Introduction The accurate representation of river network recently, serves as a decision aid for land managers for efficient monitoring and exploitation of the water resource  (Heine et al. , 2004; Ouellet Dallaire et al. , 2019). Models and many studies of hydrologic processes are sensitive to the accurate representation and density of streams  (Yan et al. , 2020; Brakebill et al. , 2011). However, in many continental databases representing rivers across Africa, information on river's characteristics is often incomplete, and small streams are largely underrepresented  (Lehner et al. , 2008). National hydrographic databases contain reliable hydrographic information in this sense, either localized or nationwide. However, accessibility to these national hydrographic databases remains difficult in Africa because of the cost required and the reluctance of some countries to share this information.
Generally, for stream extraction A min is defined rather arbitrarily and uniformly over large areas due to lack of information, resulting in the generated streams having a rather uniform spatial distribution and often not corresponding to those observed in the field  (Passalacqua et al. , 2010; Papageorgaki and Nalbantis, 2017). This is the example of the Hydro1k database  (US Geological Survey, 2000)  and AQUAMAPS  (FAO, 2014) , which proposes a mapping of rivers extracted from a 30 (approx. 1 km × 1 km) and 15 (approx. 500 m × 500 m) DEM for a constant A min value of 1000 and 100 km 2 , respectively. In a natural environment, however, drainage density varies spatially as a function of multiple environmental variables (climate, lithology, slope, vegetation cover) and to capture this variability, there is a need to stress the use of spatially variable A min  (Tucker et al.
, 2001; Da Ros and Borga, 1997). Recently,  Schneider et al. (2017)  proposed an approach to generate a series of variable A min values as a function of environmental variables. These A min values are calibrated on different landscape types (constituted of the spatial intersection of Lithology, Climate, and Slope) with a 500 m DEM from HydroSHEDS database to match observed hydrographic information from national hydrographic databases of France and Australia. The calibrated A min values were extrapolated at the global scale, given the local type of landscape, such as to extract a global hydrographic network, called LCS. Despite satisfactory validation in Brazil and USA against the national hydrographic databases, this LCS network shows difficulties in reproducing very high observed drainage densities  (Schneider et al.
, 2017). The LCS network, for example, partially captures the spatial variability of the drainage density of the national hydrographic network of Burkina Faso, which is probably due to the coarse scale of the lithology layer used. Based on the methodology used by  Schneider et al. (2017) , our objective is to use the accuracy of local information collected from national geographic institutes and widely available climatic, landscape, DEM information to establish relationships between the minimum contributing area (A min ) and environmental variables (lithology, climate, geology, vegetation cover) to constitute a spatial model. A first step in setting up the spatial model is to define a relationship between the observed drainage densities (Dd obs ) from a national hydrographic database and calibrated minimum contributing areas (A min ) in Burkina Faso.
The same process is applied in Benin and France to evaluate the effect of the scale of a national hydrographic database in the relationship between A min and Dd obs. The second step will be to define relationships between the observed drainage densities (Dd obs ) and environmental variables (lithology, climate, geology, vegetation cover), which should ultimately allow us to establish relationships between A min and the environmental variables. This study covers the first step of the model calibration in Burkina Faso, and the other steps are ongoing work.
Description of data The DEM used in this study was extracted from the Hy-droSHEDS database  (Lehner et al. , 2008) , which is the most widely used database currently for hydrological applications at a resolution of 3 (about 90 m × 90 m), i. e. the smallest resolution proposed by the database. The reference hydrographic database (national hydrographic database) taken into account in Burkina-Faso is the BNDT available at the scale of 1 : 200 000 and distributed by the IGB (Institut Géographique du Burkina). The reference hydrographic database considered in France and Benin are CARTHAGE and BDI, respectively, available at the same scale of 1 : 50 000, and distributed by the IGN (Institut Géographique National) France and IGN Bénin.
Methods All maps and spatial processing are made using ArcGIS software (10. 6. 1) and the graphics using R software ver- sion 3. 6. 1. The ArcGIS tool has been used to extract 400 river networks in Burkina Faso using the DEM from which the direction and accumulation grids are also progressively extracted. To span a large range of drainage densities, the number of pixels defined to extract these networks (N p ) ranges between 1 to 40 000, corresponding to A min between 0. 0081-324 km 2. The territory of Burkina Faso has been divided into 5 grid cells (about 64 km 2 at the equator) for this analysis. For each of the 400 extracted networks, the drainage density is calculated as the total river length (km) inside a 5 grid cell, divided by the surface area of the grid cell (km 2 ). The same procedure is used to calculate the observed drainage density (Dd obs ) using the river network information contained in the reference hydrographic database (BNDT) of Burkina Faso (Fig.
2a ). For each grid cell, the calibrated A min value is the one defining the extracted network (among the 400 possibilities) that best approximates the observed drainage density (Dd obs ), based on the BNDT river layer, in this grid cell. Thus, the selected A min is the one minimizing the difference in drainage density between the extracted networks and the observed BNDT network in the grid cell. We randomly selected several grid cells that were excluded from the calibration process (Fig. 2b ) and that represents 14 % of the territory. It is on these grid cells that validation of the relation-ship established between calibrated A min and Dd obs was carried out. The same calibration process was applied in Benin and France to evaluate the effect of the scale of hydrographic database in the relationship between A min and Dd obs.
Results and discussions The relationship between A min and Dd obs , based on the first step spatial model in Burkina, the calibrated values of A min (Fig. 2c ) obtained vary between 0. 084-318 km 2. The minimum value obtained (0. 084 km 2 ) is much lower than those used in the continental databases (Hydro1k and Aquamaps at constant A min values of 1000 and 100 km 2 , respectively, LCS with variable values falls between 0. 3-193 km 2 ). This may reflect consideration of higher drainage densities in Burkina compared to the values obtained from the databases mentioned above, given that the lower A min becomes, the higher Dd obs will. The results of this first stage of the model implementation allowed us to establish a relationship (Fig. 2c ) with very satisfactory performances (coefficient of determination r 2 = 0.