dataesr/scientific-paragraphs-categorization huggingface dataset:
Building the European Atlas of artificial instream barriers We collected and cross-referenced barrier records from 120 databases from 36 countries, including 65 local and regional databases, 52 national databases and four global ones  52. After quality checking, we harmonised records into a single relational database (the AMBER Barrier Atlas) and removed duplicates (see below). We classified over 1,000 different barrier types into six main functional groups that capture variation in barrier size and use  23, 53  : dam, weir, sluice, ramp/bed sill, ford, and culvert, plus 'other' (e. g. , groynes, spillways) and 'unknown' (Table  1 ). We included country, river name, geographical coordinates, and barrier height if known, as well as database source. These attributes were available in most databases and provided the information required to allow us to estimate barrier densities and compare them to ground-truthed values.
To map barriers consistently across Europe we used 86,381 functional subcatchments with an average area of 58. 2 km 2 (SE = 0. 24) derived from the European Catchment and Rivers Network System database (ECRINS  35  ). This database and the associated river network are derived from a 100 m resolution digital elevation model (DEM) and covers 1. 65 million km of river length across the study area. Although ECRINS may underestimate river length by up to 74% compared to more detailed river networks  36  , it is the only consistent river network that can currently be used for global comparisons across Europe. The consequences of underestimating river length for estimates of river fragmentation are difficult to predict. Underestimating river length can overestimate river fragmentation if the observed number of barriers is in reality distributed over a longer river network, but it can also underestimate it if undetected barriers are more likely to occur in poorly mapped first order streams.
Excluding duplicated barrier records We chose a maximum Euclidean distance of 1,000 m between neighbouring barriers within the same ECRINS sub-catchment to investigate potential duplicates; we had previously determined for a smaller database that few or no duplicates may be expected beyond 500 m  23. To derive exclusion distances, three people working independently assessed up to 200 potential random duplicates per country, or all potential duplicates if the number was less than 200. Each person visually assessed 25% of duplicate records using Google and Bing satellite imagery, and all assessed a common subsample comprising 25% of the records. The distance between each potential duplicate was measured in QGIS 3. 10 54. We used bootstrapping  55  to calculate a mean and 95% CI distance that excluded 80% of potential duplicates and showed 80% or better agreement between the three people working on the common subsamples using an optimised algorithm  53  (Extended Data Table  3 ).
For each barrier we encountered we recorded its coordinates, type, height class, status (abandoned or in use), and span width (full or partial river width). The influence of survey length on barrier discovery rate was determined via bootstrapping  23, 53  using R version 4. 0. 0  57. This showed an asymptotic relationship in most cases indicating that a sufficient river length had been sampled to derive robust correction factors for barrier density in each country, as well as a single correction factor across all countries (Extended Data Table  1 ). These results were used to inform the choice of calibration datasets for modelling barrier numbers using random forest regression (see below). Field-derived correction factors were applied in each country to adjust existing barrier records and derive more realistic barrier densities (Fig.
2b ; Table  1 ). To obtain corrected barrier densities for the 10 countries that had not been surveyed in the field we applied a mean correction factor of 0. 35 barriers/km, derived from the 26 surveyed countries. We employed the Likelihood Ratio Test (two-tailed) implemented in the DescTools R 4. 0 package 58 to assess the level of under-reporting, comparing the frequencies of barrier types and barrier height classes in existing databases and in walkover river surveys. Barrier reporting error (e) was calculated as 𝒆 = 𝑵𝒂 -𝑵𝒇 𝑵𝒇 * 𝟏𝟎𝟎 where Na is the number of barriers recorded in the barrier atlas and Nf the number of barriers detected in the field in the same test reaches.
Modelling barrier density through random forest regression We employed random forest regression to model barrier densities based on anthropic and environmental predictors that were expected to be associated with breaks in river connectivity. For example, culverts tend to be associated with roadcrossings  59  , small weirs with water mills in headwaters  32  , and storage dams with nearby cities, agriculture and hydropower  60. Similarly, the location of barriers is also determined by topography, geology and climate 7. For each ECRINS sub-catchment we extracted information on 11 variables (Extended Data   35, 63  , and number of road crossings in the river network divided by catchment area (No. /km 2 )  64. We used a data-driven, nonparametric Random Forest Regressor 65 developed using the scikit-learn library in Python.
g. existence of canals for navigation, or pipes and aqueducts for water diversion) operating at finer spatial scales than the predictors. Comparisons of model performance at different sub-catchment sizes (Extended Data For model training, we selected barrier records from six countries (Austria, France, Hungary, Poland, Sweden and Germany) that fulfilled five criteria: (1) together, they had relatively low levels of barrier under-reporting (mean correction factor = 0. 28); (2) were representative of different geographical areas; (3) showed wide variation in ground-truthed barrier densities; (4) there was a national barrier database (or detailed regional ones) built with a broad purpose (for example, the EU WFD) that covered all barrier types; and (5) at least five rivers where surveyed in the field.
Acknowledgements This study was funded by the  EC Horizon 2020 Research & Innovation Programme, AMBER -Adaptive Management of Barriers in European Rivers Project , grant agreement No. 689682  led by C. G. L. B. B was partially supported by  EUR H2O'Lyon (Sweden) ;  Federal Office for the Environment FOEN (Switzerland) ; Geocat. ch(Switzerland);  Environment Agency (UK) ;  Natural Resources Wales (UK) ;  Scottish Environment Protection Agency (UK). We thank  G. Luoni  for help developing the random forest regression model, and many students and colleagues who helped with field surveys across Europe.
Data availability Data for the AMBER Barrier Atlas (Fig. 1 ), observed barrier densities (Fig. 2a ), ground-truthed barrier densities (Fig. 2b ) and modelled barrier densities (Fig. 2c ) are freely available at https://amber. international/european-barrier-atlas/ as well as in figshare https://doi. org/10. 6084/m9. figshare. 12629051 under a CC-BY-4. 0 license. Data for ground-truthed surveyed reaches (Extended Table  1 , Extended Data Fig. 3 ) are also available at https://doi. org/10. 6084/m9. figshare. 12629051 under a CC-BY-4. 0 license.
To estimate barrier densities we used a four-step approach (Extended Data Fig. 1 ) consisting of (1) compiling a georeferenced atlas of barrier records from local, regional and national barrier databases (the AMBER Atlas), (2) cleaning and removing duplicate records, (3) ground-truthing barrier densities with field surveys, and (4) modelling fragmentation at the pan-European scale via random forest regression. This allowed us to identify nearly 630,000 unique barrier records (Fig. 1 , 2a), and to estimate the extent of longitudinal fragmentation in Europe from fieldcorrected (Fig. 2b ) and modelled barrier densities Fig. 2c ). As per above, we used the ECRINS sub-catchment as our spatial modelling unit. This allowed us to make use of all barrier records and avoid errors that would have resulted from snapping accurate barrier locations to the less precise, low resolution ECRINS river network.
Fish. Mgmt. Ecol. 17, 297-322 (2010). 10 Lehner, B. et al. High-resolution mapping of the world's reservoirs and dams for sustainable river-flow management. Front. Ecol. Env. 9, 494-502 (2011). 11 Lehner, B. et al. Global Reservoir and Dam Database, version 1 (GRanDv1): Palisades, NY: NASA Socioeconomic Data and Applications Center (2011). 12 Mulligan, M. , Soesbergen, A. v. & Sáenz, L. GOODD, a global dataset of more than 38,000 georeferenced dams. Sci. Dat. 7, 1-8 (2020). 13 Garcia de Leaniz, C. , Berkhuysen, A. & Belletti, B. Beware small dams as well as large. Nature 570, 164-164 (2019). 14 Mantel, S. K. , Rivers-Moore, N. & Ramulifho, P. Small dams need consideration in riverscape conservation assessments: Small dams and riverscape conservation. Aqua. Cons. Mar. Freshw. Ecosys. 27, M.
649,489 61,521 192,403 8,111 110,944 2,201 198,591 28,326 27,858 629,955 FIGURE LEGENDS Fig. 1. Artificial instream barriers in Europe (AMBER Barrier Atlas). The map shows the distribution of 629,955 unique barrier records compiled from 120 local, regional, and national databases after duplicate exclusion. Red dots represent the new barrier records assembled in this study, whereas black dots represent large dams (>15m in height) from existing global databases. The full georeferenced data can be downloaded from figshare https://doi. org/10. 6084/m9. figshare. 12629051. Country and sub-basin boundaries were sourced from the European Environment Agency 35. 0. 38 0. 74 1,213,87 4 Sum 1,194,62 9.
93), however, this correlation was not along the identity line but showed a much lower predicted age than actual age suggesting that skin tissue ages slower than the liver. This change in rate accounts for the larger RMSE. To make this approach useable for other researchers we have provided the primer sequences that span the CpGs, and an online tool that can be downloaded as an NMR age predictor based on these aDMPs (https://github. com/ ralowe/NMRAgePrediction; Supplementary Material).
Bisulfite PCR sequencing (Bis-PCR-Seq) DNA from tissues was diluted to a concentration of 11 ng/µl and 45 µl of each sample was used for generation of targeted bisulfite sequencing data by the Genome Centre Facility at the Blizard Institute, Queen Mary University of London. DNA was bisulfite converted in a 96 well plate format using the EZ-96 DNA Methylation™ Kit (Zymo, Cat. D5003). Target amplification was performed using the FastStart High Fidelity PCR System, dNTPack (Sigma-Aldrich, Cat. 4738284001) in the 48. 48 layout on the Fluidigm® C1 system (Fluidigm®, USA), a microfluidics platform. Library preparation was performed using the same kit including 4 μl of Access Array Barcode Library Primer and 1 μl of PCR product diluted 1:100. Libraries were sequenced with Illumina MiSeq sequencing using v2 chemistry (150 bp, paired-end).
Primers used for targeted bisulfite sequencing are listed in Supplementary Table  2. Raw FASTQ files were mapped to the reference hetGla2 using BISMARK (v0. 16. 3)  [35]  and Bowtie2 (v2. 2. 8)  [36]. Reads that mapped outside of the targeted regions were discarded from analyses, and methylated and unmethylated counts for each CpG were calculated using the custom C++ program (https://bitbucket. org/ lowelabqmul/methylation-extractor/src/master/). Those CpGs with a coverage < 50× were also discarded from analyses. Data created for this manuscript are available from GEO with accession number GSE86059 (sample set 1) and GSE137957 (sample set 2), and provided in Supplementary File 1.
Emission model Vehicle emissions can be obtained by emission models. Here, we use macroscopic emission rules that provide the emission rate (in [g/km]) of a reference vehicle depending on the mean speed v t ( ). These rules come from the COPERT IV framework  (Ntziachristos et al. , 2009)  and have been integrated with the fourth-degree polynomial for simplicity  (Lejri et al. , 2018). In this work, we obtained the Emission Factor (EF) model of (NO ) x and (CO ) 2 from COPERT IV based on the reference emission data recorded for the mean speed v ( ) profile of personal cars. Then, the evolution of the emission level E t ( ) dt  (in [g] ) of one pollutant between t and + t dt is calculated as: = × × × E t EF v t n t v t dt ( ) ( ( )) ( ) ( ) , dt (15) where EF v t ( ( )). On the bypass, n t ( ) is the bypass accumulation n t ( ) F , and v t ( ) is the bypass free-flow speed v F.
Implementation The proposed two-layered framework based on optimal routing and NMPC perimeter gating strategy with the MFD model were implemented and synthesized in a MATLAB. The NMPC objective function is to reduce network-wide emission via optimal splitting coefficients t ( ) i while minimizing the traffic flow at the six gates of the perimeter. In the implementation, time step of 1 s is considered to measure the traffic states + x ( ) t 1 of the system whereas the optimal control actions u t ( ( )) 0 and references t ( ) i r are updated at each 60 s. In this work, we used the MATLAB environment to synthesize simulations and LP and NLP are solved using the Interior Point Method (IPM) method. Prediction horizon (N), output penalty (Q), and input penalty (R) were set to 10, 0. 001×.
NEMO and WW3 are coupled using the OASIS Model Coupling Toolkit (OASIS3-MCT,  Van Roekel et al. , 2012; Craig et al. , 2017)  which is widely used in the climate and operational communities. The various steps for our implementation are the following (i) inclusion of all wave-induced terms in NEMO, only neglecting the terms relevant for the surf zone which is outside the scope here (ii) modification of the NEMO subgrid scales physics (including the bulk formulation) to include wave effects and a parameterization for Langmuir turbulence (iii) development of the OASIS interface within NEMO and WW3 for the exchange of data between both models (iv) test of the implementation based on a realistic global configuration at 1/4 o for the ocean and 1/2 o for the waves. To go into the details of those different steps, the paper is organized as follows.
The coupler : OASIS3-MCT The practical coupling between NEMO and WW3 has been implemented using the OASIS3-MCT  (Valcke, 2012; Craig et al. , 2017)  software primarily developed for use in multi-component climate models. This software provides the tools to couple various models at low implementation and performance overhead. In particular, thanks to MCT  (Jacob et al. , 2005) , it includes the parallelization of the coupling communications and runtime grid interpolations. For efficiency, interpolations are formulated in the form of a matrix-vector multiplication where the matrix containing the mapping weights is computed offline one for all. In practice, after compiling OASIS3-MCT, the resulting library is linked to the component models so that they have access to the specific interpolation and data exchange subroutines.
Then the wave model computes the momentum flux transferred to the ocean τ oce ww3 as well as the dimensionless Charnock parameter α ch which characterizes the sea surface roughness  (Charnock, 1955; Janssen, 2009). Using the latest available values of α ch , τ atm ww3 , τ oce ww3 , and u atm 10 , the oceanic model computes an atmospheric wind-stress τ atm using its own bulk formulation and the local value of the momentum flux going into the water column is τ oce = τ atm -(τ atm ww3 -τ oce ww3 ) (3. 6) where the τ ww3 quantities are interpolated from the wave grid to the oceanic grid. In NEMO, the windstress is computed using the IFS 1 bulk formulation such as implemented in the AeroBulk 2 package  (Brodeau et al. , 2016). In particular the roughness length which enters in the definition of the drag coefficient is function of the Charnock parameter α ch z 0 = α ch u 2 g + α m ν u where α m = 0.
In the northern hemisphere (Fig. 8 , a), there is only a slight improvement compared to data during winter and late summer when implementing the coupling with waves. In the southern hemisphere (Fig. 8, b ) the situation is rather different. From January to July, the deepening of MLD induced by the wave coupling significantly reduces the bias between the model and ARGO data. From July to December, results in the coupled case show an overestimation of MLDs which were already too deep in the uncoupled case, therefore increasing the bias between data and model. Since mesoscale activity make direct comparisons to data unreliable for such a short period of time, we compare the normalized distribution of MLD between the different simulations and available ARGO data. Results are presented in Fig. 9  for year 2014 (panel (a)) and during summer only (panel (b)).
Conclusions In this paper we have described the implementation of an online coupling between the oceanic model NEMO and the wave model WW3. The impact of such coupling on the model solutions has been assessed from the oceanic point of view for a global configuration. In particular, the following steps to set up the coupled model have been discussed in details (i) inclusion of all wave-induced terms in NEMO primitive equations, only neglecting the terms relevant for the surf zone which is outside the scope of the NEMO community, (ii) modification of the subgrid scales vertical physics (including the bulk formulation) to include wave effects and a parameterization of Langmuir turbulence, (iii) development of a coupling interface based on the OASIS3-MCT software for the exchange of data between both models, and (iv) tests of our developments on a realistic global configuration at 1/4 0 for the ocean coupled to a 1/2 0 resolution wave model.
Compared to an ocean-only simulation, the coupling with a wave model (with a resolution twice coarser than the oceanic model) leads to an additional computational cost of about 20%. Following  McWilliams et al. (2004)  and  Ardhuin et al. (2008) , in the weak vertical current shears The development of a coupling infrastructure based on OASIS3-MCT has several advantages as it allows for an efficient data exchange (including the treatment of non-conformities between the computational grids) but also for versatility in the inclusion of a wave model in existing ocean-atmosphere or ocean-only models. At a practical level, the OASIS interface we have implemented in NEMO is similar to other interfaces (e. g. toward atmospheric models) existing in the code which is important for maintenance and for further developments.
Second, using the mixed layer depth as an indicator to assess the amount of vertical mixing, the modifications to the NEMO turbulence scheme (i. e the new boundary condition for TKE and for the mixing length, the addition of the Stokes shear in the TKE equation, and the modified  Axell (2002)  parameterization for Langmuir cells) lead to an important extra mixing contributing to a deepening of the surface mixed layer particularly in the southern hemisphere. When compared to ARGO data it shows a significant improvement during the summer, while during the winter the extra mixing induced by waves deepens the already too deep mixed layer. Note that the  Fox-Kemper et al. (2008)  parameterization to account for the restratification induced by mixed layer instabilities  (Boccaletti et al. , 2007; Couvelard et al.
, 2015)  during the winter was not used in our experiments. This parameterization induces even more shallow summer mixed layer depths. As far as the northern hemisphere is concerned, coupled results show an improvement when compared to ARGO for winter with a deepening of the mixed layer while in summer results are similar to the uncoupled case. Since the comparison with ARGO data can be tricky due to the scarcity of the data, we looked at the results in terms of mixed layer depths (MLD) probability density functions. This allowed to highlight the significant improvement in MLD distribution when coupling with the waves. Furthermore, we noticed that all components of the ocean-wave coupling act to deepen the mixed layer and therefore have a cumulative effect. However the main contributor is the modified Langmuir cell parameterization of  Axell (2002)  which is consistent with recent results obtained by  Reichl et al.
Code and data availability The changes to NEMO code have been made on the standard NEMO code (nemo v3 6 STABLE). The code can be downloaded from the NEMO website (http://www. nemo-ocean. eu/, last access: 11 July 2019). The NEMO code modified to include wave-ocean coupling terms and the OASIS interface is available in the zenodo archive (https://doi. org/10. 5281/zenodo. 3331463, Couvelard (2019) ). The WW3 code version 6. 02 has been used without further modifications and can be downloaded from the NOAA github repository (https://github. com/NOAA-EMC/WW3, last access: 11 July 2019). Our modifications of the OASIS interface in the WW3 code have already been integrated in the official release. The OASIS3 MCT code is also freely available (https://portal. enes. org/ oasis/, last access: 11 July 2019).
Acknowledgements Xavier Couvelard ,  Florian Lemarié  and  Jean-Luc Redelsperger  acknowledge the support by  Mercator-Ocean  and the  Copernicus Marine Environment Monitoring Service (CMEMS)  through contract  22-GLO-HR -Lot 2  (High-resolution ocean, waves, atmosphere interaction). Numerical simulations were performed on Ifremer HPC facilities DATARMOR of  "Pôle de Calcul Intensif pour la Mer" (PCIM)  (http://www. ifremer. fr/pcim). Mixed Layer Depth Data were graciously provided by Clément.
τ atm τ oce Variable description units u h (z = η) Oceanic surface currents O→W m s -1 u atm 10 10 m-winds from external dataset O→W m s -1 - u s h (z = η) Sea-surface Stokes drift W→O m s -1 T s norm of the Stokes drift volume transport W→O m 2 s -1 Φ oc TKE surface flux multiplied by ρ 0 W→O W m -2 α ch Charnock parameter W→O - τ ww3 w Wave-supported stress W→O N. m -2 p J wave-induced pressure W→O m 2 s -2 H s Significant wave height W→O m Table ww3 τ atm ww3 1 Integrated Forecasting System: https://www. ecmwf. int/en/forecasts/ documentation-and-support/changes-ecmwf-model/ifs-documentation 2 http://aerobulk. sourceforge. net/.
We designed single-guide RNAs (sgRNA) for CRISPR/Cas9 using the online CRISPR-P 2. 0 software  49. We annealed the oligo pairs to generate double-stranded DNA and cloned the sgRNAs into the BbsI site of the gateway compatible entry vector U6-sgRNA. We then introduced the U6-sgRNA cassette into a modified pCambia1300 binary vector, in which hspCas9 expression is driven by the YAO promoter  50. To construct ProCTI1:CTI1:uidA, ProCTI2:CTI2:uidA, and ProCTI3:CTI3:uidA transgenes, gene fragments extending up to 2 kb upstream of the ATG of the gene to the last coding codon were amplified with the Pfu Ultra DNA polymerase (Stratagene) from Col-0 genomic DNA. The PCR products were introduced by BP recombination into the pDONR207 entry vector (Invitrogen) and transferred into the destination vector pBI101-R1R2-GUS 51 by LR recombination.
Yeast one-hybrid assay. Construction of the ProCTI1-500:HIS reporter plasmid, like that of the ProBCCP2-180:HIS reporter plasmid  29  , relied on the amplification by PCR of promoter fragments, that were digested by EcoRI and SacII, and inserted into the pHISi vector between the EcoRI and SacII sites. The plasmids thus Fig. 5  CTIs interacting with α-CT regulate fatty acid synthesis in a light-dependent manner. a, b 14 C-acetate incorporation experiments carried out on rosette leaves aged 4 weeks and subjected to different light/dark regimes. Data are means ± SEM, n = 3 biologically independent plants. c Leaf total fatty acid content of rosette leaves from plants grown under different light regimes as determined by gas chromatography. Plants were grown in short-day (SD), long-day (LD) or constant light (CL) conditions for 4 weeks before analyses.