dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments This work was supported by the research projects  CGL2017-82216-R  and  CGL2017-83866-C3-1-R  and  PCI2019-103631 , financed by the  Spanish Commission of Science and Technology and FEDER ;  CROSSDRO  project financed by the  AXIS (Assessment of Cross(X)-Sectorial climate Impacts  and pathways for Sustainable transformation), JPI-Climate co-funded call of the  European Commission  and  INDECIS  which is part of  ERA4CS , an  ERA-NET  initiated by  JPI Climate , and funded by  FORMAS (SE) ,  DLR (DE) ,  BMWFW (AT) ,  IFD (DK) ,  MINECO  ( ES ),  ANR (FR)  with co-funding by the  European Union  (Grant  690462 ).
Data Availability Statement In our study, we use the data from Agencia Catalana de l'Aigua (http://aca-web. gencat. cat/), Centro de estudios y experimentación de obras públicas, Confederación Hidrográfica del Guadalquivir (https://www. chguadalquivir. es), Environmental Protection Agency (https://www. epa. ie/), Ministère de l'Ecologie, du Développement durable et de l'Energie (http://www. hydro. eaufrance. fr/), Ministerio para la Transición Ecológica (http://ceh-flumen64. cedex. es/), National River Flow Archive (https://nrfa. ceh. ac. uk/), Sistema Nacional de Informacao de Recurso Hídricos (https://snirh. apambiente. pt/), Global Runoff Data Centre (GRDC, https://www. bafg. de/GRDC/EN/ Home/homepage_node. html). In addition, we use the R platform (R Core Team, R Development Team Core, 2017: A Language and Environment for Statistical Computing).
We assess the large-scale circulation by comparing a baseline climatology with the ERA5 data (e. g. 1995-2014), using a similar approach to  McSweeney et al. (2015). We use the analysis of  Priestley et al. (2020)  to assess the NA storm track over Europe in individual CMIP6 models. Blocking by high pressure weather systems is known to cause of periods of cold dry weather in the winter and summer heatwaves. Blocking is typically under-represented in GCMs and this is still the case large parts of Europe in CMIP6, although https://doi. org/10. 5194/esd-2022-31 Preprint. Discussion started: 3 August 2022 c Author(s) 2022. CC BY 4. 0 License. there has been some improvement in the bias in many CMIP6 models  (Davini and d'Andrea, 2020; Schiemann et al. , 2020). We use the results of the analysis carried out by  Davini and d'Andrea (2020)  to assess the performance of the CMIP6 models based on RMSE, bias and correlation.
Weighting for performance against global trends and model independence with the climWIP method We also compare our results with the Climate model Weighting by Independence and Performance (ClimWIP) method  (Knutti et al. , 2017; Lorenz et al. , 2018; Brunner et al. , 2019 Brunner et al. , , 2020b;; Merrifield et al. , 2020)  to assess differences between our processbased filtering and an assessment based on historical warming. ClimWIP combines model performance weighting based on one or more metrics with an assessment of model independence (i. e. , overlaps in the models' source code or development history). Here we use an adaptation of the approach described in  Brunner et al. (2020b)  and publicly available via the ESMVal-Tool (https://docs. esmvaltool. org/en/latest/recipes/recipe_climwip.
html). Performance weights are calculated based on global temperature trends compared to ERA5 in the period 1980-2014. Independence weights are based on global model output fields for temperature and sea-level pressure which have been shown to reliably identify model dependencies  (Brunner et al. , 2020b; Merrifield et al. , 2020). Here we use ClimWIP in two setups: one only based on performance weights and one only based on independence weights as detailed later. The assessment for each of the CMIP6 models is collated into two figures (Fig. 4  and Fig. 5 ), with the classification for each of the criteria, where the relevant data and/or analysis are available. The tables create a summary of each model's performance against a range of criteria, that are essential for a meaningful representation of the European climate.
The models from the process performance subset (table  1 ) are placed into clusters of models that had clear dependencies (table  2 ). The Euclidean distance of the models is determined using the ClimWIP method (see section 3. 3), for the comparison of model independence  (Brunner et al. , 2020b). Fig. S3  shows the independence matrix for the different models, which was used to create clusters of models that had dependencies, models with a Euclidean distance of =<0. 6 were combined into clusters. Three models were found to not have a sufficient dependency to the other models to be placed in any cluster (see table  2 ). In most cases many of the models with similarities are from the same institution or are known to share significant code components, such as the same atmosphere model in the HadGEM-GC31 models and ACCESS-CM2.
It was found to be the case that the flags for exclusion did not occur in isolation, severe errors (red flags) for large scale circulation, storm tracks and blocking frequency often occurred in more than one criteria (or in some cases alongside multiple orange flags). Severe errors (or Inadequate, i. e. those flagged red) in the AMOC, another criteria where data was limited, also did not occur in isolation and were found to occur in a few models that had also been flagged as Inadequate (red flagged) due to poor circulation features and in particular the poor representation of storm tracks. For example, in the case of a red flag for a very weak AMOC in the NESM3 model, severe winter cold bias (red flagged) in the NA SST were also found to be present. Filtering from the CMIP6 ensemble by excluding the least realistic models for Europe leads to the removal models throughout the projected temperature range, but removes more of the models that have more modest response.
Acknowledgements. This work was carried out as part of the  EUCP  project which is funded by the  European Commission  through the  Horizon 2020 Programme for Research and Innovation : Grant Agreement  776613. We acknowledge the  World Climate Research Programme , which, through its Working Group on  Coupled Modelling , coordinated and promoted CMIP5 and CMIP6. We thank the climate modeling groups (particularly those listed in tables 1 and S1) for producing and making available their model output, the Earth System Grid Federation (ESGF) for archiving the data and providing access, and the multiple funding agencies who support CMIP,  CMIP6  and ESGF. Terms of use and further instructions can be found at https://pcmdi. llnl. gov/. Matthew B. Menary  was supported by the  European Union   Horizon 2020 project 4C,   Climate-Carbon Interactions in the Coming Century  (grant  821003 ) and the  ANR-Tremplin ERC  project  HARMONY  ( ANR-20-ERC9-0001 ).
Code and data availability. The code used to apply the climWIP method is publicly available via the ESMValTool (https://docs. esmvaltool. org/en/latest/recipes/recipe_climwip. html) The data used in this study is available through the ESGF data portal at https://esgf-node. llnl. gov/projects/esgf-llnl/. Details of the models are available from https://wcrp-cmip. github. io/CMIP6_CVs/docs/CMIP6_source_id. html A version of the assessment figures used in this paper is available on github https://github. com/tepmo42/cmip6_european_assessment as a full spreadsheet of all available assessments (for Europe) carried out for CMIP6 models to date.
Overall approach 95 We analyse potential flood impacts from physically plausible magnitudes of outburst floods along a 40-km long reach of the Seti Khola. Our assessment is built on combining scenario-based inundation modelling with current land use and land cover (LULC) and data of buildings and the road network to identify areas of potential flood impact (Fig. 2 ). We also selected two informal settlements close to the river and estimate how the growth of their built-up area between 2008 and 2021 affected their exposure to outburst floods. Stream-gauge data is unavailable for the Seti Khola, hence we validated our model with mapped 100 damage and sediment traces caused by the May 2012 flood.
Scenarios for numerical flood routing We simulated ten flood scenarios with the Hydrologic Engineering Center's River Analysis System, HEC-RAS (version 5. 0. 7; https://www. hec. usace. army. mil/software/hec-ras/). HEC-RAS uses a step method for simulating steady, i. e. constant but gradually varied, open-channel flow in one dimensionbased on the Saint-Venant equations  (Brunner, 2020a; Westoby et al. , 2014). The standard step method computes water-level profiles between two river cross-sections that describe the geometry of the channel and the adjacent overbank areas by iteratively solving the Energy equation (Eq. 1): 𝑍 2 + 𝑌 2 + 𝑎 2 𝑉 2 2 2𝑔 = 𝑍 1 + 𝑌 1 + 𝑎 1 𝑉 1 2 2𝑔 + ℎ 𝑒 (1) For cross-sections 1 and 2,  1 and  2 describe their main channel bed elevations,  1 and  2 the corresponding flow depths,  1 and  2 the mean flow velocities, and  1 and  2 the weighting coefficients;  is the gravitational acceleration.
The energy head loss ℎ  specifies the effects of friction, as well as expansion and contraction caused by the channel geometry. At each crosssection, Manning's equation is used to compute discharge Q (Eq. 2): 𝑄 = 1 𝑛 𝐴𝑅 2/3 𝑆 𝑓 1/2 , ( 2 ) where  is cross-section area,  is the hydraulic radius,   is the energy gradient, and n is a hydraulic loss (or roughness) coefficient  (Brunner, 2020a). Geometric data for our HEC-RAS runs came from the 5-m resolution ALOS digital elevation model, projected to UTM Zone 44N (Fig. 2 ). We also acquired field data with a TruPulse 360 laser range finder and a Garmin eTrex handheld GPS during two campaigns in October of 2016 and 2019. We used these field data to derive 572 channel cross-sections of the Seti Khola and its major tributaries from the DEM, especially in the narrow gorges (Fig.
Land-use and land-cover data Using 1-m Maxar satellite imagery acquired in 2020 (ESRI and Maxar Technologies, 2021), we manually mapped ten land cover (LC) classes in our model domain with an area of 136 km² (Fig. 1 ). Following the USGS National Land Cover Database classification, we assigned the LC classes "developed, open", "developed -low (intensity)", "developedmedium (intensity)", "developed -high (intensity)", "(open) water", "barren (land)", "grassland(/herbaceous)" (might include grazing of livestock), "(cultivated) crops", "shrub (scrubs)", and "(mixed) forest". The different levels of "developed" LC classes are based on the percentage of area covered by impervious materials (built-up areas), i. e. <20% in "developed, open" and highest (>80%) in "developed -high (intensity)" areas (Multi-Resolution Land Characteristics Consortium, 2019).
We also mapped "revegetated floodplain", delineating the May 2012 flood's deposits and included in the "grassland(/herbaceous)" LC class, as well as the two land use (LU) classes "airport" and "sand and gravel mining". The latter is assigned to the "barren (land)" LC class. We derived object-scale data on Pokhara city's buildings, as well as roads and footpaths from the OpenStreetMap (OSM) collaborative project (https://www. openstreetmap. org/) via the Overpass Turbo tool (https://overpass-turbo. eu/) to capture the situation in September 2021. We mapped built-up areas in two informal settlements from medium-to high-resolution satellite imagery of April 2008 (Google Earth), November 2012 (Google Earth and RapidEye), and November 2021 (Google Earth and PlanetScope). In these images, we also identified sand and gravel mining activities based on the presence of artificial groynes and gravel heaps at the active channel margins.
Potential flood impact analysis We used a geospatial overlay of our modelled flood inundation boundaries with the LULC data to assess on a semi-quantitative basis the likely impacts of ten peak discharge scenarios. We defined ten flood hazard classes by assigning areas and objects of the lowest scenario (Qp = 1,000 m³ s -1 ) to the highest hazard class (HC) 10. Conversely, the lowest hazard class 1 is attributed to areas and objects that would be inundated in the highest magnitude scenario (Qp = 10,000 m³ s -1 ). To check the plausibility of our modelling results, we mapped the uppermost reach of the Seti Khola between Karuwa and Kharapani (Fig. 1 ), where the 2012 flood caused most damage. We used high-resolution Google Earth satellite imagery from December 2011 (pre-flood) and June 2012 (post-flood) to map man-made structures, including houses, huts, and temples.
Flood scenarios and their potential impact Our results offer a first comprehensive set of outburst flood scenarios along the Seti Khola, and thus expand on previous studies of a more qualitative  (Rimal et al. , 2015 (Rimal et al. , , 2018) )  or local focus  (Gurung et al. , 2021; Thapa et al. , 2022). By intersecting modelled inundation extents with spatial data on individual buildings and the route network, we are able to outline relative hazard zones on the assumption that smaller outburst flood magnitudes are more frequent than larger ones. We show that zones with higher relative flood hazard (HC >7) are mostly along the Seti Khola's upper reach. Despite the May 2012 flood's intense damage to infrastructure in this area, a number of infrastructure projects have been developed close to the Seti Khola, including several road bridges and a run-of-the-river hydroelectric station  (Gurung et al.
Acknowledgements This research was supported by the  Deutsche Forschungsgemeinschaft (DFG)  via the graduate research training group NatRiskChange at the  University of Potsdam  (https://www. natriskchange. de). The authors thank  Georg Veh ,  Elisabeth Schönfeldt , and  Narayan Gurung  for their support during fieldwork and  Natalie Lützow  for data support with  HEC-RAS.
Data availability All field data was submitted to the PANGEA Data Publisher for Earth & Environmental Science (https://www. pangaea. de/) on 01/12/2021 and will be freely available after publication. ALOS WORLD 3D Topographic Data was provided by the Remote Sensing Technology Center of Japan (©NTT DATA, RESTEC/ Included©JAXA). PlanetScope and RapidEye satellite imagery was freely provided through the Education and Research Program of Planet Lab Inc. (https://www. planet. com/explorer). Data from the OpenStreetMap project are freely available via https://www. openstreetmap. org/ and https://overpass-turbo. eu/. We further used the freeware HEC-RAS 5. 0. 7, which is available under https://www. hec. usace. army. mil/software/hec-ras/, and Google Earth Pro (https://www. google. com/earth/versions/#earth-pro).
Spore Size Measurement To measure the diameter of spores produced at 45 • C and 55 • C, SEM images were analyzed using ImageJ analysis software (v. 1. 51j8, National Institutes of Health, Bethesda, MD, USA). A total of 73 and 78 spores from the four batches produced at 45 • C and 55 • C, respectively, was measured. The normality of the data distribution and the homogeneity of variance were tested using a Shapiro-Wilk test and a Fisher test, respectively. A Student's t-test was performed to compare mean diameter values (Table  S1 ). All statistical tests were conducted using XLSTAT (v. 2017. 7) or utility software (Anastats Scop SARL, Rilly-sur-Vienne, France).
Including the regeneration and the equilibration steps, a single run took 93 min. Eluted peptides were analyzed with a Q Exactive PLUS mass spectrometer (Thermo Fisher Scientific, San Jose, CA, USA) using a nanoelectrospray interface. Ionization was performed with a 1. 3 kV spray voltage applied to an uncoated capillary probe (10µm i. d. , New Objective, Littleton, MA, USA). Acquisition was performed in data-dependent mode and fragmentation occurred in HCD (Higher energy collisional dissociation). This included a full MS scan covering a mass-to-charge ratio (m/z) of 300 to 1400 with a resolution of 70,000 and an MS/MS step (normalized collision energy, 27%; resolution, 17,500). Dynamic exclusion was set to 40 s. The mass spectrometry proteomics raw data were deposited in the PRIDE partner repository  [64] , as a ProteomeXchange dataset  [65] , with the identifier PXD029621 (http: //www.
Identification and Quantification of Proteins Data were converted to mzXML format using MS convert (ProteoWizard v 3. 0. 8934). Proteins were identified using X!Tandem v. 2015. 04. 01. 1  [66]  by matching peptides against the UniprotKB Moorella thermoacetica ATCC 39073 database containing 2452 entries (http: //www. uni-prot. org/proteomes/UP000007053, accessed on 29 September 2021). Proteins were filtered and grouped using the X!TandemPipeline software v. 3. 4. 2  [67] , open-source software developed by PAPPSO (http://pappso. inrae. fr/bioinfo/xtandempipeline/, accessed on 29 September 2021). To eliminate the spectra created by the contaminants, data were also compared to a contaminant database. Proteome identification was carried out with a precursor mass tolerance of 10 ppm and a fragment mass tolerance of 0.
Protein abundance values were calculated using spectral counts from three independent biological replicates. We used a rough semi-quantitative method, which allows us to detect large variations in abundance, including presence/absence variations  [68] [69] [70]. In addition, normalization was done according to the method n × Ms Ts (1) where n refers to the number of spectra identified per protein in the sample, Ms refers to the mean sum of spectra identified for each sample, and Ts refers to the total number of spectra per sample. MassChroqR (version 0. 3. 7), an R toolbox developed by the PAPPSO platform (http: //pappso. inrae. fr/, accessed on 29 September 2021), was used to check the quality of the data and perform statistical analysis on the proteomic data. The significance of variation was determined by an ANOVA (analysis of variance) test in all analyses.
Acknowledgments: The authors thank  Isabelle Martin-Verstraete ,  Mohammed Jebbar  and  Frédéric Carlin  for helpful discussions and  Bénédicte Doublet  for providing help with protein extraction. Part of this work was supported by the microscopy facilities of Platform  3A , funded by the  European Regional Development Fund , the  French Ministry of Research, Higher Education and Innovation , the  Provence-Alpes-Côte d'Azur region , the  Departmental Council of Vaucluse  and the  Urban Community of Avignon.
thermoacetica ATCC 39073 LocusS a Protein ID b M. thermoacetica Protein Identification b Putative Protein Name c Hypothetical Function d Putative Localization d ProteinsS of Bs/Bc/Ba/Cd/Cp c,d moth_1518 Q2RIB2 Ferredoxin-NADP(+) reductase subunit alpha - Nd e Exosporium CD1536 moth_1286 moth_1879 Q2RIY9 Q2RHB0 Rubrerythrin Rbr Likely to play a role in spore resistance Exosporium CD0825 CD1524 moth_0602 Q2RKV7 Uncharacterized protein - nd Exosporium CD2434 moth_1747 moth_1815 Q2RHP1 Q2RHH4 Uncharacterized protein/Agmatinase Arginase nd Exosporium BAS0155 BAS2260 moth_2167 Q2RGI3 Alanine racemase Alr Spore protection Exosporium BSU17640 BAS0238 moth_1405 Q2RIM1 Nucleoside recognition FeoB nd Exosporium CD1517 Cation diffusion moth_1683 Q2RHV4 facilitator family - nd Exosporium CD0902 transporter moth_0034 Q2RMG5 2-oxoglutarate synthase - nd Exosporium CD0117 Pyruvate moth_0033 Q2RMG6 flavodoxin/ferredoxin oxidoreductase-like - nd Exosporium CD0116 protein 2-oxoacid: acceptor moth_0035 Q2RMG4 oxidoreductase, gamma subunit, pyruvate/2- - nd Exosporium CD0118 ketoisovalerate moth_0266 Q2RLT8 Enolase Eno nd Coat/exosporium CD3170 moth_0837 Q2RK85 Stage V sporulation protein D SpoVD mother-cell specific protein (spore cortex) penicillin-binding Coat/exosporium BC3915 moth_2301 Q2RG51 Kynurenine formamidase - Metal-dependent hydrolase Coat/exosporium BC0395 moth_0739 Q2RKI2 Polysaccharide deacetylase YlxY or Pda or PdaB PdaA/PdaB:spore synthesis cortex peptidoglycan Coat/exosporium CD2598 moth_1414 Y1414_MOOTA UPF0597 protein moth_1414 - nd Coat/exosporium CD630 Peptidase M1, moth_0738 Q2RKI3 membrane alanine - nd Coat/exosporium CD3652 aminopeptidase moth_1693 Q2RHU4 Protein translocase subunit YajC YajC nd Coat/exosporium BC4410 moth_1319 Q2RIV7 Stage IV sporulation protein A SpoIVA Anchors the spore coat to the spore surface via SpoVM Spore coat basement BSU22800 BC1509 CD2629 moth_1782 f Q2RHK6 CoatF - nd Coat - moth_1059 Q2RJL6 Peptidase M16-like protein YmxG nd Coat BC3786 moth_1391 Q2RIN5 Spore coat peptide assembly protein CotJB CotJB nd Inner coat BSU06900 BC0822 CD630 moth_1392 Q2RIN4 Manganese containing catalase CotJC May protect against oxydative stress Inner coat BSU06910 BC0821 CD0598 CD2401 CPR0934.
thermoacetica Protein Identification b Putative Protein Name c Hypothetical Function d Putative Localization d ProteinsS of Bs/Bc/Ba/Cd/Cp c,d moth_0806 Q2RKB5 Small, acid-soluble spore protein, alpha/beta family SspF Protection of the spore DNA Core BSU24210 moth_1875 Q2RHB4 Small, acid-soluble spore protein SASP Small, acid-soluble spore protein Core CPR1870 NADH:flavin moth_2056 Q2RGT7 oxidoreductase/NADH YqiG nd nd BSU24210 oxidase moth_1272 Q2RJ03 N-acetylmuramoyl-L-alanine amidase CwlC Sporulation-specific amidase N-acetylmuramoyl_L-alanine nd BSU17410 moth_1828 Q2RHG1 Uncharacterized protein YckD nd nd BSU03400 moth_1059 Q2RJL6 Peptidase M16-like protein YmxG Control of proteolytic activity nd BSU16710 Spore maturation moth_1356 Q2RIS0 Nucleoside recognition SpmA protein, spore core dehydration, involved nd BSU23180 BC1470 in germination Spore maturation moth_1355 Q2RIS1 Nucleoside recognition SpmB protein, spore core dehydration, involved nd BSU23170 BC1471 in germination Alanine moth_1064 Q2RJL1 dehydrogenase/PNT-like SpoVFA Dipicolinate synthase (subunit A) nd BSU16730 BC3801 protein moth_1065 Q2RJL0 Flavoprotein SpoVFB Dipicolinate synthase (subunit B) nd BSU16740 BC3800 [37]orella thermoacetica ATCC 39073 database[28]; b Information from the UniProtKB database (http://www.
3 ± 1. 2 2. 7 ± 1. 2 0. 5072 Q2RKV7 Hypothetical protein 2. 7 ± 1. 2 3. 3 ± 1. 5 0. 8454 Q2RLU7 HAD-superfamily hydrolase subfamily IIB (YhaX) 5. 7 ± 1. 2 6. 3 ± 1. 2 0. 8787 Q2RHB0 Rubrerythrin (Rbr) 20. 0 ± 3. 2 21. 3 ± 2. 3 0. 9068 Q2RJK6 Ribonuclease J (RnjA) 3. 0 ± 0. 0 3. 3 ± 0. 6 0. 9207 a Information from the UniProtKB database; b Spectral counting performed on three independent biological replicates; proteins whose abundance varies significantly between 45 • C and 55 • C are in bold (p adjust_value < 0. 05) (n = 3); c Overlap of standard deviations; d Protein annotated as CoatF protein in the M. thermoacetica genome not homologous with the spore coat proteins of B. subtilis; e No spectra found in one of the three biological replicates.
Results Crystal structures of A. thaliana PGLR and ADPG2 reveals conserved β-fold ADPG2 was produced as an active recombinant proteins in the yeast Pichia pastoris and subsequently purified, similarly to PGLR  8. PGLR and ADPG2 present similar biochemical characteristics, with an optimal activity at acidic pH, temperatures ranging from 25 to 50 °C and on pectic substrates with low degree of methylesterification (DM, Extended data Fig. 1 and ref. 8  ). Using PGA as a substrate, at 25°C, PGLR and ADPG2 differ in their Km (14. 57 versus 3. 0 mg. ml -1 ) and Vmax (30. 8 versus 391. 7 nmol of GalA. min -1. µg -  ). Protein structures were determined by X-Ray crystallography with the final models' geometry, processing and refinement statistics summarized in Table  1. We solved the crystal structure of PGLR (429 aa , 1-18 and 409-429 aa not modelled, PDB: 7B7A) at a resolution of 1.