dataesr/scientific-paragraphs-categorization huggingface dataset:
In particular, it can realistically represent the spatial variability of the basin, which will provide a global view of the whole basin. Many physically based hydrological models had been used  [24] [25] [26] [27] , such as MIKESHE  [28] , HSPF  [29]  and Soil and Water Assessment Tool (SWAT)  [30]. Among these models, SWAT has been proved to obtain good hydrological predictions with a little direct calibration in many different basins around the world  [26, 27, 31] , and more applications can be found in SWAT literature database: https://www. card. iastate. edu/swat_articles/. Although SWAT has been applied to many Asian basins, and also to subtropical or/and tropical areas, most of them were at a scale of 77 to 105,000 km 2  [31] [32] [33] [34] [35]. The Red River is a typical Asian river system, combining different land uses, affected by human activities such as intensive dam implementations and agriculture  [36, 37].
More explanations and details about TRMM is presented in Section 2. 3. 2. Temperature changes follow a classic orographic pattern: the mean annual temperature upstream in China varies from 15 to 21 • C  [52] , while in Vietnam it ranges from 14 to 27 • C  [50]. Temperature is lower in valley areas. Potential evapotranspiration (PET) ranges from 880 to 1150 mm year -1 , and its mean value was 1040 mm year -1  [37]. Simons et al. [54]  who used global satellite-derived data to calculate actual evapotranspiration in the whole basin showed values in the range of 860 to 1117 mm year -1. The hydrology in this region is affected by the monsoon climate and the runoff is mainly recharged by precipitation, which led to large inter-seasonal variations in river flows  [51, 53, 55]. From the hydrology data that we collected, the mean annual discharge in Son Tay during 2000-2015 was 3082 m 3 s -1.
Corresponding to temporal precipitation distribution, the runoff is also uneven in intra-annual distribution: flood season occurs from June to November during which time the accumulated runoff accounts for more than 80% of the total annual runoff; low water seasons occur from December to May. The lowest discharge of the upstream Thao River in China usually occurs in March, and the minimum discharge of the Thao River observed near the border between China and Vietnam was 28. 7 m 3 s -1 in 1963  [56]. The lowest discharge at Son Tay generally showed up in March, and from the discharge data we collected, the minimum daily discharge at Son Tay during 2000-2015 was 493 m 3 s -1 (in February 2010). Peak runoff usually occurs in August, and the maximum flood was 8050 m 3 s -1 observed at the gauge station near the boundary in China in 1986  [52] , while it was 37,800 m 3 s -1 at Son Tay in 1971  [44].
Meteorological Data Daily temperature data were obtained from the Global Weather Data in SWAT file format for a given location and time period. These data come from the daily Climate Forecast System Reanalysis (CFSR). Daily precipitation data was obtained from the Tropical Rainfall Measuring Mission (TRMM, product 3B42 V7), which is a research satellite designed to provide needed information on rainfall by covering the tropical and sub-tropical regions of the Earth. Simons et al. [54]  compared several satellite-based precipitation and actual evapotranspiration products in the Red River basin in order to demonstrate that these datasets can be merged to examine hydrological processes before applying a numerical simulation model, and they found that TRMM rainfall product could provide reliable values in both space and time at this basin.
Dam Implementations SWAT requires basic information, such as date of impoundment, reservoir surface area, emergency volume, principal volume and initial volume. The volume of outflow can be calculated by one of the following methods: measured daily outflow, measured monthly outflow, average annual release rate and controlled outflow with target release. As it is impossible to get the detailed outflow of dams, and in order to avoid the complex conditions of release operations and to enable the model to be applied for future hydrology regime prediction, the average annual release rate method that releases the water whenever the dam volume exceeds the principal spillway volume  [57]  was selected. A minimum and a maximum average daily releases for the month were limited for the model according to the Q data we collected and to the release information from reference  [37].
Calibration and Validation Process The model was calibrated at a daily scale using Q and SSC from 1 January 1998 to 31 July 2014 with a two-year warm-up. Parameters were mainly calibrated manually, and some were automatically calibrated by using SWAT-CUP  [80]. SWAT-CUP is a tool that allows SWAT users to perform automatic calibrations  [81]. Five algorithms are proposed for calibration purpose  [80, 82]. The SUFI-2 (Sequential Uncertainty Fitting 2) algorithm  [82] , which can identify appropriate parameters sets in a limited number of iterations, was selected in this study. Calibration of Q was first carried out, followed by SSC. Observed data of daily Q and SS concentration from 2000 to 2014, obtained from the Vietnam Ministry of Natural Resources and Environment (MONRE) at Lao Cai, Yen Bai, Vu Quang, Hoa Binh and Son Tay stations, were used to calibrate the model.
Conclusions This is the first study trying to use a modeling approach to analyze hydrology regime and suspended sediment concentration at a daily scale for a long period in the Red River basin, including considering the successive implementations of dams all along the period. The SWAT model provided some insights on discharge and suspended sediment concentration at daily and monthly scales, respectively, in such a large basin. This study allowed us to understand, characterize, and quantify the discharge and suspended sediment concentration with spatial and temporal variations. What is more, using a modeling approach helped us to separate the impacts from climate variability and anthropogenic impacts. However, some improvements are needed, such as dam information and management, observed rainfall data, discharge and suspended sediment dataset of more stations and longer period, high frequency dataset, to gain a better estimation and understanding of the impacts of climate variability and human interferences.
5 µL of ultrapure water. The reaction conditions were as follows: 95°C for 30 sec followed by 40 cycles of 94°C for 5 sec, 55°C for 30 sec and plate read. and a final step at 95°C for 10 sec followed by a melt curve from 55°C to 95°C with 0. 5°C per sec. The reaction was performed in a CFX Connect Real-Time PCR Detection System and analyzed with the software Bio-Rad CFX Manager. Each sample was run in triplicates. If the triplicates showed too much variability (SD > 0. 3), the furthest triplicate was discarded. If the two remaining triplicates still showed too much variability (SD > 0. 3) the sample was discarded. The samples were subjected to a relative quantification and normalization. First for each sample and for each reference gene per brain region, the relative quantity (Q r ) was computed using the difference between the mean Ct value of each sample and the highest mean Ct value (ΔCt), using the following formula: Q r = (1 + E) ΔCt (with E = efficiency of the reaction).
Data analysis Latencies, velocities, and crossing events of behavioral experiments were extracted automatically by video analysis of the conditionings and memory tests. Learning curves were analyzed by performing two-way repeated measure analyses of variance (ANOVA) followed when necessary by post-hoc analysis using Dunnett's multiple comparisons test. If the sphericity criterion was not met for the repeated-measurement ANOVA, the Geisser-Greenhouse's correction was applied, thus resulting in corrected degrees of freedom for some Fischer statistics. Memory tests were analyzed by performing Kruskal-Wallis tests (or Mann-Whitney tests) on the difference between the latencies to cross toward blue and green-illuminated compartments (Δ latency = latency bluelatency green ) followed when necessary by post-hoc analysis using Dunn's multiple comparisons test.
Additionally, we assessed the presence of memory for each group by comparing the Δ latency to a theoretical value of zero (indicating equal latencies to cross toward conditioned and unconditioned stimuli, hence the absence of memory) using Wilcoxon signed-rank test. Statistical differences between the gene expression of the paired and the unpaired groups were assessed for a given gene and brain region using a Student t-test after transformation of the data to improve normality and homoscedasticity (or Welch t-test in case of heteroscedasticity). No cross-comparisons between brain regions or genes were performed. Statistical analyses were performed using GraphPad Prism 8 software. Genes of interest Amdop1 5 ′ -GAGTTATCCGAAGAATGTT (forward) 148 5 ′ -CGTTGAAGTTGATTATGAC (reverse) Amdop2 5 ′ -GGATCAACAGCGGAATGAAT (forward) 151 5 ′ -GCGAATCTTTGACTCGGTTT (reverse) Amdop3 5 ′ -CGTTGCAAACTGTCACCAAT (forward) 155 5 ′ -GACGTCCATTGCGATGTAAA (reverse) AmoctαR1 5 ′ -GAACGCCATCCAAGTGTTCT (forward) 124 5 ′ -GTGCTTCCAACCCTGTTGAT (reverse) Am5-HT1a 5 ′ -GCCTCGATATTGCATCTGGT (forward) 168 5 ′ -AGGATCCTTCCATCCGAGTT (forward) Reference genes Rps8 5 ′ -ACGAGGTGCGAAACTGACTGA (forward) 176 5 ′ -GCACTGTCCAGGTCTACTCGA (reverse) Rp49 5 ′ -AAAGAGAAACTGGCGTAAACC (forward) 126 5 ′ -CAGTTGGCAACATATGACGAG(reverse) Ef1a 5 ′ -AAGAGCATCAAGAGCGGAGA (forward) 149 5 ′ -CGTACCTTAATGACGCCCACA (reverse) The length of the amplicons resulting from PCR is given.
Climate projections. The SAFRAN meteorological reanalysis dataset at hourly time resolution was used as a reference over the historical period 1958-2016  38. This reanalysis is based on geographic areas assumed to be homogeneous (massifs) within which atmospheric variables depend only on elevation (provided by 300 m steps), slope angle and aspect. SAFRAN reanalysis data from 1980 to 2010 were used by the ADAMONT method  21  as an observation database to statistically adjust to the massif-scale by elevation bands, and disaggregate to hourly time resolution, the surface atmospheric fields of the ensemble of 13 pairs of global and regional climate models from the EURO-CORDEX database  20, 39  used in Verfaillie et al. 22. This spans historical (1950-2005) and future (2005-2100) time periods for scenario RCP 4.
5, RCP 8. 5, and for a subset of 4 GCM/RCM pairs for RCP 2. 6  22. spatial representations of ski resorts. The geographical location of ski lifts was used to infer an estimate of ski slope in ski resorts  17, 24. The French "Services Techniques de Remontées Mécaniques et Transports Guidés" (STRMTG) manages an exhaustive database of cable transportation equipments in France, which includes technical characteristics of each ski lift. This includes the ski lift power (SLP) of each ski lift, which is defined as the product of the elevation difference between the bottom and the top of a ski lift (in km) and its flow of persons per hour (pers h -1 ). It is expressed in pers km h -1. These data are completed with geographical information from the database BDTOPO (25 m of resolution) developed by the French Geographical Institute (IGN).
For any given winter season, the snow reliability index, the frequency of snow-scarce seasons and the water volume were computed based on the simulated data for 15, 30 and 45% snowmaking coverage and on the value for this specific winter season (linear interpolation). Variability, uncertainties and relationship to global warming. The time evolution of the snow-related indicators (fractional snow reliability index, frequency of seasons below the Q20, water volume for snowmaking) was computed for groomed snow conditions with and without snowmaking, for historical and future climate scenarios. The 15-yr multi-model percentiles of annual values (5%, 17%, 50%, 83% and 95%, respectively referred to as the Q5, Q17, Q50, Q83 and Q95) were computed and are displayed in Fig. 3. This illustrates not only the long-term trend but also the interannual variability.
Acknowledgements This study was funded by  Région Rhône-Alpes  (PhD grant of  Pierre Spandre ,  2013-2016 ), and benefited from funding from the  French Ministry for Ecology (MTES)  to the  ADAMONT  project through GICC and  ONERC , from the  Interreg  project  POCTEFA/Clim 'Py, from the  IDEX Univ. Grenoble Alpes Cross Disciplinary Project  " Trajectories ", and from the  Conseil Départemental de l'Isère -Isère Tourisme. The  CGET -Comité de Massif Alpes  funded the creation of the BD Stations database. CNRM/CEN and Irstea are part of  LabEX OSUG@2020  ( ANR10 LABX56 ). The authors wish to thank  G. Evin  ( Université Grenoble Alpes, Irstea, UR ETGR ) for fruitful discussions. Deborah Verfaillie 's work has been partly funded by the  European  project  EUCP  ( H2020-SC5-2016-776613 ).
Code Availability The code of the ADAMONT v1. 0 method is available as an open git repository after free registration at https:// opensource. cnrm-game-meteo. fr/projects/adamont. The version of the open source code of SURFEX/ISBA-Crocus used in this study is available as a specific branch of an open git repository, after free registration, at https:// opensource. cnrm-game-meteo. fr/projects/surfex_git2.
A progressive approach for blanket operation in DEMO It is currently foreseen that DEMO will utilise a first blanket with a 20 dpa damage limit in the first-wall steel (EUROFER) and conservative design margins and then switch to a second set of blankets with a 50 dpa damage limit with an optimized design (i. e. , with somewhat reduced design margins), and if available, improved structural materials that need to be qualified in advance. As it is unfeasible to change the Balance of Plant (BoP), the same coolant must be used while switching from the first set to the second set of blanket. This type of approach has been used for the fuel cladding in fission reactors for many years; limiting the maximum exposure level of the replaceable cladding to below the regulatory limit, while data for higher exposure operation is generated in test reactors or load test assemblies  [17].
The selection of the 20 dpa value as a target for the 'starter' blanket is discussed in  [19]. Irradiation of structural materials up to 20 dpa can be simulated, with sufficient accuracy, in existing MTRs, because the level of the He production up to this fluence foreseen in a 14 MeV fusion spectrum is deemed to be still relative modest (∼few hundred appm) to significantly affect material properties  [19]. Fusion irradiation data to be provided in IFMIF-DONES  [5]  foreseen to become operative by the end of the decade will be important to validate data collected in MTRs and extend irradiation data at higher fluences, relevant for the second set of blanket. It is also currently envisaged that DEMO act as a Component Test Facility for the breeding blanket. While operating with a near-full coverage "driver" blanket, which must be installed by day-1 to achieve tritium self-sufficiency and extract the thermal power and convert this in electricity), it must be used to test and validate in a limited number of dedicated segments of more advanced breeding blanket concept(s) that have the potential to be deployed in a future FoaK FPP.
The thermo-mechanical performances of the BSS and the SMS were assessed in normal operation conditions and in case of central major disruption loading scenarios, by means of the ABAQUS v. 6. 14 commercial FEM code (steady state analysis). The modelling approach and the analyses addressed both the segment structure stand alone and the overall 20°s ector including three outboard and two inboard segments and the VV attachment scheme. The preliminary verifications, according to ITER Structural Design Criteria for In-Vessel Components (SDC-IC), were satisfied  [46, 47]. Recent studies have been devoted to analyse a number of design alternatives to optimize the breeding blanket internals, the PbLi distribution, draining capability and minimize the associated MHD issues  [46] : the goal is to explore a large number of options and minimize the technical risks of the current WCLL configuration.
Acknowledgements Most of the strategy aspects described in this paper are the result of an effort that has involved a large number of individuals. The authors wish to thank and acknowledge, on behalf of the  EUROfusion Consortium and Fusion for Energy , the outstanding effort of the member of the independent expert panel composed of M. Gasparotto (Chair),  H. Ait Abderrahim ,  G. De Antoni ,  W. Korn ,  E. Salpietro ,  F. Wagner ,  S. Zinkle. We are also very thankful to  A. Grosman  (CEA),  K. Hesch  (KIT),  A. Ibarra  (CIEMAT),  A. Pizzuto  (ENEA),  M. Shannon  ( CCFE ),  G. Veres  (Wigner),  L. Vala  ( CV Rez ),  D. Maisonnier  ( EU Commission DG RTD ),  L. Petrizzi  ( EU Commission DG ENER ) for supporting the work of the expert panel and endorsing their recommendations, as well as for setting the foundation to address important management and governance aspects that support this proposal and ultimately will contribute to the efficient implementation of the program.
The quality of their work and the important recommendations that they have provided has played an important role in reshaping and strengthening the DEMO breeding blanket / TBM Programs in Europe. This work has been carried out within the framework of the  EUROfusion Consortium  and has received funding from the  Euratom  research and training programme 2014-2018 and  2019-2020  under grant agreement No  633053. The views and opinions expressed herein do not necessarily reflect those of the  European Commission.
Furthermore, for 11 of the 13 complete HEV genomes information was obtained by next generation sequencing (manuscript in preparation) or was available from previous studies (Table  1 ). No discrepancies between the RdRP sequences and the complete genome sequences were observed. Therefore, complete genome data was used for phylogenetic reconstructions, using a GTR nucleotide substitution model and a Maximum Likelihood algorithm with 1000 bootstrap in Geneious 11 (http://www. geneious. com). Filling and lyophilization of 0. 5 ml volumes of the bulk samples was as previously described  [3]. Vials were stored at -20 °C.
Qualitative assays For qualitative data analysis, results from all the three assays for each laboratory were pooled to give the number of positives out of the total number tested at each dilution. Assuming that a single 'detectable unit' will give a positive result, with the probability of a positive result following a Poisson distribution, the EC63 (the dilution at which 63% of the samples are expected to be positive) was chosen as the end-point. For each dilution series, this end-point was estimated by means of a probit analysis. Within the same evaluation, relative potencies were also estimated. For assays reporting C T values, these were evaluated for both qualitative and quantitative methods (relative to WHO IS) using a parallel line model for each laboratory and assay run, as well as combined for all evaluable (i.
e. valid) assay runs. Qualitative sample cut-off values from Laboratory 5 and 7 were evaluated by means of a sigmoid dose-response model. Samples with less than 3 dose values and/or non-linear or non-parallel behaviour were excluded from the analysis. Parallel line and sigmoid evaluation model as well as a combination of assays were performed according to methods as described in chapter 5. 3, "Statistical analysis of results of biological assays and tests", of the Ph. Eur. The statistical analysis was performed with SAS®/STAT software, version 9. 4, SAS System for Windows, and CombiStats, version 5. 0, EDQM, Council of Europe.
Discussion and conclusion In this study, a wide range of quantitative and qualitative assay formats were used to evaluate the candidate HEV RNA genotype panel of samples in parallel with the WHO IS. Approximately half of the assays had been developed in-house with the rest being commercially available, this is in contrast to the initial study to evaluate laboratory performance where only one commercial assay was included  [2]  and Fig. 1. Maximum-likelihood phylogeny of the complete coding sequences of reference Orthohepevirus A strains and genotype (gt) subtypes defined by Smith et al. [16] , and HEV strains used in this study (given in red), the WHO IS strain is given in blue. Taxon names of all reference sequences include genotype, subtype ("x" if not available), and GenBank accession (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article).
Acknowledgements ____________________________________________________________ The authors gratefully acknowledge the financial support for this project by the  'Agence de l'Eau Rhin-Meuse ' and the  'Zone Atelier Moselle '. We sincerely thank  E. Arce ,  P. Chaud ,  B. Le Carrer  and  Y. Namokel  for their field and laboratory work, as well as the fish farmers, the  'Office National des Forêts ' for providing us with access permits for sampling. We are also extremely grateful to the  INRA of Champenoux  for allowing us to use the laboratory facilities and conduct the stable isotope analysis at PTEF OC 081 from the  UMR 1137  and  UR 1138. The PTEF facility is supported by the  French National Research Agency through the Laboratory of Excellence ARBRE  ( ANR-11-LABX-0002-01 ). We also want to thank the three anonymous reviewers and the associate editor,  M.
Quantification and statistical analysis The fluorescence signal intensities of gCENP-C-GFP or Ndc80, CENP-T, and gCENP-C on kinetochores were quantified using Imaris (Bitplane). The fluorescence signals on ∼50-100 kinetochores in each of five  (Fig. 1, D and G; Fig. 3, B and C; and   For pull-down assays, the intensities of MBP-gCENP-C or MBP were measured with Fiji  (Schindelin et al. , 2012). The intensities of all histones in the CENP-A nucleosome were measured as the signals for the CENP-A nucleosome and normalized by the intensities of MBP-gCENP-C or MBP or MBP-hCENP-C. The intensity values were normalized with the value of CENP-A/gCENP-C 601-864 WT (CDK1-) or CENP-A/ hCENP-C 426-943 WT (CDK1-) of each assay. The graphs of the intensities were made using GraphPad Prism (GraphPad Software).
All chemical assays were conducted as previously described  [24, 28]. The microscope OLYMPUS BX53 (Olympus Corporation, Japan) equipped with OLYMPUS cellSens Dimension Desktop 1. 16 software (Olympus Corporation, Japan) was used to collect the images of fungal pellets. All imagerelated analytical procedures were performed as previously described  [26]. The definitions of projected area, elongation, and convexity parameters can be found in the previously published report  [26]. Additionally, the present study involved a parameter referred to as the shape factor, predefined in the OLYMPUS cellSens Dimension Desktop 1. 16 (Olympus Corporation, Japan) software as "the area relative to the area of a circle with an equal perimeter".
It should be noted that the above-described chemical outcome was dependent on the size of Al 2 O 3 particles ("micro" or "nano") but not on their concentration (Figure  4 ). It cannot be excluded that the addition of Al 2 O 3 nanoparticles was responsible for inhibiting the biosynthetic route leading to a yet unidentified secondary metabolite of A. terreus. To verify this idea, the natural products database Antibase : e Natural Compound Identifier  [30]  was extensively screened in an attempt to identify the underlying molecule, under an assumption that the peak at m/z=388. 1821 represented the pseudomolecular [M+H] + ion. However, this attempt proved to be unsuccessful and the structure of the substance remains unknown. Further chemical studies are necessary to discover its origin and identity.