dataesr/scientific-paragraphs-categorization huggingface dataset:
Simulated dataset We selected all individuals having STRUCTURE q-value (hereafter, q STR ) ≥0. 900 in their respective cluster at K = 2 (102 T. pityocampa and 98 T. wilkinsoni individuals) to generate a simulated dataset consisting of 1,000 T. pityocampa (sim Pit ), 1,000 T. wilkinsoni (sim Wil ), and 4,000 hybrid individuals (1,000 for each of the first-and second-generation hybrids [F 1 and F 2 ], backcrosses with parental T. pityocampa [Bkc Pit ] and T. wilkinsoni [Bkc Wil ]) using HYBRIDLAB  (Nielsen, Bach, & Kotlick, 2006). For each simulated category, we estimated the range of assignment indices obtained with STRUCTURE, INTROGRESS, and NewHybrids to support the interpretation of the field dataset.
NewHybrids This Bayesian software estimates posterior probabilities (q NH ) of each individual to fall into one of the six genotypic classes described above. We first ran NewHybrids on the simulated dataset alone using Jeffreys-like priors for allele frequencies and mixing proportions. We used 5 iterations of this analysis to check consistency of results and obtain averaged q NH values across runs. We used the "majority assignment" method to assign each individual to the class corresponding to the highest q NH , as described in  Burban et al. (2016). We compared the known genetic class of each simulated individual to the corresponding assignment result and used  Vähä and Primmer's (2006)  measures of efficiency, accuracy, and overall performance to estimate the power of assignment of the method with our dataset.
NewHybrids Efficiencies, accuracies, and overall performances were generally high for the simulated dataset, except for F 2 (Table  3 ). The levels of misassignment for each class of simulated individuals are shown in Table  3. Among field individuals, 17 were assigned as hybrid (6 F 2 , 8 Bck Pit , and 3 Bck Wil , Table  2 ). To sum up, we found a high but not strict consistency of results between the three methods. The 11 individuals identified as hybrids from INTROGRESS were also identified as such by NewHybrids, and 10 of them by STRUCTURE (Table  2 ). However, the methods used did not allow to test more complex ancestries, such as recurrent backcrosses. All individuals considered as hybrid from these analyses were collected in a restricted region comprising the Turkish Strait System (Table  2 ).
we followed the strategy developed in Burban et al. (2016)  using the field dataset together with simulated genotypes of known ancestry (see below). Briefly, identification of hybrid individuals is based on complementary methods whose respective performance is dependent both on the power of the data set available (type and number of markers, sampling size) and of the level of differentiation and reproductive isolation. Therefore, using a combination of methods and a case-specific evaluation of their performances through F I G U R E 2 Networks and geographic distribution of (a) COI haplotypes 1-13 [green]: T. pityocampa; 14-51: T. wilkinsoni (14-42 [yellow]: western wilkinsoni lineage; 43-49 [orange]: eastern wilkinsoni lineage; 50-51 [brown]: Cypriot wilkinsoni lineage), (b) Pho alleles 1-3: T.
Plot of PCA scores based on the complete microsatellite data and geographic distribution of populations color-coded according to the PCA plot. Green: main T. pityocampa populations, yellow: main T. wilkinsoni populations, orange: T. wilkinsoni populations in southeastern Turkey, black: T. wilkinsoni populations in Cyprus, dark blue: T. pityocampa and T. wilkinsoni populations occurring at the same locality without any trace of hybridization found, light blue: T. pityocampa and T. wilkinsoni populations occurring at the same locality with some traces of hybridization, red: population 26 with individuals positioned more centrally along the PC1 F I G U R E 4 Individual clustering for K = 2 and K = 3 based on STRUCTURE analysis of microsatellite data from the complete field dataset. Numbers above and names below are codes and names of the sampling localities, respectively found in western and northern Turkey corresponded to a star-like pattern, consistent with a typical range expansion phenomenon from southern refugia (possibly including the islands) to the north (Avise, 2000; Slatkin & Hudson, 1991) , which suggests a recent postglacial recolonization of these territories.
ACK N OWLED G M ENT We thank  N. T. Saner  and  İ. K. Sağlam  for help during fieldwork and sampling;  J. Rousselet  ( URZF, INRA Orléans, France ) for providing samples from Bulgaria and Greece;  I. Pivotto  for help in early analyses;  M. Uemura  for language editing, and  Course on Programming for Evolutionary Biology in 2017 in Leipzig (Germany)  for guidance in analyses. Genotyping was performed at the  Genomic and Sequencing Facility of Bordeaux  (grants from the  Conseil Regional d'Aquitaine  no. 20030304002FA  and  20040305003FA ; the  European Union ,  FEDER  no. 2003227; Investissements d'avenir, Convention attributive d'aide No. ANR-10-EQPX-16-01 ). Six-month visit of Kİ to  INRA (Cestas, France ) in 2010 was supported by the  Turkish Higher Education Council (YÖK). One-month visit of Kİ to CBGP (Montpellier, France) in September 2012 was supported by the  Mediterranean Facility  of the European Forest Institute (EFIMED).
DATA AVA I L A B I L I T Y S TAT E M E N T Sequence data available from GenBank: Accessions for COI haplotypes MH742427-MH742477, Accessions for Pho alleles MH742478-MH742487. Microsatellite data available from the Dryad Digital Repository: https ://doi. org/10. 5061/dryad. 1vhhm gqpn O RCI D Kahraman İpekdal https://orcid. org/0000-0001-9968-3013 R E FE R E N C E S.
F I G U R E 3 3 Plot of PCA scores based on the complete microsatellite data and geographic distribution of populations color-coded according to the PCA plot. Green: main T. pityocampa populations, yellow: main T. wilkinsoni populations, orange: T. wilkinsoni populations in southeastern Turkey, black: T. wilkinsoni populations in Cyprus, dark blue: T. pityocampa and T. wilkinsoni populations occurring at the same locality without any trace of hybridization found, light blue: T. pityocampa and T. wilkinsoni populations occurring at the same locality with some traces of hybridization, red: population 26 with individuals positioned more centrally along the PC1 F I G U R E 4 Individual clustering for K = 2 and K = 3 based on STRUCTURE analysis of microsatellite data from the complete field dataset.
(a) (b) TA B L E 1 Geographic locations, sampling dates, host tree species, sample size (n), mitochondrial COI haplotypes, and nuclear Pho alleles. Codes for COI haplotypes and Pho alleles are the same as in Figure2 Distribution of the two processionary moth species in the Mediterranean basin (green: Thaumetopoea pityocampa; yellow: T. wilkinsoni) (dashed circle: Turkish Straits System; dashed line: Anatolian Diagonal) (b) Sampling localities used in the present study. Different circle sizes correspond to different sampling sizes from 2 to 18 individuals. Numbers correspond to the locality codes given in Table 1 (red frame: microsatellite dataset used for hybrid detection analyses, see text for details).
Data and Code Availability The model code used in this study is available at https://doi. org/10. 14768/ 20190423002. 1. Table  2  lists all forcing data used in the simulations. For climate variables, reconstructed historical and projected future data from ISIMIP2 have been used, i. e. , model outputs of IPSL ESM following the Representative Concentration Pathway (RCP) 6. 0 (cf. Frieler et al. 26  ) (see Figure  4 ). These data are available at https://esg. pik-potsdam. de/search/isimip/. Land cover was taken from the fifth Coupled Model Intercomparison Project (CMIP5) and is available at https://luh. umd. edu/data. shtml. As in Lauerwald et al. ,  11  the bankfull discharge at which floodplain inundation starts was derived as medium discharge running ORCHILEAK over the period 1980-2000 (see  Lauerwald et al.
Model Description The model ORCHILEAK is a new branch of ORCHIDEE,  50  the land-surface scheme of the Institut Pierre Simon Laplace Earth System model. The new features of ORCHILEAK were described validated in detail by Lauerwald et al. 11  The only update of the model code used here concerns the simulation of dissolved CO 2 exports from the soil column via surface runoff (considered here to represent shallow subsurface runoff as well) and drainage. In the original version of ORCHILEAK, the CO 2 concentrations in runoff and drainage were fixed to 2 and 20 mg C L À1 , respectively, following observed values (see Lauerwald et al. 11  ). Here, we scale those concentrations to heterotrophic soil (Rh terr ) and root respiration (Ra root ), using simulated values for the period 1991-2000 as reference (4.
ACKNOWLEDGMENTS R. L. acknowledges funding from the  European Union 's  Horizon 2020 Research and Innovation program  under  Marie Sklodowska-Curie  grant agreement no. 703813  ( C-LEAK  project), the  Cland Convergence Institute , and  ISIpedia. All  authors acknowledge funding from the  European Union 's  Horizon 2020 research and innovation program  under  Marie Sklodowska-Curie  grant agreement no. 643052  ( ITN C-CASCADES ). P. F. and P. R. received funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreements  821003  ( 4C  project) and  776810  ( VERIFY  project), respectively. For their roles in producing, coordinating, and making available the ISIMIP model output, we acknowledge the modeling groups that provided data for this study (ORCHIDEE-DGVM, 51 LPJ-GUESS, 52 LPJmL, 53 VISIT, 54 CARAIB,  55 WaterGAP2 46  ) and the ISIMIP coordination team.
At 6 h post-infection (p. i. ) the TER values for each cell line were comparable to those of the respective uninfected control values at 24 h, indicative of an intact epithelium. However, at 16 h all cell lines showed significant decreases in TER, while at 24 h, TER values were comparable to those of an empty filter without cells (dotted line in Figure  2A ). Visual inspection of the Transwell filters under a light microscope confirmed Disruption of the epithelial layer. To determine if the detached Calu-3 cells 24 h p. i. were still viable, we collected and stained these cells with trypan blue. More than 84% of detached Calu-3 wild type cells were stained by Trypan blue compared to 92% of uninfected Calu-3 cells but treated with lactate dehydrogenase lysis buffer. This indicated that detached cells were mostly dying cells resulting from infection with PAO1 (data not shown).
We next compared the PAO1 apical growth on the Calu-3 wild type, CTL and cftr-epithelial layers by CFU counting. PAO1 showed similar growth kinetics on the three cell lines starting from 10 3 CFU/well and reaching 10 7 to 10 9 CFU/well at 24 h p. i. (Figure  2B ). The 2-to 3-log increase in bacterial density at 6 h p. i. did not affect TER, while progressive cell layer destruction and cell detachment occurred thereafter for the three cell lines (Figure  2A ). We also tested twelve CF-isolates from six patients on the Calu-3 wild type and cftr-cells. These isolates caused little or no damage to the epithelial cell layer during 24 h incubation (data not shown). Since this would have limited the evaluation of phages to prevent damage to epithelial cells, we decided to pursue the study with PAO1.
Sample and participant selection The data used in this study were obtained from areas at risk of coastal flooding located in the city of Cartagena (Colombia). The sample was composed of 589 adult participants with 218 males and 371 females (mean age = 43; standard deviation = 15). Participation was voluntary, and all responses were confidential and anonymous. Instructions on how to complete the questionnaire were given to the participants before they were given the statements. Details on the composition of the sample are presented in Table  1. An ethics approval was not required for this study as per institutional and national guidelines and regulations. However, the authors have complied with APA ethical standards, in accordance with the University ethics guideline and ethics was checked at the laboratory level.
Continental and global systems can address various user needs, notably in areas of sparse observational networks. Since they are based on global climate datasets, they also offer consistency among the meteorological data used in the setup and running of catchment-based hydrological models, even though they may lack accuracy at local scales due to local anthropogenic influences that are not usually taken into account in global models  [14]. Although often accompanied by stakeholders' consultation and user needs assessments, large-scale hydrological forecast systems have not yet been thoroughly validated in real-time, operational conditions. Besides, they often lack a comprehensive analysis of the main drivers of hydrological forecast skill (e. g. , what influences forecast performance and how performance can be improved to go beyond climatological information and increase the accuracy and usefulness of hydrological predictions).
For example, there are spatial gaps in hydrological records stored in hydrological databases such as the Global Runoff Data Centre, and downward trends in river flow data availability have been observed since the 1980s. An IMPREX-led study by Lavers et al. [15]  highlighted this issue by comparing the lack of data sharing in the hydrological sciences to the NWP community, a situation partly arising from geopolitical contexts and the voluntary nature of the data upload process  [15] [16] [17]. In addition to the challenges of maintaining large hydrological data archives, hydrological data must also be updated in unstable rivers. Extreme flows may cause changes in riverbed morphology which, in turn, cause changes in the river stage-discharge relation, introducing nonstationary behaviours in the data time series  [18].
e. , at about 1-1. 5 km of altitude. As such, accurate wind observations over the ocean made regularly would benefit the modelling of the global hydrological cycle. The paucity of global surface observations of the terrestrial branch of precipitation and river discharge (and the unknown anthropogenic influences, such as irrigation and reservoir regulation) hamper the undertaking of many verification studies at the global scale. Currently, there is sparsity in spatiotemporal coverage (e. g. , fewer precipitation gauges or radar imagery in mountainous regions), a lack of consistent global discharge datasets and inadequate hydrometeorological data sharing between countries (e. g. , a lack of standardisation protocols and legal and financial mechanisms to support shareable databases). As a consequence, it is currently not possible in many regions to accurately evaluate the skill of predictions from coupled large-scale hydrometeorological models.
An example is given by the ongoing efforts toward building a global dataset of (near) real-time daily discharges. Discharges come from the GloFAS-ERA5 river discharge reanalysis dataset based on runoff from the ECMWF ERA5 global reanalysis coupled to a hydrological and river routing model. It thus corresponds to a proxy for observed discharges and has the advantage of offering a coherent and homogeneous vision over the entire globe, providing a unique benchmark dataset against which to verify the forecast skill and identify key areas for model improvement (Figure  2 ;  [20] ). The performance of large-scale hydrological predictions for local (catchment-based) applications in the water sector needs particular attention, acknowledging the fact that it varies widely according to the physiographic characteristics of the location, the use of water resources in space and its variability in time (e.
g. , storage, diversions, withdrawals) and users' needs for information in their decision-making process (e. g. , nature of information and resolution). Pathways for facing these challenges have recently merged. For instance, in IMPREX, a high-resolution (both temporally and spatially) dataset of area-average precipitation, temperature and potential evapotranspiration (based on satellite downwelling shortwave radiation) was developed for the Rhine River and used to verify the ECMWF ensemble weather forecasts  [21, 22]. These high-resolution datasets have the advantage of better representing the heterogeneities that are not captured by the relatively coarse grid scale of the atmospheric model.
To improve the estimation of the initial conditions of the hydrological model, and thus improve the hydrological forecasts, data assimilation methods (most commonly based on the Ensemble Kalman Filter approach) are applied. Another way to improve hydrological forecasting skill is to improve hydrological modelling (e. g. , using better historical forcing datasets), which Imhoff et a. l  [25]  investigated for the Rhine River as part of IMPREX. Finally, statistical post-processing methods, which mainly aim to increase reliability of probabilistic predictions (e. g. , Bayesian Model Averaging, BMA; Ensemble Model Output Statistics, EMOS), have also been applied to hydrological ensemble forecasts to reduce biases in the output  [3, 26]  prior to IMPREX. It is only when a well-calibrated predictive uncertainty is provided to the end-user that rational decision-making based on a costbenefit analysis is possible.
To improve the estimation of the initial conditions of the hydrological model, and thus improve the hydrological forecasts, data assimilation methods (most commonly based on the Ensemble Kalman Filter approach) are applied. Another way to improve hydrological forecasting skill is to improve hydrological modelling (e. g. , using better historical forcing datasets), which Imhoff et a. l  [25]  investigated for the Rhine River as part of IMPREX. Finally, statistical post-processing methods, which mainly aim to increase reliability of probabilistic predictions (e. g. , Bayesian Model Averaging, BMA; Ensemble Model Output Statistics, EMOS), have also been applied to hydrological ensemble forecasts to reduce biases in the output  [3, 26]  prior to IMPREX. It is only when a well-calibrated predictive uncertainty is provided to the end-user that rational decision-making based on a cost-benefit analysis is possible.
Interaction between Forecasters and Users to Improve Forecasts The approach needed for improved forecast skill requires information exchange and engagement between forecast providers and users. Improved forecasts will be used more often by stakeholders if there is regular interaction with modellers (e. g. , on forecast developments/improvements), with increased feedback to modelling systems to complete the loop in the 'system-user' interface. Users in the water sector have different needs and expectations toward NWP-based hydrological forecasts. In many situations in Europe, these forecasts do not yet have the skill necessary for their immediate and routine use by water managers and stakeholders, especially at seasonal scales. Seasonal forecasts are often available from large datasets, which first require the adequate infrastructure and technology to transfer them to local applications.
3 ). It recognized the permissive host by ∼2 orders of magnitude more efficiently than the WT phage, whereas it reduced its recognition of the K-12 strain by ~3 orders of magnitude compared to the WT phage (Dataset S1). At the starting point of the experiment, most phages recognize the non-productive strain efficiently, while recognition of the permissive host is minimal. After evolving, most of the phage recognize the permissive strain most efficiently, while avoiding the non-productive host. (B) T7 phage was propagated in a continuous evolution experiment in the presence of K-12ΔwaaC permissive host and K-12ΔtrxA restrictive strain. Samples were collected at the indicated time points (dots) and the T7 plaque formation was determined on permissive isogenic strains encoding trxA. EOPs were normalized to the EOP of the permissive host.
Strains. The bacterial strains used in this study are listed in Table  S1. Single knock-out mutants, BW25113ΔwaaC, BW25113ΔwaaF, BW25113ΔwaaG, BW25113ΔwaaR, BW25113ΔwaaO, BW25113ΔtrxA, and BW25113ΔydhQ were acquired from the Keio collection  12. IYB5709 is a strain lacking trxA from the Keio collection  12  that was flipped for its resistance cassette using pCP20 as previously described  19. It was then used as an ancestor strain in homologous recombination to produce double mutants as described below. The WT bacteriophage T7 used in this study has a Gp12 S694P substitution compared to the published sequence (GenBank accession no. CAA24430. 1), but is otherwise similar to GenBank AY264774. Phage-generation time during continuous evolution was calculated as previously described  20. Homologous Recombination.
1  and 2 ). U-Th and 14 C dating of its shorelines constrain the timing of the Lake Tauca cycle, i. e. the transgression (18-16. 5 ka BP), highstand (16. 5-15. 5 ka BP) and regression phases (14. 5-13. 5 ka BP,  Blard et al. , 2013a). During its highstand, the lake level reached a maximum altitude of 3,770 m asl and covered 52,000 km 2. The Coipasa Lake cycle is characterized by a transgression from 13. 3 to 12. 9 ka BP, a highstand from 12. 9 to 11. 8 ka BP and a regression from 11. 8 to 10. 2 ka BP  (Blard et al. , 2011a; Placzek et al. , 2006; Sylvestre et al. , 1999). Placzek et al. (2013)  reported that the highstand lake level reached 3,700 m asl, covering 28 400 km 2. To expand the existing age dataset, we sampled two new bioherms on Isla Incahuasi (20. 24°S -67. 62°W, Table  2 ), a rocky hill in the center of Salar de Uyuni (Fig.
Lake model We used the lake model of  Condom et al. (2004)  as modified by  Martin et al. (2018). This model divides the Altiplano Basin into lake and soil pixels. All soil pixels are considered to be a reservoir that can be filled until its overflow threshold (CapaS) is reached. When precipitation fills the reservoir above CapaS, water flows directly to the lake. The flow path of water from the watershed pixel to the lake is not taken into account. As in  Blard et al. (2009) , the equation used to calculate evaporation is derived from the generalized equation of  Xu and Singh (2000)  that relies on a simplified energy balance budget. The model is run at a quarterly time resolution and a 5-km spatial resolution. Because gridded products are necessary for the lake model, the temperature and precipitation inputs are derived from the dataset of  New et al.
Coupled lake and glacier modeling The complete inversion workflow is presented in Fig. 9  and operates as follows. For a given late-glacial moraine of a paleoglacier, we have access to its age via CRE dating and its ELA via the AAR method. From this ELA, the relation of  Condom et al. (2007)  provides a set of local P-T solutions that respect the glacial extent (green path in Fig. 9 ). From the moraine age, the algorithm considers the synchronous lake shoreline in the updated dataset of the lake level chronology (see Section 4. 1. 1). The paleolake extent corresponding to the selected level is then used to compute the correct hydrologic balance for different cooling and precipitation values (blue path in Fig. 9 ). The precipitation field used in the model includes a local anomaly of enhanced rainfall over the lake region (see Section 3.
3. 5 and Supplementary Information). The intersection of these two computed P-T curves (sets of solutions) then defines the precise climatic conditions (i. e. a single P-T pair) associated with the glacial extent. We note that, despite major lake-size variations, both the continuous sediment accumulation in the Coipasa Basin throughout the study period  (Nunnery et al. , 2019)  and the presence of shoreline records indicate that the lake did not completely dry prior to the Holocene; this algorithm is therefore applicable to the entire last deglaciation. gridded products (lake model inputs) and station data  (Vuille et al. , 2008)  for site-specific data (ELA(P,T) inputs). We used the NASA-USGS SRTM 1-arcsecond DEM. Details on the Precipitation Anomaly routine are provided in Section 3. 3. 5.