dataesr/scientific-paragraphs-categorization huggingface dataset:
Indeed, this type of thermal hydraulic model has been shown to perform poorly when compared to currently available CFD techniques  [18] , especially in significantly deformed fuel bundles  [19]. Additionally, the effect of the deformation on the fuel assembly coolant mass flow rate is not considered in this approach, even though the authors acknowledge the existence of this phenomena. As we will show in this work, this effect is can be significant. On the other hand, the methodology simulation employed in this paper, firstly introduced in  [4] , is based on the numerical coupling between a detailed RANS model of SFR subassemblies, implemented in the CFD code STAR-CCM+, and a SFR bundle thermomechanical model using the code DOMAJEUR2 (CEA inhouse code). In this novel approach, the cladding temperature distribution is computed by means of a detailed RANS model in which the diametral strain and the helical flexion of the claddings can be explicitly considered to compute the temperature field in the deformed geometry.
Insights from primary sequences The development of sequence databases leads to the detection of novel enzymes related to phage λ integrase and the identification of their functional domains by comparative analysis. Despite distant relationships in protein primary sequences, all bacteriophage site-specific recombinase C-terminal ends could be aligned with the yeast 2μ plasmid FLP protein  (Argos et al. 1986). In particular, the perfect conservation of the three residues (HXXR. Y) highlighted a potential active site. The presence of a tyrosine in that region suggested a catalytic function for this residue in DNA cleavage  (Argos et al. 1986). A subsequent analysis revealed yet another conserved arginine residue upstream leading to the consensus tetrad R. HXXR. Y (Abremski and Hoess 1992). These enzymes defined a new family of proteins, the tyrosine recombinases.
Afterward, the alignment of all the tyrosine recombinases available in the databases underlined an important sequence diversity among these enzymes and the conservation of important residues composing the catalytic domain that spans ∼180 residues as confirmed by mutational analysis  (Esposito and Scocca 1997; Nunes-Duby et al. 1998). The study of archaeal organisms also revealed integraseencoding genes from Sulfolobus shibatae spindle-shaped virus (SSV1)  (Palm et al. 1991 ) that split upon integration (Fig. 3A ) and from Sulfolobus NOB8-H2 pNOB8 plasmid  (She et al. 1998). On the basis of a slightly divergent consensus, two subfamilies were identified: the SSV1-type integrases (R. KXXR. Y) and the pNOB8-type integrases (R. YXXR. Y)  (She, Chen and Chen 2004 ) (Fig. 3B ). IntSSV1 and XerA from Pyrococcus abyssi (PaXerA) were shown to form a covalent intermediate with the substrate DNA and the implication of the tyrosine Y314 was evidenced for IntSSV1  (Serre et al.
Extending the archaeal tyrosine recombinase diversity Archaeal tyrosine recombinases belong to the NCBI DNA BRE C superfamily (cl00213 or cd00397) that groups the DNA breakingrejoining enzymes with a catalytic domain in C-terminal position. In addition to tyrosine site-specific recombinases such as integrases, this superfamily also includes Type IB topoisomerases, as they share conserved active site residues and the same fold in their catalytic domain  (Cheng et al. 1998). This DNA BRE C superfamily is composed of five major Pfam domains: Phage integrase (PF00589), Phage integr 3 (PF16795), Topoisom I (PF01028), DUF3504 (PF12012) and Integrase 1 (PF12835). The DUF3504 and Integrase 1 domains are specific to eukarya and bacteria, respectively. In order to propose a comprehensive overview of archaeal integrase diversity, we undertook a phylogenomic analysis based on an exhaustive similarity network to assign all these enzymes to their respective families.
This classification also illustrates the phylogenetic relationships between the families and enlightens the evolutionary links between the three domains of life. For this deep investigation, we extracted from the conserved domain database of the NCBI all archaeal protein sequences belonging to the cl00213 superfamily. To this dataset, we added the previously reported integrases encoded by archaeal viruses  (Pauly et al. 2019 ) and the reconstructed suicidal integrases from the pTN3 and pT26-2 plasmid families  (Cossu et al. 2017; Badel et al. 2019 Badel et al. , 2020)). Partial sequences <150 residues were eliminated from the initial dataset and redundancy was reduced using the UCLUST program  (Edgar 2010)  to eliminate sequences sharing >90% identity (Fig. S1 , Supporting Information). Due to the reported high divergence in tyrosine recombinase sequences especially in the N-terminal region, molecular phylogeny tools are ill-adapted to infer the evolutionary relationships between the members of this superfamily  (Esposito and Scocca 1997).
In a previous report, we successfully used a different methodology based on protein similarity networks to establish relationships between different archaeal integrase families  (Badel et al. 2020). A similar approach was conducted on the present archaeal integrase dataset using the SiLiX program  (Miele, Penel and Duret 2011)  in order to define families sharing 25% identity covering at least 60% of the protein. Among the 4341 archaeal sequences of the cl00213 superfamily, 93. 6% were distributed into three major groups (Fig. 4 ; Table  S1 , Supporting Information). The first group, tentatively named pR1SE1, contains integrases encoded by Haloarchaea and their MGEs such as the pR1SE plasmid. The second group TopoIB corresponds to the eukaryotic-like topoisomerase I (TopoIB) encoded mostly by Thaumarchaea, Bathyarchaea and Aigarchaea.
2018; Badel et al. 2019). The TopoIb and TopoIb 2 families both contained the Topoisom I central domain (PF01028). The TopoIb 2 family is related to viral/bacterial origin whereas the TopoIb family comprises the additional eukaryotic domains Topoisom I N (PF02919) Topo C assoc (PF14370) (Brochier-Armanet, Gribaldo and Forterre 2008). The major families XerA/XerC/pNOB8 or SNJ2 corresponded to proteins with both Phage int SAM (PF02899, PF13495) associated to the Phage integrase domain (PF00589). In the families HSTV2like, fam30-07, fam30-07, fam30-17 and fam30-28 we detected the single Phage integrase domain (PF00589). A particular family, fam30-04, composed of larger proteins carried the Phage integrase (PF00589) BAT (PF15915) and HTH 10 (PF04967) domains with some of them also containing the GAF 2 domain (PF13185).
We did not detect known Pfam domains in the fam30-08 and STSV/SMV families. Overall, the diversity of archaeal tyrosine recombinases is explained by the highly variable domain composition of the 17 major families as they only share a single Pfam domain, Phage integrase (PF00589) (Figs  5  and 6 ). Archaeal integrases are not equally distributed among archaeal species, as for example the pR1SE, HSTV2 and SNJ2 families are restricted to Halobacteria and their MGEs  (Wang et al. 2018). Our analysis confirmed this limited distribution, while other integrase families are spread among different classes or phyla such as XerA/XerC/pNOB8 and fam30-08. Further phylogenomic studies will be required to understand how horizontal gene transfer generated the patchy distribution of tyrosine recombinases among archaeal phyla.
A first observation of our network map (Fig. 7 ) indicated that all archaeal tyrosine recombinases are not connected through a single network, therefore highlighting the inability to perform a robust phylogeny on this dataset. Early reports already discussed the difficulty to compare the primary sequence of archaeal, bacterial and eukaryotic tyrosine recombinases due to the presence of large portions devoid of detectable homology  (Esposito and Scocca 1997; Nunes-Duby et al. 1998). A number of recombinases diverged greatly from the main group (Esposito and Scocca 1997) and many singleton integrases could not be ranked in any subfamily  (Williams 2002). Our network analysis also confirms two previously reported evolutionary relationships, the first connecting the SSV, pTN3 and pT26-2 integrases families and the second connecting the XerA/C recombinases and the SNJ2, Met26-2 and pNOB8 integrase subfamilies  (Wang et al.
2019)  with additional manual curation. The archaeal tree was obtained from the Genome Taxonomy Database (GTDB Release 03-RS86)  (Parks et al. 2020) , generated from 122 core proteins and exported using taxonomic orders as resolution level. The presence/absence profiles for each family were visualized using iTOL  (Letunic and Bork 2019). especially while bound to their DNA substrate might highlight further archaeal peculiarities.
The process is repeated leading to a total of four recombination reactions. (Miele, Penel and Duret 2011)  and visualized with Igraph (https://igraph. org) was used to assign tyrosine recombinases from the superfamily Superfam25-02 to families and subfamilies. The similarities of all against all proteins of the dataset were assessed using BlastP (expect >0. 001, with an identity threshold of 30% among 60% of the protein). Protein clustering was achieved by a random walk algorithm. Each circle corresponds to an individual protein colored according to the clustering. Depending on the topology linkage of the two DNA sequences, the outcome of the recombination varies (Fig. 9A ). Recombination between two sites carried by two independent circular molecules results in their integration. The newly formed chimeric circular molecule harbors the specific site in two copies in direct orientation.
1989). Experimental evidence using mutated and chimeric enzymes from bacteriophages λ and HK022 suggested a multistep process in which integrase specificity first broadens then narrows to permit co-evolution of the target site  (Dorgai, Yagil and Weisberg 1995; Yagil, Dorgai and Weisberg 1995). Despite these efforts, the mechanisms underpinning integrase evolution remains somewhat murky. The special case of suicidal integrases presented in the following sections could shed some light on this fundamental process.
Figure6. Distribution of the 17 major tyrosine recombinase families among the archaeal diversity. Each tyrosine recombinase family corresponds to a dot. In order to visualize the distribution of the major tyrosine recombinase families on the archaeal diversity, we used data from AnnoTree (Mendler et al. 2019)  with additional manual curation. The archaeal tree was obtained from the Genome Taxonomy Database (GTDB Release 03-RS86) (Parks et al. 2020) , generated from 122 core proteins and exported using taxonomic orders as resolution level. The presence/absence profiles for each family were visualized using iTOL (Letunic and Bork 2019).
Monochromatic cross sections are available only to certain research groups (e. g. the Montpellier-Montréal code,  Turcotte et al. 1998). The OP also provides online 2 calculations of Rosseland opacity tables for single mixtures. In addition, the monochromatic data and the codes to handle them  (Seaton 2005)  can also be downloaded. Most of the underlying atomic data are also available through the TOPbase database  (Cunto et al. 1993). Sets of opacity data (monochromatic as well as Rosse-1 https://opalopacity. llnl. gov/ 2 http://opacities. osc. edu/ land means) have also been released by the Los Alamos group 3  (OPLIB;  Colgan et al. 2016, and references therein). Many physical processes lead to departures from the chemical composition of the medium where a star forms. For stars in nuclear burning phases, the chemical composition obviously changes in the layers where nucleosynthesis occurs.
This computation makes use of the monochromatic cross sections of each element, weighted by its abundance. Several evolution codes can compute RMOs from monochromatic data, at least for some parts of the stellar structure. For instance, STARS  (Hu et al. 2011) , TGEC  (Théado et al. 2009) , MESA  (Paxton et al. 2019) , and CESTAM  (Deal et al. 2020)  use the OP data. Instead, the OPAL data are used in the Montpellier-Montréal code  (Turcotte et al. 1998). Using the OP routines to compute Rosseland opacities dynamically requires a huge amount of time, even if they are optimised (e. g. by using parallelisation, as in Hui-Bon-Hoa & Vauclair 2018). Nevertheless, with a suitable computing strategy, we are now able to evolve a star with RMOs that are consistent with the chemical mix everywhere in the star, within a reasonable time.
Stellar evolution code The computations are carried out with the Toulouse-Geneva evolution code (TGEC; Hui-Bon-Hoa 2008;  Théado et al. 2012; Hui-Bon-Hoa & Vauclair 2018). This 1D code implements the nuclear reaction rates from the NACRE compilation  (Angulo 1999) , and the OPAL2001 equation of state  (Rogers & Nayfonov 2002). The energy transport in convective zones is estimated using the mixing-length theory, with a ratio of the mixing length to the pressure scale height equal to 1. 8. On-the-fly calculation of the Rosseland opacities using the Opacity Project OPCD v. 3. 3 data and original routines  (Seaton 2005)  was introduced by  Théado et al. (2009) , but only in the outermost regions (log T < 6. 8), and OPAL tables were used in hotter layers down to the core. In the present study, the RMOs are computed with the OP cross sections throughout the whole star.
9% of the public forested surface area), fir (Abies spp. ; 12. 9%, mainly silver fir Abies alba), Norway spruce (Picea abies; 11. 7%), and Scots pine (Pinus sylvestris; 7. 6%). Sampling design and site selection procedure. We implemented a paired-sampling design, each pair consisting of (i) one coniferous-dominated stand and (ii) one deciduous-dominated or mixed stand, located along a landscape composition gradient. Stand selection was conducted at three spatial scales. At the stand scale (i. e. forest management unit), we first selected, from the National Forest Office database, mature stands (presence of standing/lying dead trees and very large trees with diameter at breast height (DBH) > 60 cm) dominated by Fagus sylvatica, in association with either Abies alba or Picea abies. Then, we matched the selected stands with mature coniferous stands (> 80% of coniferous trees) dominated by Abies alba or Picea abies that were located at the same altitude (mean altitude differences: 33.
98 m, range: 0-157 m) and within roughly 1 km range (mean distance: 497 m, range: 262-1067 m). Stands of the same pair occurred in a relatively similar landscape context with respect to forest cover and proportion of deciduous forest within the forested landscape at different spatial scales (Fig. 1 ; Supplementary Table  S5 ). At the scale of the stand pairs, we aimed at selecting pairs that were separated by a minimum distance of 2 km (mean distance: 2332 m, range: 1900-4377 m) to ensure spatial independence between pairs. Finally, at the landscape scale, we calculated the amount of forest cover and its composition within 1 km radius buffer around the sampling points (i. e. centre of the stands where bat detectors and light-traps were placed) using the CES OSO land cover data 2018 (10 m resolution; osr-cesbio.
Assessment of plot-and landscape-scale variables. We conducted field measurements of structure and composition on fixed-angle plots (~ 30-50 m radius buffer area around the sampling points, depending on location of the largest tree) in July 2019. Using a Bitterlich relascope  65  we recorded basal area (m 2 /ha) of (i) living deciduous and coniferous trees and (ii) large (diameter at 1. 3 m from the thickest end > 40 cm) standing and lying dead trees  64. Stand composition was inferred using the percentage cover of deciduous trees, calculated with basal area measurements  27. Cover of deciduous trees ranged from 30 to 100% in plots located within deciduous/ mixed stands and from 0 to 20% in plots in coniferous-dominated stands (Fig. 1 ). To estimate fine-scale canopy openness and understorey shrub cover (vegetation at 1-8 m height), we established two perpendicular transects of 20 m centred on the sampling point and collected the data at 5-m intervals (i.
e. at 9 survey points). While we acknowledge that this scale does not represent the species-specific bat detection range, we assumed that the data collected were representative of the whole stand. Canopy openness at each survey point was derived from hemispherical pictures taken at 1. 80-2. 10 m height using a fished-eye lens 235° connected to a tablet. We then used the "Sky" package 66 implemented in R v3. 6. 0 67 to calculate the percentage of canopy openness. Shrub cover was estimated using the stratiscope method  68  which consists of noting at each survey point the presence/absence of foliage at 1-8 m height within a virtual vertical cylinder of 50 cm diameter. The cover (%) is then calculated based on the number of points in which the shrub layer was contacted divided by the total number of survey points.
To describe the forest at the landscape scale we calculated the amount of forest cover, number of forest patches, forest edge density, and mean forest patch area. We used the "landscapemetrics" package  69  and the CES OSO land cover data 2018 to compute these variables within 1 km radius buffer around the sampling points. We chose the same spatial grain as for the computation of the landscape composition (see above). Bat echolocation call recording and identification. We conducted bat surveys from 24th June to 24th July 2019 using passive acoustic sampling. We simultaneously surveyed ground and canopy levels by allocating two detectors per site: (i) one Batlogger A/A + (Elekon AG, Lucerne, Switzerland) mounted on a pole at 1. 70 m height; and (ii) one AudioMoth (https:// www. opena coust icdev ices.
This sampling effort represents a good trade-off to adequately assess bat habitat use and characterize local bat assemblages while at the same time maximizing the number of sites surveyed during the same period. We sampled between one and four stand pairs simultaneously during each sampling night. Sampling took place only when weather conditions were optimal for bats to forage, i. e. with no rain, wind speed < 30 km/h, and temperature at sunset > 12 °C. We monitored temperature at night (°C) and relative humidity (%) every 15 min using a data logger (HOBO H08-004-02; accuracy: ± 0. 7 °C for temperature and ± 5% for humidity; Onset Computer Corp. , Pocasset, MA) shielded within a multiplate meteorological shelter placed at 1. 5 m height. Recordings were cut into 5 s audio sequences using Kaleidoscope v5.
1. 9 (Wildlife Acoustics, Maynard, MA) and were then analysed using the Tadarida software  71. Based on a random forest algorithm, this software assigns with a confidence index value (from 0 to 1) each sequence containing bat echolocation calls to a bat species. Following recommendations from Barré, et al. 72  to account for potential automated identification errors  73  , we retained two separate sets of bat sequences: (i) sequences with a score ≥ 0. 90 (i. e. data with maximum error risk tolerance of 10%); and (ii) sequences with a score ≥ 0. 50 (i. e. data with maximum error risk tolerance of 50%). While the former threshold is very conservative by discarding a high number of false negatives, the latter one allows to increase sample size but is less cautious and may include false positives.
Bat activity per night was measured by counting the number of 1-min intervals in which bat sequences of a given species (or species group) were recorded  9, 10  , regardless of the detector type. Using 1-min intervals allowed us to pool the acoustic data from ground and sub-canopy levels to get a single activity index per site and per night and avoiding double counting calls that were simultaneously recorded by the two detectors  6. Nevertheless, this method does not allow to differentiate commuting from foraging behaviour. Further analyses focused on the activity of the main species (or species groups) recorded over sites. Bat species having a diet dominated by Lepidoptera (i. e. > 65% of diet composition) were considered as a moth-feeding specialist (Supplementary Table  S6 ). Moth sampling.
Statistical analysis. We assessed bat responses to (i) deciduous-conifer mixture at both stand and landscape scales, (ii) stand-and landscape-scales variables, and (iii) moth abundance using generalized linear mixedeffects models (GLMMs; "glmmTMB" package  79  ) with Poisson, quasi-Poisson, or negative binomial distribution family to handle over-dispersion. Activity of bat species (or species group) (i. e. activity of B. barbastellus, M. nattereri, H. savii, Nyctalus spp. , P. kuhlii/nathusii, P. pipistrellus, P. pygmaeus/M. schreibersii, Plecotus spp. , and R. hipposideros) identified with a maximum error risk tolerance of 50% was introduced as a response variable into the models. We considered site identity nested within stand pair as a random effect to account for both pseudoreplication (bats being surveyed during two consecutive nights) and the paired-sampling design.
Acknowledgements This study was funded by the  INTERREG SUDOE  " CERES " project (Connectivity of forest and riparian ecosystems of the SUDOE space,  SOE2/P5/F0551 ). We thank  Marvin Graham-Fonseca  and  Jérôme Wilm  for their help during fieldwork,  Marie Gigou  for her help in sorting moths, and  Alexis Laforge  for its advices during the study. We are grateful to the  National Forest Office (Gard and Lozère)  and  Jocelyn Fonderflick  from  Cévennes National Park  for granting us permission to sample. We thank  IN2P3 Computing Centre  for providing facilities to process and archive in the long-term all the acoustic recordings of this study, and  Didier Bas  for help in this process. We are grateful to two anonymous reviewers for their comments on an earlier version of the manuscript.
We then present the results of three years of high-resolution spectroscopic observations, primarily obtained at the Apache Point Observatory, with complementary support from the Haute-Provence Observatory. The objectives of the present paper are: 1) to test and improve the calibration of the asteroseismic scaling relations of RGs; 2) to study the properties of oscillations and surface activity 4 of stars under the influence of a close stellar companion; and 3) to provide new benchmark stars for tidal evolution the-ories. We first present our sample and describe the photometric and spectroscopic data sets we use (Sect. 2). We then detail the methods employed to perform the seismic, dynamical, and orbital analyses of each binary we considered (Sect. 3). Results and discussions about each objective of the paper are covered in Sect.
Observations and data reduction In total, we observed and analyzed the data of 17 stars: 16 were new dectections and one, KIC 4663623, had already been published by  Gaulme et al. (2016a). We found these systems by crossing catalogs of variable stars (e. g. , the General Catalog of Variable Stars;  Samus' et al. 2017) , EBs (e. g. , the Kepler Eclipsing Binary Catalog;  Prša et al. 2011; Abdul-Masih et al. 2016) , and RGs (e. g. , the APOKASC catalog;  Pinsonneault et al. 2014). We used two different types of data: photometric light curves obtained by the Kepler satellite and stellar spectra obtained at ground-based telescopes. We processed the Kepler data in three different ways to optimize the light curves to study the eclipses, stellar oscillations, and surface dynamics. From the spectroscopic observations, we derived the atmospheric parameters, chemical composition, and radial velocities of the stars in our sample.