dataesr/scientific-paragraphs-categorization huggingface dataset:
Year-to-year climate-growth relationships To identify effects of interannual climate variability on year-to-year ring-width variability in the three species, we computed bootstrapped correlation functions (BCFs) between the spline-detrended and residual chronologies on the one hand and mean monthly air temperature ( • C), precipitation totals (mm) and melt-out dates on the other hand using the R (R Core Team, 2016) Treeclim package  (Zang and Biondi, 2015). In addition, we computed a 30-year moving window only for the monthly/seasonal climate parameters that were found to be significant in the BCF to determine possible changes in the strength, sign and significance of correlations among themselves. We extracted monthly meteorological and snowpack variables from the SAFRAN-Crocus reanalyses dataset for the period 1959-2017 (S2M,  Durand et al.
2009; Vionnet et al. 2012 ). This dataset combines in situ meteorological observations with synoptic scale meteorological fields to provide continuous time series of meteorological variables at hourly resolution for elevation bands of 300 m, different slope aspects and angles within spatial units referred to as "massifs" (i. e. Couserans in our case). In addition, S2M also provides snow depth values. Vegetation and microtopography are simplified in the simulations and a continuous grass cover is assumed to simulate typical snow conditions for open areas  (Vionnet et al. , 2012). The simulated snow depth was found to be very comparable to observations in a recent S2M evaluation  (Vernay et al. , 2022) , ensuring reliable snow data for our study. We included monthly predictors from June of the year preceding ring formation (n-1) to October of the year of actual growth (n).
We also considered mean temperature of the vegetative period (i. e. the period without snow cover, starting around May and ending in October), mean temperature of the growing season (around July and August, based on unpublished dendrometric data) and precipitation accumulated from December to February, falling mainly in the form of snow, as indicators of snow accumulation. Finally, we derived snow melt-out dates from S2M according to elevation, slope aspect and slope angle at the study sites. Melt-out dates correspond to the date by which thickness of continuous snowpack falls below 10 cm (see Fig. S1 ). All climate data were detrended using the same approaches as for ring widths (see Chapter 2. 4), as the focus of this analysis was on the interannual radial growth response to climate.
Chronology statistics A total of 15, 13, and 20 individuals were included in the chronologies of R. ferrugineum (hereafter referred to as Rf), J. communis (Jc) and P. uncinate (Pu), respectively, corresponding to rejection rates (i. e. the percentage of misdated individuals) of 32, 38, and 5%. The average ring widths are 0. 16, 0. 47 and 1. 31 mm for Rf, Jc, and Pu, respectively (Table  1 ). The oldest individual sampled was a 212-year-old Rf shrub. Signal strength statistics computed for the standardized, residual chronologies (mean intercorrelation, Rbar, EPS and SSS) show lower values for the Rf and Jc chronologies as compared to the Pu chronology (Table  1 , Fig. 2 ). For instance, the Rbar amounts to 0. 26 for both shrub chronologies, and to 0. 37 in the case of Pu. Yet, between 1960 and 2017, that is the period for which climate data is available, all species exceed the 0.
Acknowledgments This research was supported by the  LabEx DRIIHM   ANR-11-LABX0010  ( Dispositif de Recherche Interdisciplinaire sur les Interactions Hommes-Milieux ),  INEE ,  CNRS , through the  SHRUB  and  CRYOSHRUB  project. We thank the  OHM Haut-Vicdessos Human-Environmental Observatory  and its director,  Didier Galop , for the financial support. We thank all who contributed to the meteorological series data used in the analyses, especially  Samuel Morin  ( Météo-France -CNRS, CNRM, CEN ). We are grateful to  Brigitte Girard  for her help during the laboratory phase.
Code availability. The CHIMERE v2020 model is available on its dedicated web site https://www. lmd. polytechnique. fr and for download at https://doi. org/10. 14768/8afd9058-909c-4827-94b8-69f05f7bb46d. Data availability. All data used in this study, as well as the data required to run the simulations, are available on the CHIMERE web site download page https://doi. org/10. 14768/8afd9058-909c-4827-94b8-69f05f7bb46d.
Results The cnidarian Tlx ortholog shares a highly conserved genomic structure with bilaterian Tlx. Cnidarian and bilaterian Tlx genes are remarkably conserved in structure, including an EH1 domain near the N-terminus, the homeodomain, and its signature motif, RRIGHPY just upstream of the homeodomain, called the N-terminal arm (Figure  1A ). In our search of public databases for Tlx, the EH1 domain, as well as the N-terminal arm were invariably found in complete sequences of Tlx in both cnidarians and bilaterians. However, these conserved regions could not be found in the putative Tlx orthologs of sponges and placozoans, and although ctenophore sequences had an EH1 domain, they lacked the N-terminal arm. In addition to low sequence identity in the homeodomain, a Bayesian phylogenetic analysis did not recover the putative Tlx-like gene previously identified in ctenophores  (Pang and Martindale, 2008 and Derelle and Manuel, 2007) , within the strongly supported bilaterian and cnidarian Tlx orthology group (Figure  S1 ).
Cnidarian genomes and transcriptomes assembly. Most of the genome and transcriptome sequences were obtained from the NCBI sequence read archive (SRA) (Table  S1 ) and required assembly in order to screen for Tlx. Each library was trimmed of low-quality reads and adapters using fastp  (Chen et al. , 2018). For those transcriptomes from different libraries, filtered reads were combined into a single dataset followed by de novo transcriptome assembly using Trinity v2. 8. 5  (Grabherr et al. , 2011). Genome assemblies were carried out using Spades v3. 13. 1  (Bankevich et al. , 2012). Genomes that did not require assembly were obtained from NCBI or from unpublished work shared by collaborators. The source of all the genomes and transcriptomes used in this study can be found in Table  S1. In silico search of Tlx and phylogenetic analyses.
Frey (1968)ocoryna carnea de novo transcriptome assembly used in this study, planula and early budding polyp libraries were sequenced and the sequences submitted to NCBI Sequence Read Archive (BioProject ID PRJNA744579) in addition to updated libraries for non-reproductive polyps, budding polyps and medusae (BioProject ID PRJNA245897), and used along with previously generated P. carnea libraries described in Sanders and Cartwright, 2015. Stage 2 planulae (elongated, swimming, non-competent) and stage 1-2 early budding polyps, seeFrey (1968), were flash frozen and sent to KUMC-GSF for RNA library preparation using the TruSeq RNA Sample Preparation Kit (BoxA). All libraries were 100 bp paired-end with an average insert size of 170 bp.
Acknowledgments We thank  A. Baxevanis  and  E. S Chang  for providing draft genome sequences,  A. Klompen  for help in data collection and  R. Helm  and  Catriona Munro  for providing access to transcriptome sequences. Genome sequencing services was provided by the  University of Kansas Medical School Genome Sequencing Center. Computing support was provided by the  University of Kansas Center for Research Computing. This work was supported by the  National Science Foundation (NSF) Division of Environmental Biology 095357 the University of Kansas General Research Fund , and the  University of Kansas Research Excellence Initiative  (to PC) and support to PC while serving at the National Science Foundation MT was supported by the  Chancellor's Doctoral Fellowship  at the  University of Kansas  and received research support from the  KU Graduate Research Fund.
Frey (1968)ocoryna carnea de novo transcriptome assembly used in this study, planula and early budding polyp libraries were sequenced and the sequences submitted to NCBI Sequence Read Archive (BioProject ID PRJNA744579) in addition to updated libraries for non-reproductive polyps, budding polyps and medusae (BioProject ID PRJNA245897), and used along with previously generated P. carnea libraries described in Sanders and Cartwright, 2015. Stage 2 planulae (elongated, swimming, non-competent) and stage 1-2 early budding polyps, seeFrey (1968), were flash frozen and sent to KUMC-GSF for RNA library preparation using the TruSeq RNA Sample Preparation Kit (BoxA). All libraries were 100 bp paired-end with an average insert size of 170 bp. Gajewsky et al, 1996. Animals were fixed in ice cold fix (3.
7%PFA and 0. 25% glutaraldehyde in 1X PBS). Hybridization was carried out at 50˚C for 18 hours with a probe concentration of 1 ng/µl. DIG labeled riboprobes localization was detected by immunostaining with anti-DIG-Fab-AP (ROCHE) and NBT/BCIP. De novo transcriptome assembly for P. carnea. v2. 8. 5 (Grabherr et al. , 2011), yielding 472366 transcripts total with an average length of 714. 62 and a G+C content of 36. 76%. Transcripts were blasted against a Mus musculus transcriptome dataset (GCA_000001635. 9 GRCm39) with an e-value threshold of 1. e -100 , and best hits were removed from the assembly. The longest ORFs from the transcriptomes were predicted using Transdecoder version 5. 5. 0 (http://transdecoder. github. io), duplicate sequences and isoforms were removed by clustering sequences with a 95% identity threshold using CD-HIT version 4.
8. 1 (Fu et al, 2012) and a BUSCO analysis (Simão et al, 2015) against the metazoan database (metazoa_odb10) was performed to assess the completeness of the transcriptome, estimating a 96. 2% completeness of the transcriptome (82. 9% single copy Buscos and 13. 3% duplicated Buscos), 1. 5% of fragmented Buscos and 2. 3% of missing Buscos on a total of 954 BUSCO markers. (Dobin et al, 2013) barcoded, pooled, and multiplexed on a single lane of an Illumina NovaSeq 6000 S1 flow cell at KUMC-GSF. Low -quality reads were trimmed and adapters using fastp(Chen et al. , 2018). Reads from all libraries but the planula ones were mapped to the draft genomes of their respective strains of Podocoryna carnea (Chang and Baxevanis, pers. comm. )using STAR(Dobin et al, 2013). Uniquely mapped reads (61. 28%) and reads mapping on multiple loci (20.
When accreting X-ray pulsars (XRPs) undergo bright X-ray outbursts, their luminosity-dependent spectral and timing features can be analyzed in detail. The XRP GRO J1750-27 recently underwent one such episode, during which it was observed with NuSTAR and monitored with NICER. Such a data set is rarely available, as it samples the outburst over more than 1 month at a luminosity that is always exceeding ∼5 × 10 37 erg s -1. This value is larger than the typical critical luminosity value, where a radiative shock is formed above the surface of the neutron star. Our data analysis of the joint spectra returns a highly (N H ∼ (5-8) × 10 22 cm -2 ) absorbed spectrum showing a Kα iron line, a soft blackbody component likely originating from the inner edge of the accretion disk, and confirms the discovery of one of the deepest cyclotron lines ever observed, at a centroid energy of ∼44 keV corresponding to a magnetic field strength of 4.
6 keV, and plasma optical depth τ = 6. 4). During the 2021 outburst, we initiated a monitoring campaign with NICER and NuSTAR. Preliminary results from those observations allowed to identify an electron cyclotron resonant scattering feature (CRSF) for the first time in this source at ∼43 keV  (Malacaria et al. 2022b; Devaraj & Paul 2022; Sharma et al. 2022 ). Here we further investigate the implications of the spectral analysis carried out with phenomenological and physical models, and take advantage of NICER data to explore the timing behavior and pulse-profile evolution of the source throughout the outburst.
NuSTAR NuSTAR  (Harrison et al. 2013 ) was launched in 2012 and is currently the only X-ray mission with a telescope able to focus hard X-rays up to 79 keV. NuSTAR consists of two identical coaligned telescopes that focus X-ray photons onto two independent Focal Plane Modules, FPMA and FPMB. At the focus of each telescope module are four (2 × 2) solid-state cadmium zinc telluride (CdZnTe) imaging detectors. These provide wide-band (3-79 keV) energy coverage with a FWHM of 18 and a spectral resolution of 400 eV at 10 keV, for a field of view of 10 arcmin at 10 keV. NuSTAR observed GRO J1750-27 on September 27, 2021 (ObsID 90701331002, MJD 59484. 2) following a Director's Discretionary Time (DDT) request. The filtered total exposure time was about 30 ks. NuSTAR data were reduced with NUSTARDAS v2.
1. 1 and using the CALDB 20211020  (Madsen et al. 2022). Cleaned events were obtained following the standard NuSTAR guidelines. Source spectra were extracted through the NUPRODUCTS routine. The source extraction region was a 65 radius circular region centered on the Chandra source position. Due to the position angle (PA) observational constraints and the vicinity of other bright sources, the NuSTAR field of view for both modules is contaminated by stray light and ghost rays. A careful background extraction was therefore applied, in concert with the instrument team. Given that NuSTAR internal background varies from detector to detector, the background should be extracted from the same detector as the source when possible  (Madsen et al. 2015). We therefore extracted the background from a source-free and contamination-free region of comparable radius to that of the source extraction region on the same detector (Det 0) and compared it to the background spectrum of other detectors.
This allowed us to verify that the background spectrum was free from artificial, detector-dependent features. Moreover, a spectrum of the contaminating flux was extracted and analyzed. Compared to the source spectrum, the contaminating spectrum was found to be about 300 times fainter, free from prominent features, and dominated by the background counts above 20 keV. We therefore concluded that our NuSTAR source spectrum can be safely considered free from contamination. The energy band of NuSTAR spectral data was limited to the 4-60 keV energy range, above which the background counts dominate the spectrum, and to prevent calibration uncertainties in the 3-4 keV energy band.
However, simultaneous NICER observations were prevented by ISS visibility constraints. NICER started observing GRO J1750-27 on September 24, 2021 (ObsID 4202350101, MJD 59481. 8), and continued to monitor the source with variable cadence until November 4, 2021 (ObsID 4202350121). The total exposure time was about 24 ks after data cleaning. Hereinafter, NICER ObsIDs are identified by their last two digits, that is 42023501XX. We also verified that NICER data were not contaminated by other sources in the relatively large field of view. In fact, the field of view includes the supergiant fast X-ray transient IGR J17503-2636  (Ferrigno et al. 2019). However, the same sky region was also monitored by INTEGRAL, and IGR J17503-2636 was not X-ray active during our NICER observations. NICER data were processed with HEASoft version 6.
29c and the NICER Data Analysis Software (nicerdas) version 8 (2021-08-31_V008c) with Calibration Database (CALDB) version xti20210720, adopting standard calibration and screening criteria from the nicerl2 tool. The adopted version of nicerdas also produces proper response files (RMF and ARF) for each ObsID. The background spectrum was obtained using the nibackgen3C50 v7 tool 2  (Remillard et al. 2022). Hot detectors and additional off detectors in a few ObsIDs were also identified and manually excluded during the data-reduction pipeline. ObsIDs 02 and 03 were not considered here due to their null exposure time. As advised by the instrument team, a systematic error of 1% has been applied for all NICER spectra. The energy band of NICER spectra was limited to 0. 7-10 keV, below which background counts dominate the spectrum, and to prevent calibration uncertainties in the 10-12 keV energy band.
1 × 10 22 cm -2 (HI4PI Collaboration 2016). We performed spectral analysis both for the joint NICER and NuSTAR spectra (using data from the neighbor NICER ObsID 04, see Sect. 3. 1. 2), and for the single NICER observations (see  Sect. 3. 1. 3 ). In the following, errors are reported at 1σ c. l. in tables and figures. Errors for the joint NICER and NuS-TAR spectral analysis are calculated through MCMC simulations using the Goodman-Weare algorithm of length 2 × 10 5 with 20 walkers and 10 4 burn-in steps. For the joint fit, the goodnessof-fit parameter is also reported as resulting from the goodness tool from XSPEC (with fit and nosim options), indicating the percentage of simulated spectra with a test statistic better than the one obtained from real data. In those cases, for each relevant model, we also report the null-hypothesis probability (NHP) as resulting from the XSPEC fitting procedure, which represents the probability of the data being drawn from the best-fit model.
NICER ObsID spectral analysis NICER spectral data from each ObsID have also been modeled to investigate spectral variations throughout the observed outburst stages. Due to the restricted energy band, only a relatively simple model was tested, namely an absorbed cutoff power-law model with the addition of a Gaussian emission line for the Fe Kα around 6. 4 keV. Results are reported in Table  3. The model can fit the data at all ObsIDs, returning best-fit spectral continuum parameters that are roughly consistent throughout all observations. The iron Kα line in GRO J1750-27 as observed by NICER has an energy of about 6. 3-6. 6 keV. This is consistent with emission of fluorescent lines from neutral or weakly ionized iron. The only observation that did not require an iron line component (i. e. , normalization consistent with zero) is the ObsID 01, at the lowest flux observed by NICER.
The nondetection of the iron line in ObsID 01 was also tested through Monte Carlo simulations. To this aim, the XSPEC simftest routine was employed, which allows the user to simulate a given number of spectra based on the actual data and test the resulting ∆χ 2 between each instance of the fit with and without the model component to be tested (the Gaussian line in our case). A total of 10 4 simulations were carried out. The obtained ∆χ 2 from fitting the actual data with and without the iron line is about 6, but about 10% of the simulations show a higher ∆χ 2 , thus suggesting that the observed improvement is likely due to statistical fluctuations (significance <2σ). To further investigate the spectral behavior, we explored the spectral evolution in a model-independent way, that is by means of the hardness ratio (HR).
Such an estimate represents a valid approximation if the spectral emission does not change its shape, a condition that is well satisfied here (see  Sect. 3. 1. 3). By applying this conversion factor, and assuming a distance value of 14 kpc (see Sect. 3. 1. 1), a corresponding luminosity value was associated to each Swift/BAT data point and therefore to each correspondent NICER observation. This information was included in the NICER pulse-profile heat map in Fig. 5. A selection of representative NICER pulse profiles is also shown in Fig. 6. Finally, we also investigated the evolution of the pulsed fraction (PF) during the outburst. For this, the PF was defined as (I max -I min )/(I max + I min ), where I max and I min are the maximum and minimum pulse-profile count rates, respectively. The observed PF evolution is shown in Fig.
Summary We performed spectral and timing analysis of the accreting XRP GRO J1750-27 as observed by NICER and NuSTAR at the peak of its latest outburst toward the end of 2021. Our main results can be summarized as follows: -We tested unexplored spectral models and confirm the discovery of a cyclotron line from this source at about 44 keV, implying a magnetic field strength of ∼4. 7 × 10 12 G (consistent with recent independent findings). The cyclotron line depth is possibly amongst the largest ever observed. Our results also highlight a highly absorbed source (N H ∼ (5-8) × 10 22 cm -2 ), and a soft blackbody component likely originating from reprocessing at the inner edge of the accretion disk. -The spectral data can be fitted with a physical model based on bulk and thermal Comptonization (bwcyc), favoring a distance of 14 kpc and supporting the magnetic field strength derived by the cyclotron line.
Acknowledgements. This research has made use of data and software provided by the  High Energy Astrophysics Science Archive Research Center (HEASARC) , which is a service of the  Astrophysics Science Division at NASA/GSFC  and the  High Energy Astrophysics Division of the Smithsonian Astrophysical Observatory. We acknowledge extensive use of the NASA Abstract Database Service (ADS). This work used data from the NuSTAR mission, a project led by the  California Institute of Technology , managed by the  JPL , and funded by  NASA , and has utilised the NUSTARDAS software package, jointly developed by the  ASI Science Data Center, Italy , and the  California Institute of Technology, USA. DA acknowledges support from the  Royal Society. This work was supported by  NASA  through the  NICER mission and the Astrophysics Explorers Program.
| Soil and water sampling The sampling scheme used in this study was designed to provide the data that are needed for calculating values of E a and T a per the mass balances that the previous authors had applied to grass vegetation and soybean fields  (Wenninger et al. (2010)  Specifically, the samples were taken during February and June (bare soil, during the dry season); in December (after harvest, during the dry season); and during the August wet season, which corresponds to the growing stage of groundnut crops and pasture. At Site 1, samples were collected from the upper, middle, and lower slopes. Only the middle and lower slopes were sampled at Site 2. Continuous soil cores were collected with a hand auger (Eijkelkamp, Netherlands). At Site 1, two core soil profiles were carried out at each slope location, from a depth of 5 cm down to the capillary fringe (500 cm below ground level at the upper and middle slopes, and 400 cm below ground level at the lower slope).
| Soil physical properties To determine their textures and dry bulk densities (ρ b ), soil profiles from Site 1 were sampled with a calibrated cylinder (100 cm 3 ). The ρ b values were then determined in Dakar by the "Laboratoire d'Ecologie Microbienne des Sols et Agrosystèmes Tropicaux", LMI IESOL, by weighing the soil samples after oven-drying for 48 h at 105 C. Porosities (p) of soil samples were calculated following  Black et al. (1965) , assuming a particle density of 2. 65 g cm À3. The particle size distributions (PSDs) of samples were determined via the laser diffraction method at the laboratory of "LEHNA-ENTPE," in Lyon, France. With these PSD results as inputs, the GRADISAT V9. 1 package  (Blott, 2001)  was used to compute the fractions of sand, silt, and clay, and thus to classify the soil textures according to the USDA.
For Site 2, we used soil-texture data from  Faye, Diallo, et al. (2020 ), Faye, Fall, et al. (2020) , which covered the 0-100 cm depth on the mid-slope locations as well as on the lower-slope locations, near the pond. The gravimetric water content (GWC) of each soil sample (all had been stored in double Ziplock bags; Section 2. 2) was determined by weighing and drying before and after isotope analyses. The volumetric water content (VWC) was calculated according to  Gardner (1965) , using the measured ρ b and assuming a water density (ρ w ) of 1 g cm À3. ET a values for the groundnut crop at Site 1 were derived from latent heat (λE) flux analysis via the eddy-covariance technique. For that purpose, an antenna equipped for eddy-covariance measurements (Li-7500A (LiCor) + Windmaster Pro (Gill)) had been installed in Site 1, at a height of 4.