dataesr/scientific-paragraphs-categorization huggingface dataset:
Thus, we decided to perform spectral fitting despite the low number of counts. The spectral analysis was carried out using the five detectors with the on-chip aluminium filter (telescope modules 1, 2, 3, 4 and 6), avoiding the light leak in the other two detectors  56. The eSASS  57  users version 211214 was used to process the data. Only single-pixel events without any rejection or information flag set were selected, using the eSASS task evtool. With the eSASS task srctool, a circular source region with radius 100″, centred on the coordinates RA (2000. 0) = 05 h 20 min 16. 6 s, dec. (2000. 0) = -65° 44′ 27″ was defined to select source events. A background region of the same size and shape centred on RA (2000. 0) = 05 h 21 min 9. 4 s, dec. (2000. 0) = -65° 46′ 0″ was defined, so as to lie at the same ecliptic longitude as the source region and hence in the scanning direction of eROSITA.
The corresponding ARF and RMF files were created by the same eSASS task. Spectra were constructed by combining all events within the respective regions for each of the five epochs of observation. An absorbed blackbody was fitted to each of the spectra using 3ML. The priors of the free parameters were chosen on the basis of the XMM-Newton fit results. For the absorbing column, a Gaussian centred at μ = 2. 7 × 10 21 cm -2 and with a width of σ = 0. 4 × 10 21 cm -2 was used. The prior on kT was a Gaussian with μ = 45 eV and σ = 4 eV, truncated at zero, and the prior on the normalization was a log-normal distribution with μ = log(400) and σ = 1. For the eROSITA data, the background was not modelled because of the low number of counts; rather, the data were binned to have at least one background photon in every bin and a profile Poisson likelihood was used.
Acknowledgements R. W. is supported by the  German Research Foundation (DFG)  under contract  GR1350/17-1  and J. M. B. by the DFG-funded Collaborative Research Center SFB 1258. We thank  M. Freyberg  ( MPE Garching ) for discussion of the XMM-Newton background modelling and S. Rappaport (MIT) on the TESS time-series analysis. J. G. is grateful to the  Swift team  for the rapid scheduling of the UVOT observation. This work is based on data from eROSITA, the soft X-ray instrument aboard SRG, a joint  Russian-German  science mission supported by the  Russian Space Agency (Roskosmos) , in the interests of the  Russian Academy of Sciences  represented by its  Space Research Institute (IKI)  and the  Deutsches Zentrum für Luft-und Raumfahrt (DLR). The SRG spacecraft was built by  Lavochkin Association (NPOL)  and its subcontractors and is operated by NPOL with support from the  Max Planck Institute for Extraterrestrial Physics (MPE).
with the support of the  DLR  and the Max also participated in the science preparation for eROSITA. The eROSITA data shown here were processed using the eSASS software system developed by the  German eROSITA Consortium. This paper uses public-domain data obtained by the  MACHO  Project, jointly financed by the  US Department of Energy  through the  University of California ,  Lawrence Livermore National Laboratory  under contract no. W-7405-Eng-48, by the  National Science Foundation  through the  Center for Particle Astrophysics of the University of California  under cooperative agreement  AST-8809616  and by the  Mount Stromlo and Siding Spring Observatory , part of the  Australian National University. Some of the observations reported in this paper were obtained with the  SALT  under programme  2018-2-LSP-001.
Data availability Data from the ROSAT, XMM-Newton, Swift and TESS missions, as well as from the OGLE and MACHO projects, are publicly available. eROSITA data of the first survey (eRASS1) of [HP99] 159 will be made public as part of the eRASS1 data release, scheduled for April 2023 at present. Data of the subsequent eROSITA surveys (eRASS2 and later) will be made public according to the plan as provided at https://erosita. mpe. mpg. de/erass/. The optical spectra taken with the SALT telescope are available at https://cloudcape. saao. ac. za/index. php/s/g8M1q1ya8ef7Fzd.
Acknowledgements The authors are thankful to  D. Rontani  and  K. Knobloch  for providing feedback on the manuscript and fruitful discussions. This study was supported by the  German Research Foundation (DFG)  within programs  SCHU 2922/1-1  (H. S. , T. H. , C. H. ),  KA 5069/1-1 , and  KA 5069/3-1  (L. K. ), as well as by the  French Research Agency (ANR)  under contract No. ANR-20-CE24-0012  ( MARIN ) (S. T. , J. V. K. ). The project has received funding from the  EU Research and Innovation Programme Horizon Europe  under grant agreement no. 101070290  ( NIMFEIA ) (K. S. ). Support by the  Nanofabrication Facilities Rossendorf (NanoFaRo)  at the  IBC  is gratefully acknowledged.
Contributing to filling this gap is the goal of  Sanderson et al. (2023) , based on a compilation of both trait means and variances measured in context of environmental changes. Means and variances are globally positively correlated, and the distribution of genetic and phenotypic variances is also very similar. No general patterns for phenotypic variance emerge among different types of environmental changes, or traits, rendering prediction of future phenotypic trends difficult. Sanderson et al. (2023)  conclude that much has still to be done to build more ambitious datasets on a wide range of organisms, but also to link this variance to environmental variance, a pre-requisite to EED studies. Part of this variance is due to phenotypic plasticity, and here again, a plethora of studies have been conducted  (Pfennig, 2021).
Materials and Methods Botanical exploration and ecological studies were conducted from September 2018 to August 2021 in Ossudu (Figure  1 ) along the Coromandel Coast, Villupuram district of Tamil Nadu. Ossudu Bird Sanctuary falls in two political boundaries, viz. , Union Territory of Pondicherry and Villupuram district of Tamil Nadu. During the survey an unfamiliar sedge was found and collected from the eastern shores of Ossudu Lake and the collected specimen was processed and deposited at Herbarium, French Institute of Pondicherry (HIFP) for further studies. Camera attached light microscope was used to examine the morphological features of glume and nutlets to understand the morphological differences. Recent publications  (Mao & Dash 2020; Narasimhan & Sheeba 2021)  and consultation of herbaria (Madras Herbarium (MH), Rapinath Herbarium Tiruchirapalli (RHT), Deccan Reginal Centre (DRC), Auroville Herbarium (AURO)) for the occurrence and international datasets (GBIF-the Global Biodiversity Information Facilityhttps://www.
JWST observed WASP-39b as part of its Transiting Exoplanet Community Early Release Science Program (ERS Program 1366), with the goal of explaining its atmospheric composition 2,3. Data from the NIRSpec PRISM and G395H instrument modes showed a distinct absorption feature between 4. 0 μm and 4. 2 μm, peaking at around 4. 05 μm, that atmospheric radiative-convective-thermochemical equilibrium models could not explain with metallicity and C/O values typically assumed of gas giant planets (1-100× solar and 0. 3-0. 9, respectively  8, 9  ). After excluding instrument systematics and stellar variability, a thorough search for gases has shown SO 2 as a promising candidate with the best-fit absorption feature (see Methods), although ad hoc spectra with injected SO 2 were used in the analysis. Sulfur shares some chemical similarities with oxygen but uniquely forms various compounds with a wide range of oxidation states (-2 to  +6 (ref.
Article H 2 S, S 2 , SO 2 and SH produce a sharp opacity gradient shortward of 0. 38 μm (Extended Data Fig. 7 ), at which the room-temperature UV cross-sections are used except those at 800 K for SH. The discrepancy between the models and previous HST STIS and VLT/FORS2 observations  19  (see Fig. 3 ) within 0. 38-0. 5 μm could potentially be because of enhanced UV opacities at high temperatures and/or aerosol particles. Further characterization of the sulfur species spectral features in the UV is promising with the scheduled HST/UVIS observation (Program 17162, principal investigators: Z. Rustamkulov and D. Sing). SO 2 has recently been suggested as a promising tracer of metallicity in giant exoplanet atmospheres  16. To test this and show trends in atmospheric properties, we have conducted sensitivity analysis on metallicity as well as temperature and vertical mixing using VULCAN (see Methods for details and further tests on C/O and stellar UV flux).
Simulated transmission spectra from gCMCRT To post-process the 1D photochemical model output and produce transmission spectra, we use the 3D Monte Carlo radiative-transfer code gCMCRT  40. For processing 1D columns, gCMCRT uses 3D spherical geometry but with a constant vertical profile across the globe in latitude and longitude. In this way, spectra from 1D outputs can be computed. We process the morning and evening terminator vertical 1D chemical profiles of each photochemical model separately, taking the average result of the two transmission spectra to produce the final spectra that are compared with the observational data. In the transmission spectra model, we use opacities generated from the following line lists: H 2 O (ref. 61).
Description of photochemical models We use the following 1D thermo-photochemical models to produce the steady-state chemical abundance profiles for the terminators of WASP-39b. All models assume cloud-free conditions and adopt the same temperature profiles, stellar UV flux, eddy diffusion coefficient profile (Extended Data Fig. 2 ) and zero-flux (closed) boundary conditions. A zenith angle of 83° (an effective zenith angle that matches the terminator-region-mean actinic flux for near-unity optical depth) is assumed for the terminator photochemical modelling. VULCAN. The 1D kinetics model VULCAN treats thermochemical  78  and photochemical  6  reactions. VULCAN solves the Eulerian continuity equations, including chemical sources/sinks, diffusion and advection transport, and condensation. We applied the C-H-N-O-S network (https://github.
com/exoclime/VULCAN/blob/master/thermo/SNCHO_ photo_network. txt) for reduced atmospheres containing 89 neutral C-bearing, H-bearing, O-bearing, N-bearing and S-bearing species and a total of 1,028 thermochemical reactions (that is, 514 forward-backward pairs) and 60 photolysis reactions. The sulfur allotropes are simplified into a system of S, S 2 , S 3 , S 4 and S 8. The sulfur kinetics data are drawn from the NIST and KIDA databases, as well as modelling  5, 79  and ab initio calculations published in the literature (for example, ref. 80). For simplicity and cleaner model comparison, the temperature-dependent UV cross-sections  6  are not used in this work. The pathfinding algorithm described in ref. 81 is used to identify the important chemical pathways. We note that the paths presented in this study are mainly based on VULCAN output (see Extended Data Table  1 ).
86), a package included in the open-source software RMG v3. 1. 0 (refs. 87,88), with atomic-energy corrections, bond corrections and spin-orbit corrections, based on the CBS-QB3 level of theory as the model chemistry. The reaction rate coefficients and photolysis cross-sections for these S and Cl species are derived from Venus studies  [89] [90] [91] [92] [93] [94]  , interstellar medium studies  95  , Io photochemical models  96, 97  , Jupiter cometary-impact models  98, 99  , the combustion-chemistry literature  [100] [101] [102] [103]  , terrestrial stratospheric compilations  104, 105  and numerous individual laboratory or computational kinetics studies (such as refs. 106-110). ARGO. The 1D thermochemical and photochemical kinetics code ARGO originally  111, 112  used the Stand2019 network for neutral hydrogen, carbon, nitrogen and oxygen chemistry.
We first tested the sensitivity to the assumed stellar spectra by performing the same models with the solar spectrum (close to WASP-39) and found negligible differences in the photochemical results. Because the UV spectrum shortward of 295 nm is constructed from stellar proxies rather than directly measured, we then focused on varying the stellar flux in the FUV (1-230 nm) and NUV (230-295 nm) separately. Extended Data Fig. 8  shows that the resulting sulfur species abundances are almost identical when the UV flux is reduced by a factor of 10, broadly consistent with what Zahnle et al. 5  suggested that the photochemical destruction of H 2 S only becomes photon-limited when the stellar UV flux is reduced by about two orders of magnitude (for a directly imaged gas giant). On the other hand, although SO and SO 2 are not sensitive to increased NUV, they are substantially depleted with increased FUV.
Opacities of sulfur species The opacities of sulfur species illustrated in Extended Data Fig. 7  are compiled from UV cross-sections and infrared line lists. The room-temperature UV cross-sections are taken from the Leiden Observatory database  120   Alternative SO 2 production pathways S 2 formation can compete with SO 2 production, as we will explore in detail in the next section. On WASP-39b, reactions involving S 2 are found to be important in oxidizing S at high pressures at which less OH is available. S and SH would first react to form S 2 by SH + S → H + S 2 before getting oxidized through S 2 + OH → SO + SH. The scheme is similar to that in equation (1) except SH and S 2 play the role of the catalyst to oxidize S into SO, whereas SO can also self-react to form SO 2 in this regime (references of important reactions are listed in Extended Data Table  1 ).
39, in which an internal temperature of 100 K with perfect heat redistribution and gravity g = 1,000 cm s -2 are assumed. Apart from the thermal profiles, we keep the rest of the planetary parameters the same as the WASP-39b model in this work, including stellar UV irradiation. Extended Data Fig. 10b  reveals the observation of sulfur photochemistry on other irradiated exoplanets, summarizing the averaged abundances of the key sulfur molecules produced by photochemistry as a function of equilibrium temperature. For 10× solar metallicity, the sweet-spot temperature for producing observable SO 2 is 1,000 K ≲ T eq ≲ 1,600 K. For T eq ≲ 1,000 K, SO 2 production below the 0. 01-mbar level ceased and S x (sulfur allotropes; mainly S 2 and S 8 here) is more favoured. For T eq ≳ 1,600 K, SH becomes the predominant sulfur-bearing molecular (apart from atomic S) around mbar levels.
Acknowledgements This work is based on observations made with the  NASA/ESA/CSA JWST. The working groups are associated with programme  JWST-ERS-01366. The initial manuscript was improved by the constructive comments from  L. Mancini ,  J. Mendonça ,  A. Saba  and  X. Tan. S. -M. T. is supported by the  European Research Council  advanced grant EXOCONDENSE (no. 740963 ; principal investigator:  R. T. Pierrehumbert ). E. K. H. L. is supported by the  SNSF   Ambizione Fellowship grant  (no. 193448 ). X. Z. is supported by  NASA Exoplanet Research  grant  80NSSC22K0236. O. V. acknowledges funding from the  ANR  project  'EXACT'  ( ANR-21-CE49-0008-01 ), from the  Centre National d'Études Spatiales (CNES)  and from the  CNRS/INSU Programme National de Planétologie (PNP). L. D. acknowledges support from the  European Union   H2020-MSCA-ITN-2109  under grant no.
Data availability The data used in this paper are associated with JWST ERS Program 1366 and are available from the Mikulski Archive for Space Telescopes (https://mast. stsci. edu), which is operated by the Association of Universities for Research in Astronomy, Inc. , under NASA contract NAS 5-03127 for JWST. The chemical networks and abundance output of the photochemical models (ARGO, ATMO, KINETICS and VULCAN) presented in this study are available at https://doi. org/10. 5281/zenodo. 7542781.
Thus, data relevant to an elemental, mechanistically focused decomposition ecology are available in freshwaters, but have not been fully applied to that purpose. We synthesized published detrital nutrient and stoichiometry measurements at a global scale, yielding 4038 observations comprising 810 decomposition time series (i. e. , measurements within a defined cohort of decomposing material through time) to build a basis for understanding the temporality of elemental content in freshwater detritus. Specifically, the dataset focuses on temporally and ontogenetically (mass loss) explicit measurements of N, P, and stoichiometry (C:N, C:P, N:P). We also collected ancillary data, including detrital characteristics (e. g. , species, lignin content), water physiochemistry, geographic location, incubation system type, and.
We mention that B = (b 1 , b 2 , b 3 , b 4 , 0, 0, 0) t , b i > 0, i. e. only the cortical layers are stimulated with weights b i. The observation y captures the activity of the cortical excitatory populations  [48, 50]  with C = (c 1 , 0, c 3 , 0, 0, 0, 0), c i > 0. For more details, please see the Appendix. The time domain simulations of the cortico-thalamic model is done by numerical integration using the fourth-order Runge-Kutta method implemented by the scipy library in python with a maximum simulation time step of 1 ms. The resting state activity of the cortico-thalamic brain model is shown in Fig. 2B ). The second row shows the power spectral densities estimated from the time series and the last row shows the estimated αand γ-activities computed from the spectral densities and averaged over 50 simulations.
(2007) ,  Moros et al. (2009 , 2021 ), De Deckker et al. (2012 , 2020 ), and Perner et al. (2018) , and is an updated version of the latest chronology proposed by De Deckker et al. (  2020 ) which used the Marine13 curve for age calibrations  (Reimer et al. , 2013)  within the CALIB 7. 0. 2 software  (Stuiver and Reimer, 1993). For this study, AMS 14 C dates were calibrated using the Marine20 calibration curve  (Reimer et al. , 2020) , whilst the age model was constructed using Bayesian modelling with the Rbacon package for R  (Blaauw and Christen, 2011). As suggested by De Deckker et al. (  2020 ), we used a Delta R (local reservoir age) of 440 years throughout the Holocene back to 14 14 C ka BP, and a Delta R of 600 and 700 years from 14 to 18 14 C ka BP and older than 18 14 C ka BP, respectively.
Group average hierarchical clustering was performed on all 75 samples using the Bray-Curtis similarity index on untransformed relative abundances of all identified species (%). This analysis separated two clusters which were then subjected to two separate group average hierarchical clustering, using the same approach as previously. Benthic foraminiferal species discriminating between clusters were identified by Similarity Percentage analyses (SIMPER). All hierarchical cluster and SIMPER analyses were performed using the PRIMER6 software  (Clarke and Gorley, 2006). A detrended correspondence analysis (DCA) was carried out with the R software (decorana and envfit functions; R Team, 2022) on all samples using untransformed relative abundances of cluster discriminating species formerly identified by SIMPER analyses.
, 2018) , and hence considered allochtonous at the core site. These species, noticeably belonging to the Ammoniidae, Elphidiidae, Glabratellidae, and Haynesinidae families, are referred to in the text as "shelf species". Their influence on the implemented statistical analyses was assessed by comparing the abundance of the main cluster-discriminating species when excluding or not the former from the analysed dataset. The comparison of abundances and the associated paired student t-tests show that the inclusion of shelf species does not change the observed trends in benthic foraminiferal distribution (Supplementary Material 4 and 5). All specimens throughout the core showed a relatively good preservation without any signs of dissolution, but with some visible borings and test damage (holes and etching; Fig.
Data availability The benthic foraminiferal data presented in this work together with the clay mineralogy first published by  Gingele et al. (2004)  are available at the PANGEA online repository following these links: https://doi. org/10. 1594/PANGAEA. 960585 (benthic foraminiferal census data), https://doi. org/10. 1594/PANGAEA. 960587 (clay mineralogy). Previously published data used in this study is available following this link: https://doi. org/10. 1594/PANGAEA. 911846.
Those in the CM mainly included small particles with a diameter between 50 and 150 nm, while MBVs mostly consisted of large EVs, with major peaks at 150 nm and 200 nm (Fig. 4C-D ). We molecularly characterized pCAF-derived EVs using MS proteomics (Data S3). This analysis confirmed that both types of particles contain common EV markers, such as tetraspanins CD63, CD81, CD9  (30) , and syntenin-1 (SDCBP)  (50) , but also highlighted differences, such as the relative abundance of some EV markers and the presence of ADP-ribosylation factor 6 (ARF6) and tumor susceptibility gene 101 protein (TSG101) only in MBVs and CM-EVs, respectively (Fig. 4E  and Data  S3 ). Hence, our data have identified distinct traits of CM-EVs and MBVs. To investigate this further, we compared the proteome of pCAF-derived EVs with the proteome of large-medium and small EVs of three publicly available datasets (Fig.
S2A-B )  (51) (52) (53). For each dataset, we selected proteins unique to each EV subpopulation and those with abundance significantly different between the two. Then, we matched this subset to EV proteins whose abundance was significantly different between CM-EVs and MBVs (Data S3). This analysis showed that proteins typically found in large-medium EVs were generally more abundant in MBVs. Proteins typically found in small EVs, instead, were more abundant in CM-EVs (Fig. S2B ). This observation was consistent across the three datasets (Fig. S2B ). Furthermore, proteins identified only in CM-EVs displayed enrichment for endosome-related GOCC terms (Fig. S2C ) that is one of the documented intracellular origins of small EVs (Fig. S2A ). Conversely, MBV unique proteins displayed enrichment in GOCC terms associated with plasma membrane, cytosol, ER and mitochondria (Fig.
S2C ), which are expected in large-medium EVs because of their biogenesis  (30). Overall, these results further support that each extracellular compartment contains different subsets of EVs. The majority of pCAF proteins transferred to ECs during co-culture was identified in both EV types (Fig. 4F , Data S1 and S3), and their abundance positively correlated to the amount measured in the EVs (Fig. 4G , Data S1 and S3). Notably, the majority of the transferred plasma membrane receptors and membrane-bound ligands were more abundant overall in the MBVs (Fig. 4H  and Data S3). Next, we measured whether pCAF-derived CM-EVs and MBVs could transfer proteins to ECs. As these EV types exist in different extracellular sites, we measured protein transfer when CAFs were co-cultured in physical contact (direct co-culture) or not (indirect co-culture) with HUVECs (Fig.
S5A-B ). Conversely, we did not detect significant differences in mRNA levels of stromal cell-derived factor 1 (SDF1, also known as C-X-C motif chemokine 12 or CXCL12) and interleukin-6 (IL6), which are highly expressed in mammary iCAFs  (7, 16, 54)  (Fig. S5A-B ). Similarly, decorin (DCN), which has been found expressed at similar levels in all CAFs  (7, 54) , had similar mRNA levels in our two sorted populations (Fig. S5A-B ). These data suggest that high levels of the TNFRSF12A receptor are found in CAFs with myCAF phenotype. Consistent with this observation, in two publicly available single-cell RNA sequencing datasets of CAFs isolated from patients with BC (7, 16), we found that both ACTA2 and TNFRSF12A mRNA levels were high in the subpopulation defined by the authors as myCAFs (Fig. 8A-B ).