dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical Analysis Descriptive statistics were used to describe data with a 95% confidence interval and a 5% alpha risk. The categorical variables extracted from the questionnaire were related to sociodemographic information and current surgical practices. For statistical analysis, both strongly and weakly positive opinions were grouped as positive, and both strongly and weakly negative opinions were grouped as negative. The Chi-squared test of independence was used on the descriptive statistics to test whether the main criterion (i. e. , positive or negative) was related to the categorical variables. Logistic regression was used to describe the influence of the categorical variables on the primary outcome of interest. The variables were adjusted for each other. ANOVA was used to assess the significant effect of the variables on the model.
In principle, hotter and drier forests may be either more resistant to hotter and drier climates, protected by evolutionary adaptation to extreme conditions, or more vulnerable due to operating closer to physiological thresholds  17, 18. The 2015-2016 El Niño event caused record heat and drought across South America  19  and presented a unique opportunity to evaluate both the impact of long-term climate baselines and short-term climate anomalies on South American forests. In this Article, we assessed the impact of the 2015-2016 El Niño on South American tropical forests using 123 long-term monitoring plots in the RAINFOR and PPBio networks, the largest on-the-ground dataset yet mobilized to address the impact of a single tropical drought. These structurally intact forest plots span Amazonian and Atlantic forests including those that are transitional with dry forests and Cerrado.
The climatically driest forests were significantly more prone both to El Niño-enhanced carbon losses from biomass mortality and to reduced gains from lower growth and recruitment. Consequently, these drier forests lost biomass during the El Niño period. The drier plots in our dataset were widespread geographically, and we captured the negative carbon impacts of drying in three distinct regions-north Colombia, the Brazilian State of Minas Gerais and the Southern Amazon-including many of the plots both furthest north and furthest south of the equator in our dataset (Fig. 1 ). These three regions are biogeographically distinct from one another and climatically out of phase, that is, the timings of their wet and dry seasons differ, and yet they exhibited similar negative carbon responses to the climate anomaly.
Inclusion and ethics ForestPlots. net is a global community of researchers striving to make long-term plot monitoring more equitable. Our partners independently access, analyse and manage the information from their own plots, and can choose to join and initiate their own collaborative projects across countries and continents. We seek to ensure that both the production and the use of forest data becomes more equitable, that the many less-visible colleagues who contribute vital work receive proper recognition, and that we support fair, long-term collaboration across geographical, socio-economic and cultural divides. Further information on how we promote sustainable and equitable collaborations among researchers is set out in the Code of Conduct  38.
Online content Any methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at https://doi. org/10. 1038/s41558-023-01776-4. Equations for models are listed in Supplementary Table  2. See Extended Data Fig. 8  for an alternative analysis excluding censuses before 2000, and Extended Data Fig. 9  for an alternative analysis with carbon changes in absolute terms. Content courtesy of Springer Nature, terms of use apply. Rights reserved.
The mean pre-El Niño monitoring length was 13. 3 years and the mean length of the El Niño interval was 2. 7 years. We checked that temporal consistency among plot censuses was adequate by repeating our analysis only with a shorter baseline, excluding censuses before 2000. These alternative analyses yielded similar results and are available in the extended data (with Extended Data Figs. 4-9 directly comparable to Figs. 2 3 4 ). All data are curated at ForestPlots. net  41  version 2020. 1 downloaded on 19 October 2020, data and code associated with the analyses can be accessed using ref. 42. For plot details, see Supplementary Tables  3  and 4. In each plot, all trees ≥100 mm diameter were measured, tagged with a unique identifier and identified to species, where possible. Tree diameter was measured at 1.
3 m along the stem from the ground, or above buttresses, if present, using standardized methods for all plots  43. In some cases, the point of diameter measurement had to be moved due to upward growth of buttresses or deformities. For these trees, we calculated a single common estimate of growth before and after the point of diameter measurement change  8, [44] [45] [46] [47]. Stems that reached a diameter ≥100 mm during the census interval were recorded as new recruits. Field data were checked against rules to identify potential errors, identically for all 123 plots, consistent with previous large-scale analyses  7, 8, 44, 48. We assessed trees that increased in diameter >40 mm per year or shrunk >5 mm over an interval, to determine if they could have been inaccurately measured in the field.
(2013) (ref. 50. Wood density measurements were compiled mostly from the Global Wood Density Database on the Dryad digital repository (datadryad. org)  51, 52  and each individual stem in a plot was matched to a species-specific mean wood density value, where possible. For incompletely identified individuals or individuals belonging to species not in the wood density database, we use the mean wood density value for genus if available, then family. For unidentified individuals, we used the mean wood density value of all individual trees in the plot  47, 53. Tree heights were measured in 108 plots; typically the 10 largest trees and 10 trees in each of the diameter classes 100-199 mm, 200-299 mm, 300-399 mm, 400-499 mm and 500-599 mm, with trees selected only when the top was visible 54. To estimate height for plots or trees a three-parameter regional height-diameter Weibull equation was fitted using the local.
heights function in the BiomasaFP R package  55. The regions were Amazonia: Brazilian Shield, East-Central, Guiana Shield and West, and the parameters were used to estimate tree height from tree diameter for all stems for input into the allometric equation. We estimated the aboveground biomass in live stems (AGB), in Mg dry mass ha -1 , at each census of each plot; the additions of biomass to each plot over the census interval, as aboveground wood productivity (AGWP), in Mg dry mass ha -1 per year, and the losses of AGB from the plot, termed AGB mortality, also in Mg dry mass ha -1 per year. Plot-level carbon gains and losses are increasingly underestimated as census length increases, so to avoid census-interval effects we corrected using the method outlined in Talbot et al. 46. We thus accounted for the carbon additions from trees that recruited and then died within the same interval (unobserved recruitment), and the carbon additions from trees that grew before they died within an interval (unobserved growth).
Carbon losses are affected by similar processes, so we added the estimated growth before tree death within the interval (unobserved growth), and the deaths of stems that were newly recruited within the interval (unobserved mortality). We used the BiomasaFP R package to calculate AGB, AGWP and AGB mortality, including the calculation of the census interval corrections. Pre-El Niño means of these variables are time-weighted on the basis of the census interval lengths. Where AGB, AGWP and AGB mortality are expressed in carbon terms (net change in carbon stocks, carbon gains and carbon losses) we use the mean carbon fraction for tropical angiosperms, 45. 6% (ref. 56). The difference between the mean of the pre-El Niño monitoring period and the mean of the El Niño census interval is ∆ net carbon, ∆ carbon gains and ∆ carbon losses for each plot.
Selected weights were: Δ net carbon, Area 1/2 + monitoring length 1/3 - 1; Δ carbon gains, Area 1/7 + monitoring length 1/7 - 1; Δ carbon losses, Area 1/3 + monitoring length 1/4 - 1. Instantaneous stem mortality rates were calculated and corrected for census interval effects using equation 3 from Lewis et al. 57. We used linear models, Kendall's τ correlation tests and rank-based regression lines of best fit to determine significant relationships Content courtesy of Springer Nature, terms of use apply. Rights reserved https://doi. org/10. 1038/s41558-023-01776-4 between carbon dynamics and climate. Rank based regression lines were plotted using the rfit function from the Rfit R package  58. We checked whether the explanatory climate variables covary, and as a rule of thumb we assumed correlations above 0.
7 should not be included in multiple regression  59. Multicollinearity was not an issue as correlation coefficients between explanatory variables were low (r < 0. 24) and variance inflation factors from fitted models were ≤1. 4, and thus well below the common cut-off of 5 (ref. 60), allowing us to include them in multiple regression. Mixed effects models of factors associated with tree death were performed using the glmer function from the lme4 R package  61. The multiple linear models test the impacts of pre-El Niño climate (pre-El Niño temperature and pre-El Niño MCWD) and climate anomaly (Δ temperature, Δ MCWD and their interaction) on biomass carbon (Δ net carbon, Δ carbon gains and Δ carbon losses) (full model equations listed in Supplementary Table  1 ). We included pre-El Niño climate in our models to test whether plots that were already hotter (pre-El Niño temperature) or plots that were already drier (pre-El Niño MCWD) were more or less resistant to climate extremes (trees in hotter or drier pre-El Niño climates may contain more hot or dry adapted species, but also may be closer to physiological temperature or moisture thresholds).
Climate analysis Temperature, precipitation and drought estimation. We required a continuous climate record from 1970 to 2018 over South American forests, which meant combining products with different timespans and scales. We used (1) the mean monthly 2 m temperature (30 km resolution) from the ERA5 dataset for 1979 to 2018 (ref. 64) and (2) monthly temperature (0. 5° resolution) from the Climate Research Unit (CRU) ts. 4. 03 dataset for 1970-1978 (ref. 65). The CRU dataset was resampled to match the resolution of ERA5 and harmonized to Celsius units. For each month of the overlapping period ERA5 and CRU we plotted the regression of CRU on ERA5 (1979-2018, that is, January CRU versus January ERA5) for all neotropical forest pixels, and the fit was used to correct the CRU data to match ERA5.
The 1970 to 2018 temperature record (T) includes the monthly adjusted CRU data (1970 to 1978) and monthly ERA5 data from 1979 to 2018. For temperature data at a finer spatial resolution, we also downscaled our temperature data to 1 km 2 using WorldClim v2 (ref. 66)  by first resampling the 1970-2018 temperature record to match the resolution of WorldClim. We then use the static 1970-2000 WorldClim temperature to correct the 1970-2018 record for each plot location by calculating the mean monthly temperature (μ) for the CRU-ERA-I record for the period 1970-2000. The monthly difference (T diff = T μ - T WorldClim ) of the mean climate, T diff was then used to create a plot-level monthly temperature 1970-2017: T plot = T - T diff. Temperature values were then additionally adjusted for any difference in altitude between the plot and the altitude of the 1 km grid cell used for WorldClim interpolation, using a lapse rate, so that T plotalt = T plot + 0.
005 × (A WorldClim - A plot ), where T is temperature (°C) and A is altitude (m). Similarly, continuous precipitation records were required from 1970 to 2018. We used (1) monthly precipitation (0. 25° resolution) from the Tropical Rainfall Measurement Mission (TRMM product 3B43 V7) for 1998 to 2018 (ref. 67) and (2) monthly precipitation (0. 5° resolution) from the Global Precipitation Climatology Centre (GPCC, Version 7) database for 1970 to 1998 (ref. 68). The GPCC dataset was regridded to match the resolution of TRMM. TRMM and GPCC were then regressed for the overlapping time period (1998-2003, that is, January TRMM versus January GPCC) for all neotropical forest pixels, and the fit is used to correct the GPCC data to match TRMM. The 1970-2018 precipitation record (P) includes monthly adjusted GPCC data (1970-1998) and monthly TRMM data from 1998 to 2018.
Again, for a finer spatial resolution we downscaled precipitation data for plot locations followed a procedure similar to that used for temperature: upscaling to 1 km 2 resolution using WorldClim (v2 (ref. 66)). The GPCC-TRMM precipitation record is resampled to match the resolution of WorldClim, and the mean (μ) GPCC-TRMM precipitation for the period 1970-2000 is calculated for each month. The monthly ratio (P ratio = P μ /P WorldClim ) of the mean climate is calculated, and P ratio used to adjust monthly precipitation 1970-2017: P plot = P × P ratio. The drought intensity experienced by plots was estimated as the MCWD. The MCWD was calculated as in ref. 69 and accounts for plot seasonality as calculations take place for each pixel: if WD n-1 -ET n + P n < 0; then WD n = WD n-1 -ET n + P n ; else WD n = 0.
Acknowledgements This paper is a product of the  Amazon Forest Inventory Network  (RAINFOR) and the  Programa de Pesquisa em Biodiversidade (PPBio) , and their combined plot data curated at ForestPlots. net. We gratefully acknowledge field assistants who contributed to the El Niño remeasurement campaigns:  D. Aguilar Flores ,  L. Alfaro Curitumay ,  K. Alves Cavalheiro ,  W. Alves da Cruz ,  E. Alves Hernandez ,  G. Aramayo ,  J. Araújo de Souza ,  J. Ataliba Mantelli Aboim Gomes ,  M. Baisie ,  E. Ballesteros Miguel ,  J. Betancourt ,  M. Betancur Nuñez ,  E. Borges Brito ,  A. Cardoso ,  J. Manuel Castro ,  J. Carlos Chore Egues ,  E. Ignacio Sebastian Ciriaco ,  A. Cuevas ,  J. da Costa Freitas ,  D. da Cruz Viana ,  M. da Silva Melo ,  B. de Oliveira ,  T. de Souza Valente Júnior ,  J.
We acknowledge the contributions of the ForestPlots. net Collaboration and Data Request Committee ( B. S. M. ,  E. N. H. C. ,  O. L. P. ,  T. R. B. ,  B. Sonké ,  C. Ewango ,  J. Muledi , S. L. L. and  L. Qie ) for facilitating this project and associated data management. The development of ForestPlots. net and curation of data has been funded by several grants including  NE/B503384/1 ,  NE/N012542/1- ' BIO-RED ',  ERC Advanced  Grant  291585-'T-FORCES ',  NE/F005806/1- ' AMAZONICA ',  NE/N004655/1- ' TREMOR ',  NERC   New Investigators Awards , the  Gordon and Betty Moore Foundation  (' RAINFOR ', ' MonANPeru '),  ERC Starter  Grant  758873 - ' TreeMort ',  EU  Framework 6, a  Royal Society University Research Fellowship , and a  Leverhulme Trust   Research Fellowship. For supporting A. C.
Surviving recipient cells were then cultured in selective medium to measure the Te. Here, we used antibiotic for selective recovery, but any specific culture conditions excluding the attacker could be applied. To serve as standards, a recipient cell culture was serially diluted, and Te was measured. Figure  5C  shows a linear regression of the Te values plotted as a function of the dilution factors (grey data points). Here, again, the data with the various T6SS mutant strains used as attackers were consistent with previous reports showing an absence of antibacterial activity of ∆sci1 cells (i. e. , comparable to the recipient incubated in absence of attacker) and intermediate activities of ∆tagA and ∆clpV cells. b. Pseudomonas aeruginosa H1-T6SS P. aeruginosa strains usually carry several copies of T6SS gene clusters  (Chen et al.
Acknowledgments The LAGA protocol was adapted from previous studies (Vettiger and Basler, 2016;Sgro et al. , 2018;Bayer-Santos et al. , 2019;Schneider et al. , 2019;Seef et al. , 2021). We thank the members of the  Cascales, Mignot , and  Bordi laboratories  for discussions and support,  Kathleen Postle  ( Penn state University, USA ) and  Denis Duché  ( LISM, Marseille ) for providing strain KP1344 and purified colicin D, respectively,  Moly Ba ,  Annick Brun , and  Audrey Gozzi  for technical support, and the four anonymous referees for their very constructive comments and suggestions. This work was supported by the  Centre National de la Recherche Scientifique , the  Aix-Marseille Université , and by grants from the  Agence Nationale de la Recherche  ( ANR-20-CE11-0017 ), the  Fondation pour la Recherche Médicale  (FRM,  DEQ20180339165 ) and the  Excellence Initiative of Aix-Marseille University  -A* MIDEX , a  French "Investissements d'Avenir" program  ( A-M-AAP- ID-17-33-170301-07.
Carbonaceous matter has been characterized by Raman spectroscopy. Raman data were obtained using a Horiba Jobin Yvon LabRAM HR800 spectrometer (ISTE, UNIL) in a confocal configuration equipped with an Ar + 532 nm laser and a CCD detector. Measurements were performed at room temperature on thin sections, with a laser power delivered at the sample surface <1 mW to prevent thermal damage. This technique allows to document the structural organization of the Fig. 1. a) Log of the LWC section with focus on Units B and C (modified after  Grosjean et al. , 2018). b) Early Triassic paleogeographic map showing the location of the Sonoma Foreland Basin (SFB; modified after  Brayard et al. , 2013). c) Position along a ramp system and thin section pictures of studied samples (scale bar for thin sections: 2.
Samples LWC39 and LWC41 are MISS (FA2, yellow star). Please note that sample LWC41 encloses two facies in which pyrite is observed. The first is the silty part (reported as LWC41 Siltst. ) and the second is the OM-rich laminae (noted LWC41 OM). F3 (green star) includes samples LWC47 and LWC49. Sample LWC55 is part of FA4 (blue star). Samples LWC72 and LWC88 are included in FA5 (purple star). Fe isotope compositions are reported using outlier box plot representation. Line through the box is the median and the edges of the box represent the quartiles (lower line is the 1st quartile Q1, i. e. the 25th percentile, and upper line is the 3rd quartile Q3, i. e. the 75th percentile). Lower and upper whiskers represent 1. 5*IQR (interquartile range, i. e. the range between Q1 and Q3). The minimum and maximum are marked by small lines at the end of whiskers and represent respectively the lowest and highest values of the distribution, excluding outlier values of the dataset (filled and unfilled dots below and above whiskers).
Acknowledgements K. G. Bylund ,  J. F. Jenks ,  D. A. Stephen ,  E. Fara ,  G. Escarguel ,  E. Vennin ,  G. Caravaca  and  A. S. Grosjean  are thanked for their work in the field, and notably for their participation to the sample collection. We gratefully thank  V. Pasquier  for fruitful discussions,  J. Alleon  for his assistance during Raman analyses and  M. Robyr  for his help during EPMA analyses. We thank private landowners (Scott Reessouth side of  Weber River  and  Mrs. Monte Broughnorth  side of  Weber River ) for allowing access to their lands. We thank  Prof. T. Algeo  and an anonymous reviewer for their helpful and constructive reviews of this paper. This research was supported by the  European Union 's  Horizon H2020 research and innovation program ERC (STROMATA , grant agreement  759289 ).
This work is also a contribution to the  ANR  project  AFTER  ( ANR-13-JS06-0001-01 ) and was also supported by the  French "Investissements d'Avenir" program , project  ISITE-BFC  ( ANR-15-IDEX-03 ) and received funding from the  programme TelluS  of the  Institut National des Sciences de l'Univers, CNRS.
Because of the sequential kinetics S 2 !T 2 !T 1 formation of Xn T 1 state, there were two possible excited states involved, S 2 or T 2. To reveal the nature of the excited state, the photophysics of the PGBs and acids were studied by femtosecond pump-probe spectroscopy. The evolution of the XnAAH transient spectra as a function of time is shown in Figure  3a. There was a rapid decay in the blue region from 475 nm to 550 nm, where an isobestic point was observed indicating A!B ultrafast kinetics. On the red side, the signal increased quickly, and then more slowly. Global analysis of the spectrotemporal data revealed three characteristic time constants of 0. 6 ps, 40. 5 ps, and a very long time constant > 10 ns due to a formed permanent species on the experimental timescale (attributed to T 1 ).
Identification of TR target genes in developing GABAergic neurons in rodents Since RNA-seq has advantageously replaced microarray analysis, a growing number of datasets of gene expression linked to TH signaling has accumulated [reviews in  (87)  and  (41) ]. These results are theoretically suitable for identifying genes which are putative TR target genes in GABAergic neurons and identifying the molecular mechanisms that lead from TH stimulation to neuronal maturation. However, this remains a difficult task. To start with, although early studies have shown that a few genes, notably Hr and Klf9, are T3-responsive in many cell types  (88, 89) , more recent studies have mainly demonstrated that the repertoire of THresponsive genes widely varies across cell types and brain areas  (41). Thus, it finally appears that the overlap between sets of TR target genes in different types of cells might be limited.
Another notable example is the demonstration that an up-regulation of Klf9 by TH is a key event in the postnatal loss of the regenerative capacity that Purkinje cells display after axotomy  (99). When analyzing genome-wide datasets, a special attention has often been paid to the Pvalb gene, which encodes PV. Indeed, histological analyses have consistently shown a reduction in PV expression in rodent models with altered TH signaling (see section 3 in the present review). In a microarray analysis conducted in the cortex and striatum of 21-day-old hypothyroid mouse pups, Pvalb came out as one of the most strongly downregulated genes  (13). Even subclinical hypothyroidism was shown to induce a large fold decrease in Pvalb mRNA expression in 14-day-old rats (100). Moreover, Pvalb mRNA levels in the cortex and striatum were significantly lower in TRa1 -/-and TRa1 -/-TRb1 -/-mouse pups than in control mice.
Funding JR was supported by grants of the  China Scholarship Council and ENS de Lyon , within the framework of the  PROSFER program (Program of Sino-French Education for Research ). Research in our group is funded by the  European Union 's  Horizon 2020 research and innovation program , under grant agreement no. 825753  (ERGO) and by the  French Agence Nationale de la Recherche  ( Hypothyro  project,  ANR 22-CE14-0026-01 ).
• It aims to provide a seamless transition from simulators to actual photonic processors (QPU). As such, most of the programmatic interfaces are designed for QPU control. In particular, fixing a specific QPU automatically hides methods giving access to properties (such as probability amplitudes) and features which would be unavailable on the actual hardware. Perceval is a modular object-oriented Python code, with optimised functions written in C making use of SIMD vectorisation. In the following section, we give an overview of the main classes available to the user. A full documentation is maintained on GitHub and available online through the project website.