dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements This research was partially funded by the  Strategic Priority Research Program  of the  Chinese Academy of Sciences  ( XDB31030000 ) to W. W. , the  National Natural Science Foundation of China  ( 32011530072 ,  32170210  and  31770231 ) to W. W. ,  K. C. Wong Education Foundation  ( GJTD-2020-05 ) to W. W. , P. S. S. and  D. E. S. , and the state assignments performed by the  CSBG SB RAS  ( АААА-А21-121011290024-5 ) to A. S. E. Article https://doi. org/10. 1038/s41467-023-39555-6Nature Communications | (2023) 14:4021.
Data availability Newly obtained sequences have been deposited in GenBank, the accession numbers are provided in Supplementary Data 3, and vouchers are deposited in Herbarium, Institute of Botany, the Chinese Academy of Sciences, Beijing (PE). Alignments and timetrees for the 32 clades investigated are available at Zenodo (https://doi. org/10. 5281/ zenodo. 7868007) and FigShare (https://doi. org/10. 6084/m9. figshare. 23506542). Source Data are provided in the Source Data file. Source data are provided with this paper.
12. 1). GB models were fitted using the caret package, choosing the best model of nine parameter combinations (interaction depth = 1, 2, 3 and number of trees = 50, 100, 150; method = "gbm"; caret version 6. 0-86). LM models were fitted using the lme4 package (lmer function; version 1. 1-26), and the LM models with a spatial correlation term were fitted using the spaMM package (fitme function; version 3. 7. 2).
We performed these two types of random splitting 10 times to calculate means and standard errors of model performance metrics. We calculated three model performance metrics for each meta-model, the root-mean-square error (RMSE), the coefficient of determination (R 2 ), and the Akaike information criterion (AIC), by comparing the yield change predictions obtained with each meta-model (Y pred,i ) to the original simulated yield change (Y i ). AIC was calculated as: AIC = 𝑛𝑛 × ln ⎛ ⎜ ⎜ ⎜ ⎝ √ ∑ 𝑛𝑛 𝑖𝑖=1 ( 𝑌𝑌𝑖𝑖 -𝑌𝑌pred,𝑖𝑖 ) 2 𝑛𝑛 ⎞ ⎟ ⎟ ⎟ ⎠ + 2𝑝𝑝 (1) where n is the number of predictions and p is the total number of model parameters. We reported the mean and standard deviation of the three model performance metrics for each meta-model using the 10 test datasets. We chose the meta-model with the best performance for each crop using the three metrics defined above (RMSE, R 2 , and AIC in Table  S3  in Supporting Information S1) averaged over the 10 test datasets, using the second type of model evaluation (i.
e. , different locations for testing than those used for training). Here, the model rankings obtained with the three performance metrics were in agreement about the best model for each crop, so we did not have to select one performance metric over another. Partial dependence plots (PDPs) show the relationship between the yield change and each predictor variable, averaging over the other predictors. This gives a sense of the marginal effect of each predictor on the predicted yield change. To generate our PDPs, we calculated the partial contributions of each predictor variable using the FeatureEffect function (package iml, method = "pdp"), and training the model using all the data available.
Thus, Shapley values can be used to understand the contributions of each predictor to the predicted yield anomalies. We used Shapley values, calculated using the DALEX package (version 2. For each grid cell, we determined the factor that had the largest Shapley value, that is, the largest effect on the predicted yield change anomaly for that grid cell. Using this method, we created global maps highlighting regions where the yield change anomaly is most responsive to temperature, precipitation, CO 2 concentration, or adaptation.
Results Of the 10 statistical meta-models tested, the best models were generally RF and GB models (Tables  S3  and S4  in Supporting Information S1). The consistently lower performance of the LM models implies that there are non-linear relationships in these datasets which LM models cannot capture. For spatial extrapolation (Table  S3  in Supporting Information S1), Model 4 (GB) was the highest performing model for maize, while for rice and soybean the best model was Model 5 (RF), and for wheat the best model was Model 6 (GB). Although they were outperformed by RF and GB models, the LM models revealed that adaptation management practices had a statistically significant positive effect on yield changes (Table  S5  in Supporting Information S1). For spatial extrapolation, the best maize model was able to explain the greatest amount of variation in the data set (R 2 = 0.
43), significantly more than the extrapolation performance of the wheat and soybean models (both R 2 = 0. 21). The rice model extrapolation was very low (R 2 = 0. 12) although its RMSE was lower than that of other models. In other words, when forced to predict new locations, the statistical models explained at most 43% of the yield change variation in the test data set (R 2 = 0. 12-0. 43; Figure  S2  in Supporting Information S1). Nevertheless, when we split the testing and training datasets such that the testing data set only contained locations from the same half-degree grid cells as the training data set, model performances were higher (R 2 = 0. 33-0. 58; Table  S4 , Figure  S3  in Supporting Information S1), revealing that the models are better able to predict yield changes for new scenarios at the same sites than to predict new sites.
Our study focused on a single scenario, that is, RCP4. 5 in 2060. We have chosen this scenario because it seems to be the most realistic given the current socio-economic context  (Arias et al. , 2021; IEA, 2021). However, the meta-models developed in this study are not contingent on this scenario; they have been trained with a very large database including yield simulations obtained with different climate change scenarios covering a wide range of temperature, precipitation, and CO 2 concentration changes  (Hasegawa et al. , 2022). They are therefore very generic and could be easily run for any other climate change scenario. To facilitate their reuse, we have made the meta-models and analysis scripts freely available (see Data Availability Statement Section). Results clearly reveal that adaptation practice is a dominant factor with a strong, and previously overlooked, impact on global yield change predictions.
Acknowledgments The authors thank  Marcus Schiedung  for useful discussion. RZA was supported by a  Marie Skłodowska-Curie Individual Fellowship  from the  European Union 's  Horizon 2020 program  and by the  United States Department of Energy, Office of Science, Office of Biological and Environmental Research. Oak Ridge National Laboratory  is managed by  UT-Battelle, LLC , for the  United States Department of Energy  under contract  DE-AC05-00OR22725. This study was in part funded by the  Environment Research and Technology Development Fund  ( JPMEERF20S11820 ) of the  Environmental Restoration and Conservation Agency of Japan , and in part by the  JOINT LINKAGE CALL  supported by  NARO  and  INRAE  and by the project  CLAND  ( 16-CONV-0003 ).
Data Availability Statement The authors will maintain the meta-models and analysis code used in this paper as a publicly available Github repository at https://github. com/rabramoff/ProjectYield. The first release of this repository has been deposited in Zenodo and is available here: https://github. com/rabramoff/ProjectYield/releases/tag/v0, and can be cited as  Abramoff et al. (2023). The data used in this paper is publicly available from  Hasegawa et al. (2022)  and the linked figshare repository (https://doi. org/10. 6084/m9. figshare. 14691579. v4).
Preliminary Reduction with the UVES Pipeline We used the UVES pipeline  (Ballester et al. 2000)  through EsoRex (ESO Recipe Execution Tool;  ESO CPL Development Team 2015)  to process the raw 2D UVES data, which include science and calibration frames (bias, format check file, order definition flat, echelle flat, dark, and a spectrum of the ThAr wavelength calibration lamp). All of them were taken on the same night as the corresponding science frames, except for the dark frames, which were taken 3 times per month for the purposes of detector monitoring. To run the full data reduction chain, we used the UVES_OBS_REDCHAIN recipe, which performs the following steps: (i) creation of a master bias frame, (ii) implementation of the UVES instrument model, (iii) tracing of echelle orders positions, (iv) construction of a master flat-field frame, (v) wavelength calibration, and (vi) reduction of the science frame.
For Spectral type identification was done mostly using the SIMBAD database  (Wenger et al. 2000)  or, in rare cases, based on stellar atmospheric parameters (see Section 4). this reason, the correction in this segment was carried out manually by the optimal selection of parameter values. There was no degeneracy of the two parameters (i. e. , the value of the blaze shift and the value of over-or underestimation of scattered light) because the blaze shift and inaccurate subtraction of scattered light affect the orders differently: a blaze shift "tilts" an order, while inaccurate subtraction of scattered light "bends" it.
Telluric Correction Observations made with ground-based telescopes suffer from absorption through Earth's atmosphere which leads to telluric lines imprinted on astronomical spectra. In the wavelength range of UVES, there are absorption bands that mostly originate from water vapor and molecular oxygen. There are also two wide ozone bands. Correction of the spectra for telluric absorption is crucial during data processing. Telluric correction requires accurate wavelength calibration. This means that the positions of telluric lines and other features in the spectra must precisely match the theoretically computed model telluric lines. For this reason, the barycentric and topocentric corrections were here performed after the telluric correction because telluric features should not be shifted by radial velocity effects.
(1996). We performed the zero-point conversion of the Vilnius data to the Vega system by comparing the Vega magnitudes in this catalog with the value of 0. 03 m  (Bessell 2005). In addition, we corrected Gaia magnitudes of bright stars that suffered from saturation according to  Evans et al. (2018). 3. Figure  7. Spectrophotometric correction for the UVES-POP spectrum of HD 75063. Top: spectrum before correction (black line) and after correction (red line). Blue regions demonstrate the best-fitting synthetic spectrum that fills in the gaps, while the green line is an extrapolated spectrum. Bottom: black points are the ratios of observed fluxes from photometric databases and synthetic fluxes computed from the spectrum (the ratios are normalized to the maximal value). The red line is a polynomial fit used for the correction.
The correction algorithm fits a low-order polynomial that transforms an observed spectrum in such a way that the synthetic magnitudes fit the observed ones best. An example of spectrophotometric correction is presented in Figure  7. The algorithm iteratively excludes some data points by using a rejection technique for outliers, which could occur because of high photometric errors or stellar variability. In addition, a wrong cross-match of a star from the UVES-POP list against photometric catalogs could cause these outliers, which is especially relevant for high proper motion stars. In total, we performed spectrophotometric correction for 306 stars from the library. To check the quality of the spectrophotometric correction, we compared the synthetic colors in the Tycho-2 and Gaia photometric systems with the observed colors.
Interstellar Extinction Calculating interstellar extinction for sources within the Galaxy either requires the knowledge of the three-dimensional dust distribution, known to be strongly nonuniform and notoriously hard to derive, or can be computed using secondary indicators such as interstellar absorption lines of sodium (NaD) or calcium (Ca H and K), which correlate with the line-of-sight amount of dust (e. g. ,  Hobbs 1974; Poznanski et al. 2012). We computed the color excess E(B -V ) for 41 stars using the publicly available code dustmap. 12 The algorithm is based on the integration of the extinction along the line of sight using a 3D map of dust in the Milky Way reconstructed from optical-to-near-infrared photometry of stars  (Green et al. 2015 (Green et al. , 2018)). For the remaining 365 stars it was not possible to compute the E(B -V ) values because the published 3D map does not cover a substantial portion of the southern hemisphere.
Web Service for Data Access and Quality Control We publish the results of the recalibration and analysis of the spectra in the UVES-POP library in the dedicated website of the VOXAstro Stellar Libraries project: https://sl. voxastro. org/library/UVES-POP/details/. We created a user-friendly modern and interactive web interface for accessing and visualizing data. The server-side part of the application uses Python-based framework Django 13  on top of the PostgreSQL database. The interactive front-end part of the application was developed using JavaScript framework Vue. js 14  and Plotly. js library, 15  which allows us to efficiently display high-resolution spectra. A screenshot of this tool with a spectrum of HD 162889 is shown in Figure  15. In the top section of the web page, there are links to the general description of the library, buttons for downloading all spectra at once in a single archive (see the description of the FITS binary table structure of the output results in Table  5 ), and/or the table with stellar atmospheric parameters (see Table  6 ), the latter is also available in a machine-readable version online.
An interactive table and figure are also available on the website. This allows one to quickly find an object of interest in the database and sort the results by different Figure  14. Examples of 12 resolution-degraded UVES-POP spectra (red) with Gaia BP/RP spectra (black) overplotted. All spectra are normalized to unity at the maximal flux of the convolved UVES-POP spectrum. Below each plot we show a panel with the fractional flux differences between the two spectra. The left six panels present the stars with a satisfactory agreement between UVES-POP and Gaia; the right six panels show stars with obvious calibration issues in the Gaia BP/RP spectra. None of the 12 stars have significant variability according to SIMBAD. parameters. This table is related to the 3D visualization of the parameter space for stellar atmospheres in the bottom left part of the page.
This interactive 3D plot in the T eff , g log , [Fe/H] space displays the results of the full-spectrum fitting of UVES-POP spectra performed by our data analysis technique described above. Selection of a data point in this plot or a line in the data table above will display a spectrum with its best-fitting results on the bottom right panel of the web page, as well as a download button for that particular spectrum and a link to the SIMBAD database and ESO archive to the data for the corresponding star. We developed a dedicated password-protected part of the project website to perform (i) quality control for the data reduction, order and segment merging, telluric correction, flux calibration; (ii) assessment of the full-spectrum fitting quality using synthetic stellar atmospheres; and (iii) reliability of the derived stellar atmospheric parameters.
To grade the full-spectrum fitting quality, one had to (i) inspect fitting residuals in the wings of Balmer lines in intermediate to hot stars, (ii) verify whether Balmer line cores were properly modeled (does not apply to emission-line stars), (iii) check whether molecular bands in cool stars were adequately described by the model, and (iv) check the residuals in several known multiplets of iron and the most prominent absorption lines of α-elements (e. g. , Mg b) to assess the quality of the derived iron and α-abundances. Finally, to grade the quality of the stellar atmospheric parameters, one had to check the availability of literature data in the SIMBAD database or at least a spectral type and a luminosity class and conclude whether the derived parameters corresponded to the published spectral type and/or atmospheric parameters.
This research made use of the SIMBAD database and VizieR catalog access tool, both operated at CDS, Strasbourg, France. Facility: VLT:Kueyen Appendix A Description of the Columns in the Data Tables  In Table 5 , we list the columns along with their description of the FITS binary table that contains all spectra and fitting results. Table  6  describes the content of the FITS binary table with stellar atmospheric parameters. (This table is available in its entirety in machine-readable form. ) Figure  17. Spectra of 12 stars from UVES-POP (red) and NGSL (black). For comparison purposes, we degraded the resolution of the UVES-POP spectra and applied the same wavelength sampling as NGSL. Figure  18. Spectra of eight stars from UVES-POP (red) and X-Shooter DR3 (black).
The primary outcome of the study was the proportion of participants who seroconverted by the plaque reduction neutralisation test (PRNT 50 ) 28 days after vaccination for the fractional dose versus the standard dose in the per-protocol population. Secondary outcomes were assessment of adverse events and immunogenicity during the 1-year follow-up period. Participants were considered to have seroconverted if the post-vaccination antibody titre was at least 4 times greater than the pre-vaccination titre. We set a non-inferiority margin of not less than a 17% decrease in seroconversion in the fractional dose compared with the standard dose. This study is registered with ClinicalTrials. gov, NCT02991495. Findings Between Jan 29, 2019, and May 17, 2019, 303 participants were screened, and 250 participants were included and vaccinated; 126 participants were assigned to the fractional dose and 124 to the standard dose.
Research in context Evidence before this study We searched ClinicalTrials. gov and The international Clinical Trials Registry Platform for trials registered between database inception and Feb 4, 2023. Of 103 trials on yellow fever, seven were in HIV-positive participants, none of which assessed fractional dosing of yellow fever vaccination prospectively. A systematic review and meta-analysis examined the immunogenicity and safety of fractional doses and noted that there were no data from randomised clinical trials assessing use of fractional doses among HIV-infected individuals.
These intervals were then transformed to show the ratio of the fractional dose to the standard dose for GMT. The same procedure was performed for GMFI. Reverse cumulative distributions were also produced. The immunogenicity outcomes were assessed in the per-protocol and ITT populations. These analyses were repeated for 10 days and 365 days after vaccination (long term). Long-term assessment was done separately for individuals who were assessed within the planned study timelines (ie, 365 ±14 days) and those assessed outside of the study timelines (ie, >365 + 14 days). Adverse events and serious adverse events were summarised as number and percentage by dose group. This study is registered with ClinicalTrials. gov, NCT02991495.
Acknowledgments This substudy was funded by  Wellcome Trust  (grant  092654 ),  Médecins Sans Frontières Foundation , and the  UK Department for International Development. The vaccines were donated in kind by the  Federal State Unitary Enterprise of Chumakov Institute of Poliomyelitis and Viral Encephalitides (Moscow, Russia ). We wish to thank study participants and their families, and the  Scientific Committee , including staff from  WHO , the  DSMB , and the  National Institute for Biological Standards and Control (London, UK)  for crucial support to this project. We also thank the  YEFE  team in the  Kenya Medical Research Institute-Wellcome Trust Research Programme, Kilifi, Kenya.
Acknowledgments For financial support of this work, we acknowledge the  CEA ,  CNRS ,  CHARMMMAT Laboratory of Excellence , and the  European Research Council  ( ERC Consolidator Grant  Agreement no. 818260 ). N. L. and L. P. were supported by a fellowship from the  European Union   Horizon 2020 research and innovation program  under grant agreement no  768919  ( Carbon4PUR  project). We thank  Thierry Bernard  ( CEA ) for help in the conception and realization of the autoclaves.
Introduction Historical use of chlorinated organic compounds (COCs) has resulted in widespread contamination of soil and groundwater. According to the BASOL database  (MTES 2018) , chlorinated solvents are found in 17% of sites monitored by the French government. Moreover, 36% of the water resources used for drinking water supplies (domestic and public supply wells) are not in fact used due to contamination. In source zones, they are present as dense nonaqueous phase liquids (DNAPLs), which migrate vertically downward through aquifer systems, ultimately residing along low-permeability units such as aquitards and confining layers. The migration results in occurrence of residual NAPL within the porous media that can serve as long-term source of COCs that pose a risk to human health if consumed via drinking water.
Tracers Five fluorophores were selected as potential PTs (see Table  1 ). Dye concentrations in water were analyzed by UV/Visible spectrophotometry rather than by fluorescence, since the latter is easily hindered by various mechanisms  (Leibundgut et al. 2009). A Cary UV 60 spectrophotometer coupled with a Torlon® probe body (Agilent Technologies, Inc) was used for measurements. The Beer-Lambert law was checked for each compound through calibration curves carried out at the wavelength of maximum absorption. Table  1  includes ranges of published logK ow values for the fluorescent dyes selected for this study. While only one published data was registered for RWT logK ow value, the other dyes presented a deviation of at least one log unit within the available literature (F >> > EY > SRB > RB).
Acknowledgments The authors would like to thank the  French Environment and Energy Management Agency (ADEME)  and the  BRGM/DEPA  for providing the Ph. D. grant for  Sofia Visitacion. We gratefully acknowledge financial support provided to the  FAMOUS  project by  ADEME. The authors are also thankful for financial support provided to the  PIVOTS  project by the  "Région Centre -Val de Loire" , the  European Regional Development Fund (ERDF) , and the  "Région Nouvelle Aquitaine ".
46), exposures to insecticides at work (OR 1. 52) and work environments with mould/mildew (OR 1. 61)  [53]. Further analysis of the same dataset revealed that workers for suppliers of building materials, hardware and gardening materials were also at higher risk of sarcoidosis  [54]. In addition, firefighters exposed to World Trade Center 'dust' from construction and furnishing materials were found to be at significantly increased risk of developing sarcoidosis within the first 4 years after exposure  [55] , although it is difficult to identify the causative compound/s due to heterogeneity of the exposure. More recently, Beijer et al. found immunoreactivity to silica and metals (i. e. aluminium, zirconium and beryllium) in 21. 2% of patients with sarcoidosis and 0% of controls, further supporting the hypothesis that these antigens may be pathogenetic in a significant minority of sarcoidosis patients  [56].