dataesr/scientific-paragraphs-categorization huggingface dataset:
Influence of the Weight Adjustment During the calculations, the weight of the Bayesian SAXS score adjusted substantially (Figure  5 ). From the initial rigid body docking to the best structure after refinement, the weight was multiplied by 17. This means that the SAXS data was given 17 times more importance at the end of the procedure compared to the beginning. To see why this matters, we performed 20 linker refinement simulations with a fixed weight for the SAXS restraint, varying from 10 -4 -10 2 and compared it to 10 simulations using the Bayesian SAXS restraint. We then examined the χ 2 along the simulation step, for all replicates (Figure  6 ). All Bayesian SAXS simulations consistently reach low χ 2 values. In contrast, two limiting cases emerge in the fixed-weight simulations. When the weight is very large, agreement to the SAXS data is substantial, and the simulation quickly finds a local SAXS restraint minimum.
Selection of Minimal Small Angle X-ray Scattering Ensemble The above analysis assumes that a single structure or an ensemble covering a small part of conformational space represents the SAXS data. The sampling of conformational space by the free MD trajectories enabled us to try to investigate if more disperse ensembles fit the SAXS data better. For this, we used a method based on the genetic algorithm (GA) that was developed for a similar problem  Delhommel et al. (2017). This method searches for the minimal subset of conformations minimizing the error between the experimental data and computed data from the MD simulations. The χ 2 values obtained after fitting were reduced from 6. 03 to 2. 79 for an ensemble size of three. Increasing the ensemble beyond three did not reduce the χ 2 further (Figure  10A ).
If exploration leads to a structure with a smaller χ 2 , the weight will increase. The SAXS term therefore becomes more discriminant, guiding the calculation to propose structures which match the SAXS profile more closely. Bayesian formulation of SAXS structure determination therefore transforms a rugged energy landscape into a funnel-shaped landscape  Dill and Chan, 1997. Note that, the σ is being adjusted on the fly, and the maximum likelihood estimate of σ is approximately χ 2 (Supplementary Equation S4). Therefore, the proper quantity to look at is M/2σ 2   (Spill, 2013) (Section 2. 4. 8. 3, pp 171). In case of multiple datasets, it is therefore crucial that each has their own σ.
Fixed Weight vs. Bayesian Automatic Weighting The optimal weight, at which the simulation has reasonable acceptance rates and makes good use of SAXS information, is a priori unknown. It is the purpose of the Bayesian SAXS restraint to determine this optimal weight. As shown in Supplementary Equation S4 (see Supplementary Material), the number of SAXS data points and the overall agreement of data and structures will greatly influence the optimal weight. Therefore, it is expected that it will be different for each SAXS dataset, but also for each simulation setting, for example depending on which force field is used.
Ensemble Modelling The focus of this study is to illustrate the power and utility of the Bayesian SAXS score. The setup was deliberately simple, to emphasize to what degree the final conformations were driven by the data. Emphasis was also on calculation efficiency, and the molecule was deliberately described in the simplest terms by excluding volume, rigid bodies for the two domains, and rigid covalent geometry. The experimental data was limited to SAXS data up to q < 0. 37Å-1. The SAXS data do not contain any information on specific interactions between the linker and the surface of the PTP domain. In our models the linker wraps around the PTP domain but does not directly contact the domain. This is consistent with the fact that there is no experimental NMR data that indicates a specific contact, but does not explain the sequence conservation in the linker and on the surface of the PTP domain.
Rigid Body Docking In the first stage, we used  IMP Russel et al. (2012)  to perform rigid body docking of the PDB structures of PTP (PDB code 2I75; residues 638-913) and PDZ (PDB code 3NFK chain B; residues 512-604). 64 different simulations were performed with 500 steps each. Initial orientations of PDZ with respect to PTP cover a wide range of orientations both around the PTP and of the PDZ itself (see Figure  2 ). Energy terms were the SAXS restraint (Supplementary Equation S7) and a quadratic volume exclusion term. The FoXS model was used on heavy atoms  Schneidman-Duhovny et al. (2013). Each step consisted in alternating 100 Monte Carlo rotation/translation moves (510 -2 rad/Å) of PDZ with respect to PTP, and optimizing c 1 , c 2 , σ and c. σ and c were optimized by setting them to their maximum posterior (Supplementary Equation S4 and  Spill et al.
Back Calculated Small-angle X-ray scattering Profiles For every conformation of the MD simulations, the theoretical scattering profiles were calculated using CRYSOL from the ATSAS 2. 8. 3 software suite  Svergun et al. (1995) , with the default parameters. Their corresponding χ 2 values were measured using the following equation: χ 2 1 M M i 1 I calc (i) -I exp (i) σ exp (i) 2 ( 5 ) where M is the number of points in SAXS profile, I calc is the back calculated intensity, I exp and σ exp are the experimental intensity and error, respectively.
DATA AVAILABILITY STATEMENT The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: The SAXS data, refined structures, and MD simulation trajectories generated for the PTPN4 for this study are deposited in the Zenodo. org database (accession doi: 10. 5281/zenodo. 4739101). Direct link: https://zenodo. org/record/4739101.
Agronomic Data Annual prices of crop produce for the period 2000-2018 were taken from FAOSTAT, and corrected for inflation to 2018 price levels. National-level, crop-based weighted averages were calculated, with weights assigned based on the years before present, thereby giving higher importance to more recent price levels while at the same time accounting for historic price fluctuations. For India, FAOSTAT price data stops after 2009, at a time of strong price hikes. Time series were supplemented with Minimum Support Prices as reported by the Indian government for the period 2009-2019. For Pakistan, recent price information for pulses was provided by Pakistan's Agricultural Research Council. We checked FAOSTAT prices for consistency; in cases where year to year fluctuations in price exceeded 100% we explored national agricultural statistics yearbooks.
This led to the exclusion of Pakistan sugarcane data from before 2011 and the reclassification of Pakistan cotton prices from cotton lint to raw cotton. Cost of production data at state/province-level are routinely collected by national agricultural statistics departments to support Minimum Support Price level setting. They provide, per crop, costs of the main production factors, labor, machinery, land, water and other inputs such as fertilizer and seeds. Here we used the Indian "A2" price level for fixed costs, which includes all basic input costs and the rent paid for leased land, but no rental value of owned land or the imputed value of family labor  (Sen & Bhatia, 2004). As such, it is a lower-end estimate of the actual cost of production but one routinely used by for example the Indian government.
AcknowledgmentsThis work was carried out by the Himalayan Adaptation, Water and Resilience (HI-AWARE) consortium under the Collaborative Adaptation Research Initiative in Africa and Asia (CARIAA), with financial support from the UK Government's Department for International Development and the International Development Research Centre, Ottawa, Canada. It received further support under the SustaIndus project, funded by the Dutch Research Council (NWO, grant W07. 30318. 002). The views expressed in this work are those of the authors and do not necessarily represent those of NWO, the UK Government's Department for International Development, the International Development Research Centre, Canada or its Board of Governors. The first author was also supported by the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie programme (grant agreement No.
Data Availability Statement The crop-hydrological model LPJmL is open source (it can be downloaded from www. pik-potsdam. de/research/projects/activities/biosphere-water-modelling/lpjml/versions, together with model description and parameterization). The economic data are open source and can be downloaded from FAOSTAT (prices; http://www. fao. org/faostat/en/#data/PP) and national databases (cost of production, for India; eands. dacnet. nic. in/Cost_of_Cultivation. htm, and for Pakistan; http://www. amis. pk/Surveys. aspx).
Methodology We employ first-principles molecular dynamics simulations to study the pressure and temperature dependence of a series of realistic silicate melts whose compositions approximate the global magma ocean. In molecular dynamics, the particles (i. e. , the atoms) move under the action of interatomic forces according to Newtonian mechanics. With first principles we accurately compute these interatomic forces without relying on any approximations and fits to experimental data. We use the density functional theory in the planar-augmented wavefunction flavor as implemented in the VASP package  (Kresse & Furthmüller, 1996; Kresse & Hafner, 1993)  to compute the interatomic forces. The kinetic energy cutoff of the planewaves was set at 550 eV and the augmentation charge at 800 eV. The electronic density is sampled in the reciprocal space in the Gamma point.
Partial Molar Volumes We calculated the partial molar volumes as a function of pressure and temperature by subtracting the EOS of hydrated and carbonated pyrolite melts from the EOS of dry pyrolite. Our calculated partial molar volumes at 2000 K are generally in good agreement with experiments (Figures  3  and 4 ). Experimental data in Figure  3    of H 2 O in albite at 1 bar  (Ochs & Lange, 1997)  where we use their reported temperature dependence of the partial molar volume of H 2 O to extrapolate to 2000 K, as no other data is available at ambient pressure and ∼2000 K. At ambient pressure and 2000 K, we calculate a partial molar volume of 31. 7 cm 3 /mol, similar to the experimental value of 31. 3(6) cm 3 /mol in albite melt  (Ochs & Lange, 1997) , despite the different melt compositions.
Classical simulations at 2273 K by  Guillot and Sator (2011)   derestimation of absolute densities, yet there is an excellent agreement for the partial molar volumes of CO 2 and H 2 O in ultramafic melts using the local gradient approximation  (Ghosh et al. , 2017; Mookherjee et al. , 2008)  and the generalized gradient approximation  (Karki et al. , 2020; this study). Thus, the partial molar volume of H 2 O and CO 2 provide a useful tool for benchmarking and discerning subtle differences between volatile-bearing melts when other properties may differ due to differences in methodology. The partial molar volumes of H 2 O and CO 2 in pyrolite melt as a function of pressure and temperature are available at https://doi. org/10. 5281/zenodo. 4334000.
Acknowledgments This work was supported by the  European Research Council (ERC)  under the  European Union   Horizon 2020 research and innovation program  (grant agreement number  681818 IMPACT ), and by the  Research Council of Norway through its Centres of Excellence funding scheme , project number  223272. We acknowledge access to the  GENCI  supercomputers through the stl2816 series of eDARI computing grants, the Fram supercomputer through the  NN9697K  grant, and to the Irene AMD supercomputer through the  PRACE RA4947  project.
To evaluate the association of HIV-related factors with diagnostic accuracy of screening strategies, diagnostic accuracy for CIN2+/CIN3+ was evaluated separately among women on prolonged ART (>2 years), women on short-duration ART (�2 years), and ART-naïve women at baseline (Analysis 2). Diagnostic accuracy was also evaluated according to age at screening (Analysis 2). The cumulative incidence of CIN2+ at endline was calculated among women who screened negative for each of the screening strategies at baseline (Analysis 3). Analyses for diagnostic accuracy were conducted for discrete outcomes of CIN2+ and CIN3+. Data are presented separately for each country. Data were analysed using Stata (version 16) and according to the study statistical analysis plan (S1 Text). This article was reported according to the Standards for Reporting of Diagnostic Accuracy (STARD) statement (S1 Checklist)  [29].
Acknowledgments careHPV test kits and testing systems were obtained through a  Qiagen donation programme. Contributing members of the  HARP Study Group  included the following:  A. Chikandiwa ,  E. Cutler ,  S. Delany-Moretlwe ,  D. A. Lewis ,  M. P. Magooa ,  V. Maseko ,  P. Michelow ,  B. Muzah ,  T. Omar , and  A. Puren  (Johannesburg, SA);  F. Djigma ,  J. Drabo ,  O. Goumbri-Lompo ,  N. Meda ,  B. Sawadogo ,  J. Simpore ,  A. Yonli , and  S. Zan  ( Ouagadougou, BF );  V. Costes ,  M. N. Didelot ,  S. Doutre ,  N. Leventoux ,  N. Nagot ,  J. Ngou , and  M. Segondy  (Montpellier, France); and  A. Devine ,  C. Gilham ,  L. Gibson ,  H. Kelly ,  R. Legood ,  P. Mayaud , and  H. A. Weiss  ( London, UK ). The  HARP Study Group also wishes to thank its international scientific advisory group , consisting of  Prof.
Results The Mg and O isotope compositions were measured using NanoSIMS at the Museum National d'Histoire Naturelle in Paris (cf. Table  S2  for instrumental setup). A selection of the data is reported in Table  S3  Tables and Figures. NanoSIMS analyses of the majority of the plasma deposited carbonaceous material (PDCM) exhibit small isotopic variability, on the order of 10-30‰, likely due to analytical effects caused by surface sample roughness. In the following, PDCM will refer to this large fraction of condensed organic macromolecules that does not show any significant MIF effect. The measured isotopic variations are thus expressed in d units using the average isotopic compositions (for O and Mg) of the PDCM as reference values. This procedure allows to minimize analytical matrix effects and to express the isotopic fractionation relative to the non-fractionated compounds produced from the same gas.
The instrumental parameters of the NanoSims are reported in Table  S2. In order to have comparable topographic and matrix effects for the isotopic reference and for the samples, all data are reported using the PDCM as the reference value. In others terms, the isotopic fractionation is expressed relative to the carbonaceous deposits that do not show MIF effects in excess of »±2s relative to their average value. When analyses were performed randomly, each area (» 20×20 µm) was divided in 9 regions (i. e. ROI » 6×6 µm; noted "Random Area" in Tables  S3. 3 and S3. 5). Data reported in Tables  S3. 2, 3 For magnesium, a synthetic sample (Methylmagnesium chloride evaporated in the air) was used to verify the MDF relations between the electron multipliers. For oxygen, these relations are checked routinely for rock sample analyses.
Acknowledgments This work was supported by grants from  CNRS MITI  (ISOTOP 2020, COSMIFs), from  ANR  ( ANR-20-CE49-0011-01 ). F. R. acknowledges support (Overheads) of  ERC   Advanced Grant PaleoNanoLife  (PI: F. Robert; 161764). F. R. designed the project and run the experiments. F. R. and M. C. write the paper and interpretation. S. M. and A. G. analyzed the isotopic composition with the NanoSIMS. We would like to thank  Florie Lopis  for her availability and efficiency without which the realization of these experiments would not have been possible. Sylvain Pont's contribution was appreciated for SEM analyses.
Proteomic analysis and data processing Plant samples (root and leaves) were sampled at 50 dpi and pulverized with liquid nitrogen. The used protocol for total protein extraction was based on SDS and phenol extraction  (Wu, Xiong, Wang, Scali, & Cresti, 2014). Then, samples were digested and analysed by Liquid Chromatography-Mass Spectrometry (LC-MS/MS) as described previously  (Garcia-Seco et al. , 2017b). Mass spectrometer raw files were analysed by MaxQuant (version 1. 6. 2. 3, default parameters) against UniProt T. aestivum (Version 2017-1, 150,716 entries), Uniprot Rhizophagus irregularis (Version 2015-10, 29,847 entries), Uniprot P. graminis (Version 2015-10, 6,732 entries) and Uniprot A. brasilense (Version 2015-10, 7,636 entries). On January 2019, the UniProt T. aestivum database has been updated.
Therefore, we obtained the updated protein IDs by BLAST search of our dataset against the Uniprot T. aestivum 2019 database (143,020 entries). Unknown proteins were annotated by BLAST search against the Uniprot viridiplantae database (Version 2019-01, 6,913,939 entries), taking the first hit with a valid annotation. All MS proteomic data have been deposited in the ProteomeXchange Consortium via PRIDE partner repository with the Username: reviewer04430@ebi. ac. uk and Password: 2vlyEEVZ. MaxQuant output files were processed as described earlier  (Vannini et al. , 2019). Only proteins detected in at least two of the three biological replicates (75%) sharing the same treatment and tissue were considered. To compare the differences among analytical groups we performed an ANOVA based multiple samples coupled with Tukey test using the R package LIMMA.
Only proteins with false discovery rate (FDR) below 0. 01 were considered Differentially Abundant Proteins (DAPs) within the various comparisons. In order to produce a reliable and robust dataset, all proteins which gave one nonzero and two zero outcomes (two-time imputation) in at least one of the samples in each comparison were considered unreliable and therefore eliminated. In order to use bioinformatic tools available only for A. thaliana, a local BLAST of T. aestivum proteins against the TAIR10 database (version 2012-05-07) was performed. The enrichment analysis was performed using the Gene Ontology Resource (http://geneontology. org), running PANTHER algorithm with A. thaliana as background and FDR < 0. 05 or using the AgriGO Singular Enrichment Analysis (SEA) compare tool (http://bioinfo.
Statistics Phenotyping data of plant weight, CFU and lesion length were analyzed, depending on normality of data, by ANOVA followed by Tukey post-hoc test or Kruskal-Wallis test followed by Mann-Whitney pairwise comparisons, in R Environment (rstatix) and figures produced by ggplot. Data from quantification of amino acid, total glucose and nitrogen were subjected to statistical analysis by ANOVA and Tukey post-hoc test.
Acknowledgments V. F. , D. G. S. , P. B. , L. M. , and C. V designed the study; V. F. , D. G. S. , M. N. , and D. G. carried out the majority of experiments; G. D. , M. M. , and C. V performed the bioinformatic analysis of proteomic data; C. V. , V. F. , P. B. L. M. , F. W. -D. , and M. B. interpreted the data and wrote the manuscript. ACKNOWLEDGMENTS This work was supported by the  MIC-CERES  ("Microbial eco-compatible strategies for improving wheat quality traits and rhizospheric soil sustainability") Project ( FC  Project  ID 2013-1888 ;  AF  Project  ID 1301-003 ) jointly supported by the  Agropolis Foundation  (through the  "Investissements d'avenir" program , with the reference number  ANR-10-LABX-0001-01 ) and  Fondazione Cariplo. The authors thank the  Functional Genomics Center Zurich (FGCZ)  for providing highly valuable technical support.
This work was supported by the  MIC-CERES  ("Microbial eco-compatible strategies for improving wheat quality traits and rhizospheric soil sustainability") Project ( FC  Project  ID 2013-1888 ;  AF  Project  ID 1301-003 ) jointly supported by the  Agropolis Foundation  (through the  "Investissements d'avenir" program , with the reference number  ANR-10-LABX-0001-01 ) and  Fondazione Cariplo.
Morphological data We obtained the body mass for each of the 22 focal taxa. We found body mass data in the literature for tropical mainland and all temperate taxa (Regulus madeirensis: Billerman et al. 2020; Madeiran robin and blackbird: Hounsome 1993; Madeiran chaffinch: Grant 1979; Madeiran blackcap:  Dietzen et al. 2008). For São Tomé island taxa, body mass data were collected on the field by MM (available on SAFRING database, Univ. of Cape Town). When only ranges were reported or when masses were reported separately for males and females, we assumed the midpoint median was the mean of the two values. Body mass did not differ significantly between the island and mainland species (pairwise comparisons using Wilcoxon rank-sum test, p > 0. 35).
Recordings We made recordings of each of the study species by using Fostex FR2 recorders [Fostex, Tokyo] coupled with unidirectional microphones Sennheiser ME66/K6  [Sennheiser, Hanover]  (flat frequency response between 20 Hz and 20 kHz). The signal was sampled at 44. 1 kHz with a 16 bits digitisation, and files were saved in. wav format. Data were collected during the breeding season in 2016 and 2017 (temperate mainland: Apr 2017; temperate island: Mar 2017; tropical mainland: Nov 2016; tropical island: Sept 2016). Each recording was selected from different locations: in each of the four communities, recordings were obtained in three distinct sites; within each site, recordings were obtained at least 200 m apart to ensure they came from different individuals. We recorded the typical vocalisations usually recognised as 'song' by the ornithologist community based on our own experience and on xeno-canto (<www.
xeno-canto. org/>) and Macaulay (Cornell Lab of Ornithology, <www. macaulaylibrary. org/>) online-libraries. We restricted the analyses to the highest quality files, and, on average, we measured 7. 04 (± 3. 02) songs per individual, 10. 27 (± 2. 19) individuals per species and 72. 32 (± 29. 46) songs per species (1591 songs analysed in total; sample sizes are presented in the Supporting information). These recordings were obtained at less than 30 m (mostly between 5 and 20 m) from the focal bird. Using Avisoft software [Avisoft Bioacoustics, Berlin], we computed spectrograms and oscillograms with fast Fourier transforms (FFT 1024 points; Frame 50%; Hamming window; Overlap 75%). We determined song duration using cursor placement on oscillograms. On oscillograms and spectrograms, we visually identified the number of different syllables (short repeated units as defined by  Catchpole and Slater 2008)  within each song as a measure of syllable diversity.
Statistical analyses All three dependent variables (song duration, syllable rate and syllable diversity) were log-transformed to ensure their normality (based on graphical assessment of residuals). We used linear mixed-effects models (LMM) using the R package nlme to explore patterns of variation. For each of the three response variables, we ran a model testing insularity (island/mainland), latitude (tropical/temperate), the interaction between latitude and insularity, body mass and altitude as fixed effects. We included bird mass (from HBW, Billerman et al. 2020) as a proxy for syrinx mass, long recognised as influencing the timing of sound production  (Brackenbury 1978 , Gaunt 1987). Syrinx mass primarily determines the minimum frequency that a bird can achieve  (Ryan and Brenowitz 1985, Potvin 2013) , but it may also affect other variables such as syllable rate as a result of trade-offs between rate and frequency bandwidth  (Podos 1996 (Podos , 1997)).