dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2019; Schuh et al. , 2019). The inversion systems prescribe global fossil fuel emissions. For the first time in this year's budget, most (five of the six) inversion systems prescribed the same estimate for E FOS , specifically the GCP's Gridded Fossil Emissions Dataset version 2020. 1 (GCP-GridFEDv2020. 1), which is an update to 2019 of the first version of GCP-GridFED presented by  Jones et al. (2020). GCP-GridFEDv2020. 1 scales gridded estimates of CO 2 emissions from EDGARv4. 3. 2 (Janssens-Maenhout et al. , 2019) within national territories to match national emissions estimates provided by the GCP for the years 1959-2019, which were compiled following the methodology described in Sect. 2. 1 with all data sets available on 31 July 2020 (Robbie  Andrew, personal communication, 2020). A new feature in this edition of the global carbon budget is the use of a consistent prior emissions data set for E FOS across almost all inversion models, which avoids the need to correct the estimated land sink (by up to 0.
5 GtC in the northern extra-tropics) for most models. Only the UoE inversion used an alternative data set and required a postprocessing correction (see Table  A4 ). Further, the use of GCP-GridFEDv2020. 1 for E FOS ensures a close alignment with the estimate of E FOS used in this budget assessment, enhancing the comparability of the inversion-based estimate with the flux estimates deriving from DGVMs, GOBMs, and pCO 2 -based methods. The land and ocean CO 2 fluxes from atmospheric inversions contain anthropogenic perturbation and natural preindustrial CO 2 fluxes. On annual timescales, natural preindustrial fluxes are primarily land CO 2 sinks and ocean CO 2 sources corresponding to carbon taken up on land, transported by rivers from land to ocean, and outgassed by the ocean. These pre-industrial land CO 2 sinks are thus compensated over the globe by ocean CO 2 sources corresponding to the outgassing of riverine carbon inputs to the ocean.
Model evaluation The evaluation of the ocean estimates (Fig. B1 ) shows an RMSE from annually detrended data of 0. 5 to 1. 6 µatm for the five pCO 2 -based flux products over the globe, relative to the f CO 2 observations from the SOCAT v2020 database for the period 1985-2019. The GOBM RMSEs are larger and range from 3. 5 to 6. 9 µatm. The RMSEs are generally larger at high latitudes compared to the tropics, for both the flux products and the GOBMs. The five flux products have RM-SEs of 0. 4 to 1. 9 µatm in the tropics, 0. 6 to 1. 9 µatm in the north, and 1. 5 to 2. 8 µatm in the south. Note that the flux products are based on the SOCAT v2020 database, and hence the latter is no independent data set for the evaluation of the flux products. The GOBM RMSEs are more spread across regions, ranging from 2.
Data availability The data presented here are made available in the belief that their wide dissemination will lead to greater understanding and new scientific insights into how the carbon cycle works, how humans are altering it, and how we can mitigate the resulting human-driven climate change. The free availability of these data does not constitute permission for publication of the data. For research projects, if the data are essential to the work, or if an important result or conclusion depends on the data, co-authorship may need to be considered for the relevant data providers. Full contact details and information on how to cite the data shown here are given at the top of each page in the accompanying database and summarized in Table 2. The accompanying database includes two Excel files organized in the following spreadsheets.
, 2020). The fit includes a multilinear regression against environmental drivers to bridge data gaps, and interannually explicit corrections to represent the data signals more completely. Two-step neural network method wherein a first step the global ocean is clustered into 16 biogeochemical provinces using a selforganizing map (SOM). In a second step, the non-linear relationship between available pCO 2 measurements from the SOCAT database  (Bakker et al. , 2016)  and environmental predictor data (SST, SSS, MLD, CHL a, atmospheric CO 2 -see  Landschützer et al. , 2016)  is established using a feed-forward neural network (FFN) for each province separately. The established relationship is then used to fill the existing data gaps (see  Landschützer et al. , 2013 Landschützer et al. , , 2016)).
Three regression algorithms are used: (1) gradient boosted decision trees, (2) a feed-forward neural network, (3) support vector regression. The product of the cluster configurations and the regression algorithms results in an ensemble with six members. Derived from the SO-CAT(v2020) pCO 2 database but corrected to the subskin temperature of the ocean as measured by satellite, using the methodology described by  Goddijn-Murphy et al. (2015). A correction to the flux calculation is also applied for the cool and salty surface skin. In other respects the product uses interpolation of the data using the two-step neural network based on MPI-SOMFFN: in the first step the ocean is divided into a monthly climatology of 16 biogeochemical provinces using a SOM, In the second step a feed-forward neural network establishes nonlinear relationships between pCO 2 and SST, SSS, mixed layer depth (MLD), and atmospheric xCO 2 in each of the 16 provinces.
Acknowledgements. We thank all people and institutions who provided the data used in this carbon budget and  Ian G. C. Ashton ,  Matthew Chamberlain ,  Ed Chan ,  Laique Djeutchouang ,  Christian Ethé ,  Liang Feng ,  Matthew Fortier ,  Lonneke Goddijn-Murphy ,  Thomas Holding ,  George Hurtt ,  Joe Melton ,  Tristan Quaife ,  Marine Remaud ,  Shijie Shu ,  Jamie Shutler ,  Anthony Walker ,  Ulrich Weber , and  David K. Woolf  for their involvement in the development, use, and analysis of the models and data products used here. We thank  Ed Dlugokencky  for providing atmospheric CO 2 measurements; We thank  Benjamin Pfeil ,  Steve Jones ,  Rocío Castaño-Primo , and  Maren Karlsen  of the  Ocean Thematic Centre of the EU Integrated Carbon Observation System (ICOS) Research Infrastructure  for their contribution, as well as  Karl Smith  of  NOAA's Pacific Marine Environmental Laboratory  and  Kim Currie ,  Joe Salisbury ,  Doug Vandermark ,  Chris Hunt ,  Douglas Wallace , and  Dariia Atamanchuck , who contributed to the provision of ocean pCO 2 observations for the year 2019 (see Table  A5 ).
This is NOAA-PMEL contribution number  5167. We thank the institutions and funding agencies responsible for the collection and quality control of the data in SOCAT, and the  International Ocean Carbon Coordination Project (IOCCP)  for its support. We thank the  FAO  and its member countries for the collection and free dissemination of data relevant to this work. We thank data providers ObsPack GLOBALVIEWplus v5. 0 and NRT v5. 2 for atmospheric CO 2 observations. We thank  Trang Chau  who produced the CMEMS pCO 2 -based ocean flux data and designed the system together with  Marion Gehlen ,  Anna Denvil-Sommer , and  Frédéric Chevallier1. We thank the individuals and institutions that provided the databases used for the model evaluations introduced here, and  Nigel Hawtin  for producing Figs.
Material and methods We recruited 47 boys, aged 8 to 12 years old. This age range was chosen to capture a large developmental window at school age. Data from one participant were lost due to technical issues during EEG recording. To match the groups on verbal and performance IQ (VIQ, PIQ), four participants (two from the TD group and two from the ASD group) were excluded from the reported analyses, resulting in a sample of 21 typically developing (TD) boys (mean age = 10. 2 years ± SD = 1. 3) and 21 boys with ASD (mean age = 10. 6 ± 1. 3, Table  1 ). However, inclusion of these four participants did not change any of the reported results of the analyses. The sample in this study is identical to the one in previous studies  [97, 98]  where frequency-tagged social and non-social stimuli were presented.
EEG analysis Preprocessing All EEG processing was performed using Letswave 6 (https ://www. letsw ave. org/) and Matlab 2017 (The Mathworks). EEG data were segmented in 67-s segments (2 s before and 5 s after each sequence), bandpass-filtered (0. 1-100 Hz) using a fourth-order Butterworth filter, and down-sampled to 256 Hz. Next, electrodes were visually inspected, and noisy electrodes were linearly interpolated from the 3 spatially nearest electrodes (not more than 5% of the electrodes, i. e. , 3 electrodes, were interpolated). All data segments were re-referenced to a common average reference. While in frequency-tagging studies we typically apply blink correction (using ICA) for any participant blinking more than 2 standard deviations above the mean (e. g. ,  [25, 92] ,  [96] ), in the present study we did not perform any blink correction as none of the participants blinked excessively, i.
5 Hz) (e. g. , 6 Hz, 12 Hz, 18 Hz; 7. 5 Hz, 15 Hz, 22. 5 Hz, etc. ). We used two measures to describe the response in relation to the noise level: signal-to-noise ratio (SNR) to better visualize the data (e. g. ,  [52] ) and baselinecorrected amplitudes  [75]  to quantify it. SNR spectra were computed for each electrode by dividing the value at each frequency bin by the average value of the 20 neighboring frequency bins (12 bins on each side, i. e. , 24 bins, but excluding the 2 bins directly adjacent and the 2 bins with the most extreme values). We computed baseline-corrected amplitudes in a similar way by subtracting the average amplitude of the 20 surrounding bins. For group visualization of topographical maps (Fig. 3 ), we computed across-subjects averages of the baseline-corrected amplitudes for each condition and electrode separately.
Statistical analysis We analyzed the baseline-corrected amplitudes in the ROI at each presentation frequency for each face part at the group level using a Bayesian general linear mixed model, relying on the R (v 4. 0. 2, [18] ) package brms  [11]. brms is a software package providing a convenient front end to STAN where Bayesian models are fitted using Hamiltonian MCMC methods  [13]. In Bayesian general linear mixed models, one has to explicitly put prior distributions on the model parameters. For the parameters associated with the fixed effects, we used a normal distribution prior with mean 0 and standard deviation 1. For the standard deviation parameters associated with the random effects, we used a half-normal distribution prior with mean 0 and standard deviation 2. For the residual standard deviation, we used a half-normal prior with mean 0 and standard deviation 2.
Eye-tracking recording Eye-tracking data were collected using a Tobii X3-120 screen-based remote eye tracker and Tobii Pro software (Tobii Pro). The sampling rate was 120 Hz. Binocular gaze precision at ideal conditions is estimated at 0. 24° of visual angle and binocular gaze accuracy at 0. 4°. However, for many experiments these ideal conditions are not met  [59]. With a remote eye tracker, participants are free to move their head within the "headbox" allowing eye tracking  [59]. Due to this freedom of movement, as well as the use of pediatric and/ or patient populations, the precision and accuracy of the actual data may differ from those marketed by the manufacturers. In the standard calibration procedure of the Tobii X3-120, participants have to follow a red dot moving across the screen, yielding a merely qualitative index of calibration quality based on visual inspection.
In order to obtain a subject-specific quantitative measure of eye-tracking data quality, we implemented an additional calibration validation paradigm, preceding the data registration. In this additional calibration procedure, participants had to fixate on the center of nine consecutive fixation crosses appearing on different locations on the screen. Calculation of the angle between the vectors to the displayed fixation cross versus the actual gaze point yields a quantitative index of error angle (mean and variance) and resulting accuracy. These values were used in the analysis to attribute gaze points more accurately to particular areas of interest (AOIs). For two participants (one from the ASD group and one from the TD group), eye-tracking data were not recorded due to technical failure.
Fixation filter, definition of areas of interest (AOI), gaze attribution Eye-tracking data were analyzed by means of a series of custom-built MATLAB (Matlab 2019b, The Mathworks) scripts (see https ://osf. io/mv45x /). We used the I2MC algorithm (identification by 2-means clustering  [37] ), to filter the raw eye-tracking data (i. e. , deleting random noise, interpolating missing data, identifying fixations). In the current study, the three AOIs (eyes, mouth, and nose) were defined using the limited-radius Voronoi tessellation (LRVT), as it has been shown to be the most noise-robust and objective method for defining AOIs for facial stimuli  [36]. In addition, we defined the area "outside AOI" to comprise all the fixation points that were not assigned to either of the AOIs. Fixations are attributed to the AOIs by means of a probability weighting, taking into account the subject-specific data quality as obtained via the additional calibration validation procedure.
Statistical analysis We analyzed the eye-tracking data using a Bayesian general linear mixed model, relying on the R package brms  [11]. Similarly to the statistical analysis of the EEG data, we used a normal distribution prior with mean 0 and standard deviation 1 for the parameters associated with the fixed effects. For the standard deviation parameters associated with the random effects, we used a half-normal distribution prior with mean 0 and standard deviation 2. For the residual standard deviation, we used a half-normal prior with mean 0 and standard deviation 2. We used 4 MCMC chains each including 8000 iterations of which 4000 were considered warm-up, resulting in 16,000 posterior samples across all 4 chains. The sampling algorithm was NUTS (the No-U-Turn Sampler variant of Hamiltonian Monte Carlo).
Face exploration dynamics We modeled the temporal dynamics of the gazing behavior using Observable Markov Models (OMMs), using a custom python implementation. The OMMs consist of a transition matrix T, where element T ij represents the probability of finding a fixation point in AOI j at time t + 1, given that the fixation point at t falls inside AOI i. The Markov property assumes this transition probability to be independent of the previous states. To estimate the elements of the transition matrix T, we counted all transitions from AOI i to AOI j for each participant. In a preprocessing step, we interpolated the data using a second-order spline fit. For each participant, we constructed a single transition matrix. We retained the fixation data falling within the four AOIs (left eye, right eye, nose, mouth) and removed fixations outside of these AOIs by constructing a limited radius Voronoi tessellation (with a maximum radius equivalent to 100 pixels, see Additional file 1: Figure  S1 ).
Additionally, to remove the data of participants gazing at the edge of the image or outside the image, we limited the dataset to those fixation data falling inside the face image, by applying a margin of 50 pixels from the mean point of the AOI (see vertical cutoff in Additional file 1: Figure  S1 ). To fully characterize the face exploration dynamics, in addition to the OMM transition probabilities, we computed the mean fixation duration, the mean saccadic amplitude and the mean intraparticipant dispersion  [20]. The mean fixation duration and mean saccadic amplitude are obtained by averaging the fixation durations and saccadic amplitudes, respectively, that are given in the output of Tobii Pro, for the data falling inside the four AOIs. The intraparticipant dispersion is defined as the mean Euclidean distance between all data within a trial.
As a result, the actual property vectors contained the z-scores of the respective features. Finally, to account for the strong correlation between these 12 features, we carried out a group-independent principal component analysis (PCA), with an 0. 98 explained variance ratio cutoff. The latter corresponds to keeping 13 principal components, using the default values of the scikit-learn package (v 0. 22. 1, [65] ), resulting in the retention of 13 orthogonal principal components. Based on the local outlier factor method, two participants (from the ASD group) were detected as outliers and were consequently removed from the MANOVA group comparison analysis. For the individual-level classification analysis, we opted for a linear discriminant analysis (LDA), applied to the 15 data features. We chose LDA since the model is simple and gives a robust estimate even without tuning any hyperparameters.
Availability of data and materials No part of the study procedures or analysis plans was preregistered in an institutional registry prior to the research being conducted. The conditions of our ethics approval do not permit public archiving of individual anonymized raw data. Readers seeking access to the data should contact the lead author Sofie Vettori and/or the senior author Bart Boets at the Center for Developmental Psychiatry, Department of Neurosciences, University of Leuven. Access will be granted to named individuals in accordance with ethical procedures governing the reuse of sensitive data. Specifically, requestors must complete a formal data sharing agreement to obtain the data. Stimuli and analysis scripts can be found in https ://osf. io/mv45x /.
31The reactions of phenylacetylene with complexes 1 and 2 provide complexes 6 and 8, respectively. The values in brackets are the Gibbs free energy. notobtainedas the compound decomposed when it was dried under vacuum. Calculations were carried out with Gaussian09 at the DFT level, with the hybrid functional B3PW91. The synthetic procedures of H 2 PP{N(DIPP)CH 2 CH 2 N(DIPP)} and 1 are listed as below, their NMR and elemental analyses data are also listed. The synthetic procedures and characterization of 2-9 are generally similar to those of 1, and are provided in Supplementary Methods. See Supplementary Methods for the single crystal XRD analysis, the kinetic study and the theoretical calculations; see Supplementary Data for the datasets of Cartesian coordinates in calculations. J P-H = 183.
Acknowledgements This work was supported by the  National Natural Science Foundation of China  (Nos. 21732007 ,  21890721 , and  21821002 ),  K. C. Wong Education Foundation , the  Program of Shanghai Academic Research Leader , the  Strategic Priority Research Program  of the  Chinese Academy of Sciences  (Grant No. XDB20000000 ), and  Fujian Institute of Innovation, Chinese Academy of Sciences. L. M. acknowledges the  Chinese Academy of Sciences President 's  International Fellowship Initiative.
Data availability Crystallographic data for the structures reported in this article have been deposited at the Cambridge Crystallographic Data Centre (CCDC) under deposition nos. CCDC 1945178 (H 2 PP{N(DIPP)CH 2 CH 2 N(DIPP)}), 1945179 (1), 1958510 (2), 1945180 (3), 1945181 (4), 1945182 (5), 1945183 (  6 ), 1945184  (7) , 1958511  (8) , and 1958512  (9). These data can be obtained free of charge from the Cambridge Crystallographic Data Centre via www. ccdc. cam. ac. uk/data_request/cif. All other data supporting the findings of this study are available within the Article and its Supplementary Information, at the Oxford University Research Archive (https://ora. ox. ac. uk) and from the corresponding authors upon reasonable request.
This approach allowed the identification of 142 putative IRT1 interactants (Supplemental Dataset 1). Among these, 31 were found in the two independent experiments whereas 111 were detected in only one of the two replicates. Since the control of IRT1 trafficking is of the utmost importance for the regulation of this transporter, we specifically looked for IRT1-interacting proteins that are linked to the secretory or endocytic pathways (Table1). Clathrin was found as putatively interacting with IRT1, which is in accordance with our previous results showing that IRT1 undergoes clathrinmediated endocytosis  (Barberon et al. , 2011; Barberon et al. , 2014). Interestingly SEC13a and SEC31b proteins were identified as IRT1 putative interactants, suggesting a role of coat protein complex II (COPII) machinery in the export of IRT1 from the endoplasmic reticulum (ER) to the Golgi apparatus  (Chung et al.
, 2016). Besides, co-IP was successfully used in the past to identify interactants of channels and receptors  (Karlova et al. , 2006; Bellati et al. , 2016). One must consider that an interactome provides an overview of proteins putatively interacting with a bait, but that each candidate protein has to be validated by an independent proteinprotein interaction technic before further investigation to avoid putative artifacts. Here, we decided to (i) use the IRT1 endogenous promoter to maximize the detection of interactions between IRT1 and its interactants in relevant cell types, i. e. root epidermal cells, and (ii) validate each candidate of interest using the split-ubiquitin technic. Two independent co-IP/mass spectrometry experiments were performed, which provides qualitative data on proteins interacting with IRT1.
Tryptic peptides from the two or three SDS-PAGE fractions from each eluate were analyzed separately by nanoLC-MS/MS with the Triple-TOF 4600 mass spectrometer (ABSciex) coupled to the nanoRSLC ultra performance liquid chromatography (UPLC) system (Thermo Scientific) equipped with a trap column (Acclaim PepMap100C18, 75 μmi. d. × 2 cm, 3 μm) and an analytical column (Acclaim PepMapRSLCC18, 75 μmi. d. × 50 cm, 2 μm, 100 Å). Peptides were loaded at 5 µl/min with 0. 05% (v/v) TFA in 5% (v/v) acetonitrile and peptide separation was performed at a flow rate of 300 nl/min with a 5-35% (v/v) solvent B gradient in 40 min. Solvent A was 0. 1% (v/v) formic acid in water, and solvent B was 0. 1% (v/v) formic acid in 100% acetonitrile. NanoLC-MS/MS experiments were conducted in a Data Dependent acquisition method by selecting the 20 most intense precursors for CID fragmentation with Q1 quadrupole set at low resolution for better sensitivity.
Protein identification was performed by processing raw data with MS Data Converter software (AB Sciex) for generating. mgf data files and protein identification were performed using the MASCOT search engine (Matrix science, London, UK) against the Swissprot and TAIR10 databases with carbamidomethylation of cysteines set as fixed modification and oxidation of methionines as variable modifications. Peptide and fragment tolerance were set at 20 ppm and 0. 05 Da, respectively. Results were analyzed with Scaffold 3. 6. 5 software (Proteome Software). Proteins were validated when identified with at least two unique peptides and 95% probability levels for both peptides and proteins.
This work has benefited from a  French State grant  (reference  ANR-10-LABX-0040-SPS , to E. Z. ) managed by the  French National Research Agency  under an  Investments  for the  Future program  (reference n°ANR- 11-IDEX-0003-02 ). This work was also funded by  Marie Curie Actions  ( PCIG-GA-2012-334021 , to G. V. ), by the  French National Research Agency  ( ANR-13-JSV2-0004-01 , to G. V. ) and the  French Laboratory of Excellence  project " TULIP"  ( ANR-10-LABX-41  and  ANR-11-IDEX-0002-02  to G. V. ). Guillaume Dubeaux  is supported by an  EMBO  long-term postdoctoral fellowship ( ALTF334-2018 ). main actors of iron acquisition in Arabidopsis root, namely  IRT1 ,  FRO2 , and  AHA2 , assemble into a dedicated protein complex. www. plantphysiol. org on September 2, 2020 -.