dataesr/scientific-paragraphs-categorization huggingface dataset:
A spectral resolution of at least 10 cm -1 seems to be necessary to clearly detect the various spectral shifts produced by ion irradiation. The separation between the spectral trend linked to the original composition of the meteorites and the irradiation trend is even clearer if the whole IR spectrum is considered. In the bottom panel of Fig. 4 , we show the results of the Principal Component Analysis (PCA) of the whole spectral dataset (in the range 100-1200 cm -1 , 8. 33-100 µm). A simple 2components PCA is able to separate the experimental points into four main sectors, and thus to easily distinguish anhydrous from hydrated chondrites, and irradiated from unirradiated samples. Similar results are obtained by other clustering methods such as hierarchical clustering or multidimensional scaling (not shown here).
3 ). To focus on the effect of different compositions, we include here only meteorites irradiated with exactly the same ion fluence. A Murchison pellet sample, irradiated at lower fluence, provided qualitative similar results. It will be worth comparing the effects of irradiation on different surface preparations in a further dedicated study. Bottom: PC1 and PC2 scores from a principal component analysis performed on the combined MIR and FIR spectra (and baseline corrected, as presented in Fig. 2 ) of Bennu and the studied CCs. The PC1 and PC2 are not linked to a specific mineralogy, but they rather represent two convenient axes containing the majority of the spectral information. They allow to quickly visualize the spread of our dataset, while reducing its dimensionality. The meteorites data have been resampled to Bennu's resolution for better comparison.
Acknowledgements The meteorite samples were kindly provided by the  Natural History Museum of Vienna , the  Vatican Observatory ,  J. Brucato ,  D. Cruikshank , and  L. Folco. We are grateful to  F. Capitani ,  A. Celeste , and  C. Sandt  for their precious help with the bolometer measurements at SOLEIL, and to  P. Boiardi  for useful suggestions on the manuscript. We warmly thank  R. D. Hanna  and  M. S. Thompson  for constructive reviews and discussions. PCA analysis was performed in Quasar (https://quasar. codes)using the Orange Spectroscopy toolbox (Toplak et al. 2017). The irradiations were performed using the INGMAR setup, a joint  IAS-CSNSM (Orsay, France)  facility funded by the  P2IO LabEx  ( ANR-10-LABX-0038 ) in the framework Investissements d' Avenir  ( ANR-11-IDEX-0003-01 ). The MIR micro-spectroscopy measurements were supported by grants from  Region Ile-de-France  ( DIM-ACAV ) and SOLEIL.
We thank  J. P. Bibring  for supporting the FIR spectroscopic activities at SOLEIL. This work has been funded by the  Centre National d'Etudes Spatiales (CNES-France ,  Hayabusa2  mission and OSIRIS-REx mission) and by the  ANR  project  CLASSY  (Grant  ANR-17-CE31-0004-02 ) of the  French Agence Nationale de la Recherche.
, 2020). The relative effect of height on Hg accumulation in Hölstein spruce needles is therefore investigated with data from the growing season 2019. Samples at Hyltemossa and Svartberget were cut from tree canopies using a 20 m telescopic scissors and at Hurdal using a 3 m telescopic scissors. At Norunda samples were shot from the tree canopies using a shotgun. At Schauinsland, Schmücke, Råö and Bredkälen, we used a 5 m telescopic scissors for cutting the branches in the lower half of the crown. At Pallas and Råö branches were cut from low-growing trees at breast height. We collected intact leaves at three to six time points during the 2018 growing season. Samples from at least three different branches of the same tree were pooled to a composite sample. We sampled at least three trees per species (one to four species) with the exception of Råö where only one oak and one spruce tree were available.
Standard measurement procedures included running a quality-control pre-sequence consisting of three method blanks, one process blank (wheat flour) and three liquid primary reference standards (PRSs; 50 mg of 100 ng g -1 NIST-3133 in 1 % BrCl). Daily performance of the instrument was assessed based on the three liquid PRSs and all data were corrected accordingly if the measured PRSs were within 90 % to 110 % of the expected value. If PRSs were outside this acceptable range, the instrument was recalibrated. Each sequence consisted of four SRMs, one process blank consisting of commercial wheat flour and 35 samples. Sequences were rejected if one SRM value was outside of the certified uncertainty range (NIST-1515) or 10 % of the respective target concentration (ICP Forests spruce B) or if the absolute Hg content of the flour blank was > 0.
(1) Figure  2  illustrates this flux calculation schematically. We used species-specific LAIs retrieved from a global database provided by  Iio and Ito (2014). In total, 205 values of onesided LAIs measured in central Europe and Scandinavia between latitudes of 46 and 63 • N and published in peerreviewed journals were selected for calculating an average LAI value of each species. Species-specific average LAI values are displayed in Table  S2. All LAI values for each species are peak-season values. To calculate the foliar uptake flux over the growing season, the average daily uptake flux was multiplied by the length of the growing season in days. For each site, the growing season length in days, which depends on the latitude of the site, was obtained from  Garonna et al. (2014)  and  Rötzer and Chmielewski (2001)  (Table  S1 ).
Special thanks go to Jann Launer for drawing Fig. 2. Finally, we thank Christian Körner for his helpful answers to questions on plant physiology. Financial support. The work of this paper was funded by the Swiss National Science Foundation (SNSF) (project 174101). The crane at the SCCII site is funded by the Swiss Federal Office for the  Environment (FOEN). The Swedish research infrastructures, ICOS and SITES, are both financed by the Swedish Research Council and partner universities.
yields an average foliar Hg uptake flux for Europe of 10. 4 ± 2 µg Hg m -2 over the 2018 growing season (weighted mean ± SE). Extrapolation of this weighted mean to the land area of European forests (192. 672 × 10 6 ha) results in a foliar flux of 20 ± 3 Mg Hg during the 2018 growing season (see Sect. S14 for details on flux extrapolation and Sect. S15 for error propagation). Under the assumption that tree species in the global temperate zone are distributed equally to tree species in Europe, we estimated an approximate foliar flux of 108 ± 18 Mg Hg to the area of global temperate forests (1. 04 × 10 9 ha) (Tyrrell et al. , 2012) during the 2018 growing season. This global extrapolation is at the lower end of global Hg litterfall deposition flux (163 Mg Hg yr -1 ) estimated for temperate forests based on a Hg litterfall flux database of measurements be-.
Data availability. Foliar Hg uptake fluxes at all sites are given in the Supplement. Hg concentrations and metadata of all foliage samples collected in this study are available at: https://doi. org/10. 5281/zenodo. 3957873 (Wohlgemuth, 2020). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/bg-17-6441-2020-supplement.
Funding: This research was funded by  French National Research Agency , grant number  ANR-18-CE35-0008  to C. L. and K. G. and  ANR-19-ARMB-0006-01  to C. L. , S. B. , and  S. D. , and the  University of Lyon  through funding to C. V. C. L. also acknowledges the  Schlumberger Foundation for Education and Research  ( FSER 2019 ) and the  Foundation for Innovation in Infectiology FINOVI  ( AO-2014 ) for financial support.
HILS is known to be undergoing non-linear VLM  (Featherstone et al. 2015) , while  Featherstone et al. (2015)  has suggested FREM is also undergoing non-linear VLM (cf. Thompson and Merrifield 2018; Burgette et al. 2013). Other SAR scenes are available over the test site, e. g. , C-band scenes from the European Space Agency's Envisat and Sentinel-1 satellite missions, but neither provide a sufficiently long time series nor the number of scenes that we have available from TSX. For example, Sentinel-1A started acquisitions in 2014 and observed the test site for an eight month period (e. g. ,  Parker et al. 2017) , while Sentinel-1B did not begin observations until 2016. Comparisons between X-band and C-band displacements in  Parker et al. (2017)  indicated reasonable agreement between these data although over a very short period, so are not conclusive.
Methods and data The data used and processing methods described in this section are arranged with a subsection for each technique. The cGPS in the test site (International GNSS Service (IGS) code PERT; Figure  1 ), is used to transform the InSAR displacement time series to a terrestrial reference frame (TRF) -IGS08 for this experiment -which is aligned with the International reference Frame 2008 (ITRF2008;  Altamimi et al. 2011). Derived TSX IGS08 rates can then be compared to cGPS IGS08 rates at other locations for validation, and the TSX rates at each tide gauge are then in a known TRF. We use PERT as the single reference point because it is considered the most reliable site for (1) cGPS time series (since 1995) and (2) the TSX reference pixel nearest to the cGPS. This is compared to HIL1 which is co-located with HILS but located on the roof of the tide gauges shed on a jetty in a boat harbour full of moored yachts with tall masts (possibly causing multipath) and has a break in its time series (Figure  2B ).
InSAR We first processed the 141 TSX scenes using the small baseline multi-temporal InSAR (MTI) method implemented in the Stanford Method of Persistent Scatterer (StaMPS;  Hooper et al. 2012 ) software as described in  Hooper (2008). The interferograms were processed using Doris (Delft object-oriented radar interferometric software; Kampes and Usai 1999), forming 705 small baseline interferograms. The small baselines interferogram pairs were selected manually, with the objective of keeping perpendicular (i. e. , the distance between repeat satellite positions, orthogonal to the LoS) and temporal baselines as small as possible, but retaining redundancy in the network so that each scene was connected by at least three small baseline interferograms. The high redundancy small baselines help reduce the effect of temporally variable tropospheric noise  (Williams 1998; Agram and Simons 2015; Fattahi and Amelung 2015; Cao et al.
Using the time series uncertainties in Table  3  for the PERT TSX (nominally zero) and cGPS, we propagate these to the FREM TSX rate, getting ±0. 69 mm/yr, while for HIL1 we obtain ±0. 87 mm/yr, and CUAI ±0. 88 mm/yr. The CUAI and HIL1 sites include their cGPS uncertainty in the linear error propagation. These may be an upper bound, but suggest that the differences between cGPS and TSX at HIL1 and CUAI are within the uncertainty when the TRF RP is taken into account. Longer period cGPS rates are shown in Table  4  that were computed using CATS and NGL data for this study (shown in Figure  5 ), and also from the University of La Rochelle processing (ULR6A, Santamaría-Gómez et al. 2017) and the Median Interannual Difference Adjusted for Skewness (MIDAS) method  (Blewitt et al. 2016). These are shown to provide an additional comparison for the longer term cGPS and altimetry minus tide gauge VLM rates thus serving as (1) a check for the other rates of similar periods, and (2) give an indication whether there is non-linearity at the VLM at these sites when compared to different time periods.
Acknowledgments We would like to thank the following organisations for providing and/or funding these data. TerraSAR-X scenes are acquired under the  German Space Center's (DLR's) Science  Project  LAN1499 , funded by  AuScope  under the Australian Geophysical Observing System (AGOS), and  Australian Research Council (ARC) Linkage  Projects  LP 110100284  and  LP140100155. The Ssalto/Duacs altimeter products were produced and distributed by the  Copernicus Marine and Environment Monitoring Service (CMEMS)  (http://marine. copernicus. eu). Dynamic atmospheric Corrections are produced by  CLS  using the Mog2D model from Legos and distributed by  Aviso+ , with support from  CNES  (https://www. aviso. altimetry. fr/). GPS data was obtained from  Nevada Geodetic Laboratory  at http://geodesy. unr.
Data availability The TerraSAR-X scenes are available to us under licence through DLR Science Project LAN1499, so cannot be made publicly available. ULR6 and NGL rates obtained from SONEL (https://www. sonel. org/ ). Processed GPS data are freely available at Nevada Geodetic Laboratory http://geodesy. unr. edu/, and also SONEL https://www. sonel. org/-ULR-112-. html. Monthly sea level records are freely available at PSMSL https://psmsl. org/. Altimetry data is available at CMEMS http://marine. copernicus. eu Global and Planetary Change [accepted]    Dynamic atmospheric corrections (DAC) are available from Aviso+ at https://www. aviso. altimetry. fr/.
Model Decro-3 consists of alternating silica powder and sand layers. All models are 5 cm in height except Decro-4, which is 4 cm in height. Two different pulling rates are tested: 1 cm/h and 3 cm/h. The change in rate modifies the strength of the silicone putty: the higher the rate, the stronger the silicone. The rate does not modify the mechanical properties of the sand  [Kirby, 1983 , Ranalli, 1987 , Kohlstedt et al. , 1995]. Surface and oblique pictures are taken during the experiment. Finally, the models are cut into several vertical cross-sections every 4 cm to provide 3D data sets. The properties of the five experiments are   Table 1. Physical properties and scaling are described in Tables  1  and 2. Mean deviatoric stresses 4. 8 3. 15 2. 2 2. 3 0. 9.
In the Country-1 cohort, predictions of AD and bvFTD became maximally improved upon inclusion of cognitive screenings outcomes combined with atrophy levels. Multimodal training data from this cohort allowed predicting both AD and bvFTD in the other two novel datasets from other countries with high accuracy (>90%), demonstrating the robustness of the approach as well as the differential specificity and reliability of behavioral and neural markers for each condition. In sum, this is the first study, across centers and countries, to validate the predictive power of cognitive signatures combined with atrophy levels for contrastive neurodegenerative conditions, validating a benchmark for future assessments of reliability and reproducibility.
First, works testing the classification properties of the ACE and the IFS rely on simple statistical methods to establish cut-offs for maximizing true positives and minimizing false positives, but no machine-learning approaches have yet been applied to evaluate the performance of these screenings, alone or in combination with neural measures  (Larner and Mitchell, 2014; Moreira et al. , 2017; Hsieh et al. , 2013; Torralva et al. , 2009a). Second, although a few neuroimaging studies have obtained high classification rates via sophisticated machine-learning approaches (such as support-vector machines), none of them has assessed the generalizability of their findings to new datasets  (Zheng et al. , 2016; Bron et al. , 2017; Zhang et al. , 2013; Dukart et al. , 2011; Kuceyeski et al. , 2012; Tahmasian et al.
In sum, computational-decision methods combining cognitive screenings and atrophy measures to detect dementia patient profiles prove promising, but key tests of their robustness remain to be determined. Here, we implemented machine-learning algorithms to evaluate the power of cognitive parameters and brain atrophy measures for classifying AD and bvFTD patients across three countries, including Latin-American and Anglo-Saxon samples. In addition, we applied a leave-two-out cross-validation approach to test the accuracy rates within a reference dataset (within-country analysis), and then performed a cross-country validation to further evaluate the generalization of our findings. We predicted that the ACE, given its target domains, would contribute more than the IFS for the classification of AD.
2. 3. 2. Voxel-based morphometry-Structural images from each center were analyzed via voxel-based morphometry (VBM) with the DARTEL Toolbox of the Statistical Parametric Mapping software (SPM12), following validated procedures  (Sedeño et al. , 2017). Images were segmented into white matter (WM), grey matter (GM), and cerebrospinal fluid (CSF). Then, based on the segmented GM and WM images, we created a template from each center's complete data set with the "DARTEL (create template)" module. Next, the final template from the previous step was affine-registered into the MNI space with the "Normalize to MNI Space" module from DARTEL Tools, and then this transformation was applied to all segmented GM scans to translate them into standard space (images were modulated by Jacobian determinants).
Finally, an isotropic Gaussian kernel of 12-mm full width at half maximum (FWHM) was applied to all images. Following previous procedures  (Dukart et al. , 2013) , we used a mask resembling the characteristic atrophy pattern of AD and bvFTD, respectively, to extract the GM volume for each participant. Given that we aim to test cognitive and atrophy features with a cross-center validation strategy, we used this 'a priori' feature selection to avoid the potential bias of a data-driven approach, which might find optimum classification features for an specific dataset that do not necessarily enable high classification rates in another one  (Dukart et al. , 2013). As in a previous work, the general bvFTD atrophy mask was defined using the Automated Anatomical Labeling (AAL)-Atlas  (Tzourio-Mazoyer et al.
Classification analysis To test the power of cognitive and brain atrophy features for classifying AD and bvFTD patients, we first implemented machine-learning algorithms within a reference dataset. For this analysis (from now on referred to as "within-country" approach), we selected the largest dataset available with full completion of cognitive screenings, namely, the one from Country-1. Then, we performed a cross-country classification analysis to further validate the generalization and prediction power of our findings with the within-country approach. In short, the classifier was trained with Country-1 subjects, and tested on participants from Country-2 or Country-3. For both classification approaches, six features of interest were included in the analysis: IFS and ACE scores, the volume of atrophy, and demographic data including sex, age and number of years of formal education.
Next, the hypothetically most informative features were inspected by graphing pairs of dimensions for Country-1 -the reference dataset (see 2. 4. 1 Clustering, below). This exploration was completed by principal component analysis (PCA), an exploratory technique that finds the most informative combination of features as measured by the explained variance of the data. We used MATLAB's default implementation of PCA with a singular value decomposition of feature correlation matrix. Indeed, the PCA showed that for Country-1 AD (C_1-AD) [vs Country-1 FTD (C_1-FTD)] the first principal component explains 46% (43%) of variance (of the standardized data), and 82% of this component (97%, calculated as the norm of the coefficient vector) comprises roughly equal shares of IFS, ACE, and atrophy scores.
2. 4. 1. Clustering-Clustering is an exploratory technique used when the actual groups (like disease and HC, here) are not known. The objective was to assess whether the subjects from the reference dataset (Country-1) could be clustered into two separate groups, without any knowledge about the meaning of the features. Such an exploration might provide additional insights into the distribution of the data, and help predict difficulties at the classification stage, and also show potential errors in the diagnostic label or the presence of potential sub-groups of participants. Given that the clustering analysis was conducted solely for exploratory purposes and that its results were not used in any form for the classification analyses (see sections 2. 4. 2 and 2. 4. 3), we used the classic k-means algorithm (see, e.
Within-country classification- We used a default logistic regression classifier  (Hastie et al. , 2009)  implemented in Matlab, (see Supp. Data 1) to discriminate patients from HC for Country-1, our reference data-set. Logistic regression is a type of regression specifically designed to model probabilities -in this case, the probability of a subject belonging to a condition group (AD or bvFTD). Since probability is always limited to the interval from 0 to 1, predictions of linear regression are ill-defined, because they may lie outside of this interval. On the other hand, logistic regression uses a logistic function, which is a smooth step function whose values range exactly from 0 to 1, and is thus well fitted to the classification problems. This scheme was used, since the sample of this country was the largest one compared to the other ones.
, 2011; Kuceyeski et al. , 2012; Tahmasian et al. , 2016; Salvatore et al. , 2016). This is especially true for data-driven studies in which anatomical feature selection was based on the minimum number of areas providing optimal separation of samples for a specific dataset, which was later used for the validation process  (Zheng et al. , 2016; Bron et al. , 2017; Zhang et al. , 2013; Dukart et al. , 2011; Kuceyeski et al. , 2012; Tahmasian et al. , 2016; Salvatore et al. , 2016). Although this procedure can yield large accuracy rates, it does not necessarily enable the same performance for independent cohorts, given that features might be specific for the initial data  (Huys et al. , 2016). To overcome this potential bias, we first selected hypothesis-driven cognitive and atrophy features  (Dukart et al.
Conclusion Our study is the first to use machine-learning algorithms to show the high classification rates (>0. 91) obtained from the combination of cognitive screenings and quantitative neuroanatomical measures for identifying bvFTD and AD patients across three countries. Moreover, our results presented a robust generalization power (>0. 91), validated with two independent samples from different countries, which underscores the reliability of these measures to new, unseen data from heterogeneous contexts. Therefore, although further research is needed, our work supports the implementation of computer-based methods combining these measures as a potential affordable and complementary tool with clinical value for individual diagnosis and treatment decisions. Standardize (grey box): data were standardized by converting them to z-scores, so that each feature in the control group had a zero mean and standard deviation of one.
Data exploration (light orange boxes): these procedures were used only to explore and obtain knowledge about the behavior of the data. Clustering: we used a k-means algorithm with k = 2 to separate groups in two clusters to explore data distribution, and evaluate the presence of potential sub-groups of participants (details in section 2. 4. 1). Visualization and inspection: the hypothetically most informative features (cognitive screenings and atrophy) were inspected by graphing pairs of dimensions for the reference dataset (Country-1) (details in section 2. 4). Principal component analysis (PCA): We used MATLAB's default implementation of PCA to explore the most informative combination of features as measured by the explained variance of the data. Classification (light green boxes): Withincountry classification: we implemented a logistic regression classifier with cognitive and brain atrophy features within the Country-1 dataset, given that it was the largest one with full completion of cognitive screenings.