dataesr/scientific-paragraphs-categorization huggingface dataset:
Structural T1-weighted and ASL images were analyzed using SPM8 (http://www. fil. ion. ucl. ac. uk/spm) implemented in Matlab (Mathworks Inc. , Sherborn, MA, USA). Briefly, structural T1 images were segmented into grey matter, white matter and cerebrospinal fluid and spatially normalized in the MNI space using the cat12 toolbox (http://dbm. neuro. uni-jena. de/vbm/). The ASL images were co-registered to the corresponding native grey matter images and spatially normalized to the MNI space using the deformation matrices from the T1 normalization step with a final isotropic resolution of 1. 5 x 1. 5 x 1. 5 mm. The resulting ASL images were smoothed using an isotropic Gaussian filter of 10 mm. All images were visually inspected for proper realigning and spatial normalization.
Anatomical studies using voxel-based morphometry (VBM) showed reduced grey matter volume in the superior temporal regions  [28] [29] [30] [31]. Recently, a large database study described decreased cortical thickness in the temporal cortex  [32]. Studies using diffusion tensor imaging (DTI) have indicate disrupted structure within white matter tracts in the brain, particularly in tracts connecting the temporal regions  [33]. In addition, a large series of fMRI studies have described a lack of activation in the STS during socially relevant tasks, such as perceiving eye gaze as well as hearing human voice or performing socially relevant tasks  [34] [35] [36] [37]. Today, we know that the STS is highly implicated in processing social information, ranging from the perception of visual and auditory social-relevant stimuli (eye-gaze and voice) to the more complex processes of understanding the mental state of others (theory of mind)  [38, 39].
the global signal in resting-state functional MRi data is considered to be dominated by physiological noise and artifacts, yet a growing literature suggests that it also carries information about widespread neural activity. the biological relevance of the global signal remains poorly understood. Applying principal component analysis to a large neuroimaging dataset, we found that individual variation in global signal topography recapitulates well-established patterns of large-scale functional brain networks. Using canonical correlation analysis, we delineated relationships between individual differences in global signal topography and a battery of phenotypes. The first canonical variate of the global signal, resembling the frontoparietal control network, was significantly related to an axis of positive and negative life outcomes and psychological function.
Non-parametric permutation analysis for null-hypothesis testing revealed a single statistically significant population correspondence between GS topography and behavioral profiles (r = 0. 667, p < 0. 001; Fig. 3 ). The pattern of canonical modes for the GS closely followed spatial patterns that recapitulate the frontoparietal control network (positive weights) and sensorimotor and visual networks (negative weights). In assessing the similarity between the first principal component (Fig. 2C ) and the first canonical variate (Fig. 3B ), a vertex-to-vertex correlation showed high convergence in topography (r = 0. 81, p < 0. 001). The pattern of canonical component weights for the cognitive and behavioral subject measures followed a general positive/negative gradient of life outcomes and psychological function, similar to that observed in a previous CCA of interregional resting-state functional connectivity and behavior in the HCP dataset  24.
Methods Dataset. Our population neuroscience study utilized the Human Connectome Project (HCP) S1200 release  53. Participants (N = 1094) were healthy young adults (ages 22-37) drawn from a population of twins and siblings. All imaging data were acquired on a customized Siemens 3 T Skyra at Washington University in St. Louis using a multi-band sequence. The structural images were 0. 7 mm isotropic. The resting-state fMRI data were 2 mm isotropic with TR = 0. 72 s. Two sessions of rs-fMRI data were collected on consecutive days for each subject, and each session consisted of one or two runs. The length of each rs-fMRI scan was 14. 4 min (1200 frames). Details of the data collection can be found elsewhere  53, 54. Informed consent was obtained from all subjects. Details about behavioral measures can be found in HCP S1200 Data Dictionary and  55.
All methods were carried out in accordance with relevant guidelines and the University of Miami Institutional Review Board approved the study. preprocessing of resting-state fMRi data. Preprocessing details of HCP data can be found elsewhere (HCP S1200 manual)  53, 54, 56. ICA-FIX  57, 58  was applied for denoising. The surface (fs_LR) data were aligned with MSM-All  59. Motion censoring was performed to remove subjects and runs with high motion. The to-be-censored frames (outliers) were identified by three stages. First, volumes with FD > 0. 2 mm or DVARS > 75 were marked as outliers. Second, one frame before and two frames after these volumes were also flagged as outliers. Finally, remaining segments of data that lasted fewer than five contiguous volumes were censored. All censored frames marked in these three steps were discarded when computing global signal beta maps.
It is worth noting that there are multiple ways of computing the GS in the literature. While we computed the GS using just the cortex  34, 63  , one can also compute the GS using the whole brain, or all gray matter voxels  51. However, all definitions of GS lead to very similar results. For example, in the current data, the average correlation between GS computed from all gray matter locations (i. e. , grayordinates) and the GS computed from just the cortical surface was 0. 95. Thus, it is unlikely that alternate definitions of the GS would significantly alter our results. canonical correlation analysis. To interrogate the relationship between the GS and behavior, we conducted a CCA. CCA is a natural choice of method because this machine-learning algorithm computes linear combinations of the original variables for each of two multivariate datasets that, together, maximize the linear correspondence between both variable sets.
Data reduction. To avoid overfitting in the CCA analysis, and to contend with the high dimensionality of the datasets, we conducted a principal component analysis (PCA) individually on the pre-processed behavioral measures and on the global signal beta maps (z-score normalized), analogous to previous work  24. Since we obtained a global signal beta map for each subject, we treated every subject as an observation and each vertex as a feature dimension in PCA. For example, the first principal component of global signal beta maps captured the most inter-subject variance in the global signal spatial topography. 100 principal components were derived from each of the behavioral and global signal beta maps. As in  24  , the covariance matrix of the behavioral measures was projected onto the nearest positive-definite covariance matrix, which avoided any imputation of missing observations.
Acknowledgements This work was supported by the  National Institute of Mental Health  ( R01MH107549  and  R01MH107549-03S1 ) and the  Canadian Institute for Advanced Research  to LQU; the  Deutsche Forschungsgemeinschaft (DFG ,  BZ2/2-1 ,  BZ2/3-1 , and  BZ2/4-1 ;  International Research Training Group   IRTG2150 ),  Amazon AWS Research Grants  ( 2016  and  2017 ), the  German National Merit Foundation , as well as the  START-Program  of the  Faculty of Medicine  ( 126/16 ) and  Exploratory Research Space  ( OPSF449 ),  RWTH Aachen , to DB; Natural Sciences and  Engineering Research Council of Canada and Canadian Institutes of Health Research  grants to RNS, a  Research Scholar  supported by  Fonds de recherche du Québec -Santé  and a  Singapore National Research Foundation (NRF)  Fellowship (Class of 2017) to BTTY.
Model simulations Six CTMs were used to estimate wet deposition in Europe for the period 1990-2010: Chimere  (Couvidat et al. , 2018) , CMAQ  (Byun and Schere, 2006) , EMEP MSC-W  (Simpson et al. , 2012) , LOTOS-EUROS  (Manders et al. , 2017) , MATCH  (Robertson et al. , 1999)  and MINNI  (Mircea et al. , 2014 (Mircea et al. , , 2016)). The shortened model names CHIM, CMAQ, EMEP, LOTO, MATCH and MINNI are used throughout the article. An overview of the model chemistry schemes and parameterisations for wet and dry deposition can be found in the Supplement (Table  S1 ). In order to assess the differences in model estimates, due only to model structure and parameterisations, the modelling domain and input data used in the simulations were the same for all models, wherever possible. The models were run on a domain that covers most of Europe (Fig.
S1 of the Supplement for more details). From this analysis we estimate that the errors in wet deposition due to the errors in emissions are less than 0. 5 % for most of the modelling domain with maximum errors of less than 2. 5 %. These errors are small compared with the overall uncertainty of the model estimates and the uncertainty of the observations. Biogenic and natural emissions were not prescribed and each model used their own set-up. Emissions from wildfires were not included and SO 2 emissions from volcanoes were only included in the EMEP (Etna and Stromboli) and MATCH models. The boundary conditions used in the models were a simplified version of those used in the standard EMEP MSC-W model based on a climatology of observational data  (Simpson et al. , 2012). Full details of the models, input data and boundary conditions used can be found in  Colette et al.
Observations Estimates of accumulated seasonal and annual WNOx, WNHx, WSOx (total and non-sea-salt component) and precipitation (at the same sites as the wet deposition) from the EMEP network over the period 1990-2010 were used to evaluate the model estimates. The seasonal and annual wet deposition was estimated by multiplying the volume-weighted mean concentration in precipitation by the total precipitation in the period. The concentrations for days with missing precipitation data were assumed to be equal to the volumeweighted average of the period  (Hjellbrekke, 2016). For the evaluation of modelled atmospheric concentration estimates, the EMEP network data of mean annual concentrations of total nitrate, ammonium and sulfate (non-sea-salt component) were used. Although data are available for the individual gas and particulate species for many sites, the filter pack measurement methods used do not reliably estimate the partitioning of the gas and particulate N species and, therefore, the total (gas plus particulate) is used for the evaluation.
Model evaluation The modelled wet deposition, precipitation and atmospheric concentration estimates were statistically evaluated using the package "openair"  (Carslaw and Ropkins, 2012)  for R (v3. 3. 2; R Core Team, 2016). Six metrics (proposed by  Chang and Hanna, 2004)  were used to assess model performance: fraction of model estimates within a factor of 2 of the observed values (FAC2), fractional bias (FB), geometric mean bias (MG), normalised mean square error (NMSE), geometric variance (VG) and the Pearson's correlation coefficient (r) (Table  1 ). The modStats function in openair was modified to include the metrics FB, MG, NMSE and VG. Note that the convention of positive values of FB and values of MG > 1 was used to indicate model overestimation. Model evaluation was carried out for the full time series 1990-2010 for the five models that simulated all years and for the years 1990, 2000 and 2010 for all models.
For modelled WSOx, the CMAQ estimates were evaluated using the observations of total sulfate wet deposition and the estimates from the other models were evaluated using the observed non-sea-salt sulfate wet deposition. To provide a reference for model performance, the metrics were also compared with the acceptability criteria of  Chang and Hanna (2004) : FAC2 ≥ 0. 5, |FB| ≤ 0. 3, 0. 7 ≤ MG ≤ 1. 3, NMSE ≤ 1. 5 and VG ≤ 4. It should be noted, however, that these criteria were developed to evaluate the atmospheric concentrations estimated by air quality models using specially designed model evaluation field experiments. They may not, therefore, be an appropriate tool for evaluating operational wet-deposition estimates using monitoring data and can only be used as an indicator of model acceptability.
Acknowledgements. We would like to express our thanks to all those who are involved in the EMEP monitoring efforts and have contributed through operating sites, performing chemical analysis and by submission of data. This work was supported by the  Co-operative Programme for Monitoring and Evaluation of the Long-range Transmission of Air pollutants in Europe (EMEP)  under the UNECE. The Ineris coordination of the EURODELTA-Trend exercise was supported by the  French Ministry in charge of Ecology  in the context of the Task Force on Measurement and Modelling of the EMEP programme of the LRTAP Convention. The GAINS emission trends were produced as part of the  FP7 European Research Project ECLIPSE  ( Evaluating the Climate and Air Quality Impacts of Short-Lived Pollutants ), grant no. 282688.
The MATCH participation was partly funded by the  Swedish Environmental Protection Agency  through the research programme  Swedish Clean Air and Climate (SCAC)  and  NordForsk  through the research programme  Nordic WelfAir  (grant no. 75007 ). The computing resources and the related technical support used for MINNI simulations have been provided by  CRESCO/ENEAGRID High Performance Computing infrastructure  and its staff. The infrastructure is funded by  ENEA , the  Italian National Agency for New Technologies, Energy and Sustainable Economic Development and by Italian  and European research programmes (http://www. cresco. enea. it/english,last access: 21 December 2018). MINNI participation to this project was supported by the "Cooperation Agreement for support to international Conventions, Protocols and related negotiations on air pollution issues", funded by the  Italian Ministry for the Environment, Land and Sea.
In particular, the recognised importance of water pressure for basal sliding must be explicitly accounted for through the use of an effective pressure-dependent law. Because it implies a Coulomb friction regime at low effective pressure, which is best suited to model till deformation, we suggest using the Schoof law rather than the Budd law. However, the use of such a law adds the further difficulty of estimating the spatial distribution of water pressure at the ice-bed interface, which seems to be satisfactorily overcome only through a coupling of the ice flow model to a physically based subglacial hydrological model. Code and data availability. Elmer/Ice code is publicly available through GitHub (https://github. com/ElmerCSC/elmerfem, last access: 17 January 2019;  Gagliardini et al. , 2013).
All the simulations were performed with the version 8. 2 (Rev: 997cb45) of Elmer/Ice. All scripts used for simulations and post-treatment as well as model output are available upon request from authors. The data used are listed in the references, except the surface mass balance, which is available upon request from the authors. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/tc-13-177-2019-supplement. Author contributions. JB, FGC and OG designed the study. FGC collected the data and prepared the experimental setup. JB con-ducted the inversions, the identifications and the prognostic simulations. JB wrote the paper with contributions from all co-authors. Competing interests. Olivier Gagliardini is a member of the editorial board of the journal.
Acknowledgements. We would like to thank the editor,  Carlos Martin , as well as the two referees,  Mathieu Morlighem  and  Thomas Kleiner , for their insightful and helpful comments. This work was funded by the  French National Research Agency (ANR)  through the  TROIS-AS  ( ANR-15-CE01-0005-01 ) project. Julien Brondex ,  Fabien Gillet-Chaulet  and  Olivier Gagliardini  are part of  Labex OSUG@2020  ( ANR10 LABX56 ). Most of the computational resources were provided by  CINES  through the gge6066 project. Some of the computations were performed using the Froggy platform of the CIMENT infrastructure (https://ciment. ujf-grenoble. fr, last access:  17 January 2019 ), which is supported by the  Rhône-Alpes region  ( GRANT CPER07-13 CIRA ), the OSUG@2020 labex (reference  ANR10 LABX56 ), and the  Equip@Meso project  (reference  ANR-10-EQPX-29-01 ) of the programme Investissements d'Avenir supervised by the  ANR.
Creation of the Smartphone Application and Blue Color Software Analyzer First, a smartphone application was created using OpenCV library (Intel, Santa Clara, CA, USA) to provide repeatable image acquisition using multiple time exposure. We decided to use a smartphone device (Google Nexus 5X ® , Mountain View, CA, USA) instead of a powerful professional camera because the aim was to allow widespread use of this diagnostic tool. The smartphone application allowed us to anonymize the pictures and to collect clinical and biological data for each patient. During a preliminary study conducted using the first nine patients enrolled  [22] , we tested the application in real conditions in our department, to determine an easy-handling and homogeneous image acquisition procedure. Briefly, several parameters were considered: creation of an automated sequence of multiple-time exposure image acquisition, deactivation of automated white color correction and white balance, and use of natural daylight with all artificial lights turned off.
To measure the true color of a picture, we needed a white color reference. We first tried to use the reflection of natural daylight in the sclera, but this method was too aleatory. We finally decided to use a white reference provided by a color-checker (X-rite ® , Grand Rapids, Michigan, USA), which was placed by the physician next to the eye, to take a picture including both the eye and white patch of the color-checker. Second, postprocessing software was created to analyze the blue color of the sclera: the computer code used a Python (Python software foundation, Wilmington, Delaware, USA) script fully available online at https://github. com/lasarry. com and was created by the department of one of the authors (L. S. ). To obtain pictures with a highly dynamic range, images with multiple time exposures were merged into a single image.
The software analyzed the percentage of each color (red, green, and blue) in scleral pixels after white correction. The software uses several data sets, including acquisition parameters of the images and coordinates of points inside the white color square and inside the sclerae, which are manually entered. Briefly, the script extracts the white color square and the sclera by region growing and gives output images. Then, the script takes the created output images as input, normalizes color inside the sclera with the white square value, and produces the 75% percentile of blue (BP) for each image.
Data Collection The pictures were taken by the same physician throughout the study, who collected, at the same time, clinical (age, sex, height, weight, body mass index; BMI) and biological data. The physician was not involved in the later scleral color assessment process. Exposure conditions were standardized: the patient sat next to a window in natural daylight. There was no direct contact with the patient, so the process was completely noninvasive. Iris color was graded as light (grey, green, or blue) or dark. Clinical assessment of blue sclera was conducted by three well-trained physicians of our department who were familiar with the study protocol. We decided to include only three physicians for three reasons: first, to be as close as possible to the process described in previous published studies  [15] [16] [17] [18] ; second, because increasing the number of raters risks decreasing the statistical power because the inter-rater agreement in subjective assessment of clinical features is usually poor; third, because as a consequence of the previous point, it appears necessary that the physicians are familiar with scleral color evaluation.
Results From November 2014 to July 2018, 83 consecutive inpatients were enrolled. A flowchart is available in the Supplementary Figure  S1. As previously stated, we adjusted the technical settings on the camera to provide adequate picture quality using the first nine patients enrolled. These patients were not included in the final analysis because of the non-homogeneous shooting procedure: 6 of them had no pictures with white patches identified by the color-checker, and 3 of them did not have the multiple time-exposures required to create high dynamic range images. During the second phase, we tested 74 patients using the same procedure for shooting and the same camera settings. We excluded 7 patients: 4 because of poor picture quality (blurred images, over-or underexposed images), and 3 because of a lack of biological data regarding iron stores.
Lower δ 13 C calcite values during these periods suggest greater soil development, whereas lower δ 18 O calcite (Extended Data Fig. 4 ) indicate a more-positive precipitation-evaporation balance  18. Thus, aquatic and terrestrial datasets suggest that higher temperatures along with maxima in annual precipitation amount and potential shorter summer aridity occurred during interglacial periods (Extended Data Fig. 5 ). To provide a better understanding of the observed precipitation variability from the Lake Ohrid record in a regional context, we analysed a climate-data time series derived from a transient 784,000-year simulation using the Earth system model LOVECLIM  22, 23  (Extended Data Fig. 7 ) as well as NOAA (National Oceanic and Atmospheric Administration) reanalysis precipitation data of the Lake Ohrid region for the time period 1979-2017.
The temperature time series of the 5° × 5° Lake Ohrid grid cell simulated by the LOVECLIM Earth system Fig. 1  | Chronology and location of the Lake Ohrid DEEP site record. a, Tuning of total organic carbon (TOC) minima in the DEEP site record compared with the inflection points (green dots) in insolation (black line) and winter season length (orange line). b, The age model is based on the tephrostratigraphic correlation of the 16 tephra layers to their radiometrically dated proximal deposits (red ellipses, first-order tie points; ages and errors in Extended Data Table  1 ; see Methods), tuning points (green dots from a, second-order tie points; error of ±2,000 years; see Methods) and cross-evaluation of two palaeomagnetic age reversals (dashed lines). Ellipses indicate error ranges. The age modelling followed a previously published methodological approach  20  (see Methods).
c, Location of Lake Ohrid and the approximate position of the ITCZ in summer and winter. m c. d. , m composite depth. Letter reSeArCH model closely resembles the records of the first-order global ice volume (Extended Data Fig. 4 ), such as the LR04 oxygen isotope stack of marine benthic foraminifera  24  (r = -0. 8737 or r 2 = 0. 76 based on 1,000year averages of both datasets). The fact that it closely matches changes in the amount of detrital siliciclastic sediments and tree pollen (arboreal pollen excluding pine) confirms the sensitivity of the Lake Ohrid record to global-scale climate fluctuations (Fig. 2  and Extended Data Figs. 4, 5 ). The highest amplitudes in the precipitation time series occur during phases of reduced ice volume, with prominent peaks during NHSI maxima. The significant positive relationship between simulated precipitation and our precipitation proxy time series (r 2 = 0.