dataesr/scientific-paragraphs-categorization huggingface dataset:
Co-alignment IRIS and MSDP data A two step procedure to co-align IRIS raster and MSDP data was undertaken. First, we reduced the IRIS SJI 2796 Å images resolution to the pixel size of MSDP (0. 5 arcsec. ). Then, we used a cross-correlation method to co-align an IRIS SJI image with a MSDP intensity map of Hα at line center. The co-alignment was done with the solar disk using the structures of the network. We obtained a precision of alignment better than 1 arcsec between IRIS SJI and MSDP image. In the second step, we co-aligned IRIS SJI 2796 Å images with IRIS raster data. To achieve this, we used the IRIS raster.
Electron density and optical thickness The electron density map has been computed with the Levens and Labrosse radiative transfer code for all the pixels even with a relative low weight for the pixels where the best fit was not found (Fig. 16  middle panel). Using the good co-alignment between the MSDP maps and IRIS rasters, we show an Hα inten-sity map with a superimposed electron density map (Fig. 16  right panel). The red contour limits the Hα prominence (hell brown). By comparing maps in right and left panels we note that the Hα prominence corresponds nearly to points where the fit of Mg ii lines was not correct due to their complex profiles. In order to get parameter quantitative values concerning the prominence and its top we analysed the theoretical results obtained for the two selected slit positions for example mean electron density, optical thickness of Mg ii k, and optical thickness of Hα (Figs.
Discussion and conclusion We present the data concerning a tornado-like prominence observed on April, 19, 2018 with three instruments (SDO/AIA, IRIS, MST/MSDP). The top of the prominence is active and shows twist and rotation in AIA 171 and IRIS SJI 2796 movies, suggesting evidence for a tornado. We analysed carefully the evolution of the prominence intensity and velocity using IRIS spectra and SJIs. The SJIs show that the top of the prominence consisted of horizontal fine structures where material was going away or towards the sky plane alternatively. We noted the formation of blobs at the top which escape along long field lines with large transverse velocities. We mentioned the similarity of this prominence with previous LFFF magnetic extrapolations obtained for different prominences  (Aulanier & Schmieder 2002).
Acknowledgements. We thank the observation team at the Meudon solar tower for providing the MSDP data: Pierre and  Nicole Mein ,  Daniel Crussaire  and  Guiping Ruan. We thank  Clara Froment  for chairing a fruitful discussion of our observations during the COSPAR 2021 session. Figure 13 has been provided by  Guillaume Aulanier. This study benefited from financial support from the  Programme National Soleil Terre (PNST)  of the  CNRS/INSU , as well as from the  Programme des Investissements d'Avenir (PIA)  supervised by the  Agence nationale de la recherche. The work of KB is funded by the  LabEx Plas@Par  which is driven by  Sorbonne Université. AWP acknowledges financial support from the  Science and Technology Facilities Council (STFC)  via grant  ST/S505390/1. NL acknowledges support from  STFC  grants  ST/P000533/1  and  ST/T000422/1.
Hundreds of catalytic methods are developed each year to meet the demand for high-purity chiral compounds. The computational design of enantioselective organocatalysts remains a significant challenge, as catalysts are typically discovered through experimental screening. Recent advances in combining quantum chemical computations and machine learning (ML) hold great potential to propel the next leap forward in asymmetric catalysis. Within the context of quantum chemical machine learning (QML, or atomistic ML), the ML representations used to encode the three-dimensional structure of molecules and evaluate their similarity cannot easily capture the subtle energy differences that govern enantioselectivity. Here, we present a general strategy for improving molecular representations within an atomistic machine learning model to predict the DFT-computed enantiomeric excess of asymmetric propargylation organocatalysts solely from the structure of catalytic cycle intermediates.
Reaction and organocatalysts database Asymmetric allylations  [75] [76] [77] [78]  and propargylations  79  of aromatic aldehydes are key C-C bond forming transformations, providing access to optically enriched homoallylic and homopropargylic alcohols, respectively, which serve as valuable building blocks for the synthesis of complex chiral molecules. 80  Catalysts that are selective for allylations are generally not highly stereoselective for propargylations, which has led to a dearth of stereoselective propargylation catalysts. [81] [82] [83] [84] [85]  Tools to screen dozens of allylation catalysts to nd promising Scheme 1 Library of axially chiral bipyridine N,N 0 -dioxide organocatalysts. R ¼ H or Me. Adapted from ref. 74. candidates for propargylation reactions are therefore highly valuable.
9  To this end, Wheeler and co-workers have investigated 76 Lewis base organocatalysts (Scheme 1) 74 and used the computational toolkit AARON  20  to build a database of 760 stereocontrolling transition states to predict their enantioselectivity in the propargylation of benzaldehyde (Scheme 2). 14, 74, 86  Large databases of kinetic data for asymmetric catalysis generated in silico are scarce. 63  Therefore, this library constitutes an ideal training and validation set for the development of an atomistic ML model with reaction-based representations capable of predicting the e. e. of organocatalysts readily from the structures of intermediates. Note that the workow presented below would improve the ML performance independently of the size of the training data. The target of the ML model is the DFT-computed relative forward activation energy (E a , i.
General ML workow The general workow exploited and improved herein relies on a physics-based ML model for the prediction of the e. e. of the asymmetric catalytic reactions, as illustrated in Scheme 3 and described hereaer. It comprises two parts: part (  1 ) is a training procedure that relies on the following steps: (1) Database construction: a library of 3D geometries and energies of catalytic cycle intermediates is curated. Here, the structures of 754 pairs of intermediates 2 and 3 are optimised with DFT (see the next section) and used to train the ML model. As shown in our previous work,  43  accurate geometries are not necessarily needed as inputs for atomistic ML models; thus, rough-coordinate estimates (e. g. , obtained directly from SMILES strings) or low-cost DFTB structures could potentially be used to generate suitable molecular representations.
(4) Hyperparameter optimisation and cross-validation: the full dataset is split randomly 100 times into 90/10 training/test sets (678/76 datapoints) to optimise the KRR hyperparameters and obtain the learning curves. In part (  2 ), the trained ML model is used to predict the activation energy of out-of-sample organocatalysts. The model requires as input the 3D structures of 2 and 3 and delivers the corresponding E a value. Using the energy of 2 as reference, the relative energies of the enantiodetermining (R)-and (S)-TSs can be calculated, and the e. e. of the catalyst under investigation computed (vide infra).
Quantum chemistry Catalytic cycle intermediates 2 and 3 were optimised at the B97-D/TZV(2p,2d) level of theory,  [96] [97] [98]  accounting for solvent effects (dichloromethane, 3 ¼ 8. 93) using the polarizable continuum model (PCM)  [99] [100] [101]  with Gaussian16, 102,103 in analogy with the study by Wheeler and co-workers. 74  Density tting techniques were used throughout. The structures of 1508 intermediates were obtained via intrinsic reaction coordinate calculations (IRC) 104 from the TS database curated by Wheeler et al. 74  754 target E a values (11 catalysts of type 1, 16 type 2, 15 3, 11 4, 13 5, and 10 of type 6, each in 5 distinct pro-(R) and pro-(S) ligand arrangements) 87 were computed (relative to the lowest-lying intermediate 2 ligand arrangement) at the same level, which was shown to provide the best compromise between accurate predictions of low-lying TS energies and stereoselectivities for allylation and propargylation reactions.
In this way, we obtain a more faithful representation of the reaction step, which corresponds to a more unique ngerprint of E a. Although the construction of SLATM DIFF requires the SLATM representations of both intermediates (2 and 3), the computational cost associated with its generation is negligible. As depicted in the dissimilarity plot (Fig. 1b , middle), the reaction-based representation (SLATM DIFF ) is signicantly better than SLATM 2 : the difference in E a values tends to zero as the Euclidean distance between their representations tends to zero. In line with this observation, the learning curve (shown by the orange line in Fig. 1a ) is signicantly improved. The MAE of SLATM DIFF is reduced to 0. 31 AE 0. 2 kcal mol À1 , roughly 50% better than SLATM 2 and up to 60% better than that of SLATM 3 using 90% of the data for training (i.
Notice that in our ML model, the Euclidean distance is used as a measure of similarity between representations. This means that features with high variance (i. e. , that change the most between molecules) dominate the notion of similarity, as they contribute the most to the Euclidean distance between representations. By feature, we mean each of the terms in the molecular representation, which, for SLATM, consist of two-(London dispersion) and three-(Axilrod-Teller-Muto) body potentials computed on groups of atoms closer than a certain cut-off (here, 4. 8 Å). The results of these potentials are averaged over their atom-type sets (e. g. , all C-C interactions for the two-body terms, all the C-C-C for the three-body terms), which are then concatenated to generate the SLATM vector. The size of the SLATM representation depends on the existing atom-type sets in the database.
Given that our dataset contains the elements C, H, O, N, F, Cl and Si, the total number of features of the SLATM representations is 27 827. In SLATM DIFF , features with high variance dominate the notion of similarity, measured through the Euclidean distance. However, we are using SLATM to predict a property that is very different from the single-molecule properties for which it was originally designed. Consequently, features with high variance in SLATM are not necessarily the most important ngerprints of E a. In pursuit of the best possible ngerprint of the activation energy, we assign importance scores to each feature and attempt to focus on the most relevant ones. The linear correlation coefficient (r 2 ) between each feature and the target property is used as an estimate of the importance of the different terms in the representation.
The results, presented in Fig. 4 , show that in SLATM DIFF there are only a few highvariance features, while the computed importance scores are spread over many other features that have relatively small variances. Simply put, the variances in the features of the SLATM DIFF representation are not well correlated with their real importance in this application. Based on this observation, an improved representation, labelled SLATM DIFF+ , is generated by selecting only the N f most important features of SLATM DIFF (specically, N f ¼ 500) and discarding the rest. This feature selection was done using only the training data at each train/test split of the cross-validation step, as otherwise it could lead to severe overtting. Nevertheless, the importance scores were consistent across the crossvalidation splits thanks to the robustness of the linear regressions.
2 and 1. 3 for 7j and 7k, respectively, vs. 0. 2 and 0. 5 kcal mol À1 ). In summary, we provide a logical route to improve atomistic ML methods for enantioselectivity prediction of asymmetric catalytic reactions, which are limited by both the required accuracy and the small amount of data generally available. Firstly, the intermediates associated with the enantiodetermining step (2 and 3 in Scheme 2) must be identied, and their SLATM representations generated. Secondly, using the difference between the two SLATM representations (SLATM DIFF ) as input, a set of features that map the activation energy accurately can be obtained. Finally, feature engineering can be used to improve SLATM DIFF , keeping only the most relevant features that relate to the target property. The results show that the ML workow presented herein is able to accurately predict enantioselectivity from the molecular structures of catalytic cycle intermediates.
Acknowledgements The authors are grateful to the  EPFL  for nancial support. S. G. , R. F. and C. C. acknowledge funding from the  European Research Council (ERC , Grant Agreement No. 817977 ) within the framework of European Union's  H2020. The  National Centre of Competence in Research (NCCR)  " Sustainable chemical process through catalysis (Catalysis) " of the  Swiss National Science Foundation (SNSF)  is acknowledged for nancial support of R. L. S. B. acknowledges the  NCCR "Materials' Revolution: Computational Design and Discovery of Novel Materials (MARVEL) " for providing a " INSPIRE " master fellowship. The authors thank  Benjamin Meyer  for helpful discussions at the origin of the project.
predictions were done with the same SLATM DIFF+ models trained in the cross-validation scheme. Additionally, out-ofsample predictions were done re-training the model on the entire dataset (see Fig. S6 †), although this did not lead to noticeable improvement. While simpler representations (e. g. , CM, 68 BoB 107 ) were tested, SLATM performs signicantly better (see Fig. S2 †). 105   was used to construct standard SLATM representations. Feature selection and the construction of the reaction-based representations SLATM DIFF and SLATM DIFF+ were done using the Python package Scikit-learn. 106    To generate the learning curves and the e. e. predictions, a crossvalidation scheme was used with 100 different 90/10 training/ test sets (678/76). The KRR hyperparameters (the width of the Gaussian kernel s and the ridge regularization l) were optimised for each train/test split, systematically obtaining essentially the same results for each split (see the ESI †).
, 2007)    adaption in MS  (Enzinger et al. , 2016). For example, there is an ongoing debate how to interpret increased connectivity in different brain regions as this might represent compensatory mechanisms or maladaptation  (Penner and Aktas, 2017; Rocca and Filippi, 2017; Silemek et al. , 2020). A better localisation of cortex regions that are important for cognition in MS might help to interpret altered connectivity or activation in taskbased fMRI results. The aim of the present study was to explore associations of regional cortical thickness in MS patients in relation to cognitive performance on an extensive battery of neuropsychological tests. We aimed to identify regions specific for single tests and those with a broader association pattern with cognitive performance. Moreover, we assumed that resulting association patterns from a single representative database with a single processing pipeline would allow to reduce the uncertainties about localisation from different studies.
Participants The present study was a retrospective analysis of clinical and neuroimaging data available through the database of the Institute of Neuroimmunology and Multiple Sclerosis (INIMS) at the University Medical Center in Hamburg. We first identified all patients between January 2010 and December 2015 who had received a clinically indicated neuropsychological assessment at the MS outpatient clinic in Hamburg-Eppendorf at initiation of the study. Inclusion criteria were a confirmed diagnosis of MS according to the McDonald Criteria 2010  (Polman et al. , 2011a)  and that the MRI examination at the university clinic was not more than 12 months apart from the neuropsychological assessment (91% of the final sample included MRI examinations and neuropsychological assessments both conducted within a period of 6 months); it was further ensured that MRI data was collected using the same MRI scanner and that the respective MRI protocol included identical sequences (T1 MPRAGE, FLAIR).
These criteria were met by 99 data sets, of which 2 further data sets had to be excluded due to FreeSurfer segmentation failures at a later stage, leaving a final sample of N = 97 for an explorative data set (Flow-chart, Fig. 1 ). To provide information about the specificity of our results and to validate findings in an independent dataset, we included also data from 30 relapsing-remitting MS patients and 29 healthy controls from a prospective natural history study, which underwent the same MRI assessments and the main part of the exhaustive neuropsychological assessment (details see below). The validation and healthy control cohort had been recruited between March 2013 and January 2015 at our MS outpatient clinic. Patients had to have a relapsing remitting (RR) MS in agreement with McDonald criteria 2010  (Polman et al.
Neuropsychological assessment A comprehensive battery of neuropsychological tests was used to evaluate cognitive performance in the domains attention/information processing, memory, spatial processing and executive functioning. Assessments were only performed in a stable phase of disease avoiding relapse or steroid impact on the performance. The assessment was optimized for clinical use and research  (Pöttgen et al. , 2013; Silemek et al. , 2020; Stellmann et al. , 2015; Stellmann et al. , 2017). Selected tests cover in depth cognitive functions typically impaired in MS (e. g. processing speed / attention) but also other domains allowing a differential diagnostic in the clinical setting: The tests are as follows (the abbreviation VAL indicates availability of test data in all three cohortsexploration, validation and healthy controls): We employed three subtests of the Test battery of Attentional Performance (TAP;  (Zimmermann and Fimm, 2002) ) to evaluate attentional capacities: The alertness task (tonic and phasic alertness, VAL), the selective attention task (Go/NoGo), and the divided attention task (dual task).
MRI protocol For all cohorts, MRI data were acquired with the same 3 T MRI scanner (Skyra, Siemens Medical Systems, Erlangen, Germany). The MRI protocol included the following sequences used in all cohorts: a 3D magnetization prepared rapid acquisition gradient-echo (MPRAGE) T1 weighted sequence (TR/TE = 2500 ms/2. 12 ms; TI = 1100 ms; 256 slices, voxel size 0. 8 × 0. 8 × 0. 9 mm, matrix = 288x288, FOV = 240 mm) and a T2 FLAIR sequence (TR/TE = 9000 ms/90 ms; 43 slices, voxel size 0. 7 × 0. 7 × 3. 0 mm, matrix = 320x320, FOV = 230 mm). All MRI were scheduled at least 4 weeks after the last steroid treatment.
Image analysis Images were processed with the functional imaging software library  (FSL, version 5. 0, www. fmrib. ox. ac. uk). Images were reoriented to standard MNI space and T1 and FLAIR images were registered for lesion mapping and filling. We applied first the lesion growth algorithm  (Schmidt et al. , 2012)  as implemented in the LST toolbox version 2. 0. 1 (www. statistical-modelling. de/lst. html) for SPM on FLAIR images and extracted total lesion volume. To minimize segmentation errors, we performed lesion filling on T1 weighted images by filling marked lesion areas with mean intensity values from the lesions' surrounding parenchyma. Lesion-filled T1-weighted images were automatically processed with FreeSurfer software (Version 5. 2. 0) for cortical reconstruction and volumetric segmentation (surfer.
nmr. mgh. harvard. edu/). To minimize segmentation errors, one individual supervised by JPS manually corrected brain masks and white / grey matter segmentation in all cases. However, two data sets had to be excluded from the analysis due to persisting segmentation failures (see Fig. 1 ). Spatial normalization was performed vertex-wise and a cortical thickness map was created for each subject. The individual maps were registered to the FreeSurfer 'fsaverage' template and smoothed with a Gaussian kernel of 10 mm full-width at half maximum (FWHM).
All resulting association maps (single tasks, domains and summary map) are freely available for download as FreeSurfer annotation and volume files (github. com/oneeq/ms_cognit ion_atlas). The data that support the findings of this study are available from the corresponding author upon reasonable request. For the validation and control cohorts we applied the same imaging processing pipeline with FSL and FreeSurfer. Afterwards we applied the atlas from the explorative cohort on control and validation images to extracted cortical thickness values. We included all clusters with significant results in the explorative cohort and task results in the validation and control cohort (four tests). We used statistics in R to compute Pearson's correlations between cortical thickness and tasks scores (corrected for age, sex and education level) to confirm associations between cognitive performance and cortical thickness in every single cluster.
Demographic data Demographic and clinical data are displayed in table 1. The validation cohort showed a tendency towards higher education (p = 0. 05) but did not differ from the explorative cohort concerning age and sex. In contrast to the explorative cohort, patients in the validation cohort had a lower disability level (p = 0. 001), while disease duration, lesion numbers and lesion volumes were comparable between the groups. The healthy controls group was matched by sex, age and education to the validation cohort and did not differ from the explorative cohort for these baseline characteristics.
, 2005) , which could explain why cortical thickness in this region was associated with different cognitive subdomains. The temporal pole has been suggested to be a multimodal area associated with semantic processing  (Snowden et al. , 2004) , whereas the fusiform region has been related to high-level visual perception, such as face, object and word recognition, and reduced grey matter density of the area has been linked to dyslexia  (Kronbichler et al. , 2008). The multimodal functional relationship of temporal regions is mirrored by our results and support the reliability of our approach. We observed associations with mainly language related tests from the domains of attention (e. g. SDMT and TMT-A), memory (digit backwards and VLMT) and executive functioning (both RWT subtests) while there was an only association with spatial processing.
However, the causal relation between white matter lesions, regional atrophy and cognition should be addressed in future research. For example, DTI data that are not available in our dataset would allow defining local dependencies based on tractography. Unfortunately, we were not able to analyse the impact of cortical lesions in our study. They have been identified as highly important for disability and cognition in MS  (Magliozzi et al. , 2018). However, their detection in a clinal setting is not yet easy to accomplish and might rather be a domain of ultra-high field imaging  (Faizy et al. , 2017; Harrison et al. , 2015).
Acknowledgements Analysis of the data was partially funded by a research fellowship from the  Deutsche Forschungsgemeinschaft  ( STE 2593/1-1  to JPS) and a research grant from the  Bundesministerium für Bildung und Forschung (BMBF)  with the  Biopharma Initiative  ( Neu2  Grant  161A130  to CH and SMG). SMG is supported by a  Heisenberg Professorship  from the  Deutsche Forschungsgemeinschaft  ( GO1357/5-2 ).