dataesr/scientific-paragraphs-categorization huggingface dataset:
At the same time, CLC 2 and CLC 3 areas have shown values typical of sparse cities. Results suggest that it is possible to build datasets of morphological parameters averaged over a grid of a chosen resolution (100 m × 100 m in this case), which can be employed to calculate the parameters over wider areas. The methodology followed here can represent the first step towards (i) building LCZ maps of levels 1 and 2, as performed in this paper for the Lecce region, useful for UHI/microclimate studies; and (ii) developing parameterizations for mesoscale models to represent the dynamic effect of urban areas.
A. E. acknowledges the PhD financial support of the  Italian Ministry of University and Research (MUR)  by the  PON "Ricerca e Innovazione 2014-2020-Asse IV"-PhD course in  " Scienze e Tecnologie Biologiche ed Ambientali"-XXXVII cycle-University of Salento. R. B also acknowledges the financial support of the  ICSC-Centro Nazionale di Ricerca in High Performance Computing ,  Big Data and Quantum Computing , funded by  European Union-NextGenerationEU. Institutional Review Board Statement: Not applicable.
The next challenge will be making AI patient-centric for a more effective deployment into the clinical routine and potentially benefiting patients' quality of life. 41 o drive this challenge forward, we propose a modelling approach that employs a morphospace to predict neuropsychological assessments of one of the most common neurological disorders: stroke. 42 We first mapped the distribution of 1333 brain disconnection patterns in stroke-the disconnectome morphospace. A second dataset (training set) with rich neuropsychological measures 1 year after stroke was imported into this disconnectome morphospace. This second dataset enriched the morphospace with clinical symptoms obtained from 86 neuropsychological assessments. An out-of-sample validation set with the same neuropsychological data assessed prediction accuracy.
This procedure, referred to as Disconnectome Symptoms Discoverer (DSD), reliably predicted the performance of patients with a mean absolute error below 20%. To make the DSD tool readily available to the clinical-academic community and facilitate its translation and incorporation into the clinic, we provide an open-access web application (http:// disconnectomestudio. bcblab. com), in which individual disconnection patterns can be uploaded to predict the expected 1-year neuropsychological scores. We also demonstrated the DSD model generalizability by including three external cohorts. The web application will be interactively updated, thanks to future crowdsourcing, informing the DSD model with any newly available datasets.
Stroke lesions Information on datasets is summarized in Table  1. Lesion data were derived from five different international centres (datasets 1-5). Across all five datasets acute MRI data was acquired within 2 weeks of stroke onset. Dataset 1: (n = 1333 participants) MRI scans acquired at University College London Hospitals. 37, 43 MRI scans (1. 5 T and 3 T) were acquired across several scanners during the routine clinical care of patients presenting with acute ischaemic stroke. Patients were on average 64 ± 16 years old (age range: 18-97 years) and 56% were male. This cohort was recruited as part of a study approved by the West London and GTAC Research Ethics Committee. Dataset 2 was recruited at the School of Medicine at Washington University in St. Louis and included both MRI and neuropsychological assessments.
25 The MRI clinical scans were acquired with a standardized 3 T protocol. The MRI clinical scans were acquired with a standardized 3 T protocol. The neuropsychological assessments evaluated for dataset 2 were conducted on average 1 year post-stroke onset (see Table  1  for more information). All dataset 2 participants provided informed consent following the Declaration of Helsinki (2013) and procedures established by the Washington University in Saint Louis Institutional Review Board. Dataset 2 was divided into two independent subgroups: dataset 2-training and dataset 2-validation. From dataset 2 a subgroup of patients (dataset 2-validation) was randomly selected with the constraint that the subgroup's lesion variability map was balanced across brain hemispheres and vascular territories (Supplementary Fig.
2 ) and the dataset 2-validation gender and age distributions were comparable to dataset 2-training. As such, dataset 2-validation was used as a patient population representative external dataset. For dataset 2-training (n = 119 participants), the patient average age was 54 ± 11 years (age range: 19-83 years) and 54. 6% were male. The average education level was 13 ± 2. 5 years and 91. 6% were right-handed. For dataset 2-validation (n = 20 participants), the average age was 58 ± 12 years (age range: 34-95 years), with 60% male; average education level was 14 ± 2. 6 years and 85% were right-handed. Dataset 3 contains n = 26 stroke patients [average age 55 ± 15. 7 years (age range: 14-92 years), with 46% male, average education level was 12. 4 ± 2. 7 years and 96% were right-handed] selected from the Iowa Neurological Patient Registry (USA).
44  years], with 65% male and 92% righthanded) recruited from the Centre Hospitalier Universitaire Grenoble Alpes (France). MRI clinical scans were acquired with a standardized 1. 5 T protocol within 15-30 days after stroke onset. In these datasets the Bells Test was performed between 30 and 90 days after stroke onset. 45 Each cohort was approved by the local institutional review board (CHU de Lille for DISCONEGLECT, CHU Grenoble Alpes for DOBRAS) and registered at the National Committee for Informatics and Freedom (Commission Nationale Informatique et Liberté). According to French Law, observational studies do not require approval by a national ethics committee.
Neuropsychological scores A total of n = 86 neuropsychological scores were available for dataset 2. The details of each neuropsychological evaluation (grading, test battery, administration) are reported in the Supplementary material, Section C. In brief, motor abilities (Section C. 1) were assessed for upper limb hand grasping, gripping, pinching, grip strength, peg replacement, motion shoulder flexion, wrist extension, and lower limb walking. Language abilities (Section C. 2) were assessed using picture naming, non-word repetition, commands, sentence reading, sentence comprehension and semantic fluency. Visuospatial abilities (Section C. 3) were tested for using discrimination accuracy, reaction time, subbing, behavioural inattention, and unstructured symbol cancellation. Visuospatial memory (Section C.
4) was evaluated using abstract figures retrieval scores and verbal memory (Section C. 5) for listed word recognition scores. A pain scale during the MRI scanning was recorded (Section C. 6) and a stroke sickness questionnaire administered, investigating physical and psychosocial daily sickness (Section C. 7). Despite some scores' collinearity, most evident for visuospatial abilities, we chose to use single score measures instead of combined score indices,  46, 47  to remain data-driven and clinically compatible with individual patients' measures. Semantic fluency (animals) was the only comparable test between dataset 2 and dataset 3 and, accordingly, was chosen to perform the external validation of the prediction model. On average, the time of assessment after stroke was 11. 1 ± 7. 2 months (3.
Disconnectome Patients' diffusion-weighted data were not required for the disconnection analyses. Instead, white matter disconnection maps were derived from a normative diffusion-weighted dataset composed of n = 176 healthy participants, 45% males with 7 T MRI diffusionweighted scans (HCP). Whole-brain tractography was reconstructed using the same procedure reported in Thiebaut de Schotten et al. 21 Briefly, the default HCP preprocessing pipeline (v3. 19. 0),  49  including TOPUP and EDDY corrections (https://fsl. fmrib. ox. ac. uk), was applied to the participants' diffusion-weighted images, selecting the 65 volumes of uniformly distributed gradient directions with b-value = 2000 s/mm 2 (1. 05 mm isotropic voxel, 131 near-axial slices, acceleration factor of 3, echo time = 71. 2 ms, relaxation time = 7000 ms and phase encoding direction paired anterior-posterior and reverse).
Deterministic tractography was performed in the native diffusion MRI space using the software StartTrack (Version ST_20170905, https://www. mr-startrack. com). For the damped Richardson-Lucy algorithm,  50  a fixed fibre response factor of α = 1. 5 × 10-3 mm 2 /s was set with a geometric damping parameter of 8 and 200 interactions. For the spherical fibre orientation distribution, an absolute threshold of three times a grey matter isotropic voxel was set, and a relative threshold of 8% of its maximum amplitude. 51 Whole-brain tractography streamline propagation was performed with a modified Euler algorithm (angle threshold of 35°, step size of 0. 5 mm and minimum streamline length of 15 mm). Subsequently, first the subject tractography was converted into streamline density volumes where the intensities corresponded to the number of streamlines crossing each voxel.
Second, a study-specific template of streamline density volumes was generated using the Greedy symmetric diffeomorphic normalization (GreedySyN) pipeline distributed with the Advanced Normalization Tools (ANTs) library. 52 The template was then co-registered to the MNI152 space (2 mm resolution). Third, individual streamline density volumes were registered to the streamline density template in the MNI152 space and the same transformation was applied to the individual whole-brain streamline tractography using the trackmath tool distributed with the software package Tract Querier 53 using ANTs GreedySyN. This step produced whole-brain streamline tractographies in the standard MNI152 space. Stroke lesions were manually delineated in MRI scans and subsequently normalized to the MNI152 space (2 mm resolution) using the enantiomorphic normalization tool in the BCBtoolkit (http:// toolkit.
The disconnectome morphospace Dimensionality reduction of patients' disconnectome was obtained using the Uniform Manifold Approximation and Projection (UMAP) method,  56  a non-linear embedding method that distributes data variability along major axes. Specifically, UMAP projects data into a newly constructed manifold while preserving the original pairwise distance between the input data structure over the global distance. The UMAP manifold obtained will follow the theoretical framework of Riemannian geometry and algebraic topology. Accordingly, patients with a similar disconnection profile cluster together in the UMAP morphospace and patients with different disconnection profiles are located further apart. For dataset 1 the three-dimensional disconnectome maps were vectorized and imported as features of the embedding methods.
UMAP parameters were set to default parameters (i. e. an approximation of 15 neighbours and a minimum 0. 1 Euclidean distance to obtain a twodimensional embedding of dataset 1). A two-latent variable configuration was preferred to provide a more intuitive space facilitating clinically meaningful interpretation, a space locally connected as Riemannian manifold that we addressed in the paper as the disconnectome morphospace. 56 The UMAP embedding transformation was stored as a Python object, using the Pickle library, to apply the same low-dimensional transformation when new patients are imported into the model. Subsequently, coordinate scales were shifted to only have positive coordinates with zero as origin.
Pixel-wise Pearson correlations between the patient probability of localization and neuropsychological scores were conducted with iterative loops in Python (python numpy. corrcoef). Medium effect size correlation results only were considered informative (R > |0. 2|). Subsequently, because multiple clusters of voxels survived the threshold, a principal component analysis (PCA) was run to compress the patient coordinate distribution variability. Three main principal components have been considered (Python sklearn. decomposition. PCA). The number of principal components was chosen considering the amount of variance explained (always higher than 80%) and the number of morphospace clusters surviving the |R| > 0. 2 threshold that included patients' UMAP projection datapoints (no more than three).
Subsequently, patients' principal components have been entered, as dependent variables, in the multiple regression model (Python sklearn. linear_model. LinearRegression) to predict neuropsychological scores: score 1-year prediction = c + 􏽘 3 i=1 w i PCA patient score ( 1 ) where c is the intercept of the linear regression model, w i are the model weights and PCA patient score the model variables obtained as the inner product between the patient distribution of localization and PCA components. The multiple regression formula was trained with dataset 2-training disconnectomes and validated using the out-of-sample dataset 2-validation and dataset 3 patients, and independently trained with dataset 4 and tested with dataset 5. Accuracy of prediction was assessed as the mean absolute error (MAE)  57  normalized by the maximum score obtained in the neuropsychological evaluation (MAE %): MAE % = 􏽐 N i=1 |measured -predicted|/N max(score) (2) Such normalization allows the comparison of prediction accuracy across different clinical scales, and it offers an intuitive interpretation for error measures of individual scores-the per cent of error (MAE %).
Negative and positive values correspond to the Pearson correlation between the lesion and other areas of the brain. 46 or the SD methods (DSD, L-SD and VolAge-SD), for which multiple regressions used a training set, a k-fold validation was assessed by randomly assigning 70% of the Washington St. Louis cohort (dataset 2) as the training set in each iteration (n = 100). For the voxel-based (VB) approaches (D-VB, L-VB, f-VB) we ran 5000 permutations and estimated the goodness of fit of the model (R 2 ) for the most significant voxels (98th percentile). This code is openly available in a previous publication of our group 21 : https://github. com/chrisfoulon/BCBlib/blob/devel/bcblib/scripts/effectsize_T2R. py. VB correlations included all the Washington St. Louis patients (dataset 2). Finally, we compared the DSD R 2 results to the other methods using a two-tailed paired t-test and applying the Bonferroni correction for multiple comparisons.
Disconnectome studio web application The DSD web application (http://disconnectomestudio. bcblab. com) was built using the Django framework (https://www. djangoproject. com). This web framework allows database manipulation and is Python-based. The DSD front end was created with standard Javascript and css templates, whereas the backend is hosted in a DigitalOcean web server (https://www. digitalocean. com). Gunicorn and Ngnix are used for the web application live production.
The Neuropsychological White Matter Atlas To create a white matter atlas of the evaluated neuropsychological assessments, white matter disconnectomes (dataset 1) were correlated with patients' PCA scores, evaluated by running the prediction model on dataset 1. The disconnectome data were used in defining the UMAP space, whereas the DSD model weights as variables of the multiple regression model to predict long-term neuropsychological symptoms. Using randomise (FSL libraries) a generalized voxel-based linear regression model was run, with disconnectome maps as independent variables and PCA scores as dependent variables. To address the result of replicability this procedure was repeated twice, splitting the dataset 1 into two halves of n = 666 subjects each. The randomise T-maps obtained were used to calculate the correspondent effect size maps (f 2 , python code available in the open data section at http://www.
bcblab. com). For each neuropsychological score, the three PCA scores, obtained from the DSD model, were evaluated and the maximum effect size across the components was considered. Subsequently, the highest effect size across neuropsychological assessments was reported in the NWMA summary map (FSL libraries find_the_biggest function). The replicability of the NWMA was quantified by means of Pearson correlations between the two dataset 1 summary maps.
The disconnectome morphospace The latent disconnectome configuration was defined based on n = 1333 stroke patients  43  (see dataset 1 in Table  1 ) because its numerosity of >1000 stroke patients allowed for an ecological description of lesion variability for clinical translation. Dataset 1 stroke lesions were processed to obtain disconnectome maps. Disconnectome maps quantify the pattern of white matter connections interrupted by each lesion based on the high-resolution tractography of a healthy population. 21, 61, 62 ubsequently, the UMAP  56  method was used to embed the disconnection complexity. A latent two-dimensional configuration of the disconnectome maps was obtained. Subsequently, external patient cohorts were imported into the dataset 1 latent configuration to train (datasets 2-training and 4) and test (datasets 2-validation, 3 and 5) the morphospace ability in predicting neuropsychological performance 1 year after the stroke.
Patient disconnectome profiles were distributed based on cortical lesion location and commonly disconnected white matter tracts. Patients with major left or right hemisphere disconnections were embedded in the right and left half of the morphospace, respectively. Similarly, patients with posterior or anterior disconnections were localized at the top or the bottom of the embedded space. For instance, patients with a prominent disconnection of the right inferior fronto-occipital fasciculus (IFOF) were located at the top left extremity of the morphospace. In contrast, left corticospinal tract (CST) and arcuate fasciculus (AF) disconnections were located relatively more central and toward the bottom right side of the morphospace (Fig. 2 ). Importation of the different datasets showed consistency in the distribution of the disconnectivity pattern.
These results demonstrate that the morphospace appropriately segregated the different profiles of disconnections (see Supplementary Fig. 3  for more details). 33, 63 e composite morphospace The extent to which the disconnectome morphospace can predict different neuropsychological performances is currently unknown. To answer this question, we took advantage of the second independent dataset of stroke patients  25  (n = 119; see dataset 2 in Table  1 ) who were extensively tested with standard neuropsychological assessments (n = 86, Supplementary Table  1 ). For each patient of the second dataset, disconnectome maps were calculated and imported into the disconnectome morphospace using the UMAP-defined transformation. To tackle embedding uncertainty, patient coordinates were spatially smoothed in the morphospace (see the 'Materials and methods' section).
We did not perform a simple linear association between the morphospace coordinate scale and neuropsychological scores. However, to extensively capture data variance in the morphospace, patients' probability of localization within the significant clusters was modelled by a PCA (later referred to as spatial PCA). For each patient, the first three components of the spatial PCA were entered into a multiple regression analysis to predict single-patient neuropsychological scores 1 year after symptom onset. The multiple regressions created equations, modelling the relationship between each patient's potential localization in the disconnectome morphospace (i. e. as defined by the first three components of the spatial PCA) and their neuropsychological scores. In so doing, we obtained a composite morphospace that takes advantage of the joint strengths of the two datasets.
Disconnection Symptoms Discovery web application To make this resource and method available to the clinicalresearch community, we deployed an interactive web application platform, DSD (http://disconnectomestudio. bcblab. com). The DSD platform requires the input of disconnection maps. The DSD tool returns the expected 1-year neuropsychological scores for individual disconnectome maps (see the user guide in the Supplementary material, Section E). The prediction model relies on the databases presented in this study and can be updated ondemand with new neuropsychological assessments and patients' disconnectomes.
Disconnectome morphospace component mapping In the next level, we brought the score prediction results back to the neuroimaging space to explore the neuroanatomical patterns leading to symptoms. The first dataset was split in half (2 × 666 disconnectome maps) to assess reproducibility. Latent patterns of predicted neuropsychological performances were statistically associated with brain disconnection maps of the two halves of the first dataset using voxel-wise linear regressions. In doing so we obtained two sets of maps of brain disconnection for each neuropsychological score [see example in Fig. 3A (ii)-C(ii) and all maps together with their full discussion in Supplementary material, Section C]. We were able to produce a comprehensive atlas of the brain disconnections associated with neuropsychological scores and the statistical comparison of the two sets of maps indicated an excellent level of reproducibility (Pearson R = 0.
External cohorts model validation To assess the accuracy of the predictions, data derived from a third independent dataset  25  (20 stroke patients withheld from the original dataset 2; see dataset 2-validation in Table  1 ) were projected into the morphospace. From there, equations derived from the composite morphospace were applied to predict individual neuropsychological scores. Prediction accuracy of individual neuropsychological scores was assessed using the MAE, which reflects the difference between the observed and predicted scores normalized by the maximum score (MAE %; Fig. 5 ). The MAE is a standard metric for assessing machine learning accuracy (https://scikit-learn. org/stable/modules/model_evaluation. html) and it provides a clinically meaningful measure. 57, 64 Individual scores were predicted with an average MAE of 16.
1 ± 7 (range 4. 4, 39. 2) %. More than three-quarters (n = 65) of all scores available (n = 83) were predicted in this third independent dataset with an MAE < 20% (see Supplementary Table  3  for comprehensive statistics). From a personalized clinical perspective, the neuropsychological profiles of these 20 patients were assessed by comparing the measured and predicted scores for each patient. Figure  5  displays a colour map of the predicted and measured scores for dataset 2-validation. Additionally, Fig. 6  illustrates three representative patients derived from dataset 2-validation. The radar plots demonstrate the correspondence of the DSD model prediction with the measured scores. The DSD prediction agreement with the patient's recorded performances has been reported and discussed in the Supplementary material, Section F (Supplementary Figs  58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 ).
The DSD model was further tested in a third external cohortdataset 3 (see Table  1  for demographics) for semantic fluency (animals) scores, the only test comparable across their different neuropsychological routine assessments. The DSD model predictions for dataset 3 achieved R 2 = 0. 201 and MAE % of 13. 71% (Fig. 7B ). Further, we demonstrated the DSD model generalizability by training and validating it in two different cohorts (datasets 4 and.