dataesr/scientific-paragraphs-categorization huggingface dataset:
We found that the level of noise in these regions is one of the largest, a fact that may explain inconsistencies across studies. AD Course Map fits data with an error of the order of the measurement uncertainty. AD Course Map estimates not only a typical progression of clinical and imaging data, but also a range of possible trajectories. These trajectories derive from the typical scenario by adjusting the age at onset, pace of progression, appearance of the images, the shape of meshes, and the temporal spacing among clinical assessments. After model calibration, each subject is positioned on one of these possible trajectories. The positioning amounts to the estimation of subject-specific time-shift, acceleration factor and a series of space-shift for each modality. The space-shifts determine the subject-specific trajectory.
Mean and standard errors are given in Supplementary Table  S1. Images were obtained using the freely available software FSLeyes v. 0. 22. 6 (https:// fsl. fmrib. ox. ac. uk/ fsl/ fslwi ki/ FSLey es) and Paraview v. 5. 2. 0 (www. parav iew. org). variability and subject's condition. Research protocols tend to reduce these variations as much as possible. Nevertheless, data are known only within an error margin which is difficult to evaluate. For MRI data, we estimate this uncertainty by measuring differences between test and re-test MRI sessions, two images acquired on the same subject one right after the other. For PET data, there is no such test / re-test data. As a proxy, we measured the difference between the baseline and follow-up image after 18 months for amyloid negative cognitively normal subjects.
It is an over-estimation of the measurement uncertainty as this proxy neglects the effect of normal aging on the change in FDG uptake. Nevertheless, studies have not shown statistically significant longitudinal changes in FDG uptake over such a short period of time for normal ageing individuals  27, 28. Another study reports significant longitudinal changes over an average period of 7. 8 years in the anterior and posterior cingulate cortex with annual rates of change of 0. 58% and 0. 57% respectively  29. For the neuropsychological assessments, we perform a review of the literature assessing their reproducibility (see Methods). From these estimates, we deduce an empirical distribution of the noise in the data. Fig. 3  shows the superimposition of the empirical distribution of reconstruction errors with the empirical distribution of the noise for all data types.
Overall, the two distributions largely overlap, and the standard error is of the same order than the measurement noise (see Supplementary Table  S1 ). This result shows that our model explains the variance seen in the data up to the noise level. Therefore, for the metric on the data that we used, these results could not be improved without over-fitting. AD Course Map fits new subjects' data equally well. Once the AD Course Map has been calibrated on a representative data set, we aim to use it to analyse the data of new subjects. We estimate here the accuracy of AD Course Map to fit new subject's data. We use a five-fold cross validation procedure. The model is calibrated using 80% of the subjects, and the remaining 20% subjects are then positioned on the map. In the positioning phase, we use the typical scenario of progression and the covariance among subject specific parameters, which were estimated during the calibration phase.
For each of the unseen subjects, we only estimate one time-shift, one acceleration factor and a series of space-shifts that minimize the discrepancy between the predicted data and the true subject's data. We call this discrepancy a generalisation error. Distributions of the generalisation errors are essentially identical with these the reconstruction errors (see Supplementary Fig. S2 ). Only hippocampus shows a slightly higher generalisation error but still below the noise level estimated with test / re-test data. The goodness-of-fit is therefore equally good whether the subject has been included in the training set or in the test set. AD Course Map calibration is robust against resampling. After the cross-validation procedure, each subject has been used exactly once as a test subject. We consider the subject-specific parameters that were estimated at that time.
We compare them with the parameter values that were estimated when calibrating the model on the whole data set, and thus when the considered subject was used in the training set. The difference between the two estimates is small with r 2 comprised between 0. 93 and 0. 99 (see Supplementary Fig. S3 ). Eventually, we use the cross-validation procedure to assess the robustness of AD Course Map against resampling. After the procedure, we have estimated five different course maps. Each course map is determined by the parameters of the typical model of progression and the variance of the subject-specific parameters. We compare these parameters estimated using 80% of the subjects with the ones resulting of the calibration of the model on the whole data set. These parameters show very little variations, thus showing the robustness of the estimation algorithm against resampling in the training set (see Supplementary Table  S2 ).
AD Course Map predicts the future progression of new subjects. We evaluate the ability of AD Course Map to predict the disease progression in the distant future for the subjects at risk of developing AD. For this purpose, we select subjects and visits in the ADNI database based on criteria that can be assessed from present and past visits only, without the need to know the whole disease history of the patients as previously. We select all the visits of all the subjects in ADNI, which met the following conditions: • the subject is labeled as Mild Cognitive Impairment at this visit, • the MMSE of the subject is smaller or equal to 27 at this visit, as a confirmation of the MCI stage, • the subject is amyloid positive at this visit, • the sequence of diagnosis labels in the past visits is monotonic, meaning we exclude subjects showing rever- sion to cognitively normal, or having AD label in the past.
These criteria target patients with MCI due to AD according to NIA-AA criteria  37  , a population of interest for clinical trials  38. For each subject, we select the visits for which there is at least one follow-up of the same subject 3 or 4 years later in time. We hide the follow-up visits after the considered visit. We position the subject onto the AD Course Map using subject's data until the considered visit only. We read the subject's data at the next hidden visits on the subject's trajectory. We compare the predicted data at the time-point of the follow-up visits with the data that were hidden. We call this difference a "prediction error" (see Methods). If the selected subject is not part of the previous training data set, we use the AD Course Map estimated in the previous section. If the selected subject is part of it, we use the AD Course Map that was estimated during the cross-validation procedure, using the fold in which the considered subject was a test subject.
These results advocate for longer term predictions, and possibly more sensitive imaging markers  40. For MRI derived data, the noise distribution presents a very heavy tail that is due to the large heterogeneity of the image quality and its consequence in data processing, at least using the standard acquisition protocol and processing pipelines of ADNI. The constant prediction does not show such a heavy tail, as images of best quality 3 or 4 years apart show less variability than the test and re-test image acquired the same day. Our model shows steady performance at 3 and 4 years, whereas the constant prediction worsens as time-to-prediction increases. Comparing such predictions with alternative methods is difficult, as they are often evaluated on different sets of subjects  41, 42. Prediction errors might be easily increased by selecting more stable subjects and calibrating the method to predict little to no changes.
AD Course Map simulates cohorts of virtual patients. We use AD Course Map to simulate entirely synthetic patients developing Alzheimer's disease. After model calibration, we estimate the empirical posterior distribution of the subject-specific parameters. We sample then random parameters from this distribution. These parameters are used to transform the reference trajectories into a series of trajectories reproducing the heterogeneity seen in the original population. We pick randomly an arbitrary number of time-points in these trajectories to create a synthetic longitudinal data set. We perform this procedure independently for men and women (see Methods). To validate such simulations, we aim to replicate the original ADNI data set by picking the same number of time-points and at the same frequency as in the ADNI data set, and by simulating men and women subjects with the same sex ratio.
We then compute in the simulated data the regional SUVR, cortical thickness, hippocampus volume and neuro-psychological assessments. We superimpose them with the distributions of the original and reconstructed data. The superimposition of the distributions in Figure  5  shows that the simulated data closely replicate the reconstructed data for all modalities. For the hippocampus volume, the simulated data have the same bias than the reconstructed data in comparison to the real data. This fact is expected as the simulation reproduces the variability learned by the model. This experiment shows that AD Course Map can be used as a simulator of subjects developing Alzheimer's disease, which replicates the heterogeneity in disease progression of the studied population. We have released a virtual cohort of one million of subjects developing AD that is freely available at www.
Discussion AD Course Map charts the possible trajectories of progression to AD. Few and interpretable parameters adjust a reference scenario of progression to the data of a subject. They allowed us to highlight strong sexual dimorphism and possible compensatory mechanisms at play during AD progression. The personalized subject's trajectory predicts the future image and clinical data at any time-point in the future. Such predictions are competitive with the state-of-the-art machine learning methods when tested on the same population. The prediction errors are of the order of the measurement uncertainty. Alternative methods are designed to more directly inform about possible biological mechanisms, such as the propagation of misfolded proteins in the brain  44, 45. Nevertheless, our approach is not specific to certain types of data, and may be applied easily to other therapeutic areas.
One key open question remains the potential use of these disease modeling techniques in clinical decision support systems. The promise of AD Course Map is to target the right patient at the right time. Whether such selection tools eventually lead to more powered clinical trials still needs to be shown. Figure  5. of the virtual cohort. Superimposition of empirical distributions for simulated data (blue), reconstructed errors (red, as in Fig. 3 ) and real data (orange). Images were obtained using the freely available software FSLeyes v. 0. 22. 6 (https:// fsl. fmrib. ox. ac. uk/ fsl/ fslwi ki/ FSLey es) and Paraview v. 5. 2. 0 (www. parav iew. org).
Methods Data set. We use data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) to reproduce the natural history of the disease from the pre-clinical to the clinical stage. We thus selected the 322 subjects in the ADNI, ADNI-GO and ADNI-2 databases who were included as cognitively normal or with mild cognitive impairments and who had a confirmed clinical diagnosis of Alzheimer's disease at a later time-point in the study. Definitions of these categories are as in the ADNI protocol. Whenever available, we use at each visit: • regional measurements of standard uptake value ratio (SUVR) of fluorodeoxyglucose (FDG)-positron emis- sion tomography (PET) to build models of hypometabolism across brain regions, • maps of cortical thickness defined on a mesh of the cortex and extracted from T1-weighted Magnetic Reso- nance Images (MRI) to build models of cortical thinning, • surface meshes of the hippocampus of both hemispheres segmented also from T1-weighted MRI to build models of hippocampal atrophy, and • scores of the Mini-Mental State Examination  46  (MMSE) and Alzheimer's Disease Assessment Scale -Cogni- tive Subscale with 13 items  47, 48  (ADAS-Cog), the latter being divided into four sub-scores assessing memory, language, concentration and praxis, to build models of cognitive decline, which amounts to 687 visits with PET images, 1993 visits with MRI data and 1235 visits with neuro-psychological assessments (See Supplementary Table  S3  for summary statistics).
The color of the parameter denotes its type and its position the modality. The image was obtained using the software Microsoft PowerPoint v. 16. 43. -a Standard Updake Value ratio (SUVR), normalised by the entire cerebellum, greater than 1. 1 in a PET image acquired with Florbetapir (AV-45) compound  49, 50  ; -an average SUVR, normalised by the cerebellum, greater than 1. 47 in a PET image with a Pittsburgh compound B (PiB)  50  ; -a level of beta amyloid 1-42 (Aβ42) (measured with the Roche Elecsys assays (see http:// www. adni. loni. usc. edu/ new-csf-a% CE% B21-42-t-tau-and-p-tau181-bioma rkers-resul ts-from-adni-bioma rker-coreusing-elecs ys/) in the cerebrospinal fluid (CSF) lower than 1098 pg/mL 51 ; unknown if no values of CSF biomarkers and no AV45 or PiB PET images were available at any visit in the ADNI-merge file; and negative otherwise.
Data pre-processing. MMSE total score, ADAS-cog total score and sub-scores were all normalized to a scale of zero to one, such that normal values correspond to zero and the maximum pathological value to one. Sub-scores were obtained by adding the following items of the ADAS-Cog questionnaire: items 1, 4, 7, 8 and 9 for memory, items 2, 5, 10, 11 and 12 for language, items 3 and 6 for praxis, and item 13 for concentration. Regional FDG-PET SUVR were extracted using the second version of the Automated Anatomical Atlas 52,53 (see http:// www. gin. cnrs. fr/ fr/ outils/ aal-aal2/) (AAL2) with 120 regions covering the cortex and the main subcortical structures, using the open-source community software Clinica  54  (see http:// clini ca. run/ doc/ Pipel ines/ PET_ Volume). The software performs intra-subject registration of the FDG-PET image into the space of the subject's T1-weighted MRI image using Statistical Parametric Mapping 55 (see www.
fil. ion. ucl. ac. uk/ spm/ (SPM) software (version 12)). The PET image is then spatially normalised into MNI space using DARTEL deformation model of SPM, and its intensities normalised using the average uptake value in the pons as reference region. The SUVR map is obtained by averaging resulting intensities in each region of the atlas  56. The MRI images were first processed independently with the cross-sectional pipeline of the FreeSurfer  57, 58  (see https:// surfer. nmr. mgh. harva rd. edu software (version 5. 3. 0)). The longitudinal FressSurfer pipeline is then used to create subject-specific templates from the successive data of each subject and refine image segmentations  59. These segmented images are used then to extract a cortical thickness map, and a mesh of the left and right hippocampus.
We used the cortical surface mesh projected onto the average space called FSaverage with 163,842 vertices. For dimensionality reduction purposes, we then • inflate the FSAverage mesh to a sphere using FreeSurfer, on which 3658 vertices (called patch-nodes) are selected to map the whole sphere uniformly, • associate each vertex to its closest patch-node, resulting in a parcellation of the cortical mesh into 3658 patches that are uniformly distributed over the surface, where a patch contains on average 44 vertices, • compute the average value of the cortical thickness in each patch. We also aligned the skull-stripped images with an affine 12-degrees-of-freedom transformation onto the Colin27 template brain (see http:// www. bic. mni. mcgill. ca/ Servi cesAt lases/ Colin 27, using the FSL 5. 0 softwarecitewool-rich2009bayesian (see https:// fsl.
fmrib. ox. ac. uk/ fsl/ fslwi ki/). Mesh representations of the geometry of the left and right hippocampus result from the following steps: • the volumetric segmentations of the hippocampi obtained by FreeSurfer are transformed into meshes using the aseg2srf software( see https:// brain der. org (version of July 2009)), • the resulting meshes are decimated by a 88% factor using Paraview, 5. 4. 1  60  (see www. parav iew. org), • then aligned using the previously-computed global affine transformation estimated with the FSL software, • residual pose differences among subjects are then removed by rigidly aligning the meshes from the baseline image of each subject to the corresponding hippocampus mesh in the Colin27 atlas image, this transformation with 6 degrees of freedom being computed with the GMMReg software  61  (see https:// github.
Acknowledgements We thank professor  Alan Tobin  for the insightful discussions during the preparation of the manuscript. This work has been partly funded by the  European Research Council (ERC)  under Grant Agreement No. 678304 ,  European Union 's  Horizon 2020 research and innovation program  under Grant Agreement No. 666992  (EuroPOND) and No  826421  ( TVB-Cloud ), and the program " Investissements d'avenir "  ANR-10-IAIHU-06  ( IHU-A-ICM ) and  ANR-19-P3IA-0001  ( PRAIRIE 3IA Institute ). Data used in preparation of this article were obtained from the  Alzheimer's Disease Neuroimaging Initiative (ADNI) database  (http:// www. adni. loni. usc. edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report.
FAIR data infrastructure for the ATHLETE Exposome cohort (WP1) At present, exposome data are scattered across hard-to-find and hard-to-access databases. A prerequisite for exposome research into the future is to bring data together in openly accessible data platforms that will allow pooling of data for larger sample size and replication of findings. ATHLETE will implement an early life exposome data infrastructure by building on the data sharing platform that has already been developed as part of LifeCycle for European birth cohorts (https://lifecycle-project. eu/for-scientists/variable-catalogue/) and that implements FAIR principles to enable findability, accessibility, interoperability, and reusability of cohort data. 62 This infrastructure makes cohorts and datasets findable for project partners and outside researchers in an easy-to-use open access web-based catalog, enabling quick assessment of available data suitable to answer specific research questions.
No actual data are given in the online catalog. ATHLETE will add to the existing data catalog by proposing a new set of exposome modules with harmonized data for the participating cohorts, including, among others, data on the chemical exposome that is not currently available in the catalog. Importantly also, the richest exposome database within this project, the HELIX subcohort, will be transferred in to the FAIR infrastructure as a separate entity to make it easily accessible. The catalog structure will be based on international standards, most notably the Minimum Information About BIobank Data Sharing (MIABIS) standard,  63  and those defined in http:// fairsharing. org. The catalog software will build upon the open source Molecular Genetics Information System (MOLGENIS) project,  64  which has been proven for many catalogs including the EU catalog of biobanks (http://directory.
bbmri-eric. eu). ATHLETE will implement harmonization protocols to make the exposome data interoperable. Syntax files for harmonization of exposome variables will be developed, tested, and applied to cohort data. Each cohort will harmonize and store their own harmonized data on secure local servers and make the metadata findable through the data catalog. ATHLETE will implement "federated" (data stays on local servers and is analyzed remotely) and "centralized" (data analyzed centrally by analyst) systems for cohort owners to make their datasets accessible to project partners and outside researchers in a secure and controlled manner. For some of the exposome analyses, we expect to deploy "DataSHIELD"  65, 66  as one of the federated access protocols, which enables access from the "R" statistical environment using MOLGENIS  64  or Opal software.
67 The federated system overcomes governance restrictions that prohibit the release or sharing of some of the required data, or render data access slow. Because we do not expect that all exposome analyses can be done through DataSHIELD or similar protocols, the local servers will also enable cohorts and database owners to submit their data centrally, where data are then analyzed centrally on a trusted facility with strict data access policies (managed by the project steering committee). In all cases, the cohort and data owners will be in full control of data access.
84 n all strategies, we will take account of issues inherent to exposome data, such as correlation between exposures, missing data, cohort effects, and exposure measurement errors that differ between exposures. In addition to developing these analytical tools, ATHLETE will develop open-access software, front-end applications, tutorials, e-learning material and courses, targeted at varying levels of expertise. These are being made available through our online toolbox (https://athleteproject. eu/toolbox/). As part of this, we aim to extend DataSHIELD tools for remote and nondisclosive data analysis (http://www. datashield. ac. uk/) by incorporating new functionalities to deal with exposome data visualization and analysis. To this end, our recent development of the "resources" architecture in DataSHIELD will facilitate handling complex big data, including omics, within DataSHIELD through the Opal data warehouse.
ATHLETE will include the gut microbiome as a new omics layer in exposome research. Using these datasets together with existing toxicological databases and models, we aim to discover key molecular events and biological pathways associated to the exposome that are of specific interest in early life. New data to be generated in all participants in the new follow-up of the HELIX subcohort (12-18 years, N = 1,100) include shotgun whole metagenomic sequencing (~5 gigabases/sample) of fecal DNA and untargeted serum and urine metabolomics profiling data generated by HR-MS and nuclear magnetic resonance spectroscopy (NMR). This will allow us to define gut metagenomic signatures at the functional level that associate with different exposures, with our health outcomes, and with metabolic mediators. Further, we will use the ATHLETE omics data resource to build a poly-environmental score for risk prediction, aggregating environmental risk factors and multiomics data, and including genetic background.
Health and economic impact of the exposome (WP8) Health impact assessment (HIA) is a crucial tool to translate the knowledge generated from environmental health research into information relevant for policy making. So far, several approaches have been used in HIA, including those developed in the context of the Global Burden of Disease project (e. g. ,  103  ) and in our own assessment of the environmental burden of childhood disease. 104 These assessments, however, are limited to the consideration of environmental factors with a strong level of evidence such as particulate matter, lead, and radon. ATHLETE will employ a weight of evidence approach to calculate health and economic impact of a wider set of key chemical and urban exposures possibly or more certainly related to child health. To achieve this, a plausibility database will synthesize the overall level of evidence regarding the effect of many environmental factors (urban exposome and chemicals) on child health, incorporating all mechanistic, animal-and human-based evidence.