dataesr/scientific-paragraphs-categorization huggingface dataset:
Positron emission tomography/functional imaging biomarkers For ADNI participants, we used regional standardized uptake value ratios (SUVR) extracted from Amyloid PET scans ([ 18 F]-Florbetapir and [ 18 F]-Florbetaben radiotracers), and, starting from ADNI 3, Tau PET scans ([ 18 F]-AV-1451 radiotracer). Each PET scan was registered together with the MRI for the subject performed as close as possible to the PET scan in terms of time. For Amyloid PET, we used a cortical-summary region consisting of the frontal, anterior/posterior cingulate, lateral parietal, and lateral temporal regions; data were normalized with a composite reference region consisting of the whole cerebellum, brainstem/pons, and eroded subcortical white matter  [57] [58] [59]. These PET SUVR values were converted to the centiloid scale (CL)  60  using equations from the literature 61 listed in Supplementary Table  6.
In the AIBL cohort, the processed Amyloid PET SUVR data that correspond to the published centiloid conversion equations were not publicly available. In the MEMENTO cohort, Amyloid PET SUVR data are not directly comparable with ADNI data and equations for centiloid conversion were not available. Therefore, we used Amyloid PET data on these cohorts only to define the Amyloid status of the participants, using pathological thresholds provided by these studies. For Tau PET, we used a volume-weighted average SUVR value for all anatomical Braak regions of interest (I-VI)  62  , normalized against the inferior cerebellum gray matter  63.
It learns instead how the values of the endpoints will change in the next month given the values of the endpoint at a given time-point. The 1-month transition is assumed to be a non-linear function of the current value of the endpoints and the current diagnosis (e. g. artificial neurons). parameters of this transition function are estimated using a training data set containing the repeated measurements of a multitude of participants. After the training phase, the measurements of one test participant (outside the training set) at one or several visits are used as input of the model. The model then computes the values of all the endpoints at each month in the future. AD Course Map can be trained and tested with missing data: the likelihood is optimized using the available data only. Model training is robust to missing data  6  , so we did not perform data imputation.
By contrast, RNN-AD needs complete data at the baseline visit. We imputed missing data with the mean value of the endpoint in the training set, following authors' recommendations;  24  missing data at subsequent visits are imputed recurrently using model predictions. Both models also need an internal step of data normalization. For AD Course Map, cognitive assessments were normalized to a 0 to +1 scale according to the theoretical minimum and maximum values of each assessment, 0 representing the theoretical best value (unaffected participants) and +1 the worst possible value. Harmonized amyloid PET data are clipped between 0 and 100 and converted to a (0,1) scale. MRI, tau PET, and Harmonized CSF data were clipped at the first and last centile, and then linearly mapped to a (0,1) scale. For RNN-AD, normalization consists in a z-score transformation estimated from training data.
Validation procedure We split the data sets in two (see Supplementary Fig. 1 ). We first considered the ADNI participants who were amyloid-positive according to CSF or PET data on at least one visit (shown in red in Supplementary Fig. 1 ). We then kept the other ADNI participants and all participants from the four other cohorts as an external validation set (shown in blue in the Supplementary Fig. 1 ). We then split the amyloid-positive ADNI participants into five random folds and trained AD Course Map and RNN-AD using all available data of the participants in four out of the five folds, e. g. , the training set. We repeated this procedure with another split, so that we ended up with 10 instances of each model. Each participant has been counted twice as a test subject in the left-out fold.
Forecasting endpoints We aimed to assess the accuracy of each model to forecast the values of the endpoints of a participant in the test set or the external validation set. The general principle is to blind the latest data of the participant, use the unblinded data as input of the model, and compare the predicted value with the blinded data. We used a combinatorial procedure to generate prediction tasks, as described in Supplementary Fig. 1. Because we have multiple followup visits, we assessed several forecast errors for a single participant: we blinded the data of the participant except at one to three consecutive visits, we predict the individual trajectory using the unblinded data, and forecast the data at the blinded visits after the latest unblinded visit. We required that the participants are between 50 and 90 years old and have a CDR global of at most 2 at the latest unblinded visit to exclude severely demented participants, and that the blinded visits used to assess the forecast fall between 1.
Supplementary Table  8  summarizes the characteristics of participants included in all the simulated trials. We first evaluated our prognostic enrichment strategy from a diagnostic test standpoint. For each trial, we forecast the value of the primary endpoint at the follow-up visit from the baseline data only, using the procedure described above. We calculated the median value of the outcome (i. e. the annual rate of change between baseline and follow-up visit). Participants above this threshold were considered to be fast progressors and formed the target population to be identified. A threshold for predicted outcomes was used to split the population into two groups: one considered at high risk of progression and the other at low risk of progression. We let the low-risk vs. high-risk threshold vary and calculated the resulting receiver operator characteristic (ROC) curve.
Statistics and reproducibility No statistical method was used to predetermine the sample size. We considered all available data from all the cohorts and excluded only the data of the participants with less than one year of follow-up. The experiments were not randomized since only observational data were used. The investigators were not blinded to allocation during experiments and outcome assessment since only observational data were used. Simulations of clinical trials included a random unblinded allocation into treated and control arms with assessment of biases in sex, center, level of education, and APOE genotype.
Acknowledgements E. M. , I. K. , J. O. , S. E. , and S. D. received funding from the  European Research Council (ERC)  under grant agreement no. 678304 , the  Agence Nationale de la Recherche (ANR)  under the " Investissements d'avenir" program , grants  ANR-10-IAIHU-06  ( IHU ICM ) and  ANR-19-P3IA-0001  ( PRAIRIE 3IA Institute ). E. M. , I. K. , J. O. , C. B. , M. H. A. , and S. D. received funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement no. 826421  ( TVB-Cloud ). D. A. and S. D. received funding from the  joint program in neurodegenerative diseases (JPND)  under grant agreement  ANR-19-JPW2-000  (E-DADS). S. E. received funding via an  APHP-Inria collaboration grant  (poste d'accueil) within the  ARAMIS  project-team. The data used in the preparation of this article were obtained from: -The Alzheimer's  Disease Neuroimaging Initiative  (ADNI) database (adni.
AIBL core funding was provided by the (CSIRO). The cohort was also supported by the  University of Melbourne ,  Neurosciences Australia Ltd ,  Edith Cowan University, Mental Health Research Institute, Australia, National Ageing Research Institute, Austin Health, University of WA, CogState Ltd. , Macquarie University, Hollywood Private Hospital, Sir Charles Gairdner Hospital. J-ADNI was supported by the following grants:  Translational Research Promotion Project  from the  New Energy and Industrial Technology Development Organization of Japan ; Research on Dementia,  Health Labor Sciences Research Grant ;  Life Science Database Integration Project  of  Japan Science and Technology Agency; Research Association of Biotechnology  (contributed by  Astellas Pharma Inc. ,  Bristol-Myers Squibb, Daiichi-Sankyo ,  Eisai ,  Eli Lilly and Company ,  Merck-Banyu ,  Mitsubishi Tanabe Pharma ,  Pfizer Inc.
,  Shionogi & Co. , Ltd. ,  Sumitomo Dainippon , and  Takeda Pharmaceutical Company), Japan , and a grant from an anonymous foundation. The  IMI-PharmaCog/E-ADNI  project was funded by the  European seventh framework program  and  European Federation of Pharmaceutical Industries and Associations (EFPIA)  for the  Innovative Medicine Initiative  (Grant no. 115009 ; http://www. pharmacog. org). The MEMENTO cohort was funded through research grants from the  Fondation Plan Alzheimer  (Alzheimer Plan  2008-2012 ), and the  French Ministry of Higher Education, Research and Innovation (Plan Maladies Neurodégénératives   2014-2019 ). This work was also supported by  CIC1401-EC, Bordeaux University Hospital (CHU Bordeaux , sponsor of the cohort),  Inserm , and the  University of Bordeaux. The MEMENTO cohort has received funding from  AVID, GE Healthcare , and FUJIREBIO through private-public partnerships.
Data availability The ADNI and AIBL data used in this study are available in the database of the laboratory of neuroimaging at the University of Southern California under accession code at http://adni. loni. usc. edu. The J-ADNI data used in this study are available in the NBDC Human Database under accession code at http://humandbs. biosciencedbc. jp/en/. The PharmaCog data used in this study are available in the NeuGRID2 platform under access code at https://www. neugrid2. eu/ (https://doi. org/10. 17616/R31NJN1E). The MEMENTO data used in this study are available in Dementia Platform UK under accession code at https:// portal. dementiasplatform. uk/CohortDirectory/Item?fingerPrintID= MEMENTO. Raw data and patient-level data that were generated in this study are protected and are not available due to data privacy laws and data use agreements.
Code availability The statistical analysis of the forecast errors and the simulation of clinical trials were performed in Python. We used the Leaspy opensource software (https://gitlab. com/icm-institute/aramislab/leaspy) for training and testing AD Course Map, and the corresponding opensource software for RNN-AD https://github. com/ThomasYeoLab/ CBIG/tree/master/stable_projects/predict_phenotypes/Nguyen2020_ RNNAD. Linear mixed models were trained using the open-source statsmodels package  69. The frozen versions of the Python libraries that were used to generate the results of this article can be found in a dedicated Zenodo repository  68.
9, 81. 1] ADAS-Cog13 4. 5 years Janssen tau levels -CDR global = 0. 5 ± 6 months Autonomy -p-Tau+ (*) (NCT04619420) MCI probably due to AD or -Age [54. 9, 86. 1] MMSE 3 years / mild AD -CDR global = 0. 5 or 1 ± 9 months -From AD data sets Six trials were simulated because we considered two possible primary outcomes for the trial targeting early Alzheimer's disease (AD) with high brain amyloid levels (third row). MCI mild cognitive impairment, AD Alzheimer's disease. *CSF or PET (worst visit to date).
Introduction Omics approaches allow analyzing genomes, transcripts, lipids, proteins, and metabolites to an unprecedented level of depth. Each is informing cellular processes and 2 molecular responses at particular time and size scales, making them highly complementary. The next big step forward is thus to combine them into multi-omics approaches. Correlating multi-layered datasets comes with major technical, statistical and conceptual challenges  (Krassowski et al. , 2020; Tarazona et al. , 2021). One basic challenge not to be neglected, particularly relevant to microbiologists, is the fact that these approaches are often destructive, and thus cannot be routinely performed from a single small sample and require parallel extraction on split samples (e. g. (Clerissi et al. , 2023) ). One of the most commonly applied approaches in microbial ecology is the characterization of microbial community compositions using high-throughput sequencing of ribosomal RNA-encoding genes from extracted DNA  (Alivisatos et al.
Metabolome composition can vary quickly, within seconds, in response to stimuli, stress, changes in the environment or overall health status including age, sex, disease and treatments  (Khemtong et al. , 2015; Rauckhorst et al. , 2022). Metabolomics datasets are usually difficult to interpret on their own, in particular owing to the lack of proper databases that could link peaks identified in mass spectrometry to particular metabolites, even more so in the microbial world  (Gertsman and Barshop, 2018). However, these are particularly relevant when combined in multi-omics approaches, for they can provide a quick measure of functional variations that can be linked to another omics, as for example recently performed for transcriptome analysis  (Madaj et al. , 2023). Once metabolites are extracted from a sample, for example with classical methanol extraction procedure  (Prasad Maharjan and Ferenci, 2003) , other types of molecules can be recovered from the remaining pellet including proteins, lipid membranes and DNA  (Roume et al.
, 2013; Valledor et al. , 2014). However, the effect of prior methanol extraction of metabolites on the datasets obtained when analyzing these other molecules has rarely been investigated so far. Interestingly, a recent study has shown that RNAseq dataset quality was only marginally affected by prior metabolite extraction with 80% methanol in mice model, suggesting that single sample combination of metabolomics and transcriptomics was tractable in this case  (Madaj et al. , 2023). For microbiologists, being able to produce various omics data from a single sample is particularly relevant. Indeed, microbial habitats are heterogeneous at a micrometer scale, for example in the multiple layers of a biofilm, in micro niches in soils, or in different regions of a host's gut  (Nunan, 2017; Shaffer et al.
, 2022). Splitting samples prior to applying different omics approaches to distinct subsamples thus potentially hampers our ability to apply correlation algorithms on results from the obtained datasets if samples are heterogenous. Single sample characterization of metabolites and prokaryotic community compositions could offer a quick and efficient way to correlate changes observed in the composition of communities and metabolome, linking community structure to functional fingerprinting. Besides this evident complementarity, both methods are time-and cost-effective, allowing comparison of numerous samples, and thus replication levels that allow proper hypothesis testing  (Singh et al. , 2019). Various recent studies have emphasized the potential of combining these approaches, one remarkable example identifying taxa and metabolites specific to different environments in a large scale study of earth microbiome  (Shaffer et al.
Sequencing and analysis of the V3-V4 region of prokaryotic 16S rRNA The V3-V4 region of the 16S rRNA encoding gene was amplified using primers 341F (5'-CCTACGGGNGGCWGCAG -3') and 806R (5′-GGACTACVSGGGTATCTAAT-3′)  (Parada et al. , 2016)  and sequenced on an Illumina MiSeq 250×2 bp platform (GenoToul, Toulouse, France). Reads were deposited into the Sequence Read Archive (SRA) database (accession number PRJNA995525 (samples SRR25302593 -SRR25302641); Table  S1 ). Sequence analysis was performed using the QIIME2-2022. 8 pipeline  (Bolyen et al. , 2019). Forward and reverse reads were trimmed at 230 and 225 bp, respectively. Amplicon Sequence Variants (ASVs) were obtained with the DADA2 plugin (default parameters) and affiliated with the SILVA 138-99 database. Diversity metrics were computed in RStudio (v4.
1. 3) with the R package phyloseq (v1. 38. 0)  (McMurdie and Holmes, 2013). Statistical analyses were performed using R packages vegan (v2. 6-4)  (Oksanen et al. , 2022)  and pairwiseAdonis (v0. 4)  (Arbizu, 2022). All values are displayed as median ± standard deviation unless otherwise indicated.
Funding The study was funded by  Agence Nationale de la Recherche  project  COM2LIFE  ( ANR-20-CE32-0006 ). P. F. and M. G. are funded through the  ANR  project  COM2LIFE , A. G. is funded through a Ph. D. grant from  Ecole Doctorale 227 "Sciences de la Nature et de l'Homme" ,  MNHN.
The April 2021 La Soufrière of St. Vincent eruption generated several pyroclastic density currents (PDCs) during the two weeks of the crisis, from April 9 th to April 22 nd. To support the hazard assessment team during this eruption, numerical simulations were performed in real time and generated rapid scenario-based PDC invasion maps with the two-phase version of the code VolcFlow, which was able to simulate both the concentrated and dilute regime of PDCs. To generate the maps, only the source properties (shape and location) and the initial volume used to generate the PDCs were varied, all other input parameters were kept constant and estimated from previous simulations. New simulations were then performed based on the field-based deposit map to assess the code's ability to simulate such PDCs.
The study is therefore organized in two steps: (i) a syn-crisis phase, in April 2021, during which PDC hazards were assessed with the aim of supporting the UWI-SRC by making rapid invasion maps, and (ii) a post-crisis phase during which syn-crisis maps were reanalyzed in the light of new data from fieldwork. For the first phase, we ran numerical simulations of PDCs at La Soufrière using the two-phase version of VolcFlow, during the two weeks after the onset of explosive activity on 9 th April. We chose this code for: (i) its capacity to simulate the extent of both dilute and concentrated regimes of PDCs during a single simulation, (ii) its rapidity, i. e. , simulation run < 5h, (iii) the simplicity to generate invasion maps of concentrated and/or dilute PDCs from simulation outputs. Cumulative PDC invasion maps are created for two sectors of La Soufrière, i.
Rapid scenario-based approach: procedure and parameters To generate an inundation map, a scenario was defined, and a series of simulations was run for which the value of one or several input parameters of the code were varied. To model the complex dynamics of PDCs with the two-layer version of the code VolcFlow, 13 input parameters must be defined prior to each simulation, as detailed in Table  1. Note that the reader is referred to  Kelfoun et al. (2017)  and  Gueugneau et al. (2019, Supplementary Material)  for more information on the influence of each parameter on the model dynamics. In the case of the April 2021 crisis, none of VolcFlow's input parameters were known prior to the eruption. As for the preliminary simulation, the value of each fixed input parameters were estimated and calibrated from values used in previous studies (Table  1 ).
The last two parameters of the table, a. k. a. supply duration and total volume, are specific to each eruption and cannot be estimated from previous work. Therefore, we chose to vary the source position/geometry and the total volume to create our scenarios and keep the twelve other parameters fixed in all simulations. Three different volumes were explored to define three PDC-type scenarios: (i) S1, with a volume of 10 Mm 3 , typical of small-volume PDCs (FlowDat database, Ogburn, 2012), similar to the June 25 th , 1997 PDCs at Soufriere Hills, Montserrat  (Loughlin et al. , 2002) ; (ii) S2, with a volume of 20 Mm 3 , twice the targeted volume ; (iii) S3, with a volume of 30 Mm 3 , three times the targeted volume, similar to the May 8 th , 1902 PDCs at Mount Pelée, Martinique  (Lacroix, 1904; Gueugneau et al.
1C , 3, 4 and 5). And the volume estimate from  Cole et al. (2023)  demonstrate that our syn-crisis volumes were very realistic. A C C E P T E D M A N U S C R I P T The comparison between the syn-crisis and post-crisis simulations also highlights the successful choices made when selecting inputs for all simulation scenarios, and the ability of the code to accurately model small-volume PDCs. This is also the case for most of VolcFlow's input parameters, such as the particle properties (i. e. , particle size and density, gas properties…) that remained mostly unchanged between the two sets of simulations. It is also true for the source properties (position and shape), that were correctly chosen since the first simulation owing to the known position of the new crater, even though the shape had to be slightly refined for the post-crisis simulation.
Positioning the source along the crater rim at the beginning of each inundated valley was found to be the best configuration and fits well with a fountain or a column collapsing inside a specific sector of the crater, for a short time period. To summarize, this exercise demonstrates that VolcFlow has the potential to accurately model such PDCs during a crisis when the correct input and source parameters are selected. Data from this eruption are a new addition to VolcFlow input parameters database that can be used for future crisis to reduce the preparation time or help to define ranges of value to explore in case of probabilistic hazard assessment. Most of the work should now focus on better constraining input parameters as soon as an eruption begins, which relies on (i) discretizing each PDC pulse in time and space to facilitate the determination of the most at risk zones; and (ii) estimating their parameters simultaneously (volume, inundated area, mass flux) to avoid the use of simplified source conditions.
Conclusion We Georgetown can be reach by a PDC but only in case of extremely voluminous PDC (> 30 Mm 3 ), not likely to occur with the current activity of the volcano at the time, and (iv) while the PDC hazards are satisfactorily estimated in the 'very high risk' zone of the volcanic risk map of St. Vincent, the Chateaubelair sector must be closely monitored. In the second modeling step conducted months after the crisis, the newly acquired data from the eruption, such as a PDC deposit map, were used for a new set of simulations aiming at best reproducing the observed PDCs. Input parameters used for the bestfit simulation are then compared with those of the syn-crisis simulation. Results showed that ashcloud surges in syn-crisis simulations were overestimated (two order of magnitude for the surge production coefficient), and that the volume was also too high (5.
The new set of input parameters used for the best fit simulation are a new addition to a database constituted of other PDC modeling projects with this code. The different sets of input parameters help to define range of values more and more precise that can be used for future similar PDC hazard assessment projects at other volcanoes, prior or during an eruption. Satellite images are provided by R. Grandin (Capella Space) and the TerraSAR-X imagery (acquired by DLR) can be requested through the CEOS Volcano Demonstrator Program, by contacting either the demonstrator leads or the WGDisasters secretariat (https://ceos. org/ourwork/workinggroups/disasters/). Figures 2A and 2B are copyright of Capella Space -2021. Important lessons were learned on: (i) how to best estimate and represent the PDC invasion potential (cumulative maps), (ii) how crucial the DEM resolution selection is, (iii) how radar images can provide essential syn-crisis data, and (iv) how VolcFlow's simulation results can be improved when input parameters are well constrained.