dataesr/scientific-paragraphs-categorization huggingface dataset:
34%) and summed feature 10 (C18:1 c11/t9/t6 FAME) (30. 93%) were the major components. The   2 ). API ZYM strips showed that S. dextrinosolvens Z6 was positive for the alkaline phosphatase, esterase lipase, α-chymotrypsin, acid phosphatase, naphthol-AS-BI-phosphohydrolase, leucine arylamidase, valine arylamidase, cysteine arylamidase, and N-acetyl-β-glucosaminidase enzymes, while lipase, trypsin, galactosidase (α and β), glucosidase (α and β), β-glucuronidase, and α-fucosidase tests were negative. A MALDI-TOF spectrum of our strains was compared with spectra in the database and the score values of our strain was less than 1. 7 as shown in Supplementary Figure  1. Nitrogen Metabolism of S. dextrinosolvens Z6 Grown With Different Nitrogen Sources S. dextrinosolvens Z6 was grown in media containing either urea, ammonia, or amino acids as a nitrogen source, and preferential amino acid utilization was observed (Figure  6A ).
Data collected and averaged once performance plateaued (after session #15 for short GT, between session #20 to #30 for control, and last 5 sessions for the sharp condition experiments). Short GT vs. Control: p < 0. 0001 (permutation test, see Methods); Sharp short GT vs. Sharp control: p < 0. 0001 (permutation test); Short GT vs. Sharp short GT: Non significant (non-parametric paired comparison); Control vs. Sharp control: p = 0. 79 (permutation test). f) Similar to panel a, for another group of animals that were trained to wait for 3. 5 s while the speed of the treadmill was 20 cm/s. g) Similar to panel b, for animals trained in short goal time 20 cm/s condition (panel f). Dashed line's slope shows the treadmill speed (20 cm/s). Dotted line's slope indicates control treadmill speed (10 cm/s).
h) CV for short GT and short GT 20 cm/s conditions (same colors as in panel b,g). Data collected and averaged once performance plateaued (after session #15). Short GT vs. Short GT 20 cm/s: Asterisk indicates significant difference (10,000 resamples with replacement, see Methods). The above results suggest that, in a task requiring animal to produce a motor response according to a fixed temporal constraint, the possibility to perform a stereotypical motor sequence adapted to salient features of the environment (here, taking advantage of the full treadmill length and its physical boundaries) critically determines temporal accuracy. To further investigate this idea, we trained a variability in ET (Figure  6c ). We also noticed that correct trials preferentially occurred when animals crossed the treadmill from the rear wall to the reward area (Video 3, Figure  6a, d, e ).
5 kHz, 65 db) to signal incorrect behavior (see below). Two strips of LED lights were installed on the ceiling along the treadmill to provide visible and infrared lighting during trials and intertrials, respectively (see below). The animals' position was tracked via a ceiling-mounted camera (Basler scout, 25 fps). A custom-made algorithm detected the animal's body and recorded its centroid as animal's position. The entire setup was fully automated by a custom-made program (LabVIEW, National Instruments). Experimenter was never present in the behavioral laboratory during the experiments.
Data Analysis Data from each session was stored in separate text files, containing position information, entrance times, treadmill speeds, and all the task parameters. Position information was scaled to the treadmill length, and smoothed (Gaussian kernel, σ = 0. 3 s). The entire data processing pipeline was implemented in python, using open-source libraries and custom-made scripts. We used a series of Jupyter Notebooks to process, quantify, and visualize every aspect of behavior, to develop and run the reinforcement learning algorithms, and to generate all the figures in this manuscript. All the Jupyter Notebooks, as well as the raw data necessary for full replication of the figures and videos are publicly available via the Open Science Foundation (https://osf. io/7s2r8/?view_ only=7db3818dcf5e49e88d708b2597a21956).
Highlighted areas indicate the reward window. e) Coefficient of variation (CV) for short GT and control experiments with normal (first two boxes), and sharp (last two boxes) reward profiles. Data collected and averaged once performance plateaued (after session #15 for short GT, between session #20 to #30 for control, and last 5 sessions for the sharp condition experiments). Short GT vs. Control: p < 0. 0001 (permutation test, see Methods); Sharp short GT vs. Sharp control: p < 0. 0001 (permutation test); Short GT vs. Sharp short GT: Non significant (non-parametric paired comparison); Control vs. Sharp control: p = 0. 79 (permutation test). f) Similar to panel a, for another group of animals that were trained to wait for 3. 5 s while the speed of the treadmill was 20 cm/s. g) Similar to panel b, for animals trained in short goal time 20 cm/s condition (panel f).
Dashed line's slope shows the treadmill speed (20 cm/s). Dotted line's slope indicates control treadmill speed (10 cm/s). h) CV for short GT and short GT 20 cm/s conditions (same colors as in panel b,g). Data collected and averaged once performance plateaued (after session #15). Short GT vs. Short GT 20 cm/s: Asterisk indicates significant difference (10,000 resamples with replacement, see Methods).
Study Cohorts Study participants (total N=26 654) were drawn from cohorts in the CHARGE and ENIGMA consortia and the UKB. Detailed methods are in the Data Supplement. All cohorts followed standardized procedures for participant inclusion, genotype calling, phenotype harmonization, covariate selection, and study-level analysis. Participants were included if they had phenotype, genotype, and covariate data available and were aged 45 years and over without stroke, dementia, or any neurological abnormality at the time of magnetic resonance imaging scanning. All participants provided written informed consent, and each study received ethical approval to undertake this work.
Statistical Analysis Each study fitted linear regression models to test the association of DWMH and PVWMH (continuous measures) with individual SNPs. Additive genetic effects were assumed, and the models were adjusted for age (years), sex, and ICV (where applicable). In addition, principal components for population stratification and other covariates, such as familial structure, were included if necessary. Models were also fitted with hypertension as an additional covariate. Fixed-effects, inverse variance-weighted meta-analysis was carried out in METAL,  23  with correction for genomic control. Two meta-analyses were carried out: all cohorts excluding UKB (discovery, phase I) and all cohorts (phase II). Post metaanalysis QC was also performed (see in the Data Supplement).
Finally, we investigated local regions of a shared genetic variant between the WMH subtypes and stroke (Table XV in the Data Supplement). A region on chromosome 7 (encompassing the PVWMH NOS3 exonic SNP) exhibited shared genetic influence of all stroke with both phenotypes. Other regions of shared influence with all stroke were observed for PVWMH only. For the subtypes of stroke, significant regions were identified for DWMH and PVWMH, but none were found for both phenotypes except the chromosome 7 region for ischemic stroke (also identified for all stroke). Similar to the GW level correlation, a positive regional level genetic correlation was observed between the WMH subtypes and stroke (all stroke, all-ischemic, cardio-embolic and small vessel), by using HESS. 27.
Sources of Funding This work is supported by the  National Institute of Neurological Disorders and Stroke, National Institutes of Health ,  R01AG059874 ,  P41EB015922 ,  R56AG058854 ,  U54 EB020403 ,  R01AG022381. Medical Research Council, Age UK, Scottish Funding Council ,  Row Fogo Trust , The  Welcome Trust, Age UK ,  Cross Council Lifelong Health and Wellbeing Initiative, Leverhulme Trust, National Institute for Health Research, Biotechnology and Biological Sciences Research Council, UK Medical Research Council, Icelandic Heart Association , and the  Althingi, Austrian Science Fund (FWF) ,  Australian National Health and Medical Research Council ,  Austrian National Bank, Anniversary Fund ,  European Commission   FP6 STRP, European Community 's 5th and 7th  Framework Program ,  Netherlands Organization for Scientific Research, Netherlands Consortium for Healthy Aging, Russian Foundation for Basic Research, Russian Federal Agency of Scientific Organizations, Liaison Committee between the Central Norway Regional Health Authority  and the  Norwegian University of Science and Technology, Norwegian National Advisory Unit  for functional magnetic resonance imaging (MRI),  Leipzig Research Center for Civilization Diseases (LIFE) ,  Bristol-Myers Squibb ,  Netherlands Heart Foundation ,  French National Research Agency (ANR) ,  Foundation Leducq ,  Joint Programme of Neurodegenerative Disease research ,  Bordeaux University ,  Institut Pasteur de Lille , the  labex DISTALZ  and the  Centre National de Génotypage.
PaCaOmics patient's cohort Seventy-six patients with a confirmed PDAC diagnosis were included in this study. Clinical data was collected until July 2017 (supplementary Tables  I  and II ). Tumour samples were obtained from pancreatectomy in 40 patients (52. 6%), EUS-FNA in 25 patients (32. 9%) and carcinomatosis or liver metastasis during explorative laparotomy in 11 patients (14. 5%). The study was approved by the local ethics committee following patient informed consent. The PaCaOmics study is registered at www. clinicaltrials. gov with registration number NCT01692873. All samples were xenografted in immunocompromised mice producing PDX samples. Animal experiments were approved by the local ethics committee and performed following the guidelines of our centre (CRCM).
BACAP patient's cohort The BACAP (Base Clinico-Biologique de l'Ad enocarcinome Pancr eatique) cohort is a prospective multicenter pancreatic cancer cohort (ClinicalTrials. gov Identifier: NCT02818829. Registration date: June 30, 2016) with a biological clinical database. Treatment naive tumour biological samples from endoscopic ultrasound-guided fineneedle aspiration (EUS-FNA) were available for 60 patients. Survival analysis was performed on the 47 patients with locally-advanced or metastatic diseases that subsequently received chemotherapy. BACAP project was approved by the "Sud-Ouest et Outre-Mer I" March 2014 ethics committee. All the patients were informed of the study and voluntarily agreed to participate. All the patients who agreed to participate provided written consent.
Transcriptomic profiling and analysis to derive the PAMG RNA was obtained from all PDX and BACAP cohort samples, for more details see supplementary material and methods. Next Generation Sequencing (RNA-seq) was performed on these samples. The Pancreatic Adenocarcinoma Molecular Gradient (PAMG) was derived from the PDX RNA-seq profiles using an Independent Component Analysis (ICA) on 50% most variant genes (n = 20,434), after gene-wise zero-centring (no unit scaling). The independent component that best correlated with PDX histology was identified as the PAMG. Further details are available in the supplementary information. RNA sequencing of pancreatic adenocarcinoma (PDAC) xenograft samples accession number E-MTAB-5039. The PAMG is available as an online application (http://cit-apps. ligue-cancer.
Reproducibility of the pamg in resectable human primary PDAC To evaluate the robustness of the PAMG, we tested whether an equivalent RNA signature could be blindly reproduced in independent PDAC series with transcriptomic data. Two large series of PDAC were used for this purpose 269 resected tumours from the Australian ICGC  16  profiled on Illumina microarrays from frozen samples, and the multi-centric cohort of 309 consecutive patients from Puleo et al. 10  , profiled on Affymetrix arrays from paraffin-embedded samples. To assess the reproducibility of the PAMG in these series of samples, a blind deconvolution of the transcriptomes was performed using ICA with increasing number of components resulting in ICA spaces of Fig. 2. Reproducibility of the PAMG in PDAC. a. Schematic illustration of the identification of the PAMG in public datasets.
ICA (independent component analysis) blind deconvolution was used on three different datasets of whole transcriptome profiling, generating spaces of independent components of increasing sizes (2 l 25). The PAMG was first obtained from PDX by selecting the component most associated with PDX histology. The gene weights of this initial PDX-based independent component was then correlated to the gene weights of all extracted independent components in the other datasets, with the spearman correlation represented in a grid. The highest correlating component of each dataset was selected as the PAMG. b. Density plot of the PAMG gene weights of common genes found in each pair of datasets. Marker genes are highlighted. c. Scatter plots comparing the three versions of the Molecular Gradient (PDX, ICGC and Puleo) on four datasets.
Each point is a sample, coloured by its PAMG score as defined by the PDX version. Pearson correlation is shown. up to 25 unsupervised independent components (Fig. 2a ). Once components were extracted, a component matching the PAMG from the PDX was sought by correlating gene weights of both the reference PDX ICA space and the new ICA spaces to be evaluated. This analysis aimed at evaluating whether a component biologically similar to the PAMG could be extracted from the human tumour datasets. A molecular component equivalent to the PDX-derived PAMG was found in virtually all ICA component spaces in both datasets despite the difference in measurement technologies and in tissue preservation (Figure  S3 ). The component with the highest gene weight correlation to the PDX-based PAMG was selected from each dataset.
Fig. 2b  illustrates the overall consistency in the gene weights defining each of the components of three PAMGs. Overall, three components were selected from an unsupervised gene-expression deconvolution analysis applied to three independent datasets representing diverse technological (microarrays and RNAseq) and tissue (FFPE, Frozen, PDX) options to profile PDAC resulting in three biologically equivalent implementations of the PAMG. While the three independently identified PAMGs share a similar gene expression basis, we next sought to evaluate the extent they define the same PDAC heterogeneity. The samples from the three different datasets were each projected on all three PAMGs. Fig. 2c  shows a high correlation between the three PAMGs in all three datasets, demonstrating that the signatures measure a common biological diversity independent of the types of samples profiled and the technologies used.
To validate this high reproducibility, the same analyses were applied to a PDAC cohort consisting of 60 RNAseq profiles from RNA obtained by rinsing EUS-FNA diagnostic biopsies. The three versions of the PAMG gave highly similar results on FNA-derived samples (R>0. 97). Overall, these results show that the three versions of the PAMG are essentially identical biological signatures despite the broad differences between the samples in each dataset (e. g. primary tumour with typical stromal content versus PDX).
The pamg is associated to tumour aggressiveness Several studies using only resectable tumours show molecular diversity of the epithelial compartment of PDAC is associated with tumour aggressiveness and patient prognosis  10, 11, 16. Our next goal was to determine if the PAMG could be predictive in primary PDAC tumours by analysing large series of patients with thorough clinical data and follow-up. To assess the prognostic value of the PAMG, association with overall survival was first evaluated on the ICGC series  16  which consisted of 267 resected patients with follow-up, and 230 samples with histological characterization. The continuous value of the PAMG (as extracted from the ICGC transcriptome dataset) was strongly associated to patient's overall survival (univariate Hazard Ratio: uHR=0.
This approach extracted a specific RNA signature robustly identified in PDX and human primary tumours with a minor effect of tissue preservation (FFPE vs. frozen), RNA profiling platform (microarrays or RNAseq) or of the algorithm's parameter (the total number of extracted components). The use of a deconvolution approach to define a gene signature is especially appropriate for highly heterogeneous and stromal tumours as it is able to extract signatures independently of other biological signals (e. g. stroma, normal tissue). This RNA signature, termed PAMG, provides a score measuring the molecular level of differentiation of a given sample derived from a whole-transcriptome profile. We show the robustness of our approach to derive an RNA-phenotyping signature by consistently identifying the same signature from multiple independent dataset, all providing highly similar results (average correlation of >97%).
The PAMG introduces a simple framework, based on a simple RNA signature compatible with all previously proposed PDAC classifications. The genes previously described as defining PDAC subtypes were in fact better explained by the PAMG than by the two-class classifications themselves. Molecular classifications of PDAC and, in particular, the basal-like/classical dichotomy, are a major prognostic factor in most datasets and are typically shown to correlate response to FOLFIRINOX. Our results showed the PAMG holds superior clinical value that could be ascertained prior to entering any curative protocols, using any current diagnostic material including EUS-guided biopsy needle flushing. The PAMG can be applied to a single PDAC sample, resulting in a score which is an estimation of the overall survival, with a low PAMG corresponding to poor outcome (high relative risk) and a high PAMG corresponding to an improved outcome (low relative risk).
The Action Database (ADB) was developed during the ESPREssO project (Enhancing Synergies for Disaster Prevention in the European Union) in order to store and analyze relevant ideas emerging during the project to deal with the challenges. It provides the opportunity to formalize discussions and to store their content in a synthetic format, as well as to collect experiences and evaluate the impacts they had at their respective scales of implementation and on different parameters. The major aim of the ESPREssO-ADB tool, and its main innovation, is to deal with multi-hazard and multi-challenge actions, in an international context and notably in a cross-border initiative. The ESPREssO project aimed at contributing to a new strategic vision on Disaster Risk Reduction (DRR) and Climate Change Adaptation (CCA) in Europe.
Introduction Global change is modifying risk profiles in Europe and all over the world. Exposure is increasing due to population growth and urbanization, whereas climate change affects some hazards' occurrences and intensities  [1]. The ESPREssO project (Enhancing Synergies for disaster PRevention in the EurOpean Union) is a Coordination and Support Action that was funded by DG RESEARCH under the H2020 Programme. It addresses the European Union's Horizon H2020 topic (DRS-10-2015) on "Disaster Resilience & Climate Change" regarding topic 2 for "Natural Hazards: Toward risk reduction science and innovation plans at national and European level". ESPREssO aims at contributing to a new strategic vision on disaster risk reduction (DRR) and climate change adaptation (CCA) in Europe. The project focusses on the policy sphere and barriers identified as three main challenges: 1 -Integration of CCA and DRR to foster resilience 2 -Integration of Science and Legal/Policy issues in DRR and CCA 3 -Improvement of national regulations to prepare for trans-boundary crises.
To overcome these three challenges, the ESPREssO project looked at the needs and opinions of stakeholders working with either CCA or DRR (or both) in Europe, in order to identify measures boosting adaptation or societies' resilience. To gather such information, the ESPREssO project has organized one Stakeholder Forum, at the beginning of the project and as basis structure for the application of a bottom-up approach, and three Think Tanks events (each one focusing on one of the ESPREssO challenges) during which three board games (see Ref. [2]  Fleming et al. in this issue  [3] ,  Booth et al. in  this issue  [4] ,  Abad et al. in  this issue  [5] ,  Schueller et al. in  this issue) where played. In order to gather, store and then analyze the proposed measures raised during these events a database of actions, or Action Database (ADB), has thus been developed during the project.
It is obvious that many databases exist, worldwide and in Europe, and several of them are fully accessible online on web portals (i. e. , US Geological Survey database, NOAA/WDS Global Historical Tsunami Database,  [6] ; etc. ). They are generally different in terms of content (events, losses, damages, risk mapping, solutions, etc. ), scales (from local to world) and shapes (presented as a catalog or inviting people to contribute). It should be noted that many of these databases concern either a single country or a single topic or issue. Their formats are conditioned by the typology of projects' activity, most of the time focused on participatory sciences and involving scientific, educational and general public spheres. Databases gathering data on disaster events and induced losses are numerous, at national or regional levels and for specific or multiple hazards.
There are few multi-hazards and international level databases, e. g. , the International Disaster Database (https://www. emdat. be/; [7, 8]. Some of them are expected to be sustainable, continuous, credible, publicly accessible, and in the same time quality assured. They can be either government-hosted or non-government hosted  [9]. Most of them are often (in whole or in part) funded by major international funders, such as the World Bank or the United Nations Development Program (https://www. adaptation-undp. org/action-database), or major reinsurance companies such as Swiss Re (Sigma Explorer 1 ) and Munich Re (NatCatSERVICE 2 ). Similarly, global partnerships (such as those managed by the World bank), could lead to proactive disaster risk management (DRM) and investing to ensure DRM continuity by building technical capacity at a federal, state, or municipal levels.
For example, the grant-funding mechanism of the Global Facility for Disaster Reduction and Recovery (GFDRR) supports worldwide disaster risk management projects and contributes to the implementation of the Sendai Framework for Disaster Risk Reduction (DRR) by helping countries to integrate DRR and CCA into development strategies and investment programs and recover from disasters more quickly and effectively. From climate change adaptation perspective, databases are focused on proposing solutions (e. g. , http://www. climateapp. nl/, https://platfo rm. think-nature. eu/, etc. ). As synthesized by Sanderson et al. [10]  data could be shared globally via specific web portals  [11]  dedicated to sharing and linking knowledge and facilitating adaptation measures. In 2015, the European Environment Agency report  [12]  provide an extensive overview of European adaptation portals as well.
Because all these databases and web-portals contain different levels of information, stored in many heterogeneous formats, a need was recognized for a system that could store, filter, evaluate the ideas and measures proposed by stakeholders during the ESPREssO project. The ESPREssO-ADB is a new tool that aims at collecting and evaluating the feedback from stakeholders in disaster risk reduction, climate change adaptation and cross border crisis management. It provides the opportunity to formalize discussions during workshops and to store their content in a synthetic format, as well as to collect experiences and evaluate the impacts they had at their respective scales of implementation and on different parameters. The major aim of the ESPREssO-ADB tool, and also its main innovation, is to deal with multihazard and multi-challenge actions, in an international context and notably in a cross-border European initiative.