dataesr/scientific-paragraphs-categorization huggingface dataset:
When the number of probes in the first group is too large, the analysis becomes slow because the program needs to re-estimate the variance components whenever one or more probes are removed from the first group (to avoid proximity contamination)  [24]. This may also cause convergence problem because of too much variation explained by the first randomeffect component. However, OSCA implements a version of MOMENT where an additional stepwise selection procedure to reduce the number of probes in the first group. Simulation shows that this method has approximately the same level of false positive rate, but slightly higher power compared to the initial MOMENT implementation (https:// cnsgenomics. com/software/osca/#EWAS). We thus performed MWAS for the AUS, NL, KCL, SGPD, PEG, AIBL, SCZ1, SCZ2, and rheumatoid arthritis cohorts separately and for consistency always used the --moment2-beta function for MOMENT analyses, as implemented in the OSCA software.
Meta-analyses of MOMENT MWAS within and between neurodegenerative disorders to identify DMPs We conducted inverse-variance weighted meta-analyses using the MOMENT results from each individual cohort, using METAL  [91]. We only kept probes in common between all datasets in the analyses. We performed meta-analyses within ALS (AUS, NL, and KCL cohorts, N cases = 3035, N controls = 1524), within PD (SGPD and PEG, N cases = 1133, N controls = 998), and between neurodegenerative disorders (ALS, PD, and AD, N cases = 4329, N controls = 2993). We conducted meta-analyses to identify shared DMPs using MOMENT results, since this method as shown to be more robust to (un)observed confounders  [24].
Meta-analyses of publicly available meta-GWAS summary statistics between neurodegenerative disorders To assess the potential overlap of the MWAS meta-analyses results with methylation quantitative trait loci (mQTL) and GWAS SNPs, we first performed a meta-analysis of three publicly available meta-GWAS summary statistics, for AD (N = 368,440)  [38] , ALS (N = 80,610)  [39] , and PD (minimum N = 520, maximum N = 482,730, excluding 23andMe SNPs)  [40]. We conducted sample-size weighted meta-analyses, using META L  [91] , for all SNPs in common between datasets. Sex-linked SNPs, SNPs with minor allele frequency (MAF) < 0. 01, and SNPs with incongruent MAF or base pair position between datasets were excluded.
Availability of data and materials All code used for analyses and generate figures in this manuscript can be found at https://github. com/m-nabais/202 001_cross_disorder_DNA methylation and Zenodo  [94]. The DNA methylation data sets are available as follows: • Australian ALS cohort: The DNA methylation data have been submitted to dbGAP under accession number: phs002068. v1. p1, excluding the OATS samples (N = 84)  [20]. For OATS samples, data are available to researchers by request to the OATS Governance Committee. • SGDP Parkinson's cohort: The DNA methylation data are publicly available at GEO under accession number GSE145361  [95]. • PEG Parkinson's cohort: The DNA methylation data are publicly available at GEO under accession number GSE111629  [96]. • KCL ALS cohort: The DNA methylation data have been submitted to EGA under accession number: EGAC00001000703  [97].
• Netherlands ALS cohort: The DNA methylation data have been submitted to EGA under accession number: EGAC00001000703  [97]. • AIBL cohort: The DNA methylation data are publicly available at GEO under accession number GSE153712  [98]. • ADNI cohort: The DNA methylation data are available to researchers by request as outlined in the ADNI access policy  [73]. • AddNeuroMed cohort: The DNA methylation data are publicly available at GEO under accession number GSE144858  [99]. • UCL schizophrenia cohort: The DNA methylation data are publicly available at GEO under accession number GSE80417  [100]. • Aberdeen schizophrenia cohort: The DNA methylation data are publicly available at GEO under accession number GSE84727  [101]. • Epidemiological Investigation of Rheumatoid Arthritis (EIRA): The DNA methylation data are publicly available at GEO under accession number GSE42861  [102].
looking for overlap with GWAS signals, we analyzed summary statistics provided by the International Genomics of Alzheimer's Project (IGAP), in addition to the summary statistics from the GWAS above. IGAP is a large three-stage study based upon GWAS on individuals of European ancestry. In stage 1, IGAP used genotyped and imputed data on 11,480,632 single nucleotide polymorphisms (SNPs) to meta-analyze GWAS datasets consisting of 21,982 Alzheimer's disease cases and 41,944 cognitively normal controls from four consortia: The Alzheimer Disease Genetics Consortium (ADGC); The European Alzheimer's disease Initiative (EADI); The Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium (CHARGE); and The Genetic and Environmental Risk in AD Consortium Genetic and Environmental Risk in AD/Defining Genetic, Polygenic and Environmental Risk for Alzheimer's Disease Consortium (GERAD/PERADES).
2. 1335, and OSCA version 0. 45. and the Mater Foundation. Support also came from ForeFront, a large collaborative research group dedicated to the study of neurodegenerative diseases and funded by the NHMRC (Program Grant 1132524, Dementia Research Team Grant 1095127, NeuroSleep Centre of Research Excellence 1060992) and ARC (Centre of Excellence in Cognition and its Disorders Memory Program CE10001021). The Queensland Parkinson's Project (QPP) was supported by a grant from the Australian National Health and Medical Research Council (1084560) to GWM. The New Zealand Brain Research Institute (NZBRI) cohort was funded by a University of Otago Research Grant, together with financial support from the Jim and Mary Carney Charitable Trust (Whangarei, New Zealand). We thank Allison Miller for processing and handling of NZBRI samples.
AddNeuroMed cohort: The AddNeuroMed data are from a public-private partnership supported by EFPIA companies and SMEs as part of InnoMed (Innovative Medicines in Europe), an Integrated Project funded by the European Union of the Sixth Framework program priority FP6 2004-LIFESCIHEALTH-5. Clinical leads responsible for data collection are Iwona Kłoszewska (Lodz), Simon Lovestone (London), Patrizia Mecocci (Perugia), Hilkka Soininen (Kuopio), Magda Tsolaki (Thessaloniki), and Bruno Vellas (Toulouse) and imaging leads are Andy Simmons (London), Lars-Olad Wahlund (Stockholm) and Christian Spenger (Zurich) and bioinformatics leads are Richard Dobson (London) and Stephen Newhouse (London). ADNI cohort: Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012).
Acknowledgements We would like to acknowledge the people who have directly contributed their data, for their participation in this project. We would like to thank the clinical nurses and lab personnel that directly handle and process the data. MFN would like to acknowledge the  University of Queensland/University of Exeter (QUEX)  PhD studentship. AJS and RFH are supported by funding from the  Wellcome Trust 4 -year PhD in Translational Neuroscience-training the next generation of basic neuroscientists to embrace clinical research [ 108890/Z/15/Z ]. REM is supported by  Alzheimer's Research UK  major project grant  ARUK-PG2017B-10. SJGL was supported by an  NHMRC-ARC   Dementia Fellowship  ( 1110414 ) and GH was supported by an  NHMRC  Fellowship (1079679). We thank the  International Genomics of Alzheimer's Project (IGAP)  for providing summary results data for these analyses.
The investigators within IGAP contributed to the design and implementation of IGAP and/or provided data but did not participate in analysis or writing of this report. IGAP was made possible by the generous participation of the control subjects, the patients, and their families. The i-Select chips were funded by the  French National Foundation on Alzheimer 's disease and related disorders. EADI was supported by the  LABEX (laboratory of excellence program  investment for the future) DISTALZ grant,  Inserm ,  Institut Pasteur de Lille, Université de Lille 2 , and the  Lille University Hospital. GERAD/PERADES was supported by the  Medical Research Council  (grant no. 503480 ),  Alzheimer's Research UK  (grant no. 503176 ), the  Wellcome Trust  (grant no. 082604/2/07/Z ), and  German Federal Ministry of Education and Research (BMBF) :  Competence Network Dementia (CND)  grant no.
We build a spatially-explicit IBM that integrates up-to-date information on lynx ecology while accounting for habitat preferences and the risk of collisions with vehicles. We implement our model in the R programming language and share it publicly with open-source code allowing full reproducibility and its adoption by ecologists and conservationists. We use this model to forecast the fate of lynx populations over a 50-year time horizon. Finally, we provide several population metrics that help in diagnosing the long-term species persistence in its most western range area.
After the complete decline of the Vosges-Palatinian population in the 18 th century, a reintroduction of 21 lynx occurred in the southern part of the Vosges Mountains (France) between 1983 and 1993  (Vandel et al. , 2006). Only 10 individuals contributed to the lynx local establishment  (Vandel et al. , 2006)  without conclusive stabilization  (Charbonnel & Germain, 2020). Since 2016, the lynx has been back in the Palatinate Forest (Germany) thanks to a new reintroduction program finalized in 2020 with a few individuals that already moved to the Vosges Mountains  (Scheid, Germain, & Schwoerer, 2021; Schwoerer, 2021). Finally, only a few male individuals have been observed in the Black Forest area since 2013, most of them coming from the Swiss Jura Mountains (Mitarbeitende der  Stiftung KORA, 2017; MLR, 2019; Wölfl et al.
Acknowledgements We thank all the volunteers from the " Réseau Loup-Lynx " who collected data on the field. SB and OG were funded by  French National Research Agency  ( ANR 16 CE02 0007 ). SB was funded as well by  OFB  and OG was funded by  CNRS  and the " Mission pour l'Interdisciplinarité" through the "Osez l'Interdisciplinarité" initiative. CEFE, Cerema and CROC were funded by  CIL&B, MTES (ITTECOP)  and  FRB  through the research program  ERC-Lynx. Cerema received support from the  " Direction générale des infrastructures de transports et de la mer  ( Ministère de la transition écologique ) ". CROC was funded in 2019/2020 by the  European Union  within the framework of the  Operational Program FEDER-FSE "Lorraine et Massif des Vosges 2014-2020 ", the  "Commissariat à l'Aménagement du Massif des Vosges" for the FNADT ("Fonds.
0125 and 0. 3 mg/mL. The sample was prepared by dissolving 10 mg of freeze-dried extract in 1 mL of ultrapure water. Then dilutions (10 to 20 times) were prepared with ultrapure water based on the TPC results obtained using the F-C method. The dilution was done to achieve a final CGA concentration < 0. 3 mg/mL. The samples were centrifuged at 15,000 × g for 10 min to remove the insoluble part of the extracts. The injection volume was 10 μL and the flow rate was maintained at 1. 0 mL/min. The peak areas of the standard solutions and the extracts were measured at a wavelength of 295 nm using a Chromeleon Chromatography Data system (Thermo Fisher). The results are expressed as mg CGA/g DSK on a dry basis and each sample was measured in duplicate.
Statistics analysis The statistics in this paper were analysed using SPSS software, version 25. 0 (IBM, Armonk, NY, USA). A univariate general linear model with the least significant difference test was carried out to investigate the significant differences with respect to Table  1  and Table 2 as   Correlation between the TPC and CGA content is shown in Fig. S1. A Pearson correlation factor was generated, and the significance differences were analysed at the P < 0. 01 level.
When designing a biological regulatory network, new information or wet experiments can require adding variables or interactions, inside a previously validated model. They can result in complete reconsiderations of established behaviours. Fortunately, formal methods allow for fully automated verification of properties, and TotemBioNet is an efficient software integrating a collection of formal approaches for regulatory networks. It allowed us to develop a multidisciplinary methodology for designing large dynamical models in an incremental way, including non regression proofs (preservation of important biological properties).
Selection Technics TotemBioNet inherits from SMBioNet  (Bernot et al. , 2004)  and combines two filtering approaches to identify the exhaustive set of models consistent with biological knowledge: "genetically modified Hoare logic" and temporal logic. Hoare Logic. Biological experiments prove that a set of traces must exist in the model. The genetically modified Hoare logic  (Bernot et al. , 2019; Folschette, 2019)  produces the constraint on parameters that characterizes the models in which these traces exist. We firstly transcript the observed experimental traces into a so-called Hoare triple (observed pre-condition, path and post-condition) and Hoare logic constructs the weakest pre-condition that must be fulfilled. This constraint throws away parameter values which do not enable the observed traces.
Modelling Context We proceed by successive small enrichments of the model under design. At each step, we start from a regulatory network model together with a set of parameter settings (non empty but of reasonable size) that is considered relevant w. r. t. biological knowledge and expertise. For the new enrichment, it is essential to keep track of the fundamental properties of the previously designed network models and to guarantee that they are preserved (possibly reformulated or enriched). Our TotemBioNet enrichment framework relies on a Modelling context, formed by: IG: An Influence Graph that describes the influence of each variable and multiplex in the system, BK: Some Biological Knowledge on the dynamics of the system, collected from biologists and literature, and given in natural language, PM: A Property Matrix that formalizes, (CTL or Hoare logic) the dynamics under different conditions.
When an enrichment is done, all the properties of the enriched version of a Validation PM must be satisfied. By analogy with software engineering, a Validation PM can be seen as a set of non regression tests, (2)Prediction Property Matrices (Fig. 3 -bottom) focus on fixed parameter settings. They are PM whose properties have been automatically generated and validated with TotemBioNet from simple property patterns such as oscillations, homeostasis, attraction basins and so on. There are as many prediction property matrices as parameter settings: a subset of selected prediction property matrices can then become the starting point of a new enrichment process. Fig. 3  shows a schematic vision of the usage of PMs along an enrichment of a model, where some variables have been added/removed. A Validation PM may be sparse, as the set of established biological properties is generally small.
CONCLUSION We defined an incremental methodology for developing large formal models of biological regulatory networks. Starting from an initial formal model, the enrichment is first described in natural language EBK, then the formalisation of this information leads to the EIG, EPS and validation EPM. Our process involves, at each round of this methodology, a manageable number of parameter settings. In practice, we observe that after a few enrichment steps, the ab initio design of the enriched model, without using the proposed methodology, would be impossible. The example outlined in this article (that contains notably more than 100 parameters) would lead to a number of parametrizations (1. 45 × 10 60 ) that is so huge that TotemBioNet or any similar platform would have taken more that 4. 6 × 10 50 years of computation to model check them, at the rate of 100 proofs per second! One of the key points of our incremental methodology consists in limiting, at each enrichment step, the number of parameter settings to enumerate, preserving a maximum number of known parameters from the previously validated model.
A common theme revolves around integrating the tools of a platform and its assets like data and data management. [14] Materials Acceleration Platforms  [3, 15]  integrate these elements that can lead to a greater pace in research. A foundational building block for the success of any MAP is therefore not "just" making data Findable, Accessible, Interoperable and Re-usable FAIR (Findable, Accessible, Interoperable and Reusable),  [16]  which is a challenge in its own right, but also making the tools transferable and externalizable to the battery community at large, for example, through the BIG-MAP app store (https://big-map. github. io/big-map-registry/) and GitHub repository (https:// github. com/BIG-MAP), in such a way that new data can be acquisition, generated and analyzed on-the-fly. [17] ATTERY 2030+ focuses on developing tools and methods for obtaining the necessary understanding of the spatiotemporal evolution of interfaces and interphases across relevant scales in the most cost-efficient manner.
This is done to enable the inverse design of superior battery materials and interfaces in particular, as it is a challenge that transcends all battery chemistries and concepts. In addition to a holistic and FAIR data infrastructure, adhering to specific ontologies such as BattINFO and data schema spanning all parts of the battery discovery value chain, two aspects are critical for the development of a fully autonomous and versatile battery MAP, that is, modularity and transferability to new battery chemistries and concepts (Figure  1 ). The tools developed should ultimately be fully integratable to facilitate experiments by autonomously launching simulations and simulations initiating and running experiments through the BIG-MAP orchestration infrastructure.
Transferability While specific approaches and methods are traditionally developed to solve a particular scientific challenge, say developing a neural network potential capable of describing long time-and length-scale dynamics of a given electrolyte formulation in the presence of positive (negative) electrode and under an applied bias, this tool in itself will not be transferable to other electrolyte compositions or electrode materials, but the workflows themselves needed to develop, train and test the potential utilizing the shared multi-sourced data will be made available to the community through the BIG-MAP app store and GitHub repository.
Modularity As new low-TRL battery chemistries and concepts are introduced and investigated, new and specialized modules may likely be required, such as specific synthesis techniques (e. g. , hydrothermal synthesis) or characterization modules (aqueous organic redox couples), but these modules should be easily exchangeable and integratable in the battery MAP and the underlying data infrastructure (Figure  1 ). By focusing on the key scientific and technological challenges in the five low-TRL battery chemistries concepts outlined above, we will illustrate how the AI-based toolkits currently under development in BATTERY 2030+ can be adapted and applied to these cases.
This leads to critical slowdowns when going from elemental materials to 3-4-5 different elements and effectively prevents the construction of good models with 7-8-9 elements, which are often needed for simulations of, for example, realistic battery electrode-electrolyte interface systems. There are efforts toward Figure  1. Data exchange within the BIG-MAP project highlighting the backbone for inverse materials design and AI accelerated materials discovery to be successfully deployed. Core research expertise on the one-off type of method deployments exists across different workgroups. Still, the interconnection through a shared ontology, compliance to standards, and operation on a commonly shared and interoperable data infrastructure are necessary for AI and machine learning methods to be deployed across BIG-MAP.
One class of such interface-invoked hurdles is long-range anisotropic electrostatic interactions, polarization, and charge transfer, which will occur at the electrode-electrolyte interface and between solid-solid interphases affecting not only reactions but also the atomic/ ionic structure and dynamics. In a longer-term perspective (see Roadmap paper by Edström et al. in this issue),  [21]  these effects need to be properly accounted for in the generation of the QM training data and the ML potentials. Furthermore, the different chemical bonding nature of the various compounds and molecules that meet, interact, and react at the interface makes the development of adequate QM methods non-trivial. It necessitates efficient benchmarking protocols, efforts that are ongoing within BIG-MAP. One of the most significant difficulties added when going from electrolyte modeling to battery interface modeling is the lack of solid knowledge of (most aspects) of the interface structure.
Generative Models and Inverse Design Deep generative models can learn the underlying probability distributions in the input space  [38]  and thus be used to encode physics-driven constraints in materials structures at the atomic  [39]  as well as the continuum scale  [40]  from large, validated datasets. Trained models can then be utilized to create new and novel structures while retaining plausibility as potential battery materials. For a detailed description of the current state of the art, please refer to SOA Fichtner et al. in this issue. [41] hile generative models can help create large structure libraries autonomously from smaller ones, screening processes to search for candidates that satisfy property requirements are needed during the design process. Alternatively, true inverse design can be achieved with conditional generative models that learn the probability distribution in structural space conditioned to the properties.
[42] Thus, structures can be probabilistically generated for target property value. With appropriate training structure-property datasets, conditional variational autoencoders  [43] [44] [45]  and conditional generative adversarial network  [46, 47]  can be trained for this purpose. Additionally, simulation environments that provide property estimations and structural validity can be used for training reinforcement learning (RL) agents for targeted structure generation. [48, 49] In principle, target-property conditioned structure generation can be done for multiple properties at once, allowing Pareto-style inverse design for multiple target properties. Nonetheless, it is very data-intensive to learn distributions across multiple properties unless the properties are inter-correlated. While generative models including conditional variants for molecules and bulk solids have seen initial success, batteries are interfacial systems incorporating numerous liquid and condensed matter phases.
The observed properties are controlled by structures at multiple length scales  [50, 51]  and their evolution over time. Thus, the inverse design of high-performance batteries needs a new class of generative models that are universal to solid/liquid and organic/inorganic bonds and phases and work with multiscale structural representations and properties. In BIG-MAP, we investigate a possible path forward using hierarchical VAEs  [52, 53]  that work with multiple latent spaces for multiple length scales conditioned to each other (Figure  3 ). [54] As it learns structural correlations across time and length scales from large datasets that describe battery electrochemical interfaces and properties, such a multiscale generative model can also perform scale bridging through oneshot generation of multiscale structures without the need for step-by-step multi-scaling.