dataesr/scientific-paragraphs-categorization huggingface dataset:
seismo. ethz. ch;  Michel et al. 2014; Poggi et al. 2017) , Italy with the Italian Accelerometric Archive (ITACA, http:// itaca. mi. ingv. it; D'amico et al. 2020), Turkey with the Turkish Accelerometric Database (http:// kyhda ta. deprem. gov. tr). At European level, some national seismological networks make use of European web portals to disseminate data and information on seismic stations, namely the Earthquake Strong Motion database (ESM, https:// esm-db. eu/;  Luzi et al. 2020; Lanzano et al. 2019) , the ORFEUS station book (http:// orfeus-eu. org/ stati onbook/), the European Geotechnical Database (EGD, http:// egd-epos. civil. auth. gr/). Nowadays, the most complete European database is ESM which exposes site condition information at 2071 strong motion sites, among which EC8 ground type is available at 1455 stations (about 70% of the number of stations).
2 of WP7-NA5 "Networking databases of site and station characterization" http:// www. sera-eu. org/ en/ activ ities/ netwo rking/; Di  Giulio et al. 2019 ). At international level, the USA based COSMOS Consortium (https:// stron gmoti on. org/ Proje cts/ Chara cteri zatio nGuid elines/) shares similar goals on the strong-motion data dissemination and on the definition of standard procedure for site characterization and reporting. Because of the combination of this concern and of various recent studies and papers, discussing the limitations of V S30 taken as a single indicator and proposing other proxies  (Trifunac 2016; Boudghene-Stambouli et al. 2017; Derras et al. 2017; Zhu et al. 2020; Bergamo et al. 2021; Felicetta et al. 2021) , we thought it timely to question the scientific and engineering community about the optimal site proxies to be used in the future for improved ground motion predictions.
The international questionnaire Despite some sparse efforts, there is not a customized site characterization procedure among the networks operators, and the site metadata of the permanent networks appear highly heterogeneous, if not completely absent. The identification of a set of indicators, able to catch the main site effects at a seismic station, represents the first step towards standardized site information to be included in seismic databases. This goal has been pursued by involving a wide scientific community dealing with site effects, from both seismological and earthquake engineering viewpoints. We first collected the existing bibliography on site effect estimation and related methodologies of data analysis, through a preliminary survey among research experts in this field. We involved both partners of the SERA project (ISTERRE-CNRS, France; ETH, Switzerland; INGV, Italy; AUTH, Greece) and a few other expert groups dealing with site characterization (Caltech-USGS, USA; AFAD, Turkey; Virginia Tech, USA; GFZ, Germany; ITSAK, Greece; University of Potsdam, Germany; UoT-University of Texas, USA).
We collected answers from a large number of experts in different fields (from geotechnical engineering to seismic risk) and from many countries within Europe and worldwide. Their analysis led to rank the site indicators according to various criteria, and to propose a limited set of recommended ones for site characterization at seismic stations. Finally, the proposed indicators have been adjusted following the feedback from an international workshop where we shared the project's results  (Cultrera et al. 2019). In summary, for each indicator of Table  2  we asked for: (1) the preferred method of estimation; (2) the difficulty level for obtaining it, considering both data acquisition and analysis (so called "Feasibility index", that can be "easy", "intermediate" or "difficult"); (3) the approximate cost range for deriving it, including again both data acquisition and processing; (4) free comments.
Finally, we asked each participant to rank the indicators according to a 3-degrees priority scale, i. e. whether she/he thinks it is a "mandatory", "recommended" or "optional" indicator to be included in site characterization databases. Figure  1  shows the screenshots of the online questionnaire for two indicators: the fundamental resonance frequency (f 0 ) and the shear-wave velocity of the seismic bedrock (V s_seis_bed ). In the f 0 case, the proposed data acquisition and processing options are noise (i. e. ambient vibrations), earthquake, modelling or unknown procedure, whereas for the V s_seis_bed the choice was limited to non-invasive (e. g. surface, passive or active, seismic methods) or invasive (e. g. seismic down-hole) methods. In addition, we asked for a preferred definition of the seismic bedrock, because some comments received from the preliminary survey pointed out that the definition of this indicator is not unique (see definition in Table  2 ).
Most recommended indicators First of all, we ranked the indicators according to the degree of importance for site characterization at seismic stations, as assigned by each respondent (Fig. 3 ). Almost all of them are considered useful (i. e. , at least "optional") for a reliable site characterization, whereas only few are given the highest priority ("mandatory") to be reported in site characterization databases by more than 50% of all respondents: f 0 (89%), V S (72%), V S30 (63%), Surface geology (61%), Depth of seismic bedrock H seis_bed (58%), Soil class (56%), Depth of engineering bedrock H eng_bed (55%). We thus decided to focus on these 7 consensual indicators-that we refer as to the "most recommended indicators" in the following-, to be used for the metadata of seismic stations. Nevertheless, in order to better understand what are the key aspects that drive, or not, the survey results (for instance, physical relevance, practical availability, measurement reliability, etc.
Summary report In the previous section we defined the most recommended indicators of the site behavior at the seismic station. However, their values alone are not enough to assess the reliability and the associated uncertainty of the single indicator and, more in general, of the site characterization for the station site as a whole. The background information about data acquisition and processing used to infer the site indicators are then necessary to evaluate an overall quality index for the station site, increasing the quality of seismological data (see the companion paper, Di  Giulio et al. 2021 ). (3) DI tot = m ∑ j=1 DI j * w j (4) CI tot = m ∑ j=1 CI j * w j Some seismic networks databases expose site condition characteristics with very detailed information and reports. In general, the reports can be very detailed when dedicated measurements at the site are carried out.
For instance, the site characterization metadata of the accelerometric stations in the Italian Accelerometric Archive (ITACA; http:// itaca. mi. ingv. it) are stored in three main thematic levels (topographic features, geological features and geophysical measurements), including seismic signal analysis and seismic classification according to Italian and European seismic codes  (Felicetta et al. 2017 ): out of 743 stations in ITACA database, less than 30% of them have also an exhaustive report on V S profile and 20% have a detailed geological survey. Another example is the Site Characterization Database for seismic stations of the Swiss Seismological Service (Swiss Seismological Service (SED) at  ETH Zurich, 2015) , that collects very detailed information and reports on seismic survey and V S analysis, together with topographical map, geological map, housing and current instrumentation  (Michel et al.
Discussion and conclusion This paper illustrates part of the activities carried out within the SERA EU project (WP 7-NA5 "Networking databases of site and station characterization"; http:// www. sera-eu. org/ en/ activ ities/ netwo rking), with the aim of proposing a common, reliable and efficient framework for site characterization at European seismic stations. To achieve this goal, we identified the indicators that are used in site characterization analysis (Table  2 ) and organized an online survey to get an informed feedback about their usefulness, including cost and difficulty of their measurement (Online Resource 1). We collected the respondent answers to propose a list of the most recommended ones: seven indicators (f 0 , V S , V S30 , H seis_bed , H eng_bed , surface geology and soil class) are considered as "mandatory" by a majority (> 50%) of respondents, and are found to represent a good compromise between physical relevance, practical usefulness and availability for the metadata of seismic stations.
It finally allows to homogenize information for high-quality metadata of seismic station recordings. It is a first attempt that can help not only to increase the awareness about the need for higher quality site characterization, but also to shape the evolution of the structure of site metadata in strong and weak ground motion databases. The list of basic indicators can be modified according to the outcomes of new studies for evaluating other proxies for site effect evaluation, and/or their mutual correlation and their correlation with site amplification, together with a broader discussion into an enlarged scientific community involved in seismic networks and site characterization. We hope however that the outcomes of the present survey already constitute a significant step forward in merging both the "state-ofknowledge" (what is available) and the desirable evolution of "state-of-practice" (what is needed).
Acknowledgements The authors are grateful to the colleagues that answered to the surveys and to all the participants to the  SERA L'Aquila  workshop in March 2019 (https:// sites. google. com/ view/ site-chara cteri zation-works hop/ home; Cultrera et al. 2019). We also acknowledge  Donat Fah ,  Paolo Bergamo  and  Alan Yong  for useful discussions, and the Editor and the two anonymous reviewers that largely improved the quality of the work.
Study design and participants This is a prospective, multicentre, observational cohort of patients with S. aureus community-acquired pneumonia (CAP) requiring intensive care. All French adult and paediatric intensive care units (ICU) were contacted via the main learned medical societies (Société de Réanimation de Langue Française: ∼200 adult ICU; Groupe Francophone de Réanimation et d'Urgence Pédiatrique of the Société Française de Pédiatrie: 41 paediatric ICU) and invited to participate. Patients with S. aureus CAP (see criteria in supplementary methods) and admitted to a participating ICU were included between January 2011 and December 2016. Exclusion criteria were HIV-positive status, hospital-acquired pneumonia or admission to hospital in the past 3 months. Clinical, laboratory and therapeutic data were collected prospectively at admission to ICU and on day 1, 3 and 7.
Given the large number of determinants, we used sparse regression (least absolute shrinkage and selection operator (LASSO)) as implemented in the glmnet R package, to identify the determinants retained in sparse models  [19]. Briefly, LASSO models are built sequentially by modifying a L1 penalisation constraint such that more penalised models include fewer predictors. The best-fitting models (using death, rash, haemoptysis or leukopenia as the response variable) were selected using leave-one-out cross-validation. Analyses were conducted using R software v3. 6. 0 (www. R-project. org/). Anonymised data and software code required to reproduce the results are available at github. com/rasigadelab/severecap.
Interested researchers may access the data sets by contacting Clinical Data Manager Aurelie Ponz (aurelie. ponz@univ-amu. fr) at the Institut de Neurosciences des Systèmes, Aix-Marseille Universite. The code is available at https://github. com/ins-amu/ddmisp. dynamics. Indeed, most of the model inversion studies related to epileptic seizures so far dealt with a single neural mass, small networks of neural masses, or uncoupled neural masses  [26] [27] [28] [29] [30] [31] [32]. Only recently have studies on large-scale network model inversion appeared  [33] , however, inversion of large systems of coupled differential equations can still be prohibitively computationally expensive and requires a careful balance of the model parameterization, choice of the prior distributions of the parameters, and settings of the inversion method.
Onset time detection and mapping First step in applying the model to real patient data is the detection of the onset times of seizure activity in the recorded SEEG signals  (Fig 3A)  and the subsequent mapping of the channel onset times to the brain regions defined by the brain parcellation (Fig  3B ). In our cohort of 50 patients, 45 of them had at least one seizure satisfying the inclusion criteria (total 141 seizures) to which the onset detection was applied. Among them, there was between ten to thirty-five regions observed (i. e. with at least one assigned channel) out of 162 regions in the brain parcellation (Fig 3C ); these observed regions have predominantly less than four channels assigned (Fig  3D ). The channel to region mapping may lead to one region having multiple channels assigned with different seizing/non-seizing status or different onset times.
Patients and data acquisition Fifty epileptic patients who underwent standard clinical evaluation for surgery candidates at La Timone hospital in Marseille were selected for the inclusion in this study. Details of the subjects are given in Table  3  in S1 Text. The evaluation included non-invasive T1-weighted imaging (MPRAGE sequence, either with repetition time = 1. 9 s and echo time = 2. 19 ms or repetition time = 2. 3 s and echo time = 2. 98 ms, voxel size 1. 0 x 1. 0 x 1. 0 mm) and diffusion MRI images (DTI-MR sequence, either with angular gradient set of 64 directions, repetition time = 10. 7 s, echo time = 95 ms, voxel size 1. 95 x 1. 95 x 2. 0 mm, b-weighting of 1000 s mm -2 , or with angular gradient set of 200 directions, repetition time = 3 s, echo time = 88 ms, voxel size 2. 0 x 2.
Structural model of the brain The structural connectome was built with a reconstruction pipeline using generally available neuroimaging software. The current version of the pipeline evolved from a previously described version  [68]. First, the command recon-all from FreeSurfer package  [69]  in version v6. 0. 0 was used to reconstruct and parcellate the brain anatomy from T1-weighted images. For reasons of conformity with established implantation and interpretation practices at the epileptology department of AP-HM, we employed a custom brain parcellation in this study  [70]. This custom parcellation is based on the default FreeSurfer segmentation of the brain tissue  [71]  with the Destrieux cortical parcellation  [72] , where some regions are merged together, some are split into multiple parts, and yet others are split and then merged with existing ones.
The full list of the performed operations is given in Table  1  in S1 Text. The resulting parcellation contains 162 regions with 72 cortical and 9 subcortical regions per each hemisphere (Table  2  in S1 Text). Due to the splitting of the largest cortical regions from the Destrieux atlas, the resulting parcellation contains cortical regions with sizes on the same order of magnitude  [70]. While the method can be applied to parcels of uneven size, vast differences in sizes could lead at the same time to high computational costs (spent on the small regions) and low precision (limited by the large regions). Next, the T1-weighted images were coregistered with the diffusion weighted images by the linear registration tool flirt  [73]  from FSL package in version 6. 0 using the mutual information cost function with 12 degrees of freedom.
The MRtrix package in version 0. 3. 15 was then used for the tractography. The fibre orientation distributions were estimated from DWI using spherical deconvolution  [74]  by the dwi2fod tool with the response function estimated by the dwi2response tool using the tournier algorithm  [75]. Next we used the tckgen tool, employing the probabilistic tractography algorithm iFOD2  [76] , to generate 15 millions fiber tracts. The connectome matrix was then built by the tck2connectome tool. Each element ŵij of the generated connectome matrix Ŵ represents the number of fibers from region j to region i; note that we did not use the log-count of the fibers. The elements of the matrix were then scaled by the volumes of the target regions, wij ¼ ŵij =V i , where V i is the volume of i-th region. This way, the elements wij represent the density of the fibers projecting from region j in region i.
Resection masks and surgery outcome For the patients who underwent resective surgery, the extent of surgical resection with respect to the anatomical parcellation according to the in-house custom atlas was defined using the EpiTools software suite  [77]. In brief, patient's specific 3D maps with SEEG electrodes were created and all the contacts were assigned to the anatomical regions of the atlas projected in the pre-operative MRI space. The co-registration of the post-implantation CT scan with electrodes with the post-operative MRI scan, and that of the post-and pre-operative MRI scans were then performed. The resected contacts were identified and assigned to the respective regions as defined by the pre-operative MRI. The regions resected but not explored by depth electrodes were then identified on the pre-operative/post-operative MRI co-registered images manually (SMV, JS).
Validation on real data The leave-one-out cross-validation and the validation against the resection masks consisted of the following steps: First, we detected the onset times in all recorded seizures longer than 30 seconds, and excluded the seizures where no seizing region was detected. Next, the 44 subjects for which at least one seizure was available were divided in two folds (each with 22 subjects) and for both of these two folds the multi-seizure model was fitted independently of the other. We included at most two seizures from a single patient in the learning data set. We opted for two seizures as an imperfect compromise between the need for good generalization of the trained model (to which including large amount of possibly similar seizures from a single patient would be detrimental) and the wish to include as much data as possible.
The inferred states and onset times of the excluded regions were then compared to the left-out observations and evaluated using the measures described below. The hyperparameter values used for the single-seizure fitting in one fold were those obtained from the other fold, thus preventing reusing the same data for the model training and validation. To get the excitabilities used in the validation against the resection and surgery outcome, all seizures from both folds were fitted using the single-seizure model once more, this time with no observations left out. Again, the hyperparameter values c � Normalð0; 1Þ t ¼ P W;q ðcÞ tseizing � Normalðminðt seizing ; t lim Þ; s t Þ t lim � Normalðminðt nonseizing ; t lim Þ; s t Þ were those obtained from the other fold. The entire computational pipeline was constructed and run using the Snakemake workflow manager  [83].
This work was funded by the  French National Research Agency (ANR)  as part of the second " Investissements d'Avenir" program ,  ANR-17-RHUS-0004 , EPINOV (https://anr. fr)to VJ, FB, and MG, by  European Union 's  Horizon 2020 Framework Programme for Research and Innovation  under the  Specific Grant  Agreement No. 785907  and  945539 ,  Human Brain  Project  SGA2  and  SGA3  (https://ec. europa. eu/programmes/horizon2020) to VJ, by  European Union 's  Horizon 2020 Framework Programme for Research and Innovation  under the  Specific Grant Agreement  and No. 826421 , VirtualBrainCloud (https://ec. europa. eu/programmes/horizon2020) to VJ, and by  SATT Sud-Est ,  827-SA-16-UAM  (https://www. sattse. com) to VJ, FB, and  MG. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Analysis of Hi-C data The experimental Hi-C data sets were downloaded from the NCBI Sequence Read Archive (SRA; https://www. ncbi. nlm. nih. gov/sra) (Table  1 ) using "fastq-dump" (https:// github. com/ncbi/sra-tools/ wiki). Each experiment has been processed (i. e. , FASTQ quality checks, Mapping to reference genomes, Filtering to remove noninformatic reads, and Merging data together) through the TADbit pipeline (https://github. com/3DGenomes/TADbit) (Serra et al. 2017). Then, we normalized data using the "Vanilla" method at 10 kbp resolution  (Lieberman-Aiden et al. 2009). After generating the Hi-C map, we used the "IC-Finder" tool to find TAD boundaries  (Haddad et al. 2017 ). The A/B compartments have been identi-fied by the "hic_data. find_compartments" tool from the TADbit suite, using Principal Component Analysis (PCA) of the observed/expected matrices.
Fitting of the diffusion exponent and constant To extract the diffusion constant D i,c and exponent α i,c for each trajectory c and each monomer i, we fitted the time-averaged meansquared displacement MSD i,c by a power-law D i,c Dt ai,c using the MATLAB function polyfit(LogMSD i,c ,LogΔt,1) over more than three decades. The unit of D i,c depends on α i,c and is mm 2 /sec ai,c (Supplemental Figs. S2, S3 ). For the ensemble-averaged meansquared displacement MSD i , we assumed that the diffusion exponent is a function of the time lag Δt: MSD i (Dt) = D i Dt ai (Dt) or, in logarithmic scale log MSD i = log D i + α i logΔt. α i (Δt) is then given by the local slope of the log-log MSD curves: Because a i (Dt 0) 0. 5, the unit of D i is μm 2 /sec 0. 5.
Rowley et al. (2019)in this study Drosophila melanogaster Kc167 were published inBonev et al. (2017)andRowley et al. (2019), respectively. Data set, NCBI Gene Expression Omnibus (GEO; https://www. ncbi. nlm. nih. gov/geo/) accession number, organism, cell type, restriction enzyme, number of independent runs, and reference genome to map data are given.
Acknowledgments We thank  Cédric Vaillant ,  Geneviève Fourel ,  Haitham Shaban ,  Kerstin Bystricky , and the members of the  Jost laboratory  for fruitful discussions. D. J. acknowledges  Agence Nationale de la Recherche  ( ANR-18-CE12-0006-03 ,  ANR-18-CE45-0022-01 ) for funding. We thank the  Pôle Scientifique de Modélisation Numérique (PSMN)  of the  ENS de Lyon  for computing resources. M. D. S. acknowledges a  Short Term Scientific Mission Grant  from  COST Action   CA17139  supported by  European Cooperation in Science and Technology (COST).
Region CS38-40 is a sub-TAD boundary of the HoxD locus. Through series of deletions and inversions in vivo, we recently showed that region CS38-40, located within the large T-DOM TAD flanking the HoxD cluster, was capable to act as a topological border  (18). We further evaluated the properties of this region by generating Hi-C datasets using mouse limb cells at embryonic day 12. 5 (E12. ). Because transgenes often integrate in multiple copies  (30) , we used a loxP/Cre system (31) to try and reduce the copy number to one and obtained a stable transgenic mouse line termed TgN  (38) (39) (40).
TLA TLA was performed as in  (32)  with the following adaptations. Limb cells were dissociated using collagenase type XI (Sigma-Aldrich, C7657) and the cell suspension was strained. Transgene-positive (TgN(38-40)/-) samples were identified by PCR and two E12. 5 brains were used as starting material. TLA inverse PCR was performed using the viewpointdirected inverse PCR primers listed in S2 Table. TLA library preparation was achieved using the Nextera DNA Flex Library Prep (Illumina) protocol, independently for each viewpoint. Libraries were sequenced as 100 bp single-end reads with an Illumina HiSeq 4000. TLA data analysis was performed using the custom pipelines described hereafter. In brief, all alignments were produced using Bowtie 2 (version 2. 3. 5) (45) and were sorted with SAMtools (version 1.