dataesr/scientific-paragraphs-categorization huggingface dataset:
shinyapps. io/shiny-cov idpathways. Results of text mining were accessed by the curators in the form of molecular interactions with references to the articles and to sentences from which these interactions were derived. We systematically aligned the C19DMap with assembled INDRA Statements, both to enrich and to extend the map (see "Crosstalk analysis" below). The content of INDRA and AILANI COVID-19 was accessible via interfaces that allow users to provide natural language queries, such as "What are COVID-19 risk factors?" or "What are the interactors of ACE2?", facilitating extracting knowledge from the results of text mining workflows. The results of the INDRA workflow were visualised using the COVIDminer project (https://rupertoverall. net/ covidminer). Each extracted statement describes a directed interaction between two gene products, small molecules or biological processes.
Crosstalk analysis Crosstalk analysis was performed for the list of C19DMap diagrams (Table  EV1 ). The code is available at: https://gitlab. lcsb. uni. lu/ covid/models/-/tree/master/Resources/Crosstalks. Individual diagrams were accessed via the API of the MINERVA Platform, WikiPathway diagrams via the rWikipathways package (https://github. com/ wikipathways/rWikiPathways) and Reactome diagrams via the Reactome API. Text mining interactions are from the INDRA EMMAA Collection (https://emmaa. indra. bio/dashboard/covid19), dataset timestamp: 2020-12-01-21-05-54. Verified molecular interactions for quality control of the text mining data were obtained from Omni-Path using the OmnipathR package (https://github. com/saezlab/ OmnipathR). We filtered text mining interactions of the EMMAA dataset for "belief" of 0.
8 or higher and retained those matching the direction and interacting molecules to the OmniPath dataset. We call this filtered group of interactions "EMMAA-OP interactions". Crosstalk between C19DMap diagrams was calculated based on the HGNC identifiers of their elements. For simplification, all elements of the same diagram were considered to be interacting with each other. Three types of networks were constructed: existing crosstalk, new crosstalk and new regulators. Diagram groups followed the scheme in the list of C19DMap diagrams (Table  EV1 ). The networks were visualised using Cytoscape  (Shannon et al, 2003). The colour code is common for the networks: light green for nodes representing a diagram or a diagram group, light blue for nodes having one or two neighbours, yellow for nodes having three or four neighbours and red for nodes with five or more neighbours.
Diagram interoperability and translation for computational modelling Bidirectional translation of curated diagrams between CellDesigner and SBGNML formats is supported by the MINERVA Conversion API https://minerva. pages. uni. lu/doc/api/15. 1/converter/ (Hoksza et al, 2020) , and cd2sbgml converter https://github. com/sbgn/ cd2sbgnml  (Balaur et al, 2020). The MINERVA Conversion API supports bidirectional translation between CellDesigner, SBML and GPML. Unidirectional translation from Reactome format to GPML is supported by the Reactome-to-WikiPathways converter  (Bohler et al, 2016). Diagrams in the RNEF format of Elsevier Pathway Studio were translated to SBGNML using a dedicated rnef2sbgn software (https://github. com/golovatenkop/rnef2sbgn). The C19DMap diagrams (Table  EV1 ) in CellDesigner format were translated using CaSQ  (Aghamiri et al, 2020)  into executable Boolean networks.
Conversion rules and logical formulae were inferred according to the topology and the annotations of the diagrams. SBML-qual files  (Chaouiya et al, 2013)  generated with CaSQ  (Aghamiri et al, 2020)  retained their references, annotations and layout of the original CellDesigner file. They can be used for in silico simulations and analysis with CellCollective  (Helikar et al, 2012) , GINsim  (Naldi et al, 2018a)  or MaBoSS  (Stoll et al, 2017). CaSQ was adapted to produce SIF files necessary for HiPATHIA  (Hidalgo et al, 2017)  and CARNIVAL  (Liu et al, 2019)  pipelines. The C19DMap GitLab repository (https://gitlab. lcsb. uni. lu/covid/ models) was configured to translate stable versions of diagrams into SBML qual and SIF files. The diagrams were translated to XGMML using Cytoscape and GINSim.
Calculation and visualisation of single-cell RNA-Seq expression profiles RNA-Seq transcriptomic single-cell profiles were calculated for (i) non-infected airway cells  (Lukassen et al, 2020; Data ref: Lukassen et al, 2020)  and (ii) SARS-CoV-2-infected intestinal organoids  (Triana et al, 2021; Data ref: Triana et al, 2021). The Seurat package  (Hao et al, 2021)  was used to calculate cell-specific transcriptional profiles. For dataset (i), differential expression was calculated using every cell type against remaining cell types and applying the FindAllMarkers function of the Seurat package with min pct 0. 25 and log fold change threshold 0. 25. For dataset (ii), the cells were classified into bystander or infected based on the absence or presence of SARS-CoV-2 mRNA measured by scRNAseq  (Triana et al, 2021; Data ref: Triana et al, 2021).
Differential expression was calculated by contrasting the mock organoids with the bystander or infected cells after 12 h or 24 h of treatment. Expression profiles for the following cell types and conditions were selected for visualisation and enrichment analysis: for dataset (i), three types of secretory cells; and for dataset (ii), infected and bystander immature enterocytes 24 h post-infection versus mock. The datasets were selected to recapitulate the findings in the original papers and demonstrate the capability of the C19DMap for cell-specific data interpretation. Selected differentially expressed genes (DEGs) were prepared for visualisation in the MINERVA Platform as follows. Differential expression values were normalised to [À1,1] range by dividing by three and setting the outliers to their respective border values.
Expression values and their corresponding HGNC symbols were used to create visual overlays in the C19DMap in the MINERVA Platform (https://covid19map. elixir-luxembourg. org/ minerva/index. xhtml?id=covid19_map_17Jun21, "Overlays" tab). On-the-fly exploration and enrichment analyses using the GSEA plugin  (Hoksza et al, 2019)  are described in a dedicated guide (https://covid. pages. uni. lu/minerva-guide/). Complete expression analysis and transformation scripts are available in RMarkdown files at https://gitlab. lcsb. uni. lu/covid/models/-/tree/master/Reso urces/Omics%20analysis. RNA-Seq-based analysis of transcription factor activity RNA-Seq transcriptomic profiles of SARS-CoV-2 infection come from SARS-CoV-2-infected Calu-3 cells measured 24 h after infection, Gene Expression Omnibus reference GSE147507  (Blanco-Melo et al, 2020; Data ref: Blanco-Melo et al, 2020).
RNA-seq-based analysis of pathway signalling The CoV-HiPathia  (Rian et al, 2021)  web tool was used to calculate the level of activity of the subpathways of the apoptosis diagram (https://fairdomhub. org/models/712) from the C19DMap. RNA-Seq transcriptomic profiles come from a public dataset of nasopharyngeal swabs from 430 individuals with SARS-CoV-2 and 54 negative controls, Gene Expression Omnibus reference GSE152075  (Lieberman et al, 2020; Data ref: Lieberman et al, 2020). RNA-Seq gene expression data with the trimmed mean of M-values (TMM) normalisation  (Robinson et al, 2010)  were rescaled to the range [0;1] for the calculation of the signal and normalised using quantile normalisation  (Bolstad et al, 2003). Normalised gene expression values and the experimental design (case/control sample names files) were uploaded to CoV-Hipathia to calculate the level of activation of the signalling in the selected diagram.
A case/control contrast with a Wilcoxon test was used to assess differences in signalling activity between the two conditions. To reproduce the results, files with normalised gene expression data and the experimental design can be generated using the code https://gitlab. lcsb. uni. lu/covid/models/-/tree/master/ Resources/Hipathia/data_preprocessing. These files can then be used in CoV-HiPathia at http://hipathia. babelomics. org/covid19/ under the "Differential signalling" tab. Diagrams from the C19DMap can be selected in the "Pathway source" section, under "Disease Maps Community curated pathways".
Data availability COVID-19 Disease Map diagrams are available via: -the GitLab repository (https://gitlab. lcsb. uni. lu/covid/models). -WikiPathways collection (http://covid. wikipathways. org). -Reactome collection (https://reactome. org/PathwayBrowser/#/R-HSA-9679506). Workflows, executable models and network models are available via: -the GitLab repository (https://gitlab. lcsb. uni. lu/covid/models). -FAIRDOMHub (https://fairdomhub. org/projects/190). Expanded View for this article is available online.
Acknowledgements We would like to thank  Andjela Tatarovic , architect, and  Gina Crovetto , a researcher in the field of cancer, for their help with the design of the top-level view diagrams. We would like to acknowledge the Responsible and  Reproducible Research  ( R3 ) team of the  Luxembourg Centre for Systems Biomedicine  for supporting the project and providing necessary communication and data sharing resources. The work presented in this paper was carried out using the ELIXIR Luxembourg tools and services. This study was supported by the  Luxembourg National Research Fund (FNR)   COVID-19 Fast-Track grant programme , grant  COVID-19/2020-1/14715687/CovScreen  ( E. Glaab );  European Commission, INFORE  grant  H2020-ICT-825070  ( A. Montagud ,  M. Ponce de Leon ,  M. Vazques  and  A.
Software CellDesigner v4. 4. 2 http://www. celldesigner. org (Matsuoka et al, 2014) Newt v3. 0 https://newteditor. org SBGN-ED Czauderna et al (2010) ySBGN https://github. com/sbgn/ySBGN The MINERVA Platform v15. 1. 4 https://minerva-web. lcsb. uni. lu (Gawron et al, 2016) Reactome https://reactome. org (Jassal et al, 2020) WikiPathways https://www. wikipathways. org (Slenter et al, 2018) PathVisio v3. 3. 0 https://pathvisio. github. io (Kutmon et al, 2015) INDRA Gyori et al (2017) AILANI COVID-19 https://ailani. ai BioKB https://biokb. lcsb. uni. lu/topic/DOID:0080599 OpenNLP + GNormPlus https://opennlp. apache. org/ (Wei et al, 2015) COVIDminer https://rupertoverall. net/covidminer rWikipathways v 1. 12 10. 18129/B9. bioc. rWikiPathways OmniPathR https://github. com/saezlab/OmnipathR The MINERVA Conversion API v15.
1 https://minerva. pages. uni. lu/doc/api/15. 1/converter/ (Hoksza et al, 2020) cd2sbgml https://github. com/sbgn/cd2sbgnml (Balaur et al, 2020) rnef2sbgn https://github. com/golovatenkop/rnef2sbgn Seurat v4. 0 https://satijalab. org/seurat/ (Hao et al, 2021) DESeq2 1 0. 18129/B9. bioc. DESeq2 (Love et al, 2014).
Candidate regulator elements are marked with a solid black border. See Materials and Methods for details. A Existing crosstalk between individual diagrams of IFN-I and RELA-related mechanisms. B Candidate crosstalk between pathway groups. C Candidate regulators of existing diagrams from text mining and interaction databases. The network structure of the diagrams and their interactions based on existing crosstalk (shared elements), candidate crosstalk, and candidate regulators. Colour code: green-pathways or pathway groups; blue-proteins with one or two neighbours; yellow-proteins with three or four neighbours; and red-proteins with five or more neighbours. Candidate molecular interactions are shown as directed edges.
The workflow users may supply omics datasets to interpret them in the context of the map or test their hypotheses about how disease models will behave under specific perturbations. a Example: see Results, Case study-analysis of cell-specific mechanisms using single-cell expression data. b Example: see Results, Case study-RNA-Seq-based analysis of transcription factor activity. c Example: see Results, Case study-RNA-Seq-based analysis of pathway signalling. d https://colomoto. github. io/colomoto-docker/ ª 2021 The Authors Molecular Systems Biology 17: e10387 | 2021.
This fills the gap left by  [DF20] , as their approach was not feasible for LED. Our implementations are written in a mix of Python and C++, featuring performance and a convenientAPI (an excerpt is provided in Appendix A). All the source code is made publicly available. For details, see: https://github. com/CryptoExperts/AC21-DivProp-Convexity Outline Section 2 provides the necessary background with a focus on the partial order on bit-vectors. In Section 3, we briefly reintroduce traditional division property and develop its theory, culminating in a new compact representation. As a byproduct, we exhibit a direct link between division property and graph indicators. The following Section 4 focuses on CNF/MILP modeling aspects of the new representation. Section 5 presents our algorithmic framework for manipulating dense sets of binary vectors.
Reusing trails Usually, one may expect that many linear combinations of output bits have similar ANFs. Therefore, a trail ¬u F -→ v satisfying conditions of Theorem 3 for a pair of masks (α, β), may satisfy the conditions for some other pairs of masks (α , β ) as well, even if both pairs correspond to unique and non-redundant ANFs. This condition can be checked much faster than solving a SAT instance. This suggests the following optimization: before solving the SAT instance for a pair of masks (α, β), check whether any previously found trail satisfies the condition. This approach works well for the 8-round LED. For each combination of input/output Super-Sbox, about 30 trails are sufficient to show that the Super-Sbox model of 8-round LED does not allow to find integral distinguishers. All computed trails are provided in the code repository of the paper.
Summary Using the described techniques, we managed to show that integral distinguishers for the 8-round LED (and, by Proposition 6, for its inverse), if any exists, can not be found using traditional bit-based division property even with perfect Super-Sbox modeling and arbitrary linear masks applied to Super-Sboxes at the input and at the output. To do this, we found a small set of division trails through 8-round LED that, together with Theorem 3, proves the claim. These trails are provided in the code repository of the paper.
McCluskey algorithm for Boolean minimization Due to dense packing of bits, one such set occupies only 512MB of RAM. For more details, see https://github. com/CryptoExperts/AC21-DivProp-Convexity. Boura and Coggia reformulated the Quine-McCluskey [Qui55, McC56] algorithm for minimization of Boolean Functions, previously used in [AST + 17] to optimize modeling. from subsets import DenseSet, DenseBox from divprop import SboxDivision, Sbox from divprop. ciphers import SSB_LED sbox = SSB_LED(). MC divcore = sbox. graph_dense(). Mobius(). MaxSet(). Not() divcore # <DenseSet hash=473e100dda56d8d9 n=32 wt=177643913 | 16:177643913> dc = SboxDivision(sbox) dc. divcore # <DenseSet hash=473e100dda56d8d9 n=32 wt=177643913 | 16:177643913> dc. invalid_max # MaxSet(I_S) # <DenseSet hash=6f370f1ff06cbe0a n=32 wt=33412 | 15:2596 16:5442 17:6876 # 18:6415 19:5108 20:3469 21:1896 22:942 23:423 24:161 25:68 26:15 27:1> dc.
80 eV, while a half-loop nucleates for y i /L = 0. 50 with a higher energy barrier ΔE = 1. 11 eV. In-between, all MEPs approximately converge to a saddle energy of ΔE = 0. 98 ± 0. 01 eV, characterised by the emission of a half-loop at middistance between both aforementioned nucleation sites. During the relaxation, the nucleation centre, which was initially located at y i may move to reach a relaxed position called y c , which is the true physical center of nucleation. A specific protocol was designed to extract y c from the MEPs as described in the Supplementary Methods. This first dataset allows us to draw a preliminary conclusion: several energy paths for dislocation nucleation with close activation energies but different nucleation sites co-exist in nano-objects.
Softwares and interatomic potentials The LAMMPS code  40  is used for both NEB and MD simulations. Atomic structures are designed using ATOMSK  41  and visualised using OVITO  42. Atomic interactions are described using the rigid ion model of Ball and Grimes  43  successfully employed to describe MgO surface diffusion  43  , elastic constants and dislocation properties  44  as well as nanocube compression tests using classical MD  6. Cut-off and summation radii of 8 and 10 Å are used for short-and long-range interactions, respectively, the latter being computed using the multi-level summation method of Hardy  45  , with a relative error on the forces of about 10 -5.
ACKNOWLEDGEMENTS We thank  Pr. Masenelli-Varlot ,  Dr. Joly-Pottuz  and  Dr. Issa  for the original TEM image of the MgO nanocube under compression adapted Fig. 1b. The HPC resources were provided by the  Fédération Lyonnaise de Modélisation et Sciences Numériques (FLMSN) , partner of the EQUIPEX EQUIP@MESO.
, 2019). Because recordings from superficial and deep neurons in our dataset showed differences in baseline membrane potential and intrinsic membrane properties (Figures  2E-2G ), we analyzed superficial and deep recordings separately (Figures  S5  and S6 ). This analysis revealed more pronounced changes in membrane potential and firing rates after behavioral training in deep compared with superficial neurons (Figures  S6A-S6C ), whereas premovement membrane potential dynamics were comparable across layers (Figures  S6D-S6I ).
The virtual reality setup was implemented as described previously  (Schmidt-Hieber and Ha ¨usser, 2013). Briefly, motion on the treadmill was read out as described above and linearly converted to one-dimensional movement along the virtual reality corridor. The virtual environment was projected onto a spherical dome screen (120 cm diameter), covering nearly the entire field of view of the animal, using a quarter-sphere mirror (45 cm diameter) and a projector (Casio XJ-A256) located below the mouse. The virtual linear corridor was 1. 2 m long, enriched with objects placed along the linear track and vertical or oblique grating textures on the walls. A reward zone was located near the end (1. 0-1. 1 m) of the corridor. The Blender Game Engine (https://www. blender. org/) was used in conjunction with the Blender Python API to drive the virtual reality system.
The animal was illuminated by an array of infrared LEDs. Whisker movements were analyzed with the markerless pose estimation software DeepLabCut  (Mathis et al. , 2018). The bases and tips of 4 whiskers, i. e. , a total of 8 labels, were identified across all captured frames (Figure  S4A ). To quantify whisker movement, across all captured frames we computed the sum of the Euclidean distances, in units of pixels, that were covered by each of the 8 whisker labels between adjacent frames. A whisker motion index was obtained by z-scoring the data, subtracting any offset, and low-pass filtering at f c $0. 1 Hz. Onset of whisker movement preceding onset of running were detected where the whisker motion index continuously exceeded 10% of its maximal value within a time window of 15 s before running onset (Figure  S4C ).
5 KCl, 10 HEPES, 2 CaCl 2 , and 1 MgCl 2 (pH 7. 2, 289 mOsm) was perfused on top of the craniotomy through a round plastic chamber (4 mm diameter). In vivo extracellular electrophysiology Extracellular recordings of population activity were made using Neuropixels probes  (Jun et al. , 2017). Probes were lowered into the dorsal part of the MOs region to a depth of $2. 0 mm measured from the brain surface via a small craniotomy ($500 mm) at an angle of 15 from vertical using a micromanipulator (Sensapex uMp-4). A silver/silver chloride wire, which was soldered to the external reference of the probes and connected to ground, was positioned in an additional small craniotomy. Recordings were performed from head-fixed mice placed on the treadmill as described above. Electrophysiological data were recorded with SpikeGLX (https:// billkarsh.
github. io/SpikeGLX/) and signals from the AP channels (0. 3-10 kHz bandwidth, sampling rate of 30 kHz) were used for further processing. Spike sorting for unit identification was performed with Kilosort 2 (https://github. com/MouseLand/Kilosort). Identified units were manually curated using Phy (https://github. com/cortex-lab/phy). Putative cell types were assessed based on peakto-valley ratio and half-valley width of the spike waveforms for each neuron by fitting a Gaussian mixture model  (Kim et al. , 2016b; Stark et al. , 2013). Units with low classification confidence (p < 0. 95) were unassigned. The interneuron population was further split based on average firing rate: interneurons with a mean firing rate > 10 Hz were classified as fast-spiking interneurons (Figure  S3 ).
QUANTIFICATION AND STATISTICAL ANALYSIS In vivo whole-cell electrophysiology data analysis Input resistance was calculated from the steady-state voltage response to a small hyperpolarizing 500-ms current pulse from baseline membrane potential (Figure  2G ). Only data from mice that were resting during this period were used (speed < 0. 5 cm/s). Baseline membrane potential was measured before current pulse injections at the beginning of the recording. Spontaneous firing rate and membrane potential were measured across recordings with durations exceeding 60 s. To analyze subthreshold membrane potential and its variance, traces were digitally low-pass filtered at 5 kHz and resampled at 10 kHz. Action potentials were then removed by thresholding to determine action potential times and then masking values 2 ms before and 10-20 ms after the action potential peak.
Membrane potential oscillations were analyzed by bandpass-filtering membrane potential traces after removal of action potentials (Figure  S2 ). Power spectra were computed from subthreshold membrane potential traces using Hanning windowing over data windows of $328 ms. Membrane potential variance time series were computed in rolling time windows with a width of 1000 ms. Changes in subthreshold membrane potential (DVm) were computed by subtracting the mean of subthreshold membrane potential traces after spike removal (see above). To compute movement-aligned mean traces of DVm (Figures 4, 6, S6, and S7), we aligned traces to movement periods spanning 15 s before movement onset until 5 s after movement onset. After alignment, we computed the mean of each trace between 15 and 10 s before the onset of movement, and subtracted this baseline from each trace.
This selection further reduced the number of recordings to 11 of 23 during spontaneous movement, and 10 of 12 during goal-directed movement (Figure  4 ). In Figures  6  and S7 , we selected recordings with resting periods > 6 s preceding movement onset and recording durations > 5 s after movement onset. This selection further reduced the number of recordings to 8 of 14 during PV+ interneurons inactivation, and 7 of 12 during SOM+ interneurons inactivation (Figure  5 ). Spike rates in whole-cell recordings were computed from the time points of action potential peaks (see above) in time bins of 500 ms and filtered using a sliding average with a width of 3 s. Shuffled spike rate data were obtained by generating 100 artificial datasets. Each artificial dataset was created from the original dataset by shifting it circularly by a random number of sampling points.
ACKNOWLEDGMENTS We thank  Ian Duguid  and  Josh Dacre  for comments on the manuscript. We thank  Lucile Le Chevalier-Sontag  and  Claire Lecestre  for their technical support. This work was supported by grants from the  ERC (StG 678790 NEW-RON  to C. S. -H. , MSCA 800027 FindMEMO to M. A. , Human  Brain  Project  SGA2 785907  to J. -P. C. and F. K. , and  Human Brain  Project  SGA3 945539  to J. -P. C. ), the  Pasteur Weizmann Council  (to C. S. -H. ), the  INCEPTION program  ( Investissement d'Avenir  grant  ANR-16-CONV-0005  to C. O. ), the  Fondation pour la Recherche Medicale  ( EQUIPE 2019  to U. M. ),  ERA-NET Neuron  (iPS&BRAIN to U. M. ), and a  Pasteur-Roux fellowship  to  M. A. Schematic  drawings in the graphical abstract and in Figure 2 were adopted from https://scidraw. io/under a creative commons license (CC-BY).