dataesr/scientific-paragraphs-categorization huggingface dataset:
Consistent with BP enriched in FSHD, several active modules retrieved by MOGAMUN in FSHD1 links the down-regulation of factors involved in the organization of the sarcomere, thin filament, and contractile activity or component of the extracellular matrix (ECM) (Figures  3A,B  and S2A-C ). A majority of these active modules highlight the down-regulation of ACTN2 encoding the muscle specific actinin alpha 2 cytoskeletal proteins localized to the sarcomeric Z-disc where it contributes to the anchoring of the myofibrillar actin filaments. In these different active modules, ACTN2 is connected (pathways and protein-protein interactions) to genes encoding ECM factors involved in cell adhesion, migration or response to mechanical stimulus and tensile strength such as BGN, DCN, POSTN, LAMC3, FN1, ITGA4, COL1A1, COL1A2, COL3A1, and COL6A3 but also to genes encoding proteins involved in sarcomeric organization and muscle contraction (TNNT1, TNNT2, TTN, MYBPC1, TNNC1, DES, and NEB) (Figures  3C-F  and S2D-G ).
An additional active module retrieved by MOGAMUM associates down-regulation of sarcomeric proteins to up-regulation of GRIN2B (glutamate ionotropic receptor NMDA type Subunit 2B), also expressed in muscle and encoding a subunit of the NMDA receptor ion channel, which links excitatory synaptic transmission and calcium permeability (Figure  3D ). Other active modules correspond to genes involved in phosphatidyl inositol-mediated signalling (Figures  3G,H  and S3A-G ) and associates membrane receptors such as EGFR, ERBB3, and ERBB4, expressed in muscle progenitors and involved in regulating the balance of myogenic cell fate to signalling cascades and ECM or sarcomeric components, through co expression (yellow edges), pathways (red edges) or PPi (blue edges) nodes suggesting a decreased in proliferative progenitor cells.
In FSHD2 MFs, MOGAMUN retrieved several active modules that are similar to the ones obtained in FSHD1 cells (Figure  4A-D ) with dysregulation of genes encoding factors involved in ECM, sarcomere organization, and muscle contraction, including MURF2 (TRIM55; FSHD1: FDR 2. 81e À09 ; FSHD1 mosaic: FDR 6. 49e À06 ; FSHD2: FDR 7. 60e À17 ; BAMS: FDR 1. 09e À13 ), implicated in sarcomere formation, myogenic differentiation and required for adaptation of striated muscle to stress but also ACTN2, TTN, TNNT, or TNNC genes. In 4 out of 17 modules involving genes implicated in the 'regulation of transcription' (Figures  4C  and S5E ), TP73 that controls the early stage of myogenic differentiation is up-regulated while MYOD is decreased, revealing as in FSHD1 cells, a lower expression of factors required for muscle differentiation or functioning of the contractile apparatus, strengthening previous observations.
(G) Cellular components analysis for the list of 132 DEGs in common between FSHD1, FSHD1 mosaic, and FSHD2 MFs. Genes with the higher FC are related to muscle contraction. Twenty genes belong to 'supramolecular fibres' cellular component (P value, 2. 10e À06 ), 17 genes to 'myofibril' (P value, 2. 19e À12 ), 16 genes to 'sarcomere' (P value, 2. 63e À12 ) and 12 to 'actin cytoskeleton' (P value, 9. 4e À05 ). In all conditions, a number of pathways associate proteins involved in DNA replication (Figure  S9A-H ) with an overall decrease of a few genes required for replication initiation such as CDC6 (Figure  S9C-H   (A-H ) Representative active modules sampled out of 23 nodes for FSHD1 datasets using the MOGAMUN algorithm. Integration of protein-protein interactions (blue lines), biological pathways (orange lines), and co-expression data (yellow lines) with our lists of differentially expressed genes (DEGs) enabled the identification of active modules for each category of samples.
Up-regulated nodes are coloured in red, and down-regulated ones are in green. The intensity of the colour reflects the fold change. Thickness of the dark line around each rectangle reflects the level of significance [false discovery rate (FDR) <0. 05 and 1 < FC > 1]. Each active module contains between 15 and 16 genes. Genes corresponding to each nodes were analysed using g:Profiler to define corresponding molecular function and P value. (H) Phosphatidyl inositol-mediated signalling, P value 1. 3e À14.
*P value <0. 05, **P value<0. 005, ***P value <0. 0005, and ****P value<0. 00005. (B) Muscle contractions were recorded for 30 s with images taken every 200 ms in live innervated muscle fibres (Movie S1). (C) Scattergram displays the length of contraction determined as the shifting over time of randomly distributed point (n = 1200 data points per condition) along independent muscle fibres (y-axis) analysed using the Imaris software in the different conditions. Data were analysed using a Brown-Forsythe statistical test with a Games-Howell correction, ****P value <0. 0001. (D) Intracellular calcium signalling was measured in hiPSC-derived live myofibres using an Axio observed microscope at day 45 post-differentiation. Different regions were tracked to record the fluorescent intensity at a 10× magnification during 30 s and an interval of 200.
0 ms corresponding to 151 frames. Regions of interest (ROIs) corresponding to muscle fibres were selected and intensity of fluorescence was acquired using the Imaris software. (E) Scattergram displays the amplitude of calcium uptake and release in 50 fibres randomly selected in different fields for each sample (y-axis). Data were analysed using a Kruskal-Wallis non-parametric test (ns: non-significant; ****P value <0. 0001). muscle manifestation that is specific to FSHD. 12, 13 However, as pathways that associate DUX4 expression to the FSHD muscle pathogenesis are not defined, identification of biomarkers of FSHD muscle function remain critical for understanding the process leading to the pathology but also for the definition of readouts to be used for drug design, outcome measures, and monitoring of therapies aimed at correcting the functional defect.
Network-based approaches have been widely used for efficient identification of new diseases genes or reveal biological processes perturbed in diseases. With this perspective, we used MOGAMUN, a novel algorithm that integrates expression data with the simultaneous exploration of multiplex networks (pathways, protein-protein interactions, and co-expression) to retrieve active modules specifically dysregulated in diseases. 15 As compared with existing tools such as jActiveModules,  26  COSINE,  27  and PinnacleZ, limited to the analysis of single networks, MOGAMUN takes into account different biological sources of physical and/or functional interactions. It also optimizes both the differential expression score and the density of interactions as other tools retrieve either modules with 2-3 nodes or thousands of occurrences, limiting further interpretations.
We did not notice any enrichment in specific splice variants for transcripts encoding these different factors. This observation is also consistent with previous data that revealed defects in specific stages of myogenic differentiation in FSHD muscles  29  and implicated the actin cytoskeleton signalling in the disease. 37, 38 lectron microscopy or immunochemistry did not reveal any disease-specific features or ultrastructural changes in FSHD biopsies. 39, 40 Individual fibres myofibrillar structure is preserved with atrophic and hypertrophic fibres. 40 In severely affected muscles, the presence of small angular fibres is evocative of regeneration, inflammation or fibrosis as a sign of the myopathic process. 39 However, misalignment of the contractile apparatus with increased distance between the sarcolemma and the nearest myofibrils observed in biopsies  38  appeared to be consistent with pathways retrieved via MOGAMUN.
Data were analysed using a Brown-Forsythe statistical test with a Games-Howell correction, ****P value <0. 0001. (D) Intracellular calcium signalling was measured in hiPSC-derived live myofibres using an Axio observed microscope at day 45 post-differentiation. Different regions were tracked to record the fluorescent intensity at a 10× magnification during 30 s and an interval of 200. 0 ms corresponding to 151 frames. Regions of interest (ROIs) corresponding to muscle fibres were selected and intensity of fluorescence was acquired using the Imaris software. (E) Scattergram displays the amplitude of calcium uptake and release in 50 fibres randomly selected in different fields for each sample (y-axis). Data were analysed using a Kruskal-Wallis non-parametric test (ns: non-significant; ****P value <0.
Funding This study was funded by the  'Association Française contre les Myopathies ' (AFM; TRIM-RD) and  Fondation Maladies Rares. C. L. was the recipient of a fellowship from the  French Ministry of Education. M. D. is the recipient of a fellowship from the  French Ministry of Education. The project leading to this publication has received funding from the  Excellence Initiative of Aix-Marseille University-A*Midex , a  French 'investissement d'avenir programme '  AMX-19-IET-007  through the  Marseille Maladies Rares (MarMaRa) Institute  ( PhD fellowship  to C. L. and K. M. ).
BLV VLPs have been produced in different systems by using mammalian, silkworm, or other insect cells. These BLV VLPs were only composed of Gag polyprotein, and none of them contained Env surface glycoprotein  (37, (39) (40) (41). In this study, we describe for the first time the production and characterization of BLV VLPs containing Env and Gag immunogenic proteins expressed in Drosophila melanogaster S2 stable cell lines. Purified VLP morphology was analyzed by negative staining transmission electron microscopy (TEM) and dynamic light scattering (DLS), showing a polydisperse population with shape and sizes compatible with previously reported data for another retrovirus VLPs  (42) (43) (44) (45) (46) (47) (48) (49). Gag and Env in VLPs were detected by Western blot and further confirmed by mass spectrometry.
Construction Design for Protein Expression in Drosophila Cells DNA sequences of BLV (GenBank, access number: EF600696) encoding the precursor Gag protein (amplified from FLK-BLV cells, DSMZ #ACC 153) and Env glycoprotein (synthesized by Genscript) were cloned by RF cloning in pMT/BiP/V5-His expression vector (Invitrogen). All constructs were cloned under the control of Drosophila metal-inducible metallothionein promoter (MT), followed by a stop codon immediately after its C-terminus. All sequences were confirmed by automated sequencing (Macrogen, Korea). Two different constructs (483 aa) were designed to express BLV-Env protein: the native form, pMT/BiP/Envwt, which is processed by cellular furin proteases, giving rise to SU and TM subunits and the furin-mutated form, pMT/BiP/EnvFm, containing a double substitution at positions R301N and R302N to avoid its proteolytic processing  (50) , leading to a stable Env precursor protein.
Cell Surface Env Protein Expression Analysis by Flow Cytometry Analysis Induced or non-induced EnvFm/Gag-S2 cells were incubated with a monoclonal antibody anti-gp51 (BLV1, 1/50) (VMRD, USA) and subsequently stained with a goat anti-mouse IgG Alexa-594 secondary antibody (Invitrogen). Cells were immediately acquired, without permeabilization or fixation, using a BD FACSAria TM Fusion Cell Sorter (BD Biosciences, Franklin Lakes, NJ, USA). Data were analyzed with FlowJo software (Tree Star, Inc. ). Unstained cells were used as a control of fluorescence background.
Elution was performed using the following gradient: 1-55% B over 86 min and 55-99% B over 9 min at a constant flow rate of 200 nl/min [A: 0. 1% formic acid in water, B: 0. 1% formic acid in acetonitrile (ACN)]. The mass spectrometer was operated in data-dependent acquisition mode with automatic switching between MS and MS/MS scans. The full MS scans were acquired at 70 K resolution, and MS/MS was performed using a data-dependent top 12 method at a resolution of 17. 5 K using a dynamic exclusion list. Bioinformatics data analysis was performed using PatternLab for Proteomics software, v 4. 0. 0. 84 (http://patternlabforproteomics. org). A target-decoy database was generated using Drosophila melanogaster UP000000803 from Uniprot (https://www. uniprot. org) 2018/10/31 added with the amino acidic sequences of Gag, EnvFm, and 127 most common contaminants.
Search parameters were set as follows: tryptic peptides; methionine oxidation and asparagine glycosylation (Hex3HexNAc2 and Hex3HexNAc2dHex) as variable modifications. Peptide spectrum matches were filtered using the Search Engine Processor (SEPro) using the following parameters: precursor mass tolerance 10 ppm; FDR < 1% at the protein level. The mass spectrometry proteomics raw data have been deposited to the ProteomeXchange Consortium via the PRIDE (53) partner repository with the dataset identifier PXD027973.
Dynamic Light Scattering Purified VLPs were analyzed by DLS using a Zetasizer Nano S (Malvern Panalytical). Samples were pre-incubated at 25 • C for 10 min and measured with automatic parameter using disposable plastic cuvettes (UVette, Eppendorf). Data from triplicate measurements were averaged and analyzed with Zetasizer Software v. 7. 13 (Malvern Panalytical) to obtain size distribution of the samples.
When knowledge is lacking and cannot be acquired by experimental means, the model-based approach can help define the most efficient monitoring method. It is indeed attractive to use such approach to identify the key physical quantities required for condition monitoring (see Figure  1 ). For example, Bayer and Enge-Rosenblatt  [10]  modelled an axial piston pump with signs of wear to determine adequate features for the detection of cylinder valve abrasion and increased piston-slipper clearance. The models can be data-driven or physics-based, depending on the amount and kind of data that is available. At present, data recorded in situ for helicopter axial piston pumps is scarce. This makes the physics-based modelling approach the best candidate. Nowadays, this can be supported by numerous commercially available simulation environments.
When knowledge is lacking and cannot be acquired by experimental means, the model-based approach can help define the most efficient monitoring method. It is indeed attractive to use such approach to identify the key physical quantities required for condition monitoring (see Figure  1 ). For example, Bayer and Enge-Rosenblatt  [10]  modelled an axial piston pump with signs of wear to determine adequate features for the detection of cylinder valve abrasion and increased piston-slipper clearance. The models can be data-driven or physics-based, depending on the amount and kind of data that is available. At present, data recorded in situ for helicopter axial piston pumps is scarce. This makes the physicsbased modelling approach the best candidate. Nowadays, this can be supported by numerous commercially available simulation environments.
A-causality can be seen as the best option for a simulation environment, as the user does not need to manage sequences of computation. However, letting the software manage them on its own may sometimes increase the computation time, fail to compile (when numerous algebraic loops of high index are interlaced), or involve numerical derivations, low-pass filtering of which introduces phase lags and therefore alters the simulation accuracy. Finally, causal simulation environments remain massively used, given their maturity (and interest for realtime simulation). In the present project, the models are implemented in the commercial simulation environment, Simcenter AMESim, which is causal: once the model is implemented, according to the model architecture selected, the block causalities are frozen.
Methodology for Fault Injection in Simulation Fault injection can be implemented in hardware, software, and models  [20]. Modelimplemented fault injection is easier, faster, cheaper and more versatile than the other two options. It allows the early identification of components or system design deficiencies. In the current approach, fault injection is used to study the behaviour of a helicopter pump against degradation, through the simulation of a physics-based model. In recent years, tools have been developed to facilitate the implementation of fault injection in simulation environments. For example, van der Linden  [21]  proposed a standard code for the Modelica language, which allows centralized parameterization and triggering of faults during physics-based model simulations. Both parameter faults (ones with large time constant with regard to the simulation horizon, thus considered constant), and variable faults (ones with low time constants which generate transients in the simulated system) are supported.
Proposal for Fault Injection in Simulation Using Bond-Graphs Bond-Graphs (BG) are oriented graphs intended to show the energy transfers from one system or object to another. They were first introduced in 1961  [23]. BG are mainly meant to model the dynamics of power systems with a lumped-parameter approach. BG models can be simulated directly using BG-oriented software, e. g. ,  [24] , or after extraction of equations for implementation, e. g. , in a block diagram form. They also facilitate model building and analysis in icon-based simulation environments, which allow submodels to be connected through power links; for example, Simcenter AMESim or Dymola. The process of BG creation has been well established for decades and is not detailed in this paper. More detailed information on BG modelling can be found, for example, in  [25]  or  [26].
Neutron noise analysis addresses the description of small time-dependent flux fluctuations in reactor cores, induced by small global or local perturbations of the macroscopic cross sections due to density fluctuations of the coolant, to vibrations of fuel elements, control rods, or structural materials. The general noise equations are obtained by assuming small perturbations around a steady-state neutron flux and by subsequently taking the Fourier transform in the frequency domain. Recently, new neutron noise solvers have been implemented in diffusion and transport theory in APOLLO3 R , the multi-purpose deterministic transport code developed at CEA, and a new stochastic solver has been implemented for the neutron noise analysis in the frequency domain in the Monte Carlo code TRIPOLI-4 R , also developed at CEA.
More recently, a new stochastic solver has been implemented for the neutron noise analysis in the frequency domain in the Monte Carlo code TRIPOLI-4 R , also developed at CEA  [3]. Both solvers are under active verification and validation in the framework of the CORTEX H2020 European project  [4]. In this paper, we will compare for the first time the capabilities of these solvers by performing neutron noise simulations in a simplified UOX fuel assembly. This paper is organized as follows. In Amélie Rouchon et al. , APOLLO3 R AND TRIPOLI-4 R NEUTRON NOISE SOLVERS Sec. 2, the general neutron noise theory will be briefly introduced, then the Monte Carlo algorithm implemented in TRIPOLI-4 R and the deterministic algorithm implemented in APOLLO3 R will be presented. In Sec. 3, we will compare the capabilities of the proposed Monte Carlo and deterministic solvers by computing the neutron noise propagation in a simplified UOX fuel assembly, for both Green's function solutions corresponding to noise sources located at fixed points, and more realistic cases where the noise source comes from a steady state neutron distribution.
The deterministic algorithm implemented in APOLLO3 R In order to solve Eq. (  1 ) with the deterministic code APOLLO3 R , we apply the same iteration loops to the fission source (but the production operator is now complex) and to the scattering source as customary, and we add an iteration loop between the real and imaginary parts of the neutron noise equation to the solution of the one-group problem: details can be found in  [5]. Thus, we can use the standard one-group diffusion or transport solver and consequently benefit from all numerical methods already implemented in these one-group solvers. This solution scheme has been implemented in the integro-differential transport (IDT) solver of APOLLO3 R [2] *. The IDT solver is a lattice solver based on the S n discrete ordinates method and on the method of short characteristics (MOSC).
COMPARISON OF APOLLO3 R AND TRIPOLI-4 R NEUTRON NOISE SOLVERS Previous work has concerned the verification of the APOLLO3 R and TRIPOLI-4 R neutron noise solvers against reference analytical solutions in a homogeneous infinite medium and in an infinitelylong homogeneous cylinder (specifications taken from  [8] ) and in a one-dimensional homogeneous core surrounded by a reflector (specifications taken from  [10] ). In order to further illustrate the capabilities and the accuracy of the solvers, here we perform code-to-code comparisons in a simplified UOX fuel assembly, in two dimensions.
CONCLUSIONS In this paper, we have briefly compared the deterministic APOLLO3 R neutron noise solver with the new TRIPOLI-4 R solver for the neutron noise analysis in the frequency domain, in view of proposing the examined configurations as benchmark cases for neutron noise calculations. Future work will concern the comparison with neutron noise simulations performed with the APOLLO3 R neutron noise solver in the time domain (currently under development and testing  [11] ), and the validation of the developed solvers based on the experimental campaigns carried out in the framework of the COLIBRI program at the Crocus reactor (operated by EPFL, Lausanne, Switzerland)  [12].
Current Arctic sea ice Our main objective is to investigate LIG sea ice. However, a quick assessment of the sea ice simulated in the reference state, i. e. the pre-industrial control experiment (referred to as piControl in the CMIP6 terminology, and PI in this paper) was necessary. In the absence of extensive sea ice data for the PI, we used data for a recent period before the current sea ice cover significant decrease. We use the NOAA Optimum Interpolation version 2 data  (Reynolds et al. , 2002)  for the period 1982 to 2001. The sea ice data in this dataset are obtained from different satellite and in situ observations. We have used the monthly time series at a resolution of 1 •. This dataset is termed "NOAA_OI_v2" in the rest of this paper.
Marine records of LIG Arctic sea ice We focus here on records of sea ice from marine cores. Table 1 provides a summary of LIG sea ice information and data obtained from marine sediment cores collected in the Arctic Ocean, Nordic Seas and northern North Atlantic. South of 78 • N, the records show ice-free conditions. Most of these sea ice records are derived from quantitative estimates of sea surface parameters based on dinoflagellate cysts (dinocysts). North of 78 • N the sea-ice-related records are rare and dif-ferent types of indicators were used. In addition to dinocysts, the records are based on biomarkers linked to phototrophic productivity in sea ice and on foraminifers and ostracods that both provide indication on water properties and indirectly on sea ice  (de Vernal et al. , 2013b).
Between 78 and 87 • N, the faunal data have been interpreted as indicating seasonal sea ice cover conditions during the LIG. Among sea ice cover indicators, dinocyst assemblages have been used as quantitative proxy based on the application of the modern analogue technique applied to a standardised reference modern data base developed from surface sediment samples collected at middle to high latitudes of the Northern Hemisphere  (de Vernal et al. , 2005 (de Vernal et al. , , b, 2013b (de Vernal et al. , , 2020)). The sea ice estimates from dinocysts used here are from different studies (see references in Table  1 ) and reconstructions based the new database, including 71 taxa and 1968 stations  (de Vernal et al. , 2020). The reference sea ice data used for calibration are the monthly 1955-2012 average of the National Snow and Ice Data Center (NSIDC)  (Walsh et al.