dataesr/scientific-paragraphs-categorization huggingface dataset:
For mCherry measurements, 40 acquisitions of 5 s duration were realized compared to 20 acquisitions of 10 s for EGFP measurements. Autocorrelation curves were calculated by a home-made Matlab program. The following 3D and 2D diffusion models were used for cytoplasmic proteins and membrane proteins, respectively. (3D model) G(τ) = 1 N 1 ( 1 + ( τ τD ) ) 1 ̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅ ̅ 1 + ( r0 ZZ ) 2 ( τ τD ) √ (2D model) G(τ) = 1 N 1 ( 1 + ( τ τD ) ) where N represents the mean number of fluorescent particles in the focal volume, while r 0 and Z z correspond to the lateral and axial dimensions of the focal volume, respectively. τ D corresponds to the transit time. The diffusion coefficient of the proteins was calculated by: D sample = τ reference τ sample × D reference where τ reference and D reference represent respectively the correlation time and the diffusion coefficient of TMR or ATTO 665.
Both the fluorescence brightness and the diffusion coefficient of fusion proteins can be measured using FCS  [57]. After analysis using our custom Matlab program, the autocorrelation curves provided information about the brightness and number of molecules. By comparing their relative brightness to the brightness of the fluorescence protein alone (EGFP or mCherry), one can infer the degree of protein oligomerization from their brightness. This method was previously utilized by us to assess MET's oligomerization. Briefly, fluorescent proteins were produced in cells, and brightness (B) was evaluated by the formula B=Intensity signal/N, where N is the average number of fluorescent particles identified from each autocorrelation curve (Fig. 3 ). A monomeric distribution corresponds to a brightness of 0.
The slides were left to dry at RT for 24 h. They were then imaged on confocal laser scanning microscope (LSM700, Zeiss) equipped with a Plan-Apochromat 63 × 1. 40 oil immersion objective. Alexa fluor 555 was excited with a 555 nm laser and DAPI was excited with a 405 nm laser line. Images of 1024 px × 1024 px were processed by the Fiji software  [59].
Acknowledgements We are thankful to  Ermanno Gherardi  ( University of Pavia, Pavia, Italy ) for providing HGF domains. Fig. 6 and the graphical abstract were created on Biorender. com. This work and R. M. T. were funded by the  Deutsche Französische Hochschule (DFH) , the  international doctoral program  of the  European doctoral college of UNISTRA , and the  doctoral school of life and sciences  ( ED 414 ) of  UNISTRA. The PhD of RMT was a co-tutelle between UNISTRA and KIT. Support was obtained from the  French National Centre for Scientific Research (CNRS)  and the  Hemholtz program   Information -Materials Systems Engineering (MSE), Germany. Y. M. is grateful to the  Institut Universitaire de France (IUF)  for support and providing additional time to be dedicated to research. Imaging was supported by the  Imaging Center PIQ-QuESt  (https://piq.
For the counterstaining, Hematoxylin was used. Quantification of DAMPs. DAMPs-DAB staining images were obtained and digitally captured using slide scanner VS200 (OLYMPUS, Germany) with a 20× objective lens. Digital image analysis was performed with commercial software HALO (IndicaLabs, Corrales, NM, USA). DAB and hematoxylin signals were detected using object colocalization-based algorithms, and the amount of extracellular DAMPs were assessed by subtracting the hematoxylin-colocalizing DAB signals from the total DAB signals. Quantitative PCR assay. Total RNA from ankle joint was extracted using TRIzol Reagent (Thermo Fisher Scientific, MA, USA) and purified with Nucleospin RNA (MACHEREY-NAGEL). Thereafter, cDNA was synthesized with 1 μg of total RNA by using SuperScript IV VILO Master Mix (Thermo Fisher Scientific, MA, USA).
4, Social Survey Research Information Co. , Ltd. ) or JMP® Pro 16. 2. 0 (SAS Institute Inc. , NC, USA). The level of significance was set at P < 0. 05. *P < 0. 05, **P < 0. 01, and ***P < 0. 001, unless specified. Information on the reproducibility of experiments, including the sample sizes and number of replicates and how replicates were defined in the legends of figures.
Acknowledgements We thank  Dr. K. Kurokawa  for helpful advice and discussion. We also thank to  Dr. Kaoru Atsui  ( Atsui Orthopaedic and Rheumatic Disease Clinic ),  Dr. Shinichiro Ijiri  ( Ijiri Orthopedic Clinic ),  Dr. Shuichi Ota  ( Ota Orthopedic Clinic ),  Dr. Takeshi Katayama  ( Katayama Orthopedic Clinic ),  Dr. Kei Kuzuhara  ( Kuzuhara Orthopaedic Clinic ),  Dr. Masanori Taketomi  ( Taketomi Orthopaedic Clinic ),  Dr. Hiroya Tanaka  ( Daiya Tanaka Orthopaedic Clinic ),  Dr. Yoshiharu Nakabayashi  ( Nakabayashi Orthopaedic Clinic ),  Dr. Kunihiro Araki  ( Araki Orthopaedic Clinic ),  Dr. Tadashi Yasuda  ( Kobe City Medical Center General Hospital ) for collecting and providing patients' sera and synovial fluids. This work was supported by  AMED -LEAP 23gm0010006h0005  (to T. M. ) and  22gm0010006h0504  (to S.
Acknowledgements For the EPR experiments financial support from the  IR INFRANALYTICS   FR2054  is gratefully acknowledged. The authors thanks  H. Ahouari  ( Chevreul Institute, Villeneuve d'Ascq ) for technical assistance in recording CW spectra and troubleshooting. Prof. C. Hobartner  ( University of Wuerzburg ) is kindly acknowledged for providing the cytosine TEMPO-labeled oligomers. MD simulations were performed using the HPC time granted via the  UK High-End Computing Consortium  for Biomolecular Simulation, HECBioSim (http://hecbiosim. ac. uk), supported by  EPSRC  (grant no. EP/R029407/1 ).
SARS-CoV-2 alters organelles cross-regulations. We next investigated the dependencies between LD, mitochondria, nuclei, nucleoli, and cytosol dry masses using comparative Bayesian networks (BN). BN are an established tool for modelling biological datasets  48  in fields such as signaling  49  , genomics  50, 51  , or immunology  52  , but not, to the best of our knowledge, to model the hierarchy and regulation existing between cell organelles. Established BN methods  53  allow to search for the conditional relationships and probabilities between factors of interest. These methods provide an intuitive visual representation under the form of a directed acyclic graph. We thus investigated the likelihood of discretized organelles dry mass, given the dry mass of the other organelles. Considering that the dry mass variation of a subcellular compartment reflects regulated variations of its protein, lipid, and/or nucleic acid contents, we propose that the causal relationship between organelle dry mass captures an integrated level of organelledependent regulation.
Our object detection strategy transforms the limitation of HTM, which is the complex nature of the images it provides, into an advantage: having access to biologically relevant, direct, simultaneous measurements of organelles within single cells. This opens a vast landscape of possibilities for system's investigations. Such dataset is particularity adapted for causal investigations due to the reasonable number of dependencies to evaluate. We could thus define in this study organelle cross-regulation (OCR) networks. There are pending questions: will the same OCR networks topologies be observed in other cell types or in response to other viruses? We believe that a comparative OCR analysis of different cellular conditions is essential. Future work will help determining whether we have uncovered conserved regulation networks for synchronizing organelles.
Virus The strain BetaCoV/France/IDF0372/2020 was supplied by the National Reference Centre for Respiratory Viruses hosted by Institut Pasteur (Paris, France) and headed by Pr. S. van der Werf. The human sample from which the strain was isolated has been provided by Dr. X. Lescure and Pr. Y. Yazdanpanah from the Bichat Hospital, Paris, France. The viral strain was supplied through the European Virus Archive goes Global (Evag) platform, a project that has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement no. 653316. Titration of viral stocks was performed on Vero E6, with a limiting dilution technique allowing a calculation of DCP50.
Nucleus: Our nucleoli model was trained with a dataset of 655 holotomographic microscopy images of mammalian cells images randomly split into a training set (589 images) and a testing set (66 images). These images come from 58 acquisitions and include multiple cell lines. Each image contains from 1 to 23 nuclei, all have a size of 480x480 pixel achieved by cropping or zero-padding. Probability maps provided by the model are binarize with a 50% probability threshold. All objects smaller than 10 pixels are rejected. The training took 4 hours and 19 minutes.
Nucleoli: Our nucleoli model was trained with a dataset of 495 holotomographic microscopy images of mammalian cells randomly split into a training set (445 images) and a testing set (50 images). Each image contains at least a dozen of nucleoli and have a size of 480x480 pixel achieved by cropping or zero-padding. Probability maps provided by the model are binarized with a 50% probability threshold. All objects smaller than 10 pixels are rejected. Potential unspecific signal outside the nucleus is removed thanks to the nucleus masks predicted by our nucleus model. The training took 4 hours and 13 minutes.
Cells segmentation: The first part of our cell segmentation process aims at obtaining a rough cell segmentation without a precise cell border detection. This U-Net model was trained with a dataset of 1445 holotomographic microscopy images of mammalian cells randomly split into a training set (1295 images) and a testing set (150 images). Each image contains from 1 to 10 cells and have a size of 480x480 pixel achieved by cropping or zero-padding. Probability maps provided by the model are binarized with a 50% probability threshold. All objects smaller than 10 pixels are rejected. The training took 51 minutes. The second part of our cell segmentation process takes the U-Net produced cell blobs binary masks as a seed for a precise cell edges detection using a propagation algorithm approach.
Bayesian network structure learning: We performed our Bayesian network (BN) structure learning using the bnlearn  53  python package, to determine which BN captures the best the causal links that exists between our dataset variables, lipid droplets, mitochondria, nucleus, nucleoli and cytosol dry mass densities, as well as the nature of the single cell, normal or syncytium. The best BN for each condition was defined using a greedy score-based structure learning strategy. For this study we used the K2 scoring function and the hill climb search algorithm, which incrementally tests BN alternatives in order to improve the K2 scoring that itself determines the probability of the BN structure given its training data.
Acknowledgments We thank  Timothée Bruel ,  Julian Buchrieser  and  Sasha Legrosdidier  for critical reading of the manuscript, members of the  Virus and Immunity Unit  for discussions and help,  Perrine Bomme  and the  Ultrastructural Bioimaging Unit (UBI)  and  UtechS Photonic BioImaging (UPBI) core facilities (Institut Pasteur). NS is funded by the  ministère de l'Enseignement supérieur et de la Recherche. OS laboratory is funded by  Institut Pasteur ,  Fondation pour la Recherche Médicale (FRM) ,  ANRS-MIE , the  Vaccine Research Institute  ( ANR-10-LABX-77 ),  HERA European program  ( DURABLE consortium ),  Labex IBEID  ( ANR-10-LABX-62-IBEID ),  ANR / FRM Flash Covid PROTEO -SARS-CoV-2  and  IDISCOVR. We are grateful for support for equipment from the  French Government Programme Investissements d'Avenir France BioImaging  ( FBI , N°  ANR-10-INSB-04-01 ) and the  French gouvernement (Agence Nationale de la Recherche)   Investissement d'Avenir programme ,  Laboratoire d'Excellence "Integrative Biology of Emerging Infectious Diseases"  ( ANR-10-LABX-62-IBEID ).
Quantification of extracellular metabolites by NMR Glucose, gluconate and 2-KG were identified and quantified by nuclear magnetic resonance (NMR). Culture samples (1 mL) were collected every 0. 5-1 h during growth, centrifuged at 14,500 g for 3 min, and the supernatants were stored at -20 • C until analysis. The supernatants (180 μL) were mixed with 20 μL of an internal standard consisting of 10 mM deuterated trimethylsilylpropanoic acid (TSP-d4) diluted in D 2 O. Proton NMR spectra were recorded on an Avance III 500-MHz spectrometer equipped with a 5-mm z-gradient TXI ( 1 H, 13 C, 31 P) probe (Brucker, Rheinstatten, Germany). Quantitative 1 H NMR analysis was performed at 286 K, using a zgpr30 sequence with a recycle delay of 10 s, and presaturation at 4. 7 ppm for water suppression. Thirty-two scans were accumulated (32 k data points with a spectral width of 10 ppm) after 4 dummy scans.
Inverse gated 13 C decoupling was used for samples containing 13 C-Glucose to ensure the spectra were quantitative. The zgigpr sequence was used with 13 C decoupling during 1 H acquisition (396 ms), a recycle delay of 10 s, and presaturation at 4. 7 ppm for water suppression. Thirty-two scans were accumulated (8 k data points with a spectral width of 20 ppm). The spectra were processed using Topspin 3. 1 (Bruker, Rheinstatten, Germany). The reported results are the average values obtained from at least three biological replicates.
Calculation of growth rates and extracellular fluxes Growth rates and extracellular metabolite fluxes were determined in the exponential growth phase from the time courses of biomass, glucose, gluconate and 2-KG concentrations, as measured by NMR. All calculations were performed using PhysioFit 1. 0. 2  (Peiro et al. , 2019)  (https:// github. com/MetaSys-LISBP/PhysioFit) from culture profiles obtained with unlabeled or 13 C-labeled glucose (Fig. S2 ). MBPeGFP and mCherry production rates (q MBPeGFP and q mcherry ) were calculated by multiplying the respective yields by the growth rate.
Fourier transform mass spectra were recorded in full-scan negative ion mode at a resolution of 60,000 at m/z = 400. The IC and mass spectrometry experiments are described in greater detail elsewhere  (Vogeleer and Létisse, 2022). In total, three samples were analyzed for each of three independent biological replicates. Isotopologues were quantified from 11 metabolic intermediates covering the central metabolism (6-phosphogluconate, glucose-6phosphate, fructose-6-phosphate, fructose-1,6-bisphosphate, 2/3-phosphoglycerate, phosphoenolpyruvate, ribose-5-phosphate, sedoheptulose-7-phosphate, citrate/isocitrate, malate and succinate). The mass fractions were corrected for naturally occurring isotopes, using IsoCor 2. 2. 0  (Millard et al. , 2019 , https://github. com/MetaSys-LISBP/IsoCor) and high resolution spectra (60,000 at m/z = 400), with corrections for the natural abundance and isotopic purity (99 %) of the tracer element.
13 C-metabolic flux analyses 13 C metabolic fluxes were calculated using the software influx_s  (Sokol et al. , 2012) , based on a flux model that included the mass balances and carbon atom transitions of P. putida's main central metabolic pathways  (Kohlstedt and Wittmann, 2019; Nikel et al. , 2015) : glucose uptake, EMP pathway, PP pathway, ED pathway, tricarboxylic acid cycle (TCA), anaplerotic reactions, and gluconate and 2-KG secretion. Following  Kohlstedt and Wittmann (2019) , the flux through the Kdgk reaction was fixed at 4. 9 % of the flux through the Gntk reaction. Precursor requirements for biomass synthesis were estimated based on the composition of the biomass  (van Duuren et al. , 2013)  and growth rates. Similarly, precursor requirements for the synthesis of heterologous protein were estimated from the protein sequence and from protein production rates.
Intracellular fluxes were estimated by fitting i) extracellular fluxes (glucose uptake, and gluconate, 2-KG, biomass and protein production) and ii) the 13 C-labelling patterns of intracellular metabolites measured by IC-MS. A chi-square statistical test was used to assess the goodness-of-fit (based on 95 % confidence intervals) for each condition. For comparison, the estimated fluxes were then normalized to the rate of substrate uptake, set to 100. For each condition, the mean fluxes and the corresponding standard deviations were determined from three independent biological replicates. The isotopic data and metabolic fluxes for each independent biological replicate are given in Table  S2  in the supplementary material. All model files and flux calculation results are available at https://github.
Metabolic flux analyses reveal metabolic rearrangements in the central metabolism of P. putida We employed 13 C metabolic flux analysis to measure the alterations in the central metabolism of P. putida in response to heterologous protein production. To minimize the impact of culture history, we quantified 13 C incorporation in central metabolites that have a faster turnover rate compared to the end products of the carbon metabolism. Data was collected from the cultures grown in the presence of 13 C-labeled glucose (Fig. S2 ).
P. putida generates an apparent excess of ATP under heterologous protein production conditions The rearrangement of central carbon fluxes for heterologous protein production point to an important adjustment of energy metabolism. We therefore calculated redox and ATP fluxes from the measured flux distributions as described in the Materials and Methods section. In agreement with previous studies  (Kohlstedt and Wittmann, 2019; Nikel et al. , 2015) , our data indicate that P. putida WT generates a catabolic excess of NADPH (Fig. 5A ). Isocitrate dehydrogenase (Idh) supplies roughly half of the NADPH, and malic enzyme a third. The oxidative branch of the PP pathway contributes very little, despite it Fig. 4. Relative carbon flux distributions of KT2440 WT (A) and P. putida CAP (pSEVA438_MBPeGFP) (B) grown on glucose with or without 3-MB.
Acknowledgements The authors thank  MetaToul (Metabolomics & Fluxomics Facilities, Toulouse, France , www. metatoul. fr) and its staff for technical support and access to the NMR facility. MetaToul is part of the  French National Infrastructure  for Metabolomics and Fluxomics (www. metabohub. fr), funded by the  ANR  ( MetaboHUB-ANR-11-INBS-0010 ). Edern Cahoreau  is gratefully acknowledged for assistance with NMR spectroscopy. The  French National Research Agency (ANR)  and the  German Research Foundation (DFG)  are acknowledged for funding of the  CHEAP  project and the  ANR  for funding a post -doctoral fellowship  to P. V.
Based on the author's experience in safety systems, the project team decided to undertake in-house the development of the safety system, with the valuable support and expertise of an international inspection and certification company. This article focuses on the technical, practical and regulatory aspects of the safety system of RECIF installation. Readers can also refer to other research works for a more in-depth understanding of ventilation aspects in relation to other hydrogen installations, such as the studies performed by Dixit  [24] , Lee  [25] , Cerchiara  [26]  and Patel  [27]. In the former three articles, the ventilation design and sensor placement are analyzed with the aid of computational fluid dynamics (CFD), while the latter takes a more experimental approach. Other researchers focus their attention on the analysis of incidental events databases and the lessons learned from them.
ISO 13849-1 standard provides safety requirements and guidance on the principles for the design and integration of safety-related parts of control systems; it defines a performance level (PL) of a SIF in terms of probability of failure per hour and gives guidance in the determination of the required PL (PLr) based on the severity of injury (S), the frequency and/or exposure time to hazard (F) and the possibility of avoiding hazard or limiting it harm (P). The analysis of the PL of SIF #1a and #1b was conducted using SISTEMA tool  [49] , a free piece of software developed by DGUV (German Social Accident Insurance) which provides support in the evaluation of safety in the context of ISO 13849-1 standard. The SIS-TEMA project is available for the reader from RECIF safety system GitHub repository  [46].
of the background concentration of IEC 60079-10-1  [41] , eq. C. 1, where the factor f is defined). Fig. 20  shows the block diagram of all the equations presented so far, with the addition of the safety system that changes the extractor flow rate when the gas concentration X g reaches the alarm level X alarm. This model was implemented in MATLAB/Simulink with the parameters presented in Table  10. Fig. 21  shows the simulation results for the case of a H 2 leak in the fuel cell circuit (left) and electrolyzer (right). The case of a NH 3 leak is not presented in this article; however, the complete study which includes the case of NH 3 is available in Ref. [46]  and shows that the values of X g remains very low (X g <2. 5% LFL throughout the release of ~9 kg of NH 3 ), so the danger of explosion is negligible in comparison to the toxicity hazard.
Acknowledgements This study was founded by the  French National Research Agency (ANR)  under project identifier ' ANR-18-CE05-0043 '. The author wishes to acknowledge the partners of  RECIF  project:  H 2 SYS (fuel cell integrator, France) ,  FCLAB (Fuel cell laboratory ,  CNRS, Franche-Comté University, France)  and  PROMES laboratory (CNRS, France). Special thanks to  Pascal Ortega  ( GEPASUD laboratory, University of French Polynesia ) and  Fabien Harel  (fuel cell research engineer, H 2 SYS scientific advisor).
This genetic alteration is associated with more aggressive tumors, higher risk of recurrence after surgical resection and lower overall survival rates  (Miura et al, 2012; Ahn et al, 2014; Schulze et al, 2015; Kang et al, 2019). Moreover, mice with forced FGF19 overexpression develop hepatic tumors  (Nicholes et al, 2002; Zhou et al, 2014a Zhou et al, , 2017c) )  while targeting of FGFR4, the main FGF19 receptor in the liver, reduces tumor growth  (Hagel et al, 2015; French et al, 2012). Importantly, an inhibitor of FGFR4, Fisogatinib (BLU-554), has demonstrated clinical activity in HCC patients with aberrant FGF19 expression  (Kim et al, 2019)  (phase I clinical trial, NCT02508467). The interconnection between FGF19 oncogenic and metabolic activities, as well as the relative contributions of the downstream effectors of the hormone, remain poorly characterized.
These first trials generated mixed results. A phase 2b randomized clinical trial involving 160 patients with NASH compensated cirrhosis treated with Aldafermin for 48 weeks is ongoing (NCT04210245). Here, we investigated the effects of FGF19/15 and Aldafermin in the framework of oncogenic cooperation with several common HCC drivers. We reasoned that a diseased, inflamed liver is subjected to microenvironmental insults that may result in increased somatic mutation incidence, as shown in the context of cirrhosis  (Rebouissou & Nault, 2020). It was therefore of interest to assess possible tumor promoting effects of oncogenic combinations involving FGF19 and its analogs.
Plasmids All plasmid sequences were validated by whole-plasmide sequencing (SNPsaurus). pCMV(CAT)T7-SB100 was a gift from Zsuzsanna Izsvak (Addgene plasmid #34879). SgTp53 sequence (from Zhang Lab database): 5'-ATAAGCCTGAAAATGTCTCC-3', was cloned into LentiCRISPRv2 vector. Plasmids constructs pSBbi-RN-FGF19 and pSBbi-BB-FGF15 were respectively generated by cloning the human FGF19 amplified from Huh7 cDNA and the mice Fgf15 amplified from ileum cDNA onto the pSBbi-RN (Addgene #60519) and pSBbi-BB (Addgene #60521) plasmids digested by the SfiI restriction enzyme. Murine CCND1 and human CTNNB1 S33Y open reading were cloned into pSBbi-BB and pSBbi-RN, respectively, using the same strategy. Aldafermin (NGM282) coding sequence was generated from pSBbi-RN-FGF19 plasmid using site directed mutagenesis (Q5® Site-Directed Mutagenesis -New England BioLabs) using the primers FOR 5'-ATCGGGCCTCTGAGGCCATGCGCGACTCGTCGCCCCTCGTGCACTACGGCTGG-3' and REV 5' -CGATGGCCTGACAGGCCTTACTTCTCAAAGCTGGGACTCC-3'.