dataesr/scientific-paragraphs-categorization huggingface dataset:
We then released the pressure at the inlet and recorded the decrease of intracavity pressure. In the second step of the experiment, collagen is in compression because the pressure in the cavity is greater than that in the inlet (bottom inset in Fig. 3A , and curves in the light gray sectors of Fig. 3, A  and B ). We note that these experiments could be reproduced at least three times consecutively with the same kinetic response in the cavity. Our data indicate a marked slowdown of the relaxation dynamics in compression for native versus CL collagen gels, whereas the responses in tension are comparable. While presented experiments were conducted with pepsin solubilized collagen, this response was detected independently of the collagen purification method, including acid soluble collagen (fig.
Fluid injection with a step in pressure is associated to a diffusion-like propagation of the pressure field in the material  (25). The effective diffusion coefficient D scales as κM/μ, resulting in a consolidation layer of comparable , and 10 -15 m 2 ) of the permeability κ. The three black curves correspond to the limit of a rigid porous material described by Eq. 5. (B) The scheme on the left is the configuration of the collagen slab for finite element simulations. The collagen is clamped on its sides and at the bottom (black solid lines), and the injection tube and top interface are free (dashed lines). The colormap shows the simulated normalized pressure field. (C) The plot shows the comparison of the 1D kinetic model for the same parameters as in (A) and the numerical simulations (solid curves and dotted datasets, respectively).
The resulting data were fitted by coding the analytical solution (Eq. 4) in MATLAB and using the "fit" function to retrieve M and κ. Simulations were run with COMSOL Multiphysics 6. 0 using the linear poroelastic module, which allowed coupling of the Darcy's law and the solid mechanics modules. We set the tube radius r 0 to 100 μm and placed it 300 μm above the glass coverslip and 0. 8 mm from the top outlet of collagen. The collagen slab was 2. 5 mm in width and 1. 1 mm in height. An inlet pressure P M was imposed onto the collagen gel channel border, creating a double boundary condition defined as a pressure in the Darcy's law module and a boundary load in the solid mechanics module. We used the "boundary ODEs (ordinary differential equations) and DAEs (differential-algebraic equations)" module to solve the boundary condition in the air cavity.
Acknowledgments: We thank  T. Ando  for artwork and  R. Tan  for English proofreading. We also thank  P. Assemat  for help in setting up the COMSOL simulations. Funding: J. C. acknowledges the  JSPS  for a postdoctoral fellowship. D. A. acknowledges the  WINGS-QSTEP program   for doctoral fellowship. We thank the  LIMMS (CNRS-Institute of Industrial Science, University of Tokyo)  for financial support. This research was partly supported by the  Grant-in-Aid for JSPS Fellows  ( 20F20806 ),  AMED P-CREATE  ( JP18cm0106239h0001 ), and  JSPS Core-to-Core Program  ( JPJSCCA-20190006 ). Author contributions: Conceptualization: J. C. , A. B. , and J. O. -M. Formal.
ACKNOWLEDGMENTS This work received funding from the  European Union   H2020  Programme under a  European Research Council   Consolidator grant  [MOLEMAT, 726360]. Support from the  Spanish Ministry of Science and Innovation  ( PID2019-111774RB-100/AEI/10. 13039/501100011033  and  INTERACTION {PID2021-129085OB-I00} ) and the  French ANR  ( ANR-18-CE05-0021 ) is also acknowledged. The authors are grateful to  Mr. Julien Hunel  for assistance with the SPM instrumentation.
Evaluation of the EM input scenario The EM input loads data come from a global JT-60SA EM calculation (of the whole 20 • sector, details in Fig. 2 )  [5, 6]. Specifically, the data provided are the magnetic field B values in the three coordinate directions for a representative point during EM transients: these input data will be used to evaluate the Eddy Currents in the component and, hence, the EM loads. Being that the OVT target is rather small in comparison with the overall sector (hence no substantial B field spatial variations are foreseen inside its volume), the input point has been chosen to be in the centre of a HHF OVT target (crossed point in the bottom part of Fig. 2 ). Moreover, these input data have been provided for two different disruptions: a Major Disruption (MD) down 4 ms as well as a Vertical Displacement Event (VDE) 30 ms.
In particular, the radial and vertical fields (Bx and Bz, respectively) are given in terms of time distributions (example in Fig. 3 ). On the other hand, the toroidal By field is static and it varies with 1/R (with B = 2. 25 T at R = 2. 96 m), reaching By = 2. 505 T at the chosen point. This local EM analysis technique, that uses a space-uniform, timevarying B field as input, has been developed using the Ansys Maxwell  code and it has been already benchmarked and employed in other applications for Fusion community (for example in  [7] ). It is a simplified method: since the analysed domain is an isolated system inside a uniform magnetic field, the resulting forces are null, hence it is not appropriate to calculate net forces. However, it also presents some interesting advantages: setting-up the model is rather simple (prior to a proper "cleaning" of all inconsistencies in the CAD geometry), the mesh is automatically done (only with tetrahedrons) and might also be automatically optimised, calculations are rater fast, the results are easily transferred to the Ansys Workbench mechanical suite.
Acknowledgments This work has been carried out within the framework of the  EUROfusion Consortium , funded by the  European Union  via the  Euratom Research and Training Programme  (Grant Agreement No. 101052200 -EUROfusion ). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Commission. Neither the  European Union  nor the  European Commission  can be held responsible for them.
Recording and Stimulation Performances of Planar Microelectrodes The effect of PEDOT:PSS coating on PtSi planar electrodes was investigated through Electrochemical Impedance Spectroscopy (EIS), Cyclic Voltammetry (CV), and Voltage transient experiments (see details in the Experimental Section). Uncoated PtSi electrodes of identical diameters were also tested as controls. EIS was performed on coated electrodes to assess their recording performance. Data were acquired in a two-electrode configuration using a Pt electrode as the counter electrode in the frequency range 10 0 -10 5 Hz with an amplitude of 20 mV in a 0. 9%w NaCl solution. Figure  2a ,b shows the impedance modulus (|Z|) and phase spectra of bare PtSi, GS-coated, and PD-coated Ø30 μm electrodes. The PtSi electrode possesses a high impedance modulus, and fluctuations within the frequency range 10 0 -10 2 are due to a lack of sensitivity of the equipment in low frequency range.
Equivalent Circuit Model To further interpret these results, we conducted a theoretical analysis of the equivalent circuit model corresponding to our system, as shown in Figure  4d. Here, we demonstrate that our data can be fairly well described and modeled using a simple fiveelements circuit, composed of an electrolyte resistance (R s ), a charge-transfer resistance (R ct ), a double layer capacitance (C dl ), as well as a system capacitance (C p ) and system resistance (R p )  [54]  (see Section S4, Supporting Information, for the resulting equation). The electrolyte resistance, R s , is measured between the working electrode and the platinum reference electrode; hence, it also includes all resistances originating from the access lines and contact pads. [33, 62] R ct and C dl are both associated with phenomenon at the electrode/electrolyte interface.
Acknowledgements This work was supported by the by the  European Commission  through the  NEUREKA  project (grant agreement number  863245 ) and by the  LAAS-CNRS  micro and nanotechnologies platform, a member of the  Renatech French national network. The authors would like to thank  Dr. Aurélie Lecestre  and  Adrian Laborde  for their help during the fabrication process,  Samuel Charlot  for his assistance with the packaging of the NEAs,  Dr. Venkata Suresh Reddy Vajrala  for his guidance during electrochemical characterizations and  Matthieu Sagot  for his experience in data analysis.
Foam formation was avoided by automatic addition of a 10% (v/v) anti-foam aqueous solution (Biospumex 153 K, PMC Ouvrie, France). The added masses of carbon source (i. e. , propionic acid, NH 4 OH and anti-foam solutions were monitored in a real time course by weight recording. Data acquisition and online monitoring were handled by the homemade software BossView implemented using LabVIEW 6. 1 (National Instruments, USA). The CD medium composition for biomass production in the bioreactor was slightly modified: (i) the concentration of glucose was increased to 20 g. L -1 (and not higher to avoid significant inhibition by glucose  [40, 41] ), targeting 8-10 g. L -1 of biomass according to the growth yields reported in the literature (0. 4-0. 5 g Xt. g Glc -1  [6, 40, 41] ); (ii) the total phosphorus concentration was designed to provide only the quantity of P sufficient to form 8 g Xr.
Acknowledgement The authors are very grateful to  Claudine Charpentier  and  Véronique Perrier  for technical assistance,  Véronique Martinez  for everyday-life troubleshooting at the biotech lab,  Eric Dubreucq ,  Laurent Rouméas  and  Robin Boiron  for chemical lab equipment resources and  Alexis Bessière  for polymer extraction and DSC measurements. The authors also thank  Jérome Lecomte  for scientific discussion on chemical extraction of polymers,  Philippe Dieudonné -George  for WAXS analysis,  Cédric Totee  for NMR measurements,  Stéphanie Dupoiron  and  Quentin Subrenat-Margariti  for SEC analysis and the Carnot 3BCar institute (consolidation project  CARAPOLSEC ) who supported the SEC-MALS method development. We finally thank  Etienne Paul ,  Elise Blanchet ,  Mattéo Castiello  and  Fanny Allayaud  for scientific discussions in the frame of the  LOOP4PACK  project.
Moreover, the Fig. 8. SEM images of gold-silica LSPR nanoparticles obtained from heteroaggregation using 8 nm gold nanoparticles with silica nanoparticles with a size of 600 nm (a), 400 nm (b) and 150 nm (c) SEM pictures were taken with the as-synthesized silica-gold NPs, without additional metallization. literature does not provide many data concerning the influence of the kinetics parameters (temperature and reaction time). It resulted in an inhomogeneous and non-reproducible coverage of the silica nanoparticles. On the other hand, the heteroaggregation method allowed to show that the coverage of silica nanoparticles with gold nanoparticles is quite dense, homogeneous and reproducible. There is an influence of the silica particle size, or more precisely of the surface curvature, on the ability of the metallic particles to deposit onto the surface to be grafted.
Acknowledgments Financial support of this work from the M-era. Net is gratefully acknowledged (contract number  M-ERA. NET2/2019/8/2021 ). The postdoctoral position granting was also possible thanks to the  National Centre for Scientific Research (CNRS) in France. We also would like to acknowledge the support of the  National Centre for Research and Development (NCBR)  in Poland. Acknowledgments to  Prof. Vincent Sol  and  Prof. Vincent Chaleix  for providing access to the microwave equipment used in this study. Acknowledgement to  Dr. Amandine Magnaudeix  for providing access to the Quickdrop equipment. Acknowledgements to  Mr. Pierre Carles  and  Mrs. Eloise Hyvernaud  for TEM and SEM observations, respectively.
ACKNOWLEDGMENTS L. R. -S. , J. T. , and C. C. acknowledge the  Spanish Agencia Estatal de Investigación  (Project No. PID2019-105089GB-I00/AEI/10. 130397501100011033 ) and the  U. S. Army Research Laboratory Cooperative  Agreement No. W911NF1920279  issued by the  U. S. ARMY ACC -APG-RTP.
Interestingly, the operon TERMP_(00275-00281) includes a transcriptional regulator gene (TERMP_00277) that encodes TrmBL4 (TrmB-like protein 4), which is a family member of the global transcriptional regulator TrmB, known for its regulation of sugar metabolism. TrmB is especially known to be a repressor and/or activator of sugar ABC transporters and as a histone-like chromosomal stabilizer in Archaea  (Gindner et al. , 2014; Kim et al. , 2016). Proteins of the TrmB family  (TrmB, TrmBL1, TrmBL2, TrmBL3, TrmBL4, and TrmBL5)  are present in 13 groups of Euryarchaeota  (Lee et al. , 2008). Pyrococcus furiosus has been shown to possess the genes trmB (PF1743), trmBL1 (PF0124), trmBL2 (PF0496), and trmBL3 (PF066;  Lee et al. , 2008). Transcription assays show that TrmB of P. furiosus acts as a transcriptional repressor for the trehalose/maltose transport system (TM system) with high DNA binding affinity and for the MD system with lower affinity; this inhibition can be removed by the addition of specific sugars  (Lee et al.
Recovery and concentration of extracellular proteins by ultrafiltration To recover the secretome, the WT, and the mutant cells were cultured in 1-L bottles with 330 mL TRM medium with and without sugars and then incubated at 85 C under P atm with shaking at 200 rpm. As noted above, each of the three sources of sugar used was added to give a final concentration of 0. 5 g/L except for the pectin, for which this was 0. 15 g/L, because pectin interferes with the good separation and visualization of proteins. This minimum concentration of pectin was determined after several kinetic studies. For this, growth tracking of the cells was carried out in TRM medium in the presence of different concentrations of pectin (0. 1, 0. 15, 0. 2, 0. 25, 0. 3, and 0. 5 g/L) at 85 C (data not shown). Sulphur was added (0.
Gel lanes were sliced into pieces and sent to the PAPPSO platform (Plateforme d'Analyse Protéomique de Paris Sud Ouest) for LC-MS/MS analysis on a 1D gel track (shotgun); the cytosolic concentrated proteins were rather used as a control for the secretion process. The gel slices were digested with 200 ng trypsin, then the samples were dispersed in 20 μL loading buffer and 4 μL were injected on a Fusion Lumos Orbitrap Tribrid mass Target gene Primer sequence spectrometer. Quantitation of the proteins in all samples was performed using a Nanodrop 8000. Data processing was done with the X!TandemPipeline (0. 4. 40). TERMP_00276 Fwd 5 0 ATTGCCTCATTGTTAATGCCGTATTCTCC 3 0 TERMP_00276 Rev 5 0 TTAATTATGTCTCCGGTAAGCCGCG 3 0 TERMP_00278 Fwd 5 0 CCACCTACAACAGTGACAACAACAAA 3 0 TERMP_00278 Rev 5 0 GGGGAATTACCTCATATTTAACGCCGA 3 0 TERMP_01652 Fwd 5 0 GCTTAAGCAGGGTTATCAGCGTTCT 3 0 TERMP_01652 Rev 5 0 TTGGAGCCCTTCCAAGGGATAGA 3 0 TERMP_01654 Fwd 5 GGAGGCGGAACAACCACTC 3 0 TERMP_01654 Rev 5 0 GATTCAAAAACCTGCGCCTCATTTG 3 0 TERMP_01835 Fwd 5 0 CCTTCAATCTCTACAGGCTCCTTGC 3 0 TERMP_01835 Rev 5 0 TTGCTGTTCATGTTTGACAGGTACTTCT 3 0 TERMP_00342 (pcna) Fwd 5 0 GCATGAGGGCAATGGATCCAA 3 0 TERMP_00342 (pcna) Rev 5 0 GGGTTACCTCAAGGAAGTTCTCCTCA 3 0 TERMP_00128 (30S) Fwd 5 0 GGCATAAACTTTGCCACGATGGTG 3 0 TERMP_00128 (.
Analysis of secreted proteins To properly filter the potentially secreted proteins, two procedures were used. The first of these was the detection of signal peptides in silico with the OutCyte 1. 0 program  (Zhao et al. , 2019). The second was the determination of the probability that a protein is secreted or not based on the results of MS analysis of the cytosolic proteins used as a control of secretion. In the calculations, detailed in Data S2, the determination of the secretion probability is based on the ratio of the amount of the protein in the supernatant to its presence in the cytosol while the cells are grown in the presence of the three tested sugars. Consequently, the secretion probability for each protein was determined and a heatmap was constructed based on a colorimetric scale showing the difference of normalized spectra (at 10,000/sample), which intuitively indicates the proteins that are the most abundant in the secretome of the tested samples.
Hypothetical proteins with putative domain annotations were analysed using the Pfam 34. 0  (Mistry et al. , 2021)  wrapper around the HMMER package, which analyses biosequences using hidden Markov models  (Durbin et al. , 1998) , and the UCSC Archaeal Genome Browser database  (Schneider et al. , 2006). In the latter, the basic gene annotation is derived from the NCBI GenBank/RefSeq entries, with conservation overlays of sequences across multiple species and nucleotide/protein motifs, as well as noncoding RNA and operon predictions. The hierarchically clustered heatmap was constructed with the cluster map API of the statistical data visualization Python library seaborn  (Waskom, 2021). The weighted linkage method was used for calculating clusters.
Reverse transcription quantitative polymerase chain reaction Quantitative PCR analysis was carried out on three clusters of genes, each with a representative gene, and primers were designed using SnapGene software (Figure  1  and Table  1 ). qPCR reactions with pcna and the 30S gene were used as an internal control. Three biological replicates were performed for each treatment. Data were recorded and processed using Bio-Rad CFX Maestro software. The analysis was performed using the ΔΔCt method to calculate expression changes compared with the control  (Livak & Schmittgen, 2001; Schmittgen & Zakrajsek, 2000). The control gene corresponds to a gene in the genome expressed under all growth conditions and is, therefore, unregulated. This means that the expression of this gene should be relatively stable under all the studied conditions.
Consequently, the secretome of ΔtrmBL4 cells in the presence of pectin was not studied. To obtain a description of the dataset generated by the LC-MS/MS, a hierarchical clustering heatmap was constructed based on the values, showing the difference of normalized spectra (at 10,000/sample). This method reveals which of the secreted proteins are the most abundant in the secretome of the tested samples (Figure  4  and Data S3). The detected cytosolic proteins were removed from the overall data (Data S1) to focus on the secretome. A total of at least 33 proteins were secreted in the supernatant of the WT and mutant cells cultured in the presence of sugars (Figure  4 , Table  2 , and Data S2). The corresponding heatmap highlights a WT-mutant dichotomy. Figure  4  shows the mutant cells on the left and the WT cells on the right, cultivated in the presence or absence of sugars.
This binding motif includes the second half of the TGM sequence GTGATA. Moreover, the binding site (TACTN 3 AGTA) was detected upstream of TERMP_01968 (TM system) at the positions 1,762,048-1,762,068 and 1,762,230-1,762,240. Two additional binding sites  (TATTCTGAGTT, pos. 232, 146 and TACTN 6 AGATA, pos. 1, 624, 624, 836)  were detected upstream of TERMP_00278 and TERMP_01840. Three additional binding sites (TACTTAGTT, pos. 1,295,540-1,295,548; TACTGCTT, F I G U R E 4 Hierarchical heat map for the visualization of the differential expression of the secreted proteins in each sample. The heat map was produced with the seaborn program using the liquid chromatography with tandem mass spectrometry data and shows only the secreted proteins (Data S3) from Data S1. The samples are shown in the columns and the ordered loci for the genes encoding the secreted proteins in the rows.
Regardless of the mutation, the cells in a sulphur-free medium relied more on the to grow and seemed to use sugars as a source of carbon for growth. These results are supported by studies showing that sulphur may play a role in the metabolism of sugars in Archaea  (Adams et al. , 2001). T A B L E 2 (Continued) The proteomic results highlight critical proteins of the T. barophilus secretome, which are probably at least regulated by the trmBL4 gene product, and others might be impacted by cultivation under non-optimal growth pressure in T. barophilus. The STRING database search for protein-protein interactions  (Szklarczyk et al. , 2019)  of the hypothetical amylopullulanase encoded by TERMP_01372, repressed in the mutant cells, revealed that the adjacent interacting proteins correspond to a secretion system.
This pyruvate could thus be mobilized under stressful pressure conditions, as in the case of glutamate biosynthesis in Thermococcus piezophilus  (Moalic et al. , 2021). Additionally, some proteins were found to be overexpressed in the mutant cells. These included a methyltransferase encoded by TERMP_02130, which may play a role in controlling the profile of gene expression impacted by the mutation. In addition, the search made in the STRING database  (Szklarczyk et al. , 2019 (Szklarczyk et al. , , 2023) )  on another overexpressed key protein encoded by TERMP_00597, showed that the adjacent interacting proteins correspond to ABC transporters, which may indicate their disposition in a potential operon involved in sugar transport. Contrastingly, the expression of the DNA protection during starvation protein and the vWFA domaincontaining protein in all tested conditions may indicate a stress response due to the alteration of nutrient uptake under non-optimal pressure conditions (0.
, 2010; Megaw & Gilmore, 2017). The phenotypic shifts in the behaviour of Archaea caused by stressful growth conditions require many regulatory processes and lead to the development of biofilms  (An & Parsek, 2007). Here, in T. barophilus, the mutation of the trmBL4 gene, addition of sugars, and growth under non-optimal pressure conditions may have altered the biofilm formation by repressing genes exhibiting an enhanced ability to form biofilms. In S. solfataricus, the products of βgalactosidase and α-mannosidase can control the saccharide volume in the biofilm matrix  (Koerdt et al. , 2010). Based on the proteomic results and the KEGG pathway database  (Kanehisa, 2002) , it is clear that maltose and maltodextrin share some enzymes and thus some common pathways, but pectin may not share the main assimilation systems with maltose and maltodextrin due to its complexity.
ACKNOWLEDGEMENTS We thank  Nadège Quintin  for providing the strains used in this work, which are part of the UBO culture collection. We are indebted to  C. Henry  of the  Paris Sud Ouest PAPPSO proteomics core facility  (supported by  INRA ), the  Ile-de-France regional council ,  IBiSA , and the  CNRS  for the LC/MS analyses. We are grateful to  Helen McCombie  of the  University Translation Office  for improving the language. This work was conducted by  Maria Batour  as part of her PhD work co-supervised by the  University of Lebanon  and the  University of Brest. Maria Batour  was supported by a PhD fellowship from the  Lebanese University  and the municipality of  Kadaa Zgharta. Maria Batour  also received support from the  IsBlue , an  Interdisciplinary Graduate School  for the  Blue Planet  ( ANR-17-EURE-0015 ) and cofunded by a grant from the  French government  under the program ' Investissements d'Avenir' , which is part of the  France 2030  plan.
The supernatant only represents the proteins found only in the supernatant, not in the cytosol; the secretion probability is calculated according to the equations in Data S2 and Mal/Max for maltose/maltodextrin. a Prediction of N-terminal signal sequence using the OutCyte 1. 0 program (Zhao et al. , 2019). b Significant hit obtained after a search in Pfam 34. 0 (Mistry et al. , 2021)  and the UCSC Archaeal Genome Browser database for hypothetical proteins (Schneider et al. , 2006). c Search for the TrmB-like binding motif performed in silico on the whole genome of T. barophilus, based on the known TrmB binding motifs in certain Thermococcales. Comparison of the relative expression of the genes of interest representative of the target clusters under different conditions in the presence and absence of maltose, showing the effect of trmBL4 gene mutation (A) and the effect of sugar (B).