dataesr/scientific-paragraphs-categorization huggingface dataset:
This procedure was repeated twice, resulting in four measures of threshold: two of detection and two of disappearance. Data were collected specifically for Exp. I, or prior to Exp. II or III. If one participant participated in both Exp. II and III, then only the first measure was kept (i. e. , from Exp. II). The threshold measurement procedure involved a prolonged exposure to tactile stimulations, and might thus be significantly influenced by sensory adaptation  93. If so, detection of a change should be biased differently, depending on whether the stimulus intensity is being increased or decreased. Precisely, detection of the stimulation should occur at a lower intensity than the feeling of its disappearance. We assessed the sensory adaptation effect by computing, in each participant, the averaged difference between the disappearance and detection intensities.
Model fitting To implement, fit and compare models between experimental conditions and groups, as well as estimating relevant parameters using Bayesian Model Averaging (BMA), we used the VBA Matlab toolbox (http://mbb-team. github. io/VBA-toolbox/)  96. We also assessed the reliability of our modeling approach in performing simulation-based confusion and model identifiability analyses (see Supplementary Methods 1). Data were fitted on the two first context-setting blocks to estimate the sensory precision and on the three last blocks to capture the transition between the third context-setting block and the two TOE blocks. When fitting the models on the behavioral data, we excluded the trials whose RTs were outliers (i. e. , more than three standard deviations away from the participant's mean).
Statistical analyses Descriptive statistics are reported as mean ± standard deviation. Demographic data and tactile detection thresholds were compared between groups using unpaired t-tests. Psychometric functions were estimated in the context-setting blocks using a general linear model with a logit link function. Differential thresholds were determined as the difference in frequency between F 1 and F 2 to reach 75% of correct answers. Within-group repeated measure ANOVAs were used to assess the accuracy and response times over the five blocks, in Exp. II and III, and Tukey's t-tests were used post-hoc. TOE blocks were also analyzed with two-way nested ANOVAs, with the factor participant nested in the factor group and two main factors: group and TOE blocks. To estimate the intercept of the TOE, we first assessed the performance bias for each frequency F 1 , by computing the difference in response accuracy between F 2 > F 1 trials and F 2 < F 1 trials.
20) effect sizes  97, 98. Additionally, we computed Bayes factors (BF) for the main effects of interest using the BayesFactor package in R (with default settings). To interpret the magnitude of this effect, we refer to Jeffreys (1961)  99  and Kass & Raftery (1995) 100 classifications. Unanswered trials were excluded from all the above analyses (they represented less than 0. 01% of all recorded trials). All statistical analyses were performed using R (version 2. 15. 3, http://www. r-project. org/). The threshold for statistical significance was set to p < 0. 05.
ACKNOWLEDGEMENTS We thank all the participants for their precious contribution and feedback. We would like to thank  Judith Vergne  for her help in data acquisition. This study was supported in part by a  Scientific Research Council grant  from the  Vinatier Hospital Center. The work was performed within the framework of the  LABEX CORTEX  ( ANR-11-LABX-0042 ) of  Université de Lyon , within the program " Investissements d'Avenir"  ( ANR-11-IDEX-0007 ) operated by the  French National Research Agency (ANR).
23 C2 and 46 TM2 mirror couples were used to produce G3 eggs (TM2 couples were doubled to produce TM3 and TM2C1 eggs). Finally, 8 C3, TM3 and TM2C1 mirror couples were used to produce G4 eggs that yield the 4 th generation animals, i. e. , C4, TM4 and TM2C2. This breeding strategy was relaxed for the production of generations 5 and 6 where mating within each line were decided in order to minimise average inbreeding. An optimisation program using a simulated annealing approach was used for this purpose (SYSAAF, program available on request). The pedigree of each individual was known 3 generations before the base generation. Divergence between populations was measured using pairwise F ST values between populations using pedigree information as described previously  [15]. Efficiency of the breeding strategy to limit genetic divergence between lines was verified by comparing the observed pairwise F ST values to the 95% confidence interval obtained after 1,000 simulations where pedigree was randomised within each line (Additional file 1: Supplementary Fig.
Statistical analyses Statistical analyses were performed with R software  [16]  version 4. 2. 1 using the packages base, lsmeans  [17]  version 2. 30-0 and multcomp  [18]  version 1. 4-20. Chisquare tests of homogeneity and hypothesis tests in the linear models were performed at a 5% statistical significance level. All statistical parameters including tests used, variables involved in tests, specific comparisons of treatments and sample size are summarized in Additional file 1: Supplementary Table  1. Briefly, three types of analysis were performed: multigenerational analysis ("multig. ") comparing only TM and C treatments (from G1 to G4); intergenerational analysis ("interg. ") only in G3 comparing C3, TM2C1 and TM3 treatments; and transgenerational analysis ("transg. ") comparing C4-7, TM4-7 and TM2C2-5 conditions from G4 to G7 (Additional file 1: Supplementary Table  1 ).
Acknowledgements The authors are grateful to all the members of the  MOQA team at INRAE  for assistance with sample collection and to the staff of the  INRAE PEAT  (2018, https:// doi. org/ 10. 15454/1. 55723 26250 88729 2E12 ) Poultry Experimental Facility for animal care and management. The authors thank  Christine Leterrier  and  Ludovic Calandreau  for their support during the transgenerational rearing of animals.
Availability of data and materials All data supporting the findings in this study are available within the article and its supplementary information files. Raw phenotypic datasets are available from the Recherche Data Gouv public repository entitle "Multigenerational analysis of thermal manipulation of Japanese quail", https:// doi. org/ 10. 57745/ D38TFS. Correspondence and material requests should be addressed to Vincent Coustham (vincent. coustham@inrae. fr).
Discussion In this study, novel MSI markers were selected from blood WGS to enhance an existing amplicon sequencingbased cMSI assay, achieving excellent separation of CMMRD samples from controls. MNRs were used, as these showed increased instability in WGS data from both MSH6and PMS2-associated CMMRD blood samples, whereas increased instability in longer motif microsatellites was found in the PMS2-associated CMMRD blood samples only. This is consistent with MSH6 being involved in the repair of single base (but not larger) indel loops, whereas PMS2 is active across MMR as the endonuclease within the MutL complex. 1, 2 Hence, increased MSI is typically observed in MNRs only in MSH6-deficient tissues. 22, 42 The new MSI markers were longer than the original set, ranging between 11 and 15 bp, which is equivalent to the most sensitive and specific A-homopolymers identified in The Cancer Genome Atlas tumor exome sequencing data.
Acknowledgments The authors thank all of the patients and their families who provided samples for this study. The authors thank the  Genomics Core Facility and Bioinformatics Support Unit, Newcastle University, Newcastle upon Tyne ,  United Kingdom , for their support of genome and amplicon sequencing, and genome sequence analysis, as well as the research group of  Prof Joris Veltman ,  Newcastle University, Newcastle upon Tyne, United Kingdom , for providing a panel of control blood whole genome sequencing data for variant calling. The authors thank  Cancer Research UK  for their support through the  Aspirin for Cancer Prevention (AsCaP)  ( C569/A24991 ) and  CaPP  ( C1297/ A15394 ) groups, and  The Barbour Foundation  (UK charity 328081). Collection of clinical data and biological samples for French patients was supported by la  Fondation Gustave Roussy campaign :  Guérir Le Cancer  de l'Enfant au 21ème siècle.
The study received nonfinancial support from the  European Reference  Network on genetic tumour risk syndromes (ERN GENTURIS) -Project  ID No 739547. ERN GENTURIS is partly co-funded by the  European Union  within the framework of the  Third Health Programme "ERN-2016-Framework Partnership Agreement 2017-2021 ". The authors thank the AsCaP steering committee members for their support:  Prof Jack Cuzick ,  Queen Mary University of London  (chair),  Prof Frances Balkwill ,  Queen Mary University of London ,  Prof Tim Bishop ,  University of Leeds ,  Prof Sir John Burn ,  Newcastle University ,  Prof Andrew T. Chan ,  Harvard School of Medicine ,  Dr Colin Crooks ,  University of Nottingham ,  Prof Chris Hawkey ,  University of Nottingham ,  Prof Ruth Langley ,  University College London ,  Ms Mairead McKenzie ,  Independent Cancer Patients' Voice ,  Dr Belinda Nedjai ,  Queen Mary University of London ,  Prof Paola Patrignani ,  Università G.
Methods We compiled oceanographic data from DSDP, ODP, and IODP expeditions that cover the late Miocene and early Pliocene. Data mining was performed by automatically collecting the Pangaea data sets that correspond to the selected time interval and that have at least one of the following variables: sedimentation rate, dry bulk density, MAR, CaCO 3 accumulation rate, bSiO 2 accumulation rate (biogenic SiO 2 ), %CaCO 3 , %bSiO 2. The compilation was then improved by manually adding data sets absent from Pangaea but relevant to our study. The data compilation contains 154 data sets (122 are from Pangaea) from 118 different ocean drilling sites (Table  1 ). We assumed that in each publication, one site represents one data set, which means that in a publication there are as many sites as data sets.
Bottom, compilation of some publications related to the LMBB. 2020 ) time scale to be able to perform a temporal comparison of the data sets. This step was performed using the Neptune Sandbox Berlin database  (Renaudie et al. , 2020)  and NSB Companion software  (Renaudie, 2019) , following a procedure similar to the one in  Dutkiewicz and Müller (2022). In addition, a geographical and temporal averaging system was implemented to plot time series. For a given oceanic basin (Pacific, Atlantic, and Indian) and a given variable, all values of the corresponding data sets were grouped and segmented into 500 kyr bins to obtain a single time series. This segmentation implies that the values of the bins are dated at ±250 kyr. 500 kyr was chosen because many data sets-in particular  Lyle (2003) -had a resolution of 500 kyr.
Acknowledgments The authors are grateful to the associate editor and the two anonymous reviewers for their relevant comments and suggestions that clearly improved the manuscript. The authors thank  Johan Renaudie  for his help in using Neptune Sandbox Berlin. The authors also thank  Weimin Si  for access to data and  Jean-Baptiste Landant  for discussions. This research was funded by the  French ANR  project  MioCarb awarded  to  BSM  ( ANR-20-CE49-0002 ).
Data Availability Statement All data used in this study have been previously published. However, because of the reevaluation of age-depth models, all data sets as used in this study have been deposited on SEANOE  (Pillot, 2023). The metadata and the original citations are available for each data set in Table  S1  and on SEANOE  (Pillot, 2023). The time series of all variables in all data sets are also available in this repository.
The models are applied to Lobodice town gas storage (Czech Republic), where a conversion of hydrogen to methane was measured during seasonal gas storage. Under abiotic conditions, no reaction is simulated. When the classical thermodynamic approach for aqueous redox reactions is applied, the simulated reactivity of hydrogen is too high. The proper way to simulate hydrogen reactivity must include a description of the kinetics of the aqueous redox reactions. Two models are applied to simulate the reactions of hydrogen observed at Lobodice gas storage. One modeling the microbial activity by applying energy threshold limitations and another where microbial activity follows a Monod-type rate law. After successfully calibrating the bio-geochemical models for hydrogen reactivity on existing gas storage data and constraining the conditions where microbial activity will inhibit or enhance hydrogen reactivity, we now have a higher confidence in assessing the hydrogen reactivity in future UHS in aquifers or depleted reservoirs.
Calculation code, thermodynamic database and kinetic rates The modelling tool used is the widely used PHREEQC 3. 0  (Parkhurst and Appelo, 2013)  which include the relevant processes and their kinetics. The database used is a modified version of the phreeqc. dat database included in the installation. The phreeqc. dat database includes data to allow solubility computation of gases at high pressure with the Peng-Robinson equation of state. As considered in PHREEQC, without a calibration of binary interaction coefficients, hydrogen solubility in water can be slightly underestimated at increasing pressure (see Supplementary Material). Also, specific volumes of aqueous species are calculated as a function of the dielectric properties of water and the ionic strength of the solution, which allows calculation of pressure effects on chemical reactions and on solution density, potentially significant at the high pressures relevant for hydrogen storage.
Model B: Abiotic reactions, without reactivity of hydrogen As highlighted by the literature review, if the redox reactions involving hydrogen are not catalyzed by bacterial activity, these reactions are almost negligible at the time scale of gas storage operations. Therefore, assuming abiotic conditions, it is reasonable to consider hydrogen as being non-reactive. Using PHREEQC, it is possible to have two separate, decoupled versions of hydrogen. The first type of hydrogen is the classical hydrogen system, involved in the water dissociation, pH and many chemical reactions. The second type of hydrogen is a chemically inert hydrogen that is only involved in gas-liquid dissolution equilibrium. In the phreeqc. dat thermodynamic database, the decoupled unreactive type of hydrogen is called "Hdg".
(1998) , when the reaction is written so that 1 mol of H 2 reacts. Similar energy thresholds were obtained in a thermodynamic model of microbial community dynamics  (Delattre et al. , 2020). In the threshold energy model used in this work, the kinetics of the redox reaction process is coupled to thermodynamic constraints. A 0order kinetic law is considered, using reaction rates determined by  Berta et al. (2018)  or fitted to Lobodice gas composition evolution (Table  3 ). The metabolic energy thresholds for each terminal electron accepting process are considered by calculating effective equilibrium constants, adding the energy threshold to the Gibbs free energy of the reaction  (Jakobsen, 2007). To simulate a kinetic control on the reactions of sulfate-reduction, methanogenesis and acetogenesis using PHREEQC, it is necessary to use a thermodynamic database where oxidized S (+VI) and reduced S (-II) sulphur as well as CH 4 , CO 2 and acetate are uncoupled so that the transformations are controlled by kinetics.
The phreeqc. dat, thermodynamic database was adapted for this context. Only the formation of methane from hydrogen and carbon dioxide was included in the model, but not from hydrogen and carbon monoxide. The study of  Berta et al. (2018)  reports kinetics for the consumption of hydrogen by the acetogenesis and sulphate-reduction reactions measured in the laboratory under conditions corresponding to UHS. Methanogenesis was not observed in this study. Under conditions where the H 2 partial pressure is high (2-15 bar) and much higher than the natural H 2 content in a reservoir, the kinetics of hydrogen consumption turn out to be similar, regardless of the H 2 partial pressure or salinity of the solution. Although probably catalyzed by bacterial activity, the kinetics of consumption of hydrogen does not depend on bacterial dynamics in these experiments.
Gas composition evolution Since the gas evolution (injected and produced) is the information available from Lobodice gas storage, the presentation of simulation results is primarily focused on the evolution of the gas composition. Comparison between the different model results and field data for hydrogen, methane and carbon dioxide contents and the evolution of the relative gas volume is shown in Figure  1. Using Model A, the reactive model considering redox reactions at thermodynamic equilibrium (Figure  1A ), the gas composition evolves to its final composition during the first time-step, corresponding to a total consumption of hydrogen. The content of methane reaches 70% in this simulation, compared to 40% of methane at the end of the seasonal storage at Lobodice. The carbon dioxide content decreases to 5.
9% in this simulation, while 8. 8% was measured. This simulation leads to a decrease to less than the half of the gas volume (46%) (Figure  1D ). Using the dissolved sulfate ions in solution, about 0. 01% of hydrogen sulfide is forecasted when redox reactions occurs at thermodynamic equilibrium. The hydrogen sulfide content is then calculated to increase at about 1. 25% after 7 months of storage, because of the progressive dissolution of gypsum, controlled by kinetics, reacting with the formed methane. No data on hydrogen sulfide is available at Lobodice  (Smigan et al. , 1990; Buzek et al. , 1994). The assumption made in the abiotic model (model B) is that redox reactions involving hydrogen are not catalyzed by microbial activity implying that hydrogen is inert. Accordingly, gas evolution simulated with the abiotic model shows no changes, with the hydrogen, methane and carbon dioxide contents remaining at their initial value (Figure  1A ).
Despite uncertainties on the kinetic reaction rates, methanogenesis and hydrogen sulfide production were mainly limited by the availability of carbon dioxide and sulfates, respectively. Only small mineral changes were simulated, including anhydrite and calcite dissolutions. Hassannayebi et al. (2019)  used batch simulations to simulate the reactivity of hydrogen in a reservoir corresponding to the formation hosting the injection of the Underground Sun Storage project in Austria. They considered a kinetic limitation on the rate of hydrogen dissolution in water rather than on the aqueous redox reactions. Focus was on sulfate reduction and pyrite to pyrrhotite reduction, as the reaction of methanogenesis and acetogenesis were decoupled in the thermodynamic database and, therefore not occurring.
11% at 10 keV neutron energy  [14] ). The 77 Se sample consisted of 1 g of 99. 66% enriched Se metal powder which was pressed into a cylindrical shape of 2 cm diameter and 0. 7 mm thickness. The sample was glued on a thin Mylar backing foil fixed onto an aluminum ring. In addition, a 197 Au sample was used in the experiment to normalize the data, and an empty sample holder and natural carbon sample were used to estimate the background. The dimensions and composition of all samples used in the experiment are listed in Table  I. The prompt γ radiation emitted after neutron capture was detected with a set of four liquid scintillation detectors filled with deuterated benzene (C 6 D 6 ), which were optimized to achieve an extremely low sensitivity to scattered neutrons  [15]. Data were recorded by means of 14-bit digitizers operated at a sampling rate of 1 GHz.
Detection efficiency The detection efficiency of a C 6 D 6 scintillator depends on the γ -ray energy, hence the efficiency of detecting a neutron capture event depends on the deexcitation pattern of the compound nucleus. To compensate for its variation from different resonances, we used the total energy detection principle in conjunction with the pulse height weighting technique (PHWT)  [17]. It is based on applying a pulse height dependent weight to each signal, resulting in a detection efficiency that is proportional to the excitation energy of the compound nucleus. The weighting function was calculated using simulations of the detector response to monoenergetic γ rays ranging from 100 keV to 10 MeV using GEANT4 Monte Carlo code  [18] , including a detailed geometry of the experimental setup.
B. Parameters of isolated resonances Neutron resonances in the capture yield determined in equation (1) were analysed using the multilevel multi-channel R-matrix code SAMMY  [22]. SAMMY fits resonance parameters, i. e. , resonance energy E R and neutron and radiative widths for a given resonance spin J and parity π. Experimental effects, such as resonance broadening due to thermal motion of atoms (Doppler broadening) and the resolution of the experimental facility, as well as multiple interaction and self shielding of neutrons in the sample material, are taken into account. In general, capture data alone do not allow one to reliably determine all resonance parameters, γ , n , E R , J, and π. Thus the table in the Supplemental Material  [23]  provides only resonance energy E R and capture kernel K, defined as K = g γ n γ + n , ( 2 ) with g = 2J + 1 (2I + 1)(2s + 1) , ( 3 ) where I is the spin of the ground state of the target nucleus (I π = 1 2 -for 77 Se), and s = 1 2 is the neutron spin.
Values for all identified resonance structures up to 51 keV are tabulated in the Supplemental Material  [23]. The uncertainties listed in the table are only those coming from the fit. Systematic uncertainties comprise 3% from PHWT, 1% from normalization, 1% to 5% from flux, and 0. 01% in sample composition and mass. The total systematic uncertainty is thus 3. 3% below neutron energy of 100 eV, 3. 7% between 100 eV and 10 keV, and 5. 9% above 10 keV. Figure  2  then compares the SAMMY fit to the data for several energy regions. The resonance spins, considered in SAMMY fitting, are known only for a very restricted set of resonances. For resonances listed in the ENDF/B-VIII. 0 database  [12] , we used the spin assignment from this database for the majority of them. Three s-wave resonances at 483, 1780, and 2033 eV  [6, 7]  had their spins reassigned based on analysis of the γ -ray energy spectra deposited in C 6 D 6 detector.
We compared the observed number of resonances with a kernel higher than a threshold with predictions from aforementioned simulations based on the statistical model. Several different thresholds and maximum neutron energies were adopted in testing. All their combinations yielded results consistent with D 0 = 110(15) eV. The deduced D 0 was found to be largely independent of exact values of S and parameters of γ distributions if their values were consistent with those mentioned above. Our D 0 value is in agreement with 110  (11)  eV reported in Ref. [11]. We also used URR cross section data in the range E n = 20-200 keV to estimate average resonance parameters using the FITACS package in SAMMY  [22, 29]. Fixing D 0 to 110 eV and setting channel radius to R = 6. 03 fm yielded S 0 = 1. 38(34) × 10 -4 , S 1 = 2.
The experimentally determined MACS can be also used for constraining models used in reaction codes for cross section calculation. TENDL 2021 library  [28]  provides predictions of MACS for kT = 30 keV obtained with different models used in the TALYS 1. 96 reaction code  [33]. These predictions cover a broad range of values from 200 to 700 mb. TENDL also offers a set of 12 preferred models with calculated MACS range of 295-527 mb. Our experimental value agrees within the uncertainty only with two of these 12 models. 1  We quantified the impact of our new MACS * for a 15M star with a subsolar metallicity of Z = 0. 006. The stellar structure was computed in Ref. [40] , and the choice of this specific metallicity was motivated by the peak in weak sprocess elements production that occurs at Z = 0.