dataesr/scientific-paragraphs-categorization huggingface dataset:
Clinical Signs, Rectal Temperature and Body Weight Gains No respiratory clinical symptoms were observed in any pig after vaccination or after challenge. During the first week after challenge, the rectal temperature in the REC group exceeded those of the other groups, however, using a threshold of 40. 0 °C, only very few pigs developed fever during the post-challenge period (data not shown). The average daily body weight gain (ADWG, kg per day) for the post-vaccination (dpv 0-28) and post-challenge (dpv 28-59) periods on the group level are shown in Figure  The average daily body weight gain (ADWG, kg per day) for the post-vaccination (dpv 0-28) and post-challenge (dpv 28-59) periods on the group level are shown in Figure  2  and the data for each study week are shown in Figure  S1. Overall, there were no significant differences between the ADWG of the groups; however, the REC group had a significantly decreased ADWG during the first week after challenge when compared to the UNI group only.
These results confirmed that the vaccinated animals indeed were infected by the REC strain after challenge. Oral fluids (OF) were collected at dpv 24 (dpc -4) and dpc 3, and subsequently, every week after challenge until the end of the study (dpc 8, 14, 21 and 28) (Table  2 ). PRRSV-1 was detected in one pen of each vaccinated group at dpc -4. After challenge, the PORCI group had at least one pen positive for PRRSV-1 until dpc 21, where all OF samples in all three vaccinated groups tested negative for PRRSV-1. At dpc 28, one pen of all three vaccine groups tested positive. The REC and REF groups tested positive in both pens at dpc 3 and 8, but only the REC group stayed positive in both pens until dpc 21. At necropsy at dpc 28, all OF samples were negative in the REC and REF groups. Tests of samples from dpv 24 with the RT-PCR assay specific for the REC strain revealed negative results, whereas tests of serum samples from dpv 36 (dpc 8) generated positive results (data not shown).
Clinical Signs, Rectal Temperature and Body Weight Gains No respiratory clinical symptoms were observed in any pig after vaccination or after challenge. During the first week after challenge, the rectal temperature in the REC group Vaccines 2021, 9, 417 6 of 12 exceeded those of the other groups, however, using a threshold of 40. 0 • C, only very few pigs developed fever during the post-challenge period (data not shown).
3. 2. 2 Clinical Signs, Rectal Temperature and Body Weight Gains No respiratory clinical symptoms were observed in any pig after vaccination or after challenge. During the first week after challenge, the rectal temperature in the REC group Vaccines 2021, 9, 417 6 of 12 exceeded those of the other groups, however, using a threshold of 40. 0 • C, only very few pigs developed fever during the post-challenge period (data not shown).
Raman data were obtained using a Renishaw INVIA spectrometer (IMPMC, Paris, France) in a confocal configuration equipped with a 514. 5-nm wavelength 50-mW Modulaser Ar laser, on a surface of freshly fractured chert sample, at room temperature. The configuration used yields a horizontal resolution of ≈1 μm for a laser power delivered at the sample surface below 1 mW, thereby preventing irreversible thermal damage  73, 74. Extraction of spectral parameters from peak fitting procedures and estimation of peak temperature experienced by organic carbon were done following the methodology described by Beyssac, et al. 40. Scanning electron microscopy. SEM was used to document the texture of the organic films measured using Raman before their extraction by FIB milling. Chert fragments analyzed with Raman were mounted on aluminum stubs without any additional preparation, except Pt coating.
Stack alignment and extraction of XANES spectra were done using the aXis2000 software (ver2. 1n). The XANES spectra shown here are the sum of hundreds of spectra collected over homogeneous organic-rich areas of several hundreds of square nanometers. N/C atomic ratio values and quantitative spectral parameters were estimated using both the methodology outlined by Alleon, et al. 80  and using the QUANTORXS freeware  44.
Acknowledgements This research was supported by the  European Union 's  Horizon H2020 research and innovation program ERC (STROMATA , grant agreement  759289 ;  PI Johanna Marin-Carbonne ) and by the  Laboratory of Excellence ClerVolc. Special thanks go to  Mpumalanga Tourism  and  Parks Agency , especially  Mr. Johan Eksteen  and  Sappi Forest Products , which allowed us access to lands, to  Jean-Francois Moyen  and  Robert Moritz  for their assistance during field work, to  Olivier Beyssac  for his help with Raman spectroscopy, to  David Troadec  for the preparation of the FIB foils investigated, to  Imene Esteve  for her expert support with the FEG SEM at IMPMC, to  Jean Michel Guigner  for his expert support with the FEG TEM at IMPMC, and to  Stefan Stanescu  and  Sufal Swaraj  for their expert support with the HERMES STXM beamline at SOLEIL.
The SEM facility at IMPMC is supported by  Region Ile de France  grant SESAME Number  I-07-593/R ,  INSU-CNRS ,  INP-CNRS  and  UPMC-Paris 6 , and by the  Agence Nationale de la Recherche (ANR)  grant number  ANR-07-BLAN-0124-01. The HERMES beamline (SOLEIL) is supported by the  CNRS , the  CEA , the  Region Ile de France , the  Departmental Council of Essonne  and the  Region Centre.
This is consistent with the increase of the Caesar index over the beginning of the 20 th century (Fig. 3a ). We now check whether the members identified previously present realistic trends in North Atlantic SST or are characterized by excessive cooling in comparison to the observations and the Fig. 1  Sensitivity of IPSL-CM6A-LR over the historical period. a Global mean near-surface air temperature (GSAT) anomaly (K) relative to the 1880-2018 period for the ensemble average (black), individual members (gray), and member #14 (green) of the IPSL ensemble of extended historical simulations (IPSL-EHS), the infilled HadCRUT4-CW  41, 42  (blue) and the Berkeley  45  observational datasets (brown). b The climate sensitivity calculated over the historical period (S_hist, in K) for each individual member of the IPSL-EHS ranked from the lowest to the highest value (see Methods).
The black solid line represents the least square regression between these two variables in IPSL-EHS, with a determination coefficient r² = 0. 82 (p < 0. 1, see Method). The color scale represents the RMSE between simulated and observed (HadCRUT4-CW  41, 42  ) annual GSAT anomalies over the period 1900-2018. b Time evolution of the low-pass filtered AMOC strength anomaly from IPSL-EHS (gray), the AMOC forced signal (or ensemble mean, red), and the subset of 6 members labelled in a (light blue), with the subset average (dark blue). The anomaly is calculated for each member with respect to its 1850-2018 average. A Lanczos low-pass filter with a cutoff period of 11 years is used. Fig. 3  Evaluation of Atlantic Meridional Overturning Circulation fingerprints. a Time evolution of the Caesar index computed as the low-pass filtered sea surface temperature anomaly (K) averaged over the North Atlantic Subpolar Gyre from November to May from the ERSSTv5 observational dataset  54  (blue), the 32 historical members (gray) with the ensemble mean (black) and member #14 (green).
b, Same as a but for the Atlantic Multidecadal Variability index (see Methods). c Same as a but for the Ocean Heat Content (OHC) anomaly (10 9 J m -2 ) between 0 and 700 m averaged over the Newfoundland region minus the OHC averaged over the North Atlantic Subpolar Gyre region from the IAP observational dataset  55, 56  (blue) d Same as a but for the interhemispheric near-surface air temperature difference (K), with the HadCRUT4-CW observational dataset  41, 42  (blue). A Lanczos low-pass filter with a cutoff period of 11 years is used in c and d and the anomalies are computed over the displayed period. real world since the middle of the 20th century may have masked a fraction of the anthropogenic global warming, we evaluate four different observable AMOC fingerprints against our ensemble members.
Although the instrumental period is short, previous studies suggested that the Atlantic Multidecadal Variability is underestimated in CMIP5 models, with a persistence lower than the one deduced from observations  36, 37. To gain insight on this realism, paleodata provides additional information, but it usually suffers from large uncertainties concerning quantified estimations of the variability, especially at large spatial scales. While it is usually believed that model simulations might have too low multi-centennial variability as compared to proxy records  38  , a recent study  11  suggests that GSAT interdecadal variability of some CMIP6 models might be overestimated over the period 1450-1840 in comparison to the pre-industrial control simulations. Therefore, we cannot exclude that some CMIP6 models, such as IPSL-CM6A-LR have too much internal variability.
On the longer time scale of the last two thousand years, the different methods to reconstruct global mean surface temperature do show considerable uncertainty in terms of the magnitude of the multi-centennial variability  39  , while the external forcings and their impact remain poorly estimated in models. Therefore, it seems difficult at the moment to properly assess the realism of model simulations with those reconstructions. Such an evaluation of model simulations might deserve a dedicated analysis using the PAGES2K database and last millennium simulations from CMIP6, using advanced techniques like pseudo-proxy approaches  40  in order to compare model simulations and reconstructions in a coherent framework. To conclude, we have shown here that the different indications of an AMOC weakening since the mid-20th century might be mainly of internal origin coming from multi-centennial variability of the ocean.
Methods Observational data. We used the infilled Cowtan and Way  41, 42  , named HadCRUT4-CW in this study, observational surface temperature, which avoids the problem of missing values. This dataset is composed of blending sea surface temperature over the ocean and near-surface air temperature over the land. This metric of global mean temperature, however, warms significantly less than the GSAT. To take into account this discrepancy, we applied a factor of 1. 06 to the HadCRUT4-CW surface temperature, as used in a recent study  10. This factor is similar to those of 1. 05  43  , 1. 06  44  , and 1. 08 5 used in several other studies. One of the interests of this dataset is to provide an ensemble of realizations, allowing to consider the associated uncertainties. Here, the median of the dataset is considered the best estimate, and the maximum and the minimum is used for the uncertainty.
Note that the HadCRUT4-CW dataset is very consistent with the Berkeley  45  observational surface air temperature, with a Pearson correlation coefficient between the annual GSAT values of 0. 99. Very small differences are found with the infilled HadCRUT5 dataset when accounting for the missing values of this dataset. Therefore, we keep the HadCRUT4-CW in order to have a global coverage without missing values. Simulations used. The atmospheric resolution of IPSL-CM6A-LR is 1. 26°× 1. 25°w ith 79 levels (model top at 1 Pa). The model uses a nominal resolution of 1°in the ocean and 75 levels. The IPSL-CM6A-LR ensemble  12  used in this study follows the CMIP6 protocol  46  for historical simulations for the period 1850-2014. The historical simulations initial conditions are taken from different years in a long preindustrial simulation after it has reached a quasi-stationary state.
As a result, a very small drift is present in the piControl simulations, as it is often the case with climate models  51. We diagnose the drifts in the piControl for OHC (-1. 46 × 10 21 J yr -1 ) and GSAT (-0. 00011 K yr -1 ) and remove it in the historical simulations prior to the ΔQ and ΔT calculation. Calculation and analysis of the AMOC index. The AMOC is evaluated using the maximum of the annual Atlantic meridional stream function at 20 °N-50 °N. We consider CMIP6 models with Atlantic meridional stream function data available in the piControl simulation for at least 500 years. Thirteen models, including IPSL-CM6A-LR, were available at the time of our analysis. Scaling of the internal versus externally-forced AMOC variation. From the IPSL ensemble, we define the forced (or externally-driven) response of the AMOC as the ensemble mean of AMOC variations.
Acknowledgements The IPSL-CM6 experiments were performed using the HPC resources of TGCC under the allocations  2017-R0040110492 ,  2018-R0040110492 , and  2019-A0060107732  (project  gencmip6 ) provided by  GENCI  ( Grand Equipement National de Calcul Intensif ). The IPSL-CM6 team is acknowledged for data curation of the CMIP6 model output. RB and OB were supported by the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement number  820829  for the " Constraining uncertainty of multi-decadal climate projections (CONSTRAIN) " project ( 07/2019-06/2024 ). JM, GG, and DS were supported by  Blue-Action (European Union 's  Horizon 2020 research and innovation program  under grant agreement no  727852 ) and  EUCP (European Union 's  Horizon 2020 research and innovation programme  under grant agreement no  776613 ) projects.
Data availability All of the observational datasets used are publicly available online. The CMIP6 model outputs from the piControl and historical simulations are available through the Earth System Grid Federation (ESGF) portal (https://esgf-node. llnl. gov/projects/cmip6/). The data based on the extended (beyond 2014) historical simulations from the IPSL-CM6A-LR model used in the main figures of this study are available in this repertory: https:// doi. org/10. 5281/zenodo. 5159426.
Statistical Analysis Data were analyzed using IBM SPSS Statistics v. 25 (IBM Corp. , Armonk, NY, USA) and presented as means + standard error of the mean (SEM). A Shapiro-Wilk test was performed to analyze the normality of the data, and homogeneity of the variances was tested with a Levene's test. Data were analyzed by a two-way analysis of variance (ANOVA) with diet (HP, HE) and swimming activity (VS: voluntary swimming; EX: exercise) set as independent factors, and it was followed by a Tukey's post-hoc test. Differences among groups were considered significant at p < 0. 05.
Muscle texture was analyzed by the measurement of maximal strength and elasticity which are two well established parameters to determine the physical properties of the flesh. In general terms, fish fillets with certain degree of firmness and elasticity are preferred, while low values of these two factors are associated with product defects. The results of maximal strength and elasticity showed the same tendency as that observed with the other variables studied. In fish under voluntary swimming and fed with HP diet, both parameters followed a similar trend, being significantly higher in maximal strength in comparison with values of HE-fed fish. However, exercise in fish fed with HE caused an increase on muscle texture, reaching the levels of the HP-fed group. These results agree with the gene expression data of proteolytic markers, which in exercise condition do not present the differences observed in the fish under voluntary swimming.
The log I(s) versus s plot represents the primary SAXS data. The P(r) versus r profile shows that the smooth approaches zero at r = 0 and r max = 266 Å (Fig. S4D ). The shape of P(r) versus r profile and d max = 248. 6 Å suggested that the protein is partially folded. The volume calculated is shown as a gray envelope in Figure  2C  with 0. 00654 contour level (represented by Chimera X). The volume appears elongated with globular enlargements and measures 24. 6 nm long by 6 nm broad in the largest region. Interestingly, the Ty1 IN atomic model produced by SWISS-MODEL (40) using the homologous PFV integrase (PDB: 30YM-A, Fig. S5 ) assembled in the intasome fits in size and shape with the calculated volume. The CCD domain of Ty1 IN can be unambiguously located into the largest globular stretch.
cerevisiae genome (Fig. S8A ). Among the 32 copies (43) found in Saccharomyces genome database (https://www. yeastgenome. org), 27 have two distinct LTRs, two share a common LTR, two harbor an uncomplete LTR, and 1 has only one defined LTR, resulting in 60 complete LTRs. Sequence alignment of the last 30 bp of each Ty1 U3 and U5 3' ends for these 60 LTRs showed that all Ty1 LTRs end with the CA dinucleotide that is highly conserved in retrovirus and retrotransposon LTRs (Fig. S8, B  and C ). The 3 0 end of U3 is much more conserved than that of U5 (14 out of 15 bases and nine out of 15 bases strictly conserved, respectively). To investigate the potential role of the sequence composition of the 3 0 end of Ty1 U5 in substrate specificity, we performed oligonucleotide integration assays with various probes that harbored an increasing number of specific bases at the 3 0 end (Fig.
Moreover, the presence of the Ty1 IN C-terminus region in a related retroelement is sufficient to redirect its integration to Pol III-transcribed genes  (11). All these data support that the engineering of the Ty1 IN C-terminus region is a good strategy for the design of retroviral vectors with tuneable integration site specificity. The reaction was stopped by the addition of 5 μl of the stop reaction buffer (0. 25 M EDTA, 1% SDS) and 1 μl of pronase (10 mg/ ml, Sigma) and transferred at 37 C for 1 h. The samples were subjected to electrophoresis in an agarose gel (1. 5%) supplemented with ethidium bromide in a 1× TBE buffer to allow a direct visualization of the DNA using a transilluminator. The DY-682 fluorescent dye was next detected using an Odyssey CLx fluorescence near-infrared imaging system at 700 nm.
Size-exclusion chromatography (SEC) coupled with smallangle X-ray scattering (SEC-SAXS) SEC-SAXS data for Ty1 IN sample were collected at the SWING beamline, Synchrotron SOLEIL. In line SEC-SAXS was performed using an HPLC equipped with the BioSec3-300 column and 30 μl of Sso7d-IN (16 mg/ml) was loaded at a flow rate of 300 μl/min before data collection. Scattering images with an exposure of 1 s were collected every 0. 01 s using an Eiger 4M (Dectris) detector at a photon energy of 12 keV (λ = 1. 033 nm) and sample to detector distance of 2 m. The experiment was performed at 10 C. Beamline SWINGspecific software was used for subtracting background buffer signal from protein signal, with a previous averaged data. Data were analyzed using the ATSAS 3. 0. 2 (58) and RAW 2. 0. 2  (59). Ab initio reconstructions were generated with the program DAMMIF  (60).
MALDI-TOF-TOF mass spectrometry Protein masses were determined on solution samples. Ten microliter of protein at 0. 5 mg/ml was desalted on ZipTip C8 and eluted with 2 μl of a saturated solution of sinapinic acid in 0. 1% TFA/CH3CN (50:50 v/v). One microliter was then spotted on the target and analyzed by MALDI-TOF-TOF on a Ultraflex III spectrometer (Bruker Daltonics) controlled by the Flexcontrol 3. 0 package (Build 51) and operated in the linear mode, using a maximum accelerating potential of 25 kV and a 20,000 to 80,000 m/z range (LP_66 kDa Method). The laser frequency was fixed to 100 Hz and ca. 1000 shots per sample were cumulated. Four external standards (Protein Calibration Standard II, Bruker Daltonics) were used to calibrate each spectrum to a mass accuracy within 100 ppm. Peak picking was performed using the FlexAnalysis 3.
Protein identification by fingerprinting All SDS-PAGE bands were cut and digested by trypsin. Mass spectrometry analyses were carried out by LC-MSMS using a LTQ-Velos-Orbitrap online with a nanoLC Ultimate 3000 chromatography system. The peaks obtained have been compared with the theoretical digestion peak list of the expected protein added to a database containing 20,150 human sequences, 4306 E. coli sequences.
Acknowledgments-We thank  Dr Patrick Fourquet  ( CRCM, Marseille Proteomics (MaP) ) for mass spectrometry analyses. The MaP proteomic facility (marseille-proteomique. univ-amu. fr) is supported by  « Infrastructures Biologie Santé et Agronomie » (IBiSA) ;  « Plateformes Technologiques Aix-Marseille »; « Cancéropôle PACA »; « Région Sud Provence-Alpes-Côte d'Azur »; « Institut Paoli-Calmettes »; « Centre de Recherche en Cancérologie de Marseille » Biophysical and biochemical characterization of Ty1 IN J. Biol. Chem. (2021)  297(4) 101093 13  (CRCM); «  Fonds Européen de Développement Régional » (FEDER)  and  « Plan Cancer ». This work was supported by grants from the  Agence Nationale de la Recherche  through the  generic call  project  ANR-17-CE11-0025. This work has been supported by the  Bettencourt Shueller Fondation  (FBS:  AGDI-531281 ) and an  ATIP-avenir grant  ( CNRS/ INSERM ).
Nonetheless, there is limited capacity of acclimation when long-term heating occurs  26  , especially for tropical species  27  and during reproductive stages  28. In this regard, our overarching objective is to understand how and when global warming-induced changes in thermal niches over the water column will take place in the future affecting current marine ecosystems. We take advantage of comprehensive data sets of daily three-dimensional ocean temperatures from both the long-term Ocean Sites (OS) network and a state-of-the-art Earth System Model (ESM). This novel data set and its exploitation represents a major novelty to improve our understanding of how future climate change will impact marine ecosystems. To provide a reliable framework upon which to derive the future evolution of lower and upper thermal niche bounds, we select six OS stations for which at least seven years of daily temperature observations from the surface to ~1000 m depth are available (Table  S1 ).
6) and a high (SSP5-8. 5) emission pathways. To overlap observations with the model, observations were daily resampled, and remapped into the simulation's vertical levels. At each OS location, we extract a subsample of the historical + SSP5-8. 5 simulation that matches the period of the observations. Comparison between both data sets (Fig. S1 ) show simulation deviates from observations at the northernmost stations, especially at station FRAM at which observations show a warming period before 2010 that can be originated from an anomaly advection of North Atlantic waters northwards  32. This anomalous episode can also be behind the positive anomaly at station CIS-1. Observations and model data are then used to compute profiles of Tmin and Tmax over the available period. We derive the anomalies of the lower and upper thermal range boundaries by removing the mean temperature profile from Tmin and Tmax profiles.
Acknowledgements We thank  L. Kwiatkowski ,  S. Berthet  and  E. Sánchez  for comments on pre-submission drafts of the manuscript. This work was supported by the  European Union 's  Horizon 2020 research and innovation program  with the  TRIATLAS  project under the grant agreement No  817578 , the  COMFORT  project under the grant agreement No  820989  and the  ESM2025  project under the grant agreement No  101003536.
Ocean Sites observations The Ocean Sites (OS) network constitutes a worldwide effort to monitor ocean parameters through high-quality data extracted from long-term, high-frequency observations at several locations of the World ocean. Six OS stations, listed in Table  S1 , were selected because of the availability of continuous daily measurements of ocean temperature and salinity across the water column for more than seven years, allowing a robust computation of thermal range boundaries (see below). All of the six stations provide data from the surface to about 1000 m depth, that have been resampled daily at each depth, and then interpolated into the vertical grid of CNRM-ESM2-1. Observational data is accessible through http://tds0. ifremer. fr/thredds/catalog/CORIOLIS-OCEANSITES-GDAC-OBS/DATA/.