dataesr/scientific-paragraphs-categorization huggingface dataset:
For liquid chromatographytandem mass spectrometry (LC-MS/MS) analysis, the elutes were electrosprayed inside an LTQ-VELOS mass spectrometer (ThermoFisher Scientific, Illkirch-Graffenstaden, France) through a nanoelectrospray ion source. Thermo Proteome Discoverer v1. 3 was used for raw data file processing. The E. coli O157:H7 strain EDL933 database (T number T00044) was used for protein identification. For the proteomic searches, the following parameters were set up: peptide mass tolerance of 1. 5 Da, fragment mass tolerance of 0. 5 Da, and a maximum of two missed cleavages allowed. Variable modifications considered were carbamidomethylation (C) of cysteine and methionine oxidation (M). Proteins were considered valid when a minimum of two unique peptides originating from one protein showed statistically significant (p < 0.
Images were acquired using a 64 x or 100 x oil objective (1. 4 n. a. ) mounted on a Zeiss LSM710 confocal microscope. Overlapping images (merge) were assembled by using the MosaiX module of AxioVision suite software (Zeiss, Périgny, France). In the pictures, the signal from OmpA and OmpX was pseudocoloured in green, while the signals from bacteria are shown in red. Co-localisation coefficients (Cc, either absolute (Cca: 0. 289) or weighted (Ccw: 0. 285), were estimated following image analysis using ZEN 2. 3 software (Zeiss, France); values ranged from 0 to 1 (0: no pixel co-localisation, 1: all pixels colocalised). For the absolute co-localisation coefficient (Cca), the ratio of red (bacteria) pixels co-localising with the green (mucin) pixels was considered where the relative number of co-localising pixels in channel 1 (C1) or 2 (C2), respectively, were compared to the total number of pixels above the threshold.
All authors have read and agreed to the published version of the manuscript. Funding: This work was funded in part by (i) INRAE (Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement, formerly called INRA, Institut National de la Recherche Agronomique) MICA (MICrobiologie et Chaîne Alimentaire) department, (ii) EU FP7 Marie Curie Actions ITN (Initial Training Network) EID (European Industrial Doctorates) DISCo project (http://www. discoproject. eu/; n • FP7-PEOPLE-607611), (iii) Région Auvergne FRI-IRP (Fond Régional Innovation-Institut de Recherche Pharmabiotique) CoMBa project and (iv) ANR (Agence National de la Recherche) PathoFood project (n • ANR-17-CE21-0002). Institutional Review Board Statement: The animal study protocol was approved by the Italian ministerial ethics committee (autorisation code 804/2015-PR on 7 August 2015).
Acknowledgments:  Ricardo MONTEIRO  was a  Marie Curie PhD Research Fellow  granted by  ITN EID DISCo. Valentin AGEORGES  was a PhD Research Fellow granted by the  Région Auvergne-FEDER  ( Fonds Européen de Développement Régional). The authors are thankful to the anonymous reviewers and academic editor for their valuable and constructive comments, which greatly improved the quality of the manuscript.
Kinetics of reticulo-rumen parameters Reticulo-rumen pH was monitored on the 32 RFI extreme bulls continuously throughout the experiment using a wireless sensor (eCow, Exeter, UK). Each reticulo-rumen sensor was set up to record mean pH over 15 min. The reticulo-rumen sensor was orally administered to the animals at the end of the first efficiency test. The pH kinetics until slaughter were analysed, and daily relative reticulo-rumen pH indicators (NpH) were calculated following the procedure detailed by  Villot et al. (2018)  by filtering and normalising raw data to remove sensor drift and sensor noise. The average time per day where pH was below 6. 0, 5. 8 or 5. 6, and the average mean, minimum and maximum daily pH, as well as the average daily NpH time below 0. 3 and 0. 5, NpH range, and NpH SD were calculated.
For each bull, 30 randomly selected crypts were measured, in a cross-section of the ileum. The digital image processing software NDP view 2. 6. 13 (Hamamatsu, Japan) was used to calculate the area enclosed by a manually traced outline of the crypt and of the crypt lumen, and the corresponding perimeter. No attempt to correct possible over-or under-estimation was made during the image processing, so care was taken to take measurements in identical conditions by a single operator who was blinded as to which feed efficiency group the samples belonged to. In addition to the crypt area, crypt perimeter, and crypt lumen area, the number of visible nuclei, i. e. the number of cells present on each transversal image of the crypt, was counted. The mean cell size was then determined by subtracting the crypt lumen area from the total crypt area and then dividing this value by the number of nuclei.
Indicator of transit rate The transit rate was estimated from the disappearance time of TiO 2 in the faeces. Data were ln transformed and analysed by ANOVA using a MIXED model that included the fixed effects of the diet (n = 2), the RFI group (n = 2), the time (in hour) of faeces sampling (n = 30) and their interactions as fixed effects. The time of faeces sampling was treated as a repeated factor, using the animal as random factor. The time of faeces sampling when the faecal TiO 2 concentration started to significantly decrease from the initial value was set as cut-off point given by the model (38 h), as well as the time of faecal sampling after which no more significant differences were observed in the faecal residual TiO 2 (105 h). Data between the cut-offs were used to calculate a slope per animal using a linear regression procedure in which the sampling time was set as the regressive factor.
Acknowledgements The authors gratefully acknowledge the staff of the  Experimental Unit Herbipôle (INRAE, Saint-Genès-Champanelle, France ) for their diligent care of the experimental animals,  F. Anglard  and  L. Salis  for sampling and laboratory analyses,  Y. Rochette  and  Y. Gaudron  for the provision and curation of GreenFeed data,  P. Guarnido-Lopez  for data curation of visceral organ size and animal performance ( UMR 1213 Herbivores ,  INRAE, Saint Genès-Champanelle, France ), and  M. Weisbjerg  for the TiO 2 analysis ( Aarhus University, Tjele, Denmark ), and  G. Renand  ( INRAE, GABI, Jouy-en-Josas, France ) for fruitful discussions.
Here, our aim is to fill important gaps in the research linking biodiversity and stability in agricultural systems. We test whether crop diversity within countries increases the resistance of total caloric yield and economic revenues (all crops included) to weather extremes using 58 years  of annual data. We focused our study on droughts and high temperatures, two types of extremes that are recognized as major threats to food supply  (Pörtner et al 2022). Agricultural data were matched with weather data based on the specific spatial extent of individual crops. We further test whether the selection and/or the complementarity (through the insurance) effects can explain the increased resistance to weather extremes conferred by greater crop diversity at the country level.
Material and Methods Agricultural data. The FAOSTAT database provided data on annual cropland and harvested area (in ha), production (in tonnes) and agricultural revenues (in 2015 equivalent US dollars) for an initial set of 246 countries and 176 crop commodities. From the initial list of crops, we discarded aggregated crop categories (e. g. citrus or other cereals) and crops grown in fewer than ten countries. Because agricultural yields and revenues, and their response to weather extremes, are likely to be influenced by conditions linked to agricultural management, we used annual total application of nitrogen and total cropland equipped with irrigation at the country level from the FAOSTAT database as covariates. Quality of FAOSTAT data varies among countries, depending on whether they have been estimated by the FAO or communicated directly by countries.
We accounted for this difference by removing 33 countries for which at least 20% of data on area harvested or production over the entire time period had to be estimated (following Renard and Tilman 2019). We also discarded Ireland, New Zealand and Netherlands, all of which use much of their fertilizer on pastures rather than for croplands, and Egypt, which has 100% of its cropland equipped with irrigation for the entire time period. For each of the remaining countries (n = 127) and for each year, we calculated the total annual caloric yield (millions of kcal/ha) by (1) multiplying the production of each of its crops by commodity-specific kcal conversion factors from the USDA's Nutrient Database (2013); (2) summing these kcal harvests across all crops; and (3) dividing this sum by the sum of all harvested land for all these crops.
Similarly, for each country and each year, we calculated the total annual agricultural revenues per hectare by dividing the total revenues by the total harvested area (in constant 2015 USD $/ha). We quantified crop diversity using the Shannon-Wiener diversity index (H') based on the proportion of total area harvested represented by each crop grown annually by each country studied. We calculated the annual rate of application of nitrogen and irrigation per hectare by dividing their use by the total cropland area in each country and each year. Sources of all the data are summarized in Supplementary table 1. Weather data. We obtained global 0. 5° gridded temperature (in degrees Celsius) data from the Climate Research Unit of the University of Anglia  (CRU TS v. 3. 23, Harris et al 2014)  for the 1961 to 2018 period.
We used these data to identify years with high growing season temperatures (we provide details of the calculation below). In years with high growing season temperatures, crops are expected to experience heat stress that affect their metabolic processes and biomass production. Droughts are mostly related to a reduction in the amount of precipitation, but they are also influenced by other variables including temperature and evapotranspiration (Vincente-Serrano et al 2010). To quantify wet and dry annual weather events, we used the global 0. 5° gridded Standardized Precipitation-Evapotranspiration Index (SPEI) from Vicente-Serrano et al (2010), an index that is based on both precipitation and potential evapotranspiration (PET) data. The SPEI12 index was calculated for a period of one year using the monthly climatic water balance (i.
Combining weather and crop data. To match the weather data to the agricultural data, we first calculated temperature and SPEI for each country across all cropland areas (i. e all crops included). We derived monthly temperature and SPEI values for the cropland area in each country by taking the monthly values of each grid cell in a country, weighted by the proportion of cropland in each gridcell  (Monfreda et al 2008). Then, we calculated temperature and SPEI for each country across annual growing seasons by averaging monthly weather values over months of country-specific crop-growing-season calendars  (Sacks et al 2010). Second, we derived annual temperature and SPEI conditions for the 154 individual crop commodities for which the global harvested area at 5 min resolution was mapped by  Monfreda et al (2018).
We derived monthly temperature and SPEI values for each crop in each country by taking the monthly values of each grid cell in a country, weighted by the proportion of harvested area each crop occupied. Then, we calculated crop-specific temperature and SPEI across countries and annual growing seasons by averaging monthly weather values over months of country-specific growing-season calendars. Finally, we tested for the role of extreme temperatures by identifying years with a growing season temperature one standard deviation above the 1961-2018 growing season temperature average. This was made for both the cropland and the crop-specific analysis. Statistical analyses. After the data cleaning process and homogenization between agricultural and weather data, the database comprised 127 countries (see Supplementary table 2 for the complete list) and 109 crop commodities.
Using simple regression models, we tested the relationship between individual crop responses to weather and total harvested areas or average revenues, respectively. Total global area and revenues were calculated across the 127 countries in our dataset. We hypothesized that main crops worldwide were also the most sensitive to weather extremes. Second, for each country, we computed the weighted mean (Wmean) and the weighted variance (Wvar) of individual crop responses to droughts and high temperatures to approximate the roles of selection and complementarity effects, respectively. To do this, we.
The interpretation of cosmic antiproton flux measurements from space-borne experiments is currently limited by the knowledge of the antiproton production cross-section in collisions between primary cosmic rays and the interstellar medium. Using collisions of protons with an energy of 6. 5 TeV incident on helium nuclei at rest in the proximity of the interaction region of the LHCb experiment, the ratio of antiprotons originating from antihyperon decays to prompt production is measured for antiproton momenta between 12 and 110 GeV/c. The dominant antihyperon contribution, namely Λ → pπ + decays from promptly produced Λ particles, is also exclusively measured. The results complement the measurement of prompt antiproton production obtained from the same data sample. At the energy scale of this measurement, the antihyperon contributions to antiproton production are observed to be significantly larger than predictions of commonly used hadronic production models.
These results were the first to use a helium target, and, covering an energy scale where significant violation of Feynman scaling  [9]  occurs, contributed to a better modelling of the secondary p cosmic flux  [4, 10, 11]. The uncertainties on p production from weak decays still limit the interpretation of cosmic p data  [10]. The largest of these contributions is due to antineutron decays, which cannot be directly observed in LHCb but can be estimated from the antiproton measurements and the assumption of isospin symmetry. Another significant contribution, which is less constrained theoretically, comes from decays of antihyperons, H. Antiprotons produced in this way are referred to as detached in the following as they can experimentally be distinguished from prompt antiprotons in the LHCb experiment by the separation between their production vertex and the primary pHe collision vertex (PV).
This paper reports a determination of the ratio R H ≡ σ(pHe → HX → pX) σ(pHe → p prompt X) (1) of detached to prompt antiprotons in pHe collisions at √ s NN = 110 GeV with momentum, p, ranging from 12 to 110 GeV/c and transverse momentum, p T , between 0. 4 and 4 GeV/c, where X stands for any arbitrary set of unreconstructed particles. Two approaches to the measurement, presented in Secs. 4 and 5, are followed as described below. The dominant process, namely Λ → pπ + with promptly produced Λ particles, is measured relying only on the secondary vertex displacement from the PV and on the decay kinematics. The ratio R Λ ≡ σ(pHe → ΛX → pπ + X) σ(pHe → p prompt X) (2) is then determined using the prompt production result  [8] , obtained from the same dataset. In the second approach, an inclusive measurement of detached antiprotons is performed by exploiting the particle identification (PID) capabilities of the LHCb detector.
Data sample and simulation This measurement is performed on data specifically collected for p production studies in May 2016. Helium gas was injected when the two beams circulating in the LHC accelerator consisted of proton bunches separated by at least 1 µs, 40 times the nominal value. In this configuration, spurious pp collisions are suppressed. A sample of pHe collisions with a 6. 5 TeV proton-beam energy ( √ s NN = 110. 5 GeV) and corresponding to an integrated luminosity of about 0. 5 nb -1 was collected  [8]. In the proton-nucleon centre-of-mass frame, the LHCb acceptance corresponds to central and backward rapidities -2. 8 < y * < 0. 2. Selected events are required to have a reconstructed PV within the fiducial region -700 < z < +100 mm, where the z axis is along the beam direction and z = 0 mm corresponds to the LHCb nominal collision point in the central part of the VELO.
The fiducial region is chosen to achieve a high efficiency for PV reconstruction in fixedtarget collisions and a significant probability that antihyperon decays occur within the VELO. Antiproton candidates are reconstructed in the full tracking system exploiting the excellent Λ invariant-mass resolution and IP determination. The PV position is required to be compatible with the beam profile and events must have fewer than 5 tracks reconstructed in the VELO with negative pseudorapidity. This selection suppresses to a negligible level the background from interactions with material, decays, and particle showers produced in beam-gas collisions occurring upstream of the VELO. A sample of 33. 7 million reconstructed pHe collisions satisfying these requirements is obtained from the data. Simulated data samples of pHe collisions are produced with the Epos-lhc generator  [23].
To take into account the degraded resolution of the decay vertex, the selection is loosened by requiring F IP > 1 and F χ 2 IP > 3, while the pion, reconstructed using the whole tracking system, is required to be identified by the RICH detectors to compensate for the larger background. The VELO tracking efficiency is estimated from the fraction of candidates in this sample where the partially reconstructed track satisfies the quality requirements for a fully reconstructed track when including the information from the VELO. The VELO efficiencies measured in data and simulation with the same analysis are compared in Fig. 3 , notably as a function of the p 500 -0 500 [mm] z Simulated production vertex production vertex position. No significant differences are observed. The uncertainty on the p reconstruction efficiency in each kinematic interval is expected to cancel in the R Λ ratio.
The results as a function of p (p T ), integrated over the 0. 55 < p T < 1. 2 GeV/c (12 < p < 50. 5 GeV/c) region, are shown in Fig. 5  and compared to widely used hadronic collision models included in the Crmc package  [26]. The data indicate that all considered generators significantly underestimate the Λ contribution to the p production.
2. Antiproton candidates are selected from negatively charged tracks reconstructed with a high-quality fit including segments in the VELO and in the tracking stations upstream and downstream of the magnet. The analysis is Figure  5 : Measured R Λ as a function of (top) the p momentum for 0. 55 < p T < 1. 2 GeV/c and (bottom) the p transverse momentum for 12 < p < 50. 5 GeV/c. The measurement is compared to the predictions, in the same kinematic regions, from the Epos 1. 99  [33] , Epos-lhc  [23] , Hijing 1. 38  [34]  and Pythia 6  [35]  models, included in the Crmc package  [26]. Error bars on data represent the total uncertainty. performed in the (p, p T ) plane, with ten momentum intervals between 12 and 110 GeV/c and five for p T between 0. 4 and 4 GeV/c. The p identification is based on two quantities determined from the response of the RICH detectors: the difference between the logarithm of the likelihood of the proton and pion hypotheses, DLL pπ , and that between the proton and kaon hypotheses, DLL pK  [19].
and (bottom) the p transverse momentum for 12 < p < 110 GeV/c. The measurement is compared to predictions, in the same kinematic regions, from the Epos 1. 99  [33] , Epos-lhc  [23] , Hijing 1. 38  [34] , Pythia 6  [35]  and Qgsjet-ii04  [38]  models, included in the Crmc package  [26]. Error bars on data represent the total uncertainty.
Figure16: Measured R H as a function of (top) the p momentum for 0. 4 < p T < 4 GeV/c and (bottom) the p transverse momentum for 12 < p < 110 GeV/c. The measurement is compared to predictions, in the same kinematic regions, from the Epos 1. 99 [33] , Epos-lhc [23] , Hijing 1. 38 [34] , Pythia 6 [35]  and Qgsjet-ii04 [38]  models, included in the Crmc package [26]. Error bars on data represent the total uncertainty.
Image Acquisition The fluorescence of labeled brain slices was observed by confocal microscopy (SP5, Leica Microsystems, Wetzlar, Germany) and the slide scanner Nanozoomer (2. 0 HT, Hamamatsu Photonics, Shizuoka Prefecture, Japan). The Nanozoomer contains a fluorescence imaging module using objective UPS APO 20X NA 0. 75 combined with an additional lens 1. 75X. Virtual slides were acquired with a TDI-3 CCD camera. The fluorescent acquisitions were conducted with a mercury lamp (LX2000 200W-Hamamatsu Photonics, Massy, France), and the set of filters adapted for DAPI and FITC/FAM fluorescence were usable for both fluorescein isothiocyanate-dextrans and 5-carboxyfluorescein antisense oligonucleotide. The DAPI labeling, revealing the double strain of DNA in the cell nuclei, was used for the automated focus required for Nanozoomer imaging.
Fluorescence Analyses Several types of fluorescence analyses were double-blindly performed on Nanozoomer images. To evaluate the intensity level of fluorescence, the ndpi files generated by Nanozoomer were converted into tiff images with NDPI software (version 2). The tiff files were opened with Fiji software 2. 9. 0, and the intensity levels were measured in regions of interest (ROI defined as red circle of 960 µm 2 in Figures and placed on the hippocampus (HPC), dorsal thalamic nuclei (THAL) and the retrosplenial and ectorhinal cortices (DCx and LCx, respectively) on both hemispheres of the three slices. No filter settings were applied to the images and we checked that the images did not have any saturated dots. The mean of fluorescence was calculated for each mouse and reported in the statistical analysis.
A similar analysis was performed in three liver slices. Five ROI were randomly placed on each slice. Moreover, the image analysis of fluorescent spots was performed with QuPath directly on the ndpi files. The software is able to identify and localize fluorescent spots. We empirically determined parameters to segregate fluorescent spots in brain slices from 5 mice (control and centrifuged mice) and we applied these parameters to the project containing the entire sample. The parameters were: pixel size 0. 5 µm, background radius 30 µm, median filter radius 0, sigma 1, minimum area 5 µm 2 , maximal area 1000 µm 2 and threshold 7. The collected data were attributed to experimental groups (2 g vs. 1 g) and compared statistically. The analyses, reported, were performed on the ROI anatomically defined as HPC (hippocampus), THAL (grouping all medio-dorsal and lateral thalamic nuclei), DCx (containing retrosplenial cortices), SoCx (containing somatosensorial cortices) and PirCx (containing piriform cortices).
The qPCR was performed using the endothelial cell contacts by junction M96 (predesigned for use with SYBR green; Bio-Rad's plate reference 10029202) and the Sso-advanced universal SYBR green PCR kit (Bio-Rad's reference 1725275). The qPCR was performed with CFX96 thermocycler (Bio-Rad). Samples were allocated randomly in plates, and some of them were tested twice to verify the quality of the experiment. The validation of Hprt and Gapdh as reference genes was evaluated with CFX Maestro software (Bio-Rad). The analysis of gene expression was performed on Actb, Actg1, Actn1, Actn2, Actn4, Cdh2, Cdh5, Cldn1, Cldn3, Cldn5, Ctnna1, Ctnnb1, Ctnnd1, Dsp, F11r, Gja1, Gja4, Gja5, Jam2, Jup, Ocln, Tjp1, Tjp2 and Vim. The threshold of the regulation by hypergravity on gene expression was chosen at 1. 5.
Statistical Analysis The data were statistically compared using paired t-tests, non-parametric Mann-Whitney test, or one-and two-way ANOVA with post hoc comparisons when applicable. The normogravity (1 g) is the control condition. The software used was GraphPad Prism V9, which calculated the p value as the probability of observing two identical conditions. If p < 0. 05, the two compared conditions were considered statistically different.