dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. This work is part of the  MESO Project "Modelling and forecasting the secondary effects of the Antarctic ozone hole ", registered under no. 8887. 130199/2017-00. The authors would like to thank the  CAPES (Coordination of Improvement of Higher Education Personnel)  and  COFECUB (French Committee  for the  Evaluation of University Cooperation  with Brazil) program responsible for promoting this research. https://doi. org/10. 5194/egusphere-2023-1474 Preprint. Discussion started: 22 August 2023 c Author(s) 2023. CC BY 4. 0  License.
Key amino-acids for Lo18 chaperone activity The thermostability at 55 °C of a pool of proteins present in an E. coli cell lysate was assessed after the overproduction of Lo18 or the modified proteins. The relative quantity of each protein was assessed before and after the induction of heat stress, allowing the comparison of the quantities of protected proteins, which depended on the concentration of the chaperone protein present in the corresponding lysate (Supplemental data S3). In the presence of the Lo18 protein, 35% of the cells in the cell lysate aggregated under heat stress (from 3. 5 mg/mL to 2. 3 mg/mL). Conversely, lysate protein aggregation reached 67% in the negative control condition (3. 5 mg/mL to 1. 2 mg/mL). All the modified proteins had a significantly reduced protein aggregation rate compared with the Lo18 condition with the exception of R99D, 53.
5B ). This phenomenon could indicate the insertion of the protein into the lipid bilayer of the liposome. Furthermore, the interaction between the liposomes and the modified proteins was monitored and compared to Lo18. At 25 °C, no distinct shouldered spectra in the presence of liposomes were observed for E60K and R99D (Fig. 5B ). Finally, when insertion occurred, the presence of liposomes stabilized the protein structure upon temperature elevation (Supplemental data S2). All together, these results highlight the modification of the secondary and tertiary structures of the Lo18 protein after interaction with a lipid bilayer by putatively exposing specific Lo18 domains able to interact with the membrane.
In silico structure prediction and protein membrane interaction The secondary structure of Lo18 and modified proteins was predicted using the PredictProtein online software. Then, models of the tertiary structure of these proteins were generated using trRosetta and Alphafold2 online software. The impact of amino-acid present on the point mutation of all the models obtained was analyzed using the measurement functionality of PyMOL 4. 60 software. Molecular Dynamic simulations (MD) in explicit water were performed using initial structures: A0NLC2 truncated of the first 25 amino-acids of the N-terminal domain  51  , and total structure obtained using trRosetta and Alphafold2 online software. All unbiased simulations were carried out with the GROMACS software package  52. Na + ,Cl -ions were added at a concentration of 0.
Synchrotron radiation circular dichroism spectroscopy Circular dichroism spectra were collected on the DISCO beamline (Synchrotron SOLEIL, France). In detail, the instrument was calibrated using a 99% pure (+) camphor-10-sulphonic acid (Sigma Aldrich, Saint-Louis, US) at 25 °C after each beam fill  53. Protein samples (Lo18 or modified proteins) were prepared in 50 mM sodium phosphate buffer, pH 7. 0. Then, 50 µL of the sample was loaded into a 0. 02 cm pathlength demountable cylindrical quartz Suprasil cell (Hellma, Germany) and subjected to a thermal scan from 15 °C to 84 °C with a step of 3 °C. Each dataset was collected in 1 nm steps from 262 to 176 nm, with an integration time of 1. 2 s and a spectral bandwidth of 1 nm. Three scans of each sample and the equivalent baseline were collected.
At each temperature step the three collected spectra were averaged and smoothed (9 points with Savitzky-Golay smoothing), and the baseline subtracted with the averaged and smoothed spectra of the buffer without protein taken at 15 °C. Spectra were then zeroed between 250 and 260 nm. All spectra were normalized according to the protein concentration and pathlength using the CDToolX software. A similar protocol was used in the presence of liposomes (preparation described below) using 250 µM liposomes and 140 µM proteins, with a thermal scan from 25 to 55 °C with a step of 5 °C. Deconvolution of the spectra was performed with the BeStSel server  54.
Fluidity measurements Membrane fluidity was monitored by fluorescence anisotropy measurement using a Fluorolog 3 spectrofluorimeter (FLUOROLOG-3, Jobin Yvon Inc, USA). The anisotropy values (inversely proportional to the membrane fluidity) were calculated according to Shinitzky and Barenholz  56. Excitation and emission wavelengths were 360 nm and 431 nm, respectively. The measurement was performed for 30 min (1 determination every 10 s) in a quartz cuvette filled with 250 μL of liposomes prepared as described above, with 3 μM of 1,6-diphenyl-1,3,5hexatriene (DPH) as a probe (Sigma Aldrich, Saint-Louis, US). Data were recorded in the presence or absence of Lo18 or modified protein, using a mass ratio of 1:2 (m/m) between the sHSP and the liposomes. After inserting the probe (10 min at 10 C), a linear temperature gradient from 15 to 63 °C (increase of 2 °C per min) controlled by a Peltier system (QNW TC1 temperature controller, Quantum Northwest, Liberty Lake, WA, USA) and heat shock at 45 °C for 15 min were applied to the liposome suspension.
Statistical analysis At least three independent measurements were performed for each of the conditions tested on fluorescence anisotropy, thermostability and SRCD measurement. Statistical analyses were performed by the statistical RStudio software (version 1. 2. 5033). The normality of the distribution and homogeneity of the variances of each condition were tested by the Shapiro-Wilk test and the Bartlett test, respectively. Then, a non-parametric Kruskal-Wallis test was used to compare the samples with a significance level of α = 0. 05. All the statistical tests were considered significant at a p-value < 0. 05.
Acknowledgements This work was supported by the  Regional Council of Bourgogne-Franche-Comté , and the  Ministère de lʼEnseignement supérieur, de la Recherche et de lʼInnovation. The authors would like to thank the  Dimacell Platform (Agrosup Dijon, INRA, INSERM, Univ. Bourgogne Franche-Comté ,  F-21000 Dijon France ) and the  Synchrotron SOLEIL (CNRS-CEA Paris Saclay, France)  for x-ray beamtime on DISCO beamline (project #  20220067 ). We would also like to thank  Accent Europe  for proofreading the English text.
This is mandatory in a monitored neutrino beam where most of the instrumentation is located in the cylindrical walls of the tunnel. The ENUBET instrumentation is made of iron-scintillator sampling calorimeters that are longitudinally segmented into modules (see Sec. 6). This array of modules is complemented by a photon veto made of plastic scintillator tiles positioned in the innermost part of the calorimeter, as shown in Figs. 16  and 17. The tunnel instrumentation can thus record large-angle charged leptons, perform particle identification, and measure the e ± energy. The use of iron-scintillator calorimeter modules, whose light is read by SiPMs located in the outer rim of the tunnel (see Sec. 6), keeps the cost of diagnostics well below (∼ 10%) the overall cost of the neutrino beam even if the tunnel is instrumented along its whole length (40 m).
Unlike tagged neutrino beams, leptons do not need to be associated in time with neutrinos interacting at the neutrino detector. This feature relieves the time resolution requirement of the calorimeter to a few ns instead of tens of ps. Timing is only needed to associate modules belonging to the same event (charged lepton) and remove pileup. 4D reconstruction in a monitored neutrino beam is thus performed in a neighborhood of the lepton impact point, while tagged neutrino beams must provide a global event reconstruction because a ν interaction at the neutrino detector must be uniquely linked in time with a charged lepton. This lepton must be picked up from ∼ 10 6 potential candidates: a task that can be accomplished only by fast (O(100 ps)) trackers. Thanks to the static focusing system and the loose requirements on time resolution, ENUBET can monitor not only large-angle positrons but the vast majority of particles produced in the tunnel.
This pulsed beamline does not fall into the scope of the paper but is being considered for other applications at the CERN SPS. Dedicated studies on the nominal SPS slow extraction have also shown how to further reduce its frequency noise, pointing to optimized extraction configurations not only for the case of ENUBET but for any fixed target experiment with tight rate requirements  [33]. Simulation studies have proven that both for the nominal slow extraction and the burst-mode scheme (e. g. with 10 ms bursts repeated at 10 Hz) the maximum particle rate in any calorimeter module of the ENUBET decay tunnel remains well below a few hundred of kHz/cm 2 , allowing ENUBET to perform lepton monitoring with moderate pile-up effects (see Secs. 8. 1 and 8. 2).
4 ). These constraints are imposed already in the first stage of the beamline design defining the layout and optics of the transfer line from the target to the decay tunnel. Beamline optics were simulated using the TRANSPORT code  [42]  and validated with MAD-X  [43]. This design process was driven by the following goals: maximize the number of K + and π + in the momentum range of interest for ENUBET at the entrance of the decay tunnel; focus the beam so that non-decaying particles exit the decay tunnel without hitting the tunnel walls, and constrain the beam envelope to be fully contained inside the aperture of all beamline elements (quadrupole, dipoles, collimators); use field and aperture values of the magnets compatible with existing technology. In ENUBET, we only employed normal-conducting, conventional devices to reduce the cost and complexity of the neutrino beamline.
This is a fundamental step of a secondary beamline design since the optics design programs are limited to the decay-and interaction-less propagation of the particles within the momentum bite. In a real secondary beamline as the ENUBET transfer line, many undesired particles move from the target down to the decay tunnel, together with the products of interactions with collimators, shielding, and materials around the beamline. The beamline was implemented both in G4Beamline and GEANT4. G4Beamline is a standard tool used for secondary beamline simulation and development: it is built on top of GEANT4 and relies on it for all physics processes, providing a higherlevel interface and several preset configurations to speed up code development and simulation times (e. g. precise and automatic implementation of standard beamline magnets and their fields).
Using this program was thus ideal for the rapid development and first optimization of the ENUBET beamline. On the other hand, using directly GEANT4 allows for superior control and customization of the whole simulation process: the GEANT4 implementation of the ENUBET beamline allowed to retain and analyze the full particle history and better assess the systematics of the neutrino beam (see Sec. 9). This model was also used to perform the final optimization phase of the beamline. The FLUKA code was instrumental in the simulation of the meson yields after the target (Sec. 4) and to evaluate the doses at the beamline elements and the instrumentation (Sec. 7). The interaction of the primary proton beam in the target is thus simulated with FLUKA and the exiting particles are used as input for the complete transfer line simulation.
The optimization framework developed in  [32]  for the horn optimization in ENUBET was significantly upgraded for this task since the beamline simulation is much more CPU intensive. In the computing cluster that we used for the optimization (CC IN2P3  [49] ) the simulation of a single job of 10 5 protons-on-target (pot) takes from 8 to 14 hours of CPU time and must be parallelized up to 3000 jobs. The optimizer workflow that carries out this optimization task is depicted in Fig 10. It is based on a high-level instruction language (user layer) that defines the parameters and the figure of merit (FOM). The optimizer generates a set of parallel jobs by means of an optimization layer to achieve the configuration with the optimal FOM. For the case of the ENUBET beamline, the collimators most influencing the signal-to-noise ratio were the last two Inermet ones before the decay tunnel: the optimization was thus set as a 5-parameters problem consisting in the radii of the conical aperture of the last collimator, its length and the half apertures of the next to last collimator.
Optimization layer Fig. 10  Scheme of the upgraded ENUBET optimization framework: the user can fully perform optimization by using a custom high-level instruction language (user layer). Multiple physics simulations can be developed and prepared for optimization (physics layer). The optimizer code will automatically handle the physics parameters, the computing cluster operations, and the FOM evaluation and combination for the optimization algorithm. This leads to a speed-up of about a factor 10 with a statistics reduction of a factor ≲ 2. These two configurations -corresponding to signal and background respectively -were run as two separate simulations (see  Fig 10). The flux at the tunnel entrance for 8. 5 GeV/c particles in a 10% momentum bite is 0. 4 × 10 -3 /pot for K + and 4. 6 × 10 -3 /pot for π +.
Decay tunnel instrumentation The detector technology for the instrumentation of the decay tunnel was investigated by the ENUBET collaboration from 2016 to 2022 [50-55] and culminated with the construction of a Demonstrator: a 1. 7 m long section of the instrumented tunnel validated with a charged particle beam at the CERN East Experimental Area  [56, 57]. The instrumentation comprises two detectors: a modular sampling calorimeter and a photon veto ("t 0 layer"). Each calorimeter module (Lateralreadout Compact Module -LCM) is made of five slabs of iron interleaved with tiles of plastic scintillators (Eljen EJ-200). The slab and tile cross section is ∼ 3 × 3 cm 2 and the thickness is 1. 5 and 0. 7 cm, respectively. Each LCM thus samples electromagnetic and hadronic showers every 4. 3 X 0. Three radial layers of LCMs cover the walls of the tunnel at R=100, 103, and 106 cm as in Fig.
16. Each LCM covers an azimuthal angle of 31 mrad and we have 200 LCM per layer at fixed z, where z is the position along the tunnel axis. The dimension of the LCM is a compromise between the need for high-granularity modules for pile-up reduction and particle identification and the total cost of the tunnel instrumentation (< 10% of the cost of the facility). In the ENUBET Demonstrator, the light produced by charged particles in the tiles is trapped inside the tiles by a diffusive coating (Eljen EJ-510) deposited on the tile surfaces. The only areas that are not covered by the diffuser are the grooves positioned in the back of the tile. Two WLS optical fibers (Y11, Kuraray) are glued to the grooves using an Eljen optical glue (EJ-500) with a refraction index similar to the plastic scintillator.
Particles are tracked from the tunnel's entrance using the output of the GEANT4 simulation of the ENUBET beamline described above. Tracking includes decay and interactions inside the tunnel and the creation of electromagnetic and hadronic showers originating from positrons, electrons, photons, and mesons. The energy deposition (MeV/LCM and MeV/tile in the photon veto) is converted into the number of photoelectrons (p. e. ) at the SiPM using the test beam data  collected at CERN and employed to account for p. e. statistical fluctuations (mean value: ∼ 15 p. e. /MeV in an LCM  [55] ). The simulation of the front-end electronics (SiPM response, waveform generation, and peak reconstruction) is based on the GossIP software package [58] and is described in Sec. 8. The performance of the Demonstrator was measured in the T9 beamline at the CERN-PS East Experimental area and is detailed in  [57].
Doses at the instrumentation and at the quadrupoles The evaluation of ionizing and non-ionizing doses in the beamline is a prominent task for the design of ENUBET. Ionizing doses are key to evaluating the damage to the beam components and, in particular, the first quadrupole located  in front of the target. Non-ionizing doses in the decay tunnel must be estimated to assess the potential damage to the photosensors over the entire duration of the ENUBET run. Doses in ENUBET were evaluated using FLUKA and the beamline and detector geometry of Secs. 5 and 6 were ported to to this simulation framework, too. Since the FLUKA geometry description is semi-automatically generated within GEANT4 in the ENUBET software package, the FLUKA model faithfully reproduces the GEANT4 and G4Beamline ones. Based on the secondary meson yields described in Sec.
Event simulation and reconstruction in the tagger The ENUBET tagger uses G4TAG, a GEANT4 package that simulates the detectors in the decay tunnel (Sec. 6). The simulation manages the propagation and decay up to the hadron dump of particles provided by the GEANT4 simulation of the transfer line with the static focusing system (Sec. 5) and assumes a 2 s slow extraction with 4. 5×10 13 pot per spill. The detector response is simulated at hit level, with corrections from test beam data  [55]. A software framework based on GosSiP [58] simulates the sensor response and the pileup effects by generating waveforms from hits on each channel. A full waveform reconstruction is being developed by the ENUBET Collaboration to simulate and reconstruct raw data. The results in the following use a simpler algorithm that handles pile-up effects by adding the energy and averaging the time of neighboring hits with a time difference below 1 ns.
ENUBET is equipped with a dedicated event-building algorithm for muons. The expected pattern for a muon crossing the calorimeter is a set of adjacent modules, each with an energy deposit in the scintillators of ∼ 6 -8 MeV, forming a straight line that starts from the muon impact point in the inner calorimeter wall and proceeds outward the three radial layers in the forward direction. A muon reconstruction starts as soon as a visible energy deposit compatible with a mip (energy between 5 and 15 MeV) is found in an LCM in the innermost layer of the calorimeter. This energy deposit is the seed for the hit clustering algorithm, where all other LCMs and photon-veto doublets whose energy deposits have a position in space compatible with that of a muon track are grouped together in a muon event candidate.
Pseudo-data are generated out of one of the MC sets, and fitted by building an extended maximum likelihood (EML) from the model PDF. The EML fit is validated through toy-MC experiments generated with the MC sets obtained by applying the multi-universe method. The RooFit package  [70]  from ROOT is exploited to build the model PDF, perform EML fits, and generate pseudodata. Given the high correlation between the lepton observables and the produced neutrinos, the EML fit result allows us to set a strong constraint on the flux. The model PDF parametrizes the variation of the lepton observables induced by the hadroproduction and directly constrains the hadroproduction yields through the EML fit. The propagation of the residual uncertainties from the constrained hadroproduction model through the multi-universe method allows for computing the neutrino flux covariance matrix after the lepton monitoring is introduced (post-fit result).
Acknowledgements This project has received funding from the  European Union 's  Horizon 2020 Research and Innovation programme  under Grant Agreement no. 681647  and the  Italian Ministry for Education and Research (MIUR, bando FARE, progetto NUTECH). It is also supported by the  Agence Nationale de la Recherche (ANR, France)  through the  PIMENT  project ( ANR-21-CE31-0027 ) and by the  Ministry of Science and Education of Republic of Croatia  grant No. KK. 01. 1. 1. 01. 0001.
PATIENTS AND METHODS Patient recruitment. The Assessment of Systemic Signs and Evolution in Sjögren's Syndrome (ASSESS) cohort is a French nationwide multicenter prospective cohort of patients with primary SS that was established in 2006 with a 10-year follow-up to clarify disease evolution, identify predictive factors, and investigate underlying mechanisms of primary SS-associated lymphomas  (6, 31)  (see Appendix A). In addition to collection of demographic and clinical data at enrollment and every year during follow-up, serum samples and peripheral whole blood RNA and DNA samples were collected from patients at enrollment for further biologic and molecular assessments. All patients who enrolled in the cohort fulfilled the American-European Consensus Group criteria for primary SS  (32). The diagnosis of NHL was proven by biopsy, and the NHL subtype was assessed according to the World Health Organization Classification of Tumours of Haematopoietic and Lymphoid Tissues  (33).
Ethics approval. All patients included in the ASSESS cohort gave their consent for participation. The ASSESS cohort, promoted by the Assistance Publique -Hôpitaux de Paris, was approved by the institutional review board and the ethics committee of Hôpital Bichat and the "Commission Nationale Informatique et Libertés. " Laboratory techniques and microarray data generation. Total mRNA was extracted from patients' peripheral whole blood samples using the PAXgene Blood mRNA kit (PreAnalytix GmbH). Microarray data generation. The quality of samples was assessed with Bioanalyzer 2100, with an Agilent RNA6000 nanochip kit. After validation of RNA quality, 75 ng of total RNA was reverse transcribed using the GeneChip WT Plus Reagent Kit (Affymetrix). Briefly, the resulting double-stranded complementary DNA (cDNA) was used for in vitro transcription with T7 RNA polymerase (all steps are included in the wild-type cDNA synthesis and amplification Affymetrix kit).
After purification according to the Affymetrix protocol, 5. 5 μg Sens Target DNA was fragmented and labeled with biotin. Control of fragmentation involved using Bioanalyzer 2100, and cDNA was then hybridized to GeneChip human Clariom S (Affymetrix) at 45 C for 17 hours. After overnight hybridization, chips were washed on the fluidic station FS450 in accordance with Affymetrix-specific protocols and scanned using the GCS3000 7G. We analyzed the scanned images using Expression Console software (Affymetrix) to obtain raw data (CEL files) and metrics for quality control. Data were deposited in GEO (accession no. GSE140161; https:// www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE140161). Microarray data pre-processing and analysis. Microarray computational analyses were performed with R 3. 5.
0. Briefly, raw data were loaded and normalized using the Robust Multi Array algorithm. The quality of data normalization was verified by visual inspection of intensity distribution across arrays and by principal component analysis (PCA) (from FactoMineR package), with all genes used as the input. No outlier was detected by either approach. Microarray probes were annotated with use of the clariomshumanhttranscriptcluster. db package. If several probes were mapped to the same gene, the probe with the highest coefficient of variation was selected to represent the expression of the corresponding gene. All differentially expressed genes (DEGs) were included for PCA. PCA was used to identify separate clusters between primary SS-NHL and primary SS control groups. We performed differential gene expression analysis using the limma package (based on linear models and empirical Bayesian method).