dataesr/scientific-paragraphs-categorization huggingface dataset:
Temperature profile in the snow Some of the models are multilayered and simulate the evolution of the profile of the variables that characterize the snowpack (density, temperature, enthalpy, etc. ). Thus, the JULES, ISBAES, CROCUS, and LMDZ models have an identical vertical discretization of the snowpack in 19 layers as recommended by the experimental protocol, while CLM4 has a discretization in 5 layers. Few observed data are available to make comparisons with the simulations, except for snow temperature. The snow temperature profiles of the multilayer models were therefore evaluated over the 15 d period by comparison with measurements made at different depths. In order to make an identical comparison for all models, a vertical interpolation of the observed and simulated profiles was performed on a fine grid with a resolution of 1 cm.
Acknowledgements. The first author warmly thanks  Eric Brun  for the support given to this study and for the configuration of the CRO-CUS model, and  Eric Brun ,  Aaron Boone , and  Bertrand Decharme  for the constructive discussions on the snow models. Review statement. This paper was edited by  Masashi Niwano  and reviewed by  Richard L. H. Essery  and  John King.
In this high-velocity range, models are wellestablished and agree with experimental data  [19] [20] [21] [22]. In contrast, the parameter region where v p ∼ v th (Bragg peak) is theoretically more challenging. The beamplasma coupling is here determined by binary collisions as well as interactions with density waves. Their relative and absolute contributions are strongly temperature dependent, so that even for ideal or nondegenerate conditions large discrepancies between the predictions of different stopping-power models are reported  23, 24. Experiments probing the Bragg peak are also more challenging, and the few measurements carried out for classical plasmas support models that include close binary collisions in the beam-plasma interaction description  25, 26. For WDM target conditions, theoretical modelling is more difficult due to electron coupling and degeneracy, and requires more advanced theories like quantum manybody approaches and first-principles calculations.
Simultaneously, a XUV Pinhole Grating Camera (XPHG)  45, 46  measured the time-integrated X-ray emission in the XUV range from the WDM target heater side. The measurement was weighted over the whole heated area of around 500 µm diameter and had a spectral resolution of 9 nm. FIG. 2. Experimental setup. Scheme of the experimental setup for each shot: (i) selection of a 500 keV energy proton beam from an initial broadband TNSA spectrum generated by the main beam, (ii) WDM sample generation by the heater beam, (iii) measurement of the downshifted proton energy spectrum of the selected beam after passing through the WDM target and (iv) characterization of the WDM sample by the SOP and the XPHG diagnostics. Typical raw experimental data acquired for each shot are shown for the magnet spectrometer as well as for the SOP and the XPHG diagnostics.
Simulations of the WDM target The WDM conditions were simulated using the two-dimensional (2D) radiation-hydrodynamic code RALEF2D widely used for simulations of different experiments  47, 48  , assuming local thermodynamic equilibrium (LTE), over a 500 ps time span after the heater beam onset on the target. The target ionization is deduced by post-processing the density and temperature profiles with the LTE version of the FLYCHK code  50. The profiles of mass density ρ, electron temperature T e and mean ionization Z * along the target central axis are plotted in Fig. 3  for various times of the target evolution, where the target thickness is reported in areal-density units. The reached conditions are ρ ≥ 0. 1 g/cm  3  and T e between a few eV and a few tens of eV, which correspond to carbon ionization degrees Z * ≤ 4.
They are compared to the spatially and temporally integrated spectra obtained with the PrismSPECT code  52, 53  assuming LTE and using the density and temperature profiles extracted from both the RALEF2D simulation (Fig. 3 a ) and b) respectively) and from the MULTI-fs 1D hydrodynamic simulation carried out over a weighted range of intensities matching the experimentally measured focal spot. The measured spectra agree within 10-30 % with the spectra predicted by the RALEF2D and the MULTI-fs codes, which is on the order of the experimental error bar estimated as ≈ 20 %. In contrast to the SOP data, the XPHG measurement shows an X-ray emission at higher energies than simulated, which corresponds to an experimental temperature higher than simulated. Based on the RALEF2D simulation, a mass-weighted and time-integrated temperature of 7.
Stopping-power calculations For estimating the discrepancies between stoppingpower models for typical conditions of the experiment, various predictions for protons in carbon are compared in Fig. 5a  for a density ρ = 0. 5 g/cm 3 and a temperature T e = 10 eV. The proton stopping power in solid carbon according to the SRIM database  54  is plotted as a reference. Firstly, we use ad hoc calculations combining a freeelectron and a bound-electron contribution that are obtained separately knowing the target ionization  [55] [56] [57]. The free-electron term is calculated using several mod-els that have the same Bethe-like high-velocity limit determined from dielectric stopping theory: the Li-Petrasso (LP) model  58  , the Brown-Preston-Singleton (BPS) model  59  , the T-matrix (TM) model with velocitydependent screening  23, 60  , the dielectric random phase approximation (RPA) model  23, 61  and the Zimmerman parametrization  62  of the Maynard-Deutsch dielectric stopping power  63  , the latter being very similar to the RPA description.
Energy-loss results Firstly, the energy loss of the proton beam was measured in solid carbon foils over 35 shots to estimate the measurement accuracy and provide a reference energyloss value in the solid target ∆E sol. The downshifted proton energy after passing the solid target was measured to be 449 ± 5 keV, where the error σ = ± 5 keV results from the standard deviation at 1σ over all shots and from systematic measurement uncertainties as is explained in the methods. This results in an energy loss of ∆E sol = 49 ± 5 keV, which is in good agreement with the energy loss of 48. 1 keV predicted with the SRIM database  54. Subsequently, the proton energy loss in the sample was measured on shots with the heater beam driving the target, at respective time delays of -316 ± 100 ps, -116 ± 100 ps and 86 ± 100 ps relative to the onset of the heater laser pulse on the sample.
The wavelength-dependent response of the SOP system within the 3 nm bandwidth was provided by the manufacturers. The transmission of the optical system for SOP has been measured with a 532 nm continuous diode laser and the streak camera was absolutely calibrated at the selected wavelength using the calibration of Ref. 70. The latter was carried out with the same streak camera employed in this experiment and with the filter data set BG38 and FI532 that are similar to the ones we used. The data were acquired inside a time window of 2 ns. The temporal evolution of the temperature was determined using a vertical lineout of the central target region of 50 µm diameter corresponding to the proton beam size entering the WDM target. The resulting experimental curve averaged over 65 shots is presented in Fig.
XPHG and SOP diagnostic modelling For the comparison with the experimental XUV spectra obtained with the XPHG diagnostic, the Prism-SPECT atomic code was used to postprocess target pro-files extracted from the RALEF2D and MULTI-fs 1D hydrodynamic simulations. Using the RALEF2D simulation, we considered target profiles at radii r = 30, 60, 90, 120, 150 µm from the proton propagation axis (target center), over the expansion time of 0-300 ps, with time steps of 10 ps for t = 0-100 ps, and 50 ps for t = 100-300 ps. At each time step, the spatially-integrated emission spectrum was obtained by summing the areaweighted emissivity at each radius. The spectra were then integrated over time to obtain the total space-and time-integrated emission as measured by the XPHG diagnostic on the heater beam side of the target.
ACKNOWLEDGEMENTS We gratefully thank the director  Luis Roso  and the team of the VEGA II laser facility at  Centro de Laseres Pulsados  for their expert support in performing the experiment, as well as  B. Lommel  and the team of the  GSI Target Laboratory  for the preparation and the delivery of the sample targets. The research leading to these results has received funding from  LASERLAB-EUROPE V  (Grant Agreement No. 871124 ,  European Union   Horizon 2020 research and innovation program ) and from  IM-PULSE  (Grant Agreement No. 871161 ,  European Union   Horizon 2020 research and innovation program ). Support from  Spanish Ministerio de Ciencia, Innovación y Universidades  through the  PALMA Grant  No. FIS2016-81056-R ,  ICTS Equipment  Grant No. EQC2018-005230-P , from  LaserLab Europe IV  Grant No.
654148 , from  Junta de Castilla y León  Grants No. CLP087U16  and No. CLP263P20 , from the  Canadian Natural Sciences and Engineering Research Council  ( RGPIN-2019-05013 ) and from the  LIGHT S&T Graduate Program (PIA3 Investment for the Future Program ,  ANR-17-EURE-0027 ) are acknowledged. The US collaborators acknowledge funding from the  National Nuclear Security Administration  (Grant  DE-NA0003876 HEDLP ). A. J. W and L. A. C acknowledge support under Science Campaign 4 by the  U. S Department of Energy  through the  Los Alamos National Laboratory , operated by  Triad National Security, LLC for the National Nuclear Security Agency of the U. S Department of Energy  (Contract No. 89233218NCA000001 ). Research presented in this article was also supported by the  Laboratory Directed Research and Development program  of  Los Alamos National Laboratory  under project number  20210233ER.
Cell size determination and data processing Cell size determination was performed using a Z2-Coulter Counter (Beckman). C. albicans cells were grown overnight in YPD-doxycycline at 30°C, diluted 1000-fold into fresh YPD-doxycycline and grown for 5h at 30°C to reach a nal density of 5. 10 6 -10 7 cells/ml, a range in which size distributions of the WT strain do not change. A total of 100 µl of exponentially growing cells was diluted in 10 ml of Isoton II electrolyte solution, sonicated three times for 10s and used for size quanti cation in the Z2-Coulter Counter. Size distribution data were normalized to cell counts in each of 256 size bins. Data analysis and size distribution clustering were performed using custom R scripts as previously described  (Sellam et al. 2019 ).
Modulation of Start by novel and known mechanisms Previous work has shown that mutations affecting core cellular process such as ribosomal and nucleolar proteins and cell division lead to a dramatically decreased growth rate and corresponding size defect  (Tyson et al. 1979; Jorgensen et al. 2002). To identify bona de Start regulators, rather than slow growth mutants, mutations that signi cantly impacted growth rate were excluded from our dataset. We calculated the doubling times of the 51 size mutants identi ed in this work and those that had more than 10% increase in doubling time as compared to the parental WT strains were omitted. A short list of 33 ( 26 whi and 7 lge mutants) strains was retained as high con dence size mutants of C. albicans (Table  1 ). Start onset of ve potent C. albicans Start regulators Myo1, Iqg1, Chs3, Kre1 and Sun41 in addition to the conserved regulators Swi6, were characterized by assessing bud emergence in G1-elutrated cells of their mutant strains.
The selected non-linear function g was easy to implement and was executed online (<5 ms latency for a 50-ms stimulus in MATLAB), allowing for future extensions in real-time applications. Figure  2a  shows the three processing functions that were derived after applying the optimisation procedure for the three selected CS profiles. The processing functions are shown as a function of the amplitude of the input stimulus x, normalised to its maximum absolute (peak) amplitude. Thus, for each CS profile, the envelope of the unprocessed SAM stimulus of Fig. 2b  (modulating tone) can be used to compute the processing function g across time (Eq. 2), which is then multiplied elementwise by the input stimulus x to derive the processed version x = g • x. The processed signal x is able to optimally stimulate the corresponding CS-affected periphery to generate an enhanced AN response that closely matches the NH response.
20 The experimental data were collected in a sound-proof and electrically-shielded booth and the sounds were presented monaurally via Etymotic ER-2 insert earphones connected to a Fireface UCX external sound card (RME) and a TDT-HB7 headphone driver. The EEG recordings were conducted while subjects were sitting in a comfortable chair with a head rest and were watching muted movies. The processing algorithms were implemented in MATLAB and the participants' psychoacoustic responses were collected using the AFC toolbox for MATLAB  [104]. Participants were informed about the experimental procedure according to the ethical guidelines at Ghent University and Ghent University Hospital (UZ-Gent), and were paid for their participation. Written, informed consent was obtained from all participants. An overview of the three measurement sessions is shown in Table  1 , with each session explained in detail in the following sections.
EEG measurements For the first part of the EEG session, a SAM tone with carrier frequency f c = 4 kHz, modulation frequency f m = 120 Hz and m = 100% modulation was generated in MATLAB and presented using PLAYREC with a sampling rate of 48 kHz. The processing algorithms were implemented in MATLAB and were applied online to the stimulus before presenting them to the listener. The stimuli were 500-ms long and repeated 1000 times with alternating polarities. A uniformly-distributed, random, silence jitter was applied between consecutive epochs (100 ± 10 ms) of the 1000 stimulus presentations. The calibration of the SAM stimuli was carried out based on their peak amplitudes (RMS of the carrier tone), rather than the RMS of each modulated stimulus separately. The reason for avoiding re-calibrating to the RMS was that, after processing the modulated stimulus using our algorithms (Fig.
AM detection The AM detection test was implemented as a 3-AFC experiment in the AFC toolbox of MATLAB  [104]. Participants were instructed to select the signal that sounded different to the other two, each time choosing between three randomly-presented signals containing two pure tones and a modulated tone of variable modulation depth. An 1-up, 2-down adaptive procedure was used with step sizes of  [10, 5, 3, 1]  dB and a starting modulation depth of -6 dB. The task was repeated three times for each condition, and the average value and standard deviation of the three trials was calculated (Fig. 6 ). In each trial, two 70 dB-SPL, 4-kHz pure tones (reference signals) and a modulated tone (target signal) were generated. The same peak-to-peak calibration as that applied to the EFR stimuli was used, to ensure that the same carrier signal was used among the three signals within each trial.
Code availability The MATLAB implementations of the presented hearing-restoration algorithms, as well as the scripts that were used to record and analyse the EFRs, can be made available upon request or upon acceptance of the paper. The source code of the auditory periphery model  [50]  v1. 2  [108]  is available via https://doi. org/ 10. 5281/zenodo. 3717800 or https://github. com/HearingTechnology/Verhulstetal2018Model.
Acknowledgments This work was supported by the European Research Council (ERC) under the Horizon 2020 Research and Innovation Programme (grant agreement No 678120 RobSpear). We thank Heleen Van Der Biest for assisting in the participant recruitment and data collection. English language services were provided by stels-ol. de. Supplementary Figure  3. Estimated EFR noise-floor (NF) magnitudes (cf. Fig. 5 ). The magnitudes of the estimated EFR NFs are shown for the SAM and speech stimuli before and after processing, and were subtracted from the recorded EFR results to yield the EFR magnitudes of Fig. 5  (see Methods). In both panels, the individual points correspond to the mean and standard deviation of the summed EFR magnitudes (EFR summed ), computed from the EFR noise spectra of each participant (Eq.
III. COMPUTATIONAL DETAILS Unless otherwise specified, we employ scalar-relativistic energy-consistent HF pseudopotentials and the correlationconsistent Gaussian basis sets specifically constructed for these pseudopotentials  [24, 25]. For most of the calculations, we use a double-ζ basis set minimally augmented with s and p diffuse functions on the heavy atoms and denoted here as maug-cc-pVDZ. Convergence tests are performed with the fully-augmented aug-cc-pVTZ basis set. The exponents of the diffuse functions are taken from the corresponding allelectron Dunning's correlation-consistent basis sets  [26]. The HF and CASSCF computations are performed with the program GAMESS(US)  [27, 28]. When using the CASSCF wave functions in QMC, we truncate the CAS expansion for CN11 and CN13, using a threshold on the CSF coefficients so that the configurations make up respectively about 0.
[7]  and obtain the geometries for CN13 to CN19 at the same level of theory with the Gaussian 09 program  [34]. We employ the programs CFour v2. 1  [35]  and Molcas  [36]  for the approximate coupled cluster singles and doubles (CC2) and singles, doubles, and triples model (CC3), and the CASPT2 calculations, respectively, using the all-electron aug-cc-pVDZ basis set and the frozen-core approximation, unless otherwise specified.
ACKNOWLEDGMENT A. C. is supported by the " Computational Science for Energy Research and Netherlands eScience Center joint program " (project  CSER. JCER. 022 ) of the  Netherlands Organisation for Scientific Research (NWO). The calculations were carried out on the Dutch national supercomputer Cartesius with the support of SURF Cooperative. The work is also partially supported by the  European Center of Excellence in Exascale Computing TREX -Targeting Real Chemical Accuracy at the Exascale , funded from the  European Union 's  Horizon 2020 Research and Innovation program  (grant no. 952165 ).
Moreover, we have successfully confirmed the feasibility of obtaining naive Tregs from the target population of patients with ESRD and the subsequent manufacturing process for production of TX200-TR101 in a manufacturing feasibility study. This article describes the design of the STEADFAST study (registered as NCT04817774 and EudraCT 2019-001730-34), a phase I/IIa study to assess TX200-TR101 in living-donor renal transplant recipients. We anticipate that this will be the first time that CAR-Tregs are administered to humans. As of February 2022, recruitment into the study has started and 4 sites have been initiated in Belgium, The Netherlands, and United Kingdom. The study is funded by Sangamo Therapeutics France. It is performed in accordance with the Declaration of Helsinki and International Council for Harmonization Good Clinical Practice after approval by the relevant ethics committees at the clinical sites.
The biomarker panel will determine the localization and kinetics of TX200-TR101 in the graft and the effects on graft and peripheral immune responses. The localization of TX200-TR101 will be assessed based on CD4-positive cells in the renal transplant biopsy that are also positive for the HLA-A * 02-CAR. In addition, biomarker levels and changes from baseline related to the presence of HLA-A * 02-CAR-Tregs will be assessed, including changes from baseline in donor/recipient mixed lymphocyte reaction assays. The assessment of TX200-TR101 in allograft tissue will enable the confirmation of homing of TX200-TR101 to the allograft where the target antigen HLA-A * 02 is expressed and will provide valuable data on local persistence of the HLA-A * 02-CAR-Tregs.
Dose Cohorts, Staggered Dose Escalation, and Safety Monitoring A Safety Monitoring Committee will review relevant safety data and discuss continuation, termination, dosing considerations, or other modifications of the study with the sponsor. Three doses of TX200-TR101 in the range of 10 4 to 10 9 cells/kg body weight will be assessed in 3 singleascending dose cohorts, with 3 evaluable subjects per dose level (Figure  3 ). An additional 6 transplant recipients may be recruited at the recommended dose level for expansion after consultation of the sponsor and the Safety Monitoring Committee, resulting in a total of up to 15 subjects administered TX200-TR101. It is aimed to enroll at least 1 control subject per dose cohort during the dose-escalation phase of the study. Transplantation and subsequent TX200-TR101 administration will be staggered, and sentinel dosing will be applied within and between dose groups.
The first and second subjects in each dose cohort will be observed for safety surveillance for a minimum of 28 and 14 days after infusion, respectively, before the next subject may be dosed. A review of the data by the Safety Monitoring Committee will be required before starting a new dose cohort. After infusion of TX200-TR101, subjects will be monitored closely for 24 to 48 hours in an inpatient clinical setting. This includes assessment of vital signs, clinical status, inflammatory markers, and cytokines immediately before, during, and after TX200-TR101 infusion. After discharge, additional outpatient monitoring will be in place for the subsequent 12 to 13 days postinfusion. All AEs, including serious AEs, will be documented and reported throughout the study, from the time of informed consent (serious AEs) or first invasive procedure (nonserious AEs) to the end of study participation (week 84), according to Good Clinical Practice.
The timing of the TX200-TR101 administration at a defined time point post-transplantation was chosen based on clinical data and considering the immunosuppressive regimen. Several groups have demonstrated an initial decline of polyclonal Tregs in peripheral blood immediately after transplantation. 35, 36 hus, administration of TX200-TR101 at approximately 2 to 3 months post-transplant is expected to boost the number of Tregs that interact with the overall Teff population. This study aims to evaluate the short-term safety and tolerability of TX200-TR101. Assessment of TX200-TR101 in allografts will evaluate homing and local persistence of the HLA-A * 02-CAR-Tregs in the kidney. Preclinical data demonstrating the homing of TX200-TR101 Tregs to their target antigen in a mouse model are the subject of a separate article (currently under revision).
ACKNOWLEDGMENTS This study was funded and materials were provided by  Sangamo Therapeutics. The authors thank the STEAD-FAST investigators and the patient advocacy group of kidney transplant patients for reviewing the study design. The authors also thank the  University of British Columbia  and adMare Bioinnovations (formerly known as the  Center for Drug Research and Development)  for their collaboration on the development of the HLA-A * 02-CAR. Medical writing assistance was provided by  Dr. Mike F Mueller  (Scinopsis, France). The STEADFAST study is registered on clinicaltrials. govas  NCT04817774  and on EudraCT as  2019-001730-34.