dataesr/scientific-paragraphs-categorization huggingface dataset:
Conversely, in the Autumn, the freezethaw cycles are favoured by the absence of snow in the couloir. Furthermore, although a first field test was carried out during the summer of 2018 with 3 seismometers (Le3D Lennartz) installed on the right bank of the couloir and operational for 3 weeks with results comparable to 2019 (number of events, daily and seasonal distribution), the analysis presented here only covered one season. It would be interesting to compare to other seasons to assess the reproducibility of the results. In order to better quantify the triggering processes, further measurements, including pressure and crack opening/closing sensors (strain gauges, inclinometers;  McColl and Draebing, 2019; Weber et al. 2019)  would have been required. Finally, the evolution of the snow cover in the couloir, identified in this study as an important factor in the rockfall triggering, was only evaluated from snow-covered surface data which only gives biased information on the quantity of liquid water available during melting.
s. l. by 2100 based on the RCP 8. 5 scenario  (Pohl et al. 2019 ). This will reduce the daily-scale frost weathering. Snowmelt will occur ever earlier in the summer season and probably over a shorter period. As a result, a significant amount of liquid water will infiltrate into the cracks of the rock but over a shorter period of time. Although precipitation does not show a decreasing trend in the Alps, the frequency of intense episodes of precipitation will increase (Météo France data -Drias-climat). This implies that large quantities of liquid water could punctually infiltrate the cracks of the rock. It is therefore likely that rockfalls linked to liquid water infiltration and the resulting thermo-mechanical processes will be particularly frequent but possibly over shorter periods. Finally, the ongoing degradation of permafrost will continue and intensify  (Magnin et al.
These results also allow a better evaluation of climbers' vulnerability in this sector and show that climbers' awareness of the risk of rockfalls remains limited. What's more, they do not adaptor only slightlytheir behavior to this risk, despite a particularly high accident rate. Important work on prevention and dissemination of the knowledge here acquired (newsletters, training for professionals and amateurs, awareness campaigns) among mountaineers is therefore still really necessary. The study we carried out applied the recommendations of McDowel and Koppes (2017) (i) to acquire interdisciplinary, precise data at a fine scale in order to take into account the specificities of the study area and not to simply transpose dynamics visible at larger scales; (ii) to take into account the complex interactions existing between changes in the physical environment and the socio-economic, cultural and political conditions specific to a study area, the implementation of adaptation strategies being not solely guided by physical changes in the environment; and (iii) to recommend adaptation methods adapted to the context in order to lead to appropriate adaptation plans.
At the Grand Couloir du Goûter, we acquired interdisciplinary data on a fine scale in partnership with the main actors concerned (local authorities, the French Federation of Alpine Clubs, the military forces in charge of the rescue operations, and hut keepers). This has certainly contributed to the good reception of the first results which have been disseminated to the mountain community and to the actors in charge of the management of the route through a research report (available for free in French and English on the Petzl Foundation website) and a web video clip (viewed more than 500,000 times and projected at the Science and Mountains Festival in Grenoble), and has enabled the concertation process to be re-launched in order to adapt the management of the route and the prevention measures applied to mountaineers.
Acknowledgements The authors warmly thank  Olivier Moret  and the  Petzl Foundation  for their support,  Didier Hantz  (ISTerre) and  Florence Magnin  (EDYTEM) for their help in analysing the data,  Pierre Dubois  ( REQUEA ) for giving us access to data from their weather station,  Antoine Rabatel  ( IGE ) for lending the long rang Lidar and  Emmanuel Thibert  ( INRAE ) for the access to the data from the Tête Rousse weather station. We also thank  Marc Whatelet ,  Axel Jung ,  Sandrine Roussel  and Laurent Métral https://doi. org/10. 5194/nhess-2021-128Preprint. Discussion started: 27 April 2021 c Author(s)  2021. CC BY 4. 0 License.
Powder crystals were used for the temperature dependent measurements. Samples were all sealed in an airtight vacuum bag purged with Argon right up until the measurements. Powder GIWAXS measurement were performed by crushing and smearing the powder crystal onto clean glass substrates (wiped with Acetone and Isopropyl alcohol using Kim wipes). GIWAXS data processing: GIWAXS data from the APS beamline were processed using the GIXSGUI package (version 1. 7. 3) running on Matlab 2018b (Mathworks). 7 GIWAXS data from the NSLS-II beamline were processed using SciAnalysis (https://github. com/CFNsoftbio/SciAnalysis) running on Visual Studio Code using Python (version 3. 7. 3). 8 All patterns were corrected with conditions such as detector sensitivity, X-ray polarization, and geometrical solid-angle parameters.
GIWAXS analysis: To analyze the in-situ light illuminated GIWAXS measurements, numerical fitting of the diffraction profiles was performed in Matlab 2018b. Each diffraction peak range was fitted with pseudo-Voigt profiles. 6 Each fit used the built-in "fmincon" function which constrained the pseudo-Voigt profile to physical ranges in terms of peak linewidth (full-width-at-halfmaximum FWHM). This allowed the fit to discard unphysical domain boundaries/crystallite size. Due to the small residual from the "fmincon" fit, the error interval is smaller than the size of each point in the strain and lattice parameter plot. The Debye-Scherrer formula was used to extract the average grain size of the 2D perovskites. 9 The first order out-of-plane diffraction plane was used for the analysis. The shape factor of 0.
X-ray photo-electron spectroscopy X-ray photoelectron spectroscopy measurements were carried out on a PHI Quantera XPS system. Before and after light illumination XPS was performed on a large millimeter size Dion-Jacobson ACCEPTED MANUSCRIPT / CLEAN COPY and Ruddlesden Popper n=3 single crystal perovskite and film grown on a glass substrate using the procedure mentioned in section 1. Etching was not performed, and the sample environment was under 10 -8 torr for the duration of the experiment. Each peak was averaged using 20-25 spectra collected. Emission spectra were calibrated to carbon 1s photoelectron emission at 284. 80eV. The samples were illuminated using an ABB solar simulator from Newport (model 94011) for one hours. The XPS spectra were fitted to a pseudovoigt profile, and the error was extract using a the "nlparci" function in MATLAB from the Jacobian of the fit.
Transport characterization We fabricated an electron-only device with indium tin oxide (ITO)/ tin oxide (SnO2) on one side and then C60/Cu on the other side. 2D perovskite thin films was measured about 250nm in thickness. The samples were illuminated using an ABB solar simulator from Newport (model 94011) for 40mins. The arc simulator modeled AM 1. 5G irradiance of 100 mW/cm 2 and was calibrated using a NIST-certified Si solar cell (Newport 91150V, ISO 17025). The current was measured with a Keithley 2401 instrument at scan rate of. 5V /s. Device was measured in vacuum (10 -2 torr). The analysis was performed by fitting in MATLAB and using the optimization package and a numerical least square algorithm "lsqcurvefit". An error bar for conductivity and mobility was extracted by using the Jacobian of each time fit.
Theoretical structural simulation Density Functional Theory simulations: The calculations were performed using Density Functional Theory (DFT)  12, 13  as implemented in the SIESTA package with a basis set of finiterange of numerical atomic orbitals. 14 We used the van der Waals density functional with C09 exchange within the van der Waals DF2 flavor to describe the exchange-correlation term. 15, 16 This tuning was suggested to improve C09 based geometry optimization over that with DF1 flavor. 16 e latter has already provided a good description of experimental lattice constants similar to those obtained with optimized GGA-based PBEsol functional in solids. 17, 18 Norm-conserving Troullier-Martins pseudopotentials were used for each atomic species to account for the core electrons. were used for the calculations.
Acknowledgments: The work at  Rice University  was supported by the  DoD-STIR program  funded by  ARO. J. E. acknowledges the financial support from the  Institut Universitaire de France. W. L. acknowledge NSF GRFP. This material is based upon work supported by the  National Science Foundation Graduate Research Fellowship Program  under Grant No. ( NSF   20-587 ). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author and do not necessarily reflect the views of the National Science Foundation. Work at  Northwestern  on the stability of perovskite solar cells was supported by the  ONR  ( N00014-20-1-2725 ). DFT calculations were performed at Institut FOTON as well as  ISCR  and the work was granted access to the  HPC resources of TGCC/CINES/IDRIS  under the allocations  2019-A0060906724  and  2019-A0070907682  made by  GENCI.
This research used facilities of the  Advanced Photon Source , a  U. S Department of Energy (DOE) Office of Science User Facility  operated for the  DOE Office of Science by Argonne National Laboratory  under Contrast No. DE-AC02-06CH11357. This research used beamline 11BM (CMS) of the National Synchrotron Light Source II and the  Center for Functional Nanomaterials (CFN) , both of which are  U. S. Department of Energy (DOE) Office of Science User Facilities  operated for the  DOE Office of Science by Brookhaven National Laboratory  under Contract No. DE-SC0012704. We acknowledge the help of  Bo Chen  for supervising the XPS measurements. We thank  Ruipeng Li  for his assistance performing experiments at beamline CMS. The work at  Purdue University  was supported by the  National Science Foundation  under Grant No.
Cell Survival and Viability (Propidium Iodide Exclusion) The cells were seeded in triplicate in 12-well plates 24 h before the PtNPs exposure. After a 6-h exposure to 0. 5 mM PtNPs, cells were irradiated at 6 Gy, and the medium was changed. After 3 and 6 days, the supernatants and cells were harvested and centrifuged for 5 min at 300x g. The pellets were re-suspended in 500 pL of the culture medium, and the samples were further diluted to one fifth in 5 pg/mL of a propidium iodide-PBS solu tion. The cell survival was calculated from the volumetric absolute count, using an ACEA Novocyte flow cytometer (ACEA Biosciences, Agilent Technologies France SAS, Les Ulis, France), with an optical configuration (405, 488, and 640 nm). Live and dead cells were discriminated according to their propidium iodide exclusion, and the data were analyzed by using NovoSoftware (ACEA Biosciences, Agilent Technologies France SAS, Les Ulis, France).
Acknowledgments: The authors wish to thank facilities from  INRAE  and  LNE. For the electronic microscopy, this work has benefited from the facilities and expertise provided by  MIMA2 -Université Paris-Saclay ,  INRAE ,  AgroParisTech , GABI, 78350 Jouy-en-Josas, France. The ICP-MS experiments were conducted on the Platform of the  LNE laboratory  ( Laboratoire National de Métrologie et d'Essais, Paris, France ). We are grateful to  Christophe Carles  for his constant support and interest in the study. Conflicts of Interest: The authors declare no conflict of interest.
The large surface of the specimen allows easy positioning of thermocouples on both sides of it, as shown in Figure  1 , and it is also possible to film the surface of the specimen with an infrared camera. Thus, process parameters and infrared images of the welding operation are the observational data from which 3D stresses and temperatures will be estimated. The experiments are performed with a 6-axes Panasonic robotic arm and equipped with a ValkWelding torch and the tensile loading is applied with a Lloyd Instruments LS100 plus. Two parameters related to the heat source are fixed before the experiment: the speed (v) and the power (Q), which is the product of voltage (U) and current (I). The experiments are instrumented with two thermocouples and filmed with a SC7500 FLIR infrared camera.
Results In this section, we will show the use of the proposed models. First of all, the experimental configuration of a PVR hot cracking test is explained. Then, details about the parametric finite element study from which the snapshots are generated will be given. For the numerical tests, two sources of data are considered to show the case where the torch position is well known and the case where and estimation of its position is needed. The first test will use noisy synthetic data obtained from a previously calibrated Finite Element solution as input. The joint multiphysics state and unknown parameters estimation are obtained using a single PPCA model. Then, we will consider a frame of an infrared video of a PVR experiment. The torch position will be deduced from the image and the estimation computed from a mixture of PPCAs.
Identification of Wheat TaFBX Gene Family The hidden Markov models (HMM) profile of the Pfam F-box domain and FBXlike domains (PF00646, PF12937 and PF13013, PF15966) were used as queries to identify TaFBX genes in the RefSeq v1. 1 version of the annotated wheat genome released by the International Wheat Genome Sequencing Consortium (IWGSC)  [45]. This first query led to the identification of 4303 putative TaFBX genes. InterProScan analysis and manual curation of domain organization consistency eliminated 633 sequences and left 3670 fulllength, high-confidence (HC) sequences containing an F-box or an F-box-like domain (Table  S1 ). Among them, 3536 (96. 3%) TaFBX genes were mapped onto the 21 bread wheat chromosomes; the 134 (3. 7%) remaining sequences did not have chromosomal location information.
Their protein sequences were analyzed using Pfam and their domains were specified along with their truncated names (i. e. , "G499800" means "TraesCS3B02G499800"). The red lines on the bottom part represent direct duplications and the blue ones represent reverse duplications. To further investigations, the whole set of 33 genes (TaFBX and non-TaFBX) located on the 3 Mb subregion were also studied. As expected, 14 genes were found to contain the F-box domain (IPR001810) in the N-terminal and the FBD domain (IPR006566) in the C-terminal of their protein sequences. Six other proteins contained only the FBD domain, and one protein contained only the F-box domain. The 12 remaining proteins contained various domains, like MATH/TRAF domain (IPR002083) for two of them or Domain of Unknown Function DUF1618 (IPR011676) for four of them.
These results emphasize the fact that, although the distal regions contained an absolute high number of TaFBX DEGs during wheat grain development, their relative contribution is less important than the interstitial regions (R2a, C and R2b). To study the involvement of TaFBX genes in the wheat response to heat and drought stress, RNAseq data generated by  Liu et al. (2015)   [48]  were used (accession number SRP045409). In their experiment, 7-day wheat seedlings were submitted to thermal stress, drought or a combination of both for 1 h or 6 h for each treatment. As done previously, only the HC genes were retrieved, and the counts of all of the isoforms were cumulated for each gene, leading to a 107,891 gene set. After filtration, 55,801 genes were found expressed, containing 1298 TaFBX sequences.
While this manuscript was in its final stage, Hong et al. (2020) reported that the bread wheat genome contains 1796 TaFBX genes  [43]. Using the same RefSeq v1. 1 version of the annotated wheat genome released by the IWGSC  [45] , we identified 3670 TaFBX genes, which are twice larger. This discrepancy between the two results could be explained by the validation and manual curation process of the putative sequences identified. Indeed, while both Hong et al. (2020) and we identified roughly the same initial number of TaFBX putative genes using HMMR programs (around 4000 unique genes), they may have used more restrictive filters and only the Pfam database to identify their final set of TaFBX genes. In addition, those authors indicated that they removed identical sequences. In our paper, we used InterProScan and interrogated the InterPro database that integrates signatures from 13 different databases  [68] ; this may have led to retrieving more sequences.
Furthermore, the annotation of the whole proteome of wheat by the IWGSC identified 3749 unique proteins containing the F-box domain (IPR001810). Finally, in the set identified in our paper, we retrieved sequences coding for essentials proteins, such as putative receptors for hormones that were not retrieved by Hong et al. (2020)  [43] , for instance Auxin-signaling (8 TaFBX, TraesCS7D02G177500, TraesCS7B02G081200, TraesCS1D02G099900,TraesCS1A02G091300,TraesCS1B02G119100, TraesCS5B02G280100, TraesCS5A02G281100, TraesCS5D02G288000), jasmonate perception and signaling (TraesCS3D02G360400, TraesCS3B02G399200, TraesCS1A02G279100, TraesCS1B02G288100, TraesCS1D02G278400, TraesCS3A02G367500) or abscisic acid signaling (TraesCS3A02G331200,TraesCS3B02G361400,TraesCS1B02G244300,TraesCS1A02G229800, TraesCS1D02G232200).
(2020)   [43] , while this is the Kelch domain in cotton  [32]. Analysis of the remaining TaFBX genes showed the presence of supplemental domains, such as FBD, DUF, Kelch, FBA, etc. , localized in the C-terminal region of their sequence and used for their subclassification into 17 subfamilies. Among those wheat TaFBX subfamilies, we identified 67 genes carrying the phloem protein 2-like domain (or PP2, IPR025886). The latest examination of the Arabidopsis, poplar and rice genomes  [41, 46] , as well as other plant species  [30] , did not reveal any FBX protein with this domain. More recently, eight FBX genes carrying the PP2 domain have been identified in chickpea  [31]. It has been suggested that, through evolution, some PP2 proteins (typically associated with phloem functions) have acquired the FBX domain and gained the capability to interact with glycoproteins, playing a role in protein degradation  [31, 78].
Secondly, in response to abiotic stress, 525 TaFBX DEGs were identified from the data of  Liu et al. (2015)   [48]  (Table  S5 ). A focus on their chromosomal anchorage revealed that their distribution pattern along the wheat genome did not match with the expected one from the whole TaFBX gene distribution. For both datasets, we found that, although the distal chromosomal regions (R1 and R3) were richer in genes than the central (C) and interstitial regions (R2a and R2b), the latter being richer in TaFBX DEGs than expected during embryogenesis and in response to abiotic stress. Cumulatively, the R2a, R2b and C regions contributed 16. 3% more TaFBX DEGs than expected during embryogenesis and 50. 9% more in response to abiotic stress. The most significant gap between theoretical and observed TaFBX gene differential expression was noticed in the R2b region in response to abiotic stress, with 68% more DEGs than expected.
Survey of TaFBX in the Wheat Sequence The FBX proteins were retrieved from the protein database of the IWGSC (IWGS RefSeq v1. 1, cv Chinese Spring)  [45]  by using the HMMER2 program implemented in Unipro UGENE 1. 25  [81]. The FBX and FBX-like motifs (PF00646, PF12937, PF13013, and PF15966), downloaded from Pfam, were used as queries. The initial set of genes identified by HMMER2 were subjected to InterProScan analysis and manual curation.
Subfamily (Subdomain Contained by their TaFBX Members) Number of TaFBX Genes ACTIN (Plant actin-related protein 8, IPR030071) 3 ARM (Armadillo, IPR000225) 10 DUF (Domain unknown function DUF295) 234 FBA (F-box associated domain, IPR007397) 143 FBD (FBD domain, IPR006566) 359 FBX-only (F-box domain, IPR001810) 2475 Jmjc (Jmjc domain, IPR003347) 9 Kelch (Galactose oxidase/kelch, beta-propeller, IPR011043 or Kelch-type beta propeller, IPR015915) 203 LRR (Leucine-rich repeat, IPR001611) 53 LysM (LysM domain, IPR018392) 3 Other (various domains described in Table S1) 41 PP2 (Phloem protein 2-like, IPR025886) 67 QAD (Quinoprotein amine dehydrogenase, beta chain-like, IPR011044) 17 SEL1 (Sell-like repeat, IPR006597) 3 TUB (Tubby, C-terminal, IPR000007) 31 WD40 (WD40 repeat, IPR001680) 14 zf_MYND (Zinc finger, MYND-type, IPR002893) 5.
Step 2: MG0002 study exit interviews and PRO instrument completion Face-to-face exit interviews were conducted locally by study personnel at the final study visit of the Phase 2 MG clinical study, MG0002 (ClinicalTrials. gov Identifier: NCT03052751), conducted across the US, Canada and Europe (Belgium, Czech Republic, Denmark, Germany and Spain)  [26]. The objectives and design of the exit interviews were similar to those in Wave 2 of Step 1, comprising both a concept elicitation and cognitive debriefing section. MG0002 included people with moderate-to-severe gMG who were being considered for treatment with immunological therapy and had evidence of anti-AChR or anti-MuSK autoantibodies  [26]. The draft MG Symptoms PRO was completed by MG0002 participants at 13 study visits during the treatment and observation periods.
Quantitative analysis The measurement properties of the PRO item sets were examined using both modern psychometrics (RMT) and traditional psychometrics (CTT). RMT analysis was used to first evaluate the measurement properties and make decisions on item selection and refinement, whereas CTT analysis was used to produce supportive evidence on the final PRO item sets. RMT analysis was used to examine the measurement properties of each of the proposed draft item sets  [40] [41] [42]. Specifically, it examined whether item response data achieved the requirements specified by the Rasch model in relation to (1) scale-to-sample targeting, (2) item response thresholds, (3) item fit, (4) item dependency and (5) person separation index (PSI). The principles of RMT analysis have been extensively described elsewhere  [43].
We applied RMT analysis in two steps of the study design. First, a macro-level RMT analysis was performed on the data collected in the Wave 2 interviews of Step 1 to gain early insight on the item set. Second, a full RMT analysis was performed on the stacked data from all thirteen time-points of MG0002 in Step 2 using RUMM2030 software (RUMM Laboratory; Perth, Australia). In Step 2, RMT analysis was conducted in two rounds: First on the draft version of the scales; and, second on the available data of the revised version of the scales following the MMP results interpretations (Fig. 4 ). Stacked data were used to maximise the sample size of these analyses, which were repeated for comparative purposes on the first time-point of MG0002. CTT psychometric analyses were further conducted on the MG0002 study data (Step 2) to generate complementary evidence for each scale of the PRO instrument that resulted from the RMT analysis.
Investigated psychometric properties included reliability, both internal consistency (Cronbach's alpha coefficient) and test-retest reliability (intraclass correlation coefficients were calculated in various sample and at various time-points), and construct validity (association of the reviewed scales with other available clinical outcome measures: QMG, MG Composite, MG-ADL). CTT analyses were performed using SAS v9. 4 (SAS Institute, Thousand Oaks, NC, USA).