dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We thank  Jean-François Henry ,  Nadine Aguilera  and  Tiphaine Dorel  from the animal facility (PBES,  Plateau de Biologie Experimental de la Souris ,  UMS3444/CNRS ,  US8/Inserm ,  ENS de Lyon, UCBL) ,  Sebastien Dussurgey  for technical help and advices for flow cytometry as well as  Gisele Froment  for the platform of Vectorology.
Funding This work was supported by the  French ''Agence Nationale de la Recherche  sur le SIDA et les hépatites virales", the  Janssen Horizon foundation , the  Ecole Normale Supérieure de Lyon , and the  LabEx Ecofect  ( ANR-11-LABX-0048 ) of the '' Université de Lyon" , within the  program ''Investissements d'Avenir"  ( ANR-11-IDEX-0007 ) operated by the  French National Research Agency (ANR).
16  (Martin, 2011)  (options -m 15 -q 30 -a GATCGGAAGAGCACACGTCTGAACTCCAGTCAC). Reads were mapped to the mouse genome (mm10) and to the chicken genome (galGla6) using Bowtie2 (v2. 3. 4. 1)  (Langmead and Salzberg, 2012)  with standard settings. The coverage was obtained as the output of  MACS2 (v2. 1. 1. 20160309) (Zhang et al. , 2008)  Peak calling was done using MACS2 (v2. 1. 0. 20160309). CTCF motif orientation was assessed using the CTCFBSDB 2. 0 database  (Ziebarth et al. , 2012)  with EMBL_M1 identified motifs. H3K27ac ChIP-seq were normalized with the MAnorm model  (Shao et al. , 2012).
ACKNOWLEDGEMENTS We thank  Dr Lucille Lopez-Delisle  for her help with bioinformatics as well as members of the  Duboule laboratories  for comments and discussions and  Gregory Loichot  for artwork. This work was supported in part by using the resources and services of both the  Gene Expression Core Facility  at the  School of Life Sciences  of the EPFL in Lausanne and the genomic plateform from the  IGE3 centre of the University of Geneva. Transgene injections were performed at the transgenesis platform of the  University of Geneva Medical School. This work was supported by funds from the  University of Geneva , the  Ecole Polytechnique Fédérale (EPFL, Lausanne) , the  Swiss National Research Fund  (No. 310030B_138662 ) and the  European Research Council.
predict function from the chemCal package from the R project. 18 Thus,the inverse prediction uncertaintyinterval does not refer to σ reprod (BS), but the practical use of the BS as a predictor of CT bladder volume. An example of this reading is given in Figure  S1. A new calibration was conductedfor both supine and standing position in order to improve the agreement as Brouwer et al. 12  mentioned that a systematic bias can occur in supine position.
Method We prospectively included all consecutive patients who underwent cryo-PVI for the treatment of persistent drug-refractory AF from the introduction of second generation cryoballoon ablation to the Amiens University Hospital in September 2012 to November 2019. Patients provided their signed informed consent to participate in the registry and the study was approved by the local ethics committee. Data files of the registry were declared to and authorized by the French data protection committee (Commission Nationale Informatique et Libert e, CNIL) (PI2020_843_0120). The primary objectives of the study were to assess the success rates and complication rates after one cryo-PVI procedure. The secondary objective was to establish the longterm predictive factors of ATa. Patients were included if they presented symptomatic persistent AF, documented by 12-lead ECG, refractory to at least one class I or III antiarrhythmic drug.
AF was classified according to the 2020 ESC Guidelines for the management of AF. 17  The diagnosis-to-ablation time was defined by the time between the first episode of AF and ablation. Exclusion criteria were (1) prior left atrial (LA) ablation, (2) LA tachycardia at the time of cryoablation, (3) having a a Cardiac Arrhythmia Service, Amiens-Picardie University Hospital, Amiens, France; b Biostatistics Unit, Clinical Research and Innovation Directorate, Amiens-Picardie University Hospital, Amiens, France; and prosthetic heart valve, (4) being a minor (<18 years old), and (5) no follow-up after the cryo-PVI procedure. LA area was measured during echocardiography in a 4chamber view. LA volume was obtained from multidetector computed tomography and segmentation by AW Volume-Share 5 software (General Electric Company, Fairfield, CT).
For all patients, heparin was administered as an initial bolus after the trans-septal puncture and then administered continuously to maintain the target activated clotting time (>300 seconds). Data collected during the procedure included the duration of the time to effect (TTE), minimal temperature reached during application in each PV, cumulative duration of cryo-application in each PV, number of cryo-applications for each PV, and the duration of the time under fluoroscopy and fluoroscopy exposure. All adverse events occurring during the procedure or within the following month were recorded and described. Vascular complications included vascular pseudo aneurysms, arteriovenous fistulae, hematomas, and puncture-side hemorrhages. After ablation, antiarrhythmic drugs were maintained for at least the blanking period of 3 months.
Therefore, both highresolution climate data and projections at subdaily and subhourly scales are needed in order to model GI and to estimate their potential as a climate change adaptation measure. In Norway and most of the European countries, precipitation has been measured with tipping buckets in numerous cities from years to decades. Moreover, climate projections at daily resolution for future precipitation and temperature from the EURO-CORDEX project are available at 1×1 km spatial resolution in Norway  (Dyrrdal et al. , 2018)  and 12 × 12 km resolution in France  (Jacob et al. , 2014). Consequently, the use of such data by urban hydrologists to assess the resilience of GI solutions to face climate change is conditioned by the possibility to downscale them to a subhourly resolution. V. Pons et al.
: Downscaling models to forecast green roof detention performance Downscaling includes two families of methods: dynamical downscaling and statistical downscaling  (Benestad, 2016). Dynamical downscaling methods use physically based equations and are usually computationally expensive specially to generate high-resolution data. Statistical downscaling consists in improving the resolution of data based on statistical properties observed from a lower-resolution dataset. The computational cost is lower; therefore, statistical methods might still be used to fill the gap in the next decades until the computational power is sufficient for routine dynamical downscaling. In addition, the use of a stochastic approach is necessary due to the current limitation in parametrization of small-scale processes (below the truncation scale) and the lack of coupling between resolved and parameterized scales  (Sanchez et al.
, 2016). Statistical downscaling has already been extensively used to temporally downscale data for various temporal resolutions, usually hourly or daily data. Three popular methods can be mentioned: (i) the method of fragment, (ii) the method based on point process theory, and (iii) the method of multiplicative random cascades. The method of fragment  (Li et al. , 2018; Lu et al. , 2015)  is a resampling method based on k-nearest neighbours  (Kalra and Ahmad, 2011) , which has been applied to derive hourly data from daily data. It can be accurate and effective due to its resampling nature, but it requires a large dataset, and by its design it cannot ensure extrapolation from observed data. Therefore, it might not be suitable to downscale climate projections. Methods based on point process theory have been used  (Glasbey et al.
Downscaling process The MRC downscaling process consists of transforming daily rainfall depths to rainfall depths at lower timescale, e. g. 1 min, by means of successive distribution of the depth of a parent time steps between its two children time steps. The process is repeated by iteration until the desired timescale is reached. Figure  1  describes the downscaling process. In practice, the downscaling started at 1440 min (1 d) time step with eight iterations to reach a time step of 5. 625 min. The results were interpolated and scaled to a 6 min time step for comparison with observed data. The final time step of 6 min was chosen based on the resolution of original datasets in Lyon and Marseille. Three steps are necessary to downscale a parent time step to two children time steps. The occurrence of a zero weight, i.
A3a-A3e) was indicated by the letter T in the nomenclature. The   669, 965, 1256 853, 1176, 1599 117, 150, 173 133, 163, 196 -6. 1, 6. 3, 17. 9 -1. 4, 8. 6, 19. 2  temperature dependency was added in an attempt to improve the robustness of the model toward climate change under the hypothesis that the change in rainfall pattern would be correlated to the shift in temperature and that the existing observed datasets already carry the necessary information for calibration. In the models MRC S-SEP , MRC SI-SEP and MRC SIT-SEP (Table  2 ) the weights generated were permuted stochastically depending on the neighbour (indicated with SEP, Eqs. 4 and A5), while the MRC S , MRC SI , and MRC SIT model considered equal probability (0. 5) to permute the two children weights. Similarly to  Rupp et al. (2009) , the generators of the MRC models include timescale dependency through analytical formulas.
In practice it means that there is a single set of parameters per generator and not a different set at each cascade step. It ensured a relatively parsimonious number of parameters compared to other recent works on microcanonical MRC where a set per cascade step is often used (e. g. 12 to 36 total parameters by  Bürger et al. , 2019  or from 6 to 224 parameters per cascade step by Müller-Thomy, 2020). It should be noted that based on dataset analysis and as advised by  Serinaldi (2010) , despite the fact that universal and canonical MRC represent the most parsimonious approach parameterwise, their microcanonical counterpart was preferred. This choice of model that deviates from the hypothesis of multifractal scale invariance was supported by several other studies  (Koutsoyiannis and Langousis, 2011; Veneziano et al.
, 2006). The number of parameters can be lower with the use of universal MRCs which were excluded from this study due to lack of flexibility  (Serinaldi, 2010). It also allows the model to be used with any desired initial resolution lower than 1500 min. Homogeneity of the resolution in the input datasets was not required for calibration and data processing (i. e. the model can be calibrated using multiple datasets with different resolutions between 1 min and 1 d). The parameters of each generator of MRC models and each location required calibration. A single-step calibration, based on the processed data, was sufficient for generators with only timescale dependency. A multiple-step calibration with data manipulation was necessary for generators with depth/inten- Table  2. Nomenclature of the models and various quantities taken into account by each model depending on the process considered; S is the timescale, D the rainfall depth/intensity, T the temperature and N the close neighbour.
Later studies can further improve the procedure to make it more easily calibrated. The optimizations were based on non-linear least squares in the standard library scipy. optimize with default parameters in Python  (Virtanen et al. , 2020). -The parameters of zero-weight generator with only timescale dependency (Eq. A1) followed a single-step calibration against observed zero-weight proportions by non-linear least squares. V. Pons et al. : Downscaling models to forecast green roof detention performance -The parameters of the zero-weight generator with timescale and depth dependency (Eqs. A2a-A2d) followed a two-step calibration: (i) for each timescale, the proportion of zero-weight depending on depth was evaluated using a weighted running window to compensate for rare occurrence of extreme depths.
Analysis of climates properties Figure  3  presents the zero-weight proportion depending on timescale, depth and temperature for two different datasets (Bodø and Hamar). In Fig. 3a  the proportion of zero-weight decreases with increasing timescale for Bodø. In Hamar the proportion decreases until 45 min and increases for higher timescales. Based on this observation, two types of datasets were identified in terms of zero-weight occurrence. For data from Bodø, Bergen, Kristiansund and Trondheim, the proportion of weights that equalled zero decreased with increasing timescale. For the data from Hamar, Lyon, Marseille and Kristiansand, the proportion decreased until 45 min timescale and increased afterwards (Fig. B1a ). Given a timescale, the proportion of weight equal to zero was not uniform depending on the weights (e.
3c ). On the contrary, in Hamar, the subsets with lower temperature lead to a lower proportion of weights being equal to zero, compared to subsets with higher temperature (Fig. 3d ). Moreover, the higher depths were observed in subsets with higher temperature. The increase observed in Hamar can be explained by the distribution of observed values. It is consistent with the observation of different temporal distributions of rainfall for different temperature ranges such as convective rains  (Berg et al. , 2013; Zhang et al. , 2013). If, given a depth of 10 mm at resolution of 48 min, the probability to have a weight equal to zero is higher, then there is a higher probability to have an intense rainfall. The non-homogeneity of observed datasets and the shift in temperature with climate change might lead to inconsistency in time series produced by the downscaling methods that exclude depth and/or temperature dependency.
The 48 min timescale was chosen to exemplify these properties. The same properties can be observed for different timescales, but the magnitude differs and tends to lower with higher timescale (Fig. B1b, c  and d ). Developing a model without temperature dependency might prevent comparability of parameters between locations and does not necessarily lead to parameter parsimonious models. Moreover, a model such as MRC SI can result in overfitting when used with datasets like Hamar. The functions necessary to represent the behaviour without considering the temperature dependency are more complex and less explanatory. Based on this analysis it was possible to add the temperature dependency and conceptualize a more explanatory model (MRC SIT , with Eqs. A3a-A3e) with more robust results for the influence of climate change.
Evaluation of the downscaling methods An overview of the performance of the downscaling and green roof models in Bergen is presented on Fig. 4. All the downscaling models performed similarly in terms of dryperiod distribution and slightly underestimated the dry periods in observed data (Fig. 4b ). The dry periods were directly linked to the zero-weight probability. In green infrastructure modelling, the length of the dry periods influences the retention performance as it can lead to water stress hindering evapotranspiration. However, dry periods leading to water stress can be also evaluated with daily time-step series (there is no need for minute time-step series). Therefore, dry periods longer than the initial daily resolution are not significantly affected by downscaling. The distribution of precipitation (Fig.
7 × 10 1 (Fig. 4c ), due to the behaviour of the roof with rare high discharge. The hyetographs produced by downscaling probably tend to generate less favourable hyetographs for this roof. Although the discharge of the E-Green roof did not fall in the 90 % coverage interval, it can be considered as slightly underestimated since the magnitude is similar with a KS rel of 2. 0 (Fig. 4d ). However, this was not the case for all locations, as in Hamar the most extreme precipitation tended to be underestimated, while the discharge from both roofs had the same order of magnitude as the observed data but tended to be overestimated. These findings could suggest inconsistency in the temporal structure of rainfall. This hypothesis can be confirmed by the autocorrelation (Fig. 5 ) being overestimated at 6 min time step.
The same behaviour was observed with Hamar (Fig. 6 ) and Lyon datasets (Appendix C, Fig. C1 ). This suggests that the models performed worse for dryer locations, possibly due to the calibration procedure since fewer wet days are available for calibration. MRC SI and MRC SIT performed similarly, but due to its structure, MRC SI may overfit to the calibration data. It could result in an inaccurate prediction in the case of significant temperature shift between the calibration and prediction datasets. To conclude, MRC S and MRC S-SEP lead to overestimation of the natural variability, while MRC SI , MRC SI-SEP , MRC SIT , and MRC SIT-SEP give more accurate estimates.
Conclusions In this study, multiplicative random cascades models with different variable dependency were developed. They were based on a study of timescale, depth, and temperature characteristics of the datasets to ensure a consistent structure in the view to apply them to daily-resolution climate projections. The applicability of the synthetic time series to be used as input for performance modelling of green infrastructure was evaluated. They were used to predict the shift in runoff exceedance under a future climate. Six downscaling models were developed: two models with only timescale dependency (MRC S and MRC S-SEP ), two models with timescale and depth dependency (MRC SI and MRC SI-SEP ), and two models with timescale, depth and temperature dependency (MRC SIT and MRC SIT-SEP ). The models MRC S-SEP , MRC SI-SEP and MRC SIT-SEP include a rainfall continuity property with the intention of improving the Figure  8.
Acknowledgements. The study was supported by the  Klima2050 Centre for Research-based Innovation (SFI)  and financed by the  Research Council of Norway  and its consortium partners (grant no. 237859/030 ). This research was partly performed within the framework of the  OTHU (Field Observatory  for Urban Water Management -http://othu. org,last access:  26 May 2022 ) and realized within the  Graduate School H2O ' Lyon  ( ANR-17-EURE-0018 ) and  Université de Lyon (UdL)  as part of the program " Investissement d'Avenir " and by  Agence Nationale de la Recherche (ANR). The authors acknowledge the  Water Department of Lyon Metropole  and the  Water and Sewerage Department of Marseille Provence Metropole  for sharing their rainfall data. The authors would like to thank the  Norwegian Meteorological Institute  for providing the Norwegian data and especially  Cristian Lussana  and  Lars Grinde  for their advice.
Experimental details 2. 1. Materials and instrumentation methods All reagents and solvents were purchased from Aldrich, Alfa Aesar or TCI Europe and used as received without further purification. Mass spectroscopy was performed by the Spectropole of Aix-Marseille University. ESI mass spectral Please do not adjust margins Please do not adjust margins analyses were recorded with a 3200 QTRAP (Applied Biosystems SCIEX) mass spectrometer. The HRMS mass spectral analysis was performed with a QStar Elite (Applied Biosystems SCIEX) mass spectrometer. Elemental analyses were recorded with a Thermo Finnigan EA 1112 elemental analysis apparatus driven by the Eager 300 software. 1 H and  13  C NMR spectra were determined at room temperature in 5 mm o. d. tubes on a Bruker Avance 400 spectrometer of the Spectropole: 1 H (400 MHz) and  13  C (100 MHz).
Acknowledgements The authors thank  Aix Marseille University  and  The Centre National de la Recherche Scientifique (CNRS)  for financial supports. The  Agence Nationale de la Recherche  ( ANR agency ) is acknowledged for its financial support through the PhD grant of  Guillaume Noirbent  ( ANR-17-CE08-0054 )  VISICAT  project. The  Direction Générale de l'Armement (DGA)/Agence Innovation Defense (AID)  is acknowledged for its financial support through the PhD grant of  Dr. Damien Brunel.
Participants A total of 109 community-dwelling older adults aged between 65 and 85 years old were recruited. Exclusion criteria were pathologies that prohibit a maximal strength test, such as severe cardiovascular disease, artificial hip or knee, acute hernia, infection, or tumour. All participant gave written informed consent. Thirty acquisition were not well recorded during STS due to some improper Openpose detection caused by the camera angle, leading to missing data at some velocities (0, 5 or 10kgs). Also, Grubbs test  (Ghosh and Vogt, 2012)  detected 33 recurrent outliers (abnormal STS parameters) resulting in their removal. Finally, 46 participants (6men, 40women)  were included in this study (mean age = 73. 7 +/-7. 7 years; mean height = 161. 8 +/-8. 2 cm; mean weight = 61. 5 +/-11.
Analysis procedure and data recording to sit to stand protocol. Acquisitions were recorded with an Ipad (30hz, 1280×720p). The videos were processed on Python with OpenPose library  (Cao et al. , 2019). This library can detect multiple people in real time which is the first time a library can jointly detect key points on human body, face, and foot. The protocol setup was to collect measurements from iPad, to retrieve key points by applying OpenPose "Body25 model" on videos, to preprocess (denoising, calculating kinematic parameters) and finally to calculate F-v and P-v profiles. To determine the time point for starting and ending the movement during STS test, plateau were detected on knee flexion extension kinematics. A 0 • plateau corresponded to the end of the STS (full extension and a 90 • to the start of the movement.
Analysis procedure and data recording for isokinetic protocol. Sampling was performed at 100 Hz using an electronic interface card (Biodex Medical Systems Inc. , X2151, Shirley, NY, USA). The evaluation on the six different velocities allows to obtain a set of parameters such as the ratio between agonist and antagonist muscles as well as curves representing F-v and P-v relationships. Force (F) -velocity (V) relationship was described by a first order linear equation: F = S FV •V + F0 with S FV and b_linear the coefficients of the polynomial relation. Note that F0 is a force corresponding to the theoretical moment at 0 • /s velocity value. The velocity value which corresponds to 0 Nm was the maximum theoretical velocity (V MAX ). Power (P) -velocity (V) relationship was described by a second order with a_poly and b_poly coefficients of the polynomial relation (Fig.
Data analysis and statistical calculation All statistical method comparison was made on F-v and P-v parameters which are Pmax, Fopt, Vopt and V0. We also compared coefficient of F-v and P-v equation which are a_poly and b_poly defined as the quadratic and linear coefficient respectively of P-v equation and S fv and F0 defined as the slope and the constant of the linear F-v equation. All data were screened for normality using the Shapiro Wilk test. Concurrent validity between STS and BIODEX was assessed by determining Spearman's rank correlation coefficient (r). The following correlation classification was used: negligible r = 0-0. 10, low r = 0. 10-0. 39; moderate: r = 0. 40-0. 69; high: r = 0. 70-0. 89; very high: r = 0. 90-1. 0 (Schober and Schwarte, 2018). Passing-Bablok regression was used to assess the linear association between parameters that succeed to CUSUM test (p > 0.