dataesr/scientific-paragraphs-categorization huggingface dataset:
The downstream part of the catchment contains a 10. 5 km speleological network, the Milandre cave, which hosts a 4 km long perennial cave stream, the Milandrine. This is the main drainage axis of the catchment. The stream flows into a sump around ~ 500 m upstream from the Saivu spring. Two main underground tributaries feed the Milandrine and each of them contributes to 25 to 35% to the cumulated discharge of the springs  (Grasso and Jeannin, 1994). The underground stream is monitored along its course at three places for discharge, temperature and electrical conductivity. Several drillholes located around the main karst conduits, have been monitored showing the groundwater behavior around the conduit network. This data will be important for the next steps of the challenge.
Calibration data In February 2017, each team received 4 years of meteorological data, from 1. 1. 1992 to 31. 12. 1995. For the main meteorological station (Fahy), located 7 km south-west of center of the catchment area, hourly precipitation, hourly temperature and daily PET were given. Hourly precipitation data were also given for the Maira station, which is located in the middle of the catchment area. Daily precipitation were also given for a third station (Mormont) located ~ 2 km south-east of the catchment. Each team also received ~ 2. 5 years of hourly discharge data (sum of Saivu, Font and Bâme springs), from 24. 9. 1992 to  28. 3. 1995. This data set was used for a first calibration of the models, as well as for adjusting criteria for the comparison. All teams returned their results, and a first comparison was made in 2018.
Model evaluation In June 2019 a new dataset was sent to all teams with two years of data  (2014) (2015)  for the meteorological station of Fahy and for the total combined discharge rates of Saivu, Font and Bâme springs. Data from Maira and Mormont were not provided because they were declared as not useful by the teams after the analysis of the first dataset. Teams were asked to apply their model to the second dataset with the parameters calibrated with the first dataset, to run the quality criteria, and to write a comment on it. If they wished, they had the possibility to improve the calibration using years 2014-2015.
Test data Finally, all teams were invited to forecast discharge time series for the year 2016, for which they only received input data. That meant that they could use their calibration data based on the 1992 to 1995 time series data for the simulation (forecast) of 2016, or based on the 2014-2015 time series, or a combination of both. They sent their best model to the first author, who applied the same evaluation criteria to all received time series. Among the 15 teams originally interested in the challenge, 8 of them sent their results and 5 additional teams joined afterwards, making a total of 13 modelling teams comparing their approaches and results.
Modellers using neural networks, with many parameters, claimed that data delivered for calibration were not sufficient. As no basic structure is provided in their models, compared to the models based upon a more physical function, they have to build this structure using data. They require therefore at least 15 years of data without any gap, which is challenging! These observations raise the question of uncertainty and overfitting associated with numerical models. In fact, we can probably conclude that the best models are as good as the data we provided. MODFLOW-CFPv2 and KarstFLOW are fully distributed models. These models are based on the principle that the transformation of the input signal into the output one is mainly related to the characteristics of the aquifer. This approach is more deductive than for the other models because these models are constructed from the supposed characteristics of the catchment and of the aquifer.
Lumped parameters models are interesting options if they can include non-linear functions and adequate parameters for taking evapotranspiration into account. Regarding ANN models, their limitation is that they require long and complete datasets for calibration and validation, and that they usually do not provide any physical indication about the real system, even if this "knowledge extraction" has been successfully attempted (as suggested by Kong-A-Siou et al. (  2013 ) and  Darras et al. (2015) ) these models remain mainly functional. Taking into account the limited duration covered by the available data, several ANNs show an acceptable behavior. From the comparison, as well as from tests of some models on other sites, it also seems that the order and priority in which the calibration of the models is carried out has a strong effect on the result.
Acknowledgements This work was supported by the  Swiss National Research Foundation  (SNF) for Swisskarst and Thermokarst projects (Grant Numbers  406140-125962  and  200021_188636 ), the  Deutsche Forschungsgemeinschaft, DFG  for the iKarst project (Grant Numbers:  LI 727/31-1  and  RE 4001/2-1 ). The work of  E. Pardo-Igúzquiza  was supported by research project  PID2019-106435 GB-I00  of the  Ministerio de Ciencia e Innovación of Spain. This TCD-Dublin was conducted within the  Irish Centre for Research in Applied Geosciences (ICRAG) , supported in part by a research grant from  Science Foundation Ireland (SFI)  under Grant Number  13/RC/2092. The authors would like to thank the  French Karst National Observatory Service (SNO KARST)  initiative at the  INSU/CNRS  for their diffusion of KarstMod platform.
For the measure of activation, we used peak currents recorded in oocytes, assuming that desensitization would be negligible in these conditions. The putative slow-desensitized state of GLIC was thus not included in this model. In our allosteric model, we first defined a single proton binding site present in five copies with intrinsic affinities for each state named K R , K pA , and K A. The equilibria between the states at pH 7 are governed by isomerization constants L pA =   a defined fluorescence intensity F R , F pA , and F A. As the model involves numerous parameters that cannot be fitted simultaneously given the available data, we adopted a stepwise strategy (summarized in Figure  6 -figure supplement 1). We formulated several reasonable hypotheses to fit some parameters to the experimental data which were kept fixed while others were constrained to change together: 1.
To correct data for rundown, a solution with a pH value in the middle of the pH range (usually pH 5) was used as a reference at the beginning and the end of the recording and every 3/4 applications. To limit the effect of propofol that can stay in the membrane in-between applications  (Heusser et al. , 2018) , only a limited number of pH solutions were tested per oocyte. Electrophysiological recordings were analyzed using AxoGraph X and Prism was used to fit individual pH-dependent recording using the Hill equation: y ( x ) = Imax+x n H x n H +EC n H 50 where I max represents the maximal current in percentage of the response from the reference solution. n H represents the hill number and EC 50 represents the proton concentration for which half of the maximal electrophysiological response is recorded.
5 Å from the starting/previous model, in which case an intermediate structure is generated and stored. The entire process is then repeated iteratively to generate a series of intermediate states that progressively converge to the targeted structure. The geometry of the ion channel has been computed with MOLEonline webserver ( mole. upol. cz), with the 'pore' mode  (Sehnal et al. , 2013). We used the FreeRadius value computed at the level of the I9' residue. For the Bimane docking procedure on each intermediate structure, the position of side chains was first optimized with the software Scwrl4  (Krivov et al. , 2009)  while keeping the main chain rigid. This step also allowed the introduction of point mutations. The structure of the protein and bimane was converted to pdbqt files with the software open babel 2.
4. 1. Covalent docking was then performed with the software smina  (Koes et al. , 2013). The box for docking has been defined around the mutated cysteine residue, with a size of 30 Å in each direction. Covalent docking forced the bimane to be in appropriate distance with the sulfur atom of the introduced cysteine. Only the first pose was kept for further analysis.
Acknowledgements The work was supported by the  'Agence Nationale de la Recherche'  (Grant  ANR-13-BSV8-0020 , Pentagate), the doctoral school ED3C and the  'Foundation pour la Recherche Médicale'  (PhD funding to SNL), the  'Initiative d'Excellence' (cluster of excellence LABEX Dynamo ,  ANR-11-LABX-0011  to AT) and the  ERC  (Grant no. 788974 , Dynacotine). The authors would like to thank  Stuart Edelstein  for helping with MWC equations,  Marc Gielen ,  Akos Nemecz , and  Marie Prévost  for critical reading of the manuscript.
Data Management All researchers, outcome assessors, data collector, data manager, data entry personnel, and statistician will receive special training regarding the standard procedure and data management. During the recruitment period, our data collector will record the baseline characteristics of participants in case report forms and all data will be checked by the data manager. Amount is presented for total amount taken and not for each capsule. CF, Cocoa flavanols. Study data will be pseudonymized, collected, and managed using REDCap, a secure, GDPR-proofed, web-based software platform designed to support data capture for research studies, providing (1) an intuitive interface for validated data capture, (2) audit trails for tracking data manipulation and export procedures, (3) automated export procedures for seamless data downloads to common statistical packages, and (4) procedures for data integration and interoperability with external sources  (64, 65).
To assess the effect of the reactive hyperemia, the brachial artery will be visualized pre-occlusion for 90 s (mean of three individual clips comprising each 30 s) and post-occlusion for 4 min with additional 15 s before cuff release. Although it is recommended to perform an FMD measurement in fasting state  (67) , this is not feasible since the entire protocol lasts around 5 h. However, the measurements will be done 15 and 150 min after food intake (standardized breakfast at each visit). Boundaries for diameter measurement will be identified automatically by means of a boundary tracking software (Quipu, diastolic phase) and optically controlled by a single, independent and blinded investigator. When tracking is impaired, the investigator will restore this tracking with maintenance of region of interest.
Statistical Analyses Power and Sample Size Calculation Sample size calculations are based on previous research focusing on interventional studies with FMD measurements  (77, 78)  who established that in cross-over trials at least 20-30 participants are required for a minimal detectable change of 1. 5-2% in FMD (80% power, alpha-level = 0. 05). Sample size calculation was done by SAS power and sample size, indicating a minimum of nine participants in each group  (41). Data will be analyzed taking into account volunteer-and methodology-related factors  (79).
Data-Analysis Plan Effects of CF ingestion on primary and secondary outcome measures will be evaluated within and between groups. Data will be analyzed using a random intercept model in mixed models. Fixed effects will be the group, the supplementation (cocoa flavanols vs. placebo), the time (pre-vs. post-intake of capsules) during each visit for repeated measurements, as well as group × supplementation interaction and time × supplementation interactions. We will also consider the order of both visits in a first intent and this factor will be kept in analyses only if significant. As all groups are matched by age, sex, and BMI, only medication intake, level of physical activity, glycemic excursions, risk for hypo-or hyperglycemia, and glycemic variability will be considered as covariates when analyzing data.
3 m) using 5 mm increment borers. Since most of sampled dead trees at the managed stand had recently died (during the last 2-3 years before fieldwork), we also retained the cores of the recently dead trees for the old-growth forest (no longer than 10 years before sampling) (Fig. S2 ). Wood samples were air dried and sanded with sandpaper of a progressively finer grain until the tree-rings became clearly visible. Tree-rings were measured and cross-dated after scanning at high-resolution to the nearest 0. 01 mm using the CooRecorder and CDendro 7. 7 semiautomatic software (http://cybis. se). In the cases of cores without pith, the rings to the pith were estimated by fitting a template of concentric circles with known radii to the innermost rings and checked using the graphical method developed by  Rozas (2003).
The visual cross-dating of tree-rings series was checked using the COFECHA program  (Holmes, 1983)  by comparing the consistency of the different ring-width series with mean site series using moving correlations. A final number of 56 pairs of recently dead and living trees with well cross-dated and reliably age (with less than 10 estimated missing years) from each site was used for the further analyses (Table  2 ). The trend due to the geometrical constraint of an increasing volume of wood with rising stem diameter was corrected by converting the tree rings widths into basal area increment (BAI, cm 2 yr -1 ), a more biologically meaningful biological descriptor of growth trends  (Biondi and Qeadan, 2008). This relatively age-independent measure of radial tree growth in ring-porous oak trees is related to variations in hydraulic conductance  (Kint et al.
, 2012; Colangelo et al. , 2018). To quantify climate-and drought-growth relationships, we first removed age-and size-related long-term trends of individual BAI series by converting raw BAI into BAI indices (BAIi). This was done by fitting a 30 years cubic smoothing spline to raw data to preserve high-frequency growth variability, and then dividing observed by fitted values to obtain dimensionless, standardized BAI indices. The resulting indices (BAIi) were averaged into a chronology for each site (old-growth vs. managed) per living and dead trees. The resulting mean chronologies (BAIi) of each site were described for the common period of 1918-2013 by calculating dendrochronological statistic parameters: the first-order autocorrelation of raw width data (AC), the mean sensitivity (MS), the mean correlation between trees (rbt) and the Expressed Population Signal (EPS), which measures the statistical quality of the mean site chronology compared with a perfect infinitely replicated chronology  (Wigley et al.
Climate-growth relationships Due to the incompleteness and heterogeneity of local climate data (see Table  S1 ), we used gridded climatic data (mean, maximum and minimum monthly temperatures and total precipitation) to characterize the regional climate trend for the 1901-2017 period. This dataset was downloaded from the Climate Research Unit (CRU, http://ww. cru. uea. ac. uk/es) and corresponds to interpolated data obtained from instrumental records recorded by a dense network of local meteorological stations, which have been subjected to homogeneity tests and relative adjustments, and finally gridded onto a 0. 5 • network  (Harris et al. , 2014). Temporal trends in the CRU monthly temperature and seasonal precipitation were calculated to characterize the regional climate change (Fig. 1c ).
To analyze climate-growth relationships, we downloaded the homogenized and quality-checked climate E-OBS v. 20 at higher resolution for each forest (0. 25 • gridded data) for the period 1950-2017  (Cornes et al. , 2018) , which corresponds to interpolated data obtained from nearby instrumental local meteorological stations (Table  S1 ). To estimate the effect of drought intensity and duration on radial growth (BAIi) of living and recently dead trees, we used the Standardized Precipitation-Evapotranspiration Index (SPEI), as a multiscalar drought index that accounts for the negative effect of warmer temperatures on water availability by statistically modeling cumulative water balances. The SPEI is calculated using precipitation and temperature data from the same homogeneous and spatially dense dataset of local observatories as CRU climate data  (Vicente-Serrano et al.
, 2010). The SPEI varies from negative to positive values corresponding to dry and wet periods, respectively. The SPEI monthly values were calculated for the study sites considering the 1950-2013 common period and 1-to 24months long time scales. To quantify climate-growth relationships between BAIi and monthly climate variables from E-OBS database (mean, maximum and minimum temperature; precipitation and SPEI), we calculated bootstrapped Pearson correlation coefficients for the common period 1950-2013. These correlations were calculated from previous August to current October, i. e. , during the year of tree-ring formation, as well as for the annual seasons based on previous analyses of the studied species (e. g. ,  Rozas, 2001; Rybníček et al. , 2016; Stojanović et al. , 2017). To assess whether these relationships were stable through time, we calculated 20year long moving correlations overlapping by one year, taking into consideration only those climate variables strongly and significantly (P < 0.
Since the BAI is influenced by the site conditions as well as by the individual growing condition of each tree, tree identity (Z j B j ) was considered as a random effect. We included an error term (v j ) with a first-order temporal autocorrelation (ar1) structure for the serial dependency of BAI values. Calendar year was considered as a covariate and the tree age was entered as a fixed value. BAI was log-transformed [log (x + 1)] prior to the analyses. To determine the GAMMs the mgcv package in R was used  (Wood, 2017). In a second step, to test whether trees with contrasting declining status (living vs. dead trees) show different growth responses to several drivers (e. g. , climate, drought, age, competition), generalized linear mixed-effects models (GLMM) were separately fitted to BAI data of living and dead trees for the managed and old-growth study sites considering the period 1950-2013.
We adjusted linear mixed-effects models assessing competition or tree size (considered here as a proxy of the past management), and climatic variables (maximum temperature, precipitation and SPEI) as fixed effect factors. BAI was logtransformed [log (x + 1)] prior to the analyses. The predictor variables were introduced as fixed effects and tree identity and tree age as random effects. We used selected monthly and seasonal temperature, precipitation and SPEI data as climatic predictors of BAI according to the results of climate-growth relationships (see Section 2. 3). We included a first-order temporal autocorrelation (ar1) structure for the BAI values. Residuals of the models were checked for normality, homoscedasticity and temporal autocorrelation. The effects of climate, drought and competition on BAI were tested and compared with a null model considering BAI as a constant.
Acknowledgements This work was supported by  NEMKLIM  project:  Nemoral Forests under Climate Extremes (NEMKLIM Project , grant number  3517861300 ), financed by the  German Federal Agency for Nature Conservation  ( Bundesamt für Naturschutz, BfN ) and the  German Federal Ministry for the Environment, Nature Conservation and Nuclear Safety, Germany  and by project  PN 19070506  financed by  Romanian National Authority from Scientific Research and Innovation. I. C. Petritan  was supported by a grant of the  Romanian Ministry of Education and Research, CNCS-UEFISCDI , project number  PN-III-P4-ID-PCE-2020-2696 , within PNCDI III. R. Sánchez-Salguero  was supported by  VUL-BOS  project ( UPO-1263216 ,  FEDER Funds, Andalusia Regional Government, Consejería de Economía, Conocimiento, Empresas y Universidad   2014-2020 ) and LESENS ( RTI2018-096884-B-C33 ) project from the  Spanish Ministry of Science, Innovation and Universities.
We provide an unsupervised adaptive sampling strategy capable of producing ms-timescale molecular dynamics (MD) simulations of large biosystems using many-body polarizable force fields (PFFs). The global exploration problem is decomposed into a set of separate MD trajectories that can be restarted within a selective process to achieve sufficient phase-space sampling. Accurate statistical properties can be obtained through reweighting. Within this highly parallel setup, the Tinker-HP package can be powered by an arbitrary large number of GPUs on supercomputers, reducing exploration time from years to days. This approach is used to tackle the urgent modeling problem of the SARS-CoV-2 Main Protease (M pro ) producing more than 38 ms of all-atom simulations of its apo (ligand-free) dimer using the high-resolution AMOEBA PFF.
Indeed, proteases are responsible for activating viral proteins for particle assembly. Due to their importance within the replication cycle of the virus, they have been proven to be successful targets for antiviral agents and are used to treat many diseases including HIV and hepatitis. 4 In the case of SARS-CoV-2, the main protease is called M pro or 3CL pro. Many efforts have been made to rene the crystallographic structure of M pro as the number of experimental structures available in the Protein Data Bank is increasing. ] [7] [8]  Molecular Dynamics is a powerful tool for understanding the structural and dynamical details of complex biological systems. It also enhances the ability to identify promising protein inhibitors. Two main research groups, DE Shaw Research (DESRES) and RIKEN Center for Biosystems Dynamics Research, recently released multi-microsecond MD simulations of the M pro dimer.
Here, a conguration means the positions q˛ℝ 3N of all the atoms of the system. In particular, at the very beginning of the algorithm, we suppose that we start with M 0 $ 1 congurations, obtained from an initial conventional MD simulation (which is in practice non-polarizable), or previously available studies. At the beginning of iteration k, rst, the protein is aligned in all congurations, using the backbone atoms of the 6LU7 crystal structure from the Protein Data Bank. 3  principal component analysis (PCA)  28  is then performed, using the scikit-learn  29  and MDTraj 30 packages, on the protein atoms (q i ) 1#i#M k , from which the n ¼ 4 principal modes are considered. This choice was made aer a global analysis of the rst 20 PCA modes of the rst AMOEBA 0. 14 ms which showed that n > 4 modes had variance contributions below 4% (Fig.
Preparation of systems and choice of initial structures In order to perform a large scale unsupervised adaptive sampling simulation, starting structures have to be selected from a conventional MD simulation (using either n-PFF or PFF approaches). We chose the RIKEN dataset as the starting point. From their 10 ms conventional MD simulation (PDB: 6LU7, pH ¼ 8) 3 using the n-PFF AMBER14ff 10 approach and using PCA as a guiding thread, we carefully extracted 14 relevant structures that represent our starting point for the study. It is worth noting that the 6LU7 crystal structure is a holo structure including a covalently bound inhibitor. The inhibitor-unbound apo structure was initially obtained by RIKEN removing the inhibitor and relaxed over 10 ms of simulation (https://data. mendeley. com/datasets/vpps4vhryg/1).