dataesr/scientific-paragraphs-categorization huggingface dataset:
Additionally, this replication study was pre-registered on an open science platform (https://osf. io/629yx) and the methods were executed according to the pre-registered plan, except for one aspect of the data analysis where the observed structure of the final observed dataset necessitated a divergence from the pre- registered analysis. Specifically, the pre-registered analysis was a 2(training group: action/control) × 2(time: pre-test/post-test) repeated measures ANOVA based upon the expectation that perceptual learning performance at pre-test (i. e. , the baseline motion learning task) and post-test (i. e. , the orientation learning task) would correlate (and thus could be considered as "repeated measures" of a common construct). Such a correlation between the two tasks was not observed (Supplementary Note 3).
After enrollment, participants were assigned to either the experimental (action video game) training regimen or the control (life/business simulation video game) training regimen. The assignment was done in a pseudorandom fashion so as to balance gender across training groups. Six participants failed to comply with the at-home video game training protocol; one action trainee withdrew due to game-induced motion sickness; one control trainee was excluded because of technical problems with the apparatus. The final sample thus consisted of 14 participants in the action video game group (7 women; 18-34 years old, mean age 23 years) and 11 participants (9 women; 19-56 years old, mean age 24. 3 years) in the control video game group. Data from one session of the orientation learning task was missing in one action video game trainee due to a technical issue: we interpolated the missing data by duplicating the data from the preceding session.
For the control games, we initiated a completely new character in The Sims 3 and recorded how many "challenges" participants could achieve within 30 min. For Zoo Tycoon and Viva Piñata, the number of animals that participants created, purchased, and/or attracted within 30 min of play was tabulated. These game-based parameters were used to quantify the participants' gaming improvement once training was completed, and they provided an additional check that participants indeed had played their assigned games. Apparatus. The attentional control task, the baseline motion learning task, and the orientation learning task were programmed in MATLAB using Psychophysics Toolbox  30, 31. The baseline N-back and the working memory learning tasks were programmed in E-prime 1. All pre-and post-test tasks were run under a Windows XP operating system and presented on a CRT monitor (22-inch MITSUBISHI-Diamond Pro 2070SB, 1024 × 768 resolution, 85 Hz) with linearized gamma.
Finally, despite informing the participants that gaming progress was monitored through a Microsoft Xbox account from which they were assigned to play, we could not access these data and monitored the self-reported logs instead. Apparatus. All tasks were generated in MATLAB 2016a using the Psychophysics Toolbox and were run under a Windows 7 operating system and presented on a linearized high-performance industrial LCD glass monitor (22. 5-inch ViewPixx monitor, 1920(H) × 1080(V) pixels, 120 Hz). We used the Viewpixx' M16 mode allowing to combine two 8-bit output channels of the graphics card so that the display system could produce gray levels with 14 bits of resolution  32. Participants were tested in dimly lit light, with a mean display luminance of 58 cd/m 2. Monitor gamma was calibrated by fitting the best power function to the measured luminance using a photometer at 10 different gray-scale levels.
On Day 2, the baseline N-back task was administered and another four sessions of the orientation learning task were run. Third, on Day 3 to Day 5, participants completed 2 sessions of the working memory learning task per day, yielding a total of only six sessions of this task. Fourth, we did not run the transfer session of the orientation learning task as we did in the initial intervention study. Fifth, different from the initial intervention study, we reprogrammed the baseline N-back and working memory learning tasks using the Psychtoolbox 3. 0 in Matlab R2017a. Data analysis, hierarchical Bayesian analysis of orientation and working memory learning tasks. We performed a hierarchical Bayesian analysis to quantify all learning tasks. The analysis was performed separately in each group, in each task, and in each intervention study.
Acknowledgements The authors are indebted to  Olga Pikul  and  Alyse Stegman  for their invaluable help throughout these studies. We also thank  Sylvie Denkinger ,  Marta Martins ,  Patricia Poma  and  Jérémie Todeschini  for their help in data collection. We thank  Long Ni  and  Dr. Weiji Ma  for helpful discussions on computational modeling. This work was supported by an  Office of Naval Research  award  N00014-14-1-0512  (DB&CSG) and  N00014-17-1-2049  (CSG), a  Swiss National Foundation  Grant  100014_178814  (DB), a  National Eye Institute   EY020976  (DB), a  Center of Visual Science training grant   EY001319  ( U. of Rochester Center for Visual Science ), a  National Eye Institute  Grant  EY017491  (ZLL),  Natural Science Foundation of Shanghai   21ZR1434700  (RYZ), the research project of  Shanghai Science and Technology Commission  ( 20dz2260300 ) and the  Fundamental Research Funds for the Central Uni-versities (RYZ) ,  National Natural Science Foundation of China 32100901 (RYZ) , and a  National Institute on Aging  Grant  1K02AG054665  (SMJ).
Code availability All the codes used to generate the statistics provided here are available at https://osf. io/ 4xe59. Matlab R2017a and SPSS 12. 0 were used for most analyses. All Bayes factors for two-sample comparisons were calculated using the Bayes Factor Toolbox (https:// klabhub. github. io/bayesFactor/). We used the function bf. ttest2 when investigating differences between paired groups, and bf. anova with one between-subject factor when investigating differences between independent groups, because this design is equivalent to a t-test. Results were verified using the statistical software JASP 0. 14 and the two tools agreed. For the hierarchical model involving the Markov Chain Monte Carlo method, we use the statistical package Stan and its python interface.
The DIB experiment was also carried out with P454 R12E in the presence of 100 µm of CaM in the trans compartment (Figure  S6D , Supporting Information), i. e. , at a concentration well above the K d (22. 7 µm) of the P454 R12E :CaM complex formation (Table  S3 , Supporting Information). In these conditions, a significant accumulation of P454 R12E in the trans compartment was measured, indicating that this peptide was able to translocate across the lipid bilayer and to bind CaM. Taken together, these data indicate that P454 interacts with the cis lipid leaflet of the membrane, translocates across the droplet interface bilayers, and forms a peptide:CaM complex in the trans compartment, as summarized in Figure  S8 , Supporting Information.
In summary, these   [92]  of the extended conformation of CaM is shown in light green. B) Experimental SAXS curve of the P454:CaM complex (black dots) superimposed over the best fit (red curve) obtained from the structural model shown in Figure  2F. C) Comparison of the four distance distribution functions obtained using the program GNOM for CaM alone (grey), H:CaM (red), MLCK:CaM (cyan), and P454:CaM (green) complexes. D) DAMMIN models of CaM alone, H:CaM, MLCK:CaM, and P454:CaM complexes, shown with the same color code. E) Ten models fitting the SAXS curve shown on Figure  2B  obtained using the program DADIMODO  [68]  are displayed after superimposition of C s over the range 10 to 70. F) Effects of P454 on the HDX behavior of CaM. The uptake differences (∆Deuteration) measured between the free-and P454-bound CaM were extracted for each peptide at each time point, summed, and plotted on the best-fitting structural model of P454:CaM (red curve in 2B).
Sedimentation velocity-Analytical ultracentrifugation (SV-AUC) SV-AUC experiments were carried out in a ProteomeLab XL-I analytical ultracentrifuge (Beckman Coulter, Brea, CA, USA). Samples of 400 µL and 410 µL of buffer were loaded in cells equipped with 12-mm thick double-sector epoxy centerpieces and sapphire windows. P454 and CaM were tested alone (at 57 µM and 72 µM, respectively) or mixed in a molar ratio 1:1 (at 70 µM each). Samples were incubated 2h at 20°C in the ultracentrifuge and, then span at 42,000 rpm in an An-50 Ti rotor. Detection of protein concentration as a function of radial position and time was monitored with the ProteomeLab software (Beckman Coulter, Brea, CA, USA) by both absorbance at 280 nm and interferometry. Four hundred scans were collected at 3-min intervals with a radial step size of 0.
003 cm. SV scans were analyzed using the continuous size distribution model c(s) of the software Sedfit  15. All the c(s) distributions were calculated with a fitted frictional ratio f/f0 and a maximum entropy regularization procedure with a confidence level of 0. 68. All partial specific volumes were theoretically calculated with the software Sednterp (Spin Analytical, Berwick, ME, USA). Buffer density and viscosity at 20°C of 1. 0310 g. mL -1 and 1. 0061 cP respectively were also determined with Sednterp for buffer B.
Synchrotron Radiation Circular Dichroism Synchrotron radiation circular dichroism (SRCD) was performed on the DISCO beamline of the synchrotron SOLEIL (Saint-Aubin, France). Spectra were recorded at 25°C with an integration time of 1. 2 s and a bandwidth of 1 nm with a resolution of 1 nm. A far-UV spectrum represents the average of four individual scans. QS cells (Hellma, France) with a athlength o , , or m de ending on inal rotein concentrations) were used to record spectra in the far-UV range (from 190 to 250 nm). CD spectra of peptides and proteins were measured in buffers A and B, 20% of trifluoroethanol (TFE), in the presence of SUVs (POPC:POPG 8:2) or CaM. Secondary structure content was estimated using Bestsel software 16, 17.
Small angle X-ray scattering (SAXS) Data acquisition X-ray scattering data were collected at the SWING beamline of the SOLEIL Synchrotron (Saint-Aubin, France). All measurements were performed using a size-exclusion HPLC column on-line with the SAXS measuring cell, a 1. 5 mm diameter quartz capillary contained in an evacuated vessel  20. All experimental details are given in Table  S5  in accordance with the guidelines given in  21. We studied solutions of CaM in the presence of a 2-fold excess of P454. All proteins were analyzed in buffer C. Scattering of the elution buffer before void volume was recorded and used as buffer scattering for subtraction from all protein patterns. For each frame, the protein concentration (about 0. 6/2 mg/mL at the top of elution peak for CaM-P454 and CaM respectively) was estimated from UV absorption at 280 nm using a spectrometer located immediately upstream of the SAXS measuring cell.
We also studied the P458:CaM complex in solution. Due to low sample concentration, the recorded SAXS data were of much lower quality, sufficient however to appear essentially identical to that of the P454:CaM complex. Data analysis SAXS data were normalized to the intensity of the incident beam and background subtracted using the programs FoxTrot (courtesy of SWING beamline) and Primus (https://www. emblhamburg. de/biosaxs/primus. html)  22. Identical frames under the main elution peak were selected using Cormap  23  and averaged for further analysis. The radius of gyration (Rg) was evaluated using the Guinier approximation  24  and also derived from the distance distribution function P(r) calculated by the program GNOM  25. Two independent molecular masses M estimates were obtained using the SAXSMoW2 and ScÅtter programs available at the URLs http://saxs.
Ab initio shape determination The shape or envelope of the complex of CaM with P454 peptide was determined using the ab initio program DAMMIF  26. 20 runs yielded as many shapes that were compared using the DAMAVER suite of routines based on the calculation of the Normalized Spatial Discrepancy (NSD) that is lower than 0. 7 if all 20 models are similar. The final envelope is obtained using DAMMIN  27  that uses the envelope of all superimposed DAMMIF models (the union of all models) as initial volume. Rigid-body modeling SAXS models of the P454:CaM complex were obtained from the crystal structures by using the DADIMODO modeling program  28  that uses an all-atom description of the molecule and explores conformational space by random modifications of the internal degrees of freedom φ and ψ angles within the helix between the two domains of CaM.
Crystallization and diffraction data collection To obtain crystals suitable for X-ray diffraction studies, we used the procedure and the crystallization pipeline implemented in the medium-scale crystallography platform at the Institut Pasteur, Paris  29. Briefly, crystallization screening trials of calmodulin in complex with different peptides (molar ratio 1:2) were carried out by the sitting drop vapor-diffusion method with a Mosquito automated nanoliter dispensing system (TTP Labtech, Melbourn, UK) at 291K. Sitting drops of 400 nL were set up in Greiner plates for 672 commercially available screening solutions using a 1:1 mixture of protein sample and reservoir well solution (150 µL). The plates were stored in a RockImager (Formulatrix, Bedford, USA) automated imaging system to monitor crystal growth.
Structure determination and model refinement The crystal structures of the calmodulin in complex with the synthetic P458 peptide were solved by the molecular replacement technique using separately, the N-ter and C-ter lobes of calmodulin (from the PDB entry 1CTR) as search models with program Phaser  31. Peptides were manually traced into Fourier difference electron density maps. Final models of the complexes were obtained through interactive cycles of manual model building with Coot  32  and reciprocal space refinement with Buster 33. X-ray data collection and model refinement statistics are summarized in Table  S5.
Biological samples In order to determine the effect of P454 binding on CaM, the labeling was performed on CaM alone, and in the presence of a two-fold molar excess P454. HDX-MS experiments were carried out in buffer B, i. e. , in the presence of 2 mM CaCl2. The quality of each protein and peptide was assessed prior to labeling by intact mass measurement (data not shown). Waters , and se arated using an AC PLC CSH C column m, x mm). Labeled peptides were eluted with an 8 min gradient of 5-30% acetonitrile at 40 µL/min and 0°C. After each run, the pepsin column was manually cleaned with two consecutive washes of 1. 5% formic acid, 5% acetonitrile, 1. 5 M guanidinium chloride, pH 1. 6. Blank injections were performed after each sample to confirm the absence of carry-over.
Data processing Peptide maps were generated by database searching in ProteinLynx Global Server 3. 0 (Waters Corporation, Milford, MA) using the following processing and workflow parameters: low and elevated intensity thresholds set to 100. 0 and 50. 0 counts; intensity threshold sets to 750. 0 counts; automatic peptide and fragment tolerance; non-specific primary digest reagent; false discovery rate sets to 4%. Each fragmentation spectrum was manually inspected for Statistics for the highest-resolution shell are shown in parentheses. The subtraction spectrum shows that the P454:CaM complex formation induces an increase of secondary structure content. Experimental conditions: buffer B, temperature: 25°C. interactions with membranes and CaM. The P454 peptide is represented in red and CaM by two grey lobes.
The free energy of interaction of P454 with membrane and with CaM was calculated from its K X and Kd values, as described in the materials and methods section. The KX and Kd values are listed in Table  S3. Titration was performed on VP-ITC with consecutive injections of 8 L. A two-sites binding model was used to fit to the ITC data and to extract the thermodynamic parameters reported in Table  S4. S5. S8 ) using the BestSel software  17  available at http://bestsel. elte. hu/index. php.
Funding is acknowledged from  Agence Nationale de la Recherche  (grant number  CACSICE Equipex   ANR-11-EQPX-0008 ). Region Ile de France  (grant number  DIM MalInf 2016 ),  CNRS  ( UMR 3528 ),  Institut Pasteur  (grant numbers  PasteurInnoV15006-01A ,  PTR451  and  PTR166-19 ,  PPUIP program ). The funders have no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Data Availability Statement All relevant HDX-MS, X-ray and small-angle X-ray scattering (SAXS) data are available in the Supporting Information. The crystal structures have been deposited on the protein data bank (PDB) with the access codes 6YNU and 6YNS. The molecular model and experimental SAXS data have been deposited on SASBDB (Small Angle Scattering Biological Data Bank), http://www. sasbdb. org/aboutSASBDB/) under the SAS code SASDJ64 (calcium-bound calmodulin) and SASDJ74 (P454 peptide from B. pertussis.
297) CC1/2 1 (0. 946) 0. 997 (0. 875) CC* 1 (0. 986) 0. 999 (0. 966) Reflections used in refinement 11538 (1129) 21073 (2061) Reflections used for R-free 592 (52) 1074 (96) R-work 0. 246 (0. 385) 0. 221 (0. 252) R-free 0. 290 (0. 488) 0. 284 (0. 314) CC(work) 0. 893 (0. 913) 0. 960 (0. 895) CC(free) 0. 860 (0. 777) 0. 945 (0. 860) Number of non-hydrogen atoms 2665 17084 macromolecules 2656 17055 ligands 9 29 Protein residues 340 2209 RMS(bonds) 0. 010 0. 016 RMS(angles) 1. 16 1. 74 Ramachandran favored (%) 98. 19 97. 07 Ramachandran allowed (%) 1. 51 2. 51 Ramachandran outliers (%) 0. 30 0. 43 Rotamer outliers (%) 0. 00 0. 00 Clashscore 8. 85 19. 35 Average B-factor 147. 89 113. 81 macromolecules 147. 92 113. 86 ligands 138. 83 84. 03 Number of TLS groups 10 Molecules per ASU (CaM/P458) 2/2 12/24 PDB entry 6YNU 6YNS.
Computational model. The model of 2 coupled neurons, GABAergic and pyramidal, was based on the one we developed previously  (31). In the present study, we refined the model introducing the modifications described in the Supplementary Methods, and performed the numerical simulations with the software XPPAUT (http://www. math. pitt. edu/~bard/xpp/xpp. html). The code is available at http://modeldb. yale. edu/267157. Statistics. For experiments with mice, we used data pooled from at least 3 animals per condition (including negative controls) to ensure reproducibility of results. Mice were used after genotyping and littermates were negative controls. In mice expressing fluorescent proteins, systematic tests using a flashlight were performed before the experiment to confirm the genotyping results.
Acknowledgments We thank  Frederic Brau  for his skillful expertise on imaging and image processing,  Michelle Studer  and  Pascal Ezan  for advice on immunohistochemistry experiments,  Etienne Audinat  for insightful discussions and advice, and  Yuchio Yanagawa  for donating GAD67-GFP knock-in mice. Our laboratory is a member of the " Fédération Hospitalo-Universitaire "  FHU-INOVPAIN  and of the  Neuromod Institute (Université Cote d'Azur, France). This research was supported by the  Investissements d'Avenir-Laboratory of Excellence "Ion Channels Science and Therapeutics" (grant LabEx ICST   ANR-11-LABX-0015-01  to MM),  University Côte d'Azur  (grant  IDEX  Jedi  ANR-15-IDEX-01  to MM) and the  European Commission  project  DESIRE  (grant  EFP7-602531  to MM). SZ was a PhD student of the " Ecole doctorale 85 " and received a PhD fellowship from the  French Research Ministry (MENRT).
Abstract. In 2009, the International Soil Moisture Network (ISMN) was initiated as a community effort, funded by the European Space Agency, to serve as a centralised data hosting facility for globally available in situ soil moisture measurements  (Dorigo et al. , 2011b, a). The ISMN brings together in situ soil moisture measurements collected and freely shared by a multitude of organisations, harmonises them in terms of units and sampling rates, applies advanced quality control, and stores them in a database. Users can freely retrieve the data from this database through an online web portal (https://ismn. earth/en/, last access: 28 October 2021). Meanwhile, the ISMN has evolved into the primary in situ soil moisture reference database worldwide, as evidenced by more than 3000 active users and over 1000 sci-entific publications referencing the data sets provided by the network.
As of July 2021, the ISMN now contains the data of 71 networks and 2842 stations located all over the globe, with a time period spanning from 1952 to the present. The number of networks and stations covered by the ISMN is still growing, and approximately 70 % of the data sets contained in the database continue to be updated on a regular or irregular basis. The main scope of this paper is to inform readers about the evolution of the ISMN over the past decade, including a description of network and data set updates and quality control procedures. A comprehensive review of the existing literature making use of ISMN data is also provided in order to identify current limitations in functionality and data usage and to shape priorities for the next decade of operations of this unique community-based data repository.
, 2010) , boosted the establishment of new research networks  (Colliander et al. , 2017). While all networks are valuable assets for assessing the skill of soil moisture products under various conditions and scales, their usage is hampered by the diversity of sensors, data formats, quality control, and accessibility mechanisms. The need to bring together and to harmonise available soil moisture data was recognised by the international soil moisture community and expedited by the Global Energy and Water cycle Exchanges (GEWEX) project of the World Climate Research Programme (WCRP) with support of the Committee on Earth Observation Satellites (CEOS), the Global Climate Observing System (GCOS), and the Group on Earth Observations (GEO). In the advent of the SMOS mission, ESA decided to provide the financial impetus to establish a global reference database of in situ soil moisture measurements for the purpose of satellite product development and validation.
The ISMN data hosting facility Although the ISMN may be considered a mere data repository, there is much more to it. Its core functionality includes collecting data from participating data providing networks, harmonising them in terms of units, sampling rates, naming, and metadata, performing automated quality control, storing the data and metadata in a searchable database, and making them available through a web interface. And, from a system perspective, it entails even more, e. g. communication with (potential) data providers and users (Fig. B1 ).
Since the ISMN was initially founded as a validation database for satellite (surface) soil moisture data, each station in the database provides soil moisture for the upper soil layer (≤ 0. 10 m depth). Soil moisture data sets in the ISMN can go as deep as 2 m, but generally with a decreasing number of measurements locations with depth (Table  C1 ). Some stations deploy more than one sensor at a certain depth, ei- Table  1. Overview of all available temporally dynamic variables stored in the ISMN database. * Note that, for precipitation and air temperature, the measurement height above the ground surface is indicated. ther as replacement in case of failure of one of the sensors or to characterise local soil moisture variability. The availability of meteorological data, like precipitation and soil and air temperature, is even more heterogeneous, depending on the scope of the network or the data sharing policy of the data providing organisation.