dataesr/scientific-paragraphs-categorization huggingface dataset:
In their study, Smith et al. found greater upper limb asymmetry during asynchronous arm cranking compared to synchronous arm cranking in able-bodied participants. It would be interesting to extend these findings to wheelchair users and compare the upper limb asymmetry between different arm cranking configurations. Specifically, examining the upper limb asymmetry in wheelchair users during synchronous arm cranking could provide insights into whether the ISI values align more closely with those observed on HBP and SP tests. Conversely, the asymmetry on the field (SP) is lower than on other tests. One explanation for this limitation is that in the absence of a force sensor, we use the data from the right and left wheel speeds to measure asymmetry at the start, middle, and end of the sprint. These variables may potentially be less representative than force data for expressing asymmetry during propulsion.
The snail then returned to its small individual rearing box for some time. Water in the test arena was changed before to measure the next snail. After assessing refuge use and time to reach the refuge, we measured morphological defences. • Snail mass. Total fresh mass (shell + body) was measured with a precision scale at the nearest 0. 1 mg. • Shell length and width. Maximum shell length and width were measured with the software ImageJ  (Schneider et al. 2012 ) at the nearest 0. 001 mm from a photograph of the shell with its aperture upward taken by a camera mounted on binocular loup. • Shell thickness was measured at the shell aperture edge with a digital calliper at the nearest 0. 01 mm. • Shell crush resistance. The crush resistance of empty shells was measured by an automatic device designed for that purpose.
The interaction was not significant at either generation and was therefore removed from the model. To plot shell thickness corrected by snail size, we computed model predicted means using the emmeans R package  (Lenth, 2019)  in the theoretical case where all snails would be the same size. F. Shell crush resistance was analysed in a linear model with Treatment as the only fixed effect. To test significance of fixed effects, LMs used type II F-tests, LMMs used type II F-tests with Kenward and Roger's method, and GLMs used a likelihood ratio test (χ 2 ). To test significance of the random effect, LMMs used a likelihood ratio test (χ 2 ). For all models, we visually checked normality of residuals and their homoscedasticity across factor levels. We did not remove any outliers. Standardised effect sizes of fixed effects were estimated by the partial η 2 for the LMs and LMMs using the effectsize package  (Ben-Shachar et al.
, 2020) ; and by the McFadden's Pseudo-R 2 for the GLMs. Both η 2 and R 2 indicate the percentage of variance explained by a fixed effect (for example η 2 = 0. 11 for the Treatment fixed effect indicates that 11% of the variation in defence expression is explained by the predator treatment). When several exposure windows were significantly different from the control treatment, we performed custom post-hoc pairwise contrasts to test whether the defence induction was higher for one/some of these exposure windows compared to the others. The emmeans package was used  (Lenth, 2019). We used R software version 4. 0. 2 and Rstudio version 2022. 2. 3. 492 (R Development Core Team, 2022; RStudio Team, 2022). We used the ggplot2 R package for graphing  (Wickham, 2016) , ggpubr to arrange multiple plots (Kassambara, 2020), and the tidyverse suite for dataset manipulation  (Wickham et al.
Acknowledgements We are grateful to  Ludovic Guillard  who designed and built the electronic device that measures the shell crush resistance. We are also grateful to  Simon Navel  for helping us analyse the output signal of this device. We thank  Noéline Garcia  for her help in snail rearing. Many thanks to the three reviewers,  David Murray-Stoker ,  Timothée Bonnet  and  Willem Frankenhuis , for their constructive, clear and high-quality comments, which greatly improved the article. Our warm thanks also go to the PCI recommender  Benoit Pujol  for organizing such an efficient and constructive peer-review. Preprint version 4 of this article has been peer-reviewed and recommended by Peer Community In Evolutionary Biology (Pujol, 2023, https://doi. org/10. 24072/ pci. evolbiol. 100639 ).
Fundings This work was performed within the framework of the  EUR H2O'Lyon  ( ANR-17-EURE-0018 ) of  Université de Lyon (UdL) , within the program ' Investissements d'Avenir ' operated by the  French National Research Agency (ANR). This work was also funded by the  CNRS  and by the  TEATIME grant  ( ANR-21-CE02-0005 ) from the  ANR.
Materials and methods In this current section, the site characteristics and eddy covariance (EC) flux data are presented. Then, the remote sensing data (TROPOMI, MODIS Aqua and Terra, and Copernicus Land Cover classification) used in the study are described. Lastly, data analysis methods used in this study are presented. Study sites and flux tower in situ EC flux data were obtained through the ICOS Data Portal release in 2018 and 2021 (https://www. icos-cp. eu/data-services, last access: 21 December 2021). We screened over 70 ecosystem ICOS sites, relying on the availability of GPP data for each site with simultaneous TROPOMI SIF observations in the period from February 2018 to December 2020, and maintained 40 sites for analyses. The study sites encompass a latitude from 5. 27 • N to 67. 75 • N, including a diversity of plant functional types (PFTs) based on the IGBP vegetation-type classification given by ICOS PI sites: mixed forests (MFs, 2 sites), croplands (CROs, 9 sites), deciduous broadleaf forests (DBFs, 6 sites), evergreen broadleaf forests (EBFs, 2 sites), evergreen needleleaf forests (ENFs, 13 sites), grasslands (GRAs, 3 sites), open shrubland (OSH, 1 site, which is actually a young vineyard plantation), and wetlands (WETs, 4 sites).
The PFT at each site was confirmed by photointerpretation of pictures found in the ICOS Data Portal database and Google Earth. Detailed information and references of these sites are provided in Table  S1  in the Supplement. Figure  1  presents the location of these study sites, except for the GF-Guy site, located in French Guiana. In the analyses, we used daily GPP values computed as the sum of the half-hourly values estimated from each site. GPP data previously gap-filled by ICOS PI, representing for a full year, which was the case for instance at CH-Dav, FR-Bil, IT-SR2, and SE-Deg, were filtered out and were not used in the analyses.
MODIS Terra and Aqua data Time series of daily MODIS Terra and Aqua surface reflectance products (MOD09GA, MODOCGA, MYD09GA, and MYDOCGA), centered at the location of each site, were downloaded from the Google Earth Engine database. The quality assurance (QA) flag (ideal quality, QA = 0) and the cloud mask (clear state) criteria were used. Both MODIS Terra and Aqua, used in this study, contain 16 spectral bands of which the spatial resolution from band 1 to band 7 is 500 m and 1 km for the remaining bands (8-16)  (Vermote et al. , 2015). Detailed information about the MODIS data products is given in Table  S2. We used daily MODIS surface reflectance, NDVI, NIRv, and PRI. These VIs are computed according to the equation given in   (Köhler et al. , 2018) , which is comparable to those of Orbiting Carbon Observatory-2 (OCO-2) and the Greenhouse Gases Observing Satellite (GOSAT).
However, the wide swath of TROPOMI (2600 km) is larger than that of OCO-2 (10 km), which enables TROPOMI to provide almost daily spatially continuous global coverage  (Köhler et al. , 2018). TROPOMI has a spatial resolution of 7 km along track (5 km since August 2019 owing to diminished integration time) and 3. 5 to 14 km across track (based on the viewing angle) and covers the spectral range between 675-775 nm in the near-infrared with a spectral resolution of 0. 5 nm, which allows for the retrieval of far-red SIF  (Köhler et al. , 2018). To decouple SIF emissions from the reflected incident sunlight, a statistical and data-driven approach is used; see  Köhler et al. (2018)  for more details. We used instantaneous and daily ungridded soundings of TROPOMI far-red SIF at 740 nm obtained from the Caltech dataset between February 2018 and December 2020 (https://data.
caltech. edu/records/1347, last access: 14 June 2021). Instantaneous SIF data were reported in mW m -2 sr -1 nm -1. Daily SIF (hereafter referred to as SIF d ) is computed by timing instantaneous SIF with a day-length correction factor included in the dataset. The TROPOMI SIF observations corresponding to each site were determined by relying on the following criteria. Firstly, we extracted all pixels whose center locations are less than 5 km away from the flux tower sites for analyses. The latter choice was motivated by the fact that the relationship between TROPOMI SIF and tower-based GPP gradually weakened as the distance from site to the center of pixel increased (data not shown). Secondly, to reduce the cloud effects on SIF data, SIF d observations with a cloud fraction over 15 % were excluded, even though some findings reveal that TROPOMI SIF is less sensitive to cloud than surface reflectance values  (Guanter et al.
Data analysis In this study, the GPP and SIF d relationship was evaluated at a daily timescale at different spatial scales. Before investigating the link between GPP and SIF d , it was necessary to figure out a way to process outliers which were mostly associated with negative SIF d values. It has been shown that excluding directly negative SIF values could have effects on studying the relationships between satellite SIF data and GPP  (Köhler et al. , 2018 (Köhler et al. , , 2021)). Thus, to handle the outliers, an exponential model was used to account for the structural relationship between the instantaneous SIF and the SIF error included in the dataset. A threshold of ±0. 15 mW m -2 sr -1 nm -1 was then applied to the residual random error of the exponential model. We used a hyperbolic model to relate GPP to SIF d following  (Damm et al.
64 and 0. 87 (p < 0. 0001). The weakest relationships were recorded at the FI-Var, FR-EM2, and DE-RuW sites, and no significant relationship was found at GF-Guy, IT-Cp2, and FR-Mej. For each fit, the number of data points was between 160 and 1510, depending on the data availability at each site. Detailed information and statistics on the relationships between GPP and SIF d at each site are given in Table  S3. Note that the independent assessment considering the linear model to relate SIF d to GPP at each site and each PFT and data pooled across all sites revealed a relatively consistent lower goodness of fit, justifying the use of a hyperbolic model (see Tables  S4  and S5  and Figs. S1, S2, and S3 in the Supplement). To test the effects of the PFT on the relationship between GPP and SIF d at the daily timescale, data were pooled across sites of the same PFT (MF, CRO, ENF, DBF, EBF, GRA, OSH, and WET), and the hyperbolic model was applied on each PFT.
, 2018). Investigating GPP and SIF relationships based only on clear-sky data and cloudy-sky data, without the mix of both, is justified to better understand their links. Ultimately, the PFT-dependent relationships between GPP and SIF d in this study was confirmed by the weak and statistically significant relationship reported for all biomes on data pooled together across all sites. This hypothesis was further supported by the significant effects of the year, site, and PFT on the relationship between SIF d and GPP reported in the GLM. Exploring the newly launched satellite instruments such as OCO-3 and ECOSTRESS and the upcoming FLEX and GeoCarb satellite missions, which are planned to have diurnal sampling or fine spatial resolution (for instance 300 m for FLEX), along with ongoing groundbased and airborne-based SIF and GPP data altogether will improve the ability not only to better understand the GPP and SIF relationship but also to completely decouple the effects of driving factors such as vegetation physiology, canopy structure, and abiotic stress conditions that mediate their relationships at the ecosystem scale.
Synergy between SIF d , R, and VIs for estimating GPP using random forest The second goal in this paper was to explore the synergy between SIF d from the TROPOMI instrument and MODIS R and VIs namely NDVI, NIR v , and PRI for predicting GPP on data pooled across all sites. To achieve this purpose, four RF regression models were established: RF-R, RF-SIF-R, RF-SIF-R-PFT, and RF-SIF-VI. Except for the RF-SIF-R-PFT model, the main advantage of using solely remotely sensed data for estimating GPP is that we do not need to rely on land cover type, land cover change, and meteorological data  (Xiao et al. , 2019). The current results show that the RF-R (surface spectral reflectance alone), RF-SIF-R (SIFd plus surface spectral reflectance), and RF-SIF-R-PFT (SIFd plus surface spectral reflectance plus PFT) models statistically explain the same variance of GPP at the daily timescale (82 %-86%), whereas the RF-SIF-VI (SIFd plus reflectance-based indices) explains the lowest part, about 75 % of GPP across all sites.
Code and data availability. The computer codes (MAT-LAB and Python) used in this study are available upon request to the corresponding author. Observations of carbon fluxes are available through the ICOS Data Portal services (https://doi. org/10. 18160/PAD9-HQHU, ICOS RI, 2022; https://doi. org/10. 18160/YVR0-4898, Drought 2018  Team and ICOS Ecosystem Thematic Centre, 2020). SIF data from the TROPOMI instrument satellite are available through (https://doi. org/10. 22002/D1. 1347, Koehler and  Frankenberg, 2021). Daily MODIS Aqua and Terra spectral reflectance data are available through Google Earth Engine (https://earthengine. google. com/, last access: 18 October 2021; https://doi. org/10. 5067/MODIS/MOD09GA. 006, Vermote and Wolfe, 2015a; https://doi. org/10. 5067/MODIS/MYD09GQ. 006, Vermote and Wolfe, 2015b).
A total of 80 % of the data were used for training and the remaining for testing the model. It is worth mentioning that a RandomizedSearchCV technique was used (scikitlearn library for Python) to tune the model, and it took the best parameters for each model to predict GPP and applied a 10-fold cross-validation and 20 iterations on the training set to avoid splitting the dataset into training, validating, and testing sets, which could affect the number of data allocated to the training and could easily lead to model overfitting. The ensemble of decision tree models includes 200 trees for all models, but the number of splits per tree and the maximum depth varied. The relative importance of each variable, based on the mean decrease in the impurity method, was used to evaluate the part of the contribution of each input variable in predicting the canopy GPP variability.
Simulated data Two simulation studies were carried out: SIM1 to explore the influence of the temporal and spatial correlation between two sources, and SIM2 to assess the possibility of retrieving the dynamic of a 3-source network configuration. To obtain realistic MEG simulated data, we started from real imaging and MEG sensor locations from an epileptic patient with bilateral SEEG implantation and simultaneous MEG recordings. To model the head volume conductor, a boundary element method (BEM) model was obtained using OpenMEEG software in Brainstorm toolbox  ( Tadel et al. , 2011 ). The BEM was computed from the cortex surface mesh with 15,005 vertices. The model was composed of three layers modeling the scalp and at each mesh vertex a dipole was modeled with unconstrained orientation. The obtained lead field was then used to project the sources, simulated as described below, to the 248 MEG sensors space.
SEEG and MEG were offline temporally aligned by resampling the SEEG from 2500 Hz to 2034. 51 Hz  ( Badier et al. , 2017 ). For each patient, CT-scan/MRI data fusion was performed in order to find the anatomical position of each SEEG contact along the electrode trajectory. For this purpose, the in-house software GARDEL (a Graphical User Interface for Automatic Registration and Depth Electrodes Localization) was employed. This Matlab-based tool which allows the coregistration of MRI to the CT-scan, and the automatic segmentation and localization of contacts of depth electrodes by image processing  ( Medina Villalon et al. , 2018 ). Only SEEG contacts localized in the gray matter were further considered and the signals were formated in a bipolar configuration keeping only non-contiguous bipolar channels.
The latter is used in the beamformer calculation, to obtain a N * 3 spatial filter at each source location that allows estimating the source time-courses from sensor data  ( Jaiswal et al. , 2020 ; Sekihara et al. , 2002 ; Van Veen et al. , 1997 ). By applying the obtained spatial filters to the MEG data, the output of the Fig. 1. Summary of the simulation procedure. In both SIM1 and SIM2, and for each selected source position, a time series is simulated with interictal activity, and a propagation pattern is imposed among them in terms of events co-occurrences and delays. The forward model is then solved to compute the activity at the MEG sensors. The background activity was simulated in a similar way and added to the simulation at a given SNR. beamformer is thus a time series for each target location representing the activity of that source.
Simulated data analysis ICA infomax, LCMV-BF and dipole fitting were performed using Fieldtrip-based scripts  ( Oostenveld et al. , 2011 )  with the single-shell head model type as previously stated. This was decided to simulate a real situation in which the head model used is the single shell spherical one and to avoid a possible bias due to the use of the same model to produce and analyze simulations ('inverse crime',  Colton and Kress, 1992 ; Lamus et al. , 2012 ). The analysis schema is reported in Fig. 2  , where the analysis applied to simulated data is highlighted in italic. ICA was applied with a dimensionality reduction using principal component analysis (PCA) to extract 50 and 100 components. ICs presenting the simulated spiking activity were visually selected. These were then localized by means of single dipole fitting with an interval of confidence method (IoC)  ( Pizzo et al.
, 2019 ). LCMV-BF spatial filter was applied to the data scanning the 3D grid built in the brain volume with the order of 3000 positions (10 mm resolution). For each point of the grid within the brain volume, a time series was obtained representing the reconstructed activity of each dipole. For each time series the kurtosis value was computed and for comparison to the spiking ICs, the beamformer virtual electrodes associated with the highest local maxima of kurtosis values were retained. After visually checking the results, the highest two (SIM1) or three (SIM2) virtual electrodes were further considered.
Retrieval of connectivity network links The cross-correlation (R 2 ) was computed between the time series of the simulated sources (SIM2) and between the selected ICs and virtual electrodes separately, resulting in a connectivity matrix for each second (because of 1 s windows calculation) of simulated data for the three datasets (simulated sources or 'gold standard'; selected ICs; selected LCMV-BF time series). To compare the reconstructed and simulated connectivity evolution in time, the connectivity matrixes were vectorized at each time point considering the upper triangle as it is symmetric, and the temporal evolution of each reconstructed link was compared to the one of the simulated connections. To quantify the degree of agreement between the two dynamics, a partial correlation was computed: the correlation between the reconstructed link and the simulated ones is measured, controlling for the other two reconstructed links in order to remove their influence.
Retrieval of connectivity network links To assess the possibility of retrieving network organization from MEG exploiting either the sparsity of the ICA or the beamforming technique, we correlated the MEG based network (MEG-net) to the SEEG network (SEEG-net). The idea was to observe the dynamic evolution of each network link reconstructed from MEG and to identify the corresponding links of the SEEG-net thanks to the correlation between link time-courses. Of note, for real data we used classical correlation instead of partial correlation because the number of variables to control for were not fixed. As for the correlation between time-series and for a representative evaluation, the maximum positive and significant ( p < 0. 05) correlation of each MEG-net/SEEG-net link pair was identified. For each considered link, we computed the Distance 1 (mm) as the distance between the closest MEG-net/SEEG-net node of the link and Distance 2 (mm) as the distance between the remaining MEG-net/SEEG-net node.
Real data In this section, results obtained on the real dataset are described. Since the simulation analysis showed a better performance of the ICA100 with respect to ICA50, only results related to the first approach are discussed here in comparison to the LCMV-BF. In Table  2  , the details of the identified interictal sources (selected ICs and LCMV-BF) and of the SNR values estimated at the MEG sensors for each patient are reported. We verified that both methods were able to detect interictal sources from MEG data without the need for averaging or detect spikes prior to their application ( supplementary Table  S1  ). An illustrative example regarding PAT1 is shown in the supplementary Fig. S2  where a time segment of 10 s is displayed for MEG, SEEG and each source separation method.
8 ( Fig. 3 a ), thus the independence constraint seems not to strongly affect source separation. In SIM2, we simulated an interictal network with three nodes and a connectivity pattern only based on the delay between the co-occurring events. We tested the possibility to retrieve such network dynamic using cross-correlation on both the simulated and reconstructed time series (i. e. , selected ICs and LCMV-BF virtual electrodes), and correlating the time course of each link between nodes. We mainly used this simulation as a proof of concept to test whether the retrieval was possible and, in the used time and spatial configurations, ICA showed better performance in terms of SIM/MEG links time course correlation. Encouraging results were also obtained by applying the same pipeline to real data of eight drug-resistant epileptic patients who underwent a simultaneous SEEG/MEG recording.
We excluded the ICA50 approach since ICA100 showed better results on simulated data, probably due to the higher percentage of original data variance retained  ( Artoni et al. , 2018 ). Indeed, the need of maintaining as much as possible the full rank of the data has been suggested for a 69 channels EEG dataset, even if, stereotyped sources (as we can consider the IED sources) seem not to be degraded by the application of the PCA before ICs decomposition  ( Artoni et al. , 2018 ). With an approach similar to the one adopted in previous studies  ( Fahimi Hnazaee et al. , 2020 ; Pizzo et al. , 2019 ) , we first extracted, for each identified interictal source, the mostly correlated (Pearson's correlation coefficient) bipolar SEEG channel, and then computed the Euclidean distance between the SEEG channel position and the localized source.