dataesr/scientific-paragraphs-categorization huggingface dataset:
During the baseline and recovery period data were collected in seated position. During HDBR participants remained in supine position at -6 degrees head-down tilt. The project was registered with the German Clinical Trials Register (DRKS, registration number DRKS00012946), and approved by the Ethics Committee of the Northern Rhine   1. There were no significant differences in any subject characteristics (all ps > 0. 54). Resting state eyes-closed EEG data were collected for 3 min eight days prior to bed rest (BDC-8), on the seventh day of HDBR (HDBR7), on the 31st day of HDBR (HDBR31), on the 60th day of HDBR (HDBR60), and on the eighth day of the recovery period (R+7, the first day of recovery was R+0). During baseline and recovery, EEG was recorded in seated position. During HDBR data were collected in supine posture at -6 degrees head-down tilt.
The experiment was registered with the Clinical Trial. gov database under NCT03594799 and approved by the Comité de Protection des Personnes (CPP Sud-Ouest Outre-Mer I), the French Health Authorities (Agence Française de Sécurité Sanitaire des Produits de Santé), and the local Ethics Committee at Charité -Universitätsmedizin Berlin, Germany. The study conformed to all standards and ethical principles for medical research on human subjects set out in the Declaration of Helsinki by the World Medical Association. All participants were informed about the purpose, experimental procedures, and risks before giving their verbal and written informed consent to participate in the experiment.
Data Acquisition The measurements from both experiments, i. e. , RSL and COCKTAIL, were performed in dimly lit and sound-attenuated rooms in the morning between 8. 30 am and 1. 30 pm. EEG data were acquired with a 32-channel amplifier (actiCHamp, Brain Products GmbH, Germany). Electrodes were attached to an EEG cap (actiCap, Brain Products GmbH, Germany) at positions Fp1, Fp2, F7, F3, Oz, Fz, F4, F8, FT9, FC5, FC1, TP9, CP5, CP1, TP10, CP6, CP2, FT10, FC6, FC2, FC3, C3, Cz, C4, T7, T8, P7, P3, Pz, P4, P8, O1, and O2, according to the International 10/20 System  (Jasper, 1958). Signals were referenced to Fz. Electrode impedance was checked for each subject before data collection and maintained at less than 5 k. Eye movements and eye blinks were monitored via tin electrooculogram (EOG) electrodes (B18 Multitrodes, EASYCAP GmbH, Germany) placed above and below the left eye as well as at the outer canthi of both eyes.
Data Processing All data were analyzed offline using EEGLAB (version 2019. 1. 0), a toolbox embedded in Matlab (version R2015b, The MathWorks, Inc. , Natick, Massachusetts, United States). First, the EEG signals were filtered with a 0. 5 to 65 Hz bandpass filter. Sinusoidal artifacts (50 Hz line noise) were removed using the CleanLine function  (Mullen, 2012). Next, recordings were visually inspected to allow for the interpolation of bad channels. Data from electrodes with poor signal quality were replaced using spherical spline interpolation. On average, less than 2% of the channels had to be interpolated. After re-referencing to average reference, data were segmented into 4096-ms-epochs with an overlap of 10% between consecutive segments. To exclude the possibility that EEG modifications were due to eye movement artifacts or other transient effects related to opening and closing of the eyes, the first and last 5 s of each recording were excluded for the successive analysis.
Effect sizes were reported as Cohen's d and 95% confidence intervals. The level of significance was set at α = 0. 05 (two-sided) for all testing. All statistical analyses and graphical illustrations were carried out using the software package R (version 3. 5. 1, R Core Team, 2018). Mixed models were run using the packages lme4 and lmerTest  (Bates et al. , 2015). Estimated marginal means were calculated using emmeans  (Lenth, 2016). Figures were created using ggplot2  (Wickham, 2016). The eLORETA software was used to assess changes in the neural sources of electrocortical activity by performing dependent t-tests for log-transformed estimated cortical current density between baseline (before HDBR) and all subsequent time points. Statistical significance was assessed for all frequency bands using a non-parametric randomization procedure with 5000 randomizations that determined the critical probability threshold (t critical ) with corrections for multiple testing  (Nichols and Holmes, 2002).
To assess changes in EEG resting state functional connectivity between the pairs of the nineteen ROIs in each frequency band, eLORETA was used to perform dependent sample t-tests comparing the connectivity values from baseline (before HDBR) with all subsequent points in time. For each of these t-tests a total of 684 tests were performed (171 ROI connections x 4 frequency bands). A non-parametric randomization procedure with 5000 randomizations and corrections for multiple testing was used to determine statistical significance  (Nichols and Holmes, 2002). The level of significance was set at α = 0. 05 (two-sided) for all testing performed using the eLORETA software package.
This was confirmed by a mixed model ANOVA yielding no significant interaction of Time and Region for any of the investigated frequency bands (all ps > 0. 582 for RSL; all ps > 0. 615 for COCKTAIL) when including HDBR data only. To account for inter-individual and intra-individual  BDC-10 to BDC-1 refers to baseline data collection. HDBR1, HDBR30, and HDBR60 indicate first, 30th, and 60th day of HDBR. R+0 to R+10 correspond to the first and 11th day after HDBR. For RSL data were collected at BDC-7, HDBR2, HDBR28, HDBR56, and R+10. For COCKTAIL data were collected at BDC-8, HDBR7, HDBR30, HDBR60, and R+7. *p < 0. 05, **p < 0. 01, and ***p < 0. 001 compared to baseline. differences we z-transformed the absolute power values across participants and testing days and re-analyzed the data set. The analyses confirmed the previous findings.
After the cessation of HDBR electrocortical activity was not significantly different from baseline levels recorded before HDBR. The time courses of EEG spectral power and cortical source distribution were highly comparable between the RSL and COCKTAIL study, and neither exercise nor antioxidant supplementation as a countermeasure affected this response. Our results of the immediate effects of HDBR on EEG spectral power are in line with various data previously published on the acute effects of supine or HDT position on absolute power and cortical source distribution of resting state EEG, reporting decreases within high-frequency domains including alpha and beta power  (Schneider et al. , 2008; Chang et al. , 2011; Thibault et al. , 2014; Spironelli and Angrilli, 2017). Based on MRI studies accounting functional and structural brain changes in response to prolonged bed rest  (Rao et al.
, 1975 (Frost et al. , , 1977)). Cheron et al. (2006)  used one-minute resting state recordings alternating between eyes opened and eyes closed every ten seconds to assess the impact of long-duration spaceflight on event-related spectral perturbations. They reported increases in alpha power during the arrest reaction in eyesclosed state  (Cheron et al. , 2006)  compared to pre-mission levels. Recently,  Cebolla et al. (2016)  employed a normalized measure on resting EEG data collected before administering a visuo-attention task to investigate the effect of microgravity, and observed decreases in alpha power desynchronization  (Cebolla et al. , 2016)  in relation to pre-mission. According to the authors the changes observed during/after long-term space missions can be explained by an increased demand for the integration and processing of vestibular information due to the decreased gravitational reference frame in space as well as by the reduction of support related proprioceptive afferents.
In addition, EEG measures of brain connectivity can be confounded by volume conduction effects  (He et al. , 2019). We tried to minimize these effects by employing source localization-based connectivity measure. However, due to the high correlation of adjacent voxels and the relatively low spatial resolution and inherent smoothness of the eLORETA inverse solution our findings should be interpreted with caution. Finally, data were collected in -6 degrees HDT during the bed rest phase. In contrast, participants were tested in seated position during the baseline and recovery period, resulting in global reductions of electrocortical power. It is therefore possible that the filtering effects associated with postural changes have masked underlying bed rest related alterations. Future studies should therefore employ the same position on all measurement days to discriminate the impact of bed rest from the effects of posture.
Likewise, our findings could also have important implications for EEG resting state data collections performed during spaceflight or altered gravity conditions. For instance, to determine the time course of resting state EEG during spaceflight, early inflight recordings could serve as a baseline for follow-up data collections. Future studies may also be able to systematically validate the effects of brain shifts and redistribution of CSF at varying levels of gravity on resting EEG recordings, which could provide the basis to apply normalization techniques to EEG recordings performed under microgravity conditions. Collectively, such approaches could help to disentangle the neurobehavioral impact of spaceflight stressors from cephalic fluid and brain shifts, and reveal the full potential of resting state EEG recordings during human space exploration.
ACKNOWLEDGMENTS We thank  Edwin Mulder ,  Melanie von der Wiesche ,  Alexandra Noppe , and  Ulrich Limper  from  DLR , and  Marie-Pierre Bareille  and  Arnaud Beck  from  MEDES  for their medical, technical, and administrative support in implementing the study protocol, and all volunteers whose participation and dedication made this study possible. We also acknowledge the support from the  German Research Foundation (DFG)  and the  Open Access Publication Fund of Charité -Universitätsmedizin Berlin.
Finally, coverslips were mounted on microscope slides with Fluoromount-G (Invitrogen, 00-4958-02) and analyzed with a Zeiss Axio ObserverZ1 platform microscope, with a Plan Apochromat 40×/1. 3 (oil immersion) objective and an AxioCam MRm Rev. 3 camera. Images were captured with Zen Pro (Zeiss) software and analyzed with the open-source software ImageJ.
Statistical analyses The GBA study was performed using the giTools software  (27). Fisher's exact test using higher/lower than median groups was used to find significant associations. Expression data are shown as means ± standard deviation (SD) or standard error of the mean (SEM) and statistical analyses were performed using Prism 9 (GraphPad Software). A descriptive analysis was carried out to analyze the distribution of the samples with D'Agostino and Pearson normality test. Non-parametric tests were used after normality failure. Differences between two groups were analyzed using two-tailed Student's t-test or U-Mann Whitney, whereas differences between three groups were analyzed using the Kruskal-Wallis.
S1B ). NIHCOLE was found significantly upregulated in HCC samples from the TCGA in the differential expression analysis of both peritumor-tumor pairs (Fig. 1A ) and all tumors compared to peritumoral samples (Fig. 1B ). We and others have found upregulation of NIHCOLE in other tumors (colorectal, gastric, head and neck, lung and breast cancers)  (29) (30) (31) (32). NIHCOLE expression was not observed in healthy tissues (median TPM ≤ 0. 1) according to the GTEx dataset (https://gtexportal. org/home/). To validate TCGA findings, we measured the levels of NIHCOLE in an independent cohort (BCL-CUN) of HCC patient samples (Supplementary Table  S1A ). In agreement with TCGA data, NIHCOLE's levels in tumor samples are significantly higher than in the paired peritumor samples or healthy liver biopsies by qRT-PCR (Fig.
2F  and Fig. S3A ) while cells without NIHCOLE expression remained unaffected (Fig. S3B ). To validate the specificity of this effect, we cloned the cDNA of NIHCOLE in a mammalian expression vector and performed adding-back experiments by co-transfecting the NIHCOLE-expressing vector (pN) or an empty vector (pØ) with control or NIHCOLE-targeting gapmers. Of note, much higher levels of NIHCOLE were observed for N-2 as this gapmer targets an intronic sequence that is not found in the cloned transcript (Fig. 2G ). Re-expression of NIHCOLE restored the proliferation of NIHCOLE-depleted HCC cells (Fig. 2H ). As expected, this effect was not observed in cells co-transfected with a positive control gapmer (PC) that affects cell number by targeting the essential structural protein actinin alpha-1 (ACTN1) (Fig.
inefficient. Institute of Health Carlos III. J. P. U was a recipient of a University of Navarra's Asociación de Amigos fellowship. L. P. -M is a recipient of a PFIS fellowship (FI20/00074) by the National Institute of Health Carlos III and FSE "Investing in Your Future". This work was also funded by grants from the Autonomous Region of Madrid (Tec4Bio -S2018/NMT-4443 and NanoBioCancer -Y2018/BIO-4747 to O. L. and F. M. -H. ) and co-funded by the European Social Fund. F. M. -H. acknowledges support from the European Research Council (ERC) under the European Union Horizon 2020 Research and Innovation Program (grant agreement 681299). The GTEx project was supported by the NIH and by NCI, NHGRI, NHLBI, NIDA, NIMH, and NINDS.
ACKNOWLEDGEMENTS We particularly acknowledge the patients for their participation and the  Biobank of the University of Navarra  for its collaboration. We thank all members of the  Fortes Lab  for helpful discussions and  Guillermo Serrano  and  Dr. Victor Segura , from  CIMA bioinformatics unit , for excellent expert assistance. We thank  R. Ye  and  Dr. Anne Vaahtokari  for technical assistance,  S. Fang  ( University of Calgary ) for purification of XRCC4 and APLF;  Dr. Laurence H. Pearl  ( Genome Damage and Stability Centre, School of Life Sciences, University of Sussex ) for providing X4L4, and  Ku70/80  for some of the experiments in this work. We also thank the flow cytometry facility and the  Arnie Charbonneau microscopy facility  at  Cumming School of Medicine, University of Calgary  for expert assistance.
The results show different sensitivity features according to the lake type and climate. The dominant role and time varying contribution of the lake depth, radiative parameters (albedo, light extinction coefficient) and thermocline relaxation coefficient linked to the atmospheric conditions, were clearly highlighted. These findings will lead us to distinguish between different lake categories in each grid cell of ORCHIDEE in the future implementation. Plain Language Summary Lakes are often neglected in climate modeling because their spatial extent does not exceed 4% of the Earth land area. But given the ever increasing spatial resolution of climate models and the significant role of lakes on the regional climate, it becomes important to represent water bodies in climate models. Such developments have started in the French IPSL climate model with the implementation of a one-dimensional freshwater lake model (FLake).
When implemented at large scale, a lake model requires the definition of a lake fraction in each grid cell of the land surface model. Generally, the grid cell size is a few tens of kilometers for a climate model. Such information can be offered by existing global databases generally derived from remote sensing data. These databases provide water bodies at a very fine spatial resolution, according to the decametric resolution of optical and microwave sensors. As an example, the product of  Pekel et al. (2016) , derived from Landsat imagery, provides the water extent of each water body with a resolution of 30 m, during 32 years 1984-2015 and at a monthly time scale. In addition to determining the different types of water surfaces that need to be considered, the definition of the model parameters necessary to initialize and run the model is an important task.
FLake Model FLake  (Mironov, 2008)  is a 1-D thermodynamic lake model developed for NWP purposes. It is a bulk model capable of predicting the vertical temperature structure and mixing conditions in lakes, given the meteorological conditions at the atmosphere interface (incoming radiation, air temperature and humidity, and wind speed). The water temperature profile is represented by a single mixed layer with a uniform temperature above a thermocline. Extra modules are implemented to model the snow, ice, and sediment profile temperatures into specific layers. The sediment layer allows for the representation of the heat transfer at the lake bottom-sediment interface, which is not negligible in the case of shallow lakes. The structure of the lake thermocline is parameterized using the concept of self-similarity.
Experimental Design The experiments were driven by two items: our final objective to model all the different kinds of lakes in ORCHIDEE, from the boreal thermokarst shallow lakes to the large deep lakes of temperate and tropical regions, and the knowledge of the existing parameter database to estimate the parameter uncertainties. In order to do so, we separately apply the Sobol method on LWST, H L , and H S variables, calculated by FLake over a set of 12 representative lakes ranging different depths and generic climates. Therefore, Y in the previous section is alternatively LWST, H L , and H S. The Sobol method is applied separately for each test case, considering their specific parameter intervals and meteorological forcing. We choose several cases to reflect the diversity in climate and lake size found around the world.
Representative Study Cases We used the HydroLAKES database  (Messager et al. , 2016)  to investigate the global distribution of natural lakes and human-made reservoirs in extent and lake depth. This database provides polygons of water bodies with surface areas of at least 10 ha and their main morphometric features including average lake depth and volume. We analyzed the distribution of the average lake depth of each lake weighted by their surface extent (see Figure  S1 ). In this figure, a mode around 3 m corresponding to shallow lakes (lakes with depth inferior to 5 m represent about 30% of the total lake area) can be observed, as well as an important contribution of the very deep lakes to this distribution (about 30% of which will be all simulated by FLake with the recommended prescribed depth of 50 m).
, 2016; D. Mironov and G. Kirillin, personal communication, 2019). Thus, the vector X of the method section corresponds to a vector of random variables of nine-dimensions (when the sediment layer is not activated) and 11 dimensions in the other cases. The ranges of variation that have been prescribed in the SA were defined according to the following approach. When FLake is run at global scale, these parameters are prescribed to default values, constant with time and space except for lake depth and fetch for which global maps of lake extent and lake depth are available. We therefore assumed that the uncertainties are mostly the result of the non-accounted spatial or temporal variabilities. Then, we defined the parameter ranges of variation from the analysis of existing databases providing such information, as explained in the following.
The final ranges of variation are given in Table  1  for the three kinds of lakes (shallow, intermediate, and deep). BERNUS ET AL. 10. 1029/2019JD031928 • Depth: We use the HYDROWEB database  (Crétaux et al. , 2011)  available at https://www. theia-land. fr to estimate the range of uncertainty of our three categories of lakes. We estimate them as the annual lake depth amplitude. HYDROWEB provides surface water levels (and not lake depth) from satellite altimetry on large lakes and some virtual stations on the biggest rivers on the Earth. Time series of lake water levels are provided for 160 lakes, and we have used these data to estimate the maximal annual amplitude of the water level variations. By subtracting the annual minimum level to the annual maximum level, we can assess the lake depth amplitude.
The results show that the annual amplitude is quite variable between lakes, varying between 20 cm and 19. 5 m (after filtering the inconsistent data), due to the variability of the water balance processes. Given that our perspective in the future is to model the mass balance, we decided to take as uncertainty range, the average annual amplitude, which is equal to 5 m • Light extinction coefficients: for free water, the database ILEC (ILEC: World Lake Database, International Lake Environment Committee Foundation, 1999) has been chosen to estimate the uncertainty of the light extinction coefficient, following the work of  Layden et al. (2016). ILEC provides in situ measurements of the Secchi depth on 150 lakes all other the world. The Secchi depth can be related to the lake turbidity with empirical formula.
Global Modeling Perspectives The results obtained through this SA allowed us to answer our preliminary modeling issues. First, the analysis of lakes global features showed the spatial distribution of lakes and their main features according to spatial extent, seasonal dynamics and most importantly lake depth. From the HydroLAKES database, we have seen that deep lakes represent more than 30% of the total lake area globally and that shallow lakes with lake depth lower than 5 m represent about 30% of the distribution (this number could be however underestimated because of the limited resolution of the sensors used to map water bodies. As an example, this limit is equal to 10 ha for HydroLAKES  (Messager et al. , 2016). Their spatial distribution told us that the density of shallow lakes is clearly dominant in the northern latitudes, whereas the deep lakes can be distributed all over the world.
, 2016). New products of lake turbidity and albedo are also coming out in the remote sensing community, such as those derived from Sentinel-2 instruments (https://www. theia-land. fr/en/products/), and they should be helpful to better assess the light extinction coefficient as well as the albedo and their respective ranges of variation. Data assimilation of multi-datasets (albedo, turbidity, LWST, surface water elevation, and surface extent) is therefore a promising track to calibrate and update regularly lake model parameters. Third, this study highlighted as well, the contribution of the relaxation constant of the thermocline shape factor on the surface temperature of deep lakes. However, the dynamics of this shape factor is hard to model. This issue was already raised by different authors such as Salgado and Le Moigne (2010) who proposed a solution to limit its high frequency time variations.
Another solution was proposed by  Golosov et al. (2018)  but more work is needed for calibration. As a first step, we will follow Salgado and Le Moigne (2010)'s approach, which is easier to implement, in order to avoid some of the unrealistic behaviors they revealed during the lake turnover periods. Further work is currently being undertaken by G. Kirillin (personal communication, 2019) to better parameterize the relaxation constant according to lake depth. Finally, the notable impact of the sediment processes on the surface variables despite the low sensitivity of the related parameters was shown in this study. Given the large uncertainties associated with these parameters in the absence of information on the sediment characteristics, we will not activate this module in FLake in its global scale implementation.