dataesr/scientific-paragraphs-categorization huggingface dataset:
Then, for each catchment, piezometers associated with aquifers emerging inside the catchment boundaries were listed, and maps -see an example in Fig. 2  -were produced to assess the importance of each hydrogeological formation for the catchment. Piezometers associated with formations with outcropping or sub-outcropping areas representing less than 5 % of the catchment area were discarded, along with those located on the wrong sides of underground watersheds -i. e. where groundwater does not flow to the catchment outlet but to another catchment -as identified by BDLISA. After this spatial selection, the available groundwater level and streamflow data were examined. An initial visual inspection of the time series was performed to eliminate data for which the quality was too low, relying on the expertise of the database maintainers.
Dismissing the mountainous catchments to avoid the influence of snow favoured the selection of lowland catchments, although several Vosges catchments whose downstream parts are linked to the Alsace plain aquifer reach maximum altitudes above 1000 m. However, the average altitude remains low enough for the solid precipitation fraction not to overtake 10 % of the total precipitation at the catchment scale. The variability of the mean annual potential evaporation is low, since the dataset does not contain high-altitude catchments -in which low yearly PET values are observed -or catchments located in the south-east of France -where the highest PET values are reached  (Brigode et al. , 2021). Figure  3  shows a map of the selected catchments and piezometers. The northern part of mainland France, especially the Paris basin, is over-represented because of data availability; in particular, the chalk and Tertiary limestone aquifers in this basin are the hydrogeological formations that have been monitored for the longest time in the territory.
Study of the correlation of the model with piezometry To adapt the existing model structure for groundwater level simulation, we followed an approach similar to other lumped conceptual models, i. e. a store was used as a representation of the aquifer, and the water content in the store was regarded as a proxy for groundwater level. With this aim in mind, a correlation study was performed on the dataset presented in Sect. 2 in order to identify which of the three conceptual reservoirs of the model structure was the most correlated with piezometry. The production store is part of the production function, which computes the balance between rainfall and evaporation to determine the amount of water available for streamflow. The routing and the exponential stores are part of the routing function, which models the time repartition of this available water to simulate streamflow.
GR6J was calibrated for the 107 catchments of the dataset using the Nash-Sutcliffe efficiency criterion computed on the square root of streamflow. The algorithm of  Michel (1991) , as implemented in the R package airGR  (Coron et al. , 2021; R Core Team, 2021) , was used for the whole period of available climatic data. Then, the time series of model states obtained -the levels of the three conceptual stores and the simulated streamflow used as control data -and the groundwater level time series were aggregated at monthly time steps to avoid problems caused by missing piezometry measurements. Afterwards, for each of the 160 catchment/piezometer pairs, Spearman's correlation  (Spearman, 1907)  between piezometry and each state series was computed; the results are summarised as boxplots in Fig.
Split-sample test evaluation scheme To assess the effect of the new calibration scheme described above on the streamflow simulation performance, a splitsample test  (Klemeš, 1986)  is conducted for each catchment/piezometer pair in the assessment dataset described in Sect. 2. For each pair, the available data are divided into two time periods P 1 and P 2 of equal length, defined so as to encompass the same number of data points for which both groundwater level and streamflow are available. Thereby, both periods contain the same amount of information and can be equally used for calibration and validation. The exact duration of the periods depends on the pair, since the time periods with available data have diverse durations, ranging from 5. 6 to 28. 5 years by period. Before each period, a warm-up timespan of 5 years is set: the model is run on this period, but the resulting simulated values are not used to compute criteria.
To assess the influence of the geological context, the test dataset of 160 catchment/piezometer pairs was divided into six groups, as detailed in Table  3. The groups were established in accordance with the hydrogeological formation attributed to each piezometer in the BDLISA reference inventory of  Brugeron et al. (2018). This classification may look arbitrary or inaccurate, since each piezometer corresponds to an idiosyncratic local situation; however, such a subgroup analysis of the test dataset highlights the influence of geology on model performance. The model performance for streamflow simulation in validation is not improved by the proposed calibration scheme. Figure  6  shows that the distribution of Nash-Sutcliffe efficiency computed on the square root of streamflow does not appear to change significantly for values of α under 0.
The sub-group analysis yields clearer results, which are visible in Fig. 17. For the absolute streamflow simulation performance, the original calibration framework yields high values of NSE for groups 2, 4 and 6, medium ones for group 1, and lower scores for groups 3 and 5. These patterns are found again for the composite calibration framework, even though the distribution of performances for group 5 is narrower. Regarding the difference between the two calibration frameworks, a significant improvement for a small part of the dataset is observed in groups 3 and 5 too, with no deterioration of the median performance in the group, while in groups 1, 2, 4 and 6, the median performance is reduced. A significant decrease in performance is observed for a quarter of group 2 and more than a decile of group 4.
e. α = 0; value of the Nash-Sutcliffe efficiency for each pair, computed on the square root of the streamflow, for the composite calibration with α = 0. 16; value of the Z Error criterion for each pair for α = 0. 16; difference in the NSEs obtained with the composite and the original calibration frameworks. 4. 6 Are these results model specific? Or dataset specific? The results presented in the previous sections can be seen as disappointing with reference to the objective of the study. Bringing new information -observed groundwater level data -to the GR6J model yielded no improvement in streamflow simulation performance. Of course, a question arises that we do not wish to avoid in this paper: is this disappointing conclusion model specific, i. e. is it due to the conceptual nature of the GR6J model? Would a less conceptual and more descriptive model have yielded more satisfactory results? Would a more heavily parametrised model have yielded more satisfactory results? Let us first answer this second question: equifinality is a plague in all modelling efforts, and we would not claim as a success an operation that consists of marginally improving the situation of a model that was previously impossible to calibrate.
Similar studies performed on other conceptual models did not result in different conclusions:  Thiéry (1988)  does not mention an improvement in streamflow simulation when calibrating GARDÉNIA with groundwater level data; the study by  Moore and Bell (2002)  on the PDM model is not conclusive since the new model structure is not compared to a reference one; finally, the calibration framework proposed by  Széles et al. (2020)  for the HBV model gives a similar conclusion to the one of this study: using groundwater level data for calibration helped to represent aquifer storage in the model but did not improve streamflow simulation. Regarding the catchment dataset, we tried to use the largest possible set of catchments with respect to our data sources and the selection criteria discussed in Sect.
2. 3. A selection bias is possible in the present study, mainly because the aquifers regarded as important for surface water resources are the ones that have been monitored for the longest time at the largest number of measurement points: the chalk aquifer in Picardy and the Alsace plain alluvium of the Beauce Tertiary limestones. However, the catchment dataset used in this study is diverse enough to draw general conclusions, at least for climatic and hydrogeological conditions similar to the ones observed in mainland France. Further evaluation for different contexts in other countries would help to put our results into perspective.
Acknowledgements. This work is part of the  CIPRHES  project funded by the  French national research agency ANR  and a PhD funded by the  French Ministry of Environmental Transition (MTE). The stream network shown in the maps is from the  BD CARTO database  (IGN, 2021). We would like to thank  Delphine Allier  ( BRGM ) for help in selecting the test dataset and  Jean-Baptiste Boissonnat  ( INRAE Antony ) and  Benoît Génot  ( INRAE Antony , now at  URBS ) for the database maintenance. We extend our warmest thanks to  Charles Perrin  ( INRAE Antony ),  Paul Astagneau  ( INRAE Antony ) and  François Bourgin  ( INRAE Antony ) for their thorough proofreading, which considerably improved the paper. Last but not least, computing codes could not have been developed without the precious expertise and availability of  Olivier Delaigue  ( INRAE Antony ).
We would like to thank  Yue-Ping Xu , the editor, and the two anonymous referees for their work, which helped improve and clarify this paper. Financial support. This research has been supported by the  Agence Nationale de la Recherche  (grant no. # ANR-20-CE04-0009 ). Review statement. This paper was edited by  Yue-Ping Xu  and reviewed by two anonymous referees.
S2 ). The DESeq2 analysis also indicated a signi cant decline of Urotricha a genus of the class Prostomatea, two Scuticociliatia including Cyclidium, ve genus and families associated to the class Colpodea, and ve genus and families associated to the class Hypotrichia, as well as the signi cant decline of Peniculia. The regression tree analysis identi ed a signi cant split in the dataset at an elevation of 1400m, with a Bray-Curtis dissimilarity coe cient averaging 0. 37 for lakes situated above 1400m in elevation while lakes situated under 1400m in elevation displayed an average of 0. 57 (Fig. 4 ). Ciliates functional traits and lakes ecosystem functioning. The DESeq2 indicated a signi cant increase of the facultative or strict anaerobe benthic ciliates while the benthic ciliates that require oxygen signi cantly declined in the recent period (Fig.
The reads were demultiplexed and R1/R2 reads assembled into contigs by the sequencing platform who provided one fastq les per sample. The high-throughput sequencing data were then cleaned in Mothur 1. 45. 1 66. Filtering steps were used to conserve DNA sequences of 350±50 bp in length, with no ambiguities (N=0), 10 or less homopolymer (max homopolymer=10) and no mismatch was allowed in the primer sequence. The data was dereplicated in order to work with Individual Sequence Unit (ISU). ISUs were then aligned using an aligned version of the Silva 18S database restrained to the V7 region and ISUs that were not fully aligned to the Silva 18S barcode were removed. The detection and removal of chimera was done using Vsearch as implemented in Mothur with default parameters. The taxonomic assignment of the ISU was done using a curated version of the Protist Ribosomal Reference database PR2 67 "pr2_version-4.
12. 0_18S_cil_cur" (available on Zenodo repository system: https://doi. org/10. 5281/zenodo. 5163167) and using the command classify. seqs() and the method wang with a con dence score threshold of 75% and 100 iterations. Following this rst taxonomic assignment, the ISU represented with only one read or that were identi ed as "unknown" or "Eukaryota_unclassi ed" were removed. The ISUs were then clustered into molecular Operational Taxonomic Unit (OTU) using the furthest neighbour algorithm with a similarity threshold of 97%. Finally, the command classify. otu() was used to taxonomically assign the OTUs based on the rst taxonomic assignment of the ISUs with a con dence threshold of 80%. The OTUs that did not belong to the Phylum Ciliophora were removed. The taxonomic a liations were checked and harmonized manually using the classi cation from Gao et al.
Statistical analysis Analyses were done using the R software version 3. 11  71  using the vegan package  72  , the rpart package  73  and DESeq2 package  30. The normality and homogeneity of variance of the environmental variables were tested using a Shapiro-Wilk test of normality  74  and a Fligner-Killeen's test  75  , respectively. If the variables were not normally distributed even after transformation non-parametric test were used. A Spearman correlation analysis was applied to study the relationship between the environmental variables. To assess the relationship between lake topology and trophic status the Kruskal test was used; whenever the Kruskal test 76 was signi cant, to assess which categories of trophic status were signi cantly different, a Wilcoxon rank sum test as Post Hoc test was applied using the False Discovery Rate approach by Benjamini and Hochberg  77  to adjust the p value for multiple testing (Supplemental Fig.
e. continuous variables: elevation, maximum depth, surface area of the lake, surface area of the watershed) to the amplitude of changes in the β-diversity, a univariate regression tree analysis was applied on the Bray-Curtis dissimilarity between the recent and past sample of each lake. For categorical data (i. e. Trophic Status), an analysis of variance (ANOVA) was used to compare the Bray-Curtis dissimilarity coe cients between categories. The difference in abundance between the recent and past samples was also evaluated for each functional group and at the Genus level using the DESeq2 framework applied on the raw count data  30. The results were expressed as "log2foldchange" and provided an indication of the intensity of the changes between the recent and past samples. For the creation of the map, the software QGIS  79  was used, for all other gures, we used the package ggplot2 80 and the color palette from the package scico 81 to create scienti cally derived color-maps accessible for people with colour-vision de ciencies 82.
Acknowledgments This work was supported by  Suez Lyonnaise des eaux Project  " Outils innovants pour la diagnose écologique et la gestion des lacs ". B. C. was supported by the  Pole Research & Development on Lacustrine Ecosystems (ECLA)  of the  French Biodiversity Agency (OFB). We thank  OLA (Observatory on Lakes) , the information system OLA Data (Schellenberger et al. 2019), the research infrastructure  AnaEE-France  ( Analysis and Experimentation on Ecosystems , and the  Observatoire Hommes-Milieux Pyrénées Haut Vicdessos -Labex DRIIHM ANR " for providing limnological data.
Statistical analysis Data analysis was performed using IBM SPSS v23. A descriptive assessment was conducted. The mean percentage of missing data was less than 5. 0% of the database therefore we did not replace those values. Categorical variables were described using counts and percentages, whereas means and standard deviations were used for continuous variables. For categorical variables, the chi-square and Fisher exact tests were used. The Student t-test was used to compare continuous variables in two groups. An exploratory factor analysis was initially carried out for data reduction, allowing to identify groups of items (factors). Following confirmation of sample adequacy with the Kaiser-Meyer-Olkin (KMO) index and the Chi-square sphericity test by Bartlett, factors were obtained using a principal analysis method and a promax rotation as factors were associated (19).
Statistical analysis Data were analyzed on SPSS software version 23. The reliability was checked for different factors and the total scales, using Cronbach's alpha values. Descriptive analyses were done using counts and percentages for categorical variables and mean and standard deviation for continuous measures. The construct validity of the IAT was done using the principal component analysis. The promax rotation technique was used since the extracted factors were significantly correlated. The Kaiser-Meyer-Olkin measure of sampling adequacy and Bartlett's test of sphericity were calculated to ensure the model's adequacy. Factors with eigenvalues greater than one were retained, and the scree plot method was used to determine the number of components to extract. Only items with factor loading greater than 0.
Gait analysis Gait was assessed in adult A30P and WT mice (2 months and 12-13 months old) using the Noldus Cat-Walk XT system. The system consists of a 70-cm long green illuminated glass runway. Above this corridor is a lid with red LED lights to create a silhouette of the mouse running in the corridor. Below the walkway, a high-speed camera records the scattered light from the paw prints, which is digitized and analyzed by CatWalk XT software. The calibrated walkway was set to a 10 × 20 cm area. Before the behavior test, mice were weighed. Mice were allowed to walk in an unforced manner on the glass plate at least five times in each experiment after a minimum training period of five runs the day before the experimental run. Mice that failed the training were excluded from the study. Footprints were classified automatically as right forepaw, right hindpaw, left forepaw, and left hindpaw.
All preparations were allowed to equilibrate for 10 min followed by a 10 min spontaneous motility period. GI motility was assessed by video recordings at 25 frames/s. Videos were analyzed and quantified by MotMap (www. smoothmap. org) and a custom written LabVIEW program (LabVIEW 2019, National Instruments). The video analysis software MotMap was used to set 30 imaginary dots along a 2 cm gut segment on the lower and upper border of the gut as seen in Fig. 3a. The up and down movements of these dot pairs were tracked automatically to calculate the positions to each other for every frame, representing the amplitude of gut activity (Δ). Before the analysis, the program automatically applies a low frequency baseline correction to each of the 30 sections to compensate for slow gut movements. To validate amplitude changes, all values were normalized to their initial diameter.
The motility data represent dilatations as positive values vs. the average diameter and contractions as negative values. Motility was converted to high-resolution spatiotemporal heatmaps, while all values were specified in a color range using the custom written LabVIEW software. Gut contractions were counted using spatiotemporal heatmaps. The mean interval was defined as the duration of one contraction and one dilatation (Fig. 3a ) and indicates the corresponding frequency. The contraction duration was calculated as the time between adjacent leading edge to leading edge zero crossings. To suppress a noise-induced false triggering, a hysteresis threshold was set at the order of 0. 05 to 0. 1 mm. Each of the 30 dots over the 2 cm was separately analyzed by determining the duration of all full cycles followed by averaging over all detected cycle durations.
05% Tween20 in PBS) for 30 min, tissues were incubated with respective secondary antibodies (1:500 in 0,01% NaN 3 + 1% Triton-X-100 in PBS) overnight at 37 °C on an orbital shaker (Supplementary Table  1 ). Again, samples were rinsed with PBST four times for 30 min and then counterstained with DAPI (1:1000) for 2 h at RT. Samples were washed in PBS five times for 10 min at RT. For the clearing procedure, Histodenz (Sigma) was diluted with melted N-methylacetamide (40% in PBS, Sigma) to 86% (w/v) concentration and incubated at 37 °C until dissolved. Then 0. 1% Triton-X-100 and 0. 5% 1-thioglycerol were added to the clearing solution. After being incubated in completed clearing solution for 1-5 min, the outer muscle layers were mounted in fluorescent mounting medium. Detection and image processing were performed with CellObserver Z1 using the Apo-Tome technology and Axiovision software (Zeiss).
The quantification of the immunofluorescence images was performed using ImageJ software. To quantify the expression per picture section, fluorescence images were converted to binary pictures, where the immunostained areas were presented in white and the percentage of positive area per picture section was calculated. Full-thickness samples were incubated in completed clearing solution overnight and imaged on a Leica SP8 confocal microscope (Leica Microsystems CMS GmbH, Germany). For imaging, samples were positioned in a glass-bottomed petri dish, submerged in the clearing solution and covered by a golden ring with nylon mesh to prevent the tissue from floating. 3-D renderings from intestinal wall samples were obtained using the 3-D Viewer of the Leica LAS X software (Leica microsystems, Wetzlar, Germany).
1% β-mercaptoethanol, P/S and 10 ng/μl human glial cell-derived neurotrophic factor (Immuno Tools) before exposure to monomeric and aggregated A30P α-synuclein. Culture medium was replaced by medium with B27 without antioxidants, containing 0. 5 μM A30P α-synuclein in Tris-HCl. After αsynuclein treatment for an additional 5 days, cells were fixed with 4% formaldehyde for 15 min at RT and were washed 3 times with 0. 01 M PBS. To determine cell viability after α-synuclein treatment, a live-dead-assay was performed. Cells were incubated with 1 μg/ml calcein and 0. 5 μg/ml propidiumiodide in 0. 01 M PBS for 15 min at 37 °C and analyzed with the CellObserver Z1 and Axiovision software (Zeiss). Total numbers of living and dead cells were calculated per picture section.
Immunocytochemistry Cells were fixed with 4% formaldehyde and permeabilized with 0. 1% Triton-X-100 for 10 min, followed by 30 min blocking in 0. 01 M PBS with 10% normal donkey serum. Primary antibody incubation (Supplementary Table  1 ) was performed for 1 h at RT in 0. 01 M PBS. Samples were washed three times in PBS and further incubated with respective secondary antibodies (1:500, Supplementary Table  1 ) in 0. 01 M PBS for 1 h at RT. Samples were washed three times in PBS and once in distilled water. Cells were then mounted on slides with fluorescent mounting medium with DAPI. Detection and image processing were performed with CellObserver Z1 using the ApoTome technology and Axiovision software (Zeiss). To determine the ratios of Nefl and Calb2 positive cells to the total number of neurons, numbers of cells positive for each protein were manually counted and divided by the number of PGP9.
Using the standard nCounter miRNA assay protocol, expression profiles of 578 mouse miRNAs derived from miRbase v. 22 were determined. Only miRNAs with at least 100 counts were considered as robustly expressed and included in differential expression analysis of psA30P vs. WT tissue. Data were analyzed using the nSolver software version 4. 0 (Nano-String Technologies). Stably expressed housekeeping genes such as beta-actin (Actb), beta-2-microglobulin (B2m), glyceraldehyde 3-phosphate dehydrogenase (Gapdh), and ribosomal protein L19 (Rpl19) were used for data normalization. To investigate a potential impact of the dysregulated miR-NAs on target gene translation, the top-regulated miRNAs were subjected to target gene prediction using mirWALK 3. 0 integrating the algorithms of multiple miRNA target prediction tools  [32].
Statistical analyses For statistical analysis, GraphPad Prism Software 8 was used. Normality of the sample populations was tested with the Shapiro-Wilk test. For normally distributed data, group differences were analyzed with the Student's t-test and the associated effect sizes with the Cohen's test  [33]  or two-way ANOVA. Data are displayed as means ± SD and p-values ≤ 0. 05 (*), ≤ 0. 01 (**) and ≤ 0. 001 (***) were considered statistically significant.