dataesr/scientific-paragraphs-categorization huggingface dataset:
Separate trait distance matrices may be produced to quantify phylogenetic constraints for different groups of traits. The analysis is repeated by sampling species-level branches in the phylogenetic tree a large number of times (represented by grey phylogenetic trees in the background of Figure  1b ) to quantify the sensitivity of results to the mixed taxonomic resolution of the trait database. For instance, in the simplified example presented in Figure  1b , taxon s2 in the trait database corresponds to three possible tips (species) in the phylogenetic tree (s2a, s2b, s2c), one of which would be selected in each sample. For studies using traits assigned at the species level, sampling from the phylogenetic tree is not necessary. Spatial variability is assessed by first fitting species distribution models for each taxon in the trait database, then randomly sampling a large number of grid cells (x) within the study area to generate predicted probability occurrences (p occ ) in each sampled cell (Figure  1c ).
| Model organism group In Europe, trait-based freshwater ecology is set to play an increasingly important role in biomonitoring  (Reyjol et al. , 2014). All macroscopic (macro)invertebrates, including insects, crustaceans, molluscs and other major groups are considered within the scope of monitoring. This role for trait-based ecology using macroinvertebrates is supported by well-established trait databases (Schmidt-Kloiber & Hering, 2015). The most comprehensive of these databases, compiled by Tachet, Richoux, Bournaud, and Usseglio-Polatera (2010), contains a set of 63 functional trait modalities in 11 categories, covering 484 taxa classified at mixed taxonomic levels and coded using a fuzzy scoring system  (Chevenet, Dolédec, & Chessel, 1994 ; Table  S1 ). For clarity, we use the term 'trait category' (e.
F I G U R E 1 Overview of the framework, using a hypothetical example of four taxa (s) and four fuzzy coded trait modalities (t) assigned at mixed taxonomic resolutions. (a) Trait correlations are assessed by resampling the observed s × t matrix a large number of times, respecting the rules of the fuzzy scoring system used to assign traits (Figure  S1 ), to produce null distributions of correlation coefficients for each pair of traits (r null ). Observed pairwise correlations (r obs ) are then ranked among the null distributions to derive p values. (b) Phylogenetic constraints are quantified by constructing a phylogenetic tree with tips corresponding to species represented in the trait database. A trait distance matrix is produced from the observed traits and the constraint quantified as the cumulative correlation between traits and phylogenetic distance.
The analysis is repeated by sampling species-level branches (e. g. s2a, s2b, s2c) in the phylogenetic tree a large number of times to quantify the sensitivity of results to the mixed taxonomic resolution of the trait database. (c) Spatial variability is assessed by first fitting species distribution models for each genus represented in the trait database, then randomly sampling a large number of grid cells (x) within the study area to generate predicted probability occurrences (p occ ) in each sampled cell (x = 4 shown here for demonstration purposes). Community (occurrence probability)-weighted means of each trait (p trait ) are then calculated and gradient analysis used to quantify the turnover of taxonomic and trait structure in space. The analysis may be simplified where binary or continuously measured traits are available at the species level (a) (b) (c) Previous work in North America found that many of the available traits for freshwater insects were highly collinear and tightly linked to phylogeny among the 311 taxa considered  (Poff et al.
After removing taxa with incomplete trait information, a total of 443 taxa in 23 orders and 152 families remained. Of these, most had traits assigned at the genus (52%) or species (39%) level. The remainder (mostly Diptera) were assigned at the tribe, subfamily or family levels (9%). This trait database was used as the basis for three sets of analyses designed to assess trait correlations (Table  1 ), phylogenetic constraints (Table  2)  and spatial variability (Table  3 ). More details on the steps involved are found in the corresponding tables and the Methods S1. The analyses were conducted in R version 4. 0. 0 (R Core Team, 2020) unless otherwise specified. Key R scripts for estimating trait correlations, phylogenetic constraints and spatial gradients in taxonomic and trait structure are available in the macroTraits GitHub repository (https:// github.
TA B L E 1 Steps involved in quantifying trait correlations Step Description Source data Method (package) a1 Assess the fuzzy scoring system used to quantify traits Steps b3-b5 Estimate branch lengths using aligned sequences of species represented in the synthetic tree (MrBayes v3. 2. 6). Settings are generated using the selected nucleotide substitution model (step b5) and by constraining tree topology using the synthetic phylogenetic tree (paleotree v3. 1. 3 in R) b7.
| Re-analysis of alpine macroinvertebrate community data To For all analyses, we compared the results obtained using all 11 trait categories to those obtained after excluding trait categories that most strongly violated our criteria for trait independence. We calculated commonly used functional diversity (FD) indices for each site  (Villéger, Mason, & Mouillot, 2008)  and estimated the importance of deterministic community assembly processes (dispersal-and niche-based) along the gradient of glacier cover using the same approach as the original paper  (Brown et al. , 2018). We also computed turnover (mean Bray-Curtis dissimilarity) in taxonomic and trait structure within and between regions. Finally, we tested for relationships between linear combinations of traits and glacial ice cover using the fourth corner (individual trait-glacial ice cover relationships) and RLQ (overall trait-glacial ice cover relationship) methods  (Dray et al.
| Trait correlations The maximum possible number of unique trait combinations, expressed as the product of unique combinations of resampled scores within each trait category (Figure  S4 ), was >2 × 10 36. Thus, if all traits were truly independent, we would expect all 443 taxa to exhibit their own unique trait profile, yet only 408 unique trait combinations existed in the trait database. This finding indicates trait non-independence, as previously found for stream insects in North America  (Poff et al. , 2006). Statistically significant correlations between trait modalities were widespread (Figure  2 ). Negative correlations were only found between trait modalities within the same trait categories, whereas correlations between different trait categories were always positive. This is because the fuzzy scoring system typically assigns each taxon nonzero values in a limited number of trait modalities within each trait category, damping any signal from negative trait correlations.
We therefore re-examined the alpine dataset from  Brown et al. (2018)  to test the hypothesis that trait non-independence confounds trait-based applications at large scales. We compared trait-based indicators calculated using the full set of traits to those calculated using only body size, resistance forms and food. Functional richness (related to the number of traits represented in the community) and functional evenness (related to both the number of traits and the abundance distribution) were highly sensitive to the set of traits used (Figure  5a, b ). In contrast, there was a close relationship between functional dispersion (related to the abundance distribution only) values calculated using the two alternative sets of traits (Figure  5c ), showing that purely abundance-based FD indices are robust to variation in the traits included.
test our hypothesis that trait correlations and phylogenetic constraints confound trait-based analyses, we re-analysed macroinvertebrate community (abundance) data from the European subset of alpine sites studied by Brown et al. (2018). The data were drawn from 261 alpine stream sites located across the Scandinavia Mountains, the Pyrenees and the European Alps, with varying proportions (0%-80%) of glacial ice cover in their upstream catchments. The original paper used a more focused trait database which was harmonized across alpine regions globally, whereas the comparative analyses presented here are based upon the European database of Tachet et al. (2010).
isric. org/explo re/soilg rids). Crop layers to the study area and aggregate to a common resolution of 2. 5 km (aggregate function, raster v2. 8-19 in R) Search GBIF using generic names for taxa assigned at species or genus level in the trait database, retaining only georeferenced records within the study area with coordinate uncertainties ≤ 2. 5 km (gbif function, dismo v1. 1-4 in R) Fill gaps in GBIF data coverage using national or regional datasets (e. g. RCS monitoring data from France), retaining only genus and species-level occurrence records Given the environmental layers, use MaxEnt to predict the occurrence probability in every 2. 5 km 2 grid cell for each genus represented in the augmented GBIF data (enmtools. maxent function, ENMTools v0. 2 in R) Generate an ensemble of 1,000 random samples of 1,000 grid cells (without replacement) from within the study area.
Retrieve the geographical coordinates, elevation and predicted occurrence probabilities for every genus in each sampled grid cell. See function spatial_gradient in GitHub repository a For each sampled grid cell in the ensemble, multiply the predicted occurrence probability by the corresponding trait score for each trait modality and genus. See function spatial_gradient in GitHub repository a For each of the 1,000 spatial gradient samples, fit gradient forest models to occurrence probabilities and community-weighted mean traits (gradientForest v0. | 7261 TA B L E 3 1-17 in R) to quantify the turnover of taxonomic and trait structure along spatial gradients (northing, easting, elevation). See function spatial_gradient in GitHub repository a a https://github. com/wilke sma/macro Traits.
500 yr given measurement uncertainties, (ii) a group of three boulders with exposure ages ranging from ca. 3. 5 and 6. 5 ka, and (iii) two late Pleistocene boulders, one of which is the largest surveyed boulder in our dataset. Based on these exposure ages and considerations presented in the previous sections, three main scenarios can be proposed for the emplacement of the surveyed boulders: (1) repeated boulder transport events, (2) excavation of boulders from large, older fill deposits, and (3) catastrophic events that resulted in long-range fluvial transport of the boulders. These scenarios are further discussed in the following paragraphs. The slow transport of the boulders during repeated catastrophic events requires reoccurring exceptional discharges throughout the Holocene, as suggested by the flow velocities that are required to mobilize these boulders.
Such a scenario would explain the age distribution of boulders with the existence of at least one event in the Trishuli and at least two events in the Sunkoshi/Balephi Khola reaches. Such catastrophic events are also likely to induce downstream landsliding by undercutting channel banks  (Cook et al. , 2018)  and hence to entrain boulders from different lithologies along its flow path while resulting in similar exposure ages across lithologies once deposited. It would also explain the observation in the Sunkoshi/Balephi Khola reach that the smallest boulder has the youngest exposure age and the largest the oldest, which is consistent with the fact that the boulders of older, larger events are not remobilized by subsequent smaller ones (also suggesting that our dataset is likely biased towards large events as the smaller events are likely erased).
g. ,  Pratt-Sitaula et al. , 2004)  and agrees with the provenance of a number of gneiss boulders originating from the glaciated higher Himalayan crystalline. It should also be noted that abrupt climatic shifts such as the one following the EHCO, occurred overall less frequently since the last glacial maximum (LGM) compared to the recurrence time of great earthquakes. The older exposure ages derived in the Sunkoshi catchment, including the largest boulder surveyed in this study (NEQ/161 03 with 29. 9 m intermediate diameter; Figs. 2  and 4 ; Table  1 ), could be attributed to an LGM or post-LGM glacial retreat that led to events being preserved in the channel till today (see  Schaefer et al. , 2008; Fig. 6 ). If correct, our dataset reflects that long-term climate-modulated LOFs can alter Himalayan valleys on 10 3 to 10 4 yr timescales.
Code availability. All paleo-hydrologic discharge calculations can be repeated using a user-friendly MATLAB script accessible via the URL https://gitlab. com/mlh300/bouldersforpaleohydrology/ (Huber, 2019)  and detailed in the affiliated explanatory file. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/esurf-8-769-2020-supplement.
Behavioral monitoring Prior to the MR imaging studies, we recorded the repetitive ticlike behavior  ( Aida et al. , 2015 ). Mice were put into a cylindrical, acrylic chamber (diameter: 12 cm, height: 22 cm) and their behavior was recorded for 10 min with a video camera. Tic-like movements were counted manually and quantified as the total number of tics observed in  /s (S b1000 and S b1800 , respectively) were obtained from GLT1-KD and control mice in resting state and awake conditions. The time series data for the ADC were computed with the time series data from S b1000 and S b1800. The ADC was used to investigate resting state brain activity and to define the driving events ignited by neural excitation in the IDMI analysis. The S b1800 was used to analyze the FC and to calculate the phase connectivity for the IDMI analysis.
Images of the sections were captured using an inverted light microscope (BZ-X710, Keyence, Osaka, Japan). ImageJ software ( http://rsb. info. nih. gov/ij/ ) was used for image analysis to calculate the percentage of the area expressing GLT1. All images were binarized. The positive area of GLT1 expression was defined by a threshold to distinguish GLT1 positive versus negative areas. Then, the percentage of the GLT1 expression area was calculated. We estimated the ratio of GLT1 expression as (GLT1 KD -GLT1 ctl )/GLT1 ctl × 100, where GLT1 KD is the mean percentage area of GLT1-positive cells in GLT1-KD mice.
Preprocessing of BOLD-fMRI and DfMRI images Preprocessing and statistics were performed using SPM12 (Welcome Trust Centre for Neuroimaging, London, UK) and in-house software written in MATLAB  ( Abe et al. , 2017a ). Initial image preprocessing was per-formed individually for each animal. The 3D-RARE reference images were co-registered and spatially normalized to a standardized structural brain template for mice  ( Hikishima et al. , 2017 ). The time-series images (DfMRI obtained with b1000 and b1800 mm 2 /s, and BOLD-fMRI) were realigned to correct for residual head motion, corrected for slice timing, and co-registered to the reference structural images. Then, all images where spatially normalized and co-registered to the standardized brain structure template using the normalized parameters of the structural image.
Finally, these images were resliced to a resolution of 0. 2 × 0. 2 × 0. 2 mm 3 and smoothed with a full width at a half maximum (FWHM) Gaussian kernel of 0. 6 mm. For the BOLD-fMRI, we decreased the sampling rate of the time-series data from 0. 5 Hz (equal to TR = 2s) to 0. 25 Hz (equal to TR = 4s) to allow for the comparison between the DfMRI and BOLD-fMRI. Then, the pre-processing was performed using this down sampled BOLD-fMRI data in the same way as the DfMRI. To provide some indication of the overall signal level in our image data, the signal-noise ratio (SNR) was estimated for the whole brain of each control animal and for each of the first 5 raw time course images before preprocessing. The SNRs were calculated by dividing the signal of the whole brain by the signal outside of the brain for each animal.
html ) to show regional ADC changes between ctl and GLT1-KD mice. For the region of interest (ROI)-based comparisons of the ADC time course data, the ADCs were extracted for each ROI, which included 59 bilateral brain loci defined in our flexible annotation atlas (FAA) of the mouse brain (See Fig. S1A  and Table  S1 )  ( Takata et al. , 2020 ). The FAA is based on the Allen Mouse Brain Atlas, which can be reconstructed with a text-based information and Python codes ( https://github. com/ntakata/flexible-annotation-atlas/ tree/master/FAAs/others/FAAocd ). The ADCs were first averaged at each time point over all voxels for each ROI in individual animals, then averaged for each ROI over all animals. When we compared the magnitude of the BOLD signal, we performed the statistical analysis in the same manner as described above.
Functional connectivity (FC) analysis The pre-processed time series data from S b1800 were used for the FC analysis with the functional connectivity toolbox (CONN, available at: https://www. nitrc. org/projects/conn ) and in-house software written in MATLAB. The time-series data were detrended and temporally filtered with 0. 01-0. 1 Hz. The global mean signal was regressed out from the preprocessed time series data in order to reduce non-neural signal correlations. A seed-based FC analysis was performed using the pre-processed time-series data. The ROIs from 59 bilateral brain loci were defined in the FAA. Pair-wise Pearson's correlation coefficients between the time course of a predetermined ROI and all other ROIs were estimated after de-noising and de-spiking and were transformed into Fisher's z-scores.
An ADC reduction and a BOLD increase was considered to be neural excitation, respectively  ( Abe et al. , 2017a ; Zhong et al. , 1997 ). Particularly, the time-series data from the ADC and BOLD images were transformed to z-score time-series data. An ADC intrinsic driving event was defined as an event where the z-score time series exceeded the threshold of one standard deviation from the time series, and the time series of the ADC decreased (See the blue lines in Fig. 5  A)  ( Tagliazucchi et al. , 2012 ). In contrast, a BOLD intrinsic driving event was defined as an event where the z-score time series exceeded the threshold of one standard deviation of the time series, and the time series of the BOLD signal increased. Intrinsic driving events were estimated in the 59 bilateral brain loci defined from the FAA.
m " in the Brain Connectivity Toolbox ( https://sites. google. com/site/bctnet/ ). Finally, the IDMI value was computed as the area under the curve (AUC) for all integration values at all threshold values (0 to 1). The IDMI values of the S b1000 and ADC for Figure  S6  were calculated in the same manner. To assess the validity of the IDMI analysis, we tested whether the IDMI values obtained from the measured data were significantly higher than those obtained with shuffled, randomized data. To achieve this, the IDMI value was calculated after random permutation of the phase time series from the S b1800 or BOLD-fMRI. We repeated this procedure 50 times and compared the average IDMI values obtained with the measured versus permutated data. To compare the frequency of the ADC-driving events, we counted all driving events for all brain loci.
Statistics Statistical processing was performed using MATLAB (MathWorks, MA, USA) and Excel software (Microsoft, USA). Familywise error (FWE) correction was conducted for the voxel-based analysis of ADC-DfMRI. Bonferroni corrections or false discovery rate (FDR) corrections were applied to correct p-values for multiple comparisons. Values are shown as the mean ± standard error of the mean (SEM) or as scatter diagrams with crossbars showing the average values.
Funding This work was supported by a  Grant-in-Aid for Research Fellowships  of the  Japan Society for the Promotion of Science (JSPS Research Fellow)  under grant number  18J00922  (Y. A. ),  Early-Career Scientists  of  JSPS KAKENHI  under grant number  19K16298  (Y. A. ),  Grant-in-Aid for Scientific Research (C)  under grant number  16K07032  (N. T. ),  Grantin-Aid for Scientific Research on Innovative Areas  under grant number  18H04952  (N. T. ), the joint  Usage/Research Program  from  MRI of TMDU  (K. T. , T. A. , K. F. T. ),  Takeda Science Foundation  (Y. A. , T. A. ), and research support of  Okinawa Institute of Science and Technology Graduate University  for the Neural Computation Unit (K. D. ).
The IL22RA2 locus is associated with risk for multiple sclerosis (MS) but causative variants are yet to be determined. In a single nucleotide polymorphism (SNP) screen of this locus in a Basque population, rs28385692, a rare coding variant substituting Leu for Pro at position 16 emerged significantly (p = 0. 02). This variant is located in the signal peptide (SP) shared by the three secreted protein isoforms produced by IL22RA2 (IL-22 binding protein-1(IL-22BPi1), IL-22BPi2 and IL-22BPi3). Genotyping was extended to a Europe-wide case-control dataset and yielded high significance in the full dataset (p = 3. 17 × 10 -4 ). Importantly, logistic regression analyses conditioning on the main known MS-associated SNP at this locus, rs17066096, revealed that this association was independent from the primary association signal in the full case-control dataset.
Patients and Controls All patients were diagnosed with definite MS  [21, 22]. Written informed consent was obtained from all subjects, and the study was approved by the local ethics committees. Table  1  shows the clinical and demographic data of the patients and controls enrolled in this study. The fine-mapping was completed in the Bilbao dataset, comprising patients registered at the Basurto hospital (Bilbao, Basque Country, Spain) and controls provided by the Basque BioBank for Research-OEHUN (www. biobancovasco. org). Additionally, genotyping data of three SNPs (rs276466, rs10484798 and rs6570136) in the Bilbao cohort were available from the aforementioned screening  [3] , and these were included in the haplotype and logistic regression analyses. The non-synonymous SNP, rs28385692, was further genotyped in additional patients and age-, gender-and ethnicity-matched controls from Donostia (Instituto Biodonostia, Basque Country, Spain), Barcelona (Hospital Vall d'Hebron), Madrid (Hospital Clínico S.
Carlos), Andalucía (Instituto de Parasitología y Biomedicina "Lopez-Neyra"), Germany (various centers) and France (various centers) (Table  1 ). Considering the SNP with the lowest MAF (rs28385692, 1000G European frequency = 0. 032) and combining all datasets, this study had over 80% statistical power to detect allelic odds ratios ≥ 1. 18 with a type-1-error rate of alpha = 0. 05 (calculated with the Genetic Power Calculator allelic test  [23] ). When considering only the Bilbao dataset, we had over 80% power to detect odds ratios ≥ 1. 8, assuming a MAF = 0. 032 and a type-1-error of alpha = 0. 05. The lead SNP from the 2011 GWAS  [6] , rs17066096, was also genotyped in all the above-mentioned validation cohorts, excluding France (Table  1 ). Additionally, data on the most significantly associated SNP in the fine mapping, rs202573, were available from Andalucía, Barcelona and Madrid from our previous study  [3] , and this SNP was newly genotyped in the Donostia and Germany cohorts.