dataesr/scientific-paragraphs-categorization huggingface dataset:
ME-ICA identifies in an automated fashion high ρ and low κ non-BOLD components and removes them from the optimally combined time-series dataset to produce the final multi-echo denoised dataset. This procedure has been shown to be very effective in removing various types of non-BOLD artefact from rsfMRI data, including head motion artefact, flattens DVARS traces induced by head motion, and increases tSNR by a factor of 4 over and above traditional single echo EPI data  31, 32, 34, 35. The final multi-echo denoised datasets were used in further connectivity analyses. Head motion estimates and DVARS were estimated in order to show the impact of denoising on reducing non-BOLD artefact due to head motion (see Supplementary Figure  3  for examples). In the EU-AIMS LEAP data, groups did not differ in mean FD (see Table  2 ).
EU-AIMS LEAP Functional Connectivity Analyses To assess large-scale intrinsic functional organization of the brain we input the multiecho denoised data into a group-ICA analysis. Dual regression was then utilized to back-project spatial maps and individual time-series for each component and subject. Both group-ICA and dual regression were implemented with FSL's MELODIC and Dual Regression tools (www. fmrib. ox. ac. uk/fsl). For group-ICA, we constrained the dimensionality estimate to 30. Of the 30 final components, 11 were discarded after visual examination of spatial maps which indicated that they did not correspond to well-known rsfMRI networks and instead resembled white matter or other artefacts  36. See Supplementary Figure  4  for visual depiction of the 19 ICs used in further analysis.
Time courses for each subject and each independent component (IC) were used to model between-component connectivity. This was achieved by constructing a partial correlation matrix amongst all 19 components using Tikhonov-regularization (i. e. ridge regression, rho=1) as implemented within the nets_netmats. m function in the FSLNets MATLAB toolbox (https://fsl. fmrib. ox. ac. uk/fsl/fslwiki/FSLNets). The aim of utilizing partial correlations was to estimate direct connection strengths in a more accurate manner than can be achieved with full correlations, which allow more for indirect connections to influence connectivity strength  [37] [38] [39]. Partial correlations were then converted into Z-statistics using Fisher's transformation for further statistical analyses. The lower diagonal of each subject's partial correlation matrix was extracted for a total of 171 separate component-pair comparisons.
To identify replicable subtype effects on functional connectivity, we partitioned the EU-AIMS LEAP dataset into Discovery and Replication sets. This was achieved via a random half split of the subtypes within each scanning site and balancing for sex. TD comparison groups for Discovery and Replication sets were also selected via a random split balancing sex and achieving an age-match (achieved using the MatchIt library in R with the default method of nearest neighbor matching). Models implementing the main hypothesis tests of subtype differences were computed as linear mixed effect models (lme function from the nlme library in R), whereby connectivity was the dependent variable, and subtype, sex, and age were used as fixed effect independent variables and site was modeled with random intercepts as a random effect.
Gene expression decoding analyses To identify genes whose spatial expression pattern is similar to subtype-specific ICs, we used the Gene Expression Decoding feature embedded within Neurosynth  23  to identify genes that are statistically similar in their expression profile in a consistent manner across all 6 donor brains within the Allen Institute Human Brain Atlas  22. The analysis first utilizes a linear model to compute similarity between the observed rsfMRI IC map and spatial patterns of gene expression for each of the six brains in the Allen Institute dataset. The slopes of these subject-specific linear models encode how similar each gene's spatial expression pattern is with our rsfMRI IC maps. These slopes were then subjected to a one-sample t-test to identify genes whose spatial expression patterns are consistently of high similarity across the donor brains to the rsfMRI IC maps we input.
All tests of enrichment were conducted with custom code written in R that computes enrichment odds ratios and p-values based on hypergeometric tests. The background total for these enrichment tests was set to 20,787, which is the total number of genes considered by the gene expression decoding analysis in Neurosynth. FDR was computed amongst all of the enrichment tests done and only tests that survived FDR q < 0. 05 were interpreted further as statistically significant enrichments. Underpinning this shared connectome phenotype are a myriad of differing functional genomic mechanisms. At the level of the transcriptome, we identify genes linked to macroscale functional networks by identifying genes whose spatial expression pattern across the brain is similar to the spatial topology of the macroscale functional network.
Acknowledgments We thank all participants and their families for participating in this study. We also acknowledge the contributions of all members of the  EU-AIMS LEAP group. Members of the  EU-AIMS  LEAP group are as follows:  Jumana Ahmad ,  Sara Ambrosino ,  Bonnie Auyeung ,  Tobias Banaschewski ,  Simon Baron-Cohen ,  Sarah Baumeister ,  Christian F. Beckmann ,  Sven Bölte ,  Thomas Bourgeron ,  Carsten Bours ,  Michael Brammer ,  Daniel Brandeis ,  Claudia Brogna ,  Yvette de Bruijn ,  Jan K. Buitelaar ,  Bhismadev Chakrabarti ,  Tony Charman ,  Chris Chatham ,.
, 2009). Long term ecological research (LTER) programs have been established in several regions such as North America and Europe over the last decades to facilitate adapting environmental policies in watersheds and protect terrestrial and aquatic resources for future generations. The Observatory on LAkes (OLA) database: Sixty years of environmental data accessible to the public Such programs are either national (e. g. , in France: Zone Ateliers and Analysis and Experimentation on Ecosystems [AnaEE France]; in the USA: Long Term Ecological Research  [LTER] ) or involved in international networks (e. g. , AnaEE Europe, Europe LTER, International LTER [ILTER] see  Mirtl et al. 2019). These programs aim to facilitate interactions and collaborations within the multidisciplinary community of researchers such as the Global Lake Ecological Observatory Network -GLEON-  (Rose et al.
Data available in the database The efficient partnership between scientists and local stakeholders allowed the collection of physical, chemical and biological data from 1957 on Lake Geneva (also called Lake "Léman"), 1966 on Lake Annecy, 1974 on Lake Aiguebelette and 1987 on Lake Bourget. More recently, since the late 90s, 24 small high-altitude lakes situated on a latitudinal gradient in the French Alps were included in the observatory, and are since then monitored. Studying these lakes, considered as 'pristine' and highly sensitive to climate change, opens perspectives on the impacts of global change on lake functioning. The location and intrinsic physical characteristics of the largest lakes monitored in the framework of OLA are shown in Tab. 1 and Fig. 1. Samples are collected from the middle of the lake above its deepest point.
Schagerl). Results are expressed in biovolume per volume of water (μL. m -3 ) or in biomass per volume of water considering that phytoplankton density is 1 kg. L -1 (µg. L -1 ). A database of species biovolumes  (Rimet and Druart, 2018)  is used to obtained these results. Over more than 50 years, this biovolumes' database has been regularly updated to include species observed in the alpine lakes in France (Lakes Geneva, Annecy, Bourget and Aiguebelette) and also in high-altitude lakes in the French Alps  (Feret et al. , 2017)  and in various other French lakes. Simultaneously, picocyanobacteria are examined in Lakes Annecy, Bourget and Geneva from samples taken at 6 discrete depths since 2003 (see details in supplemental data S2); these data will be included in the IS in the forthcoming years.
, 2015). Here we selected some of these long-term data to illustrate some of the scientific advances they recently provided. Supplemental data 2 and 3 show complementary data collected for other biological compartments (fish and picophytoplankton) that will be included soon in the OLA-IS. Tab. 4. Temporal distribution of the data available in the information system (white: no data, grey: data available in the information system).
2. Phosphorus maxima observed in the data were reached during the late 70s in Lake Geneva and during the 90s in Lakes Annecy and Aiguebelette. The maximum phosphorus concentration in Lake Bourget during the 80s is also well known but not included in the database  (Perga et al. , 2015). Effective measures for reducing the phosphorus load in the 70s resulted in strong decreases in phosphorus concentrations in the four large alpine lakes. Re-oligotrophication processes continue today  (Jacquet et al. , 2014a).
Long-term chloride concentration in Lake Geneva Chloride ion concentration in Lake Geneva increased continuously from 1973 to 2017 (Fig. 3 ). The annual average concentration reached 10. 31 mg Cl -/L. The contributions of various tributaries to the lake largely explain this trend  (Klein, 2016). Chloride concentration reflects a growing utilization of chemical chlorine-based products in the industrial sector (metallurgy and pharmaceutical sectors) and the application of deicing salt during winter  (Gumy and De Alencastro, 2001). These data have been used to consolidate a database of 529 lakes in Europe and North America  (Dugan et al. , 2017b) ; the associated study predicts that many lakes could exceed the aquatic life threshold criterion for chronic chloride exposure (230 mg L -1 ), stipulated by the US Environmental Protection Agency (EPA), in the next 50 years if current trends do continue  (Dugan et al.
INFORMATION SYSTEM STRUCTURE The OLA-IS is built around generic components offering common functionalities to various observatories dedicated to a variety of environmental topics (lakes, grasslands, forests and crops)  (Schellenberger et al. , 2019b ; https://doi. org/10. 15454/QJJJZU). However, several features dedicated specifically to lakes were added (specific datatype modules), making the OLA-IS specific (Schellen-berger et al. , 2019a; https://doi. org/10. 15454/VBWYWG). The OLA-IS was written in Java as a Maven project and is divided into several lake datatype modules. The OLA database was designed according to a relational model  (Monet et al. , 2020a , https://doi. org/10. 15454/OHXVJY) managed by PostgreSQL Release 12. Technically, the database has been designed to make the modeling as generic as possible so that each type of data (for instance: phytoplankton data type, probes data type, etc.
cvs) files. Online notifications show the user if the extraction is in progress or completed, and this does not imply the user OLA is part of the French national research infrastructure Anaee-f. Anaee-f works to publish its metadata and its data through the use of the semantic web (https://www. anaee-france. fr/en/infrastructure-services/ OLA fits in with this perspective and uses the vocabulary of thesaurus Anaee-F, data are semantically annoted using the AnaEE-F (http://agroportal. lirmm. fr/ontologies/ ANAEETHES and OBOE ontologies  (Madin et al. , 2007). In this way, OLA IS is developed according to FAIR Data principles (Findable, Accessible, Interoperable, Re-usable)  (Clobert et al. , 2018; Pichot, 2017 , Wohner et al. , 2019).
(2020)  The Observatory on LAkes (OLA) database: Sixty years of environmental data accessible to the public. Furthermore, the user is asked to transmit to the OLA-IS manager a copy of the work wherein the downloaded data were analyzed and valorized. The GCUs expire after a period of five years after which the user must destroy the data. The user must formulate a new request in case the data are required for a new project. Data cannot be shared or stored in another public database.
Recent technological developments have increased the number of variables monitored in lakes using automatic high-frequency sensors  (Marcé et al. , 2016). To facilitate posterior data handling and interpretation, it is critical that an interface as the OLA-IS provides storage capacity and sharing access for this type of data, given it is becoming essential in near-future limnological surveys. Monitoring additional matrices and habitats in lakes and their integration into the OLA-IS are already envisaged. For instance, data on fish populations, picocyanobacteria and new sampled habitats such as the littoral zone, are now routinely collected and are planned to be archived in the OLA-IS whose architecture allows to host new data. Therefore, integrating high-throughput data (DNA and sensors), new sampling sites and matrices into the OLA-IS would considerably enrich and diversify this database, offering thus new opportunities to enlarge scientific collaborations.
ACKNOWLEDGMENTS We thank CIPEL, CISALB, SILA and CCLA for their support in the OLA. We thank the  AnaEE-F program  and the national network  ALLENVI  for granting the OLA. ASTERS,  EDF-CIH, Parc National des Ecrins, du Mercantour, de la Vanoise  and  3CMA  are thanked for funding data production. The  GIS Lac Sentinelle  is also thanked.
0132 g L -1 CaCl 2 /2H 2 O and 0. 5 g L -1 MgSO 4 /7H 2 O, and as a main carbon source, 15 g L -1 (dry weight) of wheat straw (Triticum aestivum) or Wiley-milled aspen (Populus grandidentata). Cultures were incubated in the dark at 30 °C with shaking at 120 rpm. The cultures were stopped 10 days after inoculation and the culture broths (secretomes) were filtered using 0. 2 μm poly(ether sulfone) membrane (Millipore) and then stored at -20 °C until use. Recombinant Enzyme Production. The coding sequence for Geobacillus stearothermophilus abfA (GsGH51, GenBank: AAD45520) was synthesized with E. coli codon optimization and cloned into pET28a(+) with an N-terminal TEV protease-cleavable 6xhis tag by GenScript. Following transformation of BL21(DE3) Gold, the enzyme was produced in an auto induction medium (1% tryptone, 0.
5% yeast extract, 25 mM Na 2 HPO 4 , 25 mM KH 2 PO 4 , 50 mM NH 4 Cl, 5 mM Na 2 SO 4 , 0. 05% glucose, 0. 5% glycerol, 0. 2% lactose) at 37 °C. The enzyme was purified as described previously  22  with an added overnight treatment with his-tagged TEV protease S219 V  23  in pH 8 Tris-HCl, 5 mM DTT, 1 mM EDTA at RT followed by inverse histrap purification and desalting into 5 mM Tris-HCl, 1 mM EDTA, pH 8. 0. Aspergillus niger abfA (AnAbfA, GenBank: CAK43424) and Aspergillus kawachii abfB (AkAbfB, GenBank: BAB96816) were produced in P. pastoris X-33. A plasmid encoding AkAbfB in pPICZα with no purification tag was obtained from professors Takuya Koseki and Shinya Fushinobu. AnAbfA was synthesized by IDT as a GBlock and cloned into the vector fragment PCR-amplified from the AkAbfB-pPICZa plasmid using Gibson assembly.
Kinetic measurements were made in technical quadruplicate. Curve fitting and statistical analysis was performed using OriginPro graphing software. Enzymes were diluted in 50 mM sodium phosphate buffer pH 7. 0. Substrate was dissolved in DMSO to give a 100 mM stock which was diluted with ultrapure water. Putative inhibitors were dissolved in and diluted with ultrapure water with the exception of inhibitor 3 which was dissolved in DMSO to give a 50 mM stock, which was diluted with ultrapure water. Enzyme specific activity was initially assessed by monitoring the hydrolysis of 50 μM 4MU-Araf in pH 7 phosphate for 10 min. Michaelis-Menten parameters for the hydrolysis of 4MU-Araf were estimated by varying the substrate concentration from 4 to 500 μM and fitting a site-saturation kinetic model (v 0 /[E] t = k cat [S] 0 /K M + [S] 0 ) to the resulting rate vs substrate concentration data (Supplemental Table  1 , Supplemental Figures  1A  and 2A ).
Peptides were identified by mapping onto the predicted proteomes deduced from genome sequence of A. biennis BRFM 1778, F. fomentarius BRFM 1823, L. menziesii BRFM 1781, and T. gibbosa BRFM 1770. For each genome (to be published elsewhere), CAZymes were annotated as in Lombard et al. , 2014. 1  All genome and proteome data are publicly available on the Mycocosm portal (mycocosm. jgi. doe. gov/mycocosm/home). Enzyme Crystallization and Diffraction. Crystals of GsGH51 were grown essentially as described by Hovel et al. 22  Optimized crystals were grown by mixing 1. 2 μL of protein (10 mg/mL in 5 mM Tris-HCl pH 8. 0) with 0. 6 μL of well solution containing 15% PEG3350, 5% 2-propanol, 0. 1 M Tris-HCl pH 7. 5, 0. 80 M NH 4 F in a sitting drop at 293 K (Supplemental Figure  5A ). To generate inhibitor-bound complexes, crystal-containing droplets were supplemented with 0.
Journal of the American Chemical Society To determine where on this landscape the observed conformations of enzyme-bound species lie, we surveyed all of the conformations of L-arabinofuranose observed within the active sites of crystallized GH51 and GH54 enzymes. Specific α-L-arabinofuranosidases have been identified within GH families 43, 51, 54, and 62, of which only families 51 and 54 follow the anomeric stereochemistry-retaining Koshland double-displacement mechanism. The most detailed studies of α-L-arabinofuranosidase mechanisms have been performed using bacterial GH51 enzymes. Paes et al. obtained the structure of an intact branched pentasaccharide substrate bound to the active site of TxAbf, a thermostable GH51 from Thermobacillus xylanilyticus (PDB ID 2VRQ). 56 Hovel et al. reported the crystal structure of Geobacillus stearothermophilus AbfA (hereafter referred to as GsGH51) bound to 4-nitrophenyl α-L-arabinofuranoside (PNP-Araf) (PDB ID 1QW9).
57 In both of these Michaelis complexes, the α-L-arabinofuranose rings were found in the 4 E conformation (Figure  2A ). Therefore, similar to observations with GHs acting on pyranose sugars,  53  furanosidases distort the -1 sugar to a conformation that is preactivated for catalysis. Thus, the conformational catalytic itinerary for the rate limiting step of the reaction for GH51 family is expected to go through an oxocarbenium ion-like E 3 conformer to fulfill the requirement of having C4-O5-C1-C2 planarity. 58 eyond the bacterial GH51 enzymes, there is only one retaining α-L-arabinofuranosidases which has been crystallized. The structure of Aspergillus kawachii AbfB (a member of GH54 hereafter referred to as AkAbfB) with arabinose in the active site (PDB ID 1WD4), displays a product complex ring conformation of 4 E.
Soaking GsGH51 with inhibitors 2 and 6 overnight at room temperature resulted in the formation of full occupancy covalent complex between E294, the known catalytic nucleophile, and each inhibitor (Figure  3A, B ). These structures are similar to the complexes reported by Hovel et al. 57  They reported a covalent substrateenzyme intermediate trapped in a 2 E conformation (PDB ID 1PZ2); however, poor electron density at the anomeric center of the Hovel structure limits the confidence with which this ligand conformation can be interpreted. Nevertheless, it is clear that the structure of the active site varies between the Hovel complexes and the complexes with inhibitors 2 and 6 that we obtained (Supplemental Figure  9 ). Complexes with both inhibitors 2 and 6 are characterized by the positioning of the E294 side chain away from R69, toward the unliganded (PDBID: 1PZ3) position of Y246, which, in place of a hydrogen bond to the ring oxygen of α-Larabinofuranose, forms a hydrogen bond with O5 of the inhibitor (carbohydrate numbering).
This revealed enzyme recovery from surprisingly high temperature treatments (Supplemental Figure  15A, C ). Both enzymes were stable up to 60 °C. Increasing the temperature to 65 °C resulting in a complete loss of GH54 staining and raising it to 67 °C resulted in a complete loss of both GH51 and GH54 labeling, suggesting complete denaturation. However, increasing the preincubation temperature beyond 67 °C resulted in a partial recovery of GH54 staining. Preincubation at 86. 5 °C resulted in a ∼50% recovery of fluorescence intensity relative to the RT control (estimated by band integration using ImageQuant software (GE)). To determine the role of disulfide bonding in the stability AbfA and AbfB and the apparent refolding of AbfB, we repeated the experiment with 5 mM DTT (Supplemental Figure  15B, C ).
On-bead digestion of proteins pulled down following treatment of the secretome with ABP 5 yielded peptides from AbfB (GH54, GenBank: CAK42333), AbfA (GH51, GenBank: CAK43424), and AbfE (GH51, GenBank: ACE00420) as well as a small collection of other proteins not known to be α-L-arabinofuranosidases (Figure  4B ). We did not observe AbfC or AbfD in our analysis of the pull-down total secretome, indicating that these were not produced in our culture. Preincubation of the secretome with inhibitor 2 followed by treatment with ABP 5 and pull-down significantly reduced signal for peptides from AbfA, AbfB, and AbfE without causing a significant reduction in signal for any other detected proteins. Although we cannot exclude that ABP 5 has specific targets beyond arabinofuranosidases that are incapacitated by inhibitor 2, these results reveal the utility of ABP 5 in activity-based protein profiling to identify and annotate retaining arabinofuranosidases from secretomes derived from microorganisms grown on arabinofuranose-containing biopolymers.
Phylogenomic data generation and processing The data used in this study is an extension (436 additional samples) of a previously published exon-capture phylogenomic datamatrix of 1484 exons in 416 genes for 708 individual ophiuroid samples representative of the whole class Ophiuroidea  (O'Hara et al. 2019). The full dataset used here encompassed 1144 individual ophiuroid samples accounting for 826 species. Details on specimen collection, environmental parameters and list of species are available in Table  S1. The set of 416 single-copy genes were first determined in a phylogenomic analysis to resolve the phylogeny of Ophiuroidea  (O'Hara et al. 2014 ). These genes were rigorously screened to be one-to-one orthologs in 52 ophiuroid transcriptomes (representative of the entire class), in the sea urchin Stronlgylocentrotus purpuratus, the hemichordate Saccoglossus kowalevskii and the fish Danio rerio.
The subsequent exon-capture system laboratory, bioinformatic and phylogenetic procedures are described in previous works  (O'Hara et al. 2017; Hugall et al. 2016)  and summarized in dryad packages https://doi. org/10. 5061/dryad. db339/10 and https://datadryad. org/stash/dataset/doi:10. 5061/dryad. rb334. Briefly, base-calling used a minimum read coverage of five. Exon boundaries were initially based on the Strongylocentrotus purpuratus and Danio rerio genomes, and then revised using the exon-capture read mapping information. For all selection analyses, each codon immediately adjacent to exon boundaries was ignored. The primary data had IUPAC-coded heterozygous sites, which were then randomly resolved. However, these sites had little influence as both ambiguity-coded and randomly resolved datasets returned the same positive selection test results.
6°C to 28. 8°C), Ophiodermatidae (60 individuals from 38 species, depth range: -0. 5m to -1668m; temperature range: 2. 6°C to 28. 3°C), Ophiomyxidae (41 individuals from 29 species, depth range: -1. 5m to -792m; temperature range: 4. 6°C to 28. 7°C) and Ophiotrichidae (78 individuals from 62 species, depth range: -1m to -405m; temperature range: 10. 5°C to 29. 5°C) (Figure  1A ). Positive selection analyses were conducted separately per family. 1664 alignments were generated, representing each gene (416) in each family (4). In each alignment, a maximum of 30% missing data per sequence was allowed. Alignments that lacked deep species (>200m) after filtering were not used. As all these four families belong to the superorder Ophintegrida, sequences of Asteronyx loveni belonging to the sister superorder Euryophiurida (Asteronychidae) were used as outgroups.