dataesr/scientific-paragraphs-categorization huggingface dataset:
Study Population We analyzed raw data from two different experiments with participation of healthy European male volunteers: 21-day -6 • HDBR (n = 11) and 3-day DI (n = 12). A comparison of these experiments was not part of the original study design. However, since the experimental protocols were identical, conducted and processed by the same team of authors, and both exposures are the model of microgravity physiological effects, we considered that comparing these data is reasonable. Both studies were performed at the MEDES Space Clinic (Toulouse, France) and conformed to the standards set by the Declaration of Helsinki. All subjects were informed about the experimental procedures and gave their written consent. The first experiment analyzed in our paper was taken from the Medium duration Nutrition and vibration eXercise (MNX) Bed-Rest Study conducted from November 6, 2012 to December 20, 2013.
This study was organized as three 21-day HDBR sessions ("Pure exposure, " "Exercise & vibration, " or "Exercise & vibration plus nutrition, " in a random order) separated by a 3month washout. Moreover, all three sessions before HDBR did not significantly differ by hemodynamic parameters during the 80 • tilt test from each other. The same volunteers participated in all three sessions. In this paper, we present data only from the "Pure exposure" session, referred to in the text as "HDBR exposure. " Twelve volunteers were recruited, but one dropped out of the experiment; thus, the data were obtained from 11 subjects. The MNX Bed-Rest Study was approved by the local Ethics Committee (CPP Sud-Ouest Outre-Mer I) and the French Health Authorities (no. ID RCB: 2012-A00337-36). The second experiment was a 3-day head-out DI study conducted from January 13, 2015 to February 19, 2015.
This study was organized as a single session. Twelve volunteers were recruited; all of them completed the session. The study was approved by the local Ethics Committee (CPP Sud-Ouest Outre-Mer I, France) and the French Health Authorities (no. ID RCB: 2014-A 00904-43). Different volunteers participated in the 21-day -6 • HDBR or the 3-day DI. Anthropometric data for the two groups were not significantly different (unpaired t-test with Welch's correction, p < 0. 05; Table  1 ).
Statistical Analysis The results are presented as the mean ± SEM. All statistical analyses were performed using the GraphPad Prism program (8. 3. 0). Firstly, we assessed the normal distribution of anthropometric parameters by the Anderson-Darling test in groups (p > 0. 05; the distribution is normal). We compared them using the unpaired t-test with Welch's correction to ensure the groups were not significantly different (Table  1 ). Then, since the groups appeared comparable by anthropometric parameters, we compared the groups by other parameters. Three factors were used in the study: time (before and after exposure), models (HDBR and DI), and tilt (supine and 80 • tilt). For comparison of the partial water balance in -6 • HDBR and DI, we used two-way repeated measures ANOVA (time × models).
Central Hemodynamic Parameters Despite the similarity of the cardiovascular changes, we observed a number of differences in the effects of the two models. After the completion of the 21-day HDBR and 3-day DI, resting tachycardia and orthostatic intolerance were detected, accompanied by a relative decrease in upright SBP, DBP, SV, and TPR and an increase in HR in response to tilt. Our results are in agreement with literature data  (Buckey et al. , 1996; Knight et al. , 2009a)  and indicate a significant cardiovascular deconditioning after both simulations. Interestingly, supine TPR and DBP had a tendency to increase following DI, but not HDBR. These parameters may suggest an increase in initial vascular tone, the important role of which was indicated by several authors  (Convertino et al. , 1999; Vinogradova et al.
Analysis of conservation of gene neighbourhoods over different evolutionary levels is important for understanding operon and gene cluster evolution, and predicting functional associations. Our tool FlaGs (standing for Flanking Genes) takes a list of NCBI protein accessions as input, clusters neighbourhood-encoded proteins into homologous groups using sensitive sequence searching, and outputs a graphical visualization of the gene neighbourhood and its conservation, along with a phylogenetic tree annotated with flanking gene conservation. FlaGs has demonstrated utility for molecular evolutionary analysis, having uncovered a new toxin-antitoxin system in prokaryotes and bacteriophages. The web tool version of FlaGs (webFlaGs) can optionally include a BLASTP search against a reduced RefSeq database to generate an input accession list and analyse neighbourhood conservation within the same run.
, 2003). While there are a range of tools that analyse gene neighbourhood conservation or integrate this data along with other metrics for functional association prediction, these tend to be either restrictive in the genomes that can be considered (e. g. only complete genomes or those of model organisms) or require the creation of local genome databases  (Garcia et al. , 2019; Lemoine et al. , 2008; Martinez-Guerrero et al. , 2008; Overmars et al. , 2013; Szklarczyk et al. , 2015). Other tools that connect to the National Center for Biotechnology Information (NCBI; https://www. ncbi. nlm. nih. gov/) to detect operons may lack sensitive sequence searching for homology assignments of neighbourhood genes  (Gumerov and Zhulin, 2020). We felt there was a need for a tool that allows the use of the huge quantity of publicly accessible data in the NCBI RefSeq database  (O'Leary et al.
, 2016)  and is sensitive enough to answer questions about homologous proteins over any evolutionary distance, from the strain or isolate level, to inter-kingdom or even interdomain comparisons. We set out to build a Python tool that fulfils our list of essential criteria: 1. allows the user to have complete control over the input genomes being analysed; 2. has a simple input format that does not require coding, downloading of genomes or formatting of databases; 3. nevertheless, also has the option of running using locally stored genomes for offline analyses or analysing genomes that are not public; 4. can be run via a server with results emailed to the user; 5. can detect remote homology, suitable for analysing the most distant relationships among proteins and taxa as well as closer comparative analyses; 6.
The FlaGs workflow Our resulting tool that fulfils the above requirements is called FlaGs (standing for Flanking Genes) (Fig. 1A ). FlaGs takes in userdetermined NCBI accessions that link to the RefSeq database (around 170 million proteins from almost 100 000 organisms as of March 2020). Input files can be easily and quickly prepared from selected sequences in the output of an NCBI BLASTP search against the RefSeq database without any scripting (see the manual; Supplementary Materials File S1). An optional addition to the input file is the NCBI genome assembly identifier to target a particular genome. FlaGs clusters flanking gene-encoded proteins using the sensitive Hidden Markov Model-based method Jackhmmer, part of the HMMER distribution  (Eddy, 2011). There are three ways to run FlaGs: 1.
through the web server at www. webflags. se. This method can optionally include a BLASTP search against microbial RefSeq genomes or a representative genome database to identify homologues which with to run FlaGs  (Camacho et al. , 2009) ; 2. locally, with FlaGs querying NCBI as it runs, and not requiring locally stored genomes; 3. locally, using locally stored genomes in RefSeq GFF and protein FASTA format. Empty genes with grey borders are not conserved in the dataset, and grey genes with blue borders are pseudogenes. In this example, FlaGs reveals four different homologous groups of antitoxins as flanking genes, two of which (green and yellow) are antitoxins for the same cognate toxin. Group number 5 is an integrase. As FlaGs does not require complete genomes, regions can lack flanking genes on one side if the query gene is close to the end of a contig, as is the case with Arthrobacter castelli in this example FlaGs outputs information on the conservation of flanking geneencoded proteins, and their identity, in graphical and text formats (Fig.
, 2020). The web server STRING is one of the most widely used tools to study the gene neighbourhood conservation of a gene of interest  (Szklarczyk et al. , 2015). STRING's great strength is that it brings together pre-computed association data from a number of different sources to predict functional associations. It is an excellent first port of call for predicting the function of conserved genes. STRING, however, uses a limited set of around 5000 input organisms, and does not include bacteriophages. Therefore, it is somewhat limited when addressing neighbourhood conservation of genes with extremely patchy distributions as is often the case with genes belonging to the accessory component of pangenomes. The discovery of toxSASs was only possible through the access of FlaGs to the extensive cellular and viral genome resources in the RefSeq database.
We expect that FlaGs will continue to be successful in the prediction and evolutionary analysis of genomic loci with various functions, not just toxin-antitoxins, but for example, secretion systems (where it has already been used in the description of a novel system  (Palmer et al. , 2020) ), antibiotic biogenesis clusters, viral defence mechanisms, gene transfer agents, pathogenicity islands and transposons. A future direction of FlaGs is to go beyond RefSeq, taking advantage of all the genomic data stored in Genbank, which will further increase the genomes accessible to neighbourhood analysis by FlaGs by hundreds of thousands.
A. Deterministic The sequential quadratic programming (SQP) method from MATLAB Optimization Toolbox is used. All options are set to the default values. As the gradient is required, it is computed by using a forward finite-difference approximation, resulting in additional model evaluations. This method starts from an initial solution given by the user. If no good starting point is known, it may be drawn with the random uniform law. Thus, the optimization process becomes stochastic and statistics for the objective values are given in Tables I and II with the mean computing time and the convergence rate (conv). This last is defined as the number of solutions with optimal objective value below the lowest one plus 2e-5 divided by the number of trials. We use SQP with two variations (SQP and an improved SQP imp ).
, 2009). As a control, we confirmed that deleting either helix had no effect on targeting TANGO1 to accumulations of procollagen VII in the ER (Figure  3 -figure supplement 1). Both TANGO1 and TAN-GO1DIM were recruited as small, discrete puncta, which were apposed to collagen accumulations in the ER. The TANGO1DTM entirely overlapped with procollagen VII accumulations. Together, these data reveal that membrane curvature/shape sensitivity in TANGO1 membrane helices is not the dominant ERES-targeting device, instead the PRD is more important to target TANGO1 to its cellular location at an ERES. If the membrane shape sensor is not to target TANGO1 to ERES or collagen, what role does it play? TANGO1 membrane helices confer direction to TANGO1 molecules in a ring TANGO1 and procollagen VII were co-expressed in HeLa cells from which TANGO1 has been knocked out using the CRISPR/Cas9 system (2H5 cells).
Then, fluorescent lipids in the tube alone (not the GUV) were almost completely bleached by illumination with a 647 He-Ne laser at maximum power for a few seconds. A second picture was immediately acquired and the tube fluorescence intensity after bleaching was measured (i b ). Finally, a third picture was taken after 6 min, which provided the final tube fluorescence intensity (i 6 ). The recovery was established as (i 6 -i b )/(i 0 -i b ). We chose not to continuously monitor the fluorescence to avoid bleaching the fluorescent lipids as they migrate from the GUV to the tube. We used 6 min because we wanted to be close to the fluorescence recovery plateau. We also wished to avoid spontaneous bleaching to have as accurate a measurement as possible and therefore acquired only one data point.
The EGFP signal from the GUV is dim, attesting the low protein concentration. This forced us to use high laser power, 20% of the maximum value and maximum gain. One image with 100% power was sufficient to completely bleach the fluorescence from the ROI. Then, recovery was recorded, typically at two images per second, at 20% of the laser power. The recovery phase was monitored over ~15-20 s. The raw data are very noisy (blue data points in Figure  2 -figure supplement 1) but still display significant intrinsic bleaching, showing the difficulty to adjust the laser power and obtain quantifiable measurements. To resolve this issue, we performed the rolling average over three successive images (orange data points in Figure  2 -figure supplement 1). The data were normalized to one before bleach and 0 right after bleach.
Acknowledgements We thank  Ivan Lopez Montero  for discussion,  Jose Wojnacki  for discussions and assistance with data visualisation,  Isabelle Motta  for acquisition of the Atto 488/Alexa 488 calibration curves,  Hong Zheng  for assistance with protein purification. IR thanks  Robert Ernst  for discussions. We thank the  Advanced Light Microscopy Unit at the CRG. V Malhotra  is an  InstitucioCatalana de Recerca i Estudis Avancats professor at the Centre for Genomic Regulation. FC acknowledges financial support from the  Spanish Government ('Severo Ochoa' program for Centres of Excellence  in R and D,  SEV-2015-0522 , and  RYC-2017-22227 ),  FundacioPrivada Cellex, FundacioPrivada Mir-Puig , and from the  Generalitat de Catalunya  through the  CERCA program. The GUV experiments were performed at  Yale  with the support of  NIH   R35 GM118084  (JER).
| Occurrence data We extracted occurrence data of insular birds and mammals from polygon ranges of the IUCN Red List (IUCN, 2018) and the BirdLife International (Handbook of the Birds of the  World & BirdLife International, 2016). Among all species occurring on oceanic and continental islands, only insular endemic species were considered because of their key conservation interest  (Fattorini, 2017). Overall, we considered a total of 1,788 bird and 968 mammal species endemic to one or several islands (n = 4,348).
| Functional description of species To measure functional diversity, we selected five traits describing main diet, foraging niche, foraging period, habitat niche breadth and body mass that are available for both birds and mammals from the EltonTraits database  (Wilman et al. , 2014)  and IUCN (2018) (Table  S2 ). Combinations of traits govern species interactions through competition for habitat and food, for example, or species contribution to ecosystem functioning through nutrient cycling, seed dispersal and trophic control  (Hevia et al. , 2017; S ̜ ekercioğlu, 2010). We transformed all trait values into a categorical nature (nominal or ordinal) for two reasons. First, this transformation allowed us to compare traits between taxonomic groups, as some variables were qualitative for one taxon, but quantitative for another (i.
All analyses were performed with r software (version 3. 3. 1, R Development Core Team, 2008) using ggplot2  (Wickham, 2009) , gridExtra  (Auguie, 2017)  and matrixStatS  (Bengtsson, 2017)  packages. Functional space analyses were performed using the r packages apE  (Paradis, Claude, & Strimmer, 2004) , cluE  (Hornik, 2005).
High Low dplyr  (Wickham, Francois, Henry, & Müller, 2017) , gEomEtry  (Habel, Grasman, Gramacy, Stahel, & Sterratt, 2015) , gtoolS  (Warnes, Bolker, & Lumley, 2015)  and Stringr  (Wickham, 2017). We also used scripts developed to compute the functional diversity indices  (Villéger, 2017).
Description of the Seine basin Situated in northwestern France, 46 • 57 -50 • 55 N and 0 • 7 1 -4 • E, the Seine basin (∼ 76 285 km 2 ) has a temperate climate and a pluvio-oceanic hydrologic regime (Fig. 1 ). The mean altitude of the basin is 150 m a. s. l. (above sea level) with 1 % of the basin reaching more than 550 m a. s. l. in the Morvan  (Guerrini et al. , 1998). The water flow at Poses (stream order 7, basin area 64 867 km 2 ), the most downstream monitoring station free from tidal influence, averaged 490 m 3 s -1 during the 2010-2013 period (the HY-DRO database, http://www. hydro. eaufrance. fr, last access: 11 February 2020). The major tributaries include the Marne and upper Seine rivers upstream from Paris, and the Oise River downstream from Paris (Fig. 1a ). Three main reservoirs, storing water during winter and sustaining low flow during summer, are located upstream on the Marne River and the upstream Seine and its Aube tributary (Fig.
Hydro-morphological characteristics of the Seine drainage network, a averaged by Strahler order (SO) and b over the time period 2010-2013. Hydrographic network provided by the Agence de l'Eau Seine Normandie and water discharges by the national Banque Hydro database. Depth and flow velocity calculated according to  Billen et al. (1994) ; width calculated according to  Thieu et al. (2009)  Riverstrahler allows for the calculation of water quality variables at any point in the aquatic continuum based on a number of constraints characterizing the watershed, namely, the geomorphology and hydrology of the river system and the point and diffuse sources of nutrients.
Hydrology Runoffs were calculated over the whole Seine basin using water discharge measurements at 48 gauged stations (source: Banque Hydro database, http://www. hydro. eaufrance. fr/, last access: 11 February 2020). Surface and base flow contributions were estimated applying the BFLOW automatic hydrograph separation method  (Arnold and Allen, 1999)  over the recent time series of water discharges  (2010) (2011) (2012) (2013) (2014) (2015) (2016) (2017). For the study period  (2010) (2011) (2012) (2013) , the mean base flow index (BFI = 0. 71) of the Seine basin indicates the extent of the groundwater contribution to river discharge, with spatial heterogeneity following the main lithological structures (Fig. 1b ), but when summarizing the BFI criteria by Strahler order, significant differences did not appear (not shown).
Input constraints of the pyNuts-Riverstrahler model Diffuse sources from soil and groundwater Diffuse sources are calculated at the scale of each modeling unit, based on several spatially explicit databases describing natural and anthropogenic constraints on the Seine River basin. Diffuse sources are taken into account by assigning a yearly mean concentration of carbon and nutrients to subsurface and groundwater flow components, respectively. These concentrations are then combined with a 10 d time step description of surface and base flows to simulate the seasonal contribution of diffuse emissions to the river system. For nutrients, several applications of the Riverstrahler on the Seine River basin refined the quantification of diffuse sources: e. g. ,  Billen and Garnier (1999)   3. Dissolved organic carbon (DOC) input concentrations were extracted from the AESN database (http://www.
eau-seine-normandie. fr/, last access: 11 February 2020) and averaged by land use for subsurface sources (mean: 3. 13 mg C L -1 ; standard deviation (SD): 4. 56 mg C L -1 ; 3225 data for 2010-2013). For groundwater sources, concentrations were extracted from the ADES database (https: //ades. eaufrance. fr/, last access: 11 February 2020) and www. hydrol-earth-syst-sci. net/24/2379/2020/ Hydrol. Earth Syst. Sci. , 24, 2379-2398, 2020   Billen et al. , 1994, and Garnier and Billen, 1994) , with gray lines indicating the main processes simulated in the water column and at the interface with sediment (oxygen not shown), and implementation of the new inorganic module, based on total alkalinity (TA, maroon) and dissolved inorganic carbon (DIC, blue). Table  2. Stoichiometry of the biogeochemical processes, influencing dissolved inorganic carbon (DIC) and total alkalinity (TA) in freshwater, as taken into account in the new inorganic carbon module.
eaufrance. fr/, last access: 11 February 2020), CO 2 concentrations were not measured in their survey. TA values are also provided in the ADES database. To calculate DIC concentrations in groundwater, we therefore used our own CO 2 measurements, equaling on average 15. 92 mg C L -1 , with an SD of 7. 12 mg C L -1 (55 measurements in six piezometers in the Brie aquifer during 2016-2017) (see methodology in  Marescaux et al. , 2018a). DIC and TA were averaged for the 48 unconfined hydrogeological MESO units of the basin (see concentrations in Sect. S4) during the recent period  (2010) (2011) (2012) (2013) (2014) (2015) , including the simulation period. In Fig. 3 , a summary of TA and DIC inputs by MESO units is shown by grouping MESO units according to lithology and geological ages. Documenting TA and DIC diffuse sources based on MESO units ensures a representation of their spatial heterogeneity in the Seine River basin.
Observational data We selected the 2010-2013 timeframe for setting up and validating the new inorganic module. This period includes the year 2011, which was particularly dry in summer (mean annual water discharge at Poses, 366 m 3 s -1 ) and 2013, which was wet (mean annual average water discharge at Poses, 717 m 3 s -1 ) while 2010 and 2012 showed intermediate hydrological conditions (mean annual average water discharges at Poses, 418 and 458 m 3 s -1 , respectively) (data source: Banque Hydro). The pCO 2 values (ppmv) were calculated using CO2SYS software algorithms (version 25b06;  Pierrot et al. , 2006)  based on existing data collected by the AESN. TA, pH and water temperature datasets were used for the 2010-2013 selected period (8693 records for these three variables, i. e. , around 1209 stations distributed throughout the Seine basin, measurements that were taken at a fixed time, 09:00-15:00 UTC, and could not represent diurnal fluctuations).
Acknowledgements. The project leading to this paper received funding from the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Sklodowska-Curie  grant agreement no. 643052. A PhD grant was attributed to  Audrey Marescaux. Many thanks are due to  Sébastien Bosc ,  Anunciacion Martinez Serrano  and  Benjamin Mercier  for their kind participation in the fieldwork and for their assistance with chemical analyses in the lab. We thank  Emmanuel Soyeux  ( Veolia Water , France),  Muriel Chagniot  ( Veolia Water , France), and the operators of the  Veolia WWTPs  for their precious help in organizing the field campaigns. The  SIAAP (Vincent Rocher)  is also sincerely acknowledged for their contribution to sampling the largest WWTP of the Paris conurbation and the long-term view on treatments in the SIAAP WWTPs provided by their recent book (Rocher and Azimi, 2017).
Vincent Thieu (assistant professor at  Sorbonne University, Paris ) and  Josette Garnier  (Research Director at the  Centre National de la Recherche Scientifique, France ) are co-supervisors of the PhD. Nathalie Gypens is Professor at the  Université Libre de Bruxelles (Belgium). Marie Silvestre  is GIS Engineer at the  Centre National de la Recherche Scientifique (France). Financial support. This research has been supported by the  Marie Sklodowska-Curie  grant (grant no. 643052 ). Review statement. This paper was edited by  Anas Ghadouani  and reviewed by three anonymous referees.