dataesr/scientific-paragraphs-categorization huggingface dataset:
These descriptors were gathered from databases designed to describe the reproductive strategy of parasitoids  (Traynor & Mayhew 2005a; Traynor & Mayhew 2005b , Jervis & Ferns 2011; Mayhew 2016)  and are summarized in table  S1. We conducted the analysis at the genus level using genus-averaged dN/dS ratios and descriptors. This was necessary because the species-level concordance between databases was too low (only 6 species in common between the genomic database and the parasitoid life-history database). We used Pearson's linear correlation coefficient for continuous descriptors and Kruskal-Wallis tests for discrete descriptors. Finally, we tested the correlation of dN/dS ratios with four proxies of species range. For each species (and for all known synonyms) in the sample, we queried all available occurrence points from the GBIF database, using the R package rgbif.
Occurrence data was then used to calculate for classical proxies of species range. The mean latitude was calculated as a simple unweighted mean between occurrences. The maximum distance between two occurrences was calculated taking all occurrences into account, even when the species occurred on more than one continent. The circular area around occurrence was calculated by casting 100kmradius circles around each occurrence, and estimating the total land surface contained in at least one circle. The convex hull area around occurrence was calculated by estimating the total land surface contained in the smallest convex hull containing all occurrences. When a species occurred on more than one continent, a separate convex hull was used per continent.
RELAX analyses We used the RELAX procedure  (Wertheim et al. 2014 ) from the HyPhy program suite  (Pond, Frost, and Muse 2005)  to test for the presence of a systematic relaxation of selection on branches belonging to eusocial groups (thereafter called "eusocial branches"), that is all branches descending from the ancestral node of one of the eusocial clade present in the dataset. Hyphy allows, for a specific sequence alignment, to model the distribution of dN/dS ratios along the branches of a tree. The RELAX procedure consists first in defining focal and background branches, associated with one focal and one background distribution of dN/dS ratios. It then consists in comparing a model where the two mentioned distribution are identical (null model, no differences between branch sets) to a model where the focal distribution is a power transform of the background distribution (ωf=ωbk).
Data accessibility The original dataset of  Peters et al. (2017) , with alignments and trees, is provided by the its authors at http://dx. doi. org/10. 17632/trbj94zm2n. 2. Detailed tables containing data used for this paper as well as obtained results are available at Zenodo. org : https://zenodo. org/record/3999857#. X0UsBBk6-it.
Phe508del (legacy name F508del)  [4]. In this case, p. Phe508del functional restoration can be enhanced by combining Lumacaftor and Ivacaftor (Orkambi)  [5]. However, Orkambi was found to be suboptimal in clinical trials; new correctors, alone or in combination, expected to enhance treatment efficacy, are under evaluation  [6]. These variant based therapies led to the re-classification of CFTR pathogenic variants depending on their sensitivity to treatments, referred to as theratyping  [7]. These informations are implemented in the CFTR2 database (cftr2. org) which enables to define the appropriate treatment for the considered variant  [8]. While being continuously implemented, CFTR2 contains information for only a subset of variants. In fact, many of the 2000 pathogenic and non-pathogenic variants referenced in the CF Mutation Database (http://www.
g. p. [Ile148Thr;Ile1023_Val1024del] (legacy name [I148T;3195del6]) where p. Ile148Thr was shown to be neutral and p. [Ile1023_Val1024del] is the CF-causing variant  [16]. Indeed, variants referenced in the CF Mutation Database as CFcausing because they were observed in CF patients could in fact have residual or normal function and be in cis of a deleterious pathogenic variant that still needs to be identified. Such pathogenic variants could be located in another exon or in distant regulatory elements. As many of these variants have been identified in discrete cases, it would be challenging to perform retrospective genetic analysis of the complete locus to identify these cis variants. Finally, c. -234T>A (legacy name -102T>A) was shown to enhance CFTR promoter activity which reduced the severity of the phenotype associated to the p.
This last hypothesis was recently illustrated for complex alleles involving p. Phe508del, which have been described in the literature and in CF mutation databases (CFTR1, CFTR2 and CFTR-France; summarized in Table  1 ). As soon as p. Phe508del has been identified in a diagnostic setting after a first screening of the frequent CF-causing variant, other possible cis variants are not routinely searched and their frequency is probably underestimated. Nonetheless, their frequency turned out to be low  [10] , but higher in regional cohorts, e. g. the p. [Ala238Val;Phe508del] in the South of Italy  [23]  or p. [Phe508del;Ile1027Thr] in Brittany, France  [24]. Cis pathogenic variants can be associated to multiple defects, as illustrated in Figure  1. While variants altering the transcriptional activity of CFTR or the stability of the transcript have not been described yet, enhanced exon skipping was observed with c.
Study design and participants All subjects enrolled in this ancillary [ 18 F]FDG PET study were participants from the MAPT study. The MAPT protocol is registered on a public-access clinical trial database (www. clinicaltrials. gov, no. NCT01513252). MAPT-NI study protocol was approved by the French Ethics Committee in Toulouse and AFSSAPS (national agency for the safety of drugs and health products) in February 2009. One hundred seventy-eight subjects were asked consecutively to participate to MAPT-NI at their inclusion visit in MAPT and 68 subjects have been included. Written informed consent was given by all participants. Included subjects were 70 years-old and over and ful lled at least one of the following three clinical criteria: spontaneous memory complaint, limitation in one instrumental activity of daily living, or slow gait.
[ 18 F]FDG PET and MRI exams [ 18 F]FDG PET scans were acquired at the Toulouse Hospital PET centre, on a Biograph™ 6 TruePoint™ (Siemens Medical Solutions, Munich, Germany) high-resolution PET/CT scanner (3D detection mode, producing images with 1 × 1 × 1. 5-mm voxels and a spatial resolution of 5 mm full width at half maximum at the eld of view center), with a 10 min static run, 20 min after injection of 1. 85 MBq/kg weight of [ 18 F]FDG on average. For each patient, [ 18 F]FDG PET scans from all time points were rst realigned onto their mean image, and normalized using the same transformation matrix in the MNI space using a PET template in Statistical Parametric Mapping 12 (SPM12) software running on Matlab. Cortical Standardized Uptake Value ratio (SUVr) images were obtained using the grey matter of the cerebellum as reference region.
Regions Of Interest (ROIs) involved early in AD were selected based on the MetaROI approach described by Landau et al  (21). [ 18 F]FDG mean SUVr uptake was quanti ed in 6 prede ned cortical ROIs, extracted from a cortical atlas derived from the Harvard-Oxford atlas (FSL software, The University of Oxford): right and left posterior cingulate, angular gyrus, and middle/inferior temporal areas. The MRI scans were performed at baseline visit using a standardized protocol including these sequences: 3D T1weighted, T2 FLAIR, T2 TSE and T2 GRE. A local independent radiologist assessed MRI scans to detect signi cant exclusion abnormalities.
ROI-based approach Analysis was conducted in the intention-to-treat (ITT, n = 67, primary analysis) population and a sub-sample with adherence (exploratory analysis). ITT population included all randomly assigned participants who completed at least one [ 18 F]FDG PET scan at baseline, 6 months or 12 months. In the sub-sample with adherence analysis, participants were deemed adherent if they attended at least 75% of MI group sessions (n = 20) and took at least 75% of the prescribed capsules (n = 30). Linear mixed-model repeated-measures analyses were applied to baseline, 6-month, and 12-month data to assess between-group differences in the change on AD-based SUVr along time. Time was used as a continuous variable. All the models were completed with and without adjustments for gender, age, level of education, global CDR score, APOE-4 genotype and group intervention.
For each linear mixed model, we included subject-speci c random effects to take into account the intra-subject correlation: a random intercept to take into account the heterogeneity of the ADbased SUVr at baseline and a random slope to take into account the heterogeneity of the slopes between subjects if this parameter was signi cant. In the unadjusted linear mixed models, we included the following xed effects: intervention group, time, and interaction between group and time. All con dence intervals (CIs) were two-sided with a 95% con dence level, the statistical signi cance was set at a p value <. 05. All statistical analyses were performed using SAS software version 9. 4 (SAS Institute Inc, Cary, NC).
Conclusion MI had no signi cant effect on FDG-based AD biomarker. However, voxel-wise analysis showed an impact on limbic lobe at 6 months suggesting delay effect on AD progression. The lack of MI effect showed at 12 months suggests the necessity to maintain high frequency of MI sessions all along interventional program. These elements will need to be investigated further in non-demented subjects with positive amyloid status. The ongoing MIND-AD study (NCT03249688) which assess the effect of a MI and medical food on cognition in prodromal AD could provide an answer such as the Dutch study NL-ENIGMA (Effect of a speci c Nutritional Intervention on cerebral Glucose Metabolism in early Alzheimer's disease) which explore effect of multinutrient combination in early AD on a FDGbased AD biomarker  (33).
Acknowledgments We are indebted to the investigators from the  University Hospital of Toulouse, Hôpital de Tarbes, Hôpital de Foix, Hôpital de Castres , the  University Hospital of Limoges , the  University Hospital of Bordeaux, Hôpital de Lavaur , the  University Hospital of Montpellier, Hôpital de Montauban , and the  University Hospital of Nice  for their participation in this study.
Measuring bee life-history traits Of the 2100 and 1686 individually marked bees in sites A and B, respectively, life-history data were successfully recorded for 1867 (89%) and 1507 (89%) bees, i. e. with at least one exit and entrance sequence. This 11% loss (identical in the two sites) is attributable to the following: loss of barcode/ RFID tag prior to leaving the hive, rejection from the host colony by nest-mates or death during the bee's first flight. Bees without at least one exit and entrance sequence were excluded from analyses, thus 3374 bees were available for the analyses. For the analysis of flight activity, exits with durations shorter than 2 s or longer than 180 min were excluded (not considered as flights). We then calculated the age of first exit and entrance sequence (AFE, in days), the lifespan (i.
e. the age at last exit) and AOF for each bee using the aof function developed in the aof R-package  [47, 54]. The aof function is a simple statistical procedure, derived from the behavioural change point analysis approach  [55] , a wellappreciated technique of likelihood comparisons to statistically determine behavioural changes. It aims to detect a single behavioural change in univariate time series. The aof function works at the individual level thus accounting for inter-individual variation to detect, assess and quantify shifts in the temporal pattern of time-activity budgets recorded by individual lifelong monitoring. Indeed, a clear breakpoint in duration, frequency and time of occurrence in the day was found between learning and foraging flights of honeybees, so-called AOF  [47]. Thus, AOF is estimated from three daily activities, (i) the number of exit and entrance sequences per day, (ii) the duration of these sequences, and (iii) the hour of the day that they were performed.
Data analysis 2. 4. 1. Pre-foraging flight parameter effects on lifespan To assess which factors were associated with lifespan we used Cox proportional-hazard models for datasets of site A and site B separately (given the differences in study sites and monitoring devices). First, we tested for autocorrelation of the residuals using a Durbin-Watson test on a linear regression of lifespan as a function of AFE, AOF and cohort. Once independence of the residuals had been established, we used the Cox model to investigate the association between bee survival and the predictors. Both models included AFE and AOF as predictors. The site A model included the landscape as a predictor and the replicate (monthly cohort) as a grouping cluster. The site B model included bee weight as a predictor and the replicate as a grouping cluster.
We further analysed whether pre-foraging traits influence the foraging stage. We used a generalized linear mixed-effect model (GLMM) on the square root of the foraging stage for site A and site B separately (see above). The GLMMs had a Gaussian distribution of errors that considered AFE, AOF, the foraging intensity (mean min spent per day foraging), source colony (site B dataset), weight at emergence (site B dataset) and landscape (site A dataset) as the fixed effects. The cohort was considered as a random effect ða i Þ and the variance was allowed to differ among the six different cohorts ðe j Þ. ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi Foraging stage p ¼ / þ b 1 (AFE) þ b 2 (AOF) þ b 3 (intensity) þ b 4 (source colony) þ b 5 (weight) þ a i þ e j : 3.
Discussion By analysing two large datasets of honeybee flight activity, obtained in two different geographical areas and years, we found a consistent influence of the pre-foraging stage (as measured by the time between  the age at the first exit and the age at the onset of foraging) on bee lifespan and foraging performance. Specifically, the pre-foraging stage is associated with high mortality risks but contributes to shaping the individual's foraging performance and duration. During this pre-foraging stage, we observed a large portion of the worker bees dying; 40% of bees died before the age of 15 days and 50% of bees never reached the foraging stage. In fact, the distribution of bee lifespan was mostly bimodal, with a short-lived population of worker bees that never became foragers, and a second population of bees living longer and becoming foragers.
Conclusion In conclusion, the pre-foraging stage is highly critical to honeybee survival. During this transition, bees learn their environment (orientation flights) and develop a physiology adapted to flight, but also royalsocietypublishing. org/journal/rsos R. Soc. Open Sci. 7: 200998 get exposed to extrinsic mortality risks ( predation and weather conditions). This leads to a cost for the colony with a sharp reduction in the forager population. However, this learning stage is essential to accumulate flight experience and improve foraging performance and duration. Another aspect of our study is the remarkable consistency of results between the two datasets obtained in different environmental conditions and years, and using two different automatic monitoring systems, highlighting the robustness of the pre-foraging influence on bee lifespan.
Rivers that cease to flow are globally prevalent. Although many epithets have been used for these rivers, a consensus on terminology has not yet been reached. Doing so would facilitate a marked increase in interdisciplinary interest as well as critical need for clear regulations. Here we reviewed literature from Web of Science database searches of 12 epithets to learn (Objective 1-O1) if epithet topics are consistent across Web of Science categories using latent Dirichlet allocation topic Water 2020, 12, 1980;.
Data Sources We performed a comprehensive literature review using the search engine in Clarivate Web of Science (WoS; Core Collection) for papers published between 1 January, 1900 and 30 May, 2019. Our searches were not limited to the English language, though all analyses assumed that text was written in English. WoS has indexed scholarly books, peer-reviewed journals, reviews, editorials, chronologies, and reports within multiple research fields since 1900. We created two-word search phrases that included (1) an epithet (a common descriptor used to describe a non-perennial system) and (2) a waterbody term, a noun for a flowing (i. e. , lotic) freshwater system. We first generated a list of epithets of non-flowing conditions by selecting terms found in previous reviews of non-perennial literature  [1] [2] [3] 24]  and those resulting from discussions among authors for a total of 12 epithets (Table  1 ).
O1: Topical Differences among Epithets We compared the proportion of search results across categories as defined by WoS to quantify epithet use across fields (Figure  1 ). We then used a Fisher's exact test to determine if there were any significant differences between WoS categories and the number of papers per epithet. Due to the size of the matrix, we had to use a simulated p value based on over a million replicates. We then deployed topic modeling with Bayesian latent Dirichlet allocation (LDA) models in the R package "textmineR"  [28]. This model assumes that each document in a corpus is made up of multiple topics, and each topic is made up of multiple words. The model finds common clusters of words within documents, grouping them together and forming topics. For example, the topic "agriculture" is created due to the common occurrence of the following words found together throughout documents: soil *, water *, irrig *, yield *, us *, season *, crop *, and so on (* indicates the result of stemming).
We calculated the probability of finding a topic in each document in the corpus with the theta matrix using the package "textmineR"  [28]. After designating the six topics, we extracted the top 20 words used to define each topic for comparison across epithets, and named each topic based on the 20 words associated with it. We averaged the theta matrix for each epithet and performed an unconstrained ordination using non-metric multidimensional scaling (nMDS; using the R package "vegan"  [22] ) to assess similarities in topical overlap among papers categorized according to the epithets (Figure  2 ).
In this way, we assessed the degree of overlap among themes to see which epithets had the most similar definitions. We visualized epithet similarities using nMDS ordination with Euclidean distance. We used the R package 'vegan'  [30]  with the proportions of each theme in epithet definitions for the nMDS. After running the initial nMDS, we excluded two themes that lacked a large proportion of any definitions (water scarcity and phases of drying: no subsurface flow) and "irregular," which only had one definition. We then compared how various research fields use the same epithet. Using our results from definition mining, we selected five epithets whose definition analysis papers were over 80% covering non-perennial river systems (non-perennial, 100%; ephemeral, 98%; temporary, 89%; intermittent, 88%; arid, 83%; Table  2 ).
Acknowledgments: We gratefully acknowledge the help of the  University of Oklahoma  DAVIS librarians, in particular  Sarah Pugachev , for their assistance in coding. We also acknowledge those who helped with the formation of this project:  Albert Ruhi  and  Sarah Godsey. We would like to acknowledge  Caryn Vaughn ,  Tom Neeson , and  Katharine Marske  for their helpful comments and edits. We also acknowledge the assistance received from undergraduate researchers in the lab of K. H. C. :  Gianna N. St. Julien  and  Alexandra Trahan. Although  this work was reviewed by the  USEPA , and approved for publication, it does not necessarily reflect official USEPA policy. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the  U. S. Government.
The original structure of CMIP reflected these two motivations. In fact, CMIP was initially divided into two complementary phases (G. A. Meehl, Boer, Covey, Latif, & Stouffer, 1997 ): 1. CMIP1, which started in 1996, and transposed the main objectives of AMIP -measuring and understanding the ability of atmospheric GCMs to simulate current climateto coupled models. 2. CMIP2, which compared climate changes simulated by coupled models under a 1% per year CO 2 increase. Starting in 1997, this second phase was thus directly in line with the comparison performed in the Supplementary Report to the IPCC Scientific Assessment (J. Houghton et al. , 1992). Due to limitations in data processing and archiving capabilities at that time, CMIP1 and CMIP2 included only a few output fields, and at a coarse temporal resolution: for example, surface temperature, precipitation, and sea level pressure were averaged over one month.
This rough sample was a strong limitation for analyses based on experiments. Consequently, the Working Group on Coupled Modelling (WGCM) -a subgroup of the World Climate Research Programme (WCRP) playing a leading role in CMIP -launched a new phase, CMIP2+, in 1999 to include many more model fields, and daily data if possible. However, this new phase represented significant additional work for the modeling groups, and in the end CMIP2+ was able to collect only 12 complete sets of outputs (G. Meehl, Covey, McAvaney, Latif, & Ronald, 2005). CMIP1 and 2 -but not CMIP2+, which was not completed in time -played a substantial role in the Third Assessment Report (TAR) of the IPCC. In particular, detailed analyses of CMIP models were presented in the chapters "Model Evaluation"  (McAvaney & et al.
Providing assessments for the IPCC reports was thus a motivation at the core of CMIP3, whereas it was a side objective for previous phases of CMIP. In CMIP3, the role of CMIP to organize and coordinate climate research -while present in the first phases of CMIP as well -also acquired a new dimension. Indeed, CMIP3 was the first phase of CMIP to give open access to all the data from its experiments. While in CMIP1, 2, and 2+, only a few modeling groups around the world have had access to the data and analyzed them, the data of CMIP3 were made accessible to any student or researcher around the world. It represented more than 30 terabytes of data. This new openness "brought global coupled climate model intercomparison and analysis to an internationally coordinated level never before achieved in the field of climate science"  (WGCM, 2006).
39 ± 0. 79 mm/day for RCP2. 6 and 0. 95 ± 1. 13 mm/day for RCP8. 5  (Jayasankar et al. , 2015). Seemingly, this range provides a measure of the uncertainty about future climate change -in this example, it seems that there is much more uncertainty about the impacts of climate change on precipitation than on temperature. Nevertheless, such a statement raises epistemological issues. First, since the participation in CMIP is made on a voluntary basis, the set of models participating in CMIP has not been designed to span the uncertainty about future climate change. The distribution of CMIP multi-model ensembles is in a large part arbitrary. It led  Tebaldi and Knutti (2007)  to describe multi-model datasets as "ensembles of opportunity". Since these ensembles of opportunity do not systematically explore the uncertainty about future climate change, some scientists considers them only as a lower bound of this uncertainty (e.