dataesr/scientific-paragraphs-categorization huggingface dataset:
Genome properties Strain mt16 T 's genome (Accession number FAVH00000000) is 1,929,161 bp with a 29. 5% G þ C content (Table  3 , Fig. 6 ). At the final stage of assembly, it is composed of 3 scaffolds (composed of 5 contigs). There were 1864 predicted genes including 1808 proteincoding genes and 56 RNA genes. Among the RNA genes, there were 3 5S rRNA genes, 2 23S rRNA genes, a unique 16s rRNA gene and 50 tRNA genes. A putative function was assigned by COGS or NR blast to 1427 genes. 41 genes were identified as ORFans and the  remaining 274 genes were identified as encoding hypothetical proteins. The gene distribution into COG functional categories is reported in Table  4.
prevotii's gene contents (1,724, 1764 and 1806 respectively) but smaller than A. tetradius, A. hydrogenalis and  A. lactolyticus's gene contents (2,079, 2069 and 2253 respectively). The distribution of genes into COG categories was similar in all compared genomes except for the carbohydrates metabolism and transport for P. harei, for which this function is reduced (Fig. 7 ). Strain mt16 shared 1,003, 738, 1,061, 1,073, 1188 and 1091 orthologous genes with A. lactolyticus, P. harei, A. tetradius, A. hydrogenalis, A. vaginalis and A. prevotii respectively (Table  6 ). Among species with standing in nomenclature, AGIOS values ranged from 58 to Fig. 5. Phylogenetic tree highlighting the position of Anaerococcus rubiinfantis strain mt16 T relative to other close strains. The respective GenBank accession numbers for 16S rRNA genes are indicated in parenthesis.
Sequences were aligned using CLUSTALW, and phylogenetic inferences were obtained using the maximum-likelihood method within the MEGA6 software. Numbers at the nodes are percentages of bootstrap values obtained by repeating the analysis 1000 times to generate a majority consensus tree. Parvimonas micra strain 3119B was used as an outgroup. The scale bar represents a 1% nucleotide sequence divergence. 79% among compared species except strain mt16. When comparing strain mt16 to other species, the AGIOS value ranged from 65% with P. harei to 88% with A. vaginalis (Table  6 ). dDDH values estimated between strain mt16 T and the compared genomes ranged from 18. 8% to 34. 7%. Well under 70%, these values confirm strain mt16 T as a new species  [30].
Strain mt16 was susceptible to amoxicillin, amoxicillin/clavulanic acid, benzylpenicillin, clindamycin, imipenem, metronidazole and vancomycin. The genome is 1,929,161 bp long with a 29. 5% G þ C content and accessible under FAVH00000000 in the GenBank collection. The 16S rRNA sequence is also accessible in the GenBank collection under number LN881592. The type strain mt16 T (¼ CSUR P2032 ¼ DSM 101186) was isolated from the stool of a child living in Senegal and suffering from kwashiorkor. ± þ þ þ þ þ þ þ Mannitol þ e þ e e e e ± na Sucrose þ e e þ e þ e e na D-glucose þ þ þ þ ± ± þ þ þ D-fructose e na e þ w þ.
laevis. In addition, recent phylogeographic research supports the recognition of X. petersii, X. victorianus and X. poweri as separate species and therefore confines the native range of X. laevis sensu stricto to southern Africa (South Africa, Lesotho, Swaziland, Namibia, Botswana, Zimbabwe, Mozambique and Malawi)  [70]. The occurrence data set for southern Africa used in this study was adjusted accordingly. It is important to note that this is a novel interpretation of the taxonomy and native range of X. laevis in comparison to that used by Measey et al. [50]. To prevent over-fit and false inflation of model performance through spatially auto-correlated species records  [71] [72] [73] , the comprehensive dataset of 1382 records was filtered, and clustered locality records were reduced to a single point within a specified Euclidian distance in environmental space using the spatially rarefy occurrence data tool for the ArcGIS SDM toolbox  [74].
The final dataset used to build SDMs contained 826 records for South Africa, 37 for South America, 24 for North America, and 38 for Europe. SDMs built exclusively on occurrence data from the native range of invasive species tend to underestimate the potential invasive distribution, particularly when projecting onto climate change scenarios  [75]. Hence, native and invasive occurrence records were pooled  [50, 76]  for the computation of SDMs. In this way a maximum amount of information on the species' realized bioclimatic niche was integrated. As environmental predictors, 19 bioclimatic variables available through the WorldClimdatabase (  [77]  http://www. worldclim. org/bioclim) were used. They represent minima, maxima and average values of monthly, quarterly, and annual ambient temperature as well as precipitation recorded between 1950 and 2000.
The Maxent model was trained by randomly splitting the species records into 70% used for model training and 30% for model testing applying a bootstrap approach. The Area Under the receiver operating characteristic Curve (AUC)  [82]  was used to evaluate the discrimination ability of the resulting SDM. Averages across 100 replicates were used for further processing. As the selection of an appropriate background is known to affect model performance  [83] , a circular buffer of 250 km around each locality record was selected as training area following Measey et al. [50]. Ensemble SDMs were computed using the biomod2 package version 3. 2. 2 [84]  for Cran R  [85]  including the following eight algorithms: Generalized Linear Models (GLM), Generalized Boosting Models (GBM), Generalized Additive Models (GAM), Classification Tree Analysis (CTA), Artificial Neural Networks (ANN), Factorial Discriminant Analysis (FDA), Maxent, and Multivariate Adaptive Regression Splines (MARS) applying the same training background as for the Maxent analyses.
We applied a bootstrapping approach with 100 replicates randomly subdividing the locality dataset in 70% for model training, whereas the remaining 30% were used for model evaluation using the AUC  [82] , True Skill Statistic (TSS) and Cohen's Kappa  [86]. The average projection across all replicates was used for further processing. The "Minimum training presence" (mtp) referring to the lowest generated probability estimate of the training data  [87] , was applied as presence-absence threshold. While threshold selection is a potential source for biases, the mtp has been shown to represent a confident method performing well for presence only SDMs  [88]  particularly for modelling potential distributions of invasive species  [88, 89]. Areas requiring extrapolation beyond the training range of the variables were quantified using a multivariate environmental similarity surface (MESS) analysis  [90]  for Maxent and conceptually equivalent clamping masks for biomod2.
[70]  resulting in a larger coverage for the species native range. Thus, the environmental space covered by the occurrence dataset used was larger than in Measey et al. [50]. Under current climatic conditions, both the Maxent and the ensemble model identify regions with suitable climatic conditions favouring invasion in Portugal, France, Sicily, California, Chile, and Japan, where invasive populations already exist. Both SDMs predict only moderate probability for Great Britain, where populations from Wales and Lincolnshire have recently been extirpated  [53]. Furthermore, our results highlight areas in Spain (including the Balearic Islands), mainland Italy (including Sardinia), and southern France (including Corsica) to be highly vulnerable to potential invasions, as these regions exhibit suitable climatic conditions for X.
These results suggest that improved insulin signaling observed in KO MEFs comes from increased IR levels and Serum samples were collected from 6-month-old male mice fed ad libitum. Data are presented as means ± SEM of at least eight animals per condition. Significant differences from WT mice (*p < 0. 05; **p < 0. 01; ***p < 0. 001) and mice fed chow ( $ p < 0. 05; $$ p < 0. 01; $$$ p < 0. 001) are indicated. prolonged AKT phosphorylation that is due, at least in part, to a defect in proteasomal degradation of one or more proteins involved in insulin signaling. IRS1 is an important mediator of IR signaling and phosphorylation on serine, and threonine residues are required for its ubiquitination and degradation  (Xu et al. , 2012). We therefore investigated phosphorylation on Ser632 and Ser302 in the KO and WT MEFs.
Robust phosphorylation was observed 1 hr after insulin stimulation in WT MEFs, but not in KO MEFs (Figure  3C ). Surprisingly, we found similar activation of the mTOR/S6K pathway as evaluated by S6 (Figure  3C ), S6K, and 4EBP1 phosphorylation (data not shown). IRS1 Ser1097 phosphorylation, which is dependent on both S6K and PKCq  (Li et al. , 2004b) , was also impaired in KO MEFs. From these results, we concluded that the impairment of IRS1 degradation in KO MEFs was likely the consequence of low levels of phosphoryla- tion on serine and threonine. We next evaluated IRS1 ubiquitination. A strong polyubiquitination of IRS1 was observed in WT MEFs 3 and 6 hr after insulin stimulation, whereas ubiquitination was significantly lower in KO MEFs (Figure  3D ). These results suggest that the defect in IRS1 phosphorylation and ubiquitination in MEFs that lack PiT1 stabilizes IRS1 and delays the insulin negative feedback loop.
Because low-grade inflammation of the liver is observed in response to HFD, we analyzed expression of inflammation markers. Levels of Mcp1 and F4/80 were higher in livers of WT mice fed HFD compared to chow diet, as expected (Figure  S12 ). Tnfa expression also increased in response to HFD, although the increase was not statistically significant (Figure  S12A ). Il6 expression was unchanged (data not shown). Levels of none of these markers were significantly increased when TG mice were fed HFD. Similarly, levels of Pai1, a marker of fibrosis, increased in WT mice on the HFD, but not in TG mice (Figure  S12A ). Transaminases and alkaline phosphatase, markers of liver injury, were detected at lower concentrations in serum of TG mice than WT mice (Figure  S12B ). These data are consistent with low levels of triglyceride accumulation and little inflammation in livers of TG mice fed a HFD.
Glycemia was normalized to basal glucose levels. (H) HFD-fed mice were fasted overnight and then injected intraperitoneally with 2 g/kg body weight pyruvate. Blood glucose was measured in WT (solid line) and TG (dotted line) mice. For (F)-(H), data are means ± SEM of at least ten animals per condition. Significant differences from WT mice are indicated (*p < 0. 05; **p < 0. 01; ***p < 0. 001). (I) Representative western blot of liver proteins from HFD-fed mice after 5-min pulse of insulin. (J) Representative liver sections stained with PAS from WT (left) and TG (right) HFD-fed mice. The scale bar represents 1 mm. 2000;  Foretz et al. , 1999) , but TG mice, although more sensitive to insulin than WT, have lower SREBP1c hepatic levels than WT mice. Low levels of SREBP1 could explain the weak induction of Pparg in HFD-fed TG mice.
ACKNOWLEDGMENTS We thank  Nicolas Sorhaindo  at the  Claude Bernard Institute for Biochemistry and Nadege Brunel at the Saint-Antoine Hospital  ( UMRS 938 ) for multiplex assay. We are grateful to  David Bergerat  for genotyping of mice and  Nicolas Cagnard  for microarray analysis. We acknowledge the  Necker Institute Animal Facility and thank Hybrigenics and Christine Sala€ un  for the double hybrid screen. The work was supported by  INSERM ,  CNRS, University Paris-Descartes , and  Naturalia et Biologia  (to Y. S. ).
ACKNOWLEDGMENTS We thank  Nicolas Sorhaindo  at the  Claude Bernard Institute for Biochemistry and Nadege Brunel at Saint-Antoine Hospital  ( UMRS 938 ) for multiplex assay. We are grateful to  David Bergerat  for genotyping of mice and  Nicolas Cagnard  for microarray analysis. We acknowledge the  Necker Institute Animal Facility and thank Hybrigenics and Christine Sala€ un  for the double hybrid screen. The work was supported by  INSERM ,  CNRS, University Paris-Descartes ,  Agence Nationale de la Recherche  ( ANR  Grant  10-BLAN-1110-01 ) and  Naturalia et Biologia  (to Y. S. ).
State parameters and meteorological measurements State parameters and meteorological measurements were performed by an AIMMS-20 instrument, manufactured by Aventech Research Inc. , Barrie, Ontario, Canada. The instrument consisted of three modules. The Air Data Probe (ADP) measured the three-dimensional, aircraft-relative flow vector (true air speed, angle-of-attack, and sideslip). The temperature and relative humidity sensors were located in the aft section of the probe for protection. A three-axis accelerometer pack facilitated direct turbulence measurement. The inertial measurement unit (IMU) consisted of three gyros and three accelerometers providing the aircraft angular rate and acceleration. A GPS module provided the aircraft 3-D position and inertial velocity. Horizontal and vertical wind speeds were measured with accuracies of 0.
50 and 0. 75 m s -1 , respectively. The accuracy and resolution for temperature measurement were 0. 30 and 0. 01 During the ship emission measurements, the CO 2 (CO) data achieved a precision (1σ , 1 Hz) of 0. 02 ppmv (2. 3 ppbv). The stability of the instrument was calculated to 0. 62 ppmv (4. 7 ppbv), before applying the post flight data correction. Note that stability was based on the mean drift between two subsequent calibrations, which were performed during the flights. The stability was mainly affected by temperature variations. These instrumental drifts were corrected after the flights assuming a linear drift. Hence, the total uncertainty relative to the working standard of 0. 62 ppmv (5. 23 ppbv) could be regarded as an upper limit. As can be seen in Fig. 8 , the instrument precision of the CO 2 and CO measurements allowed for identifying the ship emission plumes and further calculation of emission factors.
Meteorological context Plume intercepts in the 3 consecutive days are referred to as plume 1 (19 July 2014), plume 2 (20 July 2014) and plume 3 (21 July 2014). The flights were planned in advance using WRF and FLEXPART-WRF forecasts (not shown) so that the aircraft could efficiently sample ship emissions downwind of the stack. Following the campaign, WRF was run using ECMWF (European Center for Medium-Range Weather Forecasts) analysis as initial and boundary conditions, (see Table  2  of  Wentworth et al. , 2016) , in order to refine forecast meteorology and to interpret campaign data. The quality of the meteorological data predicted by WRF has been evaluated using measurements made on-board of both the research aircraft and ship, indicating that the forecast predicted the meteorological situation during plume sampling reasonably well (wind speed and direction time series along flight tracks are shown in Fig.
Data availability Experimental data: NETCARE, which organized the aircraft flight campaign described in this paper, is moving towards a publicly available, online data archive. In the meantime, the data can be accessed by emailing the principal investigator of the network: Jon Abbatt at the University of Toronto (jabbatt@chem. utoronto. ca) Numerical data: The WRF modeling system can be downloaded from NCAR (http://www2. mmm. ucar. edu/wrf/users/download/get_source. html; WRF, 2016). The FLEXPART-WRF model is also freely available for use from FLEXPART. eu (https://www. flexpart. eu/wiki/ FpLimitedareaWrf; FLEXPART, 2016).
Acknowledgements. The authors acknowledge a large number of people for their contributions to this work. We appreciate expert internal review of the manuscript by  Pacal Bellavance  and  Paul Izdebski  ( Environment and Climate Change Canada -ECCC ). We thank  Tim Papakyriakou  and  Greg Wentworth  for providing ship track and speed information. We thank  Kenn Borek Air , in particular  Kevin Elkes  and  John Bayes  for their skillful piloting that facilitated these plume observations. We are grateful to  John Ford  and the  University of Toronto (UofT)  machine shop,  Jim Hodgson  and Lake Central Air Services in Muskoka,  Jim Watson  ( Scale Modelbuilders, Inc. ),  Julia Binder  and  Martin Gehrman  ( Alfred Wegener Institute -AWI -Helmholtz Center for Polar and Marine Research ),  Mike Harwood  and  Andrew Elford  (ECCC), for their support of the integration of the instrumentation and aircraft,  Yuan You  (ECCC), for helping with data analysis, and  Jeremy Wentzell  (ECCC), for sharing an SO 2 mixing ratio dataset that was collected on-board of the Amundsen.
We thank  Mohammed Wasey  for his expert support of the instrumentation during the integration and in the field. French authors acknowledge funding support from the  European Union   ACCESS (Arctic Climate Change, Economy and Society ) project under Grant Agreement No. 5265863  and TOTAL SA via an  ANRT  Cifre PhD grant. Computer simulations were performed on the IPSL mesoscale computer center (Mésocentre IPSL), which includes support for calculations and data storage facilities. The authors want to thank the ship commander,  Alain Lacerte , and the crew of the Amundsen for their support. We are grateful to  Stewart Cober  and  Carrie Taylor (ECCC) ,  Bob Christensen  (UofT),  Kevin Riehl  ( Kenn Borek Air ),  Lukas Kandora  and  Jens Herrmann  (AWI),  Desiree Toom ,  Sangeeta Sharma ,  Dan Veber ,  Andrew Platt ,  Anne Marie Macdonald , and  Maurice Watt  ( ECCC ) for their support of the study.
In the present study, we reproduced a murine model which displayed PCOS features leading to two phenotypes: with both hyperandrogenism and ovarian alterations, such as an increase in the number of preantral follicles and the formation of cysts. Nearly 50% of the cases of the PH group presented irregular ovulatory estrous cycles, whereas around 40% of the cases in PH group presented the anovulatory phenotype. Ovaries from PCOS women are known to contain an increased number of small follicles that have an excessive early growth but with follicular arrest, thus preventing the selection and further maturation of a dominant follicle. These data are in accordance with our results showing that androgens play a role in follicle recruitment  (Jonard & Dewailly 2004). We found that PH rats showed incipient liver damage.
These data are in agreement with previous reports that suggest that the intrauterine environment plays an important role in the development of both NAFLD and PCOS during postnatal life  (Baranova et al. 2013 , Brumbaugh & Friedman 2014). Contrary to other reports  (Demissie et al. 2008 , Hogg et al. 2011) , we found no lipid accumulation in the liver. This difference could be due to the higher doses of androgens used in those reports and is in agreement with our previous findings where higher doses of androgens induced a more severe PCOS-like phenotype as well as worse endocrine and metabolic disturbances  (Amalfi et al. 2012). To deepen the study of lipid metabolism in the liver, we evaluated the status of the de novo lipogenesis pathway and β-fatty acid oxidation mediators (Fig. 7 ). To assess the lipogenic pathway, we evaluated the role of three transcription factors involved in the regulation of de novo lipogenesis.
In summary, our data show for the first time that both ovulatory and anovulatory phenotypes that mimics PCOS features present at pubertal age, signs of incipient liver injury and an imbalance of the fatty acid metabolism mediated by the PPAR system, SREBP and CHREBP, as well as an imbalance of the lipogenic enzymes but without development of NAFLD. These derangements are related to systemic effects, dyslipidemia and decreased glucose tolerance.
c, Observed and reconstructed incubation bout and period length visualised (by colour) on the phylogenetic tree 29 using species' medians (based on population medians) and one of 100 sampled trees (see Methods). The grey circles represent phylogenetically independent contrasts  30  and hence emphasize the differences at each tree node. Despite substantial within-species variation, we found a strong evolutionary signal for both bout and period length with a coefficient of phylogenetic signal λ close to 1 (Extended Data Table  2 ). This is consistent with the notion that biological rhythms are largely genetically determined and conserved among related species  [8] [9] [10]. However, the phylogenetic effect seems unevenly distributed over taxonomic level. Suborder explained 33% of the phenotypic variance in both bout and period length, with the Scolopaci having longer incubation bouts and periods than the Charadrii (Extended Data Table  3 ; Fig.
METHODS Recording incubation. Incubation data were obtained between 1994 and 2015, for as many shorebird species (N = 32) and populations (N = 91) as possible, using six methods (for specifications of the equipment see Extended Data Table  1 ). (1) In 261 nests, a radio frequency identification reader ('RFID') registered presence of tagged parents at the nest. The passive-integrated tag was either embedded in a plastic flag  31, 32  , with which the parents were banded, or glued to the tail feathers  33. In 200 nests the RFID was combined with a temperature probe placed between the eggs. The temperature recordings allowed us to identify whether a bird was incubating even in the absence of RFID readings; an abrupt change in temperature demarcated the start or end of incubation  31. (2) For 396 nests, light loggers were mounted to the plastic flag or a band that was attached to the bird's leg  34, 35.
The logger recorded maximum light intensity (absolute or relative) for a fixed sampling interval (2-10 min). An abrupt change in light intensity (as opposed to a gradual change caused, e. g. by civil twilight) followed by a period of low or high light intensity demarcated the start or end of the incubation period (Extended Data Fig. 2 ). (  3 ) For nine nests a GPS tag, mounted on the back of the bird, recorded the position of the bird  36. The precision of the position depends on cloud cover and sampling interval  36. Hence, to account for the imprecision in GPS positions, we assumed incubation whenever the bird was within 25 m of the nest (Extended Data Fig. 2b ). (  4 ) At three nests automated receivers recorded signal strength of a radio-tag attached to the rump of a bird; whenever a bird incubated, the strength of the signal remained constant  24  (Supplementary Actograms p.
257-9  16  ). (  5 ) At 53 nests video cameras and (6) for 8 nests continuous observations were used to identify the incubating parents; parent identification was based on plumage, colour rings or radio-tag. In one of the populations, three different methods were used, in seven populations representing seven species two methods. In one nest, two methods were used simultaneously (Extended Data Fig. 2b ). Extraction of incubation bouts. An incubation bout was defined as the total time allocated to a single parent (i. e. the time between the arrival of a parent at and its departure from the nest followed by incubation of its partner). Bout lengths were only extracted if at least 24h of continuous recording was available for a nest; in such cases, all bout lengths were extracted. For each nest, we transformed the incubation records to local time as (UTC time + nest ′ s longitude 15 ).
Excluding the uncertain bouts did not change our estimates of median bout length (Pearson's correlation coefficient for median bout length based on all bouts and without uncertain bouts: r = 0. 96, N = 335 nests with both certain and uncertain bouts). Hence, in further analyses all bouts were used to estimate median bout length. Note that in some species sexes consistently differ in bout length (Figure  1b , e. g. Northern lapwing). As these differences are small compared to the between-species differences and because in 27 nests (of 8 species) the sex of the parents was unknown, we here use median bout length independent of sex. Extraction of period length. The method used for extracting the period length of incubation rhythm for each nest is described in the Extended Data Fig. 1. Extraction of entrainable periods.