dataesr/scientific-paragraphs-categorization huggingface dataset:
Is not able to metabolize N-acethylneuraminic acid, adonitol, D-lactic acid methyl ester, L-alaninamide and 3-O-bgalactopyranosyl-D-arabinose and D-arabitol. So far, K. africanensis was recovered from asymptomatic human fecal carriage in Senegal (this study) and from human clinical samples in Kenya  [13]. The type strain is strain 200023 T (= SB5857 T , {CIP and DSMZ numbers: in process}), isolated in 2016 from a fecal sample of a human asymptomatic carrier in Sokone, Senegal. The EMBL (GenBank/DDBJ) accession numbers of the rrs (coding for 16S rRNA) gene is MK040622. The genome sequence accession number is ERS2787533.
3. 4. Nucleotide sequence accession numbers. The genomic sequences of strains 1266 T and 200023 T were submitted to the European Nucleotide Archive under accession numbers ERS2787528 and ERS2787533, respectively. 16S rRNA sequences were also submitted individually under the accession numbers MK040621 and MK040622, respectively. The genome sequence data of strains 814, 885, 1283, 1375, 01-467-2ECBU and 01-310MBV were submitted to the European Nucleotide Archive under accession numbers ERS2787526, ERS2787527 and ERS2787529 to ERS2787532, respectively (under the BioProject number PRJEB29143). The tree was rooted using K. oxytoca ATCC 13182 T sequence. Bootstrap proportions obtained after 1000 replicates are indicated at the nodes. Branch lengths represent the number of nucleotide substitutions per site (scale, 0.
Acknowledgements We thank the ' P2M-Plateforme de Microbiologie Mutualisée' (PIBnet)  from  Institut Pasteur  for genomic sequencing. We acknowledge  Jean-Marc Collard  ( Institut Pasteur of Madagascar), Bich-Tram Huynh (Institut Pasteur, France ) and  Raymond Bercion  ( Institut Pasteur of Dakar ) for support in K. pneumoniae carriage studies. We also thank to  S. Wesley Long  ( Houston Methodist Hospital ) and  David A. Rasko  and  J. Kristie Johnson  ( University of Maryland School of Medicine, Baltimore ) for providing strains analyzed in this study.
Funding information This work received financial support from  Institut Pasteur  and from the  French Government 's  Investissement d'Avenir program Laboratoire d'Excellence Integrative Biology of Emerging Infectious Diseases  (grant number  ANR-10-LABX-62-IBEID ). CR was supported financially by the  MedVetKlebs  project, a component of European Joint  Programme One Health EJP , which has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under Grant Agreement No  773830.
K. variicola subsp. tropicalensisstrainsare able to metabolize tricarballylic acid, L-galactonic acid-γ-lactone, L-sorbose, 5keto-D-gluconic acid, D-psicose, 4-hydroxyl-L-proline and D-arabitol. They are not able to metabolize N-acetyl-neuraminic acid, adonitol, mono-methyl succinate, D-lactic acid methyl ester, L-alaninamide and 3-O-b-galactopyranosyl-D-arabinose. K. variicola subsp. tropicalensisisolateswere recovered from asymptomatic fecal carriage and from the environment. The type strain is strain 1266 T (= SB5531 T , {CIP and DSMZ numbers: in process}), isolated in 2016 from a fecal sample of a human asymptomatic carrier in Antananarivo, Madagascar. The EMBL (GenBank/DDBJ) accession numbers of the rrs (coding for 16S rRNA) gene is MK040621. The genome sequence accession number is ERS2787528.
Z-stacks images were taken using a super-resolution STED microscope (Abberior Instruments GmbH) and analyzed using ImageJ software. Identification and localization of punctae was done when the brightest punctae staining in the z-axis was within the dendritic spine to limit the analysis of punctae located below or above the spine. Super-resolution STED imaging in brain slices. STED imaging was performed using a custom upright STED microscope (Abberior Instruments). The microscope is based upon a Scientifica microscope body (Slice Scope, Scientifica) with an Olympus 100x/1. 4NA ULSAPO objective lens. It comprises a scanner design featuring four mirrors (Quad Scanner, Abberior Instruments). 488 nm, 561 nm, and 640 nm excitation lasers are available (Abberior Instruments, pulsed at 40/80 Mhz).
In all simulations, we kept the number of pumps in the head fixed at the value 50, as calibrated from a pure diffusion model (see S1 Fig). The number of trials for each simulation is shown in S2 Table. Our code is written in Python 2. 7 and is available at http://bionewmetrics. org/simulations-of-spine-calcium-transientswith-er/. RyRs. RyRs are activated upon the arrival of the first two ions to a small absorbing disk of size a RyR = 10 nm (see S2 Fig). We positioned n R = 36 receptors for the simulations organized in four rings in the SA neck, each containing six receptors. The other 12 are located on the SA component parallel to the dendritic shaft. After a receptor opens, it releases instantaneously a fixed number of calcium ions n Ca , which are positioned at the center of the receptors.
PaRid is essential to ensure proper sexual development The P. anserina genome contains a single gene, namely PaRid (Pa_1_19440)  [36] , which encodes a 752 amino acid protein closely related to fungal-specific DMT-like proteins (Fig  1A ). In addition of conserved DMT domain motifs (I to X) and expected Target Recognition Domain variable region (TRD), PaRid shows typical signatures of cytosine methyltransferases (InterPro domains PF00145, PS51679, PR00105) (Fig  1B ). This protein belongs to the same phylogenetic group (DMT-like fungal-specific family) as the previously described N. crassa RID protein (41% identity)  [19] , A. immersus Masc1 protein (33% identity)  [12]  and A. nidulans DmtA (31% identity) (Fig  2 ). As already pointed out for Masc1/Rid DMT-like, these proteins, including PaRid, have peculiar features  [33].
They show a non-canonical EQT (glutamate-glutamine-threonine) triad in motif VI instead of the ENV (glutamate-asparaginevaline) triad shared by all other eukaryotic C5-cytosine methyltransferases (Fig  3 ) as well as a remarkably short TRD domain (80 amino acids). An EST database generated from vegetatively grown P. anserina showed no expression of PaRid  [36]. However, the PaRid expression profile extracted from a microarray transcriptional time-course analysis performed during P. anserina's sexual development (Bidard and Berteaux-Lecellier, GEO accession no. GSE104632) showed a peak at T12 (12 hours post fertilization) followed by a decrease at T30 (30 hours post fertilization) (S1A Fig). Besides RT-PCR experiments (S1B Fig)  indicated  that PaRid transcripts could be detected up to T96 (96 hours post fertilization).
GSE104632), hypothesizing that these two time points likely correspond to the window of time that immediately precedes and/or spans the ΔPaRid developmental arrest. Doing so, we identified 451 CDS which expression was either down-regulated (217 CDS) or up-regulated (234 CDS) by a fold change (FC) � 2 (S5 This might indicate that the up-regulated set appears more divergent and species specific than the down-regulated set which belongs preferentially to the conserved fungal genome core. We then performed a FunCat analysis  [45]  to further characterize the function of the DE CDS (Fig 7A, Table  2 ). Approximately two-third of them belonged to the "Unclassified" category (category number 99) either in the up-or down-regulated sets. Among the 72 classified CDS of the down-regulated set, no FunCat categories were enriched (See Table  2 ).
Identification and deletion of the P. anserina PaRid gene The PaRid gene was identified by searching the complete genome of P. anserina with tblastn  [106] , using RID (NCU02034. 7) [13]  as query. One CDS Pa_1_19440 (accession number CDP24336) resembling this query with significant score was retrieved. To confirm gene annotation, PaRid transcripts were amplified by RT-PCR experiments performed on total RNA extracted from developing perithecia (female partner mat+/male partner mat-) at either 2 days (T48) or 4 days (T96) post-fertilization using primers 5s-PaRid/3s-PaRid (See S1 Table ). Sequencing of the PCR products did not identify any intron in the PaRid ORF, thus confirming the annotation (see S1B Fig). Deletion was performed on a Δmus51::su8-1 strain as described in  [74]  and verified by Southern blot as described in  [107].
Cytology & microscopy analysis Perithecia were harvested from 12 to 96 h after fertilization. Tissue fixation was performed as in  [110]. Pictures were taken with a Leica DMIRE 2 microscope coupled with a 10-MHz Cool SNAP HQ charge-coupled device camera (Roper Instruments), with a z-optical spacing of 0. 5 mm. The GFP filter was the GFP-3035B from Semrock (Ex: 472nm/30, dichroïc: 495nm, Em: 520nm/35). The Metamorph software (Universal Imaging Corp. ) was used to acquire z-series. Images were processed using the ImageJ software (NIH, Bethesda).
Phylogenetic analysis Orthologous genes were identified using FUNGIpath  [111]  and MycoCosm portal  [112]  and manually verified by reciprocal Best Hits Blast analysis. Sequences were aligned using MUS-CLE (http://www. ebi. ac. uk/Tools/msa/muscle/) and trimmed using Jalview to remove noninformative regions (i. e. poorly aligned regions and/or gaps containing regions). Trees were constructed with PhyML 3. 0 software with default parameters and 100 bootstrapped data set  [113]. The tree was visualized with the iTOL version 4. 3 (http://itol. embl. de/). Functional annotation was performed using InterPro 71. 0 (http://www. ebi. ac. uk/interpro/search/ sequence-search), Panther Classification System version 14. 0 (http://www. pantherdb. org/ panther/), PFAM 32. 0 (http://pfam. xfam. org/), Prosite (http://prosite.
Microarray data acquisition Microarrays were scanned using the Agilent Array Scanner at 5μm/pixel resolution with the eXtended Dynamic Range (XDR) function. Array quality and flagging were performed as described  [114]. Raw data pre-processing was performed with Feature Extraction (v9. 5. 3) software (Agilent technologies), with the GE2-v4_95_Feb07 default protocol. Microarray data reported in this paper have been deposited in the Gene Expression Omnibus database under the accession no. GSE104632.
Statistical analyses: Microarray data normalization, differential analysis and functional enrichment Raw data normalization and differential analysis were done using the MAnGO software  [115] , and a moderate t-test with adjustment of p-values  [116]  was computed to measure the significance with each expression difference. Differential analyses have been performed between the wild-type cross at 24 (T24) and 30 (T30) hours after fertilization and the mutant cross. Fold change values (FC) were calculated from the ratio between normalized intensities at each time of the time course. Differentially expressed (DE) coding sequences (CDS) in the ΔPaRid cross were defined as those whose adjusted P-values are inferior to 0,001 and the absolute value of FC higher than 2. The resulting list was called DE CDS specific of ΔPaRid cross.
1; TrireRid: AEM66210. 1. Cytosine-specific DNA methyltransferase domains: PF00145, PR00105, PS51679, PS00095, PS00094; protein-DNA interaction domains: bromo-associated homology (BAH) domain PS51038, Replication foci targeting sequence (RFTS) PF12047, Zinc finger motif (CXXC) PS51058, PWWP domain (PF00855). Because its sequence is too divergent, PaDnmt5 (Pa_4_2960) was not included. https://doi. org/10. 1371/journal. pgen. 1008086. g001.
Acknowledgments We acknowledge the technical assistance of  Sylvie Franc  ¸ois. We are thankful to  Philippe Silar  for providing resources to start the project as well as for fruitful scientific discussion and to  Ting-Fang Wang  for sharing unpublished data and stimulating scientific discussion. We thank  Nadia Ponts  for performing the DNA methylation assays,  Gae ¨lle Lelandais  for performing.
Context. The analysis of the full-sky Planck polarization data at 850 µm revealed unexpected properties of the E and B modes power spectra of dust emission in the interstellar medium (ISM). The positive cross-correlations over a wide range of angular scales between the total dust intensity, T , with E modes and, most of all, with B modes has raised new questions about the physical mechanisms that affect dust polarization, such as the Galactic magnetic field structure. This is key both to better understanding ISM dynamics and to accurately describing Galactic foregrounds to the polarization of the Cosmic Microwave Background (CMB). In particular, in the quest of primordial B modes of the CMB, the observed positive cross-correlation between T and B for interstellar dust requires further investigation towards parity-violating processes in the ISM.
In the case of 2D maps there is no loss of generality, although some concerns about the boundary conditions are required. Moreover the multipole l is replaced by the wavenumber k, and the coefficients of the spherical harmonics are replaced with the Fourier transform coefficients. In order to perform the powerspectra analysis with spherical harmonics on the sphere we make use of the healpy. sphtfunc. anafast. py routine of the HEALPix 3  healpy package. All the codes written in Python used for this analysis can be accessed and downloaded from the HBEB GitHub page: http://github. com/abracco/ cosmicodes/tree/master/HBEB.
Acknowledgements. First and foremost we would like to thank the anonymous referee for improving the clarity of the paper. We are grateful to  Francois Boulanger  for the encouraging conversations and his useful comments on the early versions of this work. We also thank  Nicolas Ponthieu  for teaching us how to play POKER. Andrea Bracco ,  Simon Candelaresi , and  Fabio Del Sordo  acknowledge  NORDITA for hospitality during the scientific program  " Phase transition in Astrophysics ", when the idea of this work was first conceived. Fabio Del Sordo acknowledges  Artemis Del Sordo  for useful discussions, and the  Astrophysics group at University of Crete  for warm hospitality. The work of  Axel Brandenburg  was supported by the  National Science Foundation  through the  Astrophysics and Astronomy Grant  Program (grant  1615100 ) and the  University of Colorado  through the  George Ellery Hale visiting faculty appointment.
Since the characteristics of sampling bias are specific to each dataset and their influence is unknown most of the time, it must be approximated in the statistical analysis. Consequently, the effect of sampling bias on prediction may often be underestimated, as many published results are not corrected for sampling bias before analysis  (Yackulic et al. , 2013). Multiple methods have already been developed to correct sampling bias and improve models and predictions. Phillips et al. (2009)  proposed the targeted background method which distributes background points in space with the same bias as the observer process. It has been used in numerous studies  (Kramer-Schadt et al. , 2013) , and it was shown to give better results than randomly distributed background points  (Warton, Renner, & Ramp, 2013).
However, when the targeted background points are generated over a too restricted area, it can also reduce model accuracy and results in lower prediction performances  (Fourcade, Engler, Rödder, & Secondi, 2014; Thuiller, Brotons, Araújo, & Lavorel, 2004; Warton et al. , 2013). As an alternative, Cardador,  Diaz-luque, Hiraldo, Gilardy, and Tella (2017)  recently proposed to include the potential causes of bias as predictor variables in the models, in combination with a random background sampling. Models created from spatially unbiased datasets and models successfully corrected for spatial sampling bias are thought to represent the species distribution over the study area, with the underlying hypothesis that all individuals are sampled with the same probability, thus covering the panel of different habitat needs within the population.
We hypothesized that using spatially biased sampling leads to biased estimates of habitat association (H1) and to a biased sex ratio within the virtual population (H2), but that these biases can be corrected by the two methods introduced above (H3) and that sex-specific models will give more accurate predictions than standard, generic models (H4). In addition, from our knowledge of previous studies on Capercaillie habitat use, we hypothesized that in the absence of a systematic survey protocol the datasets will be biased toward males (H5), but that sampling bias correction methods can correct the sex ratio estimation (H6). We also hypothesized that sex-specific models will give more accurate predictions than generic models which will be biased toward males (H7). The general hypotheses H1-H4 were assessed using a virtual species approach, whereas Capercaillie-specific hypotheses H5-H7 were tested with empirical data.
| Environmental variables The environmental variables used in the study for both the virtual species and the Capercaillie models were derived from LiDAR datasets stemming from two acquisition campaigns over distinct departments (Figure  1 ). The first LiDAR campaign was conducted in fall 2014 and covered 626 km 2 of the Ain department (France). F I G U R E The second LiDAR campaign was conducted in summer 2016 and covered a surface area of 431 km 2 in two other French departments, Jura and Doubs. LiDAR point-cloud metrics were calculated with R package lidaRtRee (https ://gitlab. irstea. fr/jean-matth ieu. monne t/lidaR tRee). First, LiDAR raw point clouds (classified in ground and vegetation) were normalized with the Delaunay interpolation method from the lidR package  (Roussel & Auty, 2016).
| Species distribution intensity Virtual species presence-only datasets were created following an inhomogeneous Poisson point process model using the three environmental variables for each sex separately. All variables were normalized by subtracting mean pixel value and dividing by the standard deviation before modeling. The intensity λ(s) represents the expected number of individuals per spatial unit (pixel) at position s, and it is computed as: where x i (s) represents the environmental covariate i at location s, and the β i are the corresponding model coefficients for the effects of the three environmental variables (slope, canopy density, and Simpson index, i. e. , p = 3), and β 0 denotes the intercept term  (Renner et al. , 2015). The coefficients β 0 and β i were chosen in order to obtain intensity values per pixel between 0 and 2 (Table  1 ).
| Virtual sampling In order to obtain different virtual datasets, three basic sampling designs (random, systematic, and subjective) were applied for each sex (Figure  2 ). The random sampling was done using homogenous sampling of 2,000 random points within the entire area. The systematic sampling was created using parallel transects separated by 400 m generated with the R package DSsim for a total length of 32 km. For the subjective sampling design, trajectories of real Capercaillie surveys over the selected area were used (total length 31. 2 km). The comparison of the density curves among the sampling designs showed a spatial sampling bias in the subjective design, where some environmental values were over-or under-represented relative to their availability in the study area  (Figure A1. 1).
In a first step, the number of observed occurrences with the different sampling schemes was calculated. For all cases with known trajectories (i. e. , the two nonrandom designs), the observation dataset was created from the realized presences (or signs) distribution by adding a distance-dependent detection probability. The detection probability was maximal on the tracks and diminished with increasing distance from the tracks. The detection probability followed a logspline distribution fitted to empirical distances of sign locations to the observer trajectory. In a second step, for all cases, a random binomial draw was applied, with the virtual species count taken as the number of trials per pixel and distance-dependent detection probability as probability of success. The resulting count data were converted into presence-only records by removing the locations with no observations.
| Modeling Maxent is arguably the most frequently used SDM method designed to handle presence-only data  (Elith et al. , 2006 (Elith et al. , , 2011;; Phillips, Anderson, & Schapire, 2006). Here, we use a recent reimplementation of the algorithm building on the equivalence of Maxent with an infinitely weighted logistic regression (Maxnet R package;  Phillips, 2017). All analyses were conducted using R 3. 4 (R Core Team, 2016). For both the virtual species and the Capercaillie dataset, models were created using female and male observations separately to create sexspecific models. In addition, generic models combining both female and male observations were created for each case. The performance of each model was assessed by the area under curve (AUC) using the presence data combined with a unique targeted background dataset, different from the one used in the models with targeted background sampling, but following the same distance distribution.
| D ISCUSS I ON The aim of this study was to evaluate the effect of two different spatial bias correction methods on species sex ratio estimations (and more generally spatially segregated subgroups), SDM parameter estimation and prediction accuracy. The correction methods were tested on two sampling designs: a transect and a subjective sampling with two datasets: a virtual species and a sexually dimorphic species (Capercaillie). The systematic sampling design allowed a better estimation of the parameters with or without corrections for potential spatial bias, as it covers a larger part of the environmental variation in the landscape and forces the observer to also visit areas with expected lower habitat quality, thus reducing possible spatial bias  (Cardador et al. , 2017). The use of real observer trajectories to sample a virtual species confirmed that a non-negligible sampling bias can occur in the absence of a predefined sampling design, in coherence with our hypothesis H1.
This may be due to a better ability to represent the spatial observation bias. The distance to trajectories method yielded fewer extreme predictions of spatial observer bias as it more closely followed the simulated decay of detectability with distance from sampling locations (Figures A5. 1 and A5. 2). The effect of sampling bias on model accuracy was also confirmed by the finding that generic models (combining female and male observations) generated distributions highly correlated with the virtual distribution of females but not males, thus yielding biased predictions (H4). The effect of the corrections was also observed on the prediction accuracy as it was higher when corrections were applied, in particular in the case of male models. As expected, our results indicate that if the difference between sexes is not taken into account when the dataset is spatially biased, it is likely that the full range of the potential habitat will not be covered.
This ratio was closer to the value for a balanced sex ratio for the observations collected with the systematic design, in line with our hypothesis H5. Proportionally, more female signs were collected with this protocol than with the subjective one. However, the number of observations available for the systematic design was very low (29), thus more data are needed to confirm this pattern. Nevertheless, the ratio of males observed with systematic sampling (0. 59) was slightly lower than the one reported by previous studies (0. 63 and 0. 625) that used subjective sampling  (Mollet et al. , 2015; Morán-Luis et al. , 2014). Only the targeted correction methods in the case of subjective sampling had the expected effect on the estimated sex ratio, whereas the other cases showed lower value than observed from the number of observations.