dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments: We acknowledge the imaging facility MRI, member of the  National Infrastructure France-BioImaging. We thank  L. Espert  and colleagues for helpful discussions and sharing reagents related to autophagy. We thank  M. Bonazzi  and colleagues for advice and reagents related to Rab7. We thank  T. Kirchhausen  for supportive discussions. GFP-Rab7A was a gift from  G. Voeltz  (Addgene plasmid # 61803 ), and  EGFP-Rab7A T22N  was a gift from  Q. Zhong  (Addgene plasmid # 28048 ). Funding: MS-related work was financially supported by the  "Agence Nationale de la Recherche" (ANR)  and the  French Proteomic Infrastructure  ( ProFI ;  ANR-10-INBS-08-03 ). This work was supported by the  Ministry of Science and Technology of the People's Republic of China  ( 2018YFA0507101 ) and the  National Natural Science.
To confirm that this colocalization was relevant to mRNA transport, we performed two-color live-cell imaging using movies recorded at a high frame rate (7. 36 fps for 52 seconds). This allowed the detection of co-transport events, in which a single molecule of KIF1C-STx24 moved with a reporter mRNA molecule in a rectilinear manner at high speed (Fig. 6C , D; Movie S9; Fig. S4C, D ; Movie S10). Kymographs confirmed that both molecules moved together in an anterograde direction toward protrusion (Fig. 6E  and Fig. S4E ), traveling an average distance of 22 microns at speeds of 2. 6 μm/second (Fig. 6F-G ). Taken together, these data demonstrate that the KIF1C kinesin actively transports this Net1 reporter mRNA to cell protrusions along microtubule cables.
Image analysis and quantifications Automated nuclear and cell segmentation was performed with a custom algorithm based on the U-net deep convolutional network  (Ronneberger et al. 2015). Nuclear segmentation was performed with the DAPI channel, cell segmentation was performed with the autofluorescence of the actual smFISH image. For segmentation, 3D images were projected into 2D images as described previously  (Tsanov et al. 2016). Messenger RNAs were detected with FISH-quant  (Mueller et al. 2013 ) by applying a local maximum detection on LoG filtered images. For calculation of Peripheral Distribution Index (PDI) a custom Matlab script was used. The code is described and is available in  (Stueland et al. 2019).
Imaging of live cells Live imaging (for dual visualization of 24bs/Net1 reporter RNA and KIF1C-STx24 protein) was done using a spinning disk confocal microscope (Nikon Ti with a Yokogawa CSU-X1 head) operated by the Andor iQ3 software. Acquisitions were performed using a 100X objective (CF1 PlanApo  1. 45 NA oil), and an EMCCD iXon897 camera (Andor). For two-color imaging, samples were sequentially excited at 488 and 540nm. We imaged at a rate of 7. 36 fps for 52 sec. The power of illuminating light and the exposure time were set to the lowest values that still allowed visualization of the signal. This minimized bleaching, toxicity and maximized the number of frames that were collected. Cells were maintained in anti-bleaching live cell visualization medium (DMEM gfp ; Evrogen), supplemented with 10% fetal bovine serum at 37°C in 5% CO2 and rutin at a final concentration of 20 mg/l.
Live imaging (for 24bs/Net1 reporter RNA tracking) was done using a Nikon Eclipse Ti2-E inverted microscope, equipped with a motorized stage, a Yokogawa CSU-X1 spinning disk confocal scanner unit, and operated using NIS-elements software. Acquisitions were performed using an Apochromat TIRF 100x oil immersion objective (N. A. 1. 49, W. D. 0. 12mm, F. O. V. 22mm) and a Photometrics Prime 95B Back-illuminated sCMOS camera with W-view Gemini Image splitter. Constant 37 o C temperature and 5% CO2 were maintained using a Tokai Hit incubation system. Cells were plated on fibronectin (2mg/ml)-coated 35mm glass bottom dishes and samples were excited using a 488nm (20mw) laser line and imaged at a rate of 6. 66 fps for 60 sec.
Acknowledgements We thank the staff of MRI imaging facility for their technical support. This project was supported by  France BioImaging  ( ANR-10-INBS-04 ), the  Agence Nationale de la Recherche  ( ANR-11-BSV8-018-02 ,  ANR-14-CE10-0018-01  and  ANR-19-CE12-0007-03 ), and the  Ligue Nationale Contre le Cancer  and the  Fondation pour la Recherche Médicale. This work was supported by the  Labex EpiGenMed , from the framework " Investissements d'avenir ". This work was supported in part by the  Intramural Research Program of the National Cancer Institute, NIH.
After 4h of incubation on a rotating wheel, beads were washed four times in HNGT with anti-protease, and twice with PBS. Beads were then incubated with Trizol to extract RNAs, and RNA purification was done as recommended by the manufacturer. The resulting RNAs were amplified and converted into cDNAs by the WT PICO kit (Thermo Fisher), and hybridized on HTA 2. 0 chip on an Affymetrix platform (Thermo Fisher). Experiments were performed in duplicates, data were normalized and averaged. Data are deposited on GEO with the following accession number (GSE161316). For GFP-APC immunoprecipitation, cells were lysed with a buffer containing 50mM Tris pH 7. 4, 1% Triton X-100, 75mM NaCl, 10mM MgCl2, 10% glycerol and a cocktail of protease and phosphatase inhibitors (Thermo Fischer Scientific, cat# 1861281).
Supplementary spreadsheets: Table  S1 : Excel file showing the KIF1C-GFP IP microarray data. Table  S2 : Human smFISH probe sequences and list of probes used for smFISH localization screen in HeLa cells using 18 of the mRNAs most enriched in the KIF1C IP. Table  S3 : File presenting the source data for the graph presented in Figure  S1E. APCdependence was specified based on reduced protrusion enrichment upon APC knockdown (described in  Wang et al, 2017). For each RNA, the fold-change of protrusion enrichment and significance values upon APC knockdown from the Wang et al study are also indicated. Table  S4 : File presenting the source data for the graphs presented in Figure  6G-H. Table  S5 : GO term enrichment for the 200 mRNAs most enriched in the KIF1C IP. Analysis was done with http://pantherdb.
All first single-organ adult kidney transplants performed in France in 2015 through 2018 for cDCD and in 2015 through 2017 for DBD were included. Because the 3-month follow-up report was mandatory only in cDCD recipients, they could be included through the end of 2018. DBD follow-up data are reported only at 1 year, and including them to the same date would have resulted in missing DGF data for nearly all of them. Transplants with missing data for the primary outcome, death during the first week after transplant, and those performed in overseas regions were excluded. Preemptive transplants were excluded from the primary outcome analysis because we could not measure delayed graft function (DGF), as defined in our study, in these patients. Allocation of organs from DBD to recipients 20 years older than the donor was prohibited.
| Data collection The French national transplant registry (CRISTAL, Agence de la Biomédicine) prospectively collects demographic, clinical, and laboratory data for all organ transplant recipients and donors in France, as well as transplant outcomes. Data are recorded at registration (placement on a transplantation waiting list), procurement, transplantation, and annually thereafter. Data collection is mandatory, and research technicians double-check its completeness and accuracy. In accordance with French law, research studies based on this national registry are part of transplant assessment and do not require additional institutional review board approval. The database has been reported to the French National Commission on Computing and Liberty.
A subgroup analysis was performed according to cold ischemia time (<18 h, ≥18 h) and/or hypothermic machine perfusion use. After the univariate analysis with all available variables known to be associated with DGF, 10 the multivariate analysis retained covariables with p <. 20 and those that are clinically significant. A sensitivity analysis was conducted by adding the transplant center as a covariate. The associations of the different cDCD transplant outcomes (primary nonfunction, DGF, eGFR at discharge) with functional warm ischemia time or asystolic period were tested with the Wilcoxon two-sample test or Fisher's exact test. Statistical analyses were performed with SAS guide 7. 1 (SAS Institute). All p values were two-tailed and p <. 05 was considered significant.
Genome sequencing and assembly B. atropi strain LUAb4 was cultured from a single colony in LB and DNA was purified using the DNeasy Blood and Tissue Kit (Qiagen). Libraries were made using Illumina Nextera DNA Library from ~700 bp fragments and sequenced on Illumina MiSeq 2x250 bp by GeneWiz Inc. Unprocessed reads were assembled with SPAdes (v3. 13. 0) using the 'careful' parameter and testing 21, 33, 55, 77, 99, and 127 kmers  35. Annotation was conducted with prokka (v1. 14. 0) with the parameters 'addgenes' 'genus Bordetella', and an evalue of 1e-06  36. This Whole Genome Shotgun project has been deposited at DDBJ/ENA/GenBank under the accession JAHPZX000000000. The version described in this paper is version JAHPZX010000000.
DATA AVAILABILITY This Whole Genome Shotgun project for B. atropi type strain LUAb4 has been deposited at DDBJ/ENA/GenBank under the accession XXXXXX. The version described in this paper is version XXXXXX. Raw reads from LUAb7 sequencing are available at Sequence Reads Archive (SRA) accession XXXXX. All data supporting this manuscript are available from the corresponding author upon request.
ACKNOWLEDGEMENT We would like to thank Prof. Aharon Oren for his expertise in prokaryotic nomenclature and assistance in naming B. atropi. Thank you to Dalaena Rivera for helping come up with the name B. atropi. We would like to thank Dr. Anca Segall and Dr. Nick Shikuma for providing bacterial plasmids, access to equipment, and assistance with bacterial genetics. Thank you to Dr. Aaron Reinke for editing the manuscript.
Third, all loci possibly do not share the same evolutionary history, because of incomplete lineage sorting (ILS) or even more problematically, hybridisation  (Maddison 1997). To sum up, the phylogenetic signal left behind by ancient radiations is both scarce and difficult to extract  (Whitfield and Lockhart 2007). Because increasing the size of the dataset is a necessary condition to resolve such radiations, phylogenomics, the use of large datasets, initially generated great hope  (Gee 2003). However, the failure of phylogenomics to resolve several of those complex cases suggests that more data may not be sufficient (see  Philippe et al. 2011). P D F P r o o f : M o l. B i o l. E v o l. This failure might be due to limitations of currently available tree reconstruction methods. Let us consider a dataset D containing n sites.
To simplify our initial reasoning, let us assume first that all loci share the same history. The genuine Phylogenetic Signal (PS) contained in it for a given branch B is by definition: (1)PS B ( D)=n * λ B (D), with λ B ( D) being the expected number of substitutions per site in branch B for dataset D. Yet, PS B (D), the total number of synapomorphies of the dataset supporting this branch, is actually unknown, as we only have access to the apparent phylogenetic signal ( PS ' )  (Baurain and Philippe 2010)  inferred by a specific method m: (2)PS B ' ( D | m)=PS B (D)+ NPS B (D | m), with NPS B (D | m)being the Non-Phylogenetic Signal generated in favor or in disfavor of branch B for dataset D and method m. This non-phylogenetic signal depends not only on the properties of the radiation (e. g.
, age) and the dataset (e. g. , global rate of evolution or taxon sampling), but also ultimately on how correctly the model of evolution infers the substitution history at each position. Limitations of tree reconstruction methods generally lead to negative values of NPS B (D | m) that are in disfavor of branch B. When a strong bias favors an alternative branching order (e. g. , a long branch attraction between two unrelated fast-evolving taxa), the absolute value of NPS B (D | m) might be larger than PS B ( D), generating an apparent phylogenetic signal in favor of an alternative, incorrect, branch. In contrast, NPS B ( D | m) might also be positive, whenever a bias favors the correct solution (e. g. , a long branch attraction between two fast-evolving taxa that are really sisters). In cases where all loci do not share the same history, non-phylogenetic signal might be further increased by other model violations, for example when using a concatenated model in the presence of ILS.
In particular, it might help phylogeneticists to determine whether they should increase n or rather work on reducing model violations. For this study, we sequenced mitochondrial and plastid genomes from five ochrophyte species belonging to Chrysophyceae, Dictyochophyceae, Pinguiophyceae and Synurophyceae. From these new data, we built three supermatrices, one for each genomic compartment, all representing most of the major ochrophyte clades. Each dataset was carefully constructed, so as to maximize matrix size and completeness, while minimizing erroneous inclusion of non-orthologous genes, contaminated sequences and sequencing errors. With these three largest stramenopiles supermatrices to date, we studied how model violations affect phylogenetic inference. We first observed incongruent topologies between the three genomes for deep ochrophyte relationships, along with surprisingly lower bootstrap support for the largest dataset when using the conventional model LG4X  (Le et al.
Results & Discussion Recovery of the major ochrophyte clades We carefully assembled three supermatrices from mitochondrial, plastid and nuclear genome sequences, containing 6,672, 21,692 and 209,105 amino acid positions, respectively (Table  1 ). They included species from eleven major clades of Ochrophyta: Bacillariophyta, Bolidophyceae, Chrysophyceae, Dictyochophyceae, Eustigmatophyceae, Pelagophyceae, Phaeophyceae, Pinguiophyceae, Raphidophyceae, Synurophyceae and Xanthophyceae. The nuclear dataset also included Synchroma pusilla, a species of Synchromophyceae, but some clades (Chrysomeridoephyceae, Phaeothamniophyceae, Schizocladiophyceae, Aurearenophyceae, Phaeosacciophyceae and Chrysoparadoxophyceae) were absent. Mitochondrial, plastid and nuclear phylogenies inferred using the LG4X model (Fig.
1A-C ) retrieved the monophyly of all major clades with maximal bootstrap support (BS) except Chrysophyceae (see Supplementary Figures  1 2 3 ). Chrysophyceae came out as a monophyletic group in the mitochondrion and plastid datasets (BS=56% and 88%, respectively), but were represented by two species only. In the nuclear phylogeny, which includes 12 chrysophycean species, they were paraphyletic, with Synurophyceae nested within Chrysophyceae, in agreement with previous studies  (Yang et al. 2012). Monophyly of Synurophyceae+Chrysophyceae (SC clade) was always maximally supported. In the nuclear dataset, their grouping with Synchromophyceae (SSC clade) was highly supported, as in  Yang et al. (2012)  and  Derelle et al. (2016). This SSC clade was also recovered in a plastid phylogeny built with partial Synchroma sequences obtained from RNAseq data  (Keeling et al.
2014 ) (data not shown). Consequently, in the following, we consider the SSC clade as one of the ten major ochrophyte clades contained in our analyses. The PX clade (Phaeophyceae and Xanthophyceae)  (Kai et al. 2008 ) was recovered using all three datasets, as well as their sister relationship with Raphidophyceae (PXR clade)  (Graf et al. 2020) , but with limited support in the mitochondrial dataset (BS=92% and 71%, respectively). Two other previously reported relationships  (Yang et al. 2012; Derelle et al. 2016; Han et al. 2018)  were highly supported -monophyly of Pelagophyceae and Dictyochophyceae (PD clade) and monophyly of Bolidophyceae and Bacillariophyta (BB clade) -but again mitochondrial support was not maximal for the PD clade (BS=85%). Inside Bacillariophyta, in our nuclear tree, Coscinodiscophyceae were paraphyletic at the base, followed by a monophyletic group composed of Mediophyceae and Bacillariophyceae, as in  Parks et al.
Incongruence between compartments for deep ochrophyte relationships Although the monophyly of the ten major clades was consistently recovered across the three datasets, the basal phylogeny of Ochrophyta showed incongruent relationships depending on which dataset was used. While the plastid tree strongly grouped Eustigmatophyceae with the SSC clade (BS=100), nuclear and mitochondrial trees separated them, and instead supported the grouping of Pinguiophyceae with SSC on one side (BS=68% and 74%, respectively) and the grouping of Eustigmatophyceae with PXR on the other side (BS=81% and 22%, respectively). Our plastid phylogeny (Fig. 1B ) was in agreement with the work of  Yang et al. (2012) , which was based on nuclear SSU rRNA and four plastid-encoded genes, suggesting that their inferences were dominated by the plastid signal.
Comparison with more recently published plastid  (Ševčíková et al. 2015) , mitochondrial  (Ševčíková et al. 2016 ) and nuclear trees  (Derelle et al. 2016 ) was more difficult, as those datasets lacked some major clades: Bolidophyceae, Dictyochophyceae and Pinguiophyceae  (Ševčíková et al. 2015) ; Bolidophyceae, Dictyochophyceae, Pinguiophyceae and Xanthophyceae  (Ševčíková et al. 2016) ; Eustigmatophyceae and Pinguiophyceae  (Derelle et al. 2016). Still, the plastid tree of  Ševčíková et al. (2015)  was congruent with our plastid tree. However, the mitochondrial tree of  Ševčíková et al. (2016)  did not recover the monophyly of the PXR clade, contrary to our mitochondrial tree (Fig. 1A ), but both trees recovered a basal position for Chrysophyceae. In our nuclear tree (Fig. 1C ), we observed the dichotomy between Diatomista (BB+PD) and Chrysista (PXR+SSC), first proposed in  Derelle et al.
(2016). Overall, although phylogenies based on the three genomic compartments yielded incongruent deep ochrophyte relationships (Fig. 1A-C ), they were each in good agreement with previously published trees based on the same compartment  (Yang et al. 2012; Ševčíková et al. 2015; Derelle et al. 2016; Ševčíková et al. 2016). Unexpectedly, statistical support for the eight deep nodes that connect the ten major lineages, displayed a surprising pattern with respect to the number of positions (Fig. 1D ). The average BS for these eight nodes increased from 73% in the mitochondrion tree (6,762 positions) to 97% in the plastid tree (21,692 positions), disregarding the fact that these two trees differed for basal relationships. With ~3 times more positions than the mitochondrial dataset, the plastid dataset thus confirmed the expectation that the apparent phylogenetic signal (as measured by bootstrap support) increases with dataset size.
In sharp contrast, the nuclear dataset (209,105 positions), which is ~10 times larger than the plastid dataset (~7. 5 times larger if taking into account missing data, see Table  1 ), did not follow that expectation, with an average bootstrap support of 86%. The deep ochrophyte phylogeny inferred from the three compartments therefore showed not only incongruent relationships but also unexpected statistical supports. F P r o o f : M o l. B i o l. E v o l.
Comparison of the apparent phylogenetic signal across the three genomes when controlling for the number of sites and the number of OTUs There was more apparent signal in the plastid than in the nuclear dataset for the deep branches connecting the major clades, PS ' (cp | LG 4 X )>PS ' (nu | m), as shown by BS values (Fig. 1D ). According to equation (  2 ), this would indicate that either NPS B (cp | LG 4 X ) is positive and NPS B (nu| LG 4 X) is negative or the absolute value of non-phylogenetic signal is lower for the plastid than for the nuclear dataset, since n cp ≪n nu. However, differences in taxon sampling (64/63 species in the mitochondrial/plastid datasets versus 124 in the nuclear dataset) can affect the amount of non-phylogenetic signal, making our comparison of the three datasets difficult to interpret.
To cancel out the impact of taxon sampling, we reduced each dataset to a common set of 23 species (22 for the mitochondrion). The phylogenies, inferred with the same LG4X model as above, (Supplementary Figure  4 ) were virtually identical to those inferred with more species (Supplementary Figures  1 2 3 ). Yet, we observed a slight decrease in the apparent phylogenetic signal when reducing the number of species: the average BS for the eight deep nodes went down from 73% to 64% for the mitochondrion, from 97% to 93% for the plastid and from 86% to 69% for the nucleus. This is in agreement with the widely recognized idea that the use of a large number of species improves phylogenetic accuracy, hence reducing the non-phylogenetic signal  (Zwickl and Hillis 2002). The higher apparent phylogenetic signal in the plastid versus nuclear compartment was thus still observed, in spite of controlling for taxon sampling.
To better characterize the apparent phylogenetic signal of the three compartments, we used the variable length bootstrap (VLB), or partial bootstrap, approach with the set of common species  (Lecointre et al. 1994; Springer et al. 2001; Baurain et al. 2010). Usually, VLB analyses are used to define the number of sites needed to reach a predefined level of apparent phylogenetic signal (e. g. , BS=95%), in order to compare the resolving power of different datasets  (Springer et al. 2001; Baurain et al. 2010 ). Here, they allowed us to study the variation in apparent phylogenetic signal between the three compartments without being affected by the different sizes of the datasets. Interestingly, VLBs revealed that the apparent phylogenetic signal of most nodes was highly similar for the nucleus, the plastid and, to a lesser extent, the mitochondrion (Fig.
2 ). For the monophyly of the major clades (Fig. 2A-F ), VLB curves always reached 100% BS below 1000 positions. For the five higher-level groupings (BB, PD, PX, PXR, and BB+PD) that were easily recovered with the three genomes (Fig. 1A-C ), the curves displayed similar increasing trends between compartments (Fig. 2G-K ). The mitochondrial dataset required more sites to reach a given BS, which could be due to an increased non-phylogenetic signal related to the high rate of evolution in this compartment  (Neiman and Taylor 2009 ). Yet, nucleus and plastid curves were virtually identical, sometimes the plastid increasing slightly faster (Fig. 2H ) or slower (Fig. 2I ) than the nucleus. In sharp contrast, support for the monophyly of E+SSC (Fig. 2L ), as well as their subsequent grouping with Pinguiophyceae and PXR (Fig.
2M ), rose much faster and higher in the plastid dataset than in the two other compartments. None of the bipartitions conflicting with E+SSC (Fig. 2N-P ) showed the same rapid increase in the mitochondrion or the nucleus, showing that a strong apparent phylogenetic signal only exists in the plastid dataset for positioning these taxa. F P r o o f : M o l. B i o l. E v o l. Hypotheses to explain the conflict between plastid and nucleus When controlling for the number of species and the number of sites, the apparent phylogenetic signal in plastid and nuclear compartments was almost identical for most nodes, suggesting that the amount of non-phylogenetic signal per site was virtually identical across most nodes. The higher average support observed over the eight deep nodes with the plastid dataset (Fig.