dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2020). In this study, we tested the coordination between leaf xylem vulnerability to embolism, leaf turgor loss point (used as a surrogate for the water potential causing stomatal closure), and the experienced dry season midday leaf water potential for 18 tropical rainforest canopy tree species. Furthermore, we resolved an extensive sequence of physiological thresholds during dehydration for rainforest canopy tree species. With regard to the broad phylogenetic diversity of tropical rainforests, we aimed to evaluate whether tree species show a functional convergence in terms of hydraulic safety margins or whether their drought-response strategies are divergent. We retrieved hydraulic trait data from published work conducted at the same site. We built vulnerability curves (VC) to quantify xylem vulnerability to drought-induced embolism which has been measured in leaves with the optical vulnerability (OV) method and in stems with the flow-centrifugation technique.
Finally, to compare data on leaf stomatal safety margins gathered for tropical rainforest tree species with species from other biomes, we performed a literature survey of the studies that also used the OV method to measure leaf xylem vulnerability to embolism. We specifically addressed the following questions: (i) Does leaf turgor loss point occur before embolism formation in the leaf xylem, and do leaves develop xylem embolism during the dry season in co-occurring Neotropical tree species? (ii) What is the sequence and interspecific variability in key water potential thresholds during dehydration for Neotropical canopy tree species? (iii) Are tropical rainforest tree species more subject to leaf hydraulic dysfunction than species from other biomes?.
| Study site, species and design The experiment was conducted in French Guiana at the Paracou experimental station (https://parac ou. cirad. fr/website; 5°16′26″N, 52°55′26″W), in a lowland tropical rainforest  (Gourlet-Fleury et al. , 2004). The warm, wet tropical climate of French Guiana is highly seasonal due to the North-South movement of the Inter-Tropical Convergence Zone. Mean (±SE) annual air temperature is 25. 7°C ± 0. 1°C and mean annual precipitation is 3102 mm ± 70 mm  (data between 2004 and 2014; Aguilos et al. , 2019). There is a dry season lasting from mid-August to mid-November, during which rainfall is <100 mm month -1. The dataset presented in this study results from the pooled datasets of  Ziegler et al. (2019)  and  Levionnois et al. (2020) , who conducted their studies on the same trees during common measurement campaigns and measured stem and leaf xylem vulnerability to embolism, leaf turgor loss point and leaf midday water potential.
F I G U R E 4 The P 50 stomatal safety margin (P 50 SSM leaf ; P close - P 50,leaf ; MPa) across major biomes based on a literature survey encompassing 97 species. TRF, tropical rainforests; TDF, tropical dry forests; MED, Mediterranean and dry forests; TEMP, temperate forests. The P-value refers to a Kruskal-Wallis test showing no significant differences across biomes. and high standard errors around the mean estimated PLC (for individual tree data, see the Dryad Digital Repository: https://doi. org/10. 5061/dryad. 7h44j 0zzs). Given the time-and labor-intensive nature of these measurements, we also did not account for withincrown variation, which may better explain leaf vulnerability patterns  (Cardoso et al. , 2020). The sometimes rather large differences we evidenced between trees belonging to the same species nevertheless suggest that the most vulnerable individuals may be susceptible to dry-season embolism-induced leaf damage and/or loss under more frequent and repeated severe droughts.
, 2013; Pugh et al. , 2020). The data we gathered in the tropical rainforest biome covers relatively abundant tree species with contrasting habitat preferences  (Allie et al. , 2015; Baraloto et al. , 2021). Our dataset therefore represents, to a certain extent, the interspecific variation in hydraulic traits found at our site. To have a more comprehensive understanding of the potential influence of species hydraulic traits on tree performance across lowland tropical rainforests, it would be valuable to conduct similar studies in different ecoregions with contrasting drought-intensities, for example, at the dry-edge of species' distribution range  (Reis et al. , 2022) , including tropical rainforests in Africa and Asia for which no leaf hydraulic data is currently available. Nonetheless, the data gathered in this study could still be useful to parameterize individual-, stand-or regional-scale mechanistic models predicting rainforest tree species' survival probability during drought  (Cochard et al.
ACK N O WLE D G E M ENTS We thank the climbing team for canopy sampling:  Jocelyn Cazal ,  Valentine Alt ,  Samuel Counil ,  Anthony Percevaux , and  Elodie Courtois. We thank the following colleagues for technical assistance during field work:  Oscar Affholder ,  Louise Authier ,  Anne Baranger ,  Maxime Bellifa ,  Benoit Burban ,  Maëlle Cario ,  Bruno Clair ,  Maxime Corbin ,  Elia Dardevet ,  Alexandre De Haldat Du Lys ,  Emilien Fort ,  Frederic Fung Fong You ,  Eva Gril ,  Thomas Saint-Germain , and  Ruth Tchana Wandji. We thank  Géraldine Derroire ,  Bruno Hérault  and  Aurélie Dourdain  for permission to use the facilities at  Paracou. Paracou Forest Research Station in French Guiana  is managed and supported by  CIRAD ,  UMR EcoFoG  (https://parac ou. cirad. fr/),and benefits from financial support by a  French Investissement d'Avenir Program (Labex CEBA   ANR-10-LABX-0025 ).
We also would like to thank the PHENOBOIS platform for embolism resistance measurements (PHENOBOIS, Bordeaux France). This study was funded by the  GFclim Project  ( FEDER 20142020 , Project  GY0006894 ). This work has benefited from an " Investissement d'Avenir " grant managed by  Agence Nationale de la Recherche  (CEBA, ref. ANR-10-LABX-0025 ; and  ARBRE  ref. ANR-11-LABX-0002-01 ). S. L. was supported by a doctoral fellowship from CEBA and C. Z. from a doctoral fellowship from Pôle  A2F ,  Université de Lorraine, France.
Among the most interesting characteristics are the formation of cloud decks (iron and silicates for L dwarfs; chlorides and sulphides for T and early-type Y dwarfs; and water clouds in the coolest Y dwarfs) and non-equilibrium chemistry (Saumon et al. 2000; Zahnle & Marley 2014). Sarro et al. (2013) estimated the expected end-of-mission number of UCDs in the Gaia archive: 600 objects between L0 and L5, 30 objects between L5 and T0, and 10 objects between T0 and T8. Later on, in Smart et al. (2017), we cross-matched the known UCDs with the first Gaia data release (DR1) and identified 321 dwarfs with spectral types between L and T, creating the Gaia Ultracool Dwarf Sample (GUCDS). This sample was then used as a starting point by the Data Processing and Analysis Consortium (DPAC) pipeline for parameter estimation purposes based on the Gaia RP spectra (resolution 50-30 between 630 and 1090 nm; see Carrasco et al.
2021; De Angeli et al. 2023). Gaia Collaboration (2018a) identified 601 UCDs by comparing very restrictive subsets of the second Gaia data release (DR2) with several all-sky catalogues, and Reylé (2018) extended this work and identified ≈13 000 sources in Gaia DR2 with spectral types ≥M7 (631 with spectral type L). Here we discuss the UCD candidate content in the Gaia catalogue. This UCD content is defined and characterised using the RP spectra (De Angeli et al. 2023) in addition to all other Gaia measurements used in previous data releases. We define this catalogue of UCDs as composed of sources in the Gaia Data Release 3 (DR3) archive with estimates of the effective temperature, T eff. This implies that they were identified as UCDs by the software module ESP-UCD, which is in charge of selecting and characterising UCD candidates in the context of the Gaia DPAC.
The Gaia catalogue of UCD candidates The Gaia DR3 archive contains entries for ∼1. 8 billion sources including astrophysical parameters produced by the Coordination Unit 8 (CU8). This is accomplished with Apsis, the CU8 software chain composed of thirteen modules, which includes the Extended Stellar Parametrizer for UCDs ESP-UCD. The ESP-UCD software module produced T eff estimates for 94 158 Gaia sources that constitute the subject of analysis of this work. The data processing in CU8 and the results included in Gaia DR3 are described in the CU8 chapter of the official documentation and in Creevey et al. (2023), Fouesneau et al. (2022), and Delchambre et al. (2023).
Since UCDs are intrinsically faint and very red objects, their BP spectra only contain noise in all but the brightest and hottest candidates and even there, only at the reddest wavelengths. Hence, we only discuss here the internally calibrated RP spectra, abbreviated hereafter as RP spectra. In this section we briefly mention the externally calibrated spectra. These are available from the Gaia archive only for sources with G magnitudes brighter than 15 mag (48 UCDs) but can be generated from the coefficients used to represent the internally calibrated spectra using the GaiaXPy software  3. The externally calibrated spectra of UCDs present problems discussed in Montegriffo et al. (2023) and illustrated in Fig. 7  below. Figure  5  shows in turquoise, simulations of BT-Settl synthetic spectra obtained using the mean instrument object generator (MIOG; briefly described in Creevey et al.
7 mas. While these filters may remove very young and bright sources that could be detected beyond this limit or subdwarfs redder than 1. 0 mag in G -G RP , we assume that their numbers (if not zero) are small enough to neglect their effect in the reconstruction of the luminosity function. However, we assume that only 89% of the UCDs have ϖ, G, and G RP measurements available (see the GUCDS analysis in Sect. 2. 2). The next selection filter is applied by the ESP-UCD module based on the RP spectrum pixels where the 33, 50 and 67 flux percentiles are attained. Since the filter definitions are inclusive definitions of the hot boundary of the UCD regime, in principle we only expect selection effects at that boundary. The dataset used for inference with the Bayesian model is cut at M G = 13. 8 mag and we do not expect significant selection effects.
The latter shows hints of residual overdensities. Some of them are due to the scanning law (that results in more transits, better quality measurements and consequently more sources passing the quality criteria in certain regions of the sky) while two of the others (shown in red and blue) are discussed later below. Figure  16  shows the various clusters depicted in the lower-left panel of Fig. 15  in the space of tangential velocities (using the same colour code). The significant overdensities identified by HMAC as distinct groups can easily be identified with well-known star-forming regions and clusters. These large groups show clear substructures discussed in the specialised literature. In Sect. 6. 1 we discuss clusters identified using the BANYAN Σ software tool (Gagné  et al. 2018) and in Sect.
Membership according to BANYAN In this section we discuss the determination of membership to star clusters and moving groups within 150 pc from the Sun as derived by the BANYAN Σ software tool (Gagné et al. 2018). We use the Gaia DR3 UCD candidates (their celestial positions, proper motions and parallaxes) as input to BANYAN Σ and find 2840 sources with membership probabilities higher than 0. 5, 80% of them higher than 0. 8. In the following we use the lower threshold of 0. 5 despite indications that this may include contamination by sources from the field (Liu et al. 2016). BANYAN Σ is based on multivariate Gaussian modelling of the groups, which in some cases can be a simplification of the true distribution of sources in the space of measurements. HMAC on the contrary is a non-parametric clustering technique that can identify groups of arbitrary shapes and we prefer to be conservative in the definition of the BANYAN probability threshold for the sake of completeness.
In fact, 728 of the 1109 UCD candidates with variability data are identified by HMAC as belonging to overdensities, and 353 by BANYAN Σ as members of the BANYAN clusters. The most numerous HMAC groups are Upper Sco (250 sources), Orion (162), Serpens (53), and the Perseus association (48). Those of the BANYAN class set are Upper Sco (203 sources), Upper Centaurus Lupus (57), and Taurus (45). We initially interpreted the sources with bright outliers as potential sources with flares, and those with faint outliers as eclipsing binary candidates. However, the UCD candidates with outliers significantly fainter that the median (the examples to the right, which we had interpreted initially as potential eclipsing binaries) are all classified in the vari_classifier_result as young stellar objects (YSOs) except for one source that is classified as long period variable, albeit with a probability of 0.
Acknowledgements. This work has made use of results from the  European Space Agency (ESA) space mission Gaia , the data from which were processed by the Gaia Data Processing and Analysis Consortium (DPAC). The Gaia mission website is http://www. cosmos. esa. int/gaia. This work was supported by the  MCIN  ( Spanish Ministry of Science and Innovation)  through grant  PID2020-112949GB-I00 ; the  MINECO (Spanish Ministry of Economy ) through grants  AyA2017-84089 ,  ESP2016-80079-C2-1-R ,  ESP2014-55996-C2-1-R , and  RTI2018-095076-B-C22  ( MINECO/FEDER, UE). This research has been funded by the  Spanish State Research Agency (AEI)  Projects No. PID2019-107061GB-C61  and No. MDM-2017-0737  Unidad de Excelencia "María de Maeztu"-Centro de Astrobiología (CSIC/INTA ). Ground based spectra is from observations made with the  Gran Telescopio Canarias (GTC) , installed in the  Spanish Observatorio del Roque de los Muchachos  of the  Instituto de Astrofísica de Canarias , on the island of  La Palma.
Patients An international consortium composed of 14 pathology expert centers from 13 countries was initiated to evaluate the standardized Immunoscore assay in primary tumors from 1885 patients with Stage I/II CC. Patients were split into two cohorts (North America (USA + Canada) and Europe + Asia). Patients who received preoperative treatment were systemically excluded. Clinical data from North America and Europe and Asia datasets are presented in Table  S1. Outcomes of interest were TTR, defined as the elapsed time from surgery until the first recurrence of disease, OS stands for the delay from surgery to death due to any cause and DFS is the overall patient survival time without any symptoms, from surgery until the first event of relapse or death. High-risk Stage II patients were defined as VELIPI+ (venous emboli or lymphatic invasion or perineural invasion) or T4 Stage II tumors, or perforation, or poor differentiation or less than 12 lymph-node (LN) evaluated, whereas low-risk correspond to T1-3 stage tumors without any high-risk feature.
Immunoscore Determination For each slide, CD3+ and CD8+ cell densities in CT and IM regions were converted into percentiles  [12]. The mean of the four percentiles obtained (two markers, two regions) was calculated and translated into the Immunoscore scoring system. The Immunoscore categories were previously pre-defined, independently of clinical data  [12]. These predefined categories were used. Immunoscore 3 categories, in which the mean percentiles are the following: Low (Lo) 0-25%, Intermediate (Int) (>25-70%) and High (Hi) (>70-100%). Additional analyses were performed with the pre-defined Immunoscore 2 categories: Lo (0-25%) and Int + Hi (25-100%) and Immunoscore 5 categories: I0 (0-10%), I1 (>10-25%), I2 (>25-70%), I3 (>70-95%) and I4 (>95-100%).
Immunoscore and the Outcome of Stage I/II Colon Cancer Patients Biomarker data from 1885 colon cancer patients with AJCC/UICC-TNM Stage I/II part of the Immunoscore international validation study were investigated (see consort diagram in Supplementary Information). Patients were divided into two datasets: cohort 1 (North America) and cohort 2 (Europe and Asia) with balanced demographic and clinical characteristics (Table  S1 ). Overall, 52. 6% of patients were males, with 68 years as a median age (IQR 60-77). The mean number of lymph-nodes examined was 19. 0. Among all analyzed patients, 223 relapses (11. 8% of patients) and 588 deaths (31. 2% of patients) were observed. Relapses were observed for 24/451 (5, 3%) of stage I and 199/1434 (13, 9%) of stage II patients. The median follow-up time for censored patients was 69.
Acknowledgements T. L. and S. C. contributed equally to this work. The authors thank the CMEBA platform ( Francis Gouttefangeas and Loic Joanny)  from  UMS   2001 ScanMAT CNRS-Université Rennes 1  for the FESEM images and analyses (Figure 2, 8, and  11 ). Raman investigations were performed using facilities available on  SIR Plateforms  from  UMS 2001 CNRS-Université Rennes 1  ( Figure S2, Supporting Information ). The authors are very grateful to  Bertrand Lefeuvre   ISCR UMR 6226 CNRS-Université Rennes1. The authors thank  Rennes Metropole  for its financial support via AIS funding.
Cell viability was assessed using LIVE/DEAD Fixable Near-IR (775) stain (Thermo Fisher, L34976) and the cell suspensions were analyzed with an LSRFortessa (BD Biosciences). Results were analyzed using FlowJo software (Tree Star). For scRNA-seq and bulk RNA-seq, lung myeloid cells were sorted using a FACSAria III (BD Biosciences). The full list of antibodies used can be found in the Supplementary Table  1.
The Cell Ranger (v3. 0. 2) application (10x Genomics) was then used to demultiplex the BCL files into FASTQ files (cellranger mkfastq), to perform alignment (to Cell Ranger human genome references 3. 0. 2 GRCm38/build 97), filtering and unique molecular identifier counting and to produce gene-barcode matrices (cellranger count). Filtered matrix files were used for further scRNA-seq analyses with R Bioconductor (3. 12) and Seurat (3. 2. 1)  51. The cells from pooled hashtag-labeled samples were demultiplexed with the barcode detected in each cell. Filtered matrices containing cell IDs and feature names in each sample were used to build a Seurat object. We performed quality control by filtering out the cells with less than 200 detected genes, the genes detected in less than three cells and the cells exhibiting more than 10% of mitochondrial genes.
Single-cell RNA velocity estimation The counts for unspliced and ambiguous transcripts were calculated from CellRanger output using the velocyto command-line tool (http:// velocyto. org/)  52 and saved in loom files. The single-cell RNA velocities were estimated using scVelo toolkit (https://scvelo. readthedocs. io/)  53. Briefly, the loom files were used as input for scVelo analysis. Genes with a minimum of 20 of both unspliced and spliced counts and on the top list of 2,000 genes were filtered, normalized and log transformed (scv. pp. filter_and_normalize with default parameters). Thirty principal components and 30 neighbors obtained from Euclidean distances in principal-component analysis space were used for computing first-order and second-order moments for each cell. We used generalized dynamical modeling to recover the full splicing kinetics of spliced genes, and the single-cell RNA velocities were plotted with the same cluster labels and embedding as in Fig.
Gene Ontology enrichment analysis with differentially expressed gene signatures The DEG lists for enrichment analyses were calculated using Seurat function FindMarkers with only. pos = TRUE to output only positively regulated genes. Thresholds logfc. threshold of 0. 2 and adjusted P value of 0. 01 were applied to filter the gene lists. Gene Ontology (GO) enrichment analyses were made using enrichGO functions from clus-terProfiler package  54  with default arguments. Only biology process terms of ontology were shown in the final results.
1% Sytox blue nucleic acid stain (Invitrogen, S11348) on glass slides and stored at RT in the dark overnight. All samples were analyzed by spectral fluorescence microscopy. Images of full lung sections were acquired on an LSM 980 with Airyscan 2 inverted confocal microscope (Zeiss) using a LD C-Apochromat ×40/1. 1 W objective and Zen Black software. All fluorophores were excited simultaneously, and the emission spectra were collected with a spectral detector 32-channel GaAsP photomultiplier tube in lambda mode at 8. 8-nm bins from 411 to 694 nm. A spectral unmixing was performed based on the monospectral spectra. Images were processed with the Zen Blue software. For quantification, the numbers of CD11b + CD206 lo MHC-II hi IMs (CD206 -IMs), CD11b + CD206 hi MHC-II lo/int IMs (CD206 + IM) and CD11b + CD206 -MHC-II lo Ki67 hi cells were counted blindly and manually on a total surface of 12-16 mm 2 per mouse section.
Single-cell regulatory network inference and clustering analysis To predict the potential active transcription factors, Ly6C + cMo, Tr-Mo, CD206 -IMs and CD206 + IMs were subjected to SCENIC analysis using the SCENIC package  33. The normalized counts, nFeature_RNA and nCount_RNA in merged Seurat object were used for the initial SCENIC analysis. The genes expressed with a value of 3 in 0. 5% of the cells and detected in 1% of the cells were kept for following SCENIC analysis. Coexpression network analysis was made with GENIE3 in the SCENIC package. To represent the SCENIC results, the results of the '3. 4_regu-lonAUC' output were added to the metadata of Seurat object so that regulon AUC scores could be plotted using the FeaturePlot function. The top 50 regulons with highest variance are shown with their z-scores in the heat map.
Monocle, tradeSeq and pseudotime analysis during interstitial macrophage development To evaluate trajectory-based differential expression analysis during IM development in IM DTR mice, Ly6C + cMo, Tr-Mo, CD206 -IMs and CD206 + IMs were subjected to Monocle 28 analysis. The Monocle CDS object was built with counts and metadata from Seurat object and converted using SeuratWrappers package. Cells were clustered with the cluster_cells function using calculated UMAP coordinates and a resolution of 0. 51 × 10 -3. The trajectories along pseudotime were built using learn_graph and order_cells functions. The DEGs across trajectories were calculated using Moran's I test (graph_test function) and only the genes with a q value of 0 and Morans's I value over 0. 25 were kept as significant DEGs and subjected to further analyses.
Mac, macrophage. https://doi. org/10. 1038/s41590-023-01468-3 Extended Data Fig. 6  | Tr-Mo are BM-derived cells in transition between cMo and IMs and whose proliferation is inhibited by Csf1r antagonists. a, Expression of the indicated markers in lung cMo, EdU + CD64 + and EdU -CD64 + cells from EdU-pulsed IM DTR mice at day 2 post-DT. Values obtained from individual mice are connected with a dashed line. b, Bar graph showing the % of EdU + cells in CD45. 1 donor and CD45. 2 host cells from lung cMo and CD64 + cells from lethally irradiated, thorax-protected CD45. 2 IM DTR mice reconstituted with CD45. 1 BM wild-type cells, injected i. p. with DT and evaluated 2 days post-DT and 4 hours after EdU i. p. treatment by flow cytometry. c, Representative pictures of CD11b -CD206 hi MHC-II -Ki67 + AMs, CD11b + CD206 lo MHC-II hi IMs (CD206 -IM), CD11b + CD206 hi MHC-II lo/int IMs (CD206 -IM) and CD11b + CD206 -MHC-II lo Ki67 hi cells, identified by confocal microscopy on lung sections from untreated and DTtreated IM DTR mice, at day 2 post-DT.