dataesr/scientific-paragraphs-categorization huggingface dataset:
As previously observed by Zilionis et al. (22) , the number of detected transcripts was much lower in neutrophils compared to other myeloid cells (we detected a median of 4260 genes/cell in monocyte/macrophages in the same dataset). In a first clustering analysis, we observed a substantial enrichment in genes encoding ribosomal proteins in some neutrophils. Expression of these transcripts appeared timedependent, as their proportion was higher in cells originating from the day 3 and day 5 time points (Figure  S1C ). However, proportions were much lower in neutrophils compared to other cardiac CD11B + cells at all time points (Figure  S1D ). Given the low likeliness of technical bias in our experimental design, and consistent findings in a separate experiment (see below), this differential expression of transcripts encoding ribosomal proteins most likely represents true biological variability, and could indicate increasing protein synthesis ability of neutrophils over time in the ischemic heart.
In this dataset, we could recover the major neutrophil states we had observed in the main dataset. A neutrophil cluster enriched for e. g. Cd177, Cxcl3, Lcn2 and Fpr1, was found at day 1 post-MI (proximal to cluster 4 in the main dataset). At day 3 to 7, we observed a cluster enriched for Siglecf, Icam1, Tnf or Dusp2 (proximal to cluster 1/3 in the main dataset), and a cluster enriched for e. g. Ifitm1, S100a11 or Slpi (proximal to cluster 2 in the main dataset) (Figure  S2E-G ). We also reanalyzed a previously published scRNA-seq dataset of cardiac CD45 + cells collected 4 days after MI (27) (Figure  S3A-B ). Despite low transcript coverage (median genes detected/cell in total CD45 + cells=490; Figure  S1B ), we could detect two major populations of granulocytes: one was enriched for Siglecf, Tnf, Icam1 and Dusp2 expression, and the second for Slpi, S100a11, and Ifitm1 (Figure  S3C-D ).
scRNA-seq of atherosclerotic aortas reveals two distinct neutrophil subsets To evaluate whether similar neutrophil diversity could be observed in a distinct cardiovascular disease context, we analyzed scRNA-seq data from 2106 CD45 + cells isolated from control (371 cells) and atherosclerotic (1735 cells) aortas of Low densitiy lipoprotein receptor deficient (Ldlr-/-) mice (corresponding to a previously published dataset combined with novel data, see ref  (16) , and methods) (Figure  S8 ). Although only 66 cells corresponding to neutrophils were observed (Figure  S8A ), expression of the characteristic transcripts Siglecf and Icam1 appeared to localize to a subpopulation with segregated tSNE coordinates (Figure  S8B-D ). This cluster was only observed in atherosclerotic aortas (Figure  S8 ), and besides Siglecf and Icam1, these cells were enriched for the expression of Tnf, Dusp2 or Cxcr4.
The other neutrophil population (Figure  S8B-D ) was enriched for genes associated with cardiac Siglecf low neutrophils (i. e. cluster 2 and 4 in our main dataset) such as Wfdc21, Cd177, Slpi, Hp and had higher proportions of Sell exressing cells (Figure  S8D ). Despite low cell numbers in this dataset, these findings suggest that the major neutrophil dichotomy (i. e. Siglecf hi versus Siglecf low ) may be also present in atherosclerotic aortas. Expr. Expr.
Thus, new methods able to capture the true immediate dynamics of transcription such as scSLAM-seq (  46 ) may be better suited to investigate in situ phenotypic modulation of neutrophils in the ischemic heart. In summary, our work provides a high-resolution census of dynamic neutrophil heterogeneity within the infarcted mouse heart. This dataset may constitute a valuable resource for further investigating the functional implications of neutrophil temporal heterogeneity in the infarcted myocardium, and how it may affect ischemic heart repair.
Atherosclerosis Diet-induced atherosclerosis was performed in Ldlr -/-mice as previously described in ref  (16). After 10 weeks of high fat diet feeding, macroscopically visible plaques were mechanically removed from aortas. The plaque tissue (as well as the rest of the aortas) were processed for scRNA-seq as described in ref  (16) , generating two new datasets that were pooled with data from ref  (16).
Flow cytometry analysis Cells were incubated for 10 minutes at 4°C with 10μg/ml of purified rat anti-mouse CD16/CD32 (Biolegend TruStain FcX anti-mouse) to block non-specific binding of antibodies to Fc Receptors and then incubated at 4°C for 30 minutes with Fixable Viability Staining e780 and fluorochrome/biotin conjugated antibodies (purchased from BD Bioscience, Biolegend, Thermofisher Scientific or Miltenyi biotec, and employed at concentrations recommended by the manufacturer) against mouse CD45 (clone 30-F11), anti-CD11B (M1/70), LY6C (HK1. 4), LY6G (1A8), ICAM1 (YN1/1. 7. 4), SIGLECF (1RNM44N), CD49d (R1-2), CXCR4 (REA107), CXCR2 (SA044G4), CD62L (MEL-14), CD64 (X54-5/7. 1). Specific fluorochromes conjugated to antibodies are indicated on flow cytometry panels in the figures. Data was acquired using a FACS Celesta (BD Biosciences) and analyzed with FlowJo.
Single-cell transcriptomics Cells were loaded in the 10x Genomics Chromium at concentrations recommended by the manufacturer. Libraries were prepared with Chromium Single Cell 3' Reagents Kit v2 Chemistry (day 0-1-3-5-7 kinetics dataset) or v3 Chemistry (main dataset: CITE-Seq/Hashing day 1-3-5 multiplexed post-MI dataset). The v2 chemistry samples were processed following the standard 10xGenomics protocol. The v3 sample was prepared for CITE-Seq/Hashing according to the manual until the cDNA amplification step in which 1 μl (0. 2 μM) ADT PCR additive primer to capture Antibody-derived tags (ADTs) and 1 μl (0. 1 μM) HTO PCR additive primer to capture Hashtag oligos (HTOs) were added. After cDNA amplification 60 μl (0. 6x) SPRI beads (Beckman Coulter) were added to separate the supernatant fraction that contains the ADT/HTO-derived cDNAs (<180bp) and the bead fraction that contains the mRNA-derived cDNAs (>300bp).
Sequencing was performed with S1 or S2 100bp flowcell with Novaseq 6000 platform (Illumina) and the reads for CITE-Seq/Hashing sample were allocated as follows: 5% for the hashtags, 10% for the ADTs and 85% for the mRNAs. Single-cell transcriptomics: data analysis 10X Genomics data was demultiplexed using Cell Ranger software (version 3. 0. 1) and HTO and ADT libraries were demultiplexed using a homemade script. Mouse GRCm38 reference genome was used for the alignment and counting steps. To evaluate the expression of the cell surface proteins alongside the transcriptome level in our main data set, --feature-ref flag of Cell Ranger software was used which creates a matrix that contains gene expression counts alongside the expression of cell surface proteins. The gene-barcode matrix obtained from Cell Ranger was further analyzed using Seurat (version 2.
3. 4). For the day 0-1-3-5-7 kinetics dataset, data obtained from each time point was integrated using Cell Ranger. To reduce the batch effect introduced by different sequencing depth, the read depth was equalized between libraries before integrating them. In the main dataset (including cell hashing  (17)  and CITE-seq (  19 )), quality control was performed as follows: cells with less than 500 or more than 7500 detected genes per cell were filtered out. Moreover, cells with more than 5% UMIs mapped to mitochondrial genes were filtered out as well. The transcriptome of remaining cells was normalized and log transformed and highly variable genes were identified using the default settings of Seurat. Dimension reduction and clustering analysis were performed using the first 20 principal components.
SCENIC analysis SCENIC was applied on the main dataset as described in  (34). For the analysis, mm9 motif ranking database supplied in SCENIC package was used to score potential regulons. The input matrix genes that were not present in motif ranking database were excluded from further analysis. Target genes that did not show a positive correlation based on the GENIE3 co-expression algorithm (> 0. 03) in each module were filtered out. TF-modules having less than 20 genes were filtered out and the remaining TF-modules were further examined.
Velocity analysis To count the spliced and unspliced UMIs in our data set, the velocyto package  (33)  was used. To be able to infer the future state of cells in our data set, we employed scvelo  (47). Normalized and log transformed data was used to calculate first and second order moments for each cell across its nearest neighbors. Next, the velocities were estimated and the velocity graph constructed using the scvelo. tl. velocity() with the mode set to 'stochastic' and scvelo. tl. velocity_graph() functions. Velocities were visualized on top of the previously calculated tSNE coordinates using Seurat.
Acknowledgements The work was supported by the  Interdisciplinary Center for Clinical Research (IZKF [Interdisziplinäres Zentrum für Klinische Forschung]), University Hospital Würzburg  (Project  IZKF-E-353  to C. C. and project Z-6 to P. A. ), by the  German Ministry of Research and Education within the Comprehensive Heart Failure Centre Würzburg  ( BMBF 01EO1504  to C. C. and A. -E. S. ), the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) (Projektnummer  374031971 -TRR 240  to. A. Z).
Data Availability Datasets that were generated for this report have been deposited in Gene Expression Omnibus (GSE135310) and will be made freely available upon publication. The additional data of atherosclerotic aorta scRNA-seq have been deposited in Gene Expression Omnibus together with data from ref (  16 ) (GSE97310). The previously published dataset from King et al. that was reanalyzed in this paper (Figure  S3 )  (27)  is available under GSE106473.
Geometry and manikin The geometrical model was realised in SolidWorks and then imported in Ansys Design Modeler. The geometrical model is based on the replica of the Crew Quarter built by our team and present at the Faculty of Building Services of Bucharest (see Figure  2 ). Like the real CQ, the total deployed volume of the Crew Quarter replica is approximately 2. 1 m 3. Inside the geometrical model we introduced a thermal manikin having the theoretical posture of human body in a zerogravity environment  [7]. The manikin is split into 9 zones with different temperatures. We also imposed a reference temperature of 18 ° C for the internal volume and for the introduced air corresponding to the temperature in the common area (CCAA) of the Harmony module (Node 2). A temperature of 23 ° C has been incorporated for the walls, this is equivalent to a heat flux of 150W in the Crew Quarter, corresponding to the heat flux generated by the electronic devices and other equipment present in the cabin.
Data sets To evaluate our approach at different levels (in terms of information precision) and on different floras (from temperate to equatorial), four data sets of specimens from American herbaria were used in this study. Three data sets consist of selected specimens from herbaria located in different geographic and environmental regions. Each specimen of these three data sets was annotated with the following fields: family, genus, species name, fertile/non-fertile, presence/ absence of flower(s), and presence/absence of fruit(s). Based on a data curation pipeline, our resulting data set was composed of 163,233 herbarium specimens belonging to 7782 species, 1906 genera, and 236 families. Specimens were annotated as "fertile" if any reproductive structures were present, such as sporangia (ferns), cones (gymnosperms), flowers, or fruits (angiosperms).
Non-fertile specimens were those that lacked any reproductive structures. Most herbarium specimens in this study were annotated by herbarium assistants, curators, technicians, or other personnel responsible for digitizing specimen label data (e. g. , trained undergraduate student workers), often long after the collection event. Collectors may have included the phenological status of the sampled plant or population on the specimen label or in the field notes used to create the label, in which case the digitization technician may have annotated the specimen record accordingly. More often than not, however, the digitization technician must determine from the specimen whether reproductive structures are present. Occasionally, specimens are annotated after digitization, e. g. , for specific research projects, in subsequent data quality steps, or with further identifications of the specimen.
We took a ResNet50 network  (He et al. , 2016)  pre-trained on tens of millions of images from the ImageNet data set  (Deng et al. , 2009)  and fine-tuned it on our data sets. ResNet50 was chosen because it is a state-of-the-art model, and it is widely used in image classification tasks. Moreover, pre-trained parameters can be easily found for most Deep Learning frameworks, in particular, for PyTorch (https://pytorch. org/), which we used for the experiments. Although the input image size of the original ResNet50 model was fixed to 224 × 224 pixels, herbarium specimens are usually digitized at high resolution to record fine visual information. To cope with the rectangular shape of these specimens and to preserve as much detail as possible, we adapted the ResNet50 model to use higherresolution images.
Description of experiments Assessing performance of deep learning models-Three experiments were conducted in this study to evaluate the performances of four different models for automated annotation of phenology on herbarium specimens. The first two experiments used the three herbarium data sets (NEVP, FSU, CAY), and the last used the PHENO data set. 1. The first experiment (EXP1-Fertility) aimed to evaluate the capacity of the CNN to detect fertile material (i. e. , specimens with reproductive structures present), based on the analysis of three test sets (A, B, C). Test set A (Random-split) was a random set of herbarium specimens that were not used as training data of the CNN model but that belonged to species and collections represented in the training data. This test set of 13,415 specimens allowed us to evaluate a scenario in which one herbarium collection uses its annotated specimens to train a model that automatically annotates the un-annotated specimens.
This experiment extends one step beyond the previous one in terms of information precision, as it evaluated whether fertility is related to the presence of flower and/or fruit. 3. The third experiment (EXP3-Pheno) dealt with the automated phenophase evaluation, which involved a higher number of visual classes. The test set of this experiment consisted of a random sampling of 20% of the original PHENO data set (the remaining 80% being used for training). Data and models used and produced for this study are accessible on Zenodo  (Lorieul, 2019; Lorieul et al. , 2019) , a free and open platform for preserving and sharing research output.
Results from test set A for angiosperms, gymnosperms, and ferns are provided in Table  4. Despite a low number of training images, the model achieved high performance with gymnosperms and ferns, with 100% and 95. 7% correct detection, respectively. Figure  4  shows results from test set A for the NEVP, FSU, and CAY data sets. Detection of reproductive structures was more effective on specimens from the CAY data set than on specimens from NEVP and FSU. This can probably be explained by a combination of complementary factors such as: (1) a higher number of specimens per species in the CAY data set than the FSU data set (with a mean of 21 specimens per species in CAY and 14 specimens per species in FSU), and (2) highly visible reproductive structures of tropical and equatorial species compared to flowers and fruits of temperate species in the NEVP data set.
It is noteworthy that five of the six herbarium specimens annotated as "non-fertile" by P. B. because no reproductive structures were visible (case 1) were similarly annotated by our trained model. Likewise, four of the five herbarium specimens annotated as "fertile" by P. B. in contrast to the incorrect original annotation (case 3) were annotated similarly by the trained model. This illustrates the potential of this technology to potentially detect incorrect annotations in herbarium databases. It should be noted that even if P. B. achieved 80% accuracy, the error rate in the original annotations of the very difficult subset of test set A is only 7% (cases 1, 2, and 5 cannot be considered as errors). Human annotations of 100 randomly selected specimens from the PHENO test set by P. B. , a non-expert of that flora, offered complementary results.
As most herbarium data sets present a long tail distribution of their data (with most species represented by a small number of specimens), it is important to confirm high capacity of correct annotations even for rare species for which training data are often not available. It is interesting and perhaps surprising that the models were more successful at detecting specimens with flowers present than specimens with fruit present. This result may be explained by several potential factors: (1) flowers can be more conspicuous because of their lighter colors, compared to dry fruits which are often darker than or a similar color to leaves or stems; (2) for a particular species, numbers of flowers on an inflorescence may be greater than the number of fruits, as several flowers can abort before developing into fruits; (3) mature fruits are often less well attached to the rest of the plant compared to flowers and can thus be easily lost during specimen preparation or handling; and (4) less data were available for the training phase of the fruit detection than for the flower detection task.
Considering the difficulty of the task, the good performances achieved by models using data set C (Herbarium-split) indicate that new data sets coming from herbaria that were not in the training data set can still be correctly annotated for phenology. Because managing conditions, specimen preparations, digitization parameters, and taxonomic annotations can vary considerably among different herbaria, this result is promising. The similar results on the three different data sets (NEVP, FSU, and CAY) representing three distinct American floras demonstrate that our approach can be efficient for diverse plant groups from different environments and habitats and with highly distinct morphologies. The efficiency among the three studied clades (angiosperms, gymnosperms, and ferns) is also of great importance, demonstrating that this approach is effective on distant clades with highly dissimilar reproductive structures.
g. , flowers, inflorescences, leaves, fruits), especially in agricultural contexts for yield evaluation; (2) pathology detection (for plant pathologists interested in investigating disease on herbarium specimens, e. g. ,  Meineke et al. , 2018) ; and (3) annotation of morphological features of specimens, e. g. , for rapid selection by professional taxonomists. New and enriched visual botanical  The global accuracy on the whole test set is computed using the average of the accuracy on each subset weighted by their proportion in the whole test set, i. e. , 84. 1%, 1. 7%, 12. 2%, and 2. 0%, respectively, for the true positive, false positive, true negative, and false negative subsets. APPENDIX 1. Details of the herbarium specimen annotation process. Data collected on herbarium specimens in this study were obtained according to the following method: 1.
This can result in a difference between the description on the specimen label and the visible information on the specimen duplicate in the herbarium. 2. Collector sends his specimens to one or several herbaria, accompanied by a label with the collector-provided data mentioned above. http://www. wileyonlinelibrary. com/journal/AppsPlantSci © 2019 Lorieul et al. 3. Physical specimens are mounted on herbarium sheets with a herbarium label. Information from the herbarium label is then transcribed by herbarium personnel into an electronic database. In some workflows, details about the reproductive condition reported on the label are recorded (e. g. , into a specific field or into a field containing the entire plant description). 4. The specimen-level information recorded in the herbarium database system can be updated when the herbarium specimen or image of the specimen is studied after its initial preparation.
These updates can include annotations related to identification (based on opinions of experts who have analyzed the specimen). Annotations related to the reproductive condition of the specimens can also be performed if, for example, that information was not provided on the label or was not recorded in the initial database transcription step. In some rare cases, an annotation may report that a herbarium specimen is without reproductive structures, while the label notes that plant was observed in the field with reproductive attributes. This second example can be understood by several possibilities: (1) the collector was not able to collect a fertile part of the plant (e. g. , the fertile material was too high or too far from the collector, which can be often the case in tropical forests), (  2 ) not all of the duplicates have fertile material, or (3) flowers and fruits can be fragile and become separated from the specimen when specimens are old or frequently manipulated.
ACKNOWLEDGMENTS The authors thank the herbarium staff, field botanists, students, and others who have invested considerable time to collect, prepare, store, curate, and annotate the thousands of herbarium specimens that are used to conduct these studies. This work was supported in part by  iDigBio (U. S. National Science Foundation [NSF ] grants  EF-1115210  and  DBI-1547229 ), the  New England Vascular Plant project  ( NSF DBI-1209149 ), and an " Investissement d' Avenir " grant managed by  Agence Nationale de la Recherche  (CEBA, ref. ANR-10-LABX-25-01 ).
5. 1. 2. Sparse multiclass support vector machine using group lasso (MSVMGL): Let W be a d × C matrix, where d and C represent the number of features and the number of classes, respectively. Denote by W :k ∈ R d the k-th column of W. In multiclass support vector machine, an observation x is classified to one of the C classes using the following rule: y = arg max k∈{1,. ,C} W T :k x. Given n training observations (x i , y i ), i = 1,. , n, the goal of  Blodel et al. (2013)  is to estimate a row-wise sparse solution W by solving the following problem. min    n i=1 k =yi max(1 -(W T :yi x i -W T :k x i ), 0) 2 + λ W 2,1   . (54) In  Blodel et al. (2013) , the authors use the block coordinate descent method for solving this problem. The code is published at https://github. com/ mblondel/lightning.
Datasets We evaluate the performance of comparative algorithms on two synthetic datasets and a collection of real world datasets. The datasets are generated in the following ways. For the first setup S1, we generate a three classes classification problem. Each class is assumed to have a multivariate normal distribution N (µ k , Σ), k = 1, 2, 3 with dimension q = 500. All elements on the main diagonal of covariance matrix Σ are equal to 1 and all other elements are equal to 0. 6. The first 35 components of µ 1 are 0. 7, µ 2j = 0. 7 if 36 ≤ j ≤ 70 and µ 3j = 0. 7 if 71 ≤ j ≤ 105 and 0 otherwise. For each class, we generate 100 training samples, 100 tuning samples and 500 test samples. For the last setup S2, we generate a three-class classification problem as follows: i ∈ C k then X ij ∼ N ((k -1)/2, 1) if j ≤ 100, k = 1, 2, 3 and X ij ∼ N (0, 1) otherwise, where N (µ, σ 2 ) denotes the Gaussian distribution with mean µ and variance σ 2.