dataesr/scientific-paragraphs-categorization huggingface dataset:
Results We recovered a total of 151 red-billed tropicbird foraging trips with both GPS and auxiliary data, and another 933 trips with GPS data only (Table  1 ). Within the dataset informed by auxiliary sensors, we were able to classify 83. 7% of the GPS positions to either resting, foraging or travelling, representing 10. 4% of the complete dataset (including birds equipped only with GPS loggers). After leaving out 10% of positions with known behaviours for model validation, the maximum percentage of supervision within the informed and complete GPS datasets were 75% and 9%, respectively. From left to right, auxillary sensor set-up, total number of tracked birds with specified auxillary sensor set-up alongside total sensor set-up deployments, total number of foraging trips, total number of registered GPS positions registered, and the number and percentage of GPS positions with known resting, foraging, and travelling states based on the combination of sensors used.
Accelerometers detected more foraging positions than TDR, recording dives that were shallow (0. 78 ± 0. 36 m) and short (1. 41 ± 0. 55 s) (Tables  1  and 2 ). Wet-dry loggers detected the most resting and travelling positions (Tables  1  and 2 ). Given the conditions for known states used, we did not predict foraging based on wet-dry data alone nor did we predict resting or travelling based on the TDR data alone (Table  2 ). The total number, percentage, and the number of positions uniquely identified as known resting, foraging, and travelling based on accelerometry (ACC), wet-dry state (WD) and time depth recorders (TDR). Percentages were calculated based on the total number of GPS positions with each sensor type. The unique number of positions indicates the number of positions that were uniquely identified as a given behaviour by each sensor type given that some positions were informed by more than one sensor simultaneously.
As in the auxiliary datasets, the HMM results of all models consistently suggest that tropicbirds spend most of their time resting on water, followed by travelling and foraging (Fig. 2, S4 ). The transition probabilities between behavioural states also indicate that the probability of remaining in resting from one position to another (0. 82 ± 0. 05) is much higher than remaining as travelling (0. 76 ± 0. 05) and foraging (0. 59 ± 0. 09), and this relationship becomes even stronger with the inclusion of known states (leading to 0. 90 ± 0. 02, 0. 79 ± 0. 05, 0. 47 ± 0. 16 for resting, travelling and foraging respectively with the inclusion of 75% known states, S6). While the turning angle distribution remains similar for the three states with increasing semi-supervision, the distribution of step lengths for the foraging state changed, becoming more overlapped with that of travelling (Fig.
96 ± 0. 01 to 0. 97 ± 0. 01, and remaining at 0. 96 ± 0. 01), while the sensitivity of foraging decreased (from 0. 26 ± 0. 14 to 0. 21 ± 0. 08). However, these values of sensitivity and specificity were influenced by an uneven number of known resting, foraging and travelling positions, with far more resting and travelling positions than foraging. Therefore, despite the overall improvements to the model, the precision of foraging (the proportion of correctly identified foraging positions) remained low (increasing from 0. 03 ± 0. 01 to 0. 13 ± 0. 05), with a high number of resting or travelling positions misclassified as foraging (86 ± 16 and 10 ± 3 respectively) in comparison to the number of positions correctly classified (26 ± 9). Restricting the dataset with HMM classification probability resulted in an increase in model accuracy (Fig.
Improving behavioural classification for opportunistic foragers Whilst semi-supervised learning can improve association between observed movement metrics and desired behavioural states, limitations exist. In such instances, the inclusion of additional auxiliary sensors, such as TDR, accelerometers, and/or cameras, may be necessary across the full dataset to identify less frequent behaviours such as prey-capture attempts, and achieve satifcatory model performance. If the sampling resolution of the GPS positions is greater than the duration of certain behaviours, the signal of these behaviours may be obscured by others associated to the same GPS fix, and thus the application of auxiliary sensors may need to be coupled to increases in the temporal resolution of GPS locations. Although HMMs have been shown to be relatively robust against reductions in resolution in comparison to other methods, such as deep learning  [16, 17] , the infrequency of diving behaviour may make it especially difficult for the models to correctly identify  [52].
Future research In the present study, we highlight the benefits of semisupervision in HMMs while creating awareness of possible misclassifications and the importance of cross validation. Whilst using real world tracking data allowed us to demonstrate the applied ramifications of this in a biological context, we were unable to measure the absolute increase in accuracy related to semi-supervision and suggest that a follow-up simulation study could greatly improve our overall understanding of limitations of HMMs. Such a study would comprise of creating datasets with increasing levels of overlap between state distributions, and measuring how HMMs of these datasets react to increasing semi-supervision. This would allow researchers to create guidelines based on the initial distribution of data to understand if, and/or how much semi-supervision is needed to improve the overall classification.
Conclusions Semi-supervision increased model accuracy, even when positions with inferred behaviours represented a small proportion of the dataset. This increase was uneven among the three basic behaviouralstates, with stronger increases in the sensitivity of resting and the specificity and precision of foraging, while travelling remained relatively stable. Despite these improvements, the behavioural inference levels of foraging remained low compared to those of species using commuting foraging trips, and may not be enough for the analysis of foraging habitat use and preferences. Precaution should be taken in the identification and use of foraging behaviour states in opportunistic foragers, such as species searching for prey across a homogeneous environment. The nature of the foraging behaviour of species foraging on the go may lead to an over-fitted identification of foraging behaviour.
Acknowledgements We are thankful to the field teams from Project Biodiversity,  BiosCV  and  Projecto Vitó  as well as to all technicians and volunteers involved in data collection and field coordination, notably  Carolino dos Rei Fernandes ,  Marcos Hernández-Montero  and  Vania L. B. Varela ,  Ivandra S. G. C. Gomes  and  Herculano A. Dinis.
Cell biology assays Cell proliferation was evaluated by bromodeoxyuridine (BrdU) labeling assay (Roche) according to the manufacturer's instructions. BrdU pulse was of 24 h and cells were counted in triplicate and the number of BrdU positive nuclei was annotated. Apoptosis was detected by terminal deoxynucleotidyl transferase-mediated UTP nick end labeling (TUNEL) assay with the In Situ Cell Death Detection Kit Fluorescein (Cat. 702 No. 1684795, Roche Applied Sciences), according to the manufacturer's instructions. Images were acquired with Carl Zeiss microscope (Axio Observer Z1) and AxioVision Digital Image Processing Software. Cells were counted in triplicate and the number of TUNEL-positive nuclei was annotated. For overexpression experiments, a double-labeling assay for detecting apoptotic cells, using the terminal deoxy-nucleotidyl transferase (TdT)-mediated dUTP nick end-labeling (TUNEL) assay, and antigens of interest (pcDNA3 β-arrestin1 HA) using anti-HA (sc-7392 Santa Cruz), by immunofluorescence.
Chromatin immunoprecipitation Chromatin immunoprecipitation (ChIP) analyses were performed on chromatin extracts according to manufacturer's specifications of MAGnify Chromatin Immunoprecipitation System kit (Invitrogen). Sheared chromatin was immunoprecipitated with 5 µg of the following antibodies: anti-β-arrestin1 (Clone 10 cat. 610550 BD Biosciences) or normal mouse IgG, provided by the kit, was used as negative control. Eluted DNA was amplified by qPCR using EpiTect ChIP qPCR Assay (Qiagen) for the indicated genes  (Mouse Cdc25a, Trp73, Cdkn1b, Casp3, Casp7, Zeb1, Zeb2, Birc5, Vim and Fn1). As control we used Actin and Gapdh gene. Data are presented as input percentage enrichment over background.
Immunohistochemistry (IHC) For immunohistochemical staining, 5 μm FFPE sections were used. Mouse FFPE sections from different stage of cerebellum development (p2, p4, p7, p10, p15) were stained with the following primary antibodies: anti-CASPASE-3 (D3R6Y, Cell Signaling), anti-CDC25A (PA5-77902), anti-P27 (sc-1641; Santa Cruz Biotechnology) and then stained with hematoxylin and eosin (H&E). CASPASE-3 and CDC25A indexes were generated based on the count of DAB positive cells on the total number of cells, quantified using the bioimage analysis software QuPath  (Bankhead et al. , 2017).
Our machine learning feature sets were derived from genome annotation data of carbohydrate-active enzymes (CAZymes), peptidases, secondary metabolite clusters (SMCs), transporters, and transcription factors. We found that machine learning could successfully predict fungal lifestyles and traits across taxonomic groups, with the best predictive performance coming from feature sets comprising CAZyme, peptidase, and SMC data. While phylogeny was an important component in most predictions, the inclusion of genomic data improved prediction performance for every lifestyle and trait tested. Plant pathogenicity was one of the best-predicted traits, showing the promise of predictive genomics for biosurveillance applications. Furthermore, our machine learning approach revealed expansions in the number of genes from specific CAZyme and peptidase families in the genomes of plant pathogens compared to non-phytopathogenic genomes (saprotrophs, endoand ectomycorrhizal fungi).
These lifestyle and trait categories encompass generalizations about the pathogens that make up each group, including how they infect their hosts and spread disease through ecosystems. However, it is often challenging to assign species to discrete lifestyle categories due to the complex behaviours of many fungi and their ability to exhibit multiple lifestyles; this becomes particularly problematic when trying to understand the mechanisms of fungal pathogenicity and plant disease resistance  17, 18. Given the subjectivity in assigning pathogens to lifestyle and trait categories, there has been increased research to explore these categories at the genome level, especially given the increasing availability of whole genome sequences  [19] [20] [21]. Additionally, the continued growth of online fungal genomic resources and databases such as FungiDB  22  , Ensembl Genomes  23  , NCBI RefSeq  24  , and MycoCosm  25  are providing researchers with powerful resources to compare fungi with different lifestyles and traits at the genome scale.
Phytopathogenic lifestyles and traits can be predicted from phylogenetic and genomic data Our fungal lifestyle database, FunLifeDB, comprising information on 533 fungal species (582 genomes; https:// biosa fe. cs. mcgill. ca/ funli fedb/) was the source of lifestyle and trait data for the subset of 387 published genomes (from 355 species) we analysed (Suppl. Data S1, Fig. 1 ). We observed a strong phylogenetic signal in the genomic data. All PCAs using the genomic features showed a clear separation of the Ascomycota (Pezizomycotina and Taphrinomycotina) and Basidiomycota (Pucciniomycotina and Agaricomycotina), particularly on the first two principal components (Fig. 2 ). Still, the obligate biotrophs of both Ascomycota and Basidiomycota clustered together on the PCAs for CAZymes and peptidases, indicating common genomic features for this lifestyle (Fig.
Expansions and contractions of specific CAZyme, peptidase and secondary metabolite cluster gene families drive DendroNet's predictions of phytopathogenic lifestyles and traits The three top-performing genomic feature sets across phytopathogenic lifestyles and traits were CAZymes alone, CAZymes + MEROPS, and CAZymes + MEROPS + SMCs (Suppl. Data S2). Patterns in specific gene families arose as drivers for DendroNet's predictions (Suppl. Data S4).
This finding validates recent results obtained with a subset of the MycoCosm database  21  , which demonstrated that within the Dothideomycetes, plant pathogens could be distinguished from saprobes with greater than 95% accuracy using machine learning on genomic data. Our analyses, performed on a larger number of species with a broader range of both taxonomic and lifestyle groups, confirm that not only can plant pathogens be distinguished from saprotrophs, but they also have a unique profile relative to other pathogens. Additionally, the high predictive performance of DendroNet with plant pathogen host type (angiosperms vs. gymnosperms), and particularly with foliar pathogens, indicates that there are also strong genomic signals associated with phytopathogenic traits. These findings related to plant pathogenic fungi have important implications for biosurveillance, specifically for predicting the lifestyles and traits of novel or unknown species.
Our results provide strong support for the functionality of predictive genomics, but as the lifestyles and traits of more fungi are curated and updated, DendroNet should continue to be trained and tested to determine whether its predictive power extends to a broader range of taxonomic groups and lifestyles. While we tested all possible combinations of the five genome annotations included in this study, using CAZyme data alone produced the best prediction results for most of the phytopathogenic lifestyles and traits, and CAZymes were also included in all three of the top-performing feature sets. This result is not surprising given that CAZymes are crucial for most fungal plant pathogens, allowing them to colonize their hosts by overcoming the barrier of the plant cuticle, remodeling the fungal cell wall to avoid recognition, and deconstructing the host cell wall  [45] [46] [47].
Fungal lifestyle database We created a lifestyle database for 533 fungal species (582 genomes) with data available from the Joint Genome Institute's (JGI) MycoCosm Fungal Portal  25. The species in our database span two ascomycete subphyla (Pezizomycotina, Taphrinomycotina) and two basidiomycete subphyla (Pucciniomycotina, Agaricomycotina). Each species was given taxonomic labels for phylum, class and order using the fungal nomenclature of Index Fungorum 76. To categorise each of the species in our database into its respective lifestyle(s) and identify important ecological traits, we used both the information and references from MycoCosm as well as information found in an independent literature search. We used 1014 peer-reviewed publications in assigning lifestyles and traits to fungi in the database.
Genome analyses We used both principal component analysis (PCA) and machine learning to perform genomic comparisons amongst a subset of species from the database with available data. For machine learning, we used DendroNet, a phylogeny-aware method of training machine-learning models that incorporates both phylogenetic and genomic data  77  ; this allowed us to separate the signals from phylogeny and gene content and determine which genomic features were associated with specific lifestyles and traits.
Genome annotations The genomic data for each MycoCosm species can only be used if there is an associated genome reference, or with explicit permission from the principal investigator (PI). Therefore, we downloaded the genome annotation data (gene counts) available from MycoCosm for a subset of 355 fungal species (387 genomes) from our lifestyle database consisting of 362 published and 25 unpublished genomes (used with PI permission). We obtained data from the five annotation groups available from MycoCosm: secondary metabolite clusters (SMCs-7 clusters), carbohydrate-active enzymes (CAZymes-220 families, subfamilies not included), peptidases (MEROPS-144 clans/families), membrane transport proteins (transporters-522 families), and transcription factors (transfactors-65 families). The gene counts from each of these five groups were then aligned to their respective species in the lifestyle database for comparative genomic analyses.
Phytopathogenic lifestyles and traits We performed the genomic analyses on the gene count data for all 387 genomes, but we used only a subset of lifestyles relevant to plant pathogenicity for labelling: pathogenic (includes animal, fungus, and plant pathogens), plant pathogenic (plant pathogens only), biotrophs (B), obligate biotrophs (OB), hemibiotrophs (HB), and necrotrophs (N). We also included saprotrophs (S) as a non-pathogenic lifestyle comparison, as well as ectomycorrhizal (ECM) and endomycorrhizal (ENM) species to compare plant-interacting, but non-pathogenic, fungi to phytopathogenic fungi. The ENM category comprised both ericoid mycorrhizal species and ectendomycorrhizal species. The phytopathogenic traits (gymnosperm, angiosperm, stem, foliar, and root pathogens) were also included as labels for the analyses.
Principal component analyses PCAs were performed using R software (ver 3. 5. 1)  80. Pre-processing of the data for each PCA was performed as follows: all species rows with NA values (no gene annotation) were removed, variables with zero variance and near zero variance were removed from the data using the function nearZeroVar from the caret R package  81  , and the data were scaled to unit variance and centered. The function PCA was used from the R package factoextra  82. We performed PCAs separately for each genome component (i. e. CAZymes, MEROPS, SMCs, transporters, and transfactors) to avoid excessive noise observed for joined data analyses  20.
Dataset preprocessing Membership in each of the lifestyles and phytopathogenic traits described above was considered as a separate binary-classification problem for the machine learning analyses, and the corresponding gene counts from each annotation group were used as features. For each of the target lifestyle/trait classes, we used a total of 31 genomic feature sets to train a predictive model: each of the five annotations (CAZymes, MEROPS, SMCs, transporters, and transfactors) as individual genomic feature sets, and every possible combination of the annotations (26 possible combinations). We performed the machine learning analyses for the phytopathogenic traits only on species belonging to the plant pathogen class (subset of 138 species).
DendroNet architecture DendroNet models have two components. The first component is a base model architecture, used to make predictions given a set of input data and a target output. The base model architecture used in this study was a logistic regression classifier. The second component is a neural network with the same topology as the phylogenetic tree that relates all the samples in the dataset. This neural network is used to determine the optimal weights to be used for the base classifier's predictions at each location in the phylogenetic tree. Regularization is used to encourage the use of similar weights in species that are closely related. For this study, we retrieved the Dikarya (Ascomycota + Basidiomycota) phylogenetic tree from MycoCosm  25  , which was pruned to the species being analysed prior to input into DendroNet models.
A pruned version of this tree with the 387 fungal genomes we analysed is included in Supplemental Data S6 in Newick format. To make the tree, proteins from all 387 genomes were clustered using MMseqs2 83 (Version: 0188988235c6f1a8e90f327827c73f981db8a19a). Orthologous proteins were identified from the clusters allowing for paralogs and up to 100 missing genomes per cluster. When paralogs were present, only one copy from each genome was retained for alignment. Proteins from 2580 selected clusters were aligned using MAFFT  84 (v7. 123b ). Divergent regions and poorly aligned positions were cleaned using Gblocks  85  with options '-t = p-e =. gb -b4 = 5 -b5 = h'. The resulting cleaned alignments were concatenated and used for tree building with FastTree 86.
Feature importance evaluation We investigated the predictive power of each genomic feature set towards the lifestyle and trait classes. To separate the significance of a genomic feature set from the phylogenetic signal, we used the following process: first, the baseline predictive power of phylogeny was established for each target lifestyle and trait. This was done by training a DendroNet model that used only a bias term as a feature value, which produced predictions that were made using solely the phylogenetic placement of each species in the dataset, similar to a maximum parsimony tree. We therefore refer to this baseline phylogenetic prediction as the parsimony model. Next, for each genomic feature set, we trained a DendroNet model using both that feature set and a bias term, allowing for the use of phylogenetic placement information.
Statistical analyses Prior to performing statistical analyses, we determined the distributions of the relevant data with Shapiro-Wilk normality tests. To compare DendroNet's performance across all 31 genomic feature sets, we performed a nonparametric analysis of variance with repeated measures (Friedman test) on the AUC scores for the lifestyles and traits (separate analyses) followed by a Nemenyi test for post-hoc analysis. To determine whether the feature + parsimony models resulted in a significantly increased AUC score compared to the respective parsimony models, we performed paired, one-tailed tests: either a paired t-test or a Wilcoxon signed-rank test depending on whether the paired differences followed a normal or non-normal distribution. To compare the gene counts from the top CAZyme and peptidase predictors for plant pathogenicity, we performed independent t tests on the gene counts from phytopathogenic genomes ('Plant Pathogen' = YES in FunLifeDB) relative to non-phytopathogenic genomes ('Plant Pathogen' = NO in FunLifeDB).
Acknowledgements This work was funded by  Genome Canada 's  Large-Scale Applied Research Project  ( LSARP  project # 10106 ,  bioSAFE :  Biosurveillance of Alien Forest Enemies ) with additional funding from  Genome B. C. ,  Genome Québec ,  Canadian Food Inspection Agency, Natural Resources Canada , and  FPInnovations. We also acknowledge the support of the  Natural Sciences and Engineering Research Council of Canada (NSERC CGS -D). The work conducted by the  U. S. Department of Energy Joint Genome Institute  (https:// ror. org/ 04xm1 d337), a  DOE Office of Science User Facility , is supported by the  Office of Science of the U. S. Department of Energy  operated under Contract No. DE-AC02-05CH11231.
Single-cell transcriptional mapping identifies distinct MLP1 and MLP2 subsets To characterize lymphoid differentiation trajectories at a clonal resolution, CD34 hi HSPCs from HSC-xenografted mice at week-3 were subdivided into four compartments including CD127 -and CD127 + ELPs before analysis by single-cell RNA sequencing (Figure  S5A ). This procedure ensures an equal representation of the corresponding cell fractions, thereby maximizing the capture of discrete cellular states. After quality control and doublet exclusion the final dataset was composed of 37,092 cells which were projected on a 2D space (UMAP) (Figures  6A  and 6B ). Graph-based Louvain clustering disclosed 20 clusters (C0-19) associated with 13 cell types identified based on a set of 42 manually selected lineage gene markers among the 3,844 DEGs (Figure  6C ).