dataesr/scientific-paragraphs-categorization huggingface dataset:
Behavioral data. Behavioral sampling Behavioral observations were made by observers blind to the focus of this study. We considered only dyadic interactions between adult females and adult or subadult males. Ad libitum observations and 5-min focal sampling  (Altmann, 1974)  were performed daily by recording, inter alia, affiliative and agonistic interactions between group members. In this study, we used four submissive behaviors: (i) "avoidance": when an individual A walks away from an individual B who is approaching, (ii) "displacement": when A walks away from B and B takes the place of A, (iii) "escape": when A flees away from B who expresses aggression and (iv) "submissive vocalization": when A emits a typical submissive vocalization after B expresses aggression. We calculated intra-and intersexual hierarchies based on two different datasets: a "full dataset" that comprised all submissive interactions (which were preceded by an aggressive event, or not) and a "ritualized dataset" that included only submissive interactions that were not preceded by an aggressive event.
Behavioral dataset We considered a total of 2,768 h (40,678 focal observations) of focal data in addition to ad libitum observations. For the analyses based on the full dataset, we used interactions from both focal and ad libitum observations to increase our sample size. In the ritualized dataset, we used only interactions from focal observations to filter out submissive behavior that followed an aggressive event. In the linear regression models performed below, we used only the full dataset given the similarity of the results based on these two different datasets (see results). For the analysis of intersexual conflicts, we selected from the full dataset all the dyadic interactions between an adult female and an adult male, recorded during focal observations, for which the outcome was unambiguous, i.
Dominance hierarchies We used the functions DS and ISIranks from the R package EloRating  (Neumann et al. , 2011)  to compute the intersexual hierarchies with both normalized David's score  (David, 1987)  and I&SI (de Vries, 1998) to evaluate whether these two rank estimates yielded different results suggesting potential methodological biases. David's score is calculated for each individual based on the observed dyadic proportions of wins. Namely, the number of dyadic wins (where the opponent submits to the focal individual) is divided by the sum of dyadic wins and losses (where the focal individual submits to the opponent) over other groupmates (for a formal definition, see  David, 1987). As such, the difference among two individuals' scores is more informative than the difference between their ordinal ranks, and reflects the extent of asymmetry in dominance-based power between these individuals.
We used normalized David's scores because they correct for the possibility that the observed outcomes occur by chance. Such a possibility is calculated on the basis of a binomial distribution with each animal having an equal chance of winning or losing each agonistic interaction  (de Vries et al. , 2006). This correction is crucial when the number of interactions greatly differs among dyads, like in our study group. However, David's score may be sensitive to missing data (non-interacting dyads;  Neumann et al. , 2011). Indeed, in our dataset, almost two out of three (62 ± 15%; ±SD) intersexual dyads never interacted agonistically, on average, while this figure was 32 ± 17 and 71 ± 10%, respectively, in male-male and female-female dyads (Supplementary Table  S1 ). Consequently, intrasexual agonistic interactions may be highly influential in inference of intersexual hierarchies.
Social integration and a liation rates During focal samplings, grooming events and their duration were recorded. For each 6-month time block, we used the total time of grooming recorded among females to create a female-only grooming directed network (function graph_from_data_frame from the package igraph;  Csardi and Nepusz, 2006). We calculated the in-degree of each female from the above networks, as a proxy of social integration and support. We used only the number of females grooming (and not groomed by) a female, because we consider these individuals more likely to offer their support during agonistic interactions. Similarly, we calculated the total (in and out) degree of each male in networks including only intersexual grooming interactions (number of females grooming or being groomed by each male).
Statistical analyses First, at the level of the population, we ran a Spearman's rank correlation test to study whether the intersexual hierarchy differed when using either David's score or I&SI. We then compared the percentage of males dominated by an average female in the two datasets (including all submissive interactions vs. only ritualized submissive interactions) using a Spearman's rank correlation test. Second, at the dyad level, we ran a generalized linear model (GLMM) with a binomial distribution and a logit function to test whether the probability of a given female to outrank a given male was influenced by the following fixed factors: female and male intrasexual ranks and ages, female's in-degree in the female-only social network and its corresponding quadratic term (suggested following a graphical exploration of the data), and male's total (in and out) degree in the social network including only intersexual grooming interactions and either season or sex ratio.
Metrics of dominance hierarchy and robustness tests Female mandrills elicited male submissive behaviors in 2. 4 ± 2. 1% (±SD) of intersexual agonistic interactions. However, we found that females can outrank males, with a female outranking, on average, 11. 3 ± 6. 2 % (±SD) of males (results based on the full dataset and David's score or 18. 2 ± 8. 1% based on I&SI). We found a positive correlation between David's score and I&SI metrics across 6-month time blocks (Spearman's rank correlation, rho = 0. 53, p = 0. 03; see also  Kappeler et al. , 2022). When we calculated the hierarchy 500 times using David's score, each time randomly selecting 50% of all agonistic interactions, an average female outranked 12. 1 ± 0. 8% (mean ± SD) of males (Supplementary Figure  S1A ). When we calculated the hierarchy 500 times using I&SI, we found that no iteration resulted in an intersexual hierarchy where all females are outranked by all males and an average female outranked 16.
3 ± 5. 1% (±SD; results based on David's score; see previous paragraph for the results based on the full dataset) of males. Despite these congruent hierarchies resulting from various metrics and datasets, the discrepancy between the percentage of intersexual interactions where a male showed submission toward a female and the percentage of males dominated by an average female appears puzzling. In addition, in 92. 7 ± 6. 0% of female-dominant intersexual dyads, the two individuals never interacted agonistically, while in male-dominant dyads the corresponding percentage was 60. 3 ± 14. 4%. From a total of 5,433 intersexual dyads, only 1,844 interacted agonistically at least once, including 1,805 male-dominant dyads and only 39 female-dominant dyads. In these 39 dyads, males showed submission, on average, in 18.
Discussion In this study, we find that adult female mandrills can occasionally elicit male submissive behaviors, and can outrank males, despite being much smaller and traditionally considered strictly dominated by males. We further find that the intersexual hierarchy represents an interdigitation of the female and male intrasexual hierarchies, is not sensitive to the behavioral dataset used for its construction, and fluctuates with sociodemographic variables such as breeding seasonality and group-sex-ratio. Below, we first discuss the biological relevance of our hierarchical inferences, before envisaging potential factors affecting the dynamics of female-male dominance relationships. We used various methods, including David's score  (David, 1987)  and I&SI (de Vries, 1998), and different datasets to establish intersexual hierarchies.
The average percentage of males outranked by an average female revolved around 9-16% independently of the dataset or method used to infer hierarchies. However, such a result is at odds with the percentage of agonistic interactions where females elicited male submissive behaviors, which is closer to 2% overall. Such discrepancy may reflect methodological problems or biological processes potentially specific to our study system, or a combination both; although similar discrepancies have also been reported in strictly femaledominant species, like Verreaux sifakas where females win the minority of intersexual agonistic interactions suggesting that females can indeed outrank males though they lose most fights against them  (Lewis et al. , 2022). Regarding methodological problems, mandrills live in exceptionally large groups and in dense forests with low visibility, making it difficult to observe interactions between all group members.
While males may often direct aggression to subordinate females in male-dominant dyads in a context of sexual coercion  (Smit et al. , 2022) , females may not bother about harassing those males they outrank. Indeed, male-dominant dyads interact agonistically over twice as often as female-dominant ones (3. 3 ± 4. 8 vs. 1. 6 ± 1. 1 times; ±SD). Altogether, these results highlight the caution needed when interpreting the biological relevance of hierarchies emerging from datasets with high number of non-interacting dyads and in our case, female mandrills may occasionally outrank males but potentially in a lower frequency than our results indicate. Nevertheless, our results show that females can occasionally elicit male submissive behaviors and suggest that strict male dominance is unlikely in this species.
, 2006a). Such coalitions have also been observed in other primates living in polygynandrous groups where females are philopatric (geladas:  Dunbar, 1975 ; Guinea baboons (Papio papio):  Goffe et al. , 2016)  and may often contribute to counter-balance male-biased dominance in species where physical asymmetries between sexes are extensive. This study contributes to a growing body of evidence that draws a more dynamic landscape of female-male dominance relationships, where intersexual dominance can fluctuate across time and contexts. Our results suggest that females may outrank a small proportion of males in a highly dimorphic nonhuman primate, although this remains to be confirmed due to limits in the resolution of our datasets. We further found that females outrank more males during the mating season and when they are high-ranking and more socially integrated; while they preferentially outrank poorly socially integrated males with low competitive abilities.
Acknowledgments We thank (i) all past and present field assistants who collect daily behavioral and physiological data on the study population of the Mandrillus Project, (ii)  Patrícia Izar  and two reviewers and (iii) the  SODEPAL-COMILOG  society for their logistical support. This is a Project Mandrillus publication number 29 and ISEM 2022-301 SUD.
Plasmid Dataset The dataset used in this study consists of 13,525 complete genomes from 2,421 species retrieved from NCBI RefSeq database of high-quality complete nonredundant prokaryotic genomes (ftp://ftp. ncbi. nlm. nih. gov/genomes/refseq/, last accessed in May 2019). These genomes contain 11,805 plasmids. To avoid the misidentification of ICEs as conjugative plasmids in chromids or secondary chromosomes, we excluded from further study the 419 plasmids larger than 500 kb  (Harrison et al. 2010; Smillie et al. 2010 ). The complete list of 11,386 plasmids can be found in supplementary table  S1 , Supplementary Material online.
HMM-HMM Alignments The protein alignments of the relaxases were retrieved from  Guglielmini et al. (2011)  and then used to produce HMM profiles with hhmake (command line: hhmake -I ,msa. -o HHM -cons) from HH-suite3. 0 program v. 3. 3. 0  (Steinegger et al. 2019). The alignment of HMM profiles was performed using hhalign from the same suite of programs with default parameters (command line: hhalign -t HMM1 -i HMM2 -M 50 -add_cons). We took into consideration only bidirectional alignments with a probability superior to 70% and an e-value inferior to 10 -4.
ANI Calculation ANI scores were calculated using the ani. rb Ruby script from Enveomics Collection (https://github. com/ lmrodriguezr/enveomics/tree/master/Scripts), as detailed in  Redondo-Salvo et al. (2020). Briefly, pairwise ANI scores were obtained by splitting every genome into fragments of 1. 0 kb using a sliding window algorithm with a step of 200 bp. The resulting sets of fragments were compared by all against all BLASTn, with a 70% identity threshold over 70% of the fragment length. BBH were selected and, if the number of selected fragments covered at least 50% of the smallest plasmid, an ANI score was assigned. The network layout was obtained using Gephi  (Bastian et al. 2009).
Cumulative Probability of Transitions For each tree, we computed the patristic distance matrix using the cophenetic. phylo function from the package ape (v5. 3) for R (command line: PatristicDist Matrix,-cophenetic[tree])  (Paradis and Schliep 2019). Based on the patristic distance matrices, we measured the patristic distance separating each relaxase in our dataset from the closest relaxase in the phylogenetic tree that was associated with a different mobility type. For relaxases not present in the tree, that is, those that are in a cluster but were not included in tree as the cluster representative, we used the patristic distance separating the representative of the cluster and the closest relaxase in the tree having a different mobility type. When a relaxase had a different mobility than its cluster representative we used a patristic distance of 0.
This provides for each plasmid a patristic distance to the closest plasmid of a different type. The empirical cumulative distribution function (CDF) of these patristic distances was computed for each MOB class and each mobility type, using the ECDF built-in function for python (v3. 6. 10). Empirical cumulative distribution plots were computed using the ecdfplot function from the seaborn package (v0. 11. 0) for python (command line: sns. ecdfplot())  (Waskom 2021).
Partition Proteins and Toxin-Antitoxins Proteins Detection To detect partition proteins carried by plasmids, we used HMMER v. 3. 2. 1 (e-value , 0. 001 and coverage of 50%) with HMM protein profiles for partition proteins described in  Cury et al. (2017). To detect toxin and antitoxin proteins in plasmids, we used HMMER v. 3. 2. 1 (e-value , 0. 001 and coverage of 50%) with toxin and antitoxin HMM profiles from the TASmania database  (Akarsu et al. 2019).
Hence, mobilizable and conjugative plasmids are present at similar frequencies and make up about half of all plasmids, consistent with previous results with smaller datasets  (Smillie et al. 2010). The average size of plasmids is correlated with the type of mobility, with pCONJs (median size of 111. 7 kb) being slightly, but significantly, larger than pdCONJs (96 kb, P = 0. 0015) and the latter being much larger than pMOBs (37 kb, P , 0. 001) or pMOBless (39 kb, P , 0. 001, Wilcoxon tests, fig. 2B ). Although the average size of plasmids is different depending on the class of relaxase, the trends in terms of plasmid size in relation to the mobility type are similar across MOB classes (fig. 2C ). Many plasmids encode insertion sequences (IS) that are known to promote recombination, cointegration, and transposition of genes between and within DNA molecules.
We identified ISs in all plasmids using ISEscan v1. 7. 2. 2 default options and found the highest densities in pdCONJs and the lowest in pMOBless (fig. 2D ). The dataset used in this work includes 5,666 relaxases of all the different MOB classes (fig. 2E ). The classes of relaxases are usually described as MOB X where X is the class identifier  (Garcillan-Barcia et al. 2009 ). However, some of the subclasses of relaxases are very divergent. Hence, here we will use the nomenclature MOBX to refer to the proteins retrieved by the HMM protein profile for the relaxases class or subclass X, and MOB X when we refer generically to the class. The most prevalent classes are MOB F and MOB P (particularly those retrieved by the MOBP1 HMM profile), which account for more than 62% of all relaxases and are present in both pCONJs and pMOBs.
Early Specialization of Relaxases and Frequent Recent Changes in Mobility To study the evolution of plasmid mobility we focused initially on the relaxases identified by MOBP1 (from the   1E ), they rarely cooccur in the same plasmid (fig. 4A ), they are sufficiently conserved to be good phylogenetic markers, and they are frequent in both mobilizable and in conjugative plasmids (fig. 1E ). Hence, we used the protein sequences of these relaxases for phylogenetic inference. Some of the relaxases were nearly identical in protein sequence. We removed this redundancy to accelerate phylogenetic reconstructions and to avoid uncertainty in the inferred ancestral states (which is inevitable when proteins are identical). To keep most of the genetic diversity of the dataset, we clustered the proteins that MBE aligned over at least 99% of the length with at least 99% identity.
To quantify these observations, the next sections focus on the quantification of the variation of plasmids' mobility and gene repertoires. Plasmid Size and Gene Repertoire Evolve Fast Relative to the Relaxase Sequence Gene repertoires of plasmids are known to vary very fast. To quantify their rates of change, we analyzed the differences of gene repertoires between pairs of plasmids in the light of the divergence of their relaxases (see schema in fig. 6 ). These analyses used all relaxases, that is, we did not remove those in clusters of very similar proteins, to maximize the statistical power of the analysis. We assessed the variations in gene repertoires between pairs of the 3,501 plasmids of our dataset encoding relaxases identified by the protein profiles MOBP1 and MOBF using the wGRR.
Acknowledgements Eugen Pfeifer  for providing the wGRR data and  Marie Touchon  for providing the ISEScan analysis. Manuel Ares-Arroyo  for comments and suggestions. Jorge Moura de Sousa ,  Matthieu Haudiquet , and  Olaya Rendueles-Garcia  for scientific discussions. INCEPTION  project ( PIA/ ANR-16-CONV-0005 ),  Equipe FRM  ( EQU201903007835 ),  Laboratoire d'Excellence IBEID  ( ANR-10-LABX-62-IBEID ) to E. P. C. R. and C. C. PID2020-117923GB- I00  project funded by  Spanish Ministry of Science and Innovation to FdlC and MPG-B.
Main text Recent progress in deep learning has led to significant advances in bioimage analysis  [1] [2] [3] [4]. As deep learning is data-driven, it is adaptable to a variety of datasets once an appropriate model architecture is selected and trained with adequate data  1. In spite of its powerful performance, deep learning remains challenging for non-experts to utilize, for three reasons. First, pretrained models can be inadequate for new tasks and the preparation of new training data is laborious. Because the quality and quantity of the training data are crucial for the performance of deep learning, users must invest significant time and effort in annotation at the start of the project  1. Second, an interactive user interface for deep learning, especially in the context of cell tracking, is lacking.
Third, deep learning applications are often limited by accessibility to computing power (high-end GPU). We have addressed these challenges by establishing ELEPHANT (Efficient learning using sparse human annotations for nuclear tracking), an interactive web-friendly platform for cell tracking, which seamlessly integrates manual annotation with deep learning and proofreading of the results. ELEPHANT implements two algorithms optimized for incremental deep learning using sparse annotations, one for detecting nuclei in 3D and a second for linking these nuclei across timepoints in 4D image datasets. Incremental learning allows models to be trained in a stepwise fashion on a given dataset, starting from sparse annotations that are incrementally enriched by human proofreading, leading to a rapid increase in performance (Figure  1a ).
ELEPHANT is implemented as an extension of Mastodon (https://github. com/mastodon-sc/mastodon), an open-source framework for large-scale tracking based on Fiji  5. It implements a client-server architecture, in which the server provides a deep learning environment equipped with sufficient GPU (Supplementary Figure  1 ). ELEPHANT employs the tracking-by-detection paradigm  6  , which involves initially the detection of nuclei in 3D and subsequently their linking over successive timepoints to generate tracks. In both steps, the nuclei are represented as ellipsoids, using the data model of Mastodon (Figure  1b, c ). In the detection phase, voxels are labelled as background, nucleus center or nucleus periphery, or left unlabelled (Figure  1b , top right). The nucleus center and nucleus periphery labels are generated by the annotation of nuclei, and the background can be annotated either manually or by intensity thresholding.
Sparse annotations (e. g. of a few nuclei in a single timepoint) are sufficient to start training. A U-Net convolutional neural network (U-Net CNN;  7, 8  , Supplementary Figure  2 ) is then trained on these labels (ignoring the unlabelled voxels) to generate voxel-wise probability maps for background, nucleus center, or nucleus periphery, across the entire image dataset (Figure  1b , bottom right). Postprocessing on these probability maps yields predictions of nuclei which are available for visual inspection and proofreading (validation or rejection of each predicted nucleus) by the user (Figure  1b , bottom left). Human-computer interaction is facilitated by color coding of the annotated nuclei (as true positive, false positive, true negative, false negative, or unevaluated, see Supplementary Figure  3 ) based on the proofreading.
The cycles of training and prediction are rapid because only a small amount of training data are added each time (in the order of seconds, see Supplementary Table  1 ). As a result, users can enrich the annotations by proofreading the output almost simultaneously, enabling incremental training of the model in an efficient manner. In the linking phase, we found that nearest neighbour approaches for tracking nuclei over time  9  perform poorly in challenging datasets when the cells are dividing; hence we turned to optical flow modeling to improve linking  [10] [11] [12]. A second U-Net CNN, optimized for optical flow estimation (Supplementary Figure  4 ), is trained on manually generated/validated links between nuclei in successive timepoints (Figure  1c , top left). Unlabelled voxels are ignored, hence training can be performed on sparse linking annotations.
The flow model is used to generate voxel-wise 3D flow maps, representing predicted x, y and z displacements over time (Figure  1c , bottom right), which are then combined with nearest neighbour linking to predict links between the detected nuclei (see Methods). Users proofread the linking results to finalize the tracks and to update the labels for the next iteration of training (Figure  1c , bottom left). We evaluated the performance of ELEPHANT using two types of 4D confocal microscopy datasets in which nuclei were visualized by fluorescent markers: the first type of dataset captures the embryonic development of Caenorhabditis elegans (CE datasets), which has been used in previous studies to benchmark tracking methods  13, 14  , and the second type captures limb regeneration in Parhyale hawaiensis (PH dataset, imaging adapted from  15  ), which presents greater challenges for image analysis (see below, Supplementary Video 1).