dataesr/scientific-paragraphs-categorization huggingface dataset:
Candidate comparison In order to illustrate the effectiveness of candidates in terms of memory consumption, we plotted the relationship between the number of candidates and trace arrows, against Fig. 5  We plot the results of SparseRNAFolD-Standard against two state of the art algorithms: RNAFold and LinearFold when given RNA sequence only as input against each other and an "always dangle" model on our dataset and the dinucleotide shuffled version of our dataset. (a) Memory Usage (maximum resident set size in KB) versus length (log-log plot) over all benchmark instances. The solid line shows an asymptotic fit (c 1 + c 2 n x ) for sequence length n, constants c 1 , c 2 , and exponent x for the fit. We ignored all values < 1000. (b) Run-time (s) versus length (log-log plot) over all benchmark instances.
For each tool in both plots, we report (in parenthesis) the exponent x that we estimated from the benchmark results; it describes the observed complexity as Θ(n x ). 1  We tabulate the results of the comparison between RNAFold and SparseRNAFolD-Standard when given only sequences with length > 2500 from our dataset as input and using the "exclusive dangle" model. We looked at time (s) and memory (maximum resident set size in KB) for the minimum, median and maximum length sequence within the constrained dataset.
Run the quadratic space, using the dataset that includes dinucleotide shifted elements. To emphasize the upper limit of candidate usage when executing SparseRNAFolD-Standard, we employ the "exclusive dangle" model. For a more meaningful comparison, we juxtapose the counts of candidates and trace arrows with the count obtained from a single quadratic matrix. It is important to note that the majority of algorithms employing quadratic space make use of multiple quadratic matrices. Considering this aspect, we discovered that, on average, the disparity in count between the number of candidates and trace arrows with quadratic space was approximately a factor of 100. Figure  6  highlights that the increase in candidates is consistent with the increase in length. Fig. 6  We plot the results of the number of candidates and trace arrows compared to quadratic space.
The same process is then applied to the right side, and the results are combined using a bitwise OR operation. If both sides permit pairing, the result is 0; otherwise, it is -1. This result is subsequently bitwise ANDed with a predefined large value representing infinity. Consequently, if the result was 0, the outcome is 0, and if the result was -1, it is set to infinity. Finally, we add our energy value to this outcome. This sequence of operations ensures that both sides permit pairing, all without the need for explicit branching via an if statement -see Algorithm 2. We observed an approximately 17% reduction in prediction time for the largest sequence in the dataset (10241) when comparing the original SparseRNAFolD to SparseRNAFold V2. 0. Run-time (s) versus length (log-log plot) over all benchmark instances.
Comparing these three versions on a large dataset, we concluded that the SparseRNAFolD-Triplet implementation is the most efficient in terms of memory, and SparseRNAFolD-Trace is the most efficient in terms of time. These two versions showcase how space and time trade-offs can improve performance for a specific application. The SparseRNAFolD-standard version provides a middle ground for improvement in both time and space and has been chosen as the standard implementation of our algorithm. While guaranteeing the MFE structure and matching the energy of RNAFold, our SparseRNAFolD is on par with LinearFold on memory usage and run time for sequences up to about 1000 bases. This provides a promising starting point to bring dangles contributions to pseudoknotted MFE structure prediction methods in which memory usage is the prohibitive factor  [17].
While this study and others  (43)  confirm that GPVI is a promising target for impairing metastasis, its relevance to human pathology is missing. Thus, better preclinical models and humanized therapeutic tools, with ideally no thrombocytopenic effects, are needed. To do so, we exploited our previously established genetically modified mouse strain expressing human GPVI (hGPVI)  (45). While these mice are viable with no hematological defects, they express a knockin copy version of hGPVI that makes it the best candidate to test anti-GPVI compounds. To counteract GPVI function, we decided to exploit Glenzocimab (previously ACT017, Acticor Biotech  (46). Glenzocimab is a humanized antibody fragment (Fab) that has recently completed a phase II trial in stroke patients and is currently examined in a phase II/III trial (ACTISAVE, ClinicalTrials.
gov Identifier: NCT05070260). This compound binds to hGPVI to block ligand binding, which occurs without causing thrombocytopenia or GPVI membrane depletion, and is not associated with bleeding events  (47). Building on the exciting observation that lateTCP could reduce the growth of established metastases (Fig. 4 ), we subjected hGPVI mice to an experimental metastasis protocol targeting the growth of established metastases (mimicking lateTCP, Fig. 5G ). The half-life of Glenzocimab in mice is rather short (data not shown) when compared to humans  (47). We thus administrated Glenzocimab progressively using osmotic pumps that were implanted subcutaneously (Fig. 5G ). We confirmed that Glenzocimab did not affect platelet counts (Fig. 5H ) and was found to be successfully bound to mouse platelets (Fig.
cer of AVIESAN (Alliance Nationale pour les Sciences de la Vie et de la Santé, National Alliance for Life Sciences & Health) within the framework of the Cancer Plan (France). Work and people in the lab of JGG are mostly supported by the INCa (Institut National Du Cancer, French National Can- cer Institute), charities (La Ligue contre le Cancer, ARC (As- sociation pour la Recherche contre le Cancer), FRM (Fonda- tion pour la Recherche Médicale)), the National Plan Cancer initiative, the Region Grand Est, INSERM and the Univer- sity of Strasbourg. This work has been directly funded by the INCa grant (PLBIO 2014-01) and by the support of the Ligue contre le Cancer (labelisation) and the SATT Conec- tus (Strasbourg). MGL has been funded by the University of Strasbourg (IdeX, investissements d'Avenir), the INCa and the SATT Conectus (Strasbourg).
Recent progress in image-based spatial RNA profiling enables to spatially resolve tens to hundreds of distinct RNA species with high spatial resolution. It hence presents new avenues for comprehending tissue organization. In this context, the ability to assign detected RNA transcripts to individual cells is crucial for downstream analyses, such as in-situ cell type calling. Yet, accurate cell segmentation can be challenging in tissue data, in particular in the absence of a high-quality membrane marker. To address this issue, we introduce ComSeg, a segmentation algorithm that operates directly on single RNA positions and that does not come with implicit or explicit priors on cell shape. ComSeg is thus applicable in complex tissues with arbitrary cell shapes. Through comprehensive evaluations on simulated datasets, we show that ComSeg outperforms existing state-of-the-art methods for in-situ single-cell RNA profiling and cell type calling.
Such a subset consists of pre-defined marker genes that are specific for the cell states or types of interest. Since not the entire transcriptome is imaged, we will refer to these approaches as RNA profiling. These imaging-based approaches are variants of single-molecule fluorescence in situ hybridization (smFISH) methodologies and can detect RNAs with single-molecule sensitivity with high spatial resolution, several orders below the scale of a single cell. A typical dataset consists of 2D or 3D point clouds of the imaged RNA species. One challenge in the analysis of these data is the correct assignment of these RNAs to their cells of origin. Indeed, in contrast to single-cell RNA sequencing (scRNA-seq), the information on which RNA molecules belong to the same cell has to be inferred from the image.
To alleviate these issues, we propose our new method ComSeg. ComSeg uses as input only the coordinates of the RNA molecules and optional spatial landmarks, such as nuclei. ComSeg then groups RNAs with similar expression profiles, optionally aided by these landmarks. It does not necessitate scRNA-seq data or cytoplasmic markers, nor does it make implicit use of any prior assumptions regarding cell morphology. Instead, the method describes RNA point clouds as graphs weighted by gene co-expression and relies on graph community detection. Our method is easy to apply by design as it does not require complex machine learning model training. Furthermore, we provide the tool as an open-source Python package (https://github. com/tdefa/ComSeg) with extensive documentation: https://comseg. readthedocs.
io, compatible with the scverse environment  (22). Development of such analysis approaches requires annotated ground truth to assess their performance. Experimental ground truth can be obtained in some cases by employing membrane markers, from which the cytoplasmic membrane might be segmented with deep neural networks or manual annotation  (23). However, the ground truth quality is affected by the heterogeneity of the staining quality which is low on complex and dense tissue  (15). Furthermore, experimental data does not permit a more systematic exploration of parameters such as the expression level or cell morphology. Hence, similarly  (16, 17) , we address the lack of high-quality ground truth by generating simulated data allowing us to control the complexity of the input data. We develop SimTissue (https://github.
com/tdefa/SimTissue), an open-source Python simulation package to reproduce in-silico fluorescent-based spatial transcriptomic experiments. We used the SimTissue framework to validate ComSeg on simulated data with increasing complexity in terms of abundance of RNA, number of marker genes, and tissue morphology. ComSeg outperforms other methods in terms of Jaccard index for RNA-cell association in most of the tested scenarios. We also validate ComSeg on an in-house experimental dataset of mouse lung tissue imaged with smFISH and human embryonic lung tissue  (24)  imaged with HyBISS  (25). On these experimental data, ComSeg estimates accurate RNA profiles that match established scRNA-seq datasets. Overall, the shape agnostic approach of ComSeg demonstrates notable efficacy for complex tissue composed of cells with non-convex shapes.
Spatial correlation computation: For    ()    () = Σ ∈(), () =  (  -|| -||) /   (1) By calculating for all marker genes, we get the PE vector defined as:   () () (2) () = [ 1 () ,  2 (),. ,   ()] By stacking the for each molecule , we get a proximity weighted expression matrix ()   where is the number of detected RNA and the number of marker genes.  ∈ ℝ   ×      From this, we can finally compute the co-expression matrix , where each element ∈ ℝ   ×  is the Pearson correlation of the columns of corresponding to the expression of genes  ,   and :  (3)  , = ( ,   ) The computation of these co-expression values is implemented by the Python class ComSegDataset of our package. Of note, the co-expressions could alternatively be computed from external data, e.
Simulation Simulation Python package In order to validate our method and perform quantitative benchmarks against other approaches, we designed a simulation framework SimTissue capable of creating ground-truth data with tunable complexity for tissue morphology, cell type compositions, and marker-gene expression levels. Our simulation framework can be divided into two steps: 1) Simulation of tissue morphology. 2) Simulation of RNA composition and spatial distribution. SimTissue is implemented in Python and available at https://github. com/tdefa/SimTissue.
Finally, we removed 20% of nuclei in our simulation to simulate nuclei missed during sample preparation, as explained above. We used a list of 34 cell type marker genes with the objective to classify 19 different cell types present in mouse lung tissue. The list of marker genes was selected using the NS-forest algorithm (36) on our external scRNA-seq dataset of mouse lung tissue  (32). Then, we associated each individual cytoplasm with an expression vector sampled from our scRNA-seq dataset so that all the RNA profiles in our simulation are taken from real experimental data. This dataset can be found at https://zenodo. org/records/10172316.
Experimental evaluation We applied the benchmarked methods on two datasets of lung tissue. The first one exhibits 6 different marker genes in 3D mouse lung tissue acquired with a home-built sequential smFISH system. This mouse lung tissue was irradiated with 17 gy five months prior to mouse sacrifice as described in  (32). The second dataset was acquired with HybISS (25), consisting of a human embryonic lung and 147 genes in 2D  (24). In both cases, we perform a nucleus segmentation with Cellpose  (35)  and use this segmentation as initialization in our benchmark.
Reference clusters from scRNA-seq for embryonic lung tissue In the original study  (24) , the authors applied pciSeq to perform single-cell spatial RNA profiling. Their final expression matrix provided in the study displays 89 genes over the 147 genes map in the HyBISS data. We also keep the same subset of 89 genes for evaluation. We use the same reference scRNA-seq dataset and clustering annotation as provided by the authors. Matching in-situ single-cell RNA profile and scRNA-seq As for simulation, we normalize both scRNA-seq data and the count expression matrix from spatially resolved RNA profiling data with scTransform  (37). Many methods exist to match single-cell spatial transcriptomic data and scRNA-seq  (38). We chose to leverage the cosine distance as it is robust with respect to different capture rates among the two modalities.
State-of-the-art methods for cell segmentation We benchmarked ComSeg against methods that can be used in an equivalent setting i. e single-cell spatial RNA profiling approaches requiring no external dataset. As a first baseline, we calculated the Watershed transformation  (39)  as this method is often used for RNA-nuclei association in tissue  (40, 41). This method effectively calculates a Voronoi tessellation with the nuclear regions as markers, and is thus equivalent to assigning RNA transcripts to their nearest nucleus. The method hence perfectly works on convex cell shape, if all nuclei are detected, but may fail otherwise. Second, we benchmarked Baysor  (18) , a cell-marker-free segmentation method optimizing the joint likelihood of transcriptional composition and prior cell morphology.
Validation on simple simulations of regular patterns In this section we aim to better understand the strengths and limitations of each benchmarked method. To this end, for validation and benchmarking, we generated five types of simulated datasets of gradually increasing complexity and specific cases. To study the effect of the different possible expression profiles without any cell shape complexity, we simulate a checkerboard (see Material and Methods). This is a similar cell size scale to what we can observe in mouse tissue  (32, 42). To test the effect of non-convex shapes, we also simulate L-shaped cells (see Material and Methods). Each simulated set contains 10 images of 110 cells except the last one containing 144 cells per image. 1) Simulation 1 (Sim 1). Variation of expression level: The objective of this simulation is to benchmark the methods when markers have the same expression level versus the case where one marker is sparsely expressed.
Application to experimental data We applied ComSeg to two different experimental lung datasets, each with a specific challenge for the analysis. The first dataset was created in-house. In this experiment, we visualized 6 different marker genes in 3D mouse lung tissue. Our approach has a very high RNA detection efficiency, but many cells display no RNA transcripts as only a subset of cell types is targeted. The second dataset is from a recent study of human embryonic lung mapping 147 genes in 2D  (24). The HybISS approach used for this dataset has a lower capture rate (15) but enables the visualization of many more genes. For such experimental data, no ground truth is available. In tissue with complex morphology such has lung, having no ground truth makes it particularly challenging to assess the method's quality.
An existing validation strategy is based on the correlation of segmented regions from different methods  (15, 43). However, this validation method compares methods between themselves. Errors in existing methods could thus be propagated to the next generation of methods. In the absence of direct access to ground truth for imaging datasets, we chose to leverage available scRNA-seq datasets obtained from identical organ samples. These scRNA-seq datasets serve as a means to assess the consistency of single-cell spatial RNA profiling. As for simulations, we calculate the cosine distance between cell expression vectors derived from images and the nearest scRNA-seq cluster centroid. Consequently, the cosine distance between single-cell RNA profiles from the image dataset and scRNA-seq clusters serves as a surrogate measure for the quality of single-cell spatial RNA profiling.
Similarly to what we did on simulations, we applied Baysor, pciSeq, Watershed and ComSeg on the 3D mouse lung tissue dataset (Figure  4A ) and on the 2D embryonic lung tissue dataset. On mouse lung tissue, ComSeg identifies more cells than the other tested methods, for which an RNA profile can be assigned. (Supplementary Figure  2 , left). We obtain similar results for human embryonic lung tissue (Supplementary Figure  2 , right) where ComSeg also detects more cells than the three other methods we tested. We took into account only cells with more than 5 RNAs. Importantly, the number of cells with matching expression profiles in the scRNA-seq data is higher for ComSeg for both mouse (Figure  4B , right panel) and human lung tissue (Figure  4B , left panel) at the different cosine distance threshold.
Second, ComSeg does not require external datasets, such as scRNAseq, which makes it also applicable in small-scale studies, where such data is not available. Lastly, ComSeg does not rely on annotated data, which is very tedious and sometimes impossible to provide. Its modular structure makes it easy to tailor ComSeg to particularities in the datasets. One of the limitations of ComSeg is that it is dependent on the choice of cell type marker genes. The model may fail if the spatially resolved RNA species are not discriminative of cell type or cell state. A potential improvement of ComSeg would be to incorporate in the model several landmark stainings. In all, we believe that this model will be of great interest to the community and has the potential to overcome current shortcomings in cell segmentation and cell type calling from spatial RNA profiling data.
Data availibity The implementation code of ComSeg is available at https://github. com/tdefa/ComSeg_pkg. The implementation code of SimTissue is available at https://github. com/tdefa/SimTissue. The simulated dataset of lung tissue can be downloaded from Zenodo (https://zenodo. org/records/10172316). Scripts and datasets to reproduce the benchmark are available also on Zenodo at https://zenodo. org/records/10222671.
F. Mu. and C. W. acknowledge funding by Institut Pasteur. Furthermore, this work was supported by the French government under management of Agence Nationale de la Recherche as part of the ''Investissements d'avenir'' program, reference ANR-19-P3IA-0001 (PRAIRIE 3IA Institute). S. C. A. and A. L. -V. also received support from La Ligue Contre Le cancer. S. C. A. and A. M. were recipients of PhD fellowships from the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No 666003 and No 847718 respectively. H. L. is the recipient of a PhD fellowship from the International Student program from Paris-Saclay University. J. S. is recipient of a PhD fellowship from the French Ministry of Education, research and Industry.
Acknowledgements We thank laboratory members for helpful suggestions and collaborations. This work was supported by the  Ligue Régionale contre le Cancer (Comité du Rhône) (NM)  and  ANR  ( ANR-14-CE12-0003 ) (DB). DZ was supported by the  French Ministry of Higher Education and Research  and by the  Fondation pour la Recherche Médicale FRM  ( FDT201904008259 ).
Gene sequences were reconstructed based on the collared flycatcher reference genome, after masking non-callable sites from the variant calling dataset described above. Within each sample size, we masked polymorphic sites and replaced the reference allele with the alternative allele for sites where the alternative allele was fixed in the respective species. We removed genes from further analysis if more than weighted the counts of nonsynonymous and synonymous substitutions by the proportion of GCs or ATs for strong-to-strong and weak-to-weak variants, respectively. We excluded genes with an exon length below 200 and with norm values above 10 for the GC-conservative estimates. We summarized dN/dS, by dividing the average value of dN by the average value of dS. There was little evidence of sample size dependence in the estimates of dN/dS (Table  S7 ; Table  S8 ), suggesting that divergence between taiga flycatcher and collared flycatcher is deep enough for phylogenetic analysis  (86).
Data accessibility Sequencing data for all samples are available at the EMBL-EBI European Nucleotide Archive (ENA; http://www. ebi. ac. uk/ena) with the following accession numbers: PRJEB43825 (taiga and red-breasted flycatchers), PRJEB22864 (collared flycatcher), and PRJEB7359 (pied and snowy-browed flycatchers). VCF files used for analyses will be deposited on dryad after publication. Scripts used for analysis are available at https://github. com/madeline-chase/flycatcher_recom.
Acknowledgements We thank  Laurent Guéguen  for generous advice on running Bio++, and  Laurent Duret  for helpful feedback on an earlier version of this manuscript. This study was funded by grants from the  Swedish Research Council  ( 2013-8271  to  Hans Ellegren ), and the  Knut and Alice Wallenberg Foundation  ( 2014/0044  to Hans Ellegren). Computations were performed on resources provided by the  Swedish National Infrastructure for   Computing (SNIC)  through  Uppsala Multidisciplinary Center for Advanced Computational Science (UPPMAX).
ACKNOWLEDGEMENTS JC, LL and AJ were supported by the  European Research Council (ERC)  under the  European Union 's  Horizon 2020 research and innovation program  (grant agreement  StG-679116  to AJ). JC was also supported by the  Labex WhoAmI? of Université Paris Cité. GRL and HW were supported by  Agence Nationale de la Recherche  (grant RedoxActin to GRL). We acknowledge the  ImagoSeine core facility  of the  Institut Jacques Monod , member of the  France BioImaging infrastructure  ( ANR-10-INBS-04 ) and  GIS-IBiSA. For the purpose of Open Access, the authors have applied a CC BY public copyright license to any Author Accepted Manuscript version arising from this submission.
After accounting for extrinsic factors, we expected to retrieve several pathogen-pathogen associations previously identified in the literature. This included i) positive associations between cowpox virus and Bartonella infections (Microtus agrestis,  (Telfer et al. 2010 )); ii) positive associations between distinct Mycoplasma species in mammalian hosts  (Sykes et al. 2008; Tagawa et al. 2012; Fettweis et al. 2014; Volokhov et al. 2017 )); iii) associations between Bartonella and hemotropic Mycoplasma species (both positive and negative associations, as well as experimental demonstration of dynamic interactions, have been described in Gerbillus andersonii  (Eidelman et al. 2019) ). Lastly, we also expected to find previouslyundescribed associations due to the large bacteria and rodent dataset included in our study.