dataesr/scientific-paragraphs-categorization huggingface dataset:
Biological datasets To test our method, we selected 4 biological datasets from the literature. The choice of these datasets was dictated by: (1) the availability of the data in public databases, (2) the desire to cover for situations as widely different as possible in terms of the topology of the trees and the presence of multiple associations. The phylogenetic trees of each dataset can be found in Figures D to G from the Supplementary Material. As already mentioned, any dataset D containing multiple associations cannot be analysed with Coala. Thus, in order to compare the results with those obtained by Coala  (Baudet et al. , 2015) , for each real dataset D we generated a dataset D Coala which is obtained from D by randomly choosing exactly one association (among existing ones and whenever there are more than 2 such associations) for each symbiont leaf.
Notice that this is what is usually done in the literature when analysing such datasets with a method that does not allow for multiple associations. We detail here the results obtained for only two datasets, the reader can find the remaining ones in Section D. 1 from the Supplementary Material. Computing times are also presented in Section D. 2 from the Supplementary Material. Dataset 1: AP -Acacia & Pseudomyrmex. This dataset was extracted from Gómez-Acevedo et al. (2010)  and displays the interaction between Acacia plants and Pseudomyrmex, a genus of ants. Although the authors did not use a cophylogeny reconstruction tool to analyse the dataset, this is considered as a typical example of mutualism between ants and plants, and the authors show that their relationship originated in Mesoamerica between the late Miocene to the middle Pliocene, with eventual diversification of both groups in Mexico.
The host and symbiont trees include 9 and 7 leaves, respectively. The dataset has 22 multiple-associations. The corresponding dataset with no multiple association is called AP Coala. Dataset 2: SFC -Smut Fungi & Caryophillaceus plants. This dataset was extracted from  Refrégier et al. (2008). The host and symbiont trees include 15 and 16 leaves, respectively. The dataset has 4 multiple associations. The corresponding dataset with no multiple association is called SFC Coala. Notice that this is the same dataset used in  Baudet et al. (2015). In Figures  10  and 11 , we present for each of the cophylogeny events, the distribution of the inferred probabilities obtained by running AmoCoala and Coala. First notice that the results change substantially when we consider the complete dataset instead of the one obtained by removing the multiple associations.
Indeed, from the graphics in the third row of Figure  10 , we see that if we ignore multiple associations, then Coala explains the dataset using a very low cospeciation frequency and a high number of switches and losses. In general, we can say that Coala detects a high incongruence between the trees which cannot be explained by cospeciations. However, if the complete dataset is considered, i. e. the one including all the multiple associations, we see from the first two rows of Figure  10  that the dataset can be explained by only 2-3 horizontal spreads, a high number of cospeciations, a very low number of duplications and switches and also a significantly lower number of losses. Thus, the incongruence between the two phylogenetic trees can be explained by approximately 3 horizontal spreads and then most of the events correspond to cospeciations, which is an indication of coevolution.
This is in accordance with what is expected for this dataset, which, as already mentioned in the previous paragraph, is considered as a typical example of mutualism between ants and plants. Next, we considered the dataset SFC with multiple associations proposed in  Refrégier et al. (2008). From Figure  11 , we can see that both methods show similar results concerning cospeciations, duplications and host switches while AmoCoala outputs a smaller number of losses (less then 25%) compared to Coala (less then 40%). In  Refrégier et al. (2008) , the different analyses performed indicated that the most plausible reconciliations presented for the SFC dataset have from 0 to 3 cospeciations, no Figure  10 : Comparison of the results obtained with AmoCoala and Coala for the dataset AP. In each graphic, we show for each event type, the distribution of the parameter values.
Based on the analyses presented in  Refrégier et al. (2008) , we expect to have events with the following frequencies: between 0% and 20% for cospeciations (from 0 to 3 events), 0% for duplications (no duplications), between 80% and 100% for host switches (from 12 to 15 events) and between 0% and 13% for losses (from 0 to 2 events). To compare the results output by the two methods (Coala and AmoCoala) with those expected from the analyses of  Refrégier et al. (2008) , we cluster the parameter vectors output by the methods. Indeed, both Coala and AmoCoala perform a hierarchical clustering procedure to group the final list of accepted parameter vectors. We then compared the cluster patterns found by the two Figure  11 : Comparison of the results obtained with AmoCoala and Coala for the dataset SFC.
In each graphic, we show for each event type, the distribution of the parameter values. In the first two rows, the results provided by AmoCoala and in the third row, the ones provided by Coala. methods. Table  1  shows the representative vectors of each of the clusters output by AmoCoala (for the SFC dataset) and by Coala (for the SFC Coala dataset). Notice that as already mentioned in  Baudet et al. (2015) , a vector with a high frequency of host switches can generate a large space of simulated trees, many of which can have a high distance from the real symbiont tree. Thus, it is clear that such vectors are more difficult to be output by both Coala and AmoCoala. From the results in Table  1 , we have that the event vector that is most similar to the expected one according to  Refrégier et al.
(2008)  is Cluster 2 for AmoCoala run on the SFC dataset (22. 6% for cospeciations, 0. 4% for duplications, 54. 3% for host switches and 22. 8% for losses). It is also important to note that the number of vectors that are part of this cluster is high (14 out of 50 vectors accepted in the third round). Notice that Cluster 5 of Coala run on SFC Coala is also close to these values, however this cluster is supported by only 2 of the accepted vectors. Moreover, all the representative vectors of the clusters output by AmoCoala have a frequency of duplication close to 0, which is in agreement with what is expected from  Refrégier et al. (2008). Overall the results obtained with AmoCoala are closer to the result presented in  Refrégier et al. (2008)  than those that were obtained by Coala which ignores such multiple associations.
Concluding comments In this paper, we propose a method, called AmoCoala, which for a given pair of host and symbiont trees, estimates the probabilities of the cophylogeny events, in presence of spread events, relying on an approximate Bayesian computation (ABC) approach. In AmoCoala, it is possible to estimate the probabilities of the classical cophylogeny events (cospeciation, duplication, host switch and loss) and also the probabilities of horizontal and vertical spreads. These two latter events allow to study datasets that contain multiple associations. The model uses set-labelled trees and to compare them we introduced a new distance, called MAS, which we believe can be of independent interest. AmoCoala on one hand provides more confidence in the set of costs to be used for the reconciliation of a given pair of host and symbiont trees, while on the other hand, it allows to estimate the probabilities of the events even in the case of large datasets.
We evaluate our method on synthetic and real datasets. This work leads to different research directions. First, it would be interesting to define better distances for set-labelled trees. To the best of our knowledge, these types of trees have not been considered in the literature and it would be interesting to generalise (if possible) some of the well-known phylogenetic distances to set-labelled trees. Another direction is to include the vertical and horizontal spreads in the parsimonious reconciliation framework. Thus, a perspective to this work is to design a reconciliation procedure that includes these switches. A The event-based model AmoCoala relies on the event-based model presented in  Charleston (2002) ;  Tofigh et al. (2011). For the sake of completeness, we detail the model here.
A. 3 Reconciliation model allowing for spreads The introduction of spread events modifies the previous setting in the following way. Let again H and S be respectively the rooted phylogenetic trees of the host and symbiont species, both binary and full (i. e. every internal vertex has exactly two children). Now, let φ be a relation between L(S) and L(H), representing the symbiont/host associations between extant species. More precisely, let us denote P(L(H)) the set of all subsets of L(H). Then φ is now a function from L(S) to P(L(H)). For any extant symbiont species s ∈ L(S), whenever the cardinality |φ(s)| ≥ 2 (i. e. whenever the AmoCoala Supplementary Material symbiont is associated to more than one host), we say that this symbiont has multiple associations and we count the total number of multiple associations in the dataset as: Nb of multiple associations = s∈L(S).
A. 4 Pre-estimating probabilities for the spread events Given an input dataset (H, S, φ), we rely on frequency estimators for the spread probabilities that will be used in our algorithm. Note that the "classical events" (cospeciation, duplication, host switch and loss) have the same probability to occur everywhere in the tree, while the probability of a vertical or horizontal spread is specific to each vertex of the host tree. These probabilities are pre-estimated based on the input (H, S, φ) as described below rather than in the full ABC procedure. They are estimated through heuristic frequencies observed in the associations of the two trees. In Section D. 3, we explore the robustness of our results with respect to these pre-computed estimators.
B. 2 ABC-SMC inference method in AmoCoala AmoCoala is based on the same ABC-SMC method as the one developed in Coala  (Baudet et al. , 2015). For the sake of completeness, we now recall the procedure. The ABC-SMC procedure is composed of a sequence of R > 1 rounds. At each round, parameter vectors θ are sampled in a specific way, symbiont trees Sθ are generated under the reconciliation model allowing for spreads with parameter values given by θ (and relying on the simulation algorithm described in the previous section). Then, these symbiont trees are compared to the original dataset through a summary distance d whose details are given in the next section. The parameters with the smallest discrepancies are selected. For each of these rounds, we define a tolerance value τ r (1 ≤ r ≤ R) which determines the percentage of parameter vectors to be accepted.
B. 3 Distance measure in AmoCoala The discrepancy between the simulated and the original datasets is measured through a distance between set-labelled phylogenetic trees which can be calculated in polynomial time. Similarly as in Coala, this distance contains two components: (i) d 1 , that describes how much the simulated tree Sθ is representative of the vector θ, and (ii) d 2 that measures how much is Sθ (and its labels) topologically similar to S (and its labels). Let us recall the definition of this first component. For a given vector θ = p c , p d , p s , p l and for each simulated tree Sθ that was simulated according to this vector, we keep track of the vector of the number of classical cophylogeny events o c , o d , o s , o l associated to this simulation. We compute the corresponding expected vector e c , e d , e s , e l as follows ∀i ∈ {c, d, s, l}, e i = |S| × θ i = |S| × p i , where |S| is the size of the symbiont tree, i.
D Biological datasets We provide here a description of the 4 datasets used   (1997). The host and symbiont trees include 15 and 8 leaves, respectively. The dataset has 15 multiple-associations. Dataset 4: SFC -Smut Fungi & Caryophillaceus plants. This dataset was extracted from  Refrégier et al. (2008). The host and symbiont trees include 15 and 16 leaves, respectively. The dataset has 4 multiple-associations.
D. 1 Results on biological datasets We ran AmoCoala on all the real datasets and plotted in Figures S. 8 to S. 19 the histograms of the summary discrepancies and event probabilities (except for the AmoCoala Supplementary Material spread probabilities which are not inferred) obtained at the end of each one of the 3 rounds, for each of the 4 datasets. We see on the histograms that the summary discrepancies for the accepted parameter vectors decrease after each round. We recall that the summary discrepancy measures the similarity between the simulated trees and the original symbiont tree, and hence is related to the quality of the vectors. Thus, our result shows that the set of accepted vectors is refined at each round, leading to vectors which can generate trees that are increasingly more similar to the original symbiont tree (and its host associations).
D. 2 Running times Table  S. 1 shows the running times obtained on the 4 biological datasets, together with their sizes (as expressed by the number of leaves in the host and symbiont trees) and the number of multiple associations. The results have been obtained on a computer with a AMD EPYC 7542 32-Core processor and 128 CPU (2 sockets of 32 double threads cores) and 675Gb RAM. We used just one core ('nthreads 1', though Amo-Coala has a parallelized version) and AmoCoala was run with default values on these datasets.
D. 3 Robustness analysis wrt the pre-estimated spread probabilities In this section, we explore the robustness of our results with respect to the preestimated values of the spread events probabilities. On each of the 4 biological datasets, we ran AmoCoala with perturbated values of p hs (h), p vs (h). More precisely, to each non zero probability p hs (h) or p vs (h), we added a noise value uniformly drawn in [-0. 1; 0. 1] (and then took the infimum with 1 and the supremum with 0, in order to ensure the modified probabilities remain in [0, 1]). With these perturbed values, we ran AmoCoala and output (after 3 rounds) 50 accepted vectors θ = p c , p d , p s , p l. The results are presented in Figures S. 20 to S. 23. Let us recall that AmoCoala is a stochastic algorithm and any two runs will give similar but not identical results.
AmoCoala algorithm 33 B. 1 Simulation algorithm in AmoCoala. 33 B. 2 ABC-SMC inference method in AmoCoala. 35 B. 3 Distance measure in AmoCoala. 36 B. 4 A proof that dMAS is a distance. 37 B. 5 Polynomial time algorithm for computing the dMAS distance. 38 C Additional results for the self-test 39 D Biological datasets 39 D. 1 Results on biological datasets. 39 D. 2 Running times. 40 D. 3 Robustness analysis wrt the pre-estimated spread probabilities. 40 AmoCoala Supplementary Material.
The corresponding phylogenetic trees are shown in Figures S. 4 -S. 7. Dataset 1: AP -Acacia & Pseudomyrmex. This dataset was extracted from Gómez-Acevedo et al. (2010) and displays the interaction between Acacia plants and Pseudomyrmex species of ants. The host and symbiont trees include 9 and 7 leaves, respectively. The dataset has 22 multiple-associations. Dataset 2: MP -Myrmica & Phengaris. This dataset was extracted from Jansen et al. (2011) and is composed of a pair of host and symbiont trees which have each 8 leaves. The dataset has 8 multiple-associations. Dataset 3: SBL -Seabirds & Lice. This dataset was extracted from Paterson et al.
AP dataset. First row: histograms of the input parameters. Second row: histograms of the parameters after round 1. Third row: summary discrepancies of the input parameters and of the parameters after round 1. AP dataset. First row: histograms of the input parameters. Second row: histograms of the parameters after round 2. Third row: summary discrepancies of the input parameters and of the parameters after round 2. 6 0. 8 0. 0 0. 2 0. 4 0. 6 0. 8 0. 0 0. 2 0. 4 0. 6 0. 8 1. 0 2. 2 2. 3 2. 4 Figure S. 8: AmoCoala 0 5 10 15 20 25 23% 6% 9%10% 17% 15% 14% 6% 2% AP. nex round 1 -epsilon = 0. 3577 p_loss Density 0 5 10 15 20 6% 18% 15% 22% 11% 6% 5% 6% 4% 1% 2% 11% 1% 2% 1% AP. nex round 1 vertical_Spread Frequency 0 50 100 150 200 AP rou horizont Frequency 0 10 20 30 40 AP. nex round 1 -simulation Distance Density 0.
Schartner and Böhm (2020) investigated geodetic VLBI scheduling based on a brute-force approach of testing various optimization criteria based on large-scale Monte Carlo simulations. However, in most of the preceded studies, source structure effects are not properly taken into account and scheduling is not necessarily optimized for imaging purposes. With the development of a new VLBI scheduling software called VieSched++  (Schartner and Böhm 2019) , which has already been successfully used for VGOS sessions  (Haas et al. 2021; Petrachenko et al. 2022) , new possibilities of combining geodetic scheduling with a source-centric scheduling approach, suitable for imaging, arise. The main objective of this work is to propose a sourcecentric scheduling approach that produces good geodetic performance while allowing for proper source imaging.
2016 (Chael et al. , 2018) )  and difmap  (Shepherd 1997 ) was developed. Here, the visibility data were simulated at 8. 6 GHz for each schedule. The superresolution image of source 1803+784 on 2020-02-20, from the Monitoring Of Jets in Active galactic nuclei with VLBA Experiments (MOJAVE;  Lister et al. 1996) , was used as a reference to calculate the phase and amplitude of each observation. Therefore, all the radio sources in the schedules are assumed to have the same structure. The selected reference image shows an extended jet in right ascension spreading up to ≈ 1. 3 mas away from the core, which has ≈ 75% of the total flux density. This moderate structure may represent the common characteristic of the structure of the geodetic radio sources. The simulated visibility data were used to construct images in difmap, and the constructed images were evaluated based on the MOJAVE image as the ground truth model.
Moreover, another set of reference visibilities was simulated for each source in a session by assuming that the network is only observing the said source all the time. This dataset provides the best possible UV coverage for each source and therefore represents the best possible imaging capability for the given network. The corresponding beamsize, which is source-and network-dependent, was calculated based on those reference visibilities and is further utilized during the astronomical analysis as described in Sect. 3. 5. For investigating the dynamic range, the simulations were performed using a dedicated processing pipeline that was originally built when VGOS was designed  (Petrachenko et al. 2009). A full description of the initial version of this pipeline is available in  Collioud and Charlot (2012).
Patient and samples We conducted a prospective non-interventional study in our institution (Rennes University Hospital), including only patients with a histologically or cytologically confirmed chemotherapy-naïve SCLC and eligible to a systemic treatment, starting from April 2016. The data lock was planned in December 2021 for all patients that achieve a minimal follow-up of at least 6 months. The recruitment is still active beyond this date. Any active neoplasia other than SCLC or known HIV, HCV or HBV infection, were considered as exclusion criterion. CD56+CTC were numerated and isolated from a whole blood sample, before the first administration of any cytotoxic agent and when disease relapsed (Fig  1A ). Paired tumor tissue samples were obtained for four patients in order to compare CD56+CTC and biopsies genomic characteristics.
Pathway analysis and comparison of genomic mutational patterns between biopsy and paired CD56+CTC Detailed methods for PBMC isolation, extraction of DNA, whole exome sequencing and variant annotation workflow can be found in the supplemental data. Complete analysis was performed utilising various state-of-the-art tools. Unless stated otherwise, all bioinformatics analyses were performed using shell interface command line, on a Linux-based 64GB RAM/ 12 processor cluster server (Ubuntu 18. 04). Initially, to unravel the common mutational patterns between CTCs and matched biopsies, a simple Venn diagram/overlap analysis was performed for each patient separately at the variant level (based on SNVs & INDELs) and at the gene level. The final variants that were common in the remaining variant lists of CTC and paired biopsy samples for each patient, were imported to the BioInfoMiner platform (https://bioinfominer.
com) for the functional interrogation of the perturbed pathways in the CTC and biopsy samples utilizing different hierarchical and ontological vocabularies (Gene Ontology & Reactome Pathways)  (16). Additionally, in order to investigate the mutational relatedness of CTC and biopsy samples, based on their mutational profiles in the six base substitution catalogues, the R package MutationalPatterns (v 1. 10. 0) was used  (17). Finally, semantic similarity (GO functional similarity) based on the aforementioned pathway enrichment analysis was performed between CTC and biopsy samples for each patient, to identify any common molecular mechanisms.
2F ). This analysis also stressed the importance of understudied processes in SCLC: sumoylation and transcriptional regulation by AP-2 and RUNX2. Subsequently, three genomic databases of SCLC (Cancer Gene Census, Intogen driver genes and MycancerGenome) were analyzed with BIM leading to the identification of 22, 17 and 9 hub genes respectively (Supplemental Table  1 ). Interestingly, 23 of the 75 hub genes from CD56+CTC, such as ERBB2, TP53, CREBBP and NF1, were found enriched in at least 1 of the 3 queried databases, illustrating the high affinity of the liquid biopsies to recapitulate the molecular genomic landscape of small cell lung cancer. The REACTOME pathway analysis of the 23 hub genes further confirmed that the altered biological mechanisms found in CD56+CTCs correspond to those found in SCLC, with the addition of the p53 signaling pathways (Supplemental Fig.
Moreover, intra-and inter-patient heterogeneity was observed for EMT markers in both CTCs and circulating tumor microemboli in lung cancer  (23)  and could partially explain these discrepancies. To our knowledge, this study is the first to report genomic features of EpCAM-independent CTC in SCLC. Beyond the confirmation of typical genomic alteration of SCLC in CD56+CTC, we show that the mutational load was high compared to paired tumor biopsies or published data on CTC of SCLC. This suggests that our EpCAM-independent method allows us to capture more exhaustively CTC heterogeneity and altered signaling pathways. To support this hypothesis we report a set of 75 hub genes altered in CD56+CTC, comprising 23 core genes shared with public SCLC genomic databases. Unique CTC genomic signatures have been described in others solid malignancies  (24, 25) , highlighting cell populations with different functional or metastatic potential.
In the context of SCLC, larger genomic studies considering single-cell sequencing of EpCAM-independent CTC are needed to confirm the new pathways we identified as altered in our dataset. Further longitudinal data from sequencing of CTC at diagnosis and relapse are underway to establish firmly the relevant common mechanisms that might drive resistance to therapy. Prognostic value of CTC from SCLC has been extensively reported before. Indeed,  Hou et al. reported that more than 50 CTCs/7. 5 ml of blood at diagnosis has a detrimental effect on PFS and OS  (6). Similarly,  Su et al. and Carter et al. reported that copy-number-alteration (CNA) analysis from single cell CTC could predict chemosensitity status and PFS  (7, 10). In our study, no difference of CD56+CTC numeration was observed based on the chemosensitivity status of tumors and no association with prognosis was found based on the pre-specified cutoff value of 7 CD56+CTC/ml.