dataesr/scientific-paragraphs-categorization huggingface dataset:
In contrast to sequence composition, most standard phylogenetic analyses place no constraints on rates of sequence evolution among branches of a phylogenetic tree. Nevertheless, variable evolutionary rates can result in "long branch attraction" (LBA)-an artificial grouping of taxa with higher rates of sequence evolution [105]. This phenomenon occurs because models of sequence evolution typically underestimate its complexity and thus undercount the number of changes occurring along long branches [106]. One common example of LBA is grouping of fast-evolving in-group taxa with an outgroup, the latter, by its nature, forming a long branch on a phylogentic tree. Thus, the choice of the model and the choice of outgroups are critical for phylogenomic analysis. The systematic biases discussed above can explain unusual results obtained in two recent studies of demosponge relationships that utilized mitogenomic datasets  [38, 82].
Because Keratosa are also represented by a long branch in a mitogenomic analysis, its inferred position is likely the result of the LBA between this taxon and Hexactinellida. While one might argue that any phylogeny is just a hypothesis of evolutionary relationships, and that having diverse hypotheses could stimulate discussion and expedite progress, this is often not the case. Conflicting phylogenies derived from the same or similar datasets can diminish confidence in molecular phylogenetics, particularly among biologists who are not specialists in the field. This lack of agreement can also complicate efforts to combine information from multiple phylogenetic datasets, as attempted by projects like OpenTree [108] and TimeTree  [109]. Moreover, when inaccurate phylogenies propagate through the biological literature, they can result in misinterpretations of comparative data.
Conclusion In this study, we acquired 64 complete or nearly complete and six partial mt-genome sequences, more than doubling the number of demosponge species available for phylomitogenomic analysis. We compiled these new data with pre-existing records to build a dataset of 136 demosponge species and to test the high-rank phylogeny in the class Demospongiae, in particular the order composition of the subclasses and the relationships between subclasses. Our phylogenetic reconstruction is consistent with a subdivision of Demospongiae into either three subclasses (Verongimorpha, Keratosa, Heterosclermorpha) or four subclasses (Verongimorpha,Keratosa, Heterosclermorpha, Haploscleromorpha). However, we argue that classifying the current heteroscleromorph order Haplosclerida as a fourth subclass of Demospongiae provides better agreement with estimated times of diversification and observed genomic diversity within this group.
We confirmed that Heteroscleromorpha forms the sister group to the rest of the demosponges (Haploscleromorpha (Keratosa, Verongimorpha)) and that a recent result claiming closer relationships between Heteroscleromorpha and Verongimorpha is likely an artifact of long branch attraction between Keratosa and the selected outgroup. Analysis of molecular data from Myceliospongia araneosa, a highly unusual skeletonlacking demosponge, surprisingly placed it within the heteroscleromorph order Tetractinellida, as a sister taxon to the highly skeletonized Microscleroderma sp. and Leiodermatium sp. In general, we argue that mt-genomes provide an informative dataset for studying demosponge phylogenetic relationships but that one must be careful with the choice of outgroups and models of sequence evolution.
Acknowledgments We are grateful to our colleagues  Steven Cook ,  Alexander Ereskovsky , April and  Hill ,  Gisele Lobo-Hajdu ,  Michael Nickel ,  Julie Reveillaud , and  Gert Wo ¨rheide  for samples contributed to this project;  Malcolm Hill ,  Jenna Moore ,  Bernard Picton , and  John Reed  for help with specimen collection;  Andrzej Pisera  and  Sven Zea  for help with species identification. We also thank the following former graduate and undergraduate students in the  Lavrov Lab  for their help with the molecular work:  Andrea Bekic ,  Jenessa Filler ,  Ehsan Kayal ,  Philip Lange ,  Katrina Lutap ,  Benjamin Sheller ,  Xiujuan Wang , and  Katherine Wilson. We are grateful to  Nicole.
The number of available spike protein structures has increased rapidly, explaining the structural mechanisms that allow SARS-CoV-2 entry into the host cell. According to cryo-electron microscopy (cryo-EM) structures of the spike protein, RBD exists in two states: a closed (or "down") and an open (or "up") state. In the latter state, the RBD is less buried and can interact directly with ACE2  [24]. At the beginning of this study, three crystal structures of the wild-type ACE2/RBD complex were available: PDB IDs 6M0J  [25] , 6LZG  [26] , and 6VW1  [27]. With the release of the experimental structures of the SARS-CoV-2 RBD-ACE2 complex, numerous studies have been performed to elucidate the molecular and dynamic mechanisms involved in this protein-protein interaction. Considering the flexibility of the protein partners improved the identification of hot spot residues.
They identified four to seven RBD pockets from three RBD-ACE2 complex experimental structures (PDB IDs 6VW1, 6LZG, and 6M0J) without considering protein flexibility. They selected two pockets characterized as druggable, observed in the three RBD structures. Carino et al. [33]  identified 300 putative pockets in the whole trimeric structure of the spike protein using the Fpocket estimation program  [36]. They selected six pockets on the RBD structure based on their potential druggability, structural rigidity, and sequence conservation between the SARS-CoV-2 and SARS-CoV-1 RBD. They identified two continuous pockets in the central β-sheet core of the spike RBD that were targetable by steroidal molecules based on virtual screening of FDA-approved drugs on these six RBD pockets. Using in vitro assays, they confirmed that several compounds highlighted by virtual screening can reduce the ability of the RBD to bind to ACE2.
Protein preparation Three crystal structures of the SARS-CoV-2 RBD complexed with the human ACE2 receptor were considered (PDB IDs 6VW1  [25] , 6M0J  [26] , and 6LZG  [27] ). Overall, the three structures are very similar, with a maximum backbone root mean square deviation (RMSD) of 1. 25 Å. However, 6VW1 is based on a chimeric SARS-CoV-2 RBD protein and displays poorer resolution than the 6M0J and 6LZG structures. Coordinates of 6M0J and 6LZG crystal structures were determined at 2. 45 Å and 2. 50 Å, respectively. Therefore, our study focused only on the 6M0J and 6LZG structures. Each complex was protonated at physiological pH (7. 4) using the PROPKA tool  [43].
MD simulations MD simulations were performed on these two crystallographic structures to study the dynamic behavior and stability of the ACE2-RBD complex. They were also performed to study the dynamic behavior of the unbound state of the RBD initially extracted from the ACE2-RBD complex. MD simulations were performed using the GROMACS software package  [44]  with the CHARMM36m all-atom additive protein force field  [45]  under periodic boundary conditions. A dodecahedron water box of TIP3P water molecules was used to run the simulations. The full simulation system consisted approximately of 12,500 atoms. Non-bonded interactions were truncated at a cut-off distance of ten Å for electrostatic twin-range cut-off and van der Waals cut-off. Neighbor searching was performed every 10 steps. The energy of the system was minimized over approximately 1000 steps using the steepest descent algorithm for energy minimization after the ion addition and neutralization of the systems.
9 and 1. 5 Å. Thereby, we found dominant clusters that captured > 70% of the trajectory for each MD simulation. Thus, for every dominant cluster (seven clusters/MD), we extracted one representative conformation, which was subjected to MM-PBSA calculations. A free energy decomposition analysis was performed using MM-PBSA residue decomposition to retrieve the contribution energy of each amino acid represented on the binding interface of both RBD and ACE2. The total free energy and its individual components for each individual system (6M0J and 6LZG) were averaged and weighted based on the cluster populations, that is, a higher weight was assigned to the conformations extracted from more populated clusters. We used the g_mmpbsa package for MM-PBSA computations  [56]. The dielectric relative constant ε was set to eight for proteins and 80 for water  [57].
Clustering of pockets and identification of druggable binding sites We performed unsupervised hierarchical classification using residue pocket similarity to identify binding sites frequently observed along MD simulations, corresponding to main pocket clusters. The similarity of the RBD pockets can be quantified using binary distance based on common residues. A binary distance of 0 corresponds to two identical pockets in terms of residues. Hierarchical classification of the pockets was performed using the binary distance, Ward metric (ward. D2)  [58] , and R Hclust package  [59]. Dendrogram visualizations were performed using the Heatmaps2 package in R39  [51]  to illustrate pocket similarity in terms of residues. The dendrogram lengths between the pockets and/or pocket clusters are proportional to their binary distances.
The resulting pocket classification allows the identification of the main clusters of pockets similar in terms of residues composition, corresponding to binding sites frequently observed along MD simulations. The dissimilarity between the pockets or pocket clusters increased with dendrogram lengths between pockets or clusters. Main pocket clusters were compared i) between pocket sets extracted from two PDB IDs (6LZG and 6M0J) to assess the impact of the initial PDB structure and ii) extracted from bound and unbound RBD conformations to assess the influence of ACE2 interaction. The number of similar pockets within one cluster indicates the frequency of the appearance of its corresponding binding site. The flexibility of the binding site is described by the residue variability of its corresponding pockets.
Tracking of RBD pockets during the MD simulations We first examined pockets that were extracted from the unbound RBD system from static PDB. From the initial 6M0J and 6LZG PDB IDs, we estimated four and seven pockets on the unbound RBD, respectively, using the PockDrug program  [41, 42]. This is consistent with the observation of a variable number of pockets estimated by Trigueiro-Louro et al. on the same PDB structures  [32]. We secondly examined pockets that were extracted from the unbound RBD systems considering their flexibility. We extracted two sets of pockets from two datasets of 1000 conformations sampled from each of the unbound RBD structures 6M0J and 6LZG during the MD simulations. The average number of RBD pockets by conformation in these two sets were highly similar (5. 1 versus 5.
We do not consider this cluster of pockets a priority to be targeted by therapeutic molecules due to the presence of N-acetylglucosamine. Cluster III was observed in half of the conformations (52. 4%) and mainly included druggable pockets (85. 9%) (Table  S2 ). The pockets were located on the interface region between two RBD proteins in the closed state of the spike protein trimer (Fig. S6 ). This cluster regroups similar pockets (with 17 residues observed in more than 15% of its pockets) (Table  S2 ). It is noteworthy that Carino et al. highlighted one potential druggable pocket from the static spike protein trimer (PDB ID:6VSB), but it corresponded only to a partial sub-pocket of ten residues of our cluster of pockets  [33]. Therefore, it may be of interest to design molecules that bind this cluster of 17 residue pockets to stabilize the closed state and avoid the activation of the spike protein trimer.
Acknowledgments This work was supported by the  Agence Nationale de la Recherche  ( PIF21   Project  no. ANR-19-CE18-0023 ). The authors gratefully acknowledge the financial support of the  Université Paris Cité, CNRS Institute , and  INSERM Institute. This study was performed using HPC resources from  GENCI-CINES. The authors are thankful to  Patrick Fuchs ,  Audrey Deyawe Kongmeneck , and  Rachel Blot  for their helpful comments on this manuscript.
Assuming that genomic features are conditionally independent given echolocation, the likelihood ratio of the combined features (ES value) is equal to the product of the likelihood ratios for individual features. Upon calculation, it was found that the ES was greater than 0 for all 28 echolocating mammals and less than 0 for the remaining 57 nonecholocating mammals in the training dataset (Fig 1C; Table  S4 ). We pursued a stricter cutoff by randomly selecting an amino acid from each of 14 convergent sites among 28 echolocating mammals to create a simulated dataset and calculate its ES. This procedure was repeated 10,000 times to obtain a null distribution of ES values from the simulated datasets. According to the statistical test, the probability that a species with ES > 6. 97 is not an echolocator is less than 1% (Fig.
1B ; Fig. S5 ). To screen new echolocating mammalian species, we focused on 97 mammalian species, of which 74 species with relatively high-quality genomic data from the Zoonomia Project (Table  S5 )  (19)  and 23 were nocturnal species from the database OrthoMaM (Table  S6 )  (20). Notably, this dataset included 12 known echolocating mammals, such as echolocating bats, toothed whales, and the European shrew (Sorex araneus), which were used as positive controls. By calculating ES values of these species, we found that the ES values of 9 out of 12 known echolocating mammals were larger than the cutoff 6. 97 and those of the remaining 3 echolocating mammals were close to the cutoff (Fig. 1D ; Table  S7 ), further indicating the validity of the machine learning model. In addition to these echolocating mammals, two species had ES values greater than 6.
Discussion In this study, we developed a machine learning model based on molecular convergences among echolocating mammals and trained it using a test dataset to screen for unknown echolocating species among 97 mammals with available high-quality genomic data. Three additional mammalian species, the gracile shrew mole, the ninebanded armadillo, and the solenodon, were identified as potential echolocators. We employed echolocation-related behavioral experiments  (6, 24)  to verify that not only the gracile shrew mole, but likely all shrew moles (Uropsilinae) have evolved echolocation. It has been suggested that echolocation has independently evolved at least six times in mammals. Unfortunately, due to the unavailability of living animals, we were unable to perform the same behavioral experiments for the nine-banded armadillo and the solenodon.
Second, the experimental evidence has suggested that the adaptive convergent amino acids largely contributed to the functional convergence related to echolocation among echolocating mammals  (7, 8, 17, 32). Nevertheless, we acknowledge that the caveat of the small number of convergent sites could potentially distort the predictive accuracy of our approach, which could be further enhanced as additional molecular elements or genes related to mammalian echolocation are incorporated to the training dataset. A fundamental question in evolutionary genetics is the extent to which adaptively convergent phenotypes are caused by convergent changes at the molecular sequence level  (9, 33). This raises the question of whether adaptative molecular convergence allows for the genetic prediction of similar phenotypes adapted to similar environmental challenges in independent lineages.
Identification of molecular convergences The high-quality genomic data was collected from the OrthoMam (v10c) database  (20) , GenBank  (34)  and previous studies  (6, 19, 35) , including 35 high-quality echolocating mammalian genomes from four orders: one rodential soft-furred tree mouse (Typhlomys cinereus), one tenrecid lesser hedgehog tenrec (Echinops telfairi), 14 insectivorous bats, and 19 toothed whales. To largely reduce the bias of the unequal number of closely related species against the subsequent evolutionary analyses, we chose a representative species for each non-echolocating mammalian genus in the dataset. Finally, a total of 93 mammalian species with high-quality genomic data were retained in our dataset (Tables  S1,  and S2 ) for the subsequent evolutionary analyses. We obtained a total of 9,554 one-to-one orthologous protein-coding genes across 93 species using Inparanoid v4.
To largely include the amino acid substitutions related to echolocation, we also identified convergent sites among the soft-furred tree mouse, the lesser hedgehog tenrec, any of 14 echolocating bats, and any of 19 toothed whales. By combining the two datasets above, we identified a total of 497 convergent amino acid substitutions that are putatively related to mammalian echolocation. To further ensure the relevance of these molecular convergences to echolocation, we refined these candidate convergent sites according to the flowing criteria. A convergent amino acid substitution was removed if it (i) occurred in the closest nonecholocating species or on the ancestral branches of non-echolocators; (ii) occurred in less than 5 echolocators; (iii) occurred in any of 58 non-nocturnal species or known non-echolocators; (iv) occurred randomly among echolocators, which was estimated through a statistical test by comparing with a null hypothesis derived from neutral evolution  (39).
Resampling simulation for the confidence intervals of echolocation scores To further improve the prediction accuracy, we randomly picked a site from 14 convergent amino acid substitutions among 28 echolocating mammalian species in the training dataset to generate a simulated dataset and estimated its ES. Such progress was repeated 10000 times to obtain a null distribution of ES values for echolocating mammalian species. Then, the confidence intervals of the echolocation scores can be estimated based on the null distribution (Fig. S5 ).
Using the echolocation score to predict new echolocators The genomic data used for predicting new mammalian echolocators was collected from the Zoonomia Project (3) and the OrthoMam (v10c) database (1). To make sure the reliability of the results, we only considered the relatively high-quality genomic data with single copy > 60%. After filtering, we obtained the genomic data of 74 species from the Zoonomia Project and of 23 nocturnal species in the OrthoMam database due to lacking sufficient evidence to determine whether they are capable of echolocation (Tables  S5,  and S6 ). This prediction dataset included 12 known echolocating mammals that were used as positive controls to assess the accuracy of the predictions. To calculate the ES values of these 97 mammalian species, we first retrieved the protein-coding sequences of 14 genes containing the convergent amino acids from the genomic data using genewise v2.
4. 1  (42)  and further obtained the amino acids corresponding to the 14 convergent amino acid substitutions (Table  S13 ). Then, the ES value of each species in the prediction dataset was estimated based on the above pipeline. If the sites at the position corresponding to the convergent amino acid substitutions were unavailable in the species with ES > 0, we used PCR to amplify the sequences to obtain these sites and re-estimated the ES of the species. In the list, only U. gracilis with ES > 0 had four such sites unavailable in the genomic data and thus we designed the following primers to obtain these sites in the species. COL11A1-F AGCAATCAAGATCTCTCC COL11A1-R TATCATTCTGATGATACA EFL1-F GCTGCCTGGAAGACCTGACG EFL1-R TCCACCRCGTCRCGCCACTG OTOF-F GCCTATTTAGCAGAACTCAGTCC OTOF-R TAGGATCTTCTTGACCAGGTAGC ZNF316-F GCCCTGGTTCAATGAGCTCCAAATGAG ZNF316-R GCTGCCCAGCTGGAGAGGGCAGAGG Based on the completed sequences, we obtained the final ES results for 97 mammals (Table  S7 ).
The audio and video recordings were analyzed synchronously to examine how the animals emitted ultrasonic calls during their movements in the apparatus. We analyzed video recordings with the VisuTrack software (Shanghai XinRuan Information Technology Co. , Ltd. ) that automatically documented the time when the animals entered and left the monitored sector, as well as the total exploration time on the center disc. The ultrasonic emissions in the monitored sector and in its adjacent sectors were counted from the audio recordings using the software and methods above. We calculated the percentages of exploration time and pulse rate when the animals were exploring in the monitored sector. The data of the behavioral experiments above were shown in Tables S9, S10, S11.
Micro-computed tomography (MCT) imaging The images of the thyroid cartilages were taken with a stereomicroscope (SMZ18, Nikon). MCT images of the spatial relationship between the stylohyal and tympanic bones were obtained for three shrew mole species (U. gracilis, U. investigator, and U. Soricipes). The MCT scanner (Bruker Skyscan 1176) was used to collect images with a resolution of 9 μm at 65 kV and 310 μA. We used three-dimensional analysis software (CT-VOX) to reconstruct and visualize the data.
Acknowledgements: We gratefully acknowledge  Chang-Zhe Pu ,  Ming-Jing Pu ,  Kang Luo  and  Jing Luo  for their assistance in the field. We thank the  Jinfoshan National Nature Reserve , the  Qinling Mountain National Nature Reserve , the  Yulong Snow Mountain Provincial Nature Reserve , and the  Gaoligong Mountain National Nature Reserve  for approving and facilitating our fieldwork.
: Fig. 1. Combine molecular convergences with machine learning to predict potential new mammalian echolocators. (A) Principal component analysis used 14 amino acid substitutions from known echolocating mammals can differentiate the echolocating and nonecholocating mammals. (B) Schematic diagram shows workflow of machine learning and algorithm of Echolocation scores (ES) (see details in materials and methods). (C) Echolocation scores with stricter cutoff ES > 6. 97 can stand out all echolocating species from training dataset based on machine learning. (D) Combine machine learning with molecular convergences predict three putative echolocating mammalian species. The red dots represent echolocators, black dots represent non-echolocators, pink dots represent putative echolocators, red pentagrams mark orders possess the ability of echolocation up to now.
Quality assurance was performed by concurrently testing control strains: MRSA MW2  (Baba et al. , 2002)  and S. aureus ATCC 29213. Interpretive criteria published by CLSI were applied for the comparator antibiotics (Clinical and Laboratory Standards Institute, 2018). CD437 and CD1530 were acquired from R&D Systems (Minneapolis, MN, USA). Analog 2 was synthesized and tested as previously described  (Kim et al. , 2018). MIC 50 and MIC 90 were calculated using SPSS software (version 24, IBM, Armonk, New York).
Primary outcome was the R0resection rate. Secondary outcomes included lymph node yield, major complication rate, conversion rate, and overall survival. At each participating center, a local coordinator was responsible for the communication with the central study coordinators (JC, SL). Participating centers provided anonymized data on a password secured database. The central study coordinators combined the data. This study was performed according to the principles of the Declaration of Helsinki (64th Fortaleza Brazil, October 2013) and in accordance with the Medical Research Involving Human Subjects Act (WMO) and STROBE guidelines on reporting on observational studies. 14 Due to the retrospective design, the ethical board from Amsterdam UMC waived the need for informed consent.