dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements Thanks are due to the  Portuguese National Plant Protection Authority (DGAV)  for sending us the reports about the invasion of T. erytreae. We also thank  Amílcar Duarte  ( University of Algarve ) and  Celestino Soares  ( Direção Regional de Agricultura e Pescas do Algarve ) for providing information on the invasion of the coastal area of Alentejo and Algarve by T. erytreae and  Luis Catela Nunes  for advice in the statistical analysis. Thanks are also due to the editor and reviewers for their comments and suggestions, which allowed us to improve an earlier version of the manuscript.
Methods. We initiated a pilot survey of bright O-type stars (H < 6. 5 mag) observable with MIRC-X. We observed 29 O-type stars, including two systems in average atmospheric conditions around a magnitude of H = 7. 5 mag. We systematically reduced the obtained data with the public reduction pipeline of the instrument. We analyzed the reduced data using the dedicated python software CANDID to detect companions. Results. Out of these 29 systems, we resolved 19 companions in 17 different systems with angular separations between ∼0. 5 and 50 mas. This results in a multiplicity fraction f m = 17/29 = 0. 59 ± 0. 09, and an average number of companions f c = 19/29 = 0. 66 ± 0. 13. Those results are in agreement with the results of the SMASH+ survey in the Southern Hemisphere. Thirteen of these companions have been resolved for the first time, including the companion responsible for the nonthermal emission in Cyg OB2-5 A and the confirmation of the candidate companion of HD 47129 suggested by SMASH+.
Lanthermann). We also used one night that studied O-type stars during which O-type stars were observed as backup targets because the original program could not be executed, in December 2017 (PI: S. Kraus). During this campaign, we could obtain good quality data on 29 O-type stars, listed in Table  1  with information on the spectral type, distance, and the number of detected companions in various separation ranges, from the literature as well as those detected by this study. Figure  2  shows the histogram of O-type star systems for which we obtained good-quality data as a function of their magnitude in the H-band. As the official magnitude limit in typical conditions is H = 6. 5 mag, the bulk of observed objects is around this limit. But as we advanced in time, the improved knowledge of the MIRC-X instrument acquired during commissioning allowed us to push for fainter targets, beyond the initial magnitude limit of the instrument.
Data analysis To analyze the reduced data, and look for companions in the interferometric signal, we used the CANDID 4 software  (Gallenne et al. 2015). CANDID is a tool developed to look for binarity signals in interferometric data as well as to determine the position and flux ratio of the companion(s). For nondetections, CANDID also provides upper limits on the contrast of potential companions. More information about the algorithms and methods can be found in  Gallenne et al. (2015). To streamline the analysis of the data given the relatively large size of our sample, we established an automated procedure to analyze all the data consistently and uniformly. This procedure is summarized here. First, for each observation of a star, we input all the reduced data to CANDID. We then perform a first search for a companion, fixing the maximum separation to 50 mas, which is approximately our OWA (see Eq.
(  1 )). We fixed the step of the search grid to 1. 0 mas. This step size is fine enough to find the global minimum in the range of separation we are looking for, while it is large enough to preserve reasonable computation time (approximately an hour per observation). If a companion is detected (nσ > 5, with nσ being the significance of the binary model compared to a uniform disk model fitting the data), we use the bootstrap function of CANDID around the position of the found companion. This gives us errors on the position and flux ratio of the companion that are more realistic than those computed by the initial grid search method. Then, if a first companion is detected, we analytically remove the signal of this first companion from the 4 https://github. com/amerand/CANDID interferometric data, and we perform a search for a second companion, using the same parameters as for the first search.
Acknowledgements. We would like to thank  Carine Babusiaux , from  IPAG , for her help in determining the capability of Gaia to help in the constraint of orbital parameters and  Laetitia Rodet  in her explanation of the relevance of the study of the Kozai-Lidov cycle. We also thank the referee for their constructive comments that resulted in a better paper. L. M. and E. G. acknowledge the  European Space Agency (ESA)  and the  Belgium Federal Science Policy Office (BEL-SPO)  for their support in the framework of the prodex programme. This work is based upon observations obtained with the  Georgia State University Center for High Angular Resolution Astronomy Array at Mount Wilson Observatory. The CHARA Array is supported by the  National Science Foundation  under Grant No. AST-1636624  and  AST-2034336.
DLV-772225 :  MULTIPLES ). This research has made use of the  Jean-Marie Mariotti Center Aspro  service (available at http://www. jmmc. fr/aspro). This work was supported by the  Programme National de Physique Stellaire (PNPS) of CNRS/INSU  co-funded by  CEA  and  CNES. This work has been partially supported by the  LabEx FOCUS   ANR-11-LABX-0013. This research has made use of the SIMBAD database, operated at  CDS, Strasbourg, France.
Data The ChEMBL database (v. 27) 12 was used for the experiments. It was standardised with ChemAxon JChem's utilities 13 using the following procedures: 1) dearomatisation, 2) isotopes removal, 3) stereo marks removal, 4) explicit hydrogens removal, 5) small fragments removal, 6) solvents removal, 7) salts strip, 8) neutralisation of charges, 9) functional groups transformation, 10) selection of canonical tautomer form of the molecule, 11) aromatisation, 12) duplicates removal, 13) dearomatisation. The standardised dataset consisted of 1. 6M structures with 15 atomic types (C, O, N, S, F, Cl, N+, O-, Br, P, I, N-, B, Si, and Se). The number of heavy atoms in molecules was no more than 50. The canonical order of atoms in molecules was obtained from a breadth-first search (BFS) algorithm, similar as it was done by Mercado et al.
Encoding The input graph is a hydrogen-count labelled graph (HLG)  8  where each atom is encoded in a vector with the following properties: element number, period, group, number of electrons on the last subshell + atom's charge, number of last shells, the total number of hydrogens, whether or not the atom is in a ring, number of neighbours and counts of single, double, triple and aromatic bonds near current atom. Each property is normalised with a natural logarithm for numerical stability. As a result, the input for the encoder consists of atom feature vectors with dimension 12 and the graph's adjacency matrix. The scheme of the encoder is shown in Figure  2. The first operation is an increase of the dimensionality of atoms features by a simple linear operation. Then, these vectors are passed to a Graph Convolution Network (GCN) layer  15  implemented in the Pytorch Geometric package  16.
Similarity search analysis Similarity search is one of the most common methods in chemoinformatics that heavily relies on the neighbourhood principle. This approach is one of the fundamental methods for virtual screening, which is still widely used in drug discovery research. In this work, similarity search was used in a ranking setup, where the goal is to find the most similar structures in a data set for a given molecular query. First, the molecules from the ChEMBL data set were encoded in their vector representation using algorithmic fragment-based and deep learning approaches. It is important to note, that the ordering of atoms was randomised before encoding of molecular structure in a vector representation. Then a group of 33 molecules were taken from the ChEMBL v. 30 data set 13 , which were registered in the US Accepted Names (USAN) in 2021, so these molecules were not present in the ChEMBL v.
27 standardised data set. The query molecules were also converted to vector representations with the same parameters as for ChEMBL structures. The 500 most similar structures from the ChEMBL database were identified for each query using the studied vectors. These sets of molecules were then used to compare each approach pairwisely and by visual analysis. The choice of similarity metrics was based on the nature of descriptors. Thus, for fragment-based approaches, Tanimoto coefficient was used, and for continuous vectors, Euclidian distance was employed  23.
QSAR benchmarking workflow One of the tasks where learned molecular features can be used is a Quantitative Structure-Activity Relationship (QSAR) problem. Based on the neighbourhood principle, QSAR can be used to assess the quality of the obtained molecular representation. In this way, it is expected that the predictions on latent vectors of order-dependent autoencoders will be lower than those of order-independent autoencoders. The data used for the QSAR benchmarking was collected from the ChEMBL v. 27 database. More specifically, the activity information about targets for "Homo sapiens" organism with assay type "Binding assay" and target type "Single protein" was extracted. The obtained data consisted of 387K activity records for 1805 targets. Then, activity records were filtered if 1) they were measured for compounds that did not pass standardisation; 2) if the standard deviation of several IC50 concentrations for the same target and compound was more than 20 nM.
The final number of the activity records remaining was 189K for 149K compounds and 532 targets. The thresholds for each target, the number of compounds and activity analysis are given in the Supplementary information. The machine learning algorithm for benchmarking was Random Forest  25  , implemented in the Scikit-learn Python package  26. The following training procedure was used: the activity class labels and corresponding descriptors were extracted for each target, and then data was split into five folds using the StratifiedKFold algorithm  26. The standard scaling was fitted on the training folds for each cross-validation iteration and used to transform the validation fold, except for fingerprints. To explore different parameters, the grid search was used. The metrics were balanced accuracy (BA) and receiver operating characteristic area under the curve (ROCAUC).
Analysis of fragments learned by VQGAE In the methodology section it was assumed that centroids of atoms vectors after the encoder of VQGAE represent vectors of learned fragments. However, it is not possible to retrieve structure of a fragment by passing a single codebook vector to the decoder, as VQGAE will only generate a single atom. Therefore, we decided to extract fragments in an "indirect" manner. In this approach, substructures were extracted from molecules that contained atoms encoded in the same codebook vector, with these atoms serving as the centres of the substructures. The main assumption was that a small size of the environment around the central atom would result in the same extracted substructures that correspond to the learned fragment. The standardised ChEMBL dataset was used to extract fragments.
First, VQGAE was trained on this dataset and achieved a reconstruction rate of 94. 5%. Fragment analysis was then performed on the same dataset. The environment around central atoms was used as two (i. e. only the alpha and beta atoms around the central atom were included). Several features of the atoms were kept in the fragment structures: presence of atom in the ring and maximum bond order around the atom. During analysis of the fragments, we discovered that for one codebook vector there are several corresponding fragments. For example, in Figure  9a , for a single codebook vector, 6 fragments were extracted from 4783 molecules. The most frequent structure corresponds to the C(sp2, central atom)-C(sp2)-C(sp3) acyclic fragment. The other fragments look quite similar to the first one. At the training stage, VQGAE learns enough fragment vectors to maximise the reconstruction rate.
QSAR benchmarking Another way to assess the quality of latent space concerns their application as variables in machine-learning models linking chemical or biological properties with molecular structure. Here, the VQGAE latent and feature vectors and previously described molecular descriptors were benchmarked in classification models obtained with the random forest method on 532 datasets corresponding to different biological activities extracted from ChEMBL. Statistical analysis of model performances (balanced accuracy) for different descriptors is given in Figure  12. One can see the model trained on HyFactor latent vectors demonstrated the worst results due to the atoms order dependence in the latent space. Other models showed similar performances, especially model trained on latent vectors of VQGAE showed the same performance as those trained on fragmentbased descriptors.
Inverse QSAR In the previous sections the performance of VQGAE to capture structural features was studied. However, the combination of VQGAE with the ordering network can also be used for the discovery of new chemical structures, particularly for the generation of new structures in the context of inverse QSAR. As a proof of concept, the VQGAE was used to design new antagonists of the adenosine A2A receptor. The adenosine A2A receptor is an example of the G protein-coupled receptor (GPCR) family, which is important in mediating vasodilation, supporting the synthesis of new blood vessels and protecting tissues from collateral inflammatory damage. It is a well-studied receptor with several known molecules for which affinities have been measured. The standardised ChEMBL v. 27 dataset contains 3300 molecules with measured inhibition constants (ki) for the A2A target.
The inverse QSAR setup consisted of genetic algorithm, ordering network and VQGAE architecture. Genetic algorithm (GA) implemented in PyGAD v. 3. 0. 0 python library  29  was used as the method to generate new latent vectors of VQGAE. The fitness or scoring function used was a random forest regressor trained on previously extracted molecules with measured ki for the A2A target. The ordering network was used to generate structures from the VQGAE latent vectors. It was trained separately from the VQGAE as it requires a fixed set of fragment vectors. The training and generation statistics are given in Supplementary Information. In the result, 5 new latent vectors were selected as potentially highly active (predicted ki < 10 nM), of which 4 were successfully converted into molecular structures via combination of ordering network and decoder of VQGAE.
The 4 generated molecules are shown in Figure  13. Among them, 1-3 share the same Bemis-Murcko framework and highly similar to each other. During analysis of these structures, it was discovered that the structure 1 is present in the SureChEMBL patent database, but not in the ChEMBL. This compound was registered as A2A antagonist with measured ki = 43nM. Therefore, this result confirms that our approach was able to identify molecules with desired properties.
To improve species identification, multiple marker/primer pairs are often recommended and remains an expensive method for screening of large samples  [39]. In the last decade, an innovative and cheaper proteomic tool named MALDI-TOF MS profiling, emerged for rapid mosquito species identification  [40]. The principle is based on the matching of species-specific protein signatures of a specimen with reference spectra database. This promising tool has been reported to distinguish cryptic species of Anopheles genus with high efficiency  [41]. Already used successfully for different taxonomic groups of insects, including culicids  [42]  and phlebotomids  [43] , standardization of protocols and optimized procedures could enable sharing all MS spectra reference and lead to the creation of an international MS database  [44].
Molecular identification of mosquitoes DNA was individually extracted from the abdomen of each mosquito specimen using the QIAamp DNA tissue extraction kit (Qiagen, Hilden, Germany) following the manufacturer's instructions. Sequences of a fragment of the cytochrome c oxidase subunit I (COI) gene from extracted DNA of each mosquito were amplified using DNA-barcode primers forward LCO1490 (5'-GGT CAA CAA ATC ATA AAG ATA TTG G-3') and reverse HC02198 (5'-TAA ACT TCA GGG TGA CCA AAA AAT CA-3'), according to protocols previously described  [50]. After expected amplified DNA fragment size (658pb) verified by electrophoresis (1. 5% agarose gel), samples were sent for sequencing. Sequences were edited using the Molecular Evolutionary Genetics Analysis (MEGA) X software, assembled and aligned in Clustal ω2 algorithm using the default parameters.
A reference database of Culex COI sequences obtained from sequences accessible in the Barcode of Life Data Systems (BOLD)  [51]  and in GenBank ®  [52]  was used for identification and to construct a Maximum Likelihood tree using a General Time Reversible (GTR) model +G+I (Gamma distribution ; evolutionarily Invariable). Support for internal nodes was estimated using the nonparametric bootstrap method with 1000 replicates. By using the Nucleotide Basic Local Alignment Search Tool (BLAST)  [53] , the identity percentage and query coverage parameter was also extracted for each sample. Only specimens with identity >97% were selected for this study. Each specimen was then assigned to a mosquito species name. Species identification through DNA-barcoding was used for all specimens including undetermined Culex female specimens but also for confirmation of specimens already identified by morphology.
MALDI-TOF MS parameters Protein mass profiles were obtained using a MicroFlex LT MALDI-TOF Mass Spectrometer (Bruker Daltonics, Germany), with detection in the linear positive-ion mode at a laser frequency of 50 Hz within a mass range of 2-20 kDa. The setting parameters of the MALDI-TOF MS apparatus were identical to those previously used  [57]. MS spectra were acquired automatically using the AutoXecute of the Flex Control v. 2. 4 software (Bruker Daltonics).
MS spectra analysis MS spectra profiles were controlled visually first with flexAnalysis v. 3. 3 software (Bruker Daltonics), then exported to ClinProTools v. 2. 2 and MALDI-Biotyper v. 3. 0 software (Bruker Daltonics) for data processing (smoothing, baseline subtraction, peak picking). MS spectra reproducibility was assessed by the comparison of the average spectral profiles (MSP, Main Spectrum Profile) obtained from the four spots for each specimen according to body part with MALDI-Biotyper (Bruker Daltonics). Additionally, mosquito body part MS spectra reproducibility and specificity were assessed using cluster analyses (MSP dendrogram) and the composite correlation index (CCI) tool. The MSP dendrograms were performed from clusters of the specimens, based on comparison of their protein mass profile (i.
Database creation and blind tests The MS reference database (DB) was created using spectra from paired legs and thorax of one to four specimens per species using MALDI-Biotyper (Bruker Daltonics). MS spectra were created with an unbiased algorithm using information on the peak position, intensity and frequency. A total of 58 MSP from thoraxes or legs, were included in the reference MS spectra DB (Table  1 ). The raw MS spectra from legs and thoraxes of Culex mosquitoes are provided for free use (Additional file S1). MS spectra from mosquito legs and thoraxes of the 111 remaining specimens were tested against this in-house MS reference spectra DB. The reliability of species identification was estimated using the log score values (LSVs) obtained from the MALDI Biotyper software v. 3. 0, which ranged from 0 to 3.
declarator, Cx. nigripalpus, Cx. quinquefasciatus and Cx. usquatus), and the nine remaining species were classified in the Melanoconion subgenus (Cx. adamesi, Cx. dunni, Cx. eastor, Cx. idottus, Cx. pedroi, Cx. phlogistus, Cx. portesi, Cx. rabanicolus and Cx. spissipes). According to our data set availability, 1 to 34 specimens per species were acquired. Thirty four Culex specimens encompassing the 13 species and used for MALDI-TOF MS reference database were selected for the phylogenetic analysis based on COI gene sequences (Additional file S2). Specimens are successfully assembled and classified by species.
Creation of the MS reference spectra database and validation steps MS spectra from 12 and 13 Culex species for legs (n=24) and thoraxes (n=34), respectively, including one to four specimens per species, were selected for database creation (Table  1 ). MS spectra from the remaining specimens (legs, n=145; thoraxes, n=135) were queried against the DB. The LSVs ranged from 1. 31 to 2. 71 for legs (Figure  3A ) and from 1. 03 to 2. 66 for thoraxes (Figure  3B ). For a reliable identification, a LSV upper than the 1. 8 threshold is required  [42, 56]. In these conditions, a correct identification (LSVs>1. 8) could be considered for 95. 9% (n=139/145) of legs MS spectra and 94. 8% (n=128/135) for thoraxes. Among these MS spectra reaching the LSVs threshold, 100% of the identification results were concordant with the molecular results.
Moreover, the skill and regular training of entomological experts is also another factor playing a role in the capacity of correct specimen identification  [70]. The genus Culex is recognized as a highly diverse group of mosquitoes for which delimitation and identification of species is particularly difficult. Complementary methods are therefore required for improving species identification. DNA barcoding using the mitochondrial COI gene is widely used for species identification in molecular taxonomy  [71]. A first COI barcoding database, available in the BOLD system, established based on morphological classification of male Culex specimens of FG  [3]  was used to validate morphological identification and to classify the unidentified females of this study. The COI gene has been recorded as an effective and accessible DNA barcode that provides a high performance in delimiting species within the subgenus Melanoconion of Culex species, a subgenus displaying the highest species diversity in tropical regions  [66].
In order to develop an accessible mosquito spectra database of Culex species, the present study employed MALDI-TOF MS double spectra biotyping strategy. Here, for each specimen tested and for DB creation, legs and thorax from the same specimen were independently submitted to MS analysis, whenever possible. Specific MS spectra from the 13 Culex species per body part were obtained. The cluster analysis on MSP dendrogram confirmed this species specificity. Besides, similar ordination of the specimens for both body parts with the COI phylogenetic tree, revealed a relationship between molecular taxonomic classification and MS spectra profiles. However, as MS spectra from closely-related species were grouped in the same branch of MS dendrogram, a mismatching could occur. To prevent misidentification, the legs and thorax were independently query against the DB.
Three species from the Melanoconion subgenus, Cx. idottus, Cx. phlogistus and Cx. rabanicolus, presenting isomorphic traits among females and which could not be accurately distinguished morphologically, were unambiguously identified by this proteomic tool. The present spectral database, created using thoraxes and legs from females of 13 Culex species from FG, represents the first attempt to create a MALDI-TOF MS database for identification of neotropical Culex species. However, the database encompass only 12. 5% (n=13/104) of the total number of Culex species known in FG and solely two out of the eight subgenera from the Culex genus of the mosquitoes actually inventoried in the territory  [3]. The widening of this MALDI-TOF MS spectra reference DB with missing Culex species, become compulsory for application of this tool in the monitoring of Culex genus vectors in FG.