dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We thank Genomics core, especially  David Reynolds , Director of the Genomics Core,  Shahina Maqbool , Director of the Epigenomics core as well as the  Flow Cytometry and Gene modification facilities at Einstein. We appreciate all the help in data analysis and training by  Masako Suzuki at Einstein. We thank  Drs. Bin Zhou ,  Cedric Blanpain , and  Peter Scambler  for reading this manuscript and providing helpful suggestions. We also thank  NYU Center for Genomics and Systems Biology Genomics Core; NYU Langone's Genome Technology Center  is partially supported by the  Cancer Center Support  Grant  P30CA016087  at the  Laura and Isaac Perlmutter Cancer Center , especially  Peter Meyn. This work was supported by grants from the  National Institutes of Health   P01HD070454  (B.
Data availability The datasets generated during this study are available at the GEO repository under the accession numbers: scRNA-seq "GSE167493", scRNA-seq "GSE167491", ATAC-seq "GSE173700", and ChIP-seq "GSE173521". The scRNA-seq data can be viewed at https:// scviewer. shinyapps. io/heartMLP. All other relevant data supporting the key findings of this study are available within the article and its Supplementary Information files or from the corresponding author upon reasonable request.
Multiple alleles of these genes are annotated in the IMGT database, including 14 IGHG1, 17 IGHG2, and 29 IGHG3 alleles encoding 6, 7, and 21 distinct protein variants, respectively, as of August 2021 (www. imgt. org). Differences between alleles include single nucleotide polymorphisms (SNPs) as well as variation in the number of IGHG3 hinge exons. These data show that IGHG variation involves more amino acid positions than those identified serologically. Functional consequences of IGHG polymorphism are largely unknown, but a few have been elucidated. An IGHG3 variant with histidine at position 435 (435H) in the CH3 domain was shown to have prolonged half-life compared to that containing the arginine variant (435 R) due to higher affinity of the IgG3-435H variant to FcRn at low pH  [4]. Differential FcRn binding has also been detected for IgG1 allotypes, which are fixed for the 435H variant, but differ from one another at positions 214 (CH1) and/or 356/358 (CH3)  [8].
These data were compared to publicly available whole genome sequences by extracting variants in corresponding regions from two 1kGP datasets: phase 3  [21]  (1kGP-ph3) and the newly assembled high coverage version, 1kGP-30X  [22]. Of 87 SNPs detected in our cohorts, 71 were present in 1kGP-ph3, and 83 were detected in 1kGP-30X (Table  S1 ). While the vast majority of the SNPs that were present in one dataset and not the other were of low frequency, there were several common SNPs that were discrepant in this regard (Table  S1 ). For example, variants at positions 161 and 311 in CH3 exons of IGHG2 and IGHG1, respectively, were present in our dataset and 1kGP-30X (16-29% allelic frequency), but absent in 1kGP-ph3. On the other hand, 1kGP-30X detected SNPs at positions 168 and 313 of IGHG1 CH3 and IGHG2 CH3, respectively (19-24% allelic frequency), and these SNPs were not found in our dataset nor 1kGP-ph3.
The possibility of errors in the 1kGP datasets with regard to common SNPs is evident when comparing 1kGP data to that in the IMGT for IGHG alleles, which represent cDNA clones deposited by various research groups (Fig. S2 ). The common SNPs missed in 1kGP-ph3 and detected in our cohorts are present among the IMGT alleles. Further, SNPs present in 1kGP-30X with frequency >3%, but missing in our data are also missing in IMGT. Thus, our genotypes more closely reflect those compiled in the IMGT dataset than does either version of the 1kGP datasets. We used Haploview software to phase the identified SNPs and predict IGHG alleles, which we then matched to alleles annotated in the IMGT database (Tables  S2-S4 ). Several putatively new alleles were identified, with allelic frequencies of up to 12%.
IGHG allotypes IGHG allotypes (i. e. , protein variants) were estimated by phasing only amino acid changing SNPs in our dataset. In order to compare our data to the 1kGP data, we extracted phased nonsynonymous IGHG SNP data from 1kGP-ph3 for three superpopulations, African (AFR, N = 661), European (EUR, N = 503), and East Asian (EAS, N = 504), and considered variants present with frequencies >1%. As indicated above, a number of discrepancies were evident between both versions of the 1kGP data and our data. We focused here only on the older 1kGP-ph3 because the 1kGP-30X data contains several SNPs with high frequencies that are likely to have been assigned to IGHG genes inaccurately, and it is preferable to analyze a dataset that may miss some SNPs rather than one that contains falsely assigned variants.
Interestingly, one SA individual possessed three nucleotide variants at the corresponding position, suggesting a gene duplication of the IGHG2 locus (Fig. S3 ). Although the gene duplication may have been present in more individuals, this is likely to be a rare event since the SNP data were, overall, consistent with Hardy-Weinberg equilibrium. The IGHG3 allotypes demonstrated the highest level of diversity among the three loci (Table  3 ). IGHG3*11 is the most common allotype in IED, whereas IGHG3*01 is the most common allotype in IAD and EAS. These two allotypes differ only at position 296 (Y/F) and cannot be distinguished serologically. IGHG3*03 was not present in the 1kGP-ph3 dataset, which misses SNP encoding the IGHG3 419Q/E polymorphism, likely due to sequence assembly error (G3_CH3_234, Table  S3 ).
The IGHG3 allotypes containing histidine at position 435, which is known to increase the IgG3 half-life and facilitate placental transport  [4] , were rare in our dataset (IGHG3*17 and IGHG3*n5). The corresponding SNP rs4042056 (G3_CH3_283) was not detected in our EA sample (i. e. , fixed for arginine at this position), but was present at frequencies of 3% and 10% among the AA and SA individuals, respectively (Table  S1 ). This SNP was detected at higher frequency in the 1kGP-ph3 dataset (10-16% in EUR, AFR and EAS) giving rise to multiple haplotypes (Table  3  and S5 ). The presence of four IGHG3 hinge exons was by far the most common length variant across the genotyped populations followed by presence of three exons, which showed higher allelic frequency in AA/SA compared to EA (Table  4 ).
LD between the IGHG genes Serological data implicate a high level of LD between the IGHG genes  [7] , and to test this in our dataset, multigenic haplotypes  were generated using Haploview based on amino acid changing variants (Fig. 2 , S4 and Tables  S6-S7 ). There were 10 haplotypes in the EA group and 18 haplotypes in each of the AA and SA groups restricting to those observed at frequency of >1%. Haplotype frequencies differed substantially between population samples with no common haplotypes observed between EA and SA except for one (Fig. 2A , Table  S6 ). This is a reflection of the differential allotype frequencies across the groups. Nine haplotypes were shared by the AA and SA groups, and four haplotypes were shared between AA and EA, highlighting admixture between these two groups.
DISCUSSION IgG allotype diversity has been characterized across various populations by serological methods  [7, 17, 18] , whereas characterization of polymorphisms and their frequencies in the corresponding IGHG genes remains sparse due to the homology between the genes and related difficulties in analyzing genotypes. Here, we determined IGHG variation in three population groups from the U. S. and South Africa, representing the first comprehensive genetic analysis of the IGHG locus in IAD, and compared these to the 1kGP data. Overall, our data were in agreement with the 1kGP data, but certain distinctions were observed. These included a few common SNP discrepancies between the two datasets and greater diversity of IGHG alleles in the 1kGP dataset than in ours. There are likely several causes of these discrepancies.
The 1kGP data is obtained using next generation sequencing (NGS), which relies on assembly of short overlapping reads (~100 bp) that can phase variants inaccurately  [24] , particularly in regions of high sequence similarity, such as the IGHG genes. These issues probably resulted in drop-out of low-confidence SNPs in 1kGP-ph3, and erroneous mapping in the less conservative approach of 1kGP-30X. Although our data may be prone to allele drop-out if a variant occurs within the site of primer annealing, it is strengthened by sequencing of long genomic fragments (600-1800 bp) derived from gene-specific amplification, which increases confidence in accuracy of gene-specific calling of the variants. Of note, with regard to common SNPs that were discrepant between our data and the 1kGP datasets, we found that our frequencies were generally closer to that of gnomAD data generated by NGS (http://www.
Chu et al. [10]  suggested that decreasing the hinge length diminishes phagocytic activity of IgG3 based on studies using hinge variants on the backbone of the IGHG3*01 allotype. However, our genotyping data indicate that the three-exons variant does not occur on a IGHG3*01 backbone, but rather, is present primarily in an IGHG3*03 allotype (Table  3 ). De Taye et al. [9]  tested only natural variants for ADCC activity and did not detect clear difference between three-and four-exon hinge allotypes, but the IGHG3*04 allotype with shorter hinge, encoded by two exons, demonstrated higher ADCC capacity compared to other IGHG3 allotypes. This allotype has been observed only in one individual in our dataset. Further, the    1 2 3. B Gm haplotypes corresponding to the genetic haplotypes according to Lefranc and Lefranc  [7].
PCR and sequencing products were purified using AMPure XP and CleanSEQ magnetic beads (Beckman Coulter) according to the manufacturer's protocol. Sequencing data were analyzed using Sequencher software (Gene Codes). Variability in the number of exons encoding the hinge region was analyzed by PCR fragment size resolution using a LabChip GX instrument (Perkin Elmer). Polymorphic positions within each gene were labeled to denote gene name, exon name and nucleotide position within the exon: for example, G1_CH1_68. The Eu numbering system was used for amino acid positions  [33]  (www. imgt. org). To avoid variant calling errors due to background noise and sample handling, genotyping was repeated for >10% of samples, including all variants that were present in single individuals. Table  S10  contains all genotyping data generated in this study.
The 1kGP data and Fst analysis The IGHG exon variant data for the 1kGP-ph3 dataset  [21]  and the newly assembled high coverage version, 1kGP-30X  [22] , were extracted from https://www. internationalgenome. org/data-portal/data-collection/grch38 and https://www. internationalgenome. org/data-portal/data-collection/30x-grch38, respectively. The 1kGP genotypes were obtained using DNA from lymphoblastoid cell lines, which are oligoclonal regarding Ig class switching. The data were processed using Plink v1. 9. 0, BCFtools v1. 9 and VCFtools v0. 1. 16 [34]. The 1kGP-ph3 data were used for haplotype comparison and the F ST analysis. Weir and Cockerham's F ST  [23]  values were calculated for nonsynonymous IGHG SNPs having a global minor allele frequency >10% using VCFtools v0. 1. 16. Empirical p values were determined on the F ST values of 1000 randomly sampled SNPs across all autosomes from the 1kGP-ph3 data  [21]  for super-population pairs, including AFR, EUR, and EAS.
names correspond to IMGT nomenclature (TablesS2-S4), letter "n" designates newly identified allotypes in this study, and allotypes found only in 1kGP-ph3 dataset are labeled "1K". Amino acid positions are shown in accordance with the Eu numbering system. Allotypes are listed in bold when present at frequencies >10% in at least one group, and amino acid positions are bolded when distinguished by serological typing. Gm allotypes were defined according to Lefranc and Lefranc [7]. EA -European Americans (N = 95), AA -African Americans (N = 94), AFR -Africans (N = 661), EUR -Europeans (N = 503), EAS -East Asians (N = 504). Allotypes with estimated frequencies >1% are shown.
The sequence similarity between TAS2Rs and class A GPCRs is in the range of 14%-29%  11. Structure-based sequence alignment has placed TAS2Rs in the class A family, which contains the olfactory chemosensory receptors sub-family  12. TAS2Rs have been recently labelled as class T in the GPCR database (GPCRdb) (Fig. 1a )  13. Structurally, GPCRs are made up of seven transmembrane (TM) helices named TM1 to TM7 that form a bundle across the cell membrane. How GPCRs achieve specific robust signaling and how these functions are encoded in their sequences are pending fundamental questions. GPCR activation relies on so-called molecular switches, which allosterically connect the ligand binding pocket to the intracellular G protein coupling site in order to trigger downstream signaling  14. In class A GPCRs (including olfactory receptors, ORs), these molecular switches consist of conserved sequence motifs (Fig.
Sequence alignment Automatic multiple sequence alignment (MSA) of TAS2Rs was performed with class A and class F templates (labelled ClustalO and classF, respectively) using ClustalO  31  with default settings in the Jalview interface (v2. 11. 0)  32. These MSAs were not modified. Another MSA, labelled Chemosim, was completed using class A templates, 339 class II ORs and TAS2Rs. The Chemosim alignment was then manually refined using constraints from functional assays in the literature (as described in the results section). We specifically focused on the 339 class II ORs because they contain relevant motifs for TAS2Rs alignment and because TM sequence conservation is higher than in a mixture of class I and class II human ORs. TM segments were predicted by the PPM webserver  33. The final Chemosim MSA is provided as an additional supporting file (TAS2R-OR-templates.
Template selection for comparative modeling of bitter taste receptors Class A GPCR templates were selected by submitting each of the 25 human TAS2Rs UniprotKB accession numbers to the Swiss-Model modeling server  34. From the proposed templates for human TAS2Rs, 46 with at least 10% sequence identity were kept. Templates were then grouped by protein name and sorted by resolution and average sequence identity with TAS2Rs. The highest resolution template from each group was retained, resulting in 19 templates. Finally, six GPCR class A templates were selected to maximize structural diversity. As TAS2Rs have been suggested to be part of the same family as the frizzled receptors  35  , 3 class F GPCR templates were also considered: the human FZD4 receptor  36  and 2 structures of the human SMO receptor  37.
The PDB code for the six class A templates were as follows: rhodopsin (6FUF)  38  , β1-adrenergic (4BVN)  39  , β2-adrenergic receptor (5JQH)  40  , angiotensin II type 1 (4YAY)  41  , chemokine receptor CXCR4 (3ODU)  42  , serotonin receptor 5-HT2C (6BQG)  43.
Evaluation of the model pocket score: To identify residues oriented toward the binding pocket, the following protocol was implemented in Python: i/ For each of the 25 human TAS2Rs, a reference 3D model was selected from the Chemosim models. All reference models were then structurally aligned to the TAS2R16 reference. ii/ A unique grid of points broadly covering the binding site of class A GPCRs was generated and aligned to the coordinates of the TAS2R16 reference. iii/ Each TAS2R model was aligned to its reference based on the alpha carbons of the TM residues. iv/ Residues whose sidechain center of mass (SCM) was within 8. 0 angstroms of any grid point, and whose angle between the SCM, the alpha carbon, and any grid point was lower or equal to 30 degrees, were considered as oriented towards the pocket.
Predicted tertiary structure of TAS2Rs Based on this refined alignment, we tested various protocols and structural templates to build accurate 3D homology models of TAS2Rs. Among the TAS2Rs, receptors TAS2R14, 16, and 46 were selected to evaluate the approach, as previous work on these receptors involving site-directed mutagenesis provides data to determine the residues within their binding pocket. According to our meta-score, the best models of these three receptors were obtained using the Chemosim approach and a single template, either the β2-adrenoceptor (PDB 5JQH) or the β1-adrenoceptor (PDB 4BVN) structure (Fig. 2  & S3). The performance of each protocol is compared in Fig. S3  and S4. Gomodo and ClustalO approaches led to comparable models, with slight improvement over BitterDB and, in most cases, substantial improvement over GPCRdb.
Conclusions This study elucidates key residues and consensus functional motifs of bitter taste receptors (TAS2Rs) using a combination of bioinformatics, molecular modeling, and in vitro assays. The consensus sequence motifs match well-known ones in class A GPCRs. Further, we performed sequence alignment of human TAS2Rs with olfactory and non-olfactory class A GPCRs, including residue conservation and experimental data as constraints. Using site-directed mutagenesis, we then evaluated the functional roles of these motifs in TAS2R16 as a case study. In addition to the residues lining the binding pocket, we identified the "toggle/transmission switch" (the YF  6. 48  L motif in TM6) and the "hydrophobic connector" (P 5. 50 A 3. 40 F  6. 44  ) for agonist sensing. Other molecular switches were identified in the intracellular regions of TM6 and TM7 that are suggested to be involved in G protein selectivity or in receptor activation.
Acknowledgements The authors thank  Dr. Xiaojing Cong  for fruitful discussion and critical review of the manuscript. This work was funded by the  French Ministry of Higher Education and Research  [PhD Fellowship to CB], by the  National Research Foundation of Korea (NRF)  [grant number  NRF2020R1A2C2004661 ], by  GIRACT (Geneva, Switzerland)  [9th European PhD in  Flavor Research Bursaries  for first year students to CB], and the  Gen Foundation (Registered UK  Charity No. 1071026 ), a charitable trust that primarily funds research in natural sciences, particularly food sciences/technology [grant to CB and JT]. We also benefited from funding by the  French government  through the  UCAJEDI  " Investments in the Future " project, managed by the  ANR  [grant No. ANR-15-IDEX-01  to SF and JG].
Many of them are characterized by a small body and cryptic life-style, are very rare, inhabit some extreme environments, or everything mentioned. There are numerous examples among chordeumatidans where only one or several individuals are known, on the basis of which a taxon is described, while repeated attempts to collect additional material from the type localities were unsuccessful". Enghoff et al. (2015)  agreed that probably too many families are currently recognized in the order, and that multiple major changes are foreseen. Phylogenetic analyses involving molecular data will hopefully help, but so far there have been very few such. An exception is the analysis of  Mock et al. (2016) , and if their results are representative, major changes are indeed to be expected: in European Journal of Taxonomy 769: 1-244 (2021)   (Leach, 1814).
5. The exception concerns Serbia (SB) and Montenegro (MN) which in Fauna Europaea are treated as one unit ("Yugoslavia", YU). Despite recent political changes, records from Kosovo are ascribed to Serbia (SB), and records from Crimea are ascribed to Ukraine (UA). We have found several publications in which distributional data are erroneous with regard to present-day geopolitical units, including some which have been reiterated even as recently as 2012 in national inventories of species made after boundary changes. The Balkan countries have been particularly affected in this respect; this has complicated our work considerably and may have resulted in errors. European Journal of Taxonomy 769: 1-244  (2021)  Table  1  (continued on next page). Suborders, superfamilies and families of Chordeumatida C.
For referring a locality to the correct 50 km square, physical UTM maps were used, as well as GISsurfer (https://mappingsupport. com/p2/gissurfer. php). Where a locality was given with longitude and latitude, Legal Land Converter (https://legallandconverter. com/p50. html) was used. For batch conversion of lat long coordinates, Earthpoint (https://Earthpoint. us) was used. The maps were made by André J. van Loon (EIS Kenniscentrum Insecten, Leiden, Netherlands), based on lists of 50 × 50 km MGRS coordinates, using the 'Klasse' software distributed by the Nederlandse Entomologische Vereniging (Netherlands Entomological Society).
dsmz. de/). This work reveals the misidentification of B. cereus group species by MALDI-TOF and shows that several species are responsible for bacteremia in preterm infants. Data availability. The three genome sequences and related metadata have been deposited in the GenBank database under accession numbers CAJNDP000000000, CAJNDR000000000, and CAGVOV000000000 for strains 18, 7, and 20, respectively. The raw reads were deposited in the SRA database under accession numbers SRR15372309, SRR15372310, and SRR15372308 (Illumina) and SRR15372306, SRR15372307, and SRR15372305 (Oxford Nanopore Technologies; ONT), respectively, for strains 18, 7, and 20.
Genomic characteristics and accession numbers of the bacterial isolates Bacillus cereus strain 18, Bacillus luti strain 7, and Bacillus paranthracis strain 20 a Strain identification by $96% similarity or $70% with digital DNA-DNA hybridization (dDDH; dDDH values of. 70% are considered an indication that the tested organism belongs to a different species than the type strain[s] used as the reference[s]).
ACKNOWLEDGMENTS M. B. K. and F. N. benefited from a Ph. D. and a postdoc grant, respectively, from the  Fondation Méditerranée Infection, Marseille, France. This work was financed by the  French government  through the  UCAJEDI Investments  in the  Future  project managed by the  National Research Agency (ANR) , with the reference number  ANR-15-IDEX-01.
To implements the spectral demixing data processing for multi-color SMLM we developed a software, SplitViSu, which is freely available and is integrated into a workflow of image analysis, reconstruction and segmentation of SMLM data within the SharpViSu platform  33. We demonstrate the performance of our methods by imaging the nuclear pore complex (NPC), a reference standard for super-resolution microscopy  34  , revealing simultaneous localization of individual proteins within the nuclear envelope and providing new findings on their colocalization.
2A , Suppl. Fig. S2 ). For each fluorophore within a dataset, we calculate its simple and weighted mean positions, and their difference: Δx = x wm -x m. Δx has a random component due to the limited localization precision and due to fluctuations in the photon counts, and a component that depends on the position x due to the chromatic aberrations: Δx = Δx rand + Δx chro. The typical values for our setup and fluorophore triplet are: -20 nm ≤ Δx rand ≤ 20 nm, -4 nm ≤ Δx chro ≤ 4 nm (Suppl. Fig. S4 ). We fit the chromatic component Δx chro as a function of the coordinates with a polynomial, separately for x and y coordinates, and subtract it from the "weighted mean" positions: X = x wm -Δx chro (Suppl. Fig. S4 ). The resulting localizations have the best possible resolution, as determined by Fourier ring correlation  36  (Fig.