dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2019). However, since reference genomes with complete coverage of the Ig locus are lacking for both rhesus and cynomolgus macaques, we could not rely upon published information for the design of comprehensive 5 0 MTPX primer sets in this study. Therefore, we generated IgM libraries using 5 0 RACE (Table  S1A ) from 2 animals from each of the 4 sub-groups and deep sequenced these libraries on the Illumina MiSeq platform. We analyzed the runs with IgDiscover and used the upstream module to obtain sequence information from the leader and the upstream 5 0 untranslated region (5 0 UTR) of the IGHV genes, enabling the design of 2 independent sets of 5 0 MTPX primers: one targeting the IGHV leader (comprising 25 primers) and the other targeting the 5 0 UTR (comprising 35 primers) (Table  S1B ).
Optimization of the input database used for IGHV allele inference The IgDiscover germline inference process requires an input database for the initial sequence assignments. We used a recently described macaque Ig database generated from genomic sequencing of the IGH locus of a single Indian-origin rhesus macaque, comprising 66 IGHV, 37 IGHD, and 6 IGHJ genes and 103 IGHV, 37 IGHD, and 10 IGHJ alleles, hereafter referred to as the ''starting database'' (SDB)  (Cirelli et al. , 2019). While this assembly offers the most complete information of the macaque IGH locus available to date, it has significant shortfalls if used as a comprehensive source of IGH genes and alleles since it was generated from a single macaque. In the case of the IGH locus in humans, genetic diversity occurs at both the allelic and structural levels  (Watson et al.
, 2013) , and this is likely to be the case also in macaques. An Ig gene/allele database generated from a single macaque will therefore lack the range of allelic diversity present in a population of macaques. It may also lack a number of IGHV genes due to two reasons: structural diversity within the species, with some animals containing either deletions or duplications of parts of the IGH locus, and incomplete sequence coverage, leading to errors in the assembly that fail to include V genes that actually are present in the animal. During the IGHV germline inference process, it is of critical importance to exclude false positive candidate germline sequences. A major source of these false positives is the presence of sequences that differ from genuine germline sequences due to low-level somatic hypermutation or instrument-associated sequence errors.
, 2019). However, this poses a major problem when identifying germline sequences from species in which the initial database does not contain all of the genes. In the case of germline alleles from genes that are not present in the SDB, the alleles may be mis-assigned to the next closest gene. If there is a discrepancy of usage-for example, that the novel allele is used at considerably lower levels-this may result in the exclusion of alleles that fail to meet the default allelic ratio. One means to investigate whether additional genes and alleles are present in a species is to derive a dendrogram of known IGHV germline sequences from that population. Allelic variants of a given gene will tend to cluster together, revealing individual clusters that correspond to allelic variants of each gene present within the population analyzed.
An individual IGHV genotype can also be compared to the population level tree to reveal differences at the structural level. To investigate this, we curated a combination of IGHV alleles from a previous study  (Corcoran et al. , 2016) , as well as alleles from preliminary analyses of the 45 animals in this study, and identified 67 detached clusters that did not contain sequences present in the Cirelli et al. database. This indicated that these alleles were variants of genes that were not identified in the  Cirelli et al. (2019)  study. From each of the 67 detached clusters, we selected a representative sequence referred to as ''not located'' (or ''not linked''; IGHV-NLs), which we added to the SDB. This resulted in a more diverse SDB, reducing the risk that IgDiscover would inadvertently exclude real alleles due to misassignment and allelic ratio filtering.
IGHV allele inference Using the IDB, we analyzed the libraries generated by the leader and UTR 5 0 MTPX primer sets for the 45 macaques, using version 0. 10b of IgDiscover (https://gitlab. com/gkhlab/igdiscovermacaca). Inspection of the inferred IGHV alleles revealed that the majority of the alleles, 632 of 762 (83%) for rhesus macaques and 547 of 615 (89%) for cynomolgus macaques, were identified in both the leader and UTR libraries (Figure  S1 ), providing robust validation of these alleles. Identification of a given IGHV allele by only one of the 5 0 MTPX primer sets may be explained by polymorphisms in the primer binding site affecting annealing of the primer targeting that gene in the other set. We hypothesized that we should be able to detect variation in the upstream regions in cases in which we see amplification with the UTR 5 0 MTPX primer set and a lower level of amplification with the leader 5 0 MTPX primer set.
To investigate this for a specific case, we used the IgDiscover module called ''upstream'' that enables the extraction of the leader sequences of individual alleles. Analysis of allele IGHV1-NL_2*01_S5797 in rhesus macaque E04 indicated the presence of a variant G/A nucleotide at position 18 of the leader sequence. This variant nucleotide falls within the binding site of leader primer Ma_VHL_MTPX_1 that targets this gene. The allele amplified efficiently with the UTR primer set, demonstrating the utility of using two independent libraries with different primer binding sites. In collating the individual IGHV genotype, we therefore used the union of alleles identified with the two Rep-seq libraries (generated with the leader and UTR primer sets) for each of the animals. When inspecting the IgDiscover output, we identified a total of 1,198 IGHV alleles, 670 alleles of which were found in at least 2 animals.
S1  and S2. The high degree of allelic diversity in macaques was illuminated by inferred haplotype analysis of animals Rh4 and D07 (Figure  S3 ). We used the IgDiscover plotalleles module to infer a phased allelic output for animals with heterozygous J genes. We analyzed 2 animals that contained heterozygous IGHJ5-4 alleles Rh4 and D07. This analysis demonstrated both a high level of allelic heterozygosity and evidence of structural variation between animals. Most genes showed either heterozygous or homozygous allelic content, with similar counts in each animal for heterozygous alleles of the same gene. However, it was also clear that both animals demonstrated evidence of copy-number variation (CNV) differences. For example, Rh4 showed the hemizygous presence of IGHV2-7, IGHV3-177, IGHV2-118, IGHV1-138, and IGHV1-NL_1, while D07 showed the hemizygous presence of  See also Figure  S2  for the corresponding analysis in cynomolgus macaques, and Figure  S3.
(2019) , several of which were found in both rhesus and cynomolgus macaques (Figure  3B ; Table  S4 ). Most of the validated alleles belonged to the IGHV3 family (36 alleles) or the IGHV4 family (81 alleles), reflecting the large size of these families. For an overview of the different types of validation approaches provided for each IGHV alleles, we generated a summary showing the alleles that were validated by targeted genomic PCR, which were found previously reported by BLAST query of GenBank-deposited macaque germline sequences or previous genomic assemblies, and which were found in both leader and UTR primer-generated Rep-seq libraries (Table  S5A ). Table  S5A  also shows in how many animals each allele was found, as well as whether it was found in Indianor Chinese-origin rhesus macaques or Indonesian-or Mauritiusorigin cynomolgus macaques, or whether it was found in several of these sub-groups.
To further characterize the diversity, for all sub-groups, we calculated the average number of unique IGHV alleles identified as an additional macaque is added to the database (Figure  5A ). We found that the number of alleles identified in the Mauritian cynomolgus macaques were closest to reaching a plateau, with Indonesian cynomolgus macaques demonstrating the opposite trend, while Indian-origin and Chinese-origin macaques fell between the two cynomolgus groups. Another way to study the population diversity is to examine how many IGHV alleles are exclusive to a single animal. We calculated the average in each population and obtained a result that was consistent with the previous diversity result (Figure  5B ). Next, to examine the variation within the groups of macaques in more detail, we randomly selected five macaques with comparable numbers of IGHV alleles from each sub-group and produced Venn diagrams of their allelic overlap (Figure  5C ).
Rhesus and cynomolgus databases Incomplete germline databases result in inflated estimates of SHM. To investigate the extent of this, we assigned VDJ sequences (using IgBlast) to the Cirelli et al. database and to the database generated from all 45 macaques in this study, except the alleles unique to the test animal, referred to as a ''44 animal DB. '' This benchmark test was intended to evaluate how the full database performed when assigning Rep-seq data from a ''novel'' test animal performed in terms of SHM estimation. Taking all reads with low SHM levels (<1% against the individualized database for the test animal, which is treated as baseline for these purposes), we compared estimates of SHM at the nucleotide level when sequences were assigned to the Cirelli et al. database or the 44 animal DB.
This procedure was performed with 2 randomly selected animals from each of the 4 groups. With each 44 animal DB, the degree of estimated nucleotide-level SHM is low, with a median of 0% and with only 10% of the sequences above 0. 3%. With the less-complete Cirelli et al. database, SHM appeared considerably inflated, with a median overestimate of 3%, and a 10% of sequences above 6. 3%. These latter values are in the range of SHMs typically observed in immunization studies, suggesting that incomplete germline gene databases dramatically hinder the accurate quantification of SHM in such studies. Figure  7.
DISCUSSION Comprehensive germline Ig gene databases for both macaque species are lacking, thereby hampering immunological studies and vaccine evaluation in these models. Germline Ig genes in macaques remain poorly characterized, despite their importance in pre-clinical immunology research. Work from several groups over the past decade that have begun to characterize Ig genes in macaques suggests that the level of diversity in macaque germline VDJ alleles is greater than that in humans  (Bible et al. , 2003; Corcoran et al. , 2016; Fu et al. , 2017; Helmuth et al. , 2000; Howard et al. , 2005a Howard et al. , , 2005b;; Ramesh et al. , 2017; Rosenfeld et al. , 2019). However, to date, only a small subset of the reported alleles is available in the public database of Ig germline genes, The International ImMunoGeneTics Information System (IMGT)  (Alamyar et al.
, 2012; Lefranc, 2014). We have taken the view that the major requirement of such a database is to include representative sequences for all expressed IGHV genes and to ensure that the vast majority of allelic variants present in such a reference database are validated. To this end, we produced 2 independent libraries for each animal in this study, using two 5 0 MTPX primer sets annealing to the 5 0 UTR or leader sequences, respectively. We applied stringent filtering of candidate germline sequences to ensure that only high-confidence alleles remain in the output of each analysis. As expected, we found that a minority of alleles are identical to macaque sequences already present in the publicly deposited nucleotide sequence databases. Thus, most of the Ig allelic variation identified in this study was novel.
One question our study wished to address was whether it is advisable or even necessary to use population-specific germline reference databases for Rep-seq Ig gene assignment purposes. Although our results demonstrate that each population contained different frequencies of Ig allele variation, the level of admixture between the macaque species means that an individual germline allele of interest in a single experimental animal may be present at a higher frequency in a population different from the animal in question. For example. the IGHV4-79*02_S9501 allele was found in 8. 3% (1/12) of the Chinese-origin rhesus macaques, but it was also present in 40% (6/15) of the Indian rhesus macaques analyzed. Likewise, IGHV1-124*01_S6748 was found in 2/12 Chinese-origin rhesus macaques, yet it was also present in 100% (12/12) of the Mauritian cynomolgus animals.
There are multiple examples of these type of admixed alleles that fail to neatly assort into specific population groupings. While the generation of an individualized germline Ig database for each animal under study is an approach that would solve many of the assignment issues identified here, it is not a trivial process. The availability of an alternative means of high-quality germline gene assignment would therefore be of benefit to many investigators engaged in macaque Rep-seq analysis and/or studies of macaque-derived monoclonal antibodies (Abs). To this end, we devised a benchmark test to demonstrate that high-quality IGHV allelic assignment was possible without the need for individualized database production. Comparison of the results from the 44 animal DB to the results obtained when using a less complete database, in this case that from  Cirelli et al.
(2019) , show a significant reduction in the estimations of average SHM calculated at the nucleotide level. For more advanced Rep-seq applications, such as studies involving Ab lineage tracing, the generation of individualized databases is recommended for precise calculations of SHM in given animals. In the present study, we used IgDiscover to infer IGHV and J alleles in 45 animals comprising 4 sub-groups of macaques. For each of the rhesus and cynomolgus macaque species, we included animals from 2 different geographic regions. We report high levels of allelic variation between animals both within and between sub-groups. The Mauritian cynomolgus macaques displayed notably more shared IGHV alleles among themselves than the animals in the other sub-groups, consistent with the separate cluster observed in the PCA plot.
The Indian-origin macaques included here were from two different breeding centers in the United States, which may have more restricted breeding populations compared to the facilities from which the Chinese rhesus Each plot shows the distribution of nucleotide differences (calculated using sequences with SHM levels lower than 1% against the individualized database of the test animal) when sequences were assigned to the  Cirelli et al. database (red)  or the 44 animal DB (blue). The 44 animal DB is the 1,198 allele database presented in this study minus the animal-specific alleles present in the test animal. See also Figures  S4  and S5. macaques were imported. To our knowledge, we did not include close relatives in the groups here, as our aim was to obtain an overview of allelic diversity and to determine whether there was a significant overlap in allelic content between the different groups of macaques.
In the present study, we used the set of IGHV genes from a genomic assembly of an Indian-origin rhesus macaque  (Cirelli et al. , 2019). However, the ability to identify a comprehensive set of unique genes is limited by issues of overall sequence coverage and structural variation in the population, including gene duplication and deletion events. Analysis of the output of IgDiscover indicated that many of the newly identified germline sequences could be better explained as being alleles of genes that were not present in the Cirelli et al. database. This set of NLs was used to supplement the Cirelli et al. database, which improved the inference process. This is likely because the germline filter of IgDiscover removes false positive germline candidates based on the ratio of sequence counts between alleles of a given gene.
The NLs were identified through a combination of phylogenetic distance from neighboring gene sequences and Sanger sequence analysis of the surrounding genomic sequence. It is important to note, however, that the current database does not aim to resolve the question of the total number of genes present in the two macaque species. The designation of the NL category, while based on sequence divergence that can be consistent with the different NLs being different genes, is also consistent with several NLs being allelic variants of the same underlying IGHV gene, albeit variants that differ in sequence far more than the usual allelic variation we see in IGHV alleles. The extensive validation we have performed on these sequences indicates that they are real germline sequences. While the NL designation allows these germline variants to be used efficiently by IgDiscover, we believe the ultimate nomenclature of these various alleles will coalesce through the combination of germline inference techniques and more extensive genomic assembly production.
Recently, we and others applied IgDiscover to studies of vaccine-induced antibody responses in rhesus macaques by defining individualized IGHV germline gene databases from the specific animals studied. This allowed stringent examination of antigen-specific antibody lineages and precise definition of affinity maturation of vaccine-induced HIV-1 neutralizing Abs  (Kong et al. , 2019; Martinez-Murillo et al. , 2017; Phad et al. , 2020). The availability of more complete macaque Ig genetic information in the database we have amassed will greatly facilitate such studies in the future. Not the least, during the current coronavirus disease 2019 (COVID-19) pandemic, there is a need for stringent analysis of vaccination protocol outcomes, many of which are using primate models, including animals from rhesus and cynomolgus macaque populations.
Limitations of study This study does not cover Ig Kappa (IGK) and Ig Lambda (IGL) alleles, which are also important in the analysis of antibody ontogeny. The generation of such databases can be performed with the same approach as described here, but this requires further studies. Moreover, our databases do not cover all of the IGHV alleles present in rhesus and cynomolgus macaques since the Ig diversity in these populations is very high. Finally, the alleles present in this study are inferred from rearranged VDJ segments, which introduces a certain level of uncertainty in the last few nucleotides of the V alleles. Further analysis of antibody germline genes at the genomic level will help identify potential polymorphisms in the 3 0 end of the macaque V genes that were overlooked here.
STAR+METHODS Detailed methods are provided in the online version of this paper and include the following: et al. genes or NL clusters using a custom script (available upon request). The naming of the novel alleles identified was computationally determined using two processes. Sequences that were close variants of known genes from the Cirelli et al. database were provided with a suffix by the IgDiscover program. The naming of sequences at the gene/NL cluster level was performed using a custom script. The IgDiscover plotalleles module was used to infer a phased allelic output for animals with heterozygous J genes. The cases analyzed, Rh4 and D07, both contained heterozygous IGHJ5-4 alleles and the module was run with conditions of-logscale-cfilter 0. 15 -order, with gene order based on identity to the rheMAC10 assembly.
QUANTIFICATION AND STATISTICAL ANALYSIS The population structure between animals was visualized by performing principal component analysis (PCA) of the binary matrix. The allele presence or absence was projected into a new coordinate system with axes being rotated to render the largest variation in the data. The input matrix was an n by p matrix X, with rows representing the n macaques, and columns representing the p alleles. Presence or absence of an allele was indicated either with 1 or 0. The input matrix was centered by subtracting the mean. Given the meancentered matrix, eigenvalues and eigenvectors of the sample covariance matrix were computed by applying the prcomp function in the R statistical package (v3. 6. 1). The proportion of variance explained by each component was computed by squaring each corresponding to it eigenvalue and dividing that by the sum of squared eigenvalues which represents the total variance.
ACKNOWLEDGMENTS We thank all of the personnel at the  Astrid Fagraeus Laboratory at Karolinska Institutet  for assistance with work related to NHPs. We also thank  Nancy Haigwood ,  Ann Hessell , and  David Spencer  at the  Oregon National Primate Research Center ;  Roger LeGrand  at the  CEA, IMVA-HB/IDMIT ,  Fontenayaux-Roses & Le Kremlin-Bice  ˆtre; and  Karin Loreat Karolinska Institutet  for generous sample contributions. This work was funded by a  European AIDS Vaccine Initiative (EAVI)   2020  ( 681137 ) grant, a  Distinguished Professor grant  from the  Swedish Research Council  (grant agreement  2017-00968 ), grants from the  National Institutes of Health (NIH)  ( P01 HIVRAD grant  agreement  AI104722  ( Wyatt and Karlsson Hedestam ),  AI078064 (Haigwood) ,  P51  grant agreement  OD011092  ( Oregon National Primate Research Center ), and a '' Programme Investissements d'Avenir'' (PIA)  grant managed by the  ANR  reference  ANR-11-INBS-0008 , funding the  Infectious Disease Models and Innovative Therapies  ( IDMIT, Fontenay-aux-Roses, France ) infrastructure at  CEA (LeGrand).
Data and code availability All the libraries generated and analyzed for this study were deposited in the European nucleotide archive (ENA). The accession numbers for the RACE libraries are ERR4250665-ERR4250672, both the MTPX leader primer set and the MTPX UTR primer set libraries are ERR4238026-ERR4238115. All alleles were deposited in GenBank; The accession numbers for the novel inferred alleles reported in this paper are Genbank: MT561887-MT563069, Genbank: MT643213-MT643227, Genbank: MT672244-MT672249 and.
► Caducy Medical Device Software is the evaluated measurement system using rPPG. This software is based on an algorithm developed by the company I-Virtual. To make a measurement, the algorithm is based on three successive steps  16  : Phase 1: Region of interest (ROI) determination: analysis area (head/face). 7 8 The ROI is the area of skin on the face that will allow analysis. Phase 2: Each image from the ROI will benefit from a measurement of the red, green and blue colour spectra. Phase 3: Measurement of the fluctuation of these spectra during a defined period with the intervention of an algorithm to define the physiological parameters (HR, RR and SpO2).
Statistical analysis To determine the HR, RR and SpO 2 , descriptive analyses will be conducted according to the nature and the distribution of the variables. Qualitative variables will be described with frequencies and percentages; quantitative variables will be reported as mean±SD. The subclass correlation coefficient with the 95% CI will be used to measure the concordance between the two measuring systems. The ICC with a 95% CI will be used to measure the concordance between the two measuring systems. The analysis will be adjusted according to age, sex and the six skin phototypes. 15 Analyses were performed using IBM SPSS Statistics V. 23, and p values <0. 05 were considered statistically significant.