dataesr/scientific-paragraphs-categorization huggingface dataset:
We filtered sources with non-negligible free-free contamination and corrected fluxes from line contamination, resulting in a W43-MM2&MM3 catalog of 205 getsf cores. With a median deconvolved FWHM size of 3400 au, core masses range from ∼0. 1 M to ∼70 M and the getsf catalog is 90% complete down to 0. 8 M. Results. The high-mass end of the core mass function (CMF) of W43-MM2&MM3 is top-heavy compared to the canonical IMF. Fitting the cumulative CMF with a single power-law of the form N(> log M) ∝ M α , we measured α = -0. 95 ± 0. 04, compared to the canonical α = -1. 35 Salpeter IMF slope. The slope of the CMF is robust with respect to map processing, extraction software packages, and reasonable variations in the assumptions taken to estimate core masses. We explore several assumptions on how cores transfer their mass to stars (assuming a mass conversion efficiency) and subfragment (defining a core fragment mass function) to predict the IMF resulting from the W43-MM2&MM3 CMF.
Observations and data reduction Observations were carried out between December 2017 and December 2018 as part of the ALMA Large Program named ALMA-IMF (project #2017. 1. 01355. L, see  Motte et al. 2022). The 12 m and 7 m ALMA arrays were used at both 1. 3 mm and 3 mm (central frequencies ν c 228. 4 GHz in band 6 and 99. 66 GHz in band 3, see Table  1 ). The W43-MM2 and W43-MM3 fields have the same extent and were imaged by the ALMA 12 m and 7 m arrays with mosaics composed of 27 (respectively 11) pointings at 1. 3 mm and 11 (respectively 3) pointings at 3 mm. For the 12 m array images, the maximum recoverable scales are ∼5. 6 at 1. 3 mm and ∼8. 1 at 3 mm  (Motte et al. 2022) , corresponding to 0. 15-0. 2 pc at 5. 5 kpc. At 1. 3 mm and 3 mm, eight (respectively four) spectral windows were selected for the ALMA-IMF setup; they sum up to bandwidths of 3.
7 GHz and 2. 9 GHz, respectively. Table  1  summarizes the basic information of 12 m array observations for each field and each continuum waveband. A more complete description of the W43-MM2 and W43-MM3 data sets can be found in Paper I  (Motte et al. 2022 ) and Paper II  (Ginsburg et al. 2022). The present W43-MM2 and W43-MM3 data sets were downloaded from the ALMA archive before they were corrected for system temperature and spectral data normalisation 2. This, however, has no significant impact on the continuum data as shown in Sect. 2 of Paper II  (Ginsburg et al. 2022). The data were first calibrated using the default calibration pipelines of the CASA 3  software. We then used an automatic CASA 5. 4 pipeline script  4 developed by the ALMA-IMF consortium and fully described in Paper II  (Ginsburg et al.
Extraction of compact sources Since our goal is to extract cores from their surrounding cloud, we need to use software packages that identify and characterize cores as emission peaks, whose size is limited by their structured background and neighboring cores. Many source extraction algorithms have been used in star formation studies (see  Joncour et al. 2020; Men'shchikov 2021 ). Here we use two completely independent methods, getsf and GExt2D. The getsf 7  method (Men'shchikov 2021) employs a spatial decomposition of the observed images to better isolate various spatial scales and separate the structural components of relatively round sources and elongated filaments from each other and from the background. The new method has many common features with its predecessors getsources, getfilaments, and getimages  (Men'shchikov et al.
Source detection With the objective to build the most complete and most robust core catalog in the W43-MM2&MM3 protocluster cloud, the core positions and footprints should be defined in the detection image that provides the optimum image sensitivity. This corresponds to the bsens image at 1. 3 mm (see Sect. 2). To further improve the sensitivity of the image chosen to detect cores, we removed the noise associated with cloud structures, which are incoherent from one scale to another. To do this we used the Multi-resolution non-Gaussian Segmentation software (MnGSeg) that separates the incoherent structures, referred to as Gaussian, of a cloud from the coherent structures associated with star formation  (Robitaille et al. 2019 , see also Appendix A). The removed Gaussian component corresponds to structural noise associated with the small-scale structures of cirrus that lie along the line of sight to the W43-MM2 and W43-MM3 protoclusters.
20 M (see Eq. (  5 ) and adopted assumptions). Hereafter the master source catalogs will be those from the extraction performed with getsf (v210414), using the listed input images for the following: -detection: 1. To facilitate core extraction, the noise level of the detection image is flattened, using images that are uncorrected by the primary beam. Table  E. 1 lists the sources detected by getsf at 1. 3 mm and identified by their peak coordinates, RA and Dec, along with their characteristics measured at 1. 1. 3mm and σ int 3mm ; two tags to identify cores also extracted by GExt2D and cores identified as suffering from line contamination (see Sect. 4. 1. 2). The getsf package extracted 208 cores that passed the basic recommended filtering 8  (Men'shchikov 2021). Table  2  gives the number of sources extracted by getsf when using different detection and measurement images, from the cleanest to the bsens and finally denoised & bsens images, at 1.
Summary and conclusion We used ALMA images of the W43-MM2&MM3 mini-starburst to make an extensive census of cores and derive its CMF. Our main results and conclusions can be summarized as follows: -We combined the 12 m array images of the W43-MM2 and W43-MM3 protoclusters that were individually targeted by the ALMA-IMF Large Program (see Sect. 2 and Table  1 ;  Motte et al. 2022; Ginsburg et al. 2022). At 1. 3). This new strategy proved to be efficient both in increasing the number of sources detected and in improving the accuracy of their measurements, when applied to present observations and synthetic images (see Table  2  and Appendix A). In the end, it allows the 5 σ detection of point-like cores with gas masses of ∼0. 20 M at 23 K (see Fig. 1a ); -We extracted 1. 3 mm compact sources using both the getsf and GExt2D software packages.
(2021)  from a threedimensional numerical simulation of an Orion-sized turbulent molecular cloud  (Ntormousi & Hennebelle 2019). For our purpose, we assumed it represents a typical cloud complex of the ALMA-IMF Large Program like G338. 93, located at 3. 9 kpc. The simulated density cube was projected along a single axis and a core extraction was performed with getsf to remove the flux contribution of simulated cores, thus creating an image only made of their background. These cores are not suitable for our purpose due to their ill-constrained sizes and masses. A total of 306 synthetic sources were then added where the column density exceeds 4 × 10 22 cm -2. They have masses arranged in ten logarithmic bins spanning 0. 2 -3. 2 M and, for simplicity, they are set to be unresolved by the simulated ALMA beam (here 0.
Acknowledgements. This paper makes use of the ALMA data ADS/JAO. ALMA# 2017. 1. 01355. L. ALMA is a partnership of  ESO  (representing its member states),  NSF (USA)  and  NINS (Japan) , together with  NRC (Canada) ,  MOST  and  ASIAA (Taiwan) , and  KASI (Republic of Korea) , in cooperation with the  Republic of Chile. The Joint ALMA Observatory is operated by  ESO ,  AUI/NRAO  and  NAOJ. This project has received funding from the  European Research Council (ERC)  via the  ERC Synergy Grant  ECOGAL (grant  855130 ), from the  French Agence Nationale de la Recherche (ANR)  through the project  COSMHIC  ( ANR-20-CE31-0009 ), and the  French Programme National de Physique Stellaire and Physique et Chimie du Milieu Interstellaire (PNPS and PCMI) of CNRS/INSU  (with  INC/INP/IN2P3 ). Y. P. acknowledges funding from the  IDEX Université Grenoble Alpes  under the  Initiatives de Recherche Stratégiques (IRS) "Origine de la Masse des Étoiles dans notre Galaxie" (OMEGa).
Y. P. and G. B. acknowledge funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme , for the Project "The  Dawn of Organic Chemistry" (DOC) , grant agreement No  741002. R. G. -M. and T. N. acknowledge support from  UNAM-PAPIIT  project  IN104319. R. G. -M. is also supported by  CONACyT Ciencia de Frontera  project  ID 86372. T. N. acknowledges support from the postdoctoral fellowship program of the UNAM. S. B. acknowledges support from the  French Agence Nationale de la Recherche (ANR)  through the project  GENESIS  ( ANR-16-CE92-0035-01 ). F. L. acknowledges the support of the  Marie Curie Action of the European Union  (project  MagiKStar , Grant agreement number  841276 ). A. Gi. acknowledges support from the  National Science Foundation  under grant No.
2008101. P. S. and B. W. were supported by a  Grant-in-Aid for Scientific Research  (KAKENHI Number  18H01259 ) of the  Japan Society for the Promotion of Science (JSPS). P. S. and H. -L. L. gratefully acknowledge the support from the  NAOJ   Visiting Fellow Program  to visit the  National Astronomical Observatory of Japan in 2019, February. A. S. gratefully acknowledges funding support through  Fondecyt Regular  (project code  1180350 ), from the  ANID   BASAL  project  FB210003 , and from the  Chilean Centro de Excelencia en Astrofísica y Tecnologías Afines (CATA) BASAL  grant  AFB-170002. T. B. acknowledges the support from  S. N. Bose National Centre for Basic Sciences  under the  Department of Science and Technology, Govt. of India. G. B. also acknowledges funding from the  State Agency for Research (AEI)  of the  Spanish MCIU  through the  AYA2017-84390-C2-2-R grant  and from the  PID2020-117710GB-I00  grant funded by  MCIN/ AEI /10.
13039/501100011033. T. Cs. has received financial support from the  French State  in the framework of the  IdEx Université de Bordeaux Investments  for the future Program. L. B. gratefully acknowledges support by the  ANID BASAL  projects  ACE210002  and  FB210003. K. T. was supported by  JSPS KAKENHI  (Grant Number  20H05645 ). D. W. gratefully acknowledges support from the  National Science Foundation  under Award No. 1816715.
Multiple methods have recently been developed to reconstruct full-length B-cell receptors (BCRs) from single-cell RNA sequencing (scRNA-seq) data. This need emerged from the expansion of scRNAseq techniques, the increasing interest in antibodybased drug development and the importance of BCR repertoire changes in cancer and autoimmune disease progression. However, a comprehensive assessment of performance-influencing factors such as the sequencing depth, read length or number of somatic hypermutations (SHMs) as well as guidance regarding the choice of methodology is still lacking. In this work, we evaluated the ability of six available methods to reconstruct full-length BCRs using one simulated and three experimental SMARTseq datasets. In addition, we validated that the BCRs assembled in silico recognize their intended targets when expressed as monoclonal antibodies.
We observed that methods such as BALDR, BA-SIC and BRACER showed the best overall performance across the tested datasets and conditions, whereas only BASIC demonstrated acceptable results on very short read libraries. Furthermore, the de novo assembly-based methods BRACER and BALDR were the most accurate in reconstructing BCRs harboring different degrees of SHMs in the variable domain, while TRUST4, MiXCR and BASIC were the fastest. Finally, we propose guidelines to select the best method based on the given data characteristics.
Since LC does not have a D segment, only the V-J recombination occurs. During these processes, random nucleotides are added into the V(-D-)J joining regions, resulting in a higher 2 NAR Genomics and  Bioinformatics, 2022, Vol. 4, No. 3  number of HC and LC than it would be possible by simply joining gene segments available at each HC and LC gene's locus. This process, in fact, can generate an immunoglobulin repertoire of >5 × 10 13 different antigen specificities  (5). The introduction of these new nucleotides is particularly challenging for BCR assembly algorithms because of the randomness of such introductions and their absence in the reference genome and in immunoglobulin annotation databases. The characterization of BCR repertoires from scRNAseq data has been instrumental in the investigation of groups of B cells sharing a common ancestor, called clonotypes.
Two plate-based ap-proaches, namely SMART-seq  (13, 14)  and a modification of it named SPEC-seq  (15) , have been instrumental in BCR sequencing and full-length BCR reconstruction due to their ability to obtain full-length transcripts of HC and LC genes allowing the reconstruction of the variable domain of an HC-LC pair set in a single cell. The droplet-based 10x Genomics Chromium Single Cell Immune Profiling Solution [CG000148 10x Technical Note(ctfassets. net)] also enables sequencing of the V-D-J genes of a B cell to obtain paired HC and LC. However, this comes with the cost of losing the full-length BCR information for a considerable number of single cells  (16). Although it is not feasible to determine a pair of HC and LC constituting a BCR of a cell using bulk RNA-seq techniques, several methods have been proposed to delineate BCR composition from complex datasets.
The first developed method was MiXCR  (24) , which consists of a collection of algorithms based on a proprietary aligner that perform clustering to accomplish BCR reconstruction and annotation. BASIC, a semi de novo algorithm  (25) , was the second method that was made available with the advantage of being able to process libraries as short as 25 bp. Lately, several algorithms based on a de novo assembly but using different approaches to map reads and assign V-D-J genes such as BRACER  (26) , BALDR  (27) , VDJPuzzle  (28)  and TRUST4 (29) have emerged, increasing the choice but also the difficulty in selecting the best tool for a given dataset. Importantly, BASIC  (25) , MiXCR  (24) , VDJPuzzle  (28)  and TRUST4 (29) can be used for both BCR and T-cell receptor (TCR) assembly, making them suitable for more elaborate immune repertoire studies.
Finally, the comparison of these methods using several datasets will reveal the stability of their performance across different library preparations. To address these questions, we designed a comprehensive analysis framework (Figure  1 ). First, we selected two publicly available BCR sequencing datasets of plasmablast origin with the available ground truth  (25, 27)  and generated an additional dataset with the ground truth, named 'Leiden', using sorted isotype-switched memory B cells that either recognize tetanus toxoid (TT) or have an unknown antigen specificity. Second, using random sampling, we created multiple libraries with different levels of coverage and read length using the above-mentioned experimental datasets. Third, we simulated a fully synthetic dataset (see the 'Materials and Methods' section) in which HC and LC harbor different amounts of SHMs.
Subsequently, we used these datasets to test six available BCR reconstruction methods. We evaluated their abilities to obtain productive HC and LC, defined by the absence of stop codons or out-of-frame V-J junctions (see the 'Materials and Methods' section). This was carried out using the sensitivity as a metric for the experimental data and accuracy as a metric for the synthetic data (see the 'Materials and Methods' section). In addition, we experimentally validated the benchmarked methods by determining that antibodies, generated using the productive sequences, can recognize their intended antigens. Moreover, we measured the time needed to run these algorithms to provide information on time and scalability. Finally, we propose a final performance score for each method by aggregating the results of all the experiments and provide recommendations for the selection of the method that best suits a given dataset and scientific question.
Leiden dataset (SMART-seq2). Cell sorting, cDNA synthesis, ARTISAN PCR and Sanger sequencing. TT-specific B cells and B cells with unknown specificity were isolated as described before  (9). In short, peripheral blood mononuclear cells were isolated by Ficoll-Paque gradient centrifugation and stained with Fixable Violet (405 nm) Dead Cell Stain Kit (Thermo Fisher), CD3 Pacific Blue (clone UCHT1, BD Pharmingen), CD14 Pacific Blue (clone M5E2, BD Pharmingen), CD19 APC-Cy7 (clone SJ25C1, BD Pharmingen), CD20 AlexaFluor 700 (clone 2H7, BD Pharmingen), CD27 PE-Cy7 (clone M-T271, BD Pharmingen), IgG BV510 (clone G18-145, BD Horizon), IgD FITC (clone IA6-2, BD Pharmingen) and APC-and PE-labeled TT. CD19 + CD20 + CD27 + IgG + IgD -B cells were considered TT-specific if they stained double positive for fluorescently labeled TT with two different fluorochromes: TT-APC and TT-PE.
These were HC and LC that did not contain a stop codon in variable and constant regions as defined by IgBLAST and Change-O  (34). Furthermore, 38 HC, 48 LcK, 8 LcL and 27 paired HC + LC (K or L) were classified as productive. IgBLAST and Change-O (34) define a chain productive if it is an assembled chain with in-frame V-J junctions. These 27 single cells with paired HC + LC were used as the ground truth to compute the sensitivity of each method for this dataset (Supplementary Figure  S1A  and Supplementary Table  S3 ).
Canzar dataset (SPEC-seq). We retrieved the dataset included in the publication of BASIC  (25)  from GEO (https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi? acc=GSE116500). Out of 295 single plasmablast cells published in this dataset, we selected 190 that were sequenced using a 50-bp paired-end mode. We further filtered for 113 samples that had at least 1. 25 million reads. Besides the fastq files, we obtained fasta sequences generated using Sanger sequencing, which we used as the ground truth   (25, 27)  consisting of plasmablasts with unknown antigen specificity were obtained from the corresponding publications. In addition, an scRNA-seq dataset including TT-specific B cells and B cells with unknown antigen specificity was generated in this work (Leiden). All the datasets were downsampled to achieve different read coverages and lengths.
(B) An additional dataset was simulated to investigate the effects of different levels of SHMs in the variable domains of BCRs on the performance of each method. Sensitivity and accuracy were used as metrics to evaluate each method depending on the type of used data. Antibodies were produced using a subset of clonotype-forming patient-specific BCRs and their specificity was experimentally validated. Finally, the execution time was investigated, and a final score was calculated to give a final recommendation on the method choice. for this dataset. Afterward, we ran the Immcantation framework v4. 0  (34)  to assess the number of assembled and productive HC and LC (K or L) in each single cell and to determine germline genes in each sequence. As a result, we assembled 113 HC, 62 LcK and 57 LcL, including 102 cells having complete HC + LC pairs.
Upadhyay dataset (SMART-seq2). We retrieved the human AW1 plasmablast dataset included in the BALDR publication  (27)  from SRA (https://www. ncbi. nlm. nih. gov/ sra/?term=SRP126429). This dataset, consisting of 51 single cells, was first investigated for the read quality using fastQC v0. 11. 9 (Babraham Bioinformatics; FastQC: a quality control tool for high-throughput sequence data). During this quality control step, we observed that libraries contained duplicated reads, overrepresented sequences and kmers despite the quality of the reads being adequate at 3 and 5 ends (Supplementary Data S1 and Supplementary Figure  S3 ). After downloading fastq files and fasta sequences of the ground truth that were generated using the Sanger method, we ran the Immcantation framework v4. 0  (34)  to assess the number of assembled and productive HC and LC in each single cell and to determine germline genes in each sequence.
As a result, we reconstructed 34 HC, 19 LcK and 22 LcL, including 23 paired HC + LC that were termed as assembled. Out of these, 34 HC, 19 LcK and 21 LcL were labeled productive, including 23 HC + LC pairs (BCRs). Consequently, we used these 23 single cells with full-length paired and productive HC and LC to compute the sensitivity of each method for this dataset (Supplementary Figure  S1C ).
Simulation of experimental datasets with different read lengths and coverages We used seqtk [lh3/seqtk: toolkit for processing sequences in FASTA/Q formats (github. com)] version 1. 3-r106 with the option '-s100' to perform random sampling without reintroduction of additional reads to generate libraries with different levels (from 50 000 up to 1. 25 million reads) of coverage. Afterward, the sampled reads were trimmed using seqtk with the option 'trimfq' to obtain final libraries with read lengths ranging from 25 up to 50 nucleotides for the Canzar dataset  (25) , and from 25 up to 100 nucleotides for the Upadhyay dataset (  27 ) and for the Leiden dataset. Bioinformatics, 2022, Vol. 4, No. 3 5  In total, we simulated 70 distinct libraries covering different read lengths, coverages, types of B cells (memory and plasmablasts) and plate-based techniques (SMART-seq2 versus SPEC-seq).
Sensitivity as a performance metric for experimental data In this study, ground truth was considered as a set of HC and LC sequences coming from different single cells and obtained by Sanger sequencing. In principle, every cell should have a pair of HC and LC in the ground truth. However, we realized that only ∼40% of single cells in each of three analyzed datasets had paired HC and LC that were also defined as assembled and productive using the criteria mentioned earlier (Supplementary Figure  S1A-C ). Therefore, the ground truth information was available only for a subset of single cells (and thus productive BCRs) that could be used as a ground truth for all the single cells in each dataset. A metric such as specificity could not be used to evaluate the performance of the tested algorithms for BCR reconstruction due to the nature of the data (every antigen-specific B cell must have an HC and an LC), which prevents calculation of the false positive values.
In other words, if Sanger sequencing misses one HC or one LC, assigning a false positive to a productive HC and LC assembled by a tool would be a wrong assumption since the absence of the chain would reflect a technical problem with Sanger sequencing and not the true biology. Therefore, we used sensitivity as a metric to compare different tools. First, we identified genes of productive HC and LC (K or L) of the ground truth for each cell in each dataset as described earlier. Second, we used different tested tools to assemble and annotate HC and LC (K or L) in different experimental libraries that we generated as described earlier. Then, we compared genes of each productive chain of the ground truth to the corresponding productive ones obtained by a tested method. As IgBLAST and tested tools can sometimes output more than one of each V-D-J germline gene, an HC was defined as a true positive (TP), if at least one of each of the V-D-J genes of a productive HC in the ground truth matched one of each of the corresponding V-D-J genes obtained by the computational method.
Accuracy as a performance metric for simulated synthetic BCRs In the simulated synthetic dataset described earlier, we counted both productive and nonproductive HC and LC (Supplementary Figure  S2A-D ). In humans, mature antigen-specific B cells have one productive HC and LC after SHM, as cells that fail to display functional BCRs are negatively selected and undergo apoptosis. However, the introduction of SHMs by immuneSIM returned several HC and LC with out-of-frame V-J junctions, which we interpreted as a proxy of the SHMs in the variable regions, resulting in nonproductive chains that would be negatively selected in the real world. Having assumed this, we counted true positive (TP), false negative (FN), false positive (FP) and true negative (TN) chains for each tool to compute the accuracy.