dataesr/scientific-paragraphs-categorization huggingface dataset:
Data Collection We collected our data in two waves between September 2021 and November 2021, with a gap of four weeks in between. We reached out to the human resource department of our prospective sample firms and targeted managers involved in the recruitment of gig workers. We approached firms across all sectors, and tried to achieve a balanced sample of firms from each. We sourced our firms from the HR database of a head-hunting market research firm and sent emails to their respective HR managers to seek their advice in contacting the right teams and managers handling gig workforces. We sent out a total of 3,548 copies of the questionnaire and received 753 responses. We cross-verified the responses by following up with the respondents to check their backgrounds and their portfolios in regard to their past handling of gig worker.
We thus finalized a total of 523 responses for our study. The data were collected in two waves with a gap of four weeks. This approach helped us improve the overall response rate using Dillman's (2011) total design test guidelines. The demographic distribution of the respondents is presented in Table  2. We also performed a non-response bias test following the guidelines laid down by  Armstrong & Overton (1977). We compared the mean scores of demographic categories using a t-test. We found the significance value to be greater than 0. 05, thereby confirming the absence of any significant difference between the two groups regarding data collection. The t-test results confirmed that the data did not suffer from non-response bias, and thus proceeded to perform the data analysis, as discussed in the next section.
Measurement Validation Before processing the data and testing our hypotheses, we checked for reliability and validity. Additionally, we checked for the goodness of fit of the model. We referred to the guidelines laid down by  Kock, Josiassen, and Assaf (2019)  to test for nomological validity using the PLS-SEM software. We adopted Warp PLS 7. 0, which uses a partial least square structural equation modelling. Hair, Black, Babin, Anderson, and Tatham (1998)  discussed the rationale for using PLS-SEM over covariance-based SEM and listed multiple criteria. Following those criteria, we were able to confirm that the research was an extension of existing structural models. We thus decided to use PLS-SEM over CB-SEM. We further referred to the arguments and recommendations to choose between CB-SEM and PLS-SEM made by  Hair et al.
But, precisely, reporting N e estimates based on data sets that were not originally intended for this purpose may represent a frequent source of negative bias in the literature  (Marandel et al. , 2019) , which we intentionally wanted to highlight. Individual samples were genotyped using nine nuclear microsatellites (SSRs), an Illumina Gold-enGate assay (177 single nucleotide polymorphisms, SNPs) and an Illumina Infinium assay (3514 SNPs) (see Online Appendix 1 for details). SNP sets included only one randomly selected locus per contig to minimize physical linkage among loci, so that N e estimates using the LD method reflect contemporary rather than past demographic history  (Wang, 2005; Saura et al. , 2015). Most of the SNPs from the Illumina GoldenGate assay were also included in the Infinium assay.
We estimated contemporary N e using the bias-corrected LD method of  Waples and Do (2008) , as implemented in NeEstimator v2. 0 software  (Do et al. , 2014). We obtained separate N e estimates for the nine SSRs, for the 177 SNPs (lower SNP genotyping effort), and for the 3514 SNPs (higher SNP genotyping effort). Contemporary N e estimates based on nine SSRs were infinite for five (26 %) of the 19 large maritime pine populations considered, while estimates for the other 14 populations ranged from Ne = 19 to 2084 (mean 431) with the upper bound of 95 % confidence intervals (CIs) being infinite for all but one populations (Table  1 ). Estimates based on SNP markers were finite for all populations, and lower on average than those obtained with SSRs, with values ranging across populations from 19 to 2772 (mean 219) for the small subset of 177 loci, and from 39 to 1608 (mean 374) for the larger set of 3514 loci.
Acknowledgements This work was supported by the  Spanish Ministry of Agriculture, Fisheries and Food (MAPAMA)  in the frame of the measure 15. 2 "support to the conservation and use of forest genetic resources" and under Regulation (EU) No  1305/2013  of the European Parliament and of the Council of 17 December 2013 on support for rural development by the  European Agricultural Fund for Rural Development (EAFRD)  with 75 % co-financing. This paper was also supported by the  European Union 's  Horizon 2020 research and innovation programme  [grant agreement No  862221  (FORGENIUS)]. We thank two anonymous reviewers for their constructive criticism.
Numerical simulation results In this section, we present 2D hydrodynamic simulation results obtained employing a 2D hydrodynamic computer code, BIG2  [71] , considering the four different beam-target cases noted in Table  1. This code is based on a Godunov type numerical algorithm  [72]  and considers an Eulerian numerical scheme that uses curvilinear rectangular moving grid. The code can treat multi-layered targets comprised of different materials and can handle complicated target geometries. The ion beam energy deposition in the material is calculated assuming a cold stopping model described in  [73]. Since the target temperature in these calculations is below 10 eV, the level of ionization is negligible, and thus, the cold stopping model is a valid approximation. A semi-empirical equation-of-state (EOS) model  [74, 75]  is used for tungsten, while for water, the SESAME data  [76]  is considered.
Design input The main design input to develop the pre-concept design of the individual DEMO systems such as magnets, blankets, divertor and containment structures is the so-called 2017 DEMO baseline  [5]. This set of input data contains both the output of the systems code PROCESS  [2] , i. e. the main machine and plasma parameters, as well as a 3D-CAD configuration model based on these values, which defines the space allocation for the major tokamak systems. The reference design was updated in the so called 2018 magnets baseline  [6] , to account for more realistic structural stresses in the TF coils. The 2018 magnet reference design serves as the main and necessary input for the TF WP and conductor design. As additional input, the operating scenarios of the PF and CS coils are needed for the calculation of the total magnetic field in the TF WP.
The maximum value at cycle 30,000 was 2. 7 nΩm for the intra-petal R c and 7. 0 nΩm for the inter-petal R c. One issue of the ITER TF CICCs is the degradation of the DC performance with thermal cycling and electromagnetic loading. Though a solution of the problem has been found for a 45 kA class CS conductor, the degradation of the 68 kA TF conductors may lead to the magnet operation in current-sharing mode, i. e. with a slight resistance and ohmic heating  [18]. The goal of the DEMO TF conductor R&D program is to develop a conductor that does not exhibit any DC performance degradation. This was not the case in the first RW conductor prototype, in which a slight degradation of the order of 0. 3 K was observed  [11]. In the final stage of the second RW prototype testing, the degradation was eliminated by applying a transverse pre-compression at the cable assembly during the jacketing process.
Acknowledgements The authors gratefully thank the chair of the WPMAG panel of experts,  M. Huguet , and all the panellists,  A. Bonito-Oliva ,  L. Bottura ,  M. Gasparotto , and  N. Mitchell , for providing guidance and their many precious recommendations which have much improved the work plan for the next phase. This work has been carried out within the framework of the  EUROfusion Consortium  and has received funding from the  Euratom  research and training programme 2014-2018 and  2019-2020  under grant agreement No  633053. The views and opinions expressed herein do not necessarily reflect those of the  European Commission.
Such a single measure might not capture the different characteristics of datasets, whereas a multi-objective approach might be more appropriate. Here we propose MobiLLe, a Multi-Objective Based clustering for Inferring BCR clonal lineage from high-throughput B ceLL rEpertoire data. MobiLLe requires IGHV and IGHJ gene annotations and a fixed CDR3 amino acid identity threshold to form initial groups. Next, we merge groups if this minimizes intra-clonal diversity and maximizes inter-clonal differences. Thus, MobiLLe optimizes two objective functions in parallel that continually evaluate groups' consistency until no improvement is observed in their cohesion or separation. By minimizing intra-clonal diversity, we improve each group's cohesion, which measures how similar sequences are within the clonal lineage.
MobiLLe Multi-objective clustering (MOC) decomposes a dataset into related groups, maximizing multiple objectives in parallel. Several frameworks exist to implement MOC, MobiLLe relies on multi-run clustering, where a clustering algorithm runs multiple times to optimize different objectives that capture a compound fitness function  [19]. MobiLLe proceeds through two main steps: pre-clustering and refinement. Fig  1  shows its flowchart, and Algorithm 1 and 2 the pseudo-code for the refinement step. 2. 1. 1 Pre-clustering. The pre-clustering step aims at grouping similar sequences to form initial clonal lineage groups that can be refined later. Note that MobiLLe takes only IGH sequences as input since they are more diverse than IGL chains, providing a reliable signature for immune repertoire studies  [20].
Data sets To evaluate MobiLLe and compare it with existing BCR clonal lineage grouping methods, we used three types of BCR repertoire data: simulated, artificial, and experimental. 2. 2. 1 Simulated repertoires. IGH simulated sequences are largely used to evaluate clonal lineage grouping methods  [13, 14, 28]. Some repertoire simulators have been proposed as part of such tools, but an independent B cell repertoire simulator that could produce different types of IGH repertoires (clonal, non-clonal), to the best of our knowledge, does not exist. In order to create simulated repertoires, we adapted GCTree  [29] , a B cell lineage simulator. We ran GCTree several times to produce independent B cell lineages, which were then assembled in a single repertoire. To produce a B cell lineage, GCTree randomly selects IGHV, IGHD, and IGHJ germline genes from the IMGT database  [4] , then nucleotide(s) can be added to or removed from the IGHV-IGHD and IGHD-IGHJ junction regions.
Experimental repertoires. In order to evaluate our approach on realistically-sized repertoires, we used a dataset produced at the Pitie-Salpêtrière hospital (Paris-France), and two public datasets from IReceptor repository  [32] , totalizing 26 experimental repertoires. The first dataset contains nine samples of human peripheral blood mononuclear cells collected during routine diagnostic procedures at Pitie-Salpêtrière hospital. We picked three samples from these repertoires to systematically compare MobiLLe's results and existing clonal lineage grouping methods. DNA sequences were obtained and assembled as described in Section 2. 2. 2 Table  2  shows the number of reads (sequences), the number of unique sequences (clonotypes), and the clonality status defined by standard PCR amplification and capillary electrophoresis of amplicons (Genescan analysis)  [33].
S5 Fig shows the output plot of Genescan profiles for each experimental repertoire. The second dataset contains five experimental repertoires selected from a previous work that performed high-throughput sequencing to characterize the B cell populations in several lymphoproliferative diseases  [34]. Samples come from different tissues such as blood, lymph nodes, liver, and bone marrow. From this study, we selected one healthy donor (HD) and four patients diagnosed with different lymphoproliferative diseases. Sequence data were obtained through the sequencing platform 454 GS-FLX. Table  3  summarizes the main properties of selected repertoires, for more details report to  [34]. The third dataset contains 12 repertoires selected from a previous work that conducted immune profiling studies of several individuals with different trajectories of SARS-CoV-2 infection and COVID-19 (moderate and severe) and compared them to healthy donors  [35].
BRILIA. B cell Repertoire Inductive Lineage and Immunosequence Annotator (BRILIA) builds up lineage tree reconstruction, clonal grouping, and V(D)J annotation into a single algorithm  [18]. From a collection of IGH sequences, BRILIA first provides initial V(D)J gene identification according to the IMGT database  [4]. Then, it groups sequences with the same IGHV and IGHJ gene subgroups and the same CDR3 sequence length. It proceeds by reconstructing lineage trees that will determine groups of clonally-related sequences. For that, it determines parent-child sequence relationships within each group for further reconstructing lineage trees. Evolutionary relationships are based on an adjusted hamming distance that penalizes dissimilarities in the N regions. Next, BRILIA determines the root of each obtained tree as the sequence involved in a cyclic dependency having the smallest distance to all other sequences in that cluster.
SONAR. For SONAR (Software for the Ontogenic aNalysis of Antibody Repertoire)  [17] , a clonal group contains all IG reads that share a common ancestor. This tool focuses further on seeded lineage assignment, where the sequences of one or more known antibodies are used as seeds to find all sequences in the dataset from the same lineage while leaving the rest of the sequences unclassified. In addition, it can perform "unseeded lineage assignment," which consists of classifying sequences into component lineages without any additional information. In order to perform an unseeded lineage assignment, SONAR separates sequences based on their assigned IGHV and IGHJ genes. The sequences in each group are then clustered based on their CDR3 nucleotide identity (by default, 90% of CDR3 sequence), using the UCLUST algorithm in USEARCH  [38].
Results We first evaluated and optimized MobiLLe on simulated repertoires, where clonally related sequences are well defined for all clonal lineage. Next, we compared MobiLLe with state-of-art methods on simulated, artificial, and experimental repertoires. Finally, we demonstrated the usefulness of the MobiLLe tool by applying it to experimental repertoires obtained in various clinical situations, including chronic lymphocytic leukemia and COVID-19. All datasets are described in Section 2. 2.
For IGHJ, the k-mer based distance produced the best results, but we also observed its participation in the 'worst' set. Binary distance seems more beneficial than disadvantageous, while we observed the opposite with Levenshtein and GIANA distances. Binary distance produced the worst results for CDR3, while GIANA distance produced more 'best' than 'worst' results. For the coefficients, we observed that the arithmetic mean produced satisfactory results, and higher α produced more 'best' than 'worst' results. Generally, the method was very robust; on average, 77% of parameter configurations achieved F-score �0. 98. Thus, we choose an equally performing and time-efficient configuration as default parameters to further apply it to experimental datasets. For the rest of this work, we used s = 70%, a binary distance for IGHV, Levenshtein for IGHJ and CDR3 distances, and arithmetic means for combining later distances.
46, SONAR detected four times more clonal groups than expected, obtaining its lowest recall and F-score, 0. 03 and 0. 06, respectively. Most of the time, BRILIA achieved the lowest pairwise performances across all simulated repertoires generated with different mutation rates. BRILIA removes sequences that it cannot annotate, thus reducing the original dataset, which impacts the accuracy calculation. We also observed that BRILIA over-split repertoires, producing the highest number of clonal lineages for most simulated benchmarks. The best performance was obtained on polyclonal repertoires generated with lower mutation rates (λ 0 = {0. 16, 0. 26}, S4-S7 Tables) and the lowest performance on monoclonal repertoires with higher mutation rates (λ 0 = {0. 36, 0. 46}, S8 and S11 Tables). Closeness performances.
Artificial experimental repertoires. To investigate the tools' performance on experimental benchmarks, we created artificial repertoires from BCR high throughput sequencing data as described in Section 2. 2. 2. We generated three artificial monoclonal repertoires (AMR1, AMR2, and AMR3) by mixing sequences from a pure B cell lineage (10%) and a polyclonal background (90%). Each benchmark contains 10k sequences, and the performance measures clonal grouping tools' ability to identify memberships within the most abundant group (the pure B cell lineage). Accurate tools might group sequences from the most abundant group and separate those from the polyclonal background in different clusters. Thus, we counted the number of split clusters (SC) and false positives (FP) of the most abundant group to measure the tools' accuracy.
It can be helpful since SHM also introduce insertions and deletions (indels) in the junction region at a low-frequency  [42, 43]. It was the case of the AMR1 benchmark where we observed indels of a tyrosine residue at the IGHD-IGHJ junction region. Thanks to this flexibility MobiLLe reconstructed the clonal lineage of the AMR1 benchmark better. Other rules or exceptions can be easily included within MobiLLe framework upon specific needs in the clustering process. After validating our method on simulated and experimental-based benchmarks, we applied it to experimental datasets where clonal lineage groups are unknown. We considered 26 experimental repertoires from different sources, sampled from diverse tissues, representing several human clinical conditions such as leukemia (CLL) and infection (COVID-19).
We observed that monoclonal repertoires presented the highest Gini indices, indicating a disequilibrium in the clonal population. On the other hand, non-clonal repertoires presented lower Gini indices, showing a more diversified dataset. Three experimental repertoires were used to compare MobiLLe with existing clonal lineage grouping tools. MobiLLe clonal distributions were often closer to those of Partis and SCOPe, the most performing tools. The pairwise evaluation confirmed that the clustering results of Partis and SCOPe were closer to MobiLLe with a F-score superior to 0. 93 for all analyzed repertoires. However, MobiLLe is computationally more efficient than Partis and does not require an optimized distance threshold as SCOPe, which can be time-consuming, especially when analyzing monoclonal repertoires.
In order to validate our method on IGH sequences originating from other sources, we applied it to two independent datasets. The first one contained five B-cell repertoire samples originating from several lymphoproliferative diseases, previously published in  [34]. MobiLLe reproduced the results of that study, identifying clonal populations in patients with different lymphoproliferative disorders. The second dataset included 12 repertoires selected from a study that identified B cell oligoclonal expansions in severe/moderate COVID-19 patients. MobiLLe achieved the same conclusion, detecting perturbations in the repertoires associated with the severity of the disease. Accurate identification of clonal lineage members is essential for many repertoire analyzes. However, the design and development of BCR clonal lineage grouping methods present several challenges, such as determining sequence similarities, choosing threshold distances, and maximizing computational efficiency.
Conclusion BCR lineage clonal grouping is at the core of BCR repertoire analysis. All downstream investigations, such as repertoire diversity estimation and intra-clonal analysis, depend on the correct grouping of BCR sequences. Several BCR clonal lineage grouping methods have been proposed, but the most performing tools are either time-consuming or unstable when higher divergent repertoires are analyzed. MobiLLe is a fast and accurate tool for BCR lineage clonal grouping with low runtime and memory requirements and does not require a training process or hyper-parameter optimization. It can easily be applied to (very) large-scale experimental repertoires, providing useful plots that could help interpret BCR lineage groups. An implementation of MobiLLe is freely available in Github https://github.
2. 1. To quantify clustering accuracy, we used precision, recall and F-score computed in two ways: pairwise and closeness (Section 2. 3. 1). Pairwise performances. MobiLLe achieved the best pairwise performance across all simulated datasets, Fig 7 Top. SCOPe and Partis outperformed SONAR and BRILIA. All tools achieved a precision close to 1, with few false positives. However, most tools over-split clonal lineages, detecting many false negatives that considerably decreased their recall and F-score values.
GeneScan profiles of human peripheral blood samples. IGH-VDJ rearrangements were amplified using conventional methods and PCR products were further analyzed by capillary electrophoresis. (A-C) Samples from individuals with monoclonal B cell malignancy: monoallelic profile (A and C) or biallelic profile (B); (D-I) non-malignant samples: regular polyclonal profile (D, E, G, H, I) or irregular polyclonal profile (F). (TIFF) S6 Fig. Clustering performance measurements. A) Pairwise B) Closeness. (TIFF) S7 Fig. Circle representation of the 100 most abundant clonal lineages for the experimental dataset produced at the Pitie-Salpêtrière hospital. Each circle symbolizes a clonal lineage, and the circle area its abundance. The ordinate represents cluster uniformity Eq 2, while the abscissa the clonal lineage abundance in %.
B) Number of sequences in each group, all clonal lineages are represented, vertical axis is in log scale. C) Lorenz curve and Gini coefficient. A Lorenz curve shows the graphical representation of clonal inequality. On the horizontal axis, it plots the cumulative fraction of total clonal lineages when ordered from the least to the most abundant; on the vertical axis, it shows the cumulative fraction of sequences. D) Clonal size distribution (percentage) of the 100 most abundant clonal lineages. (TIFF) S17 Fig. Circle representation of the top 100 ranked clonal lineages for the experimental dataset containing repertoires from patients with different lymphoproliferative diseases and a healthy donor. Each circle symbolizes a clonal lineage, and the circle area is proportional to clonal group abundance.
The ordinate represents cluster uniformity (Eq 2), while the abscissa the clonal lineage abundance in %. (A) HD, (B) P3-FL-SLL, (C) P4-C/SLL, (D)P5-BM, and (E) P5-L. Report to Table 3 for repertoires' properties and individuals' labels. (TIFF) S18 Fig. Circle representation of the the top 100 ranked clonal lineages for the experimental dataset containing repertoires from patients with severe COVID-19. Each circle symbolizes a clonal lineage, and the circle area is proportional to clonal group abundance. The ordinate represents cluster uniformity (Eq 2), while the abscissa the clonal lineage abundance in %. (A) S20, (B) S21, (C) S22, (D) S23, (E) S24, and (F) S26. Report to Table 4 for repertoires' properties and individuals' labels. (TIFF) S19 Fig. Circle representation of the the top 100 ranked clonal lineages for the experimental dataset containing repertoires from patients with moderate COVID-19 and healthy donors.