dataesr/scientific-paragraphs-categorization huggingface dataset:
) historical growth. i, Ratio of observed to historical (longitudinal data) and late to expected (phylogenetic data) growth (n = 37 clones detected in phylogenies (top); n = 633 in longitudinal data (bottom)). j, Differences between the median observed and historical growth per year for each gene. k, Projected ages at onset for all clones, assuming stable lifelong growth at the same fixed rate we observed during older age. Boxes in d, i, represent the 25th, 50th (median) and 75th percentiles of the data; the whiskers represent the lowest (or highest) datum within 1 interquartile range from the 25th (or 75th) percentile. risk. To confirm our findings in larger studies and include myeloid malignancies other than AML, we analysed large published datasets of AML  41  (n = 1,540) and myelodysplastic syndromes  42  (MDS) (n = 738) using a site-specific extension of the dNdScv algorithm to formally quantify the extent to which individual hotspots are under the influence of positive selection in these cancers  25  (Supplementary Tables  9, 10 ).
Online content Any methods, additional references, Nature Research reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at https://doi. org/10. 1038/s41586-022-04785-z. Publisher's note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Open Access This article is licensed under a Creative Commons Attribution 4. 0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Study participants Ethical permission for this study was granted by The East of England (Essex) Research Ethics Committee (REC reference 15/EE/0327). The SardiNIA longitudinal study recruited individuals from four towns in the Lanusei Valley in Sardinia, capturing 5 phases of sample and data collection  26  over more than 20 years. Informed consent was obtained from all participants. We analysed serial samples from 385 individuals in the SardiNIA project.
Targeted sequencing and variant calling Target enrichment of whole-blood DNA was performed using a custom RNA bait set (Agilent SureSelect ELID 3156971), designed complementary to 56 genes implicated in clonal haematopoiesis and haematological malignancies (Supplementary Table  1 ). Libraries were sequenced on Illumina HiSeq 2000 and variant calling was performed as we described previously  5, 50. In brief, somatic single-nucleotide variants and small indels were called using Shearwater (v. 1. 21. 5), an algorithm designed to detect subclonal mutations in deep sequencing experiments  51. Two additional variant-calling algorithms were applied to complement this approach: CaVEMan (v. 1. 11. 2) for single-nucleotide variants, and Pindel (v. 2. 2) for small indels  52, 53. VAF correction was performed using an in-house script (https://github.
com/cancerit/vafCorrect). Finally, allele counts at recurrent mutation hotspots were verified using an in-house script (https://github. com/cancerit/allelecount). Variants were filtered as we described previously  5, 50  , but were not curated with regard to existing notions of oncogenicity, that is, all somatic variants passing quality filters were retained for analysis. If a variant was identified in an individual at any time point in the study, this site was re-queried in the same individual at all other time points, using an in-house script (cgpVAF) to provide pileup (SNV) and Exonerate (indel) output (https://github. com/cancerit/vafCorrect). No additional filters were applied to these back-called variants.
Selection analyses To quantify selection, we used the dNdScv algorithm, a maximumlikelihood implementation of dN/dS, which measures the ratio of non-synonymous (N) to synonymous (S) mutations, while controlling for gene sequence composition and variable substitution rates  27. We first applied this method to the mutation calls from the longitudinal SardiNIA cohort in order to identify which genes are under positive selection in the context of clonal haematopoiesis. For this analysis, any mutation that was present in a single individual at multiple time points was counted only once. We also compared dN/dS ratios at the beginning and end of study, and found the latter to be higher, consistent with stronger cumulative effects of selection at older ages (Supplementary Note 2). To characterize patterns of selection in AML and MDS, we applied dNdScv to two published data sets.
The AML set was derived from 1,540 patients enrolled in three prospective trials of intensive therapy  41. The MDS set included 738 patients with MDS or closely related neoplasms such as chronic myelomonocytic leukaemia  42. Both used deep targeted sequencing of 111 cancer genes, which overlapped with 13 of the 17 genes of interest in our longitudinal clonal haematopoiesis study (PPM1D, CTCF, GNB1 and BRCC3 were not sequenced in the AML or MDS studies). We called and filtered variants in the 13 overlapping genes using the strategy described above ('Targeted sequencing and variant calling'). Variants were identified in all 13 genes in both AML and MDS datasets (Supplementary Table  10 ). We calculated dN/dS values both at the level of individual genes, and at single-site level for hotspots, the latter using the sitednds function in the dNdScv R package.
Reads were aligned to the human reference genome (NCBI build37) using BWA-MEM. Variant calling and filtering. Single-nucleotide variants (SNVs) and small indels were called against an unmatched reference genome using the in-house pipelines CaVEMan and Pindel, respectively  52, 53. 'Normal contamination of tumour' was set to 0. 05; otherwise, standard settings and filters were applied. For all mutations passing quality filters in at least one sample, in-house software (cgpVAF, https://github. com/ cancerit/vafCorrect) was used to produce matrices of variant and normal reads at each mutant site for all colonies from that individual. Copy-number aberrations and structural variants were identified using matched-normal ASCAT  59  and BRASS (https://github. com/cancerit/ BRASS). Low-coverage samples (mean <4×) were excluded from downstream analysis (n = 1, PD41305).
Samples in which the peak density of somatic mutation VAFs was lower than expected for heterozygous changes (in practice VAF < 0. 4) were suspected to be contaminated or mixed colonies, and were also excluded from further analysis (n = 3, PD41305; n = 9, PD41276; n = 3, PD34493). Multiple post-hoc filtering steps were then applied to remove germline mutations, recurrent library prep or sequencing artefacts, and in vitro mutations, as described previously  60  and detailed in custom R scripts (https://github. com/margaretefabre/Clonal_dynamics). Buccal samples were used as an additional filter; mutations were removed if the variant:normal count in the buccal sample was consistent with that expected for a germline mutation (0. 5 for autosomes and 0. 95 for X and Y chromosomes, binomial probability >0.
01), and were retained if (1) the variant:normal count in the buccal sample was not consistent with germline (binomial probability <1 × 10 -4 ) and (2) the mutation was not present in either of 2 large SNP databases (1000 Genomes Project and Kaviar) with MAF > 0. 001. Phylogenetic tree construction and assignment of mutations back to the tree. These steps were also performed as described previously  60  and are detailed here: https://github. com/margaretefabre/Clonal_ dynamics. In brief, samples were assigned a genotype for each mutation site passing filtering steps ('present' = ≥2 variant reads and probability > 0. 05 that counts came from a somatic distribution; 'absent' = 0 variant reads and depth ≥6; 'unknown' = neither 'absent' nor 'present' criteria met). The proportion of 'unknown' genotypes going into tree-building was low: 1.
5% (PD34493), 1. 4% (PD41276) and 1. 3% (PD41305; Extended Data Fig. 5a-c ). A genotype matrix of shared mutations was fed into the MPBoot program  61  , which constructs a maximum parsimony phylogenetic tree with bootstrap approximation. The in-house-developed R package treemut (https://github. com/NickWilliamsSanger/treemut), which uses original count data and a maximum likelihood approach, was then used to assign mutations back to individual branches on the tree. Since individual edge length is influenced by the sensitivity of variant calling, lengths were scaled by 1/sensitivity, where sensitivity was calculated as the proportion of germline variants called (mean sensitivity: 85. 4%, 87. 0% and 83. 5% for PD41305, PD41276 and PD34493, respectively). The approaches we used to validate the phylogenies, including comparison of MPBoot with an alternative phylogeny-inference algorithm, SCITE  62  , are detailed in Supplementary Methods.
Acknowledgements This work was funded by a joint grant from the  Leukemia and Lymphoma Society  ( RTF6006-19 ) and the  Rising Tide Foundation for Clinical Cancer Research  ( CCR-18-500 ) and by the  Wellcome Trust  ( WT098051 ). M. A. F. is funded by a  Wellcome   Clinical Research Fellowship  ( WT098051 ). J. G. d. A. is supported by the  NIHR Cambridge BRC  and their opinions are not necessarily those of the  NHS , the  NIHR  or the  Department of Health and Social Care. G. S. V. is funded by a  Cancer Research UK   Senior Cancer Fellowship  ( C22324/ A23015 ) and work in his lab is also funded by the  European Research Council ,  Kay Kendall Leukaemia Fund ,  Blood Cancer UK  and the  Wellcome Trust. E. F. M. is supported by the  Wellcome Trust and Beit Foundation  ( 104064/Z/14/Z ) and by the  EC H2020.
Data availability The data files necessary to run the analysis in https://github. com/josegcpa/clonal_dynamics are freely available at https:// doi. org/10. 6084/m9. figshare. 15029118. All sequencing data have been deposited in the European Genome-phenome Archive (EGA) (https://www. ebi. ac. uk/ega/). Targeted sequencing data have been deposited with EGA accession numbers EGAD00001007682 and EGAD00001007683; WGS data have been deposited with accession number EGAD00001007684. Data from the EGA are accessible for research use only to all bona fide researchers, as assessed by the Data Access Committee (https://www. ebi. ac. uk/ega/about/access). Data can be accessed by registering for an EGA account and contacting the Data Access Committee.
Digital Twins, which tend to intervene over the entire life cycle of products from early design phase to predictive maintenance through optimization processes, are increasingly emerging as an essential component in the future of industries. To reduce the computational time reduced-order modeling (ROM) methods can be useful. However, the spread of ROM methods at an industrial level is currently hampered by the difficulty of introducing them into commercial finite element software, due to the strong intrusiveness of the associated algorithms, preventing from getting robust and reliable tools all integrated in a certified product. This work tries to circumvent this issue by introducing a weakly-invasive reformulation of the LATIN-PGD method which is intended to be directly embedded into Simcenter Samcef TM finite element software.
Some recent papers are interested in this issue  [7] [8] [9] [10] [11] [12]. Most of the time, it results from a coupling between industrial software used as a black-box and in-house software that drives the ROM methods for parametrized problems on specific applications. Another point lies in the management of time-dependent non-linear problems with ROM methods. In this context, the LATIN-PGD method  [13, 14 ] is a worthwhile one, which has been the subject of many works highlighting its assets: ostensible reduction of computation time  [15, 16] , construction of virtual charts  [7] , ability to address all types of non-linearities  [17, 18]  among others. Hence, the LATIN-PGD method is attractive to Siemens Digital Industries Software, as a promising tool in the Digital Twins landscape. As a software company, Siemens Digital Industries Software wants to integrate this method into the finite element industrial software Simcenter Samcef TM part of the Simcenter 3D TM package.
The idea is to get the easiest possible implementation, even if it results in slightly intrusive parts at some places when necessary. This is one of the benefits of having access to the core routines of the software. Three main elements drive the approach: the robustness, the performance and the ease of use by external customers. This work proposes a strategy to use the Proper Generalized Decomposition (PGD) method  [19]  directly within an industrial software capable of handling any time-dependent non-linear problem in solid mechanics. We develop a weakly-invasive version of the LATIN-PGD method, which differs from the one most commonly used in recent years based on the internal variables description of materials  [13, 15, 20]. Instead, it can be seen as an extension of the first functional version  [21] [22] [23] [24].
Numerous numerical treatments have been made to ensure a maximum generality of the presented approach designed to deal with any non-linearity (material, contact, large deformation,. ). In addition to performance aspect, robustness remains an essential requirement to be able to deal with any industrial case. While until now the LATIN-PGD method has been used to solve specific applications (see appendix in  [25]  for related topics), the presented approach shows the possibilities offered by the method with an industrial software where no particular application is targeted. The novelty is the methodology put in place to use the PGD natively and as seamlessly as possible in an industrial software, thus providing an end-to-end process in one single software environment. The LATIN-PGD method treats the problem simultaneously in space and time.
Regarding parameters of the Digital Twin, they are managed externally via a classical non-intrusive snapshots approach leveraging that the LATIN-PGD algorithm can be initialized from any previous computation  [15, 26]. In general, the (extra-)parameters cannot be known in advance for all possible particular applications as they vary from one to another. Hence, by robustness concerns, only a space-time PGD is managed by the Simcenter Samcef TM software itself because space and time are always present in non-linear computations. Then, if required, a space-time-parameters PGD can be obtained a posteriori following classical non-intrusive technics  [8, 10]  based on snapshots. Simcenter Samcef TM is organized as different modules to be able to simulate different physics: non-linear mechanical problems, high-speed rotating machines, articulated mechanisms or thermal simulations.
As these modules are all part of the same family, it is possible to switch from one analysis to another, combine analyses or conduct cosimulations. In this work, we focus on the module dedicated to non-linear mechanical solver as an example. However, because of its generality, our approach could be used in other modules (thermal one for example) or other finite element software. The only requirement to set up the LATIN-PGD method is to have a non-linear resolution scheme already in place (Newton-Raphson), which notably solves local non-linearities. This is the strength of the proposed version to reuse without any modification the full management of non-linear equations in the local stage. This paper focuses on the implementation-and the difficulties associated with-of the LATIN-PGD method in an industrial finite element software.
Fundamentals as convergence and robustness studies will be presented in companion paper  [27]. At the present time, what has been done fits into the framework of moderate displacements, outside instability zones. In what follows, after a brief reminder of the type of mechanical problems that are considered, the LATIN-PGD strategy and some details on its implementation in Simcenter Samcef TM software are provided. Finally, results on two industrial test-cases are given to highlight the assets.
Weakly-invasive LATIN-PGD within Simcenter Samcef TM software A wide variety of problems in many fields of physics-mechanics, thermics, rotor dynamics-can be handled with the Simcenter Samcef TM software. In this study, we focus on the mechanical solver capable of solving any non-linear evolutionary problem including potentially materials non-linearities, contacts or large deformations. One of the goals is to combine ROM methods (by PGD in this work) with all possible non-linearities that already exist in the software.
LATIN-PGD methodology The novelty of our approach is to natively set up a space-time PGD method directly in an industrial finite element software (Simcenter Samcef TM here) in order to best integrate the reduced-order modeling tools into a unified workflow that can be easily handled by customers. One of the requirements is to integrate a new non-linear solver in the software which has to be non-incremental. We choose the non-linear LATIN solver  [13, 31]  designed to work well with the PGD method. This is a non-incremental iterative solver producing at each iteration a better approximation of the solution over the whole space and time. While standard industrial non-linear solvers generally used are incremental by nature, we propose in this work a gateway to effortlessly implement a non-incremental solver directly within incremental one.
Schematically, this non-incremental iterative algorithm can be represented as shown in Fig. 1  where two sub-spaces A d and are defined. Sub-space A d groups the linear equations of the problem-equilibrium and kinematic compatibility equations-while subspace groups the local non-linear equations-behavioral laws, contact equations. The solution of the problem (1-2) then results from the fulfillment of this iterative scheme with two search directions ϒ + and ϒ -representing the parameters of the method. More specifically, Eq. (  1 ) can be identified as A d while Eq. (  2 ) defines sub-space. The unknowns in this iterative scheme are the generalized displacements q and forces F i which differ from classical variables most recently used in the LATIN framework based on internal variables. This choice is guided by the finite element software.
Kinematic conditions and equilibrium equation A d q n+1 (t) ∈ Q ∀t ∈ I ( 16 ) F d (t) = F i,n+1 t, q n+1 (t) + F c,n+1 t, v(t), λ(t) ∀t ∈ I ( 17 ) Descent search direction ϒ - H q n+1 (t)q n (t) = F i,n+1 t, q n+1 (t) -F i,n t, q n (t) ∀t ∈ I (18) λ(t) -λ(t) = h v(t) -v(t) ∀t ∈ I ( 19 ) Both search directions H and h are the parameters of the method. One can interpret h as an interface rigidity  [18]. The computation of the R operator comes directly from the hosting software as a black-box. No particular modification is required for the local stage. In this form, the LATIN-PGD algorithm can be seen as the analogue of the Newton-Raphson algorithm to which convergent loop and time steps loop have been swapped. This is a crucial aspect in terms of ease of implementation as the method could be seamlessly integrated into Simcenter Samcef TM software without breaking anything and without having to redevelop specific tools at this level.
Integration of the LATIN-PGD method into Simcenter Samcef TM software From a software developer's point of view, only a few parsimonious modifications have been necessary to introduced the LATIN-PGD method in pre-existing Simcenter Samcef TM software notably through the use of a dedicated LATIN-PGD fortran module gathering all the new functionalities in an unique place. Everything that makes up the signature and the richness of Simcenter Samcef TM software-the way of treating behavioral laws, the way of managing contacts and so on-is fully recycled. In a sense, one of the strength of our approach lies in the privileged place granted to this local stage operating at the level of each element, each gauss point of each time steps, thus guaranteeing robustness and generality. A delicate point that has required particular attention concerns data storage since complete spatial-temporal approximations have to be stored at each LATIN-PGD iteration which is quite uncommon within incremental solvers.
Thus, with only 9 modes, the low-rank approximation of the LATIN-PGD method is able to perfectly capture the high-fidelity solution as the error does not exceed half a percent on all elements. On Figs. 3  and 4  evolution of the cumulative plastic strain along the time steps are exhibited. All these calculations have been carried out to achieve a high fidelity solution. While almost 22 h are required to achieve all simulations with standard Simcenter Samcef TM Fig. 4  Equivalent cumulative plastic strain at the end of each cycle according to temperature T on the most loaded element of the structure software, only 7 h are necessary to carry out the entire parametric study with reducedorder model which leads to a speed-up of 3 times faster. Moreover, there is a great disparity in the calculation time: while it remains more or less constant at around 30 min per run with the classical approach, it can vary over a range from 4 to 18 min with new LATIN-PGD method depending on the initialization of the solution.
Because In what follows, a two parameters study is undertaken where the parameters are the Young modulus E and the thermal expansion coefficient α of the blade. Values of these parameters are assumed to be insufficiently known, mainly related to the manufacturing process. Due to loading, a small variation in the value of these coefficients can affect the maximum Von Mises stress in the contact area where creep occurs over long periods of time, which can damage the structure faster than expected. Although other parameters may be of influence, we focus on these two parameters for a sake of simplicity. Figure  6  presents the stress map obtained for a given point (E = 220 MPa and α = 14 • 10 -6 K -1 ) in the parametric space. One gives the results of the LATIN-PGD method included in Simcenter Samcef TM software and the remaining error with respect to the reference.
The latter is taken as the classical solution from the commercial software (using Newton-Raphson method). One can note a very good concordance of the solutions as the relative error remains less than one percent in the area of interest for 10 PGD modes. Figure  7  shows the response surface of the maximum Von Mises stress in the contact area according to the two parameters. This chart highlights a non-linear variation according to the Young modulus E and an almost linear one on the second parameter α. In order to achieve this, 70 computations have been carried out by scanning the parametric space sequentially in a uniform manner. Thanks to both ROM methods and LATIN-PGD algorithm allowing to restart the computation from a close previous converged point in the parametric space, only 3 h have been necessary to build the virtual chart while more than 21 h would have been necessary using classical Simcenter Samcef TM software based solely on Newton-Raphson algorithm.
Conclusions In this work, the LATIN-PGD method have been directly implemented into Simcenter Samcef TM finite element software, which allows to build and then enrich reduced-order model as desired at the industrial level. Here, a very general way of looking is adopted with PGD by making the best use of all the local behavior pre-existing in industrial softwares. The PGD method becomes in fact integrated as close as possible to the solver, enabling to combine performance, robustness and a wide range of applications that can be carried out. Moreover, a first time saving on parametric studies has been demonstrated but a significantly additional gain could still be made by exploiting hyper-reduction techniques  [35] [36] [37]  and in particular the Reference Point Method  [14, 38]  well suited for LATIN-PGD method.