dataesr/scientific-paragraphs-categorization huggingface dataset:
For the pseudo-haploid data, we used 580,589 SNPs and a killer whale genome as the outgroup. We kept the transitions as evolutionary relationships were similar with and without the transitions (Supplementary Fig. 9 ). We used the following command in ANGSD: angsd -b SP1060_Atlantic_KW_10MbpKW. bamlist -dohaplocall 1 -doCounts 1 -minMapQ 25 -minQ 30 -C 50 -baq 1 -minMinor 4 -maxMis 9 -skip-Triallelic 1 -remove_bads 1 -uniqueOnly 1 -ref /path/unplaced. scaf. fa -out Atlantic_KW_SP1060_mapKW. We then converted the data to the eigenstrat format as described above for smartpca. For the contemporary called genotype data, we used one Indo-Pacific bottlenose dolphin, Tursiops aduncus, individual to root the graph. We filtered the dataset as follows: a mapping quality of 30, a genotype quality of 20, genotype depth of at least 3x, less than 25% of missing data overall, genotype data in at least five individuals in each population, a MAF of 0.
05, bi-allelic SNPs only, no missing data in the T. aduncus individual constituting the outgroup, scaffolds of at least 10 Mbp, and a distance of at least 5 Kb between SNPs. We obtained 213,488 SNPs. We also set the missing data to less than 10% and got similar results. We ran qpBrute using the following parameters: outpop: NULL, useallsnps: YES, blgsize: 0. 05 (5 Mb which is the block size for Jackniffe), forcezmode: YES, lsqmode: YES, diag:. 0001, bigiter: 6, hires: YES, lambdascale: 1, inbreed: YES (for the pseudo-haploid data only). As we have only one individual to root the graph, we did not attempt to use the allele frequency of the outgroup to normalise the weighting of each SNP in the ingroup and we used the option output: NULL that is SNPs are flat-weighted. We used the option inbreed:YES for the pseudo-haploid data.
For the pseudo-haploid dataset, the package tried all possible 120 starting graph orders. We found only one graph with no f4 outliers among a total of 3663 unique graphs. For the called genotypes, the package tries all possible 24 starting graph orders and fitted 234 unique admixture graphs to our dataset. We found three possible graphs with no f4 outliers left, although two of them were mirror graphs. We computed the mean log-likelihoods of the three models and their Bayes Factors using the MCMC algorithm implemented in the R package ADMIXTUREGRAPH v. 1. 0. 2 89 using the default chain settings implemented in qpBrute (two chains, each with two million iterations, five heated chains, a burn-in of 50%, and no thinning). We assessed the convergence of the chains using the output from the R package CODA v.
0. 19-4  90  , also generated in qpBrute. The Bayes factors showed non-significant support of the first model in comparison to the other "mirror" two, which have similar log-likelihoods (Bayes factor of 0. 88). We increased the chains to four million iterations with a burn-in of three millions, but it did not help in discriminating between the three graphs. Inferences on the SNPs under parallel linked selection Patterns of variation. We investigated and compared patterns of variation in SP1060 and contemporary populations at sites previously identified as evolving under parallel linked selection  19. We plotted a PCA for the 2122 SNPs under parallel linked selection using the packages adegenet v. 2. 1. 5 (glPCA function) and scales v. 1. 2. 1  91  and a neighbour-joining tree using the R package ape  73.
We performed 100 bootstraps of the tree using the boot. phylo option of the package ape v. 5. 6. 2 and indicated for which nodes bootstrap support values were higher than 95%. We compared the tree for the SNPs under parallel linked selection with ten trees generated using the same number of randomly sampled neutral SNPs, also computing bootstrap support values as described earlier. We also plotted the raw genotypes of the 2122 SNPs using the R packages vcfR v. 1. 12. 0 92 and adegenet  93. Heterozygosity estimation. We estimated heterozygosity for the sites under parallel linked selection to coastal habitat by computing individual site-frequency-spectrum using ANGSD v. 0. 921, both with and without transitions. The first command was as follows: angsd -i file. bam -anc ancestral_state. fa -dosaf 1 -P 6 -gl 1 -out file -minMapQ 30 -minQ 23 -remove_bads 1 -uniqueOnly 1 We then added '-noTrans 1' to remove transitions.
Acknowledgements We especially thank  Matthias Meyer  for providing the SP1060 library, which allowed the generation of the ancient genome that was central to this study. We thank  Ramon Fallon ,  Joseph Ward  and  Peter Thorpe  from the  St Andrews Bioinformatics Unit  for help with bioinformatics. Bioinformatics and computational analyses were supported by the  University of St Andrews Bioinformatics Unit  which is funded by a  Wellcome Trust   ISSF award  [grant  105621/Z/14/Z ] and ran on cluster marvin and  Crop Diversity HPC. We thank  Kelly Roberston  for the laboratory work for the contemporary samples from the  USA  and for arranging shipment. We thank  M. Thomas P. Gilbert  for our use of the ancient DNA facilities in Copenhagen. We thank  Jazmín Ramos Madrigal  for advice on data format conversion.
Contemporary sample DNA extractions were supported by  People' s Trust for Endangered Species  and the sequencing costs were supported by the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement No. 663830  awarded to A. D. F. , by the  Total Foundation  awarded to M. L. , the  University of Groningen  awarded to M. C. F. , and the  Marine Alliance for Science and Technology for Scotland  and The  Russell Trust  awarded to O. E. G. M. N. was funded by  MASTS  and the  Crawford Hayes  fund. A. D. F. was funded by  Marie Skłodowska-Curie  grant agreement No. 663830  and the  European Research Council  grant agreement No. ERC-COG-101045346.
Data availability Raw sequencing data generated as part of this project are part of Bioproject PRJNA955223. Biosample and SRA accession numbers are provided in Supplementary Table  1  and Supplementary data file 1. Mitochondrial genome sequences are accessible on GenBank at accession numbers OR120165-OR120228 as detailed in Supplementary Data File 1. Supplementary Data file 1 also indicates where the samples are housed and who should be contacted to access the material. We also used the whole-genome re-sequencing data from Bioproject PRJNA724031. We have also included mitogenome sequences from Genbank as indicated in Supplementary Table  3. Source data are provided with this paper in the Source Data file and the data, codes and related documentations that support the findings of this study are openly available in the DataSuds repository (IRD, France) at https://doi.
Polθ repairs mitotic DSBs In mitosis, although it is widely accepted that HR and NHEJ repair activities are inhibited, the activity of alternative pathways remains largely unaddressed. Although there has not been direct evidence of DSB repair in mitosis, recent data suggests that alternative end joining  15, 16  and Polθ could be active in mitosis  [17] [18] [19] [20]. To detect DSB repair in mitosis, we took advantage of recent advances in genome editing that enable the induction of DSBs after Cas9 protein transfection. We transfected nocodazole-arrested cells with a Cas9-guide RNA (gRNA) complex to induce DSB formation at two different sites in the AAVS1 safe locus, whose mutagenic repair would destroy a nearby HphI restriction site. After DSB induction, a small genomic region around the cut site was PCR amplified and digested by HphI.
3f , Extended Data Fig. 8a  and Supplementary Video 4), similar to our observations in wild-type Polθ expressing cells under PLK1 inhibition (Extended Data Fig. 8b-g ). Of note, the phosphomimetic mutant Polθ(4D), in which all four clustered serines mediating the interaction with TOPBP1 are mutated to aspartic acid, can bypass PLK1 mitotic regulation and force TOPBP1-Polθ interaction in S phase (Fig. 3g, h ). Together, our findings indicate that the recruitment of Polθ to mitotic DSBs is regulated by the phosphorylation-mediated interaction between Polθ and TOPBP1. To evaluate the role of Polθ phosphorylation in mitotic DSB repair, we performed an in vitro end-joining repair assay  29. In brief, cells arrested in mitosis were transfected with a DNA repair substrate containing microhomologies, and end-joining efficiency was measured by quantitative PCR (qPCR).
5) in this same buffer in order to obtain the NMR sample containing the bound peptide. The NMR experiments were performed on the Polθ protein fragments (E1424-Q1503) and (E1540-S1660), either  15  N-or  15  N,  13  C-labelled, and dissolved in the NMR buffer (with in addition 7% v/v D 2 O and EDTA-free Protease Inhibitor Cocktail 1× (Roche)). Most experiments were recorded on a 700 MHz Bruker ADVANCE NEO spectrometer equipped with a triple resonance cryogenically cooled probe. 1 HN,  13  Cα,  13  Cβ,  13  CO and  15  N resonance frequencies of the non-phosphorylated fragments were assigned at 283 K using 2D 1 H-15 N SOFAST HMQC, 3D HNCA, 3D HNCACB, 3D CBCA(CO)NH, 3D HNCO, 3D HN(CA)CO and 3D HN(CA) NNH experiments. All 3D experiments were recorded using non uniform sampling (NUS: 40-50%). To monitor phosphorylation by PLK1, either  15  N-or 15 N,  13  C-labelled Polθ protein fragments were mixed with PLK1, and a series of 16 2D NMR 1 H-  15  N SOFAST HMQC experiments were recorded at 298 K during 8 h, each experiment being a 1,536 × 160 dataset acquired with 80 scans during 30 min  50.
5 ratio, each experiment being a 1,536 × 160 dataset acquired with 800 scans during 5 h. The data were processed using Topspin 3. 6 (Bruker) and analysed with CCPNMR Analysis Software 51. 1 HN,  13  Cα,  13  Cβ,  13  CΟ and  15  N resonance assignments of Polθ protein fragments (E1424-Q1503) and (E1540-S1660) were deposited at the Biological Magnetic Resonance Data Bank (BMRB) under the accession numbers 51900 and 51939 for the non-phosphorylated forms and 51920 and 51940 for the phosphorylated forms, respectively.
Beads were resuspended, digested and desalted, and then peptides were reconstituted in injections buffer before LC-MS/MS analysis, as described for Polθ binding partners identification and by using an Orbitrap Eclipse mass spectrometer. Mass spectrometry analysis was performed on five independent experiments. Pull-downs of streptavidin beads incubated in nuclear extract of HeLa cells were used as control. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium (http://proteomecentral. proteomexchange. org) via the PRIDE  55  partner repository with the dataset identifier PXD029585 (username: reviewer_pxd029585@ebi. ac. uk, password: Tv3OwInu) for Polθ binding partners and project accession: PXD036486 (username: reviewer_pxd036486@ebi. ac. uk, password: GqeYlT33) for Polθ phosphorylated sites quantification and project accession: PXD042167 (Username: reviewer_pxd042167@ebi.
Data were plotted in R using ggplot2. Extrachromosomal substrate to monitor mitotic end-joining repair. Construction of a DNA repair substrate. To assess mitotic end-joining repair, a DNA repair substrate was designed to mimic resected DNA ends containing four microhomologies, as previously described  29. The repair substrate was built by ligating a PCR product (GFP sequence) to DNA oligonucleotide duplexes on both sides. The PCR product (747 bp) was amplified from a GFP sequence (PCAG vector, Addgene) using the following oligonucleotide sequences (Fwd core GFP: CAAGTGGTCTCA GACTGTGAGCAAGGGCGAGGAGCTG, Rev core GFP: GCCGAGGTCTC CGTCAGCTTGTACAGCTCGTCCATGCCGAG), digested by the BsaI-HF v2 enzyme (NEB) to reveal 4 nucleotides (nts) complementary to the DNA oligonucleotide duplexes and finally, purified (Gel and PCR cleanup kit Macherey-Nagel).
Images were taken at 60× or 100× magnification using Upright Spinning disk Confocal Microscope (Roper/Zeiss) or at 40× magnification using DeltaVision (Deconvolution) Image Restoration Microscope. All images were taken with a Z-stack of 0. 4 μM on 8 μM range around focus and projected using maximum intensity projection. Images were analysed using the Fiji software  56. All scale bars represent 5 μm unless stated otherwise.
16-bit images of six fields in each well were acquired, each containing channels for Hoechst 33242 (DAPI, wave 1) and intrinsic GFP-Polθ (FITC, wave 2). Computational image processing operations were performed using INCell Analyser 3. 7 Workstation software (GE Healthcare). Polθ was then identified as organelles in the nuclei area and quantified by the average intensity of pixels within the defined organelles region. A binary threshold was applied to discriminate cells containing at least of 5 Polθ foci resulting in "% cells multi-foci" (Supplementary Table  1 ). For each feature or phenotypic class depicted from the applied cell threshold described, plate positional effects were corrected using Tukey's two-way median polishing  57, 58  , which iteratively subtracts row, column and well median computed across all plates within a replicate.
Time-lapse video microscopy To determine Polθ foci formation during cell cycle progression, cells expressing GFP-Polθ and PCNA-mCherry were plated on 35 mm Ibidi μ-Dishes (Ibidi, 81156) and imaged every 10 min for 16 h on an Inverted Eclipse Ti-E (Nikon) microscope equipped with a stage-top incubator and CO 2 delivery system, 40× (1. 4 NA) oil objective, a Spinning disk Yokogawa CSU-X1 unit integrated in Metamorph software by Gataca Systems equipped with Camera EMCCD. Images were mounted in SoftWoRx and analysed using the Fiji software (1. 53c). Aphidicolin (1. 6 μM unless stated otherwise) was added to the cells for 24 h and removed 3 h before acquisition. For nuclear bodies resolution, palbociclib was added 30 min before starting acquisition. To determine mitotic timing by phase-contrast video microscopy, 250,000 cells were seeded in 35 mm Ibidi μ-Dishes (Ibidi, 81156) 24 h before imaging.
Cells were arrested in RO3306 for 4 h with or without IAA (Polθ depletion) and released in normal growth media before imaging. Cells were immediately imaged every 5 min for 16 h, at 20× using an inverted video microscope (Deltavision) equipped with CoolSNAP HQ2 camera controlled by Softworx software. Cell rounding (mitotic entry) and flattening (mitotic exit) were used as landmarks to calculate mitotic timing. In addition, mitotic timing was also measured from condensation to anaphase. In that case, DNA was stained 30 min prior acquisition by incubating cells with SiR-DNA (250 nM, Spirochrome) and acquisition was performed an Inverted Eclipse Ti-E (Nikon) microscope equipped with a stage-top incubator and CO 2 delivery system, 40× (1. 4 NA) oil objective, equipped with Intensilight Lamp Hg 130W (Nikon) and Camera CoolSNAP HQ (Photometrics).
Data availability The NMR chemical shift data are publicly available: 1 HN,  13  Ca,  13  Cb,  13  CO and  15  N resonance assignments of Polθ fragments (E1424-Q1503) and (E1540-S1660) were deposited at the Biological Magnetic Resonance Data Bank (BMRB) under the accession numbers 51900 and 51939 for the non-phosphorylated forms and 51920 and 51940 for the phosphorylated forms, respectively. Mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium under the identifiers PXD029585, PXD036486 and PXD042167.
Acknowledgements This work has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No  714162 ),  Emergence Programs  from the city of  Paris  ( DAE137 ) and the  Cancéropole Île-de-France  ( 2018-1-EMERG ) to R. C. In addition, S. M. , R. G. and S. Z. -J. were supported by funding from the  French Infrastructure for Integrated Structural Biology  ( ANR-10-INSB-05-01 ) and the  European Community   H2020 Program  under the project  iNEXT Discovery  (grant no. 871037 ). S. M. acknowledges funding by the  Institut National de la Santé et de la Recherche Médicale (INSERM). Mass spectrometry analysis was performed with financial support from  la Région Île-de-France  (no. EX061034 ) and  ITMO Cancer of Aviesan and INCa  on funds administered by  Inserm  (no.
21CQ016-00 ). The authors thank members of  BioPhenix laboratory Aurianne Lescure ,  F. Richard  and  M. Corbe  for their contribution in generating data in Fig. 1a;  N. Brandon  for cloning assistance;  M. Chartier  and  J. Loeuilliette  for their help during NMR sample preparation and NMR data analysis;  P. Legrand  for AlphaFold calculations;  M. H. Stern  and  B. Lopez  for helpful discussions;  J. -S. Hoffmann  and  V. Bergoglio  for sharing the Polθ antibody and helpful discussions;  D. Chowdhury  for sharing the wild-type and (T1609A/S1618A) 53BP1 plasmids;  J. Stewart-Ornstein  for the eFlut plasmids;  J. N. Mark Glover  for providing plasmids encoding the TOPBP1 BRCT domains; CurieCoreTech Platforms at  Institut Curie : Cell and Tissue Imaging Platform (PICT-IBiSA/UMR144 and 3215), member of the  French National Research Infrastructure France-BioImaging  ( ANR-10-INBS-04 );  A.
Initially present only in functional languages such as OCaml and Haskell, Algebraic Data Types (ADTs) have now become pervasive in mainstream languages, providing nice data abstractions and an elegant way to express functions through pattern matching. Unfortunately, ADTs remain seldom used in low-level programming. One reason is that their increased convenience comes at the cost of abstracting away the exact memory layout of values. Even Rust, which tries to optimize data layout, severely limits control over memory representation. In this article, we present a new approach to specify the data layout of rich data types based on a dual view: a source type, providing a high-level description available in the rest of the code, along with a memory type, providing full control over the memory layout.
Complex optimizations on nested and rich types are even more error-prone. But how does one actually pick a representation for ADTs? The Rust Reference  [2023]  famously says of the representation of values: "The only data layout guarantees made by [the Rust] representation are those required for soundness". OCaml, Java and Scala make similar non-guarantees about the art and craft of representing structured types. Even the C standard gives very little information about concrete choices made on a given platform. Most languages similarly keep a prudish veil on memory representation to allow for optimization and language evolution, thus denying programmers the ability to fine-tune data layout. Alternatively, GHC Haskell provides a rich algebra of types with various unboxing annotations. This, however, exposes internal representation choices to the full program, polluting every value manipulation such as construction and pattern matching.
We develop our approach in the context of immutable, monomorphic data types, for sequential code emission. Our contributions are as follows: • A DSL, dubbed ribbit and presented in Section 2, to describe the memory layout of monomorphic, immutable ADTs, with support for well-known memory tricks: struct packing, bit-stealing,. • The formalization of a simplified language (Section 3) with validity criteria for types and their representation (Section 4), novel compilation algorithms for ADT value constructors and pattern matching (Sections 5 and 6), along with their soundness proofs. • A sketch of extensions of our formalism, including irregular data layouts and arrays (Section 7). • A full implementation of our DSL ribbit, with code generation targeting LLVM IR (Section 8). • Some initial performance evaluation and comparison (Section 9).
Real World: Red-Black Trees In Linux As a final case study, let us look again at RBTs, but this time with a highly optimized representation originally found in the Linux kernel to model device trees  [Torvalds 2023 ]. The representation is hand-tweaked to take as little space as possible by embedding both the tag and the color in pointer alignment bits. The original version keeps a pointer to the parent node, which we eschew for pedagogical purposes. The ribbit code is shown in Fig. 3a. The source type is the same as in our first representation in Fig. 1a. The main novelty is that we combine with and subterm types in line 8 to store the Color subterm inside a second alignment bit. Since the source type does not change, the rest of the program, such as the cardinal function we showcased, will work unmodified.
The next section focuses on core features of our input language. In Section 7 and Appendix B, we describe extensions that allow our formalism to capture more intricate layouts, such as WebKit-like NaN-boxing, which we present in Appendix A. 𝜏 ::= 𝑡 | Int ℓ | &𝜏 | ⟨𝜏 0 ,. , 𝜏 𝑛-1 ⟩ | 𝐾 0 (𝜏 0 ) +. + 𝐾 𝑛-1 (𝜏 𝑛-1 ) (Type expressions) Γ ::= {𝑡 1 ↦ → 𝜏 1 ;. ; 𝑡 𝑛 ↦ → 𝜏 𝑛 } (.
Memory Specifications The second ingredient of our language is a memory description for every source type. This memory type describes how the given type will be represented in memory. We consider an abstraction of memory similar to LLVM IR's types, shown in Fig. 5. At this representation level, we are bit-precise, yet abstract away some architecture-dependent details such as endianness and machine pointer size and alignment. Handling of these details is delegated to the underlying compiler backend (in our case, LLVM: see Section 8). For the purpose of this formalization, each pointer solely consists of an address and unused alignment bits at the lower end. 3 et us first ignore the "Split" alternation, and focus on the rest of the grammar. As a convention, all memory elements are given a hat, for instance  for memory types.
EXTENSIONS So far and for simplicity, we have described our formalism for a language with "plain" Algebraic Data Types (only sums, products, and references), and comparatively simple split types, with only head constructors in provenances (which, in effect, limits inlining) and only one split discriminant. We have extended our formalism in several ways, including constants in patterns and multiple split discriminants, which are described in Appendix B. We now focus on two extensions we consider essential for the expressivity of our approach: irregular specifications, and arrays. Irregular specifications, along with several other extensions, are included in the formalism detailed in appendices and implemented in ribbit.
CODE GENERATION In the previous sections, we have described compilation steps to lower constructors (i. e, value allocation) and destructors (i. e, pattern matching) to a low-level description operating on memory values. This section deals with the last step of our compilation scheme: producing executable LLVM IR code to build memory values, and to deconstruct such memory values using decision trees. The global code generation procedure is rather straightforward: constructors are turned into memory allocation and initialization code and decision trees are turned into LLVM control flow graphs. Two difficulties remain: deal with LLVM's explicitely typed IR and generate code for memory paths.
Lowering Memory Types Our compilation procedure discards type information, yielding a decision tree whose input is a single untyped root memory value. Backend targets such as LLVM IR require each memory value (including the root value, bound subterm values and intermediate switch discriminants) to be explicitely typed. This section describes the process of building an adequate LLVM IR type for each memory value, using information retrieved from the initial memory type. Since every memory value in the decision tree is expressed as a position within the root memory value, we can deduce their types from the root memory type. However, our memory types contain splits, which are not expressable as LLVM types [LLVM Language Reference Manual 2023]. Indeed, LLVM types are unambigous, in that they only describe fully concrete types (in terms of bit width, legal operations, etc.