dataesr/scientific-paragraphs-categorization huggingface dataset:
Analysis of Met content in P. cinnabarinus, fungi, metazoans, and plants proteins. P. cinnabarinus proteins were predicted secreted if they fulfilled three conditions: (i) presence of a secretion signal peptide, (ii) absence of endoplasmic reticulum retention motif and (iii) absence of transmembrane helix outside the signal peptide, as described  (26). Experimentally validated fungal secreted proteins were retrieved from proteomics analyses  (7, (35) (36) (37) (38) (39) (40) (41) (42) (43) (44) (45) (46) (47) (48) (49) , from Uniprot (www. uniprot. org) and FunSecKB2  (50). Metazoans experimentally validated secreted were retrieved from Uniprot, MetazSecKB (51) and the Human Protein Atlas  (52, 53). Experimentally validated plant secreted proteins were retrieved from Uniprot, PlantSecKB (54) and the SUBA database  (55).
The datasets of fungal, metazoans and plants non-secreted proteins were made using proteins retrieved from Uniprot, the Human Protein Atlas and the SUBA database. For all datasets, only manually curated proteins, and those found with least 5 SC in proteomics, were conserved. Membrane proteins, proteins shorter than 50 amino acids, or not starting with Met were discarded. Statistical analyses were unpaired t-test with Welch's correction.
Optimized methods allowed to obtain secreted proteins representative of early days decay. To identify oxidizable Met in the secreted proteins of P. cinnabarinus, we focused on the early days of wood decay, during which production and consumption of H2O2 are likely to occur  (42). We performed a time-course growth of the fungus on aspen wood and purified the proteins from the culture supernatants at days 3, 5 and 7 on successive anion and cation exchange chromatography to avoid artifactual oxidation. Then, we oxidized the secreted proteins with H2 18 O2 and identified them and their potential oxidative Met modifications using proteomics (Fig. S1 ). We identified 308 secreted proteins, among which CAZymes and proteases represented 41% and 10%, respectively (Fig. 1 ; Table  S1 ; Dataset 1). Among the CAZymes, GH was by far the most represented class, with 89 proteins (71%) spread over 34 families (Fig.
1 ; Table  S1 ). We identified most of the CAZymes expected during wood degradation, i. e. cellulases (GH3, GH5_5, GH5_9 and GH7, GH12, GH45), hemicellulases (GH10, GH43, GH51, CE15, CE16) (4), as well as pectinases (GH28, CE8) and also some CAZymes targeting fungal cell wall components (e. g. GH18 chitinases) (Table  S1 ). Out of 23 identified AAs (18%), we found laccases (AA1) and class II peroxidases (AA2), LPMOs (AA9) and H2O2-generating enzymes (AA3, AA5). We estimated the abundance of the individual proteins using the Normalized Spectral Abundance Factor (NSAF) (Dataset 1). The mean %NSAF of all identified proteins was ∼ 0. 3% and 52 proteins were above this value. The most abundant protein categories were CAZymes (52%), proteins of unknown function (35%) and proteases (8%). Among the most abundant CAZymes, we found the three cellobiohydrolases (GH7), three chitinases (GH18) and two laccases (AA1).
Interestingly, three H2O2-generating enzymes (two AA3_2 and one AA5_1) were among these most abundant CAZymes (Dataset 1). Overall, the identity of the secreted proteins and their abundance validated our approach, indicating that the growth conditions, the proteins preparation method, and the oxidative treatment used were suitable for studying Met sensitivity to oxidation. S1 ; Dataset 1). Bar graphs represent the number of CAZymes per class.
Of note, 16 O or 18 O sulfone levels reached ∼2% (Fig. 3A ; Table  S3 ; Dataset 3). On the contrary, in day 5 and day 7 secretomes, the levels of MetO and MetO2 carrying 18 O were extremely low (∼1%), indicating that the blocking strategy failed for these samples (Fig. 3A ; Table  S3 ; Dataset 3). To evaluate whether this lack of oxidation by H2 18 O2 in secretomes of days 5 and 7 affected only Met residues, we reanalyzed our data by searching for two main forms of Pro carbonylation bearing 18 O. For the day 3 secretome, we found slightly less than 4% of Pro oxidized with 18 O. A similar content of 16 O was found, indicating that the large majority of Pro were not oxidized (∼92%) (Fig. 2B ; Table  S4 ; Dataset 4). This low level of Pro oxidation was anticipated due to its low reactivity with H2 18 O2 (61).
The percentage of 18 O oxidation decreased to ∼0. 5% in the day 5 and day 7 secretomes, while 16 O oxidation levels remained comparable to those observed for the day 3 secretome. Thus, the trend for Pro is the same as for Met indicating that the lack of oxidation observed for Met was not specific to this residue. Proteins secreted during the days 5 and 7 of decay were not oxidized by exogenous H2 18 O2, unlike those secreted at day 3. These results prompted us to search for secreted enzymes that scavenge H2O2. A catalase stood out: almost absent at day 3 (0. 02 ± 0. 02 %NSAF), but more abundant at days 5 and 7, with %NSAF values of 0. 33 ± 0. 05 and 0. 42 ± 0. 01, respectively (Fig. 2C ; Dataset 1). When we measured the H2O2-consumming activity of the secretomes, we observed no activity at day 3, but strong activities at days 5 and 7 secretomes, in a perfect consistency with the catalase abundance (Fig.
2C ). Although few potential peroxidases and LPMOs were present, there was no difference in their abundance during the time course (Dataset 1). These results indicated that this catalase was very likely responsible for most, if not all, of the H2O2-consuming activity. Altogether, these results show that, under our experimental conditions, P. cinnabarinus secreted a catalase at days 5 and 7, which, by consuming potential exogenous H2O2, protected the secreted proteins from oxidation. On the contrary, at day 3, the secreted proteins are left without any protection against exogenous oxidant. On the other hand, this highlight that the strategy consisting of using high concentrations of H2 18 O2 to block reduced Met can hardly be applied to the secretomes collected at days 5 and 7, at least in conditions where hydroxylamine was not an efficient inhibitor of H2O2-consuming activity.
Most of the oxidations occur on a few secreted CAZymes. Among the 308 secreted proteins identified, we detected 265 Met belonging to 118 proteins (Table  S3 ; Dataset 3). We selected only the Met-containing peptides present in all replicates for each secretome, with at least five spectral counts, in at least two out of the three replicates. At day 3, all selected Met were found oxidized with 18 O (Fig. 4A ; Dataset 3). The total oxidation ( 16 O and 18 O) ranged from ∼ 20% to 100% for the 46 identified Met (Fig. 4A ). Four Met, all from CAZymes, were fully oxidized and thus saturated with 18 O: Met305 of a PL35, Met378 of a GH7, Met314 of a PL8, and Met239 of a CE16 (Fig. 4A ; Dataset 3). Twenty-four proteins had oxidation levels ranging from ∼81% to 97% and 17 from 57% to ∼80%. Only the Met592 from an AA3_2 displayed a much lower value of total oxidation (∼20%) (Fig.
4A ; Dataset 3). The quantification of the 16 O-oxidation levels appeared clear for the four 18 O-saturated Met and should correspond to the levels "naturally" found during the wood decay. However, for all other Met with an oxidation value less than 100%, the saturation with 18 O was incomplete and some artifactual oxidation may have occurred during the sample preparation, and thus the level of 16 O might be overestimated. For these proteins, we assumed that the percentage of 16 O found during wood decay was necessarily equal to or lower than the measured one. The percentage of oxidation as 16 O ranged from 0% to 47% with a mean of 18% (Fig. 4A ; Dataset 3). This value is consistent with the mean percentage of Met 16 O oxidation calculated considering all Met containingpeptides found (see Fig.
3A ) and indicated that most of the oxidation was concentrated on these few secreted proteins. Concerning day 5 and day 7 secretomes, the very low levels of 18 O oxidation precluded the use of the blocking strategy. However, to find proteins with Met 18 O may allow to consider H2 18 O2 as a probe for highly reactive residues, because of the low H2 18 O2 concentration they were exposed to, due to the catalase activity. We found eight and nine Met bearing 18 O in the proteins secreted at day 5 and day 7, respectively, and five were common to both samples (Fig. 4B ; Dataset 3). The percentages of 18 O ranged from ∼2% to ∼14%, and from ∼1% to ∼23%, for the secreted proteins of day 5 and day 7, respectively. Among the 11 oxidized Met, only three were uniquely found at day 5 or day 7: Met533 of a GH95 at day 5, and Met284 and Met160 belonging to a PL35 and a GH3, respectively, at day 7 (Fig.
4B ; Dataset 3). This indicates that most of the proteins found oxidized with 18 O at day 5 or 7 were also oxidized at day 3, highlighting their high reactivity to H2O2. We found a total number of 49 oxidizable Met belonging to 28 proteins, representing ∼24% and ∼19% of all the identified Met and proteins from the secretome, respectively (Fig. 4C ; Dataset 3). Interestingly, the 18 identified CAZymes represented most of them (64%), while proteases and proteins of unknown function both represented 18% with five identified proteins. The most represented CAZymes were GHs with 10 identified proteins. These results indicates that the proportion of oxidized CAZymes was enriched compared to the different types of proteins present in the secretomes (compare Fig. 4C  with Fig. 1 ).
Oxidation occurs in multiple sites and hotspots of homologous proteins. Interestingly, among the five most oxidized, three Met belong to the three GH7 (A0A060SFM6, A0A060SMU8, A0A060ST00, named A, B, and C, respectively), at position 286 of the GH7 A and B, and at position 287 of the GH7 C (Fig. 4A ). A sequence alignment showed that these positions are conserved between these GH7 homologs (Fig. 5A ). These Met were oxidized with similar levels of Met 16 O (between 28% and 38%) and Met 16 O2 (between 9% and 13%). We also detected seven other oxidized Met on these GH7s, four were uniquely on the GH7 A, one on the GH7 B, and two were in a position conserved in GH7 A and B. Since we had assessed the overall level of Pro oxidation, we quantified the percentage of oxidation of individual Pro, considering only those labeled with 18 O (Dataset 4).
Acknowledgments The authors kindly thank the  CIRM-CF  for providing the P cinnabarinus CIRM-BRFM145 strain,  Dan Cullen  ( Forest Product Laboratory, USDA, Madison, WI, USA ) for providing Populus tremuloides wood, and  Marie-Noëlle Rosso  ( Aix Marseille Univ. , INRAE, Biodiversité et Biotechnologies Fongiques ) for critical reading of the manuscript. This work received support from the  French government  under the  France 2030 investment plan , as part of the  Initiative d'Excellence d'Aix -Marseille Université -IM2B  ( AMX-19-IET-006 ) for L. M. and L. T. -A* MIDEX  ( AMX-21-PEP-028 ) for L. T. The authors would like to thank the  NovoNordisk foundation  ( OxyMiST  project, grant number  NNF20OC0059697 ). CRediT author statement  Lise Molinelli : Conceptualization, Methodology, Validation, Formal analysis, Investigation, Data Curation, Writing -Original Draft, Writing -Review & Editing, Visualization,  Elodie Drula : Software, Methodology, Formal analysis, Investigation, Data Curation,  Jean-Charles Gaillard : Formal analysis, Investigation,  David Navarro : Investigation, Resources,  Jean Armengaud : Formal analysis, Resources, Data Curation, Writing -Review & Editing,  Jean-Guy Berrin : Resources, Writing -Review & Editing, Funding acquisition,  Thierry Tron : Conceptualization, Resources, Writing -Review & Editing, Supervision, Funding acquisition,  Lionel Tarrago : Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data Curation, Writing -Original Draft, Writing -Review & Editing, Visualization, Supervision, Funding acquisition.
∑ (%𝑀𝑒𝑡 𝑝𝑟𝑜𝑡𝑒𝑖𝑛 𝑛 𝑋 𝑁𝑆𝐴𝐹 𝑝𝑟𝑜𝑡𝑒𝑖𝑛 𝑛 ) ∑ 𝑁𝑆𝐴𝐹 𝑝𝑟𝑜𝑡𝑒𝑖𝑛 𝑛 With %Metprotein n and NSAFprotein n corresponding to the percentage of Met and the NSAF value of the individual proteins identified, respectively. The percentage of modification of Met or Pro residues was calculated by dividing the number of spectral counts (SC) of the residue with the modification by the total number of SC for the residue and then multiplying the result by 100. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE (34) partner repository with the dataset identifier PXD046271 and 10. 6019/PXD046271.
4 ; 4 ;Fig. 5 ; 445 Fig. S2). This represented 11 proteins among the 33 proteins identified having oxidized residues (28 with Met with or without Pro and 5 with Pro only) (Fig. 4; Fig. S2; Datasets 3; 4). Moreover, 19 out of the 33 identified proteins were oxidized on multiple residues, from two to 10. A GH152 was oxidized on 8 Pro, while the GH7 A was the protein with the highest number of oxidation sites (10) , including six Met and four Pro (Fig. Fig. S2; Datasets 3; 4).
Hotspots correspond to the number of times that two oxidizable sites were located within a window of ten amino acids on the protein sequence. All data are presented in Datasets 3 and 4. C) 3D model of the GH7 A0A060SFM6 downloaded from Alphafold database (62). The position of the cellonoanose substrate (in blue) was modelized using the experimentally determined structure of Trichoderma reesei Cel7A (PDB #4C4C). Met and Pro found oxidized are represented as cyan and orange sticks, respectively. Catalytic residues are represented as red sticks. Percentage of oxidation at day 3 are in brackets. Some parts of the protein were rendered transparent for clarity.
We numerically investigate an adaptive version of the parareal algorithm in the context of molecular dynamics. This adaptive variant has been originally introduced in [1]. We focus here on test cases of physical interest where the dynamics of the system is modelled by the Langevin equation and is simulated using the molecular dynamics software LAMMPS. In this work, the parareal algorithm uses a family of machine-learning spectral neighbor analysis potentials (SNAP) as fine, reference, potentials and embeddedatom method potentials (EAM) as coarse potentials. We consider a self-interstitial atom in a tungsten lattice and compute the average residence time of the system in metastable states. Our numerical results demonstrate significant computational gains using the adaptive parareal algorithm in comparison to a sequential integration of the Langevin dynamics.
We are thus going to use the same time-step to integrate both dynamics, the difference in cost stemming from the different complexity for evaluating the potential. In this setting, an adaptive version of the parareal algorithm has recently been introduced in  [1]. It is shown there that this adaptive version leads to significantly improved gains (in comparison to the standard version of parareal) on some toy examples. The main goal of this article is to investigate the performances of the adaptive parareal algorithm for realistic problems of physical interest. To that aim, we focus on the MD simulation of the diffusion of a self interstitial atom (SIA) in a body-centered cubic (BCC) tungsten lattice. The simulation is performed using the LAMMPS  [7]  molecular dynamics software (Large-scale Atomic/Molecular Massively Parallel Simulator), a software which is very broadly used within the materials science community.
Implementation in LAMMPS In order to provide a non-intrusive implementation of the Algorithms 1 and 2 in LAMMPS, we did not modify the source code of LAMMPS but proceeded as follows. The logic of the parareal algorithm is implemented in a so-called master code written in Python, while the force calculations and timestepping is carried out in LAMMPS. For each given (q k n , p k n ), the Python code calls LAMMPS through an API in order to request an advance of the system (using either V F or V C ) over a time range ∆t (by making L time-steps of length δt ), thereby computing F ∆t (q k n , p k n ) and C ∆t (q k n , p k n ). The jumps F ∆t (q k n , p k n ) -C ∆t (q k n , p k n ) are then computed by the master code. In the sequential part, Python first requests LAMMPS to compute C ∆t (q k+1 n , p k+1 n ) and then adds the jump to obtain (q k+1 n+1 , p k+1 n+1 ).
To circumvent this difficulty, we proceed as follows to reach the final time T = N ∆t : • in Python, we draw a list of N random numbers that we denote {S n } 1 ≤ n ≤ N and that will be used as seeds by LAMMPS. Since LAMMPS expects the seed to be an integer number in a given range, we draw {S n } 1 ≤n ≤ N as a random sequence of i. i. d. integers uniformly distributed within that range. • when we enter LAMMPS to compute F ∆t or C ∆t to integrate the system from time (n -1) ∆t to n ∆t (at any parareal iteration k), we provide the LAMMPS random number generator with the seed S n. On the basis of that seed, the random number generator of LAMMPS provides the Gaussian increments G ℓ,n for any 0 ≤ ℓ ≤ L. Because the seed is the same for F ∆t and C ∆t and at all parareal iterations, both schemes make use of the same sequence of increments {G ℓ,n } 0 ≤ ℓ ≤ L (which of course remains the same at each parareal iteration).
We have made sure that the pseudo-random number generators of Python and of LAMMPS are of different type (by default, Python uses Mersenne Twister 1  while LAMMPS uses a Marsaglia random number generator 2  ). The procedure that we have adopted, which is constrained by practical considerations, hence seems to be reasonable. We however note that a perfectly clean procedure would require to use parallel pseudo-random number generators (see e. g. [27] ), in order to avoid correlations between the sequence of numbers used by LAMMPS on the different intervals [(n -1) ∆t , n ∆t ]. The Python code corresponding to Algorithm 2 can be found in the GitHub repository at https://github. com/OlgaGorynina/Parareal_MD.
Kinetic temperature In our implementation of the parareal algorithm, LAMMPS is called N times from Python, each of these calls asking LAMMPS to perform L steps of the scheme (2)-(  3 ). This procedure generates a trajectory over a time interval of total length N ∆t = N L δt. We explain in this section that, surprisingly enough, this procedure is not equivalent to performing directly N L time-steps of (  2 )-(3), and actually introduces a bias in the observed kinetic temperature. This is because the first step of the scheme (namely (2)) reads differently from the subsequent ones (performed following (3)). In particular, one has to implement a carefully chosen temperature schedule in order to recover the correct kinetic temperature. Consider the scheme which consists in making L steps of the BBK scheme (that is, we start with (2) and next perform L -1 steps of (  3 )).
Funding. Part of this work has been completed while DP was visiting Paris-Est Sup as an invited professor. The hospitality of that institution is gratefully acknowledged. The work of FL and TL was partially supported by  ANR  through grant  ANR-15-CE23-0019-06  (project  CINE-PARA ). This project has also received funding from the  Agence Nationale de la Recherche (ANR, France)  and the  European High-Performance Computing Joint Undertaking (JU)  under grant agreement  955701  (project Time-X). The JU receives support from the  European Union 's  Horizon 2020 research and innovation programme  and  Belgium ,  France ,  Germany ,  Switzerland. This work was also partially funded by the  European Research Council (ERC)  under the  European Union 's  Horizon 2020 research and innovation programme  (grant  810367 ; project EMC2).
All transcriptomic data were deposited to ENA (https://www. ebi. ac. uk/ena/browser/view/GCA_902825105. 1) with accession numbers PRJEB36587 and PRJEB36588 for RNA-seq and Iso-Seq, respectively. Together these data have been used to provide a high quality annotation of the buffalo assembly which can be accessed through the Ensembl Rapid Release genome browser: https://rapid. ensembl. org/Syncerus_caffer_GCA_902825105. 1.
In Western/Central Africa, S. c. aequinoctialis, S. c. brachyceros and S. c. nanus sub-populations also formed separate clusters, although the S. c. nanus and S. c. brachyceros populations clustered closely together. Samples were initially grouped by sub-species and country of sampling. However, based on PCA results, the Tanzania and Kenya, and Botswana and Zimbabwe samples were grouped together, reflecting their geographic proximity. This resulted in nine subgroups for downstream analyses; referred to hereafter as S. c brachyceros, S. 1 ), leaving a total of 163 samples for the phylogenetic analyses. In order to explore the relationship between these populations further, and to mitigate the different sample size between subpopulations resulting in over-representation of populationspecific variation in the dataset as far as possible  [38] , we downsampled the larger groups to 15 representative samples (for those with less, all samples were included).
African buffalo genome The genome generated in this study represents a substantial improvement on current genomic resources available for S. caffer, with greater contiguity and much improved assembly and annotation -this, and the allied gene expression datasets, will hopefully serve as useful resources for the bovid and African buffalo research communities. The genome assembly is currently at the scaffold rather than chromosomal level, and so karyotype and features such as centromeres remain undefined, and the genome also contains Y chromosome and mitochondrial sequences that have not been completely resolved. There is therefore clearly scope for further improvement of the reference genome. An interesting finding was the African buffalo-specific sequence, which was identified after aligning the African buffalo genome to eight existing high quality bovid genome assemblies (cattle, water buffalo, yak and goat  [29] [30] [31] [32] [33] [34] ).
This may have resulted in some bias in our population analyses. However, we did attempt to mitigate this bias to some extent by reducing populations to 10 samples per population where relevant and possible. Notwithstanding this fact, the present database provides genome-level and -wide resolution on variation (based upon 23,454,419 identified variants relative to the assembled reference genome); a much more robust basis for identifying genetic differentiation than previous methods used to identify genetic substructuring in this species. These insights have parallels with previous genome/multilocus genetic data studies on African ungulates with similar pan-Sub-Saharan distribution, the giraffe (Giraffa camelopardis) and zebra (Equus quagga), which indicated a lack of correlation of genetic data with morphology-based speciation, in those cases resulting in the identification of cryptic speciation  [45, 46].
The scaffold-level P4 genome assembly contains a total of 3,351 scaffolds, of which 1,381 scaffolds are greater than 10 kb. Quality values (QV) representative of the single-base accuracy were computed using Merqury (v1. 1)  [66] with the K-mer counts generated by Meryl (v1. 2; https://github. com/marbl/meryl). For downstream analysis we selected 1,381 contigs with a length of 10 kb or greater, representing 99. 68% (2. 653 Gb) of the total length of the assembled genome. This subset of contigs were used for downstream analyses.
Detection of novel genomic sequences Following completion of the assembly, we identified the novel sequences in the genome in comparison with other ruminant species. We selected a set of nine genome assemblies for five species, and calculated the distances among them using mash v2. 2  [67] , using a K-mer size of 32. We used the following genome assemblies to generate the alignment graph: Syncerus caffer (accession number GCA_902825105. 1), Bubalus bubalis Mediterranean (GCF_003121395. 1)  [29] , Capra hircus San Clemente (GCF_001704415. 1)  [34] , Bos grunniens (GCA_005887515. 2)  [30] , Bos taurus indicus Brahman (GCF_003369695. 1), Bos taurus taurus Angus (GCA_003369685. 2)  [31] , Bos taurus taurus Hereford (GCF_002263795. 1)  [33] , Bos taurus taurus N'Dama (GCA_905123515) and Bos taurus indicus Ankole (GCA_905123885)  [32].