dataesr/scientific-paragraphs-categorization huggingface dataset:
GRMZM2G138198 are indicated with boxes in red (candidate genes) and TGA1 in black. (C) Zoom within the region with XP-CLR (dots) and D values (lines with the same color code as in A) presented along with the genome-wide 5% quantile (dark blue and green dotted line for D within CBDs and NFs respectively, and black dotted line for XP-CLR between CBDs and NFs). GRMZM2G138198 and SU1 positions are indicated by red boxes. https://doi. org/10. 1371/journal. pgen. 1006666. g004 breeding. We have generated whole genome sequencing to produce a dataset of SNPs with reduced ascertainment bias and allele frequencies that include low frequency variants (S4 Fig).
The de-excitation process of primary fission fragments can be simulated with the FIFRELIN Monte Carlo code leading to an estimation of prompt fission observables such as neutron/gamma multiplicities and spectra in correlation with fission fragments. De-excitation cascades are simulated using the notion of nuclear realization following Becvar terminology generalized to neutron/gamma coupled emission. A nuclear realization is a random set of nuclear levels (energy, spin, parity) in association with partial widths for neutron, gamma or electron emission. Experimental data related to electromagnetic transitions in the discrete level region are taken from RIPL-3 database. When nuclear level structure is completely unknown (in the continuum region), level density and strength function models are used.
In between these regions, our partial knowledge of nuclear structure is completed by models up to a fixed maximum level density. In this way the whole available experimental information is accounted for. FIFRELIN is ruled by five free input parameters driving the excitation energy sharing, the rotational energy and the spin distribution of primary fission fragments. These five free parameters are determined to match a target observable such as the average total prompt neutron multiplicity (ν). Once this procedure is completed, the whole set of fission observables can be compared with experimental results. Obviously the number of observables obtained within this code is higher than what is available from measurements. This code can therefore provide useful insights into the compatibility between models and a whole set of fission observables.
Introduction More and more experiments are performed in the field of nuclear physics to improve our knowledge of fission phenomenon and complete the international nuclear databases used in neutron/gamma transport codes. The well known average prompt neutron multiplicity (ν) used to determine the effective multiplication factor in a reactor core can be measured as a function of the primary fission fragment mass ν(A) and total kinetic energy ν(TKE). Furthermore recent measurements were performed at IRMM concerning total kinetic energy dependence of the multiplicity for given mass splits ν(TKE;A)  [1]  and prompt fission gamma spectra for different mass ranges  [2, 3]. At Lohengrin mass spectrometer of the Institut Laue-Langevin (ILL) located at Grenoble (France), recent measurements of kinetic energy dependent isomeric ratio are achieved.
More than a single average value or a global spectrum, these data allow us to constrain our models, selecting a parameter value among others or accepting/rejecting an hypothesis. The FIFRELIN Monte Carlo code  [4]  has the capability to simulate all the post-scission fission observables starting from a restricted set of parameters a e-mail: olivier. litaize@cea. fr (devoted to the excitation energy sharing between the two complementary fragments and to the initial spin distribution) and input data such as pre-neutron fission fragment mass yields and kinetic energy distributions. In this paper we have focused the discussion around the average prompt neutron multiplicity as a function of total kinetic energy for a given mass split (ν(TKE;A)) and prompt fission gamma spectra for a selected mass range (P FG S(A)).
In Sect. 2 we briefly remind the models implemented in FIFRELIN and highlight the influence of shell corrections and input data on selected fission observables. Shell corrections have been changed in the present work impacting level density calculation and neutron observables such as the prompt fission neutron spectrum (PFNS) for instance. Average prompt neutron multiplicity is discussed in Sect. 3 while gamma spectra is discussed in Sect. 4. Finally an outlook of the ongoing work is briefly presented.
Influence of shell corrections Both of the shell correction data sets mentioned in Sect. 2. 1 are available from RIPL-3 database  [10]. Following a comment from a referee, we should explain here that the whole set of parameters involved in level density calculation (including pairing , asymptotic level density parameter ã) is consistent with Mengoni-Nakajima shell corrections. We have used Myers-Swiatecki shell corrections in previous works because this was recommended in RIPL-2 database and because it is stated in this database that shell correction estimations is less essential for the systematics of the level density parameters (compared to deformations of highly excited nuclei) and in addition the shell corrections that result from MN parameterization do not differ much from the original MS parameterization.
The spectrum calculated by FIFRELIN with GEF data (mass yields Y(A), charge yields Y(Z) and total kinetic energy distributions P(TKE;A) is systematically softer than the spectrum calculated with experimental data from  [13]. such as the prompt fission neutron spectrum. This effect can be seen in Fig. 3  where input data from GEF code  [5]  have been used instead of experimental data from Vivès et al. [13]  in the case of 238 U(n,f) reaction at 2. 0 MeV recently measured by Sardet at CEA/DAM/DIF  [14]. Calculated average TKE is 170. 83 MeV in GEF code compared to 170. 01 MeV in experimental data from  [13]  then the resulting fission spectrum is slightly softer.
Note that these masses are pre-neutron emission masses. Post-neutron emission masses 123, 122 as well as 121 do not emit any gamma near 500 keV (whatever the nuclear charge). If we go further we can show that the postneutron fragment mass A post = 120 associated with a nuclear charge Z = 48 emits a prompt gamma at 506 keV. Of course we can verify that the nuclear level scheme of 120 Cd coming from RIPL-3 database and used in the code contains a high transition at 506 keV. This is the case of the ground state band (partly reproduced in the right side of Fig. 7 ) where the intraband transitions from 12 + , 10 + , 8 + , 6 + , 4 + and 2 + members are present. The intense transition at 506 keV corresponds to the (2 + → 0 + ) transition observed in the calculated spectrum (Fig. 7 ) as well as in the measured spectrum.
Conclusion and outlook We have shown in this work that the impact of a recent modification in the code (Mengoni-Nakajima shell corrections from RIPL-3 database) allows reproducing level densities of 239 U compound nucleus and improving prompt fission neutron spectrum calculations with FIFRELIN. An excellent agreement can be reached by adding a scission neutron component to the fission fragment evaporation process  [15]. Average prompt neutron multiplicity in correlation with total kinetic energy and mass of fission fragments have been calculated for the spontaneous fission of 252 Cf and compared with recent measurements from IRMM. We can now address if an overestimation or an underestimation is due to the light or the heavy fragment of a fragment pair and which TKE range is affected. Sometimes the calculation fails over the whole TKE range indicating that the temperature ratio law fails for the mass considered.
Structures appearing for light fragments are clearly related to the nuclear level scheme (neutron separation energy and energy/spin/parity of levels). Prompt fission gamma spectra measured for selected mass ranges have been calculated and compared to recent measurements from IRMM. For instance the structure at 500 keV observed in measurements and calculations is predominantly due to pre-neutron mass 123 corresponding to emitting fragment 120 Cd (after 3 neutron emission). The nuclear structure used in the code coming from RIPL-3 database is good enough for this nucleus to reproduce the gamma spectrum. All the calculations performed in this work do not use the tree of events that FIFRELIN can generate on the fly. A specific software has been developed by A. Chebboubi to extract any kind of information from this tree.
• Analog fission in neutron transport codes  [19]. Shielding calculations are performed with the standard route of TRIPOLI-4 code using nuclear data libraries containing average prompt fission neutron spectra and multiplicities and with the use of fission events generated by FIFRELIN. Fission events consisting in the whole set of caracteristics of a neutron, gamma and conversion electron cascade inside the nuclear level scheme of each fragment allow to consider neutron energy event by event instead of a global spectrum. An impact can be seen on fast reaction rates at surface.
MODOMICS is a database of RNA modifications that provides comprehensive information concerning the chemical structures of modified ribonucleosides, their biosynthetic pathways, the location of modified residues in RNA sequences, and RNAmodifying enzymes. In the current database version, we included the following new features and data: extended mass spectrometry and liquid chromatography data for modified nucleosides; links between human tRNA sequences and MINTbase -a framework for the interactive exploration of mitochondrial and nuclear tRNA fragments; new, machine-friendly system of unified abbreviations for modified nucleoside names; sets of modified tRNA sequences for two bacterial species, updated collection of mammalian tRNA modifications, 19 newly identified modified ribonucleosides and 66 functionally characterized proteins involved in RNA modification.
Moreover, a number of previously unknown RNAmodifying enzymes have been identified and characterized. New important roles for long-known RNA modifications were also discovered. Prominent examples include the involvement of N 6 -methyladenosine (m 6 A) in regulating gene expression by influencing transcript stability, splicing, translation efficiency and cap-independent translation, and in promoting circular RNA translation [review:  (7) ]. Mutations in many human genes encoding RNA modification D304 Nucleic Acids Research, 2018, Vol. 46, Database issue enzymes have been linked to diseases, such as cancer, cardiovascular diseases, metabolic diseases, neurological disorders, and mitochondria-related defects [review:  (8) ]. To adequately represent the recent accumulation of knowledge, we have added both to the variety and volume of data in the MODOMICS database.
DATABASE CONTENT MODOMICS has been developed to house and distribute collections of RNA modification pathways, chemical structures of modified nucleosides, sequences of modified RNAs, enzymes responsible for individual reactions, a catalog of 'building blocks' for chemical synthesis of modified RNA, and to be expanded to include new data types. The database was created as a single resource to organize and present all these data in a convenient and straightforward way and is currently the most comprehensive source of information among all existing RNA modification databases. Information about modified residues is also available in the RNAMDB database  (9) , while information about modified nucleosides identified from high-throughput experiments like Pseudo-seq, CeU-seq, m 6 A-seq, Aza-IP and RiboMeth-seq is hosted by RMBase  (10).
Recently MOD-OMICS was linked to RNAcentral, a database of noncoding RNA sequences  (11) , and serves as a source of modified tRNA and rRNA sequences. At present, MODOMICS contains 163 different modifications that have been identified in RNA molecules. A typical entry for a modified ribonucleoside contains information about its fundamental chemical properties, chemical structure, localization in known RNA molecule types, the phylogenetic distribution with respect to Domains of Life, and known enzymes responsible for its biosynthesis. Among other available details information related to MS analyses of modified RNAs is provided (see the 'LC/MS data for modified nucleosides' section). Many of the products of modification reactions are substrates for further reactions, and the formation of hypermodified residues occurs in complex pathways, which are displayed as graphs in the PATHWAYS section of the database.
Pathways are divided into six different categories according to their starting point: four categories correspond to the standard bases (A, G, C and U), one presents the incorporation and hypermodification pathway of queuosine, and one the modifications of the RNA 5 cap. MODOMICS provides a collection of modified RNA sequences of different types. For families of homologous RNAs, multiple sequence alignments are available. Sequences are visualized with all modifications highlighted and linked to the corresponding modification records. The current set of sequences comprises 691 tRNA, 19 rRNA, 46 snRNA and 25 snoRNA sequences. The MODOMICS database currently contains information about 340 functionally characterized proteins involved in RNA modification, both functional enzymes and protein co-factors necessary for multi-protein enzymatic activities.
For each protein a set of detailed information is provided and includes: identifiers and accession numbers from relevant resources and databases such as: NCBI GI, UniProt ID, COG number, PDB ID of structure (if available); amino acid sequence; corresponding ORF; information about catalyzed reaction, the position of modification and modified RNA(s) (if available). For proteins that are parts of enzymatic complexes, the name of the complex is provided. MODOMICS also contains human and yeast snoRNAs, involved in RNA-guided RNA modification by the C/D box and H/ACA box ribonucleoproteins, linked to the corresponding modification sites in human and yeast RNAs and the catalog of 'building blocks' for the chemical synthesis of naturally occurring modified nucleosides. Several options for database searching and querying are implemented in MODOMICS, including the BLAST (12) search of protein sequences and the PARALIGN (13) search of nucleic acid sequences collected in MODOMICS, as well as a utility that sends a protein sequence from a MODOMICS entry to BLAST on the NCBI web server.
Updated modifications section Since the previous release of MODOMICS  (14) , 19 new modifications were added to the database. Among those are four types of geranylated nucleosides discovered in bacterial tRNA (3), 5-cyanomethyluridine (cnm 5 U)  (15) , and 2 -O-methyluridine 5-oxyacetic acid methyl ester (mcmo 5 Um)  (5). LC/MS analyses of tRNAs from Bacillus subtilis, plants, and Trypanosoma brucei revealed the presence of 2-methylthio cyclic N 6threonylcarbamoyladenosine (ms 2 ct 6 A), a derivative of N 6 -threonylcarbamoyladenosine (t 6 A), at position 37 of tRNAs responsible for recognition of adenosine-starting codons  (16). 3D chemical structures of modified nucleosides are now displayed with JSmol  (17). It is an open-source JS library and HTML5 viewer for 3D chemical structures which, in contrast to the previously used Jmol tool, does not require the installation of the Java software package.
LC/MS data for modified nucleosides This new MODOMICS release features a new section on modification detail page created to host the LC/MS data of the modified nucleoside. The new fields include information concerning the product ions, the protonated mass [M+H] + , the LC elution order and its characteristics, the normalized LC elution time and their literature references. The LC elution time is normalized to guanosine (G), measured with an RP C-18 column with acetonitrile/ammonium acetate as mobile phase and the elution order is based on the retention times of C, U, G, A and m 6 A to cover all areas of the chromatogram. The LC data is intended to provide the novice LC-MS user with guidance on the relative hydrophobicity of modified nucleosides and an estimated elution region using the denoted stationary and mobile phases.
Currently, only 48 modifications, for instance, m 1 A, m 3 C, and m 2 , 2 G are associated with LC information, while 138 modifications have been associated with MS information. The new system is in place to be extended to all the modifications present in the database as soon as the new data become available. LC/MS based methods allow RNA modification profiling of different organisms in a semi-quantitative manner for the newly-detected modifications along with known modifications, and one can expect that approach will be further extended. LC/MS data for MODOMICS collection of modified nucleosides provides a comprehensive source of information for mapping of the identity and position of modified residues in RNA sequences.
For each modified nucleoside the code in new nomenclature is available on the nucleoside site in the Modifications section of the database. The nomenclature was also implemented in the Sequences section by providing the option to display modifications in sequences using this nomenclature instead of one-letter symbols. As the next step we intend to develop and provide format conversion tools to allow for exporting and importing RNA sequences with modifications, and e. g. , to run sequence searches that take modifications into account. Research, 2018, Vol. 46 , Database issue tRNA sequences section update and development For this database release, 102 new tRNA sequences were added, and a major update of mammalian tRNA modifications was performed based on  (18). Among new sequences are sets of tRNAs from two bacterial species: Streptomyces griseus and Lactococcus lactis  (19, 20) , 60 and 26 sequences, respectively.
Updated collection of proteins, enzymatic activities, and pathways The MODOMICS collection of functionally characterized proteins involved in RNA modification is under constant development. Since the previous release, 66 new proteins have been added. The collections of protein sequences and enzymatic activities are updated in parallel, which resulted in 105 new enzymatic activities. Among new proteins that were added in this release there is a collection of human RNA modification enzymes, including: dimethyladenosine transferase TFB1M  (23) , tRNA pseudouridine synthase PUS3  (24) , tRNA m 5 C methyltransferases NSUN3  (25)  and NSUN6  (26) , and rRNA m 5 C methyltransferase NSUN4  (27). Apart from the addition of newly characterized enzymes, data entries for many enzymes and associated pathways were updated.
Future prospects The number of experimentally identified modifications and RNA modifying enzymes keeps growing. New modified nucleosides are being discovered in particular in RNAs from recently adopted model systems, such as extremophilic prokaryotes. Though there is a considerable amount of information available about the enzymes responsible for introducing specific modifications, there are still many modified positions in well-characterized RNA molecules, for which the responsible enzymes are not known, e. g. , m 6 Am at the 5 end of human mRNAs or m 5 U, m 4 C in 12S mitochondrial rRNA. To help us keep up with new discoveries, we encourage the users of MODOMICS to submit suggestions for additions to be included in the database. We also encourage developers of other computational resources to contact us to have our databases mutually linked to each other.
ACKNOWLEDGEMENTS We are grateful to  Henri Grosjean , the co-founder, key cosupervisor, and key co-developer of MODOMICS, for his contribution to all previous releases. We thank  Sebastian Leidel  and  Peter Dedon  for discussions and useful suggestions. We also thank all other previous contributors to MODOMICS, for their work which provided a solid basis for the present update. We thank all members of the  Bujnicki laboratory  for fruitful discussions and useful suggestions. We are indebted to the authors of primary databases and services, whose content could be reused or linked to by MODOMICS. Last, but not least, we thank all users of MODOMICS who provided feedback and made suggestions, and who cited MODOMICS in their publications.
Gene expression microarray and Gene Set Enrichment Analyses (GSEA). We used GSEA  24  to identify pathways and gene sets associated with variation in ATPIF1 mRNA expression levels across 91 hepatocellular carcinomas  25  (GSE20238). Genes were sorted by their concordance (Pearson correlation) with ATPIF1 mRNA expression levels across tumors, and GSEA was used to evaluate gene sets enriched for either negatively or positively correlated genes. Published GSE20238 was downloaded from the InSilico DB Genomic Datasets Hub  26  (https://insilicodb. com/), and analyzed by using the GSEA v2. 07 software (http://www. broad. mit. edu/gsea), as previously described  24. To account for gene-gene correlations in the enrichment analysis, GSEA p values were computed with respect to a null distribution obtained from 1,000 randomizations of the patient-phenotype labels.
RNA isolation and reverse transcription -real-time quantitative polymerase chain reaction (RT-qPCR) analysis. Total RNAs were isolated from F258 cells using the TRIzol method (InVitrogen) and were then subjected to RT-qPCR analysis, as previously described  29. Gene-specific primers for ATPIF1 and 18S were purchased from Sigma, and the sequences used for each gene were as follows: ATPIF1: forward, ACGCCGAAGATAATGGCAGG -reverse, ATCCATGCTCTCCGACGAGT; 18S: forward, CCGGTACAGTGAAACTGCGA -reverse, GATAAATGCACGCGTTCCCC. The amplification curves of the PCR products were analyzed with the ABI Prism SDS software using the comparative cycle threshold method. To assess the successful amplification of each target gene, a standard curve was performed for each primer set. The expression levels of target genes were normalized relative to the expression of an 18S RNA endogenous reference and were plotted as fold change compared to control with vehicle (DMSO).
High IF1 expression is correlated with liver cancer progression and gene expression related to glycolytic metabolism in human hepatocarcinoma. We first explored by GSEA the potential contribution of ATPIF1 expression in human liver cancer progression (91 human HCV related-hepatocarcinoma, GSE20238, as previously described  25  ). As shown in Fig. 1A , data mining revealed a positive correlation between high IF1 mRNA expression and the progression of non-tumor (L1) to well differentiated liver tumor (G1) (with a normalized enrichment score of 1. 668, and a p value of 0. 025). Consistent with the reported role of ATPIF1 in HCC metabolism in vitro  14  , further GSEA analysis of GSE20238 pointed to a positive correlation between high IF1 expression and high glycolytic metabolism in human HCC (p value of 0.
Acknowledgements We wish to thank the MRic facilities ( SFR Biosit ) for microscopy experiments, especially  Marie-Thérèse Lavault  and  Stéphanie Dutertre  for their technical assistance. We also wish to thank  Dr. Henri Schroeder  for valuable scientific discussion,  Drs Pierre Rustin  and  Paule Bénit  for complex V activity measurements, and  Elise Saunier  for glucose oxidation experiments. KH was a recipient of a fellowship from  French Ministry for Education and Research. We are very grateful to the  Faculté des Sciences Pharmaceutiques et Biologiques de Rennes  ( University of Rennes 1 ) for further financial support to KH. We also wish to thank the  Ligue Contre le Cancer  (committees 22,35,49,85; DLG, OS), the  French National Academy of Medicine (DLG) ,  ANR  ( STEATOX project ; " ANR-13-CESA-0009 ";  DLG, OS, BM) , the ITMO-cancer Plan Cancer 2009-2013 (SB), " Region Provence Alpes Côte d' Azur", "Agence régionale santé Provence Alpes Côte d' Azur ", " Direction régionale de l'Environnement, de l'aménagement et du logement Provence Alpes Côte d' Azur " (BM, plan régional santé environnement PRSE PACA n°6.