dataesr/scientific-paragraphs-categorization huggingface dataset:
Besides, the H2B-binding motif D/Exxϑ (where ϑ is F or Y, and x is any residue;  [39] ) is strongly conserved in PpSPT16 (Figure  S11C , in green). Hence, our data strongly suggests that the Physarum FACT complex is related to the yeast one. plex is overall conserved in P. polycephalum, but is more similar in domain structure to the Arabidopsis orthologue. The replication-independent assembly complex HIR preferentially deposits H3. 3/H4 in chromatin in a replication-independent manner  [32]. The Physarum complex consists of PpHIRA, PpUBN and PpCABIN1. PpHIRA presents a similar domain organization to AtHIRA, with several WD40 repeats located in the N-terminal and an additional one just before the HIRA domain (Figure  4D ). The GRRRIxPLxI motif (with x being any amino acid) involved in ASF1 interaction  [9]  (Figure  S8B ) and the HIRA domain involved in CABIN1 binding (  [33] ; Figure  S8C ) are strongly conserved in Physarum.
IDD regions are flexible and highly dynamic; they may be critical for chromatin assembly and were reported in human histone chaperones  [54]. We used RAPID (Regression-based Accurate Prediction of Protein Intrinsic Disorder content)  [55]  Figure  7. Expression profiles of Physarum histones and chaperones during the cell cycle. The heat map displays the RNA-Seq expression of histone chaperones (A) and histones (B) at five stages of the Physarum cell cycle indicated at the bottom of the map. Each row corresponds to a transcript listed on the right and each column to a cell cycle stage. The color bar at the right depicts the scale for z-score, with blue representing the lowest expression and yellow representing the highest. In Figure  7A , chaperones from group 1, i. e. , with elevated mRNA levels in early S-phase and late G2 phase are displayed in yellow on the left of the heat map, while chaperones from group 2, i.
elegans and Drosophila and also another Mycetozoan (D. discoideum). This suggests that the APLF chaperone might have been present in the last common eukaryotic ancestor, and was subsequently lost in certain lineages or species. Moreover, while the mammalian APLF protein presents an acidic tail, this domain is translocated to XRCC1 in other species  [49]. As this tail is responsible for the interaction of APLF with histones, PpAPLF and PpXRCC1 might cooperate to ensure histone incorporation at DNA damage sites and to restore the epigenomic landscape. In SPT6, we found five WG/GW repeats in PpSPT6, whereas PpaSPT6, AtrSPT6, AtSPT6L, ZmSPT6 have 8, 20, 12 and 8 WG/GW repeats, respectively (Figure  S14A ). These WG/GW repeats were not originally identified in P. patens  [56] , most likely due to the incomplete sequences of SPT6 orthologues at that time (the XP_00175668 and XP _00296188 accession numbers used for P.
Identification of Physarum Genes, Transcripts and Proteins for Chaperones Public genomic and transcriptomic data from P. polycephalum were obtained from www. physarum-blast. ovgu. de and former published data  [21, 66]. Protein sequences from H. sapiens, M. musculus, D. rerio, D. melanogaster, X. laevis, C. elegans, A. trichopoda, A. thaliana, Z. mays, P. patens, T. thermophila, D. discoideum and S. cerevisiae were obtained from UniProt  [67]  and/or NCBI  [68]. In the absence of proteins in both databases, a BLASTp with the human protein against the proteome of the organism for which no protein was recorded was performed. For APLF orthologues, one APLF protein was recorded in UniProt (NP_001097801. 1) for D. melanogaster but it does not contain the characteristic FHA (ForkHead-Associated) domain.
Besides, in C. elegans, the CeLig3 protein is only an APLF functional analog  [49]. Thus, both proteins were excluded from our study. For the DrUBN1 protein, only a fragment of the protein is available in the databases. For UBN and CABIN1 in C. elegans, no orthologues were retrieved in UniProt or by BLASTp. CePICD-1 (pry-1 interacting CABIN1 domain containing) was found in the literature as an orthologue of CABIN1  [69]. We did not identify CePQN-80 based on UBN protein sequence homology, but it was added in the phylogenetic analysis based on a recent study  [70]. However, our analysis showed that CePQN-80 is highly divergent from the other UBNs (Figure  S2I ). For the NAP family, proteins retrieved for a specific organism were cleaned from proteins identical at 92-99% on the whole protein.
The Arabidopsis orthologue of PolE3 has not been faithfully identified yet  [71]  as well as orthologues for P. patens, T. thermophila, Z. mays, so these species were not included in the phylogenetic analysis. A local BLASTn (blast 2. 6. 0) was performed to identify chaperone homologs in the public P. polycephalum transcriptomes  [21, 66]. Identified transcripts were then aligned on the Physarum reference genome  [21]  to identify the corresponding genes. A tBLASTn search was also performed on the Physarum reference genome to investigate if the chaperones with no homologs in the Physarum transcriptomes corresponded to not expressed genes. No such case was found. All used protein sequences were reported in Table  S2  and are available at https://clipperton. ufip. univ-nantes. fr/physabase/, accessed on 22 September 2022.
Phylogenetic Analyses and Protein Sequence Alignments The chaperone protein sequences were aligned with the Clustal Omega program  [80]. Phylogenetic trees were constructed with Mega [81] and the ITOL (Interactive Tree Of Life) tool  [82]. Plant, animal and Physarum proteins were depicted in green, blue and red in trees, respectively. Proteins from yeast, D. discoideum and T. thermophila were displayed in black. Using the number of proteins per chaperones families, we clustered the species using hierarchical clustering (R core team, www. R-project. org, accessed on 23 May 2022). The Phyre2  [83]  and AlphaFold  [84, 85]  web portals were used to create the 3D protein modelling and the Chimera software  [86]  for superimposition. Pairwise comparison of protein sequence identity from Physarum and the other organisms was performed by performing a NeedleMan-Wunsch global alignment, a needle command in the EMBOSS suite  [87] , of each Physarum protein sequence to all its orthologues in the studied organisms (Table  S3 ).
RNA-Seq Library Construction and Sequencing At 3 stages during S-phase (2 min after mitosis, early S-phase; 1 h after mitosis, mid S-phase; 2. 5 h after mitosis, late S-phase) and 2 stages during G2 phase (5. 5 h after mitosis, early G2-phase; ∼10 min before mitosis, late G2-phase), RNAs were isolated in triplicates from 3 mitotically synchronous plasmodia, as described in  [20]  and then treated with DNase I (NEB, New England Biolabs, Inc. , Ipswich, MA, USA) and purified with phenolchloroform extraction. Each replicate from the 5 cell cycle stages was subjected to RNA-seq library preparation using the NEBNext ® Ultra™ II Directional RNA Library preparation kit (NEB) with NEBNext ® Multiplex Oligos for Illumina ® (Dual Index Primers Set 1, NEB) following the manufacturer's protocol. All 15 libraries were pooled together and run on one single lane of an Illumina NovaSeq6000 for paired-end sequencing (GEO accession number PRJNA894126), using a paired-end read length of 2 × 150 bp.
Assembly, Quantification and Analysis of RNA-Seq Data After sequencing of Physarum samples, read quality was evaluated by checking the number of expected sequences, the GC percentage, the presence of adaptors and the overexpressed sequences using FastQC  [90]. Contamination was checked by aligning reads against E. coli, Yeast, and PhiX Illumina control genomes. Assembly was performed with Trinity  [91]  and quantification with Salmon [92] to generate expression estimation of the read count (Transcripts Per Million transcripts, TPM). From means generated from normalized read count (in CPM, count per million), heat maps were generated with RStudio (version 2022. 02. 1) with the package "pheatmap" (Version 1. 0. 12).
Processing and Analysis of Public RNA-Seq Data Data from human (HUVEC cells, Human Umbilical Vein Endothelial Cells, GSE211658; MCF-7 cells, a breast cancer cell line, GSE94479; U2OS cells, a cell line with epithelial morphology derived from a tibia sarcoma, GSE143275), tobacco TN-90 cells (GSE121032) and yeast (GSE168699) were downloaded from NCBI. Reads were filtered and trimmed using fastp (version 0. 21. 0 with length_required 20 and average_qual 20)  [93]. Data sets were aligned against the human (Hg38) or tobacco TN-90 (Nitab-v4. 5) or yeast (S288C R64) genomes using STAR (version 2. 7. 2a with "out Filter Mismatch Nmax" = 2,.
Acknowledgements We are grateful to all individuals included in the different cohort studies. We thank  F. Allegaert , and  T. Beke  ( Inserm UMR1283 ,  CNRS UMR8199 ,  European Genomic Institute for Diabetes [EGID ],  Institut Pasteur de Lille, Lille University Hospital, Lille, France ) and  M. Vieubled  ( 4P-Pharma, Lille, France ) for their technical assistance. Data from this article were obtained as part of the PhD thesis of S. Meulebrouck entitled 'Impact of rare genetic variants on the etiology of type 2 diabetes' (2020). Several data from this article have been previously exposed during an oral presentation entitled 'The impact of rare pathogenic variants of GLIS3 on type 2 diabetes' at the 58th EASD Annual Meeting (2022).
Funding We thank ' France Génomique ' consortium ( ANR-10-INBS-009 ). This work was supported by grants from the  French National Research Agency  ( ANR-10-LABX-46  [ European Genomics Institute for Diabetes ] and  ANR-10-EQPX-07-01 [LIGAN-PM ]), from the  European Research Council  ( ERC OπO -101043671 , to AB), and from the  National Center for Precision Diabetic Medicine  -Pre-ciDIAB, which is jointly supported by the  French National Agency for Research  ( ANR-18-IBHU-0001 ), by the  European Union (FEDER) , by the  Hauts-de-France Regional Council  and by the  European Metropolis of Lille (MEL).
Data availability Data regarding variants from TOPMed and 52K studies are available on the AMP Type 2 Diabetes Knowledge Portal via this link: https:// t2d. hugea mp. org/ gait. html? gene= GLIS3 & tests= burden% 2Cvt& trans cript= ENST0 00003 81971. Data regarding GLIS3 variants from the GnomAD browser (v2. 1. 1) via this link: https:// gnomad. broad insti tute. org/ gene/ ENSG0 00001 07249? datas et= gnomad_ r2_1 Other data are available upon request to the corresponding author.
Col du Dôme (CDD) Col du Dôme is a glacier saddle at 4250 m a. s. l. between the Mont Blanc main summit and the Dôme de Goûter in the French Alps. Data used in this study are from three ice cores drilled in 1994 (126 m), 2004 (124 m), and 2012 (140 m). Ice-core sites are less than 20 m apart. Dating was performed by annual-layer counting mainly based on the NH + 4 concentrations  (Preunkert et al. , 2000) , refined with reference horizons, such as the Saharan dust layers in 1936-37, 1947, and  1977; the 1963 3 H maximum; and the 137 Cs maxima in 1954 and 1986. The mean annual net accumulation rate near the surface is 2. 4 m w. e. , with a regular distribution of seasonal accumulation  (Vincent et al. , 1997). Due to changes in depositional processes upstream of the drilling site, winter layers at the CDD site were found to generally thin with depth relative to summer layers  (Preunkert et al.
Data presentation, formation of composite records, and time-of-emergence analyses The datasets for the four individual Alpine ice-core sites were compiled from values reported previously in the studies listed in Table  2. We here used data of the seven investigated species (BC, Cd, F -, NH + 4 , NO - 3 , Pb, SO 2- 4 ) published before the end of 2021. For SO 2- 4 the non-dust and non-sea-salt contribution (exSO 2- 4 ) was calculated from the total SO 2- 4 accordingly to [exSO 2- 4 ] = [SO 2- 4 ] total -0. 12[Na + ] -0. 175 [Ca 2+ ], using Ca 2+ and Na + as mineral dust and sea salt tracers, respectively  (Schwikowski et al. , 1999b) ; exSO 2- 4 thus represents the part of SO 2- 4 exclusively formed from the atmospheric oxidation of SO 2. For all ice cores, annual averages were calculated similarly from these non-equidistant data as the arithmetic mean of all data points in an annual layer.
2. 1. 4. ), concentrations of winter and summer layers were averaged  (Legrand et al. , 2020)  or winter and summer records were investigated separately  (Preunkert et al. , 2001a (Preunkert et al. , , 2003;; Preunkert and Legrand, 2013). Since for the other three study sites a disentangling of winter and summer layers was not possible especially for the preindustrial periods with low data resolution, we accordingly formed arithmetic means of total annual-layer data for all ice cores consistently as described above. In any case, for the 10year means considered in this study, CDD longer-term concentration trends for both averaging procedures were found to agree within the uncertainty envelopes for all investigated species (see Sect. S2 and Fig. S2 ). Based on the 10-year average records obtained for each site, an ice-core composite record was formed for each species.
The MATCH-BIODIV domain extends over most of Europe at 12 km resolution, and the dataset covers the period 1987-2020. MATCH-BIODIV was forced by meteorological data generated by the regional climate model ALADIN (Aire Limitée Adaptation dynamique Développement Inter-National) with EC-EARTH (European community Earth system model) on its boundaries under the influence of greenhouse gas emission scenario RCP8. 5 (Representative Con-centration Pathway)  (Belušić et al. , 2020; Lind et al. , 2022) , as well as nitrogen and sulfur emissions from ECLIPSE V6b (Evaluating the Climate and Air Quality Impacts of Short-Lived Pollutants)  (Höglund-Isaksson et al. , 2020). The output from the chemical transport model was finally averaged over the same 10-year periods as the ice-core data.
Here, the mean height of the grid cells representing the ice-core sites span 2400 to 2700 m a. s. l. As expected, exSO 2- 4 concentrations obtained from the MATCH-BIODIV dataset for the period 1990-1999 are significantly lower compared to MATCH-ECLAIRE and are comparable to the ice-core concentrations (Fig. 3 , Table  3 ). Furthermore, the simulated concentrations using MATCH-BIODIV are similar at the four sites and the high-Alpine region in general (green region in Fig. 3e ), supporting the FLEXPART results that the same source regions of pollutants impact these high-Alpine sites (see Sect. 3). Thus, based on the MATCH-BIODIV results we can exclude a high-Alpine west-east concentration gradient of anthropogenic pollutants to explain the lower concentrations at CDD.
5 ), marking the onset of substantial anthropogenic NO x emissions to the atmosphere predominantly The Cryosphere, 17, 2119-2137, 2023 https://doi. org/10. 5194/tc-17-2119-2023 4 , NO - 3 , and exSO 2- 4 (MATCH-ECLAIRE dataset)  (Engardt et al. , 2017) ; estimated BC air mixing ratios  (Fang et al. , 2019) ; F -emission estimates of France and Switzerland  (Preunkert et al. , 2001b) ; and Pb and Cd deposition estimates  (Legrand et al. , 2020)  (red lines). from motor vehicles in western Europe. The delay of 50 years between the TOE for exSO 2- 4 and NO - 3 potentially reflects the change in fossil fuel use over time in western Europe: from coal burning during the first half of the 20th century to liquid/gaseous fuel burning during the second half of the 20th century. Although air pollution control measures such as combustion catalysts have been widely implemented in recent decades in all western European countries, no prominent decline in the ice-core NO - 3 concentrations is observed for the most recent years.
et al. , 2023). Data from CG03B+CG15 ice core are available at PANGAEA, https://doi. org/10. 1594/PANGAEA. 894787  (Sigl et al. , 2018b). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/tc-17-2119-2023-supplement. Author contributions. AE, ML, and MS conceived the study and wrote the paper. AE, TMJ, and SP contributed to data analyses and interpretation. CA and ME performed the MATCH calculations. AP and SE performed the FLEXPART modelling. All authors contributed to manuscript preparation.
Acknowledgements. Tinja Olenius  ( Swedish Meteorological and Hydrological Institute ) is acknowledged for performing the MATCH-BIODIV simulations in the research project BioDiv-Support. We thank  Jacobo Gabrielli  for providing the Cd data from  Colle Gnifetti. Financial support. The research in the  BioDiv-Support  project funded through the  2017-2018 Belmont Forum  and  BiodivERsA joint  call for research proposals, under the  BiodivScen ERA-Net COFUND programme , and with the funding organisations  AKA  ( Academy of Finland  contract no  326328 ),  ANR  ( ANR-18-EBI4-0007 ),  BMBF  (KFZ:  01LC1810A ),  FORMAS  (contract no:s  2018FORMAS  (contract no:s  -02434 ,  2018FORMAS  (contract no:s  -02436 ,  2018FORMAS  (contract no:s  -02437 ,  2018-02438 )  -02438 ) and  MICINN  (through  APCIN :  PCI2018-093149 ).
2022). Those complex organics expose an average sum formula of C 1 H 1. 56 O 0. 134 N 0. 046 S 0. 017. Notably, no organic source of phosphorus has been identified in comet 67P to date. We used the same high-resolution mass spectrometric dataset as in  Hänni et al. (2022) , which was collected on 3 August 2015 at comet 67P by the Rosetta Orbiter Spectrometer for Ion and Neutral Analysis (ROSINA) instrument suite  (Balsiger et al. 2007 ), and we present an extensive study of this comet's inventory of volatile O-bearing organic molecules, which is still poorly constrained. Previous work focused on data from the time when the comet was far from its perihelion and activity was low, and hence, only a limited number of species -the rather small and abundant ones -were detected in 67P's coma (Le  Roy et al.
2015; Altwegg et al. 2017; Schuhmann et al. 2019b; Rubin et al. 2019a ). The conditions shortly before perihelion, which is when the dataset analyzed for this work was collected, were distinct. Comet 67P was reaching its perihelion at 1. 24 au from the Sun in early August 2015, and the coma was dusty. Consequently, enhanced desorption of heavier and thus more complex molecules from ejected dust particles could have occurred. Decoupled from the heat sink of the cometary surface, the temperatures of ejected particles can rise up to a few hundreds of Kelvin  (Lien 1990 ) in sunlight, whereas comet surface temperatures around that time were observed to not exceed 240 K  (Tosi et al. 2019). For the most abundant subgroup of signals of pure hydrocarbon species in the 3 August 2015 dataset,  Hänni et al.
(2022)  demonstrated a way to decompose the observed signals into contributions of individual neutral molecules. Following the same methodological approach, we present in this work an attempt to disentangle the second-most abundant group of cometary volatile organics, namely, those of the O-bearing C n H m O x hydrocarbons, where n = 1-8, m = 0-14, and x = 1-2. After giving the relevant information on data acquisition as well as data reduction and interpretation, we compare and contrast our findings with other comets, meteoritic organic matter, and the ISM.
To derive the absolute abundance of the contributing molecules, their fragmentation patterns must be taken into account. Here, this was done based on the reference mass spectra from NIST, which was obtained for 70 eV electron impact. Therefore, the measured signals have to be corrected for the ionization cross section of the analyte molecules as well as a species-dependent sensitivity, accounting for the variable sizes, masses, and forms of the ions. Unfortunately, data only for a small set of simple molecules for the cross sections are available in the literature. The same limitations account for the species-dependent sensitivities, which for the DFMS have been studied only for a limited set of the molecules investigated in this work. As we do not consider these factors, we provide the individual abundances in arbitrary units.
For the purpose of inter-comparison, it is most suitable to consider the relative abundances of a specific species rather than the absolute abundances. However, normalization with respect to water, the major cometary volatile, is not suitable for this investigation. The data we used here were collected during a time period where sublimation from ejected dust grains was strongly enhanced in comparison to sublimation from the cometary bulk material, and water is expected to be depleted. The complex organic species seem to be correlated with methanol, see  Rubin et al. (in prep. )  or Fig. 5  in  Läuter et al. (2020). Consequently, we normalized our abundance values to methanol, which itself does have a negligibly small error. The methanol signal stands alone and is fitted well, and it led to a statistical error of less than 1%.
Deconvolution of fragmentation patterns The so-called EII fragmentation pattern of a given molecule is the characteristic fingerprint of signals being registered when the m/z range is scanned while the parent and fragment ions are being collected. The pattern is usually graphed in terms of intensity on the y-axis and m/z on the x-axis. Such mass spectra are collected in reference databases such as that from the National Institute of Standards and Technology (NIST; Steins 2018), which is the most extensive, as far as we know. The electron energy used to acquire NIST standard mass spectra is usually 70 or 75 eV, which is different from the 45 eV used by the DFMS. However, the deviations in the fragmentation patterns resulting from this difference are minor  (Schuhmann et al. 2019a,b) , and the related error is small compared to other sources of uncertainty.
Furthermore, the NIST reference spectra only come in unit-resolution. For certain signals, this results in ambiguities as to whether the O atom is retained in the fragment species (O-bearing fragment) or not (pure hydrocarbon fragment) because both fragment types could, and in some cases, do exist. In the following list, we briefly describe, for the most important O-bearing chemical functional groups, how the lowresolution mass spectra available from NIST are used to interpret our high-resolution data (we use '-' to indicate chemical bonds and '-' to indicate that an atom/group of atoms was lost from the molecular ion): -Carboxylic acids (R-COOH, where R is a residual): Usually, carboxylic acids produce a detectable molecular ion signal (M signal). The acids commonly fragment under loss of the hydroxy group, leading to a prominent M-OH signal.
For each of the selected candidate molecules, we denote the characteristic signals we used for identification and list the estimated abundance relative to methanol (ARM) as a percentage. We discuss possible alternative isomers and assign every identification with a level-of-confidence indicator (LCI) according to the following scheme: 1 = maybe present, 2 = likely present, 3 = present with great certainty. However, our LCIs are based on the available reference data and knowledge on cometary and ISM species. For example, if a specific molecule is the only isomer with reference data in NIST and is in suitable agreement with our data, it will receive an LCI of 3 despite the fact that it is possible that other isomers without mass spectrum in NIST could produce an even better match. After this procedure, some observed intensity remained unexplained (i.
e. , blanks in Figs. 2  and 3 ). This intensity is mostly associated with species with a low number of hydrogen atoms (compared to the fully saturated molecule): C 3 H 2 O, C 4 HO, C 5 H 3 O, C 6 H 5 O, C 2 H 3 O 2 , and C 3 HO 2. Some of those species might be radicals for which no NIST reference data exists. Hänni et al. (2022)  observed a similar situation for the pure hydrocarbon species and provided additional comments. Once the full cometary inventory of complex organics is done, these residual intensities can be investigated systematically.