dataesr/scientific-paragraphs-categorization huggingface dataset:
Neutron reflectometry (NR) Salivary pellicles were investigated by means of Neutron Reflectometry (NR) in order to gain insight into the irreversible thickness change originated by exposure to low ionic strength revealed by QCM-D data. Reflectometry profiles and corresponding fits for salivary pellicles in deuterated solvents are shown in Fig. 4a. Corresponding scattering length density (SLD) profiles from the fits are shown in Fig. 4b. SLD is a parameter that determines how neutrons are scattered and it provides information on the composition of the sample. The results from fitting NR data are often presented as an SLD profile i. e. , a representation of how the SLD of the sample varies along the direction perpendicular to the sample surface. As detailed in Supplementary Material Section S2, fitting NR data on salivary pellicles required a two-layer model, in agreement with  [11].
Acknowledgments The  Swedish Research Council  (Grant No. 2016-06950 ),  Nordforsk  (Grant No. 87794 ) and the  Gustaf Th. Ohlsson Foundation  are acknowledged for financial support. R. B. would like to acknowledge the support of the  Royal Society Industrial Fellowship  (Grant No. SIF\R1\181005 ) and the  Engineering and Physical Sciences Research Council in the UK  (Grant No. EP/R022534/1 ).
1%), CÁ Á ÁH/HÁ Á ÁC (23. 1%), ClÁ Á ÁH/HÁ Á ÁCl (15. 2%), OÁ Á ÁH/HÁ Á ÁO (12. 8%, Fig. 6a) and CÁ Á ÁC (9%, Fig. 6b) contacts. 5. Database surveyA search of the Cambridge Structural Database (CSD version 2020. 3. 0, update of February 2021; Groom et al. , 2016) 674 Bougueria et al. C 16 H 11 ClN 2 O 2 Acta Cryst. (2021). E77, 672-676 research communications.
54 Å and a PILATUS3 detector (Dectris, Switzerland). The experiments were performed with a collimated beam size of 0. 5 Â 0. 5 mm. The sample-to-detector distance was chosen to be 2480 mm to achieve a Q range of 0. 004-0. 25 Å À1. The silica NP solution was injected into capillaries to perform a postsynthesis analysis. Scattering from empty beam, empty capillaries and dark field were measured independently and subtracted from the sample scattering. SASfit software were used to perform the modelling and fitting.
3 ) showed a new peak around À65 ppm which is assigned to T n centers. On these spectra, two resonances were observed at À58 ppm and À66 ppm which are attributed to R-Si(OH)(OSi) 2 (T 2 ) and R-Si-(OSi) 3 (T 3 ) centers respectively, where R represents an alkyl chain. MestReNova software was used to fit the multiple Gaussian distributions of the  29  Si CP/ MAS NMR spectra. Based on Gaussian deconvolution of Q n signal species, the ratio of r(Q) = (Q 2 + Q 3 )/Q 4 contributions decreased in NH 2 -SiO 2 (Table  3 , entries 1 and 2). After grafting of epoxy-MPEO, this ratio remains the same as that of NH 2 -SiO 2 (Table  3 , entry 3) since the grafting of MPEO was performed via the reaction with amine and not with silanols. The apparition of signals of T 2 and T 3 species and the decrease of Q 2 and Q 3 in favor of Q 4 suggest that silanols at the silica surface were consumed in the silanization process with APTES.
Thermal properties of PEO-grafted silica NPs The thermal transition temperatures of PEO-grafted silica were evaluated by DSC measurements. Since the content of PEO in PEO-grafted SiO 2 composites is too low when s o 0. 2, it is difficult to measure properly the thermal transitions of PEO, so the DSC analysis was only performed for samples with s value higher than 0. 2 and DSC data are summarized in Table  4. Due to the low fraction of amorphous phases and small amount of sample used in DSC experiments, neither raw PEO nor PEOgrafted silica samples show a glass transition temperature. All composite samples exhibit a melting peak of the PEO chains attached to silica NPs, which was at a temperature lower than that of the corresponding raw PEO. The thermograms obtained for the melting of PEO in the PEO5-SiO 2 and PEO10-SiO 2 series are given in Fig.
Initial database mining efforts comprised screening a broad range of genes encoding hydrolytic enzymes, including lactonases, esterases, lactamases, and hydrolases, curated from the BRENDA database across 26 enzyme classifications (ECs) (Fig. 4C  and tables S11. 1 and S11. 2)  (32). Over three rounds of genomic mining, a total of 46 genes were tested, of which 17 were active on P6 to P8, converting them to P2 (method S11. 2). The median sequence identity of the total set of tested enzymes is 7. 0%, and the median sequence identity of active enzymes is 13. 0% (Fig. 4C ), highlighting the plasticity of functionality within the esterase family through the diversity of sequences capable of catalyzing this reaction. The most active of these enzymes was 1AUR, which in its native form hydrolyzes longer fatty acid chains but has demonstrated broad specificity toward esters (fig.
Geometric optimization of putative P2 and P5 3:1 complexes with aluminum Optimizations of either P2 or P5 molecules in a 3:1 complex with aluminum were performed, starting from relevant structures of monomers obtained from extensive MD simulations and analysis, as detailed in section S10. 1. These were arranged with a C3 symmetry around Al 3+ and deprotonated at 3′ and 4′, and with Al 3+ coordinated to the oxygens at 3′ and 4′. The two chiral assemblies of the ligands, corresponding to left and right handed (labeled  and , respectively), were tried. Relaxations were then performed in two steps: (i) A preoptimization was run in vacuum with CP2K  (39) , with DZVP-MOLOPT-SR-GTH basis set and GTH-BLYP pseudopotential (40-42) corrected with the D3(0) Grimme dispersion  (43). (ii) Final optimizations were run in Quantum Espresso  (44, 45) , using ultrasoft pseudopotentials and the Perdew-Burke-Ernzerhof (PBE) exchange-correlation functional  (46)  with implicit solvent (Environ module)  (47)  in periodic cubic box of 80 bohr.
MD simulations of P2 and P5 Classical MD simulations of P5 and P2 were run using GROMACS 4. 5. 5  (48)  upon adequate equilibration with all bonds to hydrogen atoms constrained using LINCS  (49). General AMBER force field (GAFF) parameters were assigned using the antechamber module of AmberTools13  (50, 51)  with RESP charges at the HF/6-31G* level, calculated on DFT-B3LYP optimized geometries, 50-/200-Ry basis set. For each species studied, 10 replicas were simulated using Hamiltonian replica-exchange MD (HREMD)  (52)  as implemented in the PLUMED plugin (53) (method S10. 1).
Sequence alignment and construction of the phylogenetic tree of esterases of interest Sequences tested in this study (table  S11. 1) were aligned using Geneious 2017. 10. 1. 3 using multiple sequence comparison by log-expectation (MUSCLE) alignment (method S11. 1. ). The phylogenetic tree in Fig. 4C  was constructed using the Geneious Consensus Tree Builder (method S11. 1) and visualized using GraPhlAn (https://github. com/siegel-lab-ucd/blue-pigment-publication. git) (54).
Acknowledgments: We would like to thank  Mars, Incorporated  for providing direct funding to all the groups involved in a series of grants from 2010 to 2020. We want to acknowledge  C. Johnson ,  A. Laio ,  M. Micciarelli ,  A. Ramsammy ,  M. Rosa , and  J. Krupa  for contributions. We would also like to acknowledge  Kekst CNC  for their support in developing figures.
Funding: We want to acknowledge partial financial support from the EU through the MaX Centre of Excellence for supercomputing applications (project nos. 676598 and 824143). J. B. S. was partially supported by the University of California Davis, Innovation Institute for Food and Health at UC Davis, the U. S. NIH (R01 GM 076324-11), the NSF (award nos. 1827246, 1805510, and 1627539), and the National Institute of Environmental Health Sciences (NIEHS) of the NIH (award no. P42ES004699). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH, NIEHS, NSF, or UC Davis. Author contributions: T. M. C. , J. D. , and R. J. R. conceived the presented idea. S. B. , O. D. , M. G. , T. M. C. , J. D. , R. J. R. , J. B. S. , and K. Y. devised the project, the main conceptual ideas, and proof outline.
Acknowledgements. The authors thank the referee for their constructive report and useful suggestions that have significantly improved the paper. We also thank  P. Galli  for sharing sharing the membership tables for the Chamaeleon sources and  S. van Terwisga  for interesting discussions. This paper makes use of the following ALMA data: ADS/JAO. ALMA# 2013. 1. 00708. S. ALMA is a partnership of  ESO  (representing its member states),  NSF (USA) , and  NINS (Japan) , together with  NRC (Canada) ,  NSC  and  ASIAA (Taiwan) , and  KASI (Republic of Korea) , in cooperation with the  Republic of Chile. The Joint ALMA Observatory is operated by  ESO ,  AUI/NRAO , and  NAOJ. The  National Radio Astronomy Observatory  is a facility of the National Science Foundation operated under cooperative agreement by  Associated Universities, Inc.
MV, FM, MB, and GvdP acknowledge funding from  ANR of France  under contract number  ANR-16-CE31-0013  (Planet Forming Disks). MV research was supported by an appointment to the  NASA   Postdoctoral Program  at the  NASA Jet Propulsion Laboratory , administered by  Universities Space Research Association  under contract with  NASA. CC acknowledges support from  DGI-UNAB  project  DI-11-19/R  and  ANID -Millennium Science Initiative Program -NCN19_171. LC acknowledges support from  ANID FONDECYT  grant  1211656. CP acknowledges funding from the  Australian Research Council  via  FT170100040  and  DP180104235. J. P. W. acknowledges support from  NSF  grant  AST-1907486.
The profile fitting requires the best fit simulation affording nearly-perfect difference plot as shown in Figure  1. The results reveal that all synthesized materials exhibit values of lattice parameters close to those calculated from reference molecular structure from the database. It means that the original lattice structure is well-preserved, indicating that the formation of Mg-dobdc was not ruptured by the carbon doping agents. Considering average crystal sizes, the Mg-MOF-74 composites appear with larger average crystallites than the pristine material suggesting that the carbonaceous doping agent does influence the crystal growth process. Although both Mg-MOF-74 composites present slightly higher micro strain in comparison to the pristine material, the deviations observed are not really significant to definitely conclude.
shows XRD patterns of all synthesized materials. The pristine Mg-MOF-74 and both composites exhibit sharp characteristic peaks at 6. 9°, 11. 9°, 13. 7°, 15. 3°, 16. 8°, 18. 1°, 19. 4°, 20. 5°, 21. 7°, 23. 8°, 24. 7°, 25. 7°, 26. 6°, 27. 5°, 28. 4°, 29. 2°, 30. 0°, 30. 8°, 31. 6•, 33. 8° and 34. 5° being in a perfect agreement with those calculated from reference structure available in crystallographic database (Cambridge Crystallographic Data Centre (CCDC), FIJDOS 265095).
Acknowledgment: The authors thank the  Carbon Dioxide Research Center, Department of Chemical Engineering, Universiti Teknologi PETRONAS  and the  Department of Energy Systems and Environment (GEPEA laboratory), IMT Atlantique , for their support in this collaborative work. The funds granted by  University Research Internal Funding  (Grant No. URIF 0153 AA-B90 ),  Yayasan UTP  (Grant No. YUTP 0153AA-H42 ),  Prototype Research Grant Scheme  (Grant No. PRGS/1/2017/TK02/UTP/02/1 ) and  IMT Atlantique  (half-PhD scholarship).
The proteomic analysis of hairs, yarns or textiles has emerged as a powerful method to determine species of origin, mainly used in archaeozoological research and fraud control. Differentiation between the South American Camelid (SAC) species (the wild guanaco and vicuña and their respective domesticates the llama and alpaca) is particularly challenging due to poor database information and significant hybridization between species. In this study, we analysed 41 modern and 4 archaeological samples from the four SACs species. Despite strong similarities with Old World Camelidae, we identified 7 peptides specific to SACs assigned to keratin K86 and the keratin-associated proteins KAP13-1 and KAP11-1. Untargeted multivariate analysis of the LC-MS data permitted to distinguish SAC species and propose discriminant features.
MS/MS-based molecular networking combined with database-assisted de novo sequencing permitted to identify 5 new taxonomic peptides assigned to K33a, K81 and/or K83 keratins and KAP19-1. These peptides differentiate the two wild species, guanaco and vicuña. These results show the value of combining database search and untargeted metabolomic approaches for paleoproteomics, and reveal for the first time the potential of molecular networks to highlight deamidation related to diagenesis and.
Keratins are classified into two families: type I (K31 to K40, 40-55 kDa, acidic), and type II (K81 to K87, 55-70 kDa, basic or neutral). The important similarities in sequences observed for keratins within a same type make it difficult to assign a peptide to a specific protein. A second challenge arises from the absence of sequenced genomes for uncommon species (either wild or domestic), which limits identification based on database search to cross-species proteomics  [54]. This is particularly true for SAC fibres, as the NCBI database contains only sequences from one SAC (Tribe Lamini), the alpaca (V. pacos). It also contains sequences from the three Old World camelid species (Tribe Camelini): the Bactrian camel (Camelus bactrianus), the wild Bactrian camel (Camelus ferus) and the dromedary (Camelus dromaderus).
Analysis of the digests by mass spectrometry The trypsic digests were analysed by ultra-high-performance liquid chromatographymass spectrometry (UHPLC-MS) on an Ultimate 3000-RSLC system (Thermo Scientific) connected to an electrospray ionization -quadrupole-time of flight (ESI-Q-TOF) instrument (Maxis II ETD, Bruker Daltonics) following the conditions described in  [44]. Briefly, the separation was achieved using a RSLC Polar Advantage II Acclaim column (2. 2 μm, 120 Å, 2. 1 × 100 mm, Thermo Scientific) with a linear gradient of mobile phase A (milliQ water + FA 0. 1%) and B (LC-MS grade CH3CN + FA 0. 08%), at 300 μL/min. The MS detection was carried out in positive mode in the range m/z 100-2000. Analyses were performed using collisioninduced dissociation in data dependent auto-MS/MS mode.
Data analysis The LC-MS/MS data were analysed and converted to mgf files using Data Analysis 4. PTM and SPIDER algorithms) were performed against the restricted Camelidae NCBI protein database, using the same search parameters as in Mascot. The de novo scores were set to 1% FDR. We used the last proposed nomenclatures for keratins  [63]  and KAPs  [64]. For keratins, the correspondence with the previous classifications is provided in  [44]. A list of diagnostic peptides potentially distinguishing between Camelidae species was made based on the literature, alignments of the available sequences, Mascot and PEAKS results. The peptides were selected based on specificity (checked by peptide alignments with blastp, https://blast. ncbi. nlm. nih. gov/Blast. cgi), length (longer than 6 amino acids) and scores.
Targeted taxonomic peptide search First, we examined the best protein hits obtained for each of the 45 SAC samples through database search (supporting information Table  S1 , Figures  S1-S3 ). The best sequence coverage was found for type I keratins (best hit for K33a with 41 to 74% coverage), then type II keratins (best hit K83, with 25 to 69 % coverage) and finally KAPs (best hit for KAP13-1 with 10 to 51 % coverage). The best-hit taxonomy was always Camelus sp. for K33a, Camelus sp. or V. pacos for K83 and always V. pacos for KAP13-1, which only provided unambiguous identification at the family level. The available sequence referenced for K33a from V. pacos (XP_015107542. 1) is truncated (see alignment provided as supporting information Figure  S4 ), which precludes its identification in the alpaca samples.
In addition, error-tolerant search permitted to propose a diagnostic peptide sequence carrying a Cys to Trp substitution in KAP11-1, TYQQSWVSSCR, detected in all but one of the vicuña samples and in two alpaca samples. A total of 7 diagnostic peptides were identified based on database search (Table  2 , supporting information Table  S4 ). Their specificity was proposed based on distribution within the samples.
Untargeted taxonomic peptide search The peptide markers found in the targeted approach are good indicators that individual species-specific markers may exist. In order to identify the signals involved in the group segregation, we performed an untargeted search and analysed the global proteomic signature of a set of representative samples from each SAC species (30 samples in total). The LC-MS data were converted to a multivariate matrix containing intensities of the features detected (3062 variables), and multivariate analysis was performed to assess the structure of the dataset and highlight the features discriminating among SAC species. The first two components of the PCA explained 38 and 25% of variance, respectively (Fig. 2A ). The score plot showed mainly a segregation of archaeological versus modern samples, visible on the second component.
This analysis permitted generation of a list of features of taxonomic interest (supporting information Table  S5 ). Two main types of differences were observed: (i) between modern and archaeological samples and (ii) related to the taxonomy. To help in the feature assignment, we generated a molecular network, which clusters ions based on the similarities in their MS/MS spectra  [57, 58]  (Fig. 3 ). The ions of interest were annotated through database-assisted de novo sequencing. This approach was useful to identify features selectively detected in the archaeological samples, which consisted in highly-deamidated peptides (Fig. 3A-B ). In addition, it permitted to highlight important sequence similarities between keratins, with peptides from type I or type II keratins forming clusters (Fig. 3 A-C ).
Most importantly, molecular networking permitted to cluster sequences showing high similarities and differentiating between species (Fig 3. D-F). As an example, the [M+3H] 3+ ions at m/z 660. 96 and 666. 29 appeared as interesting diagnostic peptides to differentiate vicuña from the other SACs (Fig 3. D ). Automatic database-assisted de novo sequencing did not provide reliable sequences. However, the similarities in the MS/MS spectra revealed by the network permitted manual de novo sequencing, providing two sequences varying by one amino acid, attributed to KAP19-1 (Fig. 4 ). A substitution (Asp to Asn) was also highlighted in the peptide DSLENTLTETEAR from K31 (Fig 3. E ). We represented the distribution per species in the molecular network nodes based on detection of the ions in the LC-MS data (intensity above a threshold corresponding to background signal) and not based on occurrence of MS/MS data.
This permitted to revise visually the distribution of diagnostic peptides proposed from database search (Table  2 ). For example, TYQQSWVSSCR from KAP11-1 ([M+2H] 2+ at m/z 701. 31), proposed as vicuña and alpaca diagnostic peptide, was confirmed to be specific to these two species, but also appeared in sample L1, corresponding to the llama hybrid (Fig. 3F ). This peptide clustered with a [M+2H] 2+ ion at m/z 739. 79, also specific to vicuña and alpaca, assigned to the partial sequence TYQQSA by de novo sequencing. Combining untargeted analysis, molecular networking and identification through database-assisted de novo sequencing permitted proposal of 5 new diagnostic peptides (Table  2 ). In addition to TYQQSWVSSCR from KAP11-1, 4 peptides permitted differentiation between the two wild species, guanaco and vicuña.
Combining targeted and untargeted approaches in paleoproteomics The species-specific identification of SAC hair keratin proved challenging. Databasesearch approaches, which are usually used in paleoproteomics  [45] , permitted identification of diagnostic peptides informative at the tribe level, i. e. , differentiating Camelini and Lamini but failed to differentiate within Lamini. This illustrates the difficulty of proteomic-based identification at the species level when information in the databases is missing or partial. The sequence information available for keratins and KAPs is particularly poor for SAC, as compared to other clades (supporting information Table  S6 ). In this context, the use of untargeted approaches is relevant because it permitted us to highlight discriminant signals, some of which could be subsequently identified by de novo sequencing.
This circumvents the difficulty of interpreting multivariate data analysis of samples with different degrees of alteration. In addition, this method permits clustering of peptides with high sequence similarities. The clustered peptides can be highly similar fragments from different keratin chains or result from amino acid substitutions related to intra-or interspecific polymorphism (Fig. 3 ). As for natural product discovery, molecular networking applied to bottom-up proteomics is very useful to identify ions of interest. We were able to perform manual de novo sequencing based on similarities in different MS/MS spectra highlighted by the molecular network (Fig. 4 ). Using multiple MS/MS data from highly similar peptides allows de novo sequencing to be greatly simplified through matching of the b-and y-series between spectra.
Conclusion Hair fibre palaeoproteomics is still a developing research field and presents difficulties compared to that of bone. In particular, the multiple homologous keratin sequences make protein inference difficult, and for the SAC, poor sequence information available in databases and important hybridization between species further complicate interpretation. Here, a combination of different approaches including database search, multivariate data analysis, molecular networking and database-assisted de novo sequencing of modern and archaeological samples allowed us to circumvent some of these issues and provide insights for future research. Our study is the first to examine all four SAC species at the same time. It provides a bottom-up proteomic signature of the four species as well as new diagnostic peptides for the.
Diagnostic peptides proposed for the proteomics identification of Camelidae fibres and % occurrence per SAC species (A: alpaca, n = 25; L: llama, n = 6; V: vicuña, n = 7; G: guanaco, n = 6). Peptides in black are proposed from database search. The number of observations is based on the presence of MS/MS spectra for each sample (supporting information TableS4). Peptides in blue are proposed from untargeted approaches and molecular networking combined with de novo sequencing (see section 3. 2). Their occurrence is based on peak detection in the LC-MS data (supporting information TableS5). Correspondence between the two modes is shown for peptide TYQQSWVSSCR from KAP11-1. *These sequences can be used to differentiate between Camelidae but are shared with other mammals.
Acknowledgments We are grateful to all the persons who helped in the hair collection:  Norin Chai  and his team at the  Ménagerie du Jardin des plantes ,  Alexis Lécu ,  Nathanie Dogna  and their colleagues at the  Parc Zoologique de Paris ,  Baptiste Chenet  and his team at the zoo of Montpellier,  Isabel Flores  ( Directora Huaca Pucllana project ),  Christophe Moulherat  ( Musée du Quai Branly ) and  Pr. Mike Bruford  ( Cardiff University ). We thank the  Analytical Platform of the Muséum national d'Histoire naturelle (Paris, France)  for access to the mass spectrometry facilities. We.
Funding This work was supported by a grant from  Agence Nationale de la Recherche  under the LabEx  ANR-10-LABX-0003-BCDiv , in the program " Investissements d'avenir "  n\u0001 ANR-11-IDEX-0004-02  and an  Action transversale du Muséum  grant (ATM blanche, project  DCAPG -2016 ) from the  MNHN. The  Alpaca Genetic Diversity  project was supported by the  Cloud Hollow Educational Fund ,  Maine Community Foundation , and research on the El Yaral mummies by  US National Science Foundation  grant  9321377.