dataesr/scientific-paragraphs-categorization huggingface dataset:
The data acquisition protocol, parameters and treatment were those described in  [22]. This exploratory study applied the steps of the chemometric workflow from the detection of features to the calculation and selection of the most discriminant ratios between pairs of feature areas. This chemometric approach being based on the calculation of similarity measurements between pairs of samples sharing a common source or not, it was deemed adapted to tackle the question of the source inference of altered gasoline samples. ROC analysis was used to evaluate the separation between pairs of samples sharing a common source from those which did not. This separation was optimized using the Area Under the ROC curve (AUROC) to select discriminant ratios. In this work, following the definition of Besson  [14] , each unique gasoline sample was an aliquot of what is considered here to be a source, i.
Sample analysis The analysis of the samples adsorbed onto the Tenax TA tubes was performed by ATD on a PerkinElmer TurboMatrix 650 apparatus followed by a PerkinElmer Clarus 680 Gas Chromatograph (GC) coupled to a PerkinElmer Clarus SQ 8 T Mass Spectrometer (MS). Data acquisition was done in full-scan mode (30-450 amu) on a simple quadrupole after separation on a Restek Rxi-1MS Crossbond 100% dimethylsiloxane (60m x 250µm x 0. 5µm) GC column. All the ATD-GC-MS acquisition parameters used in this study were those given in  [22]. A total of 252 samples were analyzed after alteration by evaporation and combustion (36 samples at 0%, plus 72 samples for each alteration degree at 50, 90 and 99% for both evaporation and combustion). The features detected and integrated with XCMS were filtered. The filtration step consisted in using the QCs and the blanks to clean the data.
Cependant, en tant qu'aménité, le paysage est susceptible de voir sa valeur (monétaire exclusivement) exprimée par les méthodes de l'économie standard de l'environnement qui cherchent à déterminer un prix fictif pour les biens ne transitant pas par le marché. Parmi toutes ces méthodes, les méthodes des prix hédonistes et des choix multi-attributs se révèlent les plus applicables au paysage, surtout si l'on considère le paysage « ordinaire », et pas seulement les grands sites ou les paysages particulièrement remarquables, pour lesquels la méthode des coûts de transport ou la méthode d'évaluation contingente peuvent convenir. Pour autant, la méthode des prix hédonistes, bien qu'elle permette de mettre en évidence la façon dont les modifications apportées aux paysages « ordinaires » se reflètent dans les prix des actifs immobiliers, repose sur des bases de données de l'immobilier souvent peu accessibles aux chercheurs.
The output from the state estimation is an estimate of the current network state, OD trips, and behavioral parameters governing travelers' choices, including Tripod users' responses to tokens. Within the state estimation, the extended demand and supply simulators interact to produce estimated traffic conditions. In the supply simulator, vehicle trajectories from the supply simulation are fed to the energy estimation module to produce energy consumption estimates. In the demand simulator, SUO receives trip requests from simulated Tripod users and produces personalized menus to simulate users' response. SUO allocates tokens based on TEE generated in the previous roll period. The optimization module is then triggered with the estimated network state as an input. DynaMIT predicts traffic and energy conditions for the future prediction horizon, by making the supply and demand simulators interact for different candidate values of TEE.
Step 2 yields an updated list of travelers, which are aggregated in Step 3 back into mode-wise OD matrices in relation to traveler trips. For the private vehicle modes, the ODs in relation to traveler trips are converted to vehicle trips using an average occupancy. The fourth step is OD estimation utilizing the most recent surveillance data from the network. This involves adjusting or estimating OD demands so as to minimize the difference between simulated and observed traffic counts. The OD estimation module makes use of the supply simulator discussed in the next section and results in the estimated number of private vehicle ODs (vehicle trips). These are then used to compute estimates of mode-wise private vehicle ODs (vehicle trips) based on historical modal splits which, in combination with the historical transit ODs, yield the estimated mode-wise OD demands in traveler trips.
There are two main actors: (1) traveler and (2) vehicle. The bus controller operates the fleet of buses on the network (this could involve fleets of multiple operators). It obtains from a database a list of bus lines with the related stops and frequencies/headways. The existing vehicle movement models are adapted to capture appropriately the dwelling of buses at stops and their impact on the traffic stream. Since DynaMIT naturally models spillback effects and congestion through a queuing part at the downstream end of each segment, all segments containing a bus stop are split at the location of the stop to capture the queuing caused by bus dwelling. The movement of buses can be split into two parts: (i) movement between bus stops and (ii) movement into and out of a bus stop. The movement between stops is similar to that of cars: that is, the buses are moved using the speed-density model and the queuing model.
ES(e(t)) = X N n = 1 f (v n e , u n ) À X N n = 1 f (v n 0 , u n ), ð4Þ where v n e , v n 0 are the user trajectories that result from providing tokens based on token efficiency e(t) and with no tokens, respectively, u n are the travel mode parameters (e. g. , car design parameters, bus type, driving style, etc. ), N is the number of travelers, and f () is the function that computes energy consumption for each user trajectory. SO loop: The objective of the SO loop is to determine the optimal TEE for the H roll periods within the prediction horizon. For each given TEE value, the SUO module determines the menu of travel alternatives (with tokens) offered to Tripod users on the network; SP updates the demand and calculates the new network states; the TripEnergy module evaluates the energy savings relative to the no-incentive base case.
Online Optimization The goal of SO is to find the sequence e(t) of TEE values for the next prediction horizon that minimizes the energy consumption of the entire transportation system under the token budget constraint. The optimization is performed online and adapts to the evolution of network state. This implies that it does not compute e(t) just when SO is launched, but computes it continuously over time, appending new values to it each time. SO is implemented running M instances of DynaMIT in parallel, all controlled by a coordinator, whose role is to (i) synchronize the instances and ensure they work in real time, (ii) pass the information they need, (iii) orchestrate their operations, and (iv) decide at each roll period t what will be the next TEE e(t + D). The value e(t + D) will be communicated to the SUO module and will be used to compute the incentives proposed to the real users during the interval ½t + D, t + 2D.
1 There were no incomplete data for the 476 participants that comprised the final sample for this study. Table  2  provides the sample details. 1 The results do not change with the inclusion of participants who failed the attention check. We first developed the questionnaire in English using established scales from prior literature (see Table  3 ). Animosity beliefs, anger, fear, negative word of mouth, product avoidance, product quality and consumer ethnocentrism were all measured based on  Harmeling et al. (2015). Contempt was measured based on  Romani et al. (2013)  and disgust based on van  Overveld, De Jong, and Peters (2006). All items were measured on a seven-point scale. We then had it translated into Chinese and back-translated into English. We included consumer ethnocentrism, gender, and age as control variables, following common practice in extant research (e.
Acknowledgments This paper is an outcome from the 4th  International Workshop ''Bridging the Gap between Ocean Acidification Impacts and Economic Valuation -From Science  to Solutions: Ocean acidification on ecosystem services, case studies on coral reefs'' held in Monaco from October 15 to 17, The authors are particularly grateful to the workshop organizers, including the  Government of Monaco , the  Prince Albert II Foundation , the  IAEA Ocean Acidification International Coordination Center (OA-ICC) , the  French Ministry for the Ecological and Solidary Transition , the  Oceanographic Institute -Prince Albert I of Monaco Foundation , the  Monegasque Water Company  and the  Monegasque Association on Ocean Acidification (AMAO)  and the  Centre Scientifique de Monaco (CSM).
2b , Supplementary Information). Oxidative degradation of histidine (H) and conversion of arginine (R) leading to ornithine accumulation were also observed (Supplementary Information). These modifications are absent, or much less frequent, in the control sample. Similarly, unlike in the control, the peptide length distribution in the Dmanisi dataset is dominated by shorter fragments, generated by advanced, diagenetically-induced, terminal hydrolysis  23  (Fig. 2c, d ). Together all these independent lines of evidence clearly define the substantial biomolecular damage affecting the proteomes retrieved and independently support the authenticity of the sequences reconstructed. To demonstrate beyond reasonable doubt the correct peptide sequence assignments of our MS2 spectra, we performed manual validation of peptidespectrum-matches, conducted fragment ion intensity predictions, and generated synthetic peptides, for a range of phylogenetically informative and phosphorylated peptides (Methods and Supplementary Information: Key MS2 Spectra).
Dmanisi & sample selection Dmanisi is located about 65 km southwest of the capital city of Tbilisi in the Kvemo Kartli region of Georgia, at an elevation of 910 meters above sea level (Lat: 41° 20' N, Lon: 44° 20' E)  10, 18. The 23 fossil specimens we analysed were retrieved from stratum B1, in excavation blocks M17, M6, block 2, and area R11 (Extended Data Table  1 , Extended Data Fig. 1 ). Stratum B deposits date between 1. 78 Ma and 1. 76 Ma  19. All the analysed specimens were collected between 1984 and 2014 and their taxonomic identification was based on traditional comparative anatomy. After the sample preparation and data acquisition for all the Dmanisi specimens was concluded, we applied the whole experimental procedure to a medieval ovicaprine (sheep/ goat) dental enamel+dentine specimen that was used as control.
For this sample, we used extraction protocol "C", and generated tandem MS data using a Q Exactive HF mass spectrometer (Thermo Fisher Scientific). The data were searched against the goat proteome, downloaded from the NCBI Reference Sequence Database (RefSeq) archive on 31 st May 2017 (Supplementary Information). The ovicaprine specimen was found at the "Hotel Skandinavia" site in the city of Århus, Denmark and stored at the Natural History Museum of Denmark, Copenhagen.
Biomolecular preservation We assessed the potential of ancient protein preservation prior to proteomic analysis by measuring the extent of amino acid racemisation in a subset of samples (6/23)  36. Enamel chips, with all dentine removed, were powdered, and two subsamples per specimen were subject to analysis of their free (FAA) and total hydrolysable (THAA) amino acid fractions. Samples were analysed in duplicate by RP-HPLC, with standards and blanks run alongside each one of them (Supplementary Information). The D/L values of aspartic acid/asparagine, glutamic acid/glutamine, phenylalanine and alanine (D/L Asx, Glx, Phe, Ala) were assessed (Extended Data Fig. 4 ) to provide an overall estimate of intra-crystalline protein decomposition (IcPD).
Data analysis Raw data files generated during MS/MS spectral acquisition were searched using MaxQuant  43  , version 1. 5. 3. 30, and PEAKS 44 , version 7. 5. A two-stage peptide-spectrum matching approach was adopted (Supplementary Information §5. 3). Raw files were initially searched against a target/reverse database of collagen and enamel proteins retrieved from the UniProt and NCBI Reference Sequence Database (RefSeq) archives  45, 46  , taxonomically restricted to mammalian species. A database of partial "COL1A1" and "COL1A2" sequences from cervid species  47  was also included. The results from the preliminary analysis were used for a first, provisional reconstruction of protein sequences (MaxQuant search 1, MQ1). For specimens whose dataset resulted in a narrower, though not fully resolved, initial taxonomic placement, a second MaxQuant search (MQ2) was performed using a new protein database taxonomically restricted to the "order" taxonomic rank as determined after MQ1.
For the MQ2 matching of the MS/MS spectra from specimen Dm. 5/157-16635, partial sequences of serum albumin and enamel proteins from Sumatran (Dicerorhinus sumatrensis), Javan (Rhinoceros sondaicus), Indian (Rhinoceros unicornis), woolly (Coelodonta antiquitatis), Mercks (Stephanorhinus kirchbergensis), and Black rhinoceros (Diceros bicornis), were also added to the protein database. All the protein sequences from these species were reconstructed from draft genomes for each species (Dalen and Gilbert, unpublished data, Supplementary Information). For each MaxQuant and PEAKS search, enzymatic digestion was set to "unspecific" and the following variable modifications were included: oxidation (M), deamidation (NQ), N-term Pyro-Glu (Q), N-term Pyro-Glu (E), hydroxylation (P), phosphorylation (S).
The error tolerance was set to 5 ppm for the precursor and to 20 ppm, or 0. 05 Da, for the fragment ions in MaxQuant and PEAKS respectively. For searches of data generated from sample fractions partially or exclusively digested with trypsin, another MaxQuant and PEAKS search was conducted using the "enzyme" parameter set to "Trypsin/P". Carbamidomethylation (C) was set: (i) as a fixed modification, for searches of data generated from sets of sample fractions exclusively digested with trypsin, or (ii) as a variable modification, for searches of data generated from sets of sample fractions partially digested with trypsin. For searches of data generated exclusively from undigested sample fractions, carbamidomethylation (C) was not included as a modification, neither fixed nor variable. The datasets re-analysed with MQ2 search, were also processed with the PEAKS software using the entire workflow (PEAKS de novo to PEAKS SPIDER) in order to detect hitherto unreported single amino acid polymorphisms (SAPs).
Ancient protein sequence reconstruction The peptide sequences confidently identified by the MQ1, MQ2, MQ3 were aligned using the software Geneious 48 (v. 5. 4. 4, substitution matrix BLOSUM62). The peptide sequences confidently identified by the PEAKS searches were aligned using an in-house R-script. A consensus sequence for each protein from each specimen was generated in FASTA format, without filtering on depth of coverage. Amino acid positions that were not confidently reconstructed were replaced by an "X". Novel SAPs discovered through PEAKS were only accepted if these were further validated by repeating the MaxQuant search (MQ3). All isoleucine were converted into leucines, as standard MS/MS cannot differentiate between these two isobaric amino acids. For possible deamidated sites, we checked whether there were positions in our reference sequence database where both Q and E or both N and D occurred on the same position, and where we also had ancient sequences matching.
Post translational modifications Deamidation-After removal of likely contaminants, the extent of glutamine and asparagine deamidation was estimated for individual specimens, by using the MaxQuant output files as previously published  41  (Supplementary Information). Other Spontaneous Chemical Modifications-Spontaneous post-translational modifications (PTMs) associated with chemical protein damage were searched using the PEAKS PTM tool and the dependent peptides search mode  50  in MaxQuant. In the PEAKS PTM search, all modifications in the Unimod database were considered. The mass error was set to 5. 0 ppm and 0. 5 Da for precursor and fragment, respectively. For PEAKS, the de novo ALC score was set to a threshold of 15 % and the peptide hit threshold to 30. The results were filtered by an FDR of 5 %, de novo ALC score of 50 %, and a protein hit threshold of ≥ 20.
Phylogenetic Analysis Reference datasets We assembled a reference dataset consisting of publicly available protein sequences from representative ungulate species belonging to the following families: Equidae, Rhinocerotidae, Suidae and Bovidae (Supplementary Information §7 and §8). As Cervidae and carnivores are absent from protein sequence databases to a various extent, we did not attempt phylogenetic placement of samples from these taxa. Instead, we conducted our phylogenetic analysis on the five best-performing enamel proteomes (Dm. 5/154. 2 ). We extended this dataset with the protein sequences from extinct and extant rhinoceros species including: the woolly rhinoceros ( †Coelodonta antiquitatis), the Merck's rhinoceros ( †Stephanorhinus kirchbergensis), the Sumatran rhinoceros (Dicerorhinus sumatrensis), the Javan rhinoceros (Rhinoceros sondaicus), the Indian rhinoceros (Rhinoceros unicornis), and the Black rhinoceros (Diceros bicornis).
Their corresponding protein sequences were obtained following translation of high-throughput DNA sequencing data, after filtering reads with mapping quality lower than 30 and nucleotides with base quality lower than 20, and calling the majority rule consensus sequence using ANGSD  52  For the woolly and Merck's rhinoceroses we excluded the first and last five nucleotides of each DNA fragment in order to minimize the effect of post-mortem ancient DNA damage  53. Each consensus sequence was formatted as a separate blast nucleotide database. We then performed a tblastn  54  alignment using the corresponding white rhinoceros sequence as a query, favouring ungapped alignments in order to recover translated and spliced protein sequences. Resulting alignments were processed using ProSplign algorithm from the NCBI Eukaryotic Genome Annotation Pipeline  55  to recover the spliced alignments and translated protein sequences.
Construction of phylogenetic trees For each specimen, multiple sequence alignments for each protein were built using MAFFT  56  and concatenated onto a single alignment per specimen. These were inspected visually to correct obvious alignment mistakes, and all the isoleucine residues were substituted with leucine ones to account for indistinguishable isobaric amino acids at the positions where the ancient protein carried one of such amino acids. Based on these alignments, we inferred the phylogenetic relationship between the ancient samples and the species included in the reference dataset by using three approaches: distance-based neighbour-joining, maximum likelihood and Bayesian phylogenetic inference (Supplementary Information). Neighbour-joining trees were built using the phangorn 57 R package, restricting to sites covered in the ancient samples.
The libraries generated from specimen 16859 and 16635 were processed on different flow cells. They were pooled with others for sequencing on an Illumina 2000 platform (MA399_L1, MA399_L2), using 100bp single read chemistry, and on an Illumina 2500 platform (MA2481_L1), using 81bp single read chemistry. The data were base-called using the Illumina software CASAVA 1. 8. 2 and sequences were demultiplexed with a requirement of a full match of the six nucleotide indexes that were used. Raw reads were processed using the PALEOMIX pipeline following published guidelines 62 , mapping against the cow nuclear genome (Bos taurus 4. 6. 1, accession GCA_000003205. 4), the cow mitochondrial genome (Bos taurus), the red deer mitochondrial genome (Cervus elaphus, accession AB245427. 2), and the human nuclear genome (GRCh37/hg19), using BWA backtrack 63 v0.
Sample Dm. 5/157-16635 Morphological Measurements We followed the methodology introduced by Guérin  32. The maximal length of the tooth is measured with a digital calliper at the lingual side of the tooth and parallel to the occlusal surface. All measurements are given in mm (Supplementary Information §3). Peptides specific to amelogenin X (AMELX) isoforms 1 and 2 appear in the upper and lower parts of the figure, respectively. No amelogenin X isoform 2 is currently reported in public databases for the Cervidae group. Accordingly, the amelogenin X isoform 2-specific peptides were identified by MaxQuant spectral matching against bovine (Bos Taurus) amelogenin X isoform 2 (UniProt accession number P02817-2). Amelogenin X isoform 2, also known as leucine-rich amelogenin peptide (LRAP), is a naturally occurring amelogenin X isoform from the translation product of an alternatively spliced transcript.
Phosphorylation was independently observed in all three separate analyses of Dm. 5/157-16635, including multiple spectra and peptides (see Extended Data Fig. 2 ). Extended Data Figure  6. Phylogenetic relationships between the comparative reference dataset and specimen Dm. bXI-16857. Consensus tree from Bayesian inference. The posterior probability of each bipartition is shown as a percentage to the left of each node. Extended Data Table  1  Genome and proteome survival in 23 Dmanisi fossil fauna specimens. For each specimen, the Centre for GeoGenetics (CGG) reference number and the Georgian National Museum (GNM) specimen field number are reported. *or the narrowest possible taxonomic identification achievable using comparative anatomy methods. †Only collagens survive. B = Bone, D = Dentine, E = Enamel.
Acknowledgments EC and FW are supported by the  VILLUM FONDEN  (grant number  17649 ) and by the  European Commission  through a  Marie Skłodowska Curie (MSC) Individual Fellowship  (grant number  795569 ). EW is supported by the  Lundbeck Foundation , the  Danish National Research Foundation , the  Carlsberg Foundation ,  KU2016 , and the  Wellcome Trust. EC, CK, JVO, PR and DS are supported by the  European Commission  through the  MSC European Training Network "TEMPERA " (grant number  722606 ). MM and RRJ-C are supported by the  University of Copenhagen   KU2016  ( UCPH Excellence Programme ) grant. MM is also supported by the  Danish National Research Foundation  award PROTEIOS (DNRF128). Work at the  Novo Nordisk Foundation Center for Protein Research  is funded in part by a donation from the  Novo Nordisk Foundation  (grant number  NNF14CC0001 ).
MRD is supported by a PhD DTA studentship from  NERC  and the  Natural History Museum  ( NE/K500987/1 & NE/ L501761/1 ). KP is supported by the  Leverhulme Trust  ( PLP -2012-116 ). LR and LP are supported by the  Italian Ministry for Foreign Affairs (MAECI, DGSP-VI ). LP was also supported by the  EU- SYNTHESYS  project ( AT-TAF-2550 ,  DE-TAF-3049 ,  GB-TAF-2825 ,  HU-TAF-3593 ,  ES-TAF-2997 ) funded by the  European Commission. LD is supported by the  Swedish Research Council  (grant number  2017-04647 ) and  FORMAS  (grant nr  2015-676 ). MTPG is supported by  ERC   Consolidator Grant  " EXTINCTION GENOMICS " (grant number  681396 ). LO is supported by the  ERC   Consolidator Grant "PEGASUS" (grant agreement  No  681605 ). BS, JK and PDH are supported by the  Gordon and Betty Moore foundation.
Data Availability All the mass spectrometry proteomics data have been deposited in the ProteomeXchange Consortium (http://proteomecentral. proteomexchange. org) via the PRIDE partner repository with the data set identifier PXD011008. Genomic BAM files used for Rhinocerotidae protein sequence translation and protein sequence alignments used for phylogenetic reconstruction are available on Figshare (doi: 10. 6084/m9. figshare. 7212746).