dataesr/scientific-paragraphs-categorization huggingface dataset:
This information is reflected in a SAS data set current_data. With a log-linear model with 2 parameters and exponential priors with variance set at 1 (dist = 0, nb_param = 2, prior_dist = E/E and val_param = 1/1), to find the dose to give to a patient assigned after the start-up we run: During the execution of this program, this message below is shown on the log window: Conducting a CRM trial -start -up The table of posterior mean of toxicity probabilities is then produced. The dose combination at the first level of agent B with the estimated toxicity probability closest to the target is (5,1) (AgentA = 5, AgentB = 1) with the estimate of 0. 18. This dose combination is the starting combination for the two-dimensional CRM design. A cohort of three patients is then treated at the combination (5,1) and 2 toxicities are observed.
The current_data data set is updated by adding three new observations with the variables si = 5, tj = 1, y = 0 for the first patient, si = 5, tj = 1, y = 1 for the second patient and si = 5, tj = 1, y = 1 for the third patient (without any particular order). The user needs to update the SAS data set current_data after inclusion of each cohort. Note that the information containing the maximum tolerated dose combination which varies during the trial is managed by two global macro variables (current_si and current_tj) and is transparent for the user. To identify the recommended dose combination for the next cohort based on data from 23 patients, 20 accrued in the start-up and 3 after the startup, we execute the SAS macro CRM2DIM with start_up = 0. The SAS code is: The following message appears in the log window: Conducting a CRM trial -two dimensional design The table of posterior mean of toxicity probabilities is produced in the output.
The output indicates that the next recommended combination is (4,2). This process continues until the total sample size of 35 is reached. Suppose we observe the following sequence of maximum tolerated dose combinations after the startup (5,1),(4,2),(4,2),(3,3) and (4,3) with corresponding toxicity vectors (0,1,1), (0,0,1), (0,0,0), (0,0,0) and (1,1,1) for cohort of 3 patients (Figure  2 ). After updating the SAS data set current_data from the start-up (n = 20 patients) to the last cohort of patients in the two-dimensional CRM, i. e. (si, t j, y), we execute the same code than that executed during the twodimensional CRM. When the total number of patients is reached, this execution gives automatically the recommended dose combination for each dose level of second agent. The following message appears in the log window Combinations (4,1),(4,2) and (1,3) are recommended as the maximum tolerated dose combinations with the estimated probabilities of toxicity equal to 0.
Discussion This article presents a new SAS macro, CRM2DIM, for implementing and simulating the two-dimensional Bayesian CRM for phase I dose-finding trials with two agents proposed by  Wang and Ivanova [11]. The SAS macro CRM2DIM offers several options such as a startup, several dose-toxicity models with or without incorporation of historical data by using the simplest form of the power prior approach. As this macro is based on the use of the SAS MCMC procedure for Bayesian analysis, this macro benefits from the flexibility of this procedure and thus could be easily extended, for example, to accommodate various prior distributions, densities, specification of the log likelihood function. We illustrated some of these extensions by using several variances in the prior distributions and by implementing the 3 or 4-parameter logistic regression used by Riviere et al sensitivity analyses are important to guide the choice of the prior density.
We suggest performing simulations to evaluate several potential working models and prior distributions selected for the trial when designing a dual-agent phase I trial. Models and priors can be compared based on the proportion of times the correct MTDC was selected for each dose of the second agent. In addition, one can easily change the algorithm for dose-assignment, the startup rule or implement stopping rules based on the posterior probability that toxicity is higher than expected. The SAS macro CRM2DIM shares some features with the other programs reported in.
Sample selection Two modern marine mollusc shells, O. edulis and M. modiolus, were collected in northern Jutland (Denmark) by Søren H. Andersen and were selected for the following reasons: O. edulis shells had been suggested as the potential raw material for the Hornstaad-Ho ¨rnle IA assemblage (Heumu ¨ller, 2010) and are very abundant at the shell midden site of Havnø; M. modiolus is a thick-shelled mussel with a nacreous layer, therefore a suitable raw material for the Havnø ornaments (Appendix 1, section 2). Furthermore, close relatives of both species are present in public sequence databases, which is important for palaeoproteomics: O. edulis belongs to family Ostreidae (genomes available for Crassostrea gigas and C. virginica) and M. modiolus to family Mytilidae (genome available for.
Amino acid analysis The absence of recrystallization observed by SEM was consistent with the concentration, composition and relatively low D/L values for all the amino acids analysed (e. g. alanine D/L ~0. 1 for Pes ¸tera Ungueras ¸ca, ~0. 2 for Havnø and Hornstaad), except for sample HorA. This supports a non-fossil origin for the shells used to make the double-buttons, that is the makers used 'fresh' or recently dead mollusc shells. However, the extent of degradation for HorA was significantly higher and both D/L and concentration values showed a clear 'burning' signal  (Crisp, 2013; Demarchi et al. , 2011). The amino acid composition was similar to that of freshwater bivalves (Unio, Margaritifera) present in the reference database of  Demarchi et al. (2014) , although HorA and PesB appeared to be rather different from the other double-buttons (Appendix 1, section 3.
Palaeoproteomics We characterized the proteomes preserved within the seven double-buttons and performed bioinformatic searches  (PEAKS 8. 5, Bioinformatics Solutions Inc [Ma et al. , 2003] ) of the product ion spectra against both a Protein sequences and an Expressed Sequence Tags (ESTs) database, restricting the taxonomy to Mollusca (see Methods section). This resulted in the identification of 1973 and 3233 peptide sequences, respectively, which represent the 3. 5% and 5. 1% of the total number of sequences generated by the de novo algorithm of the software (excluding contaminant sequences). For comparison purposes, we also performed shotgun proteomics on the shell matrices extracted from the six reference mollusc species (Appendix 1, section 3. 5) and analysed the data using the same databases and parametres (see Table  2 -source data 1 for full results of the palaeoproteomic analyses).
Note that molecular sequence databases for molluscan species are incomplete and biased towards well-studied model organisms. The peptide sequences recovered in our study were identified using sequence homologies with proteins originally described from Hyriopsis cumingii, Crassostrea sp. , Pinctada sp. , Mytilus sp. and several others. As a result of database insufficiency, the bioinformatic search of these 'shellomes' could not identify the exact taxon of our samples, but provided a strong indication of the fact that the closest taxon to that of the ornaments (and of the freshwater reference shells) is the pearl-producing triangle sail mussel Hyriopsis cumingii (Unionoida). sequence for which we could detect peptides) is represented by different colours. Additionally, on the right hand side of the table we indicate if each of the double-button proteins also occurred in the reference shell proteomes (the list of all shell proteins identified in both the double-buttons and the reference shells can be found in Table  2 -source data 1).
Shellomes: significant protein hits The main protein sequences from the double-buttons were identified as belonging to the pearl-producing triangle sail mussel Hyriopsis cumingii  (Bai et al. , 2013). Protein Hic74 (GenBank: ARG42316. 1) was found in all of the archaeological samples, except HorA. The percentage coverage for the Hic74 sequence was highest for the Havnø beads (35-55%), where it was supported by 132, 153, 255 unique peptides in HavA, HavB and HavC, respectively (Appendix 1, section 3. 5). This protein was also securely identified in all of the freshwater unionoid reference shells (coverage varying from 34% in M. margaritifera, supported by 67 peptides, to 50% in U. crassus, supported by 203 peptides). Hic74 is an acidic, Ala-and Gly-rich shell matrix protein  (Liu et al. , 2017a).
Consisting of 19 poly-A blocks, GA repeats, short acidic motifs (that probably bind to the mineral) and a GS-rich domain at the C-terminus (which resembles that of lustrin-A), this silk fibroin-like protein is likely to play a structural role in nacre formation and in enhancing its mechanical properties  (Liu et al. , 2017a). Protein Hic52 (GenBank: ARH52598. 1) was identified in all the reference unionoid shells and in the Havnø samples, but only when less stringent parameters were used for the identification (i. e. number of unique peptides ! 1 (instead of 2) and protein score À10lgP ! 20 (instead of 40)). Hic52 is a very basic (theoretical pI > 10), Gly-and Gln-rich protein, with few poly-Q and poly-G blocks and several degenerate G-rich repeats of different lengths along the sequence. It possesses a collagenlike structure which suggests a structural role in nacre formation  (Liu et al.
These proteins showed no homologues with any other shell proteins of marine origin currently present in the NCBI database (BLASTp search), being unique to H. cumingii and suggesting their specificity to freshwater unionoid shells. We argue that their presence (where identified as the major shell matrix proteins, supported not only by RLC domains) is specific to Unionoida, freshwater mother-of-pearl shells, which in combination with the isotopic data, and supported by the microstructural and amino acid results, excludes a marine origin for the raw material used to make the double-buttons.
Comparison with reference shells We performed a search of the product ion spectra from the double-buttons and the six reference shells against the redundant EST database, so that we could recover complementary information from non-annotated sequences. For example, a search of Hyriopsis cumingii on NCBI will retrieve 246 protein sequences but 10156 EST sequences. The dataset was used to explore the similarities between double-buttons and molluscan shell proteomes, presented as a circular plot in Figure  4. This output was derived from an adjacency matrix, showing which proteins (EST sequence identifiers) occurred in two or more samples (R code and data files can be found in Figure  4 -source data 1). From this, the subset of unique identifiers, present both in the double-buttons and in any of the reference shells, was associated with its sequence coverage (%) in the archaeological samples.
This information is represented in the right-hand side of Figure  4 : the length of the circular segment for each molluscan species is proportional to the number of sequences that the shell shares with the archaeological samples, scored on the basis of the coverage. On the left-hand side of the graph, each double-button is represented by a circular segment, which is proportional to the number of unique peptides that supports each shared protein sequence. The similarity between double-buttons and mollusc taxa can be visualised through the thickness of the connecting bands. Overall, the data showed that the EST sequences shared between the ornaments and the shells were mainly from the unionoids, consistent with the results obtained by searching the annotated protein database (Table  2 ). From all of the archaeological samples, the Havnø set showed the best match to the freshwater unionoids, owing to better-preserved proteins, that is with high coverage and number of supporting peptides.
Database-independent comparison In order to provide further, independent, evidence for the origin of the raw material, we developed an in-house tool (in C language, available in Figure  5 -source code 1) for 'proteome comparison', using all the peptide sequences generated by the de novo algorithm of the software PEAKS, that is before performing any database search (Appendix 1, section 3. 6). The tool was able to provide a score for the sequence similarity between two lists of peptides and to generate a similarity matrix from all pairwise comparisons, which was then converted to a distance matrix. Multidimensional scaling (MDS;  Gower, 1966)  was used to visualise the similarity of observations (Figure  5 ) and confirmed that the Havnø set and the freshwater reference unionoids display the higher degree of proteome similarity, while the samples from Hornstaad and PesB fall in a different area of the plot from each other and from the marine reference shells.
The results were also in accordance with those obtained from another database-independent approach Appendix 1-figure  25 , based on direct product ion spectra comparison  (Rieder et al. , 2017) , which was adapted for this study (Appendix 1, section 3. 6). Overall, our study, which represents one of the few that attempts to compare molluscan proteomes within the same clade (genus, family or order), shows that unionoid shells exhibit very similar proteome profiles, sharing many sequences between species Appendix 1-figure  23. This may suggest that this group has a rather conserved, homogeneous and recognisable proteomic signature, a conclusion that is completely congruent with earlier findings  (Marie et al. , 2017). Furthermore, all analyses showed that the three sets of archaeological ornaments have similar proteome profiles (Table  2 , Figures  4  and 5 , Appendix 1-figure 24, but do not exhibit a simple correspondence to a molluscan species, at least among the Unionoida considered here, further highlighting the complexity of molluscan shell proteomes.
Sources of bias in 'palaeoshellomics' Except technical bias, inherent to standard proteomics per se and discussed elsewhere  (Marin et al. , 2016) , we identified three potential sources of bias that may hamper, limit or confound the current use of 'palaeoshellomics': 1) the intrinsic peculiarities of several shell matrix proteins; 2) the completeness of the dataset used for identification searches; 3) the diagenetic degradation of shell proteins. 1. Sequence analysis of shell matrix proteins has revealed that a large proportion exhibits unconventional primary structures, with abundant long stretches of repeated residues, the RLCDs/ LCDs (repetitive low-complexity domains;  Marie et al. , 2013). The sequences of such domains are neither taxon-specific nor do they carry phylogenetic information.
Because they play a structural role, they are ubiquitous and may be detected in phylogenetically-distant lineages. For example, our proteomic analysis identified many sequences known in marine bivalves such as glycine-rich, insoluble matrix and MSI60-related proteins, but these findings are only supported by RLC domains that can be detected in very different molluscs. More generally, silk fibroin-like domains (poly-Ala), acidic (D-rich or poly-D) or collagen-like repeats (G-X-A triplets) are widespread, ubiquitous and cannot be simply assigned to one given shell protein and/or mollusc genus. 2. As stated before, protein identification is database-dependent and the quality of the interpretation is proportional to the size of the data set. The list of known shell proteins has dramatically expanded in the past few years, with the use of high-throughput screening  (Marin et al.
, 2016). However, the 'shellomes' of only about thirty mollusc genera have been identified, in a phylum that comprises between 80000 and 100000 species. We do not know yet whether the set of sequences at our disposal is a representative sample of the sequence diversity for the whole phylum. In other words, the taxonomic origin of a given archaeological shell sample (such as the double-buttons analysed here) may be indeterminable because extant representatives of the corresponding genus (or of closely related genera) are not yet registered in molecular databases via their genomes, mantle transcriptomes or shellomes. As a consequence, palaeoshellomics, for the time being, will provide information mainly at intermediate taxonomic levels (order, family, sub-family). Nevertheless, the continuous expansion of the dataset of shell protein sequences will increase the power of palaeoshellomics: archaeological shell samples, which are currently unidentified, can be revisited and re-interpreted in the future via in silico investigations alone (i.
modiolus. Therefore, this finding suggests that the importance of freshwater mother-of-pearl be re-evaluated. Unio sp. shells were probably selected to make 'disc beads' in the Epipaleolithic of the Levant, at Eynan (Natufian, 10,000-8,000 BCE; Bar-Yosef  Mayer, 2013) , and in Europe the presence of Unio sp. beads has been recorded 259 times according to the dataset gathered from the literature by  Rigaud et al. (2015) , mainly from Neolithic sites. Despite this relative frequency of (presumed) freshwater mollusc ornaments in prehistoric Europe, a systematic study of their exploitation as raw materials is almost completely lacking. This is especially surprising since it is known that unionoid shells were exploited for mother-of-pearl until the Middle Ages  (Bertin, 2015). Indeed, North American freshwater mussels were the basis for the 'pearl rush' during the 19th century, and their overexploitation for pearl harvesting, for making nuclei to be inserted in Pinctada pearl oysters as well as button-making on an industrial scale, almost drove a high number of species to extinction  (Haag, 2012).
Conclusions The first application of 'palaeoshellomics' has demonstrated that it is possible to recover and identify ancient proteins sequences from mollusc shell, despite significant analytical challenges due to the combined effects of several factors, including low protein concentrations, small samples sizes, diagenesis and database insufficiency (Table  2 ). Our molecular data showed that molluscan proteins are similar across the four freshwater taxa we examined (U. pictorum, U. crassus, M. margaritifera, P. auricularius) and differ significantly from the two marine species (O. edulis, M. modiolus; Table  2 , Figures  4  and 5 ). We confirmed that freshwater molluscan matrix proteins are characterized by highly repetitive low complexity domains (RLCs). This is consistent with results obtained on other shell taxa, and improves our understanding of the biomineralization mechanisms within these invertebrate systems.
The FDR is calculated by the software PEAKS using an approach called decoy fusion  (Zhang et al. , 2012) , whereby target and decoy are concatenated for each protein, rather than searching a target database and a decoy database separately (which can result in FDR underestimation); the effect is that of improving accuracy without impacting on sensitivity. The de novo algorithm derives the peptide sequences from the tandem mass spectra without using a database, and it is therefore suitable for the study of organisms where molecular reference sequences are scarce, such as in the present case. Each amino acid residue in the sequence is given a score (0-99%), indicating how confident the software is for that local identification. The overall confidence of the de novo sequence is calculated as the Average of Local Confidence (ALC) score.
The Molluscan Protein Database used in this study comprised 633061 protein sequences, that is all sequences available on the National Centre for Biotechnology Information (NCBI) repository restricting the taxonomy to Mollusca (fasta database downloaded on 15/02/2018), excluding all common contaminants (cRAP; common Repository of Adventitious Proteins: http://www. thegpm. org/crap/). The ESTs database included 1149,723 expressed sequence tags, also restricting the taxonomy to Mollusca (fasta database downloaded on 15/02/2018) and including cRAP sequences. Unionoida molluscs are poorly represented in these databases: a search for Unionoidae on the NCBI Identical Protein Database retrieves 4562 entries, almost exclusively belonging to soft tissue proteins, for example cytochrome oxidase, NADH dehydrogenase and ATP synthase.
Data deposition All the mass spectrometry proteomics data have been deposited in the ProteomeXchange Consortium (http://proteomecentral. proteomexchange. org) via the PRIDE partner repository  (Vizcaı ´no et al. , 2013)  with the data set identifier PXD011985. Modiolus modiolus, Arctica islandica or (according to  Rowley-Conwy, 2014) , Ostrea edulis, given that these are the most abundant species in the midden and that the site reflects the great importance of marine environments for the Ertebølle people. Interestingly, eight doublebuttons, similar (but smaller, only around 5 mm in diameter) to the ones from Havnø and from the same period, have been found at the Nederst site, on the Djursland peninsula, 50 km south of Havnø  (Kannegaard, 2013).
Nacreous and prismatic layers of (a) HavC, (b) PesB; (c) FTIR-ATR spectra comparison between HavC nacre and prismatic layers (black) with calcitic prismatic layer of Pinna nobilis (red) and aragonitic Unio truncatosus (blue), confirming the fully each of the ones from Havnø (due to the larger size of the latter). Appendix 1-table  2  and Appendix 1-table 3 report the THAA concentration and D/L values for each of the doublebuttons. The relative composition data (Appendix 1-figure  7 ) show that all double-buttons display similar bulk amino acid signatures, except for HorA, where the %Asx is lower than in the other examples. This suggests that the proteins in this sample have undergone rapid degradation, and this is likely due to their exposure to high temperatures. PesB also displays low %Asx values, but this might be attributed to a species effect.
MS data were acquired using a data-dependent top 20 method that selects the 20 most abundant precursor ions from the survey scan (400-1600 m/z range) for HCD fragmentation. The dynamic exclusion duration was 60 s. The isolation of precursors was performed with a 1. 6 m/z window and MS/MS scans were acquired with a starting mass of 80 m/z. Survey scans were acquired at a resolution of 70,000 at m/z 400 (AGC set to 106 ions with a maximum fill time of 180 ms). Resolution for HCD spectra was set to 35,500 at m/z 200 (AGC set to 105 ions with a maximum fill time of 120 ms). Normalized collision energy was 28 eV. The underfill ratio, which specifies the minimum percentage of the target value likely to be reached at maximum fill time, was defined as 0. 3%. The instrument was run with the peptide recognition mode (i.
The injection volume was 1 mL and the flow rate 300 nL min À1. The nanocolumn was provided with the ESI source. The mass spectrometry parameters were: positive spray voltage 2300 (V), sweep gas 1 (Arb) and ion transfer tube temperature 275˚C. Full scan spectra were acquired in the range of m/z 375-1500 (resolution 120000 @ m/z 200). MS n spectra in data dependent analysis were acquired in the range between the ion trap cut-off and precursor ion m/z values. HCD collision energy was fixed at 28%, orbitrap resolution 50000 and the isolation window was 1. 6 m/z units.