dataesr/scientific-paragraphs-categorization huggingface dataset:
Domestication is a continuous and ongoing process, therefore providing cut-off values can be seen as somewhat artificial. However, the continuity of the process is reflected by the continuity of size. Accordingly, there is a risk that a relatively large number of specimens will have measurements close to the cut-off values and therefore should be kept unidentified. We recommend the use of the cut-off and threshold values presented in this study only when wider comparative analyses is required with other biomerical datasets and where more powerfull analyses of shape are not available. Table  2 : Cut-off values at some critical threshold values for relative error risks of erroneous identification based on maximum tooth length (MTL) and width (MTW). The four odds retained here, 1:100, 1:10, 1:5 and 1:2, correspond to probabilities of wrong assignment of ~99%, ~91%, ~83% and ~67%, respectively.
Then, it aims at finding some objective indicators from the input impedance which can predict the playing frequencies without taking the musician'sb ehaviour into account, as intended by Pratt and Bowsher  [18]  and previously by  Wogram [19]. This will be done by recording alarge number of notes played by several musicians on three trumpets. Section 2p resents some basic information about the acoustics of the trumpet. Section 3describes the recording of notes played by the musicians on the different trumpets and the analysis of the data. From these measurements, an analysis of the musicians' behaviour is presented in section 4. In section 5, the playing frequencies of the recorded notes are compared to the bore resonance frequencies taken from the input impedance of the trumpets. Section 6p resents the data first as an ormal distribution and then, in order to minimize the influence of the musician on the results, it focuses on frequencydifferences instead of the frequencies themselves.
T hese instruments are presented in section 3. This representation is often used by instruments makers and is for example presented in the BIAS software  1. These graphs showthat the series of resonances of the trumpet can be considered as harmonic with a[ -20, +20] cents precision interval (the resonance frequencies are almost aligned on an horizontal line), apart from the resonances corresponding to the second regime of 100, 110 and 111 fingerings that are too low. These diagrams also showthat, by increasing the length of the bore, the resonance frequencies become more distant from the frequencies of their corresponding notes in the equally-tempered scale. Consequently,i fw ea ssume that the resonance frequencies are representative of the playing frequencies, notes should be easier to play in tune with the 000 fingering than with the 111 fingering.
Data Analysis The playing frequencyofthe notes has been analysed with the YIN [25] software 2  ,which is an estimator of the fundamental frequencyspecially calibrated for speech and music. Overlapping square windows of 68 ms length were used. This is more than twice the largest expected period for all the measured notes. We noticed that musicians were not able to play ap erfectly steady note, and slight oscillations around the playing frequencyw ere observed. Figure  4shows  an example of the frequencye volution of one note, concert B 3, played by am usician with the basic fingering (000)a tm ezzo forte. At the beginning, the frequencyrapidly increases: this is atypical transient. The same effect is happening during the quiescent. After removing the transient and the quiescent, aquasi-stationary part stays, where the frequencyisfluctuating afew hertz.
Acknowledgement This research wasfunded by the  French National Research AgencyANR  within the  PAFI  project  (Plateforme d'Aide àlaF acture Instrumentale in French ). The authors would liket ot hank all the trumpet players who participated in this study as well as  A. Burke ,  K. Cedergren , J. -P. D almont,  D. Lopatin ,  A. Mamou-Mani  and  R. Piéchaud  for proofreading and valuable talks.
The sentences were presented using E-Prime ® software and were spoken aloud by a female voice. After the sentences had been listened to, two pictures were presented (420×360 pixels): one associated with the dominant meaning of the ambiguous word and the other with the secondary meaning. The participant had to choose which of the two pictures corresponded to the meaning of the sentence. The sentences were counterbalanced across two experimental lists so that both of them were presented in the two contexts. The dependent variable was the number of correct responses when the correct choice was the sentence corresponding to the subordinate meaning of the word in high and low context (the maximum score was 10). The second task was the context-processing remediation task and contained ten interactive social scenarios presented in 20-second video sequences involving two persons during a social interaction.
For instance, for the verb prescrire, the most frequent labels are the same in the two corpora: C, J , P and T terms. Traiter frequently occurs, in the two corpora, with C and D terms. The general observation is that, for a given verb, the Expert corpus shows more sophisticated syntactic structures with higher number of specialized co-occurents. Besides, some verbs may show similar or different behavior in the two corpora studied. According to the objectives of the proposed work, we consider that an important presence of specialized terms in a sentence or corpus indicates a very specialized use and meaning of the verbs. Quantitative and qualitative analysis of the data support this first study and results.
The shrinkage at the cellular level was determined in the transverse plane of wood using an environmental scanning electron microscope (ESEM). The whole procedure is based on pairs of images grabbed with the same imaging conditions (magnification, working distance, voltage) without removing the sample from the chamber: one image collected at saturated conditions (1067 Pa, 1°C) and the other at air-dry state (533 Pa, 16°C). A closed chain of the same reference points chosen from the anatomical markers was defined on both images at the external part of the zone of interest. A custom software program, called MeshPore, allowed the shrinkage coefficients to be extracted from the slight difference of shape between these two chains. Measurements of transverse shrinkage were performed on earlywood and latewood zones from Norway spruce (Picea abies), only normal wood was studied.
Perré and Huber (2007)  proposed a new method to measure free shrinkage of very small areas. To achieve this, the tissue of interest is isolated from the rest of the material (in terms of kinematics) thanks to a diamond wire sawn. An image of this zone (R-T plane) is then grabbed at a certain water content (generally at saturation) and then at a different water content (in this case, in air-dry state). The difference between the two states makes it possible to determine the relative shrinkage of different tissues, or the anisotropy factor between two directions included in the image. To determine this shrinkage accurately on these isolated zones, specific routines have been added to the software MeshPore previously developed in our team  (Perré 2005). Shrinkage is thus calculated from contour integrals over closed chains of reference points selected in the external part of the selected zone.
The software program MeshPore, developed by  Perré (2005) , was used to achieve this manual referencing on the images and subsequently performing the contour integral to extract shrinkage coefficients. The work on a pair of images started by plotting a chain of material points, chosen as anatomical marks of the tissue, on the initial image (Figure  1a ). To allow the mathematical treatment presented below, this chain must be closed. It is duplicated before moving on to the deformed image. By translating this duplicated chain, one of the reference points was placed at the correct position on the deformed image. It was then necessary to shift each point in the chain slightly to the new position of the anatomical mark (Figure  1b ). Finally, the tangential and radial shrinkages were automatically calculated by the command "strain" (Figure  1c ).
Acknowledgments We thank  Antonio Acín  and  Miguel Navascués  for comments and discussion. Research at  Perimeter Institute  is supported by the  Government of Canada through Industry Canada  and by the  Province of Ontario  through the  Ministry of Economic Development and Innovation. This work has been supported by a grant from the  John Templeton Foundation. A. B. S. was supported by the  ERC SG PERCENT  and by the  Spanish  projects  FIS2010-14830 , DIQIP and spanish  FPU : AP2009-1174 PhD grant.
The 16 S rRNA gene dataset yielded 132,000 trimmed reads and 673 Operational Taxonomic Units. Most abundant bacterial phyla observed in the 16 S rRNA gene dataset belonged to Proteobacteria, Firmicutes, Actinobacteria and Chlamydia. The Namur coprolite yielded typical gut microbiota inhabitants, intestinal parasites Trichuris and Ascaris and systemic pathogens Bartonella and Bordetella. This study adds knowledge to gut microbiota in medieval times.
Metagenomics Acridine orange staining disclosed the presence of DNA in the coprolite, suggesting that molecular biological tools can be further applied to this specimen  [10]. Accordingly, high-throughput sequencing yielded a total of 37. 5 millions base pairs and 107,470 reads, with an average sequence length of 375 bp and a GC content between 65 and 70% (MG-RAST accession number 4479942. 3). Taxonomic assignment of the reads was performed using a BLASTX comparison with the National Center for Biotechnology Information (NCBI) database, with stringent parameters as previously described  [17, 18]. A significant similarity to known sequences was obtained for 31. 9% of reads comprising 98. 98% bacterial, 0. 52% eukaryotic, 0. 44% archaeal and 0. 06% of viral reads. The most abundant bacterial phyla were Proteobacteria (58.
12%), Gemmatimonadetes, (15. 18%), Actinobacteria (6. 96%) and Bacteroidetes (5. 10%) (Figure  2 ). More precisely, the high-throughput sequencing dataset yielded Gemmatimonas, Rhizobium, Streptomyces and Burkholderia known as environmental bacteria; as well as Corynebacterium, Cytophaga, Enterobacter, Prevotella, Ruminococcus, Aeromonas, Escherichia, Lactobacillus and Bacteroides known as members of mammal gut microbiota (Table  S1  in File S1 ). In particular, contig reconstruction and annotation identified contigs belonging to human gut Bacteroides species, including Bacteroides finegoldii, Bacteroides vulgatus, Bacteroides coprocola along with Bacteroides coprosuis belonging to pig gut microbiota (Table  S2A  in File S1). Some metagenomic reads were assigned to potential pathogenic bacteria (Figure  3 , Tables  S1  and S2B  in File S1).
Reads and contigs of amoeba-resistant bacteria (i. e. bacteria resisting to killing by amoeba) Actinobacteria spp. , Pseudomonas spp. , Parachlamydia acanthamoebae, Legionella drancourtii and Legionella pneumophila were found  [19]. Moreover, contig annotations yielded Burkholderia gladioli, Granulibacter bethesdensis, Leptospira borgpetersenii, Coxiella burnetii and Mycobacterium abscessus. Contigs respectively encoding a hypothetical protein and an initiation factor 3 of Brucella abortus were also found, as well as reads and a contig encoding VapC46 toxin from Mycobacterium tuberculosis (Figure  3 , Tables S1 in File S1 and S2B in File S1). Two contigs assigned to Clostridium botulinum encode a perosamine synthetase and a methionyl-tRNA formyltransferase. Among Bordetella spp. sequences found in the metagenomic dataset (Figure  3 ), contig reconstruction and annotation identified contigs encoding a hypothetical protein of Bordetella parapertussis and a putative hydrolase of Bordetella bronchiseptica (Tables S1, S2 in File S1).
For the hydrolase, phylogenetic analyses confirmed the BLAST based annotation (Figure  S3  in File S1). The cultured P. macerans, M. luteus, S. maltophilia and E. cloacae were also found in the metagenomic highthroughput sequence dataset. In the high-throughput sequence dataset, 0. 09% of reads identified bacterial 16 S rRNA genes. For further analysis, the V6 region was amplified by PCR. This 16 S rRNA gene dataset yielded 132,000 trimmed reads. The Bayesian microbial sourcetracking approach was performed to compare the 16 S rRNA gene dataset associated with the Namur coprolite to those of modern stool samples, published coprolites, compost and soil as previously described  [4]. The results indicated that the mixture of taxa associated to the Namur coprolite had no significant matches with any of the different sources used for comparison (Figure  S4  in File S1).
A total of 673 Operational Taxonomic Units (OTUs) were assigned to the 16 S rRNA gene dataset (MG-RAST accession number 4480463. 3). Comparisons to the laboratory gut microbiota database including all 16 S rRNA gene sequences generated by the 454 FLX titanium platform showed that OTUs associated with the Namur coprolite did not clusterize with sequences previously amplified in the laboratory. The most abundant identified phyla were Proteobacteria (85. 1%), Firmicutes (7. 4%), Actinobacteria (2. 8%) and Chlamydia (1. 8%) (Figure  2 ). Furthermore, Bartonella species were detected with a 16 S rRNA gene sequence identity of 98. 7% (Figure  3 , Table  S4  in File S1); phylogenetic analyses indicated that these 16 S rRNA gene amplicons were most closely related to Bartonella henselae, Bartonella koehlerae and Bartonella quintana (Table  S3  in File S1and Figure  S5  in File S1).
Pathogen-specific PCR assays Mechanical lysis of the specimen reduced PCR inhibition as measured by PCR amplification of an internal synthetic, 135-bp oligonucleotide sequence used as a PCR control. Further, microorganisms detected by microscopy or metagenomic were tentatively amplified by using species-specific PCR amplifications. Sequencing a 120-bp band generated using Ascaris spp. -specific primer pair  [20]  (Table  S5  and Table  S6  in File S1) yielded 98% sequence identity with Ascaris sp. DHS-2010a cytochrome b gene sequence, similar to those previously reported in a human remain  [21]  (GenBank Accession No. GU339224. 1). The amplicon generated by an Acanthamoeba spp. -specific PCR showed a 99% sequence similarity with Acanthamoeba castellanii (GenBank Accession No. JF437606. 1) [22].
Bartonella species, detected in the 16 S rRNA gene rDNA dataset, were additionally amplified by PCR. The amplicon of the ribosomal RNA operon rrsC yielded 95% of sequence identity with B. henselae and 94% with B. quintana (GenBank Accession No. BX897699. 1 and BX897700. 1), respectively. Bordetella species, detected in the high-throughput sequencing dataset, were also amplified in the PCR assays (Figure  3 ). A.
Synopsis of identified microorganisms The identified microorganisms were categorized into two groups. The first group comprises microorganisms for which the identification was confirmed by at least two independent methods among microscopy, culture, metagenomic and the PCR assay. This group includes Ascaris spp. and Acanthamoeba spp. identified by microscopy and PCR assay; P. macerans, M. luteus, S. maltophilia and E. cloacae found by culture and metagenomic; and Bartonella spp. related to B. henselae and B. quintana -and Bordetella spp. found in metagenomic or 16 S rRNA gene amplicon datasets and the PCR assay (Figure  3 ). The second group comprises of microorganisms identified only in silico by contig annotations (Figure  3 ).
No positive control was used and negative controls incorporated during all the experimentations remained negative. Nevertheless, the data herein reported -the presence of amoebal cysts, plant fibers, seeds, pollens and mold remains -indicate that the coprolite obviously contained environmental flora as previously reported in other investigations  [4, 5, 10, 27]. Accordingly, source tracking of the 16 S rRNA gene amplicon dataset yielded no hits, as previously also observed for two pre-Columbian American coprolites  [4]. More coprolites need to be investigated before source tracking can be informative. Furthermore, the observation of pig-and human-infecting Trichuris spp. eggs in the coprolite indicates a possible common use of the barrel for animal and human feces. This point was supported by the results obtained in the metagenomic datasets, with bacterial sequences assigned to species found in modern gut microbiota of humans and pigs.
On this basis, the Namur coprolite was further used as a source to detect past pathogens. Modern stool specimens diagnose systemic infections in humans and primates including malaria, rickettsiosis and tuberculosis  [29] [30] [31]. Likewise, two pathogens Haemophilus parainfluenzae and Clostridium botulinum were already found in ancient fecal material  [2, 7]  and Neisseria, Yersinia, Shigella and Mycobacterium sequences were identified in WGS datasets of pre-Columbian American coprolites  [5]. To go further, it was herein attempted to ascertain the presence of potential pathogenic agents that might be associated to the analyzed specimen, in silico or by molecular tests. Bacterial pathogens belonging to Bartonella and Bordetella were detected here for the first time in a coprolite sample. Bartonella species are responsible for zoonoses  [32]  and Bordetella species cause respiratory tract infections  [33].
Pictures were taken using a Nikon digital sight DS-U1 camera with Lucia G software (Nikon Instruments, Champigny sur Marne, France). Measurements were collected using the ImageJ software (http://rsbweb. nih. gov/ij/). Further, 500 mg of the coprolite were rehydrated into 1 mL PBS (Biotechnologie Appliquee), fixed with absolute ethanol on glass slides and stained using acridine orange solution as previously described  [10].
OTU-based analysis The 16 S rRNA gene pyrosequencing data were processed using Mothur package 1. 5  [36]. No ambiguous bases 'N' and only one mismatch were allowed in the read and primer sequences. The quality read trimming used a moving window of 50 bp and required that the average quality score over the region did not drop below 35. The trimmed reads were dereplicated and aligned using the Sylva bacteria reference alignment provided by the Mothur (http://www. mothur. org/). The multiple sequence alignment was filtered using a minimum read length of 200 bp. In addition, a pre-clustering step  [37]  was performed before chimera detection using the Uchime tool in Mothur. A distance matrix was built based on multiple sequence alignment, and OTUs clustering was performed at a 97% sequence identity.
The taxonomic classification from phylum to genus level of each representative OTU sequence was performed using the RDP classifier tool and the RDP training set 9-database (http://www. mothur. org/). The relative abundance of reads per phyla was deduced from this classification. To exclude laboratory contaminations, we built an in-house gut microbiota database. This gut microbiota database contained 5,500,000 16 S rRNA gene sequences using all of the data generated by the 454FLX titanium platform of the URMITE laboratory. The database includes data from obese (SRX118214), Senegalese (SRX118212, SRX118213), HIV (SRX209782), anorexic (SRX209240), post-antibiotic treatment (SRX189054, SRX189053) and drug resistance tuberculosis (SRX204218) stool specimens. The OTUs identified in the Namur coprolite were clustered at 97% sequence identity with sequences in our gut microbiota database.
Phylotype-based analysis To detect potential pathogens in the 16 S rRNA gene pyrosequencing data, we used an alternative method that consisted of binning reads according to their taxonomic classification using BLAST searches against the Ribosomal Database Project (RDP) database. First, the OTU-based quality read trimming filter was applied. However, no multiple sequence alignment and no distance matrix were performed to increase read length and therefore, improve the ability of assignment at the species level. Two databases were created using selected criteria from the Hierarchy Browser of the RDP 16 S rRNA database, release 10 (http://rdp. cme. msu. edu/). A ''Type'' database was built with sequences labeled ''Type strains'', ''Isolates'' and size ''length. 1,200 bp'' with ''good quality''.
A ''Non-type'' database was built using ''Non-type strains'', ''Isolates'', size ''length. 1,200 bp'' and ''good quality''. The two databases were formatted using Taxcollector  [38]. The species level was defined with a minimum sequence identity of 98. 7%  [39]  with the best BLAST hit from the ''Type'' database. The multiple best BLAST hit cases were checked for the most representative species (. 50% of the multiple best BLAST hits). A second BLAST round was performed from the remaining reads with the same cutoffs but using the ''Non-type'' database. A total of 121 reads were assigned to the genus Bartonella, including 53 reads that were significantly assigned to B. quintana and 47 reads to B. henselae. For some 16 S rDNA amplicons phylogenetic trees were constructed. Multiple sequence alignment was performed using MUSCLE  [40]  and curated by Gblocks  [41].
Source-tracking analysis To determine the potential sources of bacteria contained in the coprolite sample (defined as sink) we used the SourceTracker software package  [46]  as previously reported  [47]. The Source Tracker sofware uses a Bayesian model that estimate the different source proportion found in a community sample (sink). The different sources examined (16 S rRNA datasets) correspond to 88 soil samples  [48] , 602 multiple human body sites (skin, nasal, tongues, urine) including 45 U. S. gut samples  [49] , 20 infant guts  [50] , 3 Polynesian guts from our laboratory, 60 mammal guts  [51] , eight different coprolites  [4]  and one compost  [52]. If the community tested corresponds to a mixture of taxa that do not match with any of the source environments used, that portion of the community is classified as ''unknown''.
The 16 S rRNA reads were processed for all source and sink samples using the quantitative insights into microbial ecology 1. 7. 0 release (QIIME)  [53]. Highquality read sequences (quality score. 30, exact match to primer, and containing no ambiguous characters) were trimmed from the initial dataset. When compared datasets from non-overlapping amplicons the taxonomy binning with the closed-reference OTU picking strategy and using the Greengene gg_otus-12_10 release were performed. Then, all the taxonomy tables were converted and merged, for all the mapping files of the various source/sink datasets for SourceTracker analysis with the defaults parameters (iterations for Gibbs sampling = 100, rarefaction depth = 1000, al-pha1 = 1e 23 , alpha2 = 1e 21 ). The modification of the Source tracker parameters did not change the results obtained for the analysis of the Namur coprolite.
The titration yield was 12. 31%. In total 340,000 beads per project and per region were loaded onto the GS Titanium PicoTiterPlate Kit 70675 which corresponded for this project with 383 mL of the clonal amplification and sequenced with the GS Titanium Sequencing Kit XLR70. The run was performed overnight, and then analyzed using the cluster. Using the Camera 2  [54]  QC-Filter and the 454 duplicate clustering tool, reads that were low quality (average score ,19) or that were ,60 bp and identical duplicates (default sequence identity = 0. 96) artificially produced by titanium technology, were deleted. Reads were blasted against the NCBI non-redundant protein database using a translated nucleotide query (BLASTX). The best BLASThits with $50% identity, $50 score and E-values ,1e 205  [17, 18]  were retained.