dataesr/scientific-paragraphs-categorization huggingface dataset:
M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT In summary, those results indicate that the accuracy of the D E value calculated by MCDoseE is directly dependent on the precision on the ESR intensities (e. g. the higher the precision, the smaller the random error on the D E ). This implies that the correct uncertainties in such points is probably underestimated, possibly due to the influence of systematic error. Such underreporting of errors is likely to lead to problems, and should be part of the checks. This also provides a potential justification for removing points, or increasing the associated errors. The main limitation of the program remains in the case that one or two points show a significantly higher precision than the group. By default those highly precise value(s) will carry a major weight in the fitting and may potentially induce the calculation of an incorrect D E.
Consequently, we recommend to the users of this program to double check the fitting, by comparing the results obtained using the real experimental errors with the one obtained using uniform errors of 0. 5 or 1. 0%. Ideally, all D E values should remain very close to each other. If not, then one or more associated errors might be problematic. MCDoseE 2. 0 has a function to quickly change the error to 0. 5% of the ESR intensity (for more information see the MCDoseE 2. 0 user guide included in the supplementary file of this paper).
Without those two samples, the D E ratio between the 2 programs increases to 0. 97. In other words, MCDoseE gives on average results that are in agreement within 3% with those derived from OriginPro, regardless of the weighting option considered. In terms of precision, MCDoseE yields average 1σ errors of 2. 7% using the SSE, vs 4. 2% for Origin (both 1/I 2 and 1/s 2 ). Using the DSE function for 19 samples, the average 1σ errors are 3. 5% (MCDoseE ) vs 5. 0% for Origin (1/I 2 ). In contrast, Origin using 1/s 2 yielded an unexpectedly large average error of 20. 5%. This is the result of the two outliers mentioned Similar results can be observed with the EXPLIN function on the quartz samples (Fig. 9 ). The high correlation between the D E obtained with each program is visually striking, with an average systematic deviation < 3% for both 1/s 2 and 1/I 2.
Conclusion Both the MCDoseE 2. 0 program and Origin software provide highly consistent results. The MCDoseE 2. 0 program shows similar capabilities to the Origin fitting. By design, the program puts much emphasis on the experimental errors associated with the ESR intensities. Those errors drive the fitting process and directly impact the correctness and precision of the results. A similar phenomenon occurs when using Origin with data weighting by 1/s 2. Consequently, the accuracy of the fitting results is also directly limited by the reliability of the experimental errors. The presence of an experimental point with an abnormally small error can potentially induce the MCMC to be stuck in an inaccurate local minimum solution space. To avoid this issue, we recommend that each associated error should be checked to comply with a 2-sigma deviation from the mean error.
When using data weighting by 1/s 2 or the MCDoseE program the leverage philosophy is different, as it is then considered that the most precise points in the DRC are the most important, whatever their position. Consequently, depending on the type of data weighting selected, a given experimental point will carry more or less weight and will not influence in the same way the fitting results. This has been demonstrated here with known-dose sample #1, and some random samples (e. g. Fig. 2 , 7 and 9). By definition a highly precise point will not impact the fitting by 1/I 2 , contrary to that with 1/s 2 (Fig. 9 ). In contrast, an abnormally scattered point in the first dose steps of the DRC will strongly impact the 1/I 2 , but not the 1/s 2 if its experimental error is similar or higher than those of the other points (Fig.
9 ). In any case, it should be mentioned here that if the ESR data set is good (i. e. experimental data points have little scatter, all with similar and small experimental errors), the data weighting    with MCDoseE with those from Origin (data weighting by 1/I 2 and 1/s 2 ) for 14 quartz samples. Fitting was performed using EXP+LIN function (see Table  1 ). All errors are 1σ.
Comparison of the DRCs obtained by MCDoseE 2. 0 and Origin for the known-dose enamel sample #1. Associated experimental errors are indicated. Because one point has a very small associated error (0. 16% in comparison with the others), it has a huge influence on the fitting results. When removed from the DRC, the D E increases by almost 700 Gy, i. e. to 1659 Gy. If this point is given an error of 0. 5%, then the D E remains around 1655 Gy. Figure 4-Results of MCDoseE 2. 0 on sample#1. With original experimental errors, iterations of 100,000, on a DSE calculation over the full dataset. We note that even after 100,000 iterations the solution distribution remains chaotic and poorly constrained. Figure 5-.
Results of MCDoseE 2. 0 on sample#1. With homogenised associated errors, iterations of only 50,000, on a DSE calculation over the full dataset. If we compare with figure3, we can see that the MCMC is able to accurately calculate a Gaussian distribution when the associated errors are not limiting the solution space. Figure 6-Impact of the experimental errors on the D E results (SSE function). Evolution of the systematic error (relative deviation with the expected dose) and random error (D E precision)with the magnitude of the experimental error of the ESR intensities. Calculations were performed assuming uniform relative experimental errors (circles) for all aliquots of a given sample of 0. 1%, 0. 5, 1%, 3%, 5 % and 10% (1σ errors). The results (1σ errors) for real nonuniform experimental errors (triangle) are also indicated (the mean experimental error derived from the individual error on each aliquot was used for the plot).
For this purpose, we used the ultrafast metagenomic sequence classifier, based on an exact alignment of k-mers (Kraken) and Bowtie2, an ultrafast and memory-efficient tool for aligning sequencing reads to long reference sequences. The analyses, which have been conducted on the most exhaustive genomic database possible on Trypanosomatidae, show that species assignments could be biased by a lack of some genomic sequences of Trypanosomatidae species (strains). Nevertheless, our work raises the issue of possible co-infections by multiple members of the Trypanosomatidae family in these three pre-Columbian mummies. In the three mummies, we show the presence of DNA that is reminiscent of a probable co-infection with Leptomonas seymouri, a parasite of insect's gut, and Lotmaria.
Basic Local Alignment Search Tool (BLAST) alignments, which rely on finding the best alignment to a panel of genomic sequences, were often the traditional approach to assign a taxonomic label to an unknown sequence. However, unambiguous assignment at the species level is very hard, and this tool is very expensive in central processing unit (CPU) time for NGS data analysis on local computers, even if new facilities are now available via the use of Cloud public infrastructure or computer cluster. To shed light on the causative agent of leishmaniasis infection (and more broadly on parasites belonging to the Trypanosomatidae family) in these ancient remains, we used new software dedicated to metagenomic data analysis. Kraken, a bioinformatic program  [10] , presents numerous advantages over other programs, including its speed of performing analysis on metagenomes.
Data The metagenomic data that we used of the gut microbiomes from three pre-Columbian Andean mummies (FI3, FI9, and FI12) were available in the MG-RAST server (http://blog. mg-rast. org/) (MGRAST IDs 4629033. 3, 4630170. 3 and 4626489. 3, respectively). We built a reference genome database composed of all of the complete Trypanosomatidae genomes collected from the NCBI (http://www. ncbi. nlm. nih. gov/genome/) and complemented by genomes from TriTrypDB release 37 (25 April 2018) (Kinetoplastid Genomics Resource http://tritrypdb. org/ tritrypdb/)  [12]. The total number of Trypanosomatidae genomes in the database is 79 (Table  S1 ).
Methods We analyzed the data with Kraken (version 1. 0), which is a system for ultrafast metagenomic sequence classification using exact alignment  [10]  (http://ccb. jhu. edu/software/kraken/). It relies on the development of a database that contains records consisting of a k-mer and the lowest common ancestor (LCA) of all of the organisms whose genomes contain that k-mer  [10]. We built a reference database with the default parameters of Kraken (k = 31). We downloaded the pre-processed sequences (this step filters sequences based on length, the number of ambiguous bases, and quality value) from the MG-RAST server, and then compared them with the non-redundant, custom-built Trypanosomatidae database. The results were visualized with a metagenomic visualization tool, Krona  [13]. To complete the analysis, the same set of metagenome data was analyzed with Bowtie2-a program for the rapid alignment of gapped reads-using the sensitive option  [11].
Trypanosomes and Chagas Disease The genome representativity of our database is 55% (11 genomes out of approximately 20 Trypanosoma species currently described) (Figure  1A ). The reference database includes trypanosomes responsible for American trypanosomiasis (T. cruzi) and trypanosomes responsible for human African trypanosomiasis and animal trypanosomiasis. The analysis performed with Kraken reveals that a non-negligible proportion of reads (5%, 9%, and 10% for FI3, FI9, and FI12) is attributed to the Trypanosomatidae family (Figure  2A ). The genus Trypanosoma is clearly identified in ancient DNA from the guts of the three mummies FI3, FI9, and FI12, with 63%, 69%, and 52% of the detected Trypanosomatidae community, respectively (Figure  2B ). As expected, almost all of the reads belonging to the genus Trypanosoma can be attributed to T.
cruzi (Figure  3A-C ) for the three mummies. These results corroborate the conclusion of Santiago-Rodriguez et al. [1]. Currently, T. cruzi is split into six genetic lineages or discrete typing units (DTUs) named TcI, TcIV, TcII, TcIII, TcV, and TcVI, respectively, and a seventh one called TcBat. For both FI3 and FI9, the highest number of reads matches the T. cruzi strain Tula cl2 (Table  S2 ), which belongs to TcI (DTU I). This DTU is widely represented in the genomic database that we gathered (five of the 11 genomes currently available). For FI12, reads that match Tula cl2 are scarce (Table  S2 ). Nevertheless, because of the relatively low proportion (<10%) of reads that match with a genome filed in our database, it is probable that the DTU of the infecting T. cruzi strain is not yet represented.
Analysis with Bowtie2 clearly confirmed a T. cruzi infection (53%, 49%, and 57% for FI3, FI9, and FI12, respectively) in these mummies (Figure  2C ). The majority of reads matched T. cruzi strains Y and Tula cl2 (Table  S3 ) rather than other genomes filed in the database. Concerning the alignment with T. cruzi Y, the reads matched 178 and 203 contigs in FI3 and FI9, respectively, out of 9821 (Table  S4 ). A large majority of reads (80% in FI3 and 90% in FI9) matched 52 identical contigs for both mummies. For T. cruzi Tula cl2, reads of mummy FI3 and mummy FI9 matched 42 and 39 contigs, respectively, out of 5300, and almost 100% of the reads for both mummies matched 28 identical contigs (Table  S4 ). Overall, this set of results indicates that the infecting T. cruzi strain is probably not in our reference database.
cruzi strain Tula cl2 (Table  S2 ), which belongs to TcI (DTU I). This DTU is widely represented in the genomic database that we gathered (five of the 11 genomes currently available). For FI12, reads that match Tula cl2 are scarce (Table  S2 ). Nevertheless, because of the relatively low proportion (<10%) of reads that match with a genome filed in our database, it is probable that the DTU of the infecting T. cruzi strain is not yet represented. Analysis with Bowtie2 clearly confirmed a T. cruzi infection (53%, 49%, and 57% for FI3, FI9, and FI12, respectively) in these mummies (Figure  2C ). The majority of reads matched T. cruzi strains Y and Tula cl2 (Table  S3 ) rather than other genomes filed in the database. Concerning the alignment with T. cruzi Y, the reads matched 178 and 203 contigs in FI3 and FI9, respectively, out of 9821 (Table  S4 ).
A large majority of reads (80% in FI3 and 90% in FI9) matched 52 identical contigs for both mummies. For T. cruzi Tula cl2, reads of mummy FI3 and mummy FI9 matched 42 and 39 contigs, respectively, out of 5300, and almost 100% of the reads for both mummies matched 28 identical contigs (Table  S4 ). Overall, this set of results indicates that the infecting T. cruzi strain is probably not in our reference database. Most of the genomes of T. cruzi strains are lacking: currently, only 11 complete genomes are available in the NCBI and/or TriTrypDB databases out of more than 1902 deposited strains to date  [17]. Likewise, the database we used contains no representative genomes belonging to DTUs III, IV, or VII (TcBat). Interestingly, ancient T. cruzi DNA has also been identified in human mummies dating from the same period (Chinchorro culture) and in the same geographical region, Southern Peru  [18].
In Bolivia and Peru, strains belonging to the DTU I and DTU V clades are the main T. cruzi strains isolated, followed by strains of DTUs IV, III, and VI  [15]. The knowledge of the complex evolutionary history of T. cruzi, which involved genetic exchanges  [19] , and the existence of hybrid DTUs will certainly benefit from the identification of the infecting strains at the DTU level from this ancient DNA material. Insight into the circulating strains in these mummies should generate important elements for the calibration of such a dynamically evolving scenario. Nevertheless, our analysis highlights that the ancient T. cruzi DNA present in the three mummies cannot be assigned to T. cruzi CL Brener (DTU VI). Most of the genomes of T. cruzi strains are lacking: currently, only 11 complete genomes are available in the NCBI and/or TriTrypDB databases out of more than 1902 deposited strains to date  [17].
Likewise, the database we used contains no representative genomes belonging to DTUs III, IV, or VII (TcBat). Interestingly, ancient T. cruzi DNA has also been identified in human mummies dating from the same period (Chinchorro culture) and in the same geographical region, Southern Peru  [18]. In Bolivia and Peru, strains belonging to the DTU I and DTU V clades are the main T. cruzi strains isolated, followed by strains of DTUs IV, III, and VI  [15]. The knowledge of the complex evolutionary history of T. cruzi, which involved genetic exchanges  [19] , and the existence of hybrid DTUs will certainly benefit from the identification of the infecting strains at the DTU level from this ancient DNA material. Insight into the circulating strains in these mummies should generate important elements for the calibration of such a dynamically evolving scenario.
Leishmania and Leishmaniasis All of the Leishmania genomes that are currently available in NCBI or in TriTrypDB are included in the reference database. The database represents only 38% of genomes from 21 of the 54 Leishmania species that have been currently identified  [20]. Microorganisms from the subgenera Leishmania and Viannia are well represented (Figure  1B ). Human pathogenic Leishmania spp. are far better represented in the database (14/20), with the subgenera Leishmania and Viannia well represented (80% and 83%, respectively). No genome from the subgenus Porcisia (Paraleishmania section) is available (Table  1 ). This subgenus includes some human pathogenic Leishmania sp. , such as L. colombiensis. The NGS reads analyzed with the metagenomic sequence classifier Kraken highlight a probable co-infection with a Leishmania parasite for the three mummies FI3, FI9, and FI12 (Figure  2B ), with 3%, 3%, and 10% of the reads attributed to Trypanosomatidae that belong to the genus Leishmania, respectively.
cannot be ruled out as well as infection by hybrid strains of Leishmania  [20]. From these results, it is clear that to be accurate in its species assignment, the reference database used by Kraken needs to be highly representative of the described species and strains. The more exhaustive the database, the better the assignment accuracy will be. The alignment program Bowtie2 detects the presence of Leishmania in three mummies (FI3, FI9, and FI12) (Figure  2C ). However, unlike the mummy FI12, in the mummies FI3 and FI9, the reads match a large panel of Leishmania sp. , especially in the mummy FI3 (Table  S3 ). Then, it is not possible to unequivocally assign DNA to a specific Leishmania species and L. donovani in particular due to the number of genomes of different Leishmania spp. matched. Our analysis reveals that the genome of the infecting Leishmania species, whose DNA is detected in these ancient remains, is probably not present in our reference database.
Other Leishmaniinae (Crithidiatae) Trypanosoma and Leishmania are dixenous parasites, meaning that their life cycle includes an invertebrate as a first host and a vertebrate as a second host. Several genomes of monoxenous (one-host) Trypanosomatidae are available and belong to the genera Angomonas, Crithidia, Leptomonas, Lotmaria, and Strigomonas (Table  S1 ). The database genome representativity for Leptomonas is 5%, with only two genomes available out of the 39 Leptomonas species currently described (Figure  1C ). The analysis performed on the three mummies revealed that a relatively high proportion of reads attributed to the family Trypanosomatidae is reminiscent of the genus Leptomonas (13%, 18%, and 13% for F13, FI9, and FI12, respectively) (Figure  2B ). A large majority of these reads are assigned to L.
Conclusions The strength of our approach is to work with the most exhaustive reference database of Trypanosomatidae genomic sequences that are possible, combined with two programs that are complementary in their metagenomic analysis approaches (k-mer searching versus local alignment). These allowed us to gain insight into the identity of the infecting Trypanosomatidae agent. Analysis using the sequence classifier (Kraken) unambiguously confirmed T. cruzi infection and undoubtedly Leishmania infection in the three Andean mummies. In addition, our analysis provides new information on co-infection by at least two human pathogenic trypanosomatids, Leishmania spp. and T. cruzi, in all of the mummies with available metagenomes. This type of co-infection is known to occur in humans as well as in some wild mammals  [26, 27].
Unfortunately, it is not possible to go further in the assignment to the species level for Leishmania and DTU level for T. cruzi. This may be due to the lack of a number of Leishmania genomes, particularly those of the Paraleishmania section, and of members of T. cruzi belonging to some DTUs. It also highlights a pattern of polyinfection coupled with an opportunistic trypanosomatid, i. e. , L. seymouri  [28]. Therefore, future studies with an exhaustive reference database are necessary to better understand the interrelationships that shape the microbial community and play a role in the evolution of the parasitome.
Supplementary Materials: The following are available online at http://www. mdpi. com/2073-4425/9/8/418/s1, Table  S1 : List of Trypanosomatidae genome sequences available and included in the reference database. Table  S2. (A) Results of Kraken analysis on preprocessed metagenomic data for the mummy FI3 (mgm_46. 29033. 3); (B) results of Kraken analysis on preprocessed metagenomic data for the mummy FI9 (mgm_46. 30170. 3); (C) results of Kraken analysis on preprocessed metagenomic data for the mummy FI12 (mgm_46. 26489. 3); Table  S3. Results of Bowtie2 analysis concerning the three mummies FI3, FI9 and FI12. Table  S4. Distribution of reads matching.
Our main objective was to decipher the relationships, particularly genetic links, between individuals inhumed on the same areas of the site, even in the same funerary structure. In this context, besides analyzing maternal (mtDNA HVR-1) and paternal (Y-STRs) lineages to identify potential kinship structures, autosomal STRs were examined to reconstruct more accurately the presumed close parentage relationships. Secondly, these genetic data can be correlated with archaeoanthropological data to test common assumptions about mortuary practices in the region, and discuss the social organization of the late pre-hispanic groups of northwestern Argentina.
Area 320 In this area, located on the eastern part of the site, a rescue excavation was conducted in a burial structure, which contained at least sixteen individuals (10 adult individuals and 6 immature individuals). Since the remains lacked anatomical connections or burial goods, the context was interpreted as an ossuary  (Seldes, 2012). Seven individuals of this burial structure were analyzed, i. e. , samples LA-4, LA-5, LA-6, LA-7, LA-8, LA-9 and LA-20. Although the context was not dated, the chronological data available for this part of the site indicates that it was occupied during the late phase of the RDP (1250e1430 CE), exclusively. We can note that an osteological study carried out on cranial data allowed to highlight that half of the individuals analyzed have nutritional and/or metabolic stress linked to anemia or nutritional deficiencies during the childhood.
mtDNA analysis Mitochondrial DNA analyses were performed on the first hypervariable segment of the control region (16,024e16,383 bp) divided in two overlapping sub-regions. The used primers and PCR conditions are described in a previous study  (Carnese et al. , 2010). All PCR products were analyzed on an ABI Prism 3500 automated DNA sequencer (Applied Biosystems). Mitochondrial sequence analysis was performed using the Sequencher 4. 8 software (GeneCodes), and mutations determined by comparison with the revised Cambridge Reference Sequence (rCRS)  (Anderson et al. , 1981; Andrews et al. , 1999). For each ancient sample, mitochondrial consensus HVR-1 sequences were determined from the analysis of various PCR products (at least four). To confirm the mitochondrial haplogroup deduced from the sequencing of HVR-1, several coding region mitochondrial SNPs (mt-SNPs) [characterizing founding Native American haplogroups A2, B2, C1b, C1c, C1d, and D1  (Tamm et al.
Y-chromosome analysis For the ancient male individuals 17 Y-chromosomal STR loci (DYS19, DYS385a/b, DYS389I/II, DYS390, DYS391, DYS392, DYS393, DYS437, DYS438, DYS439, DYS448, DYS456, DYS458, DYS635 and Y GATA H4) included in the AmpFlSTR ® Y-filer™ Kit (Applied Biosystems) were analyzed. The experimental conditions were those recommended by the manufacturer, except that 34 PCR cycles were used instead 30. The amplified Y-chromosomal STR products were analyzed on an ABI Prism 3500 Genetic Analyzer (Applied Biosystems) using the GeneMapper software, version 4. 1 (Applied Biosystems). Consensus haplotypes were determined from several amplifications (at least four amplifications) by preserving the alleles having been typed at least twice. To determine the Y-hap- logroups several Y-chromosomal SNPs (Q-M242, Q-M346, Q-M3, Q- M19, Q-M194, Q-M199, Q-SA01 , and C-M217)  (Jota et al.
Kinship determination To estimate the probability of putative genetic relationship between individuals, we calculated the likelihood ratio (LR) thanks to the Familias v. 2. 0 software  (Egeland et al. , 2000). To calculate these probabilities we generated a personal database collecting allele frequencies of ancient and contemporary Andean populations  (Albeza et al. , 2002; Berardi et al. , 2003; Toscanini et al. , 2003 Toscanini et al. , , 2007;; Marino et al. , 2006a Marino et al. , , 2006b Marino et al. , , 2006c;; Crossetti et al. , 2008; Borosky et al. , 2009 Borosky et al. , , 2014;; Carnese et al. , 2010; Sala et al. , 2010; Vullo et al. , 2010; Callegari-Jacques et al. , 2011; Baca et al. , 2012 Baca et al. , , 2014;; Muñoz et al. , 2012; Parolin et al. , 2014; Fehren-Schmitz et al.