dataesr/scientific-paragraphs-categorization huggingface dataset:
For ancient DNA analysis, 44 bone and teeth samples were selected from Kesslerloch (Switzerland), Kraków Spadzista (Poland), Předmostί (Czech Republic), and additional individuals from Geißenklösterle and Hohle Fels to those analysed above. A summary of analysis performed per sample can be found in Supplementary Data S1. In-solution capture of mammoth mitochondrial genomes was successfully carried out with capture baits generated by long-range PCR using three pairs of newly designed primers (Supplementary Data S4) on DNA extracted from elephant blood (see Methods and Supplementary Information). Of the forty-four samples genetically tested, after capture twenty produced mitochondrial genomes meeting the 'relaxed' criteria of Chang et al 3. (see Table  1  and methods), resulting in 5. 3 to 522.
005%-0. 024%, Supplementary Data S5), suggesting bad DNA preservation at the site. Both Kraków Spadzista and Předmostί are open air rather than the cave environments of the other sites, the former type making specimens more susceptible to weathering, temperature fluctuation, pH and precipitation changes, which could lead to variable levels of DNA preservation  24. To confirm isotopic observations and ZooMS taxonomic identification, all shotgun libraries were mapped to a white rhinoceros assembly (Ceratotherium simum simum; NCBI Accession: GCA_000283155. 1). A ten-fold increase in DNA mapping to the rhinoceros genome sequence versus a hybrid elephant/mammoth genome sequence (see Methods) identified the morphologically identified "mammoth/rhino size" Hohle Fels rib fragment sample HF_67565_89_1632_Va as rhinoceros (Supplementary Data S5), in agreement with isotopic and ZooMS observations.
While the BEAST coalescence tree from Enk et al. 4  showed stronger support on this node (in contrast to ML), the Chang et al. 3  tree that includes many more clade III mitochondrial sequences again shows low support. This low support could be considered to represent a trifurcation in the relationship between the three clades as explicitly presented in the ML tree presented by Enk et al. 4  , thus making these relationships difficult to resolve. This was further explored with our dataset by generating Neighbour-Joining (NJ), Maximum Parsimony (MP), ML, and Bayesian trees (see Methods and Supplementary Information) which again showed a low statistical support on the node connecting clade II and III (Fig. 1 , Supplementary Figures  S3-S6 ). As this suggested that there are potential difficulties in the ability to resolve the relationships between the three clades, an additional test to assess sequence relationships between each possible pairing of clades was performed by comparing the number of 'shared' derived SNPs between each clade (see Supplementary Information).
More recent BEAST analysis on whole Eurasian datasets by Palkopoulou et al  2. did not detect any noticeable reduction of the effective population size during this period. Nuclear DNA would potentially allow to test for functional differences between all three populations, defined by mtDNA clades, in particular why clade I seems to be more 'dominant' than the other two clades. This type of data would be particularly pertinent due to the 'contradictions' identified in our study in the homoplasies observed between the different clades along the mitochondrial genome, thus questioning our ability of current models to elucidate the intra-population dynamics of the woolly mammoth. Generated from highly fragmentary remains, the 20 central European Late Pleistocene mitochondrial genomes produced here increases the number of mitochondrial genome sequences from this region.
Methods Sample Selection and Zooarchaeological Identification. Forty-four samples spanning ~34 ka to 13 ka BP (38 ka -15 ka cal BP) were obtained from archaeological collections or previous stable isotopic analysis  7, [11] [12] [13]  from the Late Pleistocene sites: Kesslerloch (Switzerland, n = 1), Geißenklösterle (Germany, n = 12), Hohle Fels (Germany, n = 10), Předmostί (Czech Republic n = 6), and Kraków Spadzista (Poland, n = 15) (See Supplementary Data S1). Three further collagen samples from Geißenklösterle that were previously radiocarbon dated  10  were analysed for stable isotopic and ZooMS analysis. An additional collagen sample from Hohle Fels from previous dating was also analysed for carbon and nitrogen stable isotopes. For samples from Geißenklösterle and Hohle Fels, a zooarchaeological identification of woolly mammoth was not always possible due to high fragmentation of remains (especially of ribs, an important raw material for the production of tools at the site  13  ).
International standards for  13  C (V-PDB) and  15  N (atmospheric air) were used and measurements were normalised to δ 13 C values of USGS24 (δ 13 C = -16. 00) and to δ 15 N values of IAEA 305 A (δ 15 n = 39. 80). Analytical error, based on within-run replicate measurements of laboratory standards (albumen, modern collagen, USGS 24, IAEA 305 A) was ±0. 1 for δ 13 C and ±0. 2 for δ 15 N. Collagen from the isotopic analysis of specimens yielding mitochondrial genomes (see below) and that had not been previously dated was additionally sub-sampled for radiocarbon dating at the Oxford Radiocarbon Accelerator Unit (ORAU). 14 C dates were converted to F 14 C dates and then calibrated in OxCal 4. 2 using the IntCal13 calibration curve. Calibrated 14 C dates are indicated by 'cal BP' (Supplementary Data S1).
ZooMS Identification. Collagen extracted from samples from Geißenklösterle and Hohle Fels during isotopic analysis was subsampled (<1 mg dry collagen) for collagen fingerprinting by Zooarchaeology by Mass Spectrometry (ZooMS  40  ). Eleven samples either identified as "mammoth" or "rhino" were chosen to provide within-site spectral references when assessing samples assigned "mammoth/rhino size" from morphological and isotopic observations. These included seven samples from Geißenklösterle and four from Hohle Fels (Supplementary Data S2). ZooMS MALDI-TOF-MS analysis was conducted following Welker et al. 41  , (see Supplementary Information) and peptide markers were identified against the ZooMS sequence database presented by Welker et al. 20. ZooMS relies on collagen type I (COL1) amino acid sequence variation to differentiate between closely related species.
All laboratory consumables and reagents are UV irradiated when possible. Full clean room suits with hairnets, masks and double pairs of gloves are worn. Positive extraction controls in the form of an internal well-characterised cave bear bone were included, and one negative control during each extraction and two controls per library preparation session. aDNA extraction was performed using a modified version of a silica-based protocol  45. Extracts were converted to double indexed sequencing libraries  46, 47  and transferred to a modern DNA lab for amplification (Supplementary Data S4). Enrichment of mitochondrial DNA was performed following the protocol of Maricic et al. 29. Template material was provided by Wilhelma Zoo (Stuttgart, Germany) as a subsample of blood taken from two Asian elephants after routine veterinary care by the in-house Veterinary practitioner.
Blood was drawn using standard venipuncture techniques in accordance with the relevant veterinary regulations and practises of the Zoo. No experimentation was performed on the elephants for the purpose of this study. The DNA extracted using a QIAamp DNA Blood Mini kit (Qiagen). Three new primer pairs (Supplementary Data S4) were manually designed to produce three ~5. 5 k bp amplicons spanning the entire Elephas maximus mitochondrial genome (NCBI Accession: EF588275. 2) through Long-Range PCR (LR-PCR). Amplicons sonicated to an length of <1000 bp were converted to biotinylated bait libraries and in-solution bead capture was performed  29. Pre-and post-enrichment libraries were then sequenced on an Illumina HiSeq 2500 (University of Tübingen). More information about methods from all laboratory protocols can be found in the Supplementary Information.
aDNA Pre-Processing, Consensus Calling and Phylogenetic Analysis. Endogenous DNA quantification, aDNA authentication and mitochondrial genome reconstruction were performed with an in-house version of the NGS data processing pipeline EAGER v1. 92 48 using a hybrid reference consisting of the African elephant nuclear genome and the woolly mammoth mitochondrial genome (NCBI Accession: GCA_000001905. 1 and NC_007596. 2 respectively). The final consensus sequences were called by GenConS (Generate Consensus Sequence, v1), a new consensus caller tool designed here to reduce the risk of false positive SNP positions that may be erroneously called due to typical aDNA damaged molecules  49, 50. In brief, taking a SNP and optionally an InDel VCF file as input (both were used in this study), a position is analysed to assess whether an aDNA miscoding pattern  49  is present prior to SNP calling.
If a damage pattern is detected, the coverage of the miscoding base is artificially reduced in order to reduce the chance of a false-positive SNP call due to damage. Additional thresholds apply: a minimum coverage for a position to be called (5 in this study) and a minimal allele frequency for a call to made (75% in this study). When either are not met an 'N' is called. The resulting consensus sequence is written in FASTA format. Furthermore, a file in Consensus Call Format is generated giving detailed information of each consensus call (further information about GenConS can be found in the Supplementary Information). GenConS is available in the TOPAS package at www. github. com/subwaystation/TOPAS. Previously published mitochondrial genomes  [3] [4] [5] [25] [26] [27] 51  were downloaded from the NCBI SRA database, and the consensus sequences generated here were aligned with these using 'Consensus Align' in Geneious vR8  52.
Evolutio ary models were estimated using modelgenerator v85 53 , jModelTest2 v2. 1. 10 54 and IQ-Tree v1. 4. 2  55  , and phylogenetic analysis performed using Geneious vR8 for Neighbour Joining, MEGA for Maximum Parsimony  56  , IQ-Tree v1. 4. 2 for Maximum Likelihood  57, 58  , and MrBayes v3. 2. 6 for Bayesian 59 methods (Supplementary Information, Supplementary Data S6). Accession Codes. Se uencing libraries have been deposited in the European Nucleotide Archive (ENA) with the project accession PRJEB21582. Fi al mitochondrial sequences have been stored in the NCBI GenBank database with accession numbers MF579931-MF579950.
Acknowledgements We thank  Maria Malina  for assistance with the Geißenklösterle and  Hohle Fels  collections. We also express our gratitude to  Elephants Pama and Zella , as well as  Annika Weigold  and  Tobias Knauf-Witzens  at  Wilhelma Zoo, Stuttgart  for donation of elephant blood. We also acknowledge  Maria Spyrou  for help with primer design. Matthew J. Collins ,  Annabell Reiner  and  Luke Spindler  are thanked for support in the contexts of the proteomics work. We thank  Dan Chang  and  Beth Shapiro  for providing sequences from their study. M. L-G. is funded by a grant from the  Czech Science Foundation  ( GACR 15-06446S ) entitled "The relationships Between humans and larges canids -the dogs and wolves of the Gravettian Pˇredmostί site (Moravia)". This work was partly supported by a  DAAD One Year Postgraduate Scholarship  to J.
Knowledge of the gene expression signatures in CTCs and reconstruction of the inherent CTC signaling network could offer a valuable resource for further functional exploitation, such as in the design of targeted therapies, and in the identification of potential markers to monitor tumor progression and the initial lodging of CTCs in distant organs. This information may ultimately offer a new perspective to target CTCs to prevent tumor cell dissemination and metastasis  23. In the present study, we collected, reanalyzed and mapped the gene expression data from several published studies in CTCs to the signaling pathways and networks available to date in an attempt to reveal new insights into CTC signaling. We integrated the transcriptomic changes observed in CTCs across various cancer types to identify with high confidence putative candidate markers involved in extravasation of CTCs.
Results and Discussion CTC phenotying and genotyping studies have expanded our knowledge on tumor progression and tumor evolution  24, 25. Though challenging, the emerging clinical applications of CTCs in cancer detection and disease management are indisputable  26. We sought to explore unresolved biological and functional facets of CTCs and understand their molecular underpinnings in a clinical context. To this end, we collated and integrated data from gene expression profiles of CTCs within the existing literature, as depicted in the workflow in Fig. 1. Our literature survey using a relevant keyword search in PubMed resulted in 3,242 publications (Supplementary Fig. 1a, b ). This was further screened and filtered to a total of 98 papers that were annotated for gene expression data in CTCs [see Methods].
Differentially expressed genes (DEGs), taken from qualitative and semi-quantitative experiments, resulted in a gene list of 7,572 molecules (denoted as 'CTC_ALL'; Supplementary Table  S1 ). Out of these, a list of 7,209 differentially expressed genes from large-scale transcriptomics studies from five cancers were compiled separately (denoted as 'CTC_FC'; Supplementary Table  S2 ; Supplementary Fig. 1c ). Fold change (FC) values for gene expression in CTCs were available only for the CTC_FC list  19, 20, [27] [28] [29]  (Table  1 ). Despite major advances in CTC isolation techniques, obtaining pure CTCs from peripheral blood is still challenging. We leveraged expression data from large-scale transcriptomics studies that reported DEGs in CTCs and compiled our gene list for molecular characterization of CTCs predominantly from five such studies based on the quality of their isolation techniques.
These five studies have used one or more advanced techniques to overcome the problem of contamination of CTC isolates with hematological and non-viable cells, almost successfully. The details of isolation techniques used to obtain relatively pure CTC populations for these five studies have been compiled in Supplementary Table  S3. Improved isolation methods to yield pure populations of CTCs will unfold new opportunities for global gene expression analysis and molecular characterization. Ingenuity Pathway Analysis (IPA) of the overexpressed genes (n = 3,251) in the CTC_ALL dataset showed significant enrichment of the 'Leukocyte Extravasation Pathway' at 44   [30] [31] [32] [33] [34] [35] [36]. The top predicted upstream regulator was TGFβ1(p = 2. 99E-39), and the IPA gene view indicated TGFβ1 as the upstream regulator of many genes that play a vital role in cancer progression, such as SERPINE1, ACTA2, CDKN1A, SMAD3, SMAD2, CTGF, FN1, CDH1, SMAD7, COL1A1, FOXP3, CDKN2B, COL1A2, VIM.
The top diseases were 'Cancer' (total no. of associated molecules = 2,871) and 'Organismal Injury and Abnormalities' (2,886), both with a p-value range of 2. 53E-08 to 5. 08E-33. The top molecular functions were 'Cell Death and Survival' (908; p-value range, 1. 39E-08 to 1. 42E-61) with a predicted decrease in cell death and predicted increase in cell survival, and 'Cellular Growth and Proliferation' (941; p-value range, 1. 3E-08 to 7. 19E-55). Similarly, IPA analysis of the CTC_FC dataset of overexpressed genes (n = 2,652) positioned the leukocyte extravasation pathway as third in the rank of enrichment (p = 2. 6E-12; z-score: 6. 260). The top ten enriched canonical pathways associated with each dataset are illustrated in Fig. 2.
To explore this further, we expanded the annotated leukocyte extravasation pathway map and built a complete map of the leukocyte/CTC extravasation pathway by integrating information from current pathway resources, review articles and other literature on CTCs (Supplementary Table  S4 ). The entities in our dataset were then mapped to the expanded pathway using the GeneSpring Pathway Architect software tool (version 13. 0). The enriched map of the Leukocyte/CTC extravasation pathway generated is shown in Fig. 3. The pathway map illustrates all the upregulated molecules in our datasets (shown in blue colored boxes) that have been identified in CTCs across different cancers that may use a common mode of extravasation and metastatic colonization. The mechanistic roles of the different molecules identified and enriched in the integrated leukocyte/ CTC pathway are highlighted and discussed below in greater detail.
In our combined pan-cancer datasets, we found upregulation of several integrins, including ITGB1, ITGB2, ITGAL, ITGAM and ITGA4 and cell adhesion markers such as ICAM3, PECAM1, JAM3 and F11R. The signal then travels via a chain of phosphorylation events that ultimately lead to phosphorylation and activation of the myosin light chain (MLC), and this causes retraction of the actin cytoskeleton in the tail-end of the migrating leukocyte or CTC  42. We found several MLC molecules to be upregulated in our datasets (Fig. 3 ). Cancer cells and leukocytes both use a group of adhesion receptors called galectins. Galectin-3 (LGALS3), known to be overexpressed in circulation of cancer patients, interacts with MUC1 resulting in exposure of cell surface adhesion molecule such as CD44 enhancing cancer cell adhesion  43.
Correspondingly, signaling events within the endothelial cell stimulate endothelial cell retraction followed by localized disassembly of the endothelial adherens junctions comprising cadherins and CAMs by the action of matrix metalloproteases (MMPs), actin remodeling and focal adhesion kinase (FAK) activation. This is consistent with the upregulation of several MMPs observed in our datasets and illustrated in Fig. 3. On the endothelial cell, ICAM1 signals activate calcium flux and protein kinase C (PKC), which are required for leukocyte migration. Similarly, vascular cell adhesion molecule (VCAM1) ligation is required to open the "endothelial passage" by activating the production of reactive oxygen species (ROS) by NADPH oxidase. This in turn activates MMPs, which causes a loss of VE-cadherin-mediated Pathways identified are represented on the y-axis.
Tumor-secreted C-terminal fibrinogen-like domain of angiopoietin-like 4 (ANGPTL4) instigates vascular 'leakiness' and endothelial disruption by binding to integrins and TGFβ via the SMAD signaling pathway  47, 48. In our datasets, we found elevated expression of ANGPTL2 and ANGPTL4. In sum, the enrichment observed in our analyses of the pan-cancer datasets will advance our current understanding of the biological mechanisms behind cancer cell dissemination and allow for a more targeted validation, with the hope to improve the management and prevention of metastatic progression. Often pathways do not depict all the interconnections and cross-talk, hence in addition to pathways, we constructed a LEP-related network using available protein-protein interactions in STRING database (version 10. 0; http://string-db.
PIK2/Akt signaling pathway is activated in VM, which in turn activates MMPs. These proteases cleave Ln-5ϒ 2 chains, and the resulting fragments create plasticity within the extracellular matrix (ECM)  59. FAK, which we observed to be overexpressed in the pancreatic and melanoma CTC datasets, activates ERK1/2, which induces VM via MMPs  60, 61. We also observed overexpression of HIF1A-a master regulator of responses to hypoxia-in the prostate CTCs dataset. HIF1A activates VEGF expression, which in turn promotes VM by inducing EMT  62, 63. Further in-depth investigations and molecular characterizations of VM in tumor cells may reveal novel insights into the metastatic process. The leukocyte extravasation pathway was built using the KEGG  96, 97  pathway (hsa04670) as a template and further appended with reactions represented in IPA (IPA, Qiagen, Redwood City, http://www.
Unsupervised clustering of differentially expressed genes in CTCs. To understand the gene expression profiles observed in CTCs among various cancers and identify signatures, we performed unsupervised clustering (using GENE-E version 3. 0. 206; http://www. broadinstitute. org/cancer/software/GENE-E/index. html) and principal component analysis (using GeneSpring GX software version 13. 0) on differentially expressed gene datasets from the CTC_FC list (Fig. 4a, b , respectively). The top gene transcript, secreted protein acid and rich in cysteine (SPARC), codes for an important ECM protein reported to be differentially expressed and a modulator of several cellular processes required for metastasis initiation in the tumor microenvironment  64, 65. In colorectal, ovarian and prostate cancers, SPARC acts as a tumor suppressor by repressing angiogenesis by inhibiting vascular endothelial growth factor (VEGF); whereas, in pancreatic, breast, melanoma and glioblastoma cancers, its overexpression leads to the activation of MMPs, which in turn promote EMT  66.
PLS3 is strongly associated with prognosis of metastatic cancers; yet, others show that PLS3 expression is unaffected during the EMT process  75, 76. Accordingly, we noted an overexpression of PLS3 mRNA in breast and colorectal CTCs datasets but downregulation in prostate CTCs. Patterns of differentially expressed genes in CTCs when compared to primary tumors or peripheral blood mononuclear cells (PBMCs) or normal tissue (Supplementary Fig. 2 ) were visualized by unsupervised clustering of DEGs for each sub-group (Fig. 5 ). We observed that colorectal CTCs and pancreatic CTCs showed similar gene expression profiles at the transcriptomic level and in their primary tumors. This could mean that CTC expression profiles could be used to reveal distinct expression patterns in some cancer types; understanding the diversities or similarities in CTC profiles across various cancer types may offer insights for the design of new therapeutic strategies  77, 78.
In a benchmark study, circulating breast cancer cells were shown to express both epithelial and mesenchymal markers  17. This observation was supported in a subsequent study by our group  80  , where we employed RNA-FISH to identify transcripts encoding various EMT markers (epithelial: E-cadherin, CK5, CK7, CK18, CK19, and EPCAM; and mesenchymal: VIM and FSCN1). We found heterogeneous expression of both epithelial and mesenchymal markers in the cultured CTCs. In concordance, others also suggest that CTCs from various cancer types may exhibit a dynamic EMT profile, and possibly showing an intermediate phenotype rather than a complete epithelial or mesenchymal state  6. The plasticity between epithelial and mesenchymal states in CTCs across cancers was assessed in two ways; 1) we employed the EMT scoring method  18  [see Methods] to compute the generic EMT scores using transcriptome datasets from the five cancer studies (same studies used to generate CTC_FC dataset; Table  1 ).
The distribution of expression values observed for the EMT signature genes in the CTC_FC dataset is shown in Supplementary Fig. 3. The swarm plot shows co-expression of epithelial and mesenchymal markers in CTCs derived from the five cancers. The observation from both analyses is in concordance with earlier studies based on experimental  86  and mathematical models  87  , indicating an intermediate EMT phenotype in CTCs across cancers. The hybrid intermediate state may act as a crucial driver for tumor progression and metastatic colonization. In addition, we computed the EMT scores for each cancer CTC dataset and compared them with their respective tumor and cancer cell lines EMT scores [see Methods]. The EMT score spectrum of CTCs exhibit similarities with our published  18  cancer-specific EMT landscape of respective cell lines and tumors, albeit the average EMT score of CTCs is slightly higher in the case of colon, breast and pancreatic cancer datasets.