dataesr/scientific-paragraphs-categorization huggingface dataset:
Pharmacological screen for drugs that selectively eliminate BRCA2-deficient cells In order to identify drugs in clinical use that can target specifically BRCA2-deficient cells, we performed a viability screen using the Prestwick chemical library (http://www. prestwickchemical. com/lib raries-screening-lib-pcl. html) containing 1,280 FDA-approved drugs. Since all drugs are suitable for human testing, any compounds identified in this screen could rapidly be repurposed for the treatment of BRCA1/2-mutated patients. We conducted two independent screens, each in triplicate, at drug concentration of 5 lM (Dataset EV1, Appendix Fig S1) using hamster BRCA2deficient VC8 cells and control BRCA2-complemented cells  (Kraakman-van der Zwet et al, 2002). We demonstrated previously  (Chaikuad et al, 2014; Zimmer et al, 2016 ) that these BRCA2-deficient cells are hypersensitive to PARP inhibitors, ERK1/2 inhibitors and pyridostatin, when compared to BRCA2-proficient counterparts.
A similar chemical library screen aiming to identify drugs that target BRCA2-deficiency was previously performed  (Evers et al, 2010)  using Brca2 À/À mouse mammary tumour-derived cell lines and the LO-PAC â 1280 Sigma library of pharmacologically active compounds (Dataset EV1). The chemical composition of this library was different from that of the Prestwick library used here, with the two libraries having approximately 25% compounds in common. Among the top scoring hits in our Prestwick library screens (Dataset EV1, Appendix Fig S1 ), we identified chlorambucil, a bifunctional alkylating agent used in the past for the treatment of breast and ovarian cancer  (Williams et al, 1985; Senn et al, 1997) , irinotecan, a topoisomerase I inhibitor in use for the treatment of cancer patients with BRCA1 mutations  (Kennedy et al, 2004) , and disulfiram, an aldehyde dehydrogenase inhibitor used in the clinic as an alcohol deterrent.
A Wild-type Balb/c mice were injected intraperitoneally with solvent (daily) or 3 mg/kg chlorambucil (daily for 5 days) or 3. 3 mg/kg cisplatin (daily for 3 days). Uptake of the apoptosis tracer 99m Tc-Duramycin 2 h after intravenous injection was quantified in selected organs using SPECT imaging in the indicated organs. Representative maximum intensity partial projections showing tracer distribution are shown. B Immunohistochemical analyses of cH2AX staining in organs from mice treated as in (A). Scale bar, 25 µm. Data information: (A) Each experimental group included n = 5 mice. Error bars represent SEM. P-values were calculated using one-way ANOVA followed by Tukey's multiple comparisons test. ****P < 0. 0001; *P < 0. 05. (B) Organs from n = 3 mice were analysed for each treatment. Error bars represent SD.
Spheroid cultures Ten thousand cells of colorectal adenocarcinoma DLD1 cells were plated in a well of a 96 round-bottom well, ultra-low attachment plate (Costar) before being centrifuged at 210 × g for 10 min. Spheroids were cultured for 4 days as in published protocols  (Friedrich et al, 2009)  before removal of half of the media and addition of the drugs diluted in that same volume. For untreated control spheroids, complete media containing the corresponding solvent was added. For drug-treated spheroids, fresh media containing the drug were added every 72 h. Pictures of the spheroids were acquired using a Nikon TE2000-E microscope, and volumes were analysed with MATLAB R2014a.
FACS analysis BRCA2 +/+ and BRCA2 À/À DLD1 cells treated for 48 h with 1 lM of cisplatin or 1 lM chlorambucil were incubated with 10 lM EdU for 1 h at 37°C. Cells were harvested by trypsinisation and processed for EdU staining using the Click-iT Plus Alexa Fluor 647 Flow Cytometry Assay Kit (Thermo Fisher Scientific). Cells were incubated with 20 lg/ml propidium iodide and 10 lg/ml RNase A (Sigma) in PBS. At least 10,000 cells were analysed by flow cytometry (FACSCalibur, BD Biosciences). Data were processed using FlowJo (FlowJo, LLC) software.
Acknowledgements We are grateful to  Dr. Violeta Serra  ( Vall d'Hebron Institute Oncology, Barcelona ) for providing the VHIO179 PDTX and to  Dr. Pavitra Kannan  ( Department of Oncology, University of Oxford ) for her technical help with spheroid cultures. The work in A. B. laboratory was supported by  Ministry of Health  (Ricerca Corrente 2018) and  Italian Association for Cancer Research (IG AIRC  grant  21579 ). This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement No. 722729. Research in M. T. laboratory is supported by  Cancer Research UK, Medical Research Council and University of Oxford.
Furthermore, it has been discovered that next to, or perhaps even rather than, carrying out a monooxygenase reaction (R-H + O 2 + 2e À + 2H + ! R-OH + H 2 O), LPMOs carry out peroxygenation of their substrate (R-H + H 2 O 2 ! R-OH + H 2 O)  [24 ]. Both reaction mechanisms have been intensely studied using computational methods  [25,26,27 ,28 ]  and kinetics  [29 ,30 ]. In a comprehensive review on oxidoreductases (potentially) involved in lignocellulose conversion, Bissaro et al. have discussed these recent discoveries in both a biological and applied perspective  [4 ]. All LPMOs described so far have a similar overall threedimensional structure and a conserved, highly characteristic surface-located catalytic mono-copper site  [31 ]. As to date, the sequence-based classification system of the CAZy database  [32]  places LPMOs into six families of socalled 'auxiliary activities' (AA9-11 and AA13-15).
The large sequence diversity and observed differences in substrate specificity (Figure  1 ), as well as the fact that LPMOs occur in several clades of the tree of life, indicate that LPMOs may be involved in biological processes Phylogenetic tree of LPMOs. The tree was built from 68 sequences, which represent the large majority of functionally characterized LPMOs. The underlying sequence alignment was based on catalytic domains only and on structural information from three selected LPMOs (TaAA9A, PDB: 2YET; SmAA10A, PDB: 2BEM; AoAA13, PDB 4OPB; underlined in the figure). The 68 sequences were aligned using the T-Coffee Expresso online tool. The resulting MSA was employed as input to build the final phylogenetic tree using PhyML available via the online platform Phylogeny. fr. The names of LPMOs with a known three-dimensional structure are printed in bold face.
Acknowledgements We thank current and former team members and collaborators for their contributions to our LPMO research, which has been supported by the  Research Council of Norway , most recently through grants  240967 ,  243663 ,  243950 ,  257622 ,  256766 ,  268002 ,  262853 ,  221576  and  226244. Additional support was received from the  Marie-Curie   FP7 COFUND People Programme , through the award of an  AgreenSkills fellowship  (under grant agreement n  267196 ), and from the  French Institut National de la Recherche Agronomique (INRA).
RESULTS AND DISCUSSION Diversity and distribution of Asgard archaea. In order to study the environmental distribution and diversity of known Asgard archaea, all affiliated 16S rRNA gene sequences were retrieved from the SILVA database (v. 132). The initial phylogenetic analysis of 4,458 putative Asgard 16S rRNA gene sequences proved challenging at first, since no monophyletic groups were retrieved at the phylum level (see Fig. S1  in the supplemental material). However, conclusive results were obtained after excluding 388 potentially chimeric or low-pintail-quality sequences (see Table  S4  posted at figshare [https://doi. org/10. 6084/m9. figshare. 9258947]). The remaining 4,070 sequences were filtered for a length greater than 1,400 bp, resulting in 2,857 unique Asgard sequences. After further clustering at a 99% identity threshold, 246 operational taxonomic units (OTUs) were obtained.
In addition to these, 32 additional Asgard 16S rRNA gene sequences from available genomes (including one new full-length 16S rRNA gene obtained in this study) and other studies (16) that were not part of the SILVA database were included in phylogenetic reconstructions. The obtained backbone tree of the Asgard archaea (Fig. 1A ; see Fig. S2  in the supplemental material) exhibited generally a similar clade organization for the Lokiarchaeota lineages as reported in earlier studies on smaller data sets  (7, 8, 17). After mapping the remaining 1,213 shorter sequences to the reference sequences in this backbone tree, 229 sequences were found to have a different taxonomic affiliation than was reflected in our phylogenetic analyses (see.
Metagenomes of Marine Sediment Loki-and Thorarchaeota Table  S4  posted at figshare). For example, there were 36 sequences that had a different taxonomic affiliation in the SILVA database (v. 132) than here with the refined phylogeny, and moreover, 193 of the good-quality shorter sequences did not match any of the reference sequences in the refined phylogeny with the given threshold (see Table  S4  posted at figshare). One of two distinct groups of sequences earlier assigned as the DAS (domain archaeal sequences) group (  16 ) was found to be a sister group of the Thorarchaeota clade, and the second branched in between two Heimdallarchaeota clades. Further inferences regarding the phylogeny and naming of these groups should await full genome sequence analyses. A thorough literature survey of metadata identified Lokiarchaeota as the group with the widest occurrence in different ecosystems compared to the other Asgard groups (Fig.
1B ). However, out of the 4,070 16S rRNA gene sequences used in this analysis, environmental information was only available for 531 (13%). Of these, 22% (117) matched to one particular Lokiarchaeota OTU (group 3) found in hypersaline environments. A previous study reported that 60% of all Lokiarchaeota (then termed DSAG) sequences available at the time in the SILVA database (v. 104) originated from hypersaline ponds in Guerrero Negro (Baja California, Mexico)  (8). Lokiarchaeota are also found in the widest pH range (5 to 9) of any Asgard archaea, whereas Odinarchaeota were only found in neutral to moderately alkaline (pH 7. 5 to 8. 5) environments (Fig. 1C ). However, the picture of the environmental distribution of Asgard lineages might change when more sequence data become available that are specifically dedicated to the detection of this superphylum in various environmental microbiomes.
Novel genomes of Loki-and Thorarchaeota. We were able to reaffirm the presence of Lokiarchaeota (DSAG) in hypersaline environments by a diversity study of the prokaryotic community in another hypersaline environment in the Baja California region, a closed salt lagoon near Puertecitos, Mexico  (15). Here, we used differential coverage binning from a metagenomic analysis of one of the sediment samples from that location (1-cm depth) and were able to acquire four new metagenome assembled genomes (MAGs) of Asgard archaea. One of these was of high quality and affiliated with the Thorarchaeota, while three additional MAGs were of medium quality and affiliated with Lokiarchaeota (according to MAG standards) (18) (Table  1 ). The Thorachaeota genome from this study (assigned as "Baja_Thor") was the most complete of all Asgard genomes available in the database and had the least contamination according to CheckM analysis.
The taxonomic affiliation of all four genomic bins that was based on their single-copy protein matches to the NR database was confirmed with the phylogenetic analysis of concatenated ribosomal proteins (Fig. 2 ). Published Lokiarchaeota genomes are generally larger than the available Thorarchaeota genomes, a trend that was also observed in this study, with the Thorarchaeota (Baja_Thor) genome size of was most closely related to the Thor_MP9T genome obtained from mangroves  (13). The three Lokiarchaeota genomes obtained in this study were more closely related to each other than the Lokiarchaeota genomes previously published (2), with their OrthoANI values ranging from 66. 8% to 68. 2%. Pangenomic analysis of Loki-and Thorarchaeota. Since a number of genomic similarities between Lokiarchaeota and Thorachaeota had been pointed out earlier and since they often share the same environment and can even be found in the same sample (as in our study), we were interested in performing a general pangenomic comparison.
5 ; see Fig. S4  in the supplemental material). The thorarchaeotal sequences, on the other hand, contain an additional 4Fe-4S domain (pfam00037) near the N terminus, a domain organization that has neither been observed neither among proven-function RDases (Fig. 5 ) nor by data mining and characterization of RDases in public databases  (26). A TAT signal sequence responsible for the translocation of the enzyme across the membrane is present in all experimentally verified respiratory RDases  (27)  but is absent in catabolic RDases studied to date  (25). TAT signals are not found in any of the Thor-or Lokiarchaeota genome sequences (Fig. 5 ; Fig. S4 ), suggesting that these putative RDases are not involved in a respiratory process; however, since archaeal signal peptides are less characterized, their absence does not prove that translocation across the cytoplasmic membrane does not occur.
A phylogenetic analysis comparing the Asgard archaeal putative RDases with the most closely related ortholog groups in the EggNOG database  (28)  revealed that the enzymes encoded on the Thor-, Lokiarchaeota, and Ferroglobus placidus genomes cluster among bacterial RDases rather than the most closely related groups of archaeal proteins (Fig. S3 ). Notably, the putative Asgard RDases cluster together, but separately from the RDase found in F. placidus. The most closely related functionally characterized enzyme (ϳ30 to 33% amino acid identity) is PceA from Sulfurospirillum multivorans. The PceA RDase is involved in organohalide respiration with tetrachloroethene as electron acceptor and reductive dechlorination of other organohalogens, including halogenated phenolic compounds, has been demonstrated  (29, 30).
MATERIALS AND METHODS Asgard 16S rRNA gene diversity. The reference sequences of the 16S rRNA genes that belong the Asgard Archaea superphylum were downloaded from the latest SILVA rRNA database (v. 132; 20 December 2017). The following filtering steps were implemented to these downloaded sequences to make a better reference phylogeny and taxonomic annotation of Asgard archaeal 16S rRNA gene sequences. (i) The sequences were filtered based on their "pintail" values (Ͻ95) from the Silva database and then were checked for "chimeric" regions based on the "Gold" reference sequences obtained and implemented through the UCHIIME pipeline  (49). (ii) Then the longer sequences (Ͼ1,400 bp) were clustered at 99% sequence identity using UCLUST  (50). (iii) These representative sequences (246 in total) along with 16S rRNA gene sequences from all available Asgard genomes, including this study (2, 3, 12, 13) together with longer Asgard 16S rRNA gene sequences from a primer-free method (16) (34 in total), were aligned using MAFFT (L-INS-i) (51) against a reference alignment from many representatives throughout TACK and Bathyarchaeota phylogeny (65 in total) (see Table  S1  posted at figshare [https://doi.
org/10. 6084/m9. figshare. 9257885]). (iv) The alignment was trimmed using Trim-Al operated under the "-gappyout" configuration  (52) , from which the maximum likelihood (ML) phylogenetic tree was calculated using IQ-TREE  (53)  with a minimum of 1,000 iterations (-bb 1000 -alrt 1000) and GTR-I-G4 as the best model. (v) Then all other high-quality shorter sequences (pintail value of Ͼ95, length of Ͻ1,400 bp) were mapped to the reference sequences of the backbone tree using USEARCH (-usearch_local -id 0. 95 -evalue 1eϪ06)  (50). (vi) Finally, all the taxonomic affiliations of sequences that differed in the SILVA database from the taxonomic affiliation based on this refined phylogeny (Fig. 1A ) were corrected. An in-house script was then used to obtain the respective GenBank file for each of these sequences from the NCBI database  (54).
Trimmed reads from both lysozyme-treated and untreated samples were coassembled using both metaSPAdes  (57)  and MEGAHIT  (58). The trimmed reads from each of the samples were then mapped to the coassembly using BBMap (59), and the average coverage for each scaffold in each sample was calculated further. The taxonomy of each scaffold was then predicted based on their universally conserved marker proteins (60) from each scaffold matching to the NCBI protein database (NR) as explained for multimetagenome binning strategies  (61, 62). The differential coverage binning targeting the assembled scaffolds/contigs of the Asgard archaea was established using the "mmgenome" toolbox (62) from the assembled metagenomes resulting from the two different assembly programs. The reads mapping to the Asgard archaea from these assemblies were then extracted and reassembled together using metaSPAdes.
The Asgard archaeal bins (metagenome assembled genomes [MAGs]) were then checked for completeness and contamination using checkM  (63). Genome annotation and orthologous groups. The genes from the MAGs were predicted using Prodigal  (64). The MAGs in this study were also analyzed with the support of MaGe (65) for various genomic features, including synteny. The COG and arCOG annotations of the proteins in the MAGs were predicted using COGsoft  (45, 66). The proteins were also annotated for functions using KEGG (Blast-KOALA) (67), BLASTP against nonredundant proteins, and the RefSeq protein database from NCBI (E value of Ͻ1eϪ10). The subcellular localization and the transmembrane domains of the proteins were predicted using multiple tools, including PSORTb, Phobius, and PRED-SIGNAL  (68) (69) (70).
The orthologous group analyses of the MAGs in this study were carried out along with all available genomes from Loki-and Thorarchaeota (April 2018) in the NCBI database together with the genomes from this study using OrthoFinder  (20)  (see Table  S3  posted at figshare [https://doi. org/10. 6084/m9. figshare. 9258926]). Analysis of putative reductive dehalogenase genes. The amino acid sequences of the putative Asgard RDases were queried against the EggNOG database (28) with a likelihood threshold of 1eϪ40. Representative sequences of the resulting orthologous groups were collected from the UniProt database (The UniProt Consortium, 2019) via their accession numbers. Epoxyqueosine reductase (QueG), which shares sequence similarity with RDases (25), was selected as an outgroup for phylogenetic reconstruction.
All QueG protein sequences were collected from the KEGG database orthology group for QueG (K18979), and a representative set was generated by application of CD-hit (73) at 50% similarity clustering. The putative Asgard RDases, EggNOG orthogroup representatives, and QueG representatives were aligned with MAFFT -auto  (51). The alignment was trimmed using TrimAl -gappyout  (52) , and subjected to maximum likelihood phylogenetic reconstruction using RaxML rapid bootstrap analysis under the PROTGAMMALG substitution model with 500 iterations. The resulting phylogenetic tree was visualized using the Interactive Tree of Life  (74). Conserved protein domains of the putative Asgard RDases were compared to those of RDases with demonstrated reductive dechlorination activity as described previously  (75)  and the putative RDase of Ferroglobus placidus.
S5. BLASTp alignment results were visualized using the genoPlotR package  (79). KEGG orthology annotations were assigned using Ghost-Koala  (80) , and COG and TIGRFAM annotations were assigned using the WebMGA server with E value thresholds of 1eϪ3. The protein sequences in MAGs predicted to be involved in the pathways of anaerobic degradation of aromatic amino acids were inferred through the pathways in Ferroglobus placidus  (33)  (BLASTp [Ͼ35% amino acid identity and Ͼ85% query coverage]). Annotation of lipid biosynthesis pathways. The different enzymes involved in the lipid biosynthesis pathway(s) identified in all MAGs from Loki-and Thorarchaeota were predicted based on the different functional annotation tools, including HMMER3 and orthogroup analysis. These enzymes were inferred based on the known enzymes and pathways  (40).
Data availability. The genome bins from this study can be found at NCBI BioProject ID PRJNA521734. The MAGs from this study have been deposited under the following accession numbers: Baja_Thor ("Candidatus Thorarchaeota archaeon" BC), SHMX00000000; Baja_Loki1 ("Candidatus Lokiarchaeota archaeon" BC1), SHMU00000000; Baja_Loki2 ("Candidatus Lokiarchaeota archaeon" BC2),.
Although each method has its own advantages, several previous studies have demonstrated the complementary power of stable isotopic and dental health data when used together  [15] [16] [17]. Such approaches can further enhance the resolution of the reconstruction, thus permitting a better understanding of the general trend of subsistence economy through time. We have collated stable isotopic and dental caries data from a total of 77 sites, spanning over a period of 7250 years and traversing eight modern provinces. From the data, we report diachronic trends in terms of isotopic measurements as well as dental health in response to the incorporation of C 3 crops into northern and northwestern Chinese diets, and discuss these patterns within their larger socio-political and geographic contexts.
Temporal and spatial span of study The time period under scrutiny in this study spans from the mid-Neolithic period (c. 9000 BP), to after the collapse of the Eastern Han Dynasty (220 AD) (Table  1 ). This timeline of our dataset is constrained firstly by the paucity of data (both isotopic and osteological) from sites prior to 9000 BP. Secondly, by the profoundly fragmentary political climate in Northern China after the collapse of the Eastern Han Dynasty. This chaotic period, known as the Sixteen Kingdoms Period (304-439 AD) was a marked departure from the relative stability of the Han Dynasties, in which the formerly unified northern China was split into 16 different states ruled by several different ethnic groups. The political heterogeneity in this period effectively eliminates the ability to sensibly constrain and account for factors when looking at changes in dietary practices in this region.
As very few site reports provided coordinates, most of the site locations are estimated from textual descriptions. Consequently, most site coordinates and elevations should only be treated as a general reference. All in all, we hope to control for as much geographic variations as possible, so that the differences in our collected data can be isolated and attributed to differences in subsistence economies. All sites involved are listed in Table  2  and plotted in Fig  1. Data were collected from archaeological sites that span the spectrum of subsistence economies from Neolithic early agriculturalists to dynastic intensified agriculturalists, with varying levels of crop cultivation development. In order to search for relevant datasets, a systematic literature review was conducted using Google Scholar and China National Knowledge Infrastructure (CNKI 中國知網).
Only papers or reports published before September 2017 are included. To the best of our knowledge, our sample adequately represents all of the indexed literature on this subject. Statistical analyses of the results were conducted using R (version 3. 0. 3). Graphs were created using the package ggplot2  [120]. To help visualize the trends in our stable isotopic data, we used LOESS (locally weighted scatter-plot smoother), a non-parametric regression analytical tool to create smoothed regression lines for the time-series data. These lines were created with the function "geom_smooth" in ggplot2, using the method "loess", with a confidence interval set at 0. 95. Detailed R scripts for all the analyses performed in this study are provided in S1 File-R scripts.