dataesr/scientific-paragraphs-categorization huggingface dataset:
Using academic search engines such as Google Scholar, the Chinese National Knowledge Infrastructure (CNKI), and ScienceON, a total of 3,304 previously published archaeological human and faunal stable isotope data from 136 archaeological sites in East and Northeast Asia, spanning over a period of 8,0 0 0 years (c. 70 0 0 BC to AD 10 0 0) are collected. This compilation only includes data that are reported alongside atomic C/N ratios. For those that are published along with any additional collagen quality control (QC) criteria, only those that has passed the conventional criteria are included: %C between 15. 3% and 47%; %N between 5. 5% and 17. 3%; and atomic C/N ratio between 2. 9 and 3. 6  [1] [2] [3] [4]. Sulfur isotope data are not screened, and are included with %S, C/S, and N/S ratios as reported (please refer to the main text for more discussion on the QC for sulfur isotope measurements).
Data Description This collection is consisted of a total of 3,304 entries of stable carbon and nitrogen isotope data, of which 3,224 (2,343 human, 881 fauna) come from unique individuals, with additional 249 entries of stable sulfur isotope data from 241 (190 human, 51 fauna) unique individuals. Multi-tissue measurements are available from 80 individuals. All stable carbon and nitrogen isotope data come from 136 archaeological sites, where 15 sites also have corroborating stable sulfur isotope data ( Fig. 1  ). An interactive map showing the locations of all sites is also available on IsoArcH ( https://database. isoarch. eu/map. php ). The dataset is deposited in IsoArcH  [5]  under the following DOI: https://doi. org/10. 48530/isoarch. 2021. 001. Table  1  provides a summary of all the sites involved, describing the locations, archaeological cultures and time periods, and numbers of human and/or faunal samples from each respective site.
All reported time periods are gathered from the studies reporting the stable isotope data, more refined chronology of the sites may be available in other associated reports. Geographically, this area is consisted of several distinctive geological features, including plains, mountains, steppes, plateaus, deserts, and islands. A general description of the geographic Table  1  Site ID, names, references, cultural phases and time periods, coordinates (latitudes and longitudes), elevations, general description of geographic zones, and numbers of δ 13 C and δ 15 N data from all sites included in the database. Bracketed numbers are the number of samples with corroborating δ 34 S values. Site ID corresponds to the numbers shown on Fig. 1. * indicates that the faunal assemblage is not contemporaneous with the human assemblage.
A key to site IDs is provided in Table  1. The black star indicates the location of Nanzhuangtou, the site with the earliest evidence of domesticated crop in Northeast Asia (no isotope data is available from this site). Modern geopolitical borders included for reference. zones, altitude, and cultural affiliations are included in Table  1. Few coordinates are exact. For a vast majority of the sites, coordinates are extrapolated from textual descriptions, therefore only represent the approximated locations of the sites. Elevations are obtained from mapcoordinates. net ( https://www. mapcoordinates. net/en ) using the approximated coordinates described above. Distances to the coast (online database only, not shown in Table  1  ) are estimated using the "measure distance" function on Google Maps.
Note that the "coast" refers to the modern coastline as shown on the satellite view on Google maps. Therefore, all these measurements should only be treated as a general reference. Meta-datasets from archaeological contexts should not be constrained by modern geopolitical borders, therefore, the area chosen for data collection is not limited by such. The data included in this dataset come from archaeological sites traversing several modern countries. Therefore, wherever possible, bibliographic information (i. e. author names, article/book/thesis titles), and site names are provided in both the original published language as well as English. This is an important point, as Romanization of site names can be inconsistent across publications: e. g. can be spelled as Ganguya  [18 , 119]  or Ganguai  [7]  ; and was spelled as Sunhung Mural Tomb in one study  [101]  , and as Sunheung Mural Tomb in others  [125 , 126].
By providing bibliographic information and site names in its original language, users of this dataset should be able to locate the original publication(s) regarding particular site(s), or to pursue more information on particular site(s) beyond the references provided here. Note that the original site names cannot be recovered for two of the sites, Xinhua (Shaanxi, PRC) and Zhanguo (Shaanxi, PRC), due to the lack of precise information provided in the original report  [22]. Three other site names from the same report: Lintong, Lixian, and Xunyi are county names and therefore offer little information about the sites. Hence, the coordinates of these five sites are only estimated from the figure provided in the original report. Data are systemically collected using academic search engines Google Scholar ( https://scholar.
google. com/ ), the China National Knowledge Infrastructure ( https://www. cnki. net/ ), and Sci-enceON ( https://scienceon. kisti. re. kr/main/mainForm. do ), with keywords such as "archaeology", "stable isotope analysis", "collagen", "carbon isotope", "nitrogen isotope", "palaeodiet", in English, Chinese, and Korean. Search results included publications in peer-reviewed journals, research dissertations/theses, and book chapters. Focusing on reports published before December 2019, only collagen (bone and dentine) stable isotope data are collected. In some instances, the same dataset is published in multiple languages. In that case, only one publication is cited. This collection is by no means an exhaustive list of all stable isotopic data from within the described geographical and temporal zones.
It is hoped that this effort can serve to initiate more active research sharing and collaborations among Asian archaeologists, and more importantly, to encourage more scholars to contribute their research data from the region to help filling in the temporal and geographical gaps in the current dataset. Descriptions of the collagen extraction protocol are provided in all reports, mostly following the standard procedure after the Longin method  [127]  , with various minor modifications. Not all studies report conventional QC criteria for collagen. In this collection, only studies that include at least the atomic C/N ratios are included. All data in this dataset has atomic C/N ratios between 2. 9 and 3. 6, and has passed all other QC criteria, whenever included  [1] [2] [3] [4]. While there are certain advantages to also include data that is not reported with, or did not pass the QC criteria, the key priority of this database is to allow users to obtain data that is immediately comparable.
Therefore, users interested in those excluded data are recommended to seek out the original reports. For stable sulfur isotope data, even though most studies follow the QC outlined in Nehlich & Richards' report  [128]  , the efficacy of using %S to determine whether diagenetic changes have taken place is debated  [129]. Therefore, before a universally accepted QC for stable sulfur isotope measurements in collagen is established, all previously published δ 34 S values are included in this dataset, along with information of their elemental compositions (%S, C/S, N/S). Users are encouraged to check the elemental compositions associated with all S isotope data in this dataset carefully before selecting the data for analysis. Most studies report isotopic measurements to 1 significant figure. However, some report up to 3 significant figures.
Acknowledgments The author is grateful to  Dr. Kevin Salesse  for his technical support. Thanks go to  Joe C Hepburn  for making the map, and to  Drs. Christine Lee  and  Michael B. C. Rivera  for help with site names translation. The author is supported by the  Agence Nationale de la Recherche  (  Project - ANR-17-CE27-0023 704 "NEOGENRE" ).
Primary multiple myeloma cells Patients' MMCs were purified using anti-CD138 MACS microbeads (Miltenyi Biotec, Bergisch Gladbach, Germany) and their gene expression profile (GEP) obtained using Affymetrix U133 plus 2. 0 microarrays as described  [20] [21] [22]. We also used publicly available Affymetrix GEP (Gene Expression Omnibus, accession number GSE2658) of a cohort of 345 purified MMC from previously untreated patients from the University of Arkansas for Medical Sciences (UAMS, Little Rock, AR), termed in the following UAMS-TT2 cohort. These patients were treated with total therapy 2 including high-dose melphalan (HDM) and autologous stem cell transplantation (ASCT)  [23]. We also used Affymetrix data from total therapy 3 cohort (UAMS-TT3; n = 158; E-TABM-1138)  [24]. Mouse 5T33MMvv cells originated spontaneously in aging C57BL/KaLwRij mice and have since been propagated in vivo by intravenous transfer of diseased marrow in young syngeneic mice as described  [25, 26].
Human myeloma cell lines (HMCLs) XG human myeloma cell lines (HMCLs) were cultured in the presence of recombinant IL-6 as previously described  [28]. XG7 Melphalan-resistant cell line was derived from the sensitive parental XG7 cells  [29]. JJN3 was kindly provided by Dr Van Riet (Brussels, Belgium), JIM3 by Dr MacLennan (Birmingham, UK) and MM1S by Dr S. Rosen (Chicago, USA). AMO-1, LP1, L363, U266, OPM2, and SKMM2 were purchased from DSMZ (Braunsweig, Germany) and RPMI8226 from ATTC (Rockville, MD, USA). HMCLs were authenticated according to their short tandem repeat profiling and their gene expression profiling using Affymetrix U133 plus 2. 0 microarrays deposited in the ArrayExpress public database under accession numbers E-TABM-937 and E-TABM-1088  [28].
RNA sequencing HMCLs were cultured for 16 h with or without 5 µM of UNC-0379, or infected with shControl or shRNA SETD8 pSIREN vectors for 48 h. RNA samples were collected as previously described. The RNA sequencing (RNA-seq) library preparation was done with 150 ng of input RNA using the Illumina TruSeq Stranded mRNA Library Prep Kit. Paired-end RNA-seq were performed with Illumina NextSeq sequencing instrument (Helixio, Clermont-Ferrand, France). RNA-seq read pairs were mapped to the reference human GRCh37 genome using the STAR aligner  [32]. All statistical analyses were performed with the statistics software R (version 3. 2. 3; available from https:// www. r-proje ct. org) and R packages developed by BioConductor project (available from https:// www. bioco nduct or. org/)  [33]. The expression level of each gene was summarized and normalized using DESeq2 R/Bioconductor package  [34].
Differential expression analysis was performed using DESeq2 pipeline. p values were adjusted to control the global FDR across all comparisons with the default option of the DESeq2 package. Genes were considered differentially expressed if they had an adjusted p value of 0. 05 and a fold change of 1. 5. Pathway enrichment analyses were performed using online the curated gene set collection on the Gene Set Enrichment Analysis software (http:// softw are. broad insti tute. org/ gsea/ msigdb/ index. jsp)  [34, 35].
Gene expression profiling and statistical analyses Gene expression data were normalized with the MAS5 algorithm and analyses processed with GenomicScape (http:// www. genom icsca pe. com)  [37]  the R. 2. 10. 1 and bioconductor version 2. 5 programs  [33]. Gene Set Expression Analysis (GSEA) was used to identify genes and pathways differentially expressed between populations. Univariate and multivariate analysis of genes prognostic for patients' survival was performed using the Cox proportional hazard model. Difference in overall survival between groups of patients was assayed with a log-rank test and survival curves plotted using the Kaplan-Meier method (Maxstat R package)  [38].
53BP1 staining-immunofluorescence microscopy After deposition on slides using a Cytospin centrifuge (600 r. p. m. for 10 min), cells were fixed with 4% PFA, permeabilized with 0. 5% Triton in PBS and saturated with 5% bovine milk in PBS. The rabbit anti-53BP1 (Novus Biologicals-Littleton, CO, USA-NB100304) antibody was diluted 1/300 in 5% bovine milk in PBS, and deposited on cytospins for 60 min at room temperature. Slides were washed twice and anti-rabbit alexa 488-conjugated antibody (diluted 1/500 in 5% bovine milk in PBS) was added for 60 min at room temperature. Slides were washed and mounted with Vectashield and 1% DAPI. Images and fluorescence were captured with a ZEISS Axio Imager Z2 microscope (X63 objective) (Oberkochen, Germany), analyzed with Omero (omero. mri. cnrs. fr) server and ImageJ software.
The cytotoxicity of UNC-0379 treatment is not dependent on p53 in malignant plasma cells Previous studies showed that UNC-0379-induced cell death depends on the activation of p53 in neuroblastoma 3 Gene changes in HMCLs upon genetic or pharmacological inhibition of SETD8. a Diagram of the experimental plan. b Molecular signatures of UNC-0379 and shSETD8 downregulated (blue bar plot) and up-regulated bar plot) genes compared to control were investigated using GSEA Database (all curated gene sets), and relevant pathways are presented (FDR q value ≤ 0. 05). Immunoblot analysis of indicated proteins in total lysates from or untreated XG7 HMCL. β-actin and H2A. X were as loading controls cancer models (NB), thereby rending p53-deficient NB cells more resistant to SETD8 inhibition  [41]. To determine whether the cytotoxic effects of the pharmacological inhibition of SEDT8 mediated by UNC-0379 were also dependent on p53 activation in MM cells, we compared the UNC-0379 response in p53 wild-type HMCLs (n = 7) and p53-deficient HMCLs (n = 5)  [28, 41]  that displayed similar SETD8 expression levels (Additional files 2 and 3: figure  S8A ).
05, **p value < 0. 01, ns: not significative). b Analysis of RNA synthesis by 5 min 5-Ethynyl Uridine (EU) pulse-labeling in XG7 cells depleted or not for p53 and treated with 3 μM of UNC-0379 or the vehicle DMSO (mock) for 24 h. Scale bar: 10 μm. EU intensity was quantified using CellProfiler "Human cell" pipeline. A minimum of 200 cells were quantified per condition. Graph shows the quantification of one representative experiment of 3 displayed a similar sensitivity to the combination than the shRNA-control XG7 cells (Additional files 2 and 3: Figure  S12D ). These data demonstrate that the lethal synergy between UNC-0379 and melphalan is indeed related to SETD8 inhibition and does not depend on p53 activation. As shown in Fig. 6c , the lethality induced by the cotreatment of melphalan and UNC-0379 was preceded by high levels of DNA breaks, as measured by the significant increase in the number of DNA damage-induced 53BP1 foci in combined versus single agent therapy, in both XG7 and XG1 cell lines.
Acknowledgements This work was supported by Grants from  Labex Epigenmed, Ligue Nationale Contre le Cancer (LNCC) ; from  INCa (Institut National du Cancer ;  PLBIO2018-160   PIT-MM ),  ANR  Grants  2017-CE15-0024-01  and  2020-CE12-0007 ,  SIRIC Montpellier Cancer ,  INCa_Inserm_DGOS_12553  and  Institut Universitaire de France. Institutional Support was provided by the  Institut National de la Santé et de la Recherche Médicale (INSERM)  and by the  Centre National de la Recherche Scientifique (CNRS). J. J. acknowledges the support by the Grant  R01GM122749  from the  U. S. National Institutes of Health. F. I. was supported by fellowships from  LNCC and fondation pour la recherche médicale (FRM). LH is supported by a Grant from  FFRMG.
Data Analysis Trimmomatic was used to trim for minimal trailing quality (30 PHRED score) and lter for minimum read length by removing the Illumina sequencing adapters from read and adapter clippings  [24]. The resulting read pairs were aligned to the D. immitis reference genome nDi. 2. 2 (http://www. nematodes. org/genomes/diro laria_immitis) using BWA-mem (http://biobwa. sourceforge. net/) resulting in binary alignment map les (BAM)  [25]. The alignments were processed with Picard (https://broadinstitute. github. io/picard) for the realignment of indels, mate xing, and marking of duplicate reads. BVATools (https://bitbucket. org/mugqic/bvatools/src) was used to extract base frequencies at each of the 9 SNP positions and the read frequencies were assimilated to the allele frequencies (Table  S2 ).
Acknowledgements We thank  Génome Québec Innovation Centre  for sequencing the samples on their MiSeq platform and preparation of the alignment les. We thank  TRS Lab Inc. for providing the US D. immitis isolates. We thank  Compute Canada  and  Calcul Québec  for use of the Beluga server for data analysis. We thank  Ms. Kathy Keller  for her help and in sample preparation and submission. We also thank  Dr. Cristina Ballesteros  for help and guidance in the analysis of the sequencing data. DT thanks  Prof. Antonio Frangipane di Regalbono  for the help in recovering the Italian Diro laria immitis specimens. LK thanks  Luigi Venco  for reporting the Savonna isolate as a possible resistant.
1 ). We hypothesized that the forest productivity (kg ha -1 year -1 ) is related to the forest biomass (kg ha -1 ) and RGR (g kg -1 year -1 ). The forest biomass can be decomposed in the mean tree biomass and the tree density, whereas RGR can be explained by functional traits and abiotic factors. To study this, we combined data from the Spanish National Forest Inventory (NFI) with field sampling to include functional trait data and soil characteristics. Our specific objectives are: (1) to determine how abiotic factors (climate and soil properties) affect functional traits; (2) to know the effect of abiotic factors, functional traits, and forest structure (tree biomass and density) on relative growth rate; and (3) to model forest productivity as a result of these ecological, physiological and environmental factors.
The plots were selected from the NFI database within certain criteria based on the plot accessibility, location and the Q. ilex percentage abundance in the plot. Five plots were randomly chosen within each climatic region (in a nested design), and the distance between plots of each region (13. 5 km on average) was considered to avoid pseudoreplication. On average, Q. ilex represented 90% of the total forest biomass of the plot, ranging from 60 to 100% (with a total dominance in 18 plots). The mean annual temperature varies between 10. 9 and 17. 4 °C and the annual precipitation varies from 357 to 858 mm. More details about the selected plots can be found in Table  1. We used the DBH data from the third National Forest Inventory  (1997 ( -2007, NFI3) , NFI3)  and we calculated tree biomass (kg tree -1 ) using the allometric equations from  Ruiz-Peinado et al.
(2011). For each plot, the forest biomass (Mg ha -1 ) was calculated as the sum of the individual tree biomass considering the subplot where the tree belongs (i. e. 5, 10, 15 and 25 m radius subplots)  (Ruiz-Benito et al. , 2014). Similarly, we calculated tree density of each subplot as the sum of tree density (number of trees ha -1 ) of each radius subplots. In 2018, we re-measured the DBH of all trees in each selected plot to calculate tree biomass increment and forest biomass increment in the last eleven years  (2007-2018 period). Moreover, we collected leaf and branch material from 5 random trees to measure key functional traits related to plant ecological strategy. Tree relative growth rate (RGR) was calculated as: RGR = [Ln (B 2 ) -Ln (B 1 )] / [t 2t 1 ], where B is tree biomass and t is time, with 2 and 1 referring to our sampling survey data (i.
e. 2018) and NFI3 data (i. e. 2007), respectively. RGR is a standardized measure of growth with the benefit of avoiding, as far as possible, the inherent differences in scale among trees of different size, thus their performances can be compared on an equitable basis  (Pommerening and Muszta, 2015; Villar et al. , 2017). Forest productivity (kg ha -1 year -1 ) was calculated as the difference of forest biomass between our sampling data and NFI3 for trees alive in both consecutive inventories divided by the time difference between surveys (between 9 and 11 years) with a daily resolution.
Due to the high correlation between TPI, TRI and hillshade, with slope and aspect, we decided to exclude slope and aspect from the models. Moreover, mean annual temperature (MAT) and precipitation data (MAP) were downloaded from the WorldClim database  (Hijmans et al. , 2005). Additionally, we downloaded the precipitation in the driest month (DMP) and the temperature in the driest month (DMT) in Celsius degrees to calculate the modified Martonne aridity index  (Stephen, 2005)  as AI m where AI m = {[MAP] / [MAT + 10]} + {[12 × DMP] / [DMT + 10] / 2}. Since high AI m indicates high water availability, we transformed this variable as follows: AI = 100 -AI m. The transformation of AI (100 -AI m ) is the inverse of AI m , and therefore the relationship between these two variables is completely linear and did not affect the results.
1 ). To explore the effect of abiotic factors (climate variables, topography, soil texture and nutrients) and functional traits on the relative growth rate, we carried out an LME model at the tree level (Objective 2; Fig. 1 ). All trees in our study were clustered within the plots (n = 30), and the plots were clustered within regions (n = 6). Therefore, to account for the no independence of our dataset  (Lefcheck, 2021) , we nested the plot random factor in the region random factor. We used the Gaussian family function to fit the models. Prior to any modeling analysis, data was scale transform using the basic "scale" function in the R software (which subtracts each data by the variable mean and divide it by the standard deviation). Finally, to explain the causal effect of these variables on forest productivity, and account for both direct and indirect effects, we built two piecewise structural equation models (SEM) using first tree level data to explain the RGR and, second, plot-level data to explain forest productivity (Objective 3).
The structure of the hypothesized causal relationships between the selected variables was set based on our expected relationships in Fig. 1. Since forest variables (forest productivity and forest biomass) and abiotic factors (climate variables, soil texture and nutrients, and soil topography) were only calculated at plot level and functional traits (LMA, LT, LD, BWD, BWDMC), tree biomass and RGR were calculated at tree level, we decided to split the analysis into two SEMs to avoid losing intraspecific variability data. First, we used data at the tree level to explain RGR using the results from Objectives 1 and 2. Then, mean values from the tree-level data were calculated to obtain a value of RGR and forest biomass per plot. These variables were then used in a subsequent SEM performed at the forest level to assess their effects on forest productivity.
Our theoretical model was changed based on our previous results to exclude non-significant relationships as a function of the highest statistical support according to the significance of the Fisher's C value (P > 0. 05), indicating that the relationships of the missing paths are neither significant nor related to our model  (Shipley, 2009). In both cases, the model was fit by maximizing the restricted log-likelihood. Data analysis was carried out using the R statistical computing software (R Core Team, 2017). The tidyverse package was used for data manipulation and graphic display  (Wickham et al. , 2019). Spatial representation of the NFI was done with the package "sp" and "sf"  (Pebesma, 2018). Map generation was done with the package "tmap"  (Tennekes, 2018). Worldclim data was downloaded and extracted with the package "raster"  (Hijmans et al.