dataesr/scientific-paragraphs-categorization huggingface dataset:
qgis. org). All maps used are in the public domain (CC0), sourced from Natural Earth (www. naturalearthdata. com). https://doi. org/10. 1371/journal. pone. 0218943. g001 major cultivars  [29, 81, 84, 116, 126]. Thus, this study will mainly use human bone collagen δ 13 C values as a proxy to understand the subsistence economy of a group, whether it was largely C 4 -based (i. e. millet-based), C 3 -based (i. e. wheat/barley/rice-based), or a mixture of both. A total of 1196 (ECP: n = 483; WCP: n = 331; QC: n = 382) human bone collagen C and N measurements from 53 sites (ECP: n = 15; WCP: n = 19; QC: n = 19) are included in this study (  Fig 1). While not all papers reported their data along with collagen quality indicators (collagen yield, %C, %N, and atomic C/N ratio), we only include data from papers that reported at least C/N ratios.
Dental caries analysis To examine the dental health status, and to verify whether and how the prevalence of caries is associated with the introduction of wheat and other cereal crops in ancient Chinese diet, we have taken published dental caries data from 35 sites in northern and northwest China (Fig  1 ). All identified literature was screened on the basis of the following two key factors: 1) dental caries diagnostic criteria, and 2) prevalence. Given the purpose of this study and the value of including as many sites as possible in the analysis, the frequency (%) compared the number of teeth affected by caries with the number of teeth observed in each population (tooth count). Evaluating the prevalence on each type of tooth was unfortunately not an option at this stage, nor was a comprehensive analysis on the role of age and sex, due to the incomplete reporting conventions in most of the studies.
Further discussion on these analytical obstacles, and a tentative investigation on the impact of demographic compositions in dental caries prevalence are provided in S2 File-Supporting Information. A total of 45,557 teeth from 2934 individuals (number of teeth-NCP: n = 6737; QJ: n = 32,770; QC: n = 6050; number of individuals-NCP: n = 532; QJ: n = 2104; GQ: n = 298) were analysed in this study. Unfortunately, due to the nature of data reporting, it is not possible to organize the dental caries data into the same temporal phases as the stable isotopic data. Based on the inflection points of the stable isotope data, we have chosen to divide our dental data into two phases, representing periods before and after a change in isotopic data. As a result, data are organized in two phases: <4000BP vs.
AdCre-Mediated Gene Deletion For conditional gene deletion, freshly sorted basal cells (CD31/45 À , CD24 low , CD49f high population) were incubated with an adenovirus expressing the Cre recombinase under the control of the CMV promotor (AdCre, SignaGen Laboratories) at MOI of 5,000 for 1 h at 37 C. Trypan blue staining showed that at least of 90%-95% were viable after infection. After washing, cells were resuspended in mammosphere medium and plated in low attachment 24-well plates at 5,000 cells/ well in duplicate as described previously  (Chiche et al. , 2013). For ROCK or myosin II-ATP-ase inhibition, Y27632 (1 mM, Millipore) or Blebbistatin (5 mM, R&D Systems) were added to the mammosphere medium and replenished twice a week. After 12-14 days of culture, wells were photographed and the number and size of mammospheres was analyzed using ImageJ software.
ACKNOWLEDGMENTS We are particularly grateful to  A. Di Cicco  for expert technical assistance, to  Dr. I. Grandjean ,  S. Jannet , and the personnel of the animal facilities at  Institut Curie  for taking care of the mice, to  Z. Maciorowski ,  A. Viguier , and  S. Grondin  for excellent assistance with FACS analyses and to the Cell and  Tissue Imaging  ( PICT-IBiSA ),  Institut Curie , member of the  French National Research Infrastructure France-BioImaging  ( ANR10-INBS-04 ). We also thank  Drs. D. Metzger ,  P. Soriano , and  J. Jonkers  for providing mouse strains. The work was supported by grants from  Agence Nationale de la Recherche  ( ANR-13-BSV2-0001 ),  Canceropole Ile de France  (  2014-1-SEIN-01-ICR-1 ),  La Ligue Nationale Contre le Cancer Comitede Paris  ( RS16/75-70 ), and  Labex Celtisphybio  ( ANR-10-LABX-0038 ), part of the Idex PSL.
(2006) , with the annealing temperature of 58°C for RAg1. PCR products were purified by the polyethylene glycol (Peg) method (20% Peg 8000, 2. 5M NaCl)  (Santos-Júnior et al. 2015)  and sequenced on the ABI 3130xl genetic Analyser (Applied Biosystems) using the BigDye Termination v3. 1 Cycle Sequencing kit, with the same primers used for amplification. Chromatograms were analyzed in SeqScape v2. 6 and consensus sequences were aligned using the Clustal W algorithm  (larkin et al. 2007 ) in the software MegA7  (Tamura et al. 2013). A total of 767 bp and 410 bp were sequenced for RAg1 and CR, respectively. The RAg1 haplotypes were inferred in software DNAsp 5, using the algorithm Phase (Rozas et al.
2003), and the parsimony network relationships for An Acad Bras Cienc (2019) 91(Suppl. 3) e20190325 3 | 7 CR and RAg1 haplotypes were estimated with the software POPART (leigh and Bryant 2015) using the median-joining algorithm  (Bandelt et al. 1999 ) (Figure  2 ). We also performed a Bayesian phylogenetic analysis to show the relationship of the individuals from the hybrid zone and the parental species, using concatenated CR and RAg1 fragments. We selected the best partition scheme and nucleotide substitution models using model selection implemented in PartitionFinder v. 2. 1. 1 (lanfear et al. 2016), taking the codon positions of RAg1 into account. For this, we used the linked model of branch lengths and an exact search. The model selection was made using a corrected-Akaike Information Criterion.
Data collection The percentages of induction, regenerable calli or embryos and regenerated plants per 100 florets were   1 ). For each haplomethod, three replicates per experiment were used for each genotype. All the experimental data were subjected to analysis of variance (ANOVA) using SPSS statistical software (Ver. 16. 0, 2007). In figure, graph bar represents mean ± standard error (SE). The statistical significance was tested using Tukey test at a 5% level of probability. A linear regression analysis (y = mx + c) was also performed between the percentage of regenerated plants and the percentage of induction and calli or embryos in order to establish their mutual relationship.
It may give the answer if a material is sufficiently stable to be stored for a given period of time, for example, 10 years at a specific temperature, such as ambient storage conditions, e. g. , 25 • C (t 25 ≥ 10 years), or for more specific temperature and time profiles. Based on the bootstrap sets of estimated kinetic parameters, the prediction can be enhanced by the estimation of the prediction band (PB) in the form of, e. g. , the upper and lower 95 percentiles (PB 95% confidence). The simulations presented in this study were done with the AKTS-Thermokinetics Software  [5].
The two other equally important kinetic parameters, i. e. , the pre-exponential factor in the Arrhenius equation, A, and the form of the kinetic function, f(α), are not considered in simplified kinetic approaches. A more precise kinetic description of the decomposition of biological compounds  [37, 38]  was obtained with kinetic parameters of two-step models, which better mimic the complicated decomposition of the investigated samples. The application of the autocatalytic kinetic model in the evaluation of the shelf life of pharmaceuticals was presented in the review by Brown and Glass  [39]. For illustration purposes, we used the pharmaceutical product studied by an advanced kinetic approach for which the kinetic parameters are known. A freeze-dried measles vaccine was investigated in our former study  [2]  for which the criteria for the discrimination of the best kinetic models were done using the AKTS-Thermokinetics Software  [5]  and based on the information theory introduced by Akaike  [6]  and its Bayesian counterpart  [7].
Continuous Shelf-Life Estimation by Using Data Logger The AKTS-Thermokinetics Software  [5]  allows the determination of the reaction extent, α (in our case the degree of the infectious titer change), in any temperature mode. Figure  8  shows the dependence of α on the time during temperature variations during a 3-year long storage in a cold chain (2 • C < T < 8 • C), followed by storage at ambient conditions with daily temperature fluctuations corresponding to the month of March in New Delhi (India). After three years of storage in the cold chain, the reaction extent amounts to ca. 3. 6%. After removal from the cold chain and exposure of the vaccine to ambient temperature fluctuations, the shelf life (=5%) is reached after 15 days (see Figure  8b ). Online determination of the reaction extent (degree of the infectious titer change) based on the kinetic parameters and temperature-time data is of great importance because it allows immediate evaluation of the remaining shelf life as a function of the excursion temperature.
The length of the horizontal line drawn at the temperature of the respective stage from point I' at the end of segment 1 till the intersection with the isoconversional line drawn for the maximal allowed the reaction extent (marked by α m ) amounts to RT-I' and at the end of segment 4 (point no. IV') to RT-IV'. The shelf life is reached before the end of segment no. 5. The simplified example presented in Figure  10  is used only for the basic explanation of the concept of the remaining shelf life calculations. In real applications, these values (characterized by the lengths of the arrows, RT-I' and RT-IV') are calculated continuously at the end of each segment collected by the data logger. The frequency of the data collection is arbitrarily set by the user. The typical information displayed from the data logger concerning the online evaluation of the remaining shelf life is shown in Figure  11.
Pressure Firing (PF) The PF testing method is based on the measurement of the maximum pressure during firing. In general, the measuring system employs a piezoelectric transducer flush mounted in the chamber of the test barrel. The pressure developed by the gases from the burning propellant exerts a force on the transducer through the cartridge case wall, causing the transducer to deflect, and creating a measurable electric charge which, after calibration, is converted into the pressure. The propellant aging increases the maximum pressure value. The measurements were performed with materials aged at 60, 70, and 80 • C using a home-made experimental arrangement. The data obtained for propellant aged at 50 • C were used for verification of the simulation procedure. The pressure was measured by a piezo-resistive pressure sensor while speed measurements were done by the photoelectric sensor placed in the light barrier.
Gas Evolution (VST) VST is based on the measurement of the volume of gas evolved on heating under specified conditions. The sample was heated in an evacuated tube and the volume of gas evolved was determined by either the mercury manometric method or by using a pressure transducer. Experiments were carried out using a tube Vacuum Stabilizer Tester (OZM Research, Hroch ův Týnec, Czech Republic) equipped with a Pressure Transmitter DMP 331 (BD Sensors GmbH, Thierstein, Germany). The measurements were performed with materials aged at 60, 70, and 80 • C. The data obtained for propellant aged at 50 • C were used for verification of the simulation procedure. Ultra-Performance Liquid Chromatography (UPLC) UPLC was used for stability testing by chromatographic monitoring of the stabilizer concentration which in fresh propellant amounts to ca.
U pper tract urothelial carcinoma (UTUC) accounts for 5-10% of all urothelial carcinomas (UCs)  1. UTUC is a distinct clinical entity with an aggressive clinical behavior and a more advanced presentation compared to urothelial carcinoma of the bladder (UCB)  1. Recently, the Cancer Genome Atlas (TCGA) study classified UCB into five molecular subtypes (luminal-papillary, luminal-infiltrated, luminal, basal/squamous, neuronal). Since TCGA did not include UTUC  2  , it is currently unknown whether UTUC recapitulates the same molecular subtypes  [2] [3] [4] [5] [6]. Furthermore, our understanding of the immune milieu of UTUC is incomplete. These knowledge gaps have hindered the development of effective UTUC-specific therapeutic strategies. To dissect the central biological features of UTUC's tumor and immune cell compartments, we analyzed whole-exome sequencing (WES) and RNA sequencing (RNAseq) data from high-grade UTUC tumors from patients at three different institutions [Weill Cornell Medicine (WCM), Baylor College of Medicine and MD Anderson Cancer Center (BCM-MDACC)].
We used whole-exome and RNAseq data from UCB tumors from the TCGA cohort as a comparison cohort  5. This enabled us to define the biological differences between UC arising from the upper and lower urinary tracts and to gain insights into the unique mechanisms that drive UTUC biology. We show that UTUC is predominantly luminalpapillary and T-cell depleted. We identify FGFR3 as a putative regulator of UTUC's immune contexture through attenuation of interferon gamma (IFNG) signaling. Finally, we report that the tumor mutational burden in sporadic UTUC is lower than UCB, despite reduced expression of DNA mismatch repair (MMR) transcripts and proteins.
However, it is unclear whether non-Lynch (sporadic) UTUC patients have defective DDR and an increased mutational burden. To define whether somatic dysregulation of DDR genes could play a similar role in inducing a hypermutated phenotype in non-Lynch UTUC patients, we assessed the mRNA expression level of DDR pathway genes in UTUC (WCM, BCM-MDA) and UCB (TCGA). We focused our analysis on identifying differentially expressed genes in the canonical DDR pathways [MMR, base-excision repair (BER), NER, homologous recombination (HR), non-homologous end joining (NHEJ), Fanconi anemia (FA), translesion synthesis (TLS)] comparing UTUC with UCB (Fig. 2a ). We identified a significant somatic dysregulation of 35 canonical DDR genes in UTUC (Fig. 2a ). We analyzed germline WES data from patients in our UTUC cohorts (WCM, BCA-MDA) and identified no germline mutations in the canonical MMR genes (MLH1, PMS2, MSH2, MSH6).
Interestingly, we observed significantly lower somatic mRNA expression of three canonical MMR genes: MLH1, MSH2, and MSH6 in UTUC tumors compared to TCGA UCB tumors (Fig. 2a ). To determine whether this decreased mRNA levels further translated into a low expression of MMR proteins, we used immunohistochemistry (IHC) to quantify their expression in WCM UTUC tumors (n = 16) compared to stagematched WCM UCB tumors (n = 14). We found that the levels of MLH1, PMS2, MSH2, and MSH6 proteins were significantly lower in UTUC tumors (Fig. 2b, c ). This confirmed that lower expression of these proteins is a characteristic feature of UTUC even in the absence of germline or somatic mutations in the respective genes in the WCM UTUC cohort. To determine whether downregulation of these proteins impaired DNA MMR, we examined WES data for short tandem repeats (MSI) using the MSI sensor program  12  that calculates the percentage of unstable microsatellites from UTUC and UCB tumor-normal paired WES data.
Surprisingly, the median MSI sensor scores were similar in WCM UTUC and TCGA UCB. Both were below the previously defined threshold of 3. 5, which was shown to accurately classify microsatellite unstable tumors  12  (Fig. 2d ). Furthermore, WCM UTUC samples harbored a significantly lower mean total mutational burden (TMB) compared to TCGA UCB tumors (2. 91 versus 5. 46 mutations per MB) (Fig. 2e ). This suggests that the somatic downregulation of MMR proteins is insufficient to produce MSI and is not a major driver of mutagenesis in non-Lynch UTUC. Collectively, these findings indicate that the decrease in the mRNA and protein levels of MMR genes in sporadic UTUC does not translate into MSI or a higher TMB. UTUCs are predominantly luminal-papillary. To characterize the gene expression profiles which define UTUC, we generated an RNAseq meta-dataset from 32 UTUC tumors and the TCGA UCB cohort.
First, we interrogated the same meta-dataset using two additional validated classifiers of urothelial carcinoma subtypes. When we applied the MDACC classifier which divides UC into luminal, basal, and p53-like subtypes  4  , we found that 22/ 32 (68. 7%) of UTUC tumors clustered with the luminal subtype (Supplementary Fig. 2  and Supplementary Table  2 ) versus only 47/128 (36. 7%) of UCB 2. Using the recent TCGA classifier which segregates UC into luminal-papillary, luminal-infiltrated, luminal, basal-squamous, and neuronal subtypes 2 , we confirmed that UTUC has a luminal-papillary phenotype (20/32, 62. 5%), with the majority of remaining UTUC tumors also exhibiting a luminal expression profile (8/12, 67%) (Supplementary Fig. 3  and Supplementary Table  2 ). This is in contrast to 35/128 (27.
3%) of luminal-papillary UCB tumors in the TCGA UCB cohort, with only a minority of the rest (35/93, 37. 6%) also segregating with luminal or luminal-infiltrated subtypes 2. To confirm these findings using a different approach, we used non-negative matrix factorization (NMF)  13  , a sensitive unsupervised statistical method to dissect and extract the key biological features of UTUC from our high dimensional RNAseq dataset  13. The NMF analysis revealed three principal components characterized by luminal/ carcinoma in situ (CIS)-low, basal/squamous-like, and extracellular matrix (ECM)/epithelial-mesenchymal transition (EMT)related gene sets  2. Principal component analysis (PCA) was then performed on the coefficients obtained from NMF to visualize the separation of the three components (Fig.
We then went on to assess whether FGFR3 is differentially expressed between T-cell inflamed and T-cell-depleted tumors. Indeed, we identified significantly higher FGFR3 expression in the T-cell-depleted cluster which harbored the majority of the UTUC tumors (Fig. 4c ). We interrogated the functional link between FGFR3 signaling and the T-cell-depleted phenotype observed in UTUC. We reanalyzed a previously published dataset of mRNA expression from the RT-112 UC cell line following doxycycline (dox)-inducible short hairpin RNA (shRNA) knockdown of FGFR3  17, 18. We found that several IFNG response genes including BST2, MX2, IRF9, GBP2 were upregulated after FGFR3 knockdown (Fig. 4d ). Confirming these results, we found that BST2 and IRF9 were significantly downregulated in the T-cell-depleted cluster which harbored the majority of UTUCs in our patient cohort (Supplementary Fig.
4 ). We also found a statistically significant upregulation of other IFNG response genes in the FGFR3 shRNA + dox dataset when compared to the control (ctl) + dox dataset (Fig. 4e ). To confirm this observation and define whether pharmacologic inhibition of FGFR3 will have a similar effect on IFNG response genes (Supplementary Table  4 ), we tested the effects of erdafitinib, a small molecule FGFR3 inhibitor, in three different UC cell lines (RT-112, RT-4, and SW780). These cell lines harbor FGFR3 fusions (RT-112, RT-4: FGFR3-TACC3; SW780: FGFR3-BAIAP2L1) resulting in constitutively activated FGFR3 signaling  19. We found that treatment with erdafitinib led to a significant upregulation of BST2, a hallmark of activated interferon signaling (Fig. 4f ). Collectively, these findings show that FGFR3 plays an important role in shaping the T-cell-depleted phenotype in UTUC in a cancercell autonomous manner.
In a previous study, unsupervised clustering of RNAseq data from both highgrade and low-grade UTUC was used to divide UTUC into four molecular subtypes  31. Here, we used an alternative approach to position high-grade UTUC within the continuum of UC biology. Fig. 4  FGFR3 plays an important role in the T-cell-depleted immune contexture of UTUC. a UTUC is T-cell depleted. Supervised consensus clustering of WCM UTUC, BCM-MDA UTUC, and TCGA UCB tumors according to a 170-immune gene signature classifies tumors into T-cell depleted (with lower expression of classifier genes), and T-cell inflamed (with higher expression of classifier genes) clusters (Fisher's exact test P = 9 × 10 -5 ). b FGFR3 is an expression outlier in UTUC tumors. WCM UTUC and BCM-MDA UTUC tumors are represented on the x-axis, and normalized z-scores of gene's expression represented on the y-axis.
The variants were annotated using Oncotator (version 1. 9); the dbSNPs amongst the mutation calls, unless also found in the COSMIC database, were filtered out. For the IPMs samples, the promiscuous mutation calls, previously identified internally as artifacts for Haloplex were also excluded from the final list of mutations. Tumor mutation burden (TMB) was calculated for each sample as the number of mutations divided by the number of bases in the coverage space per million. Somatic mutations were called via a standard cancer analysis pipeline at the BCM Human Genome Sequencing Center and by using VARSCAN2 for BCM-MDA samples  31. Mutational signature analysis. Somatic alterations identified from the WES analysis pipeline were used to identify underlying patterns of mutational signatures. The nonsynonymous SNVs were classified into the six base substitution classes and the bases immediately to the 5′ and 3′ of the mutated base gave 16 different mutational contexts; hence there were 96 different base substitution patterns in total.
IHC slides were scanned at ×200 total magnification using a single z-plane via an Aperio AT2 whole slide scanner (Leica Biosystems, San Diego, CA, USA). The scanned images were loaded onto the HALO TM imaging analysis platform (Indica Labs, Corrales, New Mexico, USA). Study pathologists manually selected tumor areas for automated image scoring, and the HALO TM analysis software determined the staining intensity of each tumor cell (0, 1+, 2+, 3+) and percentage of tumor cells for each intensity level. H-scores were then calculated using the formula [1×(% of cells with intensity of 1+)+2×(% cells 2+)+3×(% cells 3+)] with possible scores thus ranging from 0 to 300. Computational detection of MSI. MSI in WCM UTUC and TCGA UCB samples was detected by MSI sensor. MSI sensor is a software tool that quantifies MSI in paired tumor-normal genome sequencing data and reports the somatic status of corresponding microsatellite sites in the human genome  12.
For each variant, we collected annotations from databases including ClinVar and Exome Aggregation Consortium (ExAC, http://exac. broadinstitute. org)  36. Following the ACMG Standards and Guidelines regarding classification and interpretation of sequence variants  37  , the variants were classified into five categories: Pathogenic Likely Pathogenic, Likely Benign, Benign and variants of unknown significance. Germline samples from the BCM-MDA cohort were previously analyzed  31. None of the included BCM-MDA UTUC patients had a diagnosis of Lynch syndrome  31. RNA extraction, RNAseq, and data analysis. RNA was extracted from frozen material for RNA-sequencing (RNA-seq) using Promega Maxwell 16 MDx instrument, (Maxwell 16 LEV simplyRNA Tissue Kit (cat. # AS1280)) from WCM UTUC tumors. Specimens were prepared for RNAseq using TruSeq RNA Library Preparation Kit v2 or riboZero.
RNA integrity was verified using the Agilent Bioanalyzer 2100 (Agilent Technologies). cDNA was synthesized from total RNA using Superscript III (Invitrogen). Sequencing was then performed on GAII, HiSeq 2000, or HiSeq 2500. All reads were independently aligned with STAR_2. 4. 0f1  38  for sequence alignment against the human genome sequence build hg19, downloaded via the UCSC genome browser (http://hgdownload. soe. ucsc. edu/goldenPath/hg19/ bigZips/), and SAMTOOLS v0. 1. 19  39 for sorting and indexing reads. Cufflinks (2. 0. 2)  40  was used to estimate the expression values (FPKMS), and GENCODE v23  41  GTF file for annotation. Rstudio (1. 0. 136) with R (v3. 3. 2) and ggplot2 (2. 2. 1) were used for the statistical analysis and the generation of figures. For fusion analysis, we used STAR-fusion (STAR-Fusion_v0.