dataesr/scientific-paragraphs-categorization huggingface dataset:
Digital volume correlation The strain results were analysed to gain insights into the development of the kink-band prior to failure. Given that the combination of in-plane shear (𝜖 𝑥𝑦 ) and compressive strain (𝜖 𝑥𝑥 ) is expected to cause the fibre kink, these strains were selectively analysed and presented for both sample types. To perform the analysis, the DVC package kintsugi 1 was used to implement a global approach, which is summarised in Fig. 5. The unloaded micro-CT reconstructions of both samples were used as reference images, in which the grey-scale value at each voxel was denoted as 𝑓 (𝐱). In order to reduce processing time and focus on the region of interest, a cuboidal VOI was cropped from this reconstruction. The subsequent deformed micro-CT reconstructions of the samples were denoted as 𝑔(𝐱), and the same regions of interest were obtained via cropping.
Acknowledgements This work was supported by the  UKRI -Engineering and Physical Sciences Research Council, United Kingdom , Grant ' Certification for Design -Reshaping the Testing Pyramid '  EP/S017038/1. Jiraphant Srisuriyachot  is funded by  GKN Aerospace, United Kingdom. The  Diamond Light Source  is acknowledged for providing the beamtime on I13-2 beamline under the experiment number  MG28362 -2.
Parental populations The founder population included 835 F8-F9 winter-type bread wheat lines developed and phenotyped between 2000 and 2016 by breeders from the French National Research Institute for Agriculture, Food and Environment (INRAE) and its subsidiary breeding company Agri-Obtentions (AO)  (Ben Sadoun et al. 2020). They were genotyped with 35K SNPs  (Ben Sadoun et al. 2020 ) representative of the TaBW280K array  (Rimbert et al. 2018). For this analysis, the markers were filtered according to the missing data rate (< 5%), heterozygosity rate (< 5%) and minor allele frequency (> 10%) yielding 16,429 SNPs. Missing genotypes were imputed using the Beagle v4. 1 algorithm  (Browning and Browning 2007; Browning and Browning 2016)  implemented in the synbreed R-package  (Wimmer et al. 2012).
Each cross produced 11 F5 RILs (total progeny = 3,300), simulated with the MOBPS R package  (Pook et al. 2020). At cycles 1 and 2, only one progeny per cross was selected based on TBV. In the 3 rd cycle, the three best progenies per cross were kept, leading to a final population of 900 parental lines and called the 'selected population', from which 835 lines were sampled at random.
Unselected population + ESTIMATED QTL effects scenario Phenotypes of unselected parents were simulated with a heritability ℎ² 0 of 0. 4 by adding a normally distributed noise of variance 21 (quintal/ha)² to their TBV (ℎ² 0 = 14/(14+21) = 0. 4). Marker effects were estimated by backsolving the model using the PostGSf90 software package  (Wang et al. 2012; Aguilar et al. 2014). GEBV of progenies were computed as the cross product between estimated marker effects and allelic doses.
A mating design was defined by a vector giving the number of progenies   allocated to each candidate cross   *. The constraints were inspired from the bread wheat breeding program of the private company Florimond Desprez (personal communication): -C1: The total number of progenies was set at D = 3 300 -C6: Highly related parental lines could not be crossed. We used the LDAK software package  (Speed et al. 2012 ) to obtain a genomic relationship matrix G (2) in which SNPs were weighted according to local linkage disequilibrium (LD) in order to take into account the very heterogenous LD in bread wheat, which markedly increases from telomeres to centromeres. This variance-covariance matrix was computed as WW', where W was obtained by centering and scaling each column of the genotyping matrix Z such that 𝑊 𝑙 = 𝑤 𝑙 *(𝑍 𝑙 -𝑝 𝑙 )/√𝑝 𝑙 (1 -𝑝 𝑙 ) where   is the allelic frequency at locus l and   is the weight estimated by LDAK according to the local LD intensity.
Optimization of mating designs In the CONSTRAINTS scenarios, for all CSC but EMBV and UC3, the objective function and constraints constituted a system of linear equations. We used an integer linear programming algorithm implemented in IBM ILOG CPLEX software (CPLEX Python API, IBM 2017) to maximize (or minimize) objective functions while respecting the constraints. For criteria EMBV and UC3, the objective function and constraints did not form a system of linear equations, as the usefulness (e. g. the CSC value) of a cross actually depended on the number of progenies allocated to the cross. To optimize mating designs for EMBV and UC3 criteria, we used a genetic algorithm (GA). GAs are population-based metaheuristics inspired by Darwinism  (Goldberg 1989 ). The GA description used in this study and the tuning parameters are given in Supplementary Protocol S2.
Progeny simulation The F5 RIL progenies of each mating design were simulated using the MOBPS R package  (Pook et al. 2020). Each mating design was simulated 20 times to account for the possibility that progeny genotypes might vary due to Mendelian gamete sampling. Progeny TBV were then computed as the cross product between QTL effects and the allelic dosage at QTL loci.
Acknowledgments The authors would like to thank the  GenoToul bioinformatics platform Toulouse Occitanie  ( Bioinfo Genotoul , doi:  10. 15454/1. 5572369328961167E12 )for providing support, computing and storage resources. The authors are also grateful to  Hélène Rimbert  for her help in identifying SNP positions on refSeq v1. 0. The doctoral contract and activities of ADDD were funded by the  INRAE metaprogram SELGEN and Florimond Desprez (Cappelle-en-Pévèle, France). Genotyping was supported by the  Breedwheat grant  ( ANR-10-BTBR-03 ). The authors.
Material And Methods The material consisted of high-resolution full-body CT scans of 308 humans (157 females and 151 males) from infancy to late adulthood (Table  1S ) with information on sex, age in months at death, living weight, and height. The number of live births of 43 females aged 25-45 was also collected. The volumetric, demographic, and medical data were acquired from the New Mexico Decedent Image Database  43. Next, body mass index (BMI) was calculated for each individual using the equation: 2 BMI = body mass (kg) / body height (m). In individuals over 20 years of age, BMI was classi ed based on the following ranges: underweight (≤ 18. 5), healthy weight (18. 5-24. 9), overweight (25. 0-29. 9), and obese (≥ 30. 0). Individuals under 20 years of age were classi ed using sex-speci c growth charts as underweight (≤ 10th percentile), healthy weight (10-85th ), overweight (85-95th ), and obese (> 95th ).
Whole body CT scans were acquired using a standard protocol with 0. 5 mm slice thickness. 3D reconstruction and mesh cleaning of the pelvic girdle was performed using 3D Slicer (v. 4. 11) and Gom Inspect (v. Hot x 4), respectively. One pelvis on which 61 landmarks (LM) were manually digitized was selected as a template (Table  2S ). Next, 476 surface semilandmarks (SLM) were automatically generated from the template mesh. Then, SLM sliding was performed based on the manually digitized 61 LM on each pelvic girdle using the minimum bending energy criterion of the SlicerMorph extension in 3D Slicer. The LM set contained 10 LM pairs that fuse during pelvic development. Therefore, in individuals with completed fusion, the mean position was calculated for each of these 10 LM pair. The raw Procrustes coordinate data were divided into two groups, young (individuals aged 0-25) and old (individuals aged > 25), to separately analyze changes in the pelvic shape during stages of intense and complete skeletal development, respectively.
This geometric morphometric analysis focused on females in a narrow age range since the female pelvis shows relevant obstetrical dimensions during this period  11. Statistical analyses were performed using MorphoJ (v. 1. 07a) and R software (v. 4. 2. 0). All results with p < 0. 05 were considered statistically signi cant. The author con rms that all stages of the research (collection, CT scanning and analysis) were performed in accordance with the fundamental ethical principles and regulations on the analysis of human remains. All analysed CT scans were derived from the New Mexico Decedent Image Database and no new patients were scanned for this study. The acquisition of the medical image series and biological data of deceased persons was done retrospectively in compliance with the Declaration of Helsinki for the protection of data privacy.
CT scans of decedents are not regarded as human subjects under U. S. federal laws pertaining to research on human subjects. Further, Health Insurance Portability and Accountability Act (HIPAA) protections do not apply to data obtained in the investigation of a person's death. Moreover, the scans do not comprise any personal identi able information, and because the next of kin interviewed were not subjects of research, no review was required by the Institutional Review Board (IRB) of the New Mexico Decedent Image Database. However, the project descriptions and research design were reviewed by the College of Arts and Sciences, Department of Anthropology who considered the research to collect the derivatives was exempt from IRB review.
Patient characteristics and tumor samples Eight HPV-positive patients with OSCC samples and available transcriptomic data were included in this study. Patient description is shown in Supplementary Table  S1. Patients' mean age is 59 years old and median follow-up is 130 months. Patients underwent initial surgery (Sainte Barbe clinic, Strasbourg) followed by chemoradiotherapy (Centre Paul Strauss) between 1993 and 1995. Transcriptomic data was previously generated using an Affymetrix GeneChip approach, as described in  (18). Gene expression analysis, immunohistochemistry and survival analyses were carried out on a cohort of 77 patients that were treated in Strasbourg, France (N=34; they include the 8 HPVpositive OSCC that generated the transcriptomic data), and Liège, Belgium (N=43) between 1993 and 2014.
Data sets and bioinformatic analyses 2. 2. 1 Transcriptomics analysis R and Bioconductor were used for the analysis of all data sets. The expression data obtained from microarrays Affymetrix HG-U133 plus 2. 0 were obtained from HPV+ samples. Unsupervised analysis: A hierarchical classification allowed the observation of the distribution of samples. Ward's aggregation criterion and correlation distance were used. The Principal Component Analysis was realized with the R FactomineR (V1. 29) package  (20).
Differential analysis The transcriptomic differential analysis was evaluated with a moderate t-test (R package limma V3. 18. 13)  (21). The Bonferroni method was used for multiple corrections. Genes with a log fold change > 1 and an adjusted p-value< 0. 05 were selected as differentially expressed. Results are presented as a heatmap (function heatmap, R package stats V3. 0. 2. R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, https:// www. R-project. org/. ).
Meta-analysis Public data sets were acquired from the public database Gene Expression Omnibus (GEO) where only the HPV+ samples extracted from the upper aerodigestive tract (oropharynx, hypopharynx, larynx and buccal cavity) were selected. Based on these criteria, three data sets were obtained. Two of these data sets come from GEO Database, Slebos et al. (22)  (GSE3292) and Pyeon et al. (  23 ) (GSE6791). The last one was from a collaboration. The data sets were preprocessed to ensure that they are comparable, and clusters of highly interconnected genes were found in the reference data set using the WCGNA R package (V1. 70-3)  (24, 25)  based on an unsupervised analysis of gene expression profiles. Next, we investigated how these clusters are conserved in the three other data sets using the Preservation function module of the WGCNA package.
Gene expression assays Gene expression assays in tumor samples were performed by extracting total RNA from 77 OSCC frozen tissues using the RNeasy kit (Qiagen), according to the manufacturer's instructions. The integrity of extracted RNA was verified on an Agilent 2100 Bioanalyser (Agilent Technologies). RNA was retrotranscribed using the Goscript reverse transcription system (Promega), and real-time quantitative PCR was performed using the LightCycler® 480 real-time PCR system (Roche). Specific primer pairs were used to measure the expression of the genes that encode DNp63, S100A9, THBS4, CD8a, GZMK and CD68 (see Supplementary Table  S2  for primer sequences). RT-qPCR data was analyzed using LightCycler® 480 software. The expression levels of each gene were normalized to the geometric mean Ct values of 2 internal controls, Ribosomal Protein Long P0 (RPL0) and Ubiquitin B (UBB).
2. 9 In vitro cell viability analysis 2. 9. 1 MTT cell viability assays A total of 1E 04 cells were seeded per well in 96-well microplates (Falcon Multiwell, Dutscher), 24 h prior to treatment. Cisplatin (Mylan) at different concentrations (0; 0. 1; 0. 5; 1; 2. 5; 7. 5; 15; 30; 100 mM) was applied for 48 h in 100 µL of fresh medium. MTT assay was performed as previously described by replacing the cisplatin solution with fresh medium supplemented with 5 mg/L MTT (Sigma) for 1 h  (34). Cells were lysed in DMSO 100% (100 mL/ well). Absorbance measurements were performed at 550 nm with the LB942 Tristar2 Multimode Reader (Berthold Technologies). The calculation of the IC 50 and IC 75 was performed with the GraphpadPrism V5. 02 software (Graphpad, Software, USA) using non-linear regression. 2. 9.
RNA-sequencing and transcriptomic data analysis RNA was extracted from SCC90 cells transfected with siRNA against DNp63 and TRIzol-mediated cell lysis. After extraction and RNA precipitation, supernatants were removed and the RNA pellet was washed with 75% EtOH, centrifuged at 9000× g for 5 min at 4°C , and again, 75% EtOH was added. Then, the RNA was resuspended in nuclease-free H 2 O and quantified using a NanoDrop Spectrophotometer (Thermo Scientific, Waltham, MA, USA). To identify the deregulated genes, RNASeq was performed on extracted total RNAs. RNA-Seq libraries were generated from 600 ng of total RNA using TruSeq Stranded mRNA Library Prep Kit and TruSeq RNA Single Indexes kits A and B (Illumina, San Diego, CA), according to manufacturer's instructions. Briefly, following purification with poly-T oligo attached magnetic beads, the mRNA was fragmented using divalent cations at 94°C for 2 min.
Image analysis and base calling were performed using RTA 2. 7. 3 and bcl2fastq 2. 17. 1. 14. The quantification of gene expressions was performed with the Kallisto algorithm (v 0. 46. 2) and default options. RNA-Seq analysis was assessed with R software (R version 4. 0. 3). The differential analysis was performed with DESeq2 R package (version 1. 30. 0). A significantly differentially expressed gene corresponds to a p-value inferior to 0. 005 and a log2 fold change ≥ 1. Pathways enrichment analyses were performed using multiple databases (e. g. , DAVID, STRING, Reactome, TRAP, Biomarkers).
Statistical and survival analyses Survival analyses were performed using MedCalc statistical software (http://www. medcalc. be/). A two-sample Wilcoxon rank-sum (Mann-Whitney) test was used to evaluate the association between the gene expression level of S100A9 and THBS4 and the occurrence of metastatic spread at 3 years, and the Liu method (maximization of the product of sensitivity and specificity)  (35)  was used to determine optimal cut-off values. A prediction score was then constructed by entering THBS4 and S100A9 cut-off values in a logistic regression model. This score was used to stratify patients and evaluate the impact of this stratification on metastasis-free survival at 3 years using a univariate  Kaplan.
HPV-positive OSCC display molecular and prognostic heterogeneity is linked to differential expression of DNp63 We analyzed transcriptomic data from 8 tumor samples from patients with locally advanced HPV-16 positive OSCC. Unsupervised hierarchical clustering analysis distinguished two molecular groups (Figure  1A ; Cluster 1 and Cluster 2), based on the differential expression of 148 genes (Supplementary Table  S4 ). Interestingly, analysis of follow-up clinical data of the corresponding patients (Table  1 ) showed that Cluster 1 is associated with metastatic recurrence within 18 months and death within 3 years, and Cluster 2 with delayed metastatic spread (59 and 117 months after treatment) and longer survival (4/6 patients were still alive after 129 months). This shows that HPV-related OSCC can be separated into two clusters with different prognosis and molecular signatures.
DNp63 regulates the expression immune response genes and is linked to differences in the tumor immune landscape In order to gain further insights into DNp63-dependent mechanisms in HPV-positive OSCC, we investigated the transcriptional program regulated by DNp63 in SCC90 cells. RNA-sequencing (RNA-seq) analysis was carried out on cells transfected with either scrambled or DNp63 siRNA. Using a log 2 (fold change)>1 and adjusted p-value<0. 05 as cut-offs, 690 genes were found to be differentially expressed (293 genes were downregulated and 397 upregulated) upon DNp63 inhibition. Pathway enrichment analysis using normalized transcriptomic data and the String software identified repressed (Figure  3A ) and activated (Figure  3B ) cellular functions. Most of the up-regulated genes were found to be involved in either development, epithelium/ skin differentiation or cornification (Figure  3B ; red and pink bars), as expected given the known role of DNp63 in keratinocyte differentiation and skin epithelium stratification.
Acknowledgments We thank  Prof. Susan Gollin  and  Prof. Thomas E. Carey  for sharing cell lines. RNA-sequencing was performed by the GenomEast platform, a member of the " France Geńomique " consortium ( ANR-10-INBS-0009 ). The authors express their gratitude to  Aureĺien De Reynies  and  Sylvie Job  of the " Carte d'Identitédes Tumeurs" program  ( Ligue Nationale contre le Cancer ) for their valuable contribution to the bioinformatic characterization of the transcriptome of HPV-positive OSCC samples. We thank  Dr. Gilles Travéfor  critically reading the manuscript. We thank  Rob Simmons  of  IUT Louis Pasteur, Universitéde Strasbourg , for proofreading the manuscript.
Raw data were deposited in the NCBI Sequence Read Archive (SRA) database under BioProject PRJNA601278. The quality of the raw data was evaluated by FastQC v. 0. 11. 5 (Babraham Bioinformatics). Low-quality bases and remnant adapters were trimmed using Trimmomatic v. 0. 32  (Bolger et al. , 2014). The reads of both reference and resistant strains were aligned to the previously assembled sequence of S. cerevisiae CEN. PK113-7D (GCA_000269885. 1) (Nijkamp et al. , 2012)  using the Burrows-Wheeler aligner MEM v. 0. 7. 1  (Li and Durbin, 2009). Mutations were called by using Genome Analysis Toolkit (GATK) v. 3. 8. 0  (DePristo et al. , 2011)  and were manually inspected with GenomeBrowse v2. 1. 2 (GoldenHelix). The variants with low quality and common in both reference and the evolved strains were filtered out using in-house R scripts.
Internal controls were provided by Agilent One-Color RNA Spike-In Kit. cRNA samples were labeled with cyanine 3 (Cy3), and purified using Absolutely RNA Nanoprep Kit (Agilent). Hybridization of the samples on microarray slides was done at 65°C for 17 h, using Microarray Hybridization Chamber Kit (Agilent). Following washing steps, the microarray slides were scanned, and the data were analyzed by using Agilent Feature Extraction Software (Version 10. 7) and GeneSpring GX Data Analysis Software (Version 12. 0). Student's t-test and Benjamini and Hochberg's p value correction and false discovery rate (FDR)  (Benjamini and Hochberg, 1995)  were used were used for reliability assessment of gene expression data. For significantly different gene expression analysis, corrected p values <0. 05 were considered.
Additionally, genes with two-fold or higher expression changes (increase and decrease) and a p value of less than or equal to 0. 01 were considered for cluster analysis. For all samples, the microarray experiment was performed in triplicate, with three independent cultures. FunSpec online software  (Robinson et al. , 2002)  and FunCat database 1   (Ruepp et al. , 2004)  were used for data classification into clusters and functional categories. To compare the gene expression data with Environmental Stress Response (ESR)induced and ESR-repressed gene sets in yeast, "Yeast Growth Rate Homepage" 2   (Brauer et al. , 2008)  was used. Pathway enrichment analyses were performed using the Limma package in R/Bioconductor  (Ritchie et al. , 2015)  based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database.
Stress resistance characteristics of selected evolved strains The reference strain, the final population and the ten individual colonies (C1 to C10), were tested for their 2-PE resistance by spot assay on YMM plates. It was found that C2, C3, C5, C9, and C10 were able to grow in the presence of 3 g/L 2-PE (Supplementary Figure  S1 ), while the reference strain could barely grow at this 2-PE concentration. At 5 g/L of 2-PE on the YMM plates, neither the final population nor the isolated clones were able to grow (data not shown). Additional quantitative estimation of 2-PE resistance of the reference strain, final population and individual clones C2, C3, C9 and C10 was done using the MPN method by growing them in microtiter plates in YMM containing 3 g/L 2-PE for 72 h at 30°C. As shown in Figure  2 , the survival rate of the final selection population was approximately 59 times higher than that of the reference strain.
Comparative transcriptomic analysis of the evolved strain with the reference strain To complement the genome sequencing of the 2-PE-adapted strain, we performed a comparative transcriptomic analysis of this evolved strain with the reference strain to infer potential genetic targets of 2-PE-induced toxicity. This transcriptomic analysis showed that about 30% of all genes in the genome of C9 strain was differentially expressed with about 1,000 genes (17%) upregulated and 800 genes (13%) downregulated when a two-fold change and adjusted p values <0. 05 were used as criteria for differential gene expression. Gene clustering using the FunCat database (Figure  6 ) showed that metabolic energy (39%) and cell rescue and defense (22%) were the most enriched functional categories among upregulated genes, whereas protein synthesis (33%) as well as energy (32%) were the major functional categories identified in the downregulated genes.
Many genes associated with ATP production were also upregulated in the evolved strain, according to KEGG pathway analysis of the Oxidative Phosphorylation Pathway (KEGG ID sce00190; Supplementary Figure  S5 ). Genes encoding several subunits of membrane proteins were upregulated in C9, including the SDHC and SDHA subunits of succinate dehydrogenase, CYT1, COR1, and QCR7 subunits of cytochrome reductase, and COX1, COX2, COX3, COX4, and COX6A subunits of cytochrome c oxidase (Supplementary Figure  S5 ). Furthermore, the alpha (ATP1), beta (ATP2), epsilon (ATP15), a (ATP6), and j (ATP18) subunits of ATP synthase were significantly upregulated (Supplementary Table  S2 ), suggesting that the 2-PE resistance may involve ATP-related activities in the oxidative phosphorylation pathway. Gene ontology analysis of these transcriptomic data was also carried out using FUNspec.