dataesr/scientific-paragraphs-categorization huggingface dataset:
This is determined by maximising the adjusted R 2. It represents the variance in the data that can be explained through the model and can be interpreted as goodness of fit. This understanding of the process leads to two important conclusions. Firstly, the temporal changes in our observable-i. e. the CPD-seq data-can be non-linearly 5/38 dependent on time. This becomes clear when taking the derivative and re-arranging Eq 1. We obtain df (t) = mθt m-1 τ m 1 - 1 θ f (t) dt. (3) When m -1 ̸ = 1, df (t) changes non-linearly with time. This plays a crucial role for analysing the repair kinetics, e. g. when making conclusions about the expected repair time for a gene. Whilst it is no new finding by itself that repair is changing non-linearly in time, it is usually neglected in the actual data analysis.
Correlating CPD Repair Dynamics With Genomic Properties The parameters of Eq 1 can be used to set repair into the context of other biological and nuclear properties in the cell. As databases provide a large variety of data signals, it is reasonable to opt for a data driven approach. However, finding such a correlation is not straightforward. Many of the NGS histogram distributions peak sharply around a low value whilst also including far distant outliers. Moreover, it can be assumed that sequencing signals include a large amount of noise. This makes it difficult to find a correlation using a continuous regression approach. We circumvented these issues by transforming the mapping into a binary classification problem. This reduces the impact of noise. Due to using equally many values for both classes, we removed any distribution specific bias.
Data Treatment All experimental data that were analysed in this study comes from public databases (see overview in Table  1 ). CPD-seq data was taken from  [28]. It contains two time courses with samples taken at t 1 ∈ {0, 60}min and t 2 ∈ {0, 20, 120}min, respectively. The location of transcribed areas was taken from  [32]. Data signals were partitioned into different segments, depending on the experimental procedure (see schema in  Fig 3). We distinguished between two schemes. The gene setup categorises the data into TS and NTS as well as the plus and minus strand of intergenic regions. The TCR configuration uses the notion of TCR regions (see S1 Appendix). All areas that are non-TCR regions are grouped together. We furthermore partition the TS and NTS of TCR regions into beginning, centre, and end.
With the exception of nucleosome density, all biological data values are seemingly Poisson distributed. Some of them strongly peak around a low value, but contain large positive tails. To remove a potential bias introduced by outliers, we limited our analysis to the lower 95th percentile. As this procedure was applied to all data (except nucleosome density), we did not introduce a bias towards a certain model. Rather, we improved comparability. The only exception is the MNase-seq signal, as it is approximately normally distributed. We consider that trimming could introduce a bias rather than removing one. We furthermore assessed the biological data distribution for different region models (e. g. the beginning of the TS and NTS of TCR regions) after removing parameters outside defined ranges as well as excluding the outliers.
Parameter Optimisation and Definition of Correlation We presume that temporal changes in CPD-seq data can be explained by Eq 1. Parameters m and τ were determined by applying Eq 2. During the parameter search for θ, we started with 0. 5 as minimal value for transcribed/TCR regions and 0. 4 for all other. We increased it thereafter by ∆θ = 0. 01, until we reached θ = 1. 0. We hence constrained the parameter search such that at least 40% of all cells-and in transcribed/TCR regions at least 50%-must be able to repair their lesions. We conjecture that data within this range is reasonably well described by Eq 1. We pursued a data driven approach in which we estimated a function between the parameters of Eq 1-namely the shape m, the characteristic time τ , and the maximum repair fraction θ-and other biological contexts.
Acknowledgments LZ was supported by the  CEA NUMERICS program , which has received funding from the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Sklodowska-Curie  grant agreement No  800945. The work was supported by the  Fondation ARC  ( PGA1 RF20170205342 ). We thank  Zoë Slattery  for proofreading the manuscript.
Hypoxia transcriptional signature clusters inv(16) AML subgroup To capture the contribution of hypoxia pathway in human AML-LSCs, we utilized two publicly available RNA-seq transcriptomic datasets (TARGET  (21) , including pediatric and adolescent/young patients, and Leucegene  (22) , including adult patients) generated from Dx samples of the following molecularly defined AML subgroups: (i) inv  (16)  (CBF-MYH11, n=20/18 patients, TARGET/Leucegene), (ii) t(8;21) (RUNX1-RUNX1T1, n=21/16 patients) and (iii) NPM1 mut (n=6/7 patients) as low risk AML; and (iv) MLL-rearranged (MLLr, KMT2A fusions, n=13/15 patients), (v) FLT3 ITD (n=4/6 patients), and (vi) normal karyotype (NK, neither chromosomal rearrangements nor NPM1 mut or FLT3 ITD n=14/10 patients) as intermediate-high risk AML  (23). AML samples mutated for TET2, IDH1 or IDH2 were excluded from analyses because such mutations have been suggested to interfere with HIF signaling  (24, 25).
A total of 78 samples (147 runs) and 72 samples (301 runs) were analyzed from TARGET and Leucegene, respectively (Table  S1 ). For initial data inspection, we used either a multidimensional scaling reduction (MDS) of the genome-wide information or a specific set of 119 hypoxia target genes (Hypoxia signature) characterized by hypoxiadependent transcriptional induction and by the presence of functional hypoxia response elements  (26)  (Table  S2 , Figure  1A ). The hypoxia signature enabled clustering of the inv  (16)  AML samples separately from the other cytogenetic groups, in both datasets (Figure  1A ). The t(8;21) AML samples also clustered partially separately in TARGET and Leucegene datasets, albeit to a lesser extent, whereas MLLr AMLs did so in Leucegene but not in TARGET (Figure  1A ).
When integrating individual samples from the same cytogenetic subgroup, we observed that cells identified as LSCs (highest LSC6 score) clustered together in the integrated UMAPs (Figure  2G ). Notably, high LSC6-scoring cells colocalized with CD34+CD38-cells across the 3 AML subgroups, including MLLr AML. We next queried the normal stem/progenitor phenotypic prediction of the LSC6 signature (Figure  2H-I ). For this, we projected our scRNA-seq data onto an existing reference annotation dataset containing 15 healthy hematologic cell types  (36). Each AML sample was first projected individually (Figure  2H  and https://github. com/JLTrincado/scAML) and then all AML samples within the same cytogenetic subgroup were integrated (Figure  2I ). We observed that phenotypically identical annotated clusters colocalized together in the UMAP, demonstrating a similar identity/phenotype across different patients.
Total CD34+ cells and high LSC6-scoring cells (identified as LSCs) were enriched for hematopoietic stem cells (HSCs) and progenitors (Figure  2I ). Data projection on an additional annotated dataset (37) confirmed the stemness phenotype of these cells, which overlapped with HSCs, multipotent progenitors (MPPs), lymphomyeloid primed progenitors (LMPPs) and myeloblasts (Figure  S2B-C ). This LSC-enriched CD34+CD38-cluster (hereinafter, LSC 34 ) was further characterized and compared with the remaining CD34+CD38-cells not identified as LSCs (hereinafter, NonLSC 34 ) and with the CD34-CD38+ cells (hereinafter, NonLSC 38 ) (Figure  2J ). Of note, in an individual inv(  16 ) sample (AML10), an additional LSC6 high cluster was identified and classified as HSC/progenitors but with high expression of HBB (LSC2 34 ) (Figure  S2D ).
Of note, the degree of transcriptional overlap between Dx-REL pairs varied from patient to patient when the total number of cells was integrated (Figure  5A  and S5A, https://github. com/JLTrincado/scAML), suggesting patient-specific transcriptional changes in Dx-REL pairs. Similarly, analysis of the LSC6 score in paired Dx-REL samples also revealed patientspecific heterogeneity with a trend towards an increased LSC6 score at REL (4/7) (Figure  5B-C  and S5C ). In addition, analysis of the hypoxia score in paired Dx-REL samples also revealed patient-specific heterogeneity, with an inverse Dx-to-REL evolution with respect to the LSC6 score (6/7) (Figure  5B-C  and S5D ). Finally, dormancy, ER stress and ROS signatures also revealed a variable, patient-specific evolution from Dx to REL in LSC 34 cells irrespective of the AML subgroup (Figure  5D-E  and S5E ).
G. Violin plots showing the expression of the significantly overexpressed genes of the hypoxia signaling pathway in the LSC 34 cluster in each cytogenetic AML subgroup. F. Expression of the indicated HIF target genes identified in the scRNA-seq analysis to be overexpressed in the LSC cluster after 48 h treatment with the indicated drugs at 5% O2 (n=6 samples, AML03, AML16-AML20, 2 per cytogenetic group). Statistical significance was calculated using the paired Students' t test. Expression is normalized respect to the BAY87 samples. G. LSC estimation in secondary recipients using ELDA software. Mice were considered leukemic when presenting >0. 1% human cells in BM (n=3 mice/dose and group).
Analysis of public bulk-RNA-seq data RNA-seq data from the publicly available databases TARGET  (21) , including a total of 78 patients in 147 RNA-seq runs, and Leucegene  (22, 43) , including a total of 72 patients in 302 RNA-seq runs, were downloaded for analysis. AML samples from specific cytogenetic subgroups without mutations in TET2, IDH1 and IDH2 were selected. Table  S1  summarizes the main clinico-biological features of the analyzed samples and the RNA-seq ID numbers. A total of 119 HIF target genes characterized by hypoxia-dependent transcriptional induction and the presence of functional hypoxia response elements were used to define the hypoxia transcriptomic signature (26) (Table  S2 ). Pre-processing and sample alignments: All samples were processed with the same pipeline and FastQC  (78)  was used for quality control and confirmation of the sequencing data from the FASTQ files.
FASTQ files SRA for TARGET samples were extracted using the SRAToolKit (v 2. 9. 0) (https://github. com/ncbi/sratools). Gene expression quantification: Illumina paired-end RNA-seq reads were aligned to the Gencode transcriptome release 27 (GRCH38. p10) (79) using Salmon (v0. 7. 2)  (80). Quantification at gene level was performed using pseudo counts from Salmon quantification and transformation to counts per gene using tximport library function from Bioconductor  (81).
cDNA sequencing libraries were prepared using the Next GEM Single Cell 3' Reagent Kit v3. 1 (10X Genomics, PN-1000268). Briefly, after GEM-RT clean up, cDNA was amplified during 12 cycles and cDNA QC and quantification were performed on an Agilent Bioanalyzer High Sensitivity chip (Agilent Technologies). cDNA libraries were indexed by PCR using the PN-220103 Chromiumi7 Sample Index Plate. Size distribution and concentration of 3' cDNA libraries were verified on an Agilent Bioanalyzer High Sensitivity chip (Agilent Technologies). Finally, sequencing of cDNA libraries was done on the Illumina NovaSeq 6000 platform using the following sequencing conditions: 28 bp (Read 1) + 8 bp (i7 index) + 0 bp (i5 index) + 89 bp (Read 2), to obtain approximately 25-30,000 reads per cell. scRNA-seq data analysis: Reads were aligned to the Hg38 Homo sapiens reference genome and quantified through CellRanger Single-Cell Software Suite (v3.
The selection of LSC clusters was done independently on each sample. We assigned an LSC6 score for each cell using the six gene signature and weights proposed in Elsayed et al, 2020  (34). Due to the sparse nature of the single-cell data, rather than selecting the cells with highest LSC6 score, we elected to cluster the data in an unsupervised manner using the Louvain clustering algorithm with resolution values ranging from 0. 5 to 1, and rank the obtained partitions according to their average LSC6 score. Those clusters above LSC6 decile 9 were determined as the more likely to be enriched on LSCs. If more than one cluster was selected under these criteria, the proportions of in-silico predictions obtained from VanGalen et al, 2019  (36)  and  Triana et al, 2021 (37)  were used. The cluster with the highest enrichment of HSC-like predicted cells was finally determined as the most likely to be enriched on LSCs.
Cell cycle phases identification was performed based on previously defined markers  (87). Scripts and plots generated on each sample are available in Github (https://github. com/JLTrincado/scAML). In-silico prediction of cell types: Some studies have reported phenotypic heterogeneity in human BM. We leveraged these annotated datasets to predict the healthy cell type closest to our leukemic clusters. The annotated healthy BM datasets from Van Galen et al  (36)  was merged and projected onto each sample using FindTransferAnchors and TransferData methods from Seurat  (86). Code for reference assembly and projection is available at Github (https://github. com/JLTrincado/scAML). For projecting the data onto healthy BM data from Triana et al  (37) , a workflow based on scmap (88) was used. Sample code for reference atlas projection is available at https://git.
embl. de/triana/nrn//tree/master/Projection_ Vignette. Integration by cytogenetic-molecular subgroup: Seurat canonical correlation analysis (CCA, number of anchors set to 2,000) was applied to correct the patient-specific bias introduced by the pooled transcriptomic information from all sequenced samples  (86). Individual clusters identified in each sample to be enriched in LSCs, were labeled in the integrated datasets as "LSC 34 ". All the remaining cells non-labeled as "LSC 34 " within the CD34+CD38-population were labeled as "NonLSC 34 ". All CD34-CD38+ cells were labeled as "NonLSC 38 ". Pathway scores and pseudotime trajectories: Different gene sets reported in the literature to be associated with LSC-enriched pathways (Table  S2 ) were used to biologically inspect each annotated cluster.
AddModuleScore from Seurat suite was used to assign a score to each cell for each gene set  (86). Resulting values were normalized between 0 and 1. Trajectory analyses were performed with the Monocle package (v2. 18. 0)  (89). The highly variable genes obtained for the integration of the data via Seurat were used for pseudotime ordering. Dimensionality reduction was applied with the DDRTree option.
Immunophenotyping and cell cycle, apoptosis and CellROX analyses Immunophenotyping: AML engraftment in mice was monitored by FACS analysis, biweekly in PB and at sacrifice in PB, BM, spleen and liver. PB was collected into EDTA tubes (Sarstedt). Mononuclear cells were stained (30 min at 4ºC) with the following monoclonal antibodies: anti-hHLA-ABC-FITC (G46-2. 6), anti-hCD45-APC (HI30), anti-hCD33-PE (WM53), anti-hCD34-PECy7 (8G12) and anti-hCD19-BV421 (HIB19) (all from BD Biosciences). Cells were then lysed and fixed with the BD FACS TM Lysing solution (BD Biosciences). Fluorescence Minus One (FMO) controls were used to set the FACS gates. A FACSCanto TM -II flow cytometer and equipped with FACSDiva TM software was used for analysis (BD Biosciences). Cell cycle analysis: Cells were stained with anti-hCD45-BV510 and anti-hCD33-BV421 for 30 min at 4ºC.
After washing, cells were fixed with 0. 4% paraformaldehyde (Alfa Aesar) for 30 min at room temperature (RT), then lysed with 0. 2% TritonX (Sigma) for 1 h at 4ºC, washed, stained with anti-Ki67-PE (1:20, BD Biosciences) for 2 h at 4ºC and finally stained with 7AAD (BD Bioscience) for an additional one hour. Cells were analyzed using a FACSCanto TM -II flow cytometer and equipped with FACSDiva TM software. Apoptosis: Cells were washed with Binding Buffer 1X (BD Pharmingen) and stained with anti-hCD33-BV421, anti-hCD45-BV510, anti-hCD34-APC and anti-hCD38-FITC for 30 min at 4ºC. Cells were then washed with Binding Buffer 1X and stained with AnnexinV-PE (BD Biosciences) and 7AAD for 15 min at RT. Cells were analyzed within an hour using a FACSCanto TM -II flow cytometer and equipped with FACSDiva TM software.
CellROX: For ROS content analysis, cells were stained with anti-hCD33-BV421, anti-hCD45-BV510, anti-hCD34-PE (581), anti-hCD38-FITC and with CellROX Deep Red Reagent (1:500, ThermoFisher) for 30 min at 37ºC. Cells were washed 3 times with PBS and analyzed using a FACSCanto TM -II flow cytometer and equipped with FACSDiva TM software.
Cells were allowed to recover for 48 h and were then treated with the corresponding drugs and maintained for 48 h at 5% O2 (hypoxic conditions). After drug treatment, AML-MS5 co-cultures were harvested and MS5 cells and T cells were magnetically depleted by AutoMACs (Miltenyi Biotec) using anti-murine Sca1 and anti-human CD3 magnetic beads (Miltenyi Biotec). Recovered cells were counted and different doses (2,000, 1,000, 500 and 250 cells) were seeded each in 15 wells of a 96-well plate pre-coated with MS5 cells in supplemented MyeloCult media and allowed to expand in 20% O2 (normoxic conditions) with media changes twice weekly. After 5 weeks, wells were score as positive if massive growth of cells were observed in the well  (33). LSC dose was determined using ELDA software  (91). The identity of the AML cells was confirmed by detection of the molecular alteration by FISH or qPCR in some of the positive wells.
Statistical analysis Data are represented as mean ± standard error (SEM). Statistical comparisons between groups were assessed using two-tailed unpaired Student's t-tests, or paired Student's t-tests (when analysing data from same AML samples subjected to different treatments), unless otherwise stated. Data distribution was assumed to normal but this was not formally tested. All analyses were performed with Prism software, version 8. 0 (GraphPad software Inc. , San Diego, CA) and P<0. 05 was considered statistically significant (*P<0. 05 and **P<0. 01). 16 ) MLLr.
Acknowledgments We thank  Dr. Aleix Prat  for technical help and  Dr. Fernando Anjos-Afonso  for technical discussions and advices. We thank the  Finnish Hematology Registry and clinical Biobank (FHRB) ,  Instituto Aragonés de Ciencias de la Salud (IACS)  and the  Blood Cancer UK Childhood Leukaemia Cell Bank  for providing AML samples. We thank  CERCA/Generalitat de Catalunya  and  Fundació Josep Carreras-Obra Social la Caixa  for core support. Competitive financial support for this work was obtained from the  Deutsche Josep Carreras Leukämie-Stiftung  ( DJCLS15R/2021 ) to PM and TV-H. This research was also supported by the  Spanish Ministry of Economy and Competitiveness  ( SAF2016-80481R ,  PID2019-108160RB-I00 ),  La Caixa Health Research Program  ( LCF/PR/HR19/52160011 ), the  Leo Messi Foundation , " Heroes hasta la médula" initiative  and  ISCIII-RICORS within the   Next Generation EU program  ( plan de recuperación ,  transformación y resiliencia ) to PM and the  Health Institute Carlos III (ISCIII/FEDER ,  PI20/00822 ) to CB.
Amplicon data reanalysis Rumen epithelial amplicon data was downloaded from two studies  22, 23  that amplified the same variable region of the 16S rRNA (V3-V5) and the data were reprocessed using the qiime2 environment (v. 2021. 4. 0)  48. The forward reads from both datasets were denoised by implementing dada2 with trimming from positions 25 to 225 and 42 to 242 for the Neubauer et al. 23  and Wetzels et al. 22  data, respectively. The resulting count tables were merged by shared, identical ASV sequences, which classified using the rRDP package in R (v. 1. 20. 0)  49. The relative abundances for each ASV in each sample were also calculated in R, where the top 10 most abundant ASVs based on their median relative abundance across all samples were plotted.
DNA extraction was then carried out using the PowerSoil Pro kit (Qiagen), pairedend libraries were prepared using the Westburg NGS DNA Library Prep Kit, and metagenomic sequencing was carried out on an Illumina Novoseq 6000 instrument with a 250 bp read length at the Vienna BioCenter Core facilities. The reads were trimmed using Trimmomatic (v. 0. 39)  51  and mapped against a BosTorus reference genome (GCF_000003055. 6) to filter out any reads obtained from the host. The remaining reads were assembled using SPAdes (v. 3. 15. 2) 52 , reads were mapped using BWA-MEM  53  , and MAGs were generated using Metabat2 (v. 2. 12. 2) 52 with a minimum contig size of 1500 bp. The MAGs were assessed for completeness and contamination using checkM (v. 1. 1. 3)  54  , and then classified using the classify workflow from the genome taxonomy database toolkit (GTDB-Tk, v.
These reads were trimmed using cutadapt (v. 2. 10) and assembled using SPAdes (v. 3. 14. 1)  52. Contigs shorter than 1 kbp were removed using seqtk (v. 1. 3). The genomes with >1 % contamination were filtered using the mmgenome2 package (v. 2. 1. 2) 57. To obtain complete genomes, DNA from three reference genomes (C. stinkeris NA3, C. noahi NE2 and VBCF_01 NA2) was also sequenced using the Oxford Nanopore platform. Libraries were prepared using the Nanopore Native Barcoding Genomic DNA by Ligation (EXP-NBD196, SQK-LSK109) protocol and sequenced on the MinION Mk1C instrument using a FLO-MIN106 flowcell. The resulting reads were basecalled using Guppy (v. 3. 0. 3+7e7b7d0, Oxford Nanopore Technologies), assembled using pomoxis (v. 0. 3. 1, Oxford Nanopore Technologies), polished using metadaka (v.
Comparative metatranscriptomics and aarC analysis Transcriptomics were mapped using the same approach as described for the metagenomic mapping above. Reads mapping to ORFs predicted with prokka (v. 1. 14. 6)  58  were counted with htseq-count (v. 0. 11. 3)  65. To be able to compare the expression of genes across populations, we aligned ORFs with blastn and compared genes with over 80% alignment identity in terms of the number of mapped reads. To ensure that genes could be clearly distinguished from each other during the competitive mapping, genes that were over 97. 5% similar were not compared. We then carried out the statistical analysis of differential expression using using the R package DESeq2 (1. 26. 0) 66. To assess the diversity of the aarC genes, those predicted by prokka (v. 1. 14.
Population tracking in vivo The DNA extracted by Neubauer et al. 23  was used to monitor populations using the digital PCR assay and method described above. This study tested the effects of feed additives using 8 cows in a change-over design where 2 cows were assigned to the control group for each of the 4 experimental runs. Each experimental run consisted of two periods where a high-grain diet was fed, which induced changes in ruminal SCFA concentrations, and papillae samples were taken at 3 time points (1 before and 2 after the high-grain periods). From 96 samples, 2 µL of the extracted DNA was added to the mastermix. The resulting count data were merged from the rumen SCFA data measured in Neubauer et al. 23  and Pearson correlations were calculated using the cor function in R. Correlations with a p value lower than 0.
Acknowledgements We first thank  Sara Ricci  for providing papillae samples remaining from recent feeding trials. We further thank the  Joint Microbiome Facility of the Medical University of Vienna  and the  University of Vienna (JMF)  for technical assistance in sample preparation and processing for sequencing. All quantitative and digital PCR experiments were further supported using resources of the  VetCore Facility (Genomics)  of the  University of Veterinary Medicine Vienna. C. R. S. and A. J. M. were partially supported by a  Fellowship  from the  Natural Science and Engineering Council of Canada   Postgraduate Scholarship-Doctoral  ( NSERC PGS -D). The competence centre FFoQSI is funded by the  Austrian ministries BMVIT ,  BMDW  and the  Austrian provinces Niederoesterreich, Upper Austria  and Vienna within the scope of  COMET -Competence Centers for Excellent Technologies.