dataesr/scientific-paragraphs-categorization huggingface dataset:
TableS6-B: Predicted effects on protein function following the amino acid variations found on the eIFiso4E amino acid sequences in the Amygdalus main group. Predictions were processed online by three different softwares: 1-Meta-SNP prediction. The software gives a prediction score from 0 to 1, with a score > 0. 5 the mutation is predicted "Disease"with an effect on protein function and/or conformation. From 0 to 0. 5, the mutation is "Neutral". "Nali" referes to the Number of Aligned sequences in the mutated site; 2-MutPred2 prediction. The software gives a prediction score from 0 to 1, with a score > 0. 5 the mutation is predicted to affect protein function and/or conformation. In this case, indication about molecular mechanisms possibly affected with a p-value ≤ 0. 05 and the associated probability found in the database are given.
Supplemental tables available at https://entrepot. recherche. data. gouv. fr/dataset. xhtml?persistentId=doi:10. 57745/EYPR SUTable S1 : S1 List of the individuals used in this study regarding the taxonomic classification in the Prunus genus, the geographic origin of the sampling, the subgroup classification used in this study and the eiF4E and eIFiso4E haplotypes obtained after the sequencing of the coding regions. Two haplotypes for one gene indicates the individual is heterozygous. Homozygous amino acid Homozygous amino acid Accession name Main group Sub-group Species eIFiso4E haplotype variation in the variation in other Remarks Phenotyping results interaction domains positions KZ_230_3 Armeniaca Wild apricots P. armeniaca eIFiso4E_Arm_P03 A127P no budsticks available not tested US_009 Armeniaca Apricot related species P.
ε i refers to the transmissibility sam- pling with ε ∼ N 0, Dσ 2 t , where D is a diagonal matrix with variances of ε relative to σ 2 t as components (δ i ) , i. e. , considering no inbreeding δ i = 1 -ω 2 s -ω 2 d if both parents of animal i are known, δ i = 1 -ω 2 d for animals of an unknown sire, δ i = 1 -ω 2 s for animals of an unknown dam, and 1 for animals for which both parents are unknown. Given this model of transmission, the inverse of the transmission relationship matrix can be easily obtained by the following decomposition: M -1 = L ′ D -1 L , where L is a lower triangular matrix with 1s on the diagonal and the negatives of the sire and dam's coefficients of transmission as off-diagonal entries  [26, 27]. Co-variance parameters of the transmissibility model ( ω s , ω d , σ 2 t , σ 2 e ) can be obtained using the ASReml software  [28]  and the program developed by David  [20].
The transmissibility model including transmitted environmental influences is then the same as Eq. (  1 ), but the transmissibility matrix M is modified. This modified transmissibility matrix is referred to as M E. Once again, its inverse can be easily obtained by the decomposition: M -1 E = L ′ D -1 E L. Shared environmental effects are transmitted from one generation to another through path coefficients of transmission. A detailed description of the way to compute M -1 E is provided in Additional file 1. Thus, in the transmissibility model with environment, compared to the traditional transmissibility model, one additional parameter has to be estimated: r. These parameters can be estimated with the restricted maximum likelihood method (REML) using ASReml  [28]  and the OWN Fortran program, freely available on the Zenodo website (https:// doi.
org/ 10. 5281/ zenodo. 82235 72), which we have developed. Consider the special case where a group of animals experiences a particular shared environment (e. g. , a stressful environment induced in an experiment) with effect θ 0 σ t , while the other animals are each in their own environment, different from each other. In that case, information about the variance of the transmitted environmental effect leads to the difference between the average transmissibility potential of the group of animals experiencing the particular environment and the other animals. Thus, σ 2 θ = θ 2 0 σ 2 t , hence r = θ 2 0 and θ 0 < (1 -ω 2 d -ω 2 s ). The reorganized D E matrix has only one block, as previously defined, and the remaining matrix is diagonal with δ i terms.
If the information is available, another solution is to test the impact of transmitted environmental effects with the transmissibility model with environment using the direct measurements of the non-genetic inherited factors, as previously described. In any case, the transmissibility model with environment, as currently programmed, is not intended to be applied routinely to large populations, due to the long computation times it requires. The CPU time for one iteration of convergence on a linux system and intel ® Xeon ® E5-2698v3 processor for 2525 individuals in the pedigree and 15 environments averaged 900 s. The model, as currently programmed, is not intended to be used routinely for large datasets, but rather to detect the existence of transmissible environmental effects, so that further investigations can be carried out to understand (and perhaps control) this transmission.
Phylogenetic trees were then constructed based on the combined data from scWGS, bulk WGS, and fluorescence in situ hybridization (FISH). The normal bone marrow displayed diploidy in 269/270 cells (99. 6%), with only one cell deviating by loss of chromosome 21, showing the high quality of the scWGS (Fig. 1 ). Of the 2577 cells in the leukaemic samples, five were normal diploid cells and the rest showed copy number changes agreeing with leukaemic cells. Overall, highly homogeneous genomes were seen for most of the leukaemias (Fig. 1 ), with predominantly whole chromosome gains being present in all cells. When assessing whole chromosome changes, 5/9 cases had the same chromosomal content in >99% of the cells, with only 1-2 cells displaying gains or losses of single chromosomes that were not seen in the other cells, suggesting a chromosome missegregation rate highly similar as observed for the normal bone marrow.
This suggests that HeH ALL with lower and higher MCN could arise through different mechanisms. Therefore, we investigated these groups separately in the following analyses. Starting with MCN 51-61, we observed that the tetraploid/ sequential model resulted in very few such cells (Supplementary Fig. 9B ). We therefore concluded that this model could not give rise to HeH with MCN 51-61 and excluded it from further testing. We then compared the pattern of trisomies and tetrasomies at different MCN (Fig. 3a ) and the pattern of trisomies and tetrasomies for each chromosome (Fig. 3b ) between the HeH ALL patient data and the simulations results by the root mean squared error (RMSE) method (Supplementary Table  3 ). The diploid/tripolar and diploid/sequential models both showed low RMSE values, indicating that they fit relatively well with the patient data.
For UPIDs that were found in diagnostic samples, remission-specific variants from the same chromosome were assigned to one chromosomal homologue and variants that showed heterozygosity in the remission sample but homozygosity in the matched diagnostic sample were assigned to the other homologue. Then variants informative for chromosomal homologue were screened in scWGS data and the homologue inheritance of chromosomes gained or lost was determined by the ratio between the number of each type of variant in the single cells.
Copy number analysis of bulk data Log R ratio (LRR) and B allele frequency (BAF) of SNP array data from Illumina (. idat files) and Affymetrix (. CEL files) intensity files were analyzed by Illumina GenomeStudio (v2. 0, Illumina, San Diego, CA) and Affymetrix Analysis Power Tools (v2. 10. 0, Thermo Fisher Scientific Inc. , Waltham, MA), respectively. Copy number alterations were called using TAPS  47  and manually reviewed in GenomeStudio or Chromosome Analysis Suite (v3. 3, Thermo Fisher Scientific Inc. , Waltham, MA). Subclonality of whole chromosomes was assessed using the TAPS software from SNP array, WES, or WGS data, considering LRR, BAF, and tumour purity. Depending on the type of subclonality (disomy/trisomy, UPID/trisomy, etc. ), the lower limit of detection of subclones was estimated to 20-30% of the cells.
The dataset included four different cohorts: from our Department 26 , Zaliova et al. 48 , Duployez et al. 49  , and The Therapeutically Applicable Research to Generate Effective Treatments (TARGET) program (dbGAP accession number phs00464) (Supplementary Data 1). Of those 577 cases, 253 (44%) were females and 324 (56%) were males, based on the absence or presence of a Y chromosome. For WES data from TARGET, paired-end reads were aligned to the human reference genome hg19 by the bwa  41. Duplicate reads marking and local realignment were performed by GATK  46. Constitutional variants of matched tumour/normal pairs were called by GATK Haploty-peCaller and the bedtools (v2. 27. 1) intersect was used to extract the variants in the regions targeted by the exome sequencing kit. After normalizing read counts of constitutional mutation sites to the sequencing depth, the LRR of the constitutional variants was then calculated by the log-odds ratio of the variant allele count in the tumour versus in the normal.
WGS data analysis and identification of BTRI and B/ATRI mutations WGS data from 14 BCP ALL cases have been previously published  10. The initial putative somatic mutations were identified by the Complete Genomics Cancer Sequencing pipeline and the data were further filtered for Somatic Score ≥0 and number of unique reads for the mutated allele >10. For Complete Genomics data generated by the TARGET program (n = 34), somatic variants were identified by the TARGET WGS analysis pipeline ([https://ocg. cancer. gov/programs/ target/target-methods#3233]). Illumina WGS sequencing libraries of nineteen matched diagnostic and remission bone marrow or peripheral blood samples diagnosed at Skåne University Hospital, Sweden, were constructed by the TruSeq Nano DNA sample preparation kit (Illumina, San Diego, CA, USA).
Mutational signatures analysis The R package MutationalPatterns 52 (v3. 4. 1) was used to decompose mutational profiles into pre-defined single base substitution (SBS) mutational signatures based on the Sanger mutational signatures (v3. 2 -March 2021) and to ascertain the relative contributions of the SBS mutational signatures for BTRI and B/ATRI mutations in trisomic chromosomes at diagnosis, and all informative relapse-specific mutations.
Cohesion assay and FISH Sister chromatid cohesion was analyzed in metaphase spreads in all nine HeH ALL patient samples subjected to scWGS. The percentage of cells with cohesion defects, measured as visible primary constriction gaps (gaps between the sister chromatids at the centromeres)  14  , was counted. FISH metaphase spreads mounted with DAPI were used for the assay, where 20-39 cells were analyzed per case. Images were captured using a Z2 fluorescence microscope (Zeiss, Germany) and the CytoVision software (v7. 4, Leica, Germany). Metaphase FISH was carried out on cases 2, 3, and 4 according to standard methods, with a total of 17-35 cells captured for each analysis. All whole chromosome paint FISH probes were acquired from Applied Spectral Imaging (Carlsbad, CA), and locus-specific probes from Vysis (Abbot Laboratories, Chicago, IL).
The S score of the given virtual cell was computed according to: S score = 0:5 + NT g1 + 2NT g2 À NT g3 23 À S aneuploidy ð1Þ where NT g1 is the number of trisomic chromosomes in group 1, NT g2 is the number of trisomic chromosomes in group 2 and NT g3 is the number of trisomic chromosomes in group 3. The S aneuploidy was calculated by using the probability density function of beta distribution from python scipy package (https://docs. scipy. org/doc/scipy/ reference/generated/scipy. stats. beta. html) with the empirically determined location parameter loc 0, scale parameter scale 1. 8, shape parameters a and b 0. 18, 0. 65, respectively. The x parameter was defined as: x = MCN À 46 46 ð2Þ For the tetraploid/sequential model, no S aneuploidy was used since no initial tetraploid cell would survive under that condition.
Statistics and reproducibility For assessing technical reproducibility, bulk WGS data from technical replicates represented by independent next generation sequencing libraries from the same DNA of 2 HeH samples (case L31 and case L74) were generated. A high correlation between the results from the two replicates was observed and over 97% of mutation sites were identified in the replication datasets. Since the reproducibility was very high, no additional replicates were generated. No statistical method was used to predetermine sample size. All cases with HeH where SNP array/ WGS/WES data were available were included in the bulk copy number analysis, except for samples where the technical quality was too poor. The sister chromatid cohesion assay and the copy number variation calling were performed independently in a blinded fashion.
Acknowledgements The results published here are in part based upon data generated by the  Therapeutically Applicable Research  to Generate Effective Treatments (https://ocg. cancer. gov/programs/target)initiative, phs000218. Figures 1, 2, 4, and 5 and Supplementary Figs. 4,7, and 8 were created with BioRender. com. This study was supported by grants from the  Swedish Childhood Cancer Foundation , grant numbers  PR2020-0033  (MY),  TJ2020-0024  (MY),  PR2018-0004  (BJ), and  PR2018-0023  (KP); the  Swedish Cancer Fund , grant numbers  20 0792  PjF (BJ) and  19-0252-Pj  (KP); Governmental funding of clinical research within the  National Health Service , grant number  ALFSKANE-623431  (KP); the  Swedish Research Council , grant numbers  2020-01164  (BJ) and  2020-00997  (KP);  IngaBritt och Arne Lundbergs Forskningsstiftelse , grant number  LU2019-0100  (KP), the  Gunnar Nilsson Cancer Foundation (MY) , the  Royal Physiographic Society of Lund (EW) , the  Czech Health Research Council , grant number ( NU20-07-00322 )) (MZ)), the  University Hospital Motol , grant number # 00064203  (MZ, JZ), and  Program EXCELES , grant number  LX22NPO5102  (MZ, JZ).
The 187 differentially phosphorylated phosphopeptides encompassed 163 unique phosphosites within 117 unique differentially phosphorylated proteins (DPPs) (Fig. 1G ) regarded as UHMK1 putative substrates. Additionally, 25 out of the 187 differentially phosphorylated phosphopeptides contained multiple phosphosites, whereas the remaining 162 phosphopeptides contained single phosphosites (Table  S2 ). Among all phosphosites identified, 91% were serine, 8% threonine, and 1% tyrosine residues (Fig. 1H ). Of note, 11 phosphosites (Table  S2 ) were not registered in the PHOSIDA and PhosphositePlus databases, suggesting that they are specifically regulated upon UHMK1 modulation, as these phosphosites were not reported in other contexts.
For these analyses, we considered the most likely direct targets of UHMK1, i. e. , proteins whose phosphosites were upregulated in UHMK1 WT and downregulated in UHMK1-KD. Additionally, we included all the phosphosites regulated in UHMK1 K54R , since many of these phosphosites were downregulated in this condition, having a similar effect of UHMK1-KD (Fig. 2D ) or presented a pattern similar to UHMK1 WT (possibly due to residual endogenous activity Fig. 2 , F and G). Using the software pLogo for motif search  (27) , we confirmed phospho-serines (S) followed by proline (P) at position +1 with high frequency (41. 67%) and statistical significance (Fig. 3A ). Besides, glutamic acid (E) was present at positions -4 (17. 71%), +2 (18. 75%), and +3 (20. 83%), while arginine (R) was common at position -3 in 16.
00112) as statistically significant (not shown). The GO analysis confirmed the UHMK1 function in some cellular processes previously reported (cell cycle, microtubule cytoskeleton organization, translation, protein transport to the cell periphery) and previously suggested (mRNA splicing, cell division, nitrogen/ aromatic compound metabolic process), as well as in a novel function, namely in rRNA processing. The interaction network of UHMK1 and the 117 putative substrates (DPPs) was accessed using the STRING database (Fig. 4B ). Proteins annotated in some of the BP terms retrieved from the GO analysis were identified as interaction clusters, from which the most prominent comprised the splicing regulatory proteins SF1, SNRNP200, hnRNP M (Hnrnpm), SRSF2, USP39, SRRM2, PRPF38A, SRSF4, and SUGP1, together with PRPF4B, PRPF38B, LUC7L, THRAP3, PPP4R2, and SON, which were presented as intercluster interactions.
Proteomic data analysis Peptide identification and quantification were performed using MaxQuant v1. 6. 2. 10 (https://www. maxquant. org) (69)  with its built-in search engine Andromeda. MS/MS spectra were searched against the UniProt mouse database (55,029 entries, downloaded on 15. 07. 2019) supplemented with common contaminants. Carbamidomethylated cysteine was set as fixed modification. Oxidation (Met) and N-terminal protein acetylation were set as variable modifications with phosphorylation (STY) as an additional variable modification for phosphoproteome data. Used channels of TMT10 were specified as label within a reporter ion MS3 experiment type. Isotope impurities of the TMT batch were specified to allow MaxQuant the automated correction of TMT reporter intensities. LysC and Trypsin/P were set as the proteolytic enzymes with up to two missed cleavage sites.
Precursor tolerance was set to ±4. 5 ppm, and fragment ion tolerance to ±20 ppm. Results were filtered to 1% FDR at peptide spectrum match and protein levels. The MaxQuant results were further analyzed on Microsoft Excel and Perseus platform (v1. 6. 1. 1) (https://maxquant. net/perseus/) (70). First, hits of the reverse and contaminant databases were removed. Data normalization was performed under the assumption that the input amount of each sample is equal across TMT channels. The reporter intensities of full proteome data were mediancentered to the overall median of the respective dataset, and the correction factors were calculated. The correction factors of corresponding channels were further applied to the phosphoproteome data. Next, a row-wise normalization was introduced to remove the batch effect as described previously  (71).
The normalized reporter intensities were log2 transformed and the relative abundances of phosphopeptides were determined. Only phosphopeptides found in all three replicates were considered for the analysis. Differentially phosphorylated phosphopeptides were defined by p-value < 0. 05 and fold change of |1|. The identified phosphosites were searched in the databases PhosphositePlus (https://www. phosphosite. org/homeAction. action) (72)  and PHOSIDA (http://141. 61. 102. 18/phosida/index. aspx) (73).
GO and network analyses of the DPPs GO analysis and Reactome pathway analysis were carried out using the Protein Analysis Through Evolutionary Relationships -PANTHER (http://geneontology. org). Terms with FDR <0. 05 were considered statistically significant. The DPPs and UHMK1 were searched for interactions using the STRING database (https://string-db. org) (75). Interactions determined experimentally, registered in curated databases, extracted from text mining or coexpression data were considered, using a medium confidence (0. 400) interaction score. Network was clustered using the MCL clustering algorithm, with an inflation parameter of 3.
cDNA from all the samples was pooled together pre-amplified using KAPA HiFi HotStart polymerase (Roche) and SingV6 primer. Nextera libraries (5 replicates) were constructed from 0. 8 ng of pre-amplified cleaned up cDNA using Nextera XT Kit (Illumina). Index PCR was carried out using the custom P5 primer (P5NEXTPT5) and the i701 Nextera primer (IDT Technologies). Libraries were size selected in a 2% E-Gel Agarose EX Gels (Life Technologies), cut out in the range of 300 to 800 bp, and extracted using the MinElute Kit (Qiagen), according to manufacturer's recommendations. Single-end sequencing was performed in the Illumina HiSeq 1500 system (Illumina) with a coverage of 74 bp. Raw FASTQ data were processed with the pipeline zUMIs, version 0. 2. 0 (78) and mapped to the mouse genome mm10 using the software STAR  (79).
RNA-seq data analyses and target validation by qPCR Differential expression between UHMK1-KD (shUHMK1#1, shUHMK1#2, and shUHMK1#3 data, combined and used as one pseudosample) and shCTRL was assessed using the edgeR/limma package  (80, 81). Gene set enrichment analysis was performed using GSEA version 6. 2 (82), with gene sets for Mus musculus obtained from GO2MSIG  (83). Results were regarded as significant with a pvalue <0. 05. RNA-seq validation was carried out in an independent transduction of NIH3T3 cells, by analyzing the expression of eight DEGs by PCR array (qPCR). The DEGs chosen for validation were as follows: Cdca4, Fam198b, Pimreg, Lox, Map3k3, Oaz1, Prrx1, and Sptbn1. The cells were cultivated in the same conditions described for RNA-seq. Total RNA was extracted using RNeasy Mini Kit (Qiagen).
Analysis of alternative splicing For ASEs detection in the mouse genome (mm10), we considered all raw sequencing reads from the replicates of each condition as four pseudosamples (UHMK1-KD, shCTRL, UHMK1 WT , and EV) and identified ASEs using Bayesian inference followed by differential analysis from vast-tools pipeline (vast-tools diff module)  (85, 86)  with |dPSI| ≥0. 2 and MV|dPSI_at_95| ≥0. 05 as significance thresholds. Additional nondefault parameters for vast-tools diff module include the following: -S 1, -e 10, -m 0. 05. ASEs were further categorized into functional classifications according to their predicted impact on the ORF: "neutral" for events that generate known functional isoforms or which do not alter the protein sequence (ex. an alternative exon); "protective" for events that reduce the occurrence of deleterious nucleotide sequences and therefore generate a functional protein (ex.
Statistical analyses Statistical analyses (of splicing reporter assays, RNA-seq validation, and functional assays) were carried out on Graph-Pad Prism version 7. 0 for MacOS X (GraphPad Software, available at https://www. graphpad. com). The function Column Statistics was used to evaluate parameters such as median, mean, coefficient of variation, kurtosis, skewness, and normality (Shapiro-Wilk test). Based on these parameters, the tests chosen to evaluate three groups or more were One-way ANOVA followed by Bonferroni's multiple comparison test or Kruskal-Wallis test followed by Dunn's multiple comparison test. For two groups, unpaired Student t test or Mann-Whitney test were used. A confidence interval of 0. 95 was set and therefore p-values lower than 0. 05 were regarded as statistically significant.
Data availability The RNA-seq data from UHMK1-overexpressing or UHMK1-depleted NIH3T3 cells are available in the Gene Expression Omnibus (GEO) repository, accession number GSE199768. The UHMK1 mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the Proteomics Identification Database (PRIDE) partner repository with the dataset identifier PXD033353. Supporting information-This article contains supporting information.
Vegetative filter strips Quantitative analysis of the effectiveness of VFS was done with classification and regression trees (CARTs)  40  using the experimental dataset (n = 115 for water and eroded sediment, n = 244 for pesticides) compiled and published by Reichenberger et al. 41  The dataset comprises event-based data on precipitation, volume of runoff water, mass of eroded sediment, site and experimental treatment (e. g. , type of vegetative filter strip, VFS dimensions, source/strip ratio), event and active substance. A full overview of the variables included in this dataset is given in Table  S5. The aim of the CART analysis was to explain the observed reduction efficiencies with the seven available independent experimental variables: vegetative filter strip width and area, clay and organic matter content in field topsoil, runon volume, precipitation and eroded sediment yield.