dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGEMENTS We thank  R. Clark  in the Flow Cytometry and Cell Sorting Facility at the  University of Dundee  for cell sorting;  A. Prescott  in the  Dundee Imaging Facility  for assistance with fluorescence microscopy and  D. Lamont ,  K Beattie  and  S. Kosto  in the  FingerPrints Proteomics Facility at the University of Dundee  for assistance with quantitative proteomics. We also thank  D. Droll  and  C. Clayton  for the CLIP-seq protocol;  J. Faria  for assistance with Artemis and  A. Schnaufer  for the F1␤ ATP-synthase subunit antibody.
Detection of PD-L1/PD-L2-involving genetic alterations SVs and focal copy number alterations (CNAs) affecting PD-1 ligands were explored using targeted-capture sequencing with a custom SureSelect library (Agilent Technologies, Santa Clara, CA, USA) designed for capturing the entire sequences of the PD-L1 and PD-L2 genes, including their exons, introns, and 5′-and 3′-UTRs (Supplemental Figure  S1 ). Sequencing data were obtained using the Illumina HiSeq 2500 platform with a standard 125-bp paired-end read protocol. SVs and focal CNAs were detected using the Genomon pipeline (https://github. com/ Genomon-Project/) and the CNACS algorithm, respectively, as previously described  [13, 17, 18]. Putative SVs were manually curated and further filtered by removing those (i) with Fisher's exact P-value >0.
Detection of EBV genome For the detection of EBV sequence, after sequencing reads were mapped to the EBV genome (NC_007605), the number of the EBV-aligned reads in proper pairs were enumerated and divided by the number of total reads uniquely mapped to the human reference genome (GRCh37). Then, the obtained ratio was evaluated for the cut-off value of 0. 00015%, which was determined so that all EBV-positive cases with DLBCL and extranodal NK/T-cell lymphoma (ENKTL) confirmed by in situ hybridization (ISH) for EBV-encoded small RNA (EBER), LMP1 immunohistochemistry (IHC), or Southern blot were included, while minimizing EBV-negative cases. RNA sequencing (RNA-seq) data from the Cancer Genome Atlas (TCGA) cohort were analyzed in a similar manner, where the detection of EBV reads was considered as positive.
Whole-exome analysis of ENKTL Whole-exome sequencing data for ENKTL (accession number SRP057085) were obtained from the National Center for Biotechnology Information Sequence Read Archive and applied for the detection of somatic alterations using the Genomon pipeline, as previously described  [18]. Candidate mutations with (i) Fisher's exact P-value ≤0. 01; (ii) ≥4 variant reads in tumor; and (iii) allele frequency in tumor ≥0. 025 were adopted and filtered by excluding (i) synonymous single nucleotide variants (SNVs); (ii) known variants listed in the 1000 Genomes Project (October 2014 release), NCBI dbSNP build 131, National Heart, Lung, and Blood Institute (NHLBI) Exome Sequencing Project (ESP) 6500, the Human Genome Variation Database (version 2. 0), or our in-house single nucleotide polymorphism (SNP) database, unless they were listed in the COSMIC database (v70).
SVs and focal CNAs were analyzed in the same manner as described above. Targeted-capture sequencing of EBV-negative and -positive DLBCLs Targeted-capture sequencing was performed using a custom SureSelect library (Agilent Technologies) for 140 lymphoma-associated genes (Supplemental Table  S2 ), as previously described  [18, 19]. Additional probes for 1999 SNPs were designed to calculate genomic copy numbers  [17]. Somatic mutations with (i) ≥5 variant reads in tumor; and (ii) allele frequency in tumor ≥0. 025 were adopted and filtered by removing (i) synonymous SNVs; (ii) known variants listed in SNP databases (as described above); (iii) variants only present in unidirectional reads; (iv) variants occurring in repetitive genomic regions. Candidate mutations were further filtered by removing missense SNVs with allele frequency of 0.
RNA-seq and expression microarray analysis RNA-seq data for endemic Burkitt lymphoma [20] (SRP062178), anaplastic large cell lymphoma  [21]  (ALCL, SRP044708), and cutaneous T-cell lymphoma  [22]  (CTCL, SRP058948) were obtained from the National Center for Biotechnology Information Sequence Read Archive. RNAseq data for peripheral T-cell lymphoma  [23]  (PTCL, phs000689. v1. p1) and primary mediastinal B-cell lymphoma  [24]  (PMBCL, EGAD00001000692) were obtained through Database of Genotypes and Phenotypes and European Genome-phenome Archive, respectively. Microarray data for normal human cells were obtained from HemaExplorer  [25]. For RNA-seq data, expression quantification and detection of fusion transcripts were performed using the Genomon pipeline, as previously described  [13, 18].
Analysis of TCGA data sets As previously reported, we analyzed 10,210 TCGA samples from 33 cancer types, for which RNA-seq data were available from the Cancer Genomic Hub (https://cghub. ucsc. edu), to search for 3′-UTR-disrupted PD-L1 and PD-L2 transcripts  [13]. To evaluate the copy number of PD-L1 and PD-L2, the level 3 segmented copy number data (Affymetrix Genome-Wide Human SNP Array 6. 0) were downloaded from the TCGA data portal (https://tcga-data. nci. nih. gov) for DLBCL and stomach adenocarcinoma samples.
Flow cytometric analysis To detect surface PD-L1 or PD-L2 expression, cells were stained with allophycocyanin (APC)-conjugated anti-CD274 (29E. 2A3) and anti-CD273 (24F. 10C12) antibodies (BioLegend, San Diego, CA, USA), and analyzed on FACS LSR Fortessa (BD Biosciences, San Jose, CA, USA). The data analyses were performed with FlowJo software (TreeStar, Ashland, OR, USA).
Acknowledgements We thank  Miki Sagou  and  Takeshi Shirahari  for technical assistance and all the members of the  TENOMIC program. This work was supported by  Grant-in-Aid  from the  Japan Agency for Medical Research and Development [Project for Cancer Research and Therapeutic Evolution  (16cm0106501) and  Practical Research for Innovative Cancer Control  (17ck0106261)],  National Cancer Center Research and Development Funds  ( 29-E-3  and  30-A-1 ), the  Naito Foundation , the  Mochida Memorial Foundation for Medical and Pharmaceutical Research , the  Princess Takamatsu Cancer Research Fund , the  Fondation ARC  pour la recherche sur le cancer (20151203514), the  France Lymphome Espoir association , and the  GEFLUC Paris-Ile de France. LC was sponsored by a grant from  ITMO Cancer AVIESAN (Alliance Nationale pour les Sciences de la Vie et de la Santé/National Alliance for Life Sciences and Health)  within the framework of the Cancer Plan and by a fellowship awarded by the  European Hematology Association  and the  Japanese Society of Hematology.
In a context characterized by a scarcity of water resources and a need for agriculture to cope the increase of food demand, it is of fundamental importance to increase the water use efficiency of cropping systems. This objective can be meet using several currently available software packages simulating water movements in the "soil-plant-atmosphere" continuum (SPAC). The goal of the paper is to discuss and optimize the strategy for implementing an effective simulation framework in order to describe the main soil water fluxes of a typical horticultural cropping system in Southern Italy based on drip-irrigated watermelon cultivation. The Hydrus-1D model was calibrated by optimizing the hydraulic parameters based on the comparison between simulated and measured soil water content values. Next, a sensitivity analysis of the hydraulic parameters of the Mualem-van Genuchten model was carried out.
Among the available models, the physically-based models ones, i. e. , based on numerical solutions of the Richards equation, are increasingly being used to simulate water and solute movement in the vadose zone for a variety of common applications in research and soil/water management (see for example  [2] [3] [4] [5] [6] ). Hydrus software packages are finite element models for simulating the one-and two-or three-dimensional movement of water, heat, and multiple solutes in variably saturated media, and since their implementation they have been extensively applied in various applications. Hydrus-1D, with related manuals and case studies as examples, can be freely downloaded from the Hydrus website (https://www. pc-progress. com/en/Default. aspx?hydrus-1d)  [7]. In the Mediterranean environment of Southern Italy, one of the first applications of Hydrus-1D concerned the simulation of water movement and solute transport in a variably saturated water flow of fine-textured soil subjected to a fluctuating saline groundwater table  [8].
Modelling Approach The Hydrus-1D software (version 4. 17)  [18]  was used to simulate one-dimensional vertical isothermal variably-saturated flow at the experimental site and below the drip line and plastic mulch, considering the soil uniformly irrigated and explored by the roots. Simulations were performed from 30 May to 25 July 2006. The one-dimensional flow domain extended to a depth of 2. 3 m, and was divided into two separate soil layers, from 0 to -20 cm and from -20 to -230 cm, comprising a total of 74 finite elements. A finer discretization was used near the soil surface to accommodate relatively steep gradients in the pressure head. Four observational nodes were seated in the soil profile in correspondence with the TDR probe at depths of -10, -30, -50, and -70 cm. As a soil surface boundary condition, we used a system-dependent atmospheric condition in accordance with the approach of Feddes  [19]  and Šim ůnek et al.
For this purpose, two soil cores (0. 075 m in height by 0. 085 m in diameter) were subjected, in sequence, to a 1D unsaturated infiltration process and to a transient stage of evaporation to estimate the hydraulic properties of the soil in the 0-10 and 11-20 cm layers. Practically, during the transient of evaporation, applied methods provides θ-h-K values which need to be interpolated to obtain the water retention curve (θ-h) and hydraulic conductivity (K-h) functions. As a consequence, hydraulic functions were simultaneously parameterized by applying the Metronia code  [23]. Details about applied methodologies, i. e. , Unit hydraulic gradient and Wind evaporation methods, can be found in Bagarello et al. [22, 24] , respectively. The Table  2  reports the parameters of the 5 approaches. For C5 a single layer was considered because lab experiments provided similar results between soil layers.
Hydraulic Conductivity Measurements Single ring infiltration measurements of the Beerkan type  [26, 27]  were carried out on a clay soil at the experimental farm of CREA-AA, Foggia (41 • 27 03" N, 15 • 30 06" E). This fine textured soil was chosen with the aim (1) to apply several models for single ring infiltration data, to estimate the saturated hydraulic conductivity of the soil, Ks, and (2) to use these Ks values as an alternative to synthetically generated data to evaluate the impact of Ks on soil hydraulic parameter optimization. Specifically, a wide range of Ks values was obtained on undisturbed and tilled plots between December and June (five sampling dates), during the crop season of durum wheat; this allowed us to consider a relatively wide variability of the physical and hydraulic properties of the soil.
For a given date or plot, 5-12 infiltration experiments were carried out, and a total of 78 cumulative infiltrations were determined. The single-ring infiltration model of Stewart and Abou Najm  [28]  and the SSBI method (steady version of the simplified method based on a Beerkan infiltration run) proposed by Bagarello et al. [29]  were used to estimate Ks from Beerkan infiltration experiments. More specifically, we considered nine different calculation approaches corresponding to scenarios vi-xiv described in detail by Di Prima et al. [5]. Briefly, those calculation approaches differed by: (1) the way they constrained the macroscopic capillary length, (2) the use of transient or steady-state infiltration data, and (3) the fitting methods applied to transient data. The obtained Ks data set ranged more than two orders of magnitude between min and max Ks values.
Parametrization Evaluation Statistical indicators were used to compare the performance of Hydrus-1D regarding the modelling of SWC and TDR observations at different depths. For this purpose, we applied the IRENE software, a tool designed to carry out statistical analysis for use in model evaluation  [30]. Specifically, the five approaches for estimating the van Genuchten parameters were compared on the basis of 24 statistical indicators. The five approaches (C1 to C5) were ranked according to the best value reached by each statistical indicator by attributing a weight of 5 and 1 for the best and worst performance, respectively. The ranking index for each approach was calculated by averaging the 24 relative weights. In this paper only 11 statistical indicator will be reported: absolute and relative root mean squared error  [31] , modeling efficiency  [32] , index of agreement  [33] , coefficient of determination and of residual mass  [34] , mean absolute error  [35] , regression coefficient with slope and intercept, and standard error.
Discussion Horticultural crops are widespread high-income crops in Mediterranean regions but need accurate and rational management for saving water resources  [38]. In this investigation, we applied an integrated experimental approach in order to optimize the water use efficiency of a typical horticultural cropping system of Southern Italy, based on drip-irrigated watermelon cultivation. For this purpose, (1) accurate and simplified methods were selected and compared to measure and estimate, respectively, the physical and hydraulic properties of the soil, (2) the model by Coelho and Or  [25]  was applied to estimate root length density distribution starting from TDR measurements, and (3) the Hydrus-1D software was used to model the system under study, and pros and cons of the applied methods were critically discussed below in light of the available options.
Therefore, this simplified solution seems to be applicable for estimating the watermelon root growth under drip irrigation and plastic mulch. Finally, findings of this investigation show that the physically based Hydrus-1D model allows us to adequately simulate the water content within the framework of a typical drip-irrigated horticultural system. As a consequence, this free-software can be a viable alternative to more complex and paid license models. Moreover, since Hydrus is a very flexible type of software and allows accurate spatial discretization the soil, depending on the real field situations, it was possible to take into account a low permeability which was set at an depth according to TDR measurements. This compact layer, which is probably linked to the pedological characteristics of the soil profile, is plausible because the measured bulk density increased by about 10% from upper layers (0-20 cm) to deeper ones  (21-40 cm).
f cut is calculated based on numbers of cut and uncut DNA fragments covering cutting sites in genomic DNA (gDNA) sequencing data (Fig. 1b , Fig. 2a , Methods), or from qPCR data (Supplementary Fig. 1 , Methods). Finally, the absolute frequency of studied DSBs, B studied , is estimated from DSB sequencing data: B studied ¼ R studied α ; where α ¼ R cut B cut ð1Þ and R studied and R cut are the numbers of labeled reads originating from studied DSBs and from enzyme cutting sites (spike-ins), respectively, and B cut ~fcut. Accuracy of cutting efficiency estimation. The number of labeled reads per DSB (coefficient α), which is used for the final DSB quantification, as explained above, is computed from enzyme cutting efficiency, f cut (Equation (1), Methods). Therefore, to calculate α accurately, we need to be able to estimate enzyme cutting efficiency precisely.
To assess qDSB-Seq performance relative to BLISS, we compared the abilities of both methods to quantify DSBs in DIvA (AsiSI-ER-U2OS) cells, in which DSBs were induced in vivo by activation of the restriction enzyme AsiSI upon 4-hydroxytamoxifen (4OHT) treatment  18  (Methods). To test qDSB-Seq in DIvA cells, we used BLESS for DSB labeling and analyzed resulting data to determine the interval size (±3 bp) around the AsiSI cutting sites, which was used to compute frequency of AsiSI-induced breaks based on gDNA sequencing data. To estimate AsiSI-induced DSBs, we calculated cutting efficiency for each AsiSI cutting sites in 4OHT-treated cells and subtracted background (Methods). qDSB-Seq quantification yielded 52 ± 65 DSBs per cell (SD was estimated as described in Methods) consistent with 57 ± 33 DSBs per cell (n = 2) based on counting γH2A.
The resulting circular DNA was then linearized by I-SceI (NEB) digestion and amplified by PCR. Purified PCR products were subsequently treated with XhoI (NEB) to cleave terminal I-SceI sequences derived from adapters. Library preparation and sequencing. Sequencing libraries for i-BLESS (yeast strains) or BLESS (DIvA cells) and respective gDNA samples were prepared using ThruPLEX DNA-seq Kit (Rubicon Genomics). i-BLESS or BLESS libraries were prepared without prior fragmentation and further size selection. Quality and quantity of the libraries were assessed on a 2100 Bioanalyzer using HS DNA Kit (Agilent) and on a Qubit 2. 0 Fluorometer using Qubit dsDNA HS Assay Kit (Life Technologies). The libraries were sequenced (at least 2 × 70 bp) on Illumina HiSeq2000/2500/4000 platforms, according to our modified experimental and software protocols for generation of high-quality data from low-diversity samples  35.
NotI-treated cells (N) by data analysis based on the ΔC T method  36  , where the ΔC T was obtained by subtraction of the threshold cycle C T in sample U from the C T in sample N: ΔC T = C T (N) -C T (U). Final cutting efficiency was calculated as mean efficiency for all dilutions according to the formula below: f cut ¼ 1 À 1 2 ΔC T : ð1Þ We used calibration data to empirically correct ΔC T. Sequencing data analysis. We used iSeq (http://breakome. eu/software. html) to ensure sequencing data quality before mapping. Next, iSeq was used to remove i-BLESS or BLESS proximal and distal barcodes (5′-TCGAGGTAGTA-3′ and 5′-TCGAGACGACG-3′, respectively). Reads labeled with the proximal barcode, which are directly adjacent to DSBs, were selected and mapped to the version of the yeast S288C genome sacCer3 (we manually corrected common polymorphisms) or to the human genome GRCh37 using bowtie 37 v0.
12. 2 with the alignment parameters "-m1 -v1" (to exclude ambiguous mapping and low-quality reads). For ribosomal DNA mapping in RFB analysis, we mapped sequencing reads using the parameter "-v1" to allow multiple mapped reads. The end base pairs of the reads were trimmed using bowtie "-3" parameter. The parameter choice was based on the iSeq quality report. For calculation of the absolute number of DSBs per cell only mapped reads were retained. Further, the reads identified as originating from telomere ends were removed. For the yeast data, the telomeric reads were identified as those exhibiting the CAC motif in the whole AC-rich strand; regular expression C{0,3}AC{1,10} in the PERL language was used to identify them. Calculation of DSB frequencies per cell. Paired-end sequencing of gDNA or qPCR was used to measure the cutting efficiency of an endonuclease.
13. SD for results of BLISS quantification was estimated assuming the Poisson distribution of UMI and cell counts and using the formula  1  2 N UMI þ ffiffiffiffiffiffiffi ffi N UMI p N cell À ffiffiffiffiffiffi N cell p À N UMI À ffiffiffiffiffiffiffi ffi N UMI p N cell þ ffiffiffiffiffiffi N cell p , where N UMI is the number of UMI reads and N cell is the number of cells used. Analysis of fragile regions and enrichment. Hygestat_BLESS v1. 2. 3 in the iSeq package was used to identify fragile regions (i. e. , regions with significant increase of the read numbers in treatment versus control samples), which were defined using the hypergeometric probability distribution and Benjamini-Hochberg correction. To evaluate the enrichment of fragile regions on nucleosomes, we used Hygesta-t_annotations v2.
0, which computed the proportion of mappable nucleotides belonging to both the fragile regions and the nucleosomes, and the proportion of mappable nucleotides belonging to both genomic regions and the nucleosomes. To estimate the P-value for the feature enrichment inside fragile regions, we used 1000 permutations to calculate the empirical distribution of the ratio under the null hypothesis. Identification of one-ended DSBs. To estimate the total number of one-ended DSBs, we performed hypergeometric test based on the number of i-BLESS sequencing reads from Watson and Crick strands using Hygestat_BLESS v1. 2. 3 in the iSeq package with a 500 nt window size. Regions with P < 1e -10 for enrichment of either Watson or Crick strand reads were classified as one-ended DSB regions. The Bonferroni correction was used to compute P-values.
Acknowledgements This research was supported by the  NIH  grant  R01GM112131  to M. R. (Y. Z. , N. D. , B. F. , J. N. , R. Y. , and M. R. ),  Polish National Science Centre  grant  2015/17/D/NZ2/03711  to M. S. , and  Foundation for Polish Science  grant  TEAM/2016-2  to K. G. This work was also supported by  Ligue contre le Cancer (Equipe labelisee) ,  Agence Nationale pour la Recherche (ANR) , and  Institut National du Cancer (INCa)  grants to P. P. (B. P. , R. F. , and P. P. ), National Science Center grant  2016/21/B/ST6/01471  to N. D. , and a training fellowship from the  Gulf Coast Consortia on the Computational Cancer Biology Training Program (CPRIT  Grant Number  RP170593 ) to Y. Z. We are grateful to  Gaëlle Legube  for kindly providing DIvA cells and for helpful discussions, to  Andrzej Kudlicki  for helpful discussions, and to  Heather Lander  of the  Sealy Center for Structural Biology and Molecular Biophysics at UTMB , for editorial services for the manuscript.
Data availability The datasets generated by DSB sequencing and gDNA sequencing were submitted to NCBI Sequence Read Archive (SRA) under the accession numbers SRP189465 and SRP125409. The BLISS data used was obtained from NCBI SRA under the accession number SRR5441980. The source data underlying Figs. 2b-g, 3d, e, 4d,  and 5b , Supplementary Figs. 1b-c, 2,  and 4 , and Supplementary Table  2  are provided as a Source Data file. All other data are available from the authors upon reasonable request.
riparia around 470 Mb. Genome sequencing of different V. vinifera varieties gave values in the same range or greater depending on the methods of sequencing and assembly. In order to verify these values, we estimated genome size of V. riparia by the k-mer method  24, 25  using data from pair-end and mate-pair Illumina sequencing. By analyzing the 21-mers depth distribution, a total of ~50 billion k-mers were estimated with a peak frequency of 100, corresponding to a genome size of 494 Mb and the estimated repeat sequencing ratio was 33. 74%. In this study, V. riparia heterozygosity was estimated to be 0. 46% (mean distance 1 SNP each 217 bp between heterozygous SNPs) from 10x Chromium Genomics data processing. De novo Genome assembly and scaffolding of the Vitis riparia genome. We employed a hybrid de novo whole-genome assembly strategy, combining both short linked-reads and PacBio long reads data.
22 software 29 using high-coverage (~106X) Illumina paired-end and mate pair data. The 10x Chromium Genomics linked-reads were used to produce a separate V. riparia assembly using the Supernova assembler option-style = pseudohap2 and created two parallel pseudohaplotypes  30. The mean input DNA molecule length reported by the Supernova assembler was 45 kb and the assembled genome size was 424 Mb with a N50 scaffold of 711 kb. Subsequently, the PacBio assembly was scaffolded with the 10x Chromium Genomics one using the hybrid assembler LINKS 31 with 7 iterations, producing 870 scaffolds spanning 500 Mb with N50 = 964 kb and L50 = 255 (Table  2 ). Finally, genome phasing was reconstituted using Long Ranger analysis pipeline that processes Chromium sequencing output to align reads and call and phase SNPs, indels, and structural variants on the basis of molecular barcodes information.