dataesr/scientific-paragraphs-categorization huggingface dataset:
Analysis of the Integration Profile of the EFS-IL2RG Vector in the BM and PB of Transplanted Mice The aim of the analysis was to compare pre-and post-transplantation integration profiles in terms of frequency and function of the targeted gene pool that might suggest positive or negative selection of cells harboring specific integration events. The ex vivo, post-transplantation EFS-IL2RG integration profile was determined on 14/20 mice surviving at 6 months. The integration profile was determined in the BM and PB of mice belonging to each transduction group as pools (2-4, 5-10, and 16 weeks). Mice 169 and 170, showing the abnormal CD4 + /CD8 + cell population in the PB (see above), were analyzed individually, as well as one control mouse (179) belonging to the same transduction group (2-4 weeks), and they were excluded from the pooled post-transplant IS datasets.
To determine whether any gene was targeted at a different frequency in post-versus pre-transplant cells, we calculated an expected targeting frequency for each gene by multiple (1,000) random sampling of a comparable number of integrations from the corresponding pretransplant IS collection. We observed no significant difference between observed and expected targeting frequency for any gene in , peripheral blood (B), and spleen and thymus (C) of Rag2 À/À /Il2rg À/À mice transplanted with wildtype C56Bl6 Lin À cells transduced with a PGK-GFP vector (WT + PGK-GFP), Il2rg À/À Lin À cells transduced with the PGK-GFP vector (KO + PGK-GFP), and Il2rg À/À Lin À cells transduced with the EFS-IL2RG vector (KO + EFS-IL2RG), 6 months after transplantation. Data are presented as individual animals and as means ± SEM.
We then generated a set of 22,570 EFS-IL2RG ISs in Lin À cells obtained from the BM of 8-week-old, WT C57BL/6J mice, which we compared with the datasets obtained in the three groups of pre-transplant Il2rg statistical analysis identified a set of 89 over-targeted genes in WT cells, the majority of which (58%-73%) were shared with the set of genes over-targeted in Il2rg À/À cells (Figure  6A ), indicating that the vector identifies essentially the same preferential target genes in WT and Il2rg-deficient hematopoietic progenitors. To compare the integration characteristics of LV vectors in murine versus human cells, we compared the integration profile of the EFS-IL2RG vector in murine Lin À cells with that obtained by transducing with the same vector granulocyte colony-stimulating factor (G-CSF)mobilized PB CD34 + HSPCs.
From these cells, we retrieved 25,682 ISs targeting 6,498 genes, 247 of which were significantly over-targeted. We also analyzed larger datasets previously obtained from BM-or umbilical cord blood-derived derived HSPCs transduced with an LV expressing the human WAS gene  18  or a GFP gene  30  in the same CCL-SIN-18-WPRE backbone, which identified 337 and 741 over- A B D C E F Figure 4. Integration Profile of the EFS-IL2RG Vector (A) Scheme of the vector integration analysis study. ISs were retrieved by LM-PCR and Illumina high-throughput sequencing in pre-transplant Lin À cells and post-transplant BM and PB of Rag2 À/À /Il2rg À/À mice transplanted with and Il2rg À/À Lin À cells transduced with the EFS-IL2RG vector (see Figure  1 ). targeted genes, respectively (Table  2 ). While a substantial number of over-targeted genes was in common between the two adult human cell samples, almost 60% of the genes over-targeted in CB-derived HSPCs were specific for this cell type (Figure  6B ).
Interestingly, when we compared the genes preferentially targeted in murine Lin À cells with the human orthologs (NCBI HomoloGene definition) over-targeted in human mobilized CD34 + cells, we found only one gene in common (ARAP2). The comparison with the other two human datasets showed again no similarity (Figure  6C ). Table  S4  lists the top 50 over-targeted genes in murine Lin À cells and humanmobilized CD34+ cells ranked by number of ISs. The human list contains most of the genes found frequently targeted in pre-and post-transplantation samples in recent LV-based human gene therapy trials (e. g. , KDM2A, PACS1, NPLOC4, GPATCH8, ASH1L, and FCHSD2),  17, 18, 20, 31  none of which is targeted at any significant frequency in murine progenitors. These data show substantial differences in the pool of genes targeted at high frequency by LVs in murine versus human cells, which may potentially impact on the predictivity of LV-driven perturbation of specific genes in mouse studies.
IVIM Assay The IVIM assay was performed as previously described. 28, 29, 44 Briefly, 1 Â 10 5 Lin À BM cells from C57BL/6J mice were transduced with gammaretroviral or lentiviral vectors, expanded for 15 days, and seeded on 96-well plates (100 cells/well). After a further incubation for 2 weeks, insertional mutants were detected by microscopic evaluation or absorbance measurement of a reduced formazan (MTT assay) in a microplate reader. 45 Positive wells were counted and the replating frequencies (RFs) calculated by the R package limdil. 46 lates with an RF R 3. 17 Â 10 À4 were defined as positive assays. The incidence of positive assays was compared by a Fisher's exact test with Benjamini-Hochberg correction for multiple comparisons. For selected clones, Mecom gene expression was analyzed as described in the Supplemental Materials and Methods.
12, minimal length of 20 bp after trimming, and a minimal match length equal to the pre-trimming sequence), and they were mapped on the reference genome (human GRCh37. 75/hg19 and murine GRCm38/mm10) by the Bowtie2 software (>95% identity). The sequences are available at GenBank Sequence Read Archive (SRA): SRP135780. The genomic coordinates of the first nucleotide in the host genome adjacent to the viral LTR were indicated as IS. ISs originated by different reads mapping in the same genomic position were collapsed, recovering the number of corresponding reads (read count). Unique ISs were annotated on the RefSeq gene database as intergenic or intragenic (exonic or intronic). Genes (defined by the most upstream TSS to the most downstream 3 0 end) hosting at least one IS were identified as target genes.
ACKNOWLEDGMENTS This work was funded by grants from the  European Commission  ( H2020 Program SCIDNET  and  FP7 program  CELL-PID), the  Wellcome Trust  ( 104807/Z/14/Z ), the  DFG (REBIRTH Cluster of Excellence) , and the  NIH Research Biomedical Research Centre at Great Ormond Street Hospital for Children, NHS Foundation Trust and University College London. F. Z. was supported by the  EC FP7  grants  CELL-PID (261387 ) and SUPERSIST (HEALTH- F4-2013-601958 ).
This was achieved using seqtk (https://github. com /lh3/seqtk) with default parameters. The sequence data were then aligned against the the human mitochondrial (rCRS) and nuclear genomes (hg19) with BWA 0. 5. 9-r26-dev (29)  through PALEOMIX  (30)  and following the procedure described previously  (31, 32). Length distributions and damage of DNA were assessed for each experimental condition using mapDamage v2. 0  (33) , disregarding bases showing quality scores (Phred) strictly inferior to 30. Determining the capture condition stringency Shotgun sequencing revealed that the endogenous content of the test sample (R23) was 0. 39%. Following exome capture, high stringency conditions were found to result in higher foldenrichment (9. 0 -14. 8x) than low stringency conditions (1. 2 -3. 2x). The reverse was true for the whole genome capture procedure (Table  S1 ).
SOM2. Archaeological Overview We obtained ancient genomic data from 41 ancient samples recovered from Vietnam, Laos, Thailand, Malaysia, Indonesia and the Philippines (Table  S3 , Fig. S7 ). Samples were dated at the Oxford Radiocarbon Accelerator Unit (ORAU). All the data obtained is reported in Table  S3. Five of the samples produced low carbon yields and/or high C/N ratios. Dates for these samples are provided with a cautionary note on the absolute accuracy of the measurement. These dates are differentiated from the others by an OxA-X Number (Table  S3 ) with the exception of sample 017727 (OxA-X-2690-33) which was obtained on hydroxyproline (see below). The 4 samples that failed producing enough collagen for dating are indicated by NA in the 14C column. Samples from the two coffins yielding only mtDNA (Griffith University) were not directly dated.
Reads were trimmed using AdapterRemoval 2. 2. 2, in order to remove adapters, terminal N's (--trimns), low quality bases (-trim qualities, --minquality 2) and short reads (--minlength 30). Mapping to the human reference genome (hg19, build 37) using BWA  (31, 78)  , library merging using picard (http://broadinstitute. github. io/picard/), local realignment using GATK  (78, 79)  and MD tags calling using samtools calmd v1. 5-2 (29) were done as described in  (80). To minimize batch effects, we obtained genotypes for all individuals from a combination of published BAM files from previous studies and BAM files produced in this study. We genotyped genomes that had low coverage or were obtained from targeted capture by selecting the majority allele for the genomic position, looking only at reads with mapping quality ≥ 30 and base quality ≥ 30.
If both alleles were present at a site with equal coverage, a random allele was selected. High-coverage genomes were genotyped as in  (81). All analyses were restricted to regions that were within the 1000 Genomes Phase 3 (82) strict accessibility mask (ftp://ftp. 1000genomes. ebi. ac. uk/vol1/ftp/release/20130502/supporting/accessible_genome_mask s/20141020. strict_mask. whole_genome. bed), and outside repeat regions (UCSC genome browser simpleRepeat table). We assembled two panels for different types of analyses. Initial analyses were undertaken using the HUGO Pan-Asian SNP Database (13) (1,744 individuals; 50,796 SNPs) and Önge from the Simons Genome Diversity Panel (SGDP)  (18) , resulting in a panel maximising populations, at the expense of a lower SNP number, with 50,136 overlapping SNPs (hereafter the "Pan-Asia panel").
SOM4. Principal Component Analysis We performed a principal component analysis using smartpca v1600 implemented in the Eigensoft package  (86)  on the SNP covariance matrix of genomes from the Pan-Asia panel, along with the Jōmon sample. We then projected all our ancient SEA samples onto the first two principal components (87) (Fig. 1A ). To identify position of the ancient SEA samples within PC2 from the lower panel of Figure  1C , we first removed Yoruba (Fig. S8 ). PC2 now separates the Melanesians from the East Asians, although we note that La368, Ma911, Önge and Jōmon do not fall within this cline. We defined the groupings in Table  S3  based on the position of the ancient samples in the PCA plots of Figures  1A  and 1B , and on their latent ancestry components fitted using fastNGSadmix.
To account for damage in the ancient samples, only transversion were included. We explored various combinations of Group 1 ancient samples to overcome limited SNP overlap between the low coverage samples. Figures S20-S29 show the graphs fitted using combinations of the base populations and additional samples: We ran qpGraph v6100 from the Admixtools package (  16 ), following parameter settings as in Lipson and Reich  (105). We used fitted graphs with chimpanzee at the root, set "outpop" to be "NULL" to prevent use from specifying a particular outgroup population in which SNPs must be polymorphic, used a block size of 0. 05 Morgans for the jackknife procedure, and used the full matrix form of the objective function, with "diag" set to 0. 0001. Finally, we set a Z-score = 3 as the cutoff to label a statistic as an outlier.
Acknowledgments: We thank the  National High-throughput DNA Sequencing Centre (Copenhagen Denmark)  for expert advice and sequencing of samples, the  Duckworth Laboratory, University of Cambridge , for access to material in their care,  K. Gregersen  for making casts of teeth before sampling, and  Paul Tacon  for assistance with sample transfer. EW thanks  St. John 's  College, University of Cambridge , for providing an inspiring environment for scientific thought. Funding: This work was supported by the  Lundbeck Foundation , the  Danish National Research Foundation , and the  KU2016 program. HMc is supported by the  University of Adelaide 's ' George Murray Scholarship '. RS thanks the  Thailand Research Fund (TRF)  for their support (Grants  RTA6080001  and  RDG55H0006 ). The excavation of the Jōmon individual was supported by  Grant-in-Aid for Scientific Research (B)  ( 25284157 ) to YY.
Similarly, 11a was treated with tert-butyl-(trans-4-aminocyclohexyl)carbamate, Ld InMac is the intramacrophage assay carried out in THP-1 cells with L. donovani amastigotes. 14 Data are the result of three replicates. b Cl i is mouse liver microsomal intrinsic clearance; nd is not determined. and the resulting compound treated with TFA to remove the Boc group, yielding 13. This intermediate could then be either acylated or sulphonylated to give 14a,b. Alternatively, a more direct route was used for synthesis of intermediates 14c-h, whereby coupling of the appropriate combinations of 10a-c with 11a-d led to the relevant intermediates. Finally, treatment of either 12 or 14a-h with hydrazine hydrate generated the desired target compounds (3-9, 15, and 17). On the basis of the SAR learnings summarized in Table 2 (notably 7 and 9), compound 15 was prepared with the aim of improving potency and metabolic stability over 4 (Table  3 ).
The property forecast index (PFI) provides a probabilistic score of the likely developability risks and solubility of compounds. 31 15 had a PFI of 7. 2 and the impact of modulating PFI by either introducing polarity (reducing ChromLogD) or incorporating saturated isosteres (reducing ∼ #Ar) are highlighted in Table  4. A collateral benefit of reducing the PFI was that 15 and subsequent molecules all displayed excellent metabolic stability. Ld InMac is the intramacrophage assay carried out in THP-1 cells with L. donovani amastigotes. 14 Data are the result of 18 replicates. b HepG2 is a human liver cancer cell line. c Cl i is liver microsomal intrinsic clearance. d CLND is kinetic aqueous solubility measured using chemiluminescent nitrogen detection. 23 e AUC 0-last is the area under the curve until the last measurement.
This compound (30) was synthesized following Scheme 4, i. e. , displacing the chloro of 27g with amine 10a. As shown in Table  5 , 30 demonstrated excellent in vitro potency and CLND solubility, additionally showing a similar profile to Ld InMac is the intramacrophage assay carried out in THP-1 cells with L. donovani amastigotes. 14 Data are the result of at least three replicates. CLND solubility vs PFI profile within this series of compounds. Ld InMac is the intramacrophage assay carried out in THP-1 cells with L. donovani amastigotes. 14 Number of biological replicates for all active compounds ≥2; PFI is the property forecast index;  31  CLND is kinetic aqueous solubility measured using chemiluminescent nitrogen detection. 23 5 (Figure  3 ) against a panel of 210 human protein kinases at 5 μM (data not shown).
In Vitro Profile of 30 a Ld InMac is the intramacrophage assay carried out in THP-1 cells with L. donovani amastigotes. 14 Data are the result of at least eight replicates. b HepG2 is a human liver cancer cell line. c Cl i is liver microsomal intrinsic clearance. d CLND is kinetic aqueous solubility measured using chemiluminescent nitrogen detection. 23 e PFI is the property forecast index. 31 he vehicle was 0. 5% HPMC, 0. 4% Tween 80 and 0. 5% benzyl alcohol using 5 mice per dose level. %CV is the % of coefficient of variation. The vehicle was 1% methylcellulose in aqueous buffer using three rats per dose level. approaching a coplanar arrangement [having a dihedral angle of 6. 18(8)°], and this allows a π-π interaction between adjacent molecules within the columns. As such, 15 exhibited a highly stacked crystal lattice, therefore potentially explaining the low FaSSIF solubility.
A number of analogues were therefore designed that would disrupt some of the hydrogen bonding seen in this crystal structure. From the past SAR, removal of N19 or N20-H (Figure  6 ) meant significant in vitro potency reduction (i. e. , the N20-Me version of compound 15 demonstrated Ld InMac pEC 50 < 4. 3 and THP pEC 50 < 4. 3), so focusing on disruption of the other hydrogen bonds via introduction of strategically placed substituents was prioritized. A second strategy involved introducing substituents to reduce the overall planarity of this series, through either chiral centers or Table  9. In Vitro and FaSSIF Solubility Profiles of 1 and 31-40 a Ld InMac is the intramacrophage assay carried out in THP-1 cells with L. donovani amastigotes. 14 Data are the result of at least three replicates.
Melting Point (mtp) Determination. Determination of melting point of compounds were measured by thermal analysis, performing differential scanning calorimetry, (DSC) experiments, which measured temperatures and heat flows associated with thermal transitions in a material. The equipment utilized was from TA Instruments (Q20, RCS Refrigerator), using the system Software Q Advantage for Q Series and Analysis Software TA universal analysis. As general operating conditions, the following were set up: start temperature 25 °C, end temperature 350 °C, heating rate 10 °C/min. The sample quantity ranged from 1 to 3 mg, placed in a hermetic pan. The corresponding analysis with the thermal transitions occurred was obtained based on a reference blank sample (empty pan) to get the difference between the sample and the reference in the energy response when heating equally both pans.
The pH of each point in the titration curve was calculated using equations that contain pK a , and the calculated points were fitted to the measured curve by manipulating the pK a. The pK a that provided the best fit was taken to be the measured pK a. Usually 0. 5-1 mg of solid material is required for the measurements. When the compound precipitated at some point during the pH titration, the cosolvent method using methanol was applied using various concentration of cosolvent. The pK a in water was calculated using the Yasuda-Shedlovsky extrapolation method. X-ray Diffraction Studies of 1 and 15. Data for both studies were collected with an Oxford Diffraction Gemini A Ultra diffractometer at 150(2) K using Cu Kα X-radiation (λ = 1. 54178 Å). Crystal Data and Refinement Summary for 1. A description of the refinements and the full tables associated with the crystal structures are given in the Supporting Information.
Crystallographic information files have been deposited with the Cambridge Crystallographic Data Centre. CCDC 1857125 (1) and 1857128 (15) contain the supplementary crystallographic data for this paper. The data can be obtained free of charge from The Cambridge Crystallographic Data Centre via www. ccdc. cam. ac. uk/structures. In Vivo Pharmacokinetics. Mouse. Test compound 15 was dosed intravenously or orally by gavage as a solution at 3 or 10 mg free base/kg respectively (dose volume, 5 or 10 mL/kg respectively; dose vehicle, 10% (v/v) dimethyl sulfoxide (DMSO), 60% polyethylene glycol 400, and 30% deionized water) to female Balb/c mice (n = 3). Blood samples were taken from each mouse at 0. 08, 0. 25, 0. 5, 1, 2, 4, 6, 8, and 24 h post dose and mixed with two volumes of distilled water. After suitable sample preparation, the concentration of test compound in blood was determined by UPLC-MS/MS using a Quattro Premier XE (Waters, USA).
Pharmacokinetic parameters were derived from the mean blood concentration time curve using PK solutions software v 2. 0 (Summit Research Services, USA). Rat. Male Sprague-Dawley rats (n = 3) were dosed with 1, 30, 31, 33, and 39 either intravenously at a target dose of 1 mg/kg or orally at a target dose of 10, 100, and 300 mg/kg. Following discrete intravenous tail vein dosing from a solution of 5% DMSO:20% Encapsin in saline, serial blood samples (∼200 μL) were taken via lateral tail vein at 0. 08, 0. 25, 0. 5, 1, 2, 4, 8, and 24 h post dose and placed into individual tubes containing potassium EDTA and thoroughly mixed. Blood samples were placed on ice immediately after collection. Within 2 h of collection, 70 μL of blood sample was transferred to a tube containing 130 μL of 0. 1N Hepes buffer.
PCA Concept Let Φ be a dataset described by N parameters subject to M observations Φ m ∈ R N. These observations Φ m are represented by M points in the parameters space R N. The PCA allows to reduce the number of parameters N to q ≤ N with a linear transformation, while retaining as much of the "variability" of the observed data as possible. In other words, the PCA method consists in determining a sub-space of q ≤ N dimensions which captures as much of the variance of the dataset as possible. Alternatively, the subspace found by the PCA method is the one that minimizes the projection error, measured as the sum of squared Euclidean norms between each point an its projection. For this purpose, a basis v of vectors v i ∈ R q is iteratively built. The first vector v 1 is obtained by maximizing the following quantity σ 2 v 1 = 1 M M m=1 (π(Φ m -µ)) T (π(Φ m -µ)) = v T 1 1 M M m=1 (Φ m -µ)(Φ m -µ) T Σ v 1 = v T 1 Σv 1 , (1) where µ = 1 M M m=1 Φ m is the barycenter of the M observations Φ 1 , • • • , Φ M , π(Φ m ) = Φ m , v 1 v 1 the projection of Φ m upon v 1 , (Φ m -µ) T denotes the transpose of Φ m -µ and Σ ∈ M N ×N (R) is the covariance matrix of the vectors Φ 1 , • • • , Φ M.
In  [7, 8]  the authors assume that the degradation model is given by a convolution, i. e. , formally I m := h m * I (see [7, equation 2, page 2]), (2) where each h m is an unknown convolution kernel at time m ∈ {1, • • • , M } and * denotes the standard discrete convolution. The convolution kernels h m are assumed to be low-pass filters. In  [7]  Li et al. provide an intuitive explanation on how the PCA can restore turbulence degraded images that we quote extensively [7, page 1]: "The first principal component can be used as such an estimate since it has maximum variance and it contributes most to the variance of the observed dataset. Intuitively, blurring is a smoothing process in which the high-frequency components are removed and the variance is reduced. Thus, deblurring should be able to boost high-frequency components to some extent to restore the image.
Variance can be viewed as a measurement of high-frequency components in an image. Previously, we developed a constrained variance maximization method for blind image deconvolution  [8]. That variance maximization is equivalent to a PCA. " Li et al. seem to identify variance of a dataset of images (the one maximized by the subspace defined by PCA) with the high frequency content in an image, although these are different things. A priori there is no evident relation between these two things. However, as we show later, for a dataset generated by applying different amounts of blur to a given image, the principal component can indeed be related to the high frequencies of the image. Having described the observation model proposed in  [7]  we are now in position to detail the numerical method as it is proposed [7, section II, pages 2-3].
Examples and Experiments We conducted two experiments using three sets of sequences marked A, B and C. Dataset A contains images as they were acquired by the camera; it is composed of six real image sequences distorted and blurred by atmospheric turbulence. Appendix A details the acquisition protocol. Each sequence contains 200 images. Dataset B (resp. C) consists of images of A which were averaged on 20 (resp. 10) frames so that each sequence contains 10 (resp. 20) images. The first experiment was to study the behavior of SCPA with respect to the Laplacian filter in the case of sequences acquired using short exposure time. The second experiment was to get closer to the assumptions of Li et al. [8]  by considering a long exposure time and to observe the influence of the SCPA filter when varying M and the exposure time.