dataesr/scientific-paragraphs-categorization huggingface dataset:
Finally, the selected reads were aligned to a single reference sequence that was chosen for each KIR gene (Figure  S3 ), and the SNP variants were ascertained with SAMtools/BCFtools version 1. 2. 35, 50 e analyzed the resulting variant call files (VCFs) with a custom R script (jSOS) to generate genotypes based on the known combinations of SNPs (i. e. , the unique KIR alleles available from IPD). A post-filtered and aligned read depth of 20 was used as the minimum for calling the genotype at any given nucleotide position. The jSOS algorithm determines all of the possible allele combinations on the basis of the genotypes of those SNPs that achieve the threshold read depth. Thus, if a specific SNP fails to reach the threshold, the ambiguity of the final allele call will increase, but an incorrect allele-level genotype will not be returned.
HLA Class I Genotypes The HLA class I allele compositions were determined with NGSengine 1. 7. 0 (GenDX software), kindly provided by Wietse Mulder and Erik Rozemuller, with the ''IMGT 3. 18. 0 combined'' reference set. There was no pre-filtering for HLA genes, and the data were analyzed directly with the software. The one exception was the removal of reads mapping to HLA-Y, an HLA class I pseudogene present in a subset of HLA haplotypes. 57 The presence or absence of HLA-Y was determined via sequence-specific string searches of the FASTQ data. For the IHWG cells, these assignments agreed with those previously determined by PCR. 43 We further validated the assignment of HLA class I alleles by analyzing the sequence data with Assign MPS 1. 0 (Conexio Genomics), kindly provided by Damian Goodridge.
Validation of the KIR Capture Method We applied our capture/NGS method to DNA extracted from 97 publically available cell lines (sample set 1, Material and Methods), originally collected by the IHWG to facilitate study of HLA genes. 40 One of these cell lines is PGF, whose KIR haplotypes have been characterized previously by standard methods. 6, 14 We therefore used the data we obtained from PGF cells to assess the quality of the KIR sequences produced. The PGF KIR sequence reads were mapped back onto the two conventionally determined haplotype sequences. For PGF KIR haplotype 1 (137,813 bp; GenBank: FP089703), our method gave 100% coverage, a mean read depth of 49. 43, and a read depth of >103 for 99. 5% of the haplotype (Figures  3A  and 3B ). For PGF KIR haplotype 2 (142,732 bp; GenBank: FP089704), we obtained 99.
99% coverage, a mean read depth of 49. 83, and a read depth >103 for 99. 5% of the haplotype. Our haplotype 2 sequence contains two short gaps, comprising 18 nucleotides in total (Figure  3C ). The missing sequences are not predicted to be of functional importance (Figure  3C ). Thus, our method gives full coverage of the KIR region when it targets haplotypes that were included in the probe design. However, for population studies, it is important that our probe sets can cope with a wider (and unknown) range of diversity without any ''allelic dropout. '' As a test, we applied our method to genomic DNA from the subject of the chimpanzee genome project. 44 We mapped the obtained reads to the two full KIR haplotype sequences (GenBank: BX842589 and AC155174) that were previously characterized from this individual.
Measurement of KIR Gene Copy Number: PING_gc The PING_gc component of PING specifically determines gene copy number. KIR3DL3, a single-copy gene common to all KIR haplotypes,  6, 14, 16  is used as the standard to which other KIR genes are compared (Material and Methods). To assess the correlation between read ratio and KIR gene content, we applied PING_gc to the sequence data generated from the 97 IHWG samples. The first PING_gc module (called KFFgc) produced 13 distinct KIR gene presence or absence genotypes (Figure  4A ), identical to those obtained by established methods. 26, 59, 60 We then applied the second PING module, MIRAgc, and used the observed clustering (Figure  2 ) to set threshold values for determining KIR gene copy numbers (Figure  S3 ). To validate these results, we studied DNA samples from 85 of the same cell lines by using an established real-time PCR method for quantifying KIR genes.
12 We observed 99. 4% concordance between the results obtained by PING_gc and those obtained from real-time PCR (Figure  4B ). Of ten discordant results from 1,700 determinations, four involved rare alleles that were not detected by the primers of the real-time PCR assay (KIR2DL2*009 and KIR3DL2*076, the latter of which was discovered during this study). Of the other six discordant results, two were due to false positives of the real-time PCR (as shown independently by a standard PCR method),  27  two were just below the threshold values of the real-time PCR (but were clearly positive with PING_gc and standard PCR), and two remain unexplained (Figure  4B ). Thus, the discrepancies were likely due to low-frequency errors in sensitivity or specificity of the realtime PCR method. We conclude that analysis of highthroughput sequencing data with the PING_gc module provides precise measurement of KIR gene content and copy number and gives almost 100% accuracy.
PING_allele was first validated with wholeexome data from a sample of 15 KhoeSan individuals. 47 or these individuals, the KIR copy-number and allele data produced by PING matched the data obtained previously by the established methods of Sanger sequencing and pyrosequencing-based genotyping of KIR genes. 16, 31 ecause KIR3DL1, KIR3DS1, and KIR3DL2 of lineage II KIR (see Appendix A) exhibit high polymorphism and structural variation,  13, 23, 62  they were chosen as a further test of PING_allele. Using the capture/NGS method, we applied the pipeline to data obtained from 30 family trios from Mali in West Africa (sample set 2, Material and Methods). In this highly heterozygous population, we identified 18 KIR3DL1/S1, 15 KIR3DL2, and 3 KIR3DL1/2v alleles (Table  S1A ). These alleles were authenticated by established pyrosequencing and Sanger sequencing methods (Material and Methods), as well as by their segregation in the trios.
KIR3DL1/2v is a KIR3DL1-KIR3DL2 fusion gene that segregates with KIR3DL1/S1 and encodes a functional protein. 13 Importantly, we correctly identified individuals with distinct combinations of KIR3DL1/S1 alleles, KIR3DL1/2v fusion genes, and KIR3DL1/S1-deleted haplotypes (Figure  5 ). To expand the analysis, we next analyzed 2,112 individuals from the 1000 Genomes dataset. 48 From their exome sequences, we identified 50 KIR3DL1/S1, 46 KIR3DL2, and 5 KIR3DL1/2v alleles (Table  S1A ), as well as 14 KIR3DL1/S1 duplication and 13 KIR3DL1/S1 deletion haplotypes (Table  S1B ). Such duplicated and deleted KIR haplotypes were detected in all 26 populations represented in the 1000 Genomes dataset (Table  S1B ). These results demonstrate that the capture/NGS method coupled with the copy-number and allele components of the PING pipeline can correctly identify the extensive and complex variation of lineage II KIR molecules.
High-Resolution KIR Allele and Copy-Number Genotypes We applied PING_allele to the KIR sequence data obtained from the 97 IHWG cells. This analysis of 13 KIR genes identified 144 different KIR sequences: 128 corresponding to established alleles and 16 representing novel alleles. The latter were all shown to be authentic by the standard methods described above for KIR3DL1/S1 and KIR3DL2 (Table  S2D ). By considering all 144 KIR variants, we identified a minimum of 104 centromeric and 42 telomeric KIR C F M C F M C F M C F M 1 4 3 2 Figure 5. High-Resolution Allele-Level Genotyping of KIR Genes Four examples of high-resolution allele and copy-number genotypes of lineage II KIR genes and their segregation in family trios: C, child; F, father; and M, mother. Colored boxes show the segregating alleles.
All members of family 1 have two alleles each of KIR3DL1/S1 and KIR3DL2. Family 2 shows segregation of the KIR3DL1/2v fusion gene (the allele named KIR3DL1*059), which consists of exons 1-6 from KIR3DL1 and 7-9 from KIR3DL2. 13, 63 or clarity, KIR3DL1/2v is shown as an allele of KIR3DL1, so there is no allele of KIR3DL2 on this haplotype. Family 3 shows segregation of a haplotype that lacks KIR3DL1/S1 and is marked by the presence of KIR3DL2*006. The gene copy numbers were determined by PING_gc, which indicated that one copy of KIR3DL1 is present in each of individuals 3C and 3M and two copies are present in 3F. Family 4 shows segregation of both the KIR3DL1/ 2v and the KIR3DL1 negative haplotypes to the child. haplotypes in the cell panel (Figure  6  and Table  S3 ). Consistent with our results are KIR genotyping data obtained previously from the IHWG cells, which achieved a limited discrimination of alleles.
6, 59 These analyses demonstrate that PING accurately processes high-throughput sequence data to give accurate high-resolution KIR genotypes. The high-resolution KIR genotypes of the IHWG cell panel have been compiled (Table  S3 ), providing a resource for future investigation. Validation of the High-Resolution HLA Class I Capture Method Because KIR and HLA class I glycoproteins are functionally interacting receptors and ligands, the capture/NGS method was designed to capture and analyze the two gene families in the same reaction (Material and Methods). To validate the HLA class I sequences obtained by this method, we first analyzed the panel of 97 IHWG cell lines. In previous studies,  [38] [39] [40]  including high-density wholegenome SNP analysis,  43  90 of the IHGW cells were judged to be homozygous for HLA-A, HLA-B, and HLA-C, and five of the other seven lines were found to be homozygous for two of the three HLA class I genes.
This knowledge of KIR polymorphism makes substantial contributions to the KIR database. 15 For example, the number of KIR3DL2 alleles was doubled and is now in excess of 100. We also show that 476 (22. 5%) of the 1000 Genomes individuals have at least one example of a structural variant or novel allele of KIR3DL1/S1 or KIR3DL2 (Table  S1C ). All of these genomic variations have potential to influence NK cell function, but they are not visible to typing at the level of KIR gene content. A strong case can therefore be made that high-resolution knowledge of KIR diversity, in all its forms, will identify additional disease associations and improve the understanding of those already known. The study of human populations and their evolutionary dynamics, ancestry, and disease has benefited from GWAS methods, which genotype numerous SNP markers in large cohorts of individuals.
To compensate for the absence of suitable SNPs within the KIR genes, a recently described imputation method should accurately re-assess the diversity of KIR gene content for many of the reported GWASs. 28 Imputation of HLA class I alleles from GWAS data has been informative for studies of immune-mediated diseases. 72 Imputation of HLA alleles varies in its efficiency, particularly in non-European individuals, partly because >10,000 alleles are described but also because imputation relies on linkage disequilibrium, which can extend for shorter genomic tracts in non-Europeans than in Europeans. 15, 72 Many of the KIR variants and polymorphisms identified by our method are not evenly distributed across human populations. For example, the KIR3DL1/2v fusion gene is restricted to Africans, who exhibit the lowest linkage disequilibrium worldwide.
Acknowledgments This study was supported by  US NIH  grants  U01 AI090905 ,  R01 GM109030 ,  R01 AI17892 , and  U19 AI119350. FASTX-Toolkit, http://hannonlab. cshl. edu/fastx_toolkit/GenBank, http://www. ncbi. nlm. nih. gov/genbank/GenDx NGSengine, http://www. gendx. com/products/ngsengineHaplo Stats, http://www. mayo. edu/research/labs/statistical-geneticsgenetic-epidemiology/softwareImmuno Polymorphism Database (IPD), http://www. ebi. ac. uk/ipd/International Histocompatibility Working Group (IHWG), http:// www. ihwg. org/OMIM, http://www. omim. org/PING executable R files, https://github. com/wesleymarin/PING scripts, https://web. stanford. edu/~n0rmski/projectH/R statistical software, http://www. r-project. org/ UCSC Genome Browser , http://genome. ucsc. edu.
reference Sort reads per KIR Make sam/bam Count reads per gene Final align -vcf Copy number Call alleles New SNPs New combinations KIR gene +/- Copy number (manual check) KIR alleles KIR copy number and allele B genotype Data To Generated Validate ValidaƟon Individuals N (used) Genotype Method 1. IHWG cell lines 97 Enrichment/NGS KIR +/- PCR KIR copy number Real Ɵme PCR KIR alleles Pyrosequencing KIR novel alleles Cloning/Sanger sequencing HLA alleles PCR-SSOP, and IHWG database 2. West African Trios 90 Enrichment/NGS KIR alleles Pyrosequencing KIR copy number SegregaƟon HLA alleles PCR-SSOP 3. Europeans 188 Enrichment/NGS HLA alleles PCR-SSOP, and Sanger sequencing 4a. KhoeSan 15 (Exome) KIR alleles Pyrosequencing 4b. 1000 Genomes 2,112 (Exome) KIR novel alleles Cloning/Sanger sequencing.
Statistical analysis Data were analysed using SPSS v. 19  Two regression analyses were performed because combination antibiotic therapy was common, potentially leading to multicollinearity. In the first analysis, all predictors except antibiotic class were included to establish the patient-and treatment-related risk factors for CDI. In the second analysis, antibiotic classes (grouped as cephalosporins, amoxicillin/clavulanic acid, macrolides, fluoroquinolones, tetracyclines, carbapenems, piperacillin/tazobactam, narrow-spectrum penicillins) were individually included in the models to determine the independent contribution of antibiotic class to CDI risk. In sub-analyses, the additive effect of antibiotic combinations compared with monotherapy was assessed using separate survival models and estimates were compared using interaction testing.
The cationic composition agreed with the nominal one for all samples. Oxygen content of our samples was inferred from macroscopic physical properties. Synchrotron x-ray diffraction patterns were measured at the MSPD beamline  [31]  at the Alba synchrotron using the MAD26 detector  [32]. The samples were loaded in a borosilicate glass capillary (diameter of 0. 5 mm) and kept spinning during data acquisition. A short wavelength, λ = 0. 4137 Å, was selected to reduce absorption. The value of λ was calibrated using standard silicon. The patterns were collected between 80 and 300 K. The standard acquisition time to refine unit cells was about 30 min, but a total acquisition time of 3 h/pattern was used to perform a full structural characterization at selected temperatures. Diffraction patterns were analyzed by the Rietveld method using the FullProf program  [33]  with the symmetry-mode analysis.
The input file with the basis modes was obtained from the AMPLIMODES program  [34]  from Bilbao Crystallographic server. AMPLIMODES/SYMMODES and ISODISTORT, the new version of the ISODISPLACE program  [35] , were used to explore the possible distortions arising from the parent structure of YFe 2 O 4. Differential scanning calorimetry (DSC) was measured using a DSC Q-20 from TA-Instruments with samples sealed in aluminum pans under a nitrogen atmosphere. The scanning rate was 10 K min -1. Magnetic measurements were carried out between 5 and 300 K by using a commercial Quantum Design (SQUID) magnetometer, and electrical dc resistivity measurements were made on rectangular bars cut from the pellets with a typical size of 2 × 2 × 9 mm 3. The conventional four-probe configuration was used, and electrodes were made using silver paint.
The strong splitting of some diffraction peaks and the occurrence of new satellites are discernible. We have used ISODISTORT software to explore possible distortions that are able to produce transitions from the parent R-cell to a triclinic cell with P 1 space group. The peak splitting can be accounted for by a wide set of triclinic cells; some of them are summarized in the Table  II. The simplest triclinic cell (primitive in fact) is obtained by the condensation of modes belonging to the irreducible representation (Irrep) GM3+. Obviously, this cell is unable to explain the new superstructure peaks, but refinements with R Bragg ∼ 8. 6% can be obtained indicating the little weight that superstructure peaks have in the global refinement of this pattern. The second cell in Table II can be considered as the triclinic cell equivalent to the distortion found in LuFe 2 O 4 (monoclinic in that case)  [11, 15].
4(b). This result resembles the solution found in a previous electron diffraction study  [27]  where the satellites can be indexed by the propagation  vector k = (1/7, -2/7,9/7). Therefore, we have explored distorted structures coming from the mode condensation belonging to the combination of two Irreps: SM(a, -2a,0) and LD(0, 0, g) with a = 1/7 and g = 9/7. ISODISTORT yields two possible solutions with P 1 space group; they are indicated in Table  III. Both solutions refer to big cells with a large amount of free parameters. The only solution accessible to our software package was the first one, and we have tested the reliability of this solution. In this unit cell, there are 25 Y, 49 Fe, and 98 O; this leads to a total of 513 coordinates to be refined, which is at the limit of current software packages.
ACKNOWLEDGMENTS We acknowledge  CELLS-ALBA  for beam time allocation and the use of  Servicio General de Apoyo a la Investigación, Universidad de Zaragoza. We are thankful for financial support from the  Spanish Ministerio de Economía y Competitividad (MINECO)  (Projects No. MAT2012-38213-C02-1-2  and No. MAT2015-68760-C2-1-2 , cofunded by the  European Regional Development Fund  [ERDF] allocated by the  European Union)  and  Diputación General de Aragón (DGA , project  E69 ).
Adapted PET cameras are used to detect the photon pairs and generate the so-called Lines Of Responses (LORs) that connect each pair. Triangulation of successive LORs should give the point in space where the annihilation occurred -the tracer location. In practice there is some corruption of data due to a mixture of Compton scattering of photons and incorrect pairing. The algorithm developed at Birmingham over many years  (Ingram et al. , 2007a)  eliminates corrupted data thorough a statistical procedure; typically aliquots of 200-500 LORs will be used to compute the tracer location to within 0. 5-1. 0 mm at a frequency between 100 and 1000 Hz. The reliability and frequency of location depends on many factors such as tracer activity, tracer velocity, size of rig and mass of material to be penetrated by the photons.
Historically, the PEPT facility has progressed from the homemade multiwire positron camera in 1984, through the ADAC Forte Medical PET camera in 1999 (giving a 20-fold increase in data frequency) to more recent developments of the flexible, modular PEPT system built from the components of redundant PET scanners. This latter development has enabled exploitation of the technique for larger and/or more complicated geometries  (Ingram et al. , 2007b). During the experimental data acquisition N e particle positions have been recorded. The time-averaged Eulerian solid velocity, volume-averaged in a cell CðxÞ, centred at x, is defined by U p ðxÞ ¼ P Ne k ¼ 1 u p ðt k ÞΔt k δ k P Ne k ¼ 1 Δt k δ k with δ k ¼ 1 if x p ðt k Þ A CðxÞ 0 otherwise & ð1Þ where x p ðt k Þ is the instantaneous particle position at the time t k.
Numerical simulation Three dimensional numerical simulations of the fluidized bed have been carried out using an Eulerian n-fluid modeling approach for gas-solid turbulent polydisperse flows developed and implemented by IMFT (Institut de Mécanique des Fluides de Toulouse) in the NEPTUNE_CFD V1. 08@Tlse version. NEPTUNE_CFD is a multiphase flow software developed in the framework of the NEPTUNE project, financially supported by CEA (Commissariat à Fig. 7. Radial profiles of time-averaged solid vertical velocity normalized by the fluidization velocity measured at z=R ¼ 3:45. Upper panels: effect of the wall boundary conditions (with the particle-particle restitution coefficient e c ¼ 0. 9), bottom panels: effect of particle-particle normal restitution coefficient, left panels: rough wall boundary conditions, right panels: smooth wall boundary conditions.
1. Make sure the optical system is still positioned correctly prior to the experiment with the sample in place (see step 2. 1. 6). 2. Take background image S bg with each spectrometer and save it. 3. Change the trigger to 'external software', depending on the software (e. g. , SpectraSuite) (other options are: 'external', and 'synchronize', with different purposes). 4. Save each spectrum when receiving a trigger pulse. 5. Install a high definition (HD) camera at any optical access if desired. 6. Place the test sample in the protection system and vacuum the test chamber using a group of three rotary-vane vacuum pumps and a roots pump. 7. Start the plasma facility and bring it to the desired test condition in terms of heat flux and pressure by adjusting the electric power input and the vacuum pumps.
Data Processing 1. Surface recession rate and spectrometer probing location(s): 1. Observe sample injection and ejection times on the HSC video file for correct test time estimation. 2. Observe pixel location of the test sample stagnation point at injection from the HSC video file. 3. Calibrate HSC magnification by exporting image taken in step 3. 17 (for instance,. tiff format) and counting the pixels in the measurement region spanning multiple chessboard squares. Compute the correlation pixels:mm (obtain a more detailed characterization of the intrinsic and extrinsic camera parameters using the camera calibration toolbox in MATLAB if desired). 4. Export the image(s) taken in step 3. 16 (for instance,. tiff format) and find the pixels of the spectrometer probing location(s) as bright spots on the image, indicating x-and y-position.
5. Export HSC images (e. g. , Multitiff-format) and perform edge detection (e. g. , using Matlab build-in function 'edge') to define the stagnation point location at each time step i (x i and y i ). 6. Subtract the position of the surface (step 5. 1. 5) from the position of the spectrometer probing location(s) (step 5. 1. 4) for each time step to obtain their distances d(t) from the surface. 2. Emission spectra processing (all post-processing can for instance be performed within Matlab). 1. Export all recorded spectrum files (wavelength vs. intensity) and calibrate the intensity response of each recorded spectrum by: S exp,cal = (S exp -S bg ) / τ exp x C, with the experimentally obtained spectrum S exp , the background file S bg (step 3. 4. 2), the experimental integration time τ exp and the calibration factor C determined during step 4.
2. 1. 2. If several spectrometers are used, assess the validity of the calibration coefficient C using the freestream spectra taken during step 3. 9. Plot all calibrated free-stream spectra together; their responses should be nearly identical because the spectrometer collection volumes in the plasma flow are very close to each other. 3. Open the file containing the wavelength vector of the calibrated spectra (e. g. , double-click the. mat file in MATLAB) and identify the row indices corresponding to wavelengths λ 1 = 370 nm and λ 2 = 390 nm (alternatively, use the "find" command in Matlab). Note: For pyrolyzing ablators phenol originated species are also available, such as C 2 , CH, H, NH, OH. 4. Integrate the emission signal of interest (here CN violet emission, 370-390 nm) between the two indices (λ 1 and λ 2 ) from step 5.