dataesr/scientific-paragraphs-categorization huggingface dataset:
Accordingly, the novel variant frequency is much higher for the amplicon datasets than the exome and targeted exon datasets (Table  S10 ). Of course, changing variant calling criteria (such as imposing stricter coverage requirements) will affect the number of false positives (and thus the novel variant rate), so this result should not be interpreted as an indication that it is impossible to get high quality results from the amplicon dataset. Instead, this result emphasizes that benchmarks will vary with different target designs, while still demonstrating the general increase in false discovery rate for novel variants (although the extent of enrichment can vary with target design and variant caller). In the example of the specific variant at position 17933286 on chromosome 20, there was a very small proportion of reads containing the variant allele.
Recovery rates among previously observed variants droped when a threshold of 50% supporting reads was used because this threshold splits the heterozygous peak in half (increasing the likelihood of encountering variants that marginally meet the criteria in one dataset but marginally do not meet the criteria in the other dataset). In short, novel variants often have an increased false discovery rate because the low likelihood of sequencing error occurring for the same nucleotide at the same position of a naturally occurring variation, where most novel variants (defined using sufficiently liberal criteria) fit the model of a sequencing error that will be observed most frequently in variants with small percentages of supporting reads. The difference in density distributions was most clear for variant with less than 20% supporting reads, but the entire peak of variants with less than 30% supporting reads violates an assumption a diploid human genome.
Reviewing Manuscript Although 1KG samples were selected on the basis of having some validated positive controls, it is useful to have quality control metrics that will be correlated with the true sensitivity and specificity for a given variant caller. Each strategy has notable caveats for interpretation, but we think it is useful to have quality control metrics that can be used to select an optimal variant caller for a given dataset. First, we assume that the total number of variants is correlated with sensitivity. Of course, the accuracy of this assumption would depend upon the false discovery rate for the variant caller. Nevertheless, we believe that the total number of called variants is a useful benchmark to compare variant callers. SNP and indel counts are shown for 1KG exome and targeted exon samples in Figure  6.
In most cases, the pre-processing pipeline had a minimal effect on the size of the resulting list of SNPs, with the notable exception of the GATK UnifiedGenotyper (although the impact was significantly decreased for high-quality SNPs). Similarly, the overlap was strong for variants called without these extra preprocessing steps (Figure  S6  and S7 ). However, it is difficult to tell how common this trend is for all datasets: for example, the exome samples from SRP019719 did not show this same difference (Figure  S8 ) and instead showed a relatively greater difference in the number of SNPs called by VarScan with default parameters. The size of the SNP lists clearly varied with different VarScan parameters: the p-value threshold considerably decreased the number of SNPs called, the conservative parameter set was even more restrictive, and both results run with non-default settings produced significantly fewer SNPs than GATK.
Among the high-quality calls, the number of SNPs was similar for GATK HaplotypeCaller versus GATK UnifiedGenotyper. The relative number of indels varied from the relative number of SNP calls: however, there were only a limited number of indel calls on chr20 for the targeted exon panel, so we think the number of 1KG exome indel calls were more reliable for assessing general trends. There was a modest but noticeable increase in the number of indels called if the indel realignment was run, and the GATK HaplotypeCaller produced a much larger number of indels than the GATK UnifiedGenotyper. Similar to the SNP calls, VarScan produces fewer indel calls when using more stringent parameters. Second, we assume that most variants should have been previously observed in large scale sequencing project (like the 1000 Genomes Project and the Exome Sequencing Project) and variant databases (like dbSNP), and that an over-representation of variants of unknown frequencies (e.
Importantly, this abnormally high proportion of novel variants matched the considerably decreased recovery rate between exome and targeted exome datasets, which we have been shown to be caused by a very low recovery rate among novel variants (Figure  3 , Tables  S8  and S11 ). Most GATK variant lists had a similar proportion of novel variants, except when the GATK UnifiedGenotyper was run without quality score recalibration in the 1000 Genomes samples (Figure  7 ). However, trend doesn't apply to the SRP019719 samples (Figure  S9 ), emphasizing that there were other factors that can influence these results. The pre-processing steps had a modest impact on the VarScan frequencies, but the VarScan parameters had a very strong impact on the results. Namely, the frequency of novel variants was extremely high when running VarScan with default parameters, which corresponded to a decrease in the overall recovery rate rate (Figure  3 ).
However, the distribution for VarScan variants called with conservative parameters looked very similar to the GATK distributions, and these variants had a high recovery rate between the 1KG exome and targeted exon samples (Figure  3 ). ANNOVAR can also annotate variant frequencies for indels. However, small indels have not been characterised as well as SNPs (for example, there are considerably fewer indels in dbSNP  (Sherry et al. 2001 ), compared to SNPs) and damaging predictions focus primarily on SNPs. Additionally, there are almost no indels in the coding regions of chromosome 20 for the 1000 Genomes targeted exon dataset. Nevertheless, Figure  S10  shows the variant frequencies for indel calls in the 1KG and SRP019719 exome datasets. Similar to the SNP distributions, VarScan-Cons contained the least number of novel indels (among the VarScan comparisons), which was likely associated with a lower false discovery rate.
Also, novel indels were more common in GATK HaplotypeCaller variants than GATK UnifiedGenotyper variants. Arguably, this could indicate that the higher number of indels called by the HaplotypeCaller was also associated with a higher false discovery rate, but the variant frequencies alone are not sufficient to prove Reviewing Manuscript this to be the case because there may have been technical limitations in discovering the indels without using the GATK HaplotypeCaller. However, lower concordance for HaplotypeCaller indels between technical replicates (Figure  S2 ) supports the hypothesis that the HaplotypeCaller indels had a higher false discovery rate. Similarly, recovery of indels was higher for UnifiedGenotyper variants than HaplotypeCaller variants in the SRP019719 sample with both exome and amplicon sequencing data (Figure  S11 ).
Overlap of High-Quality GATK and VarScan Variant Calls Given the previous comparisons, we believe the highest quality variant calls can be made by excluding GATK variants with LowQual flags (e. g. UG-HQ and HC-HQ) and using the conservative parameters defined in this manuscript when running VarScan (e. g. VarScan-Cons). Because the number of variants called increased when running the indel realignment and quality score recalibrator steps, we compared coding variant lists produced using variant callers with "Full Pipeline" pre-processing steps. In the 1000 Genomes samples, the variant caller overlap was quite high (Figure  8  and Figure  S12 ). For example, all SNPs called using VarScan-Cons were also called using the GATK HaplotypeCaller. Additionally, all indels called using VarScan-Cons were also called by either the GATK HaplotypeCaller or the GATK UnifiedGenotyper.
Because only chromosome 20 variants can be reported for the 1000 Genomes dataset in this manuscript, it may also be useful to see the overlap for the genome-wide variant lists from SRP019719 (Figure  8 ). This time, the VarScan-Cons variants were not completely recovered by using one or both GATK variant callers, but there was still only a minority of variants that were not represented in either list of GATK variants (2. 9% of VarScan-Cons SNPs and 1. 5% of VarScan-Cons indels). This emphasizes that VarScan-Cons calls robust variants that would almost always be called when using GATK. The high recovery of VarScan-Cons variants may result from high specificity with decreased sensitivity, which is potentially the biggest drawback to this strategy. The degree to which VarScan-Cons recovers the set of high-quality GATK variants varied between datasets.
Application of QC Metrics to Other Variant Callers Although we were primarily interested in comparing VarScan (with various sets of parameters) to GATK, we also examined similar metrics for other variant callers. Given that the distribution of rare and novel variant frequencies was similar for the 1000 Genomes targeted exon and exome datasets, we used the targeted exon samples to test additional variant callers. More specifically, we tested freebayes  (DePristo et al. 2011 ) and samtools  (Li et al. 2009 ), using the "No Preprocessing" and "Full Pipeline" alignments. It is clear that freebayes yields too many novel variants (Figure  S18 ), so it was not tested on any further datasets. Of course, changes in parameters and/or downstream filtering of variants may result in a decrease in the frequency of novel variants; however, these two lists of variants (called with default parameters) are meant to serve as examples of how the quality control metrics in this dataset could be applied more broadly.
Interestingly, the pre-processing steps appeared to have minimal effect on the 1000 Genomes targeted exon and exome datasets, but running the "Full Pipeline" for pre-processing Reviewing Manuscript considerably decreased the proportion of novel variants for samtools variant calls in the SRP019719 exome dataset (Figure  S18 ). Most samtools variants overlaped VarScan-Cons variants as well as highquality GATK UnifiedGenotyper and HaplotypeCaller variants (Figure  S19 ). The ANNOVAR frequency plots (like those shown in Figure  7 , S9, and S18) are a useful metric, but they only show average trends across the dataset and do not show how the frequency of novel variants compares to the total number of variants called. If the number of conditions is reduced to only those using the "Full Pipeline" for pre-processing, then one can visualize the comparison between variant callers (Figure  9 ).
There was more variability in the proportion of novel variants for the targeted exon dataset (compared to either exome dataset), but this may relate to the lower number of total SNPs and/or the lower on-target coverage (Table  S1-S3 ). While it is not necessarily safe to assume that the false discovery rate substantially varies when the proportion of novel variants is between 0 and 6% (which is the observed range for these variant callers in the exome datasets), the proportion of novel variants is universally low for all VarScan-Cons calls in all three datasets while there is more variability in the proportion of novel variants in the GATK and samtools variant calls. VarScan-Cons and the high-quality GATK variant calls showed similar distributions of supporting reads (Figure  5 ), so the larger number of novel variants was not being driven by variants that were extremely likely to be false positives (Figure  4 ).
Each variant caller was also tested with 4 preprocessing conditions: variants called using both GATK indel realignment and quality score recalibration ("Full Pipeline" -purple), indel realignment only ("Realign Only" -red), quality score recalibration only ("Recalibrate Only" -green), or neither ("No Preprocess" -blue). Concordance is reported as recovery of SNPs called in the targeted exon data, but these cannot be treated as "gold standard" variant calls. Most clearly, there was a high false positive rate when running VarScan with default parameters, so a high proportion of those variants called in the targeted exon samples could not be recovered in the exome dataset. In fact, on-target coverage is typically lower for the targeted exon samples than the exome staples (Table  S1-   Density plots for novel variants (those variants not reported in the 1000 Genomes Project, the Exome Sequencing Project, or dbSNP) are shown with red lines while density plots for previously observed variants are shown with red lines.
RT-PCR and real-time quantitative RT-PCR Total RNA (250 ng) was reverse transcribed into cDNA by using the High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems, Darmstadt, Germany). q-RT-PCR was performed on 7900ht and ht fast devices (Applied Biosystems) with milliQ H 2 O and TaqMan Universal PCR Master Mix (Applied Biosystems). Raw data was obtained by SDS 2. 3 (Applied Biosystems). Gene expression was normalized to the mouse housekeeping gene Hprt. Relative quantitation of gene expression was calculated using the DDCt method. Three technical replicates were used for each real-time q-RT-PCR reaction; a reverse transcriptase blank and a no-template blank served as negative controls. Primer sequences for real-time q-RT-PCR are listed as supplementary material (Tables  S1  and S2 ).
Microarray data processing Bead intensities were mapped to gene information using BeadStudio 3. 2 (Illumina). Background correction was performed using the Affymetrix Robust Multi-array Analysis (RMA) background correction model  [49]. Variance stabilization was performed using log 2 scaling, and gene expression normalization was calculated with the quantile method implemented in the lumi package of R-Bioconductor. Data post-processing and graphics were performed with in-house developed functions in Matlab. Hierarchical clustering of genes and samples was performed with a correlation metric and the Unweighted Pair-Group Method using Average (UPGMA) linkage method. All the raw and processed microarray data discussed in this publication have been deposited into NCBI's Gene Expression Omnibus  [50]  and are accessible through GEO Series accession number GSE57506.
Experimental scanning protocol An experienced pQCT operator (DG) performed all pQCT scans using a Stratec XCT 2000 device (Stratec Medizintechnik GmbH, Pforzeim, Germany, software version 5. 50d). A cone phantom was scanned at the commencement of data collection to confirm machine calibration. Briefly, the cone phantom used to calibrate the pQCT scanner was the Quality Assurance in Radiology and Medicine European Forearm Phantom (QRM-EFP), which consists of water-and bone-equivalent solid materials and is a standardized device that is used to test peripheral bone densitometry systems  [16]. The pQCT scanner was calibrated on a daily basis. All scans were acquired over a two day period, and the pQCT scanner was calibrated prior to the commencement of scans each day. The effective dose of radiation for this study was calculated by an independent radiation safety officer as 0.
Statistical analysis Statistical analyses were performed using SPSS version 20. 0 (SPSS, Chicago, IL, USA). Intra-class correlation (ICC) was calculated using a two-tailed mixed consistency model  [18]. Data were then natural log transformed to meet the assumptions of inferential statistical analyses and coefficient of variations (CV%s) calculated using mean square error (MSE) and expressed as a percentage (CV%). An ICC of 1. 0 indicates perfect agreement and an ICC of >0. 75 represents "excellent reliability"  [19]. CV values of 10% or below are generally considered to be an indication of "good" reliability  [20]. Means and standard deviations (SD) are presented for unadjusted values.
5 (with repositioning) to 8. 9 (all scans included). Reproducibility data at the mid-shaft (CV% range 0. 1 to 3. 2) appeared more reliable than data collected at the distal metatarsal (CV% range 2. 4 to 8. 9). We have developed a novel scanning protocol showing acceptable reproducibility for key bone properties measured at the distal and mid-shaft regions of the 2nd metatarsal. Variables selected were considered an appropriate representation of bone geometry (area), bone material (density), and bone strength (strength strain index). All precision values for scans with-and without repositioning were considered to be an indication of good reliability. To our knowledge, we are the first to demonstrate reproducibility of measurements for key bone properties of the 2nd metatarsal using pQCT. The high reproducibility of volumetric cortical bone mineral density provides potential benefits with regard to fracture prediction.
Acknowledgements The authors would like to thank  Mr Scott Wheatley  and  Mr Ilango Rajaratnam  of the  University of Western Sydney anatomy department  for their excellent technical assistance. We would also like to thank  Dr Jeffrey Chaitow  for his advice with the preparation of our grant application. This work was supported by the  Australian Podiatry Education & Research Fund (APERF)  (grant no. 21633 ).
Owing to the limited size of the Makhohlola exclosure (100 m × 240 m) two of the four transects were taken in the no-burn part of the fire experiment (since 2004) and two were taken in the part that burns in conjunction with the surrounding landscape. At N'waxitshumbe, the paired transects were taken perpendicular to the southern fence in the south-western portion of the exclosure and parallel to the drainage in that area. Species identity was recorded for each woody plant encountered in each transect using a personal digital assistant (PDA) with CyberTracker software. The abundance data were used to create an inside abundance index, whereby the proportion of a species found in the inside compared to the outside treatment was calculated. This index was calculated by dividing the number of individuals of each species found inside by the total number of individuals found both inside and outside.
Trait sampling The dominant woody plant species making up 80% -90% of the woody biomass inside and outside the three exclosures in the KNP were determined (Table  2 ). Fourteen different plant traits relating to the leaves and stems were sampled for each species. All trait data (except BSI) were measured and recorded according to the methods explained in  Cornelissen et al. (2003). For each species, leaf material was collected from five individuals and analysed for N, P and carbon (C) content using a Leco TruSpec CN Analyser (LECO Corporation, St. Joseph, MI). SLA and average leaf area were measured using four healthy and complete sun-exposed leaves from five individuals (20 leaves per species). The BSI of a species is the total dry weight of the leaves removed from two human bites taken from five individuals (total of ten bites).
Statistical analyses All analyses were performed using R (R Project 2012). Chisquare tests were used to test for differences in tree densities between treatments. Non-parametric Wilcoxon tests were used to test for differences in trait measurements as the low replication resulting from the low species diversity at each site precluded the use of statistical tests founded on the normal distribution. Analyses of variances were used to test for differences in soil nutrients between treatments using pooled data from all three depths, as the conditions for homoscedasticity were not violated (fligner test: p > 0. 05;  Conover, Johnson & Johnson 1981). Principle components analyses (PCA) were used to explore the relationships between treatments, sites and measured plant traits. The function 'PCAsignificance' available in the BiodiversityR package  (Kindt & Coe 2005)  in R was used to test for significant PCA axes using the broken-stick distribution.
Soil nutrients Although the pooled data for all depths of soil N and P showed a pattern of higher values for the three inside treatments, the only statistically significant differences were evident at N'waxitshumbe (p < 0. 05; Figures  7a  and 7c ). Soil C and K also showed a pattern of being consistently higher for the inside treatments at Makhohlola and N'waxitshumbe, with no differences evident at Nhlangwini (Figures  7b-7d ). Similar patterns were evident for N, C and P when total N, nitrogen; P, phosphorus; SLA, specific leaf area; BSI, bite size index. In Out nutrient stocks were calculated using soil bulk density data (data not shown).
Acknowledgements This work formed part of the PhD thesis of B. J. Wigley and was made possible thanks to funding from a  BDI-PED  grant from the  French National Centre for Scientific Research , as well as the  Andrew W. Mellon Foundation. The authors are hugely grateful to:  SANParks ,  Kruger National Park Scientific Services  for allowing the study and logistical support, and  Scientific Services  game guards for protection in the field. Thanks to two anonymous reviewers for their helpful comments which helped improve this manuscript.
Analysis of the obscured X-ray spectrum of NGC 5548 in 2013 We analyzed the stacked spectrum of the 12 XMM-Newton observations taken during summer 2013. We used the RGS data between 5. 68 and 38. 23 Å. We only used the first spectral order and stacked the data of both RGS detectors. Further we used the data from the pn detector in the range between 1. 24 and 12 keV. Starting in 2013, a gain problem with the pn data appeared, causing a shift of about 50 eV near the Fe-K complex. This small offset yields poor fits near the energy of the gold M-edge of the telescope mirror, and for that reason the interval between 2. 0 and 2. 4 keV was omitted from the fit. The dynamics of the WA has hardly changed over 20 years, so variations in the WA are due to ionization changes rather than changes in its hydrogen column density.
Acknowledgments: The data used in this research are stored in the public archives of the international space observatories involved:  XMM-Newton, HST, Swift, NuSTAR, INTEGRAL and Chandra. We acknowledge support by  ISSI in Bern ; the  Netherlands Organization for Scientific Research ;  NASA   HST program 13184 from STScI , which is operated by  AURA, Inc. , under  NASA  contract  NAS5-26555 ; the  UK STFC ; the  French CNES, CNRS/PICS  and  CNRS/PNHE ;  VRIDT (Chile) ;  Israel's MoST, ISF  ( 1163/10 ), and  I-CORE program  ( 1937/12 ); the  Swiss SNSF ; the  Italian ASI  grant  INAF I/037/12/0-011/13  and  INAF/PICS ;  US NSF  grant  AST-1008882 ;  EU Marie Curie  contract  FP-PEOPLE-2012-IEF-331095. We thank  Silvana Paniagua  for helping with Fig. 4.
Wine samples Thirteen commercial wines made from Tempranillo grapes were purchased from selected Spanish wineries. The wines belonged to four vintages  (2006, 2008, 2009 and 2010) , and three Spanish protected designations of origin, Ribera de Duero, Toro and Rioja (Table 1). All the samples are Tempranillo wines that have evolved during the ageing time. They were stored under cellar conditions before the analyses were conducted. Oenological parameters as pH or ethanol content were similar across the studied wines, and they were not included in the statistical data treatment.
MLR was conducted applying a backward-stepwise strategy, which involves starting with all considered variables and removing the least significant one at each step of the process. The model is refitted after each step including only the most significant variables. From the 21 initial independent variables, only nine were statistical significant in the final fitted model (p < 0. 05). The regression coefficient R 2 took a value of 0. 968, which supposes that the fitted model explains 96. 8% of the variability observed in the data (Fig. 4 ). In a previous work 87. 7% of variability of the perceived astringency was explained with the results of the proanthocyanidic composition  (Quijada-Morin et al. , 2012). The addition of the polysaccharide and oligosaccharide compositional variables implied an important improvement of the final model.