dataesr/scientific-paragraphs-categorization huggingface dataset:
Moreover, our analysis demonstrated the robustness of the proposed fingerprint to various factors, including image alignment, scan resolution and subject age. The reproducibility of results was also confirmed using retest scans from the HCP dataset, showing our fingerprint to be robust to variability in image acquisition. The usefulness of our fingerprint was assessed on the tasks of identifying incorrectly reported zygosity and retest/duplicate scans in large dataset. Results of this experiment highlighted the effectiveness of our fingerprint, with MAP values near 100% for all test cases. Moreover, analyzing the distribution of features correspondences across the brain revealed neuroanatomical regions (e. g. , cerebellum, lateral ventricles, ventral diencephalon, hippocampus and thalamus proper) with significantly different match counts in MZ twins compared to DZ twins or full siblings.
Acknowledgements Data were provided in part by the  Human Connectome Project, WU-Minn Consortium  (Principal Investigators:  David Van Essen  and  Kamil Ugurbil ;  1U54MH091657 ) funded by the 16  NIH Institutes  and Centers that support the  NIH Blueprint for Neuroscience Research ; and by the  McDonnell Center for  Systems Neuroscience at Washington University. We thank the reviewers whose comments/suggestions helped improve and clarify this manuscript. We also thank researchers processing  Human Connectome Project data  for providing the pre-processed data and rfMRI netmats.
No. 270-Kerr, Switzerland) designed for periapical radiographs. The intraoral device used was a wall-mounted X-ray scanner (Kodak 2100 Intraoral X-ray system, 60 KV-7mA, Cone 20 cm, Japan). The distance between the X-ray source and the skin (20 cm) and the exposure time (0. 35 seconds) were the same for all cases. The images were processed in a digital reader preset by a professional representative of the manufacturer (VistaScan Mini Plus: Dürr Dental, Germany). For the analysis of the images, the monitor (HP L1710 17-inch LCD Monitor USA 2008) allowed visualization through the dental imaging software DBSWIN 5. 4. 0. After precalibration of the same image processing software, the RDT was measured in millimeters, taking as reference the triangle located at the corner of the image plate Dürr (1 × 1 mm).
1, The triangle circled in black in the bottom of the radiograph was used as a reference IJCPD APS-C sensor 22. 2 × 14. 8 mm, EF-S60, Japan) with a fixed focal distance to the tooth (14. 5 cm). Before taking each photograph, a precalibrated metal ring (3 and 7 mm inner and outer diameters respectively) was placed next to the tooth as a reference when measuring the RDT (software AutoCAD 2014), as shown in Figure  2. 16  The measuring point corresponded to that used on the corresponding radiograph and was materialized by a straight line drawn with the Windows Paint program. The average of the two measurements was recorded. All measurements were made separately by two operators. To avoid any eye strain that could distort the results and to facilitate a better reading performance, each observer made only 10 measurements over a given interval of time.
DAPI was excited by a 405-nm diode laser and monitored with a band-pass filter of 460 to 500 nm. Deconvolution of acquired images was performed with Huygens Essential software (SVI, Netherlands) and using signal-to-noise ratio of 7. 3D particle segmentation analysis. To determine the average volume, total intensity, and distributions of the fluorescent capsid -containing particles as a function of the distance from the NE (n = 20 cells), a 3D watershed 3D segmentation algorithm was used  65. The volume, intensity and location of each particle were analysed using 3D Objects Counter function  66  in Fiji software  67. For every object, the distance of its center of mass from the NE was determined (closest distance to the edge of nucleus detected with DAPI). The distance values from the NE were then divided to 500 µm-wide bins and the volume and intensity of the objects were averaged over every bin.
Carpet analysis, ACF and pCF calculations were carried out using SimFCS (Laboratory of Fluorescence Figure  6. Schematic presentation of nuclear entry pathway of CPV. Upon release from the endosomal vesicles, CPV capsids are actively transported across the NE via importin β followed by disassembly in the nucleoplasm. However, the role of importin α in this process cannot be ruled out. During this transport, the motility of capsids fluctuates from cytoplasmic slow and fast, to nucleoplasmic slow and fast. The number of viral particles (purple gradient) is high distant from the nucleus and low near the NE. In the nucleus, higher amount of small particles is present suggesting for nucleoplasmic disassembly of the imported capsids. Dynamics) and MATLAB (The Mathworks Inc. , 2010). The line scan ACF data for each pixel was fitted using a Matlab routine to single component FCS equation to find an average apparent diffusion and number of particles.
N&B analysis was used to derive the number and brightness of viral capsids from laser scanned lines  15. The variables were defined as apparent number of molecules, N = <k> 2 /σ 2 and apparent molecule brightness, B = <k>/N where <k> is the average of the intensity of time and σ 2 is the variance of intensity. Number and Brightness analysis was done for two data sets. We segmented 30000 lines into a) (1 line) × 300 lines × 100 dataset categorized as fast, b) (100 line average) × 100 lines × 1 dataset categorized as slow. This slow category matches the slow category used in ACF. As tabulated under Supplementary Table  S1 , the number and brightness data is classified under the diffusion coefficient reported for that pixel. pCF image analyses were carried out in SimFCS program (Laboratory of Fluorescence Dynamics).
Acknowledgements Carmine Di Rienzo  tragically passed away on July 7 th 2016. He participated to the analysis of the data and the discussion and interpretation of the fluctuation correlation results. We thank  Juliet A. Ellis  for the fluorescent Lamin C plasmid. We are grateful to  Klaus Hedman ,  Niilo Joutsenlahti  and  Satu Hakanen  for their comments on the manuscript. First-class technical assistance by  Wendy Weichert  and  Milka Stakic  is gratefully acknowledged. This work was supported by the  National Graduate School in Information and Structural Biology. The research was financed by the  Jane and Aatos Erkko Foundation (MVR) , the  Finnish cultural foundation (EM) , the  Academy of Finland  under the award number  138388  (MVR), and  National Institute of Health (NIH)  grants  P41GM103540 ,  P50GM076516  (EG, JC).
Linkage analysis The polymorphic SSR markers between the two parents were predicted by comparing the whole-genome resequencing data of 'Thailand Chiang Mai' with the 'Chinese Tai-zi' draft genome. RAD-sequencing of the 181 F 2 individuals were generated with the HiSeq 2000 platform (Illumina). SNPs were genotyped using realSFS  (Korneliussen et al. , 2014)  and converted into bin markers using a sliding window approach developed by  Huang et al. (2009). The R/ASMap package  (Taylor and Butler, 2014) , R/onemap package  (Margarido et al. , 2007)  and the MAPCOMP pipeline  (Sutherland et al. , 2016)  were used to construct and validate the linkage maps. Detailed methods for the prediction, validation and genotyping of polymorphic SSR markers, RAD-sequencing and SNP calling, linkage map construction and validation are provided in Methods S1.
A series of assessing and filtering measures was retained to facilitate assembly. One molecule or label was removed if it matched any condition of the following conditions: (1) molecule length <100 kb; (2) molecule signal-noise ratio (SNR) <3. 2; and (3) molecule intensity >0. 8. BioNano de novo genome mapping was performed using IrysSolve scripts pipeline (version 5134) and IrysSolve tools Assembler and RefAligner (version 5122). The hybrid scaffolding was performed using IrysSolve HybridScaffold (version 5134) to generate super-scaffolds. The parameters used in BioNano de novo genome mapping and hybrid scaffolding procedures are accessible at https://de. cyverse. org/dl/d/F50BCF40-5E0B-4199-B94A-D36A8EEC63B3/DataS1_Biona no_parameters. zip.
Genome annotations The pseudo-chromosomes of 'Chinese Tai-zi' were annotated after masking the repeats. Three approaches were used for gene © 2018 The Authors The Plant Journal © 2018 John Wiley & Sons Ltd and Society for Experimental Biology. , The Plant Journal, (2018), 94, 721-734 prediction: transcript sequences-based prediction, ab initio gene prediction and homology-based prediction. Detailed methods for the annotation of the 'Chinese Tai-zi' pseudo-chromosomes are provided in Methods S1. The annotations of pseudo-chromosomes of 'China Antique' were converted from the annotations of the de novo assembly of 'China Antique' with the liftOver UCSC tool  (Kuhn et al. , 2012).
Identification of centromere regions and telomere repeats The genome-wide recombination rates were estimated from the comparison of the linkage maps and the anchored pseudo-molecules using the R/MareyMap package  (Rezvoy et al. , 2007)  by locally adjusting a polynomial curve to the plot of genetic versus physical distances, the slope of the curve was subsequently obtained using the Locally WEighted Scatterplot Smoothing (lowess or loess) method with parameters of span = 0. 1 and degree = 2. The regions that show lowest recombination rates in each chromosome were predicted as the possible centromere regions. NnCenH3 ChIP-Seq reads were aligned to the anchored pseudomolecules using SOAPaligner with default parameters and only reads that mapped to a unique position were retained for further analysis.
ACCESSION NUMBERS The RAD-sequencing data generated for this study were deposited in the Sequence Read Archive (Bioproject PRJNA417779). The genotypes of the SNPs were deposited in NCBI dbSNP (Bioproject PRJNA400121). The BioNano genome map data generated for this study is accessible at NCBI Supplementary Files (accession SUPPF_0000001138). The anchored pseudo-chromosomes of 'Chinese Tai-zi' were deposited at GenBank under the accession no. DLUA00000000, the version described in this paper is version no. DLUA01000000. The anchored pseudo-chromosomes of 'China Antique' were deposited at GenBank under the accession no. DLUB00000000, the version described in this paper is version no. DLUB01000000. The draft assemblies of 'Chinese Tai-zi' and 'China Antique' were downloaded from GenBank under accessions nos.
All cell lines were mismatch repair-deficient. P-values for Clones 1 and 2 (p=0. 0017 and p=0. 008, respectively) were calculated using an unpaired t-test relative to wt/wt. Mutation rates for Clone 1 and Clone 2 were not significantly different from one another (p=0. 4727). (B) Error rates for base pair substitutions (BPS) and small insertion/deletion frameshift mutations (FS) were calculated using the mutation rate data from Figure  1A. Exo + BPS Error Rate = 27. 6 Â 10 À7 , SEM = 8. 48 Â 10 À7 , n = 12; Exo-BPS Error Rate = 178 Â 10 À7 , SEM = 37. 8 Â 10 À7 , n = 8; p=0. 0002. Exo + FS Error Rate = 18. 4 Â 10 À7 , SEM = 5. 73 Â 10 À7 , n = 8; Exo-FS Error Rate = 22. 2 Â 10 À7 , SEM = 12. 1 Â 10 À7 , n = 1; p=0. 7759. Error rate data shown for Exo-is from Clone 1 (See Figure  1A ). The HPRT1 ORF was sequenced from independently derived isolates of 6-TG resistant clones (these included 20 mismatch repairdeficient Pol e wt/wt and 25 mismatch repair-deficient Pol e wt/exo-clones; see Materials and methods).
Sequence changes used to calculate error rates are in Figure  1 -source data 2. ***p<0. 001; n. s. , p>0. 05. (C) Errors rates were calculated using a lacZ reversion substrate that reverts via TCTfiTAT transversion. P values were calculated using chi-square tests with Yates correction. Error rates are the averages of two experiments, each conducted with independent DNA and enzyme preparations for each construct tested. indicates the value is a maximal estimate as it is identical to the assay background. DOI: https://doi. org/10. 7554/eLife. 32692. 003 The following source data and figure supplements are available for figure  1 :.
We chose to periodically measure HPRT1 mutant frequencies at increasing population doubling level (PDL), where the slope of the plotted line is equal to the mutation rate  (Glaab and Tindall, 1997). We measured HPRT1 mutant frequencies at several population doublings from PDL = 0 to PDL = 70 or 71 in Pol e wt/wt and Pol e wt/exo-cells expressing Mlh1, respectively (Figure  4A ). At each PDL we scored between 1 and 19 6-TG-resistant colonies. However, when we sequenced the HPRT1 ORF from all 6-TG-resistant colonies we saw many instances of repeat mutations in a collection from a single PDL, indicative of a single mutational event that expanded throughout the population. Plotting mutant frequency values calculated for the indicated PDL using only the unique HPRT1 mutations (Figure  4 -source data 1) returned a line with slope of ~1, suggesting that the mutation rates were at or near the level of detection of this assay.
In tumors, many CfiA transversions occur in a highly specific triplet sequence context, TCT, which we also see in the cell lines, though not to the same proportion as in the tumor genomes. Interestingly, this particular mutation is also enriched in yeast with the P286R equivalent allele  (Barbari and Shcherbakova, 2017). We also observe increased TfiG transversions in TTT (and to a lesser extent ATT and CTT) context, similar to Pol e tumors. Because of the limited number of mutational target sites we cannot at this time draw conclusions as to Pol e strand usage during replication. Experiments designed to assess strand bias in these errors are currently underway. What is notable, however, is the lack of TCGfiTTG transitions in our dataset. This is the second most frequent Pol e-specific mutation in the TCGA database.
, 2013). Lastly, we performed microsatellite stability analysis in our HCT116 cells at five mononucleotide homopolymeric run loci (NR27, NR21, NR24, BAT25, BAT26) using capillary electrophoresis, which showed instability at these loci providing a phenotypic readout consistent with the lack of Mlh1 expression in our cells (data not shown). The HCT-116 cell line is also not in the 488 commonly misidentified cell lines from the most recent ICLAS database (Version 8. 0) and tested negative for mycoplasma.
Substitution detection from next generation sequenced data All samples were processed from raw reads (FASTQ files) from paired end libraries. The reads were aligned to the human reference (GRCh37 with decoy sequences) using BWA-MEM v0. 7. 8  (Li and Durbin, 2009). Duplicate reads were identified and marked using Picard v1. 108 (https://broadinstitute. github. io/picard/). The Genome Analysis Toolkit (GATK) v2. 8. 1 (McKenna et al. , 2010)  was used to locally realign reads to known indels and recalibrate base quality scores. Quality metrics were generated from the final BAM files to ensure high quality alignment. This includes:. average coverage >90 x in whole exome data (Figure  2 -figure supplement 6 Mean_Covera-ge_Per_Sample. pdf). alignment rate to the reference genome >99% across whole exome data (Figure  2  Limitations in the genome due to low-complexity regions and incomplete areas in the genome  (Li, 2014)  prevent proper alignment resulting in sources of error.
Somatic point mutations between the tumour and matched normal were identified using MuTect v1. 1. 4 (Cibulskis et al. , 2013). In addition, we used MuTect v1. 1. 4 in single sample mode to detect all mutations in each sample. All mutations were annotated using ANNOVAR v20130823  (Wang et al. , 2010). Subsequent filtering was performed to reduce potential false positives and allow only high confidence mutations in the dataset using a custom R package (ShlienLab. Core. SNV v0. 09). Mutations were retained if they met the following criteria:. not identified in common mutation databases including: dbSNP (138), 1000 genomes (1000g2012feb), complete genomics (CG69), Exome sequencing project (ESP 6500si). for exome data, must have at least 20x normal and 30x tumour. for WGS data, must have at least 10x normal and 10x tumour (Figure  2 -figure supplement 10) To investigate the quality of somatic mutations, we also identified key metrics including:.
4727). (B) Error rates for base pair substitutions (BPS) and small insertion/deletion frameshift mutations (FS) were calculated using the mutation rate data from Figure1A. Exo + BPS Error Rate = 27. 6 Â 10 À7 , SEM = 8. 48 Â 10 À7 , n = 12; Exo-BPS Error Rate = 178 Â 10 À7 , SEM = 37. 8 Â 10 À7 , n = 8; p=0. 0002. Exo + FS Error Rate = 18. 4 Â 10 À7 , SEM = 5. 73 Â 10 À7 , n = 8; Exo-FS Error Rate = 22. 2 Â 10 À7 , SEM = 12. 1 Â 10 À7 , n = 1; p=0. 7759. Error rate data shown for Exo-is from Clone 1 (See Figure1A). The HPRT1 ORF was sequenced from independently derived isolates of 6-TG resistant clones (these included 20 mismatch repairdeficient Pol e wt/wt and 25 mismatch repair-deficient Pol e wt/exo-clones; see Materials and methods). Sequence changes used to calculate error rates are in Figure1-source data 2.
***p<0. 001; n. s. , p>0. 05. (C) Errors rates were calculated using a lacZ reversion substrate that reverts via TCTfiTAT transversion. P values were calculated using chi-square tests with Yates correction. Error rates are the averages of two experiments, each conducted with independent DNA and enzyme preparations for each construct tested. indicates the value is a maximal estimate as it is identical to the assay background. DOI: https://doi. org/10. 7554/eLife. 32692. 003The following source data and figure supplements are available for figure1:.
Acknowledgements The authors would like to thank  Dr. Fred Bunz  ( John Hopkins University ) and  Drs. Prescott Deininger  and  Victoria Belancio  ( Tulane University ) for the kind sharing of reagents. Thanks are also due to  Christine McBride  for her contribution to the rAAV construction. Additionally, the authors would like to thank  Dr. Art Lustig  and  Dr. Stuart Linn  for insightful comments and advice.
Sampling Participants were aged 2-19. From December 2014, we began our cooperation with the Health Centre Adolf Drolc in Maribor and with the Paediatric Clinic in Lenart. There we collected BP data and certain anthropometric measurements from regular check-ups, measured with an automatic (oscillometric) device during February 2015 and March 2016. Children with a previously known diagnosis of secondary (but not essential) hypertension were excluded, as they had aetiology-based treatment and their small count would not influence the overall results. Meanwhile, we began to cooperate with two nearby schools. There, we measured BP once a month with a mercury sphygmomanometer (auscultatory method) and gathered additional data on weight, height, hip and waist circumferences. The sampling procedure for the auscultatory and oscillometric measurements is described in Figure  1.
p. ), we obtained a second right upper arm BP measurement, followed by a third measurement by the other examiner for cross-checking, and a fourth measurement on the left upper arm for evaluation of possible aortic coarctation, with three-minute intervals in between. Three right upper arm measurements were used for later analysis. The sample was increased using patient records from systematic check-ups. There, repeated BP measurements on the upper right arm were taken by Health Centre staff, using an Omron M6 oscillometric upper arm blood pressure monitor. In addition, data on gender, height, weight, and BMI were collected. After collecting the data, we calculated average values of SBP and DBP, and calculated mean arterial pressures (MAP). Using height and weight data, we calculated the BMI and BMI standard deviation scores (SDS).
Anthropometric measurements served us to calculate the waist-to-hip and waist-to-height ratios. In accordance with recommendations (5), we calculated percentiles of arterial BP based on sex, age and height. More about this calculation is found in the 4th Report (Supplement) of the NHBPEP Working Group on Children and Adolescents  (6). Since this was a study on the Slovenian children, we decided to use the WHO and CDC growth curves  (11) (12) (13)  to calculate SDS scores of anthropometric parameters (weight-for-age, height-for-age, BMI-for-age). We used the European (ages 2-11) and Japanese (ages 12-19) data to calculate waist circumference SDS scores  (14, 15). Children and adolescents were classified into four groups based on their BMI percentile: underweight, normal weight status, overweight and obese.
Statistical Methods and Sample Size We used IBM SPSS statistical program (version 20), running on a PC with Windows 10 operating system. To calculate the independence of the distributions of categorical variables, we used the chi-square test. Pearson's correlation coefficient r was used in the calculation of correlations between the SDS score of systolic and diastolic BP and certain anthropometric parameter SDS scores. The sample size was assessed via an online statistical program  (18) , using an expected hypertension prevalence (10%) and the desired confidence interval width of 95%±2%. Based on this, the recommended sample size was 912 participants.
with the space group P1 ¯. A second polymorph 2 is reported here, with the space group P2 1 /c. The compounds differ in the interactions between the conformational chiral triphenylphosphine groups. In 1, there is π-π stacking between adjacent phenyl groups, whereas in 2, there are σ-π interactions instead. A search of the Cambridge Structural Database reveals that this is a relatively frequent occurrence in cis-bis-triphenylphosphine complexes and the phenomenon can be analysed by means of the C(ipso)-P-M-P torsion angles. The majority of compounds fall in the π-π stacking data area with torsion angles of 10-15°and 55-60°; however, for octahedrally coordinated metals, the optimum is a σ-π interaction at 40°/40°. This corresponds well to the values in 2: 46°/40°, but for 1, we instead find the torsion angles to be 11°/18°.
In this communication, we report on a new polymorph of cis-[MoĲCO) 4 ĲPPh 3 ) 2 ] 2 in the space group P2 1 /c, distinct from the earlier reported P1 ¯polymorph 1 in terms of both phosphine configurations and intermolecular interactions. We have also placed this finding in a broader context by analysing diphosphine complexes in the Cambridge Structural Database (CSD) and performing quantum chemical calculations on both crystals and single molecules. In principle, one could envisage four different ways in which these molecules could vary in their conformations. We could think of either σ-π interactions or π-π stacking as potentially being the most favoured intramolecular interaction between the closest phenyl groups of the two triphenylphosphine units. Added to this is the possibility of having either homo (ΔΔ or ΛΛ) or heterochiral triphenylphosphines, giving a total of four possible conformers: σπ-ΔΔ, σπ-ΔΛ, ππ-ΔΔ, and ππ-ΔΛ.
Experimental 2. 1 Materials and methods 2. 1. 1 X-ray crystallography. Intensity data were collected using a Bruker Apex II diffractometer with Mo Kα radiation (λ = 0. 7107 Å). Unit cell determinations were carried out both at ambient temperature (294 ± 2 K) and at low temperature (173 ± 2 K) in order to test whether there were any phase changes during the cooling process; none were evident. The structure of 2 was solved routinely using SHELXS-64 and refined against F 2 with SHELXL-64. 19 he structure determination details are found in Table  1 , and an ORTEP type drawing for the molecular unit is shown in Fig. 1. 2. 1. 2 Searching the Cambridge Crystallographic Database. The CSD database 5. 38 (February 2017) was used. In all runs, the Conquest software (version 1. 19) was used with the restrictions that all retrieved structures would have R values <10% and be error-and disorder-free.