Post Technique

Initially, the sequencing reads were submitted to a quality control check by using the scripts fastq_quality_filter.pl and fastq_quality_trimmer.pl from FASTX-Toolkit [12 ]. The phred value 20 was chosen as the minimum threshold for base quality. The reads having more than 80 % of low quality bases were removed or had their 3′ extremity bases trimmed when the minimum threshold was not reached. After, the reads were aligned to the human reference genome hg19/GRCh37 using BWA-MEM [13 (link)] with default parameters and Picard [14 ] was applied for post-alignment procedures as sorting, indexing, and marking duplicates. The alignments were submitted to local realignment around INDELs and base quality score recalibration (BQSR) by using the Genome Analysis Toolkit (GATK) version 3.0 [15 (link)].
MuTect [16 (link)] and GATK (Haplotype Caller) were used for the single nucleotide variant calling. GATK variants were filtered with the Variant Quality Score Recalibration tool following the best practices on the GATK website. GATK performs the variant calling and filtration in the normal and tumor samples independently, thus the subtraction between the tumor and the normal variants resulted in our first set of candidate somatic variants.
To ensure the somatic classification of the SNVs called by GATK, we adapted the MuTect algorithm and applied its LOD_N classifier after the GATK variant calling and filtering. The LOD_N is a bayesian classifier that compares the likelihood of two models: (1) the mutation does not exist in the normal sample and all non-reference bases are explained by sequencing noise, and (2) the mutation truly exists in the normal sample as a germ-line heterozygous variant. The ratio of these two likelihoods is called LOD (Log Odds) score and when it exceeds a decision threshold, the mutation can be classified as somatic. For this filtering, we considered only sites that had total read depth greater or equal than 8 in the normal sample and greater or equal than 14 in the tumor sample. Our final candidate list consisted in the union of MuTect and GATK-LOD_N results.
The variants were annotated by ANNOVAR [17 (link)], with the Ensembl Gene annotation database for human genome build 37 (http://www.ensembl.org/), and searched for matches in the dbSNP138 and 1000 Genomes data. We selected exonic single nucleotide variants (SNVs) that were non-synonymous and gain or loss of stop codon. Variants present in dbSNP138 and 1000 Genomes with minor allele frequency (MAF) greater than 0.05 were removed. Figure 1 shows the summary of the pipeline steps. The scripts for running the main pipeline steps are availabe in the link: https://bitbucket.org/BBDA-UNIBO/wes-pipeline.Fig. 1

Pipeline of SNV detection in sequencing data of cancer samples. Summary of steps and their respective tools in the detection of SNVs in paired normal-cancer sequencing data

A subset of variants from MuTect, GATK and GATK-LOD_N calls were selected for validation. Variants with allelic frequency higher than 0.2 were validated by Sanger Sequencing and those with allelic frequency lower than 0.2 were validated by using the Illumina TruSight Myeloid Sequencing Panel and Illumina MiSeq sequencing. Data were analyzed by the VariantStudio software (Illumina), according to manufacturer’s instruction.

Two different versions of TRACAB were validated: the 4^th generation TRACAB system (Gen4, installed at a height of 36m), and the 5^th generation (Gen5, installed at a height of 16m), which represents an advancement of the Gen4 system. The Gen4 system is a stereo camera system, consisting of two multi-camera units in two locations either side of the halfway line, each comprising three HD-SDI cameras with a resolution of 1920x1080 pixels (see Fig 2).
Each multi-camera unit provides a stitched panoramic picture, which is then used to create the stereoscopic view for triangulating the players and ball. The Gen5 system represents a distributed camera architecture, combining two stereo pairs on each side of the field as well as two monocular systems behind the goal areas. In total, the tested Gen5 systems comprised 16 IP-HD cameras with a resolution of 1920x1200 pixels. An exemplary still image of one of Gen5’s multiple-camera units is depicted in Fig 3.
It should be noted that optical tracking data is prone to errors introduced by occlusions and ID-swaps, which is why one human operator is required to correct these errors during measurement. For this reason, three types of tracking data can be distinguished: Type I data is considered ‘real-time’ data which is available immediately (<300ms), e.g. for live broadcasting, and therefore potentially contains several tracking errors. There is also Type II data, which is made available with approximately 15s delay and has less errors due to further processing and human interventions. Finally, Type III data describes the final data which is made available within several hours after the recordings. This final step comprises complete data correction and post-processing steps.
During our tests, the Gen4 system ran live (online) tracking during the recording session with a trained operator. The resulting Type II data was made available by ChyronHego immediately after the recording sessions. The Gen5 system recorded the same exercises. However, the video footage was later tracked offline using a real time tracking framework. When tracking the Gen5 footage, target identities were assigned at the beginning of each exercise. In all cases, the Gen5 system retained the correct target identities throughout the trial and no further operator interaction was required.
Accordingly, the data analysed in this study comprises exclusively Type II data, consisting of cartesian XY coordinates sampled with a frequency of 25 frames per second (25 Hz), which we from here on refer to as ‘raw’ tracking data, as no additional filtering methods or post-processing procedures have been applied.

To improve the early diagnosis of CoA, several diagnostic tests have been used in clinical practice. Currently, cardiac ultrasound is a routine test for CoA, and studies to improve the prenatal diagnosis of CoA have recently been conducted based on it (17 (link), 18 (link)). However, since the aortic coarctation occurs mainly in the isthmus and its physical changes are not clear, it is often examined with the help of CT and MRI. MRI is also widely used to assess CoA (19 (link)), but due to its time-consuming, costly, and low spatial resolution, it has limitations compared with CT (20 (link), 21 (link)). Therefore, in this study, CTA was used for all study subjects, and initial reconstruction of the scanned images was completed using image post-processing techniques.
Children who were hemodynamically unstable and uncooperative were sedated before CTA by oral 10% chloral hydrate (0.5 ml/kg body mass) or intramuscular sodium phenobarbital injection (5 ml/kg body mass), with careful monitoring of heart rate and saturation by the anesthesia team during sedation. A Philips Brilliance ICT machine was used to perform CT scanning from the lower neck to the level of the diaphragm, and the scanning parameters were set according to the ALARA principle: tube voltage 80–100 kV, tube current 35–85 mAs, pitch 0.2 mm, layer spacing 5.0 mm, layer thickness 5.0 mm, and image reconstruction layer thickness 1.0 mm. Iohexol 300 (mgI/ml) and iodixanol (270 mgI/ml) were injected into the dorsal vein of the hand and foot using a high-pressure syringe at a dose of 2 ml/kg and an injection rate of 0.6–3.0 ml/s. Phase II enhancement scans were performed 15–30 s and 50–60 s after drug administration, respectively.
The minimum internal diameters of the ascending aorta (AOA), proximal arch (D1), distal arch (D2), isthmus (D3), and descending aorta (DA) were measured using a double-blind method by two physicians who have been involved in cardiovascular disease research for many years, and each measurement was taken twice and averaged.