Instance Segmentation for Feature Recognition on Noncooperative Resident Space Objects

Active debris removal and unmanned on-orbit servicing missions have gained interest in the last few years, along with the possibility to perform them through the use of an autonomous chasing spacecraft. In this work, new resources are proposed to aid the implementation of guidance, navigation, and control algorithms for satellites devoted to the inspection of noncooperative targets before any proximity operation is initiated. In particular, the use of convolutional neural networks (CNN) performing object detection and instance segmentation is proposed, and its effectiveness in recognizing the components and parts of the target satellite is evaluated. Yet, no reliable training images dataset of this kind exists to date. A tailored and publicly available software has been developed to overcome this limitation by generating synthetic images. Computer-aided design models of existing satellites are loaded on a three-dimensional animation software and used to programmatically render images of the objects from different points of view and in different lighting conditions, together with the necessary ground truth labels and masks for each image. The results show how a relatively low number of iterations is sufficient for a CNN trained on such datasets to reach a mean average precision value in line with state-of-the-art performances achieved by CNN in common datasets. An assessment of the performance of the neural network when trained on different conditions is provided. To conclude, the method is tested on real images from the Mission Extension Vehicle-1 on-orbit servicing mission, showing that using only artificially generated images to train the model does not compromise the learning process

Comparison of T1 mapping techniques for ECV quantification. histological validation and reproducibility of ShMOLLI versus multibreath-hold T1 quantification equilibrium contrast CMR

BACKGROUND: Myocardial extracellular volume (ECV) is elevated in fibrosis or infiltration and can be quantified by measuring the haematocrit with pre and post contrast T1 at sufficient contrast equilibrium. Equilibrium CMR (EQ-CMR), using a bolus-infusion protocol, has been shown to provide robust measurements of ECV using a multibreath-hold T1 pulse sequence. Newer, faster sequences for T1 mapping promise whole heart coverage and improved clinical utility, but have not been validated. METHODS: Multibreathhold T1 quantification with heart rate correction and single breath-hold T1 mapping using Shortened Modified Look-Locker Inversion recovery (ShMOLLI) were used in equilibrium contrast CMR to generate ECV values and compared in 3 ways.Firstly, both techniques were compared in a spectrum of disease with variable ECV expansion (n=100, 50 healthy volunteers, 12 patients with hypertrophic cardiomyopathy, 18 with severe aortic stenosis, 20 with amyloid). Secondly, both techniques were correlated to human histological collagen volume fraction (CVF%, n=18, severe aortic stenosis biopsies). Thirdly, an assessment of test:retest reproducibility of the 2 CMR techniques was performed 1 week apart in individuals with widely different ECVs (n=10 healthy volunteers, n=7 amyloid patients). RESULTS: More patients were able to perform ShMOLLI than the multibreath-hold technique (6% unable to breath-hold). ECV calculated by multibreath-hold T1 and ShMOLLI showed strong correlation (r(2)=0.892), little bias (bias -2.2%, 95%CI -8.9% to 4.6%) and good agreement (ICC 0.922, range 0.802 to 0.961, p<0.0001). ECV correlated with histological CVF% by multibreath-hold ECV (r(2)= 0.589) but better by ShMOLLI ECV (r(2)= 0.685). Inter-study reproducibility demonstrated that ShMOLLI ECV trended towards greater reproducibility than the multibreath-hold ECV, although this did not reach statistical significance (95%CI -4.9% to 5.4% versus 95%CI -6.4% to 7.3% respectively, p=0.21). CONCLUSIONS: ECV quantification by single breath-hold ShMOLLI T1 mapping can measure ECV by EQ-CMR across the spectrum of interstitial expansion. It is procedurally better tolerated, slightly more reproducible and better correlates with histology compared to the older multibreath-hold FLASH techniques

Misalignment of hemodynamic forces in the left ventricle is associated with adverse remodeling following STEMI

Abstract Funding Acknowledgements Type of funding sources: None. Background Infarct size (IS), area at risk (AAR) and microvascular obstruction (MVO) are well known predictors of adverse remodeling (aLVr) following acute myocardial infarction, while the pathogenic role of left ventricular (LV) hemodynamic forces (HDFs) is still unknown. Recent evidence suggests the role of HDFs in negative remodeling after pathogenic events. Purpose To identify LV HDFs patterns associated with aLVr in reperfused ST-segment elevation MI (STEMI) patients. Methods Forty-nine acute STEMI patients underwent CMR at 1 week (baseline) and 4 months (follow-up) after MI. The following parameters were measured: left ventricular end-diastolic and end-systolic volume index for body surface area (LVEDVi and LVESVi), left ventricular ejection fraction (LVEF) and LV mass index, AAR and IS. LV HDFs were computed at baseline from cine CMR long axis datasets using a novel method based on LV endocardial boundary tracking. LV HDFs were calculated both in apex-base (A-B) and latero-septal (L-S) directions. The distribution of LV HDFs were evaluated by L-S over A-B HDFs ratio (L-S/A-B HDFs ratio %). All HDFs parameters are computed over the entire heartbeat, in systole and diastole. aLVr was defined as an absolute increase in LVESV of at least 15% (ΔLV-ESV ≥15%). Results Patients with aLVr (n = 18; 37%) had significant greater value of AAR (32 ± 23 vs 22 ± 18; p = 0.03) and slightly larger IS (23 ± 16 vs 15 ± 11; p= 0.07) at baseline. In patients with aLVr at FU, baseline systolic L-S HDF were lower (2.7 ± 0.9 vs 3.6 ± 1; p = 0.027) while diastolic L-S/A-B HDF ratio was significantly higher (28 ± 14 vs 19 ± 6; p = 0.03), reflecting higher grade of diastolic HDFs misalignment. At univariate logistic regression analysis, higher IS [Odd ratio (OR) 1.05; 95% confidence interval (95% CI) 1.01-1.1; p= 0.04] L-S HDFs (OR 0.41; 95% CI 0.2-0.9; p= 0.04] and higher diastolic L-S/A-B HDFs ratio (OR 1.1; 95% CI 1.01-1.2; p= 0.05) were associated with aLVr at FU (Table). At multivariate logistic regression analysis, L-S/A-B HDF ratio remained the only independent predictor of adverse LV remodeling after correction for other baseline determinants. Conclusion Misalignment of diastolic HDFs following STEMI is associated with aLVr observed after 4 months. Predictors of adverse remodeling Univariate Multivariate Parameter OR (95% CI) P OR (95% CI) P IS (%) 1.05 (1.01-1.1) 0.042 - - Systolic L-S HDF 0.41 (0.2-0.9) 0.04 - - Diastolic L-S/A-B HDF Ratio 1.1 (1.01-1.2) 0.05 1.1 (1.01-1.2) 0.04 A-B:apex-base; L-S: latero-septal; HDFs: hemodynamic forces Abstract Figure. Diastolic HDFs distribution and aLV

Homozygosity for a missense mutation in the 67 kDa isoform of glutamate decarboxylase in a family with autosomal recessive spastic cerebral palsy: parallels with Stiff-Person Syndrome and other movement disorders

Background Cerebral palsy (CP) is an heterogeneous group of neurological disorders of movement and/or posture, with an estimated incidence of 1 in 1000 live births. Non-progressive forms of symmetrical, spastic CP have been identified, which show a Mendelian autosomal recessive pattern of inheritance. We recently described the mapping of a recessive spastic CP locus to a 5 cM chromosomal region located at 2q24-31.1, in rare consanguineous families. Methods Here we present data that refine this locus to a 0.5 cM region, flanked by the microsatellite markers D2S2345 and D2S326. The minimal region contains the candidate gene GAD1, which encodes a glutamate decarboxylase isoform (GAD67), involved in conversion of the amino acid and excitatory neurotransmitter glutamate to the inhibitory neurotransmitter γ-aminobutyric acid (GABA). Results A novel amino acid mis-sense mutation in GAD67 was detected, which segregated with CP in affected individuals. Conclusions This result is interesting because auto-antibodies to GAD67 and the more widely studied GAD65 homologue encoded by the GAD2 gene, are described in patients with Stiff-Person Syndrome (SPS), epilepsy, cerebellar ataxia and Batten disease. Further investigation seems merited of the possibility that variation in the GAD1 sequence, potentially affecting glutamate/GABA ratios, may underlie this form of spastic CP, given the presence of anti-GAD antibodies in SPS and the recognised excitotoxicity of glutamate in various contexts

Extracellular volume quantification in isolated hypertension - changes at the detectable limits?

The funding source (British Heart Foundation and UK National Institute for Health Research) provided salaries for research training (FZ, TT, DS, SW), but had no role in study design, collection, analysis, interpretation, writing, or decisions with regard to publication. This work was undertaken at University College London Hospital, which received a proportion of funding from the UK Department of Health National Institute for Health Research Biomedical Research Centres funding scheme. We are grateful to King’s College London Laboratories for processing the collagen biomarker panel

A genome-wide scan for common alleles affecting risk for autism

Although autism spectrum disorders (ASDs) have a substantial genetic basis, most of the known genetic risk has been traced to rare variants, principally copy number variants (CNVs). To identify common risk variation, the Autism Genome Project (AGP) Consortium genotyped 1558 rigorously defined ASD families for 1 million single-nucleotide polymorphisms (SNPs) and analyzed these SNP genotypes for association with ASD. In one of four primary association analyses, the association signal for marker rs4141463, located within MACROD2, crossed the genome-wide association significance threshold of P < 5 × 10−8. When a smaller replication sample was analyzed, the risk allele at rs4141463 was again over-transmitted; yet, consistent with the winner's curse, its effect size in the replication sample was much smaller; and, for the combined samples, the association signal barely fell below the P < 5 × 10−8 threshold. Exploratory analyses of phenotypic subtypes yielded no significant associations after correction for multiple testing. They did, however, yield strong signals within several genes, KIAA0564, PLD5, POU6F2, ST8SIA2 and TAF1C