Road layout understanding by generative adversarial inpainting

Autonomous driving is becoming a reality, yet vehicles still need to rely on complex sensor fusion to understand the scene they act in. The ability to discern static environment and dynamic entities provides a comprehension of the road layout that poses constraints to the reasoning process about moving objects. We pursue this through a GAN-based semantic segmentation inpainting model to remove all dynamic objects from the scene and focus on understanding its static components such as streets, sidewalks and buildings. We evaluate this task on the Cityscapes dataset and on a novel synthetically generated dataset obtained with the CARLA simulator and specifically designed to quantitatively evaluate semantic segmentation inpaintings. We compare our methods with a variety of baselines working both in the RGB and segmentation domains

Complex nature of SNP genotype effects on gene expression in primary human leucocytes.

This is a freely-available open access publication. Please cite the published version which is available via the DOI link in this record.BACKGROUND: Genome wide association studies have been hugely successful in identifying disease risk variants, yet most variants do not lead to coding changes and how variants influence biological function is usually unknown. METHODS: We correlated gene expression and genetic variation in untouched primary leucocytes (n = 110) from individuals with celiac disease - a common condition with multiple risk variants identified. We compared our observations with an EBV-transformed HapMap B cell line dataset (n = 90), and performed a meta-analysis to increase power to detect non-tissue specific effects. RESULTS: In celiac peripheral blood, 2,315 SNP variants influenced gene expression at 765 different transcripts (< 250 kb from SNP, at FDR = 0.05, cis expression quantitative trait loci, eQTLs). 135 of the detected SNP-probe effects (reflecting 51 unique probes) were also detected in a HapMap B cell line published dataset, all with effects in the same allelic direction. Overall gene expression differences within the two datasets predominantly explain the limited overlap in observed cis-eQTLs. Celiac associated risk variants from two regions, containing genes IL18RAP and CCR3, showed significant cis genotype-expression correlations in the peripheral blood but not in the B cell line datasets. We identified 14 genes where a SNP affected the expression of different probes within the same gene, but in opposite allelic directions. By incorporating genetic variation in co-expression analyses, functional relationships between genes can be more significantly detected. CONCLUSION: In conclusion, the complex nature of genotypic effects in human populations makes the use of a relevant tissue, large datasets, and analysis of different exons essential to enable the identification of the function for many genetic risk variants in common diseases.Coeliac UKNetherlands Organization for Scientific ResearchCeliac Disease Consortium (an innovative cluster approved by the Netherlands Genomics Initiative and partly funded by the Dutch government)Netherlands Genomics InitiativeWellcome Trus

Difficult Scenarios for NMSSM Higgs Discovery at the LHC

We identify scenarios not ruled out by LEP data in which NMSSM Higgs detection at the LHC will be particularly challenging. We first review the `no-lose' theorem for Higgs discovery at the LHC that applies if Higgs bosons do not decay to other Higgs bosons - namely, with L=300 fb^-1, there is always one or more `standard' Higgs detection channel with at least a 5 sigma signal. However, we provide examples of no-Higgs-to-Higgs cases for which all the standard signals are no larger than 7 sigma implying that if the available L is smaller or the simulations performed by ATLAS and CMS turn out to be overly optimistic, all standard Higgs signals could fall below 5 sigma even in the no-Higgs-to-Higgs part of NMSSM parameter space. In the vast bulk of NMSSM parameter space, there will be Higgs-to-Higgs decays. We show that when such decays are present it is possible for all the standard detection channels to have very small significance. In most such cases, the only strongly produced Higgs boson is one with fairly SM-like couplings that decays to two lighter Higgs bosons (either a pair of the lightest CP-even Higgs bosons, or, in the largest part of parameter space, a pair of the lightest CP-odd Higgs bosons). A number of representative bench-mark scenarios of this type are delineated in detail and implications for Higgs discovery at various colliders are discussed.Comment: 31 pages, 5 figure

Epidermal growth factor suppresses induction by progestin of the adhesion protein desmoplakin in T47D breast cancer cells

INTRODUCTION: Although the effects of progesterone on cell cycle progression are well known, its role in spreading and adhesion of breast cancer cells has not attracted much attention until recently. Indeed, by controlling cell adhesion proteins, progesterone may play a direct role in breast cancer invasion and metastasis. Progesterone has also been shown to modulate epidermal growth factor (EGF) effects in neoplasia, although EGF effects on progesterone pathways and targets are less well understood. In the present study we identify an effect of EGF on a progesterone target, namely desmoplakin. METHODS: Initially flow cytometry was used to establish the growing conditions and demonstrate that the T47D breast cancer cell line was responding to progesterone and EGF in a classical manner. Differential display RT-PCR was employed to identify differentially expressed genes affected by progesterone and EGF. Western and Northern blotting were used to verify interactions between EGF and progesterone in three breast cancer cell lines: T47D, MCF-7, and ZR-75. RESULTS: We found the cell adhesion protein desmoplakin to be upregulated by progesterone – a process that was suppressed by EGF. This appears to be a general but not universal effect in breast cancer cell lines. CONCLUSION: Our findings suggest that progesterone and EGF may play opposing roles in metastasis. They also suggest that desmoplakin may be a useful biomarker for mechanistic studies designed to analyze the crosstalk between EGF and progesterone dependent events. Our work may help to bridge the fields of metastasis and differentiation, and the mechanisms of steroid action

Plakophilin-2: a cell-cell adhesion plaque molecule of selective and fundamental importance in cardiac functions and tumor cell growth

Within the characteristic ensemble of desmosomal plaque proteins, the armadillo protein plakophilin-2 (Pkp2) is known as a particularly important regulatory component in the cytoplasmic plaques of various other cell–cell junctions, such as the composite junctions (areae compositae) of the myocardiac intercalated disks and in the variously-sized and -shaped complex junctions of permanent cell culture lines derived therefrom. In addition, Pkp2 has been detected in certain protein complexes in the nucleoplasm of diverse kinds of cells. Using a novel set of highly sensitive and specific antibodies, both kinds of Pkp2, the junctional plaque-bound and the nuclear ones, can also be localized to the cytoplasmic plaques of diverse non-desmosomal cell–cell junction structures. These are not only the puncta adhaerentia and the fasciae adhaerentes connecting various types of highly proliferative non-epithelial cells growing in culture but also some very proliferative states of cardiac interstitial cells and cardiac myxomata, including tumors growing in situ as well as fetal stages of heart development and cultures of valvular interstitial cells. Possible functions and assembly mechanisms of such Pkp2-positive cell–cell junctions as well as medical consequences are discussed

Candidate gene prioritization by network analysis of differential expression using machine learning approaches

<p>Abstract</p> <p>Background</p> <p>Discovering novel disease genes is still challenging for diseases for which no prior knowledge - such as known disease genes or disease-related pathways - is available. Performing genetic studies frequently results in large lists of candidate genes of which only few can be followed up for further investigation. We have recently developed a computational method for constitutional genetic disorders that identifies the most promising candidate genes by replacing prior knowledge by experimental data of differential gene expression between affected and healthy individuals.</p> <p>To improve the performance of our prioritization strategy, we have extended our previous work by applying different machine learning approaches that identify promising candidate genes by determining whether a gene is surrounded by highly differentially expressed genes in a functional association or protein-protein interaction network.</p> <p>Results</p> <p>We have proposed three strategies scoring disease candidate genes relying on network-based machine learning approaches, such as kernel ridge regression, heat kernel, and Arnoldi kernel approximation. For comparison purposes, a local measure based on the expression of the direct neighbors is also computed. We have benchmarked these strategies on 40 publicly available knockout experiments in mice, and performance was assessed against results obtained using a standard procedure in genetics that ranks candidate genes based solely on their differential expression levels (<it>Simple Expression Ranking</it>). Our results showed that our four strategies could outperform this standard procedure and that the best results were obtained using the <it>Heat Kernel Diffusion Ranking </it>leading to an average ranking position of 8 out of 100 genes, an AUC value of 92.3% and an error reduction of 52.8% relative to the standard procedure approach which ranked the knockout gene on average at position 17 with an AUC value of 83.7%.</p> <p>Conclusion</p> <p>In this study we could identify promising candidate genes using network based machine learning approaches even if no knowledge is available about the disease or phenotype.</p

Burden of illness of Pompe disease in patients only receiving supportive care

Background: Pompe disease is an orphan disease for which enzyme replacement therapy (ERT) recently became available. This study aims to estimate all relevant aspects of burden of illness-societal costs, use of home care and informal care, productivity losses, and losses in health-related quality of life (HRQoL)-for adult Pompe patients only receiving supportive care. Methods: We collected data on all relevant aspects of burden of illness via a questionnaire. We applied a societal perspective in calculating costs. The EQ-5D was used to estimate HRQoL. Results: Eighty adult patients (87% of the total Dutch adult Pompe population) completed a questionnaire. Disease severity ranged from mild to severe. Total annual costs were estimated at €22,475 (range €0-169,539) per adult Pompe patient. Patients on average received 8 h of home care and 19 h of informal care per week. Eighty-five percent of the patients received informal care from one or more caregivers; 40% had stopped working due to their disease; another 20% had reduced their working hours. HRQoL for Pompe patients who only received supportive care was estimated at 0.72, 17% lower than the Dutch population at large. Conclusions: Adult Pompe disease is associated with a considerable burden of illness at both the societal and patient levels. The disease leads to substantial costs and dependency on medical devices, home care, and informal care, and has a high impact on the patient's social network. In addition, patients are limited in their ability to work and have significantly reduced HRQoL