23 research outputs found
Genomic Variation and Its Impact on Gene Expression in Drosophila melanogaster
Understanding the relationship between genetic and phenotypic variation is one of the great outstanding challenges in biology. To meet this challenge, comprehensive genomic variation maps of human as well as of model organism populations are required. Here, we present a nucleotide resolution catalog of single-nucleotide, multi-nucleotide, and structural variants in 39 Drosophila melanogaster Genetic Reference Panel inbred lines. Using an integrative, local assembly-based approach for variant discovery, we identify more than 3.6 million distinct variants, among which were more than 800,000 unique insertions, deletions (indels), and complex variants (1 to 6,000 bp). While the SNP density is higher near other variants, we find that variants themselves are not mutagenic, nor are regions with high variant density particularly mutation-prone. Rather, our data suggest that the elevated SNP density around variants is mainly due to population-level processes. We also provide insights into the regulatory architecture of gene expression variation in adult flies by mapping cis-expression quantitative trait loci (cis-eQTLs) for more than 2,000 genes. Indels comprise around 10% of all cis-eQTLs and show larger effects than SNP cis-eQTLs. In addition, we identified two-fold more gene associations in males as compared to females and found that most cis-eQTLs are sex-specific, revealing a partial decoupling of the genomic architecture between the sexes as well as the importance of genetic factors in mediating sex-biased gene expression. Finally, we performed RNA-seq-based allelic expression imbalance analyses in the offspring of crosses between sequenced lines, which revealed that the majority of strong cis-eQTLs can be validated in heterozygous individuals
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Evaluation of Motion Segmentation Quality for Aircraft Activity Surveillance
Recent interest has been shown in performance evaluation of visual surveillance systems. The main purpose of perfor-mance evaluation on computer vision systems is the statisti-cal testing and tuning in order to improve algorithm’s reli-ability and robustness. In this paper we investigate the use of empirical discrepancy metrics for quantitative analysis of motion segmentation algorithms. We are concerned with the case of visual surveillance on an airport’s apron, that is the area where aircrafts are parked and serviced by spe-cialized ground support vehicles. Robust detection of indi-viduals and vehicles is of major concern for the purpose of tracking objects and understanding the scene. In this paper, different discrepancy metrics for motion segmentation eval-uation are illustrated and used to assess the performance of three motion segmentors on video sequences of an airport’s apron.
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Visual Surveillance for Aircraft Activity Monitoring
This paper presents a visual surveillance system for the automatic scene interpretation of airport aprons. The system comprises two modules — Scene Tracking and Scene Understanding. The Scene Tracking module, comprising a bottom-up methodology, and the Scene Understanding module, comprising a video event representation and recognition scheme, have been demonstated to be a valid approach for apron monitoring. 1