Genetic and Molecular Analyses of PEG10 Reveal New Aspects of Genomic Organization, Transcription and Translation

The paternally expressed gene PEG10 is a retrotransposon derived gene adapted through mammalian evolution located on human chromosome 7q21. PEG10 codes for at least two proteins, PEG10-RF1 and PEG10-RF1/2, by -1 frameshift translation. Overexpression or reinduced PEG10 expression was seen in malignancies, like hepatocellular carcinoma or B-cell acute and chronic lymphocytic leukemia. PEG10 was also shown to promote adipocyte differentiation. Experimental evidence suggests that the PEG10-RF1 protein is an inhibitor of apoptosis and mediates cell proliferation. Here we present new data on the genomic organization of PEG10 by identifying the major transcription start site, a new splice variant and report the cloning and analysis of 1.9 kb of the PEG10 promoter. Furthermore, we show for the first time that PEG10 translation is initiated at a non-AUG start codon upstream of the previously predicted AUG codon as well as at the AUG codon. The finding that PEG10 translation is initiated at different sides adds a new aspect to the already interesting feature of PEG10's −1 frameshift translation mechanism. It is now important to unravel the cellular functions of the PEG10 protein variants and how they are related to normal or pathological conditions. The generated promoter-reporter constructs can be used for future studies to investigate how PEG10 expression is regulated. In summary, our study provides new data on the genomic organization as well as expression and translation of PEG10, a prerequisite in order to study and understand the role of PEG10 in cancer, embryonic development and normal cell homeostasis

Visual Information Retrieval in Endoscopic Video Archives

In endoscopic procedures, surgeons work with live video streams from the inside of their subjects. A main source for documentation of procedures are still frames from the video, identified and taken during the surgery. However, with growing demands and technical means, the streams are saved to storage servers and the surgeons need to retrieve parts of the videos on demand. In this submission we present a demo application allowing for video retrieval based on visual features and late fusion, which allows surgeons to re-find shots taken during the procedure.Comment: Paper accepted at the IEEE/ACM 13th International Workshop on Content-Based Multimedia Indexing (CBMI) in Prague (Czech Republic) between 10 and 12 June 201

ALK1 signalling analysis identifies angiogenesis related genes and reveals disparity between TGF-β and constitutively active receptor induced gene expression

BACKGROUND: TGF-β1 is an important angiogenic factor involved in the different aspects of angiogenesis and vessel maintenance. TGF-β signalling is mediated by the TβRII/ALK5 receptor complex activating the Smad2/Smad3 pathway. In endothelial cells TGF-β utilizes a second type I receptor, ALK1, activating the Smad1/Smad5 pathway. Consequently, a perturbance of ALK1, ALK5 or TβRII activity leads to vascular defects. Mutations in ALK1 cause the vascular disorder hereditary hemorrhagic telangiectasia (HHT). METHODS: The identification of ALK1 and not ALK5 regulated genes in endothelial cells, might help to better understand the development of HHT. Therefore, the human microvascular endothelial cell line HMEC-1 was infected with a recombinant constitutively active ALK1 adenovirus, and gene expression was studied by using gene arrays and quantitative real-time PCR analysis. RESULTS: After 24 hours, 34 genes were identified to be up-regulated by ALK1 signalling. Analysing ALK1 regulated gene expression after 4 hours revealed 13 genes to be up- and 2 to be down-regulated. Several of these genes, including IL-8, ET-1, ID1, HPTPη and TEAD4 are reported to be involved in angiogenesis. Evaluation of ALK1 regulated gene expression in different human endothelial cell types was not in complete agreement. Further on, disparity between constitutively active ALK1 and TGF-β1 induced gene expression in HMEC-1 cells and primary HUVECs was observed. CONCLUSION: Gene array analysis identified 49 genes to be regulated by ALK1 signalling and at least 14 genes are reported to be involved in angiogenesis. There was substantial agreement between the gene array and quantitative real-time PCR data. The angiogenesis related genes might be potential HHT modifier genes. In addition, the results suggest endothelial cell type specific ALK1 and TGF-β signalling

LIFER 2.0: discovering personal lifelog insights using an interactive lifelog retrieval system

This paper describes the participation of the Organiser Team in the ImageCLEFlifelog 2019 Solve My Life Puzzle (Puzzle) and Lifelog Moment Retrieval (LMRT) tasks. We proposed to use LIFER 2.0, an enhanced version of LIFER, which was an interactive retrieval system for personal lifelog data. We utilised LIFER 2.0 with some additional visual features, obtained by using traditional visual bag-of-words, to solve the Puzzle task, while with the LMRT, we applied LIFER 2.0 only with the provided information. The results on both tasks confirmed that by using faceted filter and context browsing, a user can gain insights from their personal lifelog by employing very simple interactions. These results also serve as baselines for other approaches in the ImageCLEFlifelog 2019 challenge to compare with

Overview of ImageCLEFlifelog 2018: daily living understanding and lifelog moment retrieval

Benchmarking in Multimedia and Retrieval related research fields has a long tradition and important position within the community. Benchmarks such as the MediaEval Multimedia Benchmark or CLEF are well established and also served by the community. One major goal of these competitions beside of comparing different methods and approaches is also to create or promote new interesting research directions within multimedia. For example the Medico task at MediaEval with the goal of medical related multimedia analysis. Although lifelogging creates a lot of attention in the community which is shown by several workshops and special session hosted about the topic. Despite of that there exist also some lifelogging related benchmarks. For example the previous edition of the lifelogging task at ImageCLEF. The last years ImageCLEFlifelog task was well received but had some barriers that made it difficult for some researchers to participate (data size, multi modal features, etc.) The ImageCLEFlifelog 2018 tries to overcome these problems and make the task accessible for an even broader audience (eg, pre-extracted features are provided). Furthermore, the task is divided into two subtasks (challenges). The two challenges are lifelog moment retrieval (LMRT) and the Activities of Daily Living understanding (ADLT). All in all seven teams participated with a total number of 41 runs which was an significant increase compared to the previous year

Organiser Team at ImageCLEFlifelog 2020: A Baseline Approach for Moment Retrieval and Athlete Performance Prediction using Lifelog Data

For the LMRT task at ImageCLEFlifelog 2020, LIFER 3.0, a new version of the LIFER system with improvements in the user interface and system affordance, is used and evaluated via feedback from a user experiment. In addition, since both tasks share a common dataset, LIFER 3.0 borrows some features from the LifeSeeker system deployed for the Lifelog Search Challenge; which are free-text search, visual similarity search and elastic sequencing filter. For the SPLL task, we proposed a naive solution by capturing the rate of change in running speed and weight, then obtain the target changes for each subtask using average computation and linear regression model. The results presented in this paper can be used as comparative baselines for other participants in the ImageCLEFlifelog 2020 challenge.publishedVersio