| 1. |
- Kristan, Matej, et al.
(author)
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The Ninth Visual Object Tracking VOT2021 Challenge Results
- 2021
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In: 2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS (ICCVW 2021). - : IEEE COMPUTER SOC. - 9781665401913 ; , s. 2711-2738
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Conference paper (peer-reviewed)abstract
- The Visual Object Tracking challenge VOT2021 is the ninth annual tracker benchmarking activity organized by the VOT initiative. Results of 71 trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in recent years. The VOT2021 challenge was composed of four sub-challenges focusing on different tracking domains: (i) VOT-ST2021 challenge focused on short-term tracking in RGB, (ii) VOT-RT2021 challenge focused on "real-time" short-term tracking in RGB, (iii) VOT-LT2021 focused on long-term tracking, namely coping with target disappearance and reappearance and (iv) VOT-RGBD2021 challenge focused on long-term tracking in RGB and depth imagery. The VOT-ST2021 dataset was refreshed, while VOT-RGBD2021 introduces a training dataset and sequestered dataset for winner identification. The source code for most of the trackers, the datasets, the evaluation kit and the results along with the source code for most trackers are publicly available at the challenge website(1).
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| 2. |
- Desai, Trishna A., et al.
(author)
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Identifying proteomic risk factors for overall, aggressive, and early onset prostate cancer using Mendelian Randomisation and tumour spatial transcriptomics
- 2024
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In: EBioMedicine. - : Elsevier. - 2352-3964. ; 105
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Journal article (peer-reviewed)abstract
- Background Understanding the role of circulating proteins in prostate cancer risk can reveal key biological pathways and identify novel targets for cancer prevention. Methods We investigated the association of 2002 genetically predicted circulating protein levels with risk of prostate cancer overall, and of aggressive and early onset disease, using cis- pQTL Mendelian randomisation (MR) and colocalisation. Findings for proteins with support from both MR, after correction for multiple -testing, and colocalisation were replicated using two independent cancer GWAS, one of European and one of African ancestry. Proteins with evidence of prostate -speci fi c tissue expression were additionally investigated using spatial transcriptomic data in prostate tumour tissue to assess their role in tumour aggressiveness. Finally, we mapped risk proteins to drug and ongoing clinical trials targets. Findings We identi fi ed 20 proteins genetically linked to prostate cancer risk (14 for overall [8 speci fi c], 7 for aggressive [3 speci fi c], and 8 for early onset disease [2 speci fi c]), of which the majority replicated where data were available. Among these were proteins associated with aggressive disease, such as PPA2 [Odds Ratio (OR) per 1 SD increment = 2.13, 95% CI: 1.54 - 2.93], PYY [OR = 1.87, 95% CI: 1.43 - 2.44] and PRSS3 [OR = 0.80, 95% CI: 0.73 - 0.89], and those associated with early onset disease, including EHPB1 [OR = 2.89, 95% CI: 1.99 - 4.21], POGLUT3 [OR = 0.76, 95% CI: 0.67 - 0.86] and TPM3 [OR = 0.47, 95% CI: 0.34 - 0.64]. We con fi rmed an inverse association of MSMB with prostate cancer overall [OR = 0.81, 95% CI: 0.80 - 0.82], and also found an inverse association with both aggressive [OR = 0.84, 95% CI: 0.82 - 0.86] and early onset disease [OR = 0.71, 95% CI: 0.68 - 0.74]. Using spatial transcriptomics data, we identi fi ed MSMB as the genome-wide top -most predictive gene to distinguish benign regions from high grade cancer regions that comparatively had fi ve -fold lower MSMB expression. Additionally, ten proteins that were associated with prostate cancer risk also mapped to existing therapeutic interventions. Interpretation Our fi ndings emphasise the importance of proteomics for improving our understanding of prostate cancer aetiology and of opportunities for novel therapeutic interventions. Additionally, we demonstrate the added bene fi t of in-depth functional analyses to triangulate the role of risk proteins in the clinical aggressiveness of prostate tumours. Using these integrated methods, we identify a subset of risk proteins associated with aggressive and early onset disease as priorities for investigation for the future prevention and treatment of prostate cancer.
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| 3. |
- Figiel, Sandy, et al.
(author)
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Spatial transcriptomic analysis of virtual prostate biopsy reveals confounding effect of tissue heterogeneity on genomic signatures
- 2023
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In: Molecular Cancer. - : Springer Nature. - 1476-4598. ; 22:1, s. 162-
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Journal article (other academic/artistic)abstract
- Genetic signatures have added a molecular dimension to prognostics and therapeutic decision-making. However, tumour heterogeneity in prostate cancer and current sampling methods could confound accurate assessment. Based on previously published spatial transcriptomic data from multifocal prostate cancer, we created virtual biopsy models that mimic conventional biopsy placement and core size. We then analysed the gene expression of different prognostic signatures (OncotypeDx®, Decipher®, Prostadiag®) using a step-wise approach with increasing resolution from pseudo-bulk analysis of the whole biopsy, to differentiation by tissue subtype (benign, stroma, tumour), followed by distinct tumour grade and finally clonal resolution. The gene expression profile of virtual tumour biopsies revealed clear differences between grade groups and tumour clones, compared to a benign control, which were not reflected in bulk analyses. This suggests that bulk analyses of whole biopsies or tumour-only areas, as used in clinical practice, may provide an inaccurate assessment of gene profiles. The type of tissue, the grade of the tumour and the clonal composition all influence the gene expression in a biopsy. Clinical decision making based on biopsy genomics should be made with caution while we await more precise targeting and cost-effective spatial analyses.
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