| 1. |
- Das Mahapatra, Kunal, et al.
(författare)
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A comprehensive analysis of coding and non-coding transcriptomic changes in cutaneous squamous cell carcinoma
- 2020
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Ingår i: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 10:1
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Tidskriftsartikel (refereegranskat)abstract
- Cutaneous Squamous Cell Carcinoma (cSCC) is the most common and fastest-increasing cancer with metastatic potential. Long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs) are novel regulators of gene expression. To identify mRNAs, lncRNAs and circRNAs, which can be involved in cSCC, RNA-seq was performed on nine cSCCs and seven healthy skin samples. Representative transcripts were validated by NanoString nCounter assays using an extended cohort, which also included samples from pre-cancerous skin lesions (actinic keratosis). 5,352 protein-coding genes, 908 lncRNAs and 55 circular RNAs were identified to be differentially expressed in cSCC. Targets of 519 transcription factors were enriched among differentially expressed genes, 105 of which displayed altered level in cSCCs, including fundamental regulators of skin development (MYC, RELA, ETS1, TP63). Pathways related to cell cycle, apoptosis, inflammation and epidermal differentiation were enriched. In addition to known oncogenic lncRNAs (PVT1, LUCAT1, CASC9), a set of skin-specific lncRNAs were were identified to be dysregulated. A global downregulation of circRNAs was observed in cSCC, and novel skin-enriched circRNAs, circ_IFFO2 and circ_POF1B, were identified and validated. In conclusion, a reference set of coding and non-coding transcripts were identified in cSCC, which may become potential therapeutic targets or biomarkers.
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| 2. |
- Figueiredo, Eloi, et al.
(författare)
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Does Climate Change Impact Long-Term Damage Detection in Bridges?
- 2023
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Ingår i: Experimental Vibration Analysis for Civil Engineering Structures - EVACES 2023 - Volume 2. - 2366-2557 .- 2366-2565. - 9783031391163 ; 433 LNCE, s. 432-440
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Konferensbidrag (refereegranskat)abstract
- The effects of operational and environmental variability have been posed as one of the biggest challenges to transit structural health monitoring (SHM) from research to practice. To deal with that, machine learning algorithms have been proposed to learn from experience based on a reference data set. These machine learning algorithms work well based on the premise that the basis of the reference data does not change over time. Meanwhile, climate change has been posed as one of the biggest concerns for the health of bridges. Although the uncertainty associated with the magnitude of the change is large, the fact that our climate is changing is unequivocal. Therefore, it is expected that climate change can be another source of environmental variability, especially the temperature. So, what happens if the mean temperature changes over time? Will it significantly affect the dynamics of bridges? Will the reference data set used for the training algorithms become outdated? Are machine learning algorithms robust enough to deal with those changes? This paper summarizes a preliminary study about the impact of climate change on the long-term damage detection performance of classifiers rooted in machine learning algorithms trained with one-year data from the Z-24 Bridge in Switzerland. The performance will be tested for three climate change scenarios in three future periods centered in 2035, 2060, and 2085.
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