Health monitoring of bolted joints using modal-based vibrothermography

This article presents a novel modal-based vibrothermographic approach for health monitoring of loosening bolted joints in coupled structures. In this article, the theoretical background supporting this proposed approach is firstly presented. Through finite element analyses on a simple bolted structure with varying joint conditions achieved by adjustment of bolt loads, the relationship between the bolt load and the temperature increase in the vibrating bolted joint during vibrothermographic tests was revealed. Experimental vibrothermographic tests on a more complex structure were completed to verify the observations from the finite element analyses while demonstrating the viability of the vibrothermographic approach in a laboratory environment. It has been shown that this vibrothermographic approach was able to determine the stage of a bolted joint in its progression of failure by tracing the changes in the temperature increase in relevant regions during vibrothermographic tests. Moreover, additional tests have been performed to illustrate that this approach was effective even by using only the residual responses of the structure’s vibration that were away from the resonances, which indicates it is more applicable to structures with higher damping as such structures have stronger residual responses during vibration that can be utilized. In the concluding observations of the article, the procedure for practical application of this approach is summarized, and its potential for further development is discussed

Low-Frequency Vibrothermography Using Lightweight Piezoelectric Actuators:The Location of Excitation and Application to Composite Materials

This article presents a novel infrared thermographic approach for damage detection by utilizing the heat generated around damage sites during vibrations below 1000 Hz induced by lightweight piezoelectric actuators. In this research, the optimal location of excitation was first investigated through finite element analyses, where two generalized equations were obtained to describe the relationship between the excitation and the resulting displacement response. These observations were then verified experimentally on an aerospace-grade composite plate, followed by vibrothermographic tests conducted on the same structure to demonstrate the effectiveness of the proposed damage detection process employing only a single lightweight piezoelectric disk as the actuator

Cancer LncRNA Census reveals evidence for deep functional conservation of long noncoding RNAs in tumorigenesis.

Long non-coding RNAs (lncRNAs) are a growing focus of cancer genomics studies, creating the need for a resource of lncRNAs with validated cancer roles. Furthermore, it remains debated whether mutated lncRNAs can drive tumorigenesis, and whether such functions could be conserved during evolution. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, we introduce the Cancer LncRNA Census (CLC), a compilation of 122 GENCODE lncRNAs with causal roles in cancer phenotypes. In contrast to existing databases, CLC requires strong functional or genetic evidence. CLC genes are enriched amongst driver genes predicted from somatic mutations, and display characteristic genomic features. Strikingly, CLC genes are enriched for driver mutations from unbiased, genome-wide transposon-mutagenesis screens in mice. We identified 10 tumour-causing mutations in orthologues of 8 lncRNAs, including LINC-PINT and NEAT1, but not MALAT1. Thus CLC represents a dataset of high-confidence cancer lncRNAs. Mutagenesis maps are a novel means for identifying deeply-conserved roles of lncRNAs in tumorigenesis

Analyses of non-coding somatic drivers in 2,658 cancer whole genomes.

The discovery of drivers of cancer has traditionally focused on protein-coding genes1-4. Here we present analyses of driver point mutations and structural variants in non-coding regions across 2,658 genomes from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium5 of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). For point mutations, we developed a statistically rigorous strategy for combining significance levels from multiple methods of driver discovery that overcomes the limitations of individual methods. For structural variants, we present two methods of driver discovery, and identify regions that are significantly affected by recurrent breakpoints and recurrent somatic juxtapositions. Our analyses confirm previously reported drivers6,7, raise doubts about others and identify novel candidates, including point mutations in the 5' region of TP53, in the 3' untranslated regions of NFKBIZ and TOB1, focal deletions in BRD4 and rearrangements in the loci of AKR1C genes. We show that although point mutations and structural variants that drive cancer are less frequent in non-coding genes and regulatory sequences than in protein-coding genes, additional examples of these drivers will be found as more cancer genomes become available