Continuous monitoring of residual torque of loose bolt in a bolted joint

6th Asia Pacific Workshop on Structural Health Monitoring, APWSHM, Hobart, Tasmania, Australia, 7-9 December 20162016-2017 > Academic research: refereed > Publication in refereed journal201804_a bcmaVersion of RecordPublishe

Characterization of damage in shielding structures of space vehicles under hypervelocity impact

6th Asia Pacific Workshop on Structural Health Monitoring, APWSHM, Hobart, Tasmania, Australia, 7-9 December 2016Version of RecordPublishe

Characterizing hypervelocity impact (HVI)-induced pitting damage using active guided ultrasonic waves : from linear to nonlinear

2016-2017 > Academic research: refereed > Publication in refereed journal201804_a bcmaVersion of RecordPublishe

Prediction of grout penetration in fractured rocks by numerical simulation

As fractures in rock significantly reduce the strength as well as the stiffness of the rock mass, grouting may be required to improve the performance of the rock mass in engineering or mining projects. During grouting, mortar of cement or other materials is injected into the rock mass so that the fractures can be filled up and the rock mass can act as an integral unit. Unlike water, grouts are usually viscous and behave as non-Newtonian fluids. Therefore, the equations describing the flow of grout are more complicated and the solutions are quite difficult to obtain. The problem is further aggravated by the fact that the fractures are mostly randomly distributed, and it is rarely possible to accurately define the fractures and the distribution patterns. In this paper, a numerical model is proposed for analyzing the grouting process. The model is based on the stochastic approach, and it can provide the depth of penetration and the fluid pressure due to the flow of grout, which is modeled as a Bingham fluid, in the fractured rock mass. Parametric studies have been carried out to investigate the effects of various factors on the depth of penetration, and a regression formula is developed for calculating the penetration depth. Experiments have been carried out and their results are used to validate the present method.published_or_final_versio

Protein 3D Graph Structure Learning for Robust Structure-based Protein Property Prediction

Protein structure-based property prediction has emerged as a promising approach for various biological tasks, such as protein function prediction and sub-cellular location estimation. The existing methods highly rely on experimental protein structure data and fail in scenarios where these data are unavailable. Predicted protein structures from AI tools (e.g., AlphaFold2) were utilized as alternatives. However, we observed that current practices, which simply employ accurately predicted structures during inference, suffer from notable degradation in prediction accuracy. While similar phenomena have been extensively studied in general fields (e.g., Computer Vision) as model robustness, their impact on protein property prediction remains unexplored. In this paper, we first investigate the reason behind the performance decrease when utilizing predicted structures, attributing it to the structure embedding bias from the perspective of structure representation learning. To study this problem, we identify a Protein 3D Graph Structure Learning Problem for Robust Protein Property Prediction (PGSL-RP3), collect benchmark datasets, and present a protein Structure embedding Alignment Optimization framework (SAO) to mitigate the problem of structure embedding bias between the predicted and experimental protein structures. Extensive experiments have shown that our framework is model-agnostic and effective in improving the property prediction of both predicted structures and experimental structures. The benchmark datasets and codes will be released to benefit the community

The clinicopathologic observation, c-KIT gene mutation and clonal status of gastrointestinal stromal tumor in the sacrum

<p>Abstract</p> <p>Background</p> <p>It is very rare that gastrointestinal stromal tumor (GIST) occurs in the sacrum. Only one case of GIST occuring in the sacral region, with intracranial metastasis, has been reported in the literature. Moreover, only few cases have been published in literature about its clonal origin.</p> <p>Case presentation</p> <p>In this report, we present a rare case of GIST occuring in the sacrum and describe its clinicopathologic features, c-KIT gene mutation and clonal status. Microscopically, the lesion was composed of spindle cells arranged in cords, knitted and whirlpool patterns. Trabecula of bone were found in the lesion. The cytoplasm of tumor cells were abundant, and the nuclei were fusiform. Mitotic figures were rare. Immunohistochemically, the tumor cells showed positive reactivity for CD117 and CD34. On mutation analysis, a c-KIT gene mutation was found in exon 11. The result of clonal analysis demonstrated that the GIST was monoclonal.</p> <p>Conclusion</p> <p>In summary, we showed that tumor material, phenotypically identical with GISTs was found in the sacrum. It is difficult to differentiate GISTs from other spindle cell tumors, hence the need for immunohistochemistry, the examination of c-KIT gene amplification and sequencing.</p

Molecular mechanism underlying differential apoptosis between human melanoma cell lines UACC903 and UACC903(+6) revealed by mitochondria-focused cDNA microarrays

Human malignant melanoma cell line UACC903 is resistant to apoptosis while chromosome 6-mediated suppressed cell line UACC903(+6) is sensitive. Here, we describe identification of differential molecular pathways underlying this difference. Using our recently developed mitochondria-focused cDNA microarrays, we identified 154 differentially expressed genes including proapoptotic (BAK1 [6p21.3], BCAP31, BNIP1, CASP3, CASP6, FAS, FDX1, FDXR, TNFSF10 and VDAC1) and antiapoptotic (BCL2L1, CLN3 and MCL1) genes. Expression of these pro- and anti-apoptotic genes was higher in UACC903(+6) than in UACC903 before UV treatment and was altered after UV treatment. qRT-PCR and Western blots validated microarray results. Our bioinformatic analysis mapped these genes to differential molecular pathways that predict resistance and sensitivity of UACC903 and UACC903(+6) to apoptosis respectively. The pathways were functionally confirmed by the FAS ligand-induced cell death and by siRNA knockdown of BAK1 protein. These results demonstrated the differential molecular pathways underlying survival and apoptosis of UACC903 and UACC903(+6) cell lines

A novel tumor suppressor gene ECRG4 interacts directly with TMPRSS11A (ECRG1) to inhibit cancer cell growth in esophageal carcinoma

<p>Abstract</p> <p>Background</p> <p>The esophageal carcinoma related gene 4 (ECRG4) was initially identified and cloned from human normal esophageal epithelium in our laboratory (GenBank accession no.<ext-link ext-link-id="AF325503" ext-link-type="gen">AF325503</ext-link>). ECRG4 has been described as a novel tumor suppressor gene associated with prognosis in esophageal squamous cell carcinoma (ESCC).</p> <p>Methods</p> <p>In this study, binding affinity assay in vitro and co-immunoprecipitation experiment in vivo were utilized to verify the physical interaction between ECRG4 and transmembrane protease, serine 11A (TMPRSS11A, also known as ECRG1, GenBank accession no. <ext-link ext-link-id="AF 071882" ext-link-type="gen">AF 071882</ext-link>). Then, p21 protein expression, cell cycle and cell proliferation regulations were examined after ECRG4 and ECRG1 co-transfection in ESCC cells.</p> <p>Results</p> <p>We revealed for the first time that ECRG4 interacted directly with ECRG1 to inhibit cancer cell proliferation and induce cell cycle G1 phase block in ESCC. Binding affinity and co-immunoprecipitation assays demonstrated that ECRG4 interacted directly with ECRG1 in ESCC cells. Furthermore, the ECRG4 and ECRG1 co-expression remarkably upregulatd p21 protein level by Western blot (P < 0.001), induced cell cycle G1 phase block by flow cytometric analysis (P < 0.001) and suppressed cell proliferation by MTT and BrdU assay (both P < 0.01) in ESCC cells.</p> <p>Conclusions</p> <p>ECRG4 interacts directly with ECRG1 to upregulate p21 protein expression, induce cell cycle G1 phase block and inhibit cancer cells proliferation in ESCC.</p

The use of nanocrystal quantum dot as fluorophore reporters in molecular beacon-based assays

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