miR-16-5p Is a Stably-Expressed Housekeeping MicroRNA in Breast Cancer Tissues from Primary Tumors and from Metastatic Sites

For quantitative microRNA analyses in formalin-fixed paraffin-embedded (FFPE) tissue, expression levels have to be normalized to endogenous controls. To investigate the most stably-expressed microRNAs in breast cancer and its surrounding tissue, we used tumor samples from primary tumors and from metastatic sites. MiRNA profiling using TaqMan^® Array Human MicroRNA Cards, enabling quantification of 754 unique human miRNAs, was performed in FFPE specimens from 58 patients with metastatic breast cancer. Forty-two (72%) samples were collected from primary tumors and 16 (28%) from metastases. In a cross-platform analysis of a validation cohort of 32 FFPE samples from patients with early breast cancer genome-wide microRNA expression analysis using SurePrintG3 miRNA (8 × 60 K)^® microarrays from Agilent^® was performed. Eleven microRNAs could be detected in all samples analyzed. Based on NormFinder and geNorm stability values and the high correlation (rho ≥ 0.8) with the median of all measured microRNAs, miR-16-5p, miR-29a-3p, miR-126-3p, and miR-222-3p are suitable single gene housekeeper candidates. In the cross-platform validation, 29 human microRNAs were strongly expressed (mean log2-intensity > 10) and 21 of these microRNAs including miR-16-5p and miR-29a-3p were also stably expressed (CV < 5%). Thus, miR-16-5p and miR-29a-3p are both strong housekeeper candidates. Their Normfinder stability values calculated across the primary tumor and metastases subgroup indicate that miR-29a-3p can be considered as the strongest housekeeper in a cohort with mainly samples from primary tumors, whereas miR-16-5p might perform better in a metastatic sample enriched cohort.     

Augmented semantic segmentation for the digitization of grinding tools based on deep learning

In order to analyze various process characteristics, grinding simulations can be used, which need accurate models of the tool and the individual grains. For this purpose, grinding tools can be digitized. To identify characteristic grains from a large number of measurements, each individual grain has to be analyzed and separated from the bond manually. Therefore, a deep learning-based methodology was developed to achieve a high segmentation accuracy of the grain boundaries efficiently. Additionally, a data augmentation approach was investigated to limit the data necessary for learning. The model transferability was quantified by analyzing different states of tool wear.