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For wide applications of the lacZ gene in cellular/molecular biology, small animal investigations, and clinical assessments, the improvement of noninvasive imaging approaches to precisely assay gene expression has garnered much attention. In this study, we investigate a novel molecular platform in which alizarin 2-O-β-d-galactopyranoside AZ-1 acts as a lacZ gene/β-gal responsive 1H-MRI probe to induce significant 1H-MRI contrast changes in relaxation times T1 and T2in situ as a concerted effect for the discovery of β-gal activity with the exposure of Fe3+. We also demonstrate the capability of this strategy for detecting β-gal activity with lacZ-transfected human MCF7 breast and PC3 prostate cancer cells by reaction-enhanced 1H-MRI T1 and T2 relaxation mapping.

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The features on freeform surface are common in products with complex curved surfaces, such as gas film holes attached on turbine blade surface or pressing head on the multi-spots bending press. The freeform surfaces generally have tiny geometric structure, the large number and regular arrangement, which makes it hard to extract analysis parameters from CAD structures and construct equivalent analysis features. Therefore, the analysis models generation will be accelerated if parameters for analysis could be extracted automatically rather than manually. In this paper, the analysis-oriented parameter extraction problem is proposed and analyzed. The geometry pattern parameter and physical attribute parameter are summarized and defined as the research kernel based on common analysis scenarios. Based on Hough transform and k-means clustering method, a geometry pattern parameter extraction method is proposed, which can convert analytic criterion of extraction requirements into clear pattern recognition problems. A physical attribute parameter extraction method based on rule reasoning is also put forward to extract numeric analysis parameters with physical meanings. Finally, two representative cases are taken to illustrate the implementation steps of the proposed method, and to verify the effectiveness and practicability in engineering. The instance demonstrates that the proposed method could significantly reduce the time consumption of analysis model generation and improve the integration degree of CAD/CAE.

Film hole component is one of the most important cooling components in pipe-net calculation for blade design. However, owing to a large number of gas film holes, manual parameter configuration for film hole components will affect the efficiency of pipe-net calculation. Extracting parameters from the geometric structure of film holes automatically could significantly shorten the time consumption of pipe-net calculation. In this paper, numeric analysis parameter and group data required by film hole components were summarized and analyzed, based on which a parameter configuration strategy was proposed. Aiming at the extraction of group data, a film hole grouping method was presented. By converting the grouping requirements in fluid-heat analysis into a geometric row pattern recognition problem, it further redefines the geometric row as a linear distribution by a projection method and a parametric mapping method and finally solves the grouping problem by a linear search algorithm. A numeric parameter extraction method was put forward to redefine analysis parameters with geometric properties and identify certain geometry primitives. The proposed algorithm and method are verified with instances. The results illustrate that our method could shorten parameter configuration process of film hole component into several minutes from several hours in manual, with more reliable accuracy.

We proposed a convolutional neural network for vertex classification on 3-dimensional dental meshes, and used it to detect teeth margins. An expanding layer was constructed to collect statistic values of neighbor vertex features and compute new features for each vertex with convolutional neural networks. An end-to-end neural network was proposed to take vertex features, including coordinates, curvatures and distance, as input and output each vertex classification label. Several network structures with different parameters of expanding layers and a base line network without expanding layers were designed and trained by 1156 dental meshes. The accuracy, recall and precision were validated on 145 dental meshes to rate the best network structures, which were finally tested on another 144 dental meshes. All networks with our expanding layers performed better than baseline, and the best one achieved an accuracy of 0.877 both on validation dataset and test dataset.

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