Characterization of porcine dentin sialoprotein (DSP) and dentin sialophosphoprotein (DSPP) cDNA clones

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/74726/1/j.1600-0722.2003.00009.x.pd

Modelling double emulsion formation in planar flow-focusing microchannels

Double emulsion formation in a hierarchical flow-focusing channel is systematically investigated using a free-energy ternary lattice Boltzmann model. A three-dimensional formation regime diagram is constructed based on the capillary numbers of the inner ( ), middle ( ) and outer ( ) phase fluids. The results show that the formation diagram can be classified into periodic two-step region, periodic one-step region, and non-periodic region. By varying and in the two-step formation region, different morphologies are obtained, including the regular double emulsions, decussate regimes with one or two alternate empty droplets, and structures with multiple inner droplets contained in the continuous middle phase thread. Bidisperse behaviours are also frequently encountered in the two-step formation region. In the periodic one-step formation region, scaling laws are proposed for the double emulsion size and for the size ratio between the inner droplet and the overall double emulsion. Furthermore, we show that the interfacial tension ratio can greatly change the morphologies of the obtained emulsion droplets, and the channel geometry plays an important role in changing the formation regimes and the double emulsion sizes. In particular, narrowing the side inlets or the distance between the two side inlets promotes the conversion from the two-step formation regime to the one-step formation regime

Multi-model Ensemble Learning Architecture Based on 3D CNN for Lung Nodule Malignancy Suspiciousness Classification

© 2020, Society for Imaging Informatics in Medicine. Classification of benign and malignant in lung nodules using chest CT images is a key step in the diagnosis of early-stage lung cancer, as well as an effective way to improve the patients’ survival rate. However, due to the diversity of lung nodules and the visual similarity of lung nodules to their surrounding tissues, it is difficult to construct a robust classification model with conventional deep learning–based diagnostic methods. To address this problem, we propose a multi-model ensemble learning architecture based on 3D convolutional neural network (MMEL-3DCNN). This approach incorporates three key ideas: (1) Constructed multi-model network architecture can be well adapted to the heterogeneity of lung nodules. (2) The input that concatenated of the intensity image corresponding to the nodule mask, the original image, and the enhanced image corresponding to which can help training model to extract advanced feature with more discriminative capacity. (3) Select the corresponding model to different nodule size dynamically for prediction, which can improve the generalization ability of the model effectively. In addition, ensemble learning is applied in this paper to further improve the robustness of the nodule classification model. The proposed method has been experimentally verified on the public dataset, LIDC-IDRI. The experimental results show that the proposed MMEL-3DCNN architecture can obtain satisfactory classification results