Enhanced metagenomic deep learning for disease prediction and consistent signature recognition by restructured microbiome 2D representations.

Metagenomic analysis has been explored for disease diagnosis and biomarker discovery. Low sample sizes, high dimensionality, and sparsity of metagenomic data challenge metagenomic investigations. Here, an unsupervised microbial embedding, grouping, and mapping algorithm (MEGMA) was developed to transform metagenomic data into individualized multichannel microbiome 2D representation by manifold learning and clustering of microbial profiles (e.

[Repeatability of three-dimensional measurements based on cone-beam CT images].

Objective: To investigate the repeatability of three-dimensional (3-D) cephalometric measurements for the clinical application of 3-D cephalometry.

Methods: Forty-nine measurements that widely used in traditional cephalometric analyses were defined in 3-D cone-beam CT (CBCT) images. Three examiners identified landmarks on CBCT images of 17 subjects with normal occlusion, respectively, and 3-D measurements were exported automatically by software SimPlant.

[Three-dimensional analysis of craniofacial growth in adolescents with normal occlusion in Beijing].

Objective: To analyze craniofacial growth three-dimensionally for adolescents with normal occlusion in Beijing.

Methods: One hundred and twenty-six adolescents with normal occlusion were selected according to the criteria. The sample was divided into four age groups (53 within 4 years, 30 within 7 years, 27 within 10 years and 16 within 13 years).