[Deep learning-assisted construction of three-dimensional face midsagittal plane based on point clouds]

Objective: To establish an intelligent registration algorithm under the framework of original-mirror alignment algorithm to construct three-dimensional(3D) facial midsagittal plane automatically. Dynamic Graph Registration Network (DGRNet) was established to realize the intelligent registration, in order to provide a reference for clinical digital design and analysis. Methods: Two hundred clinical patients without significant facial deformities were collected from October 2020 to October 2022 at Peking University School and Hospital of Stomatology. The DGRNet consists of constructing the feature vectors of key points in point original and mirror point clouds (X, Y), obtaining the correspondence of key points, and calculating the rotation and translation by singular value decomposition. Original and mirror point clouds were registrated and united. The principal component analysis (PCA) algorithm was used to obtain the DGRNet alignment midsagittal plane. The model was evaluated based on the coefficient of determination (R2) index for the translation and rotation matrix of test set. The angle error was evaluated on the 3D facial midsagittal plane constructed by the DGRNet alignment midsagittal plane and the iterative closet point(ICP) alignment midsagittal plane for 50 cases of clinical facial data. Results: The average angle error of the DGRNet alignment midsagittal plane and ICP alignment midsagittal plane was 1.05°±0.56°, and the minimum angle error was only 0.13°. The successful detection rate was 78%(39/50) within 1.50° and 90% (45/50)within 2.00°. Conclusions: This study proposes a new solution for the construction of 3D facial midsagittal plane based on the DGRNet alignment method with intelligent registration, which can improve the efficiency and effectiveness of treatment to some extent.

Sheep fecal transplantation affects growth performance in mouse models by altering gut microbiota

Animal growth traits are important and complex traits that determine the productivity of animal husbandry. There are many factors that affect growth traits, among which diet digestion is the key factor. In the process of animal digestion and absorption, the role of gastrointestinal microbes is essential. In this study, we transplanted two groups of sheep intestinal microorganisms with different body weights into the intestines of mice of the same age to observe the effect of fecal bacteria transplantation on the growth characteristics of the mouse model. The results showed that receiving fecal microbiota transplantation (FMT) had an effect on the growth traits of recipient mice (P < 0.05). Interestingly, only mice receiving high-weight donor microorganisms showed differences. Use 16S rDNA sequencing technology to analyze the stool microorganisms of sheep and mice. The microbial analysis of mouse feces showed that receiving FMT could improve the diversity and richness of microorganisms (P < 0.05), and the microbial composition of mouse feces receiving low-weight donor microorganisms was similar to that of the control group, which was consistent with the change trend of growth traits. The feces of high-weight sheep may have higher colonization ability. The same five biomarkers were identified in the donor and recipient, all belonging to Firmicutes, and were positively correlated with the body weight of mice at each stage. These results suggest that FMT affects the growth traits of receptors by remodeling their gut microflora.

Relationship between rumen microbial differences and traits among Hu sheep, Tan sheep, and Dorper sheep

Rumen microbes play an important role in the growth and development of ruminants. Differences in variety will affect the rumen community structure. The three excellent sheep breeds were selected for this study (Hu sheep, Tan sheep, and Dorper sheep) have different uses and origins. The sheep were raised on the same diet to 180 d of age in a consistent environment. 16S rDNA V3 to V4 region sequencing was used to assess the rumen microbes of 180 individuals (60 per breed). There were differences in microbial diversity among different sheep breeds (P < 0.05). Principal coordinate analysis showed that the three varieties were separated, but also partially overlapped. Linear discriminant analysis effect size identified a total of 19 biomarkers in three breeds. Of these biomarkers, five in Hu sheep were significantly negatively correlated with average feed conversion rate (P < 0.05). Six biomarkers were identified in the rumen of Dorper sheep, among which Ruminococcus was significantly positively correlated with body weight at 80 d (P < 0.05). In Tan sheep, Rikenellaceae_RC9_gut_group was significantly positively correlated with meat fat, and significantly positively correlated with volatile fatty acids (VFAs), such as butyric acid and isobutyric acid (P < 0.05). The Rikenellaceae_RC9_gut_group may regulate Tan mutton fat deposition by affecting the concentration of VFAs. Functional prediction revealed enrichment differences of functional pathways among different sheep breeds were small. All were enriched in functions, such as fermentation and chemoheterotrophy. The results show that there are differences in the rumen microorganisms of the different sheep breeds, and that the microorganisms influence the host.