Application of image segmentation and adaptive interpolation techniques to 3D reconstruction of the human temporal bones

xix, 168 leaves : ill. (some col.) ; 29 cm.Includes abstract.Includes bibliographical references (leaves 142-152).Three dimensional models aid otolaryngologists in understanding the complex anatomical features of the human temporal bone. Many of these models are generated by reconstructing histological sections. The goal of this thesis is to provide improvements on these existing 3D reconstruction methods. Presented are a segmentation framework and a contour finding algorithm for histological slices, followed by Gaussian filtering and error analysis. An adaptive interpolation algorithm based on monotonic piecewise cubics is used to automatically generate missing anatomical structure. Part of the algorithm development was completed on CT scans with proposals for extension to histological slices. The contour finding and Gaussian filtering algorithms output valid data points for interpolation. The adaptive interpolation algorithm produces satisfactory results with the interpolation error for the malleus being 1.80% when half of the data is used. The equivalent 3D model volume difference was 0.24%

Dynamic changes in microbial communities and flavor during different fermentation stages of proso millet Baijiu, a new product from Shanxi light-flavored Baijiu

IntroductionProso millet, a high-quality fermentation material used for Chinese yellow wine production, can produce special flavored substances; however, its role in improving the flavor and altering microbial communities of light-flavored Baijiu during fermentation remain unknown. Thus, we aimed to investigate the effect of proso millet on improving the flavor of light-flavored Baijiu and altering microbial communities during different fermentation stages.MethodsThe dynamic changes in the microbial communities and flavor of proso millet (50%) + sorghum (50%) mixed fermentation samples were analyzed through intermittent sampling on days 7, 14, 21, and 28 of the fermentation process. Microbial high-throughput sequencing and the analysis of flavor characteristics were conducted through 16S DNA/ ITS amplicon sequencing and gas chromatography (multi-capillary column)-ion mobility spectrometry, respectively.ResultsProso millet significantly changed the core flavor compound composition of traditional light-flavored Baijiu from ethyl acetate, ethyl hexanoate, ethyl hexanoate dimer, ethyl butanoate, ethyl lactate, and butyl acetate to oct-2-ene, 2-butanol, propyl propanoate, 2-pentenal, and 4-methylpentanal. The amplicon sequencing analysis revealed that the alpha diversity parameters of bacterial and fungal communities, including the Chao1, Pielou_e, Shannon, and Simpson indices, for proso millet–sorghum mixed fermentation samples were significantly higher than those for sorghum fermentation samples (p < 0.05). Of the 40 most significant microbial genera in two treatments, proso millet significantly increased the abundance of 12 bacterial and 18 fungal genera. Among the 40 most significant bacterial and fungal species, 23 bacterial species belonged to the Lactobacillus genus, whereas the 30 primary fungal species belonged to 28 different genera. The analysis of the relationship between microbial changes and the main flavor compounds of light-flavored Baijiu showed that bacteria from the Weissella, Acinetobacter, Bacteroides, Psychrobacter, Pseudarthrobacter, Lactococcus, Chloroplast, Saccharopolyspora, Psychrobacter, Saccharopolyspora, Pseudonocardiaceae, Bacteroides genera and fungi from the Thermoascus, Aspergillus, Pichia, Rhizomucor, Papiliotrema, Hyphopichia, and Mucor genera significantly inhibited the synthesis of ethyl hexanoate, ethyl butanoate, ethyl lactate ethyl lactate, and butyl acetate but increased the synthesis of ethyl acetate (p < 0.05). Moreover, these microbes exhibited a significantly greater abundance in proso millet–sorghum mixed fermentation samples than in sorghum samples. The synthesis of special flavored compounds in proso millet Baijiu was significantly positively correlated with the presence of fungi from the Rhizopus, Papiliotrema, Wickerhamomyces, Aspergillus, and Thermoascus genera but negative correlated with the presence of bacteria from the Weissella, Acinetobacter, Psychrobacter, Pseudarthrobacter, Bacteroides, and Saccharopolyspora genera. Regarding ethanol content, the low alcohol content of Fenjiu may be due to the significantly high abundance of fungi from the Psathyrella genus and bacteria from the Staphylococcus, Kroppenstedtia, Brevibacterium, and Acetobacter genera during fermentation. In summary, proso millet significantly altered the flavor of light-flavored Baijiu by inducing the formation of a special microbial community; however, it did not increase alcohol concentration.DiscussionThis study lays the foundation for future research on Baijiu fermentation. Additionally, the study findings may help improve the production efficiency and elevate the quality and flavor of the final product

Unleashing the potential of GNNs via Bi-directional Knowledge Transfer

Based on the message-passing paradigm, there has been an amount of research proposing diverse and impressive feature propagation mechanisms to improve the performance of GNNs. However, less focus has been put on feature transformation, another major operation of the message-passing framework. In this paper, we first empirically investigate the performance of the feature transformation operation in several typical GNNs. Unexpectedly, we notice that GNNs do not completely free up the power of the inherent feature transformation operation. By this observation, we propose the Bi-directional Knowledge Transfer (BiKT), a plug-and-play approach to unleash the potential of the feature transformation operations without modifying the original architecture. Taking the feature transformation operation as a derived representation learning model that shares parameters with the original GNN, the direct prediction by this model provides a topological-agnostic knowledge feedback that can further instruct the learning of GNN and the feature transformations therein. On this basis, BiKT not only allows us to acquire knowledge from both the GNN and its derived model but promotes each other by injecting the knowledge into the other. In addition, a theoretical analysis is further provided to demonstrate that BiKT improves the generalization bound of the GNNs from the perspective of domain adaption. An extensive group of experiments on up to 7 datasets with 5 typical GNNs demonstrates that BiKT brings up to 0.5% - 4% performance gain over the original GNN, which means a boosted GNN is obtained. Meanwhile, the derived model also shows a powerful performance to compete with or even surpass the original GNN, enabling us to flexibly apply it independently to some other specific downstream tasks.Comment: 13 pages, 9 figure

Treatment of Nonalcoholic Fatty Liver Disease with Total Alkaloids in Rubus aleaefolius Poir

Total alkaloids in Rubus aleaefolius Poir (TARAP) is a folk medicinal herb that has been used clinically in China to treat nonalcoholic fatty liver disease (NAFLD) for many years. However, the mechanism of its anti-NAFLD effect is largely unknown. In this study, we developed a NAFLD rat model by supplying a modified high-fat diet (mHFD) ad libitum for 8 weeks and evaluated the therapeutic effect of TARAP in NAFLD rats as well as the underlying molecular mechanism. We found that TARAP could reduce the serum triglycerides (TG), total cholesterol (TC), and low-density lipoprotein (LDL-C) levels and increase the serum high-density lipoprotein (HDL-C) level in NAFLD rats. In addition, TARAP treatment reduced expression of fatty acid synthetase (FAS), and acetyl-CoA carboxylase (ACC) and upregulated the expression of carnitine palmitoyltransferase (CPT). Our results suggest that regulation of lipid metabolism may be a mechanism by which TARAP treats NAFLD