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Data analysis helps identify changes in the microstructure of materials, but is often hindered by the cost and time requirements of experimental data generation. Data augmentation provides an in silico alternative. A recent data augmentation algorithm, known as the random persistence diagram generator (RPDG), samples a sequence of synthetic topological summaries from a possibly limited amount of data. RPDG relies on a parametric model for persistence diagrams, namely a pairwise interacting point process (PIPP). Herein, we develop a Bayesian approach to infer the PIPP parameters and call the resulting pipeline the Bayesian RPDG (BRPDG). We showcase that BRPDG exhibits higher discriminative power than RPDG in the identification of materials structural changes. [ABSTRACT FROM AUTHOR]

Topological data analysis, and in particular persistence diagrams, are gaining popularity as tools for extracting topological information from noisy point cloud and digital data. Persistence diagrams track topological features in the form of k-dimensional holes in the data. Here, we construct a new, automated approach for identifying persistence diagram points that represent robust long-life features. These features may be used to provide a more accu-rate estimate of Betti numbers for the underlying space. This approach extends the established practice of using a lifespan cutoff on the features in order to take advantage of the observation that noisy features typically appear in clusters in the persistence diagram. We show that this approach okers more exibility in partitioning features in the persistence diagram, resulting in greater accuracy in computed Betti numbers, especially in the case of high noise levels and varying image illumination. This work is motivated by 3-dimensional Micro-CT imaging of ice core samples, and is applicable for separating noise from robust signals in persistence diagrams from noisy data. [ABSTRACT FROM AUTHOR]

Boar taint is an off-putting odor found in the meat of entire male pigs due to the accumulation of androstenone and skatole in the fat. Development of boar taint is dependent on the balance between the rate of synthesis and metabolism of these compounds. Prior studies have shown that feed ingredients high in fermentable carbohydrates, including chicory root, sugar beet pulp, and Jerusalem artichoke decreased the synthesis of skatole in the hindgut and altered the metabolism in the liver, resulting in a skatole concentrations comparable with castrated males. However, the mechanism of how these dietary fermentable carbohydrates affect liver metabolism is not well understood. Metabolism of boar taint compounds takes place in a two-phase process, regulated by various enzymes and cytochrome families. Short-chain fatty acids (SCFAs) are produced from the microbial fermentation of fiber and have been shown to have regulatory effects on histone deacetylases and gene expression in humans, providing a potential mechanism in which feed high in dietary fiber reduces boar taint. The purpose of this study was to assess if SCFA directly affect the metabolism of androstenone in liver hepatocytes. Primary hepatocytes were isolated from the liver of 6 [(Yorkshire x Landrace) x Duroc] boars weighing around 90 kg. Cells were treated in triplicate with acetate, butyrate, propionate, and various combinations for 24 h to alter gene expression. Two sets of wells acted as a control, not being treated with SCFAs. The cells were then incubated with androstenone for 3 h, and media was extracted, and metabolite production was analyzed using high-performance liquid chromatography. The effects of treatments were using a one-way ANOVA with a Dunnet’s adjustment for multiple comparisons to a control. Results showed the combined acetate, butyrate, and propionate treatment had the greatest effect on androstenone glucuronide production when compared with the control, resulting in significant increases (P < 0.05) ranging from 82 ± 1.6% to 1 ± 2.4% in individual boars. Other treatments had a similar effect, significantly increasing the production of androstenone glucuronide but to a lesser degree. Butyrate increased production ranging from 40 ± 2.2% to 5±1.5% (P < 0.05), propionate from 38 ± 3.6% to 3 ± 2.6% (P < 0.05), acetate and butyrate from 61±3.9% to 1 ± 2.8% (P < 0.05), acetate and propionate from 70±3.5% to 5±1.7% (P < 0.05), and butyrate and propionate from 82 ± 2.5% to 4 ± 1.1% (P < 0.05) when compared with the control. In conclusion, this trial provided evidence that SCFAs produced by the microbial fermentation of fiber can significantly increase hepatic androstenone metabolism in some animals, with a wide range in these effects among individual boars. Future research will involve assessing the effects of SCFAs on hepatic skatole metabolism [ABSTRACT FROM AUTHOR]

The application of artificial intelligence (AI) to various medical subfields has been a popular topic of research in recent years. In particular, deep learning has been widely used and has proven effective in many cases. Topological data analysis (TDA)—a rising field at the intersection of mathematics, statistics, and computer science—offers new insights into data. In this work, we develop a novel deep learning architecture that we call TopoResNet that integrates topological information into the residual neural network architecture. To demonstrate TopoResNet, we apply it to a skin lesion classification problem. We find that TopoResNet improves the accuracy and the stability of the training process. [ABSTRACT FROM AUTHOR]