BSDA: Bayesian Random Semantic Data Augmentation for Medical Image Classification

Data augmentation is a crucial regularization technique for deep neural networks, particularly in medical image classification. Mainstream data augmentation (DA) methods are usually applied at the image level. Due to the specificity and diversity of medical imaging, expertise is often required to design effective DA strategies, and improper augmentation operations can degrade model performance. Although automatic augmentation methods exist, they are computationally intensive. Semantic data augmentation can implemented by translating features in feature space. However, over-translation may violate the image label. To address these issues, we propose \emph{Bayesian Random Semantic Data Augmentation} (BSDA), a computationally efficient and handcraft-free feature-level DA method. BSDA uses variational Bayesian to estimate the distribution of the augmentable magnitudes, and then a sample from this distribution is added to the original features to perform semantic data augmentation. We performed experiments on nine 2D and five 3D medical image datasets. Experimental results show that BSDA outperforms current DA methods. Additionally, BSDA can be easily assembled into CNNs or Transformers as a plug-and-play module, improving the network's performance. The code is available online at \url{https://github.com/YaoyaoZhu19/BSDA}

1-Phenyl-3-(pyren-1-yl)prop-2-en-1-one

The title compound, C25H16O, was prepared by the condens­ation reaction of pyrene-1-carbaldehyde and acetophenone in ethanol solution at room temperature. The phenyl ring forms a dihedral angle of 39.10 (11)° with the pyrene ring system. In the crystal structure, adjacent pyrene ring systems are linked by aromatic π–π stacking inter­actions, with a perpendicular inter­planar distance of 3.267 (6) Å and a centroid–centroid offset of 2.946 (7) Å

Enlarging Feature Support Overlap for Domain Generalization

Deep models often struggle with out-of-distribution (OOD) generalization, limiting their real-world applicability beyond controlled laboratory settings. Invariant risk minimization (IRM) addresses this issue by learning invariant features and minimizing the risk across different domains. Thus, it avoids the pitfalls of pseudo-invariant features and spurious causality associated with empirical risk minimization (ERM). However, according to the support overlap theorem, ERM and IRM may fail to address the OOD problem when pseudo-invariant features have insufficient support overlap. To this end, we propose a novel method to enlarge feature support overlap for domain generalization. Specifically, we introduce Bayesian random semantic data augmentation to increase sample diversity and overcome the deficiency of IRM. Experiments on several challenging OOD generalization benchmarks demonstrate that our approach surpasses existing models, delivering superior performance and robustness. The code is available at \url{https://github.com/YaoyaoZhu19/BSDG}

Coal gasification through microbial degradation in a low-pressure CO2 and H2 environment: An experimental study

Objective and MethodsThis study aims to investigate the characteristics of CO2 biomethanation and coal gasification through microbial degradation in a low-pressure environment. With low-rank bituminous coals (Rmax = 0.67%) as fermentable substrates, this study conducted a 96-day gas production experiment through microbial fermentation in a low-pressure CO2 and H2 environment. Using techniques including gas chromatography, 16S rRNA gene sequencing, and low-temperature liquid nitrogen adsorption, this study delved into the intrinsic variation patterns of biogenic gas production, microbial communities, and coal structures. Results and Conclusions The results indicate that compared to conventional fermentation, the injection of low-pressure CO2 inhibited CH4 production, leading to a reduced CH4 production efficiency. After the H2 injection, the injected H2 was consumed quickly, resulting in a rapid decrease in the H2 concentration and contributing to CH4 production. Meanwhile, the H2 injection changed the production mode of biogenic gas, exerting a profound influence on the structure of microbial communities in fermentable liquids. Specifically, the relative abundance of Firmicutes and Bacteroidota increased. Notably, the S50_wastewater_sludge_group in Bacteroidota always predominated, trending upward together with the unclassified_W27 genus. This occurred due to the late-stage H2 injection, which accelerated the growth and metabolism of both bacterial genera. Regarding the distribution of archaea at the genus level, Methanobacterium represented the highest proportion (47.66%‒83.05%), followed by Methanosarcina and Methanoculleus sequentially. Benefiting from the simultaneous consumption of H2, CO2, and substrates such as acetic acid, the relative abundance of Methanosarcina exhibited a significant upward trend. In contrast, Methanoculleus, which synthesizes methane via the hydrogenotrophic pathway, displayed a rapidly decreasing relative abundance due to a shortage of H2 in the later stage. Compared to the raw coals, coals with injected low-pressure CO2 exhibited a lower adsorption capacity, with the total pore volume and specific surface area decreasing. As more low-pressure CO2 was injected, fractal dimensions D1 and D2 trended downward and upward, respectively, suggesting an increase in the surface roughness of coal pores and a decrease in the complexity/heterogeneity of pore structures. This is inferred to be associated with the dual effects of microbial degradation and carbonate precipitation. The results of this study enrich the fundamental theories on the microbial degradation of coals and the biological transformation and utilization of CO2, especially providing a theoretical basis for the biological transformation and storage of CO2 in coal seams

Hand-drawn sketch and vector map matching based on topological features

In the process of addressing, when people use words to express indistinctly, they often draw simple sketches to assist expression, which helps people to form a simple spatial scene in the brain and correspond to the actual scene one by one, and finally locate and find the target address. How to establish an one-to-one mapping relationship between the spatial objects in the hand-drawn sketch and in the vector map is the key to the realization of map addressing and location, and this process is also the process of map matching. This paper aims to address difficult problems associated with the features of hand-drawn sketches and vector map matching in order to improve the use of all potential matching points designed for application in hand-drawn sketches and spatial relation matrix structures of vector maps. To accomplish this, we use the N-queen problem solving process and improve the tabu search algorithm. In the matching process under the constraint of a single spatial relationship, and the hierarchical matching process under the constraint of multiple spatial relations, this study verifies the quality of the spatial relationship and the feasibility and effectiveness of the matching method of hand-drawn sketches and vector maps using the improved tabu search algorithm

microRNA-1 and microRNA-206 regulate skeletal muscle satellite cell proliferation and differentiation by repressing Pax7

Pax7 is a target of two miRNAs that are induced during muscle satellite cell differentiation and repressed in response to muscle injury

Transgenic overexpression of miR-133a in skeletal muscle

<p>Abstract</p> <p>Background</p> <p>MicroRNAs (miRNAs) are a class of non-coding regulatory RNAs of ~22 nucleotides in length. miRNAs regulate gene expression post-transcriptionally, primarily by associating with the 3' untranslated region (UTR) of their regulatory target mRNAs. Recent work has begun to reveal roles for miRNAs in a wide range of biological processes, including cell proliferation, differentiation and apoptosis. Many miRNAs are expressed in cardiac and skeletal muscle, and dysregulated miRNA expression has been correlated with muscle-related disorders. We have previously reported that the expression of muscle-specific miR-1 and miR-133 is induced during skeletal muscle differentiation and miR-1 and miR-133 play central regulatory roles in myoblast proliferation and differentiation in vitro.</p> <p>Methods</p> <p>In this study, we measured the expression of miRNAs in the skeletal muscle of mdx mice, an animal model for human muscular dystrophy. We also generated transgenic mice to overexpress miR-133a in skeletal muscle.</p> <p>Results</p> <p>We examined the expression of miRNAs in the skeletal muscle of <it>mdx </it>mice. We found that the expression of muscle miRNAs, including miR-1a, miR-133a and miR-206, was up-regulated in the skeletal muscle of <it>mdx </it>mice. In order to further investigate the function of miR-133a in skeletal muscle in vivo, we have created several independent transgenic founder lines. Surprisingly, skeletal muscle development and function appear to be unaffected in miR-133a transgenic mice.</p> <p>Conclusions</p> <p>Our results indicate that miR-133a is dispensable for the normal development and function of skeletal muscle.</p