Transcriptome profiling of fast/glycolytic and slow/oxidative muscle fibers in aging and obesity.

Aging and obesity pose significant threats to public health and are major contributors to muscle atrophy. The trends in muscle fiber types under these conditions and the transcriptional differences between different muscle fiber types remain unclear. Here, we demonstrate distinct responses of fast/glycolytic fibers and slow/oxidative fibers to aging and obesity. We found that in muscles dominated by oxidative fibers, the proportion of oxidative fibers remains unchanged during aging and obesity. However, in muscles dominated by glycolytic fibers, despite the low content of oxidative fibers, a significant decrease in proportion of oxidative fibers was observed. Consistently, our study uncovered that during aging and obesity, fast/glycolytic fibers specifically increased the expression of genes associated with muscle atrophy and inflammation, including Dkk3, Ccl8, Cxcl10, Cxcl13, Fbxo32, Depp1, and Chac1, while slow/oxidative fibers exhibit elevated expression of antioxidant protein Nqo-1 and downregulation of Tfrc. Additionally, we noted substantial differences in the expression of calcium-related signaling pathways between fast/glycolytic fibers and slow/oxidative fibers in response to aging and obesity. Treatment with a calcium channel inhibitor thapsigargin significantly increased the abundance of oxidative fibers. Our study provides additional evidence to support the transcriptomic differences in muscle fiber types under pathophysiological conditions, thereby establishing a theoretical basis for modulating muscle fiber types in disease treatment.

Single-site bipyridine cobalt complexes covalently embedded into graphitic carbon nitride with excellent photocatalytic activity and selectivity towards CO2 reduction.

A combination of a semiconductor-based photosensitizer with molecular catalysts via covalent bonds is an effective way to utilize solar energy to reduce CO2 into value-added chemicals with high efficiency and selectivity. In this study, 2,2'-bpy-5,5'-dialdehyde functioned as organic ligands and were embedded into the skeleton of g-CN through imine bonds via thermal copolymerization. The introduction of 2,2'-bpy can not only chelate with earth-abundant Co as single-site catalytic centers but also can optimize the properties of original g-CN such as the enlarged specific surface area and extended visible light absorption range. The CO evolution rate of g-CN-bpy-Co can reach up to 106.3 µmol g-1 h-1 with a selectivity of 97% over proton reduction, which was 82-fold than that of g-CN-Co. The different coordination environments and valence states of cobalt were also studied simultaneously and the results showed that Co(II) exhibited superior catalytic activity towards Co(III). Control experiments demonstrated that the covalent linkage between g-CN and Co-2,2'-bpy plays a vital role in photocatalytic activity and selectivity. Besides, the CO generation rate demonstrated linear growth upon visible light irradiation up to 72 h and preferable recyclability. This research provides a new facile way to fabricate low-priced photocatalysts with high activity and selectivity and bridge homogeneous and heterogeneous catalysis.

Deep Learning-Based Imbalanced Classification With Fuzzy Support Vector Machine.

Imbalanced classification is widespread in the fields of medical diagnosis, biomedicine, smart city and Internet of Things. The imbalance of data distribution makes traditional classification methods more biased towards majority classes and ignores the importance of minority class. It makes the traditional classification methods ineffective in imbalanced classification. In this paper, a novel imbalance classification method based on deep learning and fuzzy support vector machine is proposed and named as DFSVM. DFSVM first uses a deep neural network to obtain an embedding representation of the data. This deep neural network is trained by using triplet loss to enhance similarities within classes and differences between classes. To alleviate the effects of imbalanced data distribution, oversampling is performed in the embedding space of the data. In this paper, we use an oversampling method based on feature and center distance, which can obtain more diverse new samples and prevent overfitting. To enhance the impact of minority class, we use a fuzzy support vector machine (FSVM) based on cost-sensitive learning as the final classifier. FSVM assigns a higher misclassification cost to minority class samples to improve the classification quality. Experiments were performed on multiple biological datasets and real-world datasets. The experimental results show that DFSVM has achieved promising classification performance.