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Chemical kinetics is a branch of chemistry that investigates the rates of chemical reactions and has applications in cosmology, geology, and physiology. In this study, we develop a mathematical model for chemical reactions based on enzyme dynamics and kinetics, which is a two-step substrate-enzyme reversible reaction, applying chemical kinetics-based modeling of enzyme functions. The non-linear differential equations are transformed into fractional-order systems utilizing the constant proportional Caputo-Fabrizio (CPCF) and constant proportional Atangana-Baleanu-Caputo (CPABC) operators. The system of fractional differential equations is simulated using the Laplace-Adomian decomposition method at different fractional orders through simulations and numerical results. Both qualitative and quantitative analyses such as boundedness, positivity, unique solution, and feasible concentration for the proposed model with different hybrid operators are provided. The stability analysis of the proposed scheme is also verified using Picard's stable condition through the fixed point theorem. [ABSTRACT FROM AUTHOR]

Background: Atherosclerosis (AS) is a critical pathological event during the progression of cardiovascular diseases. It exhibits fibrofatty lesions on the arterial wall and lacks effective treatment. N6-methyladenosine (m6A) is the most common modification of eukaryotic RNA and plays an important role in regulating the development and progression of cardiovascular diseases. However, the role of m6A modification in AS remains largely unknown. Therefore, in this study, we explored the transcriptome distribution of m6A modification in AS and its potential mechanism. Methods: Methylation Quantification Kit was used to detect the global m6A levels in the aorta of AS mice. Western blot was used to analyze the protein level of methyltransferases. Methylated RNA immunoprecipitation with next-generation sequencing (MeRIP-seq) and RNA sequencing (RNA-seq) were used to obtain the first transcriptome range analysis of the m6A methylene map in the aorta of AS mice, followed by bioinformatics analysis. qRT-PCR and MeRIP-qRT-PCR were used to measure the mRNA and m6A levels in target genes. Results: The global m6A and protein levels of methyltransferase METTL3 were significantly increased in the aorta of AS mice. However, the protein level of demethylase ALKBH5 was significantly decreased. Through MeRIP-seq, we obtained m6A methylation maps in AS and control mice. In total, 26,918 m6A peaks associated with 13,744 genes were detected in AS group, whereas 26,157 m6A peaks associated with 13,283 genes were detected in the control group. Peaks mainly appeared in the coding sequence (CDS) regions close to the stop codon with the RRACH motif. Moreover, functional enrichment analysis demonstrated that m6A-containing genes were significantly enriched in AS-relevant pathways. Interestingly, a negative correlation between m6A methylation abundance and gene expression level was found through the integrated analysis of MeRIP-seq and RNA-seq data. Among the m6A-modified genes, a hypo-methylated but up-regulated (hypo-up) gene Fabp5 may be a potential biomarker of AS. Conclusions: Our study provides transcriptome-wide m6A methylation for the first time to determine the association between m6A modification and AS progression. Our study lays a foundation for further exploring the pathogenesis of AS and provides a new direction for the treatment of AS. [ABSTRACT FROM AUTHOR]

The current instance segmentation method can achieve satisfactory results in common scenarios. However, under the overlap or partial occlusion between targets caused by the complex scenes, accurate segmentation of pigs remains a challenging task. To address the problem, the authors propose an instance segmentation method based on Mask Scoring region‐based convolutional neural networks (R‐CNN) (MS R‐CNN), which creates the adversarial network called MaskDis in the head branch of MS R‐CNN. The MaskDis is trained as a discriminator using a generative adversarial network, and the MS R‐CNN model is used as a generator during model training. The adversarial training enables the generator to learn context information and features at the pixel level, which effectively improves the segmentation quality under pigs' overlapping or dense occlusions scenes. Experimental conducted on the pig object segmentation dataset show that the proposed approach achieves a precision of 92.03%, a recall of 92.18%, and an F1 score of 0.9210. Compared with the basic MS R‐CNN model, the approach achieved a 2.25% improvement in precision and 1.18% improvement in F1 score. Furthermore, the improved approach outperformed advanced instance segmentation methods such as YOLACT, Swin Transformer, YOLOv5‐seg, and SOLOv2 on COCO evaluation metrics. These experimental results demonstrate the effectiveness of the proposed approach in instance segmentation of pigs in complex scenes, providing technical support for non‐contact pig automatic management. [ABSTRACT FROM AUTHOR]

The maximum internal spanning tree (MIST) problem is to find a spanning tree with maximum number of internal node for an undirected graph. It is a variation of the well-known minimum spanning tree problem and is NP-hard. Evolutionary algorithms (EAs) have been successfully applied to solve many NP-hard combinatorial optimization problems in numerical empirical studies. However, researchers know little about their performance guarantees from theory aspect. This paper designs a valid fitness function to guide the well-studied evolutionary algorithm, (1 + 1) EA , to optimize the MIST problem, and presents theoretical analysis to show that it can obtain a performance guarantee of 2 and 5/3 in expected runtime O (n m 2) and O (n m 4) , respectively. Moreover, this paper proves that the (1+1) EA can achieve a performance guarantee of 3 for a variation of the MIST problem, where each vertex is associated with a weight. In addition, comparison analyses on two family instance graphs are presented to show that (1 + 1) EA is better than two local search algorithms. This theoretical study provides insight into the process of (1 + 1) EA constructing a performance guarantee solution for the MIST problem. [ABSTRACT FROM AUTHOR]

This article proposes a simple yet effective multiobjective evolutionary algorithm (EA) for dealing with problems with irregular Pareto front. The proposed algorithm does not need to deal with the issues of predefining weight vectors and calculating indicators in the search process. It is mainly based on the thought of adaptively selecting multiple promising search directions according to crowdedness information in local objective spaces. Concretely, the proposed algorithm attempts to dynamically delete an individual of poor quality until enough individuals survive into the next generation. In this environmental selection process, the proposed algorithm considers two or three individuals in the most crowded area, which is determined by the local information in objective space, according to a probability selection mechanism, and deletes the worst of them from the current population. Thus, these surviving individuals are representative of promising search directions. The performance of the proposed algorithm is verified and compared with seven state-of-the-art algorithms [including four general multi/many-objective EAs and three algorithms specially designed for dealing with problems with irregular Pareto-optimal front (PF)] on a variety of complicated problems with different numbers of objectives ranging from 2 to 15. Empirical results demonstrate that the proposed algorithm has a strong competitiveness power in terms of both the performance and the algorithm compactness, and it can well deal with different types of problems with irregular PF and problems with different numbers of objectives. [ABSTRACT FROM AUTHOR]

Decomposition-based multiobjective evolutionary algorithms (MOEAs) are a class of popular methods for solving the multiobjective optimization problems (MOPs), and have been widely studied in numerical experiments and successfully applied in practice. However, we know little about these algorithms from the theoretical aspect. In this paper, we present running time analysis of a simple MOEA with mutation and crossover based on the MOEA/D framework (MOEA/D-C) on five pseudo-Boolean functions. Our rigorous theoretical analysis shows that by properly setting the number of subproblems, the upper bounds of expected running time of MOEA/D-C obtaining a set of solutions to cover the Pareto fronts (PFs) of these problems are apparently lower than those of the one with mutation-only (MOEA/D-M). Moreover, to effectively obtain a set of solutions to cover the PFs of these problem, MOEA/D-C only needs to decompose these MOPs into several subproblems with a set of simple weight vectors while MOEA/D-M needs to find $\Omega (n)$ optimally decomposed weight vectors. This result suggests that the use of crossover in decomposition-based MOEA can simplify the setting of weight vectors for different problems and make the algorithm more efficient. This paper provides some insights into the working principles of MOEA/D and explains why some existing decomposition-based MOEAs work well in computational experiments. [ABSTRACT FROM AUTHOR]

Mitochondrial dysfunction has been associated with the pathogenesis of a variety of neurodegenerative diseases. Sitagliptin is a dipeptidyl‐peptidase‐4 (DPP‐4) inhibitor that has been approved for the treatment of type 2 diabetes (T2DM). In the current study, we report that sitagliptin increased the expression of PGC‐1α, NRF1, and TFAM in human SH‐SY5Y neuronal cells. Notably, our data indicate that sitagliptin promoted mitochondrial biogenesis by increasing the amount of mtDNA, the levels of mitochondria‐related genes such as TOMM20, TOMM40, TIMM9, NDUFS3, ATP5C1, and the expression of oxidative phosphorylation subunits complex I and complex IV. Additionally, we found that sitagliptin induced a "gain of mitochondrial function" in SH‐SY5Y cells by increasing the mitochondrial respiratory rate and adenosine triphosphate (ATP) production. Significantly, our results demonstrate that sitagliptin activated the transcriptional factor CREB by inducing its phosphorylation at Ser133. Inhibition of CREB using its specific inhibitor H89 abolished the effects of sitagliptin on the expression of PGC‐1α, NRF1, and TFAM, as well as an increase in mtDNA amount and ATP production. These findings suggest that sitagliptin could become a potential agent for the treatment of neurological disorders. [ABSTRACT FROM AUTHOR]

Myocardial infarction is a leading cause of mortality worldwide, and timely blood/oxygen reperfusion may substantially improve the outcome of infarction. However, ischemia/reperfusion (I/R) may cause severe side effects through excess reactive oxygen species generation. To develop novel methods to relieve I/R induced cell damage, the present study used a component of traditional Chinese medicine. In the present study, isolated heart tissue from aged mice and H9C2 cells with chemically-induced aging were used as experimental subjects, and it was demonstrated that formononetin was able to alleviate I/R-induced cell or tissue apoptosis. By applying formononetin to I/R-damaged tissue or cells, it was demonstrated that formononetin was able to enhance autophagy and thus alleviate I/R-induced cell damage. Furthermore, it was observed that I/R was able to inhibit lysosomal degradation processes in aged tissues or cells by impairing the lysosome acidification level, and formononetin was able to reverse this process via the re-acidification of lysosomes. In conclusion, the present study demonstrated that formononetin was able to alleviate I/R-induced cellular apoptosis in aged cells by facilitating autophagy. [ABSTRACT FROM AUTHOR]

Arctiin is a lignin isolated from Arctium lappa which has been known to have anti-viral and anti-inflammatory effects. The aim of this study is to explore the protective effect of arctiin on lipopolysaccharide (LPS)-induced inflammatory responses in acute lung injury (ALI) model of mice. Male BALB/c mice were pretreated with commercial arctiin (10, 20, and 40 mg/kg) 1 h prior to LPS challenge. Twelve hours later, airway inflammation was assessed. We assessed the effects of arctiin on the LPS-induced production of TNF-α, IL-6, and IL-1β in the bronchoalveolar lavage fluid (BALF). The lung wet-to-dry weight ratio, myeloperoxidase (MPO) activity, and inflammatory signaling pathway were also detected. Our results showed that arctiin not only significantly ameliorated LPS-stimulated lung histopathological changes but also reduced the lung MPO activity. Arctiin also dramatically decreased the production of TNF-α, IL-1β, and IL-6 in the BALF. In addition, arctiin significantly inhibited LPS-induced PI3K/Akt phosphorylation as well as NF-κB activation. In conclusion, our results suggested that arctiin protected against LPS-induced ALI through inhibiting PI3K/AKT/NF-κB signaling pathway. [ABSTRACT FROM AUTHOR]