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Background: Fibroblast growth factor 21 (FGF-21) is an evolutionarily conserved protein that plays multiple roles in metabolic regulation. Over the past two decades, numerous studies have deepened our understanding of its various functions and its pharmacological value. Nevertheless, most clinical trials have not achieved the desired results, which raises issues regarding its clinical value. In this bibliometric analysis, we evaluated the state of FGF-21 research over the last 20 years and identified important topics, achievements, and potential future directions.Methods: Publications related to FGF-21 were collected from the Web of Science Core Collection-Science Citation Index Expanded. HistCite, VOSviewer, and CiteSpace were used for bibliometric analysis and visualization, including the analysis of annual publications, leading countries, active institutions and authors, core journals, co-cited references, and keywords.Results: Altogether, 2,490 publications related to FGF-21 were obtained. A total of 12,872 authors from 2,628 institutions in 77 countries or regions reported studies on FGF-21. The United States of America was the most influential country in FGF-21 research. Alexei Kharitonenkov, Steven A. Kliewer, and David J. Mangelsdorf were the most influential scholars, and endocrinology journals had a core status in the field. The physiological roles, clinical translation, and FGF-21-based drug development were the main topics of research, and future studies may concentrate on the central effects of FGF-21, FGF-21-based drug development, and the effects of FGF-21 on non-metabolic diseases.Conclusion: The peripheral metabolic effects of FGF-21, FGF-21-based drug development, and translational research on metabolic diseases are the three major topics in FGF-21 research, whereas the central metabolic effects of FGF-21 and the effects of FGF-21 on metabolic diseases are the emerging trends and may become the following hot topics in FGF-21 research.

The present study was performed to determine the effect of high fat diet in lipid accumulation, oxidative stress and autophagy, and to explore the underlying molecular mechanism of high fat diet induced hepatic oxidative damage in Chinese softshell turtle. To this end, the control group were fed a normal fat diet (NFD, 6.38% lipid) and the experimental group were bred high fat diet (HFD, 13.89% lipid) for eight weeks. Lipid accumulation, oxidative stress and autophagy, as well as the mRNA expression of genes related to the antioxidant system were determined in the liver. Results showed that high fat diet not only exacerbated lipid accumulation in the liver and serum through increasing contents of triglyceride, total cholesterol and low-density lipoprotein and decreasing content of high-density lipoprotein, but also induced liver injury through increasing activities of alanine aminotransferase and aspartate aminotransferase in the serum. In addition, the experimental subject induced oxidative injury for the increase of reactive oxygen species, malondialdehyde and protein carbonyl contents and the reduction of glutathione contents, anti-superoxide anion capacity and catalase, total superoxide dismutase, glutathione peroxidase, glutathione-S transferase activities. Meanwhile, antioxidant-related signaling molecule expression were also decreased, which might attribute to regulate antioxidant-related signaling molecule. On top of that, it indicated promote the occurrence of liver autophagy via up-regulating expressions of AMP activated protein kinase, UNC-51-like kinase 1, Microtubule-associated proteins 1A/1B light chain 3 and down-regulating gene expression of mammalian target of rapamycin. In conclusion, high fat diet could enhance lipid accumulation in the liver and serum, lead to liver injury and oxidative damage, impair liver antioxidant capacity, regulate antioxidant-related signaling molecule expression and activate hepatic autophagy.

This research illustrates the effects of high-fat diet induced oxidative stress on muscle textural properties, oxidation status and autophagy of juvenile Chinese soft-shelled turtles (Pelodiscus sinensis) and explores the molecular regulatory mechanism. A total of 120 juvenile Chinese soft-shelled turtles with the average initial body weight of (38.60 ± 1.13) g were fed two diets that contained different lipid levels, 6.38% (normal diet, ND) and 13.89% (high-fat diet, HFD), for 8 weeks. The results indicated that: (1) the survival rate (SR) and feed conversion rate (FCR) were not significantly different (P > .05); the final body weight, weight gain rate (WGR), specific growth rate (SGR), and the hepatosomatic index (HIS) of groups that were fed with HFD were significantly higher than those fed with ND (P .05), while the crude lipid content of the turtles increased, and the crude protein content decreased when the dietary lipid concentration increased (P .05), but a significant decreases in hardness, gumminess and chewiness of muscle with HFD (P

Shuffled leap frog algorithm (SFLA) is a new nature-inspired intelligent algorithm, which uses the whole update and evaluation strategy on solutions. For solving multi-dimension function optimization problems, this strategy will deteriorate the convergence speed and the quality of solution of algorithm due to interference phenomena among dimensions. To overcome this shortage, a dimension by dimension improvement based on SFLA is proposed. The proposed strategy combines an updated value of one dimension with values of other dimensions into a new solution, and that whose updated value can improve the solution will be accepted greedily. Further, a new individual update formula is designed to learn experiences both from the global best and the local best solution simultaneously. Meanwhile, they also reveal the modified algorithm is competitive for continuous function optimization problems compared with other improved algorithms.

Support vector machine (SVM), which is based on statistical learning theory (SLT), has shown much better performance than most other existing machine learning methods, which are based on the traditional statistics. The original SVM was developed to solve the dichotomy classification problem. Various approaches have been presented to solve multi-class problems. Using multi-class SVM classifier we have obtained high class rate of 95.4% in remote sensing image classification. However for the class number of remote sensing image is much great, manually obtaining of training samples is a much time-consuming work. Hence, we present a multi-class SVM based semi-supervised approach. We choose the initial cluster centers manually first, then label the samples as the training ones automatically with fuzzy C-means clustering algorithm. It is believed that this method upgrades the classification efficiency greatly with practicable class rate.

Efficient application scheduling is critical for achieving high performance in heterogeneous computing systems (HCS). Because an application can be partitioned into a group of tasks and represented as a directed acyclic graph (DAG), the problem can be stated as finding a schedule for a DAG to be executed in a HCS so that the schedule length can be minimized. The tasks scheduling problem is NP-hard in general, except in a few simplified situations. In order to obtain optimal or suboptimal solutions, a large number of scheduling heuristics have been presented in the literature. Genetic algorithm (GA), as a power tool to achieve global optimal, has been successfully used in this field. This work presents a new hybrid genetic algorithm to solve the scheduling problem in HCS. It uses a direct method to encode a solution into chromosome. Topological sort of DAG is used to repair the offspring in order to avoid yielding illegal or infeasible solutions, and it also guarantees that all feasible solutions can be reached with some probability. In order to remedy the GA's weakness in fine-tuning, this paper uses a greedy strategy to improve the fitness of the individuals in crossover operator, based on Lamarckian theory in the evolution. The simulation results comparing with a typical genetic algorithm and a typical list heuristic, both from the literature, show that this algorithm produces better results in terms of both quality of solution and convergence speed.

The tasks scheduling problem is a key factor for a parallel multiprocessor system to gain better performance. Because a task can be partitioned into a group of subtasks and represented as a DAG (directed acyclic graph), so the problem can be stated as finding a schedule for a DAG to be executed in a parallel multiprocessor system so that the schedule length can be minimized. The tasks scheduling problem is NP-hard in general, except in a few simplified situations. In order to obtain optimal or suboptimal solutions, a large number of scheduling heuristics have been presented in the literature. The most studied heuristics are based on list heuristic. Recently, genetic algorithm has received much attention as a class of heuristic. This paper presents a new genetic algorithm called TEOL (task execution order list based genetic algorithm) to solve the scheduling problem in parallel multiprocessor systems. It guarantees that all feasible schedules can be reached with some probability, and because the TEOL is based on the restrain of predecessor relationship of the DAG only, so other heuristics can be combined into it to improve the performance. Simulation results comparing with two genetic algorithms and a list algorithm, both from the literature, show that TEOL produces encouraging results in terms of quality of solution and execution speed.