Quantum-Inspired Moth-Flame Optimizer With Enhanced Local Search Strategy for Cluster Analysis.

Clustering is an unsupervised learning technique widely used in the field of data mining and analysis. Clustering encompasses many specific methods, among which the K-means algorithm maintains the predominance of popularity with respect to its simplicity and efficiency. However, its efficiency is significantly influenced by the initial solution and it is susceptible to being stuck in a local optimum. To eliminate these deficiencies of K-means, this paper proposes a quantum-inspired moth-flame optimizer with an enhanced local search strategy (QLSMFO). Firstly, quantum double-chain encoding and quantum revolving gates are introduced in the initial phase of the algorithm, which can enrich the population diversity and efficiently improve the exploration ability. Second, an improved local search strategy on the basis of the Shuffled Frog Leaping Algorithm (SFLA) is implemented to boost the exploitation capability of the standard MFO. Finally, the poor solutions are updated using Levy flight to obtain a faster convergence rate. Ten well-known UCI benchmark test datasets dedicated to clustering are selected for testing the efficiency of QLSMFO algorithms and compared with the K-means and ten currently popular swarm intelligence algorithms. Meanwhile, the Wilcoxon rank-sum test and Friedman test are utilized to evaluate the effect of QLSMFO. The simulation experimental results demonstrate that QLSMFO significantly outperforms other algorithms with respect to precision, convergence speed, and stability.

An equilibrium optimizer slime mould algorithm for inverse kinematics of the 7-DOF robotic manipulator.

In order to solve the inverse kinematics (IK) of complex manipulators efficiently, a hybrid equilibrium optimizer slime mould algorithm (EOSMA) is proposed. Firstly, the concentration update operator of the equilibrium optimizer is used to guide the anisotropic search of the slime mould algorithm to improve the search efficiency. Then, the greedy strategy is used to update the individual and global historical optimal to accelerate the algorithm's convergence. Finally, the random difference mutation operator is added to EOSMA to increase the probability of escaping from the local optimum. On this basis, a multi-objective EOSMA (MOEOSMA) is proposed. Then, EOSMA and MOEOSMA are applied to the IK of the 7 degrees of freedom manipulator in two scenarios and compared with 15 single-objective and 9 multi-objective algorithms. The results show that EOSMA has higher accuracy and shorter computation time than previous studies. In two scenarios, the average convergence accuracy of EOSMA is 10e-17 and 10e-18, and the average solution time is 0.05 s and 0.36 s, respectively.

DTSMA: Dominant Swarm with Adaptive T-distribution Mutation-based Slime Mould Algorithm.

The slime mould algorithm (SMA) is a metaheuristic algorithm recently proposed, which is inspired by the oscillations of slime mould. Similar to other algorithms, SMA also has some disadvantages such as insufficient balance between exploration and exploitation, and easy to fall into local optimum. This paper, an improved SMA based on dominant swarm with adaptive t-distribution mutation (DTSMA) is proposed. In DTSMA, the dominant swarm is used improved the SMA's convergence speed, and the adaptive t-distribution mutation balances is used enhanced the exploration and exploitation ability. In addition, a new exploitation mechanism is hybridized to increase the diversity of populations. The performances of DTSMA are verified on CEC2019 functions and eight engineering design problems. The results show that for the CEC2019 functions, the DTSMA performances are best; for the engineering problems, DTSMA obtains better results than SMA and many algorithms in the literature when the constraints are satisfied. Furthermore, DTSMA is used to solve the inverse kinematics problem for a 7-DOF robot manipulator. The overall results show that DTSMA has a strong optimization ability. Therefore, the DTSMA is a promising metaheuristic optimization for global optimization problems.

Proteomics analysis reveals the correlation of programmed ROS-autophagy loop and dysregulated G1/S checkpoint with imatinib resistance in chronic myeloid leukemia cells.

Although tyrosine kinase inhibitors (TKIs), including imatinib, have greatly improved clinical treatment of patients with chronic myeloid leukemia (CML), drug resistance remains a major obstacle. Studies on the mechanisms underlying imatinib resistance and other alternative drugs are urgently needed. Liquid chromatography tandem mass spectrometry was applied to investigate the differences in proteomics and phosphoproteomics between K562 and K562/G (imatinib resistant K562). Multiple bioinformatics analyses were performed to unveil the differential signal pathways. CCK-8 was used to detect cell proliferation. Flow cytometry was performed to analyze reactive oxygen species (ROS), cell cycle, and cell apoptosis. Western blotting and quantitative real-time reverse transcription-polymerase chain reaction (qRT-PCR) were used to observe the changes of ROS and autophagy associated with imatinib resistance in CML. Our results indicated that ROS-autophagy formed one negative feedback loop and was associated with imatinib resistance. Additionally, the limited-rate enzymes of serine synthesis pathway were escalated in K562/G, which could contribute to the increased cyclin-dependent kinases and cell proliferation index. According to phosphoproteomics data, K562/G cells exhibited abnormal phosphorylation of splicing signals. These results revealed that it could be one useful strategy to correct metabolism shift and oxidative stress, or moderately regulate autophagy. Future research should focus on the discovery of potential targets in ROS-autophagy loop.

Insulin-like growth factor-binding proteins play a significant role in the molecular response to imatinib in chronic myeloid leukemia patients.

Imatinib (IM) is successfully used in the majority of patients with chronic myeloid leukemia (CML), but some patients develop resistance to drug treatment. Insufficient apoptosis results in uncontrolled cell proliferation, which is closely associated with the occurrence of drug resistance. Therefore, it is crucial to identify new biomarkers related to drug resistance. This aim of the present study was to investigate the profile of apoptosis-related proteins in K562 and K562/G (IM-resistant K562 cells) cells, in order to identify new biomarkers. A human apoptosis antibody array was used to screen 46 proteins in the two cells lines, among which 20 proteins were found to be differentially expressed between K562 and K562/G cells. The major proteins included secreted caspase-8, insulin-like growth factor-binding protein (IGFBP)-1, IGFBP-2, IGFBP-3, caspase-3 and p27. IGFBP-1 IGFBP-2 and IGFBP-3 were selected for the follow-up study. Subsequently, reverse transcription-quantitative PCR analysis and western blotting were used to detect the expression levels of the IGFBPs. The results revealed that the expression levels of IGFBP-2 and IGFBP-3 in K562/G cells were significantly decreased compared with those in K562 cells, whereas the IGFBP-1 level was higher. Moreover, no significant correlation was observed between IGFBP-1 or IGFBP-2 and the level of the BCR-ABL fusion protein, whereas decreasing IGFBP-3 levels were associated with increasing BCR-ABL levels. These results suggested that IGFBP-1, IGFBP-2 and IGFBP-3 could be useful novel biomarkers for IM resistance in CML.

Zirconium Oxynitride-Catalyzed Oxygen Reduction Reaction at Polymer Electrolyte Fuel Cell Cathodes.

Most nonplatinum group metal (non-PGM) catalysts for polymer electrolyte fuel cell cathodes have so far been limited to iron(cobalt)/nitrogen/carbon [Fe(Co)/N/C] composites owing to their high activity in both half-cell and single-cell cathode processes. Group IV and V metal oxides, another class of non-PGM catalysts, are stable in acidic media; however, their activities have been mostly evaluated for half-cells, with no single-cell performances comparable to those of Fe/N/C composites reported to date. Herein, we report successful syntheses of zirconium oxynitride catalysts on multiwalled carbon nanotubes, which show the highest oxygen reduction reaction activity among oxide-based catalysts. The single-cell performance of these catalysts reached 10 mA cm-2 at 0.9 V, being comparable to that of state-of-the-art Fe/N/C catalysts. This new record opens up a new pathway for reaching the year 2020 target set by the U.S. Department of Energy, that is, 44 mA cm-2 at 0.9 V.