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Purpose: Cancer cells exhibit profound alterations in their metabolism (abnormal glucose and glutamine metabolism). Targeting cancer metabolism is a promising therapeutic strategy. Lymphoma can be classified into many different types and it is very complicated. Therefore, in this paper, we want to know whether the B cell lymphoma cells with different metabolic characteristics have distinct sensitivities to metabolic inhibitors.Methods: We classified 9 B cell lymphoma cell lines into different metabolic subtypes according to the dependency on glutamine and glucose. Then we detected the OCR, ECAR, glucose consumption and lactate production, mitochondrial content and growth rate. And we also determined the IC50 of these 9 cell lines to metabolic inhibitors.Results: According to the dependency on glutamine and glucose, we successfully classified three distinct metabolic subtypes in B cell lymphoma cell lines, one subtype was defined glutamine and glucose equally utilized subtype (GLN=Glu), whereas the other two subtypes were GLN-addicted and Glu-dependent. And these three subtypes showed striking differences in glucose and glutamine utilization, glycolysis and mitochondrial function, and proliferation rate. GLN-addicted and Glu-dependence subtypes also showed differences in cell sensitivity to inhibitors of glutamine and glycolysis metabolism, respectively. However, GLN=Glu subtype seems minimal sensitive to glycolytic and glutaminolytic inhibitors, and with high proliferation rate.Conclusions: The cells rely more on glucose/gltamine have a stronger sensitivity to glucose/glutamine depletion or glycolysis/ glutaminolysis inhibition and a lessened sensitivity to glutaminolysis/glycolysis inhibitors. To target tumor metabolism based on metabolic characteristics may provide a new therapeutic strategy for the treatment of B cell lymphoma.

A novel semi-supervised learning method is proposed to better utilize labeled and unlabeled samples to improve classification performance. However, there is exists the limitation that Laplace regularization in a semi-supervised extreme learning machine (SSELM) tends to lead to poor generalization ability and it ignores the role of labeled information. To solve the above problems, a Joint Regularized Semi-Supervised Extreme Learning Machine (JRSSELM) is proposed, which uses Hessian regularization instead of Laplace regularization and adds supervised information regularization. In order to solve the problem of slow convergence speed and the easy to fall into local optimum of marine predator algorithm (MPA), a multi-strategy marine predator algorithm (MSMPA) is proposed, which first uses a chaotic opposition learning strategy to generate high-quality initial population, then uses adaptive inertia weights and adaptive step control factor to improve the exploration, utilization, and convergence speed, and then uses neighborhood dimensional learning strategy to maintain population diversity. The parameters in JRSSELM are then optimized using MSMPA. The MSMPA-JRSSELM is applied to logging oil formation identification. The experimental results show that MSMPA shows obvious superiority and strong competitiveness in terms of convergence accuracy and convergence speed. Also, the classification performance of MSMPA-JRSSELM is better than other classification methods, and the practical application is remarkable.

The transient search algorithm (TSO) is a new physics-based metaheuristic algorithm that simulates the transient behavior of switching circuits, such as inductors and capacitors, but the algorithm suffers from slow convergence and has a poor ability to circumvent local optima when solving high-dimensional complex problems. To address these drawbacks, an improved transient search algorithm (ITSO) is proposed. Three strategies are introduced to the TSO. First, a chaotic opposition learning strategy is used to generate high-quality initial populations; second, an adaptive inertia weighting strategy is used to improve the exploration ability, exploitation ability, and convergence speed; finally, a neighborhood dimensional learning strategy is used to maintain population diversity with each iteration of merit seeking. The Friedman test and Wilcoxon’s rank sum test were also used by comparing the experiments with recently popular algorithms on 18 benchmark test functions of various types. Statistical results, nonparametric sign tests, and convergence curves all indicate that ITSO develops, explores, and converges significantly better than other popular algorithms, and is a promising intelligent optimization algorithm for applications.

Accurate and reliable air quality predictions are critical to the ecological environment and public health. For the traditional model fails to make full use of the high and low frequency information obtained after wavelet decomposition, which easily leads to poor prediction performance of the model. This paper proposes a hybrid prediction model based on data decomposition, choosing wavelet decomposition (WD) to generate high-frequency detail sequences WD(D) and low-frequency approximate sequences WD(A), using sliding window high-frequency detail sequences WD(D) for reconstruction processing, and long short-term memory (LSTM) neural network and autoregressive moving average (ARMA) model for WD(D) and WD(A) sequences for prediction. The final prediction results of air quality can be obtained by accumulating the predicted values of each sub-sequence, which reduces the root mean square error (RMSE) by 52%, mean absolute error (MAE) by 47%, and increases the goodness of fit (R2) by 18% compared with the single prediction model. Compared with the mixed model, reduced the RMSE by 3%, reduced the MAE by 3%, and increased the R2 by 0.5%. The experimental verification found that the proposed prediction model solves the problem of lagging prediction results of single prediction model, which is a feasible air quality prediction method.