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In the field of supervised learning, the problem of class imbalance is one of the most difficult problems, and has attracted a great deal of research attention in recent years. In an imbalanced dataset, minority classes are those that contain very small numbers of data samples, while the remaining classes have a very large number of data samples. This type of imbalance reduces the predictive performance of machine learning models. There are currently three approaches for dealing with the class imbalance problem: algorithm-level, data-level, and ensemble-based approaches. Of these, data-level approaches are the most widely used, and consist of three sub-categories: under-sampling, oversampling, and hybrid techniques. Oversampling techniques generate synthetic samples for the minority class to balance an imbalanced dataset. However, existing oversampling approaches do not have a strategy for handling noise samples in imbalanced and noisy datasets, which leads to a reduction in the predictive performance of machine learning models. This study therefore proposes a noise-adaptive synthetic oversampling technique (NASOTECH) to deal with the class imbalance problem in imbalanced and noisy datasets. The noise-adaptive synthetic oversampling (NASO) strategy is first introduced, which is used to identify the number of samples generated for each sample in the minority class, based on the concept of the noise ratio. Next, the NASOTECH algorithm is proposed, based on the NASO strategy, to handle the class imbalance problem in imbalanced and noisy datasets. Finally, empirical experiments are conducted on several synthetic and real datasets to verify the effectiveness of the proposed approach. The experimental results confirm that NASOTECH outperforms three state-of-the-art oversampling techniques in terms of accuracy and geometric mean (G-mean) on imbalanced and noisy datasets.

The majority of cases of T-cell acute lymphoblastic leukemia (T-ALL) contain chromosomal abnormalities that drive overexpression of oncogenic transcription factors. However, whether these initiating oncogenes are required for leukemia maintenance is poorly understood. To address this, we developed a tetracycline-regulated mouse model of T-ALL driven by the oncogenic transcription factor Lmo2. This revealed that whilst thymus-resident pre-Leukemic Stem Cells (pre-LSCs) required continuous Lmo2 expression, the majority of leukemias relapsed despite Lmo2 withdrawal. Relapse was associated with a mature phenotype and frequent mutation or loss of tumor suppressor genes including Ikzf1 (Ikaros), with targeted deletion Ikzf1 being sufficient to transform Lmo2-dependent leukemias to Lmo2-independence. Moreover, we found that the related transcription factor TAL1 was dispensable in several human T-ALL cell lines that contain SIL-TAL1 chromosomal deletions driving its overexpression, indicating that evolution to oncogene independence can also occur in human T-ALL. Together these results indicate an evolution of oncogene addiction in murine and human T-ALL and show that loss of Ikaros is a mechanism that can promote self-renewal of T-ALL lymphoblasts in the absence of an initiating oncogenic transcription factor.

We study the effects of contagion around the global financial crisis (GFC) and the Eurozone crisis periods using German and UK returns, each paired with returns from Central and East European (CEE) stock markets that recently joined the European Union (EU). Using bivariate vector error-correction models (VECMs) estimated in GARCH(1,1), we find strong support for long-run equilibrium conditions. This finding suggests that tests of tail dependence using differenced VARs may be mis-specified when long-run equilibrium conditions apply. Past news has more persistence on current volatility in CEE markets than in the developed markets. Past volatility has more persistence in the developed markets compared to the CEE markets. The T-V symmetrized Joe–Clayton (T-V SJC) copula outperforms all other copulas in goodness-of-fit, including, the T-V Gaussian and Student t copulas. This result is supported by a differenced VAR-GARCH (1,1). For CEE and developed market returns, no more than half of our market pairs exhibit significant increases in lower tail dependence, under the T-V SJC copula. Given the number of paired comparisons, the evidence on joint extreme dependence is weak. As such, CEE stock markets experienced little contagion effects during the GFC and Eurozone crisis periods, contrary to prior results. We find that the legal environment negatively impacts financial development, perhaps causing CEE and the EU markets to be isolated.