Correction: Impacts of multicollinearity on CAPT modalities: An heterogeneous machine learning framework for computer-assisted French phoneme pronunciation training.

[This corrects the article DOI: 10.1371/journal.pone.0257901.].

Impacts of multicollinearity on CAPT modalities: An heterogeneous machine learning framework for computer-assisted French phoneme pronunciation training.

Phoneme pronunciations are usually considered as basic skills for learning a foreign language. Practicing the pronunciations in a computer-assisted way is helpful in a self-directed or long-distance learning environment. Recent researches indicate that machine learning is a promising method to build high-performance computer-assisted pronunciation training modalities. Many data-driven classifying models, such as support vector machines, back-propagation networks, deep neural networks and convolutional neural networks, are increasingly widely used for it. Yet, the acoustic waveforms of phoneme are essentially modulated from the base vibrations of vocal cords, and this fact somehow makes the predictors collinear, distorting the classifying models. A commonly-used solution to address this issue is to suppressing the collinearity of predictors via partial least square regressing algorithm. It allows to obtain high-quality predictor weighting results via predictor relationship analysis. However, as a linear regressor, the classifiers of this type possess very simple topology structures, constraining the universality of the regressors. For this issue, this paper presents an heterogeneous phoneme recognition framework which can further benefit the phoneme pronunciation diagnostic tasks by combining the partial least square with support vector machines. A French phoneme data set containing 4830 samples is established for the evaluation experiments. The experiments of this paper demonstrates that the new method improves the accuracy performance of the phoneme classifiers by 0.21 - 8.47% comparing to state-of-the-arts with different data training data density.

Macrocyclic compounds as anti-cancer agents: design and synthesis of multi-acting inhibitors against HDAC, FLT3 and JAK2.

A novel series of macrocyclic compounds were designed and synthesized as multi-target inhibitors targeting HDAC, FLT3 and JAK2. Some of these compounds exhibited potent HDAC inhibition as well as FLT3 and JAK2 inhibition under both cell-free and cellular conditions. In vitro antiproliferative assay indicated that these compounds were interestingly more cytotoxic to MV4-11 cells bearing FLT3-ITD mutation and HEL cells bearing JAK2(V617F) mutation.

Design, synthesis and biological evaluation of di-substituted cinnamic hydroxamic acids bearing urea/thiourea unit as potent histone deacetylase inhibitors.

A novel class of di-substituted cinnamic hydroxamic acid derivatives containing urea or thiourea unit was designed, synthesized and evaluated as HDAC inhibitors. All tested compounds demonstrated significant HDAC inhibitory activities and anti-proliferative effects against diverse human tumor cell lines. Among them, 7l exhibited most potent pan-HDAC inhibitory activity, with an IC50 value of 130 nM. It also showed strong cellular inhibition against diverse cell lines including HCT-116, MCF-7, MDB-MB-435 and NCI-460, with GI50 values of 0.35, 0.22, 0.51 and 0.48 µM, respectively.