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Independent vector analysis (IVA) can be viewed as an extension of independent component analysis (ICA) to multiple datasets. It exploits the statistical dependency between different datasets through mutual information. In the context of motor imagery classification based on electroencephalogram (EEG) signals for the brain–computer interface (BCI), several methods have been proposed to extract features efficiently, mainly based on common spatial patterns, filter banks, and deep learning. However, most methods use only one dataset at a time, which may not be sufficient for dealing with a multi-source retrieving problem in certain scenarios. From this perspective, this paper proposes an original approach for feature extraction through multiple datasets based on IVA to improve the classification of EEG-based motor imagery movements. The IVA components were used as features to classify imagined movements using consolidated classifiers (support vector machines and K-nearest neighbors) and deep classifiers (EEGNet and EEGInception). The results show an interesting performance concerning the clustering of MI-based BCI patients, and the proposed method reached an average accuracy of 86.7 % . [ABSTRACT FROM AUTHOR]

Background: Eccentric instruments have been proposed as more effective and less time‐consuming for endodontic instrumentation. Aim: To compare biomechanical outcomes of different instrumentation systems and time undertaken for instrumentation in resin prototypes. Design: Sixty standardized prototypes of mandibular second primary molars were instrumented according to the following systems: K‐files, ProTaper Next (PTN), XP‐endo Shaper (XPS), XP‐endo Finisher (XPF), XP–Clean (XPC), and Sequence Baby File (SBF; n = 10/each). Irrigation was performed with saline with simultaneous aspiration, and time spent was recorded. The prototypes were micro‐CT‐scanned before and after the instrumentation, and image sets were reconstructed and registered. Non‐instrumented areas, accumulated debris, removed root material volume, and canal transportation were quantified. Data were analyzed through ANOVA, the Kruskal–Wallis test, and the Wilcoxon signed‐rank test (α = 5%). Results: K‐files and SBF resulted in more instrumentation time (p <.05). SBF, XPC and PTN removed less root dentine (p <.05), but PTN left more untouched areas (p <.05). Accumulated debris were lower for XPC and SBF (p <.05). Canal transportation was similar among the groups. Conclusion: Rotary systems reduced instrumentation time, whereas SBF and XPC resulted in more conservative instrumentation, with less debris accumulation and non‐instrumented areas. A dedicated paediatric endodontic system (SBF) outperformed eccentric instruments in terms of effectiveness. [ABSTRACT FROM AUTHOR]

Background: Molar hypomineralization (MH) is defined as a multifactorial condition, and thus, its presence may be defined by interactions between environmental and genetic factors. Aim: To evaluate the association between MH, genes involved in enamel development, and the use of medication during pregnancy in early childhood. Design: One hundred and eighteen children, 54 with and 64 without MH, were studied. The data collected included demographics, socioeconomic data, and the medical history of mothers and children. Genomic DNA was collected from saliva. Genetic polymorphisms in ameloblastin (AMBN; rs4694075), enamelin (ENAM; rs3796704, rs7664896), and kallikrein (KLK4; rs2235091) were evaluated. These genes were analyzed by real‐time polymerase chain reaction using TaqMan chemistry. The software PLINK was used to compare allele and genotype distributions of the groups and to assess the interaction between environmental variables and genotypes (p <.05). Results: The variant allele KLK4 rs2235091 was associated with MH in some children (odds ratio [OR]: 3.75; 95% confidence interval [CI] = 1.65–7.81; p =.001). Taking medications in the first 4 years of life was also associated with MH (OR: 2.94; 95% CI = 1.02–6.04; p =.041) and specifically in association with polymorphisms in ENAM, AMBN, and KLK4 (p <.05). The use of medications during pregnancy was not associated with MH (OR: 1.37; 95% CI = 0.593–3.18; p =.458). Conclusion: The results of this study suggest that taking medication in the postnatal period appears to contribute to the etiology of MH in some evaluated children. There may be a possible genetic influence of polymorphisms in the KLK4 gene with this condition. [ABSTRACT FROM AUTHOR]