EEG-Based Identification of Emotional Neural State Evoked by Virtual Environment Interaction.

Classifying emotional states is critical for brain-computer interfaces and psychology-related domains. In previous studies, researchers have tried to identify emotions using neural data such as electroencephalography (EEG) signals or brain functional magnetic resonance imaging (fMRI). In this study, we propose a machine learning framework for emotion state classification using EEG signals in virtual reality (VR) environments. To arouse emotional neural states in brain signals, we provided three VR stimuli scenarios to 15 participants. Fifty-four features were extracted from the collected EEG signals under each scenario. To find the optimal classification in our research design, three machine learning algorithms (XGBoost classifier, support vector classifier, and logistic regression) were applied. Additionally, various class conditions were used in machine learning classifiers to validate the performance of our framework. To evaluate the classification performance, we utilized five evaluation metrics (precision, recall, f1-score, accuracy, and AUROC). Among the three classifiers, the XGBoost classifiers showed the best performance under all experimental conditions. Furthermore, the usability of features, including differential asymmetry and frequency band pass categories, were checked from the feature importance of XGBoost classifiers. We expect that our framework can be applied widely not only to psychological research but also to mental health-related issues.

Identification of Risk Factors for Suicidal Ideation and Attempt Based on Machine Learning Algorithms: A Longitudinal Survey in Korea (2007-2019).

Investigating suicide risk factors is critical for socioeconomic and public health, and many researchers have tried to identify factors associated with suicide. In this study, the risk factors for suicidal ideation were compared, and the contributions of different factors to suicidal ideation and attempt were investigated. To reflect the diverse characteristics of the population, the large-scale and longitudinal dataset used in this study included both socioeconomic and clinical variables collected from the Korean public. Three machine learning algorithms (XGBoost classifier, support vector classifier, and logistic regression) were used to detect the risk factors for both suicidal ideation and attempt. The importance of the variables was determined using the model with the best classification performance. In addition, a novel risk-factor score, calculated from the rank and importance scores of each variable, was proposed. Socioeconomic and sociodemographic factors showed a high correlation with risks for both ideation and attempt. Mental health variables ranked higher than other factors in suicidal attempts, posing a relatively higher suicide risk than ideation. These trends were further validated using the conditions from the integrated and yearly dataset. This study provides novel insights into suicidal risk factors for suicidal ideations and attempts.

Three-Component Difunctionalization of Cyclohexenyl Triflates: Direct Access to Versatile Cyclohexenes via Cyclohexynes.

Difunctionalization of strained cyclic alkynes presents a powerful strategy to build richly functionalized cyclic alkenes in an expedient fashion. Herein we disclose an efficient and flexible approach to achieve carbohalogenation, dicarbofunctionalization, aminohalogenation and aminocarbonation of readily available cyclohexenyl triflates. We have demonstrated the novel use of zincate base/nucleophile system for effective formation of key cyclohexyne intermediates and selective addition of various carbon and nitrogen nucleophiles. Importantly, leveraging the resulting organozincates enables the incorporation of a broad range of electrophilic partners to deliver structurally diverse cyclohexene motifs. The importance and utility of this method is also exemplified by the modularity of this approach and the ease in which even highly complex polycyclic scaffolds can be accessed in one step.

1,2-Difunctionalization of Aryl Triflates: A Direct and Modular Access to Diversely Functionalized Anilines.

ortho-Amino difunctionalization of aryl triflates has been achieved via a three-component reaction. The cascade reaction proceeds through a zincate base-mediated deprotonative formation of a reactive aryne intermediate, in situ nucleophilic addition, and coupling with electrophilic partners. This strategy leverages the advantageous reactivity of organozincate intermediates, enabling the installation of various functionalities such as amine, azide, oxygen, sulfur, halide, alkynyl, aryl, vinyl, and alkyl groups in a modular manner for the synthesis of diverse aniline skeletons.

Site-Selective Copper-Catalyzed Amination and Azidation of Arenes and Heteroarenes via Deprotonative Zincation.

Arene amination is achieved by site-selective C-H zincation followed by copper-catalyzed coupling with O-benzoylhydroxylamines under mild conditions. Key to this success is ortho-zincation mediated by lithium amidodiethylzincate base that is effective for a wide range of arenes, including nonactivated arenes bearing simple functionalities such as fluoride, chloride, ester, amide, ether, nitrile, and trifluoromethyl groups as well as heteroarenes including indole, thiophene, pyridine, and isoquinoline. An analogous C-H azidation is also accomplished using azidoiodinane for direct introduction of a useful azide group onto a broad scope of arenes and heteroarenes. These new transformations offer rapid access to valuable and diverse chemical space of aminoarenes. Their broad applications in organic synthesis and drug discovery are demonstrated in the synthesis of novel analogues of natural product (-)-nicotine and antidepressant sertraline by late-stage amination and azidation reactions.