Phonological properties of logographic words modulate brain activation in bilinguals: a comparative study of Chinese characters and Japanese Kanji.

The brain networks for the first (L1) and second (L2) languages are dynamically formed in the bilingual brain. This study delves into the neural mechanisms associated with logographic-logographic bilingualism, where both languages employ visually complex and conceptually rich logographic scripts. Using functional Magnetic Resonance Imaging, we examined the brain activity of Chinese-Japanese bilinguals and Japanese-Chinese bilinguals as they engaged in rhyming tasks with Chinese characters and Japanese Kanji. Results showed that Japanese-Chinese bilinguals processed both languages using common brain areas, demonstrating an assimilation pattern, whereas Chinese-Japanese bilinguals recruited additional neural regions in the left lateral prefrontal cortex for processing Japanese Kanji, reflecting their accommodation to the higher phonological complexity of L2. In addition, Japanese speakers relied more on the phonological processing route, while Chinese speakers favored visual form analysis for both languages, indicating differing neural strategy preferences between the 2 bilingual groups. Moreover, multivariate pattern analysis demonstrated that, despite the considerable neural overlap, each bilingual group formed distinguishable neural representations for each language. These findings highlight the brain's capacity for neural adaptability and specificity when processing complex logographic languages, enriching our understanding of the neural underpinnings supporting bilingual language processing.

Machine learning approach for predicting post-intubation hemodynamic instability (PIHI) index values: towards enhanced perioperative anesthesia quality and safety.

BACKGROUND: Adequate preoperative evaluation of the post-intubation hemodynamic instability (PIHI) is crucial for accurate risk assessment and efficient anesthesia management. However, the incorporation of this evaluation within a predictive framework have been insufficiently addressed and executed. This study aims to developed a machine learning approach for preoperatively and precisely predicting the PIHI index values. METHODS: In this retrospective study, the valid features were collected from 23,305 adult surgical patients at Peking Union Medical College Hospital between 2012 and 2020. Three hemodynamic response sequences including systolic pressure, diastolic pressure and heart rate, were utilized to design the post-intubation hemodynamic instability (PIHI) index by computing the integrated coefficient of variation (ICV) values. Different types of machine learning models were constructed to predict the ICV values, leveraging preoperative patient information and initiatory drug infusion. The models were trained and cross-validated based on balanced data using the SMOTETomek technique, and their performance was evaluated according to the mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE) and R-squared index (R2). RESULTS: The ICV values were proved to be consistent with the anesthetists' ratings with Spearman correlation coefficient of 0.877 (P < 0.001), affirming its capability to effectively capture the PIHI variations. The extra tree regression model outperformed the other models in predicting the ICV values with the smallest MAE (0.0512, 95% CI: 0.0511-0.0513), RMSE (0.0792, 95% CI: 0.0790-0.0794), and MAPE (0.2086, 95% CI: 0.2077-0.2095) and the largest R2 (0.9047, 95% CI: 0.9043-0.9052). It was found that the features of age and preoperative hemodynamic status were the most important features for accurately predicting the ICV values. CONCLUSIONS: Our results demonstrate the potential of the machine learning approach in predicting PIHI index values, thereby preoperatively informing anesthetists the possible anesthetic risk and enabling the implementation of individualized and precise anesthesia interventions.

Augmented Recognition of Distracted Driving State Based on Electrophysiological Analysis of Brain Network.

In this study, we propose an electrophysiological analysis-based brain network method for the augmented recognition of different types of distractions during driving. Driver distractions, such as cognitive processing and visual disruptions during driving, lead to distinct alterations in the electroencephalogram (EEG) signals and the extracted brain networks. We designed and conducted a simulated experiment comprising 4 distracted driving subtasks. Three connectivity indices, including both linear and nonlinear synchronization measures, were chosen to construct the brain network. By computing connectivity strengths and topological features, we explored the potential relationship between brain network configurations and states of driver distraction. Statistical analysis of network features indicates substantial differences between normal and distracted states, suggesting a reconfiguration of the brain network under distracted conditions. Different brain network features and their combinations are fed into varied machine learning classifiers to recognize the distracted driving states. The results indicate that XGBoost demonstrates superior adaptability, outperforming other classifiers across all selected network features. For individual networks, features constructed using synchronization likelihood (SL) achieved the highest accuracy in distinguishing between cognitive and visual distraction. The optimal feature set from 3 network combinations achieves an accuracy of 95.1% for binary classification and 88.3% for ternary classification of normal, cognitively distracted, and visually distracted driving states. The proposed method could accomplish the augmented recognition of distracted driving states and may serve as a valuable tool for further optimizing driver assistance systems with distraction control strategies, as well as a reference for future research on the brain-computer interface in autonomous driving.

Research on the Effects of the Relationship between Agronomic Traits and Dwarfing Genes on Yield in Colored Wheat.

This research focuses on 72 approved varieties of colored wheat from different provinces in China. Utilizing coefficients of variation, structural equation models, and correlation analyses, six agronomic traits of colored wheat were comprehensively evaluated, followed by further research on different dwarfing genes in colored wheat. Using the entropy method revealed that among the 72 colored wheat varieties, 10 were suitable for cultivation. Variety 70 was the top-performing variety, with a comprehensive index of 87.15%. In the final established structural equation model, each agronomic trait exhibited a positive direct effect on yield. Notably, plant height, spike length, and flag leaf width had significant impacts on yield, with path coefficients of 0.55, 0.40, and 0.27. Transcriptome analysis and real-time fluorescence quantitative polymerase chain reaction (RT-qPCR) validation were used to identify three dwarfing genes controlling plant height: Rht1, Rht-D1, and Rht8. Subsequent RT-qPCR validation clustering heatmap results indicated that Rht-D1 gene expression increased with the growth of per-acre yield. Rht8 belongs to the semi-dwarf gene category and has a significant positive effect on grain yield. However, the impact of Rht1, as a dwarfing gene, on agronomic traits varies. These research findings provide crucial references for the breeding of new varieties.

Heterogeneous overtaking and learning styles with varied EEG patterns in a reinforced driving task.

Overtaking maneuvers occur when vehicle drivers pursue higher driving speeds or comfort scenarios through back-to-back lane-changing behaviors, which require active participation of mental resources and certain self-learning practices. However, few studies have investigated how brain activities change during overtaking. Moreover, the learning process, which indicates the heterogeneity of drivers from a process-based perspective, has been neglected. In this work, we studied varied overtaking and learning styles using electroencephalogram (EEG) signals collected from drivers during a simulated driving task with a possible learning process. The average speed, standard deviation of speed, steering wheel angle and lateral movement distance of overtaking behaviors are analyzed in these reinforced tasks to evaluate overtaking performance. Four types of overtaking styles (i.e., low-speed type, low-speed & strong-oscillation type, high-speed & strong-steering type, and high-speed & close-distance type) and three types of learning styles (i.e., stable, adaptive and changeful) are discovered, not only from eventual overtaking behaviors but also from behavioral changes in a certain learning process. EEG features, such as the power spectral density (PSD) in the θ, α, ß and γ bands, are extracted to characterize driver mental states and to correlate with heterogeneous learning styles. The obtained results show that fatigue and fatigue confrontation are more likely with a stable learning style, and the mental workload is reduced with an adaptive learning style, whereas no significant changes in brain activity are apparent with a changeful learning style. Understanding and recognizing heterogeneous overtaking and learning styles with varying EEG patterns will be extremely useful in the future for deep integration of advanced driving assistance systems (ADASs) and brain computer interface (BCI) systems.

Wielding and evaluating the removal composition of common artefacts in EEG signals for driving behaviour analysis.

Noninvasive EEG signals provide neural activity information at high resolution, of which human mental status can be properly detected. However, artefacts always exist in brain oscillatory EEG signals and thus impede the accuracy and reliability of relevant analysis, especially in real-world tasks. Moreover, the use of a mature artefact identification method cannot assure impeccable artefact separation; this leads to a trade-off between removing contaminated information and losing valuable information. This study addresses this problem by investigating a simulator-based driving behaviour analysis using a car-following scenario to correlate the EEG-based mental features with behavioural responses. The study develops an architecture for an artefact composition pool and proposes three integrated prediction models to evaluate the removal compositions of the EEG artefacts. Three errors (mean absolute, root mean square, mean absolute percentage) and R-squared index are considered for measuring the performance of the models. The results show that the best-performing composition outperformed the no-removal and all-removal cases by 11.75% and 4.28% improvements, respectively. Specifically, we investigate different common artefacts including eye blinks, horizontal eye movements, vertical eye movements, generic discontinuities and muscle artefacts. The gained knowledge on artefact removal, EEG spectral features and stimuli-response patterns can be further applied to properly manipulate real-world EEG signals and develop an effective brain-computer interface.

Quantitatively mining and distinguishing situational discomfort grading patterns of drivers from car-following data.

Situational discomfort awareness plays an important role in decision making among drivers and has rarely been discussed in detail in previous research. An instrumented vehicle was used to collect car-following data from multiple drivers, thereby quantitatively examining situational discomfort grading patterns using a new discomfort grading method and the latent Dirichlet allocation model. In this process, the gas pedal data and speed difference data are particularly involved in the computation for providing broader meaning to discomfort and building more comprehensive situations. The results show that individual discomfort awareness varies between drivers. More importantly, the potential patterns of situational discomfort grading are extracted, which provides knowledge for characterizing drivers in the context of discomfort awareness. The knowledge achieved can be further applied to distinguish drivers and identify the typical comfort and discomfort zones. This study has great value for promoting investigations on traffic psychology and developing more effective and customized driver assistant systems.

Similar ventral occipito-temporal cortex activations in literate and illiterate adults during the Chinese character matching task: an fMRI study.

Visual word expertise is typically associated with enhanced ventral occipito-temporal (vOT) cortex activation in response to written words. Previous study utilized a passive viewing task and found that vOT response to written words was significantly stronger in literate compared to the illiterate subjects. However, recent neuroimaging findings have suggested that vOT response properties are highly dependent upon the task demand. Thus, it is unknown whether literate adults would show stronger vOT response to written words compared to illiterate adults during other cognitive tasks, such as perceptual matching. We addressed this issue by comparing vOT activations between literate and illiterate adults during a Chinese character and simple figure matching task. Unlike passive viewing, a perceptual matching task requires active shape comparison, therefore minimizing automatic word processing bias. We found that although the literate group performed better at Chinese character matching task, the two subject groups showed similar strong vOT responses during this task. Overall, the findings indicate that the vOT response to written words is not affected by expertise during a perceptual matching task, suggesting that the association between visual word expertise and vOT response may depend on the task demand.