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Purpose: The novel coronavirus pneumonia (COVID-19) has continually spread and mutated, requiring a patient risk stratification system to optimize medical resources and improve pandemic response. We aimed to develop a conformal prediction-based tri-light warning system for stratifying COVID-19 patients, applicable to both original and emerging variants., Methods: We retrospectively collected data from 3646 patients across multiple centers in China. The dataset was divided into a training set (n = 1451), a validation set (n = 662), an external test set from Huoshenshan Field Hospital (n = 1263), and a specific test set for Delta and Omicron variants (n = 544). The tri-light warning system extracts radiomic features from CT (computed tomography) and integrates clinical records to classify patients into high-risk (red), uncertain-risk (yellow), and low-risk (green) categories. Models were built to predict ICU (intensive care unit) admissions (adverse cases in training/validation/Huoshenshan/variant test sets: n = 39/21/262/11) and were evaluated using AUROC ((area under the receiver operating characteristic curve)) and AUPRC ((area under the precision-recall curve)) metrics., Results: The dataset included 1830 men (50.2 %) and 1816 women (50.8 %), with a median age of 53.7 years (IQR [interquartile range]: 42-65 years). The system demonstrated strong performance under data distribution shifts, with AUROC of 0.89 and AUPRC of 0.42 for original strains, and AUROC of 0.77-0.85 and AUPRC of 0.51-0.60 for variants., Conclusion: The tri-light warning system can enhance pandemic responses by effectively stratifying COVID-19 patients under varying conditions and data shifts., Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Xiao Li reports financial support was provided by National Natural Science Foundation of China. NA reports a relationship with NA that includes:. NA has patent NA pending to NA. NA If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

Purpose: To develop CT-based radiomics models that can efficiently distinguish between multiple primary lung cancers (MPLCs) and intrapulmonary metastasis (IPMs)., Method: This retrospective study included 127 patients with 254 lung tumors pathologically proved as MPLCs or IPMs between May 2009 and January 2020. Radiomics features of lung tumors were extracted from baseline CT scans. Particularly, we incorporated tumor-focused, refined radiomics by calculating relative radiomics differences from paired tumors of individual patients. We applied the L1-norm regularization and analysis of variance to select informative radiomics features for constructing radiomics model (RM) and refined radiomics model (RRM). The performance was assessed by the area under the receiver operating characteristic curve (AUC-ROC). The two radiomics models were compared with the clinical-CT model (CCM, including clinical and CT semantic features). We incorporated both radiomics features to construct fusion model1 (FM1). We also, build fusion model2 (FM2) by combing both radiomics, clinical and CT semantic features. The performance of the FM1 and FM2 were further compared with that of the RRM., Results: On the validation set, the RM achieved an AUC of 0.857. The RRM demonstrated improved performance (validation set AUC, 0.870) than the RM, and showed significant differences compared with the CCM (validation set AUC, 0.782). Fusion models further led prediction performance (validation set AUC, FM1:0.885; FM2:0.889). There were no significant differences among the performance of the FM1, the FM2 and the RRM., Conclusions: The CT-based radiomics models presented good performance on the discrimination between MPLCs and IPMs, demonstrating the potential for early diagnosis and treatment guidance for MPLCs and IPMs., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

In recent years, increasing attention has been paid to the all-around development and mental health of students in education. Positive education, a rapidly developing ramification of positive psychology, has proved beneficial to students' learning and wellbeing. Meanwhile, online learning has quickly gained popularity due to the impact of COVID-19. However, there have been few reports discussing the relationship between positive education and online learning by combining theories and practices. To explore the connection between positive education and online learning, we provide a literature review for studies, mostly between 2010 and 2022, of theories and practices for both positive education and online learning. Next, we establish one-to-one links between the relevant theories and practices of online learning to each domain in the PERMA model of positive education, a theoretical framework including Positive emotion(P), Engagement(E), Relationship(R), Meaning(M), and Accomplishment(A). We aim to explore how to promote the development of positive education by applying the theoretical and practical advantages of online learning to the PERMA framework of positive education. This study aims to enrich the research perspectives of positive education and provide a reference for future research., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Lou and Xu.)

Due to its low molecular weight and abundant functional groups, water-soluble lignin (WSL) is considered as a more potent antioxidant than traditional industrial lignin in biofields. However, few studies have been conducted to evaluate its intracellular and endogenous reactive oxygen species (ROS)-scavenging ability, especially for the intervention of ROS-related disease in vivo. In this work, WSL in bamboo autohydrolysate (WSL-BM) and wheat stalk autohydrolysate (WSL-WS) were isolated and characterized to comparably analyze their bioactivities. The composition analysis and NMR characterization showed that both WSL-BM and WSL-WS contained relatively similar components and substructures, but WSL-BM contained higher contents of phenolic OH groups. Both WSL samples exhibited excellent biocompatibility with the concentration below 50 μg/mL, while WSL-BM exhibited superior ROS-scavenging ability and ROS-related ulcerative colitis treatment potential at same concentration. In addition, WSL-BM also showed better performance in ameliorating inflammation and oxidative stress in RAW 264.7 cells and colitis mice by activating Nrf2 and suppressing NFκB signaling, resulting in an overall improvement in both macroscopic and histological parameters. Overall, these results implied that WSL from gramineous biomass can be used as a novel anti-inflammatory and antioxidative agent in the biomedical field., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Background: Atypical tumor response patterns during immune checkpoint inhibitor therapy pose a challenge to clinicians and investigators in immuno-oncology practice. This study evaluated tumor burden dynamics to identify imaging biomarkers for treatment response and overall survival (OS) in advanced gastrointestinal malignancies treated with PD-1/PD-L1 inhibitors., Methods: This retrospective study enrolled a total of 198 target lesions in 75 patients with advanced gastrointestinal malignancies treated with PD-1/PD-L1 inhibitors between January 2017 and March 2021. Tumor diameter changes as defined by immunotherapy Response Evaluation Criteria in Solid Tumors (iRECIST) were studied to determine treatment response and association with OS., Results: Based on the best overall response, the tumor diameter ranged from - 100 to + 135.3% (median: - 9.6%). The overall response rate was 32.0% (24/75), and the rate of durable disease control for at least 6 months was 30.7% (23/75, one (iCR, immune complete response) or 20 iPR (immune partial response), or 2iSD (immune stable disease). Using univariate analysis, patients with a tumor diameter maintaining a < 20% increase (48/75, 64.0%) from baseline had longer OS than those with ≥20% increase (27/75, 36.0%) and, a reduced risk of death (median OS: 80 months vs. 48 months, HR = 0.22, P = 0.034). The differences in age (HR = 1.09, P = 0.01), combined surgery (HR = 0.15, P = 0.01) and cancer type (HR = 0.23, P = 0.001) were significant. In multivariable analysis, patients with a tumor diameter with a < 20% increase had notably reduced hazards of death (HR = 0.15, P = 0.01) after adjusting for age, combined surgery, KRAS status, cancer type, mismatch repair (MMR) status, treatment course and cancer differentiation. Two patients (2.7%) showed pseudoprogression., Conclusions: Tumor diameter with a < 20% increase from baseline during therapy in gastrointestinal malignancies was associated with therapeutic benefit and longer OS and may serve as a practical imaging marker for treatment response, clinical outcome and treatment decision making., (© 2021. The Author(s).)

Objectives: To develop and evaluate machine learning models using baseline and restaging computed tomography (CT) for predicting and early detecting pathological downstaging (pDS) with neoadjuvant chemotherapy in advanced gastric cancer (AGC)., Methods: We collected 292 AGC patients who received neoadjuvant chemotherapy. They were classified into (a) primary cohort (206 patients with 3-4 cycles chemotherapy) for model development and internal validation, (b) testing cohort I (46 patients with 3-4 cycles chemotherapy) for evaluating models' predictive ability before and after the complete course, and (c) testing cohort II (n = 40) for model evaluation on its performance at early treatment. We extracted 1,231 radiomics features from venous phase CT at baseline and restaging. We selected radiomics models based on 28 cross-combination models and measured the areas under the curve (AUC). Our prediction radiomics (PR) model is designed to predict pDS outcomes using baseline CT. Detection radiomics (DR) model is applied to restaging CT for early pDS detection., Results: PR model achieved promising outcomes in two testing cohorts (AUC 0.750, p = .009 and AUC 0.889, p = .000). DR model also showed a good predictive ability (AUC 0.922, p = .000 and AUC 0.850, p = .000), outperforming the commonly used RECIST method (NRI 39.5% and NRI 35.4%). Furthermore, the improved DR model with averaging outcome scores of PR and DR models showed boosted results in two testing cohorts (AUC 0.961, p = .000 and AUC 0.921, p = .000)., Conclusions: CT-based radiomics models perform well on prediction and early detection tasks of pDS and can potentially assist surgical decision-making in AGC patients., Key Points: • Baseline contrast-enhanced computed tomography (CECT)-based radiomics features were predictive of pathological downstaging, allowing accurate identification of non-responders before therapy. • Restaging CECT-based radiomics features were predictive to achieve pDS after and even at an early stage of neoadjuvant chemotherapy. • Combination of baseline and restaging CECT-based radiomics features was promising for early detection and preoperative evaluation of pathological downstaging of AGC., (© 2021. The Author(s).)

Purpose: Small-cell lung cancer (SCLC) is the deadliest form of lung cancer, partly because of its short doubling time. Delays in imaging identification and diagnosis of nodules create a risk for stage migration. The purpose of our study was to determine if a machine learning radiomics model can detect SCLC on computed tomography (CT) among all nodules at least 1 cm in size., Materials and Methods: Computed tomography scans from a single institution were selected and resampled to 1 × 1 × 1 mm. Studies were divided into SCLC and other scans comprising benign, adenocarcinoma, and squamous cell carcinoma that were segregated into group A (noncontrast scans) and group B (contrast-enhanced scans). Four machine learning classification models, support vector classifier, random forest (RF), XGBoost, and logistic regression, were used to generate radiomic models using 59 quantitative first-order and texture Imaging Biomarker Standardization Initiative compliant PyRadiomics features, which were found to be robust between two segmenters with minimum Redundancy Maximum Relevance feature selection within each leave-one-out-cross-validation to avoid overfitting. The performance was evaluated using a receiver operating characteristic curve. A final model was created using the RF classifier and aggregate minimum Redundancy Maximum Relevance to determine feature importance., Results: A total of 103 studies were included in the analysis. The area under the receiver operating characteristic curve for RF, support vector classifier, XGBoost, and logistic regression was 0.81, 0.77, 0.84, and 0.84 in group A, and 0.88, 0.87, 0.85, and 0.81 in group B, respectively. Nine radiomic features in group A and 14 radiomic features in group B were predictive of SCLC. Six radiomic features overlapped between groups A and B., Conclusion: A machine learning radiomics model may help differentiate SCLC from other lung lesions., Competing Interests: Rajesh P. ShahConsulting or Advisory Role: Genentech, Intuitive Surgical, Histosonics Artio Medical, IncResearch Funding: Merit Medical Systems Millie DasHonoraria: PlexusConsulting or Advisory Role: Jazz Pharmaceuticals, AstraZeneca, BeiGeneResearch Funding: Novartis, Verily, Varian Medical Systems, AbbVie, United Therapeutics, Cellsight Technologies Olivier GevaertResearch Funding: Nividien Inc, Lucence Diagnostics, Onc.AI Sandy NapelConsulting or Advisory Role: FoviaPatents, Royalties, Other Intellectual Property: Several patents, none currently licensedNo other potential conflicts of interest were reported.

Purpose: To propose a practical strategy for the clinical application of deep learning algorithm, i.e., Hierarchical-Ordered Network-ORiented Strategy (HONORS), and a new approach to pulmonary nodule classification in various clinical scenarios, i.e., Filter-Guided Pyramid NETwork (FGP-NET)., Materials and Methods: We developed and validated FGP-NET on a collection of 2106 pulmonary nodules on computed tomography images which combined screened and clinically detected nodules, and performed external test (n = 341). The area under the curves (AUCs) of FGP-NET were assessed. A comparison study with a group of 126 skilled radiologists was conducted. On top of FGP-NET, we built up our HONORS which was composed of two solutions. In the Human Free Solution, we used the high sensitivity operating point for screened nodules, but the high specificity operating point for clinically detected nodules. In the Human-Machine Coupling Solution, we used the Youden point., Results: FGP-NET achieved AUCs of 0.969 and 0.847 for internal and external test. The AUCs of the subsets of the external test set ranged from 0.890 to 0.942. The average sensitivity and specificity of the 126 radiologists were 72.2 ± 15.1 % and 71.7 ± 15.5 %, respectively, while a higher sensitivity (93.3 %) but a relatively inferior specificity (64.0 %) were achieved by FGP-NET. HONORS-guided FGP-NET identified benign nodules with high sensitivity (sensitivity,95.5 %; specificity, 72.5 %) in the screened nodules, and identified malignant nodules with high specificity (sensitivity, 31.0 %; specificity, 97.5 %) in the clinically detected nodules. These nodules could be reliably diagnosed without any intervention from radiologists, via the Human Free Solution. The remaining ambiguous nodules were diagnosed with high performance, which however required manual confirmation by radiologists, via the Human-Machine Coupling Solution., Conclusions: FGP-NET performed comparably to skilled radiologists in terms of diagnosing pulmonary nodules. HONORS, due to its high performance, might reliably contribute a second opinion, aiding in optimizing the clinical workflow., (Copyright © 2021 Elsevier B.V. All rights reserved.)

The COVID-19 pandemic overwhelms the medical resources in the stressed intensive care unit (ICU) capacity and the shortage of mechanical ventilation (MV). We performed CT-based analysis combined with electronic health records and clinical laboratory results on Cohort 1 (n = 1662 from 17 hospitals) with prognostic estimation for the rapid stratification of PCR confirmed COVID-19 patients. These models, validated on Cohort 2 (n = 700) and Cohort 3 (n = 662) constructed from nine external hospitals, achieved satisfying performance for predicting ICU, MV, and death of COVID-19 patients (AUROC 0.916, 0.919, and 0.853), even on events happened two days later after admission (AUROC 0.919, 0.943, and 0.856). Both clinical and image features showed complementary roles in prediction and provided accurate estimates to the time of progression (p < 0.001). Our findings are valuable for optimizing the use of medical resources in the COVID-19 pandemic. The models are available here: https://github.com/terryli710/COVID&#95;19&#95;Rapid&#95;Triage&#95;Risk&#95;Predictor .

Unlike the other studies on emotional design in multimedia learning, the present study differentiated the two confounding variables of visual interface design and structured content to manipulate the instructional material. Specifically, we investigated how the visual aesthetics of positive emotional interface design influenced learners' cognitive processes, emotional valences, learning outcomes, and subjective experience. Eighty-one college students took part in the experimental study. They were divided into the three experimental groups: a holistic layout of positive emotional design group (HPED), a local layout of positive emotional design group (LPED), and a neutral emotional design group (ND). By using a mixed approach of questionnaires and eye tracking, we further explored the differences among the three groups in cognitive processing, learning outcomes, and subjective experience. Results indicated that the LPED group invested higher cognitive effort, put more attentional focus in the relevant knowledge content module, and achieved better learning performance (i.e., retention and transfer tests) in contrast to the HPED group and the ND group. However, no significant difference in dynamic changes of emotional state among the three groups was detected. The analytical results can provide researchers and practitioners with valuable insights into the positive emotional design of multimedia learning, which allows for the facilitation of mental engagement, learning outcomes and subjective perception., Competing Interests: Competing interestsThe authors declared no potential competing interests with respect to the research, authorship, and/or publication of this article., (© The Author(s) 2021.)

Objectives: To develop and validate a deep learning model to discriminate transient from persistent subsolid nodules (SSNs) on baseline CT., Methods: A cohort of 1414 SSNs, consisting of 319 transient SSNs in 168 individuals and 1095 persistent SSNs in 816 individuals, were identified on chest CT. The cohort was assigned by examination date into a development set of 996 SSNs, a tuning set of 212 SSNs, and a validation set of 206 SSNs. Our model was built by transfer learning, which was transferred from a well-performed deep learning model for pulmonary nodule classification. The performance of the model was compared with that of two experienced radiologists. Each nodule was categorized by Lung CT Screening Reporting and Data System (Lung-RADS) to further evaluate the performance and the potential clinical benefit of the model. Two methods were employed to visualize the learned features., Results: Our model achieved an AUC of 0.926 on the validation set with an accuracy of 0.859, a sensitivity of 0.863, and a specificity of 0.858, and outperformed the radiologists. The model performed the best among Lung-RADS 2 nodules and maintained well performance among Lung-RADS 4 nodules. Feature visualization demonstrated the model's effectiveness in extracting features from images., Conclusions: The transfer learning model presented good performance on the discrimination between transient and persistent SSNs. A reliable diagnosis on nodule persistence can be achieved at baseline CT; thus, an early diagnosis as well as better patient care is available., Key Points: • Deep learning can be used for the discrimination between transient and persistent subsolid nodules. • A transfer learning model can achieve good performance when it is transferred from a model with a similar task. • With the assistance of deep learning model, a reliable diagnosis on nodule persistence can be achieved at baseline CT, which can bring a better patient care strategy.

The wave of COVID-19 continues to overwhelm the medical resources, especially the stressed intensive care unit (ICU) capacity and the shortage of mechanical ventilation (MV). Here we performed CT-based analysis combined with electronic health records and clinical laboratory results on Cohort 1 (n = 1662 from 17 hospitals) with prognostic estimation for the rapid stratification of PCR confirmed COVID-19 patients. These models, validated on Cohort 2 (n = 700) and Cohort 3 (n = 662) constructed from 9 external hospitals, achieved satisfying performance for predicting ICU, MV and death of COVID-19 patients (AUROC 0.916, 0.919 and 0.853), even on events happened two days later after admission (AUROC 0.919, 0.943 and 0.856). Both clinical and image features showed complementary roles in events prediction and provided accurate estimates to the time of progression (p<.001). Our findings are valuable for delivering timely treatment and optimizing the use of medical resources in the pandemic of COVID-19.

Physical activity may improve cognitive control and episodic memory. Cognitive control could exert positive or negative influences on episodic memory. This study aimed to test whether physical activity modulated the effect of proactive and reactive control on episodic memory. Participants reported their physical activity in the past week, encoded episodic memory incidentally in proactive and reactive conditions, and subsequently retrieved their memories of items and sources. Subsequent item memory was better when items were encoded in proactive vs. reactive condition. Smaller condition difference in subsequent item memory was related to better cognitive control ability. Cognitive control completely mediated the relation between physical activity and the condition difference in subsequent item memory. Additionally, condition difference in subsequent source memory was negatively related to cognitive control. After controlling for cognitive control, greater physical activity was positively related to the difference in subsequent source memory between proactive and reactive conditions. Altogether, the findings suggested that physical activity modulated the effect of proactive and reactive control on subsequent item memory through improving cognitive control ability, but it was independent of cognitive control for subsequent source memory., (Copyright © 2020 Shi, Geng, Hu and Xu.)

Background: Adherent perinephric fat (APF) is evaluated preoperatively with the Mayo adhesive probability (MAP) scoring system using conventional single-form computed tomography (CT) images. An objective or quantitative indicator for predicting APF is urgently needed for clinical application., Methods: A total of 150 patients with renal tumours who underwent laparoscopic partial nephrectomy (LPN) were retrospectively enrolled and divided into the APF group (n=100) and the non-APF group (n=50) according to surgical results. All patients underwent a renal contrast-enhanced dual-energy CT (DECT) scan. The obtained CT DICOM data were transmitted to the DECT post-processing workstation and adopted virtual non-contrast (VNC), Rho/Z Maps, and Monoenergetic Plus (mono+) modes separately to undergo a multi-parameter analysis. A logistic stepwise investigation was utilized to analyse the related risk factors. The cutoff value was determined by the Youden index. Fifty patients were prospectively enrolled to validate the constructed model. The area under the curve (AUC), sensitivity, specificity and accuracy of the model were calculated., Results: The study demonstrated that age, sex, body mass index (BMI), smoking status, tumour diameter, exophytic status, degree of malignancy and posterior perinephric fat thickness were related to the occurrence of APF (P<0.05). Model 1 was selected with the contrast material (CM) parameter (cutoff point 0.5), model 2 was selected with the effective atomic number (Z eff ) parameter (cutoff point 6.5), and model 3 was selected with the slope K (K) parameter (cutoff point -0.95). The AUC, sensitivity, specificity and accuracy of model 1 were 0.94, 0.94, 0.93 and 0.94, respectively; for model 2, they were 0.94, 0.93, 0.93 and 0.96, respectively; and for model 3, they were 0.92, 0.92, 0.93 and 0.92, respectively., Conclusions: Multi-mode and multi-parameter models of DECT can effectively be used to predict the occurrence of APF., Competing Interests: Conflicts of Interest: The authors have no conflicts of interest to declare., (2019 Translational Andrology and Urology. All rights reserved.)

Background: Adherent perinephric fat (APF) is evaluated preoperatively with the Mayo adhesive probability (MAP) scoring system using conventional single form computed tomography (CT) images. An objective or quantitative indicator for predicting APF is urgently needed for clinical application., Methods: Data obtained from 150 renal tumor patients with partial nephrectomy (PN) were used in the present study. Patients were divided into two groups based on the surgical results: an APF group (n=100) and a non-APF group (n=50). All patients had renal contrast-enhanced dual-energy CT (DECT) scan, and the data were brought into a post-processing workstation. Monoenergetic plus mode was adopted to analyze the spectrum curve of the region of interest (ROI). Logistic stepwise relapse investigation was utilized to analyze the related risk factors. The maximum Youden index was taken as the cut-off value. The sensitivity, specificity, accuracy, and 95% CI of the model were calculated., Results: APF is related to patients' clinical characteristics of age, gender, BMI, smoking, tumor diameter, exophytic, and benign or malignant nature of the renal masses (P<0.05). The shape of the curve of ROI 1 in the APF group was different from that of the ROI 4 in the non-APF group, and the curve slope of K1 (-0.21±0.47) was different from that of K4 (-1.30±0.14) (P<0.001). Statistical analysis showed that the slope K was solely retained in the model index. The best cut-off point for the K value was -0.95. The AUC of the cut-off point was 0.97 (95% CI: 0.96-0.99)., Conclusions: The DECT spectrum curve performed well in predicting APF, and the curve slope K can be used as an objective quantitative indicator., Competing Interests: Conflicts of Interest: The authors have no conflicts of interest to declare.

Learning constitutes a main developmental context for children everywhere. Learning-related emotions can affect cognition, motivation, and achievement and are associated with parenting. Studies on learning-related emotions and how parenting is associated with a child's emotional development in learning have been less conclusive for Chinese children. This article provides an overview of the literature on related emotions as experienced in academic settings in Chinese culture and their relations with parenting. The research on academic, achievement, and domain-specific emotions is presented from a culture-specific perspective. After stressing the functions of emotions for learning and achievement, we discuss the complex relationship between emotions and parenting in the learning context. We propose an integrated model of emotion, parenting, self, and achievement in learning to understand the mechanisms of the interactions between emotions and parenting from a culture-specific perspective. Implications for future research are identified., (© 2019 Wiley Periodicals, Inc.)

We examined two aspects of temperamental approach in early infancy, positive reactivity and anger, and their unique and combined influences on maternal reports of child surgency and attention focusing at 4 years of age. One hundred and fourteen infants were observed for their positive reactions to novel stimuli at 4 months, and their anger expressions during arm restraint at 9 months. Child surgency and attention focusing at age 4 years were assessed by maternal report. Infants who expressed more anger to restraint were rated higher in surgency during early childhood relative to infants who expressed less anger. The effects of positive reactivity to novelty on attention focusing were moderated by anger to restraint. These findings suggest that infant temperamental approach tendencies are multifaceted and have both unique and combined influences on later maternal report of attention and social behavior.

Social class demarcates sociocultural environments differing in the relative abundance and scarcity of resources, which in turn differentially afford independent and interdependent psychological processes. The relationships between social class and psychological processes are well documented in Western populations but less so elsewhere. Examining such a relationship is particularly important in China, with its unique historical and sociocultural issues surrounding social class. This research examined the relationship between social class and independence-interdependence among Chinese adolescents (N = 1184). Findings were consistent across a diverse array of psychological processes implicated by independence-interdependence: Participants with well-educated parents experienced more socially disengaging emotions and a higher level of self-esteem, and were more inclined toward dispositional attribution and focused attention, compared to participants with less well-educated parents. These findings highlight the cross-cultural commonality in the relationship between social class and independence-interdependence.

The relations among infant anger reactivity, approach behavior, and frontal EEG asymmetry, and their relations to inhibitory control and behavior problems in early childhood were examined within the context of a longitudinal study of temperament. Two hundred and nine infants' anger expressions to arm restraint were observed at 4 months of age. Infants' approach behaviors during play with an unpredictable toy and baseline frontal electroencephalogram (EEG) asymmetry were assessed at 9 months of age. Inhibitory control during a Go/No-Go task and parent-report of behavior problems were evaluated at 4 years of age. High anger-prone infants with left, but not right, frontal EEG asymmetry showed significantly more approach behaviors and less inhibitory control relative to less anger-prone infants. Although a link between anger proneness in infancy and behavior problems in early childhood was not found, a combination of low approach behaviors and poor inhibitory control was predictive of internalizing behaviors.