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This study aims to examine how job resources, demands, and self-efficacy affect American STEM teachers' job satisfaction by analyzing the US TALIS 2018 data. Multiple regression and commonality analysis were used to analyze factors' significant contributions and their detailed real unique and common contributions to STEM teachers' job satisfaction. The results show that the final model explains 29.6% of the variances of STEM teachers' job satisfaction. The commonality analysis further showed that job resources, job demands, and job self-efficacy explained 23.5%, 8.6%, and 8.0% of variances of job satisfaction, respectively. However, these factor sets uniquely contributed 15.9%, 2.9%, and 2.1% of the variance, separately. This study confirms the validity of the revised job demands-resources model for STEM teachers' job satisfaction. Furthermore, the commonality analysis reveals the unique and independent contributions of job demands, resources, and self-efficacy to job satisfaction. Results from the research identified the significance of job resources contributing to the improvement of STEM teachers' job satisfaction.

We examine recent research across countries and cultures in regard to the issues related to the formation of gifted and talented education perspectives, policies, and practices. Many modern cultures and subcultures have developed formal and informal definitions of what it means to be gifted and talented, and when we compare the perceptions, policies, and practices across nations, we discover very different constructs of intelligence and ability. These understandings of giftedness and gifted and talented education can be grouped into four binary dimensions, scholarly versus co-curricular capabilities, aptitude versus achievement, nature versus nurture, and individualistic versus collective, that have significant implications for policy and practice. These constructs can serve as a foundation for countries that are looking to formalize or expand their gifted and talented education models or can be used to challenge the norms of established systems. We put forward recommendations to address some of the challenges in advancing gifted education cross-nationally, an area that is often assumed to introduce risks of enlarging social inequity. We also provide a cross-national matrix that captures known elements of gifted education policies and programs from over 20 subnational jurisdictions, countries, and world regions.

The purpose of this study was to investigate how students' disabilities impacted their learning at the university and to measure their perceptions of universal design for learning (UDL). An online survey was administered at a large public research university in the south-central United States; 160 students with disabilities completed the survey. These students with disabilities described how their disabilities influenced their learning in the following aspects: attention issues, slow processing, absence, accessibility issues, reading and writing challenges, mental health challenges, and social interaction challenges. The results also revealed that some UDL teaching practices were not being fully utilized by instructors based on the perceptions of students with disabilities. We discussed those UDL practices which were not being used by instructors and highlighted the impact of using the practices on students' learning. These practices include effective teaching methods such as helping students organize and summarize learning content, using technology to increase accessibility, providing flexibility in assessments and assignments, providing meaningful feedback, and recruiting students' attention and engagement. [ABSTRACT FROM AUTHOR]

The formation of an academic field is evidenced by many factors, including the growth of relevant research articles and the increasing impact of highly cited publications. Building upon recent scoping reviews of journal publications in STEM education, this study aimed to provide a systematic review of high impact empirical studies in STEM education to gain insights into the development of STEM education research paradigms. Through a search of the Web of Science core database, we identified the top 100 most-cited empirical studies focusing on STEM education that were published in journals from 2000 to 2021 and examined them in terms of various aspects, including the journals where they were published, disciplinary content coverage, research topics and methods, and authorship's nationality/region and profession. The results show that STEM education continues to gain more exposure and varied disciplinary content with an increasing number of high impact empirical studies published in journals in various STEM disciplines. High impact research articles were mainly authored by researchers in the West, especially the United States, and indicate possible "hot" topics within the broader field of STEM education. Our analysis also revealed the increased participation and contributions from researchers in diverse fields who are working to formulate research agendas in STEM education and the nature of STEM education scholarship. [ABSTRACT FROM AUTHOR]

In 2022, more than 100 000 non‐Hodgkin lymphoma (NHL) diagnoses are expected, yet few risk factors are confirmed. In this study, data from six US‐based cohorts (568 717 individuals) were used to examine body size and risk of NHL. Over more than 20 years of follow‐up, 11 263 NHLs were identified. Hazard ratios (HRs) and 95% confidence intervals (CI) estimated associations with NHLs for adult body mass index (BMI), height, weight change, waist circumference and predicted fat mass. Adult height was broadly associated with NHL, but most strongly with B‐cell NHLs among non‐White participants (e.g. HRBLACK = 2.06, 95% CI: 1.62–2.62). However, the strongest association among the anthropometric traits examined was for young adult BMI and risk of diffuse large B‐cell lymphoma (DLBCL), particularly those who maintained a higher BMI into later adulthood. Individuals with BMI over 30 kg/m2 throughout adulthood had more than double the DLBCL risk (HR = 2.67, 95% CI: 1.71–4.17) compared to BMI 18.5–22.9 kg/m2. Other anthropometric traits were not associated with NHL after controlling for BMI. These results suggest that sustained high BMI is a major driver of DLBCL risk. If confirmed, we estimate that up to 23.5% of all DLBCLs (and 11.1% of all NHLs) may be prevented with avoidance of young adult obesity. [ABSTRACT FROM AUTHOR]

The findings of association between alcohol consumption and arthritis are mixed while little is known about age differences in the associations of mental health and behavioral factors with arthritis. This study aimed to estimate the prevalence and associated factors of arthritis among US adults using data from the 2012 National Health Interview Survey. In total, 8,229 adults with arthritis and 26,256 controls were selected from the adult respondents. Weighted univariate and multiple logistic regression analyses were used to estimate the odds ratios (ORs) with 95 % confidence intervals. The overall prevalence of arthritis was 22.1 %. The prevalence increased with age (6.8, 29.6, and 47.9 % for 18-49, 50-64, and 65+ years of age, respectively). The prevalence of mental problems was higher in cases than controls [4 vs. 1 % for serious psychological distress (SPD), 29 vs. 16 % for anxiety, and 26 vs. 11 % for depression, respectively]. Multiple logistic regression analyses showed that being female, older age, smoking, alcohol consumption, obesity, SPD, depression, and anxiety were positively associated with arthritis. Stratified by age, SPD was associated with arthritis only in young adults (18-49 years old) while the ORs of anxiety and depression with arthritis decreased as age increased. Alcohol consumption revealed stronger associations in middle-aged adults and elderly. Using a large nationally representative sample in the USA, alcohol consumption, smoking, SPD, anxiety, and depression were associated with arthritis, and the associations varied across different age groups. [ABSTRACT FROM AUTHOR]

Abstract: Family, twin, and adoption studies have indicated that human intelligence quotient (IQ) has significant genetic components. We performed a low-density genome-wide association analysis with a family-based association test to identify genetic variants influencing IQ, as measured by Wechsler Adult Intelligence Scale full-score IQ (FSIQ). We examined 11,120 single-nucleotide polymorphisms (SNPs) from the Affymetrix GeneChips 10K mapping array genotyped in 292 nuclear families from Genetic Analysis Workshop 14, a subset from the Collaborative Study on the Genetics of Alcoholism (COGA). A replication analysis was performed using part of International Multi-Center ADHD Genetics Project (IMAGE) dataset. Twenty-two SNPs were identified as having suggestive associations with IQ (p <10−3) in the COGA sample and eleven of the SNPs were located within known genes. In particular, NTM at 11q25 (rs411280, p =0.000764) and NR3C2 at 4q31.1 (rs3846329, p =0.000675) were two novel genes which have not been associated with IQ in other studies. It has been reported that NTM might play a role in late-onset Alzheimer disease while NR3C2 may be associated with cognitive function and major depression. The associations of these two genes were well-replicated by single-marker and haplotype analyses in the IMAGE sample. In conclusion, our findings provide evidence that chromosome regions of 11q25 and 4q31.1 contain genes affecting IQ. This study will serve as a resource for replication in other populations. [ABSTRACT FROM AUTHOR]

• A new kind of random sampling cost-sensitive MLP model(RCSMLP) was constructed by optimizing the prediction model in output level, data level and algorithm level. • The Youden index method was examined to be of the best ability to divide the prediction results and applied in the RCSMLP model. • The random under-sampling model with crash and non-crash data ratio 1:4 showed the best performance to solve the imbalanced problem. • The cost-sensitive MLP model can effectively solve the imbalanced data problem. The Active Traffic Management (ATM) system has been widely used in the United States and the European countries to improve the traffic safety of urban expressways. The accurate real-time crash risk prediction is fundamental to the system running well. Crash data are characterized by small probability, which poses a typical Imbalanced Data Classification problem. Most previous studies mainly improved the prediction methods only in data level or algorithm level, which may be inadequate to predict the crash risk accurately especially in a continuous real-time traffic data environment. The comprehensive imbalanced classification algorithm was examined in this research to build more accurate real-time traffic crash risk prediction model. At the output level, the Youden index method has been proved to be of the best ability to divide the prediction results and Probability Calibration Method was proposed to optimize the prediction results in further. At the data level, Under-sampling and Synthetic Minority Oversampling Technique(SMOTE) methods were compared to solve the imbalanced data classification problem by changing the data distribution. At the algorithm level, the cost-sensitive MLP algorithm and Adaboost algorithm were examined and finally the random sampling cost-sensitive MLP model(RCSMLP) and Rusboost model were constructed by synthesizing the optimization methods from three levels. The sensitivity of the RCSMLP model reached 78.10 % and the specificity of the model reached 81.44 %. The AUC and sensitivity of the Rusboost model reached 0.892 and 0.842 while the specificity of the model reached 0.816, which shows the better performance in dealing with the imbalanced traffic crash risk prediction problem compared to existed prediction models. The proposed method of improving prediction accuracy in this study is universal and can be applied to many other prediction models to predict real-time traffic crash risk. [ABSTRACT FROM AUTHOR]

• The wavelet soft threshold denoising (WSTD) is systematically introduced. • The problem that is hardly tackled by decomposition methods is solved via the WSTD. • A deep learning method gated recurrent unit (GRU) is successfully developed. • The WSTD-GRU has a good performance in accuracy, speed, volatility and adaptability. • The parameters of the proposed model are adjusted by cross-validated grid-search. Wind speed forecasting plays a pivotal role in the security and economy of the power system operation. However, accurate prediction on the wind speed value is still quite challenging due to three features of wind speed: randomness, fluctuation and unpredictability. Though many previous works have studied decomposition-based methods for preprocessing, these methods are difficult to use in actual prediction, since the newly acquired data will greatly affect the values of the initial decomposed subsequences. To solve this problem, a good solution referred as the wavelet soft threshold denoising (WSTD) is proposed herein, which is also systematically introduced for the first time. Moreover, a novel deep learning method gated recurrent unit (GRU) is successfully developed to be combined with WSTD to forecast the wind speed series in this paper. In the ensemble WSTD-GRU model, the WSTD is applied to filter the redundant information from the original data of wind speed series; GRU, which is particularly advantageous for learning the features in case of cooperating with WSTD, is adopted to predict the future multi-step wind speed values. Also, the parameters of the GRU are adjusted by cross-validated grid-search, and they are visualized in the paper. In experiments, thirty-five commonly used models are involved for comparison to evaluate the forecasting performance of the proposed model, through the datasets from four sites in the United States covering a wide range of longitudes. Finally, a superior performance of this methodology with relatively high accuracy, fast forecasting speed, small volatility of results and good adaptability is verified by the test results of case studies aforementioned. [ABSTRACT FROM AUTHOR]

Meta-analyses have concluded that positive emotions do not reduce appetitive risk behaviors (risky behaviors that fulfill appetitive or craving states, such as smoking and excessive alcohol use). We propose that this conclusion is premature. Drawing on the Appraisal Tendency Framework and related theories of emotion and decision-making, we hypothesized that gratitude (a positive emotion) can decrease cigarette smoking, a leading cause of premature death globally. A series of multimethod studies provided evidence supporting our hypothesis (collective N = 34,222). Using nationally representative US samples and an international sample drawn from 87 countries, Studies 1 and 2 revealed that gratitude was inversely associated with likelihood of smoking, even after accounting for numerous covariates. Other positive emotions (e.g., compassion) lacked such consistent associations, as expected. Study 3, and its replication, provided further support for emotion specificity: Experimental induction of gratitude, unlike compassion or sadness, reduced cigarette craving compared to a neutral state. Study 4, and its replication, showed that inducing gratitude causally increased smoking cessation behavior, as evidenced by enrollment in a web-based cessation intervention. Self-reported gratitude mediated the effects in both experimental studies. Finally, Study 5 found that current antismoking messaging campaigns by the US Centers for Disease Control and Prevention primarily evoked sadness and compassion, but seldom gratitude. Together, our studies advance understanding of positive emotion effects on appetitive risk behaviors; they also offer practical implications for the design of public health campaigns., Competing Interests: Competing interests statement:The authors declare no competing interest.

Abstract: Current literature demonstrates a lack of racial diversity in plastic surgery media. However, to our knowledge, no study has yet examined the racial diversity of Webpage content as if from a patient-search perspective. The objective of this study is to determine if there is a racial discrepancy between the US Census, American Society of Plastic Surgeons (ASPS) statistics, and the media featuring implied patients on US plastic surgeons' Webpages from a patient-focused approach. A Google search was completed using the term "(state) plastic surgeon." The first 10 relevant Web sites were collected for each state, and homepages were analyzed. In line with previous studies, the implied patients in media were classified into 1 of 6 skin tone categories: I, ivory; II, beige; III, light brown; IV, olive; V, brown; and VI, dark brown. These correlate to Fitzpatrick phototypes; however, the Fitzpatrick scale measures skin's response to UV exposure. Skin tone was used as a guide to measure racial representation in the media, with the caveat that skin tone does not absolutely correlate to racial identity. Categories I-III were further classified as "white" and IV-VI as "nonwhite." These data were compared with the 2020 ASPS demographics report and US Census. Four thousand eighty individuals were analyzed from 504 Webpages, the majority of which were those of private practice physicians. A total of 91.62% of individuals were classified as "white" and 8.38% "nonwhite." The distribution by category was as follows: I = 265, II = 847, III = 2626, IV = 266, V = 71, and VI = 5. Using χ2 analyses, a statistically significant difference was found between the racial representation within this sample and that of the 2020 US Census nationally (P < 0.001), regionally (P < 0.001), and subregionally (P < 0.001); the 2020 ASPS Cosmetic Summary Data (P < 0.001); and the 2020 ASPS Reconstructive Summary Data (P < 0.001). This study highlights the significant difference between racial representation on plastic surgeons' Webpages and the demographics of patients they serve. Further analyses should identify the impact of these representational disparities on patient care and clinical outcomes, as well as examine how best to measure racial diversity and disparities in patient-oriented media., Competing Interests: Conflicts of interest and sources of funding: none declared., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Background: Many factors affect outcomes after congenital cardiac surgery., Objectives: The RLS (Residual Lesion Score) study explored the impact of severity of residual lesions on post-operative outcomes across operations of varying complexity., Methods: In a prospective, multicenter, observational study, 17 sites enrolled 1,149 infants undergoing 5 common operations: tetralogy of Fallot repair (n = 250), complete atrioventricular septal defect repair (n = 249), arterial switch operation (n = 251), coarctation or interrupted arch with ventricular septal defect (VSD) repair (n = 150), and Norwood operation (n = 249). The RLS was assigned based on post-operative echocardiography and clinical events: RLS 1 (trivial or no residual lesions), RLS 2 (minor residual lesions), or RLS 3 (reintervention for or major residual lesions before discharge). The primary outcome was days alive and out of hospital within 30 post-operative days (60 for Norwood). Secondary outcomes assessed post-operative course, including major medical events and days in hospital., Results: RLS 3 (vs. RLS 1) was an independent risk factor for fewer days alive and out of hospital (p ≤ 0.008) and longer post-operative hospital stay (p ≤ 0.02) for all 5 operations, and for all secondary outcomes after coarctation or interrupted arch with VSD repair and Norwood (p ≤ 0.03). Outcomes for RLS 1 versus 2 did not differ consistently. RLS alone explained 5% (tetralogy of Fallot repair) to 20% (Norwood) of variation in the primary outcome., Conclusions: Adjusting for pre-operative factors, residual lesions after congenital cardiac surgery impacted in-hospital outcomes across operative complexity with greatest impact following complex operations. Minor residual lesions had minimal impact. These findings may provide guidance for surgeons when considering short-term risks and benefits of returning to bypass to repair residual lesions., Competing Interests: Funding Support and Author Disclosures This study was supported by grants (U24HL135691, U10HL068270, HL109818, HL109778, HL109816, HL109743, HL109741, HL109673, HL068270, HL109781, HL135665, HL135680) from the National Heart, Lung, and Blood Institute (NHLBI), National Institutes of Health (NIH). Drs. Nathan and Anderson were supported by K23 grants (NHLBI/NIH HL119600and HL133454, respectively). The views expressed in this paper are those of the authors and do not necessarily represent the views of the NHLBI, NIH, or U.S. Department of Health and Human Services. The authors have reported that they have no relationships relevant to the contents of this paper to disclose., (Copyright © 2021 American College of Cardiology Foundation. All rights reserved.)

Background: We aimed to explore whether the anatomic extent of lymph node metastases (AE-LNM) could independently predict prognosis of node-positive major salivary gland carcinoma (MaSGC)., Methods: A total of 376 pathologically node-positive MaSGC patients were identified from the Surveillance, Epidemiology and End Results database and constituted the training cohort. Using the X-Tile program, these patients were divided into three groups based on AE-LNM degrees. Discrimination of overall survival (OS) and disease-specific survival (DSS) was evaluated and compared with the 8th American Joint Committee on Cancer (AJCC) pN classification. The results were externally validated by 220 patients in a Chinese multicenter cohort (Validation cohort)., Results: Using the training cohort, AE-LNM was divided into Extent 1 (spread to parotid LNs or level I), Extent 2 (spread to level II-IV) and Extent 3 (spread to level V or bilateral LNs or rare LNs). Regarding both OS and DSS, the AE-LNM model revealed clear separation of survival curves, while the pN classification failed to discriminate the prognosis of pN1 and pN2 patients. When we incorporated both the AE-LNM model and AJCC pN classification into the same multivariate Cox analyses, AE-LNM was still an independent prognostic factor, while the AJCC pN classification lost its significance. These results were externally validated by the validation cohort., Conclusion: AE-LNM is an independent nodal prognosticator for node-positive MaSGC and may have improved discriminative ability over the current AJCC pN classification. Integration of anatomic extent of LNM into the current AJCC N classification could be considered., (Copyright © 2019. Published by Elsevier Ltd.)

Personality traits like novelty seeking (NS), harm avoidance (HA), and reward dependence (RD) are known to be moderately heritable (30-60%). These personality traits and their comorbidities, such as alcohol dependence (AD), may share genetic components. We examined 11,120 single nucleotide polymorphisms (SNPs) genotyped in 292 nuclear families from the Genetic Analysis Workshop 14, a subset from the Collaborative Study on the Genetics of Alcoholism (COGA). A family-based association analysis was performed using the FBAT program. NS, HA, and RD were treated as quantitative traits and AD as a binary trait. Based on a multivariate association test of three quantitative traits in FBAT, we observed 20 SNPs with p < 10(-3). Interestingly, several genes (TESK2, TIPARP, THEMIS, ABLIM1, RFX4, STON2 and LILRA1) are associated with three personality traits with p < 10(-3) using single trait analysis and AD. Especially, SNP rs727532 within ABLIM1 gene at 10q25 showed the most significant association (p = 6.4 × 10(-5)) in the multivariate test and strong associations with NS, HA, RD, and AD (p = 4.48 × 10(-4), 1.2 × 10(-5), 5.6 × 10(-5), 3.12 × 10(-4), respectively) in the COGA sample. In addition, the association of rs727532 with AD was confirmed in a replication study. This study reports some newly recognized associations between several genetic loci and both AD and three personality traits.

Background: Summative evaluation of medical students is a critical component of the educational process. Despite extensive literature on evaluation, few studies have centered on affiliate faculty members' attitudes toward summative evaluation of students, though it has been suggested that these attitudes influence their effectiveness as evaluators., Purpose: The objective is to examine affiliate faculty members' attitudes toward clinical clerkship evaluation using primarily qualitative research methods., Methods: The study used a nonexperimental research design and employed mixed methods. Data were collected through interviews, focus groups, and a questionnaire from 11 affiliate faculty members., Results: Themes emerging from the data fell into three broad categories: (a) factors that influence grading, (b) consequences of negative evaluations, and (c) disconnections in the grading process. The quantitative portion of the study revealed important discrepancies supporting the use of qualitative methods., Conclusions: The study highlights faculty members' struggles with the evaluative process and emphasizes the need for improvements in evaluation tools and faculty development.