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ObjectiveHypertensive response to exercise (HRE) is observed in patients with hypertrophic cardiomyopathy (HCM) with normal resting blood pressure (BP). However, the prevalence or prognostic implications of HRE in HCM remain unclear.MethodsIn this study, normotensive HCM subjects were enrolled. HRE was defined as systolic BP > 210 mmHg in men or >190 mmHg in women, or diastolic BP > 90 mmHg, or an increase in diastolic BP > 10 mmHg during treadmill exercise. All participants were followed for subsequent development of hypertension, atrial fibrillation (AF), heart failure (HF), sustained ventricular tachycardia/fibrillation (VT/VF), and all-cause death. Six hundred and eighty HCM patients were screened.Results347 patients had baseline hypertension, and 333 patients were baseline normotensive. 132 (40%) of the 333 patients had HRE. HRE was associated with female sex, lower body mass index and milder left ventricular outflow tract obstruction. Exercise duration and metabolic equivalents were similar between patients with or without HRE, but the HRE group had higher peak heart rate (HR), better chronotropic response and more rapid HR recovery. Conversely, non-HRE patients were more likely to exhibit chronotropic incompetence and hypotensive response to exercise. After a mean follow-up of 3.4 years, patients with and without HRE had similar risks of progression to hypertension, AF, HF, sustained VT/VF or death.ConclusionHRE is common in normotensive HCM patients during exercise. HRE did not carry higher risks of future hypertension or cardiovascular adverse outcomes. Conversely, the absence of HRE was associated with chronotropic incompetence and hypotensive response to exercise.

Differential item functioning (DIF) occurs when the probability of endorsing an item differs across groups for individuals with the same latent trait level. The presence of DIF items may jeopardize the validity of an instrument; therefore, it is crucial to identify DIF items in routine operations of educational assessment. While DIF detection procedures based on item response theory (IRT) have been widely used, a majority of IRT-based DIF tests assume predefined anchor (i.e., DIF-free) items. Not only is this assumption strong, but violations to it may also lead to erroneous inferences, for example, an inflated Type I error rate. We propose a general framework to define the effect sizes of DIF without a priori knowledge of anchor items. In particular, we quantify DIF by item-specific residuals from a regression model fitted to the true item parameters in respective groups. Moreover, the null distribution of the proposed test statistic using robust estimator can be derived analytically or approximated numerically even when there is a mix of DIF and non-DIF items, which yields asymptotically justified statistical inference. The Type I error rate and the power performance of the proposed procedure are evaluated and compared with the conventional likelihood-ratio DIF tests in a Monte Carlo experiment. Our simulation study has shown promising results in controlling Type I error rate and power of detecting DIF items. Even when there is a mix of DIF and non-DIF items, the true and false alarm rate can be well controlled when a robust regression estimator is used.

In this paper, the emotions of dancers are identified in combination with the integrated deep-learning model. Firstly, four initial value features with important emotional states are extracted from the time, frequency, and time-frequency domains, respectively. It was isolated using a deep belief network enhanced by neuro colloidal chains. Finally, the finite Boltzmann criterion integrates the features of higher abstractions and predicts the emotional states. The results of DEAP data show that the correlation between EEG channels can be discovered and applied by glial chains. The fused deep learning model combines EEG emotional features with temporal, frequency, and expressive qualities.

Critical thinking is one of the important higher-order skills very much treasured in education, but hard to be measured using paper-pencil tests. In line with recent recommendation to measure high-order thinking skills with interactive tasks (vs. static one set of questions), in this study we developed an interactive and automated game-based assessment of critical thinking, using the Toulmin Model. In two real-life simulation stories, through interactive tasks in progressing scenes in the stories, students chose and rated evidence and conflicting reasons as supportive or non-supportive arguments in making the eventual decision. Critical thinking scores were awarded on choosing the appropriate evidence and reasons. The psychometric quality of the game was evaluated with 185 Chinese senior secondary students. Results showed that (i) reliabilities as measured by Cronbach's a of the whole scale and subdomains were reasonable; (ii) parallel form reliability was high; (iii) its correlation and convergent validity with the popular Halpern Critical Thinking Assessment were comparable to those in other studies; (iv) it was generally not related to academic performance; and (v) the game was interesting and engaging. We also noted that students hesitated to query others and they were weak in applying critical thinking to problem-solving, which were in congruent with previous research showing students rarely used critical thinking to solve complex, real-world problems. In sum, we demonstrated successfully the use of interactive simulation tasks in measuring critical thinking. With the advancement of technology, our study suggested the possibility of assessing hard-to-measure important complex higher-order competence with dynamic games.

This study examined whether the teacher-student relationship (TSR) served as a protective factor for students from families of lower socioeconomic status (SES). It was based on data from a standard mathematics assessment and survey using student and teacher questionnaires that were developed by the Collaborative Innovation Center of Assessment toward Basic Education Quality (CICA-BEQ) in China in 2016, which included 8707 fourth-grade Chinese students nested within 164 classes. We used multilevel structural equation models (MSEM) to investigate the mediating role of self-efficacy in mathematics in the relationship between SES and mathematics achievement and the moderating role of TSR in the direct and indirect relationship between SES and mathematics achievement at both the student-level and the class-level. The results suggested that the effect of SES on mathematics achievement was mediated by academic self-efficacy in mathematics both at the student-level and the class-level. The results also demonstrated a significant interaction between TSR and SES for self-efficacy both at the student-level and the class-level. Additionally, statistics indicated that TSR moderated the indirect relationship between SES and achievement via academic self-efficacy in mathematics., Author(s): Hongyun Liu [sup.1] , Qimeng Liu [sup.2] , Xiaofeng Du [sup.2] , Jian Liu [sup.2] , Cathy Ka Weng Hoi [sup.3] [sup.4] , Randall E. Schumacker [sup.4] Author Affiliations: [...]

The action sequences of respondents in problem-solving tasks reflect rich and detailed information about their performance, including differences in problem-solving ability, even if item scores are equal. It is therefore not sufficient to infer individual problem-solving skills based solely on item scores. This study is a preliminary attempt to incorporate process data analysis into the measurement of problem-solving ability. The entire procedure consists of using information from process data as prior information for the estimation of problem-solving proficiency in an item response model. The purpose of this study is twofold: (1) to investigate the impact of adding process information on the estimation of latent ability; (2) to examine the extent to which the ability estimates obtained from the combination model can reflect the information of the problem-solving process. Seven problem-solving items from the Programme for International Assessment of Adult Competencies were used. Results indicate that the inclusion of process priors enhances the correlation between proficiency estimates and process information related to the problem-solving strategies adopted by respondents, as well as to their solution efficiency. The inclusion of process priors further reveals differences in the problem-solving performance of respondents exhibiting the same score pattern and increases precision of latent ability estimation.

The primary goals of this study are to investigate if cognitive activation increases mathematics self-efficacy, which in turn alleviates anxiety and to examine the moderating role of internal migrant status. Hierarchical linear models and multilevel structural equation models were employed to datasets of 3088 fourth graders from 57 classrooms in China. The moderated effect and the moderated mediation effect of cognitive activation at both the student and classroom levels were tested. Results show that: (a) cognitive activation was positively associated with mathematics self-efficacy and negatively associated with mathematics anxiety; (b) the association of cognitive activation with mathematics self-efficacy and anxiety was stronger in classrooms with more internal migrant children and (c) mathematics self-efficacy mediated the relationship between cognitive activation and mathematics anxiety.

The prevalence and serious consequences of noneffortful responses from unmotivated examinees are well-known in educational measurement. In this study, we propose to apply an iterative purification process based on a response time residual method with fixed item parameter estimates to detect noneffortful responses. The proposed method is compared with the traditional residual method and noniterative method with fixed item parameters in two simulation studies in terms of noneffort detection accuracy and parameter recovery. The results show that when severity of noneffort is high, the proposed method leads to a much higher true positive rate with a small increase of false discovery rate. In addition, parameter estimation is significantly improved by the strategies of fixing item parameters and iteratively cleansing. These results suggest that the proposed method is a potential solution to reduce the impact of data contamination due to severe low test-taking effort and to obtain more accurate parameter estimates. An empirical study is also conducted to show the differences in the detection rate and parameter estimates among different approaches.

The response process of problem-solving items contains rich information about respondents' behaviours and cognitive process in the digital tasks, while the information extraction is a big challenge. The aim of the study is to use a data-driven approach to explore the latent states and state transitions underlying problem-solving process to reflect test-takers' behavioural patterns, and to investigate how these states and state transitions could be associated with test-takers' performance. We employed the Hidden Markov Modelling approach to identify test takers' hidden states during the problem-solving process and compared the frequency of states and/or state transitions between different performance groups. We conducted comparable studies in two problem-solving items with a focus on the US sample that was collected in PIAAC 2012, and examined the correlation between those frequencies from two items. Latent states and transitions between them underlying the problem-solving process were identified and found significantly different by performance groups. The groups with correct responses in both items were found more engaged in tasks and more often to use efficient tools to solve problems, while the group with incorrect responses was found more likely to use shorter action sequences and exhibit hesitative behaviours. Consistent behavioural patterns were identified across items. This study demonstrates the value of data-driven based HMM approach to better understand respondents' behavioural patterns and cognitive transmissions underneath the observable action sequences in complex problem-solving tasks.

Acute myeloid leukemia (AML) is one of the most common malignancies of the hematopoietic progenitor cell in adults. Quercetin has gained recognition over the years because of its anti-cancer effect with minimal toxicity. Herein, we aim to investigate the anti-leukemia mechanism of quercetin and to decipher the signaling pathway of quercetin in HL-60 leukemic cells. We observed that quercetin induces apoptosis and autophagic cell death, in which both pathways play an important role in suppressing the viability of leukemia cells. Phosphorylated AMPK (p-AMPK) protein expressions are lower in primary AML cells, HL-60 cells, KG-1 and THP-1 cells than in peripheral blood monocular cells. After quercetin treatment, the expression of p-AMPK is increased while the expression of p-mTOR is decreased in a dose-dependent manner. Mechanistically, compound C, an AMPK phosphorylation inhibitor, upregulates the phosphorylation of mTOR and inhibits autophagy and apoptosis in quercetin-induced HL-60 cells, while silencing of CaMKKβ inhibits the quercetin-induced phosphorylation of AMPK, resulting in increased mTOR phosphorylation. Furthermore, silencing of CaMKKβ inhibits the autophagy in HL-60 cells. Taken together, our data delineate that quercetin plays its anti-leukemia role by inhibiting cell viability and inducing apoptosis and autophagy in leukemia cells. Quercetin inhibits the phosphorylation of mTOR by regulating the activity of AMPK, thus playing a role in the regulation of autophagy and apoptosis. CaMKKβ is a potential upstream molecule for AMPK/mTOR signaling pathway, through which quercetin induces autophagy in HL-60 cells.

Background: Previous studies have suggested that teachers' frequent use of cognitive activation strategies in mathematics lessons is positively associated with students' mathematics achievement. Aims: This study aims to investigate the possible mediating role of mathematics self-efficacy in the association between cognitive activation and achievement in mathematics classrooms. Besides that, we explored the moderating role of students' socioeconomic status (SES) in the relationships between cognitive activation, mathematics self-efficacy, and mathematics achievement. Samples: This study employed data from a project of the National Assessment Center for Education Quality (NACEQ) in China. A sample with 8707 fourth-grade primary students and 129 mathematics teachers was analysed. Methods: The multi-level analysis was used in this study with students representing the level-1 units and teachers representing the level-2 units. Results: Based on a large-scale standard assessment and survey in China, this study showed that cognitive activation was positively related to mathematics performance overall. Mathematics self-efficacy played a vital mediating role in the association between cognitive activation and mathematics achievement. The indirect relationship between cognitive activation and mathematics achievement via mathematics self-efficacy was moderated by SES both at the student level and the teacher level. Conclusions: Our study suggests the importance of cognitively activating instruction, especially for students with lower SES and classes with lower average SES.

In latent growth curve modeling (LGCM), overall fit indices have garnered increased disputation for model selection, and model fit evaluation based on the mean structure has becoming popularity. The present study developed a versatile fit index, named Weighted Root Mean Squared Errors (WRMSE), based on individual case residuals (ICRs) with the aim of facilitating model selection. Through comparing to various fit indices, this study demonstrated that WRMSE could provide effective model fit information beyond previous indices. Due to higher sensitivity to misspecification in the mean structure, WRMSE was suggested to be utilized as a supplement for the traditional overall fit indices in the process of model selection. The limitation of WRMSE was that for complex models beyond the commonly used polynomial models, an increased number of repeated measurement occasions was required to ensure the precision of WRMSE. [ABSTRACT FROM AUTHOR]

Although low-protein diets can improve the nitrogen utilization efficiency and alleviate economic pressures in ruminants, they may also negatively impact dairy performance. Rumen-protected lysine (RPL) supplementation can improve the health status and growth performance of ruminants without compromising nitrogen utilization efficiency and feed intake. In this study, a total of thirty-three multiparous dairy goats in the late-lactation period were randomly divided into three groups that were separately fed the control diet (namely the protein-adequacy group), the low-protein diet (namely the protein-deficient group), and the RPL-supplemented protein-deficient diet (namely RPL-supplementation group) for five weeks. Here, we investigated the molecular mechanisms regarding how low-protein diets with RPL supplementation compromise lactation phenotypes in dairy goats through cross-tissue transcriptomic analyses. Dietary protein deficiency caused an imbalance in amino acid (AA) intake, disrupted hepatic function, and impaired milk synthesis. Transcriptomic analyses further showed that RPL supplementation exhibited some beneficial effects, like mitigating abnormal lipid and energy metabolism in the liver, elevating hepatic resistance to oxidative stress, improving the mammary absorption of AAs, as well as activating mammary lipid and protein anabolism primarily through peroxisome proliferator-activated receptor (PPAR) and janus kinase-signal transducer (JAK)—signal transducer and activator of transcription (STAT) signaling, respectively. RPL supplementation of a low-protein diet contributes to maintaining late lactation in dairy goats primarily through mitigating hepatic energy disturbances and activating both lipid and protein metabolism in the mammary glands. Since RPL supplementation initiated a series of comprised events on mammary protein and lipid metabolism as well as the hepatic function and energy generation in dairy goats under protein deficiency during late lactation, these findings thus provide some insights into how RPL supplementation helps maintain milk production and health in dairy mammals especially at late lactation. [ABSTRACT FROM AUTHOR]

Summary: Recent evidence suggests that immune thrombocytopenia (ITP), a common bleeding disorder, is linked to an imbalance in macrophage polarization and impaired bone marrow mesenchymal stem cells (BMSCs). However, the relationship between macrophage polarization imbalance and functional defects in BMSCs, as well as the involvement of associated molecules in BMSCs' defects, is not well understood. This study aimed to investigate the regulatory effects of high mobility group protein 1 (HMGB1) on the physiological functions of BMSCs, specifically in relation to macrophage polarization imbalance. Patients with ITP showed dysregulation in monocyte/macrophage polarization and impaired BMSCs function. HMGB1 was found to have a negative impact on the ability of BMSCs to regulate the imbalance in macrophage polarization, especially when inflammatory factors are present. The MyD88‐dependent pathway downstream of BMSCs was found to be significantly enhanced with HMGB1 treatment. Furthermore, treatment with toll‐like receptor 4 (TLR4) inhibitors successfully restored the regulatory capacity of BMSCs in ameliorating macrophage polarization imbalance and effectively inhibited the activation of the MyD88‐dependent pathway. Meanwhile, infusion of si‐TLR4‐BMSCs reversed HMGB1‐induced platelet dysfunction and reduced over‐polarization to M1‐like macrophages in the ITP mouse model. Consequently, targeting the HMGB1‐TLR4 pathway could be a potential approach to restore the immunoregulatory function of BMSCs. [ABSTRACT FROM AUTHOR]

Linear mixed-effects models have been increasingly used to analyze dependent data in psychological research. Despite their many advantages over ANOVA, critical issues in their analyses remain. Due to increasing random effects and model complexity, estimation computation is demanding, and convergence becomes challenging. Applied users need help choosing appropriate methods to estimate random effects. The present Monte Carlo simulation study investigated the impacts when the restricted maximum likelihood (REML) and Bayesian estimation models were misspecified in the estimation. We also compared the performance of Akaike information criterion (AIC) and deviance information criterion (DIC) in model selection. Results showed that models neglecting the existing random effects had inflated Type I errors, unacceptable coverage, and inaccurate R-squared measures of fixed and random effects variation. Furthermore, models with redundant random effects had convergence problems, lower statistical power, and inaccurate R-squared measures for Bayesian estimation. The convergence problem is more severe for REML, while reduced power and inaccurate R-squared measures were more severe for Bayesian estimation. Notably, DIC was better than AIC in identifying the true models (especially for models including person random intercept only), improving convergence rates, and providing more accurate effect size estimates, despite AIC having higher power than DIC with 10 items and the most complicated true model. [ABSTRACT FROM AUTHOR]