30 research outputs found

    Data_Sheet_1_The relation between family socioeconomic status and depressive symptoms among children and adolescents in mainland China: a meta-analysis.pdf

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    Family socioeconomic status (SES) is widely believed to be associated with depressive symptoms in children and adolescents. The correlation between SES and depressive symptoms changes based on social culture and the economic development level. In China, which includes many children and adolescents, the magnitude of the relationship between SES and depressive symptoms and its potential moderators remains unclear. The current meta-analysis was conducted to determine the overall association between SES and depressive symptoms in children and adolescents in mainland China. We included 197 estimates in mainland China from 2000–2023. Among 147,613 children and adolescents aged 7–18 years, the results showed a weak but significant overall negative association between SES and depression (r = −0.076). Moderator testing showed that the composite SES indicator (r = −0.104) had a stronger association with depression than parental educational level (r = −0.065) and occupational status (r = −0.025) but not family income (r = −0.088). Additionally, the negative association between SES and depression became weaker over the past 20 years in China (β = 0.010). Furthermore, the magnitude of the relationship between SES and depression was stronger in West China (r = −0.094) than in Middle China (r = −0.065), but not East China (r = −0.075). These findings indicate that the relationship between SES and depression among children and adolescents in mainland China may vary based on social contexts. It is necessary to further explore the effect of these social factors and the underlying mechanisms.</p

    Correlations between brain activation changes and self-reported impulse changes to unknown trials between post- and pre- tests in (a) left DLPFC, and (b) right ACC.

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    <p>Correlations between brain activation changes and self-reported impulse changes to unknown trials between post- and pre- tests in (a) left DLPFC, and (b) right ACC.</p

    The timeline of the “recall and recognition” task.

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    <p>The timeline of the “recall and recognition” task.</p

    Regional brain activity changes in recall stage in post-test minus pre-test in facing the new unlearned trials.

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    <p>Regional brain activity changes in recall stage in post-test minus pre-test in facing the new unlearned trials.</p

    Universal Parameter Optimization of Density Gradient Ultracentrifugation Using CdSe Nanoparticles as Tracing Agents

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    Density gradient ultracentrifugation (DGUC) has recently emerged as an effective nanoseparation method to sort polydispersed colloidal NPs mainly according to their size differences to reach monodispersed fractions (NPs), but its separation modeling is still lack and the separation parameters’ optimization mainly based on experience of operators. In this paper, we gave mathematical descriptions on the DGUC separation, which suggested the best separation parameters for a given system. The separation parameters, including media density, centrifuge speed and time, which affected the separation efficiency, were discussed in details. Further mathematical optimization model was established to calculate and yield the “best” (optimized) linear gradient for a colloidal system with given size and density. The practical experiment results matched well with theoretical prediction, demonstrating the DGUC method, an efficient, practical, and predictable separation technique with universal utilization for colloid sorting

    How Much Do Metamemory Beliefs Contribute to the Font-Size Effect in Judgments of Learning?

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    <div><p>Evidence shows that the font size of study items significantly influences judgments of learning (JOLs) and that people’s JOLs are generally higher for larger words than for smaller words. Previous studies have suggested that font size influences JOLs in a belief-based way. However, few studies have directly examined how much people’s beliefs contribute to the font-size effect in JOLs. This study investigated the degree to which font size influenced JOLs in a belief-based way. In Experiment 1, one group of participants (learners) studied words with different font sizes and made JOLs, whereas another group of participants (observers) viewed the learners' study phase and made JOLs for the learners. In Experiment 2, participants made both JOLs and belief-based recall predictions for large and small words. Our results suggest that metamemory beliefs play an important role in the font-size effect in JOLs.</p></div

    How font size affects judgments of learning: Simultaneous mediating effect of item-specific beliefs about fluency and moderating effect of beliefs about font size and memory

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    <div><p>Numerous studies have provided experience-based or theory-based frameworks for the basis of judgment of learning (JOL). However, few studies have directly measured processing experience and beliefs related to the same cue in one experiment and examined their joint contribution to JOLs. The present study focused on font-size effects and aimed to examine the simultaneous contribution of processing fluency and beliefs to the effect of font size on JOLs. We directly measured processing fluency via self-paced study time. We also directly measured participants’ beliefs via two approaches: pre-study global differentiated predictions (GPREDs) as an indicator of preexisting beliefs about font size and memory and ease of learning judgments (EORs) as online generated item-specific beliefs about fluency. In Experiment 1, EORs partially mediated the font-size effect, whereas self-paced study time did not. In Experiments 2a and 2b, EORs mediated the font-size effect; at the same time, beliefs about font size and memory moderated the font-size effect. In summary, the present study demonstrates a major role of beliefs underlying the font-size effect.</p></div

    Studies supporting processing experience and/or beliefs as the basis for JOLs.

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    <p><b>Note</b>. ST = self-paced study time, EORs = ease of reading judgments, TTA = numbers of trials to acquisition, GPREDs = global differentiated predictions.</p

    Box plots of plasma mitochondrial DNA concentration in trauma patients stratified by the severity of injury.

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    <p>Plasma mtDNA concentrations presented as median values (line across the boxes), IQR (limits of the boxes), and 5<sup>th</sup> to 95<sup>th</sup> percentiles (whiskers). Plasma mtDNA concentrations in traumatic patients with ISS ≧ 16 were significantly higher than in patients with ISS < 16 (P<0.001).</p
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