Data_Sheet_1_Brain optimization with additional study time: potential brain differences between high- and low-performance college students.ZIP

This study investigates potential differences in brain function among high-, average-, and low-performance college students using electroencephalography (EEG). We hypothesize that the increased academic engagement of high-performance students will lead to discernible EEG variations due to the brain’s structural plasticity. 61 third-year college students from identical majors were divided into high-performance (n = 20), average-performance (n = 21), and low-performance (n = 20) groups based on their academic achievements. We conducted three EEG experiments: resting state, Sternberg working memory task, and Raven progressive matrix task. Comprehensive analyses of the EEG data from the three experiments focused on power spectral density (PSD) and functional connectivity, with coherence (COH) employed as our primary metric for the latter. The results showed that in all experiments, there were no differences in working memory ability and IQ scores among the groups, and there were no significant differences in the power spectral densities of the delta, theta, alpha1, alpha2, beta, and gamma bands among the groups. Notably, on the Raven test, compared to their high-performing peers, low-performing students showed enhanced functional connectivity in the alpha 1 (8–9 Hz) band that connects the frontal and occipital lobes. We explored three potential explanations for this phenomenon: fatigue, anxiety, and greater cognitive effort required for problem-solving due to inefficient self-regulation and increased susceptibility to distraction. In essence, these insights not only deepen our understanding of the neural basis that anchors academic ability, but also hold promise in guiding interventions that address students’ diverse academic needs.</p

Funnel plot for the assessment of potential bias in s-p53 antibody assays.

<p>The funnel graph plots the DOR (diagnostic odds ratio) against the 1/root (effective sample size). The dotted line is the regression line. The result of the test for publication bias showed publication bias (p<0.001).</p

ROC curves of network structure inference.

<p>The performance of structure inference, under 6 different numbers of perturbations (from 2 to 7), is evaluated by ROC curves. Each subplot contains the inference results for 6 benchmark networks. The average AUROC is 0.97. More specifically, the maximum AUROC value 1.0 is achieved by the n-4 network (3–7 perturbations) and the n-11 network (6–7 perturbations), while the minimum AUROC value 0.88 is obtained by the n-58 network (2 perturbations).</p

Assessment of the Potential Diagnostic Value of Serum p53 Antibody for Cancer: A Meta-Analysis

<div><p>Background</p><p>Mutant p53 protein over-expression has been reported to induce serum antibodies against p53. We assessed the diagnostic precision of serum p53 (s-p53) antibodies for diagnosis of cancer patients and compared the positive rates of the s-p53 antibody in different types of cancers.</p><p>Methods</p><p>We systematically searched PubMed and Embase, through May 31, 2012. Studies were assessed for quality using QUADAS (quality assessment of studies of diagnostic accuracy). The positive likelihood ratio (PLR) and negative likelihood ratio (NLR) were pooled separately and compared with overall accuracy measures using diagnostic odds ratios (DORs) and Area under the curve(AUC). Meta regression and subgroup analyses were done, and heterogeneity and publication bias were assessed.</p><p>Results</p><p>Of 1089 studies initially identified, 100 eligible studies with 23 different types of tumor met the inclusion criteria for the meta-analysis (cases = 15953, controls = 8694). However, we could conduct independent meta analysis on only 13 of 36 types of tumors. Approximately 56% (56/100) of the included studies were of high quality (QUADAS score≥8). The summary estimates for quantitative analysis of serum p53 antibody in the diagnosis of cancers were: PLR 5.75 (95% CI: 4.60–7.19), NLR 0.81 (95%CI: 0.79–0.83) and DOR 7.56 (95% CI: 6.02–9.50). However, for the 13 types of cancers on which meta-analysis was conducted, the ranges for PLR (2.33–11.05), NLR (0.74–0.97), DOR (2.86–13.80), AUC(0.29–0.81), and positive rate (4.47%–28.36%) indicated significant heterogeneity. We found that breast, colorectal, esophageal, gastric, hepatic, lymphoma, lung and ovarian cancer had relatively reasonable diagnostic accuracy. The remaining results of the five types of cancers suggested that s-p53 antibody had limited value.</p><p>Conclusions</p><p>The current evidence suggests that s-p53 antibody has potential diagnostic value for cancer, especially for breast, colorectal, esophageal, gastric, hepatic, lymphoma, lung and ovarian cancer. The results showed that s-p53 antibody had high correlation with cancers.</p></div

Possible sources of heterogeneity of sub-group analysis.

<p>Note: QUADAS: quality assessment of studies of diagnostic accuracy, PLR: positive likelihood ratio, NLR: negative likelihood ratio, DOR: diagnostic odds ratio. PLR (95% CI)*, DOR (95% CI)* and NLR (95% CI)* were calculated using a random effect model.</p

Bar charts of RMSE for inferred transition matrices.

<p>These charts show the results of both Step 2 and Step 3 for 6 different benchmark networks with different numbers of perturbations varying from 2 to 7. In Step 2, the RMSE values range from to with the mean value of ; in Step 3, the RMSE values range from to with the mean value of . The RMSE ratios (Step 3/Step 2) vary from 0.14% to 51% with the mean value of 17%.</p

Characteristics of the benchmark network set.

<p>Characteristics of the benchmark network set.</p

Relationship between the average variance and RMSE.

<p>Each point represents an experiment for a benchmark network under a specific number of perturbations. For example, 2-n-53 means the experiment for n-53 network under 2 perturbations. The x-coordinate indicates the natural logarithm of the average variance for all elements in the refined transition matrix, while the y-coordinate indicates the RMSE values of the refined transition matrix. The RMSE values range from to and the average variance varies from to .</p

Pooled diagnostic accuracy of s-p53 antibody for detection of 13 types of cancer.

<p>Note: PLR: positive likelihood ratio, NLR: negative likelihood ratio, DOR: diagnostic odds ratio, AUC: the area under the SROC curve; PLR (95% CI)*, DOR (95% CI)* and NLR (95% CI)* were calculated using a random effect model.</p

Relationship between noise levels and RMSE.

<p>This chart shows the RMSE values for inferred transition matrices of n-39 network under 6 perturbations at different noise levels. The standard deviations of noises vary from 10 to 1. In Step 2, the RMSE values range from to ; in Step 3, the RMSE values range from to .</p