32 research outputs found
Vocabulary Learning Strategies Used by Extroverted and Introverted Iranian EFL Students
Abstract The present study is a research which explores the relationship between introversion/extroversion and the use of vocabulary learning strategies. The participants were 80 male and female EFL students of University of Sistan and Baluchestan who had studied EFL at the university for at least two years and were randomly selected. The participants were selected after excluding those who got L scores of above 4 or 5 from the EPI personality inventory. They were then divided into two groups of introverts and extroverts based on the Eysenck Personality Inventory (EPI). The independent sample t-test was run to examine if there is a significant relationship between the students' personality type (intro/extra) and their use of vocabulary learning strategies. It was found that there is no difference between introverts and extroverts in the overall use of vocabulary learning strategies as well as in the use of cognitive, memory, and social strategies. However, a significant difference was found between these two groups in the use of determination strategies. It was also found that of the four categories, determination strategies were the most frequently used category by all the participants. The results also revealed that extroverts used vocabulary learning strategies more frequently than their introverted counterparts
Normalized root mean squared errors (NRMSE) across three different models, with four sample sizes (N = 100, 200, 500, 1000), and at three levels of additive noise (small, medium, and large).
Corresponding standard deviations are recorded in parentheses.</p
FPCA results from SPI curves in TNBC dataset.
(A) Mean function. (B) First three eigenfunctions.</p
SPI under different configuration rotations.
At each rotation, pairwise distances between all cells were calculated based on their corresponding x- and y-coordinates. Distance ranges were subsequently computed. Finally, the spatial entropy curve across all distance ranges was obtained. (TIF)</p
FPCA results from SPI curves in NSCLC dataset.
Spatial heterogeneity in the tumor microenvironment (TME) plays a critical role in gaining insights into tumor development and progression. Conventional metrics typically capture the spatial differential between TME cellular patterns by either exploring the cell distributions in a pairwise fashion or aggregating the heterogeneity across multiple cell distributions without considering the spatial contribution. As such, none of the existing approaches has fully accounted for the simultaneous heterogeneity caused by both cellular diversity and spatial configurations of multiple cell categories. In this article, we propose an approach to leverage spatial entropy measures at multiple distance ranges to account for the spatial heterogeneity across different cellular organizations. Functional principal component analysis (FPCA) is applied to estimate FPC scores which are then served as predictors in a Cox regression model to investigate the impact of spatial heterogeneity in the TME on survival outcome, potentially adjusting for other confounders. Using a non-small cell lung cancer dataset (n = 153) as a case study, we found that the spatial heterogeneity in the TME cellular composition of CD14+ cells, CD19+ B cells, CD4+ and CD8+ T cells, and CK+ tumor cells, had a significant non-zero effect on the overall survival (p = 0.027). Furthermore, using a publicly available multiplexed ion beam imaging (MIBI) triple-negative breast cancer dataset (n = 33), our proposed method identified a significant impact of cellular interactions between tumor and immune cells on the overall survival (p = 0.046). In simulation studies under different spatial configurations, the proposed method demonstrated a high predictive power by accounting for both clinical effect and the impact of spatial heterogeneity.</div
FPCA results from SPI curves in ovarian cancer dataset.
(A) Mean function. (B) First two eigenfunctions. (TIF)</p
FPCA results from SPI curves in IHC COVID-19 dataset.
(A) Mean function. (B) First three eigenfunctions. (TIF)</p
Illustration for SPI calculation.
A point pattern consisting of 30 cells, with 10 cells per each type A, B, and C, was simulated. Circles of radius 0.25 were drawn around each point to identify co-occurrences of cell types with the first distance range w1 = (0, 0.25]. Specifically, there were 1 AA, 1 BB, 3 CC, 0 AB, 0 AC, and 0 BC. (TIF)</p
Kaplan–Meier curves for the overall survival probability from the NSCLC dataset, stratified using the Mantel correlation.
Subjects were classified as clustered vs. random based on the permutation test of the Mantel correlation. P-value of 0.24 indicates non-significant difference in survival probability in two groups. (TIF)</p