Psychometric properties of the chinese version of multidimensional experiential avoidance questionnaire-30.

BACKGROUND: Experiential avoidance represents the tendency to avoid negative internal experiences, which is a key concept in Acceptance and Commitment Therapy. However, existing measures of experiential avoidance (i.e., Acceptance and Action Questionnaire-II, AAQ-II) have some limitations. This study aims to assess the psychometric properties of the Chinese version of Multidimensional Experiential Avoidance Questionnaire-30 (MEAQ-30) and provide evidence for the reliability and validity of this new instrument. METHODS: Two questionnaire surveys were conducted. The first sample (N = 546) was analyzed using classical test theory (CTT), and the second sample (N = 511) was analyzed using multidimensional item response theory (MIRT). RESULTS: CTT supported the six-factor structure of MEAQ-30, indicating good internal consistency and measurement invariance across genders. Furthermore, the Chinese version of MEAQ-30 showed satisfactory convergent and discriminant validity. The incremental validity test showed that after controlling for the effects of neuroticism and AAQ-II, the Chinese version of MEAQ-30 could still significantly predict depression, anxiety, and stress. MIRT indicated that 30 items had good discrimination and difficulty, and the six subscales were sufficiently reliable across the continuum of experiential avoidance. CONCLUSION: The Chinese version of MEAQ-30 has good reliability and validity and is suitable for assessing experiential avoidance among Chinese populations.

Migration mechanism of atrazine in the simulated lake icing process at different freezing temperatures based on density function theory.

Atrazine causes concern due to its resistant to biodegradation and could be accumulated in aquatic organisms, causing pollution in lakes. This study measured the concentration of atrazine in ice and the water under ice through a simulated icing experiment and calculated the distribution coefficient K to characterize its migration ability in the freezing process. Furthermore, density functional theory (DFT) calculations were employed to expatiate the migration law of atrazine during icing process. According to the results, it could release more energy into the environment when atrazine staying in water phase (-15.077 kcal/mol) than staying in ice phase (-14.388 kcal/mol), therefore it was beneficial for the migration of atrazine from ice to water. This explains that during the freezing process, the concentration of atrazine in the ice was lower than that in the water. Thermodynamic calculations indicated that when the temperature decreases from 268 to 248 K, the internal energy contribution of the compound of atrazine and ice molecule (water cluster) decreases at the same vibrational frequency, resulting in an increase in the free energy difference of the compound from -167.946 to -165.390 kcal/mol. This demonstrated the diminished migratory capacity of atrazine. This study revealed the environmental behavior of atrazine during lake freezing, which was beneficial for the management of atrazine and other pollutants during freezing and environmental protection.

Deep learning-based solid component measuring enabled interpretable prediction of tumor invasiveness for lung adenocarcinoma.

BACKGROUND: The nature of the solid component of subsolid nodules (SSNs) can indicate tumor pathological invasiveness. However, preoperative solid component assessment still lacks a reference standard. METHODS: In this retrospective study, an AI algorithm was proposed for measuring the solid components ratio in SSNs, which was used to assess the diameter ratio (1D), area ratio (2D), and volume ratio (3D). The radiologist measured each SSN's consolidation to tumor ratio (CTR) twice, four weeks apart. The area under the receiver-operating characteristic (ROC) curve (AUC) was calculated for each method used to discriminate an Invasive Adenocarcinoma (IA) from a non-IA. The AUC and the time cost of each measurement were compared. Furthermore, we examined the consistency of measurements made by the radiologist on two separate occasions. RESULTS: A total of 379 patients (the primary dataset n = 278, the validation dataset n = 101) were included. In the primary dataset, compared to the manual approach (AUC: 0.697), the AI algorithm (AUC: 0.811) had better predictive performance (P =.0027) in measuring solid components ratio in 3D. Algorithm measurement in 3D had an AUC no inferior to 1D (AUC: 0.806) and 2D (AUC: 0.796). In the validation dataset, the AI 3D method also achieved superior diagnostic performance compared to the radiologist (AUC: 0.803 vs 0.682, P =.046). The two measurements of the CTR in the primary dataset, taken 4 weeks apart, have 7.9 % cases in poor consistency. The measurement time cost by the radiologist is about 60 times that of the AI algorithm (P <.001). CONCLUSION: The 3D measurement of solid components using AI, is an effective and objective approach to predict the pathological invasiveness of SSNs. It can be a preoperative interpretable indicator of pathological invasiveness in patients with lung adenocarcinoma.

The Sussex Oxford Compassion for the Self Scale validity in a working sample using classical test theory, item response theory and network analysis.

Self-compassion is a relatively new construct in the scientific literature, and there is currently a lack of robust psychometric measures of self-compassion in the workplace. Therefore, validating the Sussex Oxford Compassion for the Self Scale (SOCS-S) in various cultural settings is essential to add to the existing research on the psychometric properties of the scale. This study aimed to evaluate the validity of the SOCS-S in a Chinese working sample of 1,132 participants (39.4% males) using classical test theory (CTT), item response theory (IRT), and Network Analysis. The results supported the validity of the SOCS-S's five-factor structure, with high internal consistency and measurement invariance across genders. IRT was applied using a graded response model (GRM) to assess the overall SOCS-S scale items, indicating that all 20 items had sufficient discrimination indices and acceptable difficulty indices. Moreover, it is worth noting that the results of the network analysis are consistent with those of the IRT analysis. In summary, the study confirms the validity of the SOCS-S as a scale for assessing self-compassion among Chinese occupational groups.

The Migration Rules of Malathion during Indoor Simulated Lake Freezing.

The effect of malathion in ice is a poorly researched area, and ice is an important habitat for organisms at the base of the food web. This study presents laboratory-controlled experiments designed to investigate the migration rule of malathion during lake freezing. Concentrations of malathion were determined in samples of melted ice and in under-ice water. The effects of the initial sample concentration, freezing ratio, and freezing temperature on the distribution of malathion in the ice-water system were investigated. The concentration effect and migration capacity of malathion during freezing was characterized by the concentration rate and distribution coefficient. The results showed that the formation of ice led to the concentration of malathion appearing as follows: concentration in under-ice water > concentration in raw water > concentration in ice. This implied that malathion tended to migrate from the ice to the under-ice water during the freezing process. The increase in the initial malathion concentration, freezing ratio, and freezing temperature caused a more pronounced repulsion of the malathion by the ice and increased the migration to the under-ice water. When the solution of malathion with an initial concentration of 50 µg/L was frozen at -9 °C and the freezing ratio reached 60%, the concentration of malathion in the under-ice water was concentrated to 2.34 times the initial concentration. The migration of malathion to under-ice water during freezing may pose a potential threat to under-ice ecology; therefore, the environmental quality and impact of under-ice water in icebound lakes needs to be given more attention.

The association between experience-based risky choice and mathematical ability.

Mathematical ability has always been considered an important influencing factor in description-based risky choices. Experience-based risky choices, which occur frequently in daily life, are very different from description-based risky choices. The association between experience-based risky choice and mathematical ability remains unknown. This study adopts the feedback paradigm for experience-based risky choice to explore the association between multiple mathematical abilities and experience-based risky choice. The results show that, in experience-based risky choice, mathematical ability did not influence the decision to pursue higher expected value, but it did influence preference for risky. Thus, our study contributes to a more comprehensive view of mathematical ability and risky choice.

Algebra dissociates from arithmetic in the brain semantic network.

BACKGROUND: Mathematical expressions mainly include arithmetic (such as 8 - (1 + 3)) and algebra (such as a - (b + c)). Previous studies have shown that both algebraic processing and arithmetic involved the bilateral parietal brain regions. Although previous studies have revealed that algebra was dissociated from arithmetic, the neural bases of the dissociation between algebraic processing and arithmetic is still unclear. The present study uses functional magnetic resonance imaging (fMRI) to identify the specific brain networks for algebraic and arithmetic processing. METHODS: Using fMRI, this study scanned 30 undergraduates and directly compared the brain activation during algebra and arithmetic. Brain activations, single-trial (item-wise) interindividual correlation and mean-trial interindividual correlation related to algebra processing were compared with those related to arithmetic. The functional connectivity was analyzed by a seed-based region of interest (ROI)-to-ROI analysis. RESULTS: Brain activation analyses showed that algebra elicited greater activation in the angular gyrus and arithmetic elicited greater activation in the bilateral supplementary motor area, left insula, and left inferior parietal lobule. Interindividual single-trial brain-behavior correlation revealed significant brain-behavior correlations in the semantic network, including the middle temporal gyri, inferior frontal gyri, dorsomedial prefrontal cortices, and left angular gyrus, for algebra. For arithmetic, the significant brain-behavior correlations were located in the phonological network, including the precentral gyrus and supplementary motor area, and in the visuospatial network, including the bilateral superior parietal lobules. For algebra, significant positive functional connectivity was observed between the visuospatial network and semantic network, whereas for arithmetic, significant positive functional connectivity was observed only between the visuospatial network and phonological network. CONCLUSION: These findings suggest that algebra relies on the semantic network and conversely, arithmetic relies on the phonological and visuospatial networks.

The Lateralization of Attentional Functions in Action Video Game Players.

Action video game players (AVGPs) are proven to be significantly different from non-AVGPs (NAVGPs) in attention, which is proposed to be divided into three functional networks: alerting, orienting, and execution control. However, whether the hemispheric lateralization of attentional functions is influenced by the action video game is unclear. In the present study, we examined the lateralization of the three attentional functions in a group of AVGPs (n = 33) compared to NAVGPs (n = 34). The results showed that, relative to NAVGPs, the interactions between orienting and executive control in the left hemispheres of AVGPs were higher than those in the right hemisphere. Moreover, the correlations among the functions are much more sensitive in the left hemisphere. These results suggest significant left lateralization of the attentional functions in AVGPs.

Study of low-dose PET image recovery using supervised learning with CycleGAN.

PET is a popular medical imaging modality for various clinical applications, including diagnosis and image-guided radiation therapy. The low-dose PET (LDPET) at a minimized radiation dosage is highly desirable in clinic since PET imaging involves ionizing radiation, and raises concerns about the risk of radiation exposure. However, the reduced dose of radioactive tracers could impact the image quality and clinical diagnosis. In this paper, a supervised deep learning approach with a generative adversarial network (GAN) and the cycle-consistency loss, Wasserstein distance loss, and an additional supervised learning loss, named as S-CycleGAN, is proposed to establish a non-linear end-to-end mapping model, and used to recover LDPET brain images. The proposed model, and two recently-published deep learning methods (RED-CNN and 3D-cGAN) were applied to 10% and 30% dose of 10 testing datasets, and a series of simulation datasets embedded lesions with different activities, sizes, and shapes. Besides vision comparisons, six measures including the NRMSE, SSIM, PSNR, LPIPS, SUVmax and SUVmean were evaluated for 10 testing datasets and 45 simulated datasets. Our S-CycleGAN approach had comparable SSIM and PSNR, slightly higher noise but a better perception score and preserving image details, much better SUVmean and SUVmax, as compared to RED-CNN and 3D-cGAN. Quantitative and qualitative evaluations indicate the proposed approach is accurate, efficient and robust as compared to other state-of-the-art deep learning methods.