Fixed and growth mindsets represent implicit theories about the nature of one's abilities or traits. The existing body of research on academic achievement and the effectiveness of mindset ...interventions for student learning largely relies on the premise that fixed and growth mindsets are mutually exclusive. This premise has led to the common practice in which measures of one mindset are reversed and then assumed to represent the other mindset. Focusing on K-12 and university students (N = 27328), we tested the validity of this practice via a comprehensive item-level meta-analysis of the Implicit Theories of Intelligence Scale (ITIS). By means of meta-analytic structural equation modeling and network analysis, we examined (a) the ITIS item-item correlations and their heterogeneity across 32 primary studies; (b) the factor structure of the ITIS, including the distinction between fixed and growth mindset; and (c) moderator effects of sample, study, and measurement characteristics. We found positive item-item correlations within the sets of fixed and growth mindset items, with substantial between-study heterogeneity. The ITIS factor structure comprised two moderately correlated mindset factors (ρ = 0.63–0.65), even after reversing one mindset scale. This structure was moderated by the educational level and origin of the student sample, the assessment mode, and scale modifications. Overall, we argue that fixed and growth mindsets are not mutually exclusive but correlated constructs. We discuss the implications for the assessment of implicit theories of intelligence in education.
•The ITIS structure is best represented by two correlated factors.•Fixed and growth mindset are positively correlated, ρ = .63-.65•Internal consistencies of the ITIS are high, α = .80-.82, ω = .83-.91•The factor structure is not fully invariant between school and university samples.•Educational level and scale modifications explain heterogeneity in factor models.
The digital gender divide is one of the most critical issues in education today. Digital gender gaps can exist in students’ access to and use of ICT, attitudes toward technology, and digital ...knowledge and skills. However, previous research has primarily examined these divides in isolation and largely ignored their interdependencies. Using student data from the 2013 and 2018 International Computer and Information Literacy Study (ICILS), this study examines how these gaps are related. Specifically, we synthesize evidence on gender gaps in digital knowledge and skills, controlling for differences in ICT access and technology attitudes. In addition, we examine the role that technology attitudes play in the gender gap in digital knowledge and skills. Our findings suggest that (a) girls outperform boys in digital knowledge and skills (
β
= -0.11 to -0.29); (b) gender differences in attitudes toward technology partially explain gender differences in digital knowledge and skills; (c) the variability within and between countries in the gender gap in students' digital skills is partly explained by the type of digital skill and the country's socio-economic development and gender inequality. Overall, this research shows a relationship between the gender gaps in students’ digital knowledge and skills and the gender gaps in students’ attitudes toward technology. We conclude that the first- and second-level gender digital divides are connected.
Systematic reviews and meta-analyses are crucial for advancing research, yet they are time-consuming and resource-demanding. Although machine learning and natural language processing algorithms may ...reduce this time and these resources, their performance has not been tested in education and educational psychology, and there is a lack of clear information on when researchers should stop the reviewing process. In this study, we conducted a retrospective screening simulation using 27 systematic reviews in education and educational psychology. We evaluated the sensitivity, specificity, and estimated time savings of several learning algorithms and heuristic stopping criteria. The results showed, on average, a 58% (
SD
= 19%) reduction in the screening workload of irrelevant records when using learning algorithms for abstract screening and an estimated time savings of 1.66 days (
SD
= 1.80). The learning algorithm random forests with sentence bidirectional encoder representations from transformers outperformed other algorithms. This finding emphasizes the importance of incorporating semantic and contextual information during feature extraction and modeling in the screening process. Furthermore, we found that 95% of all relevant abstracts within a given dataset can be retrieved using heuristic stopping rules. Specifically, an approach that stops the screening process after classifying 20% of records and consecutively classifying 5% of irrelevant papers yielded the most significant gains in terms of specificity (
M
= 42%,
SD
= 28%). However, the performance of the heuristic stopping criteria depended on the learning algorithm used and the length and proportion of relevant papers in an abstract collection. Our study provides empirical evidence on the performance of machine learning screening algorithms for abstract screening in systematic reviews in education and educational psychology.
Breast cancer is the second most common diagnosed type of cancer in women. Chronic neuropathic pain after mastectomy occurs frequently and is a serious health problem. In our previous single-center, ...prospective, randomized controlled clinical study, we demonstrated that the combination of serratus anterior plane block (SAM) and pectoral nerve block type I (PECS I) with general anesthesia reduced acute postoperative pain. The present report describes a prospective follow-up study of this published study to investigate the development of chronic neuropathic pain 12 months after mastectomy by comparing the use of general anesthesia alone and general anesthesia with SAM + PECS I. Additionally, the use of analgesic medication, quality of life, depressive symptoms, and possible correlations between plasma levels of interleukin (IL)-1 beta, IL-6, and IL-10 collected before and 24 h after surgery as predictors of pain and depression were evaluated. The results showed that the use of SAM + PECS I with general anesthesia reduced numbness, hypoesthesia to touch, the incidence of patients with chronic pain in other body regions and depressive symptoms, however, did not significantly reduce the incidence of chronic neuropathic pain after mastectomy. Additionally, there was no difference in the consumption of analgesic medication and quality of life. Furthermore, no correlation was observed between IL-1 beta, IL-6, and IL-10 levels and pain and depression. The combination of general anesthesia with SAM + PECS I reduced the occurrence of specific neuropathic pain descriptors and depressive symptoms. These results could promote the use of SAM + PECS I blocks for the prevention of specific neuropathic pain symptoms after mastectomy.Registration of clinical trial: The Research Ethics Board of the Hospital Sirio-Libanes/Brazil approved the study (CAAE 48721715.0.0000.5461). This study is registered at Registro Brasileiro de Ensaios Clinicos (ReBEC), and ClinicalTrials.gov, Identifier: NCT02647385.
The onset of global climate change has led to abnormal rainfall patterns, disrupting associations between wildlife and their symbiotic microorganisms. We monitored a population of pumpkin toadlets ...and their skin bacteria in the Brazilian Atlantic Forest during a drought. Given the recognized ability of some amphibian skin bacteria to inhibit the widespread fungal pathogen Batrachochytrium dendrobatidis (Bd), we investigated links between skin microbiome health, susceptibility to Bd and host mortality during a die‐off event. We found that rainfall deficit was an indirect predictor of Bd loads through microbiome disruption, while its direct effect on Bd was weak. The microbiome was characterized by fewer putative Bd‐inhibitory bacteria following the drought, which points to a one‐month lagged effect of drought on the microbiome that may have increased toadlet susceptibility to Bd. Our study underscores the capacity of rainfall variability to disturb complex host–microbiome interactions and alter wildlife disease dynamics.
Amphibian skin bacteria are known to play an important role in inhibiting the widespread fungal pathogen Batrachochytrium dendrobatidis (Bd). We monitored a population of pumpkin toadlets in the Brazilian Atlantic Forest throughout a short‐term drought and investigated links between skin microbiome health, pathogen susceptibility and host mortality during a die‐off event. Our structural equation model showed strong indirect effects of drought on Bd loads through microbiome dispersion, surpassing the magnitude of the direct effect of rainfall deficit on Bd loads.
This study aimed to assess the impact of the type of undergraduate studies and the ability to form mental images on representations of city imagery. A total of 291 Architecture and Business Studies ...undergraduates participated in the study. Participants completed a 5‐min task of drawing their own mental map of a city. The number of point marks, line marks, and area marks drawn and the system of representation were assessed. Business Studies undergraduates represented more point marks using a point than Architecture undergraduates, whereas the latter drew more point marks in plan and in 3D than the former. The ability to form spatial images had no influence on the number of point, line, or area marks drawn. Significant differences between women and men were observed in the 3D representations, and in the configuration.
The increasing availability of individual participant data (IPD) in the social sciences offers new possibilities to synthesize research evidence across primary studies. Two-stage IPD meta-analysis ...represents a framework that can utilize these possibilities. While most of the methodological research on two-stage IPD meta-analysis focused on its performance compared with other approaches, dealing with the complexities of the primary and meta-analytic data has received little attention, particularly when IPD are drawn from complex sampling surveys. Complex sampling surveys often feature clustering, stratification, and multistage sampling to obtain nationally or internationally representative data from a target population. Furthermore, IPD from these studies is likely to provide more than one effect size. To address these complexities, we propose a two-stage meta-analytic approach that generates model-based effect sizes in Stage 1 and synthesizes them in Stage 2. We present a sequence of steps, illustrate their implementation, and discuss the methodological decisions and options within. Given its flexibility to deal with the complex nature of the primary and meta-analytic data and its ability to combine multiple IPD sets or IPD with aggregated data, the proposed two-stage approach opens up new analytic possibilities for synthesizing knowledge from complex sampling surveys. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
Nicaraguan government puts mining over justice Oliveira, Rafael F; Ottoni, Felipe P; Vieira, Lucas O ...
Science (American Association for the Advancement of Science)
383, Številka:
6686
Journal Article
The purple non-sulfur bacterium Rhodopseudomonas palustris is recognized as a critical microorganism in the nitrogen and carbon cycle and one of the most common members in wastewater treatment ...communities. This bacterium is metabolically extremely versatile. It is capable of heterotrophic growth under aerobic and anaerobic conditions, but also able to grow photoautotrophically as well as mixotrophically. Therefore R. palustris can adapt to multiple environments and establish commensal relationships with other organisms, expressing various enzymes supporting degradation of amino acids, carbohydrates, nucleotides, and complex polymers. Moreover, R. palustris can degrade a wide range of pollutants under anaerobic conditions, e.g., aromatic compounds such as benzoate and caffeate, enabling it to thrive in chemically contaminated environments. However, many metabolic mechanisms employed by R. palustris to breakdown and assimilate different carbon and nitrogen sources under chemoheterotrophic or photoheterotrophic conditions remain unknown. Systems biology approaches, such as metabolic modeling, have been employed extensively to unravel complex mechanisms of metabolism. Previously, metabolic models have been reconstructed to study selected capabilities of R. palustris under limited experimental conditions. Here, we developed a comprehensive metabolic model (M-model) for R. palustris Bis A53 (iDT1294) consisting of 2,721 reactions, 2,123 metabolites, and comprising 1,294 genes. We validated the model using high-throughput phenotypic, physiological, and kinetic data, testing over 350 growth conditions. iDT1294 achieved a prediction accuracy of 90% for growth with various carbon and nitrogen sources and close to 80% for assimilation of aromatic compounds. Moreover, the M-model accurately predicts dynamic changes of growth and substrate consumption rates over time under nine chemoheterotrophic conditions and demonstrated high precision in predicting metabolic changes between photoheterotrophic and photoautotrophic conditions. This comprehensive M-model will help to elucidate metabolic processes associated with the assimilation of multiple carbon and nitrogen sources, anoxygenic photosynthesis, aromatic compound degradation, as well as production of molecular hydrogen and polyhydroxybutyrate.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
There are a considerable number of studies concerning the behavioral effects of physical exercise on the epileptic brain; however, the intracellular signaling mechanisms involved remain unclear. We ...investigated the effects of aerobic exercise on hippocampal levels of brain‐derived neurotrophic factor (BDNF), expression of its receptor tropomyosin receptor kinase B (TrkB), and activation of intracellular proteins related to BDNF‐TrkB signaling in male Wistar rats with pilocarpine‐induced epilepsy. Thirty days after the first spontaneous seizure, rats from the exercise group undertook a 30‐day physical exercise program on the treadmill. Thereafter, BDNF levels, expression of TrkB, and activation of intracellular proteins were quantified by enzyme‐linked immunosorbent assay, Western blotting, and multiplex assay, respectively. Statistical analyses were conducted using nonparametric tests. Rats with epilepsy presented decreased BDNF levels compared with control rats. BDNF levels increased significantly in the exercise group compared with the epileptic and control groups. Expression of full‐length and truncated TrkB was increased in rats with epilepsy, and physical exercise restored its expression to control levels. RAC‐alpha serine/threonine‐protein kinase, mammalian target of rapamycin, and extracellular signal‐regulated kinase activation were reduced in rats with epilepsy, and exercise increased activation compared with control and epilepsy groups. Increased cAMP response element binding protein activation was observed in the exercise group compared with the epilepsy group. Our findings indicate that the beneficial effects of exercise in the epileptic brain can be in part related to alterations in the activation of proteins related to the BDNF‐TrkB signaling pathway.
Beneficial effects of physical exercise on epilepsy have been reported in the literature. In this study, we investigated one of the possible mechanisms by which physical exercise reduces seizure frequency in animals with epilepsy. An aerobic exercise program increased hippocampal BDNF levels, restored to control levels the overexpression of fl‐TrkB and tr‐TrkB isoforms and altered the hippocampal activation of some proteins linked to BDNF‐TrkB intracellular signaling. These present results strengthen the beneficial role of physical exercise as a complementary therapy for epilepsy.
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