Search Results (398)

Objective: Fatty acids (FAs) play a pivotal role in brain development and cognitive functions during adolescence. We aimed to investigate the association of red blood cell (RBC) FA patterns and several high order neuropsychological functions in adolescents. Methods: The study followed a cross-sectional design. Principal component analysis was applied to 22 FA species previously measured in RBC membranes (exposure variable) to identify FA principal components (PCs) from two cohorts of adolescents in Catalonia, Spain (mean age = 14.53 years). Multiple linear regression was then used to examine associations between PC FAs and cognitive outcomes—working memory, fluid intelligence, and risky decision-making (gain and loss domains). Regression models were adjusted for child sex, age, body mass index, maternal education, and cohort enrollment. Results: Three FA PCs (eigenvalues > 2.0) were retained for the current study: a very-long chain FAs PC, a long-chain omega-6 FA PC and an omega-3 FA PC. The omega-3 FA PC showed a positive association with scores of fluid intelligence (${\beta }_1$ = 0.14, CI = 0.05, 0.24, p for trend = 0.003) and risky decision-making (loss domain) (${\beta }_1$ = 0.27, CI = 0.03, 0.52, p for trend = 0.030). The very-long chain FAs and long-chain omega-6 FAs patterns showed no significant associations with any cognitive outcome. The PC of omega-3 FA and fluid intelligence associations remained significant after multiple testing corrections. Conclusions: After applying an agnostic approach of multiple FAs in RBC, we found omega-3 FA patterns were positively associated with fluid intelligence among adolescents. Full article

Background: The extracellular domain of the M2 protein (M2e) and the conserved region of the second subunit of the hemagglutinin (HA2, 76–130 а.а.) of the influenza A virus, could be used to develop broad-spectrum influenza vaccines. However, these antigens have low immunogenicity and require the use of special carriers to enhance it. Virus-like particles (VLPs) formed from viral capsid proteins are among the most effective carriers. Methods: In this work, we obtained and characterized VLPs based on capsid proteins (CPs) of single-stranded RNA bacteriophages Beihai32 and PQ465, simultaneously displaying M2e and HA2 peptides. Results: Fusion proteins expressed in Escherichia coli formed spherical VLPs of about 30 nm in size. Subcutaneous immunization of mice with chimeric VLPs elicited a robust humoral immune response against M2e and the whole influenza A virus, and promoted the formation of cytokine-secreting antigen-specific CD4⁺ and CD8⁺ effector memory T cells. Conclusions: VLPs based on CPs of phages Beihai32 and PQ465 carrying conserved peptides M2e and HA2 of the influenza A virus can be used for the development of universal influenza vaccines. Full article

Background. Cancer treatment-related cognitive impairment (CTRCI) is a frequent and persistent consequence of systemic cancer therapy, adversely affecting quality of life and independence among cancer survivors. Methods. To clarify the relative effectiveness of available treatments, we conducted a systematic review and network meta-analyses of randomized controlled trials evaluating psychological, pharmacological, and other interventions for established CTRCI in adult survivors of non-central nervous system cancers. Eligible trials reported objective outcomes in one or more of eight cognitive domains, including learning, memory, processing speed, word generation, cognitive flexibility, attention, working memory, and abstraction. Results. Eighteen studies met inclusion criteria, with 14 trials (n = 1100) contributing to network meta-analyses of immediate post-intervention effects across seven domains. A therapist-guided group intervention combining patient education and cognitive rehabilitation consistently ranked highest and was associated with significantly improved learning, memory, processing speed, attention, and working memory compared with a waitlist control, although the certainty of evidence (CoE) was low to very low and largely based on a single trial. Mindfulness-based interventions were also associated with improved processing speed (low CoE). Donepezil was associated with no benefit versus placebo for any domain. Conclusions. While findings suggest that structured multimodal group interventions may represent the most promising strategy for CTRCI, CoE was low, and additional rigorous, standardized trials are required. Full article

Background/Objectives: Shift work in healthcare professionals affects performance in high cognitive processing, especially in complex environments. However, the beneficial effects that working in complex environments may have on auditory–cognitive processing remain unknown. These professionals face increased challenges in decision-making due to factors such as noise exposure and sleep disturbances, which may lead to the development of enhanced auditory–cognitive resources. This study aims to investigate the associations between shift work and auditory–cognitive processing in middle-aged healthcare workers. Methods: Thirty middle-aged healthcare workers were equally allocated to a shift worker (SW) or a fixed-schedule worker (FSW) group. Performance on a cognitive test, and in pure-tone audiometry, speech in quiet and noise, and listening effort were used to explore whether correlations were specific to shift work. Results: Exploratory analyses indicated that shift workers tended to perform better in visuospatial/executive function, memory recall, memory index, orientation, and total MoCA score domains compared to fixed-schedule workers. In the SW group, hearing thresholds correlated with memory recall and memory index. In the FSW group, hearing thresholds correlated with orientation, memory index, and total MoCA score, while listening effort correlated with naming, and speech intelligibility in quiet correlated with total MoCA scores. Conclusions: These exploratory findings suggest that shift work may be linked to distinct auditory–cognitive patterns, with potential compensatory mechanisms in visuospatial/executive functions and memory among middle-aged healthcare workers. Larger, longitudinal studies are warranted to confirm whether these patterns reflect true adaptive mechanisms. Full article

Forecasting high-volume, univariate, and multivariate longitudinal data streams is a critical challenge in Big Data systems, especially with constrained computational resources and pronounced data variability. However, existing approaches often neglect multivariate statistical complexity (e.g., covariance, skewness, kurtosis) of multivariate time series or rely on recency-only windowing that discards informative historical fluctuation patterns, limiting robustness under strict resource budgets. This work makes two core contributions to big data forecasting. First, we establish a formal, multi-dimensional framework for quantifying “data bigness” across statistical, computational, and algorithmic complexities, providing a rigorous foundation for analyzing resource-constrained problems. Second, guided by this framework, we extend and validate the Adaptive High-Fluctuation Recursive Segmentation (AHFRS) algorithm for multivariate time series. By incorporating higher-order statistics such as covariance, skewness, and kurtosis, AHFRS improves predictive accuracy under strict computational budgets. We validate the approach in two stages. First, a real-world case study on a univariate Bitcoin time series provides a practical stress test using a Long Short-Term Memory (LSTM) network as a robust baseline. This validation reveals a significant increase in forecasting robustness, with our method reducing the Root Mean Squared Error (RMSE) by more than 76% in a challenging scenario. Second, its generalizability is established on synthetic multivariate data sets in Finance, Retail, and Healthcare using standard statistical models. Across domains, AHFRS consistently outperforms baselines; in our multivariate Finance simulation, RMSE decreases by up to 62.5% in Finance and Mean Absolute Percentage Error (MAPE) drops by more than 10 percentage points in Healthcare. These results demonstrate that the proposed framework and AHFRS advances the theoretical modeling of data complexity and the design of adaptive, resource-efficient forecasting pipelines for real-world, high-volume data ecosystems. Full article

Self-regulation is widely theorized as a foundation for early mathematics achievement, yet little is known about how specific forms of teacher scaffolding advance this process in preschool classroom contexts. Drawing on sociocultural and self-regulation theories, this study conceptualizes scaffolding as a mechanism through which teachers support children’s attention, working memory, and behavioral regulation during mathematics instruction. We extend theory by distinguishing three domains of scaffolding—Instructional Strategies, Management Organization, and Warmth Responsivity—and examining how each uniquely relates to children’s math outcomes. Participants were 416 preschoolers (M age = 59.7 months) and 18 head teachers in Santiago, Chile. Teachers’ scaffolding behaviors were video recorded and coded at the beginning and end of the school year, and children’s math achievement was assessed with the Woodcock-Muñoz III. Multilevel models controlling for prior achievement, age, income, and gender revealed that Management Organization was positively associated with math achievement, while Warmth Responsivity was negatively associated, and Instructional Strategies showed no significant effect. These findings refine theoretical models by showing that organizational scaffolding plays a particularly important role in supporting math learning, whereas warmth responsivity may function as compensatory scaffolding in response to children’s difficulties. The study advances understanding of how the quality and type of scaffolding shape the developmental pathway from self-regulation to mathematics achievement. Full article

This study examined the relationships between anxiety and executive functioning in Arab Israeli female adolescents diagnosed with Attention-Deficit/Hyperactivity Disorder (ADHD), compared to their typically developing peers. The aim was to explore differences in emotional and metacognitive executive functions, as well as how anxiety correlates with these cognitive domains within a culturally specific and gender-sensitive population. Eighty adolescent girls aged 15–18 (40 with ADHD and 40 controls) completed self-report measures assessing anxiety and executive functions using the BRIEF-SR and State-Trait Anxiety Inventory. No significant group differences were found in behavioral aspects of executive functions (inhibition, shifting, emotional control, and monitoring) or in overall anxiety levels. However, the ADHD group demonstrated significantly greater difficulties in all metacognitive executive function domains—including working memory, planning, organization, and task completion—as well as higher scores on the Metacognitive Index and Global Executive Composite. Correlational analyses revealed that anxiety was significantly associated with both behavioral and metacognitive executive dysfunction in the control group. In the ADHD group, however, anxiety was only significantly related to behavioral regulation, not metacognitive functioning. These findings underscore the importance of metacognitive support in interventions for adolescent girls with ADHD. Culturally tailored educational strategies that target working memory, planning, and organizational skills may help improve academic performance and overall adaptive functioning in this underserved population. Full article

Background: Early childhood is crucial for motor and cognitive development, with physical activity playing a key role. Mini-trampoline exercises may offer an effective approach to enhance these domains. Methods: This study assessed the effects of a mini-trampoline program on executive functions and motor skills in Tunisian preschoolers. Fifty-four children (age 3.87 ± 0.47 years) participated in a 12-week intervention, divided into a control group (n = 27), following standard activities, and an experimental group (n = 27), engaging in mini-trampoline exercises. Pre- and post-tests measured motor skills like postural steadiness, balance, and coordination, as well as cognitive functions, including working memory (WM) and inhibition. Results: Significant improvements were observed in the experimental group for functional mobility, postural steadiness, lower body strength, and inhibition (p < 0.001), whereas the control group showed minimal changes. ANOVA revealed no significant group × time effects, except for a trend in postural steadiness (p = 0.062), suggesting a potential benefit of the intervention. Conclusions: These findings highlight the potential of mini-trampoline exercises to enhance motor skills and specific executive functions in preschoolers, supporting their overall development. Full article

Emerging virtual reality (VR) technologies provide objective and immersive methods for assessing cognitive–motor function, particularly in elite sport. This study evaluated the reliability and validity of VR-based cognitive–motor assessments in a large sample of elite male athletes (n = 829). Ten cognitive–motor tests, delivered via Oculus Quest 2 headsets, were used, covering four domains: Balance and Gait (BG), Decision-Making (DM), Manual Dexterity (MD), and Memory (ME). A Confirmatory Factor Analysis (CFA) was conducted to establish a four-factor model and generate data-driven weights for domain-specific composite scores. The results demonstrated that the composite scores for BG, MD, ME, and a Global Cognitive–Motor (CM) score were all normally distributed. However, the DM score significantly deviated from normality, exhibiting a pronounced ceiling effect. Test–retest reliability was high across all cognitive–motor domains. In summary, VR assessments offer ecologically valid and precise measurements of cognitive–motor abilities by capitalising on high-fidelity motion tracking and standardised test delivery. In particular, the Global CM Score offers a robust metric for parametric analyses. While future work should address the DM ceiling effect and validate these tools in diverse populations, this approach holds significant potential for enhancing the precision and sensitivity of psychological and clinical assessment. Full article

Quantum spin transport in integrable systems reveals a rich nonequilibrium phenomena that challenges the conventional hydrodynamic framework. Recent advances in ultracold atom experiments with state preparation and single-site addressing have enabled the understanding of this anomalous behavior. Particularly, the full universality characterization of exotic initial states, as well as their measurement representation, remain unknown. By employing tensor network and contrast methods, we systematically investigate spin transport in the quantum XXZ spin chain and extract dynamical scaling exponents emerging from two paradigmatic and experimentally attainable initial states, i.e., multi-periodic domain-wall (MPDW) and spin-helix (SH) states. Our results using different values of anisotropic parameters $\Delta \in [0,1.2]$ demonstrate the evident impeded transport and the difference between the two states with increasing $\Delta$ values. Large-scale and consistent simulations confirm the contrast method as a viable scaling extraction approach for exotic states with periodicity within experimentally accessible timescales. Our work establishes a foundation for studying initial memory and the corresponding relations of emergent transport behavior in nonequilibrium quantum systems, opening avenues for the identification of their unique universality classes. Full article

The random thermal switching of domain walls (DWs) in perpendicularly magnetized anisotropy nanowires (PMA) poses a significant challenge for the reliability of spintronic storage devices. In this work, we study the thermal nucleation and dynamics of DWs in PMA nanowires using micromagnetic simulations. The focus is on the effect of device temperature, with attention to uniaxial anisotropy energy (K_u), saturation magnetization (M_s), and nanowire geometry. The results show that larger K_u or M_s reduces DW thermal switching, thereby enhancing DW thermal stability and increasing the DW nucleation temperature (T_n). A wider or thicker nanowire also lowers the probability of thermally induced DW creation, further improving stability. In addition, DW velocity rises with temperature, showing a thermally assisted motion. These results provide useful guidance for designing PMA-based memory devices with improved resistance to thermal fluctuations. Full article

Automotive manufacturing quality control faces persistent challenges such as limited defect samples, cross-domain variability, and the demand for interpretable decision-making. This work presents an explainable few-shot anomaly detection framework that integrates EfficientNet-based feature extraction, adaptive prototype learning, and component-specific attention mechanisms to address these requirements. The system is designed for rapid adaptation to novel defect types while maintaining interpretability through a multi-modal explainable AI module that combines visual, quantitative, and textual outputs. Evaluation on automotive datasets demonstrates promising performance on evaluated automotive components, achieving 99.4% accuracy for engine wiring inspection and 98.8% for gear inspection, with improvements of 5.2–7.6% over state-of-the-art baselines, including traditional unsupervised methods (PaDiM, PatchCore), advanced approaches (FastFlow, CFA, DRAEM), and few-shot supervised methods (ProtoNet, MatchingNet, RelationNet, FEAT), and with only 0.63% cross-domain degradation between wiring and gear inspection tasks. The architecture operates under real-time industrial constraints, with an average inference time of 18.2 ms, throughput of 60 components per minute, and memory usage below 2 GB on RTX 3080 hardware. Ablation studies confirm the importance of prototype learning (−4.52%), component analyzers (−2.79%), and attention mechanisms (−2.21%), with K = 5 few-shot configuration providing the best trade-off between accuracy and adaptability. Beyond performance, the framework produces interpretable defect localization, root-cause analysis, and severity-based recommendations designed for manufacturing integration with execution systems via standardized industrial protocols. These results demonstrate a practical and scalable approach for intelligent quality control, enabling robust, interpretable, and adaptive inspection within the evaluated automotive components. Full article

COVID-19 infection is often accompanied by psychological symptoms, which may persist long after the end of the infection (long COVID). The symptoms include fatigue, cognitive impairment, and anxiety. The reason for these long-term effects is currently unclear. Therapeutic approaches have included cognitive rehabilitation therapy, physical activity, and serotonin reuptake inhibitors (SSRIs) if depression co-exists. The neuropsychological evaluation of subjects with suspected cognitive issues is essential for the correct diagnosis. Most of the COVID-19 studies used the Montreal Cognitive Assessment (MoCA) or the Mini Mental State Examination (MMSE). However, MoCA scores can be confusing if not interpreted correctly. For this reason, we have developed an original technique to map cognitive domains and motor performance on various brain areas in COVID-19 patients aiming at improving the follow-up of long-COVID-19 symptoms. To this end, we retrospectively reanalyzed data from a cohort of 40 patients hospitalized for COVID-19 without requiring intubation or hemodialysis. Cognitive function was tested during hospitalization and six months after. Global cognitive function and cognitive domains were retrieved using MoCA tests. Laboratory data were retrieved regarding kidney function, electrolytes, acid–base, blood pressure, TC score, and P/F ratio. The dimensionality of cognitive functions was represented over cortical brain structures using a transformation matrix derived from fMRI data from the literature and the Cerebroviz mapping tool. Memory function was linearly dependent on the P/F ratio. We also used the UMAP method to reduce the dimensionality of the data and represent them in low-dimensional space. Six months after hospitalization, no cases of severe cognitive deficit persisted, and the number of moderate cognitive deficits reduced from 14% to 4%. Most cognitive domains (visuospatial abilities, executive functions, attention, working memory, spatial–temporal orientation) improved over time, except for long-term memory and language skills, which remained reduced or slightly decreased. The Cerebroviz algorithm helps to visualize which brain regions might be involved in the process. Many patients with COVID-19 continue to suffer from a subclinical cognitive deficit, particularly in the memory and language domains. Cerebroviz’s representation of the results provides a new tool for visually representing the data. Full article

Aviation safety remains a critical area of research, requiring accurate and efficient classification of incident reports to enhance risk assessment and accident prevention strategies. This study evaluates the performance of three deep learning models, BERT, Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) for classifying incidents based on injury severity levels: Nil, Minor, Serious, and Fatal. The dataset, drawn from ATSB records covering the years 2013 to 2023, consists of 53,273 records and was used. The models were trained using a standardized preprocessing pipeline, with hyperparameter tuning to optimize performance. Model performance was evaluated using metrics such as F1-score accuracy, recall, and precision. Results revealed that BERT outperformed both LSTM and CNN across all metrics, achieving near-perfect scores (1.00) for precision, recall, F1-score, and accuracy in all classes. In comparison, LSTM achieved an accuracy of 99.01%, with strong performance in the “Nil” class, but less favorable results for the “Minor” class. CNN, with an accuracy of 98.99%, excelled in the “Fatal” and “Serious” classes, though it showed moderate performance in the “Minor” class. BERT’s flawless performance highlights the strengths of transformer architecture in processing sophisticated text classification problems. These findings underscore the strengths and limitations of traditional deep learning models versus transformer-based approaches, providing valuable insights for future research in aviation safety analysis. Future work will explore integrating ensemble methods, domain-specific embeddings, and model interpretability to further improve classification performance and transparency in aviation safety prediction. Full article

Background/Objectives: Risk taking, a crucial component of decision-making, is domain-specific. However, most literature has focused on financial risk-taking in relation to cognitive functioning. The current study investigated the association between risk-taking behaviors in five different domains and various cognitive abilities in cognitively normal older adults. Methods: Participants (mean age = 69.55 ± 7.35 years; mean education = 16.69 ± 2.19 years; 58.9% female) completed the Domain-Specific Risk-Taking Scale (DOSPERT), consisting of financial, health, ethical, recreational, and social risk-taking questions. Cognitive performance on associative memory, verbal memory, working memory, verbal fluency, processing speed, and executive function was examined. Linear regression models adjusted for age, gender, and education level were conducted. Results: Two out of five risk-taking domains were associated with various aspects of cognition. Conclusions: Financial risk-aversion was linked to better memory, while health and safety risk-taking was linked to faster processing speed. These findings have practical implications in the context of everyday decision making. Full article