324139 research outputs found
Sort by
Causal discovery methods in psychological research: Foundations, algorithms, and a practical tutorial in R
Get PDFUnderstanding causality and the mechanisms underlying psychological phenomena has been a cornerstone of psychological research with significant implications for theory development and intervention design. While traditional methods such as experimental manipulations or structural equation modelling have been extensively used to explore causal relationships, recent advances in computational techniques have introduced causal discovery methods as a powerful alternative. These methods can uncover complex causal network structures from observational or interventional data, enabling the identification of causal directions in intricate interdependencies involving numerous variables. Building on a growing body of literature, this paper provides a comprehensive survey of core causal discovery algorithms and their recent applications across various disciplines, with a particular focus on their use in uncovering psychological mechanisms. To complement this overview, we provide a tutorial using data from the Health Behavior in School-Aged Children (HBSC) study. This case study demonstrates how causal discovery can be applied to examine gender-specific mechanisms underlying bullying-related outcomes. We also discuss the opportunities and challenges of integrating causal discovery into psychological research
A Folding Magnetic Soft Sheet Robot With Real‐Time Reconfigurable Magnetization for Targeted Drug Delivery
Get PDFMagnetic soft robots have the characteristics of small size, noncable drive, and motion agility, which are suitable for medical operations in the gastrointestinal tract. However, multiangle folding and reconfigurable magnetization have yet to be fully investigated, and thus, the study of magnetic soft robots with morphological changes and medical functions is still challenging. To this end, we propose a magnetic soft sheet robot based on the magnetorheological fluids, which presents a remarkable capability for reversible folding motion, rapid real‐time reconfigurable magnetization, and targeted drug delivery functions. Furthermore, the robot has a fully soft sheet structure that is not magnetized in a zero magnetic field. After folding, the surface area of the soft sheet robot can be reduced to one third of its original area to cope with the complex gastrointestinal cavities. Five kinds of soft sheet robot prototypes with different magnetic driving abilities are fabricated, and the movement experiments of these robots are carried out on a smooth surface, a flexible fluff surface, a slope surface, underwater, and under load, respectively. The influence mechanisms of different magnetic field strengths and frequencies on robot movement are analyzed. The effectiveness of the proposed scheme is verified by ex vivo porcine stomach experiments and ultrasonic detection
Annotation efficient learning with affinity graphs
Get PDFAnnotation-efficient learning has emerged as a critical area of research due to the scarcity of labeled samples posing a substantial barrier to developing robust fullysupervised deep neural networks. When the availability of labeled samples is limited, developing an effective learning system becomes a formidable challenge. Conversely, unlabeled data is often plentiful and can be obtained at a relatively low cost. Consequently, the concept of leveraging a substantial volume of unlabeled data to train deep models, despite the paucity of labeled samples, emerges as a compelling proposition. This thesis explores novel approaches to annotation-efficient learning in machine learning, with a focus on leveraging implicit relationships within data to improve model performance in scenarios with limited labeled data. The research addresses three key challenges: effectively utilizing implicit structures in input data, integrating these structures into the learning process, and determining optimal representations for extracting and utilizing implicit relationships. The methodology centers on the development and application of affinity graph constraints across three domains: self-supervised learning for whole-slide image analysis, semi-supervised learning for medical image segmentation, and multi-modal learning for single-cell data integration. The methodology centers on the development and application of affinity graph constraints across three domains: self-supervised learning for whole-slide image analysis, semi-supervised learning for medical image segmentation, and multi-modal learning for single-cell data integration. Specifically, we propose: 1. An affinity graph constraint (AGC) for self-supervised learning on whole-slide images, which captures fine-grained features and improve existing self-supervised methods. 2. An affinity-graph-guided contrastive learning framework for semi-supervised medical image segmentation, incorporating patch-wise class-centric sampling and hard-negative reweighting. 3. A single-cell Affinity Graph transFormer (scAGFormer) for multi-modal singlecell analysis, which employs an affinity graph prior to improve modality transformation. Key findings demonstrate significant improvements over state-of-the-art methods across all three domains. The proposed affinity graph-based approaches consistently enhance model performance, particularly in scenarios with limited labeled data. The meworks show remarkable generalizability across different datasets and tasks, highhting their potential for broad application in medical image analysis and computational biology
Reimagining primary health care: a historical and contemporary scoping review of community-based primary health care models and innovations
Get PDFObjectivesCommunity-based primary health care (CBPHC) has long underpinned health service delivery in resource-limited settings. However, demographic shifts, increasing chronic disease burdens, and digital transformations challenge its sustainability. This review synthesizes historical and contemporary evidence on CBPHC to assess effectiveness, identify limitations, and outline future directions toward universal health coverage (UHC).MethodsUsing the Arksey and O'Malley framework, we conducted a scoping review of global literature from 1975 to 2025 across PubMed, Scopus, Web of Science, Google Scholar, and grey sources. Data were thematically analyzed into categories capturing evolution, achievements, challenges, and future directions.ResultsA total of 134 documents were reviewed. CBPHC improved access to essential services, particularly maternal and child health, infectious disease control, and health promotion. Programs led by community health workers and volunteers strengthened systems but faced persistent barriers such as attrition, limited funding, and weak integration. Case studies from Nepal, Ethiopia, Brazil, and Rwanda showed improved maternal and child outcomes and pandemic preparedness and resilience. Emerging challenges include syndemics, demographic shifts, and urbanization.ConclusionsCBPHC remains vital for advancing universal health coverage. Its sustainability depends on evolving into a diagonally integrated, people-centered, and digitally enabled model supported by equitable investment in governance, workforce training, and community engagement
Linking obesity with white matter microstructure highlights the importance of brainstem tracts and sex differences
Get PDFWhile obesity (body mass index ≥ 30) has been consistently associated with white matter diffusion magnetic resonance imaging (MRI) phenotypes, the contributions of common obesity phenotypes on various diffusion metrics, and the moderating effects of sex and age, require further clarification. This study aims to elucidate these body–brain connections to enhance our understanding of the comorbid link between obesity and body anthropometrics and the brain using a large-scale dataset. We analysed cross-sectional data from 40 040 participants from the UK Biobank (52.2% female; ages 44–83 years) using multiple linear regression to evaluate how obesity and body anthropometrics relate to regional white matter diffusion tensor imaging metrics (fractional anisotropy, axial diffusivity, radial diffusivity, mean diffusivity). We also examined interactions with age and sex. Our analyses revealed significant associations between individual obesity phenotypes (i.e. obesity and body anthropometrics) and diffusion tensor imaging metrics of small effects, with partial correlation coefficient |r| effect sizes ranging from 0.02 to 0.20 for most regions of interest with largest effects in brainstem tracts. We observed more widespread sex-by-obesity phenotypes than age-by-obesity phenotypes interaction effects on diffusion tensor imaging metrics. Our results link obesity and body anthropometrics with white matter phenotypes and suggests that shared body fat-related pathways link physical and brain health that may vary based on sex and age. Understanding these body–brain relationships, and the role of age and sex, could enhance the development and evaluation of targeted, personalized, treatment strategies for brain disorders that co-occur with obesity, although further longitudinal and intervention studies are needed to map the causal dynamics of these associations
Observation of Transition from Rate Law to Butler–Volmer Controlled Water Oxidation Kinetics on Hematite Photoanodes
Get PDFDespite its central role in photoelectrochemical (PEC) water splitting, the mechanistic pathway of water oxidation on metal oxides remains unresolved, with population-based and Butler–Volmer (BV) models offering distinct views on how surface valence band holes drive the reaction. Here, we bring together these two perspectives by combining operando photoinduced absorption (PIA) spectroscopy with photocurrent analyses on α-Fe2O3 (hematite) photoanodes as a function of light intensity. We find a crossover from population-controlled, rate law water oxidation at low hole densities to a BV-like, potential driven regime at high densities, triggered by band edge unpinning once surface M–OH species are fully oxidized, and excess holes accumulate without compensation. This mechanistic transition unifies competing models of interfacial charge transfer and reveals design principles for optimizing water oxidation in metal oxide photoelectrodes
Evaluation of the replicability of systematic reviews with meta-analyses of the effects of health interventions
Get PDFSystematic reviews are often characterized as being inherently replicable, but several studies have challenged this claim. The objective of the study was to investigate the variation in results following independent replication of literature searches and meta-analyses of systematic reviews. We included 10 systematic reviews of the effects of health interventions published in November 2020. Two information specialists repeated the original database search strategies. Two experienced review authors screened full-text articles, extracted data, and calculated the results for the first reported meta-analysis. All replicators were initially blinded to the results of the original review. A meta-analysis was considered not ‘fully replicable’ if the original and replicated summary estimate or confidence interval width differed by more than 10%, and meaningfully different if there was a difference in the direction or statistical significance. The difference between the number of records retrieved by the original reviewers and the information specialists exceeded 10% in 25/43 (58%) searches for the first replicator and 21/43 (49%) searches for the second. Eight meta-analyses (80%, 95% CI: 49–96) were initially classified as not fully replicable. After screening and data discrepancies were addressed, the number of meta-analyses classified as not fully replicable decreased to five (50%, 95% CI: 24–76). Differences were classified as meaningful in one blinded replication (10%, 95% CI: 1–40) and none of the unblinded replications (0%, 95% CI: 0–28). The results of systematic review processes were not always consistent when their reported methods were repeated. However, these inconsistencies seldom affected summary estimates from meta-analyses in a meaningful way
Validation of the International League Against Epilepsy ( ILAE ) Risk of Bias Tool against the Newcastle–Ottawa Scale in epilepsy research
Get PDFObjective: Systematic reviews and meta‐analyses (SRMAs) are critical for synthesizing evidence and guiding clinical and public health decision‐making. This study aims to evaluate the reliability, validity and reproducibility of the International League Against Epilepsy (ILAE) Commission on Epidemiology Risk of Bias Tool by comparing it against the Newcastle–Ottawa Scale (NOS) to inform whether the ILAE tool may serve as a valid alternative in epilepsy‐focused evidence syntheses. Methods: This study was planned a priori on three consecutive SRMAs. We assessed 54 observational studies included in these SRMAs focused on psychiatric comorbidities in persons with epilepsy. Eligible studies had ≥30 participants per group and validated criteria for diagnosing epilepsy and psychiatric conditions. Two independent raters scored all studies using both tools. The ILAE tool comprises six specific domains: (1) Source of Study Population; (2) Completeness (Sensitivity) of Epilepsy Case‐Finding; (3) Sensitivity of Comorbidity Determination; (4) Accuracy of Epilepsy Diagnosis; (5) Accuracy of Comorbidity Diagnosis; and (6) Representativeness of Study Sample. Test–retest reproducibility used intraclass correlation coefficient (ICC). Correlation used Spearman's rho. Agreement used weighted kappa. Bland–Altman analysis reported mean difference. Results: There was a strong positive correlation between NOS scores and ILAE ratings (Spearman's rho = 0.80, 95% confidence interval [CI] 0.68–89, p < 0.001). Cohen's weighted kappa was 0.68 (95% CI 0.37–0.92, p < 0.001). Bland–Altman mean difference was 0.09 with limits from −0.48 to 0.67, showing good agreement between tools. The ILAE tool showed excellent test–retest reproducibility (ICC 0.90, 95% CI 0.83–0.94). Significance: The ILAE tool demonstrated strong reliability and substantial agreement with the NOS while offering epilepsy specific rigor in diagnostic accuracy, sensitivity, case finding and representativeness. The ILAE tool offers a reliable, conceptually relevant, field‐specific alternative for quality assessment in epilepsy SRMAs
Automated cone photoreceptor detection using synthetic data and deep learning in confocal adaptive optics scanning laser ophthalmoscope images
Get PDFAdaptive optics scanning laser ophthalmoscope (AOSLO) imaging enables the cone photoreceptor mosaic to be visualised in the living human eye. Performing quantitative analysis of these images requires identification of individual photoreceptors. This is typically performed by manual labelling, which is subjective, time consuming and not feasible on a large scale. Automated algorithms to replace manual labelling are required and deep learning-based methods provide an effective way of achieving this. However, this approach requires large volumes of annotated training data that are difficult to acquire. Synthetic data may help to bridge this lack of annotated training data. A U-Net configuration was trained using a large synthetic dataset of confocal AOSLO images generated using ERICA alongside a smaller dataset of real confocal AOSLO images (Milwaukee dataset). Model performance was assessed by calculating the Dice coefficient, a metric quantifying segmentation overlap, on both a real held-out test set and an independent real dataset (Oxford dataset). Results from this evaluation were benchmarked against expert labelling and two automated cone detection methods: a confocal convolutional neural network (CNN) (1), and a combined graph-theory and dynamic programming approach (2)). The mean Dice coefficient compared to manual labelling was 0.989 (U-Net), 0.989 (confocal CNN), and 0.985 (graph-theory and dynamic programming) on the held-out test set. On the independent Oxford dataset, the U-Net achieved a mean Dice coefficient of 0.962 compared to manual labelling. Results show performance that is comparable to the gold standard of manual labelling and two automated cone detection methods. Furthermore, we demonstrate generalisability of this approach on an independent real dataset with images from higher retinal eccentricities. This approach may be useful for quantitative analysis of the photoreceptor mosaic in patients with retinal disease to provide cell-specific imaging biomarkers from AOSLO images
