Browse Articles

SwinCup-DiscNet: A fusion transformer framework for glaucoma diagnosis using optic disc and cup features

Individual and interaction effects of health determinants on health-related quality of life in Korean adults aged 50–81 years: A causal Bayesian network analysis

Health-related quality of life (HRQoL) reflects physical and mental well-being and is increasingly important in aging populations, yet traditional approaches often fail to capture the complex causal pathways among its determinants. We analyzed 2,566 adults aged 50–81 years from the Korean Genome and Epidemiology Study using the Short Form-12 (Physical Component Summary [PCS] and Mental Component Summary [MCS]). A causal Bayesian network was learned using the PC algorithm of Spirtes and Glymour with hierarchical constraints to ensure causal interpretability. We then estimated the causal effects of each variable on tail-defined outcomes— poor (bottom quartile) and good (top quartile) PCS and MCS—and quantified pairwise interaction effects. The network revealed how upstream factors propagate through direct and indirect pathways to shape HRQoL. Notably, PCS and MCS shared common upstream causes but showed no direct causal connection. Quantifying these causal pathways through relative risk (RR) estimates revealed the magnitude of individual factor effects. For poor PCS, severe insomnia (RR = 1.98), high stress (RR = 1.45), low physical activity (RR = 1.39), and multimorbidity (RR = 1.36) were the principal risk factors. For poor MCS, high stress (RR = 3.28) and severe insomnia (RR = 2.72) dominated. Notably, low BMI increased poor MCS risk (RR = 1.20), consistent with frailty pathways. The patterns for good outcomes largely mirrored these findings, with favorable levels showing protective effects. Interaction analyses revealed substantial synergistic effects: severe insomnia with high stress increased poor MCS probability by 6.44 percentage points (pp) beyond additivity, while high stress with physical inactivity added 4.77 pp. For good MCS, low insomnia with low stress (+4.72 pp) and low BMI with exercise (+4.21 pp) showed synergy, whereas stress with inactivity exhibited antagonism (–4.00 pp). These results support integrated interventions that combine sleep improvement, stress reduction, physical activity promotion, and multimorbidity management to improve HRQoL in aging populations.

Video-dominant emotion recognition for portable EEG-based devices

Abstract Electroencephalography (EEG) signals offer a promising avenue for detecting emotional responses during video viewing, enabling the automated recognition of video-induced emotions and providing an objective assessment approach. However, current approaches face two main limitations. First, emotion labels often rely on subjective self-reports that introduce personal bias. Second, most systems require high-density electrode arrays that are costly and impractical for portable applications. To address these challenges, this study explores video emotion recognition using a lightweight EEG setup. We introduce three complementary strategies: (i) a dynamic hierarchical label calibration approach that reduces labeling subjectivity through consistency modeling and boundary refinement; (ii) a multi-dimensional energy ratio analysis that compresses channel requirements while preserving discriminative information; and (iii) a saliency-guided feature selection method to improve generalization capability. By reducing 65% of the channels from the original dataset, our approach achieves 45% accuracy in four-class dominant video emotion prediction using only 11 channels, while maintaining meaningful discriminative performance under cross-subject conditions. Beyond technical advancements, these results demonstrate the potential of EEG-based systems to capture collective emotional responses to video content. This capability supports practical applications in audience sentiment analysis, media content evaluation, and emotion-aware recommendation systems.

Genome-wide identification and characterization of argonaute, dicer-like, and RNA-dependent RNA polymerase gene families in potato (Solanum tuberosum): Advancing RNA interference-based crop enhancement

In eukaryotic species, RNA silencing is a conserved mechanism for controlling gene expression. The RNA-dependent RNA polymerase (RDR), argonaute (AGO) , and dicer-like (DCL) proteins are essential for RNA silencing. The RNA interference (RNAi) system regulates eukaryotic gene expression throughout growth, development, and stress response. It is also closely linked the post-transcriptional gene silencing (PTGS) process. The potato is one of the four major food crops and a staple meal in the world that has a great potential to combat global malnutrition. However, no genome-wide analysis of the silencing gene family has yet to be conducted in the economically significant plant potato. In this study, we identified 29 (6 StDCL , 14 StAGO and 9 StRDR ) candidate genes in potato. These genes correspond to the Arabidopsis thaliana RNAi silencing genes. The analysis of the conserved domain, motif, and gene structure for StDCL , StAGO , and StRDR genes showed higher homogeneity within the same gene family. The Gene Ontology (GO) enrichment analysis exhibited that the identified RNAi genes could be involved in RNA silencing and associated metabolic pathways. A number of important transcription factors (TFs), BBR-BPC, bHLH, bZIP, C2H2, Dof, ERF, MIKC MADS, WRKY families, were identified by network and sub-network analyses between TFs and candidate RNAi gene families. Furthermore, the cis -acting regulatory elements (CREs) related to light, stress and hormone responsive functions and tissue-specific expression were identified in candidate genes. These genome wide analyses of these RNAi gene families provide valuable information related to RNA silencing which might be helpful for potato improvement in the breeding program.

SPCNNet: spiking point cloud neural network for morphological neuron classification

High accuracy breast cancer classification with BIRADS and coclustering

Breast cancer is one of the most common disease in women. Most of existing breast cancer classification methods include region segmentation, feature extraction and classification phases. It is hard for doctors to understand the conclusion drawn from low level image features. Besides, in cancer hospital more malignant cases than benign cases can be collected, in physical examination center more benign cases can be collected, causing the imbalance problem. To solve above two problems, this study designed a novel breast cancer classification method based on high level Breast Imaging Reporting and Data System (BI-RADS) features. First, an improved Synthetic Minority Oversampling Technique (SMOTE) algorithm is proposed to generate minority samples for balance. Subsequently, coclustering is adopted to mine diagnostic rules. Finally, with Adaboost, the rules can construct a strong classifier. Comparison experiment results on two public datasets shows that the accuracy, precision, recall F1 of proposed method improves more than 5% than comparison methods. Besides, under different imbalance ratios, accuracy of the proposed method is more than 5% higher than comparison methods.

Ninja optimization algorithm based ultra wideband antenna electromagnetic band gap modeling via a generative adversarial network

Building an implementation framework for directly observed feedback by attending physicians

Background Effective formative feedback from attending physicians to residents is critical for competency-based medical education. Feedback curricula commonly focus on simulated feedback delivery while actual verbal feedback delivery is unobserved by anyone other than the individuals involved. External observation of feedback has received limited attention as a novel method of improving feedback quality. Despite this, there is no research describing attitudes towards directly observed feedback. Methods We developed two surveys, one for Internal Medicine residents (IMRs) and one for hospitalists, who specialize in comprehensive care of hospitalized patients, at the University of Washington in Seattle, Washington in 2023. Survey validity evidence was gathered prior to disseminating surveys via a census sampling approach by group email listservs. Quantitative questions were analyzed by dichotomizing Likert responses as neutral/disagree vs. agree. Free text comments were qualitatively analyzed via a general inductive approach until theme sufficiency was reached. Survey development and analysis was conducted using a lens of social cognitive theory. Results The response rate was 71% (130/184) and 57% (74/129) for IMRs and hospitalists respectively. Most residents and hospitalists reported feeling comfortable with having a feedback exchange observed (105/129, 81%; 46/72, 64% respectively). Hospitalist and IMR concerns about the implementation of directly observed feedback were categorized into three themes: concerns about the relationship with the faculty observer, negative impact on learning environment, and altered feedback quality. Hospitalist and IMR suggestions for parameters to mitigate the challenges of observed feedback were categorized into three themes: the feedback observer’s relational boundaries, empower participant agency, and preserve feedback integrity. Conclusion The thematic concerns expressed by both cohorts relate to social monitoring, either of a projected self-image or to the educational safety of a learner. These themes highlight the fundamental importance of psychological safety in developing a program of directly observed feedback for attending physicians and residents.

Purification and characterization of a dihydroxy Oxocyclohexyl acetate derivative from endophytic Aspergillus Candidus targeting Candida albicans lanosterol 14α-Demethylase

Medial pivot designs result in improved patient reported outcome measures and range of motion when compared to cruciate retaining total knee replacements: A systematic review and meta-analysis

Background Knee arthroplasty remains one of the most important treatment options in improving quality of life for patients with end-stage knee osteoarthritis. However, roughly 20% of patients remain dissatisfied with their outcome. Perceived implant instability and range of motion are factors that may contribute to dissatisfaction. The medial pivot (MP) total knee replacement (TKR) is postulated to provide increased stability due to greater implant conformity and replication of anatomical function compared to cruciate retaining (CR) implants. This systematic review and meta-analysis evaluates the impact of MP TKR on patient reported outcome measures (PROMs,) range of motion (ROM), pain scores and functional assessment measures in comparison to traditional CR implants. Methods and findings An extensive literature search of multiple databases was conducted to identify eligible high quality studies which compared PROMs data for MP and CR TKR, with a minimum of 12 months follow-up. Our primary outcome was the forgotten joint score (FJS-12). Secondary outcomes included additions PROMs, ROM data and functional assessments. Risk of bias and quality of research were assessed by GRADE rating and the AMQPP tool respectively. A total of 7 articles were included in the systematic review, encompassing 675 patients aged 59–86 years. Four studies assessed FJS-12, with mean difference of 7.46 (−2.44–17.37) in favour of MP TKRs, which was not statistically significant. Overall, 1415 PROM scores from 675 patients were included, giving a statistically significant difference 0.34 (0.16–0.52) and an effect size of 3.69 (p = 0.0002) in favour of MP designs utilising a standardised mean difference analysis. ROM data demonstrated an overall statistically significant mean difference of 4.63° (1.00–8.27) in favour of MP knees. Further functional outcomes, laxity, power measures demonstrated favourable outcomes for MP knees but were ineligible for inclusion in pooled analyses. Conclusion No statistical difference was observed for the majority of PROMs. PROMs including FJS-12, range of motion and functional outcome scores trended towards favouring MP TKRs; with a statistically significance advantage seen for pooled PROMs scores and ROM. However, there remains limited data relating to functional outcome measures within the literature. Further high-powered, multicentre studies are required to analyse whether MP TKRs are superior to CR TKRs regarding functional outcomes.

A comprehensive web-based platform for calculation of abiotic stress tolerance indices in plant breeding

The WEAR-BOT checklist: A risk of bias tool for evaluating validity and reliability research in wearable technology

This paper proposes an innovative tool designed to standardize the evaluation of validity and reliability studies in the rapidly evolving field of wearable technology. We introduce the WEArable Technology Risk of Bias and Objectivity Tool (WEAR-BOT), a tool that addresses the need for a comprehensive and systematic way to assess bias in studies examining consumer-grade and research-grade wearable devices. This is the first tool designed to evaluate the risk of bias in validation and reliability studies. Other risk of bias tools like the Cochrane ROB, COSMIN, or the many other risk of bias tools are designed for different study methodologies or are overly broad and largely unnecessary for the specifics of validity/reliability testing studies. The development of the WEAR-BOT involved extensive collaboration among experts, encompassing iterative, open-ended discussions, several rounds of anonymous Delphi-style questionnaires, and pilot testing. The tool comprises detailed checklists for both validity and reliability studies, with subdivisions focusing on study design, methodology, statistical analysis methods, and other critical aspects. The tool balances the need for rigor with ease-of-use. It incorporates a variety of questions to rigorously evaluate the risk of bias in these studies and aims to enhance and standardize methodological approaches in the field. The tool is practical, easily available, and easy to use, as it is built in Microsoft Excel and contains macros that are intuitive and easy to use that allow the user to work more efficiently. The WEAR-BOT represents a significant advancement in the standardization of research methods and statistical analysis in the domain of wearable technology.

The development and evaluation of agricultural question-answering systems based on large language models

Broad immunogenicity of house dust mite proteins contrasts restricted specific IgE and IgG4 associated with allergy

Background House dust mite (HDM) allergy involves IgE and T H 2 responses to major and minor allergens. Less is known about the involvement of other immune pathways and the potential role of other HDM proteins in allergic disease. In this study, the association between HDM allergy and immune responses to the HDM proteome was investigated. Methods The HDM proteome was represented by 40 purified recombinant HDM proteins (19 known allergens and 21 novel proteins). T-cell responses to HDM proteins were determined ex vivo and antibody responses (IgA, IgE, IgG and IgG4) were measured using micro arrays and basophil activation in 21 HDM allergic donors and 16 non-allergic controls. Changes in specific IgE, IgG and IgG4 during SQ HDM SLIT-Tablet immunotherapy was assessed in 38 subjects with allergic asthma. Results HDM proteins were broadly immunogenic inducing comparable IgG, IgA, and non-T H 2 cytokine responses in both allergic and non-allergic individuals. Specific IgE, IgG4 and T H 2 cytokine responses were largely restricted to the allergic donors. IgE and IgG4 were primarily directed to known major allergens and overlapping in specificity whereas cellular T H 2 responses extended beyond the known HDM allergens. Individual proteins displayed distinct immunological profiles. HDM sublingual immunotherapy increased the levels of specific IgE and IgG4 but did not change the overall pattern of recognition. Conclusion HDM proteins are highly immunogenic and give rise to complex patterns of immune recognition also in the absence of allergy. This has potential implications for the pathogenesis of HDM allergy and the mode of action of allergy immunotherapy.

A Novel Hybrid Approach To Drought Forecasting: Leveraging Feature Engineering And Ensemble Methods

An upstream secondary DNA motif within the IL3 insulator CTCF binding site is required for enhancer-blocking insulator activity

CCCTC-binding Factor (CTCF) is the only known vertebrate protein that functions to organise the genome into distinct functional chromatin domains. A subset of CTCF sites also provide insulator activity and barrier activity to block inappropriate enhancer-promoter communication and spreading of histone modifications and non-coding transcription into loci where it is unwarranted. Paradoxically, other CTCF sites mediate enhancer and promoter communication, partly by supporting DNA loop extrusion within chromatin domains. Despite intensive study and abundant data, it remains poorly understood how CTCF directs these different functions. In this study we provide new data and mine published data that show that CTCF utilises zinc fingers 9–11 and an upstream DNA binding consensus sequence resembling CAGCTGTTCC to mediate high affinity binding and enhancer-blocking insulator activity. A single high affinity CTCF binding site from the IL3 locus is able block IL3 promoter activation by an upstream enhancer. Mutation of a CTGCAGCTTT sequence upstream of the CTCF core motif abolishes insulator activity. We propose that CTCF is able to confer insulator and barrier activity upon a specific subset of CTCF sites that contain the upstream DNA consensus binding motif, thereby allowing CTCF to function differently in different contexts.

Parameters optimization of photovoltaic systems using modified quantum inspired particle swarm method

Development and evaluation of a stepwise clinical competency development program for new nurses: A single-group repeated-measures quasi-experimental study

Background New nurses often encounter adaptation challenges in hospital settings owing to gaps in clinical knowledge and skills, leading to high turnover rates and patient safety concerns. Although various orientation and preceptorship programs exist, they remain inconsistent and rarely evaluated longitudinally. Effective training is essential to support clinical adaptation. This study aimed to develop a structured, stepwise clinical competency development program (SCCDP) for new nurses and evaluate its effectiveness. Methods This study employed a single-group repeated-measures quasi-experimental design. From September 2023 to July 2024, 49 new nurses from C University Hospital in South Korea participated in the SCCDP, which consisted of lectures, practice, and simulation validated by experts. The outcomes measured included basic and advanced clinical knowledge, clinical performance ability, and self-efficacy at four intervals: pre-intervention, immediately post-intervention, and at three and six months post intervention. Data were analyzed using repeated-measures and Friedman’s ANOVA, and effect sizes were calculated. Results The SCCDP significantly improved new nurses’ basic clinical knowledge immediately post-intervention and three and six months post-completion (F = 40.01, p  < .001), with the largest effect size observed in medical device operation (ES = 1.56–1.65). Advanced clinical knowledge also demonstrated significant enhancement across all time points (F = 26.06, p  < .001), with the greatest increase occurring immediately after the SCCDP (ES = 1.84), particularly in emergency nursing (ES = 1.22). Clinical performance ability showed notable gains at three and six months post-program (χ² = 55.92, p  < .001), with the most substantial improvement in interpersonal and communication skills (ES = 2.05–2.09). However, self-efficacy did not change significantly over time (F = 2.80, p  = .066). Conclusions The SCCDP enhanced new nurses’ knowledge and clinical performance and demonstrated sustained effects over time. These findings support the implementation of structured, competency-based education to facilitate new nurses’ adaptation and retention in clinical practice.

Identification of perception gaps between physicians and patients with neurological diseases and the prediction of these gaps using machine learning

Abstract Understanding perception and communication gaps between patients with neurological diseases and their treating physicians is essential for optimizing patient-centered care. The GAP-AI study aimed to identify these gaps in a cohort of patients with Parkinson’s disease, multiple sclerosis, or epilepsy. This single-center observational study involved patients (N = 197) and their treating physicians (N = 12) answering questionnaires (18-item Patient Satisfaction Questionnaire Short Form, 9-item Shared Decision Making Questionnaire for patients and physicians, Barthel Index, and 36-item Short Form subdomains) over two clinic visits. The primary outcome was the difference between pairwise items in the questionnaires (perception gap). Perception gaps, albeit minimal, were identified for patient satisfaction, shared decision-making, activities of daily living, and quality of life. Attributes that significantly influenced perception gaps included physician’s age, years of experience/holding a neurologist qualification, disease area, and the number of patients treated, with experienced physicians tending to provide more rigorous evaluations than their patients’ self-assessments. Multiple machine learning algorithms were used to develop predictive models based on study data. The k-nearest neighbors algorithm demonstrated the best performance in predicting a patient–physician perception gap. Insights from our study highlight the potential to recognize, predict, and ultimately address these gaps, thus enhancing clinical practice by increasing the level of understanding between patients and their physicians.

The impact of policy restrictions and mobility changes on excess mortality during the COVID-19 pandemic in The Netherlands, 2020–2022

We analyzed the impact of the COVID-19 policy restrictions on mobility patterns and excess mortality at the regional level in The Netherlands between 2020 and 2022. Our analysis combines data on public policies, mobility patterns from the Google Mobility Reports, officially registered COVID-19 cases and deaths, and new region-specific measures of excess mortality over a relatively long time period extending beyond the first wave of the pandemic. We modeled the relationships of these so that policy responds to information about the pandemic; mobility reacts both to information about the pandemic and to policy; the number of COVID-19 cases is influenced by changes in mobility and policy; and excess mortality is affected directly by the policy restrictions and indirectly via the impact of policy on mobility. The results confirm that the stringency of policy restrictions increased with the number and growth rates of COVID-19 cases and deaths. Mobility, as reflected in presence in public places (transport hubs, groceries, retail, work), decreased while presence at residential locations increased in response to stricter policies and higher COVID-19 case and death counts in preceding weeks. The number of new COVID-19 cases declined when stricter policy restrictions were enacted and with reduced presence in public places (following a two-week lag). Excess mortality decreased with stricter policy restrictions (with a five-week lag) and, to a lesser extent, with reduced presence in public places and increased presence in places of residence. Importantly, the effects of policy restrictions and mobility diminished with consecutive COVID-19 waves. Overall, the evidence shows that policy restrictions were effective in limiting the spread of the pandemic and in saving lives. While policies influenced mobility patterns, the policy impact was not fully mediated by mobility changes.