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Green synthesis of silver nanoparticles from Buddleja asiatica L. and their multifunctional applications for industrial and bio-based products

Efficient hybrid algorithm for nonnegative matrix factorization based on modified nonmonotone linear search

In this paper, we present a modified nonmonotone line search algorithm that employs a variable parameter to control the degree of nonmonotonicity. This modification enhances both the probability of identifying the global minimum and the rate of convergence. Within the framework of alternating nonnegative least squares (ANLS), we propose a hybrid algorithm that employs either the modified nonmonotone projected Barzilai–Borwein method and the block coordinate descent method to address the subproblems in each iteration. To further accelerate convergence, we integrate a technique that allows for a larger step size. Under mild assumptions, we establish the global convergence of the algorithm. Numerical experiments conducted on both synthetic and real datasets demonstrate that the proposed algorithm is efficient for nonnegative matrix factorization (NMF) and outperforms other state-of-the-art methods.

Construction and characteristics of a supply–demand network for forest ecosystem water-yield services in the Dongting Lake region, China

Navigating the potassium dilemma: a qualitative study of nephrologists’ strategies for renin–angiotensin–aldosterone system inhibitor preservation and hyperkalaemia management in Spain

Background Hyperkalaemia is a common complication among patients with predialysis chronic kidney disease (CKD) and a leading reason for modifying or discontinuing renin–angiotensin–aldosterone system inhibitor (RAASi) therapy despite its proven cardiorenal benefits. Although newer potassium binders may improve potassium control while enabling RAASi use, real clinical practice remains poorly characterised. This study explored how nephrologists in Spain manage hyperkalaemia in patients with predialysis CKD (stages 3–5), with particular emphasis on RAASi continuation. Methods Semi-structured interviews were conducted with 12 practising nephrologists across Spain who routinely manage hyperkalaemia in patients with predialysis CKD. Interviews were transcribed verbatim and analysed using ATLAS.ti. A codebook-based thematic analysis was performed, with codes iteratively refined into 15 sub-themes and grouped into five overarching themes. Results Five themes were identified: (1) hyperkalaemia was managed using a structured clinical algorithm incorporating dietary, metabolic, diuretic, and pharmacological strategies, to preserve RAASi therapy where possible; (2) newer potassium binders (patiromer and sodium zirconium cyclosilicate) were viewed as effective and better tolerated than traditional resins, supporting greater RAASi continuity; (3) system-level barriers, including visado requirements, regional formulary variation, and fragmented communication across specialties, limited timely use of newer binders were perceived to contribute to RAASi discontinuation; (4) patient engagement, underpinned by clear communication, trust, and nursing-led education, facilitated adherence, although clinician-level nutrition expertise varied; and (5) hyperkalaemia management affected patients’ daily functioning and emotional well-being, yet formal assessment of health-related quality of life (HRQoL) was infrequent. Conclusions Nephrologists in Spain prioritised preservation of RAASi therapy when managing hyperkalaemia in predialysis CKD. Although newer potassium binders supported this objective, their use was limited by access and care coordination challenges. Streamlined access pathways, improved multidisciplinary communication, and routine HRQoL assessments may enable more patient-centred and guideline-concordant care. These implications reflect qualitative clinician perspectives rather than evidence of clinical or economic effectiveness.

Spinal deformity associated with dural ectasia in individuals with Marfan syndrome: a cross-sectional comparative study (MarfanEOS)

Environmental footprint of in-center hemodialysis in Türkiye: A national, scenario-based analysis of water, energy, and waste

Background Hemodialysis (HD) is a life-sustaining but resource-intensive therapy, consuming large volumes of water and electricity and generating substantial waste. This hybrid modeling study quantified the environmental footprint of in-center HD in Türkiye using national registry data, published parameters, and measured facility-level data from two Turkish dialysis centers. Methods A hybrid modeling approach combined published per-session resource parameters with measured facility-level data from two Turkish hemodialysis centers (4,000 sessions/month combined). Literature-derived parameters (493 L water/session at 42% RO recovery, 6.2–19.6 kWh electricity/session, 1.5–8.0 kg waste/session) were scaled nationally for 2023 (9.27 million sessions). Measured Turkish data (390 L water, 10.8 kWh electricity, 1.19 kg hazardous waste per session) provided empirical calibration. GHG emissions were calculated using Türkiye-specific (0.494 kg CO 2 e/kWh for electricity) and international conversion factors. Results Using literature parameters, annual national estimates include 4.57 million m³ water, 57.5–181.8 GWh electricity, and 13.9–74.2 kt waste, with total GHG emissions ranging from 45.3 to 171.1 kt CO 2 e/year. Measured Turkish facility data yielded lower per-session water consumption (~390 L vs. 493 L, a 21% reduction) and hazardous waste generation (1.19 vs. 1.5 kg/session under good segregation), while electricity consumption (mean 10.8 kWh/session) aligned with the moderate literature scenario. Electricity was the dominant emission source (63–72%), followed by waste incineration (up to 47% with poor segregation). Conclusion In-center HD in Türkiye imposes a substantial yet modifiable environmental burden. Measured Turkish facility data demonstrated that actual water consumption is meaningfully lower than international estimates, highlighting the importance of locally validated parameters. Waste segregation and electricity decarbonization represent the highest-impact intervention points for reducing the sector’s carbon footprint.

From the ground up: Belowground herbivory and phytoplasma infection influence aboveground herbivore performance

Exploratory identification of candidate SNP markers associated with recurrent clinical mastitis in Holstein cattle

Mastitis remains one of the most prevalent and economically significant diseases in dairy cattle worldwide. Although somatic cell count (SCC) is widely used as an indicator for mastitis diagnosis, its physiological variability limits its utility for predicting individual susceptibility. In this study, we aimed to identify genomic markers associated with recurrent clinical mastitis by defining mastitis-susceptible cows as those experiencing three or more episodes within a single lactation. Whole-genome resequencing was conducted on 50 Holstein cows (25 mastitis-susceptible and 25 healthy controls), yielding 536,184 high-quality SNPs after stringent quality control. A genome-wide association analysis identified 86 SNPs surpassing the significance threshold (–log₁₀P > 5.0), and seven candidate SNPs were evaluated in an independent cohort of 100 cows. Notably, the majority of candidate SNPs were localized on the X chromosome, suggesting a potential role for X-linked variation in mastitis immune response and disease resistance. While the candidate marker panel combining SNP1, SNP2, and SNP7 demonstrated moderate sensitivity (47%), its high specificity (98%) highlights its potential utility as a preliminary screening tool for identifying individuals at increased risk of recurrent mastitis. These findings provide a foundation for further functional validation and large-scale replication studies, which are essential for implementing effective genomic selection strategies to mitigate mastitis incidence in dairy herds.

Correction: Potential surrogate endpoint for B-cell hematologic malignancy: A systematic review and meta-analysis

Structural analysis of recombinant AAV vector genomes at single-molecule resolution

Recombinant adeno-associated virus vectors are essential tools for in vivo gene therapy, yet heterogeneity in their packaged genomes remains an important safety consideration. To systematically evaluate this heterogeneity, we developed a long-read, read-level analysis pipeline that directly classifies individual AAV genomes and their structural variants from PacBio sequencing data. The workflow combines two components: a tiling step that aligns each read to reference sequences to generate positional patterns, and a parsing step that applies a formal grammar to categorize reads into five structural classes: expected, truncated, snapback, truncated snapback, and others. Each molecule is annotated with strand orientation, breakpoint coordinates, and structural arrangement, enabling precise classification of genome heterogeneity at single-vector resolution. Applied to both single-stranded and self-complementary vector genome preparations, the pipeline achieved high classification accuracy and revealed distinct patterns of genome structure between different vector constructs. In both cases, the majority of genomes were classified as expected full-length species, consistent with the dominant full peaks observed by orthogonal methods. For snapback genomes, breakpoints frequently clustered at discrete sites, with some coinciding with regions predicted to form stable secondary structures and others occurring in less structured regions. This distribution suggests contributions from both sequence-driven folding and additional replication- or processing-related mechanisms. Together, these read-level insights highlight sequence and structural features that shape AAV genome heterogeneity. Importantly, the pipeline demonstrated strong performance in structural classification, maintaining high accuracy even in the presence of sequencing error profiles such as homopolymer-associated indels (insertion or deletion). By integrating structural classification, sequence context, and secondary-structure predictions, our pipeline provides a comprehensive framework for evaluating recombinant adeno-associated virus genome diversity. This approach not only improves resolution of vector genome architecture but also offers actionable insights to guide vector design and production processes for safer and more efficacious recombinant adeno-associated virus therapeutics.

Domain-generalized representation learning for cross-chemical-family toxicity prediction

Abstract Traditional QSAR toxicity models are, in general, assessed with random train-test splits where structural overlaps between train and test compounds are allowed. This usually results in an inflated predictive performance. Therefore, this paper looks at the problem of toxicity prediction under the structural distribution shift and checks if representation-level invariance can contribute to better cross-family generalization. A set of 1792 structurally diverse organic molecules for which toxicity data (e.g., Tetrahymena pyriformis pIGC₅₀) were determined experimentally was modeled with physicochemical descriptors. In order to depict the realistic scenarios of model use, the leave-one-cluster-out (LOCO) protocol was applied to enforce strict structural separation of training and test domains. Baseline neural models lost a lot of their prediction accuracy under LOCO versus random splits, thus exposing a very large generalization gap. On the other hand, invariant learning methods such as invariant risk minimization, contrastive alignment, and domain-adversarial training managed not only to reduce the cross-domain error but also to make residual distributions more stable. The embedding of latent space further demonstrated that invariance helps to get rid of cluster-specific signals while keeping toxicity-relevant gradients intact. From a practical perspective, these results suggest that robust in silico predictive toxicology, further under structural distribution shift, can be achieved through domain-aware validation and invariant representation learning.

Effectiveness of MyKidEye in improving parents’ knowledge, attitude, and practice on children eye health care

Background Vision problem among children is the most common health concern worldwide. Parental involvement plays an important role in addressing these issues especially through their Knowledge, Attitude, and Practice (KAP) regarding vision care. MyKidEye is an innovative smartphone application that contain information on common vision problem in children, early signs and symptoms, and treatment options to enhance parent’s KAP. This study aims to assess the effectiveness of MyKidEye application in improving parental KAP related to children’s eye health care. Methods This is a quasi-experimental study conducted from January to February 2023. The MyKidEye smartphone application was developed, and efficacy testing was performed. Parents from two schools who met the study criteria were selected and divided into two groups: the control group and the study group. Both groups completed the Parental Knowledge, Attitude, and Practice in Eye Problem among Children Questionnaire (PEPC-KAPQ) and their responses were recorded as pre-intervention data. Parents in the study group were given the MyKidEye application to be used for 4 weeks. Following a four weeks period, the PEPC-KAPQ was re-administered to both groups, and their responses were recorded as post-intervention data. Result A total of 178 parents were enrolled in this study, with 89 parents in the control group and 89 in the study group. Statistical analysis was performed using two-way mixed ANOVA to evaluate changes in parental knowledge, attitude, and practice score between groups over time. The practice scores of parents in the study group showed significant improvement after using MyKidEye, compared to the control group (F (1,176) = 34.27, p < 0.01). However, no significant changes were observed in knowledge (F (1,176) = 0.02, p = 0.88) or attitude score (F (1,176) = 2.82, p = 0.10). Conclusion MyKidEye application effectively improved parental practices related to children’s eye health care. It can serve as a valuable tool for improving and sustaining parents’ knowledge, attitudes, and practices regarding children’s eye health over a prolonged period, ultimately promoting better long-term eye care and awareness.

Fraud learns too: continual graph learning under strategic adversarial drift in dynamic networks

Abstract Financial transaction networks face a persistent threat from strategic adversarial drift, in which sophisticated actors manipulate graph structure to bypass detection. Conventional temporal graph neural networks tend to fail in this setting because they forget historical patterns when retrained and generalise poorly to novel structural perturbations. We address this gap with Game Theoretic Anticipatory Continual Graph Learning (GT-ACGL), a framework that casts fraud detection as a continuous Stackelberg game between a defender and an adaptive adversary. The framework combines three components: a bilevel anticipatory optimisation step that trains the defender against simulated future attacks, an Adversarial Motif Memory that retains topologically significant historical patterns without redundancy, and a predictive smoothing module that preserves temporal fidelity during high throughput batched training. We evaluate the approach on three large dynamic graph datasets. On the financial benchmark, Elliptic Temporal, GT-ACGL improves F1 by 11.0 percentage points over the strongest baseline under adaptive attack, with smaller but consistent gains on two behavioural interaction benchmarks. The framework also reduces the observed forgetting rate to below 6 percentage points and incurs only a $$1.45\times$$ training overhead relative to a standard temporal graph network. By modelling the cost of evasion inside a Stackelberg training objective, GT-ACGL encourages decision boundaries that remain comparatively stable under strategic structural perturbation. These results are empirical observations on the studied benchmarks, obtained against the specified edge addition threat model realised by our own attack generator. They are not guarantees of equilibrium behaviour, of the economic infeasibility of attack, or of robustness to the full range of real world fraud adaptations.

Directional drift in biologically meaningful vector planes: A proposed geometric framework for early detection of subthreshold disease

Background Most conventional diagnostic systems rely on fixed thresholds to differentiate disease states from normal. However, early pathological changes may begin before these thresholds are crossed. Therefore, a system that works in this pre-threshold state can potentially lead to earlier diagnosis. Objective To propose and evaluate a geometric framework that models early disease as a directional drift from a physiological plane to a pathological plane, allowing for pre-threshold detection using biologically interpretable variables. Methods In this modeling study on synthetic data derived from published clinical trends, two clinically meaningful variables were used to define a 2D feature space. The model was applied to a synthetic dataset of 4000 eyes divided into four phenotypes: normal stable (NS), early disease stable (ED_S), early disease progressive (ED_P), and pre-threshold progressive (PT_P). A physiological plane was constructed using range-normalized values from the NS group. A canonical disease vector was derived from the ED_P group. Each subject's follow-up data was transformed into a subject-specific drift vector, and the Composite Drift Score (CDS) was calculated as the product of directional alignment (Directional Emphasis Multiplier, DEM) and a Magnitude-to-Noise Ratio (MNR). Results CDS increased significantly over follow-ups in both ED_P and PT_P, distinguishing them from the two stable cohorts (p < 0.001). DEM and MNR components showed consistent trends, with progressive cases exhibiting higher alignment with the disease vector and supra-noise magnitude of change. Visual and statistical analyses confirmed early drift detection even within numerically normal ranges. Conclusion In this early modeling study based on simulation data, we could quantify the directional drift with a unitless, interpretable metric (CDS) and its derivatives. It showed similar trends in pre-threshold groups as early disease groups, showing potential for further evaluation.

Comparison of cervical swab and urine samples for high-risk HPV infection and PAX1 methylation in detection of cervical neoplasia

Abstract Cervical cancer screening commonly requires clinician-collected cervical specimens, prompting interest in less invasive sampling approaches. This paired pilot study assessed agreement in high-risk human papillomavirus (hrHPV) detection and PAX1 methylation testing between cervical swab and urine specimens and evaluated their performance for cervical intraepithelial neoplasia grade 3 or worse (CIN3+). We enrolled 104 women with abnormal cervical cytology and histopathological confirmation. Among 94 pairs with valid hrHPV results, overall agreement was 94.7% (Cohen’s κ = 0.883; 95% CI, 0.783–0.983). Among 91 pairs with valid PAX1 results, agreement was 79.1% (κ = 0.446; 95% CI, 0.241–0.651). For CIN3 + detection, cervical swab and urine hrHPV testing showed sensitivities of 86.8% and 80.6% and specificities of 44.4% and 45.9%, respectively. Cervical swab and urine PAX1 methylation showed lower sensitivities of 52.6% and 41.2% but higher specificities of 85.7% and 90.0%, respectively. Rule-based combinations showed sensitivity–specificity trade-offs. In this referral-based cohort, urine hrHPV testing showed high agreement with cervical swab testing, whereas urine PAX1 methylation had lower agreement and sensitivity. Urine-based PAX1 testing remains exploratory and requires optimization and independent validation.

Design and validation of an immersive virtual rehabilitation system for individuals with vestibular disorders: A feasibility study

Background Vestibular rehabilitation is an essential part of managing balance disorders, but traditional exercises can often be repetitive and demotivating for patients. This can reduce adherence and limit treatment outcomes. Objective This study aimed to assess the feasibility, usability, patient satisfaction, and safety of DizzyVR, an immersive virtual reality system designed to support vestibular rehabilitation. Preliminary data on its potential effects on clinical outcomes were also collected. Methods A prospective, single-arm feasibility study was conducted with ten participants diagnosed with various vestibular disorders. Each participant completed eight weekly sessions using DizzyVR. Feasibility was assessed through session attendance and completion rates. Usability, satisfaction, and safety were measured using the System Usability Scale, the User Satisfaction Evaluation Questionnaire, and the Simulator Sickness Questionnaire. Clinical measures included the Dizziness Handicap Inventory (DHI), Timed Up and Go (TUG) test, Activities-specific Balance Confidence (ABC) scale, and Functional Gait Assessment (FGA). Results Ten participants completed the training, with a high adherence rate (91.25%). Usability scores indicated good ease of use, and satisfaction scores were high. Mild adverse events such as nausea or disorientation were reported by three participants, but improved over time. Exploratory pre–post changes were observed in gait speed (TUG: p = 0.002), balance confidence (ABC: p = 0.007), and gait stability (FGA: p = 0.012). While DHI scores demonstrated a trend toward improvement, the change did not reach statistical significance (p = 0.081). Overall, participants reported feeling safe and expressed willingness to recommend and reuse the system. Conclusions The findings suggest that DizzyVR is a feasible, usable, and well-accepted tool that may complement conventional vestibular rehabilitation. The system appears to support patient motivation and safe participation while showing promising exploratory changes in balance and gait. Further studies with larger samples, control groups, and longer follow-up are recommended to confirm and expand on these promising preliminary results.

A multi-objective optimization framework for sustainable automotive interior design integrating enhanced triple bottom line, fuzzy decision-making, and BO-BiLSTM-driven NSGA-II

Retailer’s inventory-based financing with bounded advance rates: Interplay between wholesale price contract and loan menu

We examine a supply chain comprising a capital-constrained retailer, a supplier, and a bank. The retailer adopts inventory-based financing with bounded advance rates (IBF-B) to procure products through wholesale price contracts offered by the supplier. The bank is viewed as a strategic decision-maker with market power over the retailer when designing a loan menu that includes interest rates and inventory advance rates. By analyzing how inventory advance rates influence the retailer’s financing decisions, we derive upper and lower bounds for these rates to balance profitability and credit risk. Furthermore, we explore the interplay between the loan menu and wholesale price contracts. For the retailer intending to borrow up to the loan limit, the highest feasible wholesale price is offered if the supplier can obtain more profits, while the bank consistently charges the highest feasible interest rate. The upper bound of inventory advance rates is offered only when it yields a positive margin; otherwise, the interior advance rate within medium range is preferred. Numerical results illustrate the value of IBF-B, the motivations of all participants, and the impact of key economic parameters on financing decisions and supply chain performance.

Inhalable ligustrazine powders relieve inflammation of COPD via regulation of neutrophil extracellular traps and gut-lung axis microbiota

Bayesian hierarchical mixture modelling to derive probabilistic iELISA thresholds for bovine brucellosis in endemic dairy systems

Background In endemic dairy systems, the interpretation of serological tests for bovine brucellosis is compromised using fixed diagnostic cut-offs, which fail to account for continuous antibody distributions and population heterogeneity. This study aimed to apply a Bayesian hierarchical Gaussian mixture model (BHGMM) to resolve diagnostic uncertainty by deriving probabilistic, biologically informed thresholds for indirect ELISA (iELISA). Methods A cross-sectional dataset comprising 2,696 milk samples from large-scale dairy herds was analysed. Log-transformed and standardised antibody values were modelled using a three-component hierarchical mixture representing healthy, latent, and diseased populations. Posterior class distributions, herd-specific cut-offs, and prevalence were estimated, and model performance was evaluated using convergence diagnostics, posterior predictive checks, and ROC analysis. Results Three distinct serological populations were identified. Mean antibody levels (S/P%) were 5.29 in healthy, 17.07 in latent, and 299.84 in diseased animals. Dual diagnostic thresholds were estimated at 10.7 S/P% and 82.2 S/P%. Estimated class proportions were 23.5% healthy, 43.6% latent, and 32.9% diseased. Substantial between-herd heterogeneity was observed, with confirmatory cut-offs ranging from approximately 68–133 S/P% and herd-level true prevalence varying from about 1% to 67%. The model demonstrated high diagnostic accuracy (AUC = 84.5%) and stability across prior specifications. Conclusions Bayesian modelling captures intermediate serological “gray zones” and herd-level variability overlooked by standard binary interpretations. This probabilistic approach supports targeted control strategies in complex endemic environments.