Abstract: Since the features of cross-seeding of alpha-synuclein forms may affect sensitivity and specificity of the test systems, we developed a modified approach to obtain alpha-synuclein amyloid seeds with particle sizes from 20 to 50 nm prepared from either the wild-type protein (α-synWT) or its more fibrillation-prone form A53T (α-synA53T). These seeds had optimal properties for subsequent initiation of fibrillation. Our data showed that the elevated efficiency of alpha-synuclein A53T monomers transformation was hardly affected by the type of used seeds, whereas the addition of the seeds obtained from the alpha-synuclein mutant form to wild-type protein monomers had a significantly less effect than α-synWT seeds. TEM data revealed that in the presence of α-synWT seeds the wild-type alpha-synuclein formed long and wide fibrils, while the addition of α-synA53T seeds led to the formation of long, but thin fibrils. The application of α-synA53T monomers significantly reduced the fibrillation lag period, making it a promising candidate for use in medical test systems. In the future, a set of alpha-synuclein mutant forms could be used for the differential diagnosis of synucleinopathies caused by the different mutations of this protein.

Abstract: This work explores the key capabilities of emerging sensing technologies in the context of Structural Health Monitoring (SHM) of civil infrastructures, aiming at contributing to the research on integrated and intelligent systems for more accessible and efficient monitoring solutions. As a case study, this study focuses on analysing the static and dynamic behaviour of the Edgar Cardoso stay-cable Bridge during its rehabilitation, recurring to a fully customized transducers and equipment. The developed system integrates sensors capable of measuring accelerations, displacements and temperature, which are connected to an autonomous data acquisition and transmission network. A digital interface was also developed to store, process and visualize the collected data, allowing remote access for later interpretation and analysis. The results confirmed the effectiveness of the developed system, which enabled the identification of the dynamic properties of the structure in terms of natural frequencies and vibration modes. The effects of traffic loads, as well as the correlations between temperature and structural displacements were also identified. Furthermore, the estimation of the axial forces in the stay cables permitted to study the influence of wind actions and traffic loads in these elements. The results demonstrate the potentialities of customized sensing solutions as effective tools for the management, maintenance, and long-term preservation of strategic infrastructures.

Abstract: This study examines the role of volunteers in the formation of social initiatives that utilize local resources. Previous research on volunteering has typically explained participation in terms of altruistic motives and a desire to contribute to society. However, this study focuses on intellectual curiosity—specifically, an interest in observing on-site situations and analyzing problem structures, as a factor that supports the continuity of volunteer activities.The study analyzes a local resource project that utilizes camellia leaves naturally growing on a remote island in Miyagi Prefecture, Japan. Using qualitative analysis, we examine the activities of a central practitioner, referred to as Practitioner A. Practitioner A plays a bridging role by connecting multiple actors, including local residents, companies, welfare facilities, tourism stakeholders, and researchers. This study conceptualizes such practitioners as “analytical volunteers.” Analytical volunteers are participants who are motivated not only by altruistic intentions but also by an intrinsic interest in observing real-world situations and constructing activities through problem analysis.The case analysis reveals that while the camellia project has succeeded in forming a network among diverse actors, it has not yet achieved stable commercialization. This stage is interpreted as the “growing pains” of social innovation in local communities.This study contributes to volunteer research by highlighting the presence of participants motivated by analytical thinking and by demonstrating the importance of network intermediaries in the formation of regional innovation processes.

Abstract: This research work deals with the challenges in software fault prediction (SFP) such as class imbalance in benchmark datasets, noisy features, and high-dimensional feature spaces. To overcome the above limitations, we propose a novel hybrid feature selection framework, FS-BWOA–COA, which incorporates Coati Optimization Algorithm (COA) for local exploitation and Beluga Whale Optimization Algorithm (BWOA) for global exploration. The two-phase optimization approach helps to avoid duplication and improves the stability of the classifier and also helps in maintaining the balance between exploration and exploitation. The framework was tested using several classifiers such as Decision Tree, SVM, KNN, and Naïve Bayes on eleven NASA PROMISE datasets. The hybrid outperforms single BWOA and COA, with an average accuracy of 0.9033 and peak values of 0.95 on the MC1 and JM1 datasets. The results of the statistical validation using the Friedman test, Wilcoxon signed-rank test, and paired t-tests confirm the same.

Abstract: Additive manufacturing is increasingly promoted as a pathway toward sustainable production; however, its Environmental, Social, and Governance (ESG) implications remain insufficiently defined at the operational level. This study addresses this gap by developing a governance implications assessment framework to interpret empirically validated life cycle sustainability assessment (LCSA) evidence for post-consumer recycled polylactic acid (PC-PLA)-based fused filament fabrication (FFF) within the Australian manufacturing context. The central contribution lies in demonstrating how LCSA evidence can be translated into decision-relevant ESG information without reliance on product declaration frameworks or simplified circularity claims. While LCAs and EPDs do not directly optimise production systems, sustainability-oriented governance can guide engineering and operational decisions by managing whole-system trade-offs and future risk. The framework integrates mechanical validation, service-life-normalised LCSA results, circularity metrics, and material flow modelling through a consequences analysis lens. Results indicate that a V50:R50 (vPLA:PC-PLA) blend achieves balanced performance, delivering a 57% reduction in global warming potential while maintaining functional durability. However, these benefits are governance-contingent; managing an 11.7% increase in service-life-normalised costs requires senior-level oversight, formalised material traceability, and structured workforce training. Waste diversion analysis further demonstrates that scalability is constrained primarily by upstream collection and sorting efficiency rather than fabrication performance. The study concludes with a strategic roadmap advocating regional recycling hubs, certified micro-credentials, and adoption of decision-relevant governance metrics aligned with ASRS S1 and S2 disclosure standards. The proposed framework offers a transferable approach for translating engineering-level sustainability evidence into credible governance insights, strengthening accountability and supporting Australia’s transition toward resilient, circular manufacturing.

Abstract: Motivation: Gene regulatory network (GRN) inference from single-cell RNA-seq (scRNA-seq) data remains hampered by technical noise, high false-positive rates, and extreme computational costs. Existing methods often require hours or days to process developmental datasets yet fail to capture the physical and topological constraints of regulatory interactions, essential for accurate regulatory mapping. Results: We developed AENetMoX, a multimodal autoencoder that integrates transcriptomic correlations with transcription factor (TF) binding motifs and protein-protein interaction (PPI) networks. We evaluated AENetMoX against SCENIC, GRNBoost2, and CLR across 48 independent configurations using three human brain organoid lineages. At K=100, AENetMoX achieved 7.7±5.1% precision (95.7% relative improvement over SCENIC; p=1.039×10^(-3), one-sided Wilcoxon test; Vargha-Delaney Â=0.635). ChIP-seq validation showed 51.6% precision (+9.5% over SCENIC, p=0.141, Â=0.500) and 168.4% improvement in F1 over SCENIC (p=2.883×10^(-9), Â=0.854). Ablation studies revealed PPI integration as the primary driver of performance, increasing precision by 492% (~5.9×) and ChIP-seq recovery precision by 198% (~3×) over its expression-only variant. Crucially, AENetMoX completes inference in under 5 minutes, a 24-fold speedup over SCENIC (~2 hours) and significantly outperforming CLR (>12 hours). Analysis of novel predictions identified 334 unique regulatory edges, including temporally persistent SMAD3 and SOX2 hubs that remain stable across multiple developmental stages. Availability: Source code is available at https://github.com/Shirshak52/AENetMoX. All datasets and databases used are available at OSF (Project DOI: https://doi.org/10.17605/OSF.IO/K6EHW).

Abstract: Thailand preserves one of the most extensive records of Mesozoic vertebrate footprints in Tropical Asia, yet these ichnological data have never been comprehensively synthesized. This review compiles and reassesses all known Triassic to Cretaceous vertebrate tracksites in Thailand to clarify their stratigraphic distribution, taxonomic diversity, and palaeobiogeographical significance. Published records, new field observations, and updated stratigraphic correlations are integrated to evaluate trackmaker attributions and temporal patterns. The Thai record documents diverse assemblages including chirotheriids, early theropods, sauropodomorphs, ornithopods, sauropods, and crocodilians. Upper Triassic–Lower Jurassic assemblages capture a major faunal transition, revealing the co-occurrence of non-dinosaurian archosaurs and some of the earliest dinosaurs in the region, whereas Lower Cretaceous sites are dominated by theropods, sauropods and diverse ornithopods. Comparison with other Asian ichnofaunas indicates faunal continuity across eastern Asia and supports early dinosaur dispersal into equatorial low latitudes. This synthesis also evaluates site conservation, highlighting the vulnerability of several Triassic localities and a positive trend of community-led discoveries since 2009, underscoring the need for proactive management and standardized digital documentation. Overall, the Thai ichnological succession represents the most complete Mesozoic footprint record presently known from Tropical Asia and provides key insights into vertebrate evolution, palaeoecology, and regional biogeography.

Abstract: Biometric identification has become a key element in modern security and surveillance applications; however, traditional systems based on a single biometric trait often suffer from noise, distortions, and vulnerability to manipulation, which limits their reliability in real-world environments. To overcome these challenges, this study proposes a multi-pattern biometric identification model that integrates facial features and hand gesture information extracted from video data for remote identity verification. The proposed system captures real-time video of an individual approaching a sensor, selects relevant frames, and applies advanced feature extraction techniques to both facial and hand modalities, which are then fused during the evaluation stage. Identity classification is performed using a time-delay neural network (TDNN), and the model is evaluated on diverse multimedia datasets containing static facial images and dynamic hand gestures, including American Sign Language samples. Experimental results demonstrate that the multimodal approach significantly outperforms single-modal systems, achieving an accuracy of 0.98, recall of 0.98, and F1 score of 0.97, compared to lower performance when using facial or hand features independently. These findings indicate that combining multiple biometric traits enhances robustness, reduces ambiguity, and improves recognition accuracy, making the proposed approach suitable for practical biometric verification scenarios under varying environmental conditions.

Abstract: Background: Access to prosthetic, orthotic and related assistive services remains uneven globally; this manuscript examines the systemic causes and rehabilitation consequences within the context of India. We frame service gaps as health-systems failures with measurable workforce, supply-chain, financing and data components. Methods: A narrative policy review was undertaken using targeted searches of peer-reviewed literature, government reports, professional body publications, and NGO datasets. Key themes were synthesized across governance, workforce, supply chain, financing, and monitoring domains to derive pragmatic policy interventions. (Authors should replace or update search dates and data sources as required prior to submission.) Findings/Observations: Four structural deficits drive undercoverage: (1) insufficient trained P&O workforce and uneven geographic distribution; (2) fragmented manufacturing and procurement with limited quality control; (3) inadequate public financing and poor insurance/benefits coverage for device services; and (4) absence of routine service and outcome surveillance. These deficits produce preventable functional dependency, increased caregiver burden, and inequitable access—most pronounced among rural, low-income, and disabled populations. Conclusions: Closing P&O service gaps requires integrated health-systems actions: workforce scale-up and credentialing, pooled procurement and quality standards, explicit public financing pathways, and routine service/outcome monitoring. Policy recommendations (summary): Five priority actions are proposed: national workforce strategy, accreditation and CE frameworks; standardized device procurement and quality assurance; finance and benefit design for assistive services; decentralized service hubs with tele-rehabilitation links; and a national monitoring dashboard tied to performance indicators.

Abstract:

Strawberry (Fragaria × ananassa Duch.) production faces growing pressure to reduce reliance on peat and coconut coir substrates, driven by documented life cycle liabilities including carbon losses from peat extraction and embodied transport emissions from coir. Nutrient film technique (NFT), a substrate-free recirculating hydroponic system, eliminates growing media entirely and reduces material inputs across successive crop cycles, making it an environmentally attractive candidate for controlled environment strawberry production. Despite early commercial adoption in Europe during the 1970s, NFT was largely abandoned for strawberry production by the 1980s following systematic failures whose physiological basis remains incompletely characterized. This review synthesizes evidence from hydroponic systems engineering, plant physiology, and oomycete pathology to examine the two structural constraints underlying NFT’s historical rejection: dissolved oxygen depletion dynamics within recirculating nutrient solution, and exceptional susceptibility to Pythium spp. root rot. We demonstrate that these constraints are coupled rather than independent, sharing a common pathway through root-zone oxygen status. Progressive root mat development over a six-month fruiting cycle degrades passive film aeration and creates hypoxic conditions that impair root membrane integrity, alter rhizosphere exudate profiles, and facilitate Pythium zoospore encystment and necrotrophic transition. This interaction is compounded by strawberry’s exceptional oxygen sensitivity and absence of adaptive aerenchyma formation, rendering thresholds established for tomato and cucumber inapplicable to this species. We identify two prerequisite research gaps that must be resolved before NFT can be rationally reconsidered for commercial strawberry production: characterization of root mat effects on channel hydraulic performance, and establishment of a strawberry-specific dissolved oxygen threshold under NFT-relevant conditions.

Abstract: The two spot cotton leafhopper (TSCL), Amrasca biguttula (Hemiptera: Cicadellidae), is an emerging invasive pest in the southeastern United States. Although TSCL is historically associated with cotton and vegetable crops, recent detections on ornamental hibiscus have raised regulatory concern, including “Stop Sale and Hold” orders and an emergency quarantine in Texas. Despite increasing pressure on hibiscus, no insecticide efficacy data exist for ornamental systems. We evaluated the acute (0–24 h) and residual (24–96 h) toxicity of bifenthrin, flupyradifurone, and tolfenpyrad against adult and immature TSCL using a sequential cohort leaf disc bioassay. New insects were introduced at 24 h and 72 h to isolate residue based mortality from prolonged exposure effects. Bifenthrin caused the highest acute mortality at 24 h, whereas flupyradifurone and tolfenpyrad exhibited slower initial activity but strong residual performance. Immatures were more susceptible than adults across doses. By 72 h, all three insecticides produced near complete mortality, with significant treatment and dose effects confirmed by ANOVA and binomial GLM analyses. Dose–response curves showed steep concentration dependent mortality for bifenthrin and tolfenpyrad and a time dependent response for flupyradifurone. These results provide the first insecticide efficacy data for TSCL on ornamental hibiscus and offer immediate guidance for nursery producers and regulatory agencies. The findings establish a foundation for whole plant and greenhouse evaluations to support integrated management and interstate plant movement compliance.

Abstract: The Gravity Recovery and Climate Experiment (GRACE/GRACE-FO) missions provide terrestrial water storage anomalies (TWSA) at coarse spatial resolution (300 km), limiting their application in medium-sized basins. This study develops a machine-learning framework to enhance the spatial interpretability of GRACE mascon TWSA within the 48,000 km² Hadejia-Jama’are River Basin, Nigeria. Hydroclimatic predictors derived from TerraClimate, Global Land Data Assimilation System (GLDAS), and Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) were integrated within a unified 4 km spatial framework. Four machine learning models were evaluated, including Random Forest (RF), Gradient Boosting, Histogram Gradient Boosting, and a Multi-Layer Perceptron. The RF model achieved the highest skill in reproducing mascon-scale TWSA (R² = 0.937; NSE = 0.937; RMSE = 4.36 cm). Aggregation of the 4 km fields back to the mascon scale preserved basin-integrated mass (R² = 0.94), confirming consistency with the original GRACE signal. The resulting groundwater storage anomaly (GWSA) fields resolve sub-basin spatial gradients and seasonal recharge-depletion cycles that are not discernible in the native product. Validation against 31 monitoring wells yielded moderate temporal agreement (Pearson correlation coefficient, r = 0.656), with magnitude discrepancies attributable primarily to scale mismatch and hydrogeological heterogeneity. While not a substitute for in-situ monitoring, the downscaled product enhances basin-scale groundwater assessment in data-scarce semi-arid regions. The framework is transferable to comparable basins and supports regional drought monitoring and water-resource management.

Abstract: This paper develops a hybrid quantum–classical framework for adaptive AI agents, combin- ing a self-reference-aware quantum evaluation layer with a classical candidate-generation and evolutionary optimization layer. On the quantum side, we introduce a nonlinear, memory- dependent extension of open-system dynamics through St[ρ] and derive key structural proper- ties, including trace preservation, Hermiticity, pointer-basis fixed-point behavior, and practical positivity conditions in bounded-coupling regimes. On the AI-systems side, we define measur- able response metrics (χ2, ζ), introduce a compositional synergy integral Sint, and specify an online-selection plus offline-evolution pipeline. Candidate-dependent evaluation is implemented through semantic embedding and amplitude encoding, so quantum initialization reflects linguis- tic proximity rather than hash collisions. The contribution is framed as a testable theoretical architecture rather than a universal performance claim: χ2 and ζ are structural diagnostics, while semantic-quality gains remain an empirical hypothesis requiring calibration. We also pro- vide implementation-oriented interfaces and a worked compositional example to support staged empirical validation on NISQ-era hardware.

Abstract: Contemporary theories of emotion explain affective life through biologically prepared modules, cognitive appraisal mechanisms, predictive construction, dimensional organization, neural survival circuits, attachment regulation, adaptive computation, and error minimization. While each framework offers important insight, none fully explains why emotional intensity varies dramatically across contexts, why identical informational input yields divergent responses, or why certain commitments reorganize identity and override instrumental reasoning.This paper introduces the Dynamic Love-Based Valuation (DLBV) framework, proposing that emotional diversity emerges from a unified valuation architecture grounded in identity-relevant valuation, termed love. Love is defined not as romantic behavior nor as a discrete emotion, but as a structural valuation system operating across three qualitatively distinct phases: attraction, immersion, and union. Attraction preserves instrumental rationality and goal-directed evaluation. Immersion reorganizes valuation through identity expansion and potential subordination of rational constraint. Union stabilizes valuation within integrated attachment and responsibility.Within this framework, emotions such as fear, anger, sadness, joy, jealousy, guilt, gratitude, hope, and despair are interpreted as contextual modulations of valuation under specific informational conditions, including threat, violation, deprivation, alignment, rivalry, uncertainty, or loss. Emotional tone and intensity depend not solely on appraisal content or arousal magnitude but on the structural depth at which valuation operates.To move beyond conceptual synthesis, the paper proposes an experimental paradigm designed to test phase-dependent modulation of emotional intensity. The design operationalizes identity-relevant valuation across the three love phases and examines whether identical informational stimuli elicit systematically different affective responses depending on phase-structured commitment. The Dynamic Love-Based Valuation framework therefore offers both an integrative theoretical account and a falsifiable empirical program for investigating the structural architecture of emotion.

Abstract: We report the first complete circular genome of Acetobacter cerevisiae KSO5, an indigenous strain isolated from Korean fruit vinegar, comprising a 3.3 Mb chromosome and two plasmids encoding 2,898 genes. Phylogenomics confirmed species assignment (average nucleotide identity, ANI 97%; digital DNA–DNA hybridization, dDDH 71%). Comparison with seven draft A. cerevisiae genomes revealed strain-specific genomic islands, mobile genetic elements and polymorphisms in stress-response pathways, with enrichment in acid-tolerance–associated functions, and highlighted plasmid-borne modules potentially linked to genetic stability. The genome encodes a periplasmic oxidative fermentation system with membrane-bound pyrroloquinoline quinone-dependent alcohol dehydrogenase (PQQ-ADH) and molybdopterin-dependent aldehyde dehydrogenase (Mo-ALDH), together with respiratory-chain components consistent with flexible aerobic metabolism. Three acetate-handling routes (efflux, acetyl-CoA conversion and an AarC branch) were also predicted, suggesting mechanisms to limit intracellular acetate accumulation. Consistent with these features, phenotyping under ethanol stress (5–10%) showed measurable growth and titratable acidity production up to 9% ethanol (late-stage peak acidity). These data provide a genomic and phenotypic basis for developing robust vinegar starter cultures.

Abstract: Background: Women with atrial fibrillation experience a higher lifetime risk of ischemic stroke, greater stroke severity, and worse functional outcomes than men. Preventive strategies focused on AF detection may therefore miss critical opportunities for early intervention in women; (2) Methods: We developed a decision-analytic Markov model using real-world primary care data from Catalonia (Spain) to evaluate an artificial intelligence (AI) enabled strategy for upstream thromboembolic risk detection. The intervention combined electronic health record–based risk prediction, targeted digital rhythm screening, and individualized anticoagulation. Lifetime clinical and economic outcomes were estimated for adults aged ≥65 years, with pre-specified sex-stratified analysis; (3) Results: Compared with usual care, the AI-enabled strategy reduced ischemic stroke, major adverse cardiovascular events, and long-term disability. Absolute reductions in stroke and disability were greater in women, reflecting higher baseline thromboembolic risk. Per 1,000 high-risk women, the strategy prevented more strokes and generated larger quality-adjusted life-year gains than in men. From both healthcare payer and societal perspectives, the intervention was cost-saving in women, driven by reductions in stroke-related disability and long-term care; (4) Conclusions: AI-enabled upstream thromboembolic risk detection may deliver particularly important benefits for older women and represents a promising approach to reduce sex-based inequities in stroke prevention.

Abstract: We study learnable spectral layers whose feature family is generated by an entire function, motivated by the Master Integral Transform (MIT) of [1]. In a periodic discrete model on T, we define an oversampled multi-β analysis operator A(M) g,K built from an entire generator g and show that, on the one-sided bandlimited subspace PWK (T), it is a tight frame with an explicit inverse, sharp frame bounds, and noise-stability constants governed solely by the Taylor coefficients of g. For general (non-bandlimited) signals we derive exact aliasing identities and quantify two de-aliasing mechanisms: a deterministic multi-resolution cancellation scheme and a randomized rotation estimator with unbiasedness, MSE = O(1 /m), and high-probability bounds. We extend the discrete theory to Td, allowing general multivariate entire generators G(z) = ∑α∈Nd cαzα, and obtain exact inversion and conditioning bounds on tensor bands PW(d) K (Td) with explicit constants. To connect discrete layers to continuum MIT injectivity, we formalize density control of active Taylor indices. We prove that bounded gaps imply positive one-sided interior Beurling–Malliavin density (hence, in particular, positive lower counting density), closing an end-to-end bridge from gap-regularized learning to the injectivity theorem of [1]. For bounded-gap sequences we also give a weighted-series characterization of strong a-regularity, yielding computable surrogate penalties. Finally, we prove two injectivity mechanisms that do not rely on density: (i) a β-analytic injectivity theorem (access to multiple β-channels near 0) for any nonconstant entire kernel, and (ii) a finite-band generic-shift result ensuring invertibility on PWK for nonpolynomial generators. Full-data experiments illustrate conditioning collapse without coefficient floors and confirm the predicted 1 /m de-aliasing variance decay. Contributions (take-home). (i) Certified band-invertible spectral layers: exact inversion + frame bounds + noise stability on PWK (T) and PW(d) K (Td) with constants controlled by Taylor coefficients; (ii) Provable de-aliasing: deterministic multi-resolution cancellation and randomized rotation Monte Carlo with unbiasedness, MSE = O(1 /m), and high-probability bounds; (iii) A closed discrete-to-continuum bridge: bounded-gap activity ⇒ DBM > 0 and hence the lower counting density required by MIT injectivity; (iv) Beyond-density injectivity + nonharmonic structure: two density-free injectivity mechanisms and a monomial rigidity principle. Applications. These results yield MIT-certified modules for machine learning and scientific computing: invertible FFT-like embeddings for grid data, learnable positional encodings, and drop-in replacements for Fourier blocks in neural operators with explicit conditioning control and provable de-aliasing.

Abstract: This paper offers estimates of the per-capita GDP growth trajectories consistent with zero CO₂ emissions. The focus is on six developed economies (Canada, Germany, Italy, Japan, Singapore, United States) and six emerging economies (China, India, Indonesia, Malaysia, Mexico, South Korea). To this end, we postulate model linking per-capita CO₂ emissions growth to per-capita GDP growth and technology. Parameter estimation relies on annual data from 1980 to 2022 using Ordinary Least Squares (OLS) and Instrumental Variables (IV). Given the parameter estimates, we use the model to estimate the growth rate of per-capita GDP that is consistent with zero growth in CO2 emissions.

Abstract: Bell tests and Bell's theorem used to interpret the test results opened the door to quantum information processing, such as quantum computation and quantum communication. Based on the erroneous interpretation of the test results, quantum information processing contradicts a well-established mathematical fact in point-set topology. In this study, the feasibility of quantum computation and quantum communication is investigated. The findings are as follows. (a) Experimentally confirmed statistical predictions of quantum mechanics are not evidence of experimentally realized quantum information processing systems. (b) Physical carriers of quantum information coded by quantum bits (qubits) do not exist in the real world. (c) Einstein's ensemble interpretation of wave-function not only will eliminate inexplicable weirdness in quantum physics but also can help us see clearly none of quantum objects in the real world carries quantum information. The findings lead to an inevitable conclusion: Without carriers representing quantum information, physical implementations of quantum information processing systems are merely an unrealizable myth. Examples are given for illustrating the reported results. For readers who are unfamiliar with point-set topology, the examples may alleviate difficulty in understanding the results.

Abstract:

Background. Transient ischemic attack (TIA) and minor stroke often result in excellent functional recovery but are frequently followed by substantial psychological morbidity. It remains unclear whether mood disturbances or cognitive impairment are the primary contributors to reduced health-related quality of life (HRQoL) in this population. Methods. We conducted a prospective observational case–control study including 90 patients with acute TIA or minor stroke confirmed by diffusion-weighted imaging, and 92 age-matched healthy controls. At 90 days, participants completed the Hamilton Depression Rating Scale, Hamilton Anxiety Rating Scale, Montreal Cognitive Assessment, and the EQ-5D-5L. Hierarchical multiple regression using standardized z-scores identified independent predictors of HRQoL. Bias-corrected bootstrapped mediation analyses (5,000 iterations) assessed whether cognitive impairment mediated the relationship between mood symptoms and HRQoL. Results: Compared with controls, patients exhibited markedly higher rates of depression (82.2% vs. 18.5%), anxiety (81.1% vs. 21.7%), and cognitive impairment (66.7% vs. 13.0%) (all p<0.001). Psychopathological variables explained an additional 36.6% of HRQoL variance, whereas cognitive and neuroimaging variables contributed only 1.7% (ΔR²=0.017; p=0.523). Anxiety showed the strongest predictive value (β=–0.055; p=0.064), while cognitive impairment had negligible effects (β=–0.001; p=0.947). Mediation analyses revealed no significant indirect effects, indicating that mood and cognitive complications arise independently rather than sequentially. Conclusions: Following TIA or minor stroke, depressive and anxiety symptoms are highly prevalent, persist despite good neurological recovery, and exert a disproportionately negative impact on HRQoL. Anxiety appears particularly influential in determining patient-reported outcomes, underscoring the need for routine mood screening and targeted psychological management in this population.