Advancing Open Science

Background: Inherited retinal dystrophies are a heterogeneous group of progressive disorders impacting photoreceptor function, often limiting the usefulness of standard electroretinography in advanced cases. Full-field stimulus test (FST) testing has become a sensitive psychophysical technique for detecting residual visual function when traditional electrophysiology is non-recordable. This study evaluated the ability of chromatic FST to differentiate rod–cone from cone photoreceptor dysfunction in patients with genetically confirmed inherited retinal dystrophies. Methods: Cross-sectional FST data were analyzed from 39 patients (mean age 45.7 ± 20.0 years) with genetically confirmed inherited retinal dystrophies at a tertiary academic center. All participants underwent standardized FST testing using white, red, and blue stimuli. Patients were classified into rod–cone dystrophy (n = 27) or cone dystrophy (n = 12) groups based on genetic and clinical criteria. Group comparisons focused on FST thresholds and especially blue–red threshold differences as markers of photoreceptor-mediated function. Bonferroni correction was applied to adjust for multiple comparisons across four primary FST parameters. Additional analyses by genotype were performed with nonparametric tests. Results: Eight different genetic mutations were represented, including Phosphodiesterase 6A (PDE6A) (n = 10), Rhodopsin (RHO) (n = 7), Phosphodiesterase 6B (PDE6B) (n = 6), Cyclic Nucleotide-Gated Channel Beta 1 (CNGB1) (n = 4), Cyclic Nucleotide-Gated Channel Alpha 3 (CNGA3) (n = 4), Nuclear Receptor Subfamily 2 Group E Member 3 (NR2E3) (n = 4), Guanylate Cyclase 2D (GUCY2D) (n = 2), and Cyclic Nucleotide-Gated Channel Beta 3 (CNGB3) (n = 2). Blue–red FST threshold differences exhibited moderate group discrimination in uncorrected analysis, with rod–cone dystrophies averaging −8.35 ± 10.37 dB and cone dystrophies −11.20 ± 14.60 dB. The area under the receiver operating characteristic curve for blue–red difference was 0.74 (95% CI: 0.59–0.90), with 75% sensitivity and 70.4% specificity at a −10 dB cutoff. However, no chromatic FST parameter maintained statistical significance between groups after Bonferroni correction. Inter-eye FST correlation was high (r = 0.758, p < 0.001), supporting test reliability. Conclusions: Chromatic FST testing provides a practical and sensitive means to assess photoreceptor function in advanced inherited retinal dystrophies, particularly when standard electrophysiologic methods are uninformative. Although the blue–red threshold difference offers moderate discrimination between rod–cone and cone dystrophies in uncorrected analysis, no chromatic parameter reached statistical significance after adjustment for multiple testing. Chromatic FST should be considered a supplementary approach for clinical monitoring and therapeutic studies in advanced retinal dystrophies, with further validation needed in larger cohorts.

The Moroccan locust Dociostaurus maroccanus (Thunberg, 1815) (Orthoptera: Acrididae) is recognized as a serious pest of pastures and crops in many Mediterranean countries. So far, the identification of chemical compounds involved in intraspecific communication has been elusive, with only one male-specific compound, namely (2E,7R,11R)-3,7,11,15-tetramethylhexadec-2-enal (2E,7R,11R-phytal), proposed as candidate sex pheromone component. Previous works reported that males also release the sex-specific long-chain aldehydes tetradecanal and hexadecanal, although no evidence of their role in chemical signaling has been presented so far. Here we additionally report another male-specific compound, viz. pentadecanal, and also report the electroantennographic and behavioral responses of virgin and mated females to these three aldehydes. We first demonstrated that their release is age-dependent, with emission overall peaking at 1–2 weeks after fledging. Solid-phase microextraction analyses from male hind legs suggest that these aldehydes are released from this body part. Overall, the antennae of virgin and mated females exhibited a dose-dependent response to increasing concentrations of each aldehyde, except for the response of virgin females to hexadecanal. Significant differences were observed between virgin and mated females at all doses of hexadecanal, while the mating status did not influence the response to tetradecanal and pentadecanal. Furthermore, the behavioral response of females varied according to their mating status. Specifically, mated females showed a significant preference for tetradecanal and pentadecanal, whereas a slight but non-significant attraction to hexadecanal was observed on virgin females. These findings provide new insights into the sexual communication of the species, although further research is needed to decipher their ecological function, especially on mated females.

Optical Wireless Power Transmission (OWPT) holds a significant position for enabling cable-free energy delivery in long-distance, high-energy, and mobile scenarios. However, ensuring human and equipment safety under high-power laser exposure remains a critical challenge. This study reports a vision-based OWPT safety system that implements the principle of automatic emission control (AEC)—dynamically modulating laser emission in real time to prevent hazardous exposure. While camera-based OWPT safety systems have been proposed in the concept, there are extremely limited working implementations to date. Moreover, existing systems struggle with response speed and single-object assumptions. To address these gaps, this research presents a low-latency safety architecture based on a customized deep learning-based object detection framework, a dedicated OWPT dataset, and a multi-threaded control stack. The research also introduces a real-time risk factor (RF) metric that evaluates proximity and velocity for each detected intrusion object (IO), enabling dynamic prioritization among multiple threats. The system achieves a minimum response latency of 14 ms (average 29 ms) and maintains reliable performance in complex multi-object scenarios. This work establishes a new benchmark for OWPT safety system and contributes a scalable reference for future development.

Recent advances in deep learning (DL) have enabled the integration of diverse biomedical data for disease prediction and risk stratification. Building on this progress, the overall objective of this study was to develop and evaluate a multimodal DL framework for robust multi-label classification (MLC) of major comorbidities in patients with obstructive sleep apnea (OSA) using physiological time series signals and clinical data. This study proposes a robust framework for multi-label classification (MLC) of comorbidities in patients with OSA using diverse physiological and clinical data sources. We conducted a retrospective observational study including a convenience sample of 144 patients referred for overnight polysomnography at the Sleep Medicine Center (SleepLab Split), University Hospital Centre Split (KBC Split), Split, Croatia. Patients were selected based on predefined inclusion criteria and data availability. A one-dimensional Convolutional Neural Network (1D-CNN) was developed to process and fuse time series signals, oxygen saturation ($Sp{O}_2$), derived $Sp{O}_2$ features, and nasal airflow (FP0), with demographic and physiological parameters, enabling the identification of key comorbidities such as arterial hypertension, diabetes mellitus, and asthma/COPD. The instruments included polysomnography-derived signals (SpO₂ and FP0 airflow) and structured demographic/physiological parameters. Signals were preprocessed and used as inputs to the proposed fusion model. The proposed model was trained and fine-tuned using the Optuna hyperparameter optimization framework, addressing class imbalance through weighted loss adjustments. Its performance was comprehensively assessed using multi-label evaluation metrics, including macro/micro F1-score, AUC-ROC, AUC-PR, subset and partial accuracy, Hamming loss, and multi-label confusion matrix (MLCM). The study protocol was approved by the Ethics Committee of the School of Medicine, University of Split (Approval No. 003-08/23-03/0015, Date: 17 October 2023). The 1D-CNN achieved superior predictive performance compared to traditional machine learning (ML) classifiers with macro AUC-ROC = 0.731 and AUC-PR = 0.750. The model demonstrated consistent behavior across age, gender, and BMI groups, indicating strong generalization and minimal demographic bias. In conclusion, the results confirm that $Sp{O}_2$ and airflow signals inherently encode comorbidity-specific physiological patterns, enabling efficient and scalable screening of OSA-related comorbidities without the need for full polysomnography. Although the study is limited by data set size, it provides a methodological basis for the application of multi-label DL models in clinical decision support systems. Future research should focus on the expansion of multi-center datasets, thereby improving model interpretability and potential clinical adoption.

Food systems are major drivers of global environmental change, accounting for about one-third of global greenhouse gas (GHG) emissions and contributing to land degradation, freshwater depletion, and biodiversity loss. Within this system, post-retail activities generate an estimated 18–20% of total food-related GHG emissions. In Europe, food service is responsible for roughly 12% of total food waste, making collective catering a strategic sector for sustainability interventions. Objective: Through menu design and composition, collective catering services can influence the environmental performance of thousands of meals served daily. This study introduces a novel meal-level scoring system—the App for the Environmental Impact Assessment of Dishes in Collective Catering (EcoRistApp, ERA)—designed to assess and communicate the environmental performance of institutional canteen dishes. Methods: EcoRistApp was developed and applied to a representative selection of first courses, second courses, and side dishes. Environmental impacts were quantified using Life Cycle Assessment (LCA) with SimaPro 9.5.5 software and the ReCiPe Midpoint (H) method. Normalized and weighted impact results were aggregated into a composite Environmental Impact Index (EII), which was then translated into a five-color interpretative scale to enhance usability and comprehension. Results: The analysis highlighted marked differences in environmental performance among dishes, largely driven by ingredient type and origin. Plant-based meals, such as lentil soup, consistently achieved lower impact scores, while dishes containing animal-derived ingredients, particularly beef and fish, showed higher impacts across multiple categories. Recipes combining high- and low-impact ingredients demonstrated potential for reducing overall environmental burdens. Conclusions: By converting complex LCA outcomes into an intuitive scoring system, EcoRistApp supports informed decision-making by catering operators and consumers, encourages plant-forward menu strategies, and contributes to the environmental transition of food service systems.

This paper develops an operator-oriented framework for spectral approximation in fractional calculus by introducing a fractional inner product defined through the Riemann-Liouville integral. Instead of modifying polynomial families, the proposed approach continuously deforms the underlying Hilbert space structure, with the fractional order $\alpha$ acting as a deformation parameter. A central theoretical result shows that this fractional inner product is mathematically equivalent to a classical weighted inner product with a deformed weight . This equivalence establishes a rigorous connection between fractional calculus and classical orthogonal polynomial theory and clarifies the structural role of the fractional parameter. For a canonical one-dimensional setting, explicit recurrence relations are derived and the limiting behavior as $\alpha \to 1$ is characterized, recovering the classical theory. The resulting orthogonal systems are naturally compatible with fractional operators and are used to construct spectral Galerkin methods for fractional differential equations. Well-posed variational formulations and optimal convergence rates are established. Numerical experiments illustrate the effectiveness of the framework, demonstrating spectral accuracy and improved performance in the approximation of fractional integrals and selected fractional differential equations when compared with standard polynomial bases. The proposed formulation provides a unifying operator-level perspective for spectral methods in fractional calculus.

In recent years, the methods based on convolutional neural networks have achieved significant progress in hyperspectral image super-resolution. However, existing methods still face two key challenges: (1) they fail to fully extract edge detail information from hyperspectral images; (2) they struggle to simultaneously capture local and global features. To address these issues, we propose an Edge-Distilled and Local–Global Feature Selection network (EDLGFS) for hyperspectral image super-resolution. This network aims to effectively leverage edge details and local–global features, thereby enhancing super-resolution reconstruction quality. Firstly, we design an edge-guided super-resolution network based on knowledge distillation. This network transfers edge knowledge to improve the reconstruction. Secondly, we propose a Local–Global Feature Selection mechanism (LGFS), which integrates convolutions of different sizes with the self-attention mechanism. This design models spatial correlations across features with different receptive fields, achieving efficient feature selection to more effectively capture local and global features. Finally, we propose a dynamic loss mechanism to more effectively balance the contribution of each loss term. Extensive experimental results on three public datasets demonstrate that the proposed EDLGFS achieves superior super-resolution reconstruction quality.

Background/Objectives: Pheochromocytoma (PHEO) is increasingly detected incidentally or through genetic screening; however, predictors of symptomatic presentation and its perioperative impact remain unclear. We aimed to quantify the prevalence of symptomatic PHEO, identify associated factors, compare perioperative outcomes, and develop a predictive nomogram. Methods: We retrospectively analyzed patients diagnosed and/or operated on for PHEO at a tertiary referral center between 1984 and 2021. Associations with symptomatic presentation were assessed using univariate and multivariable logistic regression analyses. A nomogram was constructed based on independent predictors and evaluated using receiver operating characteristic (ROC) analysis and the area under the curve (AUC). Results: Among 173 patients (mean age 44.4 ± 15.8 years; 53.2% women), 67.1% were symptomatic. In multivariable analysis, male sex (odds ratio [OR] 0.33; p = 0.023) and the presence of a germline mutation (OR 0.15; p = 0.004) were associated with a lower likelihood of symptoms, whereas a noradrenergic secretion profile was associated with a higher likelihood (OR 12.73; p = 0.02). Symptomatic patients had higher rates of intraoperative (OR 2.60; p = 0.032) and postoperative complications (OR 3.09; p = 0.04). The nomogram incorporating sex, genetic status, and noradrenergic profile demonstrated moderate discrimination (AUC 0.799; 95% confidence interval 0.722–0.877; p < 0.001). Conclusions: Symptomatic PHEO is associated with sex, genetic status, and noradrenergic secretion profile and is linked to increased perioperative morbidity. A simple nomogram based on readily available variables may help estimate individual risk and support perioperative management.