Calcium is widely used to improve apple postharvest performance and reduce bitter pit development, but the mechanisms linking calcium accumulation with cold storage tolerance remain unclear. This study investigated calcium-mediated responses in peel and flesh tissues of two apple cultivars, ‘King Roat’ and ‘Golden Chief’, during harvest and cold storage. Repeated preharvest calcium applications increased calcium accumulation in both tissues, maintained fruit firmness, and markedly reduced bitter pit symptoms after cold storage. Integrated cell wall, metabolomic, and transcriptomic analysis showed that calcium protection extended beyond its classical structural role. Calcium reshaped pectin-, xyloglucan-, and arabinogalactan protein-associated cell wall domains in a cultivar- and tissue-dependent manner, supporting improved wall cohesion and reduced tissue destabilization. Metabolomic profiling revealed coordinated changes in carbohydrate, organic acid, lipid, phenolic, and glutathione-related pathways, consistent with metabolic adjustment during storage. Transcriptomic analysis in ‘King Roat’ further indicated that calcium repressed genes associated with cell wall loosening, flavonoid biosynthesis, and programmed cell death, while activating redox-related components. Among the validated genes, MdGRXC11L was consistently induced by calcium across cultivars, tissues, and storage stages, indicating a potential role for glutaredoxin-mediated redox recovery in calcium-associated protection. Overall, these findings support a model in which calcium reduces bitter pit expression by reinforcing cell wall cohesion, maintaining metabolic homeostasis, and promoting glutathione-linked redox regulation during cold storage, providing potential molecular targets for predicting and managing postharvest physiological disorders in apple.
Climate chambers enable repeatable indoor boundary conditions and are increasingly used to study multi-domain IEQ. However, thermal comfort and IAQ are still often evaluated separately, limiting evidence on their coupled behavior and potential trade-offs under different ventilation and air-cleaning strategies. The present study was carried out in the climate chamber located in the laboratory facilities of the University of Western Macedonia to quantify thermal comfort and IAQ simultaneously across different experimental scenarios that vary ventilation mode, heating operation, and occupancy. The results show a correlation between subjective and objective measurements, with the comfort temperature varying around 22.2 degrees C, as estimated by the Griffiths model, while ventilation mainly affects the stability of the thermal environment. CO2 levels scaled with occupancy and ventilation rate, while PM removal was strongly strategy-dependent: after a controlled smoke event, mechanical ventilation plus air purification achieved the fastest decay and recovery toward near-background concentrations. Overall, this work represents a first step toward coupled IEQ research by jointly quantifying thermal comfort and IAQ in a climate chamber, enabling systematic comparison of ventilation strategies in terms of both perceived comfort and pollutant exposure.
Accurate and reproducible electroencephalography (EEG)-based classification of dementia remains a key challenge in computational neurodiagnostics. The open-access AHEPA dataset has become the most commonly used benchmark for Alzheimer's disease (AD) and Frontotemporal dementia (FTD) classification, yet reported results vary widely due to methodological inconsistencies. This study presents the first systematic and quantitative benchmark review of all published machine learning approaches applied to the AHEPA dataset. Forty-six studies were reviewed and stratified into three validity tiers, with Validity 1 representing the highest methodological rigor and Validity 3 the lowest.According to their evaluation rigor: (1) subject-level validation (e.g., Leave-One-Subject-Out cross-validation, LOSO-CV), (2) subject-level train/test splits, and (3) epoch-level k-fold cross-validation. Performance metrics were normalized across classification problems. The analysis revealed that methodological rigor is inversely correlated with reported accuracy: for AD versus Cognitively Normal controls, mean accuracy decreased from 90.81% overall to 82.11% in Validity-1 studies; for FTD versus controls, accuracy dropped from 86.53% to 75.18%. Linear regression analyses demonstrated that weaker validation protocols were associated with systematic increases of 7-10% points in reported accuracy, explaining more than half of the observed performance variance. Deep and hybrid models reported the highest nominal accuracies, but under proper validation, traditional algorithms performed comparably, indicating that data leakage often drives apparent improvements. The review also highlights the lack of cross-configuration generalization and the urgent need for adaptive, montage-independent methodologies. Overall, this benchmark establishes the first reproducible reference framework for EEG-based dementia classification on the AHEPA dataset, providing quantitative baselines and validity criteria against which all future studies should be evaluated.
This study examined the evolution of volatile and non-volatile compounds of a Cypriot monovarietal cultivar Maratheftiko red wine over a span of six years (2019-2024). Several physicochemical properties of the wines were evaluated. Alcohol content and volatile acidity remained stable; acidity and malic acid are the main differentiating factors among vintages. In addition, bioactive molecules in the wines showed a distinct vintage effect, with the 2024 vintage exhibiting significantly higher concentrations. For instance, the high concentration of polyphenols (3877.86 mg gallic acid equivalents per L), tannins (688.43 mg of catechin equivalents per L), flavonoids (506.90 mg of rutin equivalents per L), and anthocyanins (413.74 mg of cyanidin equivalents per L) contributed to the high antioxidant capacity of the specific vintage, as FRAP and DPPH assays were measured at 44.60 and 29.91 mmol of ascorbic acid equivalents per L, respectively. Furthermore, the intense crimson color of this red wine could be attributed to the high concentration of the abundant anthocyanin malvidin-3-O-glucoside in this vintage (21.62 mg/L). On the other hand, it was observed that the latest vintage showed high polyphenol concentration but low volatile compound concentration. This pattern was ascertained through correlation analyses and could be attributed to an unsatisfactory level of the aging process. Correlation analysis (Pearson's r) confirmed inverse relationships between polyphenol concentration and volatile compounds (r = -0.62, p < 0.05). Principal component analysis (PCA) further highlighted the 2024 as an outlier vintage, distinguished by elevated phenolic and antioxidant profiles.
Genetic sequences play a central role in biological and medical research, and mathematics provides powerful means for their representation and analysis. Conventional approaches, such as the fuzzy polynucleotide space [0, 1]12, model codons as 12-dimensional vectors, but this comes at the cost of high dimensionality. In this study, we introduce two new models, Vector-Fuzzy-I and Vector-Fuzzy-II, that map codons and genetic sequences into the 4-dimensional Euclidean space & Ropf;4 using vector algebra and fuzzy set theory. In the first model, sequence structure is represented by successive vector addition, while in the second, it is represented by positional frequencies normalized by nucleotide locations. These low-dimensional representations are unique, preserve sequence order, and allow effective measurement of similarity and difference via Euclidean metrics. Compared with the fuzzy polynucleotide space, the proposed models achieve dimensionality reduction while enhancing the resolution of sequence differentiation. Our approach offers new mathematical perspectives for sequence analysis in theoretical biology.
