This study focused on improving the efficiency of sour beer production through mixed fermentations of Lachancea thermotolerans and Kveik yeast strains, aiming to reduce fermentation time while preserving the sour profile characteristic of L. thermotolerans. While in earlier trials, mixed fermentations with S. cerevisiae (US-05) achieved low pH values (~3.65), they did not achieve the best sensory profiles at elevated temperatures. Kveik yeast strains were selected as an alternative due to their high-temperature tolerance and rapid fermentation kinetics.

Fermentations were conducted at 25 °C using Kveik yeast and L. thermotolerans at ratios of 1:10, 1:20, and 1:30 (Kveik to L. thermotolerans). All fermentations were completed in under 10 days, confirming Kveik’s ability to significantly accelerate fermentation. However, final pH values remained relatively high (~3.8) compared to previous US-05 L. thermotolerans mixed cultures.

To enhance acidification, additional fermentations were carried out with glucose supplementation, based on prior findings that glucose increases L. thermotolerans's acidifying ability. Glucose-supplemented mixed fermentation trials using both Kveik (at 25 °C, 1:30 and 1:40) and US-05 (at 20 °C, 1:10 and 1:30) led to lower final pH values while maintaining clean sensory profiles and short fermentation times.

Across all trials, pH and extract (ºPlato) were monitored daily. Final beers were analyzed for alcohol content, total acidity, FAN, and underwent sensory evaluation. These findings suggest that mixed fermentations with L. thermotolerans and Kveik yeast (particularly with glucose addition) are an effective strategy for producing sour beers efficiently, without compromising flavor balance or sour character.

Objective: The purpose of this paper is to develop a machine learning-based classification model that can predict the risk level of maternal disease during pregnancy. The risk levels are classified as low or high based on clinical and physiological features. This model can provide early warnings, helping to reduce complications that typically occur during pregnancy.

Material/method: In this study fifteen classification algorithms were implemented and evaluated: Logistic Regression, Linear SVM(L1), RBF SVM, Decision Tree, Random Forest, XGBoost, AdaBoost, Bagging, KNN, Gaussian Naïve Bayes, Bernoulli Naïve Bayes, Ridge Classifier, Linear Discriminant Analysis, LightGBM, Extra Trees, and Deep Learning algorithms such as Artificial Neural Networks (ANNs) and Convolutional Neural Networks (CNNs), which are planned for future enhancements to capture nonlinear patterns. The dataset used in this model is Mendeley's Maternal Health Risk Assessment Dataset. The target variable in this dataset is the Risk Level, which is categorized as Low or High. The datasets include nearly 1,187 patient records. The key features in this dataset are Age, Systolic and Diastolic Blood Pressure, Blood Sugar (BS), Body Temperature, BMI, Heart Rate, Previous Complications, Preexisting Diabetes, Gestational Diabetes, and Mental Disease Indicators.

Results: Among the fifteen classification algorithms tested, XGBoost achieved the highest accuracy of 99% along with strong precision and recall for high-risk cases. Feature importance analysis showed that preexisting Diabetes, Blood Sugar, BMI, Heart Rate, and Mental Disease Indicators were the most influential predictors.

Reducing fat content in mozzarella cheese often results in compromised texture, meltability, and consumer acceptability. This study aimed to enhance the physicochemical, functional, and sensory properties of low-fat mozzarella cheese (LFMC) by utilizing flaxseed mucilage (FM) and a combination of FM with konjac glucomannan (FMKGM) as clean-label fat replacers. Cheeses were formulated using low-fat buffalo milk (<2.5% fat) with varying concentrations of FM (1%, 2.5%, 5%) and FMKGM (1%, 2.5%, 5%), and compared with full-fat and low-fat controls. Results showed that FM and FMKGM significantly increased protein content and moisture retention while maintaining reduced fat levels. Treatments with 2.5% FM and 2.5% FMKGM achieved optimal performance across key parameters. These samples demonstrated superior stretchability (44.2–46.1 inches) and meltability (up to 2.0 inches), closely resembling the functional behavior of full-fat cheese. Additionally, the same treatments exhibited improved textural resistance and reduced oiling-off ratios, indicating enhanced structural integrity. Sensory evaluation confirmed higher scores in mouthfeel, flavor, and overall acceptability for FM 2.5% and FMKGM 2.5% samples. This work demonstrates the potential of FM and FMKGM as sustainable, multifunctional fat replacers in dairy reformulation, offering a viable alternative for health-conscious consumers. Their application supports functional food innovation and aligns with Sustainable Development Goals #3 (Good Health and Well-being) and #12 (Responsible Consumption and Production). The findings provide practical insights for clean-label, cost-effective cheese manufacturing using plant-derived ingredients.

Diabetic Retinopathy (DR) remains one of the leading causes of vision damage worldwide, emphasizing the urgent need for reliable and interpretable diagnostic tools. While deep learning models have demonstrated remarkable performance in DR detection, their “black-box” nature limits clinical adoption. This study explores an explainable AI (XAI) framework that integrates lesion-level analysis with retinal fundus images to improve both accuracy and interpretability. Lesions such as microaneurysms, hemorrhages, and exudates are detected and highlighted as clinically relevant biomarkers, which are then mapped to disease severity grading. The model not only classifies DR stages but also generates visual explanations that correspond to ophthalmologists’ diagnostic reasoning. Preliminary results suggest that lesion-based explainability enhances clinician trust, facilitates validation of automated outputs, and supports better decision-making in screening programs. This approach underscores the potential of combining AI precision with medical interpretability, paving the way for practical integration of DR screening tools in real-world healthcare settings.
In the primary stage of experiments, the images were categorized into multiple classes representing the severity of diabetic retinopathy (e.g., No DR, Mild, Moderate, Severe, and Proliferative DR). The MobileNet model achieved over 82% classification accuracy, indicating its ability to distinguish between different stages of the disease with reasonable reliability. This performance demonstrates the feasibility of using lesion-focused features for automated DR grading. At the same time , the lesion-based explainability maps generated during these trials showed alignment with clinically relevant structures such as microaneurysms, hemorrhages, and exudates, supporting both the accuracy and interpretability of the predictions.

The aim of this study was to investigate the effect of mullein flower extract on the oxidative stability of
linseed oil. The theoretical part discussed the composition, properties, and applications of linseed oil and
mullein. The lipid oxidation process and techniques for enriching oils with antioxidant compounds,
including the use of ultrasound, were described. In the experimental part, the ultrasound-assisted extraction
of antioxidant compounds from mullein flowers was optimized, with antioxidant activity (AA) used as the
optimization parameter. The optimized extract was added to linseed oil, determining the optimal addition
level at 5%. Both fresh linseed oil and the oil enriched with the extract were subjected to safety analysis,
and their oxidative stability and bioactive compound content were determined. The results confirmed the
effectiveness of mullein extract as a natural antioxidant, with linseed oil stability increasing on average by
26%. Moreover, the oil with the extract showed a higher degree of hydrolysis and a higher level of
secondary oxidation products. The addition of the extract increased the content of bioactive compounds in
linseed oil, especially phenolics, which increased threefold to values ranging from 311.49 to 430.41 mg
GAE/100 g of oil. The induction time of the oil was strongly positively correlated with the phenolic content
(r = 0.97), anisidine value (r = 0.95), and antioxidant activity of the oil’s hydrophilic fraction (r = 0.93).

Abstract: This paper presents the design and comprehensive analysis of a 9 GHz inset-fed rectangular microstrip patch antenna intended for X-band applications. Microstrip patch antennas are widely recognized for their low-profile structure, ease of fabrication, and seamless integration with modern microwave circuits, making them suitable for radar, satellite communication, and defense systems. The proposed antenna is designed and simulated using Ansys HFSS software on four distinct substrate materials, FR4, air, Bakelite, and Rogers RT Duroid 5880, with dielectric constants of 4.4, 1.0, 4.8, and 2.2, respectively. The study emphasizes the impact of substrate material on antenna performance by evaluating critical parameters such as return loss, gain, directivity, bandwidth, and VSWR. Simulation results demonstrate that FR4 provides the best impedance matching with a return loss of –37.13 dB, while the air substrate offers maximum gain of 9.58 dBi. Rogers 5880 achieves balanced performance in terms of gain and return loss, making it suitable for high-performance applications despite higher fabrication costs. Bakelite shows moderate performance but remains viable for low-cost solutions. This investigation highlights the trade-offs between performance and cost in substrate selection, offering antenna designers practical guidelines for optimizing X-band antenna designs. The findings contribute to improving antenna design strategies for reliable communication systems operating in the X-band spectrum.

Peptides are extensively studied bioactive compounds from food. They are analyzed e.g. using in silico strategies. The BIOPEP-UWM database (https://biochemia.uwm.edu.pl/en/biopep-uwm-2/) has become a standard tool in peptide research. Number of visits since 2024.01.01 is c.a. 56,000 (2025.07.24) and there is over 2,000 publications citing the database name or web address, as judged by Google Scholar.

The BIOPEP-UWM is publicly available without registration and includes databases of bioactive peptides, sensory peptides and amino acids and virtually bioactive peptides (BIOPEP-UWM Virtual). They annotate 5362, 587 and 499 records respectively, representing 99 bioactivities including tastes. Currently BIOPEP-UWM offers possibility of annotation of peptides with non-proteinogenic and modified amino acids or non-amino acid residues using data of monomers from the BIOPEP-UWM repository of amino acids and modifications.

Records of peptides containing proteinogenic amino acids and other residues are available to all options e.g. proteolysis simulation or conversion from sequence into SMILES code. BIOPEP-UWM enables annotation of peptides containing residues not investigated to date, such as sulfoxides with asymmetric sulphur atoms or anomers of lysine glycation products. The database is thus ahead of the state of knowledge in food science.

Acknowledgements:

This work was supported by designated subsidy of the Minister of Science and Higher Education Republic of Poland, task entitled: The Research Network of Life Sciences Universities for the Development of the Polish Dairy Industry – Research Project and by Minister of Science under “the Regional Initiative of Excellence Program”.

Among the by-products of the olive industry, olive leaves are the most abundant, yet their high content of bioactive compounds remains largely underexploited. Their valorization offers good opportunities in various fields, including food conservation. Our work aims to evaluate the performance of olive leaf extracts of ‘cv Picholine Marocaine’ as a natural additive for improving the oxidative stability of various edible vegetable oils, including sunflower, soybean, and olive oils, obtained from three extraction technologies: wo-Phase (2P), Three-Phase (3P), and Super-Pressure (SP) extraction systems. The aqueous extraction of bioactive compounds was performed using the Soxhlet instrument. Three concentrations of leaf extracts, 200, 400, and 800 ppm, were added to the studied vegetable oils. A physicochemical characterization, including free acidity (FA), peroxide value (PV), extinction coefficients (K232 and K270), chlorophyll content (Chl), carotenoid content (Cart), fatty acid composition, antioxidant activity (AA), total phenolic compounds (TPCs), and total flavonoid content (TFC), were monitored for six months. Combined analysis of variance reveals that treatment with additives, storage time, and their interaction had highly significant effects (p ≤ 0.001) on almost all investigated parameters. Moreover, storage time was the most important factor influencing the dependent variables. The results of our mean comparison show a significant improvement of the investigated parameters, PV, K₂₃₂, K₂₇₀, TPC, and TFC, following the incorporation of 400 and 800 ppm leaf extract. In contrast, no significant effect (p ≥ 0.05) was observed on the fatty acid composition of the vegetable oils. Robust simple regression was highlighted between the concentration of leaf extract and both TPC and pigment contents. Principal component analysis confirmed the ANOVA results and clustered the enriched olive oils in correlation with the low values of oxidation indices. In conclusion, olive leaves provide a potential by-product to enhance the oxidative stability of vegetable oils.

Abstract—Satellite imagery is increasingly being scrutinized through deep learning methodologies for remote sensing applications, particularly focusing on the detection of water bodies. The ability to identify and analyze rivers, lakes, and reservoirs through segmentation has now become feasible, enabling the exploration of their statistical information. During crises such as floods and changes in river pathways, real-time detection of water bodies via remote sensing proves to be highly advantageous. Nevertheless, achieving a precise segmentation of water bodies presents a notable challenge, mainly due to the necessity of high-resolution multi-channel satellite images. The existing literature predominantly relies on satellite data from multi-band satellites for water body extraction. Conversely, this current research emphasizes the segmentation of water body regions using relatively lower-resolution RGB images without the incorporation of extra multi-spectral channels. To tackle this challenge, a unique methodology is suggested, involving a customized U-Net model integrated with a Time-Frequency Attention network (TFAU-Net) for segmentation. To assess the comprehensive performance of the proposed model, it is evaluated against a publicly available Sentinel-2 satellite dataset, and the outcomes are compared against standard benchmark metrics. The model achieved a precision of 94%, sensitivity of 96%, Dice score of 93%, Mean IoU of 85%, and accuracy of 97%. The proposed architecture surpasses even the most high-performing baseline. The segmentation results obtained exhibit a significant improvement in performance compared to the cutting-edge methods used for water body segmentation from low-resolution satellite images.

Abstract

Introduction

Many chronic diseases are associated with oxidative stress imbalances. Incorporating antioxidants into biscuits could provide an effective way to protect against cell damage caused by exposure to free radicals. Various ingredients were investigated. This study aimed to identify ingredients that could enhance the antioxidant content of biscuits.

Methods

Different ingredients were incorporated into biscuit dough. Biscuit extracts were prepared using warm water (0.5% w/w), stirring for 20 minutes at 600 rpm (12 x g) and 45°C.The antioxidant capacity of the aqueous extracts was determined using the ferric reducing antioxidant power (FRAP) method. A calibration curve was constructed using FeSO₄·7H₂O and the results were expressed as µmol FeSO₄/g of dry sample. The colour of the products was measured using a colourimeter to record the L*, a* and b* values.

Results

The results showed that the colour of the biscuits was more consistent when the added ingredients were liquids rather than solids. Carob flour and cinnamon were the solid ingredients that increased antioxidant levels the most. In contrast, the antioxidant capacity did not significantly increase with the liquid extracts of Callistemon citrinus or Tropaeolum majus flowers at 0.02% w/w. The aim was to select ingredients that would significantly increase the biscuits' antioxidant content.

Conclusion

The antioxidant content of biscuits can be increased by adding certain ingredients to the formulation.