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Multifunctional POSS-based nanoparticles functionalized with silver, SPIONs, and rhamnolipid for antibacterial applications
(Elsevier, 2026) Kibar, Gunes; Kafali, Melisa; Ozonuk, Olgu Cagan; Oztas, Merve; Usta, Berk; Ercan, Batur
Nano-engineered materials, particularly those featuring bio-based surface modifications, are emerging as effective tools in combating bacterial infections. In this study, polyhedral oligomeric silsesquioxane (POSS) nanoparticles were functionalized with silver nanoparticles (Ag), superparamagnetic iron oxide nanoparticles (SPIONs), and the biosurfactant rhamnolipid (RL)-either individually or in combination-to evaluate their antibacterial and antibiofilm activities against Staphylococcus aureus (S. aureus) and Pseudomonas aeruginosa (P. aeruginosa). The modified nanoparticles exhibited sizes ranging from 127 to 227 nm and demonstrated superparamagnetic behavior, offering potential for magnetic targeting. Among the various formulations, the RLcoated, silver- and SPION-decorated POSS nanoparticles (RSMP) exhibited the highest antibacterial efficacy, reducing S. aureus and P. aeruginosa colony growth by approximately 90 % and 66 %, respectively, at a concentration of 0.01 g/L. RSMP nanoparticles also showed strong biofilm inhibition and had the lowest MIC50 values. Notably, these nanoparticles supported the proliferation of human osteoblasts at concentrations up to 0.05 g/L, indicating favorable cytocompatibility. Overall, RSMP nanoparticles present a promising platform for magnetically targetable antibacterial agents, with potential applications in biomedical fields, particularly for managing orthopedic infections.
Peas, natural resources for a sustainable future: a multifaceted review of nutritional, health, environmental, and market perspectives
(Frontiers Media SA, 2026) Nikolic, Nada Cujic; Mutavski, Zorana; Savikin, Katarina; Zivkovic, Jelena; Pavlovic, Suzana; Jones, Petra; Copperstone, Claire; Aytar, Erdi Can; Aydin, Betul; Van Bavegem, Evelien; Kunili, Ibrahim Ender; Ozmen, Ozge; Kusumler, Aylin Seylam; Unal, Derya Ozalp; Gunduz, Selin; Lara, Szymon Wojciech; Akin, Meleksen; Orahovac, Amil; Balazs, Balint; Milesevic, Jelena; Sirbu, Alexandrina; Negrao, Sonia; Knez, Marija
The pea (Pisum sativum L.) is an emerging pillar in plant-based nutrition and sustainable food systems due to its high-quality proteins, diverse bioactive compounds, and agroecological benefits. This review provides an updated synthesis of the nutritional composition, health-promoting properties, and environmental relevance of peas, emphasizing recent scientific findings. Pea seeds typically contain 20%-40% protein, 45%-55% starch, and 10%-15% dietary fiber, alongside essential micronutrients such as vitamin C (40-60 mg/100 g), folate (60-70 mu g/100 g), vitamin K (30-45 mu g/100 g), iron (1.5-2.0 mg/100 g), and manganese (0.4-0.6 mg/100 g). Their storage proteins, primarily legumin and vicilin, offer high digestibility and amino acid profiles compatible with human requirements, supporting their rapidly growing use in protein isolates and meat- and dairy-alternative products. Peas represent a valuable source of phenolic acids, flavonoids, and saponins, which contribute to notable antioxidant (50-120 mu mol Trolox/g) and anti-inflammatory activities demonstrated in preclinical studies. Compared with other legumes, peas exhibit a lower glycemic index (35-45), making them suitable for metabolic health applications. Agronomically, pea cultivation enhances soil fertility through biological nitrogen fixation (up to 150 kg N/ha), supporting reduced fertilizer inputs and improved crop rotation performance, aligning with circular economy and climate-resilience strategies. Despite these advantages, global consumption and breeding innovation remain insufficient to meet the rising demand for alternative proteins. Future opportunities include improving protein extraction technologies, valorizing processing side-streams, and exploring underutilized phytochemicals to strengthen the nutritional and sustainability profile of pea-based food systems.
Size Dependent Effects of Aluminum Oxide Particles on Tissue Accumulation and Antioxidant Enzyme Activities of Oreochromis niloticus
(Springer, 2026) Tuncsoy, Mustafa; Tuncsoy, Benay
Aluminum (Al) is a widely occurring element in nature and is the most abundant element in the earth's crust after oxygen and silicon. In this study, metal accumulation levels in the gills, liver and muscle tissues and liver SOD, CAT and GPx enzyme activities of liver tissue of O. niloticus were determined under the influence of 10 mg/L concentrations of micro and nano sizes of Al2O3 for 7 days. Aluminum levels in the tissues were measured by ICP-MS. In this study, no mortality was observed during the experiments. It was observed that there was a significant increase in the tissues examined under the effect of both forms of Al2O3 compared to the control. At the end of the 7 days exposure period, the following relationship was found among the tissues in accumulating under the effect of both forms of Al2O3 as gill > liver > muscle. It was also observed that there was a significant increase in SOD, CAT and GPx enzyme activities of liver tissue examined under the effect of both forms of Al2O3 compared to the control. Overall, Al2O3 NPs are more effective than Al2O3 MP in terms of liver enzyme activities.
Bioclimatic comfort and solar responsive urban design in the traditional street texture of Diyarbakir's Suriçi region
(Nature Portfolio, 2026) Gider, Kubra Suna; Ergin, Sefika; Yildizhan, Hasan; Ameen, Arman
People in urban areas (such as streets, parks, semi-open and enclosed spaces) are exposed to varying microclimatic conditions. These conditions change depending on environmental characteristics and directly affect individuals' bioclimatic comfort levels. The lack of climate-responsive urban planning exposes inhabitants to uncomfortable thermal stress. Establishing climate-sensitive thermal comfort conditions at the micro scale is therefore essential for creating more livable urban environments. In hot-arid climates, kabalt & imath;s, roofed passages integrated into the street network, are among the spatial elements that influence pedestrian thermal comfort. However, there is limited knowledge in the literature regarding the thermal performance of these shaded structures, which provide both protection from solar radiation and shelter from rain and wind. This study aims to reveal the impact of kabalt & imath;s, as traditional urban elements in hot-arid regions, on bioclimatic comfort, and to contribute to the development of climate-responsive urban design strategies. Due to the scarcity of research on the thermal performance of kabalt & imath;s, the findings of this study provide new insights into climate-adaptive design solutions within traditional street networks and serve as a guide for urban planning practices. The research was conducted in the historical district of Diyarbak & imath;r Suri & ccedil;i, focusing on six kabalt & imath;s and their surrounding streets located in the Ziya G & ouml;kalp, Abdaldede, and S & uuml;leyman Nazif neighborhoods. At a total of 19 measurement points, air temperature, relative humidity, and wind speed were recorded over the course of one year. Using the RayMan Pro software, Physiological Equivalent Temperature (PET) values were calculated, and Sky View Factor (SVF) values were determined for comparative analysis. The results indicate that the studied streets and kabalt & imath;s were exposed to varying degrees of heat and cold stress throughout the year. Shaded zones and kabalt & imath;s exhibited lower air temperature and PET values compared to other points. In this hot-arid setting, the presence of covered, shaded areas was found to be effective in reducing solar exposure and lowering thermal stress during summer months. The measurements further revealed that urban geometry, particularly building height and street width, influenced solar radiation access and wind speed, thereby affecting PET values. In addition, no direct correlation was observed between SVF and PET, highlighting the need to consider other parameters when assessing bioclimatic comfort.
Effects of urban street geometry and traditional kabalti passages on building surface temperature in a hot-dry climate
(Nature Portfolio, 2026) Ergin, Sefika; Gider, Kubra Suna; Seker, Ibrahim Halil; Yildizhan, Hasan; Ameen, Arman
In hot climate regions, the direct impact of solar radiation on building surfaces, including heat absorption and storage, negatively impacts outdoor comfort and the living conditions of urban residents. This study investigates the impact of urban street geometry on building surface temperatures in a hot and dry climate, focusing on the traditional Suri & ccedil;i district of Diyarbak & imath;r. Measurements were conducted at 25 locations throughout the year along streets with varying sky view factor (SVF) values and within vaulted covered passages (kabalt & imath;s). In the study, a Testo 410-2 anemometer was used to measure air temperature and a thermal camera was used to measure surface temperature. The results show smaller daily surface temperature amplitudes in regions with lower SVF values and in kabalt & imath;s with an SVF value of 0. Measured surface temperatures reached as high as 58.8 degrees C at high SVF locations, while they remained around 30 degrees C in shaded kabalt & imath;s. The findings indicate that street geometry parameters such as building height, spacing, and orientation significantly influence microclimate conditions. Differences of up to 15-20 degrees C were observed between shaded kabalt & imath;s surface temperatures and other surface temperatures at measurement points where the SVF value was close to 1. Reducing SVF through design strategies such as the use of kabalt & imath;s and planting trees can improve outdoor thermal comfort in hot climates.
Prediction of severe erectile dysfunction after penile fracture repair: machine learning analysis results from the reconstruction and trauma working group of the society of urological surgery (RAT-SUS)
(Oxford University Press, 2025) Geyik, Serdar; Onder Yilmaz, Ismail; Zubaroglu, Mehmet; Deger, Mutlu; Kavak, Rahmi; Sari, Hilmi; Danacioglu, Yavuz Onur; Sertkaya, Caglar; Yilmaz, Mehmet; Haciobey, Ibrahim; Tipirdamaz, Mustafa; Dundar, Mehmet; Duran, Mesut Berkan; Sinirsiz, Can; Bayrak, Omer; Zeytun, Onur; Albaz, Alican; Demir, Murat; Goger, Yunus Emre; Ucar, Murat; Akgul, Burak; Arda, Ersan; Akarken, Ilker; Guzel, Ahmet; Kayra, Mehmet Vehbi; Kartal, Ibrahim Guven; Girgin, Reha; Baba, Dursun; Ceker, Gokhan; Ozen, Mehmet; Gurbuz, Ahmet; Yilmaz, Ozgur; Bozkurt, Ozan
Background Erectile dysfunction (ED) is a significant complication following penile fracture repair, and early prediction is critical for clinical management.] Aim To evaluate the effectiveness of machine learning (ML) algorithms in predicting the development of severe ED after penile fracture repair and to identify complex risk factors beyond the scope of traditional statistical methods. Methods A retrospective analysis was conducted using data from 547 patients who underwent surgical repair for penile fracture between January 2020 and June 2024 at 23 urology centers affiliated with the Reconstructive Urology and Trauma Study Group of the Urological Surgery Society. Patients were categorized into two groups based on their International Index of Erectile Function-5 scores at six months postoperatively: severe ED (+) (<= 7) and ED (-) (>7). Eleven different ML classifiers were evaluated to determine the most predictive models. Four distinct resampling techniques were employed to address class imbalance in the dataset. Feature importance analysis was also performed to identify the most influential variables contributing to ED risk. Outcomes This study was conducted to enable the early identification of patients at high risk of developing severe ED following penile fracture surgery. Results Logistic Regression, Gaussian Naive Bayes, and Linear Support Vector Machine emerged as the best-performing algorithms on the original dataset, with Area Under the Curve (AUC) scores of 0.81, 0.78, and 0.76, respectively. On the Synthetic Minority Over-sampling Technique (SMOTE)-resampled dataset, Quadratic Discriminant Analysis (QDA) achieved an AUC of 0.85, while the Artificial Neural Network (ANN) reached an AUC of 0.84. On the SMOTE-resampled dataset, QDA achieved a ROC-AUC of 0.85 (95% CI: 0.75-0.93), whereas on the SMOTE-Tomek Link-resampled dataset, the ANN attained a ROC-AUC of 0.84 (95% CI: 0.71-0.94). The most critical predictors of severe ED were age, comorbidities, tunical tear length, and time to surgery. Urethral injuries were not significant contributors, as all were minor and managed conservatively without urethroplasty. Clinical Implications Integration of ML-based prediction models into clinical workflows could support early risk stratification and individualized patient care, ultimately improving postoperative functional outcomes. Strengths and Limitations This study benefits from a large, multicenter dataset and a comparative analysis of multiple ML algorithms. However, its retrospective nature and inter-center variability in data reporting may limit generalizability. Conclusion ML algorithms are effective and reliable tools for predicting severe ED after penile fracture repair and may enhance personalized postoperative management. Eliminating class imbalance in the data with resampling techniques improves model performance.
Experimental and Theoretical Study of Defect Evolution in InSb Epilayers under Gamma Irradiation: A Comparative Analysis of MOCVD vs MBE Growth Methods
(American Chemical Society, 2025) Marroquin, John Fredy Ricardo; Derc, Alex Cortes; Nascimento Lima, Erika; de Oliveira, Igor Saulo Santos; Gunes, Mustafa; Akyol, Mustafa; Archanjo, Braulio S.; de Azevedo, Walter M.; Henini, Mohamed; Felix, Jorlandio Francisco
The operational requirements of high-radiation and extraterrestrial environments highlight the need to evaluate narrow-bandgap semiconductors that remain unexplored under such conditions, among them Indium Antimonide (InSb). As a material system, InSb offers unparalleled electron mobility and a massive g-factor, making it indispensable for next-generation infrared detection, Hall sensing, and topological quantum computing architectures. However, the practical realization of these devices is frequently hindered by the necessity of heteroepitaxial growth on lattice-mismatched substrates, typically Gallium Arsenide (GaAs), which introduces a complex landscape of threading dislocations and interfacial defects. This report presents an exhaustive, multimodal investigation into the radiation hardness of InSb epilayers, specifically contrasting the microstructural evolution of films grown via Metal-Organic Chemical Vapor Deposition (MOCVD) against those synthesized by Molecular Beam Epitaxy (MBE). Utilizing an experimental framework that integrates Electron Paramagnetic Resonance (EPR), Raman spectroscopy, High-Resolution Scanning Transmission Electron Microscopy (HR-STEM), and ab initio Density Functional Theory (DFT), this study elucidates the mechanistic divergence in radiation response between the two growth methodologies. The data reveal a critical, counterintuitive trade-off: the MOCVD-grown material, despite exhibiting superior initial crystalline quality driven by a zinc-doped seed layer that passivates interfacial traps, demonstrates a heightened susceptibility to electronic degradation and stoichiometry violation under high-fluence Gamma (gamma) irradiation. In contrast, the MBE-grown material, initially marred by a higher density of dislocations, exhibits a complex survivability mode at elevated doses, characterized by defect saturation. This report details the atomic-level physics driving these behaviors, including the radiation-induced formation of homopolar Sb-Sb bonds, the symmetry-breaking anisotropy of the g-factor, and the thermodynamic instability of dopant-passivated interfaces under nonequilibrium conditions. Furthermore, these findings can be used as actionable engineering guidelines for Radiation Hardness Assurance (RHA), proposing novel nondestructive spectroscopic metrics for the qualification of semiconductors destined for space and nuclear applications.
Resource efficiency and environmental sustainability of wheat production in Türkiye
(Nature Portfolio, 2025) Ozturk, Mujdat; Yildizhan, Hasan; Ameen, Arman
The environmental impact of agricultural production varies depending on input levels. This study provides a comparative sustainability assessment of wheat production in two different provinces of T & uuml;rkiye, Samsun and Tokat, by examining the energy, exergy and environmental performance on a per ton basis. Based on exergy consumption, Cumulative Degree of Perfection (CDP) and Renewability Index (RI) indicators were determined. The results show that Cumulative Energy Consumption (CEnC) is 7262.93 MJ/ton in Samsun and 3502.97 MJ/ton in Tokat. This indicates that wheat production in Samsun is approximately twice as energy intensive as in Tokat. Cumulative Exergy Consumption (CExC) was calculated as 10514.76 MJ/ton in Samsun and 5400.88 MJ/ton in Tokat. Here, the largest component of the exergy load is irrigation, followed by diesel consumption. From an environmental perspective, Cumulative CO2 Emissions (CCO2E) was found to be 957.5 kg/ton in Samsun and 562.27 kg/ton in Tokat. The sustainability metrics, CDP and RI values, were calculated as 2.13 and 0.53 for Samsun and 4.14 and 0.76 for Tokat, respectively. Based on these findings, it is evident that Samsun has lower exergetic efficiency and a limited degree of renewability due to higher fuel and irrigation inputs. These results suggest that Tokat presents a more sustainable model for wheat production.
Effect of post-deposition aluminizing on the corrosion and mechanical behavior of WAAM-fabricated stainless steel and Ni-based superalloy
(Nature Portfolio, 2025) Gunen, Ali; Gurol, Ugur; Cakan, Ahmet; Kocak, Mustafa; Cam, Gurel; Yildizhan, Hasan; Alsaadi, Ahmed; Gomes, Joao
The demand for corrosion-resistant and mechanically reliable metallic components in marine, chemical processing, and energy conversion industries has encouraged the integration of additive manufacturing into industrial production. Wire Arc Additive Manufacturing enables the fabrication of medium- to large-scale complex metallic structures at low cost; however, the high thermal input and layer-by-layer deposition commonly lead to elemental segregation, porosity, and nonuniform microstructures that degrade corrosion performance. This study investigates the influence of a post-deposition aluminizing treatment on the surface characteristics and corrosion behavior of stainless steel ER307 and nickel-based superalloy Inconel 625 produced by Wire Arc Directed Energy Deposition. Microstructural evolution, phase transformation, hardness distribution, and corrosion behavior in a 3.5% sodium chloride environment were examined through microscopy, X-ray diffraction, hardness testing, and electrochemical analysis. The aluminizing process generated localized surface porosity and limited non-uniformity aluminide coatings of approximately 40-50 mu m thickness, reduced surface roughness, and markedly improved surface hardness. Electrochemical assessments demonstrated substantial enhancements in corrosion resistance, including a 2.3-fold improvement for stainless steel and a 13.9-fold improvement for Inconel 625. These findings reveal that post-deposition aluminizing effectively mitigates intrinsic surface defects and microchemical heterogeneity, enabling significantly improved durability in chloride-containing environments. This work provides a straightforward and scalable strategy for enhancing the corrosion resistance of wire-arc-manufactured metallic structures and promotes their application in aggressive service conditions.
Thermodynamic and environmental assessment of apple production in Türkiye: regional comparison and agrivoltaic integration
(Nature Portfolio, 2025) Ozturk, Mujdat; Kayabasi, Ramazan; Yildizhan, Hasan; Ameen, Arman
This study presents a comprehensive thermodynamic and environmental assessment of apple cultivation across three major production regions in T & uuml;rkiye: Antalya, Isparta and Ni & gbreve;de. This study is the first to provide an integrated energy, exergy and environmental assessment of agricultural voltaic systems by conducting a resource efficiency and sustainability assessment for open field apple production in T & uuml;rkiye. Using a functional unit of one ton of apple production, the analysis integrates cumulative energy consumption (CEnC), cumulative exergy consumption (CExC) and cumulative carbon dioxide emissions (CCO2E) to reveal the sustainability performance of regional farming systems. The results indicate significant spatial variations linked to climatic and operational factors. Ni & gbreve;de exhibited the highest total energy (3098 MJ/ton) and exergy (2975 MJ/ton) consumptions, mainly due to diesel-powered irrigation and mechanization, resulting in a cumulative carbon footprint of 125 kg CO2/ton. Conversely, Antalya recorded the lowest total emissions (33 kg CO2/ton) with a balanced energy profile dominated by fertilizers and electricity use. Isparta demonstrated the most thermodynamically efficient and renewable system, achieving the highest cumulative degree of perfection (CDP) (3.80) and Renewability Index (RI) (0.74) values. The integration of agrivoltaic systems (AVS) has further enhanced sustainability across all provinces, particularly in Ni & gbreve;de, by increasing CDP by up to 97%. These findings highlight the significant role that renewable energy integration plays in reducing carbon intensity and increasing resource efficiency in apple cultivation. By providing a region-specific perspective on agricultural thermodynamics, the study provides strategic insights into the transition to sustainable and climate-resilient food production systems in T & uuml;rkiye.
Production of L-asparaginase from Candida utilis by solid-state fermentation: a comprehensive assessment of its antiproliferative potential on glioblastoma cells
(Springer, 2025) Alkis, Meryem Damla Ozdemir; Ipek, Semih Latif; Tulek, Ahmet; Gunduz, Cennet Pelin Boyaci; Gokturk, Dilek
L-asparaginase (L-ASNase) is an enzyme that depletes asparagine, a key amino acid for cancer cell survival, producing aspartic acid and ammonia. Beyond its food industry applications, L-ASNase is a clinically important agent against acute lymphoblastic leukemia (ALL). In this study, L-ASNase was produced and purified from Candida utilis via solid-state fermentation. Optimization on wheat bran identified 2 mL inoculation volume, 60% moisture, and a 4-day fermentation period as the optimal conditions, yielding 172.5 U/mL activity. The purified enzyme was tested against glioblastoma (GBM) cell lines, showing IC50 values of 0.4 U/mL for U87MG and 1.8 U/mL for T98G, with minimal toxicity toward normal HaCaT cells. Apoptotic effects were confirmed by DAPI/F-actin and Giemsa staining, while wound healing and clonogenic assays revealed inhibition of cell migration and colony formation. RT-qPCR analysis demonstrated downregulation of the Survivin gene, a key survival regulator. These findings highlight L-ASNase's potent antiproliferative, anti-migratory, and pro-apoptotic effects, underscoring its potential as an adjuvant therapy for GBM.
An improved roosters algorithm for constrained 3D UAV path planning in urban environments
(Nature Portfolio, 2025) Gencal, Mashar Cenk; Ata, Baris; Kurucan, Mehmet; Kilinc, Emre
Urban environments impose complex challenges for the navigation of unmanned aerial vehicles (UAVs), including dense obstacles, no-fly zones, energy constraints, and regulatory restrictions. Addressing these challenges requires efficient and robust optimization techniques. This study introduces the Improved Roosters Algorithm (IRA), a novel metaheuristic inspired by the natural dominance behavior of roosters, tailored for constrained 3D UAV path planning in urban scenarios. Unlike existing metaheuristics, IRA introduces a spiral dancing operator, adaptive constraint handling, and a hierarchical population structure. These innovations directly target the lack of adaptive mechanisms in constraint-rich urban environments, enabling more reliable and realistic UAV path planning. The performance of IRA is benchmarked against Particle Swarm Optimization (PSO), Standard Genetic Algorithm (SGA), Differential Evolution (DE), Grey Wolf Optimizer (GWO) and the original Roosters Algorithm (RA) across three increasingly complex simulation scenarios. Experimental results demonstrate that IRA consistently outperforms the baseline methods in terms of feasibility and optimality, validating its potential as a competitive tool for UAV mission planning in realistic urban environments.
Thermal Versus Ultrasound Inactivation of Bifidobacterium animalis subsp. lactis BB-12: Functional Implications for Postbiotics
(Springer, 2025) Marsak, Nimet; Akan, Ecem; Yavas, Adem; Erbay, Zafer
This study evaluated the viability and functional properties of postbiotics derived from Bifidobacterium animalis subsp. lactis BB-12 following thermal and ultrasound processing. Sixteen processing conditions were tested, including heat treatments (65-95 degrees C for 5-90 min) and ultrasound amplitudes (98-320 mu m for 15-60 min). Postbiotics were assessed for antimicrobial activity, probiotic-stimulating effects on Lacticaseibacillus casei 431 and Bifidobacterium spp. cultures, and their capacity to enhance short-chain fatty acid (SCFA) production. Complete inactivation was achieved under all heat treatment conditions, whereas only 11 ultrasound conditions resulted in total loss of culturability, indicating greater microbial resistance at lower intensities or shorter exposures. Discrepancies between flow cytometry and colony counting in ultrasound-treated samples suggest the presence of viable but non-culturable cells (VBNC), highlighting the limitations of culture-based viability assessments, especially when assessing VBNC states. Postbiotics from heat-treatment showed significantly stronger probiotic-enhancing effects, with growth increases up to 2.9-fold for L. casei and 37.7-fold for Bifidobacterium spp. compared to controls, alongside greater antimicrobial activity, especially against Enterococcus faecalis. Both processing methods significantly increased SCFA levels (p < 0.05). In conclusion, it can be said that heat treatment was more effective than ultrasound in producing biologically active postbiotics from B. lactis BB-12. The enhanced functional properties observed in heat-inactivated preparations underscore thermal processing as a robust method for postbiotic production. These findings highlight the potential of heat-derived postbiotics as active bioingredients for microbiota-targeted functional foods and nutraceuticals. Further in vivo validation and standardization efforts are needed to fully demonstrate their therapeutic potential and support regulatory approval.
A review of deep learning architectures for plant disease detection
(Tubitak Scientific & Technological Research Council Turkey, 2025) Kaya, Yasin; Gursoy, Ercan
Background/aim: The rapid advancement of deep learning (DL) has revolutionized plant disease detection by enabling highly accurate, image-based diagnostic solutions. This review provides a comprehensive synthesis of DL-based methodologies for plant disease detection, systematically structured around the key stages of the modeling pipeline, encompassing data acquisition, preprocessing, augmentation, classification, detection, segmentation, and deployment. Materials and methods: The review focuses on evaluating convolutional neural network (CNN) architectures such as VGG, ResNet, EfficientNet, and DenseNet across diverse experimental contexts. Classification strategies are categorized according to their integration of visualization techniques (e.g., saliency maps, Grad-CAM) to enhance model interpretability, emphasizing the pivotal role of explainable artificial intelligence (XAI) in plant pathology. Object detection models are systematically examined within both one-stage (YOLO, SSD) and two-stage (Faster R-CNN) paradigms. Furthermore, critical challenges-such as environmental variability, data imbalance, and computational constraints-along with potential solutions including transfer learning, synthetic data generation using generative adversarial networks (GANs) and diffusion models, and edge computing for real-time deployment, are comprehensively discussed. Results: This review summarizes best practices for dataset selection and model optimization for mobile platforms, emphasizing their role in improving the efficiency and accuracy of plant disease detection systems. Conclusion: Deep learning-based methods show strong potential to enhance precision and resilience in real-world plant disease detection and monitoring.
Rye (Secale cereale L.) revisited-nutritional composition, functional benefits, and role in sustainable diets
(Frontiers Media SA, 2025) Zadeike, Daiva; Copperstone, Claire; Aleksandrova, Olha; Unal, Derya Ozalp; Savikin, Katarina; Zivkovic, Jelena; Guzel, Mustafa; Kalkan Yildirim, Hatice; Kunili, Ibrahim Ender; Ivanova, Teodora; Ozmen, Ozge; Bantis, Filippos; Milesevic, Jelena; Balazs, Balint; Negrao, Sonia; Knez, Marija
Rye (Secale cereale L.) is increasingly recognized as a sustainable cereal with significant nutritional, ecological, and economic potential. While previous studies have highlighted its dietary fiber (DF), bioactive compounds, and associated health benefits, this review provides an updated synthesis that integrates recent findings on rye's role in human health, food security, and sustainability. In particular, it emphasizes novel evidence on rye's functional properties, its potential contributions to plant-based dietary strategies, and its economic and social relevance. By consolidating current knowledge and outlining future directions for product development and dietary innovation, this work offers a fresh perspective that extends beyond earlier 0 reviews focused on rye.
Maturation-Dependent Changes in Volatile Aroma Profile and ?-Glucosidase Activity in Kozan Misket Orange (Citrus sinensis L.)
(MDPI, 2025) Yabaci Karaoglan, Selin
Background/Objectives: Kozan Misket orange (Citrus sinensis L.) is a regional Turkish cultivar valued for its unique flavor, yet the mechanisms underlying its aroma development remain unclear. Volatile compounds are key contributors to citrus sensory quality, and beta-glucosidase is involved in releasing glycosidically bound aroma precursors. However, no previous study has examined the interaction between enzyme activity and volatile production during maturation in this cultivar. This study aimed to characterize the dynamic changes in volatile composition and beta-glucosidase activity across different maturation stages of Kozan Misket orange. Methods: Fruits were harvested at three maturity stages (green, green-yellow, yellow). Physicochemical traits (TSS, TA, TSS/TA), volatile profiles (HS-SPME/GC-MS), and specific beta-glucosidase activity were analyzed. Volatile compounds were identified, quantified, and compared across stages. Results: A total of 47 volatile compounds were identified, with monoterpenes dominating at all stages. D-limonene was the most abundant compound, exceeding 86% of total volatiles. Total volatile content increased with maturation, particularly monoterpenes and sesquiterpenes, whereas oxygenated monoterpenes (e.g., linalool, 4-terpineol, alpha-terpineol) declined at full maturity. Specific beta-glucosidase activity decreased markedly from 20.15 to 8.25 U mg-1 protein. This shift suggests that bound precursors contribute more to early-stage aroma release, while later-stage aroma accumulation may rely on metabolic conversions. Conclusions: This study provides the first integrated insight into aroma development in Kozan Misket orange, revealing a dual-phase mechanism linking volatile formation and beta-glucosidase activity. These findings clarify cultivar-specific flavor development and offer guidance for harvest optimization and flavor management.
Predicting IVF outcomes using a logistic regression-ABC hybrid model: A proof-of-concept study on supplement associations
(Public Library Science, 2025) Ejder, Ugur; Hepsag, Pinar Uskaner
Machine learning models are increasingly applied to assisted reproductive technologies (ART), yet most studies rely on conventional algorithms with limited optimization. This proof-of-concept study investigates whether a hybrid Logistic Regression-Artificial Bee Colony (LR-ABC) framework can enhance predictive performance in in vitro fertilization (IVF) outcomes while producing interpretable, hypothesis-driven associations with nutritional and pharmaceutical supplement use. A retrospective dataset of 162 women undergoing IVF was analyzed. Clinical, demographic, and supplement variables were preprocessed into 21 predictors. Four algorithms (K-Nearest Neighbors, Classification and Regression Tree, Support Vector Machine, and Random Forest) were implemented alongside their LR-ABC hybrid counterparts. Model performance was evaluated using 5-fold cross-validation with Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance. Local Interpretable Model-agnostic Explanations (LIME) were applied to improve interpretability. Across all algorithm models, LR-ABC hybrids outperformed their baseline models (e.g., Random Forest: 85.2% -> 91.36% accuracy). LIME explanations identified omega-3, folic acid, and dietician support as influential features in individual predictions. However, given the small sample size, binary representation of supplements, and absence of external validation, the observed improvements and associations should be regarded as exploratory rather than definitive. The LR-ABC hybrid model demonstrates methodological potential for improving prediction and interpretability in IVF research. Findings regarding supplement associations are hypothesis-generating, not clinically directive. Future studies with larger, multi-center datasets including detailed dosage and dietary data are needed to validate and extend this framework.
Microfluidic rapid isolation and electrochemical detection of S. pneumonia via aptamer-decorated surfaces
(Elsevier, 2025) Babaie, Zahra; Kibar, Gunes; Yesilkaya, Hasan; Amrani, Yassine; Dogan, Soner; Tuna, Bilge G.; Ozalp, Veli C.; Cetin, Barbaros
Background: S. pneumoniae is widely recognized as a leading cause of respiratory infections worldwide, often resulting in high mortality rates. However, the advent of microfluidic technologies has brought significant advancements, including the simplified, sensitive, cost-effective, and rapid approach to pneumococcal bacteremia detection. In this study, a microfluidic magnetic platform is presented for rapid isolation, and an electrode array is utilized for the electrochemical detection of S. pneumoniae. Aptamer-decorated surfaces were employed for both isolation and detection. For isolation, silica magnetic microparticles were synthesized and decorated with aptamer. Results: Isolation performance was assessed for phosphate-buffered saline (PBS) and blood samples for different concentrations of S. pneumoniae. Electrical impedance spectroscopy (EIS) with fabricated gold interdigitated electrodes (IDEs) decorated with aptamer was implemented for the detection of S. pneumoniae at different bacteria concentrations. The microfluidic platform performed bacteria isolation at comparable isolation efficiency with batch systems but at a much faster rate (isolation took about a minute, and the aptamer-decorated electrode array exhibited a limit of detection (LOD) at 962 CFU/mL and linear range between 104 and 107CFU/mL. Significance: Our method represents a significant advancement compared to previous reports. Our microfluidic platform can efficiently isolate 60 mu L of the bacteria sample within about one minute. The entire process takes about two minutes including the detection step. Furthermore, our method achieves a notable improvement in the detection limit for S. pneumoniae compared to conventional ELISA and magnetic microfluidics ELISA.
Development and Characterization of Potato Starch-Pectin-Based Active Films Enriched With Juniper Berry Essential Oil for Food Packaging Applications
(Wiley, 2025) Bhatia, Saurabh; Jawad, Muhammad; Chinnam, Sampath; Al-Harrasi, Ahmed; Shah, Yasir Abbas; Khan, Talha Shireen; Al-Azri, Mohammed Said; Koca, Esra; Aydemir, Levent Yurdaer; Diblan, Sevgin; Mohan, Syam; Najmi, Asim; Khalid, Asaad; Khan, Mahbubur Rahman
The increasing demand for sustainable food packaging has driven the development of films based on biopolymers. However, enhancing their functional properties remains a challenge. In the current study, potato starch-pectin (PSP) composite films were fabricated and enriched with juniper berry essential oil (JBEO) to improve their physicochemical properties. The effects of incorporating different concentrations of JBEO (0.1%-1% v/v) on various properties of PSP-based films were evaluated, including surface color, transparency, barrier properties, scanning electron microscopy (SEM), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), thermal analysis (TGA and DTA), antioxidant activity, and antimicrobial effectiveness. Increasing the level of JBEO led to a significant decrease in the moisture content, film transparency, and mechanical attributes, while an increase in thickness, water permeability, and film elongation was observed. SEM analysis also revealed morphological properties such as some spherical, bubble-like configuration and cracks on the surface due to an increase in JBEO concentration. TGA and DTA revealed lower weight loss in the initial cycles due to the addition of JBEO, and the thermal stability of the films improved. The antioxidant assays revealed a concentration-dependent increase in the radical scavenging capacity of the films from 11.31% to 17.28% for DPPH and from 3.06% to 25.53% for ABTS. Moreover, significant antibacterial and antifungal activity of the bioactive films was observed against P. aeruginosa, S. aureus, and C. albicans. These findings suggest that JBEO enhances the functional properties of PSP films, making them suitable for active food packaging applications.
The expanded theory of planned behavior for energy saving among academics in Romania, Bulgaria, Turkey, and Slovakia
(Nature Portfolio, 2025) Puiu, Silvia; Yilmaz, Sidika Ece; Udristioiu, Mihaela Tinca; Raganova, Janka; Raykova, Zhelyazka; Yildizhan, Hasan; Ameen, Arman
Given the escalating global energy consumption and the concurrent economic and energy crises, energy-saving behaviour must be adopted on a large scale. Universities that are energy-intensive institutions should be one of the institutions where energy-saving behaviour is widely adopted. Academics devote a substantial portion of their time to their offices, which leads to increased energy usage. However, no study has investigated academics' energy-saving behaviours in the literature. Most studies focus on students or employees in various organizations. Our study tries to cover the gap by examining the energy-saving behaviour of academics in four countries (Romania, Bulgaria, Turkey, and Slovakia) based on the expanded Theory of Planned Behaviour. A questionnaire was distributed to 228 academics from the four countries to gather data. The research hypotheses were tested using partial least squares structural equation modelling. The findings show that individual factors (attitude and perceived behaviour control) influence the energy-saving intention of academics but not the organisational factors due to the weak identification with their universities. The study offers valuable insights for policymakers seeking to promote energy-saving programs in academic institutions. The academics can be seen as role models for their students which emphasizes the need to study more their sustainable behaviours.

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