Search Results (24,422)

Repetitive temperature fluctuations during transportation and storage promote ice crystal formation in salmon flesh, leading to protein denaturation, lipid oxidation, and quality loss. Tuna skin, a major by-product of tuna processing, is a potential source of antifreeze peptides (AFPs) but remains underutilized. This study examined the cryoprotective effects of tuna skin-derived AFPs on salmon cubes subjected to repeated freeze–thaw cycles. Cubes treated with AFPs from three groups of protein hydrolysates prepared using trypsin, pepsin, or neutral protease were evaluated for texture, color, water holding capacity (WHC), volatile odor profiles, protein conformation, biochemical indices, and microstructure. AFP treatment improved textural properties, maintained color stability, and reduced thawing, cooking, and centrifugal losses. The neutral protease-treated group exhibited the optimal cryoprotective ability and it also limited aldehyde and sulfide accumulation, preserved the retention rate of α-helix structure at 49% which was higher than 39% in controls, and enhanced Ca²⁺-ATPase activity to 1.75 μmol Pi·mg⁻¹·h⁻¹ with a 45.8% increase compared to controls, and significantly inhibited protein and lipid oxidation. Microstructural analysis showed compact fibers and intact sarcolemma in the neutral protease-treated group samples, contrasting with severe disruption in controls. This study showed that tuna skin AFPs mitigate freeze–thaw damage in salmon cubes by stabilizing proteins and reducing oxidative deterioration, highlighting their potential as natural, healthy cryoprotectants for seafood preservation, meeting the growing demand of the food industry for clean-label, low-calorie preservation solutions, while advancing the circular economy of aquatic processing via the valorization of tuna skin by-products for high-value seafood applications. Full article

P-glycoprotein (P-gp), an efflux transporter highly expressed in renal tubules, plays a crucial role in the detoxification and protection of barrier/excretory tissues from harmful xenobiotics. Xanthones and thioxanthones (TXs) are known for their antimicrobial and antitumor activities and for their ability to modulate membrane transporters such as P-gp. Previous studies have reported that (thio)xanthonic derivatives enhance P-gp expression and/or activity in intestinal cells, reducing the intracellular accumulation of toxic substrates; however, their capacity to modulate P-gp in renal cells remains poorly explored. This study aimed to predict, in silico, TXs’ binding sites within P-gp and to evaluate, in vitro, in human kidney (HK)-2 cells, the effects of selected TXs (TX1–5) on P-gp activity and expression, and protection against cisplatin-induced cytotoxicity. Computational studies identified preferential TX1–5 binding to the drug-binding pocket, particularly the rhodamine 123 (R) or modulator (M) sites, and to nucleotide-binding domain 1. In vitro, rhodamine 123 accumulation assays revealed increased P-gp transport activity after 120 min or 24 h exposure to TX1–5, except TX4. TX2 elicited the strongest effect (141% increase, p < 0.0001), upregulated P-gp expression (24 h, p < 0.0001), and significantly protected HK-2 cells from cisplatin-induced cytotoxicity (increased IC₅₀, p < 0.0001). Altogether, these findings position thioxanthones as promising scaffolds for the development of P-gp-targeted strategies to mitigate drug-induced nephrotoxicity. Full article

The increasing digitization of manufacturing and the integration of CNC and industrial control systems into the industry 4.0 environment have introduced new cybersecurity risks that directly affect operational reliability. Traditional deterministic risk-assessment methods used for securing ICS—such as SCADA, PLC, and CNC systems—struggle to address uncertainty, dynamic operating conditions, and complex dependencies between technical and organizational factors. To overcome these limitations, this study develops a Bayesian Network (BN) model that captures probabilistic relationships between machine-level configuration parameters, network conditions, and potential security incidents. The model is applied to a CNC machining center (ZPS MCG1000i), where it supports scenario-based prediction of cybersecurity risks and provides interpretable outputs suitable for operator decision-making and human–machine interaction. The results demonstrate that BNs are effective in environments with limited data availability and high uncertainty, offering transparent and quantifiable insights into how specific misconfigurations—such as active remote access or irregular firmware updates—elevate overall system exposure. The proposed approach aligns with current regulatory and standardization requirements, including the NIS2 Directive (EU 2022/2555), ISO/IEC 27001:2022, ISO/IEC 27005:2022, and Regulation (EU) 2024/2847 (Cyber Resilience Act), which define cybersecurity obligations for products with digital elements. The study provides a reproducible and future-oriented methodology for integrating cybersecurity into machinery-safety evaluation in modern industrial environments. Full article

Innovation is a central driver of competitiveness, resilience, and sustainability in the agri-food sector, particularly among small and medium-sized enterprises (SMEs). However, traditional science- and technology-based models may not fully grasp the innovation dynamics in this domain, and research explicitly addressing agri-food SMEs remains limited. This study adapts, integrates, and extends existing Innovation Capability (IC) and related constructs into a unified WI–PI–IA framework (Willingness to innovate–Propensity to innovate–Innovation Ability) for agri-food SMEs. The framework is empirically tested through a sectoral quantitative case-study based on structured questionnaires administered to twenty SMEs operating in the Sardinian sheep dairy industry. The findings confirm the framework’s validity, highlighting the role of contextual factors and revealing distinct innovation patterns between cooperatives and private firms. This study is, to our knowledge, the first to conceptualise IC in agri-food SMEs as the outcome of the three above constructs and offers a comprehensive and context-sensitive approach that contributes to academic research and directs policymakers towards factors that affect agri-food SME innovation outcomes, considering their unique structures and specific challenges they face. Full article

Stock-ranking prediction is an important task in quantitative finance because it directly influences portfolio construction and alpha generation. Recent Graph Neural Network (GNN) models provide a promising way to describe inter-stock dependencies, but many existing methods still have difficulty balancing rapidly changing market interactions with relatively stable structural relationships. They are also easily affected by financial micro-structure noise. To address these issues, this paper proposes USTGCN, a Unified Spatio-Temporal Graph Convolutional Network for stock-ranking prediction. USTGCN adopts a dual-stream temporal encoder based on ALSTM and GRU to capture short-term dynamic patterns and longer-horizon structural information, respectively. We further introduce a rolling-window correlation smoothing strategy to build a more stable dynamic graph, and then integrate the dynamic and structural graph views through a shared fusion layer. Skip connections are used to preserve original temporal information during spatial aggregation. Experiments on the CSI100 and CSI300 benchmark datasets show that USTGCN achieves IC values of 0.141 and 0.154, respectively, and exhibits improved drawdown control during stressed market periods, indicating its practical value for quantitative trading. Full article

Globally, breast cancer is one of the most prevalent tumors in women and remains a major concern due to its high mortality rate. Although treatment options for this disease have evolved over the years, there are still many cases of recurrence and metastasis. In this con-text, considering the importance of evaluating less aggressive and more efficient therapeu-tic alternatives to aid in the treatment of breast cancer, the present study critically discuss-es the cytotoxic effects of diterpenoids isolated from Croton species (Euphorbiaceae). The articles were retrieved from different databases, from the first report published in 2005 to October 2025. A total of 115 diterpenoids were isolated from 15 Croton species and inves-tigated against different breast cancer cell lines (MDA-MB-231, MCF-7, and MDA-MB-468). These compounds mainly belong to the kaurane group (40%), followed by clerodane (14%), tigliane (12%), and abietane (10%). Of this total, only 25 compounds showed prom-ising results (IC50 = <10 µM). The mechanisms of action of the compounds crokokaugenoid A, kongensin A, kongensin D, ent-16β,17α-dihydroxykaurane, and lauicyclone A have been reported. These compounds likely act by inducing apoptosis, autophagy, cell cycle arrest, inhibition of cell migration and invasion, and DNA fragmentation in breast cancer cell lines. To date, no randomized clinical trials have been conducted using Croton diterpenoids for the treatment of breast cancer. Therefore, further studies on the modula-tion of the immune response by these natural products are essential to better understand their immunotherapeutic activity in the tumor microenvironment during breast cancer progression. Full article

Hypersaline culture media used for cultivation of Dunaliella salina represent complex multicomponent aqueous systems whose cooling–heating phase behavior remains insufficiently characterized. In this study, the thermal transitions of two biologically relevant hypersaline media (Artari and Ramaraj) were investigated using differential scanning calorimetry (DSC) and cryomicroscopy. The media were examined at NaCl concentrations of 1.5, 2.0, and 4.0 M, corresponding to moderate to highly concentrated brine conditions comparable to natural salt lakes and evaporative basins. DSC analysis revealed pronounced salinity-dependent suppression of ice crystallization and modification of melting transitions relative to classical NaCl–water systems. Increased NaCl concentration reduced recrystallization during heating and shifted peak temperatures, indicating kinetic and compositional effects in the unfrozen fraction. Rapid cooling promoted formation of partially amorphous phases, consistent with limited vitrification in highly concentrated media. Cryomicroscopy directly confirmed changes in ice morphology, nucleation density, and crystal growth dynamics under varying salinity and thermal histories. The combined calorimetric and microscopic approach demonstrates that complete hypersaline cultivation media exhibit phase behavior that cannot be fully extrapolated from simplified binary systems. These findings provide new insight into the physicochemical stability of multicomponent brines during cooling and highlight the critical role of salinity and thermal history in controlling crystallization pathways in hypersaline aqueous environments. Full article

The incorporation of plant-derived oils into cosmetic formulations has attracted increasing interest due to their natural origin, skin compatibility, and multifunctional formulation roles. Argan and castor oils are widely used in cosmetic products as emollient lipid components with intrinsic antioxidant properties. However, limited studies have systematically evaluated the physicochemical stability and antioxidant performance of emulsions combining these two oils. The aim of this study was to develop and comprehensively characterize a stable oil-in-water (O/W) cosmetic emulsion based on argan and castor oils using a natural non-ionic emulsifier (C14–22 Alcohol (and) C12–20 Alkyl Glucoside). Particular emphasis was placed on formulation stability, as it represents a critical prerequisite for further product evaluation. Stability was investigated through thermal stress testing (4–37 °C), centrifugation assays, droplet size analysis, and zeta potential measurements. Complementary physicochemical and structural characterization was performed using rheological analysis and Fourier transform infrared (FT-IR) spectroscopy. The formulated emulsion exhibited good physical stability with no phase separation under the tested conditions, a skin-compatible pH, a uniform droplet size distribution (4.15 ± 0.68 µm), and pseudoplastic, moderately thixotropic rheological behavior. Antioxidant capacity was assessed using the 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging assay, yielding an IC₅₀ value of 19.21 ± 1.02 mg/mL. Overall, this study provides a formulation-oriented framework for the development and evaluation of stable natural oil-based O/W emulsions intended for cosmetic applications, supporting future optimization and biological validation. Full article

As one of the world’s leading cotton-producing countries, China frequently experiences severe yield reductions due to crop diseases and pest infestations, with losses often exceeding 20%. Although computer vision models can identify diseased plants, they currently fail to connect visual symptoms to the diagnostic reasoning process used by agronomists. This leads to text descriptions that ignore the biological causes of the damage. To fix this, we built Multimodal Knowledge Graph-Enhanced Cross Vision Transformer-18-Dagger-408 and Text-to-Text Transfer Transformer for Cotton Disease and Pest Image Captioning (MKG-CottonCapT6), a model that uses a local knowledge database to generate professional diagnostic reports from field images. The technical core consists of a Multimodal Knowledge Graph (MMKG) containing 14 types of entities (such as Pathogens and Control Agents) and 12 types of relations. We use a Cross Vision-Transformer-18-Dagger-408 (CrossViT) encoder to capture both the overall leaf shape and microscopic details of pests. Through a Visual Entity Grounding (VEG) module, the model maps visual features directly to specific triplets in the graph. These triplets are then turned into text sequences and fused with image data in a Text-to-Text-Transfer-Transformer (T5) decoder. To train the model, we collected a dataset of cotton images paired with expert descriptions of lesions, colors, and affected plant parts. Tests show that MKG-CottonCapT6 performs better than standard models, reaching an Information-based Metric for Image Captioning (InfoMetIC) score of 72.6%. Results prove that by using a specific alignment loss (𝓛_align), the model generates reports that correctly name the disease stage and recommend specific chemicals, such as Carbendazim or Triadimefon. This framework provides a practical tool for farmers to record and treat cotton diseases with high precision. Full article

To improve the efficiency and output power of the twin-screw expander used in natural gas pressure energy recovery, a hybrid NGO-SDERIME algorithm is proposed for structural optimization, with the structural parameters of the male and female rotors selected as the optimization design variables. First, the enhanced Rime Ice Optimization (RIME) algorithm is adopted to perform hybrid improvement on the Northern Goshawk Optimization (NGO) algorithm; then, the stability and superiority of the proposed hybrid algorithm are verified by using a suite of benchmark test functions; finally, the algorithm is applied to the structural optimization of the twin-screw expander, followed by numerical simulation and experimental verification. The results indicate that, compared with other existing algorithms, the proposed NGO-SDERIME hybrid algorithm shows excellent convergence and strong optimization performance. After optimization using this algorithm, the output power of the screw expander increases by 5.5%, the high-speed leakage area is significantly reduced, the isentropic efficiency improves from 75.1% to 78.1%, and the average mass flow rate increases from 18.42 t/h to 18.72 t/h. Full article

Industrial Control Systems (ICSs) are the backbone of modern critical infrastructure, such as electric power, water treatment, oil and gas distribution, and manufacturing operations. While the convergence of IT and OT has greatly increased efficiency and observability, it has also greatly expanded the attack surface of these once-isolated systems. High-profile cyber-physical attacks, including Stuxnet (2010), TRITON (2017), and the Colonial Pipeline ransomware attack (2021), have shown that ICS-targeted cyberattacks can cause physical damage, disrupt economic stability, and put public safety at risk. Despite the growing prevalence and intensity of such threats, ICS-based cybersecurity education remains largely under-resourced and underfunded. Traditional ICS training laboratories require highly specialized hardware, vendor-specific tools, and expensive licensing that significantly raise barriers to entry. Traditional labs typically require on-site participation and pose physical safety concerns when cyber-physical attack scenarios are performed. These barriers leave students unable to get necessary security training for ICSs. Therefore, this paper introduces AE${}^3$GIS: Agile Emulated Educational Environment for Guided Industrial Security—a fully virtual, lightweight, open-source platform designed to democratize ICS cybersecurity education. Based on the GNS3 network simulation tool, AE${}^3$GIS enables rapid deployment of comprehensive ICS environments containing IT and OT systems, industrial communication protocols, control logic, and diverse security tools. AE${}^3$GIS is designed to provide practical training for students using realistic ICS cybersecurity scenarios through a local or remote training platform without the cost, safety, or accessibility limitations of hardware-based labs. Full article

Sea ice segmentation based on Synthetic Aperture Radar (SAR) images has become an important technical means for polar climate change monitoring and navigation safety guarantee. However, the existing methods have limitations in the utilization of SAR polarization information and the modeling of local diversity details of sea ice, which leads to insufficient segmentation, especially in complex ice-water boundary regions. To address these issues, this paper proposes a novel Polarization-Fused Edge-Enhanced UNet (PFEE-UNet) designed specifically for sea ice segmentation from high-resolution SAR images. Specifically, we design the Cross-Polarization Channel Interaction (CPCI) module, which employs a dual interaction strategy of hierarchical inter-group cascading and symmetric cross-fusion. This approach effectively leverages the complementary features of the HH and HV polarization channels, significantly enhancing the distinction between sea ice and open water. Additionally, we present the Dense–Sparse Diversity Enhancement (DSDE) module, which combines a spatial-channel joint attention mechanism to strengthen the model’s ability to capture spatial relationships within complex ice–water structures, effectively alleviating misclassifications caused by abrupt local texture changes. Finally, we design the Selective Edge Fusion (SEF) module, which dynamically selects and integrates multi-level edge features, improving the continuity of sea ice boundaries and preserving its morphological integrity. The experimental results show that the proposed PFEE-UNet model outperforms mainstream segmentation methods on the AI4Arctic/ASIP sea ice dataset, achieving an average Intersection over Union (IoU) of 84.48%, which surpasses existing methods such as HRNet (82.52%) and DeepLabv3+ (82.40%). Additionally, PFEE-UNet was applied for end-to-end ice–water segmentation on real-world Sentinel-1 SAR scenes, demonstrating its effectiveness and robustness for practical sea ice monitoring. Full article

Real-time quaking-induced conversion (RT-QuIC) is highly sensitive for prion detection; however, inhibitory factors present in tissue homogenates readily interfere with the assay. We previously reported that recombinant cervid prion protein (rCerPrP) enabled the establishment of practical RT-QuIC for detecting chronic wasting disease and atypical bovine spongiform encephalopathy (BSE) prions, i.e., detecting low levels of prions in high concentration of brain tissue homogenates. Accordingly, the present study aimed to establish RT-QuIC for detecting classical BSE (C-BSE) and classical and atypical scrapie (C- and A-scrapie, respectively). A single-step lipid extraction using a 3:1 mixture of 2-butanol and methanol was effective as a pretreatment to remove inhibitors from brain homogenates. Among three rPrPs extensively evaluated, recombinant sheep PrP (rShPrP) was the most suitable substrate for practical detection of C-BSE prions. rCerPrP-173S/177N and rCerPrP-98S/173S/177N, which carry sheep-type amino acid substations at codons 173 and 177 and at codons 98, 173, and 177, showed excellent performance for detecting C-scrapie prions. Moreover, rCerPrP-98S/173S/177N, but not rCerPrP-173S/177N, was identified as an optimal substrate for detecting A-scrapie prions. These results suggested that combining inhibitor-removal pretreatment with the optimization of rPrP substrate for each animal prions further enhanced of RT-QuIC performance. Full article

Background: Acinetobacter baumannii (A. baumannii) is a formidable nosocomial pathogen and is classified by the World Health Organization (WHO) as a critical-priority pathogen, owing to its rapid evolution into extensively drug-resistant (XDR) and pan-drug-resistant (PDR) strains. Colistin remains one of the last-resort therapeutic options, although resistance rates are increasing in endemic regions such as Greece. In this study, we investigated the molecular basis of colistin resistance and characterized the clonal backgrounds of clinical XDR/PDR A. baumannii isolates collected between January and June 2022 from two tertiary-care hospitals in Thessaloniki, Northern Greece. Methods: We analyzed forty non-duplicate XDR/PDR clinical isolates. Antimicrobial susceptibility was determined using the VITEK 2 system, broth microdilution, and gradient diffusion methods. The lipid A biosynthesis genes (lpxA, lpxC, lpxD) and the pmrCAB operon were amplified by PCR and sequenced for all isolates. A representative subset of strains (n = 10/40) underwent multilocus sequence typing (MLST) according to the Pasteur MLST scheme. Results: All isolates proved colistin-resistant (MIC ≥ 4 µg/mL), and 95% were classified as PDR. Sequence analysis revealed multiple nonsynonymous mutations in the pmrCAB operon, with the PmrB A226V substitution predominating and extensive amino-acid changes observed in PmrC. In contrast, lpx genes exhibited limited protein-level variation, limited to lineage-associated polymorphisms (LpxC N287D, LpxD E117K). A novel six-nucleotide insertion in pmrB was identified in one isolate. MLST demonstrated a predominance of ST2 (International Clone 2), with single representatives of ST115 (IC2) and ST1 (IC1). Conclusions: In this cohort from Northern Greece, chromosomal mutations in the pmrCAB operon, within a predominantly ST2/IC2 background, were strongly associated with colistin resistance. These findings underscore the urgent need for continued molecular surveillance and targeted infection-control measures to limit further spread of PDR A. baumannii. Full article

This paper presents a low-noise analog front-end (AFE) integrated circuit (IC) circuit for capacitive micro-electromechanical system (MEMS) accelerometers that can be used for optical image stabilization (OIS) in various optical imaging systems. The AFE circuit design features a fully differential chopper stabilization technique that efficiently minimizes low-frequency 1/f noise and parasitic coupling. The AFE circuit chip is fabricated in a 0.18 $\mathsf{\mu }$m complementary metal-oxide-semiconductor (CMOS) technology and co-packaged with an x-axis capacitive MEMS accelerometer based on a silicon-on-glass (SOG) process. The SOG accelerometer has a footprint of 1000 $\mathsf{\mu }\mathrm{m}$ $\times$ 950 $\mathsf{\mu }\mathrm{m}$. The packaged system demonstrates a sensitivity of 342 mV/g and a nonlinearity of 1.1% between −1 g and +1 g, a dynamic range of 88 dB, and an equivalent noise floor of 14 $\mathsf{\mu }\mathrm{g}/\sqrt{\mathrm{H}\mathrm{z}}$. Full article