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Candidozyma (Candida) auris is a fungal pathogen associated with life-threatening invasive infections and high mortality rates. It is becoming a major global public health concern due to its ability to resist multiple antifungal drugs and spread in healthcare settings. Despite this, little is known about the mechanisms underlying drug resistance, fungal development, pathogenesis, and virulence. Among the factors contributing to these processes, transporters play a central role in fungal biology, regulating nutrient acquisition, metabolite exchange, ion homeostasis, and drug efflux. However, the composition and diversity of transporter systems in C. auris remain poorly defined. Through genomic analysis, we identified 686 transporters and 125 accessory factors involved in transport in C. auris, most of which had not been characterized. These transporters and accessory factors were classified into seven classes, 22 subclasses, and 215 families, reflecting substantial functional diversity. Comparative analyses with other pathogenic Candida species and Saccharomyces cerevisiae reveal lineage-specific divergence in several transporter families. We also integrated multiple publicly available RNA-seq datasets encompassing antifungal drug exposure and drug-resistant isolates and identified subsets of transporters that are transcriptionally responsive in distinct antifungal conditions, including members of families implicated in drug transport, metabolism, and ion homeostasis. Together, this study defines the landscape of transporter systems in C. auris and highlights transporter families that may contribute to stress adaptation and antifungal responses, providing a resource for future functional and mechanistic investigations. Full article

Background/Objectives: Multiple sclerosis (MS) significantly impairs quality of life (QoL) beyond physical disability, affecting psychosocial well-being. Although nurses play a central role in holistic, person-centered care, region-specific evidence from Western Greece remains limited. This study aimed to evaluate QoL and its biopsychosocial determinants among adults with MS in Western Greece and synthesize evidence on modifiable factors to guide nursing interventions. Methods: A cross-sectional study was conducted among 128 adults with MS (82% response rate from a pool of 156). QoL was measured with the MSQOL-54, depression with the Beck Depression Inventory-II, and social support with the Multidimensional Scale of Perceived Social Support. Data were analyzed using descriptive statistics, correlations, and multiple regression. Results: Participants reported moderate QoL impairment (Physical Composite Score = 53.6; Mental Composite Score = 57.4). Unemployment (52% of sample) was significantly associated with poorer physical QoL (p < 0.001). Fatigue, pain, and depressive symptoms showed strong negative correlations with QoL (p < 0.001). Higher perceived social support was a significant predictor of better mental health (β = 0.42, p < 0.01). The systematic review confirmed these predictors and reinforced social support as a key protective factor. Conclusions: Nurses should prioritize psychosocial aspects of MS care. Routine assessment and strengthening of social support networks, along with addressing employment barriers, are essential. Integrating targeted psychosocial strategies into standard nursing practice can effectively improve holistic well-being and mitigate QoL deterioration in individuals with MS. Full article

Background: Blockchain technology has emerged as a transformative communication solution for securing distributed systems. However, several vulnerabilities exist during transactions, including latency and network congestion issues during mempool processing, topology weaknesses, cross-chain bridge exploits, and cryptographic weaknesses. These vulnerabilities have led to attacks that have threatened system integrity, including Block Extractable Value (BEV) attacks, Maximal Extractable Value (MEV) attacks, sandwich attacks, liquidation, and Decentralized Finance (DeFi) reordering attacks, among others. Thus, implementing a robust security framework based on the Confidentiality, Integrity, and Availability (CIA) triad remains critical for addressing modern blockchain technology threats. Objective: This paper examines blockchain technology, its various vulnerabilities, and attacks to determine how criminals exploit the system during transactions. Further, it evaluates its impact on users. Then, implement a blockchain attack in a “MasterChain” virtual environment to demonstrate how vulnerable spots can be practically exploited and discuss the application of the CIA security triad through modern cryptographic primitives. Methods: The approach considers Hevner’s design science framework, which emphasizes creating innovative artifacts that address identified problems while contributing to the knowledge base through rigorous evaluation. Furthermore, we developed a MasterChain tool using Python with Flask for distributed node communication, utilizing the Elliptic Curve Digital Signature Algorithm (ECDSA) with the Standards for Efficient Cryptography Prime 256-bit Koblitz curve 1 (secp256k1) for digital signatures and Secure Hash (SHA-3) (Keccak-256) hashing for block integrity. Results: show how the CIA has been implemented to provide secure communication through ECDSA-based transactions, SHA-3 chain integrity verification, and a multi-node distributed architecture, respectively. The performance analysis shows that ECDSA provides 256-bit security with 64-byte signatures compared to 2048-bit Rivest–Shamir–Adleman (RSA)’s 256-byte signatures, achieving a 75% reduction in bandwidth overhead. SHA-3 provides immunity to length extension attacks while maintaining equivalent collision resistance to SHA-256. Conclusions: The MasterChain framework provides a practical foundation for implementing blockchain security that addresses both classical and emerging vulnerabilities. The adoption of ECDSA and SHA-3 (Keccak-256) positions the system favourably for modern blockchain applications, while providing insights into the cryptographic trade-offs between performance, security, and compatibility. Full article

Accurate gold price forecasting is crucial for economic stability and investment decision-making. In order to improve the accuracy of gold price prediction and quantify the uncertainty of gold price fluctuation, this paper proposes a hybrid model (CNN-QRLSTM) that integrates convolutional neural network (CNN) and quantile regression long- and short-term memory network (QRLSTM) and innovatively introduces news text data to quantify the media sentiment. We combine EEMD with the Hurst index to remove white noise from the original signal, and the processed data is used as the input layer of the prediction model. Furthermore, to demonstrate the impact of news sentiment on gold prices, this paper employs entropy measurement methods based on information theory to quantify the uncertainty and information content embedded within processed gold price sequences and derived sentiment indicators. The mutual information (MI) algorithm, based on information entropy, captures the nonlinear correlations between financial keywords and market sentiment. It constructs a financial sentiment lexicon (covering keywords such as economic policies and geopolitical conflicts), combines semantic rules with context-weighted strategies, calculates sentiment scores for news texts, and generates daily aggregated media sentiment indicators. This entropy-based perception method not only enhances the interpretability of emotion-driven fluctuations but also provides a theoretical foundation for reducing prediction uncertainty through multi-source data fusion. The experiment uses 2022–2025 daily London gold spot price data, Shanghai Gold Exchange gold price data, and the same period of Gold Investment Network gold market news to carry out the study. The empirical study shows that the synergy of multi-source data fusion and the quantile regression mechanism can improve the accuracy of gold price prediction and the new paradigm of risk interpretation while providing theoretical support for the formulation of quantitative investment strategies. Full article

The olive oil sector is one of the most important agri-food chains, but it is also characterised by the production of large volumes of solid and liquid by-products which, if improperly managed, have a significant environmental impact. In this context, circular economy approaches have been increasingly proposed to improve sustainability through the valorisation of by-products. This study investigates whether it is possible to conceptualise a circular business model for the olive oil supply chain by integrating by-products into the production system itself. The research adopts a systematic review of the literature supported by bibliometric techniques. The results show that current studies focus mainly on the technological and environmental aspects of by-product valorisation, particularly chemical extraction processes and environmental impact assessment, while the organisational, supply chain and governance dimensions remain fragmented. Based on these findings, the study develops an emerging conceptual framework that integrates by-product valorisation, supply chain configuration and enabling conditions, such as partnerships and political support, into a circular business model perspective. The study concludes that a circular business model for the olive oil supply chain is conceptually and technically feasible, but its implementation requires a systemic and integrated approach at the supply chain level rather than isolated technological solutions. Full article

Cotton (Gossypium hirsutum) is globally cultivated for its high-quality fiber; yet, its seed, rich in oil and protein, offers untapped potential for various applications, including food, feed, and industry. With cottonseed oil gaining renewed attention as a valuable co-product, efforts to enhance oil content must contend with long-standing breeding priorities focused on lint yield and fiber quality. A central challenge lies in the complex and often antagonistic genetic relationships between oil accumulation and key agronomic traits. Notably, negative correlations between seed oil content and fiber yield, as well as the pleiotropic nature of several regulatory genes and Quantitative Trait Loci (QTLs), present significant barriers to dual-trait improvement. This review synthesizes current knowledge on the genetic and molecular interplay between cottonseed oil content and other agronomic traits. We examine the architecture of oil-related QTLs and pleiotropic loci, co-expression patterns of shared transcriptional regulators, and metabolic trade-offs influencing carbon allocation between seed and fiber. Recent advances in genomics, transcriptomics, and systems biology are explored as tools to disentangle these trait interactions. We highlight strategies such as multi-trait genomic selection, CRISPR-based uncoupling of antagonistic loci, and the use of wild and exotic germplasm to overcome linkage drag. By providing an integrative overview of the constraints and opportunities at the intersection of oil and agronomic trait improvement, this review lays the groundwork for the development of dual-purpose cotton ideotypes. We propose a conceptual framework for breeding programs to simultaneously enhance fiber yield and oil productivity in a sustainable and climate-resilient manner. Full article

Alfalfa mosaic virus (AMV) is a devastating plant pathogen with an extensive host range, yet effective control strategies remain limited. This study investigated the prophylactic efficacy and molecular mechanisms of two plant immune inducers, the Paecilomyces variotii extract DYDS and the antiviral agent Dufulin, against AMV infection in cowpea (Vigna unguiculata). Our results demonstrate that both agents possess potent antiviral activity, with inactivation, protective, and therapeutic efficacies all exceeding 21.00%. Notably, DYDS exhibited superior overall performance. RT-qPCR and immunofluorescence assays confirmed a significant downregulation of AMV coat protein (CP) expression in treated plants. Furthermore, exogenous application of these inducers mitigated chlorophyll loss and markedly augmented the activities of key defense enzymes’ activity, including superoxide dismutase (SOD), peroxidase (POD), catalase (CAT), polyphenol oxidase (PPO), and L-phenylalanine ammonia-lyase (PAL), peaking at 5 days post-inoculation. In silico molecular docking simulations further revealed that DYDS and Dufulin interact spontaneously with the AMV-CP, yielding binding free energies of −6.5 and −5.8 kcal/mol, respectively. Gene expression analysis indicated that these inducers trigger a robust immune response through the integrated activation of the salicylic acid (SA), jasmonic acid (JA), and ethylene (ET) signaling pathways. Collectively, these findings suggest that DYDS and Dufulin provide a dual mode of action—direct viral inhibition and host immune priming—offering a promising and sustainable approach for the management of AMV in leguminous crops. Full article

Chicory (Cichorium intybus L.) is a widely used nutritional and medicinal plant, whose roots are an important commercial source of inulin, while the aerial parts are often discarded during industrial processing. This study systematically compared chicory polysaccharides (CPs) extracted from aerial parts (CP-A) and roots (CP-R) with respect to their compositional features and cytoprotective effects in an oxygen–glucose deprivation/reperfusion (OGD/R)-induced H9c2 cell injury model. CP-A and CP-R differed in molecular weight distribution and monosaccharide composition, with CP-R exhibiting a higher molecular weight and fructose content. Despite these differences, both fractions significantly improved cell viability and reduced oxidative and biochemical injury markers. Integrated proteomic and transcriptomic analyses indicated that CP-A and CP-R were associated with the modulation of stress-responsive signaling networks, prominently involving oxidative stress-linked MAPK/NF-κB pathways. These findings demonstrate comparable cytoprotective activities of polysaccharide-rich fractions from roots and aerial parts and support the valorization of chicory aerial biomass as a potential source of functional ingredients for cardiovascular health. Full article

To accurately quantify pore pressure uncertainty and associated kick risk, this paper proposes a dual-phase pre-drilling risk assessment framework based on Bayesian Long Short-Term Memory (BLSTM) networks, integrating formation pressure prediction with distribution interference analysis. First, the effects of two Bayesian layer optimization methods—Monte Carlo dropout and Bayes-by-Backprop—on deep learning networks were systematically evaluated. The optimized Bayes-by-Backprop-LSTM model was subsequently selected for uncertainty prediction of formation pore pressure. Finally, kick risk was quantified by analyzing the interference between predicted pressure distributions and the safety margin of designed drilling mud density. The BLSTM models uncertainty regression between well-log parameters and formation pore pressure labels. Using the Bayes-by-Backprop strategy, it generates probabilistic pressure predictions. By incorporating the designed drilling mud density of target wells, kick risk probability is calculated through distribution interference criteria, where the overlapping area between pore pressure distributions and mud density safety boundaries is mapped to risk probability. Validation experiments utilized five types of well-log parameters from three wells in EAST CHINA. Key results demonstrate: (1) The BLSTM regression model achieved a mean absolute error (MAE) of 0.037 on test wells, representing a 26.7% reduction compared to conventional LSTM, with the 95% confidence interval coverage reaching 69.6%. (2) In the 3893–4048 m interval of a test well, interference areas exceeding thresholds indicated 60% kick risk probability. Spatial correlation with actual kick events revealed risk points undetectable by conventional pore pressure prediction methods. This study establishes a comprehensive risk assessment paradigm encompassing pore pressure uncertainty regression prediction and probabilistic risk calculation, providing drilling engineering with a framework that combines physical interpretability and statistical reliability. Full article

This paper proposes a novel active disturbance rejection prescribed performance controller for a class of strictly feedback nonlinear systems under unknown initial tracking conditions. By introducing a novel algebraic saturation function, the initial value of tracking error is transformed into a bounded range, effectively overcoming the limitation of traditional prescribed performance control that requires prior knowledge of the initial value of tracking error. To address the differential explosion issue arising from the backstepping method, this paper employs dynamic surface processing techniques. The integration of active disturbance rejection control with prescribed performance control significantly enhances the robustness of nonlinear systems. The designed controller ensures that closed-loop systems under unknown initial tracking conditions converge to any small neighborhood near the origin within finite time. The system output satisfies the requirements of the prescribed performance function and exhibits excellent suppression capability against external disturbances. Full article

Soybean lodging severely impairs yield and quality, and its precise grading is a key prerequisite for intelligent agricultural management and loss assessment in agricultural insurance. Most existing studies have focused primarily on soybean lodging identification and lodging resistance evaluation, whereas methods for the precise differentiation of lodging grades remain to be refined. This study presents an improved AlexNet model integrated with a Local Feature Aggregation (LFA) attention mechanism and a dynamic optimization strategy for the accurate grading of soybean lodging. RGB imagery of soybean canopies during the grain-filling to early maturity stages was acquired via a multispectral unmanned aerial vehicle (UAV). A dynamic Dropout strategy was adopted to enhance model stability and mitigate overfitting, and the Particle Swarm Optimization (PSO) algorithm was employed to intelligently optimize key hyperparameters of the model. The results demonstrate that the optimized model achieved an overall accuracy of 94.23% on the test set, with an average loss of 0.0682 and an inference speed of 0.422 s/step. In independent field validation, the grading accuracies for the five lodging grades were 90.12%, 86.35%, 89.47%, 88.93%, and 92.76%, respectively, with a mean accuracy of 89.53%. The proposed model enables the rapid and precise grading of soybean lodging under field conditions, thereby providing effective technical support for intelligent field management and disaster loss assessment in soybean production. Full article

Color multi-threshold image segmentation is a non-convex, gradient-free global optimization problem. The number of decision variables increases with the number of thresholds, leading to a rapid expansion of the search space and increased computational complexity. To address this problem, this paper proposes a Multi-Mechanism Artificial Lemming Algorithm (MALA). When applied to color multi-threshold image segmentation, the original Artificial Lemming Algorithm (ALA) suffers from an imbalance between exploration and exploitation, excessive reliance on the current best solution, and rigid boundary handling, which may lead to premature convergence and suboptimal threshold selection. MALA integrates three lightweight yet structurally enhancement mechanisms to enhance the stability of the exploration–exploitation process, population-level guidance, and boundary-handling behavior. To verify its general optimization capability, MALA is evaluated on the CEC2017 benchmark suite, where it shows competitive convergence behavior and improved objective values compared with ALA and representative baseline algorithms. Furthermore, segmentation experiments on six benchmark images using Otsu’s criterion show that MALA attains competitive fitness values and generally higher PSNR, SSIM, and FSIM metrics. These results suggest that MALA can serve as a general optimization method with applicability to color multi-threshold image segmentation. Full article

Wellbore instability and sand production pose critical challenges in deep tight sandstone reservoirs, severely impairing wellbore integrity and reducing hydrocarbon recovery. This study introduces, for the first time, the combined finite–discrete element method (FDEM) to numerically simulate sand production under high in situ stress. The FDEM By seamlessly integrates continuum and discontinuum representations within a unified framework, enabling the simulation of the complete failure sequence—from matrix damage and fracture growth to granular flow. High-resolution numerical simulations are conducted to compare intact and naturally fractured formations, with a focus on the governing role of pre-existing geological discontinuities. Results show that in intact sandstone, stress concentration drives helical crack growth leading to a symmetrical V-shaped breakout, with a critical borehole pressure (CBHP) of 60.05 MPa required to prevent instability. In fractured rock, however, pre-existing fractures act as dominant weakness planes that distort the stress field and induce earlier, asymmetric failure, raising the CBHP to 64.05 MPa. A strong negative linear correlation is observed between reservoir pressure depletion and CBHP: a pore-pressure reduction of 23.75 MPa decreases the CBHP by 4.8–5.0 MPa. Notably, natural fractures amplify the destabilizing effect of depletion, raising the required CBHP by 4.0 MPa at initial reservoir pressure (95 MPa) and by 5.0 MPa under full depletion. Consequently, although fractured formations require a higher CBHP (64.05 MPa vs. 60.05 MPa), their safe operating window is effectively narrower. These findings advance the mechanistic understanding of fracture-controlled sand production and provide a validated numerical framework for determining safe production pressures in deep, fractured sandstone reservoirs. Full article

Background: Compared with lactic acid-producing probiotics, spore-producing probiotics such as Bacillus subtilis (BS) and Bacillus natto (BN) exhibited superior metabolic capacity and stress resistance and are more suitable for industrial applications. However, limited understanding of their nutritional and intestinal health mechanisms has constrained their food potential. Objectives: This review systematically expounded on the ‘triple mechanism’ of BS and BN and their effects on intestinal nutrition, immunity and metabolism benefit for the first time. Methods: We searched PubMed, Scopus, Web of Science, and Google Scholar for studies on livestock, model organisms, and human research from 2000 to 2025. After evaluating relevance and eligibility, 115 articles were included. Results: Firstly, by secreting various digestive enzymes, BS and BN directly enhanced the small intestine digestive and absorptive efficiency and promoted animal growth. In particular, BN significantly increases calcium absorption in postmenopausal women. Secondly, as the antigen carrier that induced intestinal mucosal immunity, BS and BN enhanced the host’s defense ability by strengthening the expression of tight junction proteins, mucins, and inflammatory factors and bidirectionally regulated constipation and acute diarrhea in the human body. Thirdly, they reshaped the structure of the intestinal microbiota and their metabolic profile in the form of the gut–liver/gut–adipose axis, including enriching beneficial bacteria, activating lipid metabolism pathways such as PI3K/AKT and AMPK/SREBP, and regulating liver targets such as PPAR and CD36, thereby reducing insulin resistance and liver injury and maintaining overall metabolic homeostasis. Conclusions: Bacillus subtilis and Bacillus natto mediated their probiotic benefits through a gut-centric, multi-system regulatory strategy, involving nutrient utilization, immune homeostasis, and microbial–host metabolic interactions. This integrated mechanism provided a robust foundation for their targeted application in functional formulations and fermented food science. Full article

This study analyses the educational and social impact of a series of innovative teaching projects developed at the Museum of the History of Pharmacy of the University of Seville. The initiatives—including historical video documentaries, the “student guides” programme, and the digital outreach project “Voices that Empower”—explore the pedagogical potential of scientific heritage as a learning tool and as a medium for public communication. Through experiential and service-learning methodologies, these projects have enhanced students’ communication skills, critical thinking, and awareness of cultural and gender dimensions within pharmaceutical studies. The results demonstrate that the integration of audiovisual production, museum-based learning, and digital storytelling fosters meaningful engagement between the university and society, while also revitalising the historical and humanistic dimensions of pharmacy. Furthermore, the inclusion of a gender perspective in the “Voices that Empower” initiative contributes to the visibility of women in STEM and highlights the museum as a space for empowerment and social transformation. This work concludes that university museums can act as strategic platforms for innovation in higher education, combining heritage preservation, teaching excellence, and civic outreach to promote a more inclusive and sustainable scientific culture. Full article