Search Results (2,505)

Babesia divergens, a zoonotic tick-borne pathogen, causes bovine and human babesiosis in Europe. The Elongation Factor 1 alpha (EF-1α) protein is important in many cellular processes and has emerged as a possible target for subunit vaccine development against parasitic infections, and its intergenic region (IG) is an important tool for genetic manipulation of Babesia parasites. While the EF-1α locus of B. divergens has been described, structural variation between isolates was poorly defined. In order to fill this gap, we performed a comparative analysis of the EF-1α-IG in B. divergens human (Rouen 87 and Spanish sample) and bovine (Türkiye) host isolates. Our findings revealed both conserved and variable elements, particularly in TA nucleotide repeat numbers and IG sequence length. The Spanish isolate exhibited the highest TA repeat expansion, whereas the Rouen 87 strain had the shortest IG. Given the known role of repeat-rich promoter elements in gene regulation, these differences may influence EF-1α transcription. Additionally, these findings provide insights into the evolutionary divergence of B. divergens and its host adaptation mechanisms. This study establishes a foundation for future gene editing and transfection strategies, where selecting intergenic sequences with varying TA repeats could optimize transfection efficiency and explain phenotypic differences between isolates from different hosts or regions. Full article

Anonymous communication networks such as the Invisible Internet Project (I2P) are essential for safeguarding privacy and ensuring freedom of expression, necessitating robust performance and security evaluation in controlled environments. Network testbeds offer a reliable alternative to real-world testing. This paper proposes a dynamic modeling framework based on Graph Attention Network (GAT). We introduce a Region-Centric Initialization (RCI) strategy to establish an initial observation anchor, followed by a GAT-based Locality-Aware (GAT-LA) sampling mechanism that treats representative node selection as a dynamic learning task. Experimental results demonstrate that the GAT-LA mechanism significantly outperforms static methods in maintaining long-term similarity to real-world I2P performance metrics. The integrated stability penalty mechanism effectively suppresses excessive topological fluctuations, ensuring temporal smoothness across evolutionary cycles. Furthermore, the RCI strategy provides high engineering flexibility by supporting both automated scoring and target-oriented manual configuration. This paper presents a scalable methodology for dynamic network simulation with enhanced statistical alignment, providing a practical reference for security research within resource-constrained anonymous network ranges or testbeds. Full article

This study explores the multi-objective optimization of flight sequencing in multi-runway airports using genetic algorithms. As air traffic continues to grow, airports face increasing pressure to optimize resource allocation. This research focuses on the sequencing of takeoffs and landings to minimize delays, reduce runway idle time, and enhance sequence robustness while maintaining a fair delay distribution among flights. A genetic algorithm-based approach is employed to balance these objectives while adhering to safety and operational constraints. Despite its low computational cost, this method ensures high convergence and solution diversity, leading to improved airport efficiency. Experimental evaluations compare multiple multi-objective genetic algorithms under different traffic conditions, identifying the most effective solutions for complex scheduling challenges. The results demonstrate that evolutionary multi-objective optimization can reduce total delays by up to 70% and runway idle times by 60% while maintaining fairness and robustness across flights. Among the tested algorithms, U-NSGA-III achieved the most consistent and reliable performance, confirming its suitability for real-time air traffic sequencing. This work aims to contribute practical, high-performance strategies for real-world airport operations. Full article

Metapneumoviruses comprise a genus of negative-sense RNA viruses that cause significant respiratory disease across human and avian hosts. Human metapneumovirus (hMPV) is a globally prevalent pathogen associated with acute lower respiratory tract infections in infants, older adults, and immunocompromised individuals. Avian metapneumovirus (aMPV) imposes substantial economic losses on the poultry industry through respiratory disease, reproductive impairment, and high mortality in the presence of secondary infections. Despite their distinctive host ranges, hMPV and aMPV share a conserved genomic architecture and encode homologous structural and non-structural proteins that mediate viral entry, replication, assembly, and evasion of host innate immunity. Comparative analysis highlights that both have deeply conserved polymerase and nucleocapsid functions, and yet have a wide range of diversity in the attachment glycoprotein (G) and small hydrophobic protein (SH), reflecting divergent evolutionary pressures in human versus avian hosts that have led to such distinctive differences. The recent emergence and detection of aMPV/A and aMPV/B across the previously aMPV-free United States beginning in late 2023, combined with rising cases globally of hMPV post-SARS-CoV-2 pandemic, underscore the continued challenges of metapneumovirus surveillance and control in humans and animals. This review aims to highlight the current knowledge on the history, molecular virology, pathogenesis, epidemiology, diagnostics, and control strategies for aMPV while drawing mechanistic parallels to hMPV. By contextualizing shared biology and structure alongside host-specific adaptations, we aim to identify key gaps that shape vaccine design, antiviral development, and future research priorities aimed at mitigating the health and economic burden posed by metapneumoviruses found in both birds and humans. Full article

To address the significant decline in fracture conductivity after cross-layer fracturing in the L3 sand–mudstone interbedded reservoir of the WZ Oilfield, which restricts efficient development, this study investigates three typical fracture types formed after fracturing: simple fractures in muddy siltstone, simple fractures in mudstone, and complex fractures in muddy siltstone. Based on downhole full-diameter cores, fracture conductivity plates were prepared, and long-term (50 h) conductivity evaluation experiments were conducted under a simulated formation closure pressure of 28 MPa. The interaction modes between fracture surfaces and proppants, as well as the conductivity evolution laws of different fracture types were systematically analyzed. The results indicate that the interaction modes between proppants and fracture walls vary significantly with lithology and fracture morphology. Specifically, proppant embedment dominates in simple muddy siltstone fractures, whereas hydration-induced embedding and wrapping by swelled clay particles dominate in mudstone fractures. The conductivity evolution of simple fractures in muddy siltstone and mudstone follows an exponential decay law, with attenuation amplitudes of 35% and 98% after 50 h, respectively. Complex fractures in muddy siltstone exhibit a staged decay pattern with an attenuation amplitude of 92%, and their long-term conductivity primarily depends on shear-induced self-support. The overall conductivity of cross-layer fractures is controlled by the minimum conductivity among the intersected layers. Under the specific experimental conditions of 28 MPa closure pressure and 30/50 mesh ceramic proppant, the poor long-term conductivity of mudstone simple fractures (only 2% of the initial value) becomes the key bottleneck restricting productivity. This study characterizes the evolutionary features of conductivity evolution of cross-layer fractures in sand–mudstone interbedded reservoirs and provides theoretical support and engineering guidance for optimizing fracturing fluid systems to inhibit hydration and refining stage isolation strategies in similar reservoirs. Full article

Digital transformation (DT) is reshaping manufacturing, with core enterprises (CEs) leveraging their resources to build industrial Internet platforms (IIPs) that support ordinary enterprises (OEs) in adopting DT. Differences in enterprise roles lead to varying impacts of government subsidies, necessitating careful policy design. Crucially, IIP adoption involves higher-order, multi-player interactions beyond conventional pairwise relationships—a dimension often overlooked in existing quantitative studies. This research employs hypergraph theory to model these complex interactions on IIPs and applies evolutionary game theory to analyze how enterprise decisions and government subsidies shape DT dynamics in manufacturing supply chains. The findings reveal that: (1) The network effect is the primary driver for DT via IIPs, but its promotional impact exhibits diminishing marginal returns. (2) Governments should prioritize subsidizing CEs for platform establishment, as subsidies directed at OEs for DT adoption are less effective. (3) Before withdrawing subsidies, governments must ensure a sufficiently high IIP adoption rate to sustain DT autonomously. This study introduces a novel methodology for examining DT and offers theoretical insights to guide enterprise strategy and policy implementation. Full article

Hyperparameter optimization for cryptocurrency trading strategies encounters distinct challenges owing to continuous operation, volatility rates 3–4 times higher than equity indices, and price dynamics influenced by market sentiment. Bayesian optimization (Tree-Structured Parzen Estimator, TPE) and evolutionary algorithms (Differential Evolution, DE) are great for machine learning, but there are not many systematic comparisons for trading cryptocurrencies. This research evaluates Random Sampling, TPE, and DE through 36 factorial experiments, comprising 3 trading strategies (3, 4, and 5 hyperparameters) × 3 optimizers × 4 cryptocurrency pairs (BTC/USDT, ETH/USDT, INJ/USDT, SOL/USDT), resulting in 14,400 backtesting trials with walk-forward validation. TPE won 75% of strategy–asset pairs (9 of 12), reaching 90% of optimal performance within 13–17% of trial budgets. We find strategy-specific optimizer compatibility: mean-reversion strategies show DE underperformance independent of topology (−1% to −8%), whereas trend-following strategies show consistent DE competitiveness across assets (+13% to +37%). Most notably, for the same strategy, parameter space topology differs significantly between assets (trend following: 4.6% viable on BTC to 82% on ETH = 17.8×; mean reversion: 10.8% on ETH to 92% on SOL = 8.5×), indicating that topology results from strategy–asset interaction rather than intrinsic properties. Complete testing failures and widespread severe overfitting point to regime non-stationarity as a fundamental problem. Among the contributions are: (1) evidence shows that topological effects are dominated by optimizer–strategy compatibility (DE fails on mean-reversion strategies even in 92% viable spaces, but succeeds on trend-following strategies regardless of topology, spanning 13.6–82% viable spaces); (2) this is the first systematic Bayesian versus evolutionary comparison across 4 cryptocurrency assets; (3) parameter space topology emerges from strategy–asset interaction, varying up to 17.8-fold; and (4) single-period backtests inadequately identify parameter instability. Full article

Green building incentives constitute a policy instrument for mitigating economic, technical, and behavioral barriers to the adoption of green buildings, yet existing studies remain fragmented across incentive types, stakeholders, and building life cycle stage. A coherent synthesis that explains how incentive strategies evolve and interact across these dimensions is still missing. This study addresses that gap through a systematic literature review guided by the PRISMA 2020 protocol. A total of 69 peer-reviewed journal articles published between 2016 and 2025 were identified from Scopus and analyzed using thematic synthesis. The review maps temporal trends, incentive typologies, stakeholder roles, and implementation challenges across different regional and market contexts. The findings indicate that incentive effectiveness depends on alignment between life cycle stage, market maturity, and stakeholder capacity, rather than on any single policy instrument. Financial incentives remain critical in early market phases, while non-financial and regulatory instruments gain prominence as markets mature. The synthesis also demonstrates how evolutionary game theory has been increasingly applied to analyse dynamic incentive and penalty strategies under bounded rationality, offering a structured lens for adaptive policy design. By integrating life cycle perspectives, stakeholder interactions, and game theoretical insights, this study advances current understanding of these incentive designs. The results provide a foundation for more adaptive and context-sensitive incentive frameworks and identify clear directions for future empirical and comparative policy research. Full article

Microbial bioprospecting in contaminated environments is a promising strategy for identifying biosurfactant-producing bacteria; however, translating environmentally adapted strains into predictable cultivation processes remains challenging. In this study, a microbial consortium subjected to long-term evolutionary laboratory adaptation in oily sludge was investigated to evaluate strain-specific phenotypic responses related to biosurfactant production. Phylogenetic analysis based on 16S rDNA sequencing identified three taxonomically distant isolates: Faucicola sp. strain BS5C, Pseudomonas sp. strain BS16B, and Enterobacter sp. BS14MR. Biosurfactant production was evaluated using a sequential Design of Experiments (DOE) approach, including fractional factorial and central composite rotatable designs, with the emulsification index (E24) used as a semi-quantitative response variable. Initial screening revealed a statistically significant negative effect (p < 0.10) of high dextrose concentrations for all isolates. Strain-specific differences in model adequacy were observed, with a statistically adequate quadratic model obtained for Pseudomonas sp. BS16B (R² = 0.8658, p = 0.0225), whereas the other isolates showed significant lack of fit (p < 0.05). ATR-FTIR analysis revealed spectral profiles consistent with lipopeptide-like compounds. Overall, these results indicate that isolates derived from the same long-term adapted system may differ substantially in process predictability, suggesting that productivity-based screening alone may be insufficient for selecting robust strains. Full article

River basins are not merely geographical spaces but also cultural-historical ecosystems, where the spatial patterns of Intangible Cultural Heritage (ICH) profoundly reflect the long-term interaction between human and environment, as well as contemporary transformations. While international research on ICH has evolved from conceptual clarification to interdisciplinary theory-building, and spatial quantitative methods have been widely applied to cultural heritage analysis, the spatial patterns, multi-scale structures, and “natural-human” driving mechanisms of ICH in continental arid river basins—particularly in the Tarim River Basin (TRB, China’s largest inland river and a key corridor of the Silk Road)—remain underexplored. To address this gap, this study takes 313 ICH items in the TRB as the research object. It uses ArcGIS 10.8.1 to visualize their spatial distribution and employs an integrated methodology—including global Moran’s I, kernel density estimation (KDE), DBSCAN spatial clustering, and geographical detector (Geodetector)—to systematically reveal their spatial characteristics and influencing factors. The findings indicate that: (1) The distribution of ICH exhibits a multi-scale feature of “global randomness with local clustering”: spatial autocorrelation is not significant at the county level, while at the micro-geographical scale, a dendritic structure characterized by “one axis, three cores, denser in the north and sparser in the south” emerges, which is highly coupled with the river network. DBSCAN clustering further identifies a “mainstem axis–tributary node” cluster system and a relatively high proportion of peripheral “noise” heritage points. (2) Agglomeration patterns vary significantly across different ICH categories, with traditional craftsmanship showing high clustering, while traditional sports, entertainment, and acrobatics display highly fragmented distributions. (3) The study reveals and validates a ternary “Water–Tourism–Urbanization” driving framework that predominantly shapes the spatial heterogeneity of ICH: water resources constitute a fundamental ecological threshold, whereas tourism development and urbanization have emerged as more explanatory social driving forces, with widespread nonlinear enhancement interactions between natural and human factors. This research moves beyond the traditional view of river basins as static cultural “containers,” providing empirical evidence for their dynamic nature as “cultural-ecological co-evolutionary systems.” The proposed ternary framework not only offers a new perspective for understanding the spatial resilience of ICH in arid regions and the potential risks of “spectacularization” and “spatial polarization” amid rapid changes, but also provides a scientific basis for spatial governance, culture-tourism integration, and the formulation of conservation strategies for ICH at the basin scale. Full article

This critical and selective review synthesizes the accumulating body of biological evidence supporting a process we term epigenetic–genetic coupling as a mechanistic basis for Lamarckian inheritance of somatically acquired adaptations. We propose that evolutionary processes in mammals and higher vertebrates can involve deaminase-driven, reverse transcriptase-mediated, RNA-templated targeted homologous recombination. We contrast well-established examples of “Soft”, reversible epigenetic inheritance with historical and contemporary evidence suggestive of stable, DNA-integrated “Hard” Lamarckian transgenerational inheritance. Our analysis indicates that the establishment of “Hard” Lamarckian inheritance may require specific population dynamics, including inbreeding or interbreeding among phenotypically affected offspring, together with sustained and defined environmental stimuli over one or more generations to consolidate the acquired traits at the genomic level. We also present molecular and cellular evidence supporting RNA-to-DNA genetic feedback mechanisms involving targeted genomic integration, primarily mediated by the DNA repair–associated reverse transcriptase activity of DNA polymerase η. Finally, we review diversification mechanisms in molecular and cellular immunology that now routinely employ single-molecule, real-time, long-read genomic sequencing (6–8 kb). We recommend the broader application of these technologies in future breeding and experimental programs across other somatic systems. Their deployment offers a robust strategy for securing definitive “Hard” molecular evidence of Lamarckian acquired inheritance in diverse biological contexts; including somatically acquired immunity, as well as adaptive behavioral and central nervous system phenotypes. This is compatible with our over-arching goal—to provide an experimental road map of conceptual options to drive future experimentation in acquired inheritance breeding programs. Full article

Differential wavefront sensing (DWS) is widely adopted for high-precision angular detection in interferometric systems, yet its measurement range is constrained by the nonlinear implicit phase–angle relationship. This paper proposes a strategy-enhanced differential evolution algorithm, termed Bi-inheritance and Tournament-Selection-based Differential Evolution (BiTsDE), to suppress nonlinear angular errors. The method introduces fitness-guided inheritance of mutation and crossover factors and tournament-based elite parent selection, enabling adaptive balance between global exploration and local exploitation. Unlike conventional DE variants that mainly tune control parameters, BiTsDE optimizes the evolutionary search strategy, enhancing early-stage diversity and late-stage convergence stability. Simulations demonstrate angular resolution better than 0.06 nrad within ±1 mrad. Experiments show that up to 600 μrad, BiTsDE reduces demodulation error by 99.9% compared with linear DWS, achieving 17.9 nrad precision and 42% faster convergence. These results validate BiTsDE as an effective solution for nonlinear error suppression in DWS-based high-precision optical metrology, particularly for space-based gravitational wave detection. Full article

Background/Objectives: Chloroplast genomes are essential for understanding the systematics and adaptive evolution of alpine plants, yet genomic data for high-altitude Delphinium species remain scarce. Delphinium densiflorum, a medicinal plant endemic to the Qinghai-Tibet Plateau, exhibits notable high-altitude adaptations, but its plastome features and evolutionary position are still unclear. This study aims to assemble and characterize its complete chloroplast genome and clarify its phylogenetic placement within Delphinium. Methods: Using Illumina NovaSeq data, we de novo assembled the D. densiflorum plastome, annotated it with CPGAVAS2, and compared it with 12 published Ranunculaceae plastomes. We analyzed IR-boundary dynamics, genome-wide sequence variation, and codon-usage bias and constructed a maximum-likelihood phylogeny based on 69 shared protein-coding genes. Results: The plastome is 154,161 bp (GC 38.24%) with a canonical quadripartite structure, encoding 131 genes (87 CDS, 8 rRNA, 37 tRNA). An IR expansion into the SSC region yields the shortest SSC reported among the compared Delphinium species and produces unique structural variants. Photosynthetic genes are extremely conserved (nucleotide diversity Pi ≤ 0.01), whereas several loci (e.g., ycf1 and psaC) are highly divergent (Pi ≥ 0.05). Codon usage shows a strong bias toward AU-ending triplets. Phylogenetically, D. densiflorum forms a 100%-bootstrap clade with other high-altitude congeners, supporting the non-monophyly of Delphinium. Conclusions: This study delineates the plastome architecture and putative adaptive signatures of D. densiflorum, identifies robust candidate loci for DNA barcoding, and provides molecular evidence for taxonomic revision and conservation strategies in Delphinium. Full article

Biopharmaceuticals are therapeutic drugs engineered to target specific sites within the body. Their manufacturing process comprises two primary stages: upstream processing (USP) and downstream processing (DSP). USP primarily involves cell culture and growth, whereas DSP focuses on purifying and packaging the final product. The recent literature only reports a few studies addressing production planning and scheduling in biopharmaceutical manufacturing. In this work, we address a long-term scheduling and midterm planning problem incorporating on-time or late delivery of final products with unknown finite delivery rates. Early delivery is prohibited, and late delivery incurs a penalty cost. Published models and evolutionary algorithms exhibit key limitations in areas such as shelf-life modeling, inventory management, and product delivery. To overcome these shortcomings, we propose a revised mixed-integer linear programming (MILP) model implemented using the General Algebraic Modeling System (GAMS). When applied to two illustrative examples, the model reduces optimum event counts by two to three, improving computational efficiency through fewer binary variables, continuous variables, and constraints. Furthermore, it achieves up to 7% improvement over two published benchmarks, underscoring its potential to enhance scheduling strategies for multiproduct biopharmaceutical facilities. Full article

Understanding how herbivores adjust their foraging strategies to cope with seasonal resource fluctuations has been central to the nutritional ecology. Optimal Foraging Theory (OFT) predicts that generalists should broaden their dietary niche when high-quality resources are scarce, but empirical evidence in extreme environments remains poorly understood. We used trnL-P6 metabarcoding of fecal samples (n = 10/season) and a local reference library of 120 plant species to quantify diet composition and niche metrics of free-ranging yaks (Bos grunniens) on the Qinghai–Tibetan Plateau in June (summer) and October (autumn) 2024. Yaks shifted from a diverse, forb-dominated diet (e.g., Polygonaceae, Rosaceae) in summer to a specialized diet dominated by grasses in autumn. Although dietary richness and total niche width (TNW) decreased in autumn, phylogenetic diversity remained stable, indicating a strategic shift to distinct evolutionary lineages to ensure functional redundancy. Furthermore, food network analyses demonstrated a transformation from a flexible, modular foraging pattern in summer to a highly integrated, synchronized network in autumn. These findings suggest that under the distinct quality–quantity trade-off of high-altitude ecosystems, yaks adopt an energy-maximization strategy by minimizing search costs, aligning with the opportunity cost constraints of OFT, rather than randomly expanding their niche. This insight into selective foraging dynamics is critical for developing sustainable grazing practices that accommodate the natural adaptive behaviors of alpine herbivores. Full article