Search Results (4,927)

Multi-objective feature selection provides an effective framework for high-dimensional data analysis by jointly considering classification performance and feature subset size. However, redundant and irrelevant features may degrade the quality of the initial search distribution, and population aggregation may reduce the structural diversity of non-dominated feature subsets. To address these issues, this paper proposes PGS-MODE-FS, a prior-guided and structure-aware multi-objective differential evolution method for high-dimensional feature selection. Specifically, feature–class relevance and feature redundancy are integrated into a unified feature importance measure to guide the generation of candidate solutions with different sparsity levels. The same feature-priority information is further used to construct a Top-k activated subspace, in which individuals are assigned to multiple islands according to their structural differences on informative features, thereby promoting diverse evolutionary search. Experiments on 17 public and biomedical datasets, including parameter analysis, comparative experiments, and ablation studies, demonstrate that PGS-MODE-FS achieves competitive performance in solution-set quality, classification accuracy, feature reduction, and computational efficiency. Further diversity analysis shows that the proposed multi-island mechanism effectively preserves structural diversity during evolution. Full article

Background: Prime editing has rapidly evolved from a CRISPR-based “search-and-replace” approach for precise sequence modification into a diverse family of genome editing technologies. This systematic review maps the technological evolution of prime editing, with emphasis on editor architecture, guide RNA engineering, delivery, therapeutic applications, computational approaches, and emerging capabilities. Methods: PubMed and Web of Science were systematically searched for studies in which prime editing constituted a substantive experimental, technological, computational, delivery, or therapeutic component. After deduplication and screening, candidate studies underwent manual re-screening against prespecified eligibility criteria. Reviews, corrections, plant and bacterial studies, conventional CRISPR or base editing studies without a substantive prime editing component, and other non-relevant records were excluded. A total of 294 studies were included in the final systematic evidence synthesis. Because of substantial heterogeneity in editor architectures, targets, experimental models, outcomes, and reporting, the literature was synthesized using systematic mapping and qualitative thematic analysis rather than meta-analysis. Results: The evidence demonstrates rapid diversification from the original Cas9 nickase–reverse transcriptase–prime editing guide RNA architecture through improvements in pegRNA design, Cas and reverse transcriptase engineering, DNA repair modulation, delivery, computational design, and increasingly complex sequence modification. Therapeutic studies span disease modeling, correction of pathogenic variants, ex vivo applications, and direct in vivo editing; however, high editing efficiency does not necessarily translate into functional or therapeutic rescue. Large-sequence insertion and replacement strategies further extend the capabilities of prime editing, although these approaches remain less mature than small-sequence correction and face substantial challenges in efficiency, fidelity, cargo delivery, and genomic safety. Conclusions: Prime editing has developed into a versatile precision genome editing platform, but the evidence base remains heterogeneous and predominantly preclinical. Translation to genetic medicine will require improvements in reproducibility across targets and cell types, delivery to clinically relevant tissues, product purity, genomic safety, and demonstration of meaningful functional benefit. Emerging large-sequence editing approaches broaden the potential scope of prime editing but should be distinguished from technologies with established experimental and therapeutic evidence. Full article

Idesia polycarpa Maxim. is a predominantly dioecious woody oil species valued for its oil-bearing fruits, bioactive metabolites, and distinctive reproductive biology. To provide an integrated understanding of its biological characteristics, resource utilization, and research evolution, this study combines a concise review of current knowledge with a bibliometric analysis of English-language publications from 2005 to 2025. The review summarizes current knowledge on reproductive biology, fruit and oil quality, lipid biosynthesis, phytochemical diversity, and processing utilization, while the bibliometric analysis includes 109 eligible publications from the Web of Science Core Collection using Microsoft Excel 2021, bibliometrix 4.5.0, VOSviewer 1.6.20, CiteSpace 7.0.R0, and SCImago Graphica 1.0.51. The review highlights that the resource value of I. polycarpa is determined by the interaction between reproductive characteristics, fruit development, lipid accumulation, phytochemical composition, and processing strategies, while available evidence indicates that molecular findings remain largely associated with developmental regulation and require further functional validation. Publication output rose markedly during 2023–2025 and peaked in 2025. China contributed the most publications, while institutional and author collaboration networks consisted of several relatively distinct groups with limited intergroup links. Reference co-citation and keyword analyses revealed three major bibliometric thematic domains: natural products and biological activities; fruit-oil evaluation and utilization; and genomic, developmental, and reproductive research. Fatty acids and oil-related traits remained persistent topics, whereas recent bursts of “cloning”, “allocation”, and “bisexual flower” indicated growing interest in molecular regulation and reproductive development. Overall, research on I. polycarpa has progressed from resource characterization toward molecular regulation and utilization-oriented studies. Future work should integrate germplasm evaluation, reproductive biology, oil quality, and processing traits through coordinated multidimensional approaches. Full article

Background/Objectives: Drug resistance in tuberculosis remains one of the major challenges to global disease control. Traditionally, its emergence has been attributed to factors such as treatment interruption, poor adherence, and the selective pressure exerted by antimicrobial agents. However, recent advances in bacterial genetics, within-host evolution, cellular persistence, and heteroresistance suggest that relevant biological processes may develop before treatment initiation. The aim of this critical integrative reflection was to explore the biological plausibility that diagnostic delay may act as an amplifying factor for adaptive and evolutionary mechanisms potentially associated with drug resistance in Mycobacterium tuberculosis. Methods: A structured documentary search was conducted in PubMed/MEDLINE, Scopus, Web of Science, and Google Scholar. Of the 86 documents initially identified, 29 studies were selected using predefined inclusion and exclusion criteria. The evidence was analysed through a conceptual convergence matrix, which enabled the organisation of findings into five analytical categories: diagnostic delay and programmatic determinants; mechanisms of resistance, bacterial adaptation, and clinical implications; persistence and drug tolerance; within-host evolution and genetic diversity; and heteroresistance and resistant subpopulations. In addition, four institutional reports were incorporated to contextualise the issue from a global perspective. Results: The reviewed evidence suggests that extended persistence of infection before diagnosis may favour biological opportunities for bacterial adaptation, the persistence of tolerant subpopulations, the accumulation of genetic diversity, and the emergence of variants with distinct drug susceptibility profiles. Although the available studies do not demonstrate a direct causal relationship, they support the plausibility of an evolutionary trajectory capable of influencing the dynamics of drug resistance. Conclusions: An expanded model of drug resistance in tuberculosis is proposed, in which diagnostic delay does not constitute a direct cause of resistance but rather a potential amplifying factor for biological adaptation and evolutionary change occurring before treatment initiation. This hypothesis generates new avenues for research on the interaction between timely diagnosis, bacterial evolution, and drug resistance. Full article

Marine-derived Streptomyces are among the most prolific producers of structurally diverse and biologically active natural products. Adaptation to unique marine environments, including deep-sea sediments, hydrothermal vents, mangrove ecosystems, marine invertebrates, and hypersaline habitats, has promoted the evolution of specialized biosynthetic systems capable of generating a broad spectrum of secondary metabolites. These metabolites include polyketides, non-ribosomal peptides (NRPs), ribosomally synthesized and post-translationally modified peptides (RiPPs), terpenoids, alkaloids, and hybrid compounds with significant antibacterial, antifungal, antiviral, antiparasitic, anti-inflammatory, and anticancer activities. Recent advances in genome sequencing, bioinformatics, genome mining, metabolomics, synthetic biology, and artificial intelligence (AI)-assisted discovery have substantially expanded marine natural product research by enabling the identification and prioritization of previously inaccessible biosynthetic gene clusters (BGCs). However, major challenges remain, including silent biosynthetic pathways, low cultivation efficiency, rediscovery of known compounds, metabolite yield instability, dereplication bottlenecks, and limited ecological interpretation. These constraints continue to impede the translation of biosynthetic potential into pharmaceutical applications. This review summarizes current strategies for marine natural product discovery and highlights emerging translational approaches integrating multi-omics technologies, pathway engineering, and AI-guided prioritization. Collectively, these advances provide a roadmap for advancing marine Streptomyces research from descriptive omics-based exploration toward experimentally validated and clinically relevant drug discovery. Full article

Background/Objectives: Intracranial aneurysm (IA) management has evolved from surgical treatment of ruptured lesions into a multidisciplinary field integrating diagnostics, neurocritical care, endovascular techniques, and individualized risk assessment. As the literature has grown beyond the scope of traditional narrative synthesis, bibliometric methods are needed to characterize the field’s development. We used co-word analysis to map the conceptual structure and thematic evolution of IA research over nearly eight decades. Methods: We analyzed 22,061 publications indexed in PubMed between 1946 and 2023. Co-word analysis of Medical Subject Headings tracked thematic development across six subperiods grouped into eras. Within each thematic network, topics were classified by centrality and density as motor, niche (highly developed but isolated), emerging or declining, or basic themes. Results: Four historical eras were delineated as an interpretative synthesis of the six subperiods. The open-surgery era (1946–1980) centered on ruptured aneurysms and refinement of operative techniques. The transitional era (1981–1990) was marked by proactive vasospasm management and emerging neurocritical care. The endovascular era (1991–2010) reflected the rise of evidence-based medicine and endovascular therapy. The multidisciplinary era (2011–2023) shifted toward unruptured aneurysms, molecular research, biomechanics, and individualized risk stratification. Recent themes, including hemodynamics, flow diversion, and precision medicine, reflect a growing research emphasis on predictive and personalized approaches to aneurysm management. Conclusions: IA research has evolved from descriptive, technique-oriented studies toward a data-driven landscape increasingly focused on predictive assessment and management of unruptured lesions. Its thematic evolution highlights the growing prominence of molecular and computational approaches, although their clinical and translational impact cannot be directly inferred from the present analysis. Full article

Accurate runoff forecasting serves as a fundamental basis for the scientific management of water resources and flood and drought risk mitigation. Owing to the nonlinearity, non-stationarity, and multi-scale temporal characteristics of runoff series, existing deep learning models still exhibit notable limitations in capturing long-term trends, responding to abrupt hydrological events, and ensuring model interpretability. The original Transformer relies on global self-attention, whose computational complexity increases quadratically with sequence length; it also has limited capacity to capture short-term local temporal dependencies such as rainfall–runoff relationships, and lacks prior constraints tailored to hydrological processes. To address these challenges, this study proposes a collaborative forecasting framework that integrates a gated convolutional Transformer (GCTrans) with an improved black kite algorithm (IBKA), enabling accurate, stable, and interpretable daily-scale runoff prediction. The GCTrans model consists of three customized modules: convolution-enhanced positional encoding (CEPE), which combines learnable positional encoding with local causal convolution to strengthen the temporal association of adjacent rainfall–runoff events, thereby providing a hydrologically meaningful positional reference for the attention mechanism; gated convolutional attention (GCA), which adopts a dual-path parallel architecture comprising global self-attention and local causal convolution to adaptively fuse long-term seasonal patterns with short-term storm-induced variations, thus capturing both baseflow evolution and flood peak responses; and a temporal gated output layer (TGOL), which performs adaptive feature weighting along the temporal dimension to selectively enhance the contribution of critical driving periods associated with extreme flood events, thereby improving the flood peak prediction accuracy. In addition, an improved black kite algorithm (IBKA) was developed by incorporating Tent chaotic initialization to enhance initial population diversity and introducing cosine adaptive inertia weights to dynamically balance global exploration and local exploitation, effectively alleviating premature convergence in high-dimensional hyperparameter spaces. Validation using data from the ME-Inland snowmelt-dominated watershed and the OR-Coastal storm-driven coastal watershed in the United States demonstrated that the GCTrans model consistently outperformed benchmark models including TCN, LSTM, Transformer, and Informer. After synergistic optimization with IBKA, both prediction accuracy and stability were further improved. SHAP-based interpretability analysis revealed that the model’s feature response patterns are statistically consistent with the rainfall–runoff generation mechanisms of the study basins: temperature-related drivers dominate in the inland watershed, while precipitation plays a dominant role in the coastal watershed, confirming the hydrological plausibility of the model’s decision-making logic. The integrated framework—encompassing model architecture, optimization algorithm, and interpretability—offers a valuable methodological reference for deep learning-based runoff forecasting in complex hydrological settings. Full article

Particle swarm optimization (PSO) is a widely used bio-inspired optimization method. However, global guidance can align particle trajectories, reduce population diversity, and lead to premature convergence. To address this issue, we propose GRACE-PSO, a particle swarm optimizer based on group rank assessment and cooperative evolution. The method introduces group-best learning as an intermediate layer between personal-best and global-best learning to improve the balance between exploration and exploitation. GRACE-PSO integrates three coupled mechanisms: (1) a group-learning update that provides the population with multiple group-specific search directions; (2) a rank-based group-utility assessment that evaluates relative search effectiveness through pairwise comparisons of personal-best fitness values; and (3) a utility-driven adaptive strategy that adjusts the strengths of group-best and global-best learning and selectively reinitializes a small number of underperforming particles in stagnant groups. Experiments on 29 CEC 2017 benchmark functions at 30 and 50 dimensions against seven representative PSO methods show that GRACE-PSO achieves average ranks of 1.2414 and 1.3793, respectively. Experiments on two practical flexible intelligent metasurface optimization problems further demonstrate its competitive performance and practical applicability. Full article

Metaheuristic optimizers often suffer from premature convergence, diversity loss, and stagnation in complex search landscapes. This study proposes HCQ-SGuA, a plant-growth-inspired framework that strengthens the Sowing phase through structured population initialization and regulates the biologically framed Mating, Branching, and Vaccinating mechanisms through chaotic operator control. An architecture-aware development–confirmation–holdout protocol was used to isolate initialization, chaotic control, their factorial interaction, and adaptive map selection on the 29 CEC2017 function classes available in OPFUNU 1.0.4 at D = 10 and D = 30. Faure initialization and Logistic control were the most consistent individual components, and high-budget confirmation selected HCQ-SGuA2[Faure + Logistic]. On the reserved holdout set, the frozen hybrid was competitive with its individual components and several established optimizers, but it did not significantly surpass the principal nonadaptive SGuA2 variants and remained inferior to DE and L-SHADE. The engineering experiments produced feasible and competitive best-run solutions, although mean performance and stability were weaker than those of the leading differential-evolution methods. The principal contribution is therefore a controlled biomimetic hybridization framework, showing when complementary interventions in Sowing and Growing Up cooperate and when added adaptive complexity is not beneficial. Full article

Fringe belts serve as the “growth rings” of urban expansion, bearing witness to the historical development and evolution of cities. Previous studies on urban morphology often focused on qualitative research, lacking quantitative measurement of various values in the context of high-density megacities. This study uses the main urban area of Guangzhou as a case study, reconstructs morphological periods based on the city’s history of over 2000 years, combines multi-source spatiotemporal data to accurately identify natural and artificial fixation lines, and scientifically defines and divides the spatial ranges of four urban fringe belts: inner, middle, middle-outer, and outer. This study has constructed a quantitative measurement framework integrating function and space, relying on remote sensing land surface temperature inversion, land use models, nighttime lights, and POI data to determine the ecological, economic, and social functions of the fringe belts in Guangzhou. The results demonstrate that (1) Guangzhou fringe belts exhibit distinct hierarchical spatial expansion pathways: leapfrog expansion, linear extension and stratified renewal. (2) Fringe belts constitute composite spaces with notable ecological value, exhibiting relatively favorable local thermal environmental characteristics within high-density built-up environments. Moreover, the natural patches contained within fringe belts demonstrate strong morphological resilience. (3) The economic and social functions of fringe belts strengthened while exhibiting uneven spatial changes, while spatial functions shifted from predominantly production and institutional uses toward more diverse urban functions. Nighttime light intensity increased overall, with widening absolute differences among units but declining relative dispersion, indicating overall brightening alongside persistent spatial variation in nighttime illumination. This work creates a novel paradigm for value evaluation, adapting Western urban morphological theory to a Chinese megacity while offering an effective tool for urban resilience and landscape regulation. Full article

Roegneria nutans, a premium grass on Western Sichuan Plateau, China, is vital for ecological stability and pastoral security. Studying its chromosomal architecture and environmental correlations lays a foundation for Roegneria genetic evolution research. This study used genomic in situ hybridization (GISH) and non-denaturing fluorescence in situ hybridization (ND-FISH) with two tandem repetitive sequence probes pAs1 and (AAG)₁₀) to analyze the species’ St and Y genomes of R. nutans from different altitudes. Our findings demonstrated that for both the number of probe signals and variant types, inter-population variation was higher than intra-population variation. Distinct altitudinal response patterns of (AAG)₁₀ and pAs1 FISH signals were detected between the St and Y genomes. Within the St genome, the abundance of (AAG)₁₀ loci exhibited a trend of initial decline followed by a marked rise with increasing altitude. The pAs1 probe displayed higher signal density than (AAG)₁₀, with its signal abundance peaking in medium-altitude populations, and polymorphic loci were most abundant in high-altitude populations. In contrast, medium-altitude samples harboured relatively fewer (AAG)₁₀ signals within the Y genome. Although pAs1 exhibited lower signal density than (AAG)₁₀ in the Y genome, its maximum signal number was observed in medium-altitude populations, where polymorphic loci predominated. Low-altitude populations maintained relatively conserved hybridization patterns, and chromosomal variation was mainly detected in the Y genome at medium altitudes and in the St genome at high altitudes. This study provides a valuable basis for exploring the genetic diversity of R. nutans across the Western Sichuan Plateau. Full article

It remains unclear why deformation results obtained by applying different MT-InSAR methods to the same dataset, as well as those from different SAR datasets, exhibit discrepancies. Hence, this study selects Baode County, located in the Loess Plateau, as the study area to conduct a comparative analysis of multi-source SAR data and multiple MT-InSAR techniques for surface deformation monitoring. The datasets consist of concurrent Radarsat-2 and Sentinel-1 images acquired from October 2020 to June 2024. PS-InSAR, SBAS-InSAR, and IPTA-InSAR are adopted to compare their applicability across multiple dimensions, such as point coverage, deformation correlation, and mapping performance. Furthermore, based on IPTA-InSAR, the influences of spatiotemporal resolutions from different datasets on monitoring results are analyzed. The Sequential Turning Point Detection (STPD) method is incorporated to characterize the dynamic evolution of deformation and its correlation with precipitation. The results indicate that the three techniques exhibit favorable consistency and complementarity across diverse landform types. Nevertheless, SBAS-InSAR’s superiority in point density cannot be translated into an ability to represent continuous deformation fields. In contrast, IPTA-InSAR demonstrates better adaptability to complex surface conditions. Regarding data sources, Radarsat-2 achieves superior monitoring performance thanks to its high spatial resolution, yet its monitoring capacity is sensitive to variations in spatial resolution. Multi-looking not only reduces the maximum subsidence rate by approximately half but also increases elevation uncertainty by up to 24.8% and deformation-rate uncertainty by up to 42.1%. Sentinel-1, with its shorter revisit cycle, provides temporal sampling advantages that significantly enhance the ability to capture rapid subsidence signals. Through controlled-variable experiments, a dual-comparison analysis of multi-source SAR datasets and multiple MT-InSAR techniques enables the separation of discrepancies induced by algorithms from those originating in the datasets. The multifaceted experimental design provides a relatively comprehensive assessment of the influencing factors. The findings of this study can serve as references for InSAR data source combinations, technique selection, results presentation, and reliability assessment of deformation results in complex monitoring areas. Full article

The study presents the experiences of emerging artists from the Republic of Moldova in the field of comic strips between 2000 and 2025. Comic strip creation has undergone various periods of development, as well as periods of stagnation. The evolution of current technologies, with their implications for the visual arts, alongside contemporary societal imperatives and challenges, is shaping a new attitude amongst young readers towards documentation, information and knowledge. Drawing on current global trends regarding the relevance of publications with sequential graphic content, the aim of this article is to map the new visual discourses of emerging comic strip creators in the Republic of Moldova, documenting their stylistic and thematic contributions, as well as the role of digital technologies in their creative process. The objectives focus on analysing the artistic development, visual language and messages of key figures such as Octavian Curoșu, Cristina Grati, Alex Buretz, Inna Sacali, Călin Enachi, Valentin Enachi and others. The methodology combines case studies with stylistic and semiotic analysis, examining the structure of the comics, their layout and the relationship between text and image. The results reveal significant diversity. Octavian Curoșu combines the sensitivity of traditional techniques with digital media, addressing historical and socio-cultural themes. The artist Alex Buretz uses caricature and the grotesque as tools for civic activism and social criticism. Comic strip artists Cristiana Grati and Inna Sacali explore the world of family life and human relationships, using an expressive digital style and colour palettes that are either evocative or decorative. Graphic designers Călin and Valentin Enachi bring a youthful and adventurous touch to the narrative images, with experimental and original page layouts. All the artists featured make full use of specialised software (Adobe Photoshop, Clip Studio Paint, Procreate and others) and graphics tablets, modernising the creative process. The use of these tools has enabled the emerging artists to emulate traditional techniques, as well as create complex visual effects, optimising work on layers and facilitating publication on online platforms. Thus, through stylistic diversity and digital adaptation, these artists are strengthening the presence of comic strips within the cultural landscape, confirming a vibrant and modern revival of comic art in the Republic of Moldova. Full article

The Murcia Region unrelated donor (URD) registry was established in 1992 as part of the Spanish Bone Marrow Donor Registry (REDMO). This study aimed to comprehensively characterize the long-term evolution of URD recruitment, human leukocyte antigen (HLA) genetic diversity, allele and haplotype frequencies, and the spectrum of novel HLA alleles detected over the 34 years from 1992 to 2025. Recruitment trends, URD demographic characteristics, HLA allele and haplotype frequencies, and newly detected HLA alleles were analyzed retrospectively. HLA typing was performed using serological methods (1992–1996); PCR-SSO, PCR-SSP, and PCR-SSO Luminex (1997–2018); sequence-based typing (SBT, 2018–2021); and next-generation sequencing (NGS, 2021–2025). NGS was used to analyze a subset of 650 URDs to determine high-resolution HLA allele frequencies, Hardy–Weinberg equilibrium, expected heterozygosity, and extended HLA haplotypes. Newly detected HLA alleles were further characterized by the affected HLA locus and the localization of sequence variants within exons, introns, and the 3′-untranslated region (3′UTR). Between 1992 and 2025, 33.271 URDs were recruited, of whom 27.107 remained active in REDMO at the time of analysis. URD recruitment increased substantially following the implementation of the Spanish National Bone Marrow Plan in 2012 and reached a historical high in 2017. Additional recruitment peaks were associated with major regional public awareness campaigns. High-resolution HLA analysis of the NGS cohort demonstrated extensive immunogenetic diversity across all analyzed HLA loci, with a mean expected heterozygosity of 0.914. Ten frequent extended HLA haplotypes were identified, the most prevalent being HLA-A*29:02-C*16:01-B*44:03-DRB1*07:01-DQB1*02:02. Thirty-nine novel HLA alleles were characterized, exhibiting heterogeneous distributions among HLA loci and across coding and non-coding genomic regions. Exonic variants were predominantly detected in HLA-C, whereas intronic variants were mainly observed in HLA-C, HLA-DQB1, and HLA-A. This study integrates 34 years of URD recruitment with high-resolution immunogenetic characterization of the Murcia Region URD registry, providing new insights into the evolution of donor recruitment, HLA diversity, and the discovery of novel HLA alleles. The implementation of NGS enabled precise molecular characterization of HLA diversity and substantially expanded the detection and characterization of novel HLA alleles. Together, these findings provide a robust immunogenetic framework for the Murcia URD population, supporting donor recruitment strategies, optimization of URD searches, registry management, and future research in population genetics and hematopoietic stem cell transplantation. Full article

The translocase of the outer membrane (TOM) complex is the primary entry gate for nucleus-encoded mitochondrial proteins and is conserved across eukaryotes; however, parasitic protists exhibit remarkable adaptations. This review synthesizes current knowledge on the composition, structure, and function of TOM and related import systems in apicomplexans, kinetoplastids, and anaerobic protists with reduced mitochondrion-related organelles (MROs). We highlight major evolutionary innovations, including the bacterial-origin ATOM complex in trypanosomes—comprising core channel ATOM40, receptors ATOM46/69, structural subunit ATOM19, and multifunctional pATOM36 linking protein import to mitochondrial DNA inheritance—alongside lineage-specific receptors such as Tom60 in Entamoeba histolytica and extreme reductive streamlining in microsporidian mitosomes. Beyond its canonical role in biogenesis, we explore how parasites co-opt the host TOM complex for intracellular survival and how the import machinery integrates with broader cellular processes, including immune signaling, stress responses, and organelle dynamics. Notably, the essential nature of these adapted systems, coupled with their significant divergence from host machinery, highlights their potential as targets for developing novel therapeutic strategies against major parasitic diseases. This work underscores that understanding the diversity of mitochondrial protein import pathways not only provides insights into eukaryotic evolution but may also open new avenues for the development of antiparasitic drugs. Full article