Search Results (292)

Phytophthora and allied oomycete pathogens pose a perennial challenge to global food security through their devastating impact on crop systems. While metalaxyl has demonstrated remarkable efficacy in controlling Phytophthora diseases since its introduction decades ago, the persistent emergence of metalaxyl-resistant strains has severely compromised its field efficacy. Elucidating the genetic determinants underlying resistance mechanisms is critical to developing surveillance strategies and sustainable countermeasures against evolving oomycete resistance. Through experimental evolution, we generated six metalaxyl-resistant Phytophthora sojae mutants exhibiting extreme resistance levels (resistance factor > 2000). Comparative whole-genome re-sequencing of resistant mutants versus the wild-type parental strain identified 64 candidate genes containing conserved nonsynonymous mutations across all resistant lineages. Among these, PsALG8, encoding a putative alpha-1,3-glucosyltransferase, was identified as the primary determinant, carrying a recurrent homozygous missense mutation across all resistant lineages. CRISPR/Cas9-mediated knockout of PsALG8 in both wild-type and resistant backgrounds significantly reduced metalaxyl tolerance (p < 0.01), confirming its functional involvement in resistance modulation. These results suggest that PsALG8 is associated with metalaxyl sensitivity and mycelial growth in P. sojae under laboratory conditions. The conservation of ALG8 homologs suggests that PsALG8 may have a conserved cellular function related to protein glycosylation across eukaryotes. Although this glucosyltransferase is universally conserved among oomycete species, whether its association with metalaxyl sensitivity constitutes a shared resistance adaptation pathway still requires extensive functional validation in diverse Phytophthora pathogens, which may offer insights into future fungicide resistance management strategies in P. sojae. Full article

Existing radio environment map(REM)-based emitter-detection methods suffer from high false positives and missed detections in blurred or conjoined structures, or require large annotated datasets and heavy computation. We propose an unsupervised method, persistent homology with agglomerative clustering (PH-AC), based on an improved persistent-homology algorithm. A simulated spaceborne-REM dataset is constructed via synthetic-aperture passive interferometric imaging, covering isolated, adjacent-pair, and complex-emitter distributions. Persistent homology tracks the birth, death, and merging of zero-dimensional connected components as the intensity threshold varies. To address missed detections for spatially proximate emitters, multidimensional topological features are constructed via feature-contribution analysis. Agglomerative clustering with Ward linkage then adaptively separates emitters from noise without supervision. Experimental results show that PH-AC achieves a perfect F1 score of 1.000 in isolated scenarios; for adjacent emitters, it improves F1 by 15.7% over the best image-processing method and stays within 4% of supervised deep learning methods, while requiring no annotations. In complex environments, it attains an F1 of 0.937, outperforming all compared methods. Its computational complexity is only $2.25\times {10}^6$ FLOPs, three orders lower than YOLO-based detectors. This work offers a lightweight, annotation-free topological paradigm for spaceborne-REM-emitter detection. Full article

Topological data analysis (TDA) has evolved into a flexible and robust paradigm for obtaining qualitative, geometry-inspired insights from high-dimensional, noisy, and complex data. Grounded in algebraic topology, geometry, statistics, and machine learning (ML), TDA provides multiscale descriptions through persistent homology, Mapper (a graph-based method that summarizes the shape of high-dimensional data), and related topological signatures that are often inaccessible to standard linear and metric methods. In recent years, and especially during 2024–2025, TDA has expanded rapidly across science, engineering, biomedical research, and socio-economic studies, while also being integrated with modern learning paradigms such as deep learning (DL) and graph learning. This survey summarizes recent developments in TDA using a carefully selected set of articles, with emphasis on 2024–2025. We first present the mathematical and computational foundations of TDA, covering simplicial complexes, filtrations, persistent homology, the Mapper algorithm, and computational advances such as data simplification, stability, and efficiency. We then review applications in time series and dynamical systems, biomedical imaging and precision medicine, engineering and physical sciences, finance and risk analysis, DL and interpretability, and security and critical infrastructure systems. Throughout, we highlight how TDA can extract informative features, function as a model component, and provide a conceptual lens for studying complex systems. However, the survey also emphasizes recurrent failure patterns: TDA performance is highly sensitive to filtration, embedding, and vectorization choices; aggressive simplification can dilute or remove informative topological signals; and integration into standard ML workflows still lacks uniform validation and reporting protocols. We conclude by outlining key challenges—including scalability, statistical foundations, interpretability, and compatibility with rapidly evolving artificial intelligence (AI) paradigms—and by identifying directions for future research. The survey also provides a unifying design perspective for TDA systems, highlighting methodological trade-offs and emerging research directions for integrating topology with modern ML. Full article

While classical clustering methods, particularly k-means, produce powerful and practical solutions based on metric distances between data points, they can be limited in complex, nonlinear, and structurally disordered datasets. This study proposes a hybrid topological–metric clustering framework, referred to as Hybrid-NHTSI, that integrates persistent homology-based structural information into the clustering update process. The method is based on the Topological Cluster Separation Index (TCSI), a persistent homology (PH)-based metric for topological separation. In addition to TCSI, the proposed framework uses the Normalized Topological Cluster Separation Index (NTCSI), the Hybrid Topological Clustering Index (HTCI), and the Normalized Hybrid Topological Separation Index (NHTSI) to evaluate clustering performance from both geometric and topological perspectives. In the proposed approach, while the topological separation between clusters is increased, intra-cluster geometric scattering is controlled by a regularization term. This formulation enables the extraction of clusters that are consistent not only topologically but also geometrically. The performance of the method was evaluated on synthetic circles-and-moons benchmark datasets under different noise and overlap levels, and on the UCI Human Activity Recognition real sensor dataset. The experimental results showed that DBSCAN achieved the strongest overall performance on the density-favorable synthetic benchmark, which is consistent with the nonconvex and density-separable structure of the data. However, Hybrid-NHTSI produced higher NTCSI, HTCI, and NHTSI values than classical metric/geometric baselines such as k-means, Spectral Clustering, and Agglomerative Clustering. Pairwise statistical comparisons based on NHTSI confirmed that these improvements were significant against several competing methods. In the real-data experiment, although Spectral Clustering achieved the highest ARI value, Hybrid-NHTSI obtained the highest NTCSI, HTCI, and NHTSI values and significantly outperformed all competing methods in terms of NHTSI. The findings demonstrate that considering both metric and topological information together, rather than relying solely on metric or topological information, provides a more structurally informed evaluation and optimization mechanism for complex clustering problems. Accordingly, the proposed method should not be interpreted as a universally superior clustering algorithm across all metrics, but rather as a topology-aware hybrid refinement framework that enriches metric-based clustering with persistent homology. Full article

Standard cryptocurrency transaction cost models assume flat geometry and assign execution cost as a proportional fee. This paper proposes GEODEX, a framework that models execution slippage as the geodesic arc length on the Fisher information manifold of a Markov-switching GARCH maximum-entropy model, augmented by a joint curvature–topological fragmentation alarm. The Curvature-Fragmentation Law (Proposition 2) is an analytically derived heuristic. Its empirical validity is confirmed across four crisis episodes. Ablation confirms that each geometric component contributes uniquely: removing the geodesic increases mean squared prediction error by 2.9%, removing topological data analysis by 2.1%, and removing curvature by 1.5%. On five cryptocurrency markets (BTC, ETH, XRP, LTC, and BCH), over 2253 daily observations, the framework achieves competitive prediction error and is the only single-signal model retained in the Model Confidence Set at $\alpha =0.10$ against eight benchmarks. A joint curvature–topological alarm fires a median of two days before price-based circuit breaker thresholds across four crisis episodes, including the Terra collapse (May 2022) and FTX bankruptcy (November 2022). Online inference requires under one second; full offline calibration requires approximately 28 h. The framework requires no additional data beyond the upstream estimation pipeline and supports SDG 10 (Reduced Inequalities) and SDG 16 (Strong Institutions) by enabling accessible geometric liquidity intelligence for regulators and smaller market participants. Full article

Many machine-learning tasks involve structured data whose geometry, local feature distributions, and global organization interact in ways that are not well captured by existing methods based on vectorization, graph metrics, or homological signatures. We introduce Fiber Bundle Learning (FBL), a topological framework that represents each data sample as a discrete fiber bundle and extracts a classification signature combining persistent homology, local feature geometry, and gluing structure. FBL builds a base space from the coarse geometry of each object, models local feature patches as fibers, and estimates transition maps between neighboring fibers to construct a discrete connection. From this representation, FBL computes a set of invariants: persistent homology of the base, fibers, and total space; holonomy obtained by transporting fiber states along cycles; curvature-like quantities measuring transition inconsistency; and discrete analogues of characteristic classes. These components are assembled into a fixed-length feature vector that can be used with any standard classifier. We show that FBL yields a signature with three desirable theoretical properties: stability under perturbations of geometry and local features, invariance under isometries and global fiber reparameterizations, and robustness to sampling noise. Our synthetic experiments show that FBL distinguishes twisted from untwisted bundles with identical homology, a distinction classical topological methods fail to capture. Additional tests quantify the system’s resistance to noise, its invariance to geometric transformations, and the contribution of each signature component. Taken together, our results indicate that representing data through fiber bundle structure may provide an effective tool for classifying complex, multi-level objects. Full article

The study of conformational spaces and potential energy surface (PES) functions is fundamental for understanding the structural and dynamical properties of molecules with one or more rotational degrees of freedom. In this work, the topological characteristics of conformational spaces and PES functions are investigated for a set of molecules including ethane, butane, and butadiene, which possess one rotational degree of freedom, as well as n-pentane with two rotational degrees of freedom. Sublevel-set persistent homology was applied to the potential energy functions in order to characterize the topology of the associated energy landscapes. This approach allows for the identification of topological changes during the sublevel filtration process, which can be associated with the presence of critical points in the energy landscape, including minima (index 0), transition states (index-1), and maxima (index-2). Furthermore, the method provides information about the global connectivity and structural organization of the conformational landscape. The results show that sublevel-set persistent homology successfully reproduces the energy hierarchy and connectivity between molecular conformers, providing a coherent topological description of the molecular energy landscape. These findings demonstrate that persistent homology constitutes a useful framework for studying the topology of conformational spaces and potential energy surfaces in molecular systems. Full article

Angiostrongyliasis, the primary cause of eosinophilic meningitis, represents an emerging disease caused by Angiostrongylus cantonensis larvae, inadvertently transmitted to humans. The diagnosis of human angiostrongyliasis relies on epidemiological features, clinical symptoms, medical history, and laboratory findings, notably hyper eosinophilia in blood and cerebrospinal fluid. Consequently, accurate diagnosis is challenging and prone to confusion with other parasitic diseases. The quest for an early, rapid, and specific diagnostic test for angiostrongyliasis persists, driven by the imperative for enhanced test specificity. This study focused on mapping IgM epitopes on galectin-1 (Gal-1) and galectin-2 (Gal-2) proteins derived from A. cantonensis. The specificity of the epitopes was assessed using database homology analysis. After selecting specific epitopes, researchers chemically synthesized 12 individual multi-antigen peptides (MAPs4) and one chimeric polypeptide that is 65 amino acids long. The effectiveness of these synthesized peptides was subsequently evaluated using enzyme-linked immunoassay (ELISA). A total of twelve unique IgM epitopes were discovered; five were linked to Gal-1, while seven were linked to Gal-2. An ELISA-peptide method confirmed the twelve epitopes, and then the chimeric polypeptide was employed as an antigen to coat ELISA plates. This setup was evaluated with patients’ sera to diagnose strongyloidiasis in vitro. This study provides a comprehensive representation of the IgM epitopes of Gal-1 and Gal-2 from A. cantonensis. ELISA data utilizing the chimeric polypeptide demonstrate that the selected sequences hold promise for the development of a specific immunological assay tailored for the acute diagnosis of angiostrongyliasis infections. Full article

In this study, a layer-wise point cloud representation of CT scan images is proposed, from which persistence diagrams are constructed and persistent entropy is computed as a compact topological feature for three-class lung tumor classification. Two parallel approaches are investigated: the direct computation of persistence diagrams from CT images, and computation from subsampled point clouds derived from image intensity layers. The proposed method is evaluated on the publicly available IQ-OTH/NCCD lung cancer dataset, comprising 1097 CT scan images from 110 individuals, annotated by expert oncologists and radiologists. Classification is performed using K-Nearest Neighbors (KNN), Random Forest, Support Vector Machine, and eXtreme Gradient Boosting, and compared against Convolutional Neural Network (CNN) and traditional image feature-based methods. The persistent entropy approach applied to layer-wise subsampled point clouds achieves 97.67% accuracy, a Precision–Recall AUC of 96.63%, and a ROC-AUC of 99.46% using KNN, outperforming direct image-based analysis (95.91%) and achieving comparable accuracy to the CNN method (97.21%) with a computational speedup of approximately $478\times$. These results demonstrate that persistent homology applied to subsampled point clouds provides an accurate, mathematically interpretable, and computationally efficient alternative to deep learning for lung tumor classification. Full article

Vibrio splendidus is an important opportunistic pathogen that causes diseases in aquatic animals, and its persisters increase the difficulty of aquaculture disease control. The stringent response is a central pathway in bacteria for coping with environmental stress, and the signaling molecule (p)ppGpp, synthesized under the regulation of RelA/SpoT homologs, is closely associated with persister formation and virulence modulation. However, the regulatory mechanisms linking the stringent response to persister formation and virulence in V. splendidus remain unclear. In this study, the core gene deletion strains ΔrelA and ΔrelAΔspoT were constructed via homologous recombination. Combined with D₂O single-cell Raman spectroscopy, transcriptomics, and phenotypic assays, we systematically characterized the biological effects of stringent response inactivation. The results showed that the loss of relA and spoT significantly reduced persister formation and key virulence traits while enhancing biofilm formation. Single-cell Raman spectroscopy analysis indicated that persisters remained metabolically active, accompanied by changes in different cellular components. Transcriptome analysis revealed that the absence of stringent response affected multiple pathways, including ribosomal function, energy metabolism, two-component systems, and quorum sensing. Additionally, the sigma factor RpoS may potentially exert a compensatory function in ΔrelAΔspoT strain, but this requires further validation. In conclusion, the stringent response positively regulates persister formation and virulence in V. splendidus, despite the existence of complex regulatory mechanisms. This study provides a theoretical basis for the development of anti-infective strategies targeting stringent response in aquatic pathogens. Full article

Traffic congestion poses a significant challenge in urban environments. The use of digital techniques has emerged as a pivotal trend, as it offers substantial safety to and mitigates stress and frustration for road users. The purpose of this survey was to explore the current approaches and digital techniques for managing traffic congestion. We address this through a systematic literature review (SLR) approach by adopting PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. We began by exploring the key techniques of topological data analysis (TDA), machine learning (ML) and deep learning (DL) for modeling urban traffic prediction. We evaluated the robustness of the topological data analysis technique (Persistent Homology (PH)) against deep learning frameworks (Graph Convolutional Neural Networks (GCNNs)). We found that each framework has its own strengths and weaknesses, and neither of the frameworks independently provides a complete solution. PH may offer richer structural insights and robustness to noise but may struggle with direct predictive implementation, while deep learning models do better at extracting dynamic predictive patterns but are assumed to lack interpretability and generalizability. Therefore, the integration of multiple techniques, either PH with stacking ensemble methods or deep learning with stacking ensemble methods, can improve prediction and generalization of the model while at the same time reducing over-reliance on local graph assumptions. Future research should focus not only on performance metrics or methods but also on explainability, transferability, adaptability across heterogeneous road environments and computational cost. Full article

We propose a topological data analysis framework for the study of damage evolution in anisotropic composite materials based on scalar filtrations defined on cubical complexes. Two complementary anisotropic filtrations are constructed from the structure tensor: a fibre-oriented filtration f1, capturing directional coherence, and a crack-oriented filtration f2, sensitive to isotropic and weakly oriented structures. Zero-dimensional persistent homology is analysed through merge trees built from the superlevel-set filtration via the transformation $g=1-f$, providing a hierarchical representation of connected components. Higher-order connectivity is described using skeleton-based Reeb-like graphs. From these constructions, we derive spatial and global descriptors, including a topological danger map and a Topological Damage Complexity Index (TDCI) based on one-dimensional persistent homology. The behaviour of the TDCI is examined with respect to variations in its parameters and to image perturbations, showing consistent trends across the considered configurations. The results highlight complementary structural behaviours captured by the two filtrations and show a coherent correspondence with observed patterns. Overall, the proposed framework provides a mathematically grounded description of structural organisation. It is intended as an exploratory approach, and further work is needed to clarify its relationship with the underlying physical damage mechanisms. Full article

Chronic inhalation exposure to nanoplastics, specifically polyethylene terephthalate (PET) nanoplastics (PET-NPLs) is an emerging health concern, yet the long-term consequences for genomic stability and DNA damage response (DDR) capacity in bronchial epithelial cells remain poorly characterized. For this study, human bronchial epithelial BEAS-2B cells were continuously exposed to PET-NPLs for over 20 weeks, after which elevated basal DNA genotoxic damage was observed, as assessed by the alkaline comet assay. In addition, a broad transcriptional suppression of the DDR, with 27 of 84 profiled genes involved in DDR showing reduced expression relative to passage-matched control was observed. The suppressed genes span ATM/ATR checkpoint signaling, homologous recombination (HR), base excision repair (BER), nucleotide excision repair (NER), and apoptotic pathways. To determine whether chronic PET-NPL exposure altered susceptibility to acute genotoxic challenge in a damage-type-specific manner, cells were treated with methyl methanesulfonate (MMS), ultraviolet-C (UV-C) radiation, or bleomycin. While MMS and UV-C induced comparable levels of DNA damage in control and PET-exposed cells, bleomycin produced significantly greater damage in PET-exposed cells, indicating selective sensitization to doble-strand breaks (DSB)-type and oxidative genotoxic insults. Transcriptional profiling during bleomycin challenge identified 18 DDR genes with relatively higher expression in PET-exposed cells compared to passage-matched controls, encompassing HR, BER, ATM/ATR signaling, the Fanconi anemia pathway, and apoptosis. Furthermore, PET-exposed cells retained significantly higher residual DNA damage after 3 h of bleomycin challenge, indicating a persistent early repair deficit. Together, these findings suggest that chronic PET-NPL exposure specifically compromises the bronchial epithelial DDR, with potential implications for long-term genomic stability in respiratory epithelia subjected to nanoplastic inhalation. Full article

Nonlinear dynamical systems exhibit complex behaviors such as periodicity and chaos, which are traditionally analyzed using time-series data. However, these approaches often fail to capture the intrinsic geometric structure of the system dynamics represented in the phase space. In this study, we address this limitation by proposing a topological machine learning framework that leverages phase portrait images to classify dynamical regimes. The primary objective of this study is to investigate whether the topological features extracted from phase portraits can effectively distinguish between periodic and chaotic behaviors across different nonlinear systems. To achieve this, we employed the Topological Data Analysis (TDA) technique of cubical homology, which enables the extraction of topological descriptors, such as persistence diagrams and Betti curves. We used these features to train multiple machine learning (ML) classifiers, including XGBoost, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Gaussian Naïve Bayes (GNB), and Random Forest (RF). The experimental results across benchmark systems, including the Chua, Lorenz, Mathieu–Duffing, and erbium-doped fiber laser models, demonstrate that the proposed approach achieves high classification accuracy, with performance improving from approximately 93% under $H_0$ features to 99–100% under $H_1$ and combined feature representations. These findings highlight that topological features, particularly $H_1$, effectively capture the underlying geometric structure of dynamical systems. Overall, the proposed framework provides a robust, interpretable, and generalizable approach for phase portrait classification, with potential applications in nonlinear system analysis, pattern recognition, and early detection of chaotic transitions. Full article

Food and medicine homology (FMH) substances are increasingly utilized as nutritional and medicinal resources in sustainable livestock production. Their active ingredients include polysaccharides, flavonoids, and terpenes, which may positively affect livestock meat quality by maintaining gut microbiota homeostasis, enhancing intestinal barrier function, and facilitating nutrient absorption, as well as regulating key signaling pathways such as mechanistic target of rapamycin (mTOR), AMP-activated protein kinase (AMPK), and nuclear factor-κB (NF-κB). Notably, the meat quality improvement can also be indirectly achieved via the gut–muscle axis. Gut microbiota metabolites, including short-chain fatty acids (SCFAs), bile acids (BAs), and amino acid derivatives, modulate microbial homeostasis, intestinal barrier function, and nutrient absorption through the gut microbiota–metabolite axis, gut–immune axis, and nutrient absorption–signaling axis. These processes remotely regulate skeletal muscle metabolism, inflammation, and fiber type transformation, ultimately influencing meat tenderness, flavor, juiciness, and nutritional value. Despite their potential to reduce reliance on antibiotic growth promoters and enhance meat quality, multiple challenges persist, including complex component profiles, elusive mechanisms, undefined dose–effect relationships, inadequate standardization, insufficient safety evaluation and scarce direct trials on livestock meat quality endpoints. This review summarizes FMH substances that modulate the gut–muscle axis in meat quality regulation across different animal species and outlines their application prospects, aiming to facilitate antibiotic-free agriculture, the development of green functional feeds, and sustainable animal husbandry. Full article