Search Results (1,109)

Seborrheic alopecia (SA) is one of the most common forms of hair loss with a complex pathogenesis involving multiple etiological factors. Although the scalp microbiome has been implicated in various scalp disorders, its specific role in the development and progression of SA remains incompletely understood. To characterize the scalp microbiome in SA, we performed high-throughput sequencing of the 16S rRNA gene and ITS region on scalp samples from 41 Chinese SA participants before and after a 12-week intervention with a shampoo containing herbal extracts (ginger root, Polygonum multiflorum, and Platycladus orientalis leaf) and 29 healthy controls. The untreated SA group exhibited significant microbial dysbiosis compared to the healthy controls, characterized by reduced bacterial and fungal alpha diversity and increased relative abundances of Staphylococcus, Cutibacterium, and Malassezia. LEfSe analysis confirmed the significant enrichment of these three genera. Correlation network analysis revealed a substantial restructuring of microbial interactions in the untreated SA group: Staphylococcus and Malassezia lost all positive correlations with other genera, whereas Cutibacterium displayed relatively stable topological relationships. Following the 12-week intervention, the treated SA group showed significant clinical improvement (reduced hair loss and scalp sebum content), along with a restoration of microbial diversity to levels comparable to the healthy group and a normalization of the abundances of Staphylococcus and Malassezia. Our study confirms the critical role of scalp microecological dysbiosis in SA pathogenesis and identifies Staphylococcus and Malassezia as key taxa strongly associated with this dysbiosis. These findings provide a theoretical foundation for developing microbiome-targeted strategies for SA treatment and support the use of multi-targeted, plant-based interventions to restore microbial homeostasis and promote hair growth. Full article

Early mild cognitive impairment (EMCI) is the earliest intervenable stage of Alzheimer’s disease (AD). Although graph neural networks (GNNs) have begun to exploit brain network topology, traditional fMRI-based diagnostic methods often neglect these structural patterns by relying on vectorized features. Furthermore, existing GNNs frequently disregard inter-regional functional heterogeneity and group-level discriminative patterns, leading to limited accuracy and biomarker interpretability. To address these challenges, we propose HF-BrainGNN, an end-to-end hierarchical graph learning framework for EMCI identification. Our method introduces a functional affinity region convolution (FAR-Conv) layer to learn region-adaptive kernels, a Differential Focus Pooling (DF-Pool) module to identify disease-salient brain regions by maximizing inter-group distinctiveness, and a hierarchical integration classifier (HIC) to fuse multi-level graph representations. The framework is optimized using classification, focus separation, and consistency regularization losses. Experiments on the ADNI dataset (104 EMCI, 114 Cognitively Normal) show that HF-BrainGNN achieves 86.78% accuracy, outperforming the best baseline (Hi-GCN) by 4.64%. Furthermore, the automatically identified regions, such as the bilateral hippocampus and default mode network hubs, align with established EMCI biomarkers. Ultimately, HF-BrainGNN provides an efficient, interpretable artificial intelligence tool for precise brain network characterization and early AD intervention. Full article

The Lovozero and Khibiny alkaline massifs (Kola Peninsula, Russian Arctic) are the prominent sources of REE minerals, with the Lovozero loparite deposit being the only currently active REE mine in Russia. A new ashcroftine-related mineral phase KA with the idealized chemical formula K₆(Na₄Ca)(Y₈Ca₃Mn)[Si₂₈O₆₈(OH)₂](CO₃)₈F₂·9H₂O was found in the Khibiny alkaline massif. Its empirical formula determined by electron microprobe analysis is Na_4.14K_6.11Ca_3.89Mn_0.59Y_6.10Ce_0.08 Gd_0.32Tb_0.15Dy_0.78Ho_0.19Er_0.35Tm_0.15Yb_0.12Lu_0.06Si₂₈C₈O_93.02F_2.08·9H₂O. The crystal structure was determined and refined by means of single-crystal X-ray diffraction analysis. The KA phase is tetragonal, I4/mmm, a = 24.1661(3), c = 17.5914(4) Å, V = 10,273.4(3) ų. The crystal structure contains two Y sites. The Y1 site is [8]-coordinated and hosts more heavy REEs, whereas the Y2 site is predominantly [7]-coordinated and accumulates lighter REEs and Mn. The crystal structure is based upon the [Si₂₈X₇₀] nanotubes (X = O,OH) elongated along the c-axis and composed of corner-sharing SiX₄ tetrahedra. The external diameter of the tubules is equal to ~19.54 Å, i.e., slightly less than 2 nm. The silicate nanotubes are running parallel to the c-axis and centered along the (00z) and (½½z) directions. The tubules are linked by walls of YO_n polyhedra that also involve triangular CO₃ groups. The K⁺, Na⁺, and Ca²⁺ cations, as well as H₂O molecules, are located either inside or outside the tubules. The crystal-chemical formula of the KA phase can be written as {K_6.14Na_4.30Ca_0.81}[Y_5.88Ca_3.12Dy_0.88Mn²⁺_0.60Gd_0.32 Ho_0.24Er_0.24Tb_0.16Tm_0.16Er_0.12Yb_0.12Ce_0.08Lu_0.08](Mn³⁺_0.09) [Si₂₈O_68.36(OH)_1.65](CO₃)₈F₂·8.97H₂O, which agrees well with the idealized formula. According to the information-based complexity analysis, the KA phase has a very complex structure and belongs to less than 3.5% of the very complex minerals known today. The presence of silicate tubules is the key reason for the exceptional structural complexity of the phase. It is impossible to establish exact relations between the KA phase and ashcroftine-(Y) on the basis of the currently available data, since the last chemical analysis of the latter mineral was done in 1924. Therefore, the mineralogical identity of ashcroftine-(Y) is currently an unresolved problem. The silicate tubule in the KA phase is topologically related to the Linde zeolite A (the LTA zeolite framework) and can be produced from the latter by a series of topological operations. The KA phase forms a homological row with caysichite-(Y) and miyawakiite-(Y), along which the Si content is increasing, and silicate chains in caysichite-(Y) transform into silicate tubules in miyawakiite-(Y) and into silicate nanotubules in the KA phase. Indeed, the M:Si:C ratio (where M = Y, REEs, Ca, Mn, Fe) changes from 1:1:0.75 for caysichite-(Y) through 0.75:1:0.5 for miyawakiite-(Y) to 0.43:1:0.29 for ashcroftine-(Y) (and KA). The increasing role of silica along the row results in the formation of zeolite-derived porous one-dimensional units. The KA phase possesses two important crystal chemical properties that distinguish it from other minerals known to date: it hosts a variety of REEs and is based upon nanoscale zeolite-like silicate units. The KA phase, ashcroftine-(Y), caysichite-(Y), and miyawakiite-(Y) have never been prepared under laboratory conditions. The mineralogical occurrence of the KA phase in the Khibiny massif points out to its secondary origin, i.e., its formation under relatively soft, low-temperature hydrothermal conditions. Thus, the discovery of the KA phase in nature may provide important hints toward its synthesis in the laboratory by means of a soft-chemistry approach. Full article

Chronic low-grade inflammation is a key feature of obesity-associated dysglycemia, yet substantial heterogeneity exists in inflammatory responses among individuals with normoglycemia, prediabetes, and type 2 diabetes mellitus (T2DM). Whether circulating inflammatory protein profiles define distinct patient phenotypes beyond conventional glycemic classification remains incompletely understood. In this cross-sectional analysis of 142 individuals with severe obesity, plasma inflammatory proteins were quantified using Olink proximity extension assay technology. Subjects were stratified by glycemic status (noDM, normoglycemia; PreDM, prediabetes and T2DM) while maintaining comparable distributions of metabolic dysfunction-associated steatotic liver disease. Differential expression analyses were performed across glycemic groups, and unsupervised topological data analysis (TDA) was applied to identify inflammatory protein-based patient subgroups. Several inflammatory proteins were significantly upregulated in T2DM and PreDM compared with noDM, with interleukin-8 (IL-8), Fms-relatedlike tyrosine kinase 3 ligand (Flt3L), and CUB domain containing protein (CDCP1) showing the largest significant differences. NPX distributions of these proteins exhibited gradual increases across glycemic stages with substantial inter-individual variability. TDA identified seven clusters defined by distinct inflammatory protein signatures. One cluster was enriched for individuals with T2DM and characterized by coordinated upregulation of IL-8, Flt3L, CDCP1, and additional immune- and cytokine-related proteins, whereas other clusters displayed alternative inflammatory profiles that were not explained by glycemic status alone. Inflammatory proteomic profiling in severe obesity reveals both glycemia-associated protein changes and distinct inflammatory phenotypes that transcend conventional clinical classification. Integration of differential expression analysis with TDA highlights heterogeneity in inflammatory states, supporting a hypothesis-generating framework for future studies aimed at validating these proteomic patterns and clarifying their longitudinal relevance in obesity-related dysglycemia. Full article

Bacterial and fungal airway communities play a critical role in allergic respiratory diseases, yet they are often studied independently despite evidence of cross-kingdom ecological interactions. We investigated bacterial–fungal interactions across allergic rhinitis (AR), asthma (AS), allergic rhinitis with asthma comorbidity (ARAS), and healthy controls (HC) in a cohort of 286 participants (531 samples) using network analysis. Buccal and nasal samples were sequenced for 16S rRNA and ITS amplicons and networks constructed using sparse inverse covariance estimation for ecological association and statistical inference. Inferred bacterial–fungal connections comprised approximately one-third of the network edges. Taxonomic analyses of cross-kingdom-associated ASVs from the bacterial genera Dolosigranulum, Gamella, Haemophilus, Lawsonella, and Moraxella and the fungal genus Cladosporium revealed significant (p < 0.05) differences in mean relative abundance between the disease groups and healthy controls. Network topology analysis further identified distinct high-weight and high-degree microbial hubs, predominantly comprising fungal ASVs (Aleurina, Cladosporium, Malassezia, and Vishniacozyma) acting as putative keystone taxa and with disease-specific patterns. Together, these findings demonstrate that allergic airway diseases in this cohort are characterized by altered cross-kingdom network structure and disease-specific reorganization of microbial interaction patterns; this highlights the importance of integrated bacteriome–mycobiome analyses in understanding airway dysbiosis. Full article

Meaningful partitioning of 2D binary shapes remains a challenging problem in shape analysis because many existing methods rely mainly on local geometric rules or skeleton simplification, which often struggle to separate the main body of a shape from its protruding parts in a perceptually meaningful way. This limitation becomes more evident in shapes with thin limbs, branching structures, or irregular extensions, where preserving topology while achieving human-consistent decomposition is difficult. We present a fully automatic framework for the hierarchical partitioning of 2D binary shapes into semantically meaningful core bodies and protruding limbs (tails). The pipeline begins by generating candidate structural lines through multi-directional centroid tracking along horizontal, vertical, and diagonal (±45°) bands. Three direction-specific Sugeno fuzzy controllers first evaluate these lines based on normalized length, angular alignment, and minimum distance to the boundary. A second pair of fuzzy systems then classifies segments as either tails or core parts using thickness statistics derived from the distance transform. For ambiguous merged tail groups, iterative midpoint splitting is applied until stable labeling is achieved. High-curvature boundary corners are then detected via signed turning-angle analysis, and candidate cutting rays are assessed through exact region splitting, tail area measurement, and label purity analysis. An adaptive third-stage fuzzy controller ranks these candidates according to cut length, purity, and area. The highest-scoring non-overlapping cuts are executed iteratively, progressively peeling peripheral parts while preserving the overall topology and symmetry of the shape. The proposed framework is evaluated on a targeted subset of 32 categories from the 2D Shape Structure Dataset Results on this evaluated subset indicate that the method produces coherent and topologically consistent partitions, with competitive agreement with the available human-annotated references. This training-free framework provides an interpretable tool for 2D shape analysis, with potential applications in object recognition, computer animation, and symmetry studies. Full article

Series-connected lithium-ion battery packs are widely used in electric vehicles (EVs). However, inevitable inconsistency among cells can cause charge imbalance, accelerated aging, and reduced system safety. To improve the consistency of series-connected battery packs under complex EV operating conditions, this study proposes a hierarchical active balancing system. Bidirectional Buck–Boost converters are employed for intra-group balancing, and distributed flyback converters are used for inter-group balancing. A multi-stage coordinated balancing control strategy is further developed to reduce control complexity and improve balancing efficiency. A 16-cell series-connected battery pack model is established in MATLAB R2024a/Simulink and evaluated under resting, charging, and discharging conditions. The results show that, compared with the conventional single-layer Buck–Boost balancing topology, the proposed method reduces the balancing time by 58.09%, 57.97%, and 58.06%, respectively. These results indicate that the proposed system can effectively improve the consistency and balancing performance of series-connected battery packs, providing a scalable solution for EV battery management systems. Full article

We consider Banach spaces ${\ell }_p\left(\mathcal{C}\right),$ $1\le p<\infty ,$ where the index set $\mathcal{C}$ is the classical Cantor set and study various groups of symmetries of ${\ell }_p\left(\mathcal{C}\right),$ associated with the binary representation of $\mathcal{C}.$ The main purpose of the paper is the investigation of polynomials on ${\ell }_p\left(\mathcal{C}\right)$ that are symmetric (i.e., invariant) with respect to the constructed groups $G.$ We are interested in finding systems of generators of algebras of G-symmetric polynomials for different groups G and we discuss possible applications of G-symmetric polynomials to highly composite physical systems. The generators are useful for descriptions of spectra of algebras of G-symmetric analytic functions on ${\ell }_p\left(\mathcal{C}\right),$ and for the construction of some nontrivial complex homomorphisms of these algebras. Finally, we establish the topological transitivity and hypercyclicity of some shift-like operators on ${\ell }_p\left(\mathcal{C}\right)$ and its subspaces, and translation operators on algebras of symmetric analytic functions on ${\ell }_p\left(\mathcal{C}\right).$ Full article

With advancements in Earth observation capabilities, the demand for large-scale mapping using remote sensing images has increased significantly. However, selecting an optimal image set for the area of interest (AOI) from a large collection of remote sensing images remains challenging. On the one hand, it is crucial to select images with minimal redundancy and low cloud cover to enhance production efficiency and the effective coverage of mapping products. On the other hand, adjacent selected images should transition naturally so that the resulting mapping products appear visually cohesive. Unfortunately, most existing remote sensing image selection algorithms focus only on the former, with little attention to visual consistency. Meanwhile, images from the same swath inherently offer advantages in both redundancy reduction and visual consistency. However, a larger coverage area also carries the potential for greater variation in cloud cover, and cloud distribution within a swath can be highly complex. Managing the relationships among swaths, images, and cloud cover is also challenging. To address these issues, this paper proposes a novel image selection model, SwathSel. Candidate images are grouped through a composite grouping strategy based on swaths, cloud cover, and topological connectivity, thereby expanding the fundamental unit for image selection from individual scenes to connected image subsets. A dynamic adjustment mechanism is introduced to enhance grouping flexibility. Additionally, local and global swath consistency constraints are designed to strengthen visual consistency among images, and a subset evaluation module is used to comprehensively assess swath consistency, coverage, cloud cover, and metadata information. Through a greedy strategy combined with a rapid refinement technique, the final selected image set is obtained. Experiments were conducted on four datasets, and four quantitative metrics were designed to evaluate the visual consistency of the results. Compared with baseline models, SwathSel achieves lower redundancy and cloud cover while delivering superior visual consistency. Full article

Objective biomarkers for neurodevelopmental disorders remain an unmet clinical need. The electroretinogram (ERG), a non-invasive recording of the retinal response to light, has shown promise as a physiological marker for autism spectrum disorder (ASD) and attention deficit/hyperactivity disorder (ADHD), yet existing classification approaches based on time-domain and time–frequency features achieve limited accuracy in clinically relevant multi-group scenarios. This study introduces ERG-Graph, a novel graph signal processing (GSP) framework that transforms each ERG waveform into a weighted, undirected graph through amplitude quantization and temporal-adjacency connectivity. Nine topological and spectral features, including total load centrality, clique number, algebraic connectivity, and clustering coefficient, were extracted from each graph to characterize the structural dynamics of the signal. Using light-adapted ERG recordings from 278 participants (ASD = 77, ADHD = 43, ASD + ADHD = 21, Control = 137), we evaluated these features across binary, three-group, and four-group classification scenarios using seven machine learning classifiers with 10-fold subject-wise cross-validation. The proposed ERG-Graph features achieved balanced accuracies of 0.91 (ASD vs. control, males) and 0.88 (ADHD vs. control, females). Critically, fusing ERG-Graph with time-domain features yielded a balanced accuracy of 0.81 for three-group classification (ASD vs. ADHD vs. control), representing an 11-percentage-point improvement over the previous benchmark of 0.70. Statistical analysis confirmed significant topological differences between groups (Kruskal–Wallis, p < 0.001; Cliff’s delta: large effect sizes), and SHAP analysis revealed that graph-theoretic features dominated the top-ranked predictors. These results demonstrate that graph-based topological features capture discriminative information in the ERG waveform that is inaccessible to conventional signal analysis methods, advancing the development of objective biomarkers for neurodevelopmental disorder screening. Full article

To address the limitations of conventional LC resonant battery equalization circuits, including slow balancing speed under small voltage differences, limited scalability in multi-cell configurations, and the risk of over-equalization, this paper proposes a dual-layer LC resonant equalization topology integrated with a Boost-assisted mechanism and a state-of-charge (SOC)-based dual closed-loop current control strategy. In the proposed topology, a Boost converter is introduced to actively enhance the effective voltage difference between cells, thereby improving the equalization current amplitude and accelerating the balancing process. A switched-inductor structure is further adopted to enable scalable inter-group energy transfer in multi-cell battery systems. To improve control accuracy, SOC is selected as the balancing variable, and a dual closed-loop control framework is designed, where the outer loop regulates SOC deviation, and the inner loop controls the equalization current via proportional–integral (PI) controllers. A MATLAB/Simulink model is established to evaluate the proposed method under multiple operating conditions, including idle, charging, and discharging states. The results show that the proposed topology significantly reduces the equalization time compared with conventional LC resonant circuits and improves balancing speed by approximately 49% under the dual closed-loop control strategy. In addition, the system maintains stable performance across different operating conditions. It should be noted that this study focuses on topology design and control strategy validation through simulation. Due to the focus on topology validation and control mechanism analysis, this study is limited to simulation-based verification. Experimental implementation will be conducted in future work. Full article

In large-scale offshore wind power transmission systems, the offshore converter valves are typically based on the half-bridge Modular Multilevel Converter (MMC) topology. This design leads to considerable weight and high costs, presenting a critical bottleneck for the development of offshore wind power transmission. This paper proposes a hybrid topology consisting of paralleled MMCs connected in series with a Diode Rectifier Unit (DRU) to achieve lightweight offshore converter valves. The parallel configuration enhances the steady-state current-carrying capacity of the valve group to match the DRU valve group, and power balance among the paralleled MMCs is realized through an additional DC current-sharing control loop. A calculation method for the main circuit parameters of this lightweight topology is presented, along with a complete parameter calculation process. A design example based on actual engineering capacity is provided. PSCAD simulation results verify that the electrical quantities during steady-state operation of the hybrid topology are consistent with the designed parameters, confirming the correctness of the proposed parameter calculation method. Full article

The advent of fault-tolerant quantum computers poses an existential threat to the current blockchain technology, which relies on cryptographic primitives like elliptic-curve cryptography and SHA-256 hashing. This manuscript surveys the emerging field of quantum-secure blockchains, categorizing the main research directions into two paradigms. The first, post-quantum blockchain, seeks to replace classical cryptographic elements with quantum-resistant algorithms. The second, more radical approach aims to construct an intrinsically quantum blockchain, where the ledger’s security and state are encoded directly in quantum mechanical principles. We delve into three promising intrinsic schemes: those based on Greenberger–Horne–Zeilinger (GHZ) states and entanglement in time, those leveraging multi-time states and pseudo-density matrices, and hypergraph-based approaches. As the principal original contribution of this work, we present a comprehensive theoretical framework for a topological quantum blockchain based on non-Abelian anyons, providing the first detailed encoding scheme mapping classical blockchain data to braiding sequences. We further develop the connection to Chern–Simons theory, establishing a field-theoretic foundation where the blockchain’s history is encoded in Wilson loops, and its immutability follows from topological and gauge invariance. Extending this framework, we introduce a holographic AdS/CFT interpretation, revealing that the topological blockchain can be understood as a dual description of a black hole analog in anti-de Sitter space, where the blockchain’s history is encoded in the microstates of a black hole and linking braids between blocks correspond to wormholes. We provide a detailed physical and mathematical analysis of each scheme, comparing their security assumptions, resource requirements, and feasibility in the near and long terms. The topological approach, in particular, offers a compelling new path toward a blockchain with inherent fault tolerance, where the chain’s history is encoded in the topology of anyon worldlines, making it naturally resistant to decoherence and local tampering. Full article

Fairness and minority exclusion have emerged as the central concerns of contemporary Artificial Intelligence (AI) ethics. However, standard auditing and documentation practices often fail to capture harms affecting edge cases and marginalized groups. This article argues that this failure is structural: the act of “discretization”—converting continuous reality into discrete governance categories—inevitably produces a “residual.” Drawing on German Idealism (Kant, Fichte, Schelling) and continental philosophy (Dilthey, Gadamer, Merleau-Ponty), we reconceptualize residuals not as mere noise but as “surprising facts” that should trigger abductive hypothesis revision. We critique checklist-centered governance as a form of proceduralized auditing that can obscure these residuals. This article makes three key contributions: (i) a structural diagnosis of residual production using systems theory and topology; (ii) a philosophical reconstruction of abductive revision as a hermeneutic necessity; and (iii) an institutional design proposal—specifically, the Residual Ledger and Category Revision Protocols—to operationalize “Open Schema” governance. Full article