We uncover a pronounced anisotropy in Majorana vortex topology arising from the interaction between vortex orientation and multiband topologies, exemplified by iron-based superconductors (FeSCs). This anisotropy manifests in two distinct vortex configurations: the z-vortex and x-vortex, oriented perpendicular and parallel to the Dirac axis (z-axis for FeSCs), respectively. The x-vortex exhibits a unique bifurcation, displaying two distinct topological phase diagrams. One is strikingly simple, comprising only trivial and topological superconducting phases, and remains resilient to multiband entanglement. The other mirrors the z-vortex's complex diagram, featuring alternating trivial, topological crystalline, and topological superconducting phases. The former is exclusive to the x-vortex and supports unpaired Majorana vortices across a wide parameter range, even in the presence of normal-state Dirac nodes. Notably, uniaxial strain can modulate these x-vortex phases, enabling the x-vortex to support both stable Majorana vortices and rich exotic physics in a controllable manner. Moreover, we propose that the x-vortex offers promising advantages for developing Majorana nanowire devices in FeSCs. Our findings introduce a novel paradigm in vortex topology within multiband superconducting systems, highlighting the x-vortex as a promising platform for exploring Majorana physics and advancing FeSC quantum devices.
Conventional structural design studies often prioritize mechanical metrics, yet lack a unified narrative that renders the aesthetic expression of form both quantifiable and verifiable. To address this gap, we develop a GAN-based framework for biomimetic topology fusion generation, leveraging Cycle-Consistent GANs (CycleGAN) to learn bidirectional mappings and morphological translations between two classes of natural prototypes under unpaired supervision: performance-oriented morphologies (e.g., dragonfly wing venation and leaf venation), which exhibit high structural efficiency but comparatively weak visual order, and aesthetics-oriented patterns (e.g., honeycomb cells and pinecone spirals), which display pronounced geometric regularity and proportional structure but limited load-bearing capacity. Through cross-domain translation and fusion, the model synthesizes hybrid topological textures that simultaneously encode cues of structural robustness and ordered geometric features. These synthesized morphologies are subsequently validated via flexural (bending) testing in terms of load-carrying capacity and energy absorption efficiency, and are objectively characterized by a multi-metric aesthetic quantification scheme-computed on binary, vectorized structural maps-covering symmetry, complexity, and order. Across multiple morphology-pair settings, the fusion-generated structures exhibit a more balanced overall profile in both mechanical response and aesthetic metrics, indicating effective synergy between engineering usability and visual expression. In addition, we provide an application example in conceptual form design for orthopedic exoskeletal products, illustrating the cross-domain potential of the proposed approach at the interface of engineering design and aesthetic design.
Dynamic State Tracking (DST) is pivotal for personalized recommender systems and user modeling, aiming to estimate users' evolving latent states from sequential interactions. However, existing deep sequence paradigms predominantly treat interaction entities, such as semantic tokens and items, as isolated deterministic vectors. This approach often overlooks latent structural dependencies among entities, including knowledge graph topologies, and remains limited in quantifying the epistemic uncertainty inherent in stochastic user behaviors caused by random interactions and aleatoric noise. To address these dual challenges, we propose Adaptive G-UKT (Adaptive Graph-Enhanced Uncertainty-aware Knowledge Tracing), a unified probabilistic framework for temporal sequence modeling. Unlike traditional point-estimation models, we map hidden user states into Gaussian distributions, enabling the simultaneous tracking of semantic activation levels and estimation confidence through diagonal covariance. To mitigate data sparsity, we design an Adaptive Graph Learner that autonomously infers latent semantic correlations from raw data, coupled with an Adaptive Gaussian-HGNN that propagates uncertainty information across the dynamically learned topology. Furthermore, we introduce a Wasserstein attention mechanism to perform distribution-aware sequence retrieval and an uncertainty-guided contrastive learning strategy to enhance model robustness against noisy interactions. Extensive experiments on four large-scale real-world sequential datasets, namely ASSISTments2009, Bridge2Algebra2006, Algebra2005, and NIPS34, demonstrate that Adaptive G-UKT achieves competitive performance against state-of-the-art baselines, showing particularly significant gains in sparse data regimes. Crucially, visualization analysis confirms the model's capacity to autonomously uncover intrinsic structural topologies, bridging the critical gap between high-precision deep sequence learning and interpretable knowledge graph reasoning.
Neural networks underlie complex brain information processing, yet the role of their single-neuron topology in governing computation and behavior remains unclear, particularly regarding how it shapes individual neuron function and activity evolution during learning. Using two-photon calcium imaging, we tracked functional connectivity in thousands of posterior parietal cortex neurons as monkeys learned sensorimotor associations across days. We identified small-world networks with densely connected hub neurons that dominated encoding key task variables, driving local dynamics and neural encoding evolution during the monkeys' task performance and learning. Dynamic transitions in hub/non-hub status captured how inter-neuronal interactions shaped neuronal encoding evolution during association formation. Modular structures supported specialized neuron ensembles, enabling segregated representations and interactions within local networks. Importantly, small-world network properties predicted behavioral performance, with global information processing efficiency increasing as learning progressed. These findings reveal how single-neuron-resolution brain networks, through small-world organization, orchestrate both global and modular neural computations within local network to mediate behavior and shape learning.
Phylogenetic trees are tree-like data structures commonly adopted to mathematically represent cancer clonal evolution. The information encoded by phylogenetic trees is important for clinical outcomes, but the automatic extraction of such information is still hard, also due to the fact that working directly with tree-like data structures is complex. This is especially true for machine learning tasks, where models are usually designed for vector data. We introduce CPhyT-GNN, a novel Deep Learning method to compute unsupervised embeddings of phylogenetic trees. The embeddings learnt by CPhyT-GNN are vectors that can be used for a variety of machine learning tasks. CPhyT-GNN is based on Graph Neural Networks, which allow to obtain representations that combine the information provided by the alterations present in the tumor and the topological information provided by the corresponding phylogenetic tree. Experiments with cancer data show that the embeddings learnt by our model are general-purpose and can be applied to different tasks, with results that improve the state-of-the-art. Data and code are available at the following link: https://github.com/VandinLab/CPhyT-GNN.
Computing topological invariants in two-dimensional quasicrystals and supermoiré matter is a remarkable open challenge due to the absence of translational symmetry and the colossal number of sites inherent to these systems. Here, we establish a method to compute local topological invariants of exceptionally large systems using tensor networks, enabling the computation of invariants for Hamiltonians with hundreds of millions of sites, several orders of magnitude above the capabilities of conventional methodologies. Our approach leverages a tensor network representation of the density matrix using a Chebyshev tensor network algorithm, enabling large-scale calculations of topological markers in quasicrystalline and moiré systems. We demonstrate our methodology with two-dimensional quasicrystals featuring C_{8} and C_{10} rotational symmetries and mosaics of Chern phases. Our Letter establishes a powerful method to compute topological phases in exceptionally large-scale topological systems, providing the required tool to rationalize generic super-moiré and quasicrystalline topological matter.
Cells regulate their functions through gene expression, driven by a complex interplay of transcription factors (TFs) and other regulatory mechanisms that together can be modeled as gene regulatory networks (GRNs). While the advent of single-cell sequencing has revolutionized our understanding of these networks, current GRNs inference methods rely predominantly on expression data alone, overlooking the sequence semantic context of target genes, and the intrinsic physicochemical properties of TFs. Consequently, the reconstructed networks are often riddled with false-positive connections, significantly compromising their reliability. To address these challenges, we propose CaHoT-GRN, a context-aware high-order topology learning framework for robust single-cell GRNs inference. First, we leverage pretrained biological large language models to extract deep semantic embeddings from gene and protein sequences. This allows the model to explore the potential TF-target binding affinity within a latent semantic space. Second, to model cooperative regulatory mechanisms and capture high-order gene interactions, we construct a heterogeneous information network (HIN) via meta-path generation constrained by protein-protein interactions. Furthermore, we propose a similarity co-attention module to model the topological consistency between the prior GRNs and the HIN, thereby capturing long-range associations among genes. On single-cell transcriptomic datasets across four types of networks, CaHoT-GRN yielded an average AUC of 0.846 and an AUPR of 0.420, matching or outperforming existing methods. Moreover, downstream case studies, pathway analyses, and motif matching confirmed its high biological relevance. CaHoT-GRN is publicly available at https://github.com/ydkvictory/CaHoT-GRN.
Immune checkpoint blockade has transformed cancer therapy, achieving lasting responses in some patients, yet most still encounter primary or acquired resistance. Recent evidence demonstrates that this resistance is driven not only by intrinsic cellular features but also by the spatial organization of the tumor microenvironment (TME), including physical barriers, localized immunosuppressive niches, and organized immune cell aggregates that collectively regulate anti-tumor immunity. This review synthesizes advances in Spatial AI, combining high-resolution spatial multi-omics with deep learning approaches, particularly graph neural networks (GNNs), to elucidate the topological mechanisms of immune evasion and inform therapeutic development. Technological platforms enabling spatial molecular mapping, tools for multi-modal alignment and normalization, and computational frameworks for graph-based TME representation are covered. We define spatial phenotypes associated with immune resistance, such as immune exclusion, dysfunctional inflamed regions, and maturation states of tertiary lymphoid structures, and demonstrate how Spatial AI generates interpretable topological biomarkers that surpass conventional assays. The discussion addresses translational pathways for spatial biomarker validation and highlights key obstacles, including data standardization, computational scalability, explainability, and regulatory approval. Ultimately, immune evasion is a topological challenge, and Spatial AI offers a robust computational solution to translate complex spatial data into actionable clinical strategies to overcome architectural resistance in cancer immunotherapy.
Optical links and knots have attracted growing attention owing to their exotic topologic features and promising applications in next-generation information transfer and storage. However, current protocols for optical topology realization rely on paraxial propagation of spatial modes, which inherently limits their three-dimensional topological structures to longitudinal space-filling. In this work, we propose and experimentally demonstrate a scheme for creating optical knots and links that are localized in space within a transverse plane of a paraxial field, as well as in time. These spatiotemporal topological structures arise from polychromatic wave fields with tightly coupled spatial and temporal degrees of freedom that can be realized in the form of superpositions of toroidal light vortices of opposite topological charges. The (2 + 1)-dimensional nature of a toroidal light vortex imparts spatiotemporally localized wave fields with nontrivial topological textures, encompassing both individual and nested links or knots configurations. Moreover, the resulting topological textures are localized on an ultrashort timescale, propagate at the group velocity of the wave packets and exhibit remarkable topological robustness during propagation as optical carriers. The nascent connection between spatiotemporally localized fields and topology offers exciting prospects for advancing space-time photonic topologies and exploring their potential applications in high-capacity informatics and communications.
Working memory impairments are a common late effect in survivors of childhood acute lymphoblastic leukaemia, yet the structural network substrates of these difficulties remain poorly defined. Existing connectomic studies often rely on whole-brain parcellations, overlooking working memory-associated circuitry and multiscale organization. We developed a multiscale structural connectivity framework to investigate working memory-associated networks using diffusion MRI and performed a cross-sectional study with 70 acute lymphoblastic leukaemia survivors and 70 age and sex matched healthy controls. Working memory-relevant regions were identified based on functional activation patterns, and structural connectomes were constructed at two spatial scales: a fine-scale 76-node network and a coarser 24-node network derived from spatially contiguous, architecturally and functionally coherent regional groupings, as defined in the multimodal parcellation atlas of Human Connectome Project. Graph theoretical metrics, clustering coefficient, Eigenvector centrality, local assortativity and participation coefficient were computed to assess local network topology. Group comparisons were conducted with false discovery rate correction for multiple comparisons. Compared to healthy controls, survivors exhibited marked topological shifts. Specifically, clustering and assortativity were increased in the caudate, putamen and thalamus but decreased in the frontoparietal cortex. In contrast, centrality and participation showed the opposite pattern, signalling subcortical segregation and cortical hyperintegration. These effects were consistent across both spatial scales. Additional findings included scale-specific effects unique to the fine scale, as well as heterogeneous fine-scale patterns that resolved into consistent regional changes at the coarse scale. All effects remained significant after false discovery rate correction, highlighting the robustness of the network reorganization. Our framework combining a targeted working memory network with multiscale connectomic analysis proves its worth by revealing structural changes of working memory circuitry in survivors compared to healthy controls. The results show a broad reorganization, with weakened cortical networks and strengthened subcortical circuits, possibly as a form of compensation. These insights sharpen our understanding of treatment-related structural network alterations and point to new targets for future studies of cognitive outcomes and rehabilitation.
Functional DNA modifications hold promise for nanoelectronics, yet achieving stable and conductive systems remains challenging. This work introduces thieno-expanded sulfur-substituted purine bases (tth-/ttz-G/A) and constructs Cu-modified base pairs by replacing protons in Watson-Crick regions with copper(I) (tthG3CuC, tthA2CuT, ttzG3CuC, ttzA2CuT, tthG3CuT, tthA2CuC, ttzG3CuT, and ttzA2CuC). It reveals a synergistic strategy combining thieno-expanded backbone modification with multicopper(I) coordination to overcome this challenge. Density functional theory (DFT) calculations reveal that the expanded framework provides a stable π-conjugated platform and intrinsically narrows the HOMO-LUMO gap, while multicopper substitution introduces strong σ-type Cu-N/O coordination bonds that dramatically enhance binding energies by an order of magnitude. Critically, copper coordination synergistically modulates the frontier orbitals, raising both the HOMO and LUMO levels, with a more pronounced HOMO upshift. This significantly lowers the ionization potential, narrows the energy gap, reduces the electron affinity, and enhances hole transport capability while suppressing electron capture. Red-shifted absorption spectra and increased charge-transfer transitions confirm facilitated charge migration. In triple stacks, copper modification consistently reduces the energy gap, with the extent of narrowing depending on both base identity and sequence topology (crossover versus repeat). These findings establish multicopper-modified, thieno-expanded purine base pairs as promising theoretical candidates for DNA-based molecular wires and provide a rational guideline/design principles for designing functional nucleic acid nanomaterials through the orthogonal combination of scaffold expansion and metal coordination.
When confinement approaches the scale of hydration shells, water and ions can cease to behave as continuous media and instead assemble into discrete, cooperative motifs. Experiments using graphene and other atomically thin channels have revealed quantized conductance, inverted selectivity, and nonlinear ionic responses-signatures of transport governed by molecular geometry and interfacial correlations rather than continuum electrostatics. However, the microscopic principles linking local solvation structure to collective ionic motion remain poorly resolved. Here, molecular dynamics simulations of aqueous electrolytes confined within sub-nanometre graphene slits were performed to uncover the structural origins of angstrom-scale transport, from cation-anion pairing at the channel entrance to collective interfacial cluster diffusion inside the slits. At the entrance, ion-pair formation lowers the effective entry barrier by mitigating electrostatic repulsion from surface charges while reducing desolvation penalties through shared hydration shells. Once inside, ions reorganise into dynamic cation-anion clusters whose topology and size govern their cooperative diffusivity, giving rise to enhanced transport compared with bulk electrolytes. These results reveal a continuous mechanistic pathway-from entrance pairing and interfacial clustering, to in-slit collective motion-that bridges molecular solvation geometry with emergent transport behaviour. By tracing ionic mobility back to its structural determinants, this work could advance our understanding and engineering of ionic and molecular flows in angstrom-scale channels.
This paper presents an event-triggered control strategy that incorporates extended looped-functionals to enhance communication capacity efficiency in real-time systems. Among the various methods to guarantee stability in event-triggered mechanisms, this study focuses on a looped-functional approach and extends the conventional functionals to improve performance. Event-triggered control has been widely studied in various forms to improve communication efficiency and reduce computational load, and dynamic event-triggered control is often employed to prevent Zeno behavior and enhance performance. However, many of these studies have been analyzed only theoretically, with relatively few validated on actual hardware. To bridge the gap between theory and practice, this study experimentally verifies an advanced control topology that employs the extended looped-functional method with an aperiodic event-triggered technique. The conditions are derived as linear matrix inequalities to stabilize a linear system through aperiodic sampled-data control, and their effectiveness is demonstrated through numerical examples and experiments. The approach is experimentally validated on a rotary inverted pendulum system using the control gains obtained from the proposed conditions. Through this, it is intended to demonstrate that simulation-based studies can have a significant impact on real systems.
Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality worldwide, largely due to late diagnosis, molecular heterogeneity, and limited prognostic biomarkers. Aberrant protein phosphorylation plays a critical role in cancer progression by regulating DNA damage response, cell cycle control, and signaling pathways; however, the prognostic relevance of phosphorylation events in key DNA topology-related proteins remains incompletely understood. This study aimed to investigate the prognostic significance of phosphorylation of TOP1, TOP2A, TOP2B, and C1orf35 in HCC and to characterize their associated molecular features to identify potential diagnostic and therapeutic biomarkers. Publicly available HCC phosphoproteomic and proteomic datasets were analyzed to identify significantly upregulated phosphorylation sites of TOP1, TOP2A, TOP2B, and C1orf35. Integrated bioinformatics and machine learning approaches were applied, including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), protein-protein interaction (PPI) network analysis, drug-gene interaction analysis, and survival analyses (overall and disease-free survival). A total of 11,547 phosphorylation sites corresponding to 4043 phosphoproteins were quantified from 159 HCC patients. Phosphorylation of TOP1, TOP2A, TOP2B, and C1orf35 was significantly upregulated. Enriched pathways included DNA damage response, homologous recombination repair, cell cycle regulation, SUMOylation, and TP53 signaling. PPI analysis identified these proteins as highly interconnected hub nodes. Elevated expression was significantly associated with poor clinical outcomes. Phosphorylated TOP1, TOP2A, TOP2B, and C1orf35 are strongly associated with HCC progression and poor prognosis, highlighting their potential as prognostic biomarkers and therapeutic targets. These insights not only enhance our understanding of the complex molecular mechanisms underlying HCC but also offer promising avenues for the identification of novel therapeutic targets.
Moiré flat bands in rhombohedral multilayer graphene provide a platform for exploring interaction-driven topological phases, where a single isolated band often forms a Chern band. However, non-Abelian degenerate Chern bands with internal symmetries such as SU(N) have so far been realized only in highly engineered systems. Here, we show that a doubly degenerate non-Abelian Chern band with Chern number |C|=1 emerges spontaneously at filling ν=2 in rhombohedral three-, four-, and five-layer graphene, regardless of the presence of a hexagonal boron nitride substrate. Using self-consistent Hartree-Fock calculations, we map out phase diagrams as functions of displacement field and electronic periodicity and analytically demonstrate that the Fock term drives spontaneous symmetry breaking and generates non-Abelian Berry curvature. We further show that this non-Abelian topology is characterized by SU(2) gauge flux threading the noncontractible cycles of the Brillouin zone, leading to a global non-Abelian holonomy. Our findings unveil a new class of interaction-driven non-Abelian topological phases, distinct from quantum anomalous Hall and fractional Chern phases.
Native mass spectrometry is widely used to interrogate protein systems by gently transferring them into the gas phase, where they are thought to persist in a native-like structure. However, which aspects of the solution structure survive solvent removal remains unclear. Protein folding in solution is cooperative, largely due to the solvent-mediated hydrophobic effect, and its disappearance in vacuo is therefore expected to eliminate thermodynamic cooperativity, leading to independent loss of native contacts and structurally heterogeneous ensembles. Here, we determine which features of the canonical protein holo-myoglobin persist in the solvent-free state by delineating its gas-phase denaturation and unfolding pathways. Integrating tandem-trapped ion mobility spectrometry/tandem-mass spectrometry (Tandem-TIMS) with molecular dynamics simulations reveals that native-like holo-myoglobin largely retains its α-helical fold, compact core architecture, and noncovalent heme coordination in the absence of solvent. Energetic activation in vacuo indicates a hierarchical unfolding mechanism: an initial denaturation within a compact shape, followed by global unfolding, and eventual heme loss. Notably, denaturation proceeds collectively across multiple helices and is largely insensitive to salt-bridge rearrangements, arguing against electrostatic interactions as the primary stabilizing factor of native-like protein structures in the absence of solvent. Instead, our findings propose steric and topological constraints as key contributors to the kinetic barriers that confer metastability on solution-like protein structures in vacuo. These findings bear directly on the interpretation of native ion mobility/mass spectrometry, which assumes that kinetically trapped solvent-free protein structures reflect aspects of their solution-phase topology. By clarifying the physical origin of this metastability, our results provide a structural framework for interpreting native mass spectrometry measurements.
Psoriasis vulgaris (PV), a chronic immune-mediated inflammatory dermatosis, is associated with a significant burden of systemic comorbidities. Traditional comorbidity research methods struggle to reveal its complex interconnectedness. Based on large-scale retrospective cohort data, we constructed a PV comorbidity network using the Ising model from statistical physics. Weighted network centrality analysis was used to identify core and hub nodes and elucidate shared molecular mechanisms at the multiomics level (nontargeted proteomics and lipid peroxidation metabolomics). Finally, the impact of IL-17A inhibition (IL-17Ai) on PV and atherosclerosis (assessed by carotid Doppler color ultrasound) was evaluated using a prospective intervention study. The Ising model identified atherosclerosis- coronary heart disease (CHD) as the core comorbidity (degree centrality >10), with pulmonary nodules, hypertension, and fatty liver serving as key hub nodes (betweenness centrality >60). Multiomics analysis revealed a core molecular mechanism in PV, involving immune inflammation, oxidative stress, lipid metabolism disorder, and coagulation abnormalities, where the oxidative stress molecule GPX3 acts as a critical hub. Following IL-17Ai intervention, both skin lesions and early atherosclerosis markers significantly improved, accompanied by downregulation of the proinflammatory peripheral blood factor S100A9 and upregulation of anti-inflammatory lipid peroxidation metabolites (e.g., 17(R)-RVD1). This study systematically revealed the modular hierarchical structure of PV comorbidities at the network topology and molecular mechanism levels, confirming the central role of the IL-17 signaling pathway in driving the comorbidity network. This conclusion was further clinically validated by IL-17Ai intervention outcomes. This research provides theoretical and clinical evidence for early identification, prioritized management, and "one drug, multiple targets" therapeutic strategies for treating PV comorbidities.
Lagerstroemia indica L. (Lythraceae) constitutes a globally significant ornamental resource, valued for its prolonged anthesis and vivid floral displays. Elucidating the morphological mechanisms of floral organogenesis is a prerequisite for molecular breeding programs aiming to modify floral architecture (e.g., via stamen petaloidy). While foundational ontogenetic studies have been conducted in L. indica, our understanding of its developmental landscape remains incomplete. Specifically, critical gaps regarding the precise 3-dimensional topology of organ initiation and the dynamic morphological shifts during early meristem stages have yet to be fully resolved. To bridge this gap, this study presents a detailed reconstruction of floral ontogeny in L. indica using a combinatorial approach of Scanning Electron Microscopy (SEM) and paraffin histology. We delineate a precise developmental sequence commencing with sepal initiation and enclosure, followed by the synchronous differentiation of the androecium and gynoecium. A defining feature of the Lythraceae-the delayed initiation (retardation) of petal primordia-was confirmed, with petals emerging distinctively later than reproductive organs. Crucially, our observations characterize the proliferation pattern of the inner androecium: unlike the solitary outer stamens, the inner stamen primordia appear to undergo sequential multiplication from a single initial unit in a characteristic 'zigzag' (alternate) pattern. This process results in the formation of fascicled stamen clusters, providing structural evidence consistent with the mechanism of secondary polyandry (dédoublement) in this species. These SEM-based findings offer a detailed three-dimensional interpretation of floral differentiation in L. indica. Notably, this approach enabled us to propose a "zigzag proliferation" model characterizing the development of the inner androecium. Together, these results provide a morphological basis for future investigations into the genetic regulation of floral diversity and the phylogeny of Lagerstroemia.
Eukaryotic mRNAs typically encode a single functional polypeptide, a principle challenged by the discovery of widespread non-canonical peptide-coding ORFs within 5'UTRs. However, their functional significance at the protein level remains underexplored. Using a four-layered pipeline, we identify 14 human transcripts predominantly transcribed in polycistronic forms, each encoding two conserved proteins. Focusing on the SLC35A4 transcript, we show that its 5'UTR encodes a mitochondrial inner membrane-localized microprotein that we name STREMI (SLC35A4 stress response regulating MICOS interactor). Sharing topology and motifs with the MICOS core subunit MIC10, STREMI regulates mitochondrial cristae morphogenesis in mice and human cells. Additionally, the STREMI-encoding uORF mediates stress-responsive translation of SLC35A4-a Golgi nucleotide sugar transporter-upregulating its translation during the integrated stress response. Evolutionary analyses indicate that these bicistronic transcripts likely arose through transcriptional readthrough following retroposition. We propose a mechanism of "gene symbiosis" that enables functional partitioning and coordinated translation of protein pairs from bicistronic transcripts.
Quantum chemical mapping of weak interaction networks in N2O4/HNO3/H2O and HNO3/N2O4 propellant systems is reported. Electrostatic potential analysis identifies HNO3 as the dominant hydrogen bond donor (+64.28 kcal/mol), forming the strongest complex with H2O (-9.45 kcal/mol). In the ternary HNO3···N2O4···H2O cluster, water acts as a polarization catalyst, inducing a cooperative stabilization of 1.99 kcal/mol by enhancing the HNO3 donor ability. Most significantly, in water-depleted 2HNO3···N2O4 clusters, a specific weak interaction topology prefigures a concerted double proton transfer pathway (ΔE‡ = +32.1 kcal/mol), forming [HNO2···NO2+]···NO3- ion pairs. This finding provides a new theoretical hypothesis for the source of ions beyond the dissociation of HNO3 in future studies of corrosion origin. Solvation models further confirm the persistence of these weak interaction networks and the feasibility of the proposed proton transfer pathway in the liquid N2O4 environment.