Molecular engineering has played a pivotal role in biomedical fields, driving significant advancements in gene therapy, disease diagnosis, and biosensing. However, nucleic acid molecular engineering faces various challenges including vast design spaces, complex structure-function relationships, lengthy application validation cycles, and inefficient optimization processes. Machine learning (ML), with its superior pattern recognition, multidimensional data integration, and automated optimization capabilities, offers a unique opportunity to construct predictive models of sequence-structure-function relationships, thereby enabling a paradigm shift from empirically driven to data-driven approaches. This review systematically surveys recent progress in ML applications across three major domains: nucleic acid structure construction, performance modulation, and application expansion. It also explores core challenges such as data quality, model interpretability, and experimental validation efficiency, along with potential resolution strategies. These insights are poised to propel nucleic acid molecular engineering from static structure prediction toward dynamic behavior simulation, and from single-molecule design to complex system engineering, guiding future directions in hybrid ML-quantum models and expanded applications to non-canonical nucleic acids for transformative innovation in biomedicine, environmental monitoring, and information technology.

Accurate short-term traffic flow prediction in complex urban road networks is of great significance for capacity organization and dispatch optimization in intelligent transportation systems. Using publicly available historical taxi trip records released by the New York City Taxi and Limousine Commission from January to June 2016, this study develops a spatiotemporal fusion framework for short-term traffic flow prediction. To address the nonlinearity, sparsity, and complex spatiotemporal dependencies of traffic flow sequences, the raw trajectory data are first cleaned, spatially gridded, and temporally discretized. Based on the spatial adjacency relationships among grid nodes, a graph structure is then constructed, and a serially coupled graph convolutional network and long short-term memory model is developed to capture spatial dependency features and temporal dynamic features, respectively. Experimental results on the New York City taxi dataset show that, compared with baseline models including the historical average model, long short-term memory network, graph convolutional network, and Transformer, the proposed model achieves better performance in terms of mean absolute error, root mean square error, and coefficient of determination. Furthermore, the SHAP (SHapley Additive exPlanations) method is employed to ANALYZE the differences in feature contributions across nodes in different functional zones from both temporal and spatial perspectives. The results indicate that the model exhibits heterogeneous temporal dependency depths and spatial aggregation patterns across different types of regions within the study area. In addition, regions with high feature contributions show a certain degree of spatial correspondence with the major traffic corridors in Manhattan, suggesting that the model is able to capture part of the spatiotemporal correlation structure of traffic flow in this dataset. Finally, the limitations of the proposed method in terms of static graph structure, response to extreme events, and integration of external factors are discussed. It should be noted that these findings are derived from New York City taxi data from the first half of 2016, and their generalizability to other cities, time periods, or traffic scenarios remains to be further validated.

Beyond static structure: high-throughput chemical cartography of dynamic cell wall assembly.

Abstract Atmospheric motions generate clouds and influence planetary climate systems. Venus, permanently shrouded by sulfuric acid clouds, provides a striking example. Unlike the photochemically produced upper clouds, the lower cloud layer is thought to form through condensation driven by poorly understood atmospheric dynamics. Observations by the Akatsuki spacecraft revealed a persistent, planetary‐scale massive cloud cover in the lower cloud region, moving westward with a sharply defined leading edge about 6,000 km long. This feature, unexpected from existing atmospheric models, raised fundamental questions about Venusian meteorology. Here, we show that the cloud front results from the largest hydraulic jump (bore) in the solar system. A planetary‐scale Kelvin wave becomes unstable due to a background static stability structure, generating an updraft along the front that triggers sulfuric acid condensation. Numerical simulations reproduce the observed morphology, including fine undulations. The westward momentum carried by the Kelvin wave is transferred to the mean flow through the hydraulic jump, thereby contributing to the maintenance of the planet's fast atmospheric superrotation. The resulting clouds modify the static stability, further facilitating a hydraulic jump. This previously unrecognized coupling between clouds and atmospheric dynamics represents a fundamental process likely to operate across planetary atmospheres.

AlloPath: A method for identifying protein allosteric pathway based on transfer entropy and hidden Markov model.

We investigate the classical antiferromagnetic Heisenberg model on the triangular lattice with up to third-nearest neighbor exchange couplings using the Nematic Bond Theory. This approach allows us to compute the free energy and the neutron scattering static structure factor at finite temperatures. We map out the phase diagram with a particular emphasis on finite-temperature phase transitions that break lattice-rotational symmetries, spiral spin liquids and the broad specific heat hump that is ubiquitous in the antiferromagnetic 120∘phase. We identify this specific heat hump as signaling the onset of an exponentially increasing correlation length. Further, we map out the temperature of the specific heat hump and the transition temperatures of the symmetry-breaking transitions throughout the exchange-coupling space. Along the lineJ3=J2/2, the Fourier-transformed exchange coupling exhibits a degenerate ring-like minimum, giving rise to spiral spin liquid behavior at intermediate temperatures. We investigate the structure factor of the spiral spin liquid as function ofJ2and identify the corresponding low-temperature order, which coincides with the single-q→spiral states of maximum spin-wave entropy along the degenerate ring.

Correlating the effects of hydration on the structure of polyelectrolytes and their conformational dynamics remains a long-standing fundamental challenge, with significant technological implications. Molecular-level insight, attained from atomistic molecular dynamics simulations of a fully sulfonated polystyrene polyelectrolyte with Na+ as a counterion in water, has enabled a direct correlation of the structure across atomistic to mesoscopic length scales with the corresponding dynamics. We find that as the polymer concentration c decreases, the chains extend, as observed by the shifts in the polyelectrolyte signature of the static structure factor, with qmax being the momentum transfer vector at the maximum intensity of this peak. The characteristic length scale that captures the system structure, lmax = 2π/qmax, scales with cα with α = -0.48. Concurrently, the Na+ counterion condensation decreases and the chains transition from coiled to more extended conformations. These structural changes lead to enhanced segmental and mesoscopic mobility, highlighting the coupled roles of hydration, conformation and dynamics in polyelectrolyte systems.

Static Life Cycle Assessment (LCA) meets its limit at capturing critical aspects in the climate impact of bio-based products (BBPs) and bio-energy, particularly biogenic carbon accounting and land-use change impacts. These issues depend strongly on spatial and temporal dynamics of feedstock systems. Dynamic LCA (DLCA) has been proposed as a potential improvement over conventional LCAs, however no comprehensive overview exists of its application to bio-based systems, including BBPs or bio-energy. We conducted a systematic literature review of 44 DLCA articles including a bio-related term (e.g., “biobased”, “bio-based”), yielding 83 case studies. Articles were screened by sector, feedstock, product types and dynamic inventory and impact assessment. We analysed how dynamics in biogenic carbon and land-use change (LUC) were addressed. We specifically reviewed parameters critical to biogenic carbon dynamics: sequestration models, time horizon, inventory period and modelling, and storage period. We compared SLCA and DLCA results, reflecting on the added insights of DLCA for different feedstocks (short- vs. long-rotation) and product types (short- vs. long-lived).We further examined whether and how LUC was modelled in the reviewed articles. Most DLCA case studies assess long-lived BBPs from forest biomass. Dynamic modelling is primarily used to represent biogenic carbon flows rather than time-varying foreground or background processes. Carbon uptake is modelled using approaches ranging from distribution functions and parametric models to detailed forest growth simulations, reflecting limited methodological consensus. Differences between static LCA and DLCA are not consistently reported and, when available, vary widely; DLCA often yields lower climate impacts, although results are context-dependent. Several interrelated choices contribute to this variability and the complexity of DLCA: assessment period and method, time horizon, growth vs. regrowth perspective and assumed carbon storage duration. Spatial impacts (e.g., soil organic carbon and biodiversity from LUC) are rarely included, although this reflects broader challenges in LCA. Integrating temporal (biogenic carbon) and spatial (land-use change) dynamics into LCA remains challenging due to its static structure. DLCA improves the representation of carbon sequestration and delayed emissions, but approaches vary widely. Its added value is context-dependent and introduces significant methodological complexity and choices. This study highlights key modelling choices influencing DLCA results and provides recommendations for their application, underscoring the importance of transparency to ensure results are interpretable and comparable.

Among pure metals, lithium is the one with the highest mass ratio between existing stable isotopes. In this study, we use classical molecular dynamics (MD) simulations in order to determine the sensitivity of different properties to the presence of these two isotopes. The isotopic effect occurs in two ways: the first one is the direct influence of the mass of the atom on its motion while the second one is the influence of the composition of its environment. For this purpose, we consider several samples which composition changes from 100%6Li to 100%7Li. We first focus on the static structure which, as could be expected, is not affected by the isotopic mass since the interatomic forces are independent on the isotopic species. This is not the case when considering the self-diffusion coefficients, the viscosity or the dynamic structure factor. The differences between behaviours of both species are highlighted and their influence on the measured properties is discussed. The comparison with available experimental data raises the question of the possible existence of quantum effects in lithium. Unfortunately, MD simulations cannot account for such effects. For both isotopic or quantum effects, this study brings out the need for further experimental studies.

We investigate composition-dependent orientational dynamics in hydrogen-bonded N-methylformamide (NMF)-water mixtures using time-resolved optical Kerr effect (OKE) spectroscopy, molecular dynamics (MD) simulations, and nuclear magnetic resonance (NMR) spectroscopy. Fits of the derivative of the stretched exponential to the OKE response yield average relaxation time, τβOKE, that rises sharply at a low NMF content and plateaus near equimolar compositions, while the stretch parameter, β, displays a clear maximum at xNMF of ∼0.5, indicating the most dynamically homogeneous regime. In contrast, Voronoi tessellation of MD configurations shows maximal local-density fluctuations near xNMF of ∼0.15, identifying the most structurally heterogeneous regime at a much lower NMF content. This offset establishes a robust decoupling between structural and dynamical heterogeneity. NMR chemical shifts and diffusion coefficients exhibit non-monotonic trends consistent with composition-dependent hydrogen-bond reorganization, while axis-resolved MD reorientation times corroborate the OKE trends. Our results demonstrate that orientational relaxation in NMF-water is governed not by viscosity or static structure alone but by a cooperative hydrogen-bond network whose dynamics evolve non-trivially with composition.

Intervertebral disc degeneration (IVDD) is the leading cause of chronic low back pain (LBP), driven by a pathological microenvironment marked by acidic pH, increased reactive oxygen species (ROS), and elevated matrix metalloproteinase (MMP) activity, which hinder tissue regeneration. Conventional hydrogels, while replicating the hydrophilic environment of the nucleus pulposus and enabling minimally invasive delivery, fail to dynamically adapt to the evolving pathological signals during degeneration due to their static structure. Smart responsive hydrogels overcome this limitation by integrating “sensing-response-output” functionality, achieved through molecular elements such as dynamic covalent/non-covalent bonds, enzyme-substrate peptides, and external field-responsive units, or gene circuits responsive to specific pathological cues, including pH changes, ROS levels, MMP concentrations, and mechanical stress. Recent developments highlight that these materials provide timely mechanical support (e.g., in situ modulus enhancement to mitigate fibrosis) and enable microenvironment-driven sequential therapies, including targeted delivery of anti-inflammatory/pro-regenerative factors, ROS scavenging, inhibition of enzymatic activity, immune microenvironment remodeling, and precise regulation of cell fate via endogenous stem cell recruitment/differentiation and ferroptosis suppression. Advanced fabrication techniques such as microfluidics, 3D bioprinting, and in situ self-assembly further enhance biomimetic structural and functional integration. Despite promising regenerative outcomes in animal models—such as achieving NP cell survival rates reaching 85%, a 3.3-fold increase in COL2 synthesis, and 87% recovery of disc height through spatiotemporally controlled release, ROS scavenging, and immune modulation—significant challenges remain for clinical translation. These include the need for long-term biosafety validation, the stability of delivery systems under physiological conditions, and their adaptability to the complex mechanical environment of the spine. This review systematically explores the design principles, response mechanisms, fabrication innovations, therapeutic applications, and translational challenges of smart responsive hydrogels for IVDD regeneration, providing a roadmap for future development.

Abstract. Transportation systems play a critical role in supporting economic and social sustainability, yet they are exposed to a range of vulnerabilities, including natural disasters, climate change, infrastructure failures, and human-induced disruptions. In this context, assessing the resilience and service continuity of transportation systems has emerged as a key research area. This research explores critical issues in transportation resilience, highlighting existing limitations and areas for improvement. While previous studies have addressed certain aspects of resilience (e.g., only topological indicators, single-mode network behavior, or static network structure), they often fall short of meeting the complex demands of contemporary urban transportation systems, indicating a clear need for new perspectives and approaches. To operationalize the proposed framework, a stepwise algorithm is developed that integrates heterogeneous data, monitors system dynamics, measures resilience metrics, predicts disruptions, and implements adaptive interventions. To further illustrate its applicability, the framework is demonstrated through two representative campus-scale scenarios addressing flood resilience management and air quality–driven mobility guidance. By conceptualizing transportation resilience through five key dimensions—integrate, observe, measure, predict and adapt, —this research proposes a comprehensive framework intended to advance both theoretical understanding and practical implementation in urban planning contexts.

Mapping allosteric rewiring in viral RNA: Sequence-encoded control of protein binding mechanisms.

Dynamic interplay between lateral diffusion and conformational states in a secondary transporter revealed by high-speed AFM.

STEGG: Structural TCR-pMHC ensemble generator and gallery.

• A static structural model captures finite-temperature dynamics in CsPbI₃. • Band alignment at CsPbI₃/TiO₂ interfaces depends strongly on surface termination. • PbI₂-terminated CsPbI₃ enables efficient electron transport into TiO₂. • CsI termination suppresses electron injection across the interface. • Findings provide design rules for selective charge transport in perovskite solar cells. Accurate descriptions of the electronic structure of halide perovskites at finite temperatures require structural models that capture the dynamical tilting of BX 6 octahedra. Here, we demonstrate that a single static structure—constructed by combining the low-temperature γ-phase geometry with the cubic lattice of the α-phase—reproduces results obtained from time-averaged trajectories. Using this approach, we investigate how free surfaces and interfaces modify the electronic structure of lead-halide perovskites. Focusing on CsPbI 3 as a model halide perovskite interfaced with the prototypical electron transport layer TiO 2 , we make quantitative predictions of band offsets and band bending for the two possible CsPbI 3 (001) surface terminations, PbI 2 and CsI. Our results reveal that TiO 2 acts as an efficient electron transport layer only in the case of PbI 2 termination, highlighting the termination-dependent selectivity of charge transport at halide perovskite/TiO 2 interfaces.

• Introduces CircularB-DfC, a design-stage framework for assessing and prioritising circularity in buildings • Integrates 35 circularity indicators and 20 enabling factors across design and value-chain processes • Provides Delphi-weighted scoring and a practitioner-oriented Excel tool for decision support • Demonstrates framework applicability through three contrasting regional and structural scenarios • Reveals how design strategies and enabling conditions jointly shape circular performance Circularity is increasingly recognised as a critical paradigm for sustainability in the built environment, yet existing efforts to assess it—whether focused on material flow analysis, design-for-disassembly strategies, durability metrics, or carbon accounting—remain fragmented and operate at different scales. Despite numerous indicator sets, the literature lacks an integrated framework that combines both technical design factors and the enabling organisational conditions required to support circular outcomes at the building level. This paper introduces CircularB-DfC (CircularB COST Action – Design for Circularity), a decision-Support Tool with a structured matrix for prioritising building design factors to enhance circular Material flows. The framework consolidates insights from a systematic literature review and a multi-stage expert engagement process, resulting in 35 technical indicators and 20 enabling factors . These are organised into four technical categories: Material Selection; Design for Disassembly; Embodied Energy and Carbon Footprint; Waste Minimisation, and one enabling category, Circular Construction Management, including Governance, Certification, Stakeholder Engagement, Digitalisation, and Socio-economic aspects. Indicators and enablers are aggregated into a Design Score and an Enabler Score to support early decision-making. The tool was applied to three illustrative scenarios: a reinforced-concrete industrial hall in the Western Balkans, a steel office building in Central Europe, and a timber residential project in East London. The steel scenario achieved the highest Design and Enabler Scores, the concrete scenario performed strongest in Waste Minimisation through prefabrication and site-based strategies, and the timber scenario scored lowest overall due to limited reuse and disassembly provisions in the original design. While CircularB-DfC offers a simple and transparent basis for integrating circularity in design, it is limited by the subjectivity of expert-based weighting and its static structure. Future research will focus on dynamic modelling, integration with digital tools, and broader validation to enhance applicability.

Protein engineering is a powerful tool for applications spanning synthetic biology, biocatalysis, and drug discovery. Recent advances in artificial intelligence (AI), from conventional machine learning (ML) algorithms to large-scale pre-trained protein models, have greatly accelerated enzyme engineering field entering a data-driven era. This review provides a guidance map of current enzyme engineering tasks and builds an integrative perspective on AI methods, model types, landmark tasks, and data resources. We begin by delineating the core modeling tasks in enzyme engineering, which include encompassing function annotation, structural modeling, and property prediction and by reviewing recent advances alongside dominant algorithmic frameworks. Next, we outlined the evolution of AI into enzyme engineering, tracing its progression through four stages: classical machine learning approaches, deep neural networks, protein language models (pLMs), and emerging multimodal architectures. Finally, we highlight four trends that are redefining the landscape of AI-driven enzyme design: (i) the replacement of handcrafted features with unified, token-level embeddings; (ii) a shift from single-modal models toward multimodal, multitask systems; (iii) the emergence of intelligent agents capable of reasoning; and (iv) a movement beyond static structure prediction toward dynamic simulation of enzyme function. Together, these developments are paving the way for intelligent, generalizable, and mechanistically interpretable AI platforms poised to synthetic biology.

Unraveling interference contributions to light scattering from concentrated colloids: A numerical study using density expansion

Abstract We revisit the localization tensor (LT) from geometric and probabilistic perspectives and construct extensions that are naturally compatible with periodic boundary conditions (PBC), without redefining the position operator. In open boundary conditions, we show that the LT can be written exactly as the covariance of a bivariate probability distribution built from density-density correlations. This leads to two conceptually distinct extensions to PBC: (i) a geometric one based on the Riemannian center (Frëchet mean) on the circle, and (ii) a metric-free one based on the mutual information I, which treats the configuration space purely as a probability space. We then relate the LT to the static structure factor by identifying the diagonal part, C pp , as a "localization function" C(p), whose small-momentum behavior determines the LT in the thermodynamic limit. This clarifies why the LT is sensitive to transitions out of the extended phase but by itself cannot distinguish Anderson-type localization from dimerization: both share the same low-momentum asymptotics. We show that the finite-momentum behavior of C(p), together with an inverse participation ratio (IPR) based upper bound valid in localized phases, provides a sharp criterion that discriminates localization from dimerization. These results are illustrated on the Su-Schrieffer-Heeger and Aubry-Andrë models, with and without interactions, and suggest that structure factor based probes offer robust and experimentally accessible diagnostics of localized and dimerized phases under PBC.