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Controlling 2D Nanoparticle Self-Assembly Mesophases via Symmetry Breaking Driven by Single Bottlebrush Polymer Conjugation
No full textPolymer grafting density critically influences the self-assembly of polymer-grafted nanoparticles, yet the low grafting density regime remains underexplored. Here, we investigate the thin-film self-assembly of bottlebrush polymer-grafted core/shell nanoparticles (BPGNPs) under quasi-2D confinement at near-zero grafting densities through coarse-grained molecular dynamics (CGMD). The NP core is modeled using a hard-core/soft-shoulder (HCSS) potential, and it is compared against Weeks-Chandler-Andersen (WCA) potential. While the phase behaviors of both models are well-known, the distinct phase behaviors of both models persist even with polymer grafting offering additional room for tunability. Unlike sufficiently high grafting density or bare nanoparticles (NPs), grafting a single bottlebrush polymer breaks the rotational symmetry. The resulting structural polarity of grafted NPs can be precisely controlled through bottlebrush design parameters. We demonstrate that enhanced structural polarity stabilizes specific ordered phases, enabling precise control over self-assembled morphologies such as hexagonal lattices, square lattices, and linear clusters. Lastly, we explore the impact of synthesis-induced heterogeneity by introducing bare NPs, dual-polymer-grafted particles, and unconjugated polymers as minor species, providing insights into morphological stability under realistic grafting conditions. This work advances our understanding of BPGNP self-assembly in the near-zero grafting density regime and establishes design principles for functional nanotechnology applications.
Application of Deep Neural Network to an Accelerated Prediction of a Severe Accident in Nuclear Power Plants
No full textRecent nuclear severe accidents have spurred interest in the development of advanced accident management support tools (AMSTs) to enhance decision-making during crises. This study examines the efficacy of deep neural networks (DNNs) in accelerating severe accident predictions within nuclear power plants (NPPs), focusing on a loss-of-component-cooling-water (LOCCW) accident scenario. Through analysis of 10,780 simulated LOCCW accident scenarios across varied component failures and mitigation strategy implementations, time series datasets were synthesized at 15, 30, and 60-min intervals. The evaluation demonstrated that convolutional neural network (CNN)-integrated models outperformed standalone architectures in prediction accuracy across all temporal resolutions. Notably, higher temporal resolutions in training datasets significantly improved mean absolute error (MAE) and root mean squared error (RMSE), thereby enhancing prediction precision for immediate subsequent time steps. However, the augmentation of temporal resolution did not uniformly improve overall scenario prediction performance, as assessed by dynamic time warping (DTW) distance, due to cumulative prediction error in higher resolution models. These findings elucidate the nuanced relationship between temporal resolution and predictive accuracy, offering valuable insights for the development of sophisticated AMSTs aimed at bolstering nuclear safety and accident management strategies.
Generalized valley topological phase for robustwave propagation in armchair-shaped waveguide
No full textREADRetro web: A user-friendly platform for predicting plant natural product biosynthesis
No full textNatural products (NPs), a fundamental class of bioactive molecules with broad applicability, are valuable sources in pharmaceutical research and drug discovery. Despite their significance, the large-scale production of NPs is often limited by their availability and scalability, requiring alternative approaches such as metabolic engineering or biosynthesis. To identify ideal pathways for the mass production of NPs, deep learning-based retrosynthesis models have been recently developed. Such models accelerate NP discovery; however, these tools are often not easy to use for researchers with a limited computational background, because they require complex environment configurations, command-line interfaces, and substantial computational resources. Here, we introduce READRetro web, a user-friendly web platform that integrates the READRetro machine learning (ML) model for retrosynthesis prediction. Based on modern web technologies, our web platform provides a fast and responsive user experience. READRetro Web bridges the gap between advanced ML-driven retrosynthesis and practical research workflows, making retrosynthesis prediction accessible to a broader range of researchers. Our platform demonstrates high predictive accuracy and computational efficiency, offering well-organized results to facilitate NP retrosynthetic pathway design. READRetro Web is freely accessible via https://readretro.net.
Sparse Identification of Nonlinear Dynamics-Based Model Predictive Control for Multirotor Collision Avoidance
No full textThis article proposes a data-driven model predictive control (MPC) method for multirotor collision avoidance, considering uncertainties and the unknown dynamics caused by a payload. To address this challenge, sparse identification of nonlinear dynamics (SINDy) is employed to derive the governing equations of the multirotor system. SINDy is capable of discovering the equations of target systems from limited data, under the assumption that a few dominant functions primarily characterize the system's behavior. In addition, a data collection framework that combines a baseline controller with MPC is proposed to generate diverse trajectories for model identification. A candidate function library, informed by prior knowledge of multirotor dynamics, along with a normalization technique, is utilized to enhance the accuracy of the SINDy-based model. Using data-driven model from SINDy, MPC is used to achieve accurate trajectory tracking while satisfying state and input constraints, including those for obstacle avoidance. Simulation results demonstrate that SINDy can successfully identify the governing equations of the multirotor system, accounting for mass parameter uncertainties and aerodynamic effects. Furthermore, the results confirm that the proposed method outperforms conventional MPC, which suffers from parameter uncertainty and an unknown aerodynamic model, in both obstacle avoidance and trajectory tracking performance.
Highly accurate image registration for 3D multiplexed cyclic imaging using dense labeling in expandable tissue gels
No full textMultiplexed cyclic imaging in expandable tissue gels has been extensively studied to visualize numerous biomolecules at a nanoscale resolution in situ. Previous studies have employed sparse labels, such as DAPI or lectin staining, as registration markers. However, these sparse labels do not adequately capture the full extent of deformation across the entire region of interest. To overcome this challenge, we propose the use of dense labels, specifically fluorophore N-hydroxysuccinimide (NHS)-ester staining, as registration markers to achieve highly accurate image registration. We first tested several NHS-functionalized fluorophores as fiducial markers and identified the proper candidates for three-dimensional (3D) multiplexed cyclic imaging. We analyzed the registration accuracy between DAPI and NHS-ester staining and illustrated that dense label-based registration provides a more accurate registration performance. In the multiplexed imaging of expanded specimens, we observed that repetitive expansion/shrinking processes and chemical treatments for signal elimination can induce 3D nonlinear distortion. This sample distortion can be mitigated by re-embedding the tissue gel or replacing the chemical de-staining process with photobleaching-based signal removal or computational signal unmixing. With such an optimized experimental setup, we demonstrated 3D multiplexed cyclic imaging with nanoscale precision image registration. Finally, we prove that dense biological structures, such as actin, can be used as registration markers to achieve high registration accuracy. We anticipate that the proposed dense labeling strategy will overcome the technical limitations of multiplexed cyclic imaging in expandable tissue gels, offering high-precision registration. We expect it to be widely adopted by the biological and medical communities.
Type 1 interferon signature and allograft inflammatory factor-1 contribute to refractoriness to TNF inhibition in ankylosing spondylitis
No full textAnkylosing spondylitis (AS) is a chronic inflammatory arthritis that primarily affects the enthesis and may culminate in bony ankylosis of the spine. Despite TNF inhibitor (TNFi) being foundational in managing active inflammation, 30-40% of patients with AS remain non-responsive. Through longitudinal and multi-omics profiling of peripheral blood mononuclear cells from TNFi-receiving patients with AS, here we reveal that elevated type I IFN signatures at baseline are associated with poor TNFi response, leading to a paradoxical enhancement of IFN signatures and Th17 responses following TNFi therapy. Among type I IFN-related genes, we identify and validate AIF-1 as a predictive biomarker reflecting the inherent IFN signature that differentiates responders from non-responders. AIF-1 also contributes to an inflammatory cycle by increasing IFN alpha receptor expression and Th17 responses. In summary, our findings advocate for a personalized approach to managing AS by considering individual variations in AIF-1 levels and IFN signatures.
On isomorphism of the space of continuous functions with finite p-th variation along a partition sequence
No full textWe study the concept of (generalized) p-th variation of a real-valued continuous function along a general class of refining sequence of partitions. We show that the finiteness of the p-th variation of a given function is closely related to the finiteness of p-norm pound of the coefficients along a Schauder basis, similar to the fact that H & ouml;lder coefficient of the function is connected to B infinity-norm of the Schauder coefficients. This result provides an isomorphism between the space of ca-H & ouml;lder continuous functions with finite (generalized) p-th variation along a given partition sequence and a subclass of infinite-dimensional matrices equipped with an appropriate norm, in the spirit of Ciesielski.
Review of data-driven computational guidance for unmanned aerospace vehicles
No full textThis paper explores the application of data-driven computational guidance in unmanned aerospace vehicles, emphasizing improving the optimality of guidance strategies through data-driven approaches. Unmanned aerospace vehicles are engineered to execute predetermined missions while adhering to a variety of physical and operational constraints. Both their design and operational strategies prioritize the efficient utilization of onboard resources. Data-driven methods can learn from data to develop well-trained neural networks that uncover underlying guidance patterns. These trained neural networks can rapidly generate optimal outputs in response to inputs with minimal computational cost. This characteristic of data-driven methods is particularly well-suited for guidance applications in scenarios with limited onboard computational resources. This paper reviews the state-of-the-art achievements in data-driven computational guidance. Simultaneously, we categorize these advancements based on the role of neural networks within the guidance system, referring to them as neural-end-to-end computational guidance and neural-assisted fixed-structure guidance, respectively. Additionally, the paper highlights several open problems and potential future research directions.