Anomaly Detection in Images With Smooth Background via Smooth-Sparse Decomposition

<p>In various manufacturing applications such as steel, composites, and textile production, anomaly detection in noisy images is of special importance. Although there are several methods for image denoising and anomaly detection, most of these perform denoising and detection sequentially, which affects detection accuracy and efficiency. Additionally, the low computational speed of some of these methods is a limitation for real-time inspection. In this article, we develop a novel methodology for anomaly detection in noisy images with smooth backgrounds. The proposed method, named smooth-sparse decomposition, exploits regularized high-dimensional regression to decompose an image and separate anomalous regions by solving a large-scale optimization problem. To enable the proposed method for real-time implementation, a fast algorithm for solving the optimization model is proposed. Using simulations and a case study, we evaluate the performance of the proposed method and compare it with existing methods. Numerical results demonstrate the superiority of the proposed method in terms of the detection accuracy as well as computation time. This article has supplementary materials that includes all the technical details, proofs, MATLAB codes, and simulated images used in the article.</p

Complex Archimedean Tiling Self-Assembled from DNA Nanostructures

Archimedean tilings are periodic polygonal tessellations that are created by placing regular polygons edge-to-edge around a vertex to fill the plane. Here we show that three- and four-arm DNA junction tiles with specifically designed arm lengths and intertile sticky-end interactions can be used to form sophisticated two-dimensional (2D) and three-dimensional (3D) tessellation patterns. We demonstrate two different complex Archimedean patterns, (3³.4²) and (3².4.3.4), and the formation of 2D lattices, 3D tubes, and sealed polygon-shaped pockets from the tessellations. The successful growth of hybrid DNA tile motif arrays suggests that it maybe possible to generate 2D quasi-crystals from DNA building blocks

A Bayesian Partially Observable Online Change Detection Approach with Thompson Sampling

This paper proposes a Bayesian learning framework for online change detection of high-dimensional data streams where only a subset of variables can be observed at each time point due to limited sensing capacities. On the one hand, we need to build a change detection scheme based on partial observations. On the other, the scheme should be able to adaptively and actively select the most critical sensing variables to observe to maximize the detection power. To address these two points, in this paper, first, a novel Bayesian Spike-Slab Composite Decomposition (BSSCD) is proposed to decompose the high-dimensional signals onto normal and abnormal bases, where the projection coefficients are efficiently estimated via variational Bayesian inference. Built upon it, the posterior Bayes factor is constructed as the detection statistic. Second, by further formulating it as the reward function of combinatorial multi-armed bandit (CMAB), a Thompson sampling strategy is proposed for selecting the potential changed variables with the balance of exploration and exploitation. The efficacy and applicability of our method are demonstrated in practice with numerical studies and a real case study.</p

Correlation of Solubility and Prediction of the Mixing Properties of Capsaicin in Different Pure Solvents

Using a static analytical model, experimental solubility data were obtained for capsaicin in <i>n</i>-hexane, cyclohexane, carbon disulfide, butyl ether, and isopropyl ether at temperatures ranging from 278.15 to 323.15 K. The melting temperature and fusion enthalpy of capsaicin were measured using differential scanning calorimetry. The measured solubility data were well correlated by the van’t Hoff, modified Apelblat, λ<i>h</i> (Buchowski), Wilson, and NRTL models, with the Wilson model showing the best agreement. The activity coefficients of capsaicin and the mixing Gibbs free energies, enthalpies, and entropies of the resulting solutions were predicted on the basis of the Wilson model parameters at measured solubility points. In addition, the infinite-dilution activity coefficients and excess enthalpies of capsaicin were estimated. Finally, the effects of solute–solvent intermolecular repulsive interactions on the solubility behavior and the values of mixing Gibbs free energy were discussed

Real-Time Monitoring of High-Dimensional Functional Data Streams via Spatio-Temporal Smooth Sparse Decomposition

<p>High-dimensional data monitoring and diagnosis has recently attracted increasing attention among researchers as well as practitioners. However, existing process monitoring methods fail to fully use the information of high-dimensional data streams due to their complex characteristics including the large dimensionality, spatio-temporal correlation structure, and nonstationarity. In this article, we propose a novel process monitoring methodology for high-dimensional data streams including profiles and images that can effectively address foregoing challenges. We introduce spatio-temporal smooth sparse decomposition (ST-SSD), which serves as a dimension reduction and denoising technique by decomposing the original tensor into the functional mean, sparse anomalies, and random noises. ST-SSD is followed by a sequential likelihood ratio test on extracted anomalies for process monitoring. To enable real-time implementation of the proposed methodology, recursive estimation procedures for ST-SSD are developed. ST-SSD also provides useful diagnostics information about the location of change in the functional mean. The proposed methodology is validated through various simulations and real case studies. Supplementary materials for this article are available online.</p

Three-Input Majority Logic Gate and Multiple Input Logic Circuit Based on DNA Strand Displacement

In biomolecular programming, the properties of biomolecules such as proteins and nucleic acids are harnessed for computational purposes. The field has gained considerable attention due to the possibility of exploiting the massive parallelism that is inherent in natural systems to solve computational problems. DNA has already been used to build complex molecular circuits, where the basic building blocks are logic gates that produce single outputs from one or more logical inputs. We designed and experimentally realized a three-input majority gate based on DNA strand displacement. One of the key features of a three-input majority gate is that the three inputs have equal priority, and the output will be true if any of the two inputs are true. Our design consists of a central, circular DNA strand with three unique domains between which are identical joint sequences. Before inputs are introduced to the system, each domain and half of each joint is protected by one complementary ssDNA that displays a toehold for subsequent displacement by the corresponding input. With this design the relationship between any two domains is analogous to the relationship between inputs in a majority gate. Displacing two or more of the protection strands will expose at least one complete joint and return a true output; displacing none or only one of the protection strands will not expose a complete joint and will return a false output. Further, we designed and realized a complex five-input logic gate based on the majority gate described here. By controlling two of the five inputs the complex gate can realize every combination of OR and AND gates of the other three inputs

Reconfigurable DNA Origami to Generate Quasifractal Patterns

The specificity of Watson–Crick base pairing, unique mechanical properties of DNA, and intrinsic stability of DNA double helices makes DNA an ideal material for the construction of dynamic nanodevices. Rationally designed strand displacement reactions can be used to produce dynamic reconfiguration of DNA nanostructures postassembly. Here we describe a ‘fold–release–fold’ strategy of multiple strand displacement and hybridization reactions to reconfigure a simple DNA origami structure into a complex, quasifractal pattern, demonstrating a complex transformation of DNA nanoarchitectures

Effect of DNA Hairpin Loops on the Twist of Planar DNA Origami Tiles

The development of scaffolded DNA origami, a technique in which a long single-stranded viral genome is folded into arbitrary shapes by hundreds of short synthetic oligonucleotides, represents an important milestone in DNA nanotechnology. Recent findings have revealed that two-dimensional (2D) DNA origami structures based on the original design parameters adopt a global twist with respect to the tile plane, which may be because the conformation of the constituent DNA (10.67 bp/turn) deviates from the natural B-type helical twist (10.4 bp/turn). Here we aim to characterize the effects of DNA hairpin loops on the overall curvature of the tile and explore their ability to control, and ultimately eliminate any unwanted curvature. A series of dumbbell-shaped DNA loops were selectively displayed on the surface of DNA origami tiles with the expectation that repulsive interactions among the neighboring dumbbell loops and between the loops and the DNA origami tile would influence the structural features of the underlying tiles. A systematic, atomic force microscopy (AFM) study of how the number and position of the DNA loops influenced the global twist of the structure was performed, and several structural models to explain the results were proposed. The observations unambiguously revealed that the first generation of rectangular shaped origami tiles adopt a conformation in which the upper right (corner 2) and bottom left (corner 4) corners bend upward out of the plane, causing linear superstructures attached by these corners to form twisted ribbons. Our experimental observations are consistent with the twist model predicted by the DNA mechanical property simulation software CanDo. Through the systematic design and organization of various numbers of dumbbell loops on both surfaces of the tile, a nearly planar rectangular origami tile was achieved

RNA Origami Functions as a Self-Adjuvanted Nanovaccine Platform for Cancer Immunotherapy

Peptide-based vaccines have been widely investigated in cancer immunotherapy. Despite their high specificity, safety, and low production cost, these vaccines have shown limited success in clinical studies, owing to their poor immunogenicity. Extensive efforts have been devoted to increasing the immunogenicity of peptide vaccines by mixing peptides with adjuvants and/or promoting their delivery to tumor-draining lymph nodes (TdLNs) for better antigen presentation by and maturation of dendritic cells. Among these efforts, the exploration of various nanoparticles has been at the forefront of the rational design and construction of peptide-based vaccines. Here, we present a nanovaccine platform that is built on a self-assembled RNA origami (RNA-OG) nanostructure. As previously reported, this RNA-OG nanostructure is a potent toll-like receptor (TLR)­3 agonist. In addition, due to its robust synthesis and versatility in modification, RNA-OG could be readily linked to peptides of interest. Thus, these RNA-OG nanostructures function as adjuvanted nanocarriers to construct RNA-OG-peptide nanovaccines that are uniform in size, consistent in peptide loading, and highly stable. Here, we demonstrate that the assembled RNA-OG-peptide nanovaccines induced dendritic cell maturation, reduced tumor-mediated immunosuppression, and mobilized tumor-specific CD8+ T cell responses at the tumor site. Together, these actions led to the elicitation of an effective antitumor immunity that increased the survival of tumor-bearing mice. The combination of RNA-OG-based nanovaccines with the α-PD-1 immune checkpoint blockade further enhanced the immunity. Hence, our RNA-OG nanostructures represent a robust, simple, and highly effective platform to empower peptide-based vaccines for cancer immunotherapy

Weakly correlated profile monitoring based on sparse multi-channel functional principal component analysis

<p>Although several works have been proposed for multi-channel profile monitoring, two additional challenges are yet to be addressed: (i) how to model complex correlations of multi-channel profiles when different profiles have different features (i.e., weakly or sparsely correlated); (ii) how to efficiently detect sparse changes occurring in only a small segment of a few profiles. To fill this research gap, our contributions are twofold. First, we propose a novel Sparse Multi-channel Functional Principal Component Analysis (SMFPCA) to model multi-channel profiles. SMFPCA can not only flexibly describe the correlation structure of multiple, or even high-dimensional, profiles with distinct features, but also achieve sparse PCA scores which are easily interpretable. Second, we propose an efficient convergence-guaranteed optimization algorithm to solve SMFPCA in real time based on the block coordinate descent algorithm. Third, as the SMFPCA scores can naturally identify sparse out-of-control (OC) patterns, we use the scores to construct a monitoring scheme which provides increased sensitivity to sparse OC changes. Numerical studies together with a real case study in a manufacturing system demonstrate the effectiveness of the developed methodology.</p