Programming the Nucleation of DNA Brick Self-Assembly with a Seeding Strand.

Recently, the DNA brick strategy has provided a highly modular and scalable approach for the construction of complex structures, which can be used as nanoscale pegboards for the precise organization of molecules and nanoparticles for many applications. Despite the dramatic increase of structural complexity provided by the DNA brick method, the assembly pathways are still poorly understood. Herein, we introduce a "seed" strand to control the crucial nucleation and assembly pathway in DNA brick assembly. Through experimental studies and computer simulations, we successfully demonstrate that the regulation of the assembly pathways through seeded growth can accelerate the assembly kinetics and increase the optimal temperature by circa 4-7 °C for isothermal assembly. By improving our understanding of the assembly pathways, we provide new guidelines for the design of programmable pathways to improve the self-assembly of DNA nanostructures.Natural Science Foundation of China (grants 51672022, 51302010) NSF (grants DMR-1654485 and ECCS-1807568) Semiconductor Research Corporation (grant 2836.002) EPSRC Tier-2 (capital grant EP/P020259/1

Proteomics Landscape of Host-Pathogen Interaction in Acinetobacter baumannii Infected Mouse Lung

Acinetobacter baumannii is an important pathogen of nosocomial infection worldwide, which can primarily cause pneumonia, bloodstream infection, and urinary tract infection. The increasing drug resistance rate of A. baumannii and the slow development of new antibacterial drugs brought great challenges for clinical treatment. Host immunity is crucial to the defense of A. baumannii infection, and understanding the mechanisms of immune response can facilitate the development of new therapeutic strategies. To characterize the system-level changes of host proteome in immune response, we used tandem mass tag (TMT) labeling quantitative proteomics to compare the proteome changes of lungs from A. baumannii infected mice with control mice 6 h after infection. A total of 6,218 proteins were identified in which 6,172 could be quantified. With threshold p 1.2 or < 0.83, we found 120 differentially expressed proteins. Bioinformatics analysis showed that differentially expressed proteins after infection were associated with receptor recognition, NADPH oxidase (NOX) activation and antimicrobial peptides. These differentially expressed proteins were involved in the pathways including leukocyte transendothelial migration, phagocyte, neutrophil degranulation, and antimicrobial peptides. In conclusion, our study showed proteome changes in mouse lung tissue due to A. baumannii infection and suggested the important roles of NOX, neutrophils, and antimicrobial peptides in host response. Our results provide a potential list of protein candidates for the further study of host-bacteria interaction in A. baumannii infection. Data are available via ProteomeXchange with identifier PXD020640

SeCo: Exploring Sequence Supervision for Unsupervised Representation Learning

A steady momentum of innovations and breakthroughs has convincingly pushed the limits of unsupervised image representation learning. Compared to static 2D images, video has one more dimension (time). The inherent supervision existing in such sequential structure offers a fertile ground for building unsupervised learning models. In this paper, we compose a trilogy of exploring the basic and generic supervision in the sequence from spatial, spatiotemporal and sequential perspectives. We materialize the supervisory signals through determining whether a pair of samples is from one frame or from one video, and whether a triplet of samples is in the correct temporal order. We uniquely regard the signals as the foundation in contrastive learning and derive a particular form named Sequence Contrastive Learning (SeCo). SeCo shows superior results under the linear protocol on action recognition (Kinetics), untrimmed activity recognition (ActivityNet) and object tracking (OTB-100). More remarkably, SeCo demonstrates considerable improvements over recent unsupervised pre-training techniques, and leads the accuracy by 2.96% and 6.47% against fully-supervised ImageNet pre-training in action recognition task on UCF101 and HMDB51, respectively. Source code is available at \url{https://github.com/YihengZhang-CV/SeCo-Sequence-Contrastive-Learning}.Comment: AAAI 2021; Code is publicly available at: https://github.com/YihengZhang-CV/SeCo-Sequence-Contrastive-Learnin

Monitoring of Multimode Processes Based on Quality-Related Common Subspace Separation

A new monitoring approach for multimode processes based on quality-related common subspace separation is proposed. In the model, the data set forms a larger space when the correlation between process variables and quality variables is considered. And then the whole space is decomposed: quality-related common subspace, quality-related specific subspace, and the residual subspace. Monitoring method is performed in every subspace, respectively. The simulation results show the proposed method is effective

Experimental Visualization of the Icing Process of Water Droplets on Cold Aluminum Plate Surface

Ice accretion on the cold airfoil blade surface, such as wind turbines working in winter, affects its performance and degrades its aerodynamic characteristics and efficiency. Therefore, it is necessary to study the icing characteristics on the cold blade surface. At present, many pieces of research on wind turbine blade icing have been explored on the macroscale but seldom on the microscale. In this chapter, the icing process of a single water droplet on the cold aluminum plate surface was examined by a visualized method. The effects of volume and temperature on the icing characteristics were tested and acquired. After that, the profile parameters of iced water droplets were drawn and analyzed by MATLAB software, including the contact diameter, the maximum diameter and height of iced water droplets, the contact angle, and so on. The research findings provide experimental and theoretical foundations to deeply study the icing characteristics of wind turbine blades on a microscale

Learning Transferable Adversarial Examples via Ghost Networks

Recent development of adversarial attacks has proven that ensemble-based methods outperform traditional, non-ensemble ones in black-box attack. However, as it is computationally prohibitive to acquire a family of diverse models, these methods achieve inferior performance constrained by the limited number of models to be ensembled. In this paper, we propose Ghost Networks to improve the transferability of adversarial examples. The critical principle of ghost networks is to apply feature-level perturbations to an existing model to potentially create a huge set of diverse models. After that, models are subsequently fused by longitudinal ensemble. Extensive experimental results suggest that the number of networks is essential for improving the transferability of adversarial examples, but it is less necessary to independently train different networks and ensemble them in an intensive aggregation way. Instead, our work can be used as a computationally cheap and easily applied plug-in to improve adversarial approaches both in single-model and multi-model attack, compatible with residual and non-residual networks. By reproducing the NeurIPS 2017 adversarial competition, our method outperforms the No.1 attack submission by a large margin, demonstrating its effectiveness and efficiency. Code is available at https://github.com/LiYingwei/ghost-network.Comment: To appear in AAAI-2