A Label-Free Electrochemical Immunosensor for Carbofuran Detection Based on a Sol-Gel Entrapped Antibody

In this study, an anti-carbofuran monoclonal antibody (Ab) was immobilized on the surface of a glassy carbon electrode (GCE) using silica sol-gel (SiSG) technology. Thus, a sensitive, label-free electrochemical immunosensor for the direct determination of carbofuran was developed. The electrochemical performance of immunoreaction of antigen with the anti-carbofuran monoclonal antibody was investigated by cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS), in which phosphate buffer solution containing [Fe(CN)6]3−/4− was used as the base solution for test. Because the complex formed by the immunoreaction hindered the diffusion of [Fe(CN)6]3−/4− on the electrode surface, the redox peak current of the immunosensor in the CV obviously decreased with the increase of the carbofuran concentration. The pH of working solution, the concentration of Ab and the incubation time of carbofuran were studied to ensure the sensitivity and conductivity of the immunosensor. Under the optimal conditions, the linear range of the proposed immunosensor for the determination of carbofuran was from 1 ng/mL to 100 μg/mL and from 50 μg/mL to 200 μg/mL with a detection limit of 0.33 ng/mL (S/N = 3). The proposed immunosensor exhibited good high sensitivity and stability, and it was thus suitable for trace detection of carbofuran pesticide residues

Facing Unknown: Open-World Encrypted Traffic Classification Based on Contrastive Pre-Training

Traditional Encrypted Traffic Classification (ETC) methods face a significant challenge in classifying large volumes of encrypted traffic in the open-world assumption, i.e., simultaneously classifying the known applications and detecting unknown applications. We propose a novel Open-World Contrastive Pre-training (OWCP) framework for this. OWCP performs contrastive pre-training to obtain a robust feature representation. Based on this, we determine the spherical mapping space to find the marginal flows for each known class, which are used to train GANs to synthesize new flows similar to the known parts but do not belong to any class. These synthetic flows are assigned to Softmax's unknown node to modify the classifier, effectively enhancing sensitivity towards known flows and significantly suppressing unknown ones. Extensive experiments on three datasets show that OWCP significantly outperforms existing ETC and generic open-world classification methods. Furthermore, we conduct comprehensive ablation studies and sensitivity analyses to validate each integral component of OWCP.Comment: Accepted by 2023 IEEE ISCC, 6 pages, 5 figure

In-plane mechanical behavior of novel auxetic hybrid metamaterials

We present in this paper two novel concepts of hybrid metamaterials that combine a core unit cell of re-entrant or cross-chiral shape and lateral missing ribs. The first topology is a hybrid between an anti-tetrachiral and a missing rib (cross-chiral) configuration; the second one has a variable cross-chiral layout compared to the classical missing rib square structure. Their in-plane mechanical properties have been investigated from a parametric point of view using finite element (FE) simulations. The two classes of metamaterials have been benchmarked to obtain optimized designs and specific effective properties. Nonlinear simulations and experimental tests of the new re-entrant missing rib metamaterials featuring optimized geometry parameters have been performed to understand the behavior of these architectures under large deformations

Select2Col: Leveraging Spatial-Temporal Importance of Semantic Information for Efficient Collaborative Perception

Collaboration by leveraging the shared semantic information plays a crucial role in overcoming the perception capability limitations of isolated agents. However, existing collaborative perception methods tend to focus solely on the spatial features of semantic information, while neglecting the importance of the temporal dimension. Consequently, the potential benefits of collaboration remain underutilized. In this article, we propose Select2Col, a novel collaborative perception framework that takes into account the {s}patial-t{e}mpora{l} importanc{e} of semanti{c} informa{t}ion. Within the Select2Col, we develop a collaborator selection method that utilizes a lightweight graph neural network (GNN) to estimate the importance of semantic information (IoSI) in enhancing perception performance, thereby identifying contributive collaborators while excluding those that bring negative impact. Moreover, we present a semantic information fusion algorithm called HPHA (historical prior hybrid attention), which integrates multi-scale attention and short-term attention modules to capture the IoSI in feature representation from the spatial and temporal dimensions respectively, and assigns IoSI-consistent weights for efficient fusion of information from selected collaborators. Extensive experiments on two open datasets demonstrate that our proposed Select2Col significantly improves the perception performance compared to state-of-the-art approaches. The code associated with this research is publicly available at https://github.com/huangqzj/Select2Col/

Listen to Minority: Encrypted Traffic Classification for Class Imbalance with Contrastive Pre-Training

Mobile Internet has profoundly reshaped modern lifestyles in various aspects. Encrypted Traffic Classification (ETC) naturally plays a crucial role in managing mobile Internet, especially with the explosive growth of mobile apps using encrypted communication. Despite some existing learning-based ETC methods showing promising results, three-fold limitations still remain in real-world network environments, 1) label bias caused by traffic class imbalance, 2) traffic homogeneity caused by component sharing, and 3) training with reliance on sufficient labeled traffic. None of the existing ETC methods can address all these limitations. In this paper, we propose a novel Pre-trAining Semi-Supervised ETC framework, dubbed PASS. Our key insight is to resample the original train dataset and perform contrastive pre-training without using individual app labels directly to avoid label bias issues caused by class imbalance, while obtaining a robust feature representation to differentiate overlapping homogeneous traffic by pulling positive traffic pairs closer and pushing negative pairs away. Meanwhile, PASS designs a semi-supervised optimization strategy based on pseudo-label iteration and dynamic loss weighting algorithms in order to effectively utilize massive unlabeled traffic data and alleviate manual train dataset annotation workload. PASS outperforms state-of-the-art ETC methods and generic sampling approaches on four public datasets with significant class imbalance and traffic homogeneity, remarkably pushing the F1 of Cross-Platform215 with 1.31%, ISCX-17 with 9.12%. Furthermore, we validate the generality of the contrastive pre-training and pseudo-label iteration components of PASS, which can adaptively benefit ETC methods with diverse feature extractors.Comment: Accepted by 2023 20th Annual IEEE International Conference on Sensing, Communication, and Networking, 9 pages, 6 figure

Transcriptomic Profiling the Effects of Airway Exposure of Zinc Oxide and Silver Nanoparticles in Mouse Lungs

Consumers and manufacturers are exposed to nanosized zinc oxide (nZnO) and silver particles (nAg) via airways, but their biological effects are still not fully elucidated. To understand the immune effects, we exposed mice to 2, 10, or 50 μg of nZnO or nAg by oropharyngeal aspiration and analyzed the global gene expression profiles and immunopathological changes in the lungs after 1, 7, or 28 days. Our results show that the kinetics of responses varied in the lungs. Exposure to nZnO resulted in the highest accumulation of F4/80- and CD3-positive cells, and the largest number of differentially expressed genes (DEGs) were identified after day 1, while exposure to nAg caused peak responses at day 7. Additionally, nZnO mainly activated the innate immune responses leading to acute inflammation, whereas the nAg activated both innate and adaptive immune pathways, with long-lasting effects. This kinetic-profiling study provides an important data source to understand the cellular and molecular processes underlying nZnO- and nAg-induced transcriptomic changes, which lead to the characterization of the corresponding biological and toxicological effects of nZnO and nAg in the lungs. These findings could improve science-based hazard and risk assessment and the development of safe applications of engineered nanomaterials (ENMs), e.g., in biomedical applications

In-Plane Mechanical Behavior of a New Star-Re-Entrant Hierarchical Metamaterial

A novel hierarchical metamaterial with tunable negative Poisson’s ratio is designed by a re-entrant representative unit cell (RUC), which consists of star-shaped subordinate cells. The in-plane mechanical behaviors of star-re-entrant hierarchical metamaterial are studied thoroughly by finite element method, non-dimensional effective moduli and effective Poisson’s ratios (PR) are obtained, then parameters of cell length, inclined angle, thickness for star subordinate cell as well as the amount of subordinate cell along x, y directions for re-entrant RUC are applied as adjustable design variables to explore structure-property relations. Finally, the effects of the design parameters on mechanical behavior and relative density are systematically investigated, which indicate that high specific stiffness and large auxetic deformation can be remarkably enhanced and manipulated through combining parameters of both subordinate cell and parent RUC. It is believed that the new hierarchical metamaterial reported here will provide more opportunities to design multifunctional lightweight materials that are promising for various engineering applications