Multiple DNA cycle amplification for highly efficient detection of mercury pollution in food.

Mercury ion (Hg2+), a highly toxic metal pollutant, is widely found in the environment and can enter the human body through the food chain, causing various health issues. Sensitive and accurate methods for monitoring Hg2+ are highly desirable for ensuring food safety. Herein, we propose a self-sustainable multiple amplification system (MAS) for Hg2+ determination through the reciprocal activation between catalytic hairpin assembly (CHA) and rolling circle amplification (RCA). The thymine-encoded recognition element specifically recognizes Hg2+, triggering the exposure of the initiator. The initiator then motivates the mutual activation of CHA and RCA to accelerate the production of an exponentially amplified signal. The MAS method achieved a low detection limit of 11 pM. Due to its reliable target recognition and robust amplification efficiency, the MAS circuit facilitated the highly efficient and accurate analysis of low-abundance Hg2+ in milk and snakehead samples, thus providing a potentially new tool for food safety control.

Isothermal Reciprocal Catalytic DNA Circuit for Sensitive Analysis of Kanamycin.

Inappropriate use of veterinary drugs can result in the presence of antibiotic residues in animal-derived foods, which is a threat to human health. A simple yet efficient antibiotic-sensing method is highly desirable. Programmable DNA amplification circuits have supplemented robust toolkits for food contaminants monitoring. However, they currently face limitations in terms of their intricate design and low signal gain. Herein, we have engineered a robust reciprocal catalytic DNA (RCD) circuit for highly efficient bioanalysis. The trigger initiates the cascade hybridization reaction (CHR) to yield plenty of repeated initiators for activating the rolling circle amplification (RCA) circuit. Then the RCA-generated numerous reconstituted triggers can reversely stimulate the CHR circuit. This results in a self-sufficient supply of numerous initiators and triggers for the successive cross-invasion of CHR and RCA amplifiers, thus leading to exponential signal amplification for the highly efficient detection of analytes. With its flexible programmability and modular features, the RCD amplifier can serve as a universal toolbox for the high-performance and accurate sensing of kanamycin in buffer and food samples including milk, honey, and fish, highlighting its enormous promise for low-abundance contaminant analysis in foodstuffs.

Construction of a simple dual-mode ATP-sensing system for reliable fish freshness evaluation.

Adenosine triphosphate (ATP), the main carrier of chemical energy, plays a key role in various biochemical reactions such as cellular metabolism. Currently, ATP levels are considered important indicators of microbial content in food safety, and food freshness can be determined by detecting ATP content. Some ATP sensing strategies have been applied to evaluate food freshness. However, cumbersome nanomaterial preparation, low sensitivity, and low reliability hamper their widespread application. Herein, a simple, high-performance, and reliable dual-mode sensing system based on hemin-G-quadruplex (G4) DNAzyme was established to detect ATP and assess fish freshness. Two nucleic acid probes, including subunits of the hemin-G4 DNAzyme in inactive structures and anti-ATP aptamer, self-assemble upon the input of ATP into the active hemin-G4 DNAzyme unit. The generated DNAzyme acts as a biocatalyst for colorimetric or fluorescent readout of the sensing process. The colorimetric and fluorescent dual-mode sensing system enables highly sensitive and reliable analysis of target ATP with detection limits of 71 nM and 73 nM, respectively. Moreover, the biosensor exhibited good selectivity for differentiating ATP from other interfering analytes. The proposed system was used to detect ATP in perch samples, and a linear correlation between ATP level and microbial content was confirmed. The established ATP-sensing system reliably evaluated fish freshness. Notably, in comparison with microbiological counts, the proposed DNAzyme-based dual-mode strategy for freshness evaluation is facile, highly efficient, and cost-effective, thus providing a promising method for food safety and quality monitoring.

Construction of a self-sufficient DNA circuit for amplified detection of kanamycin.

Improper use of kanamycin can lead to trace kanamycin residues in animal-derived foods, which can pose a potential threat to public health. Isothermal enzyme-free DNA circuits have provided a versatile toolbox for detecting kanamycin residues in complicated food samples, yet they are always limited by low amplification efficiency and intricate design. Herein, we present a simple-yet-robust nonenzymatic self-driven hybridization chain reaction (SHCR) amplifier for kanamycin determination with 5800-fold sensitivity over that of the conventional HCR circuit. The analyte kanamycin-activated SHCR circuitry can generate numerous new initiators to promote the reaction and improve the amplification efficiency, thus achieving an exponential signal gain. With precise target recognition and multilayer amplification capability, our self-sustainable SHCR aptasensor facilitated the highly sensitive and reliable analysis of kanamycin in buffer, milk, and honey samples, thus holding great potential for the amplified detection of trace contaminants in liquid food matrices.

HT-Fed-GAN: Federated Generative Model for Decentralized Tabular Data Synthesis.

In this paper, we study the problem of privacy-preserving data synthesis (PPDS) for tabular data in a distributed multi-party environment. In a decentralized setting, for PPDS, federated generative models with differential privacy are used by the existing methods. Unfortunately, the existing models apply only to images or text data and not to tabular data. Unlike images, tabular data usually consist of mixed data types (discrete and continuous attributes) and real-world datasets with highly imbalanced data distributions. Existing methods hardly model such scenarios due to the multimodal distributions in the decentralized continuous columns and highly imbalanced categorical attributes of the clients. To solve these problems, we propose a federated generative model for decentralized tabular data synthesis (HT-Fed-GAN). There are three important parts of HT-Fed-GAN: the federated variational Bayesian Gaussian mixture model (Fed-VB-GMM), which is designed to solve the problem of multimodal distributions; federated conditional one-hot encoding with conditional sampling for global categorical attribute representation and rebalancing; and a privacy consumption-based federated conditional GAN for privacy-preserving decentralized data modeling. The experimental results on five real-world datasets show that HT-Fed-GAN obtains the best trade-off between the data utility and privacy level. For the data utility, the tables generated by HT-Fed-GAN are the most statistically similar to the original tables and the evaluation scores show that HT-Fed-GAN outperforms the state-of-the-art model in terms of machine learning tasks.

Fast, Resource-Saving, and Anti-Collaborative Attack Trust Computing Scheme Based on Cross-Validation for Clustered Wireless Sensor Networks.

The trust computing mechanism has an increasing role in the cooperative work of wireless sensor networks. However, the computing speed, resource overhead, and anti-collaborative attack ability of a trust mechanism itself are three key challenging issues for any open and resource-constrained wireless sensor networks. In this study, we propose a fast, resource-saving, and anti-collaborative attack trust computing scheme (FRAT) based on across-validation mechanism for clustered wireless sensor networks. First, according to the inherent relationship among three network entities (which are made up of three types of network nodes, namely base stations, cluster heads, and cluster members), we propose the cross-validation mechanism, which is effective and reliable against collaborative attacks caused by malicious nodes. Then, we adopt a fast and resource-saving trust computing scheme for cooperation between between cluster heads or cluster members. This scheme is suitable for wireless sensor networks because it facilitates resource-saving. Through theoretical analysis and experiments, the feasibility and effectiveness of the trust computing scheme proposed in this study are verified.

Variation Analysis of Sphygmogram to Assess Cardiovascular System under Meditation.

In this article, we studied how meditation affects the characteristics of the cardiovascular system, mainly based on blood pressure waveforms (BPW). Four parameters derived from BPW include the rising slope (h(1)/t(1)), normalized height of T wave (h(3)/h(1)), normalized height of V(3) valley (h(4)/h(1)) and normalized height of D wave (h(5)/h(1)), where t(1) and h(i), i = 1, ... ,5 are quantitative features of the BPW waveform pattern. A larger value of h(1)/t(1) reflects better heart ejection ability and aorta compliance. A larger value of h(3)/h(1) may infer an arterial system with good elasticity. The decrease (increase) of h(4)/h(1) parameter indicates the decrease (increase) of peripheral resistance of vessels. A larger value of h(5)/h(1) indicates better artery elasticity and aortic valve function. In comparison with the control group, Zen-meditation practitioners have more after-meditation h(1)/t(1), h(3)/h(1) and h(5)/h(1) increase, with more h(4)/h(1) decrease, with statistical significance (P < 0.05). The observation allows us to infer that Zen meditation may effectively improve relevant characteristics of the cardiovascular system.