Pathogenic bacteria are pathogens widely spread that are capable of causing mild to life-threatening diseases in human beings or other organisms. Rationally organizing the simple helical motif of double-stranded DNA (dsDNA) tiles into designed ensemble structures with architecturally defined collective properties could lead to promising biosensing applications for pathogen detection. In this work, we facilely engineered multivalent hairpin aptamer probe-tethered DNA monolayers (MHAP-DNA monolayers) and applied them to build a fluorescence polarization-responsive circular isothermal strand displacement amplification (FP-CSDA) for Salmonella assay. In this system, the MHAP-DNA monolayers were constructed based on a dsDNA tile-directed selfassembly. A FAM-labeled reporting probe (RP FAM ) with an inherent low FP signal serves as the signaling unit. The presence of target Salmonella leads to the trapping of F RP FAM into the super DNA monolayers via a target-triggered CSDA to peel off the tethered hairpin-structured aptamer probes (HAPs) responsible for the binding of RP FAM . As a result, the FP signal of the FAM fluorophore can be remarkably amplified due to the recycling of target Salmonella and the capacity of structural DNA materials to strongly restrict the free rotation of the FAM fluorophore but without a fluorescence quenching effect. Experimental results demonstrate that the FP assay is able to detect Salmonella with a low limit of detection (LOD) of 7.2 × 10 0 CFU/mL and high specificity. As a proof-ofconcept study, we envision our study using DNA nanoarchitecture as the foundation to modulate CSDA-based FP assays, promising to open up a new avenue for disease diagnosis, food safety detection, and biochemical studies.

The number of words: 4645, the number of figures: 4, the number of tables: 1The outbreak of COVID-19 in December 2019 caused a global pandemic of acute respiratory disease, and with the increasing virulence of mutant strains and the number of confirmed cases, this has resulted in a tremendous threat to global public health. Therefore, an accurate diagnosis of COVID-19 is urgently needed for rapid control of SARS-CoV-2 transmission. As a new molecular biology technology, loop-mediated isothermal amplification (LAMP) has the advantages of convenient operation, speed, low cost and high sensitivity and specificity. In the past two years, rampant COVID-19 and the continuous variation in the virus strains have demanded higher requirements for the rapid detection of pathogens. Compared with conventional RT–PCR and real-time RT–PCR methods, genotyping RT-LAMP method and LAMP plus peptide nucleic acid (PNA) probe detection methods have been developed to correctly identified SARS-CoV-2 variants, which is also why LAMP technology has attracted much attention. LAMP detection technology combined with lateral flow assay, microfluidic technology and other sensing technologies can effectively enhance signals by nucleic acid amplification and help to give the resulting output in a faster, more convenient and user-friendly way. At present, LAMP plays an important role in the detection of SARS-CoV-2.

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Community detection plays an essential role in understanding network topology and mining underlying information. A bipartite network is a complex network with more important authenticity and applicability than a one-mode network in the real world. There are many communities in the network that present natural overlapping structures in the real world. However, most of the research focuses on detecting non-overlapping community structures in the bipartite network, and the resolution of the existing evaluation function for the community structure’s merits are limited. So, we propose a novel function for community detection and evaluation of the bipartite network, called community density D. And based on community density, a bipartite network community detection algorithm DSNE (Density Sub-community Node-pair Extraction) is proposed, which is effective for overlapping community detection from a micro point of view. The experiments based on artificially-generated networks and real-world networks show that the DSNE algorithm is superior to some existing excellent algorithms; in comparison, the community density (D) is better than the bipartite network’s modularity.

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