Interspecies transmission (host switching/jumping) of influenza viruses is a key scientific question that must be addressed. In addition to the vigorous research on highly pathogenic avian influenza viruses (HPAIVs), studies of the mechanism of interspecies transmission of low-pathogenic avian influenza viruses (LPAIVs) could also provide insights into host tropism and virulence evolution. Influenza A viruses harboring hemagglutinin (HA) H13 (e.g., H13N6) are LPAIVs. In this study, soluble H13 HA glycoprotein was purified, and its receptor binding activity was characterized. The results revealed that H13 exclusively binds the avian α2-3-linked sialic acid receptor; no binding to the mammalian α2-6-linked sialic acid receptor was detected. Furthermore, the molecular basis of the H13 receptor binding specificity was revealed by comparative analysis of the crystal structures of both receptor-bound H13 and H5 HAs, which might be contributed by the hydrophobic residue V186. Work with an H13N186 mutant confirmed the importance of V186 in the receptor binding specificity of H13 HA, which shows that the mutant protein reduced the binding of an avian receptor analog but increased the binding of a human receptor analog. Detailed structural analysis also demonstrated that the conserved binding sites of the recently well-studied broadly neutralizing human monoclonal antibodies targeting the HA2 domain are found in H13. Our results expand our understanding of virulence evolution, receptor binding preference, and species tropism of the LPAIVs and HPAIVs.

Bottom-up synthesis of graphene nanoribbons (GNRs) by surface-assisted polymerization and cyclodehydrogenation of specifically designed precursor monomers has been shown to yield precise edges and doping. Here we use a precursor monomer containing sulfur atoms to fabricate nanostructures on a Au(111) surface at different annealing temperatures. The nanostructures have distinct configurations, varying from sulfur-doped polymers to sulfur-doped chevron-type GNRs (CGNRs) and, finally, pristine graphene nanoribbons with specific edges of periodic five-member carbon rings. Non-contact atomic force microscopy provides clear evidence for the cleavage of C–S bonds and formation of pristine CGNRs at elevated annealing temperatures. First-principles calculations show that the CGNRs exhibit negative differential resistance.

Recently, person identification has been a prerequisite in many applications of the Internet of Things. As a new biometric identification technology, gait recognition has a wide application prospect with the advantages of long-distance recognition and difficulty to forge. However, the existing gait recognition methods have some problems, such as complex algorithm calculation, high user participation, and large equipment overhead. In this paper, we propose RF-Gait, a method that identifies a person through unobtrusive gait perception with COTS RFID. The key insight is that wireless signal fluctuation can be exploited to distinguish each person’s unique gait behavior. To this end, we first collect and preprocess the gait-induced data composed of multiple RFID tags. Furthermore, multivariate variational mode decomposition is utilized to extract the intrinsic features in the spatial multichannels cooperatively. By developing a support vector machine model, we identify a person via the intrinsic walking pattern. Finally, extensive experiments show that our method can identify a person with an average accuracy of 96.3% from a group of twenty persons in a complex indoor environment.

With the rapid development of the internet of things, radio frequency identification (RFID) technology plays an important role in various fields. However, RFID systems are vulnerable to cloning attacks. This is the fabrication of one or more replicas of a genuine tag, which behave exactly as a genuine tag and fool the reader to gain legal authorization, leading to potential financial loss or reputation damage. Many advanced solutions have been proposed to combat cloning attacks, but they require extra hardware resources, or they cannot detect a clone tag in time. In this article, we make a fresh attempt to counterattack tag cloning based on spatiotemporal collisions. We propose adaptable clone detection (ACD), which can intuitively and accurately display the positions of abnormal tags in real time. It uses commercial off-the-shelf (COTS) RFID devices without extra hardware resources. We evaluate its performance in practice, and the results confirm its success at detecting cloning attacks. The average accuracy can reach 98.7%, and the recall rate can reach 96%. Extensive experiments show that it can adapt to a variety of RFID application scenarios.

The Radio Frequency Identification (RFID) data acquisition rate used for monitoring is so high that the RFID data stream contains a large amount of redundant data, which increases the system overhead. To balance the accuracy and real-time performance of monitoring, it is necessary to filter out redundant RFID data. We propose an algorithm called Time-Distance Bloom Filter (TDBF) that takes into account the read time and read distance of RFID tags, which greatly reduces data redundancy. In addition, we have proposed a measurement of the filter performance evaluation indicators. In experiments, we found that the performance score of the TDBF algorithm was 5.2, while the Time Bloom Filter (TBF) score was only 0.03, which indicates that the TDBF algorithm can achieve a lower false negative rate, lower false positive rate, and higher data compression rate. Furthermore, in a dynamic scenario, the TDBF algorithm can filter out valid data according to the actual scenario requirements.