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BackgroundCrohn’s disease (CD) is a chronic nonspecific inflammatory bowel disease (IBD) with an increasing incidence worldwide. The etiology of CD is still obscure, but microbial dysbiosis has been recognized as an essential factor contributing to CD. However, few studies have revealed the microbiome’s signatures and reciprocal correlations between multiple sites in patients with CD over different disease stages. This study investigated the specific microbial architectures of the oral cavity, sputum, and ileum in patients with CD in the active and remission stages.MethodsMicrobial samples from the oral cavity, sputum, and ileum were collected from patients with CD in the active and remission stages and healthy controls. The microbial composition was assessed by 16S ribosomal RNA (rRNA) gene sequencing. In addition, bioinformatics methods were used to demonstrate the microbial signatures, functional changes, and correlations between microbiota and clinical data in CD.ResultsCompared with healthy controls, a distinct microbiota dysbiosis in the oral cavity, sputum, and ileum of patients with CD was identified, characterized by alterations in microbiota biodiversity and composition. The oral cavity and sputum microbiota showed significantly lower microbial diversity in patients with CD than in healthy controls. In terms of microbiota composition, the microbiota changes in the oral cavity of patients with CD were similar to those in the sputum, while they were different from those in the ileum. In the oral cavity and sputum of patients with CD, a lower relative abundance of Firmicutes and Actinobacteria was observed compared to healthy controls, which was most prominent in the active stage. In contrast, an increased relative abundance of Fusobacteria, Porphyromonas, and Haemophilus was observed in patients with CD. The predicted metabolic pathways involved in the oral cavity, sputum, and ileum were similar, predominantly involving metabolism, environmental information processing, and genetic information processing.ConclusionThe results revealed the alterations of microbiota architecture in the oral cavity, sputum, and ileum of patients with CD, which varied across disease stages. Studying microbiota dysbiosis may bring new insights into the etiology of CD and lead to novel treatments.

Wi-Fi device authentication is crucial for defending against impersonation attacks and information forgery attacks. Most of the existing authentication technologies rely on complex cryptographic algorithms. However, they cannot be supported well on the devices with limited hardware resources. A fine-grained device authentication technology based on channel state information (CSI) provides a noncryptographic method, which uses the CSI fingerprints for authentication since CSI can uniquely identify the devices. But long-term authentication based on CSI fingerprints is a challenging work. First, the CSI fingerprints are environment-sensitive, which means that the local authenticator should be updated to adapt to the changing channel state. Second, the local authenticator trained with old CSI fingerprints is outdated when users reconnect to the network after being offline for a long time, thus, it needs to be retrained in the access phase with new fingerprints. To tackle these challenges, we propose a CSI-based enhancing Wi-Fi device authentication protocol and an authentication framework. The protocol helps to collect new CSI fingerprints for authenticator’s training in access phase and performs the fingerprints’ dispersion analysis for authentication. In the association phase, it provides packet-level authentication and updates the authenticator with valid CSI fingerprints. The authenticator consists of an ensemble of small-scale autoencoders, which has high enough time efficiency for packet-level authentication and authenticator’s update. Experiments show that the accuracy of the framework is up to 98.7%, and the authenticator updating method can help the framework maintains high accuracy.

Cluster tracking is a mainstream approach for the study of time-variant channel characteristics. In the paper, we propose a power spectrum based sequential tracker (PSBST) to compensate for the disadvantages of existing cluster tracking algorithms. The proposed tracker identifies clusters via simple three-stage power spectrum processing. Furthermore, fuzzy c-means (FCM) algorithm is incorporated to separate clusters considering the overlapped clusters which may appear in the power spectrum. In terms of tracking, we implement Kalman filter to sequentially predict candidate ranges of tracked clusters in consecutive snapshots and simultaneously a novel gradient-based histogram of power (GBHOP) method is devised to determine the evolution of clusters. We also investigate the performance of the tracker by synthetic channel simulation. It demonstrates high accuracy and less computational time compared with the results derived from the existing cluster tracking algorithms. Besides, we verify applicability of the tracker by analyzing the field measurement conducted in vehicle-to-everything (V2X) scenario, and preliminary statistical characteristics for intra-cluster and inter-cluster parameters can be readily obtained in the sequel.INDEX TERMS Time-variant radio channel, cluster tracking, power spectrum, fuzzy clustering, cluster detection, V2X.

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Regeneration of gastrointestinal tissues remains a great challenge due to their unique microenvironment. Functional composite decellularized scaffolds have shown great potential in gastrointestinal repair and inducing gastrointestinal tissue-specific proliferation. In...

Clinically rare, multiple primary tumors are a growth or development of two or more neoplasms in the same individual. A 57-year-old woman with two primary cancers, namely, breast and gastric cancers, and a gastrointestinal stromal tumor was admitted. Next-generation sequencing (NGS) of the three tumors and blood was performed to determine their clonal origin and identify genetic cancer susceptibility. NGS identified that germline genetic variants potentially correlated with an individual risk of developing multiple cancers and that additional mutations are required to drive the formation of different tumors.

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