Spatial structure characteristics and effects of self-driving tourism flows before and after the new crown epidemic: Taking Yunnan Province as an example

To obtain the impact of the new crown epidemic on the spatial structure of self-driving tourism flow during the Spring Festival Golden Week, social network and spatial statistical analysis methods are used to integrate road traffic flow big data and travelogue data to analyze the spatial structure characteristics of self-driving tourism flow in Yunnan Province during the Spring Festival Golden Week in 2018 and 2021. The results show that: 1) The self-driving tourism flow in Yunnan Province during the Spring Festival Golden Week in 2021 shows the “dragonfly” spatial clustering characteristics of “two centers, one axis and two wings”, and the new sub-core area of Qujing is added to the core area of Kunming in 2018, and the self-driving tourists are affected by the epidemic. 2) During the Spring Festival Golden Week in 2021, compared with 2018, there is no significant change in the spatial structure of tourism flow into Yunnan from outside the province, but the degree of intermediary centrality of provincial boundary nodes is significantly weakened, and the self-driving tourism flow at Wenshan junction and Lijiang junction decreased by 72.54% and 87.26%. The New Crown epidemic hindered the development of self-drive tours into Yunnan from Guangxi and Sichuan. 3) During the New Crown epidemic, tourists were less willing to visit hotspot cities, and showed overall behavioral preference characteristics of avoiding crowd gathering and focusing on health and safety. Under the New Crown epidemic, the tendency of long-distance self-driving shifts to close distance self-driving between neighboring cities centered on Kunming.Keywords: self-driving tourism flow; spatial structure characteristics; COVID-19; Golden Week; social network analysis; Yunnan Provinc

Towards Open-Scenario Semi-supervised Medical Image Classification

Semi-supervised learning (SSL) has attracted much attention since it reduces the expensive costs of collecting adequate well-labeled training data, especially for deep learning methods. However, traditional SSL is built upon an assumption that labeled and unlabeled data should be from the same distribution e.g., classes and domains. However, in practical scenarios, unlabeled data would be from unseen classes or unseen domains, and it is still challenging to exploit them by existing SSL methods. Therefore, in this paper, we proposed a unified framework to leverage these unseen unlabeled data for open-scenario semi-supervised medical image classification. We first design a novel scoring mechanism, called dual-path outliers estimation, to identify samples from unseen classes. Meanwhile, to extract unseen-domain samples, we then apply an effective variational autoencoder (VAE) pre-training. After that, we conduct domain adaptation to fully exploit the value of the detected unseen-domain samples to boost semi-supervised training. We evaluated our proposed framework on dermatology and ophthalmology tasks. Extensive experiments demonstrate our model can achieve superior classification performance in various medical SSL scenarios

The non-clonality of drug resistance in Beijing-genotype isolates of Mycobacterium tuberculosis from the Western Cape of South Africa

Background. The Beijing genotype of M. tuberculosis is a virulent strain that is disseminating worldwide and has a strong association with drug resistance. In the Western Cape of South Africa, epidemiological studies have identified the R220 cluster of the Beijing genotype as a major contributor to a recent outbreak of drug-resistant tuberculosis. Although the outbreak is considered to be due to clonal transmission, the relationship among drug resistant isolates has not yet been established. Results. To better understand the evolution of drug resistance among these strains, 14 drug-resistant clinical isolates of the Beijing genotype were sequenced by whole-genome sequencing, including eight from R220 and six from a more ancestral Beijing cluster, R86, for comparison. While each cluster shares a distinct resistance mutation for isoniazid, mapping of other drug-resistance mutations onto a phylogenetic tree constructed from single nucleotide polymorphisms shows that resistance mutations to many drugs have arisen multiple times independently within each cluster of isolates. Thus, drug resistance among these isolates appears to be acquired, not clonally derived. This observation suggests that, although the Beijing genotype as a whole might have selective advantages enabling its rapid dissemination, the XDR isolates are relatively less fit and do not propagate well. Although it has been hypothesized that the increased frequency of drug resistance in some Beijing lineages might be caused by a mutator phenotype, no significant shift in synonymous substitution patterns is observed in the genomes. Conclusion. While MDR-TB is spreading by transmission in the Western Cape, our data suggests that further drug resistance (i.e. XDR-TB) at this stage is acquired.Peer Reviewe