An elevated level of viral diversity was found in some SARS-CoV-2 infected patients, indicating the risk of rapid evolution of the virus. Although no evidence for the transmission of intra-host variants was found, the risk should not be overlooked. Abstract:Background A novel coronavirus (SARS-CoV-2) has infected more than 75,000 individuals and spread to over 20 countries. It is still unclear how fast the virus evolved and how the virus interacts with other microorganisms in the lung. MethodsWe have conducted metatranscriptome sequencing for the bronchoalveolar lavage fluid of eight SARS-CoV-2 patients, 25 community-acquired pneumonia (CAP) patients, and 20 healthy controls. ResultsThe median number of intra-host variants was 1-4 in SARS-CoV-2 infected patients, which ranged between 0 and 51 in different samples. The distribution of variants on genes was similar to those observed in the population data (110 sequences).However, very few intra-host variants were observed in the population as polymorphism, implying either a bottleneck or purifying selection involved in the transmission of the virus, or a consequence of the limited diversity represented in the current polymorphism data. Although current evidence did not support the transmission of intra-host variants in a person-to-person spread, the risk should not be overlooked.The microbiota in SARS-CoV-2 infected patients was similar to those in CAP, either dominated by the pathogens or with elevated levels of oral and upper respiratory commensal bacteria.Conclusion SARS-CoV-2 evolves in vivo after infection, which may affect its virulence, infectivity, and transmissibility. Although how the intra-host variant spreads in the population is still elusive, it is necessary to strengthen the surveillance of the viral Downloaded from https://academic.oup.com/cid/advance-article-abstract/doi/10.1093/cid/ciaa203/5780800 by guest on 16 March 2020 4 evolution in the population and associated clinical changes.

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A new variant of concern for SARS-CoV-2, Omicron (B.1.1.529), was designated by the World Health Organization on November 26, 2021. This study analyzed the viral genome sequencing data of 108 samples collected from patients infected with Omicron. First, we found that the enrichment efficiency of viral nucleic acids was reduced due to mutations in the region where the primers anneal to. Second, the Omicron variant possesses an excessive number of mutations compared to other variants circulating at the same time (62 vs. 45), especially in the Spike gene. Mutations in the Spike gene confer alterations in 32 amino acid residues, more than those observed in other SARS-CoV-2 variants. Moreover, a large number of nonsynonymous mutations occur in the codons for the amino acid residues located on the surface of the Spike protein, which could potentially affect the replication, infectivity, and antigenicity of SARS-CoV-2. Third, there are 53 mutations between the Omicron variant and its closest sequences available in public databases. Many of these mutations were rarely observed in public databases and had a low mutation rate. In addition, the linkage disequilibrium between these mutations was low, with a limited number of mutations (6) concurrently observed in the same genome, suggesting that the Omicron variant would be in a different evolutionary branch from the currently prevalent variants. To improve our ability to detect and track the source of new variants rapidly, it is imperative to further strengthen genomic surveillance and data sharing globally in a timely manner.

We identified an individual who was coinfected with two SARS-CoV-2 variants of concern, the Beta and Delta variants. The ratio of the relative abundance between the two variants was maintained at 1:9 (Beta:Delta) in 14 days. Furthermore, possible evidence of recombinations in the Orf1ab and Spike genes was found.

Variational mode decomposition (VMD) is a recently introduced adaptive signal decomposition algorithm with a solid theoretical foundation and good noise robustness compared with empirical mode decomposition (EMD). However, there is still a problem with this algorithm associated with the selection of relevant modes. To solve this problem, this paper proposes a novel signal-filtering method that combines VMD with Hausdorff distance (HD) in the VMD-HD method. A noisy signal is first decomposed into a given number K of band-limited intrinsic mode functions by VMD. The probability density function is then estimated for each mode. The aim of this method is to reconstruct the signal using the relevant modes, which are selected on the basis of noticeable similarities between the probability density function of the input signal and that of each mode. Various similarity measures are investigated and compared, and the HD is shown to offer the best performance. The results of filtering of simulation signals illustrate the validity of the proposed method when compared with EMD-based methods under comprehensive quantitative evaluation criteria. As a specific example, the proposed method is successfully used for filtering the pipeline leakage signal as evaluated by the de-trended fluctuation analysis algorithm.

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In response to the current COVID-19 pandemic, it is crucial to understand the origin, transmission, and evolution of SARS-CoV-2, which relies on close surveillance of genomic diversity in clinical samples. Although the mutation at the population level had been extensively investigated, how the mutations evolve at the individual level is largely unknown. Eighteen time-series fecal samples were collected from nine COVID-19 patients during the convalescent phase. The nucleic acids of SARS-CoV-2 were enriched by the hybrid capture method. First, we demonstrated the outstanding performance of the hybrid capture method in detecting intra-host variants. We identified 229 intra-host variants at 182 sites in 18 fecal samples. Among them, nineteen variants presented frequency changes > 0.3 within 1-5 days, reflecting highly dynamic intra-host viral populations. Moreover, the evolving of the viral genome demonstrated that the virus was probably viable in the gastrointestinal tract during the convalescent period. Meanwhile, we also found that the same mutation showed distinct pattern of frequency changes in different individuals, indicating a strong random drift. In summary, dramatic changes of SARS-CoV-2 genome were detected in fecal samples during the convalescent period; whether the viral load in feces is sufficient to establish an infection warranted further investigation.

Because there are many unlabeled samples in hyperspectral images and the cost of manual labeling is high, this paper adopts semi-supervised learning method to make full use of many unlabeled samples. In addition, those hyperspectral images contain much spectral information and the convolutional neural networks have great ability in representation learning. This paper proposes a novel semi-supervised hyperspectral image classification framework which utilizes self-training to gradually assign highly confident pseudo labels to unlabeled samples by clustering and employs spatial constraints to regulate self-training process. Spatial constraints are introduced to exploit the spatial consistency within the image to correct and re-assign the mistakenly classified pseudo labels. Through the process of self-training, the sample points of high confidence are gradually increase, and they are added to the corresponding semantic classes, which makes semantic constraints gradually enhanced. At the same time, the increase in high confidence pseudo labels also contributes to regional consistency within hyperspectral images, which highlights the role of spatial constraints and improves the HSIc efficiency. Extensive experiments in HSIc demonstrate the effectiveness, robustness, and high accuracy of our approach.