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BackgroundOur understanding of SARS-CoV-2 evolution and mutation rate is limited. The rate of SARS-CoV-2 evolution is minimized through a proofreading function encoded byNSP-14and may be affected by patient comorbidity. Current understanding of SARS-CoV-2 mutational rate is through population based analysis while intra-host mutation rate remains poorly studied.MethodsViral genome analysis was performed between paired samples and mutations quantified at allele frequencies (AF) ≥0.25, ≥0.5 and ≥0.75. Mutation rate was determined employing F81 and JC69 evolution models and compared between isolates with (ΔNSP-14) and without (wtNSP-14) non-synonymous mutations in NSP-14 and by patient comorbidity.ResultsForty paired samples with median interval of 13 days [IQR 8.5-20] were analyzed. The estimated mutation rate by F81 modeling was 93.6 (95%CI:90.8-96.4], 40.7 (95%CI:38.9-42.6) and 34.7 (95%CI:33.0-36.4) substitutions/genome/year at AF ≥0.25, ≥0.5, ≥0.75 respectively. Mutation rate in ΔNSP-14 were significantly elevated at AF>0.25 vs wtNSP-14. Patients with immune comorbidities had higher mutation rate at all allele frequencies.DiscussionIntra-host SARS-CoV-2 mutation rates are substantially higher than those reported through population analysis. Virus strains with altered NSP-14 have accelerated mutation rate at low AF. Immunosuppressed patients have elevated mutation rate at all AF. Understanding intra-host virus evolution will aid in current and future pandemic modeling.

Background Our understanding of SARS-CoV-2 evolution is limited. Most estimates arise from analysis of global databases populated with unrelated sequences and is currently estimated at ∼27.7 substitutions/genome/year. SARS-CoV-2 polymerase contains a proofreading function encoded by NSP-14 limiting change. Additionally, virus evolution may be influenced by patient comorbidity. Intra-host mutational rate (MR) during infection remain poorly studied. Methods To minimize effect of vaccination and/or natural immunity on MR analysis, paired samples from adults originating from the initial pandemic wave (3/17/2020 through 5/27/2020) were retrieved and analyzed at Cleveland Clinic. Viral genome analysis was performed using next generation sequencing, and mutations between paired samples were quantified at allele frequencies (AF) ≥ 0.1, ≥ 0.5 and ≥ 0.75 and compared. MR was calculated employing F81 and JC69 evolution­­­­ models and compared between isolates with (Δ NSP-14) and without (wildtype, wt) non-synonymous mutations in NSP-14 and by comorbidity. Results A total of 40 patients (80 sample pairs) were identified. Median interval between paired tests was 15 days [range 5-32]. The estimated MR by F81 modeling was 317.2 (95%CI 312.0-322.3), 54.6 (95%CI 52.5-56.7) and 45.1 (95%CI 43.1-47.0) substitutions/genome/year at AF of ≥0.1, ≥0.5, ≥0.75 respectively. Rates in ΔNSP-14 (n=13) vs wt (n=27) groups were 557.7 (95%CI 537.0-578.2) vs 193.1 (95%CI 187.1-199.1) p-value 0.001, 50.8 (95%CI 44.3-57.3) vs 56.3 (95%CI 53.1-59.4) p-value 0.144, and 31.0 (95%CI 25.9-36.1) vs 51.3 (95%CI 48.3-54.3) p- value < 0.001 at AF ≥0.1, ≥0.5, ≥0.75 respectively. Patients with immune comorbidities (n=6) had significantly higher MR of 137.6 (95%CI 114.6-160.5) vs 40.5 (95%CI 38.4-42.7) p-value < 0.001, and 113.7 (95%CI 92.8-134.5) vs 33.4 (95%CI 31.5-35.4) p-value < 0.001 at AF ≥0.5 and ≥0.75 respectively. Similar results were obtained when using the JC69 model. Conclusion Intra-host SARS-CoV-2 mutation rates are higher than those reported through population analysis. Virus strains with altered NSP-14 have accelerated MR at low AF. Immunosuppressed patients have elevated MR at higher AF. Understanding intra-host virus evolution will aid in current and future pandemic modeling. Disclosures Frank Esper, M.D, Johnson and Johnson: Advisor/Consultant Daniel D. Rhoads, M.D. PhD, Luminex: Advisor/Consultant|Talis Biomedical: Advisor/Consultant|Thermo Fisher: Advisor/Consultant.

With the enhancement of the performance of cloud servers, face recognition applications are becoming more and more popular, but it also has some security problems, such as user privacy data leakage. This article proposes a face recognition scheme based on homomorphic encryption in cloud environment. The article first uses the MTCNN algorithm to detect face and correct the data and extracts the face feature vector through the FaceNet algorithm. Then, the article encrypts the facial features with the CKKS homomorphic encryption scheme and builds a database of the encrypted facial feature in the cloud server. The process of face recognition is as follows: calculate the distance between the encrypted feature vectors and the maximum value of the ciphertext result, decrypt it, and compare the threshold to determine whether it is a person. The experimental results show that when the scheme is based on the LFW data set, the threshold is 1.1236, and the recognition accuracy in the ciphertext is 94.8837%, which proves the reliability of the proposed scheme.

Modern object detectors rarely achieve short training time, fast inference speed, and high accuracy at the same time. To strike a balance among them, we propose single-scale TTFNet and multi-scale TTFNeXt. In this work, we use light-head, single-stage, and anchor-free designs, which enable fast inference speed. Then, we focus on reducing training time and improving accuracy. We notice that encoding more training samples from annotated boxes plays a similar role as increasing batch size, which helps enlarge the learning rate and accelerate the training process. To this end, we introduce a dense regression approach based on Gaussian kernels. We also show through experiments that deformable convolutions in our single-scale detector are not sufficient to handle the scale-variation problem. Therefore, we extend the single-scale detector to a multi-scale version. The multi-scale design will yield redundant detections from different pyramid levels, thus we introduce our cross-level NMS algorithm to efficiently eliminate redundant results. Experiments on MS COCO show that our TTFNet and TTFNeXt have great advantages in balancing training time, inference speed, and accuracy. They can reduce training time by more than three times compared to previous real-time detectors under similar detection accuracy and faster inference speed. When training 120 epochs, our TTFNeXt is able to achieve 33.7 AP/99 FPS and 41.8 AP/40 FPS with single GTX 1080Ti.

Oxygen involved fermentation is generally recognized as the critical process for the formation of quality of black tea. However, the specific role of oxygen plays in taste-related metabolites' alteration has not been illustrated clearly. In the present work, a series of fermentation systems with different oxygen concentrations were used to investigate the mechanism of the effects of oxygen on the quality and nonvolatile metabolites in black tea. The results showed that oxygen-enriched fermentation significantly improved the taste of black tea. And sixty-six metabolites, including catechins, theaflavins (TFs), proanthocyanidins, amino acids, flavonoid glycosides, and phenolic acids, were significantly different in the black teas fermented by three oxygen concentrations. Meanwhile, a 10-30% decrease in catechins, flavonoid glycosides and phenolic acids and a 5% increase in TFs, glutamate and glutamine in oxygen-enriched group, when compared to the control group, reduced astringency and bitterness and enhanced umami intensity. Furthermore, increased oxygen concentrations promoted the oxidation of catechins, flavonoid glycosides and some phenolic acids. And catechins oxidation in turn could accelerate the degradation of amino acids to form volatile aldehydes and also promote phenolic acids oxidation. Our results reveal the potential role of oxygen plays in the metabolites' alteration in black tea during fermentation, which gives a new insight into understanding the quality formation of black tea.

Background Red light withering significantly improves the sensory flavor qualities of tea, although changes in metabolites during this process have not been systematically studied until now. The present study comprehensively analyzes metabolites in withered tea leaves at 2-h intervals up to 12 h under red light (630 nm) and dark conditions using ultra performance liquid chromatography-high resolution mass spectrometry (untargeted metabolomics). Results Ninety-four non-volatile compounds are identified and relatively quantified, including amino acids, catechins, dimeric catechins, flavonol glycosides, glycosidically-bound volatiles, phenolic acids and nucleosides. The results show that amino acids, catechins and dimeric catechins are most affected by red light treatment. Ten free amino acids, theaflavins and theasinensin A increase after red light irradiation, whereas epigallocatechin gallate and catechin fall. Conclusion The present study provides a comprehensive and systematic profile of the dynamic effects of red light on withering tea and a rationale for its use in tea processing quality control. © 2021 Society of Chemical Industry.