Objectives We aimed to gain an understanding of the paradox of the immunity in COVID‐19 patients with T cells showing both functional defects and hyperactivation and enhanced proliferation. Methods A total of 280 hospitalised patients with COVID‐19 were evaluated for cytokine profiles and clinical features including viral shedding. A mouse model of acute infection by lymphocytic choriomeningitis virus (LCMV) was applied to dissect the relationship between immunological, virological and pathological features. The results from the mouse model were validated by published data set of single‐cell RNA sequencing (scRNA‐seq) of immune cells in bronchoalveolar lavage fluid (BALF) of COVID‐19 patients. Results The levels of soluble CD25 (sCD25), IL‐6, IL‐8, IL‐10 and TNF‐α were higher in severe COVID‐19 patients than non‐severe cases, but only sCD25 was identified as an independent risk factor for disease severity by multivariable binary logistic regression analysis and showed a positive association with the duration of viral shedding. In agreement with the clinical observation, LCMV‐infected mice with high levels of sCD25 demonstrated insufficient anti‐viral response and delayed viral clearance. The elevation of sCD25 in mice was mainly contributed by the expansion of CD25+CD8+ T cells that also expressed the highest level of PD‐1 with pro‐inflammatory potential. The counterpart human CD25+PD‐1+ T cells were expanded in BALF of COVID‐19 patients with severe disease compared to those with modest disease. Conclusion These results suggest that high levels of sCD25 in COVID‐19 patients probably result from insufficient anti‐viral immunity and indicate an expansion of pro‐inflammatory T cells that contribute to disease severity.

In this paper, we study the problem of approximate containment similarity search. Given two records Q and X, the containment similarity between Q and X with respect to Q is |Q∩X| |Q| . Given a query record Q and a set of records S, the containment similarity search finds a set of records from S whose containment similarity regarding Q is not less than the given threshold. This problem has many important applications in commercial and scientific fields such as record matching and domain search. Existing solution relies on the asymmetric LSH method by transforming the containment similarity to well-studied Jaccard similarity. In this paper, we use a inherently different framework by transforming the containment similarity to set intersection. We propose a novel augmented KMV sketch technique, namely GB-KMV, which is data-dependent and can achieve a much better trade-off between the sketch size and the accuracy. We provide a set of theoretical analysis to underpin the proposed augmented KMV sketch technique, and show that it outperforms the state-ofthe-art technique LSH-E in terms of estimation accuracy under practical assumption. Our comprehensive experiments on real-life datasets verify that GB-KMV is superior to LSH-E in terms of the space-accuracy trade-off, time-accuracy trade-off, and the sketch construction time. For instance, with similar estimation accuracy (F-1 score), GB-KMV is over 100 times faster than LSH-E on several real-life datasets.

In this paper, we study the problem of selectivity estimation on set containment search. Given a query record Q and a record dataset S , we aim to accurately and efficiently estimate the selectivity of set containment search of query Q over S. We first extend existing distinct value estimating techniques to solve this problem and develop an inverted list and G-KMV sketchbased approach IL-GKMV. We analyze that the performance of IL-GKMV degrades with the increase in vocabulary size. Motivated by limitations of existing techniques and the inherent challenges of the problem, we resort to developing effective and efficient sampling approaches and propose an ordered trie structure-based sampling approach named OT-Sampling. OT-Sampling partitions records based on element frequency and occurrence patterns and is significantly more accurate compared with simple random sampling method and IL-GKMV. To further enhance the performance, a divide-and-conquer-based sampling approach, DC-Sampling, is presented with an inclusion/exclusion prefix to explore the pruning opportunities. Meanwhile, we consider weighted set containment selectivity estimation and devise stratified random sampling approach named StrRS. We theoretically analyze the proposed techniques regarding various accuracy estimators. Our comprehensive experiments on nine real datasets verify the effectiveness and efficiency of our proposed techniques.