Background: Coronavirus disease (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is currently a pandemic affecting over 200 countries. Many cities have established designated fever clinics to triage suspected COVID-19 patients from other patients with similar symptoms. However, given the limited availability of the nucleic acid test as well as long waiting time for both the test and radiographic examination, the quarantine or therapeutic decisions for a large number of mixed patients were often not made in time. We aimed to identify simple and quickly available laboratory biomarkers to facilitate effective triage at the fever clinics for sorting suspected COVID-19 patients from those with COVID-19-like symptoms. Methods: We collected clinical, etiological, and laboratory data of 989 patients who visited the Fever Clinic at Wuhan Union Hospital, Wuhan, China, from Jan 31 to Feb 21. Based on polymerase chain reaction (PCR) nucleic acid testing for SARS-CoV-2 infection, they were divided into two groups: SARS-CoV-2-positive patients as cases and SARS-CoV-2-negative patients as controls. We compared the clinical features and laboratory findings of the two groups, and analyzed the diagnostic performance of several laboratory parameters in predicting SARS-CoV-2 infection and made relevant comparisons to the China diagnosis guideline of having a normal or decreased number of leukocytes (9¢5 10 9 /L) or lymphopenia (<1¢1 10 9 /L). Findings: Normal or decreased number of leukocytes (9¢5 10 9 /L), lymphopenia (<1¢1 10 9 /L), eosinopenia (<0¢02 10 9 /L), and elevated hs-CRP (4 mg/L) were presented in 95¢0%, 52¢2%, 74¢7% and 86¢7% of COVID-19 patients, much higher than 87¢2%, 28¢8%, 31¢3% and 45¢2% of the controls, respectively. The eosinopenia produced a sensitivity of 74¢7% and specificity of 68¢7% for separating the two groups with the area under the curve (AUC) of 0¢717. The combination of eosinopenia and elevated hs-CRP yielded a sensitivity of 67¢9% and specificity of 78¢2% (AUC=0¢730). The addition of eosinopenia alone or the combination of eosinopenia and elevated hs-CRP into the guideline-recommended diagnostic parameters for COVID-19 improved the predictive capacity with higher than zero of both net reclassification improvement (NRI) and integrated discrimination improvement (IDI). Interpretation: The combination of eosinopenia and elevated hs-CRP can effectively triage suspected COVID-19 patients from other patients attending the fever clinic with COVID-19-like initial symptoms. This finding would be particularly useful for designing triage strategies in an epidemic region having a large number of patients with COVID-19 and other respiratory diseases while limited medical resources for nucleic acid tests and radiographic examination.

Abstract-Graphs are popular models for representing complex structure data and similarity search for graphs has become a fundamental research problem. Many techniques have been proposed to support similarity search based on the graph edit distance. However, they all suffer from certain drawbacks: high computational complexity, poor scalability in terms of database size, or not taking full advantage of indexes. To address these problems, in this paper, we propose SEGOS, an indexing and query processing framework for graph similarity search. First, an effective two-level index is constructed off-line based on sub-unit decomposition of graphs. Then, a novel search strategy based on the index is proposed. Two algorithms adapted from TA and CA methods are seamlessly integrated into the proposed strategy to enhance graph search. More specially, the proposed framework is easy to be pipelined to support continuous graph pruning. Extensive experiments are conducted on two real datasets to evaluate the effectiveness and scalability of our approaches.

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The total number of COVID-19 patients since the outbreak of this infection in Wuhan, China has reached 40000 and are still growing. To facilitate triage or identification of the large number of COVID-19 patients from other patients with similar symptoms in designated fever clinics, we set to identify a practical marker that could be conveniently utilized by first-line health-care workers in clinics. To do so, we performed a case-control study by analyzing clinical and laboratory findings between PCR-confirmed SARS-CoV-2 positive patients (n=52) and SARS-CoV-2 negative patients (n=53). The patients in two cohorts all had similar symptoms, mainly fever and respiratory symptoms. The rates of patients with leukocyte counts (normal or decreased number) or lymphopenia (two parameters suggested by current National and WHO COVID-19 guidelines) had no differences between these two cohorts, while the rate of eosinopenia (decreased number of eosinophils) in SARS-CoV-2 positive patients (79%) was much higher than that in SARS-CoV-2 negative patients (36%). When the symptoms were combined with eosinopenia, this combination led to a diagnosis sensitivity and specificity of 79% and 64%, respectively, much higher than 48% and 53% when symptoms were combined with leukocyte counts (normal or decreased number) and/ or lymphopenia. Thus, our analysis reveals that eosinopenia may be a potentially more reliable laboratory predictor for SARS-CoV-2 infection than leukocyte counts and lymphopenia recommended by the current guidelines.

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The skyline operator has received considerable attention from the database community, due to its importance in many applications including multi-criteria decision making, preference answering, and so forth. In many applications where uncertain data are inherently exist, i.e., data collected from different sources in distributed locations are usually with imprecise measurements, and thus exhibit kind of uncertainty. Taking into account the network delay and economic cost associated with sharing and communicating large amounts of distributed data over an internet, an important problem in this scenario is to retrieve the global skyline tuples from all the distributed local sites with minimum communication cost. Based on the well known notation of the probabilistic skyline query over centralized uncertain data, in this paper, for the first time, we propose the notation of distributed skyline queries over uncertain data. Furthermore, two communication-and computation-efficient algorithms are proposed to retrieve the qualified skylines from distributed local sites. Extensive experiments have been conducted to verify the efficiency and the effectiveness of our algorithms with both the synthetic and real data sets.

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