This study is the first to characterise gut microbiome in patients with HCC and to report the successful diagnosis model establishment and cross-region validation of microbial markers for HCC. Gut microbiota-targeted biomarkers represent potential non-invasive tools for early diagnosis of HCC.

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Coronavirus disease 2019 (COVID-19) has become a public health emergency. The reverse transcriptase real-time quantitative PCR (qRT-PCR) test is currently considered as the gold standard in the laboratory for the etiological detection of COVID-19. However, qRT-PCR results could be false-negative due to the inadequate sensitivity of qRT-PCR. In this study, we have developed and evaluated a novel one-step single-tube nested quantitative real-time PCR (OSN-qRT-PCR) assay for the highly sensitive detection of SARS-CoV-2 targeting the ORF1ab and N genes. The sensitivity of the OSN-qRT-PCR assay was 1 copy/ reaction and 10-fold higher than that of the commercial qRT-PCR kit (10 copies/reaction). The clinical performance of the OSN-qRT-PCR assay was evaluated using 181 clinical samples. Among them, 14 qRT-PCR-negative samples (7 had no repetitive results and 7 had no cycle threshold (CT) values) were detected by OSN-qRT-PCR. Moreover, the 7 qRT-PCR-positives in the qRT-PCR gray zone (CT values of ORF1ab ranged from 37.48 to 39.07, and CT values of N ranged from 37.34 to 38.75) were out of the gray zone and thus were deemed to be positive by OSN-qRT-PCR, indicating that the positivity of these samples is confirmative. Compared to the qRT-PCR kit, the OSN-qRT-PCR assay revealed higher sensitivity and specificity, showing better suitability to clinical applications for the detection of SARS-CoV-2 in patients with low viral load.

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Predicting traffic conditions has been recently explored as a way to relieve traffic congestion. Several pioneering approaches have been proposed based on traffic observations of the target location as well as its adjacent regions, but they obtain somewhat limited accuracy due to lack of mining road topology. To address the effect attenuation problem, we propose to take account of the traffic of surrounding locations(wider than adjacent range). We propose an endto-end framework called DeepTransport, in which Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) are utilized to obtain spatial-temporal traffic information within a transport network topology. In addition, attention mechanism is introduced to align spatial and temporal information. Moreover, we constructed and released a real-world large traffic condition dataset with 5-minute resolution. Our experiments on this dataset demonstrate our method captures the complex relationship in temporal and spatial domain. It significantly outperforms traditional statistical methods and a state-of-the-art deep learning method. * Xingyi Cheng is the corresponding author.† main contribution