Due to the shortage of COVID-19 viral testing kits, radiology imaging is used to complement the screening process. Deep learning based methods are promising in automatically detecting COVID-19 disease in chest x-ray images. Most of these works first train a Convolutional Neural Network (CNN) on an existing large-scale chest x-ray image dataset and then fine-tune the model on the newly collected COVID-19 chest x-ray dataset, often at a much smaller scale. However, simple fine-tuning may lead to poor performance for the CNN model due to two issues, firstly the large domain shift present in chest x-ray datasets and secondly the relatively small scale of the COVID-19 chest x-ray dataset. In an attempt to address these two important issues, we formulate the problem of COVID-19 chest x-ray image classification in a semi-supervised open set domain adaptation setting and propose a novel domain adaptation method, Semi-supervised Open set Domain Adversarial network (SODA). SODA is designed to align the data distributions across different domains in the general domain space and also in the common subspace of source and target data. In our experiments, SODA achieves a leading classification performance compared with recent state-of-the-art models in separating COVID-19 with common pneumonia. We also present initial results showing that SODA can produce better pathology localizations in the chest x-rays.

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Controlling total mRNA content differences between cell populations is critical in comparative transcriptomic measurements. Due to poor compatibility with ERCC, a good control for droplet-based scRNA-seq is yet to be discovered. Normalizing cells to a common count distribution has been adopted as a silent compromise. Such practice profoundly confounds downstream analysis and mislead discoveries. We present TOMAS, a computational framework that derives total mRNA content ratios between cell populations via deconvoluting their heterotypic doublets. Experiments showed that cell types can have total mRNA differences by many folds and TOMAS can accurately infer the ratios between them. We demonstrate that TOMAS corrects bias in downstream analysis and rectifies a plethora of previously counter-intuitive or inconclusive analytical results. We argue against the opinion that doublets are undesired scale-limiting factors and revealed the unique value of doublets as controls in scRNA-seq. We advocate for their essential role in future large-scale scRNA-seq experiments.

Objective To explore the clinical value of transthoracic echocardiography (TTE) in the differentiation of Supracardiac Anomalous Pulmonary Venous Connection (SAPVC) in children.Materials and methods A total of 118 children with concurrent TTE and CT databases of cases diagnosed with SAPVCs were included. We analyzed the consistency between the two for the ability to diagnose the classi cation of SAPVC, drainage sites, ectopic pulmonary veins and the segments of superior vena cava (SVC). ResultsThe consistency between TTE and CT in diagnosing the existence of SAPVC and the classi cation were 88.1% (95% CI: 80.9%-93.4%) and 91.0% (95% CI: 84.1%-95.6%), respectively. The error rate of partial type diagnosed by TTE was signi cantly higher than that of total and mixed type (20.5% vs. 2.8%, P=0.003). The consistency between TTE and CT to determine drainage sites was 91.9% (95% CI: 85.2%-96.2%). TTE had a signi cantly higher error rate in determining pulmonary vein drainage to the SVC than in those draining into the left innominate vein (17.5 vs. 2.5%, P=0.007). The consistency of TTE and CT in judging the number of veins was 87.4% (95% CI: 79.7%-92.9%). The error rate in determining the presence of 2 and 5 ectopic pulmonary veins was signi cantly higher than those of 1 and 4 veins (P<0.05). ConclusionTTE for diagnosing partial SAPVC and identifying the drainage site of SVC has a high error rate of misdiagnosis and missed diagnosis. The extra attention should be given to these factors in clinical practice to improve the accuracy of TTE in diagnosing SAPVC.