Pulmonary lobe segmentation in computed tomography scans is essential for regional assessment of pulmonary diseases. Recent works based on convolution neural networks have achieved good performance for this task. However, they are still limited in capturing structured relationships due to the nature of convolution. The shape of the pulmonary lobes affect each other and their borders relate to the appearance of other structures, such as vessels, airways, and the pleural wall. We argue that such structural relationships play a critical role in the accurate delineation of pulmonary lobes when the lungs are affected by diseases such as COVID-19 or COPD. In this paper, we propose a relational approach (RTSU-Net) that leverages structured relationships by introducing a novel non-local neural network module. The proposed module learns both visual and geometric relationships among all convolution features to produce self-attention weights. With a limited amount of training data available from COVID-19 subjects, we initially train and validate RTSU-Net on a cohort of 5000 subjects from the COPDGene study (4000 for training and 1000 for evaluation). Using models pre-trained on COPDGene, we apply transfer learning to retrain and evaluate RTSU-Net on 470 COVID-19 suspects (370 for retraining and 100 for evaluation). Experimental results show that RTSU-Net outperforms three baselines and performs robustly on cases with severe lung infection due to COVID-19.In severe viral pneumonias, including Coronavirus disease 2019 (COVID-19), the viral replication phase is often followed by a hyperinflammatory reaction ('cytokine storm syndrome') that leads to acute respiratory distress syndrome and death, despite maximal supportive care. Preventing hyperinflammation is key to avoiding these outcomes. We previously demonstrated that alpha-1 adrenergic receptor antagonists ($\alpha$-blockers) can prevent cytokine storm syndrome and death in mice. Here, we conduct a retrospective analysis of patients with acute respiratory distress or pneumonia (n = 13,125 and n = 108,956, respectively) from all causes; patients who were incidentally taking $\alpha$-blockers had a reduced risk of requiring ventilation (by 35% and 16%, respectively), and a reduced risk of being ventilated and dying (by 56% and 20%, respectively), compared to non-users. Beta-adrenergic receptor antagonists had no significant effects. These results highlight the urgent need for prospective trials testing whether prophylactic $\alpha$-blockers improve outcomes in diseases with a prominent hyperinflammatory component such as COVID-19.We show that the COVID-19 pandemic under social distancing exhibits universal dynamics. The cumulative numbers of both infections and deaths quickly cross over from exponential growth at early times to a longer period of power law growth, before eventually slowing. In agreement with a recent statistical forecasting model by the IHME, we show that this dynamics is well described by the erf function. Using this functional form, we perform a data collapse across countries and US states with very different population characteristics and social distancing policies, confirming the universal behavior of the COVID-19 outbreak. We show that the predictive power of statistical models is limited until a few days before curves flatten, forecast deaths and infections assuming current policies continue and compare our predictions to the IHME models. We present simulations showing this universal dynamics is consistent with disease transmission on scale-free networks and random networks with non-Markovian transmission dynamics.We present a timely and novel methodology that combines disease estimates from mechanistic models with digital traces, via interpretable machine-learning methodologies, to reliably forecast COVID-19 activity in Chinese provinces in real-time. Specifically, our method is able to produce stable and accurate forecasts 2 days ahead of current time, and uses as inputs (a) official health reports from Chinese Center Disease for Control and Prevention (China CDC), (b) COVID-19-related internet search activity from Baidu, (c) news media activity reported by Media Cloud, and (d) daily forecasts of COVID-19 activity from GLEAM, an agent-based mechanistic model. Our machine-learning methodology uses a clustering technique that enables the exploitation of geo-spatial synchronicities of COVID-19 activity across Chinese provinces, and a data augmentation technique to deal with the small number of historical disease activity observations, characteristic of emerging outbreaks. Our model's predictive power outperforms a collection of baseline models in 27 out of the 32 Chinese provinces, and could be easily extended to other geographies currently affected by the COVID-19 outbreak to help decision makers.As the COVID-19 pandemic continues its march around the world, an unprecedented amount of open data is being generated for genetics and epidemiological research. The unparalleled rate at which many research groups around the world are releasing data and publications on the ongoing pandemic is allowing other scientists to learn from local experiences and data generated in the front lines of the COVID-19 pandemic. However, there is a need to integrate additional data sources that map and measure the role of social dynamics of such a unique world-wide event into biomedical, biological, and epidemiological analyses. For this purpose, we present a large-scale curated dataset of over 152 million tweets, growing daily, related to COVID-19 chatter generated from January 1st to April 4th at the time of writing. This open dataset will allow researchers to conduct a number of research projects relating to the emotional and mental responses to social distancing measures, the identification of sources of misinformation, and the stratified measurement of sentiment towards the pandemic in near real time.We are in the midst of a global viral pandemic, one with no cure and a high mortality rate. learn more The Human Leukocyte Antigen (HLA) gene complex plays a critical role in host immunity. We predicted HLA class I and II alleles from the transcriptome sequencing data prepared from the bronchoalveolar lavage fluid samples of five patients at the early stage of the COVID-19 outbreak. We identified the HLA-I allele A*2402 in four out of five patients, which is higher than the expected frequency (17.2%) in the South Han Chinese population. The difference is statistically significant with a p-value less than 10-4. Our analysis results may help provide future insights on disease susceptibility.