Breaking Down the Computational Barriers to Real‐Time Urban Flood Forecasting

Flooding impacts are on the rise globally, and concentrated in urban areas. Currently, there are no operational systems to forecast flooding at spatial resolutions that can facilitate emergency preparedness and response actions mitigating flood impacts. We present a framework for real‐time flood modeling and uncertainty quantification that combines the physics of fluid motion with advances in probabilistic methods. The framework overcomes the prohibitive computational demands of high‐fidelity modeling in real‐time by using a probabilistic learning method relying on surrogate models that are trained prior to a flood event. This shifts the overwhelming burden of computation to the trivial problem of data storage, and enables forecasting of both flood hazard and its uncertainty at scales that are vital for time‐critical decision‐making before and during extreme events. The framework has the potential to improve flood prediction and analysis and can be extended to other hazard assessments requiring intense high‐fidelity computations in real‐time.Plain Language SummaryCurrently, we cannot forecast flooding depths and extent in real‐time at a high level of detail in urban areas. This is the result of two key issues: detailed and accurate flood modeling requires a lot of computing power for large areas such as a city, and uncertainty in precipitation forecasts is high. We present an innovative flood forecasting method that resolves flood characteristics with enough detail to inform emergency response efforts such as timely road closures and evacuation. This is achieved by performing complex analysis of information on flooding impacts well before a future storm event, which subsequently allows much faster predictions when flooding actually happens. This approach completely changes the demand for required resources, replacing the nearly impossible burden of computation in real‐time with the easy problem of data storage, feasible even with a low‐end computer. Example results for Hurricane Harvey flooding in Houston, TX, show that predictions of both flood hazard and uncertainty work well over different areas of the city. This approach has the potential to provide timely and detailed information for emergency response efforts to help save lives and reduce other negative impacts during major flood events and other natural hazards.Key PointsThere is presently no means to forecast urban flooding at high resolution due to prohibitive computational demands and data uncertaintiesProposed framework combines high‐fidelity modeling and probabilistic learning to forecast flood attributes with uncertainty in real‐timeThe framework can be extended to other real‐time hazard forecasting, requiring high‐fidelity simulations of extreme computational demandPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/170850/1/2021GL093585-sup-0001-Supporting_Information_SI-S01.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/170850/2/grl63104_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/170850/3/grl63104.pd

Targeted resequencing in epileptic encephalopathies identifies de novo mutations in CHD2 and SYNGAP1

Epileptic encephalopathies are a devastating group of epilepsies with poor prognosis, of which the majority are of unknown etiology. We perform targeted massively parallel resequencing of 19 known and 46 candidate genes for epileptic encephalopathy in 500 affected individuals (cases) to identify new genes involved and to investigate the phenotypic spectrum associated with mutations in known genes. Overall, we identified pathogenic mutations in 10% of our cohort. Six of the 46 candidate genes had 1 or more pathogenic variants, collectively accounting for 3% of our cohort. We show that de novo CHD2 and SYNGAP1 mutations are new causes of epileptic encephalopathies, accounting for 1.2% and 1% of cases, respectively. We also expand the phenotypic spectra explained by SCN1A, SCN2A and SCN8A mutations. To our knowledge, this is the largest cohort of cases with epileptic encephalopathies to undergo targeted resequencing. Implementation of this rapid and efficient method will change diagnosis and understanding of the molecular etiologies of these disorders

Sex differences in immune responses that underlie COVID-19 disease outcomes

There is increasing evidence that coronavirus disease 2019 (COVID-19) produces more severe symptoms and higher mortality among men than among women1,2,3,4,5. However, whether immune responses against severe acute respiratory syndrome coronavirus (SARS-CoV-2) differ between sexes, and whether such differences correlate with the sex difference in the disease course of COVID-19, is currently unknown. Here we examined sex differences in viral loads, SARS-CoV-2-specific antibody titres, plasma cytokines and blood-cell phenotyping in patients with moderate COVID-19 who had not received immunomodulatory medications. Male patients had higher plasma levels of innate immune cytokines such as IL-8 and IL-18 along with more robust induction of non-classical monocytes. By contrast, female patients had more robust T cell activation than male patients during SARS-CoV-2 infection. Notably, we found that a poor T cell response negatively correlated with patients’ age and was associated with worse disease outcome in male patients, but not in female patients. By contrast, higher levels of innate immune cytokines were associated with worse disease progression in female patients, but not in male patients. These findings provide a possible explanation for the observed sex biases in COVID-19, and provide an important basis for the development of a sex-based approach to the treatment and care of male and female patients with COVID-19