RESUMO
Green roofs are effective tools for stormwater control in highly urbanized areas since they allow the reduction of peak runoffs and volumes discharged in sewer systems. Their design is quite standardized, except for the thickness of the growing medium layer, which is strictly related to vegetation type and rainfall regime. The paper proposes an analytical probabilistic approach that relates the climatic variables, the growing medium thickness, and the water content in the condition of fulfilled field capacity to the probability that runoff from green roofs exceeds a fixed threshold. The developed equations also consider the possibility of a reduced retention capacity due to previous rainfall events, that strongly influence the performance of these green infrastructures, especially when short dry periods and/or low evapotranspiration rates occur. This feature, neglected by the traditional design storm approach, and only partially considered by previous analytical probabilistic models, represent a great potentiality of the proposed equations that are also more user-friendly and less time-consuming than continuous simulation analysis. The focus of the paper is on the influence of climatic parameters on runoff probability. To this aim to perform the monthly analysis is fundamental, especially when there is a strong variability of the climatic parameters throughout the year. The model was tested in a case study in Milano, Italy. The application presented a good agreement between the results obtained from the proposed equations and those obtained from the continuous simulation of recorded data. The results also highlighted the importance of performing analysis on a monthly scale.
RESUMO
The pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causing coronavirus disease 2019 (COVID-19), resulting in acute respiratory disease, is a worldwide emergency. Because recently it has been found that SARS-CoV is dependent on host transcription factors (TF) to express the viral genes, efforts are required to understand the molecular interplay between virus and host response. By bioinformatic analysis, we investigated human TF that can bind the SARS-CoV-2 sequence and can be involved in viral transcription. In particular, we analysed the key role of TF involved in interferon (IFN) response. We found that several TF could be induced by the IFN antiviral response, specifically some induced by IFN-stimulated gene factor 3 (ISGF3) and by unphosphorylated ISGF3, which were found to promote the transcription of several viral open reading frame. Moreover, we found 22 TF binding sites present only in the sequence of virus infecting humans but not bat coronavirus RaTG13. The 22 TF are involved in IFN, retinoic acid signalling and regulation of transcription by RNA polymerase II, thus facilitating its own replication cycle. This mechanism, by competition, may steal the human TF involved in these processes, explaining SARS-CoV-2's disruption of IFN-I signalling in host cells and the mechanism of the SARS retinoic acid depletion syndrome leading to the cytokine storm. We identified three TF binding sites present exclusively in the Brazilian SARS-CoV-2 P.1 variant that may explain the higher severity of the respiratory syndrome. These data shed light on SARS-CoV-2 dependence from the host transcription machinery associated with IFN response and strengthen our knowledge of the virus's transcription and replicative activity, thus paving the way for new targets for drug design and therapeutic approaches.
