Plant synthetic biology promises immense technological benefits, including the potential development of a sustainable bio-based economy through the predictive design of synthetic gene circuits. Such circuits are built from quantitatively characterized genetic parts; however, this characterization is a significant obstacle in work with plants because of the time required for stable transformation. We describe a method for rapid quantitative characterization of genetic plant parts using transient expression in protoplasts and dual luciferase outputs. We observed experimental variability in transient-expression assays and developed a mathematical model to describe, as well as statistical normalization methods to account for, this variability, which allowed us to extract quantitative parameters. We characterized >120 synthetic parts in Arabidopsis and validated our method by comparing transient expression with expression in stably transformed plants. We also tested >100 synthetic parts in sorghum (Sorghum bicolor) protoplasts, and the results showed that our method works in diverse plant groups. Our approach enables the construction of tunable gene circuits in complex eukaryotic organisms.

The two sources of information commonly available for modeling the top of a structure, depth data from wells and geophysical measurements from seismic surveys, are often Miicult to integrate. W bile, the well data provide t he most accurate measurements of depths there are rarely enough wells to permit an accurate appraisal from well data alone. On the other hand, the seismic data are generally less precise but more abundant. Two geoatatistical methods, "external drift~and "collocated cokrigingfi, are proposed to integrate the two sources of information. A case study is used to document the strengths and weaknesses of both approaches for constructing contour maps cf the top structure and assessing the uncertainty on such maps through stochastic simulations.

Background To combat the COVID-19 pandemic, nonpharmaceutical interventions (NPI) were implemented worldwide, which impacted a broad spectrum of acute respiratory infections (ARI). Methods Etiologically diagnostic data from 142 559 cases with ARIs, who were tested for eight viral pathogens (influenza virus, IFV; respiratory syncytial virus, RSV; human parainfluenza virus, HPIV; human adenovirus; human metapneumovirus; human coronavirus, HCoV; human bocavirus, HBoV, and human rhinovirus, HRV) between 2012 and 2021, were analyzed to assess the changes of respiratory infections in China during the first COVID-19 pandemic year compared to pre-pandemic years. Results Test positive rates of all respiratory viruses decreased during 2020, compared to the average levels during 2012−2019, with changes ranging from -17·2% for RSV to -87·6% for IFV. Sharp decreases mostly occurred between February and August when massive NPIs remained active, although HRV rebounded to the historical level during the summer. While IFV and HMPV were consistently suppressed year round, RSV, HPIV, HCoV, HRV HBov resurged and went beyond historical levels during September, 2020−January, 2021, after NPIs were largely relaxed and schools reopened. Resurgence was more prominent among children younger than 18 years and in Northern China. These observations remain valid after accounting for seasonality and long-term trend of each virus. Conclusions Activities of respiratory viral infections were reduced substantially in the early phases of the COVID-19 pandemic, and massive NPIs were likely the main driver. Lifting of NPIs can lead to resurgence of viral infections, particularly in children.

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