Transition through cell cycle phases requires temporal and spatial regulation of gene expression to ensure accurate chromosome duplication and segregation. This regulation involves dynamic reprogramming of gene expression at multiple transcriptional and posttranscriptional levels. In transcriptionally silent oocytes, the CPEB-family of RNAbinding proteins coordinates temporal and spatial translation regulation of stored maternal mRNAs to drive meiotic progression. CPEB1 mediates mRNA localization to the meiotic spindle, which is required to ensure proper chromosome segregation. Temporal translational regulation also takes place in mitosis, where a large repertoire of transcripts are activated or repressed in specific cell cycle phases. However, whether control of localized translation at the spindle is required for mitosis is unclear, as mitotic and acentriolar-meiotic spindles are functionally and structurally different. Furthermore, the large differences in scale-ratio between cell volume and spindle size in oocytes compared to somatic mitotic cells may generate distinct requirements for gene expression compartmentalization in meiosis and mitosis. Here we show that mitotic spindles contain CPE-localized mRNAs and translating ribosomes. Moreover, CPEB1 and CPEB4 localize in the spindles and they may function sequentially in promoting mitotic stage transitions and correct chromosome segregation. Thus, CPEB1 and CPEB4 bind to specific spindle-associated transcripts controlling the expression and/or localization of their encoded factors that, respectively, drive metaphase and anaphase/cytokinesis.Nursing homes and other long-term care facilities account for a disproportionate share of COVID-19 cases and fatalities worldwide. Outbreaks in US nursing homes have persisted despite nationwide visitor restrictions beginning in mid-March. An early report issued by the Centers for Disease Control and Prevention identified staff members working in multiple nursing homes as a likely source of spread from the Life Care Center in Kirkland, WA, to other skilled nursing facilities. The full extent of staff connections between nursing homes-and the role these connections serve in spreading a highly contagious respiratory infection-is currently unknown given the lack of centralized data on cross-facility employment. We perform a large-scale analysis of nursing home connections via shared staff and contractors using device-level geolocation data from 50 million smartphones, and find that 5.1% of smartphone users who visited a nursing home for at least 1 h also visited another facility during our 11-wk study period-even after visitor restrictions were imposed. We construct network measures of connectedness and estimate that nursing homes, on average, share connections with 7.1 other facilities. Traditional federal regulatory metrics of nursing home quality are unimportant in predicting outbreaks, consistent with recent research. Controlling for demographic and other factors, a home's staff network connections and its centrality within the greater network strongly predict COVID-19 cases.With nearly every country combating the 2019 novel coronavirus (COVID-19), there is a need to understand how local environmental conditions may modify transmission. To date, quantifying seasonality of the disease has been limited by scarce data and the difficulty of isolating climatological variables from other drivers of transmission in observational studies. We combine a spatially resolved dataset of confirmed COVID-19 cases, composed of 3,235 regions across 173 countries, with local environmental conditions and a statistical approach developed to quantify causal effects of environmental conditions in observational data settings. We find that ultraviolet (UV) radiation has a statistically significant effect on daily COVID-19 growth rates a SD increase in UV lowers the daily growth rate of COVID-19 cases by ∼1 percentage point over the subsequent 2.5 wk, relative to an average in-sample growth rate of 13.2%. The time pattern of lagged effects peaks 9 to 11 d after UV exposure, consistent with the combined timescale of incubation, testing, and reporting. Cumulative effects of temperature and humidity are not statistically significant. Simulations illustrate how seasonal changes in UV have influenced regional patterns of COVID-19 growth rates from January to June, indicating that UV has a substantially smaller effect on the spread of the disease than social distancing policies. Furthermore, total COVID-19 seasonality has indeterminate sign for most regions during this period due to uncertain effects of other environmental variables. Our findings indicate UV exposure influences COVID-19 cases, but a comprehensive understanding of seasonality awaits further analysis.The last five years marked a surge in interest for and use of smart robots, which operate in dynamic and unstructured environments and might interact with humans. We posit that well-validated computer simulation can provide a virtual proving ground that in many cases is instrumental in understanding safely, faster, at lower costs, and more thoroughly how the robots of the future should be designed and controlled for safe operation and improved performance. Against this backdrop, we discuss how simulation can help in robotics, barriers that currently prevent its broad adoption, and potential steps that can eliminate some of these barriers. The points and recommendations made concern the following simulation-in-robotics aspects simulation of the dynamics of the robot; simulation of the virtual world; simulation of the sensing of this virtual world; simulation of the interaction between the human and the robot; and, in less depth, simulation of the communication between robots. This Perspectives contribution summarizes the points of view that coalesced during a 2018 National Science Foundation/Department of Defense/National Institute for Standards and Technology workshop dedicated to the topic at hand.  https://www.selleckchem.com/products/gsk963.html  The meeting brought together participants from a range of organizations, disciplines, and application fields, with expertise at the intersection of robotics, machine learning, and physics-based simulation.