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Abstract Evolutionary perspectives on the deployment of immune factors following infection have been shaped by studies on a limited number of biomedical model systems with a heavy emphasis on vertebrate species. Although their contributions to contemporary immunology cannot be understated, a broader phylogenetic perspective is needed to understand the evolution of immune systems across Metazoa. In our study, we leverage differential gene expression analyses to identify genes implicated in the antiviral immune response of the acorn worm hemichordate, Saccoglossus kowalevskii, and place them in the context of immunity evolution within deuterostomes—the animal clade composed of chordates, hemichordates, and echinoderms. Following acute exposure to the synthetic viral double-stranded RNA analog, poly(I:C), we show that S. kowalevskii responds by regulating the transcription of genes associated with canonical innate immunity signaling pathways (e.g., nuclear factor κB and interferon regulatory factor signaling) and metabolic processes (e.g., lipid metabolism), as well as many genes without clear evidence of orthology with those of model species. Aggregated across all experimental time point contrasts, we identify 423 genes that are differentially expressed in response to poly(I:C). We also identify 147 genes with altered temporal patterns of expression in response to immune challenge. By characterizing the molecular toolkit involved in hemichordate antiviral immunity, our findings provide vital evolutionary context for understanding the origins of immune systems within Deuterostomia. 

Abstract BackgroundThere are a wide range of developmental strategies in animal phyla, but most insights into adult body plan formation come from direct-developing species. For indirect-developing species, there are distinct larval and adult body plans that are linked together by metamorphosis. Some outstanding questions in the development of indirect-developing organisms include the extent to which larval tissue undergoes cell death during the process of metamorphosis and when and where the tissue that will give rise to the adult originates. How do the processes of cell division and cell death redesign the body plans of indirect developers? In this study, we present patterns of cell proliferation and cell death during larval body plan development, metamorphosis, and adult body plan formation, in the hemichordateSchizocardium californium(Cameron and Perez in Zootaxa 3569:79–88, 2012) to answer these questions. ResultsWe identified distinct patterns of cell proliferation between larval and adult body plan formation ofS. californicum. We found that some adult tissues proliferate during the late larval phase prior to the start of overt metamorphosis. In addition, using an irradiation and transcriptomic approach, we describe a genetic signature of proliferative cells that is shared across the life history states, as well as markers that are unique to larval or juvenile states. Finally, we observed that cell death is minimal in larval stages but begins with the onset of metamorphosis. ConclusionsCell proliferation during the development ofS. californicumhas distinct patterns in the formation of larval and adult body plans. However, cell death is very limited in larvae and begins during the onset of metamorphosis and into early juvenile development in specific domains. The populations of cells that proliferated and gave rise to the larvae and juveniles have a genetic signature that suggested a heterogeneous pool of proliferative progenitors, rather than a set-aside population of pluripotent cells. Taken together, we propose that the gradual morphological transformation ofS. californicumis mirrored at the cellular level and may be more representative of the development strategies that characterize metamorphosis in many metazoan animals. 

Non-luminescent, isostructural crystals of [(C 6 H 11 NC) 2 Au](EF 6 )·C 6 H 6 (E = As, Sb) lose benzene upon standing in air to produce green luminescent (E = As) or blue luminescent (E = Sb) powders. Previous studies have shown that the two-coordinate cation, [(C 6 H 11 NC) 2 Au] + , self-associates to form luminescent crystals that contain linear or nearly linear chains of cations and display unusual polymorphic, vapochromic, and/or thermochromic properties. Here, we report the formation of non-luminescent crystalline salts in which individual [(C 6 H 11 NC) 2 Au] + ions are isolated from one another. In [(C 6 H 11 NC) 2 Au](BArF 24 ) ((BArF 24 ) − is tetrakis[3,5-bis(trifluoromethyl)phenyl]borate) each cation is surrounded by two anions that prohibit any close approach of the gold ions. Crystallization of [(C 6 H 11 NC) 2 Au](EF 6 ) (E = As or Sb, but not P) from benzene solution produces colorless, non-emissive crystals of the solvates [(C 6 H 11 NC) 2 Au](EF 6 )·C 6 H 6 . These two solvates are isostructural and contain columns in which cations and benzene molecules alternate. With the benzene molecules separating the cations, the shortest distances between gold ions are 6.936(2) Å for E = As and 6.9717(19) Å for E = Sb. Upon removal from the mother liquor, these crystals crack due to the loss of benzene from the crystal and form luminescent powders. Crystals of [(C 6 H 11 NC) 2 Au](SbF 6 )·C 6 H 6 that powder out form a pale yellow powder with a blue luminescence with emission spectra and powder X-ray diffraction data that show that the previously characterized [(C 6 H 11 NC) 2 Au](SbF 6 ) is formed. In the process, the distances between the gold( i ) ions decrease to ∼3 Å and half of the cyclohexyl groups move from an axial orientation to an equatorial one. Remarkably, when crystals of [(C 6 H 11 NC) 2 Au](AsF 6 )·C 6 H 6 stand in air, they lose benzene and are converted into the yellow, green-luminescent polymorph of [(C 6 H 11 NC) 2 Au](AsF 6 ) rather than the colorless, blue-luminescent polymorph. Paradoxically, the yellow, green-luminescent powder that forms as well as authentic crystals of the yellow, green-luminescent polymorph of [(C 6 H 11 NC) 2 Au](AsF 6 ) are sensitive to benzene vapor and are converted by exposure to benzene vapor into the colorless, blue-luminescent polymorph. 

Abstract Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages. 

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub ( https://covid19forecasthub.org/ ) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks.