Enhanced survival but not amplification of Francisella spp. in the presence of free-living amoebae

Transmission of Francisella tularensis, the etiologic agent of tularemia, has been associated with various water sources. Survival of many waterborne pathogens within free-living amoeba (FLA) is well documented; however, the role of amoebae in the environmental persistence of F. tularensis is unclear. In this study, axenic FLA cultures of Acanthamoeba castellanii, Acanthamoeba polyphaga, and Vermamoeba vermiformis were each inoculated with virulent strains of F. tularensis (Types A and B), the attenuated live vaccine strain, and Francisella novicida. Experimental parameters included low and high multiplicity of infection and incubation temperatures of 25 and 30 °C for 0–10 days. Francisella spp. survival was enhanced by the presence of FLA; however, bacterial growth and protozoa infectivity were not observed. In contrast, co-infections of A. polyphaga and Legionella pneumophila, used as an amoeba pathogen control, resulted in bacterial proliferation, cytopathic effects, and amoebal lysis. Collectively, even though short-term incubation with FLA was beneficial, the long-term effects on Francisella survival are unknown, especially given the expenditure of available amoebal derived nutrients and the fastidious nature of Francisella spp. These factors have clear implications for the role of FLA in Francisella environmental persistence

Microbial diversities (16S and 18S rRNA gene pyrosequencing) and environmental pathogens within drinking water biofilms grown on the common premise plumbing materials unplasticized polyvinylchloride and copper

Drinking water (DW) biofilm communities influence the survival of opportunistic pathogens, yet knowledge about the microbial composition of DW biofilms developed on common in-premise plumbing material is limited. Utilizing 16S and 18S rRNA gene pyrosequencing, this study characterized the microbial community structure within DW biofilms established on unplasticized polyvinyl chloride (uPVC) and copper (Cu) surfaces and the impact of introducing Legionella pneumophila (Lp) and Acanthamoeba polyphaga. Mature (\u3e 1 year old) biofilms were developed before inoculation with sterilized DW (control, Con), Lp, or Lp and A. polyphaga (LpAp). Comparison of uPVC and Cu biofilms indicated significant differences between bacterial (P = 0.001) and eukaryotic (P \u3c 0.01) members attributable to the unique presence of several family taxa: Burkholderiaceae, Characeae, Epistylidae, Goniomonadaceae, Paramoebidae, Plasmodiophoridae, Plectidae, Sphenomonadidae, and Toxariaceae within uPVC biofilms; and Enterobacteriaceae, Erythrobacteraceae, Methylophilaceae, Acanthamoebidae, and Chlamydomonadaceae within Cu biofilms. Introduction of Lp alone or with A. polyphaga had no effect on bacterial community profiles (P \u3e 0.05) but did affect eukaryotic members (uPVC, P \u3c 0.01; Cu, P = 0.001). Thus, established DW biofilms host complex communities that may vary based on substratum matrix and maintain consistent bacterial communities despite introduction of Lp, an environmental pathogen

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

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

The United States COVID-19 Forecast Hub dataset

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

Differences in UV-C LED Inactivation of Legionella pneumophila Serogroups in Drinking Water

Legionella pneumophila (Lp) is an opportunistic pathogen that causes respiratory infections primarily through inhalation of contaminated aerosols. Lp can colonize premise plumbing systems due to favorable growth conditions (e.g., lower disinfectant residual, stagnation, warm temperatures). UV-C light-emitting diodes (UV-C LEDs) are an emerging water treatment technology and have been shown to effectively inactivate waterborne pathogens. In this study, the inactivation of four Lp strains (three clinical sg1, 4, and 6; and one sg1 drinking water (DW) isolate) was evaluated using a UV-C LED collimated beam at three wavelengths (255, 265, and 280 nm) and six fluence rates (0.5–34 mJ/cm2). Exposure to 255 nm resulted in higher log reductions at the lower fluences compared to exposures at 265 and 280 nm. Efficacy testing was also performed using a UV-C LED point-of-entry (POE) flow-through device. Based on the log inactivation curves, at 255 nm, the sg4 and sg6 clinical isolates were more susceptible to inactivation compared to the two sg1 isolates. However, at 265 and 280 nm, the sg1 and sg4 clinical isolates were more resistant to inactivation compared to the sg6 clinical and sg1 DW isolates. Differential log reductions were also observed using the POE device. Results indicate that although UV-C LED disinfection is effective, variations in Lp inactivation, wavelengths, and technology applications should be considered, especially when targeting specific isolates within premise plumbing systems

Fig 3 -

Culture (A) and qPCR (B) analysis of sediment samples from storage tanks ET-1 and ET-2. Culture results are expressed as mean log10 CFU g-1 for heterotrophic plate counts (using the R2A and PC methods) or MPN g-1 for P. aeruginosa and total coliform (TC). qPCR results are expressed as mean log10 gene copies (GC) g-1.</p

Processing of storage tank sediment samples.

Sediment samples from ET-1 were collected into four bottles (A) and from ET-2 into two bottles (B). The liquid phase from each bottle (C, D) was decanted into separate sterile containers (e.g., glass 1L bottle shown on the left side of panels C and E). Small aliquots of the liquid phase were placed in 50mL conical tubes. Settled and resuspended particles in the sediment liquid phase are shown for ET-1 (E) and ET-2 (F). (TIF)</p

Fig 2 -

Temporal disinfectant residual summary for distribution (A-C) and residential (D-F) sites. Sampling occurred weekly before (wk-4 to wk-1), during (wk0 to wk5), and after (wk6 to wk10) the FCC period. Monochloramine (A, D), free chlorine (B, E), and total chlorine (C, F) levels are shown in triangle, circle, and square symbols, respectively. Each sampling location is represented by different colors (EP, black; MRT, grey; STa, light green; STb, light blue; RG, pink; RT, orange; RW, dark green; RC, dark blue). nd, no data, for STa and STb during week 5, for RC during week 4, and for RW during week 7.</p

Kitchen faucet type, plumbing materials, and configuration for residential sites.

Images were taken by homeowners for the RG (A), RT (B), RW (C), and RC (D) kitchen sampling location used in this study. The under sink plumbing consists of: plastic hoses from the valve to the fixture with the valve connected to copper plumbing at RG (A); braided polymer tubing from the valve to fixture with the valve is connected to copper plumbing at RT (B); braided polymer and plastic tubing from the valve to fixture with the valve connected to copper plumbing at RW (C); and cross-linked polyethylene (PEX) and copper materials from the valve to fixture with the valve is connected to copper plumbing at RC (D). Note that drain line materials and components are not included in this plumbing materials list. Published with permission from the USEPA Region 6 participating drinking water utility. (TIF)</p

S3 Fig -

Temporal physiochemical parameters summary for distribution (A-C) and residential (D-F) sites. Sampling occurred weekly before (wk-4 to -1), during (wk0 to 5), and after (wk6 to 10) the FCC period. pH (A, E), temperature (B, F), turbidity (C, G), and hardness (D, H) levels are shown in the down triangle, hexagon, star symbols, and cross-hatched circle symbols, respectively. Each sampling location is represented by different colors (EP, black; MRT, grey; STa, light green; STb, light blue; RG, pink; RT, orange; RW, dark green; RC, dark blue). nd, no data, for STa and STb during week 5, for RC during week 4, and for RW during week 7. (TIF)</p