Ecological Niche Modeling of Francisella tularensis Subspecies and Clades in the United States

Two subspecies of Francisella tularensis are recognized: F. tularensis subsp. tularensis (type A) and F. tularensis subsp. holartica (type B). Type A has been subdivided further into A1a, A1b, and A2, which differ geographically and clinically. The aim of this work was to determine whether or not differences among subspecies and clades translate into distinct ecological niches. We used 223 isolates from humans and wildlife representing all six genotypes (type A, B, A1, A2, A1a, or A1b). Ecological-niche models were built independently for each genotype, using the genetic algorithm for rule-set prediction. The resulting models were compared using a non-parametric multivariate analysis-of-variance method. A1 and A2 are ecologically distinct, supporting the previously observed geographic division, whereas ecological niches for types A and B overlapped notably but A1a and A1b displayed no appreciable differences in their ecological niches

Exportation of Monkeypox Virus From the African Continent.

BACKGROUND: The largest West African monkeypox outbreak began September 2017, in Nigeria. Four individuals traveling from Nigeria to the United Kingdom (n = 2), Israel (n = 1), and Singapore (n = 1) became the first human monkeypox cases exported from Africa, and a related nosocomial transmission event in the United Kingdom became the first confirmed human-to-human monkeypox transmission event outside of Africa. METHODS: Epidemiological and molecular data for exported and Nigerian cases were analyzed jointly to better understand the exportations in the temporal and geographic context of the outbreak. RESULTS: Isolates from all travelers and a Bayelsa case shared a most recent common ancestor and traveled to Bayelsa, Delta, or Rivers states. Genetic variation for this cluster was lower than would be expected from a random sampling of genomes from this outbreak, but data did not support direct links between travelers. CONCLUSIONS: Monophyly of exportation cases and the Bayelsa sample, along with the intermediate levels of genetic variation, suggest a small pool of related isolates is the likely source for the exported infections. This may be the result of the level of genetic variation present in monkeypox isolates circulating within the contiguous region of Bayelsa, Delta, and Rivers states, or another more restricted, yet unidentified source pool

Nowcasting and forecasting the 2022 U.S. mpox outbreak: Support for public health decision making and lessons learned

In June of 2022, the U.S. Centers for Disease Control and Prevention (CDC) Mpox Response wanted timely answers to important epidemiological questions which can now be answered more effectively through infectious disease modeling. Infectious disease models have shown to be valuable tools for decision making during outbreaks; however, model complexity often makes communicating the results and limitations of models to decision makers difficult. We performed nowcasting and forecasting for the 2022 mpox outbreak in the United States using the R package EpiNow2. We generated nowcasts/forecasts at the national level, by Census region, and for jurisdictions reporting the greatest number of mpox cases. Modeling results were shared for situational awareness within the CDC Mpox Response and publicly on the CDC website. We retrospectively evaluated forecast predictions at four key phases (early, exponential growth, peak, and decline) during the outbreak using three metrics, the weighted interval score, mean absolute error, and prediction interval coverage. We compared the performance of EpiNow2 with a naïve Bayesian generalized linear model (GLM). The EpiNow2 model had less probabilistic error than the GLM during every outbreak phase except for the early phase. We share our experiences with an existing tool for nowcasting/forecasting and highlight areas of improvement for the development of future tools. We also reflect on lessons learned regarding data quality issues and adapting modeling results for different audiences

Ecological Niche Modeling of Francisella tularensis Subspecies and Clades in the United States

Two subspecies of Francisella tularensis are recognized: F. tularensis subsp. tularensis (type A) and F. tularensis subsp. holartica (type B). Type A has been subdivided further into A1a, A1b, and A2, which differ geographically and clinically. The aim of this work was to determine whether or not differences among subspecies and clades translate into distinct ecological niches. We used 223 isolates from humans and wildlife representing all six genotypes (type A, B, A1, A2, A1a, or A1b). Ecological-niche models were built independently for each genotype, using the genetic algorithm for rule-set prediction. The resulting models were compared using a non-parametric multivariate analysis-of-variance method. A1 and A2 are ecologically distinct, supporting the previously observed geographic division, whereas ecological niches for types A and B overlapped notably but A1a and A1b displayed no appreciable differences in their ecological niches

Ecological Niche Modeling of Francisella tularensis Subspecies and Clades in the United States

Two subspecies of Francisella tularensis are recognized: F. tularensis subsp. tularensis (type A) and F. tularensis subsp. holartica (type B). Type A has been subdivided further into A1a, A1b, and A2, which differ geographically and clinically. The aim of this work was to determine whether or not differences among subspecies and clades translate into distinct ecological niches. We used 223 isolates from humans and wildlife representing all six genotypes (type A, B, A1, A2, A1a, or A1b). Ecological-niche models were built independently for each genotype, using the genetic algorithm for ruleset prediction. The resulting models were compared using a non-parametric multivariate analysis-of-variance method. A1 and A2 are ecologically distinct, supporting the previously observed geographic division, whereas ecological niches for types A and B overlapped notably but A1a and A1b displayed no appreciable differences in their ecological niches.U.S. Department of DefenseCenters for Disease Control and Prevention (U.S.A)Microsoft ResearchDepto. de Genética, Fisiología y MicrobiologíaFac. de Ciencias BiológicasTRUEpu

Deployable Structures and Biological Morphology

IASS-IACM 2008 Session: Deployable Structures and Biological Morphology Session Organizers: Hiroshi FURUYA (Tokyo Institute of Technology), Hidetoshi KOBAYASHI (Osaka University) -- Plenary Lecture: Abstract, Slides and Video : "Folding and deployment of stored-energy composite structures" by Sergio PELLEGRINO (California Institute of Technology) -- Keynote Lecture: "Unfolding of potato flower as a deployable structure" by Hidetoshi KOBAYASHI, Keitaro HORIKAWA(Osaka University), Yoshinori MORITA (Kawasaki Heavy Industries, Ltd.) -- "Structural analysis for the multi-folding and deployable structures" by Masatoshi NAKAZAWA (Tohoku Gakuin University), Ichiro ARIO (Hiroshima University), Andrew WATSON (Loughborough University) -- "Deployment schemes for 2-D space apertures and mapping for bio-inspired design" by Christopher H. JENKINS, Jeffery J. LARSEN (Montana State University) -- "Microstructure of foldable membrane for gossamer spacecrafts" by Hiroshi FURUYA, Yasutaka SATOU, Yosuke INOUE, Tadashi MASUOKA (Tokyo Institute of Technology) -- "Natural twist buckling in shells: From the hawkmoth's bellows to the deployable Kresling-pattern and cylindrical Miura-ori" by Biruta KRESLING (Experimental Design and Bionics, Paris