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Since 2013, the Centers for Disease Control and Prevention (CDC) has hosted an annual influenza season forecasting challenge. The 2015–2016 challenge consisted of weekly probabilistic forecasts of multiple targets, including fourteen models submitted by eleven teams. Forecast skill was evaluated using a modified logarithmic score. We averaged submitted forecasts into a mean ensemble model and compared them against predictions based on historical trends. Forecast skill was highest for seasonal peak intensity and short-term forecasts, while forecast skill for timing of season onset and peak week was generally low. Higher forecast skill was associated with team participation in previous influenza forecasting challenges and utilization of ensemble forecasting techniques. The mean ensemble consistently performed well and outperformed historical trend predictions. CDC and contributing teams will continue to advance influenza forecasting and work to improve the accuracy and reliability of forecasts to facilitate increased incorporation into public health response efforts.

A wide range of research has promised new tools for forecasting infectious disease dynamics, but little of that research is currently being applied in practice, because tools do not address key public health needs, do not produce probabilistic forecasts, have not been evaluated on external data, or do not provide sufficient forecast skill to be useful. We developed an open collaborative forecasting challenge to assess probabilistic forecasts for seasonal epidemics of dengue, a major global public health problem. Sixteen teams used a variety of methods and data to generate forecasts for 3 epidemiological targets (peak incidence, the week of the peak, and total incidence) over 8 dengue seasons in Iquitos, Peru and San Juan, Puerto Rico. Forecast skill was highly variable across teams and targets. While numerous forecasts showed high skill for midseason situational awareness, early season skill was low, and skill was generally lowest for high incidence seasons, those for which forecasts would be most valuable. A comparison of modeling approaches revealed that average forecast skill was lower for models including biologically meaningful data and mechanisms and that both multimodel and multiteam ensemble forecasts consistently outperformed individual model forecasts. Leveraging these insights, data, and the forecasting framework will be critical to improve forecast skill and the application of forecasts in real time for epidemic preparedness and response. Moreover, key components of this project—integration with public health needs, a common forecasting framework, shared and standardized data, and open participation—can help advance infectious disease forecasting beyond dengue.

We consider a one-dimensional Osp(\\(N|2M\\)) pseudoparticle mechanical model which may be written as a phase space gauge theory. We show how the pseudoparticle model naturally encodes and explains the two-dimensional zero curvature approach to finding extended conformal symmetries. We describe a procedure of partial gauge fixing of these theories which leads generally to theories with superconformally extended \\({\\cal W}\\)-algebras. The pseudoparticle model allows one to derive the finite transformations of the gauge and matter fields occurring in these theories with extended conformal symmetries. In particular, the partial gauge fixing of the Osp(\\(N|2\\)) pseudoparticle mechanical models results in theories with the SO(\\(N\\)) invariant \\(N\\)-extended superconformal symmetry algebra of Bershadsky and Knizhnik. These algebras are nonlinear for \\(N \\geq 3.\\) We discuss in detail the cases of \\(N=1\\) and \\(N=2,\\) giving two new derivations of the superschwarzian derivatives. Some comments are made in the \\(N=2\\) case on how twisted and topological theories represent a significant deformation of the original particle model. The particle model also allows one to interpret superconformal transformations as deformations of flags in super jet bundles over the associated super Riemann surface.

The issue of field redefinition invariance of path integrals in quantum field theory is reexamined. A ``paradox'' is presented involving the reduction to an effective quantum-mechanical theory of a \\((d+1)\\)-dimensional free scalar field in a Minkowskian spacetime with compactified spatial coordinates. The implementation of field redefinitions both before and after the reduction suggests that operator-ordering issues in quantum field theory should not be ignored.

Radiation exposure from radiological or nuclear events, medical treatments, or spaceflight poses significant health risks, yet human-specific models to investigate radiation effects on skin remain limited. This study establishes a novel in vitro platform using a full-thickness bioengineered human skin equivalent colonized with natural mixed human microbiota (coHSEs) to assess radiation-induced biological responses. We exposed coHSEs to acute doses of up to 4 Gy with x-rays and evaluated their viability, structural integrity, and molecular responses over 25 days. The coHSE model demonstrated sustained viability without dose-dependent opportunistic microbial overgrowth when procedural optimizations were applied. Radiation-induced epidermal remodeling did not compromise tissue architecture or swabbing-based sample collection. Cell proliferation analyses revealed dose- and time-dependent dynamics, with consistent dermal cell density maintained across radiation doses. Comparative multi-omic analyses, including untargeted metabolomics, targeted lipidomics, and 16 S metagenomics, revealed conserved metabolic and microbial responses to radiation in both coHSEs and skin from irradiated mice. Enriched pathways such as arachidonic acid and fatty acid metabolism, along with shifts in microbial taxa including Lachnospiraceae , support the translational relevance of the coHSE model. This system offers a scalable, ethical, and physiologically relevant platform for radiation biology, biodosimetry, and therapeutic development, advancing terrestrial health research with promising application for space research.