Engineering periplasmic ligand binding proteins as glucose nanosensors

Diabetes affects over 100 million people worldwide. Better methods for monitoring blood glucose levels are needed for improving disease management. Several labs have previously made glucose nanosensors by modifying members of the periplasmic ligand binding protein superfamily. This minireview summarizes recent developments in constructing new versions of these proteins that are responsive within the physiological range of blood glucose levels, employ new reporter groups, and/or are more robust. These experiments are important steps in the development of novel proteins that have the characteristics needed for an implantable glucose nanosensor for diabetes management: specificity for glucose, rapid response, sensitivity within the physiological range of glucose concentrations, reproducibility, and robustness

An open challenge to advance probabilistic forecasting for dengue epidemics

This is the final version. Available on open access from the National Academy of Sciences via the DOI in this recordData Availability: Data deposition: The data are available at https://github.com/cdcepi/dengue-forecasting-project-2015 (DOI: https://doi.org/10.5281/zenodo.3519270).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

Voltage-switchable artificial muscles actuating at near neutral pH

Novel artificial muscle materials were developed, which can be electrically actuated at near neutral pH environments by low applied potential (3 V or less). The artificial muscle material is based on a hydrogel that is composed of acrylamide and a carboxylic acid derivative, such as acrylic acid, and is doped with a polypyrrole/carbon black composite. The electroactuation response of the artificial muscle is optimized in terms of the constituents of the hydrogel precursor solution and the polymerization conditions. It is shown that the introduction of the dicarboxylic acid monomer maleic acid increases the osmotic pressure arising at the hydrogel/electrolyte interface, increasing the bending response of the material. Additionally, the increase of the polymerization temperature and the use of the crosslinking agent N,N???-methylenebisacrylamide, which promotes intermolecular interactions within the hydrogel network, result in the development of artificial muscles with increased and reproducible electroactuation.close293