Characterization of a broad-based mosquito yeast interfering RNA larvicide with a conserved target site in mosquito semaphorin-1a genes

BACKGROUND: RNA interference (RNAi), which has facilitated functional characterization of mosquito neural development genes such as the axon guidance regulator semaphorin-1a (sema1a), could one day be applied as a new means of vector control. Saccharomyces cerevisiae (baker's yeast) may represent an effective interfering RNA expression system that could be used directly for delivery of RNA pesticides to mosquito larvae. Here we describe characterization of a yeast larvicide developed through bioengineering of S. cerevisiae to express a short hairpin RNA (shRNA) targeting a conserved site in mosquito sema1a genes. RESULTS: Experiments conducted on Aedes aegypti larvae demonstrated that the yeast larvicide effectively silences sema1a expression, generates severe neural defects, and induces high levels of larval mortality in laboratory, simulated-field, and semi-field experiments. The larvicide was also found to induce high levels of Aedes albopictus, Anopheles gambiae and Culex quinquefasciatus mortality. CONCLUSIONS: The results of these studies indicate that use of yeast interfering RNA larvicides targeting mosquito sema1a genes may represent a new biorational tool for mosquito control

Closed-loop optimization of fast-charging protocols for batteries with machine learning.

Simultaneously optimizing many design parameters in time-consuming experiments causes bottlenecks in a broad range of scientific and engineering disciplines1,2. One such example is process and control optimization for lithium-ion batteries during materials selection, cell manufacturing and operation. A typical objective is to maximize battery lifetime; however, conducting even a single experiment to evaluate lifetime can take months to years3-5. Furthermore, both large parameter spaces and high sampling variability3,6,7 necessitate a large number of experiments. Hence, the key challenge is to reduce both the number and the duration of the experiments required. Here we develop and demonstrate a machine learning methodology  to efficiently optimize a parameter space specifying the current and voltage profiles of six-step, ten-minute fast-charging protocols for maximizing battery cycle life, which can alleviate range anxiety for electric-vehicle users8,9. We combine two key elements to reduce the optimization cost: an early-prediction model5, which reduces the time per experiment by predicting the final cycle life using data from the first few cycles, and a Bayesian optimization algorithm10,11, which reduces the number of experiments by balancing exploration and exploitation to efficiently probe the parameter space of charging protocols. Using this methodology, we rapidly identify high-cycle-life charging protocols among 224 candidates in 16 days (compared with over 500 days using exhaustive search without early prediction), and subsequently validate the accuracy and efficiency of our optimization approach. Our closed-loop methodology automatically incorporates feedback from past experiments to inform future decisions and can be generalized to other applications in battery design and, more broadly, other scientific domains that involve time-intensive experiments and multi-dimensional design spaces

Characterizing the Adaptive Optics Off-Axis Point-Spread Function - I: A Semi-Empirical Method for Use in Natural-Guide-Star Observations

Even though the technology of adaptive optics (AO) is rapidly maturing, calibration of the resulting images remains a major challenge. The AO point-spread function (PSF) changes quickly both in time and position on the sky. In a typical observation the star used for guiding will be separated from the scientific target by 10" to 30". This is sufficient separation to render images of the guide star by themselves nearly useless in characterizing the PSF at the off-axis target position. A semi-empirical technique is described that improves the determination of the AO off-axis PSF. The method uses calibration images of dense star fields to determine the change in PSF with field position. It then uses this information to correct contemporaneous images of the guide star to produce a PSF that is more accurate for both the target position and the time of a scientific observation. We report on tests of the method using natural-guide-star AO systems on the Canada-France-Hawaii Telescope and Lick Observatory Shane Telescope, augmented by simple atmospheric computer simulations. At 25" off-axis, predicting the PSF full width at half maximum using only information about the guide star results in an error of 60%. Using an image of a dense star field lowers this error to 33%, and our method, which also folds in information about the on-axis PSF, further decreases the error to 19%.Comment: 29 pages, 9 figures, accepted for publication in the PAS

The Application of Advanced Composites for the Construction of Commercial Transport Aircraft

This individual Capstone project examined and evaluated current industry methods of testing, certification, and maintenance of advanced composite materials for the construction of commercial transport aircraft and the FAA regulations governing their use. The project critically compared and contrasted existing FAA standards and regulations governing the testing, certification, and maintenance of advanced composites for commercial transport aircraft structural applications with current industry practices to determine whether there were any areas of conflict between the two in order to accept or reject that current testing, certification, and maintenance procedures for advanced composites used in primary and secondary commercial transport aircraft structures are standardized throughout the aerospace industry and sufficiently capable of detecting damage or component failure. This was accomplished by performing a qualitative and quantitative analysis utilizing meta-analysis to contrast and compare past and current aerospace composite materials studies with non-destructive inspection (NDI) testing and structural health monitoring (SHM) data to determine statistical significance that supported or refuted the hypothesis of comprehensive process improvement throughout the industry. The results of the analysis showed that the hypothesis was accepted for testing and certification, but overwhelmingly rejected for current maintenance and repair. In addition, industry concerns were examined to determine whether limitations exist that would preclude the future use of advanced composites in structural applications based on current FAA standards and regulations. This project determined how current industry practices and FAA methodologies for the testing, certification, and maintenance of advanced composites in commercial transport aircraft structural applications may need to be modified in order to capture and address future industry use