State University of New York System

About: State University of New York System is a education organization based out in Albany, New York, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 54077 authors who have published 78070 publications receiving 2985160 citations.

Remote control of ion channels and neurons through magnetic-field heating of nanoparticles

Abstract: Recently, optical stimulation has begun to unravel the neuronal processing that controls certain animal behaviours. However, optical approaches are limited by the inability of visible light to penetrate deep into tissues. Here, we show an approach based on radio-frequency magnetic-field heating of nanoparticles to remotely activate temperature-sensitive cation channels in cells. Superparamagnetic ferrite nanoparticles were targeted to specific proteins on the plasma membrane of cells expressing TRPV1, and heated by a radio-frequency magnetic field. Using fluorophores as molecular thermometers, we show that the induced temperature increase is highly localized. Thermal activation of the channels triggers action potentials in cultured neurons without observable toxic effects. This approach can be adapted to stimulate other cell types and, moreover, may be used to remotely manipulate other cellular machinery for novel therapeutics.

Adaptive dynamic programming

Abstract: Unlike the many soft computing applications where it suffices to achieve a "good approximation most of the time," a control system must be stable all of the time. As such, if one desires to learn a control law in real-time, a fusion of soft computing techniques to learn the appropriate control law with hard computing techniques to maintain the stability constraint and guarantee convergence is required. The objective of the paper is to describe an adaptive dynamic programming algorithm (ADPA) which fuses soft computing techniques to learn the optimal cost (or return) functional for a stabilizable nonlinear system with unknown dynamics and hard computing techniques to verify the stability and convergence of the algorithm. Specifically, the algorithm is initialized with a (stabilizing) cost functional and the system is run with the corresponding control law (defined by the Hamilton-Jacobi-Bellman equation), with the resultant state trajectories used to update the cost functional in a soft computing mode. Hard computing techniques are then used to show that this process is globally convergent with stepwise stability to the optimal cost functional/control law pair for an (unknown) input affine system with an input quadratic performance measure (modulo the appropriate technical conditions). Three specific implementations of the ADPA are developed for 1) the linear case, 2) for the nonlinear case using a locally quadratic approximation to the cost functional, and 3) the nonlinear case using a radial basis function approximation of the cost functional; illustrated by applications to flight control.

Seasonal variation of cloud radiative forcing derived from the Earth Radiation Budget Experiment

Abstract: The impact of clouds on the earth's radiation balance is assessed in terms of longwave, shortwave, and net cloud forcing by using monthly averaged clear-sky and cloudy-sky flux data derived from the NASA Earth Radiation Budget Experiment (ERBE). Emphasis is placed on regional measurements, regional cloud forcing, zonal cloud forcing, and snow and ice contributions. It is shown that the global mean cooling varied from 14 to 21 W/sq m between April 1985 and January 1986; hemispherically, the longwave and shortwave cloud forcing nearly cancel each other in the winter hemisphere, while in the summer the negative shortwave cloud forcing is significantly lower than the longwave cloud forcing, producing a strong cooling. The ERBE data reveal that globally, hemispherically, and zonally, clouds have a significant effect on the radiative heating gradients.

Ideological Values and the Votes of U.S. Supreme Court Justices

Abstract: It is commonly assumed that Supreme Court justices' votes largely reflect their attitudes, values, or personal policy preferences. Nevertheless, this assumption has never been adequately tested with independent measures of the ideological values of justices, that is, measures not taken from their votes on the Court. Using content analytic techniques, we derive independent and reliable measures of the values of all Supreme Court justices from Earl Warren to Anthony Kennedy. These values correlate highly with the votes of the justices, providing strong support for the attitudinal model.