Regulation of primary production in the Gulf of California through interaction of large-scale and local ocean processes [abstract]

EXTRACT (SEE PDF FOR FULL ABSTRACT): The suppression of primary productivity observed in eastern boundary ecosystems of the Pacific during El Nino episodes does not occur throughout the Gulf of California. On the contrary, analysis of the modern siliceous phytoplankton record from annually layered sediments and compilation of available primary productivity measurements indicate that production is significantly increased in the central Gulf during El Nino years compared to anti-El Nino years. Integrated observations of biological and physical variability during the spring of 1983, under the influence of the strong El Nino, show that very high primary productivity occurred along the eastern margin of the central Gulf. This resulted from the upwelling of a nutrient rich source provided by the locally formed Gulf water mass originating in the northern Gulf. Lower productivity and phytoplankton biomass were associated with the anomalous penetration of Tropical Surface Water along the western side of the Gulf.

Risk-sensitivity and the mean-variance trade-off: decision making in sensorimotor control.

Numerous psychophysical studies suggest that the sensorimotor system chooses actions that optimize the average cost associated with a movement. Recently, however, violations of this hypothesis have been reported in line with economic theories of decision-making that not only consider the mean payoff, but are also sensitive to risk, that is the variability of the payoff. Here, we examine the hypothesis that risk-sensitivity in sensorimotor control arises as a mean-variance trade-off in movement costs. We designed a motor task in which participants could choose between a sure motor action that resulted in a fixed amount of effort and a risky motor action that resulted in a variable amount of effort that could be either lower or higher than the fixed effort. By changing the mean effort of the risky action while experimentally fixing its variance, we determined indifference points at which participants chose equiprobably between the sure, fixed amount of effort option and the risky, variable effort option. Depending on whether participants accepted a variable effort with a mean that was higher, lower or equal to the fixed effort, they could be classified as risk-seeking, risk-averse or risk-neutral. Most subjects were risk-sensitive in our task consistent with a mean-variance trade-off in effort, thereby, underlining the importance of risk-sensitivity in computational models of sensorimotor control.

Tailoring the local interaction between graphene layers in graphite at the atomic scale and above using scanning tunneling microscopy.

With recent developments in carbon-based electronics, it is imperative to understand the interplay between the morphology and electronic structure in graphene and graphite. We demonstrate controlled and repeatable vertical displacement of the top graphene layer from the substrate mediated by the scanning tunneling microscopy (STM) tip-sample interaction, manifested at the atomic level as well as over superlattices spanning several tens of nanometers. Besides the full-displacement, we observed the first half-displacement of the surface graphene layer, confirming that a reduced coupling rather than a change in lateral layer stacking is responsible for the triangular/honeycomb atomic lattice transition phenomenon, clearing the controversy surrounding it. Furthermore, an atomic scale mechanical stress at a grain boundary in graphite, resulting in the localization of states near the Fermi energy, is revealed through voltage-dependent imaging. A method of producing graphene nanoribbons based on the manipulation capabilities of the STM is also implemented.

A stochastic model of temporal variations in monthly temperature, precipitation, snowfall, and resulting snowpack

We report a Monte Carlo representation of the long-term inter-annual variability of monthly snowfall on a detailed (1 km) grid of points throughout the southwest. An extension of the local climate model of the southwestern United States (Stamm and Craig 1992) provides spatially based estimates of mean and variance of monthly temperature and precipitation. The mean is the expected value from a canonical regression using independent variables that represent controls on climate in this area, including orography. Variance is computed as the standard error of the prediction and provides site-specific measures of (1) natural sources of variation and (2) errors due to limitations of the data and poor distribution of climate stations. Simulation of monthly temperature and precipitation over a sequence of years is achieved by drawing from a bivariate normal distribution. The conditional expectation of precipitation. given temperature in each month, is the basis of a numerical integration of the normal probability distribution of log precipitation below a threshold temperature (3°C) to determine snowfall as a percent of total precipitation. Snowfall predictions are tested at stations for which long-term records are available. At Donner Memorial State Park (elevation 1811 meters) a 34-year simulation - matching the length of instrumental record - is within 15 percent of observed for mean annual snowfall. We also compute resulting snowpack using a variation of the model of Martinec et al. (1983). This allows additional tests by examining spatial patterns of predicted snowfall and snowpack and their hydrologic implications.