Size-dependent trajectories of DNA macromolecules due to insulative dielectrophoresis in submicrometer-deep fluidic channels.

In this paper, we demonstrate for the first time that insulative dielectrophoresis can induce size-dependent trajectories of DNA macromolecules. We experimentally use lambda (48.5 kbp) and T4GT7 (165.6 kbp) DNA molecules flowing continuously around a sharp corner inside fluidic channels with a depth of 0.4 mum. Numerical simulation of the electrokinetic force distribution inside the channels is in qualitative agreement with our experimentally observed trajectories. We discuss a possible physical mechanism for the DNA polarization and dielectrophoresis inside confining channels, based on the observed dielectrophoresis responses due to different DNA sizes and various electric fields applied between the inlet and the outlet. The proposed physical mechanism indicates that further extensive investigations, both theoretically and experimentally, would be very useful to better elucidate the forces involved at DNA dielectrophoresis. When applied for size-based sorting of DNA molecules, our sorting method offers two major advantages compared to earlier attempts with insulative dielectrophoresis: Its continuous operation allows for high-throughput analysis, and it only requires electric field strengths as low as approximately 10 Vcm.

Structure learning in action.

'Learning to learn' phenomena have been widely investigated in cognition, perception and more recently also in action. During concept learning tasks, for example, it has been suggested that characteristic features are abstracted from a set of examples with the consequence that learning of similar tasks is facilitated-a process termed 'learning to learn'. From a computational point of view such an extraction of invariants can be regarded as learning of an underlying structure. Here we review the evidence for structure learning as a 'learning to learn' mechanism, especially in sensorimotor control where the motor system has to adapt to variable environments. We review studies demonstrating that common features of variable environments are extracted during sensorimotor learning and exploited for efficient adaptation in novel tasks. We conclude that structure learning plays a fundamental role in skill learning and may underlie the unsurpassed flexibility and adaptability of the motor system.

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.

Use of skeletochronological analysis to estimate the age of leatherback sea turtles Dermochelys coriacea in the western North Atlantic

Although growth rate and age data are essential for leatherback management, estimates of these demographic parameters remain speculative due to the cryptic life history of this endangered species. Skeletochronological analysis of scleral ossicles obtained from 8 captive, known-age and 33 wild leatherbacks originating from the western North Atlantic was conducted to characterize the ossicles and the growth marks within them. Ages were accurately estimated for the known-age turtles, and their growth mark attributes were used to calibrate growth mark counts for the ossicles from wild specimens. Due to growth mark compaction and resorption, the number of marks visible at ossicle section tips was consistently and significantly greater than the number visible along the lateral edges, demonstrating that growth mark counts should be performed at the tips so that age is not underestimated. A correction factor protocol that incorporated the trajectory of early growth increments was used to estimate the number of missing marks in those ossicles exhibiting resorption, which was then added to the number of observed marks to obtain an age estimate for each turtle. A generalized smoothing spline model, von Bertalanffy growth curve, and size-at-age function were used to obtain estimates of age at maturity for leatherbacks in the western North Atlantic. Results of these analyses suggest that median age at maturation for leatherbacks in this part of the world may range from 24.5 to 29 yr. These age estimates are much greater than those proposed in previous studies and have significant implications for population management and recovery.

Shortspine thornyhead (Sebastolobus alascanus) abundance and habitat associations in the Gulf of Alaska

Shortspine thornyhead (Sebastolobus alascanus) abundance was estimated from 107 video transects at 27 stations recorded from a research submersible in 1991 off southeast Alaska at depths ranging from 165 to 355 m. Numbers of invertebrates in seven major taxa were estimated, as was substrate type. Thornyhead abundance ranged from 0 to 7.5/100 m2, with a mean of 1.22/100 m2, and was positively correlated with depth and amount of hard substrate. Invertebrate abundances were not significantly correlated with numbers of thornyheads. Shortspine thornyhead abundance estimates from this study were several times higher than estimates produced by bottom trawl surveys off southeast Alaska in 1990 and 1993, the two years of survey that encompassed the submersible transects; however, the trend of increasing abundance with depth was similar in the trawl surveys and in the submersible transects, suggesting that trawl surveys systematically underestimate abundance of shortspine thornyheads