Nanoelectromechanical DRAM for ultra-large-scale integration (ULSI)

A Nanoelectromechanical (NEM) device developed for dynamic random access memory (DRAM) is reported. A vertical nanotube structure is employed to form the electromechanical switch and capacitor structure. The mechanical movement of the nanotube defines 'On' and 'OFF' states and the electrical signals which result lead to charge storage in a vertical capacitor structure as in a traditional DRAM. The vertical structure contributes greatly to a decrease in cell dimension. The main concept of the NEM switch and capacitor can be applied to other memory devices as well. © 2005 IEEE.

Near optimal combination of sensory and motor uncertainty in time during a naturalistic perception-action task.

Most behavioral tasks have time constraints for successful completion, such as catching a ball in flight. Many of these tasks require trading off the time allocated to perception and action, especially when only one of the two is possible at any time. In general, the longer we perceive, the smaller the uncertainty in perceptual estimates. However, a longer perception phase leaves less time for action, which results in less precise movements. Here we examine subjects catching a virtual ball. Critically, as soon as subjects began to move, the ball became invisible. We study how subjects trade-off sensory and movement uncertainty by deciding when to initiate their actions. We formulate this task in a probabilistic framework and show that subjects' decisions when to start moving are statistically near optimal given their individual sensory and motor uncertainties. Moreover, we accurately predict individual subject's task performance. Thus we show that subjects in a natural task are quantitatively aware of how sensory and motor variability depend on time and act so as to minimize overall task variability.

Human muscle spindles act as forward sensory models.

Modern theories of motor control incorporate forward models that combine sensory information and motor commands to predict future sensory states. Such models circumvent unavoidable neural delays associated with on-line feedback control. Here we show that signals in human muscle spindle afferents during unconstrained wrist and finger movements predict future kinematic states of their parent muscle. Specifically, we show that the discharges of type Ia afferents are best correlated with the velocity of length changes in their parent muscles approximately 100-160 ms in the future and that their discharges vary depending on motor sequences in a way that cannot be explained by the state of their parent muscle alone. We therefore conclude that muscle spindles can act as "forward sensory models": they are affected both by the current state of their parent muscle and by efferent (fusimotor) control, and their discharges represent future kinematic states. If this conjecture is correct, then sensorimotor learning implies learning how to control not only the skeletal muscles but also the fusimotor system.

Inferring visuomotor priors for sensorimotor learning.

Sensorimotor learning has been shown to depend on both prior expectations and sensory evidence in a way that is consistent with Bayesian integration. Thus, prior beliefs play a key role during the learning process, especially when only ambiguous sensory information is available. Here we develop a novel technique to estimate the covariance structure of the prior over visuomotor transformations--the mapping between actual and visual location of the hand--during a learning task. Subjects performed reaching movements under multiple visuomotor transformations in which they received visual feedback of their hand position only at the end of the movement. After experiencing a particular transformation for one reach, subjects have insufficient information to determine the exact transformation, and so their second reach reflects a combination of their prior over visuomotor transformations and the sensory evidence from the first reach. We developed a Bayesian observer model in order to infer the covariance structure of the subjects' prior, which was found to give high probability to parameter settings consistent with visuomotor rotations. Therefore, although the set of visuomotor transformations experienced had little structure, the subjects had a strong tendency to interpret ambiguous sensory evidence as arising from rotation-like transformations. We then exposed the same subjects to a highly-structured set of visuomotor transformations, designed to be very different from the set of visuomotor rotations. During this exposure the prior was found to have changed significantly to have a covariance structure that no longer favored rotation-like transformations. In summary, we have developed a technique which can estimate the full covariance structure of a prior in a sensorimotor task and have shown that the prior over visuomotor transformations favor a rotation-like structure. Moreover, through experience of a novel task structure, participants can appropriately alter the covariance structure of their prior.

Discovering transcriptional modules by Bayesian data integration.

MOTIVATION: We present a method for directly inferring transcriptional modules (TMs) by integrating gene expression and transcription factor binding (ChIP-chip) data. Our model extends a hierarchical Dirichlet process mixture model to allow data fusion on a gene-by-gene basis. This encodes the intuition that co-expression and co-regulation are not necessarily equivalent and hence we do not expect all genes to group similarly in both datasets. In particular, it allows us to identify the subset of genes that share the same structure of transcriptional modules in both datasets. RESULTS: We find that by working on a gene-by-gene basis, our model is able to extract clusters with greater functional coherence than existing methods. By combining gene expression and transcription factor binding (ChIP-chip) data in this way, we are better able to determine the groups of genes that are most likely to represent underlying TMs. AVAILABILITY: If interested in the code for the work presented in this article, please contact the authors. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Integration of fisheries acoustics surveys and bathymetric mapping to characterize midwater-seafloor habitats of US Virgin Islands and Puerto Rico (2008-2010)

NOAA’s Coral Reef Conservation program (CRCP) develops coral reef management priorities by bringing together various partners to better understand threats to coral reef ecosystems with the goal of conserving, protecting and restoring these resources. Place-based and ecosystem-based management approaches employed by CRCP require that spatially explicit information about benthic habitats and fish utilization are available to characterize coral reef ecosystems and set conservation priorities. To accomplish this, seafloor habitat mapping of coral reefs around the U.S. Virgin Islands (USVI) and Puerto Rico has been ongoing since 2004. In 2008, fishery acoustics surveys were added to NOAA survey missions in the USVI and Puerto Rico to assess fish distribution and abundance in relation to benthic habitats in high priority conservation areas. NOAA’s National Centers for Coastal Ocean Science (NCCOS) have developed fisheries acoustics survey capabilities onboard the NOAA ship Nancy Foster to complement the CRCP seafloor habitat mapping effort spearheaded by the Center for Coastal Monitoring and Assessment Biogeography Branch (CCMA-BB). The integration of these activities has evolved on the Nancy Foster over the three years summarized in this report. A strategy for improved operations and products has emerged over that time. Not only has the concurrent operation of multibeam and fisheries acoustics surveys been beneficial in terms of optimizing ship time and resources, this joint effort has advanced an integrated approach to characterizing bottom and mid-water habitats and the fishes associated with them. CCMA conducts multibeam surveys to systematically map and characterize coral reef ecosystems, resulting in products such as high resolution bathymetric maps, backscatter information, and benthic habitat classification maps. These products focus on benthic features and live bottom habitats associated with them. NCCOS Centers (the Center for Coastal Fisheries and Habitat Research and the Center for Coastal Environmental Health and Biomolecular Research) characterize coral reef ecosystems by using fisheries acoustics methods to capture biological information through the entire water column. Spatially-explicit information on marine resources derived from fisheries acoustics surveys, such as maps of fish density, supports marine spatial planning strategies and decision making by providing a biological metric for evaluating coral reef ecosystems and assessing impacts from pollution, fishing pressure, and climate change. Data from fisheries acoustics surveys address management needs by providing a measure of biomass in management areas, detecting spatial and temporal responses in distribution relative to natural and anthropogenic impacts, and identifying hotspots that support high fish abundance or fish aggregations. Fisheries acoustics surveys conducted alongside multibeam mapping efforts inherently couple water column data with information on benthic habitats and provide information on the heterogeneity of both benthic habitats and biota in the water column. Building on this information serves to inform resource managers regarding how fishes are organized around habitat structure and the scale at which these relationships are important. Where resource managers require place-based assessments regarding the location of critical habitats along with high abundances of fish, concurrent multibeam and fisheries acoustics surveys serve as an important tool for characterizing and prioritizing coral reef ecosystems. This report summarizes the evolution of fisheries acoustics surveys onboard the NOAA ship Nancy Foster from 2008 to 2010, in conjunction with multibeam data collection, aimed at characterizing benthic and mid-water habitats in high priority conservation areas around the USVI and Puerto Rico. It also serves as a resource for the continued development of consistent data products derived from acoustic surveys. By focusing on the activities of 2010, this report highlights the progress made to date and illustrates the potential application of fisheries data derived from acoustic surveys to the management of coral reef ecosystems.

Integration of data for nowcasting of harmful algal blooms

Harmful algal blooms (HABs) are a significant and potentially expanding problem around the world. Resource management and public health protection require sufficient information to reduce the impacts of HABs by response strategies and through warnings and advisories. To be effective, these programs can best be served by an integration of improved detection methods with both evolving monitoring systems and new communications capabilities. Data sets are typically collected from a variety of sources, these can be considered as several types: point data, such as water samples; transects, such as from shipboard continuous sampling; and synoptic, such as from satellite imagery. Generation of a field of the HAB distribution requires all of these sampling approaches. This means that the data sets need to be interpreted and analyzed with each other to create the field or distribution of the HAB. The HAB field is also a necessary input into models that forecast blooms. Several systems have developed strategies that demonstrate these approaches. These range from data sets collected at key sites, such as swimming beaches, to automated collection systems, to integration of interpreted satellite data. Improved data collection, particularly in speed and cost, will be one of the advances of the next few years. Methods to improve creation of the HAB field from the variety of data types will be necessary for routine nowcasting and forecasting of HABs.