A miniature, handheld mass spectrometer, based on the rectilinear ion trap mass analyzer, has been applied to air monitoring for traces of toxic compounds. The instrument is battery-operated, hand-portable, and rugged. We anticipate its use in public safety, industrial hygiene, and environmental monitoring. Gaseous samples of nine toxic industrial compounds, phosgene, ethylene oxide, sulfur dioxide, acrylonitrile, cyanogen chloride, hydrogen cyanide, acrolein, formaldehyde, and ethyl parathion, were tested. A sorption trap inlet was constructed to serve as the interface between atmosphere and the vacuum chamber of the mass spectrometer. After selective collection of analytes on the sorbent bed, the sorbent tube was evacuated and then heated to desorb analyte into the instrument. Sampling, detection, identification, and quantitation of all compounds were readily achieved in times of less than 2 min, with detection limits ranging from 800 parts per trillion to 3 parts per million depending on the analyte. For all but one analyte, detection limits were well below (3.5-130 times below) permissible exposure limits. A linear dynamic range of 1-2 orders of magnitude was obtained over the concentration ranges studied (sub-ppbv to ppmv) for all analytes.

The paper describes a target tracking system running on a Heterogeneous Sensor Network (HSN) and presents results gathered from a realistic deployment. The system fuses audio direction of arrival data from mote class devices and object detection measurements from embedded PCs equipped with cameras. The acoustic sensor nodes perform beamforming and measure the energy as a function of the angle. The camera nodes detect moving objects and estimate their angle. The sensor detections are sent to a centralized sensor fusion node via a combination of two wireless networks. The novelty of our system is the unique combination of target tracking methods customized for the application at hand and their implementation on an actual HSN platform.

Recent advances in optical microscopy have enabled biological imaging beyond the diffraction limit at nanometer resolution. A general feature of most of the techniques based on photoactivated localization microscopy (PALM) or stochastic optical reconstruction microscopy (STORM) has been the use of thin biological samples in combination with total internal reflection, thus limiting the imaging depth to a fraction of an optical wavelength. However, to study whole cells or organelles that are typically up to 15 microm deep into the cell, the extension of these methods to a three-dimensional (3D) super resolution technique is required. Here, we report an advance in optical microscopy that enables imaging of protein distributions in cells with a lateral localization precision better than 50 nm at multiple imaging planes deep in biological samples. The approach is based on combining the lateral super resolution provided by PALM with two-photon temporal focusing that provides optical sectioning. We have generated super-resolution images over an axial range of approximately 10 microm in both mitochondrially labeled fixed cells, and in the membranes of living S2 Drosophila cells.

Skeletal muscle differentiation requires a cascade of transcriptional events to control the spatial and temporal expression of muscle-specific genes. Until recently, muscle-specific transcription was primarily attributed to prototypic enhancer-binding factors, while the role of core promoter recognition complexes in directing myogenesis remained unknown. Here, we report the development of a purified reconstituted system to analyze the properties of a TAF3/TRF3 complex in directing transcription initiation at the Myogenin promoter. Importantly, this new complex is required to replace the canonical TFIID to recapitulate MyoD-dependent activation of Myogenin. In vitro and cell-based assays identify a domain of TAF3 that mediates coactivator functions targeted by MyoD. Our findings also suggest changes to CRSP/Mediator in terminally differentiated myotubes. This switching of the core promoter recognition complex during myogenesis allows a more balanced division of labor between activators and TAF coactivators, thus providing another strategy to accommodate cell-specific regulation during metazoan development.

Mammalian mtDNA has been found here to harbor RNA-DNA hybrids at a variety of locations throughout the genome. The R-loop, previously characterized in vitro at the leading strand replication origin (OH), is isolated as a native RNA-DNA hybrid copurifying with mtDNA. Surprisingly, other mitochondrial transcripts also form stable partial R-loops. These are abundant and affect mtDNA conformation. Current models regarding the mechanism of mammalian mtDNA replication have been expanded by recent data and discordant hypotheses. The presence of stable, nonreplicative, and partially hybridized RNA on the mtDNA template is significant for the reevaluation of replication models based on two-dimensional agarose gel analyses. In addition, the close association of a subpopulation of mtRNA with the DNA template has further implications regarding the structure, maintenance, and expression of the mitochondrial genome. These results demonstrate that variously processed and targeted mtRNAs within mammalian mitochondria likely have multiple functions in addition to their conventional roles.

Aquaporins (AQPs) are a family of ubiquitous membrane channels that conduct water across cell membranes. AQPs form homotetramers containing four functional and independent water pores. Aquaporin-0 (AQP0) is expressed in the eye lens, where its water permeability is regulated by calmodulin (CaM). Here we use a combination of biochemical methods and NMR spectroscopy to probe the interaction between AQP0 and CaM. We show that CaM binds the AQP0 C-terminal domain in a calcium-dependent manner. We demonstrate that only two CaM molecules bind a single AQP0 tetramer in a noncanonical fashion, suggesting a form of cooperativity between AQP0 monomers. Based on these results, we derive a structural model of the AQP0/CaM complex, which suggests CaM may be inhibitory to channel permeability by capping the vestibules of two monomers within the AQP0 tetramer. Finally, phosphorylation within AQP0's CaM binding domain inhibits the AQP0/CaM interaction, suggesting a temporal regulatory mechanism for complex formation.

The relationship between a sound and its neural representation in the auditory cortex remains elusive. Simple measures such as the frequency response area or frequency tuning curve provide little insight into the function of the auditory cortex in complex sound environments. Spectrotemporal receptive field (STRF) models, despite their descriptive potential, perform poorly when used to predict auditory cortical responses, showing that nonlinear features of cortical response functions, which are not captured by STRFs, are functionally important. We introduce a new approach to the description of auditory cortical responses, using multilinear modeling methods. These descriptions simultaneously account for several nonlinearities in the stimulus-response functions of auditory cortical neurons, including adaptation, spectral interactions, and nonlinear sensitivity to sound level. The models reveal multiple inseparabilities in cortical processing of time lag, frequency, and sound level, and suggest functional mechanisms by which auditory cortical neurons are sensitive to stimulus context. By explicitly modeling these contextual influences, the models are able to predict auditory cortical responses more accurately than are STRF models. In addition, they can explain some forms of stimulus dependence in STRFs that were previously poorly understood.

Learning and memory has been studied extensively in Drosophila using behavioral, molecular, and genetic approaches. These studies have identified the mushroom body as essential for the formation and retrieval of olfactory memories. We investigated odor responses of the principal neurons of the mushroom body, the Kenyon cells (KCs), in Drosophila using whole cell recordings in vivo. KC responses to odors were highly selective and, thus sparse, compared with those of their direct inputs, the antennal lobe projection neurons (PNs). We examined the mechanisms that might underlie this transformation and identified at least three contributing factors: excitatory synaptic potentials (from PNs) decay rapidly, curtailing temporal integration, PN convergence onto individual KCs is low ( approximately 10 PNs per KC on average), and KC firing thresholds are high. Sparse activity is thought to be useful in structures involved in memory in part because sparseness tends to reduce representation overlaps. By comparing activity patterns evoked by the same odors across olfactory receptor neurons and across KCs, we show that representations of different odors do indeed become less correlated as they progress through the olfactory system.

A network of excitatory synapses trained with a conservative version of Hebbian learning is used as a model for recognizing the familiarity of thousands of once-seen stimuli from those never seen before. Such networks were initially proposed for modeling memory retrieval (selective delay activity). We show that the same framework allows the incorporation of both familiarity recognition and memory retrieval, and estimate the network's capacity. In the case of binary neurons, we extend the analysis of Amit and Fusi (1994) to obtain capacity limits based on computations of signal-to-noise ratio of the field difference between selective and non-selective neurons of learned signals. We show that with fast learning (potentiation probability approximately 1), the most recently learned patterns can be retrieved in working memory (selective delay activity). A much higher number of once-seen learned patterns elicit a realistic familiarity signal in the presence of an external field. With potentiation probability much less than 1 (slow learning), memory retrieval disappears, whereas familiarity recognition capacity is maintained at a similarly high level. This analysis is corroborated in simulations. For analog neurons, where such analysis is more difficult, we simplify the capacity analysis by studying the excess number of potentiated synapses above the steady-state distribution. In this framework, we derive the optimal constraint between potentiation and depression probabilities that maximizes the capacity.