The cell-surface-signaling protein Notch, is required for numerous developmental processes and typically specifies which of two adjacent cells will adopt a non-neuronal developmental fate. It has recently been implicated in long-term memory formation in mammals and Drosophila. Here, we investigated whether activity-dependent synaptic plasticity at the neuromuscular junctions (NMJs) of third instar Drosophila larvae depends on Notch signaling. The length and number of axonal branches and number of presynaptic sites (boutons) in NMJ vary with the level of synaptic activity, so we increased activity at the NMJ by two complementary methods: increasing the chronic growth temperature of third instar larvae from 18 to 28 degrees C and using the double-mutant ether-a-gogo,Shaker (eagSh), both of which increase NMJ size and bouton count. Animals homozygous for the functionally null, temperature-sensitive Notch alleles, N(ts1) and N(ts2), displayed no activity-dependent increase in NMJ complexity when reared at the restrictive temperature. Dominant-negative Notch transgenic expression also blocked activity-dependent plasticity. Ectopic expression of wild-type Notch and constitutively active truncated Notch transgenes also reduced activity-dependent plasticity, suggesting that there is a "happy medium" level of Notch activity in mediating NMJ outgrowth. Last, we show that endogenous Notch is primarily expressed in the presynaptic cell bodies where its expression level is positively correlated with motor neuron activity.
The metabolic cycle of Saccharomyces cerevisiae consists of alternating oxidative (respiration) and reductive (glycolysis) energy-yielding reactions. The intracellular concentrations of amino acid precursors generated by these reactions oscillate accordingly, attaining maximal concentration during the middle of their respective yeast metabolic cycle phases. Typically, the amino acids themselves are most abundant at the end of their precursor's phase. We show that this metabolic cycling has likely biased the amino acid composition of proteins across the S. cerevisiae genome. In particular, we observed that the metabolic source of amino acids is the single most important source of variation in the amino acid compositions of functionally related proteins and that this signal appears only in (facultative) organisms using both oxidative and reductive metabolism. Periodically expressed proteins are enriched for amino acids generated in the preceding phase of the metabolic cycle. Proteins expressed during the oxidative phase contain more glycolysis-derived amino acids, whereas proteins expressed during the reductive phase contain more respiration-derived amino acids. Rare amino acids (e.g., tryptophan) are greatly overrepresented or underrepresented, relative to the proteomic average, in periodically expressed proteins, whereas common amino acids vary by a few percent. Genome-wide, we infer that 20,000 to 60,000 residues have been modified by this previously unappreciated pressure. This trend is strongest in ancient proteins, suggesting that oscillating endogenous amino acid availability exerted genome-wide selective pressure on protein sequences across evolutionary time.
We characterize optical wave propagation along line defects in two-dimensional arrays of air-holes in free-standing silicon slabs. The fabricated waveguides contain random variations in orientation of the photonic lattice elements which perturb the in-plane translational symmetry. The vertical slab symmetry is also broken by a tilt of the etched sidewalls. We discuss how these lattice imperfections affect out-of-plane scattering losses and introduce a mechanism for high-Q cavity excitation related to polarization mixing.
Techniques to detect and quantify DNA and RNA molecules in biological samples have had a central role in genomics research. Over the past decade, several techniques have been developed to improve detection performance and reduce the cost of genetic analysis. In particular, significant advances in label-free methods have been reported. Yet detection of DNA molecules at concentrations below the femtomolar level requires amplified detection schemes. Here we report a unique nanomechanical response of hybridized DNA and RNA molecules that serves as an intrinsic molecular label. Nanomechanical measurements on a microarray surface have sufficient background signal rejection to allow direct detection and counting of hybridized molecules. The digital response of the sensor provides a large dynamic range that is critical for gene expression profiling. We have measured differential expressions of microRNAs in tumour samples; such measurements have been shown to help discriminate between the tissue origins of metastatic tumours. Two hundred picograms of total RNA is found to be sufficient for this analysis. In addition, the limit of detection in pure samples is found to be one attomolar. These results suggest that nanomechanical read-out of microarrays promises attomolar-level sensitivity and large dynamic range for the analysis of gene expression, while eliminating biochemical manipulations, amplification and labelling.
Proteins are dynamic molecular machines having structural flexibility that allows conformational changes. Current methods for the determination of protein flexibility rely mainly on the measurement of thermal fluctuations and disorder in protein conformations and tend to be experimentally challenging. Moreover, they reflect atomic fluctuations on picosecond timescales, whereas the large conformational changes in proteins typically happen on micro- to millisecond timescales. Here, we directly determine the flexibility of bacteriorhodopsin -- a protein that uses the energy in light to move protons across cell membranes -- at the microsecond timescale by monitoring force-induced deformations across the protein structure with a technique based on atomic force microscopy. In contrast to existing methods, the deformations we measure involve a collective response of protein residues and operate under physiologically relevant conditions with native proteins.
In the 1970s, deGennes discussed the fundamental geometry of smectic liquid crystals and established an analogy between the smectic A phase and superconductors. It follows that smectic layers expel twist deformations in the same way that superconductors expel magnetic field. We make a direct observation of the penetration of twist at the edge of a single isolated smectic A layer composed of chiral fd virus particles subjected to a depletion interaction. Using the LC-PolScope, we make quantitative measurements of the spatial dependence of the birefringence due to molecular tilt near the layer edges. We match data to theory for the molecular tilt penetration profile and determine the twist penetration length for this system.
We describe an active polymer network in which processive molecular motors control network elasticity. This system consists of actin filaments cross-linked by filamin A (FLNa) and contracted by bipolar filaments of muscle myosin II. The myosin motors stiffen the network by more than two orders of magnitude by pulling on actin filaments anchored in the network by FLNa cross-links, thereby generating internal stress. The stiffening response closely mimics the effects of external stress applied by mechanical shear. Both internal and external stresses can drive the network into a highly nonlinear, stiffened regime. The active stress reaches values that are equivalent to an external stress of 14 Pa, consistent with a 1-pN force per myosin head. This active network mimics many mechanical properties of cells and suggests that adherent cells exert mechanical control by operating in a nonlinear regime where cell stiffness is sensitive to changes in motor activity. This design principle may be applicable to engineering novel biologically inspired, active materials that adjust their own stiffness by internal catalytic control.
For biomedical applications, such as targeted drug delivery and microsurgery, it is essential to develop a system of swimmers that can be propelled wirelessly in fluidic environments with good control. Here, we report the construction and operation of chiral colloidal propellers that can be navigated in water with micrometer-level precision using homogeneous magnetic fields. The propellers are made via nanostructured surfaces and can be produced in large numbers. The nanopropellers can carry chemicals, push loads, and act as local probes in rheological measurements.
We report what we believe to be the first realization of a computer-generated complex-valued hologram recorded in a single film of photoactive polymer. Complex-valued holograms give rise to a diffracted optical field with control over its amplitude and phase. The holograms are generated by a one-step direct laser writing process in which a spatial light modulator (SLM) is imaged onto a polymer film. Temporal modulation of the SLM during exposure controls both the strength of the induced birefringence and the orientation of the fast axis. We demonstrate that complex holograms can be used to impart arbitrary amplitude and phase profiles onto a beam and thereby open new possibilities in the control of optical beams.
Power-spectral-density measurements of any sampled signal are typically restricted by both acquisition rate and frequency response limitations of instruments, which can be particularly prohibitive for video-based measurements. We have developed a new method called intensity modulation spectral analysis that circumvents these limitations, dramatically extending the effective detection bandwidth. We demonstrate this by video tracking an optically trapped microsphere while oscillating an LED illumination source. This approach allows us to quantify fluctuations of the microsphere at frequencies over 10 times higher than the Nyquist frequency, mimicking a significantly higher frame rate.
While many models of biological object recognition share a common set of "broad-stroke" properties, the performance of any one model depends strongly on the choice of parameters in a particular instantiation of that model--e.g., the number of units per layer, the size of pooling kernels, exponents in normalization operations, etc. Since the number of such parameters (explicit or implicit) is typically large and the computational cost of evaluating one particular parameter set is high, the space of possible model instantiations goes largely unexplored. Thus, when a model fails to approach the abilities of biological visual systems, we are left uncertain whether this failure is because we are missing a fundamental idea or because the correct "parts" have not been tuned correctly, assembled at sufficient scale, or provided with enough training. Here, we present a high-throughput approach to the exploration of such parameter sets, leveraging recent advances in stream processing hardware (high-end NVIDIA graphic cards and the PlayStation 3's IBM Cell Processor). In analogy to high-throughput screening approaches in molecular biology and genetics, we explored thousands of potential network architectures and parameter instantiations, screening those that show promising object recognition performance for further analysis. We show that this approach can yield significant, reproducible gains in performance across an array of basic object recognition tasks, consistently outperforming a variety of state-of-the-art purpose-built vision systems from the literature. As the scale of available computational power continues to expand, we argue that this approach has the potential to greatly accelerate progress in both artificial vision and our understanding of the computational underpinning of biological vision.
Primates can easily identify visual objects over large changes in retinal position--a property commonly referred to as position "invariance." This ability is widely assumed to depend on neurons in inferior temporal cortex (IT) that can respond selectively to isolated visual objects over similarly large ranges of retinal position. However, in the real world, objects rarely appear in isolation, and the interplay between position invariance and the representation of multiple objects (i.e., clutter) remains unresolved. At the heart of this issue is the intuition that the representations of nearby objects can interfere with one another and that the large receptive fields needed for position invariance can exacerbate this problem by increasing the range over which interference acts. Indeed, most IT neurons' responses are strongly affected by the presence of clutter. While external mechanisms (such as attention) are often invoked as a way out of the problem, we show (using recorded neuronal data and simulations) that the intrinsic properties of IT population responses, by themselves, can support object recognition in the face of limited clutter. Furthermore, we carried out extensive simulations of hypothetical neuronal populations to identify the essential individual-neuron ingredients of a good population representation. These simulations show that the crucial neuronal property to support recognition in clutter is not preservation of response magnitude, but preservation of each neuron's rank-order object preference under identity-preserving image transformations (e.g., clutter). Because IT neuronal responses often exhibit that response property, while neurons in earlier visual areas (e.g., V1) do not, we suggest that preserving the rank-order object preference regardless of clutter, rather than the response magnitude, more precisely describes the goal of individual neurons at the top of the ventral visual stream.
The human visual system is able to recognize objects despite tremendous variation in their appearance on the retina resulting from variation in view, size, lighting, etc. This ability--known as "invariant" object recognition--is central to visual perception, yet its computational underpinnings are poorly understood. Traditionally, nonhuman primates have been the animal model-of-choice for investigating the neuronal substrates of invariant recognition, because their visual systems closely mirror our own. Meanwhile, simpler and more accessible animal models such as rodents have been largely overlooked as possible models of higher-level visual functions, because their brains are often assumed to lack advanced visual processing machinery. As a result, little is known about rodents' ability to process complex visual stimuli in the face of real-world image variation. In the present work, we show that rats possess more advanced visual abilities than previously appreciated. Specifically, we trained pigmented rats to perform a visual task that required them to recognize objects despite substantial variation in their appearance, due to changes in size, view, and lighting. Critically, rats were able to spontaneously generalize to previously unseen transformations of learned objects. These results provide the first systematic evidence for invariant object recognition in rats and argue for an increased focus on rodents as models for studying high-level visual processing.