Statistical methods for localization and discrimination of acoustical signals backscattered by air bubbles

Five records of acoustic echoes backscattered by experimentally produced distinct size « monobubbles » are studied . These records were splitted into several blocks so as to detect bubble signals . We proceeded to the analysis of distances between blocks, which were expressed either by usual distances or by distances between equivalence classes of blocks (orbital distances) . The bubble detection is rendered easier by filtering records through principal component analysis . The selected blocks were then submitted to a series of discriminant analyses in five, four or three classes . The discriminant variables were either Fourier coefficients or scores stemming from various orbital distances analyses, The obtained results are discussed and validated by a resampling method.On étudie cinq enregistrements d'échos acoustiques de « monobulles », de tailles distinctes, produites expérimentalement. Ces enregistrements sont découpés en blocs afin de détecter les signaux de bulles. On procède ensuite à l'analyse des inter-distances entre blocs, exprimées soit au moyen de la distance usuelle, soit au moyen de distances entre classes d'équivalence de blocs (distances orbitales). La détection des bulles est facilitée par un filtrage des enregistrements par analyse en composantes principales. Les blocs sélectionnés sont soumis à une série d'analyses discriminantes en cinq, quatre et trois classes, les variables discriminantes étant soit les coefficients de Fourier du signal, soit les facteurs issus de l'analyse de diverses tables d'inter-distances orbitales. Les résultats obtenus sont discutés et validés par une technique de rééchantillonnage

Direct Observation of Propagating Gigahertz Coherent Guided Acoustic Phonons in Free Standing Single Copper Nanowires

We report on gigahertz acoustic phonon waveguiding in free-standing single copper nanowires studied by femtosecond transient reflectivity measurements. The results are discussed on the basis of the semianalytical resolution of the Pochhammer and Chree equation. The spreading of the generated Gaussian wave packet of two different modes is derived analytically and compared with the observed oscillations of the sample reflectivity. These experiments provide a unique way to independently obtain geometrical and material characterization. This direct observation of coherent guided acoustic phonons in a single nano-object is also the first step toward nanolateral size acoustic transducer and comprehensive studies of the thermal properties of nanowires

Femtosecond nonlinear ultrasonics in gold probed with ultrashort surface plasmons

Fundamental interactions induced by lattice vibrations on ultrafast time scales become increasingly important for modern nanoscience and technology. Experimental access to the physical properties of acoustic phonons in the THz frequency range and over the entire Brillouin zone is crucial for understanding electric and thermal transport in solids and their compounds. Here, we report on the generation and nonlinear propagation of giant (1 percent) acoustic strain pulses in hybrid gold/cobalt bilayer structures probed with ultrafast surface plasmon interferometry. This new technique allows for unambiguous characterization of arbitrary ultrafast acoustic transients. The giant acoustic pulses experience substantial nonlinear reshaping already after a propagation distance of 100 nm in a crystalline gold layer. Excellent agreement with the Korteveg-de Vries model points to future quantitative nonlinear femtosecond THz-ultrasonics at the nano-scale in metals at room temperature

Ab initio Pseudopotential Plane-wave Calculations of the Electronic Structure of YBa_2Cu_3O_7

We present an ab initio pseudopotential local density functional calculation for stoichiometric high-Tc cuprate YBa_2Cu_3O_7 using the plane-wave basis set. We have overcome well-known difficulties in applying pseudopotential methods to first-row elements, transition metals, and rare-earth materials by carefully generating norm-conserving pseudopotentials with excellent transferability and employing an extremely efficient iterative diagonalization scheme optimized for our purpose. The self-consistent band structures, the total and site-projected densities of states, the partial charges and their symmetry-decompositions, and some characteristic charge densities near E_f are presented. We compare our results with various existing (F)LAPW and (F)LMTO calculations and establish that the ab initio pseudopotential method is competitive with other methods in studying the electronic structure of such complicated materials as high-Tc cuprates. [8 postscript files in uuencoded compressed form]Comment: 14 pages, RevTeX v3.0, 8 figures (appended in postscript file), SNUTP 94-8

Natural images from the birthplace of the human eye

Here we introduce a database of calibrated natural images publicly available through an easy-to-use web interface. Using a Nikon D70 digital SLR camera, we acquired about 5000 six-megapixel images of Okavango Delta of Botswana, a tropical savanna habitat similar to where the human eye is thought to have evolved. Some sequences of images were captured unsystematically while following a baboon troop, while others were designed to vary a single parameter such as aperture, object distance, time of day or position on the horizon. Images are available in the raw RGB format and in grayscale. Images are also available in units relevant to the physiology of human cone photoreceptors, where pixel values represent the expected number of photoisomerizations per second for cones sensitive to long (L), medium (M) and short (S) wavelengths. This database is distributed under a Creative Commons Attribution-Noncommercial Unported license to facilitate research in computer vision, psychophysics of perception, and visual neuroscience.Comment: Submitted to PLoS ON

TOM40 Mediates Mitochondrial Dysfunction Induced by α-Synuclein Accumulation in Parkinson's Disease.

Alpha-synuclein (α-Syn) accumulation/aggregation and mitochondrial dysfunction play prominent roles in the pathology of Parkinson's disease. We have previously shown that postmortem human dopaminergic neurons from PD brains accumulate high levels of mitochondrial DNA (mtDNA) deletions. We now addressed the question, whether alterations in a component of the mitochondrial import machinery -TOM40- might contribute to the mitochondrial dysfunction and damage in PD. For this purpose, we studied levels of TOM40, mtDNA deletions, oxidative damage, energy production, and complexes of the respiratory chain in brain homogenates as well as in single neurons, using laser-capture-microdissection in transgenic mice overexpressing human wildtype α-Syn. Additionally, we used lentivirus-mediated stereotactic delivery of a component of this import machinery into mouse brain as a novel therapeutic strategy. We report here that TOM40 is significantly reduced in the brain of PD patients and in α-Syn transgenic mice. TOM40 deficits were associated with increased mtDNA deletions and oxidative DNA damage, and with decreased energy production and altered levels of complex I proteins in α-Syn transgenic mice. Lentiviral-mediated overexpression of Tom40 in α-Syn-transgenic mice brains ameliorated energy deficits as well as oxidative burden. Our results suggest that alterations in the mitochondrial protein transport machinery might contribute to mitochondrial impairment in α-Synucleinopathies

Fluctuation-Driven Neural Dynamics Reproduce Drosophila Locomotor Patterns.

The neural mechanisms determining the timing of even simple actions, such as when to walk or rest, are largely mysterious. One intriguing, but untested, hypothesis posits a role for ongoing activity fluctuations in neurons of central action selection circuits that drive animal behavior from moment to moment. To examine how fluctuating activity can contribute to action timing, we paired high-resolution measurements of freely walking Drosophila melanogaster with data-driven neural network modeling and dynamical systems analysis. We generated fluctuation-driven network models whose outputs-locomotor bouts-matched those measured from sensory-deprived Drosophila. From these models, we identified those that could also reproduce a second, unrelated dataset: the complex time-course of odor-evoked walking for genetically diverse Drosophila strains. Dynamical models that best reproduced both Drosophila basal and odor-evoked locomotor patterns exhibited specific characteristics. First, ongoing fluctuations were required. In a stochastic resonance-like manner, these fluctuations allowed neural activity to escape stable equilibria and to exceed a threshold for locomotion. Second, odor-induced shifts of equilibria in these models caused a depression in locomotor frequency following olfactory stimulation. Our models predict that activity fluctuations in action selection circuits cause behavioral output to more closely match sensory drive and may therefore enhance navigation in complex sensory environments. Together these data reveal how simple neural dynamics, when coupled with activity fluctuations, can give rise to complex patterns of animal behavior

Context Matters: The Illusive Simplicity of Macaque V1 Receptive Fields

Even in V1, where neurons have well characterized classical receptive fields (CRFs), it has been difficult to deduce which features of natural scenes stimuli they actually respond to. Forward models based upon CRF stimuli have had limited success in predicting the response of V1 neurons to natural scenes. As natural scenes exhibit complex spatial and temporal correlations, this could be due to surround effects that modulate the sensitivity of the CRF. Here, instead of attempting a forward model, we quantify the importance of the natural scenes surround for awake macaque monkeys by modeling it non-parametrically. We also quantify the influence of two forms of trial to trial variability. The first is related to the neuron’s own spike history. The second is related to ongoing mean field population activity reflected by the local field potential (LFP). We find that the surround produces strong temporal modulations in the firing rate that can be both suppressive and facilitative. Further, the LFP is found to induce a precise timing in spikes, which tend to be temporally localized on sharp LFP transients in the gamma frequency range. Using the pseudo R[superscript 2] as a measure of model fit, we find that during natural scene viewing the CRF dominates, accounting for 60% of the fit, but that taken collectively the surround, spike history and LFP are almost as important, accounting for 40%. However, overall only a small proportion of V1 spiking statistics could be explained (R[superscript 2]~5%), even when the full stimulus, spike history and LFP were taken into account. This suggests that under natural scene conditions, the dominant influence on V1 neurons is not the stimulus, nor the mean field dynamics of the LFP, but the complex, incoherent dynamics of the network in which neurons are embedded.National Institutes of Health (U.S.) (K25 NS052422-02)National Institutes of Health (U.S.) (DP1 ODOO3646

An Image Statistics–Based Model for Fixation Prediction

The problem of predicting where people look at, or equivalently salient region detection, has been related to the statistics of several types of low-level image features. Among these features, contrast and edge information seem to have the highest correlation with the fixation locations. The contrast distribution of natural images can be adequately characterized using a two-parameter Weibull distribution. This distribution catches the structure of local contrast and edge frequency in a highly meaningful way. We exploit these observations and investigate whether the parameters of the Weibull distribution constitute a simple model for predicting where people fixate when viewing natural images. Using a set of images with associated eye movements, we assess the joint distribution of the Weibull parameters at fixated and non-fixated regions. Then, we build a simple classifier based on the log-likelihood ratio between these two joint distributions. Our results show that as few as two values per image region are already enough to achieve a performance comparable with the state-of-the-art in bottom-up saliency prediction