Ad Hoc Microphone Array Calibration: Euclidean Distance Matrix Completion Algorithm and Theoretical Guarantees

This paper addresses the problem of ad hoc microphone array calibration where only partial information about the distances between microphones is available. We construct a matrix consisting of the pairwise distances and propose to estimate the missing entries based on a novel Euclidean distance matrix completion algorithm by alternative low-rank matrix completion and projection onto the Euclidean distance space. This approach confines the recovered matrix to the EDM cone at each iteration of the matrix completion algorithm. The theoretical guarantees of the calibration performance are obtained considering the random and locally structured missing entries as well as the measurement noise on the known distances. This study elucidates the links between the calibration error and the number of microphones along with the noise level and the ratio of missing distances. Thorough experiments on real data recordings and simulated setups are conducted to demonstrate these theoretical insights. A significant improvement is achieved by the proposed Euclidean distance matrix completion algorithm over the state-of-the-art techniques for ad hoc microphone array calibration.Comment: In Press, available online, August 1, 2014. http://www.sciencedirect.com/science/article/pii/S0165168414003508, Signal Processing, 201

Surfactant-Mediated Epitaxial Growth of Single-Layer Graphene in an Unconventional Orientation on SiC

We report the use of a surfactant molecule during the epitaxy of graphene on SiC(0001) that leads to the growth in an unconventional orientation, namely $R0^\circ$ rotation with respect to the SiC lattice. It yields a very high-quality single-layer graphene with a uniform orientation with respect to the substrate, on the wafer scale. We find an increased quality and homogeneity compared to the approach based on the use of a pre-oriented template to induce the unconventional orientation. Using spot profile analysis low energy electron diffraction, angle-resolved photoelectron spectroscopy, and the normal incidence x-ray standing wave technique, we assess the crystalline quality and coverage of the graphene layer. Combined with the presence of a covalently-bound graphene layer in the conventional orientation underneath, our surfactant-mediated growth offers an ideal platform to prepare epitaxial twisted bilayer graphene via intercalation.Comment: 7 pages, 3 figure

Metal ion type significantly affects the morphology but not the activity of lipase-metal-phosphate nanoflowers

Enzyme–metal-ion–phosphate nanoflowers are high-surface area materials which are known to show higher activity than the constituting protein. Although the synthesis of hybrid nanoflowers has been demonstrated with a variety of proteins and reaction conditions, only di-valent metal ions have been tested to date. We expand on previous findings by testing a range of metal ions of different valence in co-presence with lipase from Burkholderia cepacia: Ag(I), Fe(II), Cu(II), Au(III). All metal ions produced colour precipitates, although the type of metal caused different precipitate morphologies under comparable reaction conditions: from nanoflowers to forests of nano-plates and crystal-like precipitates. In contrast, the type of metal ion did not appear to significantly affect the product\u27s specific enzyme activity, which remained greater than that of free lipase. This indicates that the type of metal ion and the macroscopic arrangement of the petals play a secondary role to that of the co-presence of the metal and phosphate ions in determining lipase nanoflower activity. The demonstrated ability to produce metal–phosphate-protein nanoflowers with a selection of different metals also opens the way to producing a wider range of functional, nanostructured, materials

TREM2 is required for microglial instruction of astrocytic synaptic engulfment in neurodevelopment

Variants in the microglial receptor TREM2 confer risk for multiple neurodegenerative diseases. However, it remains unknown how this receptor functions on microglia to modulate these diverse neuropathologies. To understand the role of TREM2 on microglia more generally, we investigated changes in microglial function in Trem2−/− mice. We found that loss of TREM2 impairs normal neurodevelopment, resulting in reduced synapse number across the cortex and hippocampus in 1-month-old mice. This reduction in synapse number was not due directly to alterations in interactions between microglia and synapses. Rather, TREM2 was required for microglia to limit synaptic engulfment by astrocytes during development. While these changes were largely normalized later in adulthood, high fat diet administration was sufficient to reinitiate TREM2-dependent modulation of synapse loss. Together, this identifies a novel role for microglia in instructing synaptic pruning by astrocytes to broadly regulate appropriate synaptic refinement, and suggests novel candidate mechanisms for how TREM2 and microglia could influence synaptic loss in brain injury and disease

Regional desynchronization of microglial activity is associated with cognitive decline in Alzheimer’s disease

Background Microglial activation is one hallmark of Alzheimer disease (AD) neuropathology but the impact of the regional interplay of microglia cells in the brain is poorly understood. We hypothesized that microglial activation is regionally synchronized in the healthy brain but experiences regional desynchronization with ongoing neurodegenerative disease. We addressed the existence of a microglia connectome and investigated microglial desynchronization as an AD biomarker. Methods To validate the concept, we performed microglia depletion in mice to test whether interregional correlation coefficients (ICCs) of 18 kDa translocator protein (TSPO)-PET change when microglia are cleared. Next, we evaluated the influence of dysfunctional microglia and AD pathophysiology on TSPO-PET ICCs in the mouse brain, followed by translation to a human AD-continuum dataset. We correlated a personalized microglia desynchronization index with cognitive performance. Finally, we performed single-cell radiotracing (scRadiotracing) in mice to ensure the microglial source of the measured desynchronization. Results Microglia-depleted mice showed a strong ICC reduction in all brain compartments, indicating microglia-specific desynchronization. AD mouse models demonstrated significant reductions of microglial synchronicity, associated with increasing variability of cellular radiotracer uptake in pathologically altered brain regions. Humans within the AD-continuum indicated a stage-depended reduction of microglia synchronicity associated with cognitive decline. scRadiotracing in mice showed that the increased TSPO signal was attributed to microglia. Conclusion Using TSPO-PET imaging of mice with depleted microglia and scRadiotracing in an amyloid model, we provide first evidence that a microglia connectome can be assessed in the mouse brain. Microglia synchronicity is closely associated with cognitive decline in AD and could serve as an independent personalized biomarker for disease progression

Instance reduction for one-class classification

Instance reduction techniques are data preprocessing methods originally developed to enhance the nearest neighbor rule for standard classification. They reduce the training data by selecting or generating representative examples of a given problem. These algorithms have been designed and widely analyzed in multi-class problems providing very competitive results. However, this issue was rarely addressed in the context of one-class classification. In this specific domain a reduction of the training set may not only decrease the classification time and classifier’s complexity, but also allows us to handle internal noisy data and simplify the data description boundary. We propose two methods for achieving this goal. The first one is a flexible framework that adjusts any instance reduction method to one-class scenario by introduction of meaningful artificial outliers. The second one is a novel modification of evolutionary instance reduction technique that is based on differential evolution and uses consistency measure for model evaluation in filter or wrapper modes. It is a powerful native one-class solution that does not require an access to counterexamples. Both of the proposed algorithms can be applied to any type of one-class classifier. On the basis of extensive computational experiments, we show that the proposed methods are highly efficient techniques to reduce the complexity and improve the classification performance in one-class scenarios