Infants segment words from songs - an EEG study

Children’s songs are omnipresent and highly attractive stimuli in infants’ input. Previous work suggests that infants process linguistic–phonetic information from simplified sung melodies. The present study investigated whether infants learn words from ecologically valid children’s songs. Testing 40 Dutch-learning 10-month-olds in a familiarization-then-test electroencephalography (EEG) paradigm, this study asked whether infants can segment repeated target words embedded in songs during familiarization and subsequently recognize those words in continuous speech in the test phase. To replicate previous speech work and compare segmentation across modalities, infants participated in both song and speech sessions. Results showed a positive event-related potential (ERP) familiarity effect to the final compared to the first target occurrences during both song and speech familiarization. No evidence was found for word recognition in the test phase following either song or speech. Comparisons across the stimuli of the present and a comparable previous study suggested that acoustic prominence and speech rate may have contributed to the polarity of the ERP familiarity effect and its absence in the test phase. Overall, the present study provides evidence that 10-month-old infants can segment words embedded in songs, and it raises questions about the acoustic and other factors that enable or hinder infant word segmentation from songs and speech

Competing periodicities in fractionally filled one-dimensional bands

We present a variable temperature Scanning Tunneling Microscopy and Spectroscopy (STM and STS) study of the Si(553)-Au atomic chain reconstruction. This quasi one-dimensional (1D) system undergoes at least two charge density wave (CDW) transitions at low temperature, which can be attributed to electronic instabilities in the fractionally-filled 1D bands of the high-symmetry phase. Upon cooling, Si(553)-Au first undergoes a single-band Peierls distortion, resulting in period doubling along the imaged chains. This Peierls state is ultimately overcome by a competing tripleperiod CDW, which in turn is accompanied by a x2 periodicity in between the chains. These locked-in periodicities indicate small charge transfer between the nearly half-filled and quarter-filled 1D bands. The presence and the mobility of atomic scale dislocations in the x3 CDW state indicates the possibility of manipulating phase solitons carrying a (spin,charge) of (1/2,+-e/3) or (0,+-2e/3).Comment: submitted, accepted for publication in Phys. Rev. Let

Formation of atom wires on vicinal silicon

The formation of atomic wires via pseudomorphic step-edge decoration on vicinal silicon surfaces has been analyzed for Ga on the Si(112) surface using Scanning Tunneling Microscopy and Density Functional Theory calculations. Based on a chemical potential analysis involving more than thirty candidate structures and considering various fabrication procedures, it is concluded that pseudomorphic growth on stepped Si(112), both under equilibrium and non-equilibrium conditions, must favor formation of Ga zig-zag chains rather than linear atom chains. The surface is non-metallic and presents quasi-one dimensional character in the lowest conduction band.Comment: submitte

Solution of the 2-star model of a network

The p-star model or exponential random graph is among the oldest and best-known of network models. Here we give an analytic solution for the particular case of the 2-star model, which is one of the most fundamental of exponential random graphs. We derive expressions for a number of quantities of interest in the model and show that the degenerate region of the parameter space observed in computer simulations is a spontaneously symmetry broken phase separated from the normal phase of the model by a conventional continuous phase transition.Comment: 5 pages, 3 figure

Analysis of Signaling Endosome Composition and Dynamics Using SILAC in Embryonic Stem Cell-Derived Neurons

Neurons require efficient transport mechanisms such as fast axonal transport to ensure neuronal homeostasis and survival. Neurotrophins and their receptors are conveyed via fast axonal retrograde transport of signaling endosomes to the soma, where they elicit transcriptional responses. Despite the essential roles of signaling endosomes in neuronal differentiation and survival, little is known about their molecular identity, dynamics, and regulation. Gaining a better mechanistic understanding of these organelles and their kinetics is crucial, given the growing evidence linking vesicular trafficking deficits to neurodegeneration. Here, we exploited an affinity purification strategy using the binding fragment of tetanus neurotoxin (HCT) conjugated to monocrystalline iron oxide nanoparticles (MIONs), which in motor neurons, is transported in the same carriers as neurotrophins and their receptors. To quantitatively assess the molecular composition of HCT-containing signaling endosomes, we have developed a protocol for triple Stable Isotope Labeling with Amino acids in Cell culture (SILAC) in embryonic stem cell-derived motor neurons. After HCT internalization, retrograde carriers were magnetically isolated at different time points and subjected to mass-spectrometry and Gene Ontology analyses. This purification strategy is highly specific, as confirmed by the presence of essential regulators of fast axonal transport in the make-up of these organelles. Our results indicate that signaling endosomes undergo a rapid maturation with the acquisition of late endosome markers following a specific time-dependent kinetics. Strikingly, signaling endosomes are specifically enriched in proteins known to be involved in neurodegenerative diseases and neuroinfection. Moreover, we highlighted the presence of novel components, whose precise temporal recruitment on signaling endosomes might be essential for proper sorting and/or transport of these organelles. This study provides the first quantitative proteomic analysis of signaling endosomes isolated from motor neurons and allows the assembly of a functional map of these axonal carriers involved in long-range neuronal signaling

The statistical mechanics of networks

We study the family of network models derived by requiring the expected properties of a graph ensemble to match a given set of measurements of a real-world network, while maximizing the entropy of the ensemble. Models of this type play the same role in the study of networks as is played by the Boltzmann distribution in classical statistical mechanics; they offer the best prediction of network properties subject to the constraints imposed by a given set of observations. We give exact solutions of models within this class that incorporate arbitrary degree distributions and arbitrary but independent edge probabilities. We also discuss some more complex examples with correlated edges that can be solved approximately or exactly by adapting various familiar methods, including mean-field theory, perturbation theory, and saddle-point expansions.Comment: 15 pages, 4 figure

Critical phenomena in exponential random graphs

The exponential family of random graphs is one of the most promising class of network models. Dependence between the random edges is defined through certain finite subgraphs, analogous to the use of potential energy to provide dependence between particle states in a grand canonical ensemble of statistical physics. By adjusting the specific values of these subgraph densities, one can analyze the influence of various local features on the global structure of the network. Loosely put, a phase transition occurs when a singularity arises in the limiting free energy density, as it is the generating function for the limiting expectations of all thermodynamic observables. We derive the full phase diagram for a large family of 3-parameter exponential random graph models with attraction and show that they all consist of a first order surface phase transition bordered by a second order critical curve.Comment: 14 pages, 8 figure