A Monte Carlo-Based Fiber Tracking Algorithm using Diffusion Tensor MRI

Diffusion tensor magnetic resonance imaging, which measures directional information of water diffusion in the brain, has emerged as a powerful tool for human brain studies. In this paper, we introduce a new Monte Carlo-based fiber tracking approach to estimate brain connectivity. One of the main characteristics of this approach is that all parameters of the algorithm are automatically determined at each point using the entropy of the eigenvalues of the diffusion tensor. Experimental results show the good performance of the proposed approach

Universality class of fiber bundles with strong heterogeneities

We study the effect of strong heterogeneities on the fracture of disordered materials using a fiber bundle model. The bundle is composed of two subsets of fibers, i.e. a fraction 0 ≤ α ≤ 1 of fibers is unbreakable, while the remaining 1 - α fraction is characterized by a distribution of breaking thresholds. Assuming global load sharing, we show analytically that there exists a critical fraction of the components αc which separates two qualitatively diferent regimes of the system: below αc the burst size distribution is a power law with the usual exponent Ƭ= 5/2, while above αc the exponent switches to a lower value Ƭ = 9/4 and a cutoff function occurs with a diverging characteristic size. Analyzing the macroscopic response of the system we demonstrate that the transition is conditioned to disorder distributions where the constitutive curve has a single maximum and an inflexion point defining a novel universality class of breakdown phenomena

Avalanche dynamics of fiber bundle models

We present a detailed analytical and numerical study of the avalanche distributions of the continuous damage fiber bundle model CDFBM . Linearly elastic fibers undergo a series of partial failure events which give rise to a gradual degradation of their stiffness. We show that the model reproduces a wide range of mechanical behaviors. We find that macroscopic hardening and plastic responses are characterized by avalanche distributions, which exhibit an algebraic decay with exponents between 5/2 and 2 different from those observed in mean-field fiber bundle models. We also derive analytically the phase diagram of a family of CDFBM which covers a large variety of potential avalanche size distributions. Our results provide a unified view of the statistics of breaking avalanches in fiber bundle models

Impairment of mossy fiber long-term potentiation and associative learning in pituitary adenylate cyclase activating polypeptide type I receptor-deficient Mice

The pituitary adenylate cyclase activating polypeptide (PACAP) type I receptor (PAC1) is a G-protein-coupled receptor binding the strongly conserved neuropeptide PACAP with 1000-fold higher affinity than the related peptide vasoactive intestinal peptide. PAC1-mediated signaling has been implicated in neuronal differentiation and synaptic plasticity. To gain further insight into the biological significance of PAC1-mediated signaling in vivo, we generated two different mutant mouse strains, harboring either a complete or a forebrain-specific inactivation of PAC1. Mutants from both strains show a deficit in contextual fear conditioning, a hippocampus-dependent associative learning paradigm. In sharp contrast, amygdala-dependent cued fear conditioning remains intact. Interestingly, no deficits in other hippocampus-dependent tasks modeling declarative learning such as the Morris water maze or the social transmission of food preference are observed. At the cellular level, the deficit in hippocampus-dependent associative learning is accompanied by an impairment of mossy fiber long-term potentiation (LTP). Because the hippocampal expression of PAC1 is restricted to mossy fiber terminals, we conclude that presynaptic PAC1-mediated signaling at the mossy fiber synapse is involved in both LTP and hippocampus-dependent associative learning.

Prediction of subcutaneous fatty acid composition from ham of CLA fed boars and gilts using a NIRS fiber optic probe

The aim of this work was the use of NIR technology by direct application of a fiber optic probe on back fat to analyze the fatty acid composition of CLA fed boars and gilts. 265 animals were fed 3 different diets and the fatty acid profile of back fat from Gluteus medius was analyzed using gas chromatography and FT-NIR. Spectra were acquired using a Bruker Optics Matrix-F duplex spectrometer equipped with a fiber optic probe (IN-268-2). Oleic and stearic fatty acids were predicted accurately; myristic, vaccenic and linoleic fatty acids were predicted with lower accuracy, while palmitic and α-linolenic fatty acids were poorly predicted. The relative percentage of fatty acids and NIR spectra showed differences in fatty acid composition of back fat from pigs fed CLA which increased the relative percentage of SFA and PUFA while MUFA decreased. Results suggest that a NIR fiber optic probe can be used to predict total saturated and unsaturated fatty acid composition, as well as the percentage of stearic and oleic. NIR showed potential as a rapid and easily implemented method to discriminate carcasses from animals fed different diets.

Properties of nitrogen doped silicon films deposited by low-pressure chemical vapor deposition from silane and ammonia

Nitrogen doped silicon (NIDOS) films have been deposited by low-pressure chemical vapor deposition from silane SiH4 and ammonia NH3 at high temperature (750°C) and the influences of the NH3/SiH4 gas ratio on the films deposition rate, refractive index, stoichiometry, microstructure, electrical conductivity, and thermomechanical stress are studied. The chemical species derived from silylene SiH2 into the gaseous phase are shown to be responsible for the deposition of NIDOS and/or (silicon rich) silicon nitride. The competition between these two deposition phenomena leads finally to very high deposition rates (100 nm/min) for low NH3/SiH4 gas ratio (R¿0.1). Moreover, complex variations of NIDOS film properties are evidenced and related to the dual behavior of the nitrogen atom into silicon, either n-type substitutional impurity or insulative intersticial impurity, according to the Si¿N atomic bound. Finally, the use of NIDOS deposition for the realization of microelectromechanical systems is investigated.

Bipolar pulsed excitation of erbium-doped nanosilicon light emitting diodes

High quantum efficiency erbium doped silicon nanocluster (Si-NC:Er) light emitting diodes (LEDs) were grown by low-pressure chemical vapor deposition (LPCVD) in a complementary metal-oxide-semiconductor (CMOS) line. Erbium (Er) excitation mechanisms under direct current (DC) and bipolar pulsed electrical injection were studied in a broad range of excitation voltages and frequencies. Under DC excitation, Fowler-Nordheim tunneling of electrons is mediated by Er-related trap states and electroluminescence originates from impact excitation of Er ions. When the bipolar pulsed electrical injection is used, the electron transport and Er excitation mechanism change. Sequential injection of electrons and holes into silicon nanoclusters takes place and nonradiative energy transfer to Er ions is observed. This mechanism occurs in a range of lower driving voltages than those observed in DC and injection frequencies higher than the Er emission rate.

Enhancement of the photoelectrochemical properties of Cl-doped ZnO nanowires by tuning their coaxial doping profile

Arrays of vertically aligned ZnO:Cl/ZnO core-shell nanowires were used to demonstrate that the control of the coaxial doping profile in homojunction nanostructures can improve their surface charge carrier transfer while conserving potentially excellent transport properties. It is experimentally shown that the presence of a ZnO shell enhances the photoelectrochemical properties of ZnO:Cl nanowires up to a factor 5. Likewise, the ZnO shell promotes the visible photoluminescence band in highly conducting ZnO:Cl nanowires. These lines of evidence are associated with the increase of the nanowires" surface depletion layer

Response of doped 4He droplets

In the framework of a finite-range density-functional theory, we compute the response of 4HeN clusters doped with a rare-gas molecule. For this purpose, the mean field for the 4He atoms, their wave functions and effective quasiparticle interaction, are self-consistently calculated for a variety of particle numbers in the cluster. The response function is then evaluated for several multipolarities in each drop and the collective states are consequently located from the peaks of the strength function. The spectra of pure droplets approach those previously extracted with a similar algorithm resorting to a zero-range density functional. The spectra of doped clusters are sensitive to the presence of the impurity and are worth a future systematic investigation.