Wavelet Analysis of Big Data Contaminated by Large Noise in an fMRI Study of Neuroplasticity

Functional magnetic resonance imaging (fMRI) allows researchers to analyze brain activity on a voxel level, but using this ability is complicated by dealing with Big Data and large noise. A traditional remedy is averaging over large parts of brain in combination with more advanced technical innovations in reducing fMRI noise. In this paper a novel statistical approach, based on a wavelet analysis of standard fMRI data, is proposed and its application to an fMRI study of neuron plasticity of 24 healthy adults is presented. The aim of that study was to recognize changes in connectivity between left and right motor cortices (the neuroplasticity) after button clicking training sessions. A conventional method of the data analysis, based on averaging images, has implied that for the group of 24 participants the connectivity increased after the training. The proposed wavelet analysis suggests to analyze pathways between left and right hemispheres on a voxel-to-voxel level and for each participant via estimation of corresponding cross-correlations. This immediately necessitates statistical analysis of large-p-small-n correlation matrices contaminated by large noise. Furthermore, distributions that we are dealing in the analysis are neither Gaussian nor sub-Gaussian but sub-exponential. The paper explains how the problem may be solved and presents results of a dynamic analysis of the ability of a human brain to reorganize itself for 24 healthy adults. Results show that the ability of a brain to reorganize itself varies widely even among healthy individuals, and this observation is important for our understanding of a human brain and treatment of brain diseases.     

Spinful fermionic ladders at incommensurate filling: Phase diagram,local perturbations,and ionic potentials

We study the effect of external potential on transport properties of the fermionic two-leg ladder model. The response of the system to a local perturbation is strongly dependent on the ground state properties of the system and especially on the dominant correlations. We categorize all phases and transitions in the model (for incommensurate filling) and introduce “hopping-driven transitions” that the system undergoes as the inter-chain hopping is increased from zero. We also describe the response of the system to an ionic potential. The physics of this effect is similar to that of the single impurity, except that the ionic potential can affect the bulk properties of the system and in particular induce true long range order.     

2009 SSRL School on X-ray Spectroscopy Techniques in Environmental and Materials Sciences: Theory and Application

More than 50 students, post-docs, and career scientists from US national laboratories, academic institutions, and the international user community participated in this four-day school, held from June 2–5, 2009, which delved deeply into theoretical and practical aspects of synchrotron X-ray spectroscopy. The fourth annual school on synchrotron techniques in environmental and materials sciences, organized by the Stanford Synchrotron Radiation Lightsource (SSRL) at the SLAC National Accelerator Laboratory, was designed to introduce new and prospective users to theoretical underpinnings and capabilities of the techniques, data collection procedures, and data analysis approaches. More advanced topics, particularly in data analysis, were also discussed to reinforce and clarify important concepts that are fundamental to data interpretation. Although the school focused principally on applications in environmental and materials sciences, diverse and cross-cutting disciplinary backgrounds were represented, from environmental remediation science and geochemistry, to heterogeneous catalysis and bioinorganic chemistry, to materials sciences and applied physics.     

A Matched Asymptotic Expansions Approach to Continuity Corrections for Discretely Sampled Options. Part 1: Barrier Options

We propose a general framework to model equity volatility for a firm financed by equity and additional non-equity sources of funds. The stochastic nature of equity volatility is endogenous, and comes from the impact of a change in the value of the firm's assets on the financial leverage. We first present the basic model, which is an extension of the Black-Scholes model, to value corporate securities. Second, we show for the first time in the option literature, that instantaneous equity volatility is a solution of a partial differential equation similar to Black-Scholes', although it is non-linear and in general does not have any analytical solution. However, analytical approximations for equity volatility are proposed for different capital structures: (1) equity and debt, (2) equity and warrants, and (3) equity, debt and warrants. They are shown to be very accurate.     

Topology control in the mobile ad hoc networks in order to intensify energy conservation

Although studied for years, due to their dynamic nature, research in the field of mobile ad hoc networks (MANETs) has remained a vast area of interest. Since once distributed, there will be less to no plausibility of recharge, energy conservation has become one of the pressing concerns regarding this particular type of network. In fact, one of the main obligations of designers is to make efficient use of these scarce resources. There has been tremendous work done in different layers of protocol stack in order to intensify energy conservation. To date, numerous topology control algorithms have been proposed, however, only a few have used meta-heuristics such as genetic algorithms, neural networks and/or learning automata to overcome this issue. On the other hand, since nodes are mobile and thus in a different spatial position, as time varies, we can expect that by regulating time intervals between topology controls, one may prolong the network’s lifetime. The main initiative of this paper is to intensify energy conservation in a mobile ad hoc network by using weighted and learning automata based algorithms. The learning automata, regulates time intervals between which the topology controls are done. The represented learning automata based algorithm uses its learning ability to find appropriate time-intervals so that the nodes would regulate the energy needed in order to exchange the information to their neighbors, accordingly. Moreover, at first we have represented two weighted based algorithms which extend two prominent protocols, namely K-Neigh and LMST. Then these algorithms are combined with a learning based algorithm which regulates time intervals between which the topology controls are done. In comparison with approaches that are based on periodic topology controls, proposed approach shows enhanced results. On the other hand, considering the learning ability of the learning automata based algorithms, composition of the aforementioned algorithms has been proven to be enhanced, in the respect of energy consumed per data transmitted, over those compared with.     

Regio‐ and Enantio‐selective Chemo‐enzymatic C−H‐Lactonization of Decanoic Acid to (S)‐δ‐Decalactone

The conversion of saturated fatty acids to high value chiral hydroxy‐acids and lactones poses a number of synthetic challenges: the activation of unreactive C?H bonds and the need for regio‐ and stereoselectivity. Here the first example of a wild‐type cytochrome P450 monooxygenase (CYP116B46 from Tepidiphilus thermophilus) capable of enantio‐ and regioselective C5 hydroxylation of decanoic acid 1 to (S)‐5‐hydroxydecanoic acid 2 is reported. Subsequent lactonization yields (S)‐δ‐decalactone 3 , a high value fragrance compound, with greater than 90 % ee. Docking studies provide a rationale for the high regio‐ and enantioselectivity of the reaction.     

Osteoblastic cell response on magnesium-incorporated apatite coatings

Magnesium is one of the most important bivalent ions associated with biological apatite. A series of magnesium-substituted calcium apatite coatings (Ca_10−xMg_x)(PO₄)₆(OH)₂, where x = 0, 0.50, 1.00, 1.50 and 2.00, are synthesized onto Ti6Al4V substrate by sol-gel dip-coating method to determine how magnesium influences the synthesis and the resulting structural and biological properties. X-ray diffraction (XRD) analysis shows that the incorporation of magnesium helps formation of Mg-containing β-TCP (β-TCMP) phase. X-ray photoelectron spectroscopy (XPS) is used to study the chemical composition and the results show that the apatite structure can only host magnesium less than ∼2.4 wt.% beyond which magnesium aggregates on the surfaces. The incorporation of magnesium slows down the dissolution of Ca²⁺ from the coating. The in vitro behavior of the coatings is evaluated with human osteosarcoma MG63 cells for cell morphology and proliferation. Similar cell morphologies are observed on all coatings. The cell proliferation results show that the incorporation of magnesium up to x = 2 has no adverse effect on cell growth.     

Development of sound measurement systems for auditory functional magnetic resonance imaging

Auditory functional magnetic resonance imaging (fMRI) requires quantification of sound stimuli in the magnetic environment and adequate isolation of background noise. We report the development of two novel sound measurement systems that accurately measure the sound intensity inside the ear, which can simultaneously provide the similar or greater amount of scanner- noise protection than ear-muffs. First, we placed a 2.6 x 2.6-mm microphone in an insert phone that was connected to a headphone [microphone-integrated, foam-tipped insert-phone with a headphone (MIHP)]. This attenuated scanner noise by 37.8+/-4.6 dB, a level better than the reference amount obtained using earmuffs. The nonmetallic optical microphone was integrated with a headphone [optical microphone in a headphone (OMHP)] and it effectively detected the change of sound intensity caused by variable compression on the cushions of the headphone. Wearing the OMHP reduced the noise by 28.5+/-5.9 dB and did not affect echoplanar magnetic resonance images. We also performed an auditory fMRI study using the MIHP system and presented increase in the auditory cortical activation following 10-dB increment in the intensity of sound stimulation. These two newly developed sound measurement systems successfully achieved the accurate quantification of sound stimuli with maintaining the similar level of noise protection of wearing earmuffs in the auditory fMRI experiment.