Natural convection flow in a strong cross magnetic field with radiation

The problem of MHD natural convection boundary layer flow of an electrically conducting and optically dense gray viscous fluid along a heated vertical plate is analyzed in the presence of strong cross magnetic field with radiative heat transfer. In the analysis radiative heat flux is considered by adopting optically thick radiation limit. Attempt is made to obtain the solutions valid for liquid metals by taking Pr≪1. Boundary layer equations are transformed in to a convenient dimensionless form by using stream function formulation (SFF) and primitive variable formulation (PVF). Non-similar equations obtained from SFF are then simulated by implicit finite difference (Keller-box) method whereas parabolic partial differential equations obtained from PVF are integrated numerically by hiring direct finite difference method over the entire range of local Hartmann parameter, $xi$ . Further, asymptotic solutions are also obtained for large and small values of local Hartmann parameter $xi$ . A favorable agreement is found between the results for small, large and all values of $xi$ . Numerical results are also demonstrated graphically by showing the effect of various physical parameters on shear stress, rate of heat transfer, velocity and temperature.

Thermo-magnetic convection of paramagnetic fluids with non-instantaneous heating

The unsteady boundary-layer development for thermomagnetic convection of paramagnetic fluids inside a square cavity has been considered in this study. The cavity is placed in a microgravity condition (no gravitation acceleration) and under a uniform magnetic field which acts vertically. A ramp temperature boundary condition is applied on left vertical side wall of the cavity where the temperature initially increases with time up to some specific time and maintain constant thereafter. A distinct magnetic convection boundary layer is developed adjacent to the left vertical wall due to the effect of the magnetic body force generated on the paramagnetic fluid. An improved scaling analysis has been performed using triple-layer integral method and verified by numerical simulations. The Prandtl number has been chosen greater than unity varied over 5-100. Moreover, the effect of various values of the magnetic parameter and magnetic Rayleigh number on the fluid flow and heat transfer has been shown.

Time-varying bispectral analysis of visually evoked multi-channel EEG

Theoretical foundations of higher order spectral analysis are revisited to examine the use of time-varying bicoherence on non-stationary signals using a classical short-time Fourier approach. A methodology is developed to apply this to evoked EEG responses where a stimulus-locked time reference is available. Short-time windowed ensembles of the response at the same offset from the reference are considered as ergodic cyclostationary processes within a non-stationary random process. Bicoherence can be estimated reliably with known levels at which it is significantly different from zero and can be tracked as a function of offset from the stimulus. When this methodology is applied to multi-channel EEG, it is possible to obtain information about phase synchronization at different regions of the brain as the neural response develops. The methodology is applied to analyze evoked EEG response to flash visual stimulii to the left and right eye separately. The EEG electrode array is segmented based on bicoherence evolution with time using the mean absolute difference as a measure of dissimilarity. Segment maps confirm the importance of the occipital region in visual processing and demonstrate a link between the frontal and occipital regions during the response. Maps are constructed using bicoherence at bifrequencies that include the alpha band frequency of 8Hz as well as 4 and 20Hz. Differences are observed between responses from the left eye and the right eye, and also between subjects. The methodology shows potential as a neurological functional imaging technique that can be further developed for diagnosis and monitoring using scalp EEG which is less invasive and less expensive than magnetic resonance imaging.

Spectroscopic correlation analysis of NMR-based metabonomics in exercise science

Spectroscopic studies of complex clinical fluids have led to the application of a more holistic approach to their chemical analysis becoming more popular and widely employed. The efficient and effective interpretation of multidimensional spectroscopic data relies on many chemometric techniques and one such group of tools is represented by so-called correlation analysis methods. Typical of these techniques are two-dimensional correlation analysis and statistical total correlation spectroscopy (STOCSY). Whilst the former has largely been applied to optical spectroscopic analysis, STOCSY was developed and has been applied almost exclusively to NMR metabonomic studies. Using a 1H NMR study of human blood plasma, from subjects recovering from exhaustive exercise trials, the basic concepts and applications of these techniques are examined. Typical information from their application to NMR-based metabonomics is presented and their value in aiding interpretation of NMR data obtained from biological systems is illustrated. Major energy metabolites are identified in the NMR spectra and the dynamics of their appearance and removal from plasma during exercise recovery are illustrated and discussed. The complementary nature of two-dimensional correlation analysis and statistical total correlation spectroscopy are highlighted.

Quantitative estimation of bioturbation based on digital image analysis

Quantitative determination of modification of primary sediment features, by the activity of organisms (i.e., bioturbation) is essential in geosciences. Some methods proposed since the 1960s are mainly based on visual or subjective determinations. The first semiquantitative evaluations of the Bioturbation Index, Ichnofabric Index, or the amount of bioturbation were attempted, in the best cases using a series of flashcards designed in different situations. Recently, more effective methods involve the use of analytical and computational methods such as X-rays, magnetic resonance imaging or computed tomography; these methods are complex and often expensive. This paper presents a compilation of different methods, using Adobe® Photoshop® software CS6, for digital estimation that are a part of the IDIAP (Ichnological Digital Analysis Images Package), which is an inexpensive alternative to recently proposed methods, easy to use, and especially recommended for core samples. The different methods — “Similar Pixel Selection Method (SPSM)”, “Magic Wand Method (MWM)” and the “Color Range Selection Method (CRSM)” — entail advantages and disadvantages depending on the sediment (e.g., composition, color, texture, porosity, etc.) and ichnological features (size of traces, infilling material, burrow wall, etc.). The IDIAP provides an estimation of the amount of trace fossils produced by a particular ichnotaxon, by a whole ichnocoenosis or even for a complete ichnofabric. We recommend the application of the complete IDIAP to a given case study, followed by selection of the most appropriate method. The IDIAP was applied to core material recovered from the IODP Expedition 339, enabling us, for the first time, to arrive at a quantitative estimation of the discrete trace fossil assemblage in core samples.

Brain decoding based on functional magnetic resonance imaging using machine learning : a comparative study

Brain decoding of functional Magnetic Resonance Imaging data is a pattern analysis task that links brain activity patterns to the experimental conditions. Classifiers predict the neural states from the spatial and temporal pattern of brain activity extracted from multiple voxels in the functional images in a certain period of time. The prediction results offer insight into the nature of neural representations and cognitive mechanisms and the classification accuracy determines our confidence in understanding the relationship between brain activity and stimuli. In this paper, we compared the efficacy of three machine learning algorithms: neural network, support vector machines, and conditional random field to decode the visual stimuli or neural cognitive states from functional Magnetic Resonance data. Leave-one-out cross validation was performed to quantify the generalization accuracy of each algorithm on unseen data. The results indicated support vector machine and conditional random field have comparable performance and the potential of the latter is worthy of further investigation.

Enhancing MRI of prostate cancer using PSMA-targeting iron oxide magnetic nanoparticles

Introduction Novel imaging techniques for prostate cancer (PCa) are required to improve staging and real-time assessment of therapeutic response. We performed preclinical evaluation of newly-developed, biocompatible magnetic nanoparticles (MNPs) conjugated with J591, an antibody specific for prostate specific membrane antigen (PSMA), to enhance magnetic resonance imaging (MRI) of PCa. PSMA is expressed on ∼90% of PCa, including those that are castrate-resistant, rendering it as a rational target for PCa imaging. Materials and Methods The specificity of J591 for PSMA was confirmed by flow cytometric analysis of several PCa cell lines of known PSMA status. MNPs were prepared, engineered to the appropriate size, labeled with DiR fluorophore, and their toxicity to a panel of PC cells was assessed by in vitro Alamar Blue assay. Immunohistochemistry, fluorescence microscopy and Prussian Blue staining (iron uptake) were used to evaluate PSMA specificity of J591-MNP conjugates. In vivo MRI studies (16.4T MRI system) were performed using live immunodeficient mice bearing orthotopic LNCaP xenografts and injected intravenously with J591-MNPs or MNPs alone. Results MNPs were non-toxic to PCa cells. J591-MNP conjugates showed no compromise in specificity of binding to PSMA+ cells and showed enhanced iron uptake compared with MNPs alone. In vivo, tumour targeting (significant MR image contrast) was evident in mice injected with J591-MNPs, but not MNPs alone. Resected tumours from targeted mice had an accumulation of MNPs, not seen in normal control prostate. Conclusions Application of PSMA-targeting MNPs into conventional MRI has potential to enhance PCa detection and localization in real-time, improving patient management.

Mineralogy and Magnetic Properties of Basaltic Substrate Soils: Kaho'olawe and Big Island, Hawaii

Magnetic behavior of soils can seriously hamper the performance of geophysical sensors. Currently, we have little understanding of the types of minerals responsible for the magnetic behavior, as well as their distribution in space and evolution through time. This study investigated the magnetic characteristics and mineralogy of Fe-rich soils developed on basaltic substrate in Hawaii. We measured the spatial distribution of magnetic susceptibility (χlf) and frequency dependence (χfd%) across three test areas in a well-developed eroded soil on Kaho'olawe and in two young soils on the Big Island of Hawaii. X-ray diffraction spectroscopy, x-ray fluorescence spectroscopy (XFCF), chemical dissolution, thermal analysis, and temperature-dependent magnetic studies were used to characterize soil development and mineralogy for samples from soil pits on Kaho'olawe, surface samples from all three test areas, and unweathered basalt from the Big Island of Hawaii. The measurements show a general increase in magnetic properties with increasing soil development. The XRF Fe data ranged from 13% for fresh basalt and young soils on the Big Island to 58% for material from the B horizon of Kaho'olawe soils. Dithionite-extractable and oxalate-extractable Fe percentages increase with soil development and correlate with χlf-and χfd%, respectively. Results from the temperature-dependent susceptibility measurements show that the high soil magnetic properties observed in geophysical surveys in Kaho'olawe are entirely due to neoformed minerals. The results of our studies have implications for the existing soil survey of Kaho'olawe and help identify methods to characterize magnetic minerals in tropical soils.

Primary and secondary neural networks of auditory prepulse inhibition: a functional magnetic resonance imaging study of sensorimotor gating of the human acoustic startle response

Feedforward inhibition deficits have been consistently demonstrated in a range of neuropsychiatric conditions using prepulse inhibition (PPI) of the acoustic startle eye-blink reflex when assessing sensorimotor gating. While PPI can be recorded in acutely decerebrated rats, behavioural, pharmacological and psychophysiological studies suggest the involvement of a complex neural network extending from brainstem nuclei to higher order cortical areas. The current functional magnetic resonance imaging study investigated the neural network underlying PPI and its association with electromyographically (EMG) recorded PPI of the acoustic startle eye-blink reflex in 16 healthy volunteers. A sparse imaging design was employed to model signal changes in blood oxygenation level-dependent (BOLD) responses to acoustic startle probes that were preceded by a prepulse at 120 ms or 480 ms stimulus onset asynchrony or without prepulse. Sensorimotor gating was EMG confirmed for the 120-ms prepulse condition, while startle responses in the 480-ms prepulse condition did not differ from startle alone. Multiple regression analysis of BOLD contrasts identified activation in pons, thalamus, caudate nuclei, left angular gyrus and bilaterally in anterior cingulate, associated with EMGrecorded sensorimotor gating. Planned contrasts confirmed increased pons activation for startle alone vs 120-ms prepulse condition, while increased anterior superior frontal gyrus activation was confirmed for the reverse contrast. Our findings are consistent with a primary pontine circuitry of sensorimotor gating that interconnects with inferior parietal, superior temporal, frontal and prefrontal cortices via thalamus and striatum. PPI processes in the prefrontal, frontal and superior temporal cortex were functionally distinct from sensorimotor gating.

Functional brain maps of Tower of London performance : a positron emission tomography and functional magnetic resonance imaging study

Regional cerebral blood flow (rCBF) and blood oxygenation level-dependent (BOLD) contrasts represent different physiological measures of brain activation. The present study aimed to compare two functional brain imaging techniques (functional magnetic resonance imaging versus [15O] positron emission tomography) when using Tower of London (TOL) problems as the activation task. A categorical analysis (task versus baseline) revealed a significant BOLD increase bilaterally for the dorsolateral prefrontal and inferior parietal cortex and for the cerebellum. A parametric haemodynamic response model (or regression analysis) confirmed a task-difficulty-dependent increase of BOLD and rCBF for the cerebellum and the left dorsolateral prefrontal cortex. In line with previous studies, a task-difficulty-dependent increase of left-hemispheric rCBF was also detected for the premotor cortex, cingulate, precuneus, and globus pallidus. These results imply consistency across the two neuroimaging modalities, particularly for the assessment of prefrontal brain function when using a parametric TOL adaptation.

Analysis of impedance matched circuit for wireless power transfer

Typical wireless power transfer systems utilize series compensation circuit which is based on magnetic coupling and resonance principles that was first developed by Tesla. However, changes in coupling caused by gap distance, alignment and orientation variations can lead to reduce power transfer efficiencies and the transferred power levels. This paper proposes impedance matched circuit to reduce frequency bifurcation effect and improve on the transferred power level, efficiency and total harmonic distortion (THD) performance of the series compensation circuit. A comprehensive mathematical analysis is performed for both series and impedance matched circuits to show the frequency bifurcation effects in terms of input impedance, variations in transferred power levels and efficiencies. Matlab/Simulink results validate the theoretical analysis and shows the circuits’ THD performance when circuits are fed with power electronic converters.

Medical imaging and Biomechanical analysis of scoliosis progression in the growing adolescent spine

Progression of spinal deformity in children was studied with Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) to identify how gravity affects the deformity and to determine the full three-dimensional character of the deformity. The CT study showed that gravity is significant in deformity progression in some patients which has implications for clinical patient management. The world first MRI study showed that the standard clinical measure used to define the extent of the deformity is inadequate and further use of three-dimensional MRI should be considered by spinal surgeons.

Hierarchical topological network analysis of anatomical human brain connectivity and differences related to sex and kinship

Modern non-invasive brain imaging technologies, such as diffusion weighted magnetic resonance imaging (DWI), enable the mapping of neural fiber tracts in the white matter, providing a basis to reconstruct a detailed map of brain structural connectivity networks. Brain connectivity networks differ from random networks in their topology, which can be measured using small worldness, modularity, and high-degree nodes (hubs). Still, little is known about how individual differences in structural brain network properties relate to age, sex, or genetic differences. Recently, some groups have reported brain network biomarkers that enable differentiation among individuals, pairs of individuals, and groups of individuals. In addition to studying new topological features, here we provide a unifying general method to investigate topological brain networks and connectivity differences between individuals, pairs of individuals, and groups of individuals at several levels of the data hierarchy, while appropriately controlling false discovery rate (FDR) errors. We apply our new method to a large dataset of high quality brain connectivity networks obtained from High Angular Resolution Diffusion Imaging (HARDI) tractography in 303 young adult twins, siblings, and unrelated people. Our proposed approach can accurately classify brain connectivity networks based on sex (93% accuracy) and kinship (88.5% accuracy). We find statistically significant differences associated with sex and kinship both in the brain connectivity networks and in derived topological metrics, such as the clustering coefficient and the communicability matrix.

Genome-wide interaction analysis reveals replicated epistatic effects on brain structure

The discovery of several genes that affect the risk for Alzheimer's disease ignited a worldwide search for single-nucleotide polymorphisms (SNPs), common genetic variants that affect the brain. Genome-wide search of all possible SNP-SNP interactions is challenging and rarely attempted because of the complexity of conducting approximately 1011 pairwise statistical tests. However, recent advances in machine learning, for example, iterative sure independence screening, make it possible to analyze data sets with vastly more predictors than observations. Using an implementation of the sure independence screening algorithm (called EPISIS), we performed a genome-wide interaction analysis testing all possible SNP-SNP interactions affecting regional brain volumes measured on magnetic resonance imaging and mapped using tensor-based morphometry. We identified a significant SNP-SNP interaction between rs1345203 and rs1213205 that explains 1.9% of the variance in temporal lobe volume. We mapped the whole brain, voxelwise effects of the interaction in the Alzheimer's Disease Neuroimaging Initiative data set and separately in an independent replication data set of healthy twins (Queensland Twin Imaging). Each additional loading in the interaction effect was associated with approximately 5% greater brain regional brain volume (a protective effect) in both Alzheimer's Disease Neuroimaging Initiative and Queensland Twin Imaging samples.

A genetic analysis of cortical thickness in 372 twins

Imaging genetics is a new field of neuroscience that blends methods from computational anatomy and quantitative genetics to identify genetic influences on brain structure and function. Here we analyzed brain MRI data from 372 young adult twins to identify cortical regions in which gray matter volume is influenced by genetic differences across subjects. Thickness maps, reconstructed from surface models of the cortical gray/white and gray/CSF interfaces, were smoothed with a 25 mm FWHM kernel and automatically parcellated into 34 regions of interest per hemisphere. In structural equation models fitted to volume values at each surface vertex, we computed components of variance due to additive genetic (A), shared (C) and unique (E) environmental factors, and tested their significance. Cortical regions in the vicinity of the perisylvian language cortex, and at the frontal and temporal poles, showed significant additive genetic variance, suggesting that volume measures from these regions may provide quantitative phenotypes to narrow the search for quantitative trait loci that influence brain structure.