Learning-based Single-step Quantitative Susceptibility Mapping Reconstruction Without Brain Extraction

Quantitative susceptibility mapping (QSM) estimates the underlying tissue magnetic susceptibility from MRI gradient-echo phase signal and typically requires several processing steps. These steps involve phase unwrapping, brain volume extraction, background phase removal and solving an ill-posed inverse problem. The resulting susceptibility map is known to suffer from inaccuracy near the edges of the brain tissues, in part due to imperfect brain extraction, edge erosion of the brain tissue and the lack of phase measurement outside the brain. This inaccuracy has thus hindered the application of QSM for measuring the susceptibility of tissues near the brain edges, e.g., quantifying cortical layers and generating superficial venography. To address these challenges, we propose a learning-based QSM reconstruction method that directly estimates the magnetic susceptibility from total phase images without the need for brain extraction and background phase removal, referred to as autoQSM. The neural network has a modified U-net structure and is trained using QSM maps computed by a two-step QSM method. 209 healthy subjects with ages ranging from 11 to 82 years were employed for patch-wise network training. The network was validated on data dissimilar to the training data, e.g. in vivo mouse brain data and brains with lesions, which suggests that the network has generalized and learned the underlying mathematical relationship between magnetic field perturbation and magnetic susceptibility. AutoQSM was able to recover magnetic susceptibility of anatomical structures near the edges of the brain including the veins covering the cortical surface, spinal cord and nerve tracts near the mouse brain boundaries. The advantages of high-quality maps, no need for brain volume extraction and high reconstruction speed demonstrate its potential for future applications.Comment: 26 page

An Efficient Algorithm by Kurtosis Maximization in Reference-Based Framework

This paper deals with the optimization of kurtosis for complex-valued signals in the independent component analysis (ICA) framework, where source signals are linearly and instantaneously mixed. Inspired by the recently proposed reference-based contrast schemes, a similar contrast function is put forward, based on which a new fast fixed-point (FastICA) algorithm is proposed. The new optimization method is similar in spirit to the former classical kurtosis-based FastICA algorithm but differs in the fact that it is much more efficient than the latter in terms of computational speed, which is significantly striking with large number of samples. The performance of this new algorithm is confirmed through computer simulations

Modeling asymmetry and tail dependence among multiple variables by using partial regular vine

Copyright © SIAM. Modeling high-dimensional dependence is widely studied to explore deep relations in multiple variables particularly useful for financial risk assessment. Very often, strong restrictions are applied on a dependence structure by existing high-dimensional dependence models. These restrictions disabled the detection of sophisticated structures such as asymmetry, upper and lower tail dependence between multiple variables. The paper proposes a partial regular vine copula model to relax these restrictions. The new model employs partial correlation to construct the regular vine structure, which is algebraically independent. This model is also able to capture the asymmetric characteristics among multiple variables by using two-parametric copula with flexible lower and upper tail dependence. Our method is tested on a cross-country stock market data set to analyse the asymmetry and tail dependence. The high prediction performance is examined by the Value at Risk, which is a commonly adopted evaluation measure in financial market

Local breaking of four-fold rotational symmetry by short-range magnetic order in heavily overdoped Ba(Fe1−x_{1-x}Cux_{x})2_{2}As2_{2}

We investigate Cu-doped Ba(Fe$_{1-x}$Cu$_x$)$_2$As$_2$ with transport, magnetic susceptibility, and elastic neutron scattering measurements. In the heavily Cu-doped regime where long-range stripe-type antiferromagnetic order in BaFe$_2$As$_2$ is suppressed, Ba(Fe$_{1-x}$Cu$_x$)$_2$As$_2$ (0.145 $\leq x \leq$ 0.553) samples exhibit spin-glass-like behavior in magnetic susceptibility and insulating-like temperature dependence in electrical transport. Using elastic neutron scattering, we find stripe-type short-range magnetic order in the spin-glass region identified by susceptibility measurements. The persistence of short-range magnetic order over a large doping range in Ba(Fe$_{1-x}$Cu$_x$)$_2$As$_2$ likely arises from local arrangements of Fe and Cu that favor magnetic order, with Cu acting as vacancies relieving magnetic frustration and degeneracy. These results indicate locally broken four-fold rotational symmetry, suggesting that stripe-type magnetism is ubiquitous in iron pnictides.Comment: accepted by Physical Review B Rapid Communication

Evolution of Moire Profiles from van der Waals Superstructures of Boron Nitride Nanosheets

Two-dimensional (2D) van der Waals (vdW) superstructures, or vdW solids, are formed by the precise restacking of 2D nanosheet lattices, which can lead to unique physical and electronic properties that are not available in the parent nanosheets. Moire patterns formed by the crystalline mismatch between adjacent nanosheets are the most direct features for vdW superstructures under microscopic imaging. In this article, transmission electron microscopy (TEM) observation of hexagonal Moire patterns with unusually large micrometer-sized lateral areas (up to similar to 1 mu m(2)) and periodicities (up to similar to 50 nm) from restacking of liquid exfoliated hexagonal boron nitride nanosheets (BNNSs) is reported. This observation was attributed to the long range crystallinity and the contaminant-free surfaces of these chemically inert nanosheets. Parallel-line-like Moire fringes with similarly large periodicities were also observed. The simulations and experiments unambiguously revealed that the hexagonal patterns and the parallel fringes originated from the same rotationally mismatched vdW stacking of BNNSs and can be inter-converted by simply tilting the TEM specimen following designated directions. This finding may pave the way for further structural decoding of other 2D vdW superstructure systems with more complex Moire images

Molecular characters and recombinant expression of the carboxylesterase gene of the meadow moth Loxostege sticticalis L. (Lepidoptera: Pyralidae)

Insect carboxylesterases are enzymes that catalyze the hydrolysis of ester and amide moieties, which play important roles in insecticide resistance, specifically allelochemical tolerance and developmental regulation. We obtained the cDNA encoding carboxylesterase gene of Loxostege sticticalis (LstiCarE) by a cDNA library screen. The full cDNA of LstiCarE is 1,980 bp in length, containing an open reading frame (ORF) of 1,875 bp, which encodes a preprotein of 625 amino acid residues. The LstiCarE contains the catalytic triad (Ser-His-Glu), the pentapeptide GxSxG motif and GxxHxxD/E motif, which are typical characteristic of esterases. The GxSxG and GxxHxxD/E motifs of LstiCarE are modified as GCSAG and GxxHxxQ, respectively. The 3-D model structure of LstiCarE showed that Ser197, His440 and Glu321 are aggregated together, which form the catalytic triad. The recombinant LstiCarE were successfully expressed in BL21 cells using recombinant plasmid DNA, and showed high carboxylesterase activity. However, the biochemical and physiological functions of carboxylesterase gene in L. sticticalis requires further investigation.Key words: Carboxylesterase gene, Loxostege sticticalis, recombinant expression

Thermodynamic Geometry of black hole in the deformed Horava-Lifshitz gravity

We investigate the thermodynamic geometry and phase transition of Kehagias-Sfetsos black hole in the deformed Horava-Lifshitz gravity with coupling constant $\lambda=1$. The phase transition in black hole thermodynamics is thought to be associated with the divergence of the capacities. And the structures of these divergent points are studied. We also find that the thermodynamic curvature produced by the Ruppeiner metric is positive definite for all $r_+ > r_-$ and is divergence at $\eta_2=0$ corresponded to the divergent points of $C_{\Phi}$ and $C_T$. These results suggest that the microstructure of the black hole has an effective repulsive interaction, which is very similar to the ideal gas of fermions. These may shine some light on the microstructure of the black hole.Comment: 5 pages, 3 figure