Fully automatic localization and segmentation of 3D vertebral bodies from CT/MR images via a learning-based method

In this paper, we address the problems of fully automatic localization and segmentation of 3D vertebral bodies from CT/MR images. We propose a learning-based, unified random forest regression and classification framework to tackle these two problems. More specifically, in the first stage, the localization of 3D vertebral bodies is solved with random forest regression where we aggregate the votes from a set of randomly sampled image patches to get a probability map of the center of a target vertebral body in a given image. The resultant probability map is then further regularized by Hidden Markov Model (HMM) to eliminate potential ambiguity caused by the neighboring vertebral bodies. The output from the first stage allows us to define a region of interest (ROI) for the segmentation step, where we use random forest classification to estimate the likelihood of a voxel in the ROI being foreground or background. The estimated likelihood is combined with the prior probability, which is learned from a set of training data, to get the posterior probability of the voxel. The segmentation of the target vertebral body is then done by a binary thresholding of the estimated probability. We evaluated the present approach on two openly available datasets: 1) 3D T2-weighted spine MR images from 23 patients and 2) 3D spine CT images from 10 patients. Taking manual segmentation as the ground truth (each MR image contains at least 7 vertebral bodies from T11 to L5 and each CT image contains 5 vertebral bodies from L1 to L5), we evaluated the present approach with leave-one-out experiments. Specifically, for the T2-weighted MR images, we achieved for localization a mean error of 1.6 mm, and for segmentation a mean Dice metric of 88.7% and a mean surface distance of 1.5 mm, respectively. For the CT images we achieved for localization a mean error of 1.9 mm, and for segmentation a mean Dice metric of 91.0% and a mean surface distance of 0.9 mm, respectively.

Step by step analyzing, modeling and simulation of single and double array PV system in different environmental variability

This research analyses the mathematical model of the PV array with all the parameters involved, in the sequential steps. Then it is going to simulate the I-V and P-V characteristics of PV array in different climatology conditions, by using both MATLAB M-file and Simulink. Finally the capability of this method is demonstrated in simulated results, which are performed for both Single and Double Array PV system.

Affective instability and the course of bipolar depression: results from the STEP-BD randomised controlled trial of psychosocial treatment

BACKGROUND: Little is known about predictors of recovery from bipolar depression. AIMS: We investigated affective instability (a pattern of frequent and large mood shifts over time) as a predictor of recovery from episodes of bipolar depression and as a moderator of response to psychosocial treatment for acute depression. METHOD: A total of 252 out-patients with DSM-IV bipolar I or II disorder and who were depressed enrolled in the Systematic Treatment Enhancement Program for Bipolar Disorder (STEP-BD) and were randomised to one of three types of intensive psychotherapy for depression (n = 141) or a brief psychoeducational intervention (n = 111). All analyses were by intention-to-treat. RESULTS: Degree of instability of symptoms of depression and mania predicted a lower likelihood of recovery and longer time until recovery, independent of the concurrent effects of symptom severity. Affective instability did not moderate the effects of psychosocial treatment on recovery from depression. CONCLUSIONS: Affective instability may be a clinically relevant characteristic that influences the course of bipolar depression.

Attenuated total reflectance fourier transform infrared spectroscopy: an analytical technique to understand therapeutic responses at the molecular level

Rapid monitoring of the response to treatment in cancer patients is essential to predict the outcome of the therapeutic regimen early in the course of the treatment. The conventional methods are laborious, time-consuming, subjective and lack the ability to study different biomolecules and their interactions, simultaneously. Since; mechanisms of cancer and its response to therapy is dependent on molecular interactions and not on single biomolecules, an assay capable of studying molecular interactions as a whole, is preferred. Fourier Transform Infrared (FTIR) spectroscopy has become a popular technique in the field of cancer therapy with an ability to elucidate molecular interactions. The aim of this study, was to explore the utility of the FTIR technique along with multivariate analysis to understand whether the method has the resolution to identify the differences in the mechanism of therapeutic response. Towards achieving the aim, we utilized the mouse xenograft model of retinoblastoma and nanoparticle mediated targeted therapy. The results indicate that the mechanism underlying the response differed between the treated and untreated group which can be elucidated by unique spectral signatures generated by each group. The study establishes the efficiency of non-invasive, label-free and rapid FTIR method in assessing the interactions of nanoparticles with cellular macromolecules towards monitoring the response to cancer therapeutics.

One-step amino-functionalization of milled carbon fibre for enhancement of thermo-physical properties of epoxy composites

Development of new chemical approaches for preparation of engineered carbon-based fillers is critical for high-performance applications. Herein, an efficient method for covalent functionalization of polyacrylonitrile-based carbon fibre through azo radical addition under mild condition is demonstrated. In this way, isobutyronitrile radicals in situ produced from thermal decomposition of 2,2′-azobisisobutyronitrile (AIBN), were covalently grafted on milled carbon fibre (MCF) surface, assisted by microwave irradiation, as evidenced by FTIR, Raman, and TGA analysis. The grafted isobutyronitriles on MCF surface (n-MCF) were applied for further MCF amino-functionalization (a-MCF) via nucleophilic reaction of an amine-rich compound. Then, both pure MCF and a-MCF were incorporated into epoxy matrix; and its curing process and thermo-physical properties were investigated using DSC, rheometry, DMA, TGA, and flexural analysis. The Tg and flexural strength of epoxy/a-MCF composites, compared to epoxy/MCF, increased by ∼3.5% and ∼10.2%, resulting from good adhesion between a-MCF and epoxy matrix which confirmed by SEM observations.

On the fast Lanczos method for computation of eigenvalues of Hankel matrices using multiprecision arithmetics

The use of the fast Fourier transform (FFT) accelerates Lanczos tridiagonalisation method for Hankel and Toeplitz matrices by reducing the complexity of matrix-vector multiplication. In multiprecision arithmetics, the FFT has overheads that make it less competitive compared with alternative methods when the accuracy is over 10000 decimal places. We studied two alternative Hankel matrix-vector multiplication methods based on multiprecision number decomposition and recursive Karatsuba-like multiplication, respectively. The first method was uncompetitive because of huge precision losses, while the second turned out to be five to 14 times faster than FFT in the ranges of matrix sizes up to n = 8192 and working precision of b = 32768 bits we were interested in. We successfully applied our approach to eigenvalues calculations to studies of spectra of matrices that arise in research on Riemann zeta function. The recursive matrix-vector multiplication significantly outperformed both the FFT and the traditional multiplication in these studies.

Chemical structure based prediction of PAN and oxidized PAN fiber density through a non-linear mathematical model

The production of carbon fiber, particularly the oxidation/stabilization step, is a complex process. In the present study, a non-linear mathematical model has been developed for the prediction of density of polyacrylonitrile (PAN) and oxidized PAN fiber (OPF), as a key physical property for various applications, such as energy and material optimization, modeling, and design of the stabilization process. The model is based on the available functional groups in PAN and OPF. Expected functional groups, including [Formula presented], [Formula presented], –CH2, [Formula presented], and [Formula presented], were identified and quantified through the full deconvolution analysis of Fourier transform infrared attenuated total reflectance (FT-IR ATR) spectra obtained from fibers. These functional groups form the basis of three stabilization rendering parameters, representing the cyclization, dehydrogenation and oxidation reactions that occur during PAN stabilization, and are used as the independent variables of the non-linear predictive model. The k-fold cross validation approach, with k = 10, has been employed to find the coefficients of the model. This model estimates the density of PAN and OPF independent of operational parameters and can be expanded to all operational parameters. Statistical analysis revealed good agreement between the governing model and experiments. The maximum relative error was less than 1% for the present model.

One-step synthesis of graphene quantum dots from defective CVD graphene and their application in IGZO UV thin film phototransistor

Chemical vapor deposition (CVD) has recently been considered as the most reliable method to prepare high-quality monolayer graphene films, yet the as-grown graphene usually contains wrinkles and cracks or suffers from discontinuity. These defects can easily result in the shredding of large-sized graphene into small pieces even under a gentle disturbance. Herein, this work presents a cost-effective new method to produce high-quality GQDs by vigorous sonication of defective CVD graphene. The prepared GQDs can be easily and stably dispersed in organic solvents. Morphology and optical properties of the GQDs are investigated using a number of techniques. And we observed the as-prepared GQDs are highly homogeneous, mostly consisted of single-layered graphene, roughly round shapes less than 8 nm in a diameter, and exhibited a strong blue luminescence. Impressively, it is also confirmed that the as-obtained GQDs can act as a promising light absorption material for phototransistor with a hybrid film of GQDs and indium gallium zinc oxide (IGZO) as the channel layer. The GQD/IGZO phototransistor exhibited an appreciated photocurrent, which is 10 times larger than that of the IGZO one when exposed to 270 nm light.

Towards simulation of flapping wings using immersed boundary method

In this work the immersed boundary method is applied to simulate incompressible turbulent flows around stationary and moving objects. The goal is to demonstrate that the immersed boundary technique along with a large eddy simulation approach is capable of simulating the effect of the so-called leading edge vortex (LEV), which can be found in flapping wing aerodynamics. A Lagrangian method is used to approximatethe solutions in the freshly cleared cells that lay within solid objects at one time step and emerge into fluid domain at the next time step. Flow around a stationary cylinder at ReD D 20, 40, and 3900 (based oncylinder diameter D) is first studied to validate the immersed boundary solver based on the finite volume scheme using a staggered grid. Then, a harmonically oscillating cylinder at ReD D 10 000 is considered to test the solver after the Lagrangian method is implemented to interpolate the solution in the freshly cleared cells. Finally, this approach is used to study flows around a stationary flat-plate at several angles of attack and fast pitching flat-plate. The rapidly pitching plate creates a dynamic LEV that can be used to improve the efficiency of flapping wings of micro air vehicle and to determine the optimum flapping frequency.

A high embedding capacity image watermarking method with rank-based embedder and decoder

High embedding capacity is desired in digital image watermarking. In this paper, we propose a novel rank-based image watermarking method to achieve high embedding capacity. We first divide the host image into blocks. Then the 2-D discrete cosine transform (DCT) and zigzag scanning is used to construct the coefficient sets with a secret key. After that, the DCT coefficient sets are modified using a rank-based embedding strategy to insert the watermark bits. A buffer is also introduced during the embedding phase to enhance the robustness. At the decoding step, the watermark bits are extracted by checking the ranks of the detection matrices. The proposed method is host signal interference (HSI) free, invariant to amplitude scaling and constant luminance change, and robust against other common signal processing attacks. Experimental results demonstrate the effectiveness of the proposed method.