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Obstructive sleep apnea (OSA) is a common sleep respiratory disease. Previous studies have found that the wakefulness electroencephalogram (EEG) of OSA patients has changed, such as increased EEG power. However, whether the microstates reflecting the transient state of the brain is abnormal is unclear during obstructive hypopnea (OH). We investigated the microstates of sleep EEG in 100 OSA patients. Then correlation analysis was carried out between microstate parameters and EEG markers of sleep disturbance, such as power spectrum, sample entropy and detrended fluctuation analysis (DFA). OSA_OH patients showed that the microstate C increased presence and the microstate D decreased presence compared to OSA_withoutOH patients and controls. The fifth microstate E appeared during N1-OH, but the probability of other microstates transferring to microstate E was small. According to the correlation analysis, OSA_OH patients in N1-OH showed that the microstate D was positively correlated with delta power, and negatively correlated with beta and alpha power; the transition probability of the microstate B → C and E → C was positively correlated with alpha power. In other sleep stages, the microstate parameters were not correlated with power, sample entropy and FDA. We might interpret that the abnormal transition of brain active areas of OSA patients in N1-OH stage leads to abnormal microstates, which might be related to the change of alpha activity in the cortex.

Background. Totally extraperitoneal herniorrhaphy (TEP) is a therapeutic challenge because of its complex anatomical location in inguinal region. The aim of this study was to describe the related surgical anatomy through laparoscopic observation and share the lessons learned from a review of 250 primary inguinal hernia repair procedures performed at our hospital from January 2013 to November 2019. Patients and Methods. There were 245 men and 5 women (median age: 63.2 years). Right hernia (60.2%) was the most common site. Indirect hernia (60.5%) was the most common abnormality. The classification of type II (65.0%) was the most common form. Surgical techniques comprised retromuscular approach using cauterized dissection, management of variations of arcuate line, Retzius space and Bogros space dissection, hernia sac reduction, and mesh positioning. Results. The incidence of peritoneum injury was in 27 (10.1%). No epigastric vessels were injured. There were 8 (3%) hematoma and 18 (6.8%) seroma. No mesh infection, chronic pain, and recurrence were found after follow-up of an average of 35 months. Conclusion. A good understanding of the anatomically complex nature in the inguinal region can make it easier and safer to learn the TEP approach. Early and midterm outcomes after TEP are satisfactory.

First-principles computational studies under density functional theory (DFT) framework were used to investigate the structural stability, conductivity and voltage profile of LiFe1−nNnP1−mMmO4 (N, M = Si or S) electrode materials. It is found that the LiFeP7/8Si1/8O4 system has the most stable structure. After doping, the band gap values of the systems decrease gradually, and LiFe7/8S1/8PO4 system has a minimum band gap of 1.553 eV, attributed to the hybridization of the Fe-d and S-p orbital electrons. The LiFeP7/8S1/8O4 system demonstrates the characteristic of n-type semiconductor, and other doping systems have the feature of p-type semiconductor. Charge density difference maps show that the covalent property of Si–O bond is enhanced in the LiFeP7/8Si1/8O4 system. The average distance of Li and O atoms in the S doping systems increases from 0.21026 to 0.21486 and 0.21129 nm, respectively, indicating that doping broadens significantly the channel of Li ion de-intercalation in LiFe7/8S1/8PO4 and LiFeP7/8S1/8O4. Additionally, the results of lithium intercalation potential imply that the voltages of the doping systems fall into the range of 2.23–2.86 V.

Hepatocellular carcinoma is one of the most lethal cancers in the United States. Early detection of the disease is crucial for reducing the mortality of this malignancy. Recently, we identified a panel of fusion genes present in several types of human cancers, including hepatocellular carcinoma. Among 8 fusion genes, MAN2A1-FER, TRMT11-GRIK2 and CCNH-C5orf30 appear most frequently in hepatocellular carcinoma samples. In this study, we showed that the fusion transcripts of MAN2A1-FER, CCNH-C5orf30 and SLC45A2-AMACR were detected in the serum samples of liver cancer patients as circulating cell-free RNA. The distributions of these gene fusion RNA fragments largely matched those of the primary HCC samples. In contrast, the sera of all healthy individuals free of human malignancies were shown to be negative for these fusion genes. These results suggest that gene fusion RNA is frequently shed from liver cancer cells. The detection of serum cell-free fusion transcripts may provide a new approach to aid in the diagnosis, follow-up or therapy of liver cancers.

Prostate cancer remains one of the most lethal cancers for men in the United States. The study aims to detect fusion transcripts in the blood samples of prostate cancer patients. We analyzed nine fusion transcripts including MAN2A1-FER, SLC45A2-AMACR, TRMT11-GRIK2, CCNH-C5orf30, mTOR-TP53BP1, KDM4-AC011523.2, TMEM135-CCDC67, LRRC59-FLJ60017 and Pten-NOLC1147 in the blood samples from 147 prostate cancer patients and 14 healthy individuals, using Taqman RT-PCR and Sanger’s sequencing. Similar analyses were also performed on 25 matched prostate cancer samples for matched-sample evaluation. Eighty-two percent blood samples from the prostate cancer patients were positive for MAN2A1-FER transcript, while 41.5% and 38.8% blood samples from the prostate cancer patients were positive for SLC45A2-AMACR and Pten-NOLC1, respectively. CCNH-c5orf30 and mTOR-TP53BP1 had low detection rates, positive in only 5.4% and 4% of the blood samples from the prostate cancer patients. Only 2 blood samples were positive for KDM4B-AC011523.2 transcript. Overall, 89.8% patients were positive for at least one fusion transcript in their blood samples. The statistical analysis showed varied sensitivity of fusion transcript detection in the blood based on the types of fusions. In contrast, the blood samples from all healthy individuals were negative for the fusion transcripts. Detection of fusion transcripts in the blood samples of the prostate cancer patients may be a fast and cost-effective way to detect prostate cancer.

The characterization of human gene expression is limited by short read lengths, high error rates and large input requirements. Here, we used a synthetic long read (SLR) sequencing approach, LoopSeq, to generate accurate sequencing reads that span full length transcripts using standard short read data. LoopSeq identified isoforms from control samples with 99.4% accuracy and a 0.01% per-base error rate, exceeding the accuracy reported for other long-read technologies. Applied to targeted transcriptome sequencing from colon cancers and their metastatic counterparts, LoopSeq revealed large scale isoform redistributions from benign colon mucosa to primary colon cancer and metastatic cancer and identified several previously unknown fusion isoforms. Strikingly, single nucleotide variants (SNVs) occurred dominantly in specific isoforms and some SNVs underwent isoform switching in cancer progression. The ability to use short reads to generate accurate long-read data as the raw unit of information holds promise as a widely accessible approach in transcriptome sequencing., Silvia Liu et al. present LoopSeq, a synthetic long-read sequencing method that generates accurate long-read transcriptome data from short Illumina reads. As an example of possible applications, they use LoopSeq to investigate differential isoform expression, isoform-specific single nucleotide variants, and potential fusion genes in multiple stages of colon cancer. Altogether, LoopSeq is a valuable method for analyzing complex transcriptomes and investigating gene expression at the isoform level.

Considering the insufficient understanding of pulmonary cryptococcosis (PC) with consolidation, 22 patients were evaluated based on the chest computed tomography (CT) images and clinical manifestation. The clinical symptoms were mild, mainly manifesting as cough and sputum. Pulmonary lesions mostly involved a single lobe in a single lung with multiple lesions. Specifically, single lung involvement was observed in 17 cases, single lobe in 16 cases and multiple lesions in 14 case. Fifteen cases were mainly distributed in the periphery and 17 cases in the long axis in parallel to the pleura. Nineteen cases had air bronchograms. Eight cases displayed cavitation inside the lesions and 18 cases had surrounding halo signs. Seventeen cases had pleural thickening, of which 10 cases had "pasting wall" signs. The clinical symptoms of PC with consolidation were relatively mild. Comprehensive clinical and imaging performance could improve the diagnosis of the disease.

Background Previous studies have focused on the clinical characteristics of hospitalized patients with the novel 2019 coronavirus disease (COVID-19). Limited data are available for convalescent patients. This study aimed to evaluate the clinical characteristics of discharged COVID-19 patients. Methods In this retrospective study, we extracted data for 134 convalescent patients with COVID-19 in Guizhou Provincial Staff Hospital from February 15 to March 31, 2020. Cases were analyzed on the basis of demographic, clinical, and laboratory data as well as radiological features. Results Of 134 convalescent patients with COVID-19, 19 (14.2%) were severe cases, while 115 (85.8%) were non-severe cases. The median patient age was 33 years (IQR, 21.8 to 46.3), and the cohort included 69 men and 65 women. Compared with non-severe cases, severe patients were older and had more chronic comorbidities, especially hypertension, diabetes, and thyroid disease (P Conclusion Leukopenia, lymphopenia, ground-glass opacity, and fibrosis are common in discharged severe COVID-19 patients, and liver injury is common in discharged adult patients. We suggest physicians develop follow-up treatment plans based on the different clinical characteristics of convalescent patients.

Senescence-associated alterations in microglia may have profound impact on cerebral homeostasis and stroke outcomes. However, the lack of a transcriptome-wide comparison between young and aged microglia in the context of ischemia limits our understanding of aging-related mechanisms. Herein, we performed RNA sequencing analysis of microglia purified from cerebral hemispheres of young adult (10-week-old) and aged (18-month-old) mice five days after distal middle cerebral artery occlusion or after sham operation. Considerable transcriptional differences were observed between young and aged microglia in healthy brains, indicating heightened chronic inflammation in aged microglia. Following stroke, the overall transcriptional activation was more robust (>13-fold in the number of genes upregulated) in young microglia than in aged microglia. Gene clusters with functional implications in immune inflammatory responses, immune cell chemotaxis, tissue remodeling, and cell-cell interactions were markedly activated in microglia of young but not aged stroke mice. Consistent with the genomic profiling predictions, post-stroke cerebral infiltration of peripheral immune cells was markedly decreased in aged mice compared to young mice. Moreover, post-ischemic aged microglia demonstrated reduced interaction with neighboring neurons and diminished polarity toward the infarct lesion. These alterations in microglial gene response and behavior may contribute to aging-driven vulnerability and poorer recovery after ischemic stroke.

Domain adaptation (DA), a particular case of transfer learning, is an effective technology for learning a discriminative model in scenarios where the data from the training (source) and the testing (target) domains share common class labels but follow different distributions. The differences between domains, called domain shifts, are caused by variations in the acquisition devices and environmental conditions, such as changing illuminations, pose, and collecting-device noises, that are related to a specific domain, denoted as domain-specific noises in this paper. The research on stacked denoising autoencoder (SDA) has demonstrated that noise-robust features could be learned through training a model to reduce the man-made (simulated) noises. However, little research has been conducted to learn the domain-invariant features through training SDA to reduce the domain-specific noises from the real word. In this paper, we propose a novel variant of SDA for DA, called the stacked local constraint auto-encoder (SLC-AE), which aims to learn domain-invariant features through iteratively optimizing the SDA and the low-dimensional manifold. The core idea behind the SLC-AE is that both the source and target samples are corrupted due to the domain-specific noises, and each corrupted sample could be de-noised by calculating the weighted sum of its neighbor samples defined on the intrinsic manifold. Because the neighbor samples on the intrinsic manifold are semantically similar, their weighted sum preserves the generic information and reduces the domain-specific noises. To properly evaluate the performance of the SLC-AE, we conducted extensive experiments using seven benchmark data sets, i.e., MNIST, USPS, COIL20, SYN SIGNS, GTSRB, MSRC and VOC 2007. Compared to twelve different state-of-the-art methods, the experimental results demonstrated that the proposed SLC-AE model made significant improvement over the performance of SDA and achieved the best average performance on the seven data sets.

Recent development on image categorization, especially scene categorization, shows that the combination of standard visible RGB image data and near-infrared (NIR) image data performs better than RGB-only image data. However, the size of RGB-NIR image collection is often limited due to the difficulty of acquisition. With limited data, it is difficult to extract effective features using the common deep learning networks. It is observed that humans are able to learn prior knowledge from other tasks or a good mentor, which is helpful to solve the learning problems with limited training samples. Inspired by this observation, we propose a novel training methodology for introducing the prior knowledge into a deep architecture, which allows us to bypass the burdensome labeling large quantity of image data to meet the big data requirements in deep learning. At first, transfer learning is adopted to learn single modal features from a large source database, such as ImageNet. Then, a knowledge distillation method is explored to fuse the RGB and NIR features. Finally, a global optimization method is employed to fine-tune the entire network. The experimental results on two RGB-NIR datasets demonstrate the effectiveness of our proposed approach in comparison with the state-of-the-art multi-modal image categorization methods.

Objective: This study was aimed to explore the expression level of long noncoding RNA H19 in the plasma of patients with nonsmall cell lung cancer (NSCLC) and its clinical significance. Methods: A total of 66 NSCLC patients (case group) and 31 patients with benign lung disease (control group) admitted from February 2015 to February 2017 were included in this study. Real-time polymerase chain reaction assay was applied to examine the relative expression level of long noncoding RNA H19 in the plasma of the two groups. The relationship between H19 expression and clinical, pathological features was explored. Receiver-operating characteristic (ROC) curve was applied to evaluate the clinical value of plasma H19 as a tumor marker in the auxiliary diagnosis of NSCLC. Results: The relative expression levels of plasma H19 inpatients from NSCLC group and benign lung disease group were 5.62 ± 2.02 (ΔCt) and 7.74 ± 2.75 (ΔCt), respectively. The NSCLC group presented with significantly higher levels than that of the benign disease group (P < 0.05). According to the median of relative expression level of 5.54, the plasma H19 of NSCLC patients was classified into low expression group ≥5.54 (n = 34) and high expression group 0.05). With the serum H19 as a diagnosis reference, the diagnostic sensitivity of NSCLC was 67.74%, and the specificity was 63.08%. The area under the ROC curve was 0.73, and the diagnostic cutoff value was 6.62. Conclusion: Plasma level of H19 in NSCLC patients was significantly increased, which could be applied as a serological marker for the auxiliary diagnosis of NSCLC.

Neurotrophic factors and their receptors have been identified to promote tumor progression. GFRα1, the receptor for glial cell line-derived neurotrophic factor (GDNF), has been demonstrated to be predominantly expressed in adult liver tissue. Our preliminary data showed that GFRα1 is significantly downregulated in hepatocellular carcinoma (HCC) tissue, compared to the matched non-neoplastic tissue. However, the role of GFRα1 in HCC progression remains unknown. Here we found that the expression of GFRα1 in HCC tissue is inversely correlated with the poorer prognosis of HCC patients. Silencing of GFRα1 expression markedly enhances HCC cell growth, tumor metastasis, as well as shortens the survival of HCC tumor-bearing mice. Forced expression of GFRα1 in HCC cells significantly reverses the tumor-promoting effects of GFRα1 silencing, and AAV8-mediated GFRα1 transfection in HCC tumor tissues significantly impedes tumor growth and prolongs the survival of HCC tumor-bearing mice. These results are also verified in vivo in GFRα1 knock-out mice model, with increased DEN-induced HCC carcinogenesis. Mechanistically, GFRα1 could inhibit epithelial-to-mesenchymal transition (EMT) of HCC cells, by upregulating expression of Claudin-1 and ZO-1. Of note, silencing of GFRα1 expression promotes oxaliplatin-mediated HCC cell apoptosis resulting in prolonged survival of HCC-bearing mice, and forced expression of GFRα1 markedly increased oxaliplatin resistance of HCC cells. These results demonstrate that deficiency of GFRα1 promotes HCC progression but enhances chemotherapeutic anti-tumor efficacy, suggesting that GFRα1 may be a candidate prognostic biomarker and a potential therapeutic target in HCC.

Purpose Single image super-resolution (SR) is highly desired in many fields but obtaining it is often technically limited in practice. The purpose of this study was to propose a simple, rapid and robust single image SR method in magnetic resonance (MR) imaging (MRI). Methods The idea is based on the mathematical formulation of the intrinsic link in k-space between a given (modulus) low-resolution (LR) image and the desired SR image. The method consists of two steps: 1) estimating the low-frequency k-space data of the desired SR image from a single LR image; 2) reconstructing the SR image using the estimated low-frequency and zero-filled high-frequency k-space data. The method was evaluated on digital phantom images, physical phantom MR images and real brain MR images, and compared with existing SR methods. Results The proposed SR method exhibited a good robustness by reaching a clearly higher PSNR (25.77dB) and SSIM (0.991) averaged over different noise levels in comparison with existing edge-guided nonlinear interpolation (EGNI) (PSNR=23.78dB, SSIM=0.983), zero-filling (ZF) (PSNR=24.09dB, SSIM=0.985) and total variation (TV) (PSNR=24.54dB, SSIM=0.987) methods while presenting the same order of computation time as the ZF method but being much faster than the EGNI or TV method. The average PSNR or SSIM over different slice images of the proposed method (PSNR=26.33 dB or SSIM=0.955) was also higher than the EGNI (PSNR=25.07dB or SSIM=0.952), ZF (PSNR=24.97dB or SSIM=0.950) and TV (PSNR=25.70dB or SSIM=0.953) methods, demonstrating its good robustness to variation in anatomical structure of the images. Meanwhile, the proposed method always produced less ringing artifacts than the ZF method, gave a clearer image than the EGNI method, and did not exhibit any blocking effect presented in the TV method. In addition, the proposed method yielded the highest spatial consistency in the inter-slice dimension among the four methods. Conclusions This study proposed a fast, robust and efficient single image SR method with high spatial consistency in the inter-slice dimension for clinical MR images by estimating the low-frequency k-space data of the desired SR image from a single spatial modulus LR image.

Graphene-based van der Waals (vdW) heterostructures have currently emerged as a promising application in the construction of next-generation electronic and optoelectronic devices. In this present contribution, through the comprehensive first-principle calculations, the electronic characteristics along with the Schottky barrier of graphene/β-silicon nitride(0001) heterostructure are theoretically investigated, considering external strain and electric fields effects. The results concluded that the electronic performance of both the graphene monolayer and β-Si3N4(0001) surface are perfectly maintained in the heterostructure on account of a weak interlayer vdW force between them. Furthermore, the graphene/β-Si3N4 (0001) heterostructure forms a p-type Schottky contact with the SBH of 0.72 eV, which can be modified by using normal strain or perpendicular electric field. It is found that the heterostructure still maintains a p-type Schottky contact when the interlayer spacing from 2.2 A to 4.4 A or when the applied positive electric field is smaller than 0.2 V/A. Most importantly, a transformation from the p-type Schottky contact to Ohmic one is observed in the heterostructure at an electric field of +0.2 V/A. The above results are expected to provide a practical guidance for designing and fabrication of the novel nanoelectronic devices based on graphene/β-Si3N4 vdW heterostructure.

We have investigated the propagation characteristics of anisotropic a-axis hollow lithium niobate nanowire based on a three-layer-dielectric circular waveguide structure by exactly solving Maxwell's equations. The modal characteristics, waveguide dispersion, and nonlinear parameters of the hollow nanowire have been numerically investigated. The numerical results show that the lithium niobate hollow nanowires have some excellent and unique properties, such as the enhanced evanescent field, large dispersion, and high nonlinearity, which are favorable for nonlinear optics applications, such as supercontinuum generation. The results may make inroad toward the miniaturized high-performance linear or nonlinear photonic devices.

It is a crucial property for autonomous vehicle driving systems to robustly perform in different driving surroundings. However, the modules based on computer vision suffer from the performance degradation problem, when there is distribution discrepancy between the practically captured data and the training data. In this paper, we address this problem by learning an one-step transformation to bridge the discrepancy from source domain to target domain. Since the feature space learned by labeled source data is well-trained, the target data firstly are directly mapped to this feature space. With regard the domain discrepancy, the distribution of source and target features need to be further aligned. We model the aligning process as an one-step transformation and implement it as one layer convolutional neural network. In order to effectively learn the one-step transformation, a new adversarial loss function is proposed to minimize the Wasserstein distance of involving domains and the prediction error simultaneously. The experiments are conducted on six datasets, including the challenging traffic-related data,e.g. traffic sign images and the pedestrian fisheye images captured by the cameras installed in a moving vehicle. The results demonstrated the efficiency of the proposed method in comparison with other eight classical recognition methods.

Hyperspectral image (HSI) is usually composed of hundreds of bands which contain very rich spatial and spectral information. However, the high-dimensional data may lead to the curse of dimensionality phenomenon when it is used for land use classification or other applications, making it difficult to be utilized effectively. In this paper, we developed a deep learning classification framework based on the spectral and spatial information of hyperspectral image. Firstly, the deep learning features in different layers could be extracted automatically. Secondly, based on the learned deep learning features, we could obtain the classification of hyperspectral image with logistic regression (LR) classifier. Finally, we compared our approach with other methods including quadratic discriminant analysis with the multilevel logistic spatial prior (QDAMLL), logistic discriminant analysis with the multilevel logistic spatial prior (logDAMLL), linear discriminant analysis with the multilevel logistic spatial prior (LDAMLL), subspace multiclass logistic regression with the multilevel logistic spatial prior (MLRsub MLL), support vector machine on extended morphological profiles (SVM/EMP), support vector machine on expectation maximization and post-regularization (SVM-EM-PR). The experimental results showed that our method obtained the optimum accuracy, which was better than the other six approaches. And the OA was up to 99.39%. Therefore, the deep convolutional neural networks (DCNNs) is a robust method for land use classification with hyperspectral image. Index Terms — Classification; deep convolutional neural networks; hyperspectral image.

Several models based on deep neural networks have applied to single image super-resolution and obtained great improvements in terms of both reconstruction accuracy and computational performance. All these methods focus either on performing the super-resolution (SR) reconstruction operation in the high resolution (HR) space after upscaling with a single filter, usually bicubic interpolation, or optimizing parts of the reconstruction pipeline. Then the studies of network-based model advance to attempting to shrink the feature dimension of the nonlinear mapping considering the tradeoff between accuracy and time cost. In this paper, we present an improved convolutional neural network (CNN) architecture based on channels combination, which benefits from both quick training and accuracy gain. In addition, we propose that the feature maps can be extracted in the LR space and an efficient multi-channel convolution layer which learns an array of upscaling filters, specifically trained for each feature map, to upscale the final HR feature maps into the HR output. We explore different settings and evaluate the proposed approach using images from publicly available datasets and show that it performs significantly better (about + 0.3 dB margin on term of PSNR and + 0.03 on term of SSIM than previous works) with better visual appearance.

Structural stability along with the electronic and the optical properties of intrinsic 3C-SiC, [Formula: see text] and [Formula: see text] are studied by the first-principles calculations. [Formula: see text] system possesses the most considerate stability with lowest binding energy [Formula: see text] and formation energy [Formula: see text] compared to [Formula: see text]. It is observed that the non-filled impurity energy levels in the vicinity of the Fermi level can subsequently give rise to an enhancement of electrical conductivity of 3C-SiC. Through the analysis of charge difference density maps, it is found that covalence of bonds between the Na atom and nearby C atom reduces in varying degrees. In different concentrations of Na doping systems, especially for the supercell of [Formula: see text], the real and imaginary parts of the dielectric constant are visibly added in the energy range of 0–0.5 eV, demonstrating that the dielectric loss property of the 3C-SiC is improved evidently. These features confirm that the Na-doped 3C-SiC semiconductor is propitious to the wide application of 3C-SiC in the field of absorbing materials.

To effectively remove impulse noise in natural images while keeping image details intact, this paper proposes a dictionary learning based impulse noise removal (DL-INR) algorithm, which explores both the strength of the patch-wise adaptive dictionary learning technique to image structure preservation and the robustness possessed by the @?"1-norm data-fidelity term to impulse noise cancellation. The restoration problem is mathematically formulated into an @?"[email protected]?"1 minimization objective and solved under the augmented Lagrangian framework through a two-level nested iterative procedure. We have compared the DL-INR algorithm to three median filter based methods, two state-of-the-art variational regularization based methods and a fixed dictionary based sparse representation method on restoring impulse noise corrupted natural images. The results suggest that DL-INR has a better ability to suppress impulse noise than other six algorithms and can produce restored images with higher peak signal-to-noise ratio (PSNR).

In recent years Bregman iterative method (or related augmented Lagrangian method) has shown to be an efficient optimization technique for various inverse problems. In this paper, we propose a two-level Bregman Method with dictionary updating for highly undersampled magnetic resonance (MR) image reconstruction. The outer-level Bregman iterative procedure enforces the sampled k-space data constraints, while the inner-level Bregman method devotes to updating dictionary and sparse representation of small overlapping image patches, emphasizing local structure adaptively. Modified sparse coding stage and simple dictionary updating stage applied in the inner minimization make the whole algorithm converge in a relatively small number of iterations, and enable accurate MR image reconstruction from highly undersampled k-space data. Experimental results on both simulated MR images and real MR data consistently demonstrate that the proposed algorithm can efficiently reconstruct MR images and present advantages over the current state-of-the-art reconstruction approach.

This paper presents an augmented Lagrangian (AL) based method for designing of overcomplete dictionaries for sparse representation with general l"q-data fidelity term (q=

A novel approach for denoising medical images is proposed based on a reconstruction-average mechanism. First, different parts of the original complete spectrum are chosen, from each of which a signal is reconstructed using a singularity function analysis (SFA) model. We finally achieve denoising by averaging these reconstructed signals using the fact that each of them is the sum of the same noise-free signal and an additive noise of varying magnitude. The theoretical ground of such approach is mathematically formulated. The experimental results on both simulated and real monochrome images show that the proposed denoising method allows efficient denoising while maintaining image quality, and presents significant advantages over conventional denoising methods.

Sparse projections are an effective way to reduce the exposure to radiation during X-ray CT imaging. However, reconstruction of images from sparse projection data is challenging. This paper introduces a new sparse transform, referred to as S-transform, and proposes an accurate image reconstruction method based on the transform. The S-transform effectively converts the ill-posed reconstruction problem into a well-defined one by representing the image using a small set of transform coefficients. An algorithm is proposed that efficiently estimates the S-transform coefficients from the sparse projections, thus allowing the image to be accurately reconstructed using the inverse S-transform. The experimental results on both simulated and real images have consistently shown that, compared to the popular total variation (TV) method, the proposed method achieves comparable results when the projections is sparse, and substantially improves the quality of the reconstructed image when the number of the projections is relatively high. Therefore, the use of the proposed reconstruction algorithm may permit reduction of the radiation exposure without trade-off in imaging performance.

A novel all-optical microwave generation technique based on fiber birefringence effect in single-longitudinal-mode (SLM) distributed Bragg reflector (DBR) fiber lazer is presented. Owing to the birefringence-induced mode splitting, the proposed lazer could provide a microwave signal at ∼1.72 GHz with a 3 dB bandwidth of ∼30 kHz. Moreover, another microwave signal at 10.96 GHz could also be generated as transverse pressure was applied onto the proposed lazer. Similar phenomenon was observed when another DBR lazer constructed with Er/Yb co-doped fiber was used, and a microwave signal at 11.47 GHz with ∼33 kHz linewidth have also been achieved. © 2010 Wiley Periodicals, Inc. Microwave Opt Technol Lett 52: 535–540, 2010; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.24961

Short acquisition time is essential in cardiac cine magnetic resonance imaging (MRI), in particular for sick patients who have difficulties to hold their breath. This paper investigates the use of two reconstruction methods, initially proposed for static images, for reconstructing cardiac cine images from partial k-space. A new acquisition schema which makes use of temporal redundancies within data is proposed. The results show that, in comparison with the full k-space acquisition, the acquisition time can be reduced by a factor of 4 while obtaining a good reconstruction quality.

A fiber active temperature sensor based on single-longitudinal-mode distributed Bragg reflector (DBR) fiber laser is proposed and experimentally demonstrated. The dual-polarization mode DBR fiber laser could be employed as a temperature sensor through beat frequency interrogation. The temperature sensitivities of this polarimetric sensor are experimentally compared both through frequency and wavelength interrogations. By calculating the fiber birefringence at different temperature, temperature dependence of fiber birefringence is also investigated. Theoretical analysis and experiment results indicate that the fiber birefringence would linearly decrease with the increase of temperature. Based on the frequency and wavelength interrogations, respectively, the fiber active sensor would provide an effective way to achieve simultaneous measurement of multi-parameters. © 2009 Wiley Periodicals, Inc. Microwave Opt Technol Lett 52: 41–44, 2010; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.24820

A polarimetric multilongitudinal-mode distributed Bragg reflector fiber laser sensor is proposed and experimentally demonstrated. We have theoretically analyzed the mode beat mechanism of this laser sensor, which is verified by the experimental observation, and its application in strain measurement has also been studied. Experimental results indicate that both of the longitudinal mode beat (LMB) and birefringence-induced polarization mode beat signals have linear strain dependence, and the coefficients of determination reach 0.9991 and 0.9908, respectively. One possible way to improve the sensitivity of LMB-based sensing has been investigated as well. © 2009 Wiley Periodicals, Inc. Microwave Opt Technol Lett 51: 2559–2563, 2009; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.24671

In this paper, a novel all-optical microwave generation technique based on a dual-wavelength single-longitudinal-mode (SLM) distributed Bragg reflector (DBR) fiber laser is proposed and demonstrated. By exploiting spatial hole burning (SHB) effect, this laser could provide stable dual-wavelength SLM operation with a wavelength separation of ∼0.088 nm corresponding to the microwave signal at ∼10.484 GHz with a 3 dB bandwidth of ∼28 kHz. By appropriately adjusting the pump power, dual-wavelength oscillation could be maintained at different temperatures. We have theoretically analyzed the mechanism for microwave generation of the proposed DBR laser, and the calculated microwave frequency is in good agreement with our experimental results. Furthermore, experimental observation shows both of the laser wavelengths and generated microwave signals have good stability at room temperature.

Forecasting box office revenue of a movie before its theatrical release is a difficult and challenging problem. In this study, a multi-layer BP neural network (MLBP) with multi-input and multi-output is employed to build the prediction model. All the movies are divided into six categories ranged from ''blob'' to ''bomb'' according to their box office incomes, and the purpose is to predict a film into the right class. The selections of the input variables are based on market survey and their weight values are determined by using statistical method. As to the design of the neural network structure, theoretical guidance and plentiful experiments are combined to optimize the hidden layers' parameters which include the number of hidden layers and their node numbers. Then a classifier with dynamic thresholds is used to standardize the output for the first time, and it improves the robustness of the model to a high level. Finally, a 6-fold cross-validation experiment methodology is used to measure the performance of the prediction model. The comparison results with the MLP method show that the MLBP prediction model achieves more satisfactory results, and it is more reliable and effective to solve the problem.

A novel 3-dimensional fiber Bragg grating (FBG) strain and displacement sensing system based on a cylinder structure was proposed. Three FBGs, fixed in the outer surface of the cylinder at the same level according to a 120° angle interval, were used as sensing elements. In principle, the three FBGs have some fixed relationship when a strain F is put on the cylinder. Based on the principles of physics and mathematics, theoretical derivations and the experimental set-up were shown. According to the experimental results, this sensor could measure the size and angles of the strain or displacement accurately.

Microarray technology has been widely applied to the analysis of many malignancies, however, integrative analyses across multiple studies are rarely investigated. In this study we performed a meta-analysis on the expression profiles of four published studies analyzing organ donor, benign tissues adjacent to tumor and tumor tissues from liver, prostate, lung and bladder samples. We identified 99 distinct multi-cancer biomarkers in the comparison of all three tissues in liver and prostate and 44 in the comparison of normal versus tumor in liver, prostate and lung. The bladder samples appeared to have a different list of biomarkers from the other three cancer types. The identified multi-cancer biomarkers achieved high accuracy similar to using whole genome in the within-cancer-type prediction. They also performed superior than the one using whole genome in inter-cancer-type prediction. To test the validity of the multi-cancer biomarkers, 23 independent prostate cancer samples were evaluated and 96% accuracy was achieved in inter-study prediction from the original prostate, liver and lung cancer data sets respectively. The result suggests that the compact lists of multi-cancer biomarkers are important in cancer development and represent the common signatures of malignancies of multiple cancer types. Pathway analysis revealed important tumorogenesis functional categories.

The singularity function analysis (SFA) model is a mathematical tool that allows representing an image with much less coefficients than the conventional Fourier transform (FT) model while maintaining a better image quality. The performance of the SFA method for reconstructing medical images may however degrade if unwanted phase drifts are present in the acquired image. This paper proposes a new SFA-based reconstruction scheme by taking into account phase drifts. To this end, phase drifts are first mathematically formulated and corrected. The singularity function model is then applied to represent the phase-corrected image. The performance of this phase-corrected SFA reconstruction scheme is evaluated using both simulated and real brain images, and compared with the conventional FT model and SFA method without phase correction. The results demonstrate that the proposed reconstruction method achieves significant improvement in image quality.

There is substantial heterogeneity among primary prostate cancers, evident in the spectrum of molecular abnormalities and its variable clinical course. As part of The Cancer Genome Atlas (TCGA), we present a comprehensive molecular analysis of 333 primary prostate carcinomas. Our results revealed a molecular taxonomy in which 74% of these tumors fell into one of seven subtypes defined by specific gene fusions (ERG, ETV1/4, and FLI1) or mutations (SPOP, FOXA1, and IDH1). Epigenetic profiles showed substantial heterogeneity, including an IDH1 mutant subset with a methylator phenotype. Androgen receptor (AR) activity varied widely and in a subtype-specific manner, with SPOP and FOXA1 mutant tumors having the highest levels of AR-induced transcripts. 25% of the prostate cancers had a presumed actionable lesion in the PI3K or MAPK signaling pathways, and DNA repair genes were inactivated in 19%. Our analysis reveals molecular heterogeneity among primary prostate cancers, as well as potentially actionable molecular defects., National Institutes of Health (U.S.). (grant 5U24CA143799), National Institutes of Health (U.S.). (grant 5U24CA143835), National Institutes of Health (U.S.). (grant 5U24CA143840), National Institutes of Health (U.S.). (grant 5U24CA143843), National Institutes of Health (U.S.). (grant 5U24CA143845), National Institutes of Health (U.S.). (grant 5U24CA143848), National Institutes of Health (U.S.). (grant 5U24CA143858), National Institutes of Health (U.S.). (grant 5U24CA143866), National Institutes of Health (U.S.). (grant 5U24CA143867), National Institutes of Health (U.S.). (grant 5U24CA143882), National Institutes of Health (U.S.). (grant 5U24CA143883), National Institutes of Health (U.S.). (grant 5U24CA144025), National Institutes of Health (U.S.). (grant U54HG003067), National Institutes of Health (U.S.). (grant U54HG003079), National Institutes of Health (U.S.). (grant U54HG003273), National Institutes of Health (U.S.). (grant P30CA16672)

This study aims to explore the relationship between melatonin (MLT) and gastric cancer angiogenesis by nuclear receptor with ELISA, real-time quantitative RT-PCR, IHC, Western blot, and siRNA metho...

Objectives Myopodin has recently been characterized as a tumor suppressor gene whose encoded product inhibits prostate cancer growth and metastasis both in vitro and in animal models. However, the clinical evidence of tumor suppression in humans is still lacking. In this study, we conducted a large-scale analysis of myopodin expression in prostate cancer samples to examine the relationship between myopodin expression and prostate cancer grade and stage and the probability of clinical relapse. Methods Immunostaining using anti-myopodin antibodies was performed on 746 formalin-fixed paraffin-embedded tissue samples to evaluate the level of myopodin expression in normal and malignant prostatic tissue. Results The expression of myopodin was semiquantitatively determined by immunostaining. The myopodin expression levels were examined in relation to prostate cancer grade and stage, preoperative prostate-specific antigen level, surgical margin involvement, tumor volume, and clinical relapse. The mean myopodin expression score (range 0 to 3) was 2.1 and 0.88 for benign prostatic tissue and prostate cancer, respectively. Minimal variation in myopodin expression was observed among the various grades, stages, tumor volumes, and preoperative prostate-specific antigen levels of prostate cancer. However, samples with positive surgical margins were associated with lower myopodin expression. Complete inactivation of myopodin expression correlated with a greater than 86% rate of clinical relapse. Conclusions Our results suggest that myopodin is an important predictor of prostate cancer metastasis, independent of Gleason score, preoperative prostate-specific antigen level, and tumor stage.

A new approach for correcting bias field in magnetic resonance (MR) images is proposed using the mathematical model of singularity function analysis (SFA), which represents a discrete signal or its spectrum as a weighted sum of singularity functions. Through this model, an MR image's low spatial frequency components corrupted by a smoothly varying bias field are first removed, and then reconstructed from its higher spatial frequency components not polluted by bias field. The thus reconstructed image is then used to estimate bias field for final image correction. The approach does not rely on the assumption that anatomical information in MR images occurs at higher spatial frequencies than bias field. The performance of this approach is evaluated using both simulated and real clinical MR images.

We bring forward a novel FBG sensor multiplexing technique based on the combination of wavelength- and spatial-division multiplexing technique. Furthermore, the data acquisition parameter of the multiplexing sensing FBG system is defined. With this technique, the FBG sensors system not only avoid frequently switching and improve the average response speed, but also are able to enact data acquisition rule according to the data acquisition factor that is composed with priority (PRI), the delay time and the wavelength shift of FBG sensors. Therefore, the smart FBG sensors multiplexing system is more rational and smarter than the conventional counterpart. The simulate result and the experiment work demonstrated that the average delay time of smart system is less than conventional ones and immune to the scale size.