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Hantaan virus (HTNV) is asymptomatically carried by rodents, yet causes lethal hemorrhagic fever with renal syndrome in humans, the underlying mechanisms of which remain to be elucidated. Here, we show that differential macrophage responses may determine disparate infection outcomes. In mice, late-phase inactivation of inflammatory macrophage prevents cytokine storm syndrome that usually occurs in HTNV-infected patients. This is attained by elaborate crosstalk between Notch and NF-κB pathways. Mechanistically, Notch receptors activated by HTNV enhance NF-κB signaling by recruiting IKKβ and p65, promoting inflammatory macrophage polarization in both species. However, in mice rather than humans, Notch-mediated inflammation is timely restrained by a series of murine-specific long noncoding RNAs transcribed by the Notch pathway in a negative feedback manner. Among them, the lnc-ip65 detaches p65 from the Notch receptor and inhibits p65 phosphorylation, rewiring macrophages from the pro-inflammation to the pro-resolution phenotype. Genetic ablation of lnc-ip65 leads to destructive HTNV infection in mice. Thus, our findings reveal an immune-braking function of murine noncoding RNAs, offering a special therapeutic strategy for HTNV infection. Hantaan virus is carried and transmitted by rodents and results in asymptomatic infection, yet transmission to humans' results in symptomatic disease and development of hemorrhagic fever with renal syndrome. Here the authors explore the disparate effects in myeloid cells from mice and humans. [ABSTRACT FROM AUTHOR]

Rock failure is the result of internal crack initiation (CI), propagation and intersection under external loading. This paper carried out a series of experimental tests and numerical simulations that aim to reveal the failure mechanical behavior, crack development, AE event attributes and failure characteristics of oil shale under various confining pressures. The results suggest that the peak and residual strength of the oil shale under triaxial compression are both enhanced with increasing confining pressure. Splitting failure and diagonal shear failure are the main macrofailure modes of the specimens under uniaxial and triaxial compressive condition. Shear cracks play a dominant role in the rock failure process especially under high confining pressures. The AE event rate shifts from concentration to dispersion with the change from uniaxial to triaxial compression. The cumulative number of cracks, the cumulative AE event counts before the peak strength and the CI stress all first increase sharply and then increase gradually with increasing confining pressure. However, the ratio of the cumulative numbers of shear cracks to tensile cracks before the peak strength increases at a fairly steady rate as the confining pressure increases. These findings are helpful to evaluate the stability and safety of rock mass under confining pressure, and could also provide a basis for the design of rock mass reinforcement. [ABSTRACT FROM AUTHOR]

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Stress contributes to major depressive disorder (MDD) and chronic pain, which affect a significant portion of the global population, but researchers have not clearly determined how these conditions are initiated or amplified by stress. The chronic social defeat stress (CSDS) model is a mouse model of psychosocial stress that exhibits depressive-like behavior and chronic pain. We hypothesized that metabotropic glutamate receptor 5 (mGluR5) expressed in the nucleus accumbens (NAc) normalizes the depressive-like behaviors and pain following CSDS. Here, we show that CSDS induced both pain and social avoidance and that the level of mGluR5 decreased in susceptible mice. Overexpression of mGluR5 in the NAc shell and core prevented the development of depressive-like behaviors and pain in susceptible mice, respectively. Conversely, depression-like behaviors and pain were exacerbated in mice with mGluR5 knockdown in the NAc shell and core, respectively, compared to control mice subjected to 3 days of social defeat stress. Furthermore, (RS)-2-chloro-5-hydroxyphenylglycine (CHPG), an mGluR5 agonist, reversed the reduction in the level of the endocannabinoid (eCB) 2-arachidonoylglycerol (2-AG) in the NAc of susceptible mice, an effect that was blocked by 3-((2-methyl-1, 3-thiazol-4-yl) ethynyl) pyridine hydrochloride (MTEP), an mGluR5 antagonist. In addition, the injection of CHPG into the NAc shell and core normalized depressive-like behaviors and pain, respectively, and these effects were inhibited by AM251, a cannabinoid type 1 receptor (CB1R) antagonist. Based on these results, mGluR5-mediated eCB production in the NAc relieves stress-induced depressive-like behaviors and pain. [ABSTRACT FROM AUTHOR]

Blind image quality assessment (BIQA) is one of the most challenging and difficult tasks in the field of IQA. Given that sparse representation through dictionary learning can learn the image feature well, this paper proposed a method termed Discriminative Feature Representation (DFR) from the perspective of feature learning for noise contaminated image quality assessment. DFR makes use of two sub-dictionaries composed of atoms featuring desirable image structures and undesirable noise, respectively. Noise is quantified via a joint evaluation of the sparse coefficients related to the atoms in the two sub-dictionaries. The method is validated using public databases with different types of noise, a comparison with other up-to-date methods is provided. The proposed method is also applied to CT images acquired at different-level doses and reconstructed by various well-known algorithms. [ABSTRACT FROM AUTHOR]

Purpose: Acute ischemic stroke is one of the most causes of death all over the world. Onset to treatment time is critical in stroke diagnosis and treatment. Considering the time consumption and high price of MR imaging, CT perfusion (CTP) imaging is strongly recommended for acute stroke. However, too much CT radiation during CTP imaging may increase the risk of health problems. How to reduce CT radiation dose in CT perfusion imaging has drawn our great attention. Methods: In this study, the original 30-pass CTP images are downsampled to 15 passes in time sequence, which equals to 50% radiation dose reduction. Then, a residual deep convolutional neural network (DCNN) model is proposed to restore the downsampled 15-pass CTP images to 30 passes to calculate the parameters such as cerebral blood flow, cerebral blood volume, mean transit time, time to peak for stroke diagnosis and treatment. The deep restoration CNN is implemented simply and effectively with 16 successive convolutional layers which form a wide enough receptive field for input image data. 18 patients' CTP images are employed as training set and the other six patients' CTP images are treated as test dataset in this study. Results: Experiments demonstrate that our CNN can restore high-quality CTP images in terms of structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR). The average SSIM and PSNR for test images are 0.981 and 56.25, and the SSIM and PSNR of regions of interest are 0.915 and 42.44, respectively, showing promising quantitative level. In addition, we compare the perfusion maps calculated from the restored images and from the original images, and the average perfusion results of them are extremely close. Areas of hypoperfusion of six test cases could be detected with comparable accuracy by radiologists. Conclusion: The trained model can restore the temporally downsampled 15-pass CTP to 30 passes very well. According to the contrast test, sufficient information cannot be restored with, e.g., simple interpolation method and deep convolutional generative adversarial network, but can be restored with the proposed CNN model. This method can be an optional way to reduce radiation dose during CTP imaging. [ABSTRACT FROM AUTHOR]

Sparse-view sampling scans reduce the patient's radiation dose by reducing the total exposure duration. CT reconstructions under such scan mode are often accompanied by severe artifacts due to the high ill-posedness of the problem. In this paper, we use a Non-Local means kernel as a regularization constraint to reconstruct image volumes from sparse-angle sampled cone-beam CT scans. To overcome the huge computational cost of the 3D reconstruction, we propose a sequential update scheme relying on ordered subsets in the image domain. It is shown through experiments on simulated and real data and comparisons with other methods that the proposed approach is robust enough to deal with the number of views reduced up to 1/10. When coupled with a CUDA parallel computing technique, the computation speed of the iterative reconstruction is greatly improved. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel dimensionality reduction algorithm called discriminant maximum margin projections (DMMP) for face recognition. By discovering both geometrical and discriminant structures of the data points, DMMP aims at finding a subspace that optimally preserves the local neighborhood information of the data set, as well as maximizes the margin between data points from different classes at each local area. Moreover, DMMP utilizes a equilibrium parameter to adjust the significance of the locality preserving property and margin distances of the data points. Finally, with the experiments used face recognition data sets, such as the ORL, Yale, and FERET face databases, the results prove that DMMP can attain a better effectiveness than most other advanced approaches. [ABSTRACT FROM AUTHOR]

Vascular related diseases have become one of the most common diseases with high mortality, high morbidity and high medical risk in the world. Level set is a kind of active contour model, and can be used to extract vessel structures. However, the applications of level set methods in vessel segmentation suffer from two problems. The first problem is the error caused by the false inclusion of some non-vessel structures. The second one is the sensitivity of the level set evolution to the initialization condition. In this paper, we propose an algorithm termed Centerline constrained level set (CC-LS) for vessel segmentation which utilizes centerline information to improve the evolution of level set. Using centerline information as the initial level set condition leads to improved evolution efficiency and extraction accuracy. Additionally, a new centerline modulated velocity term can be used in the level set evolution function to avoid the wrong inclusion of non-vessel structures. Performance of the proposed CC-LS algorithm is well validated using both 2D and 3D coronary images in different types. The proposed method is able to attain satisfactory results on both 2D and 3D coronary data. [ABSTRACT FROM AUTHOR]

A novel efficient algorithm called unsupervised multi-manifold linear differential projection(UMLDP) is proposed to overcome the drawbacks of existing unsupervised linear differential projection(ULDP) for face recognition. Firstly, the multi-manifold local neighborhood graph and the largest global variance is constructed respectively. Next, we calculate a low dimensional manifold embedded in high-dimensional space through the multi-objective optimization. This mapping can not only get the low-dimensional manifolds embedded in a high-dimensional space but also maintain the local and the global structural information effectively. Finally, experimental results validate the effectiveness of the proposed algorithm on the ORL, Yale and AR face databases. [ABSTRACT FROM AUTHOR]

In this work, a multilevel thresholding approach that uses modified bacterial foraging optimization (MBFO) is presented for enhancing the applicability and practicality of optimal thresholding techniques. First, the diversity of solutions is considered during the reproduction step. Each weak bacterium randomly selects a strong bacterium from the healthiest bacteria, attempts to reach a location near the chosen strong bacterium, and maintains the same direction. Particle swarm optimization is subsequently incorporated into each chemotactic step to strengthen the global searching capability and quicken the convergence rate of the bacterial foraging algorithm. Finally, the optimal thresholds are obtained by maximizing the Tsallis thresholding functions using the proposed MBFO algorithm. The performance of the proposed algorithm in solving complex stochastic optimization problems is compared with other popular approaches such as a bacterial foraging algorithm, particle swarm optimization algorithm, and genetic algorithm. Experimental results show that the optimal thresholds produced using MBFO require less computation time. The devised algorithm generates more stable results, and the proposed method performs better than the other algorithms in terms of multilevel thresholding. In addition, MBFO method can achieve significantly better results than other compare algorithms on a set of benchmark functions. [ABSTRACT FROM AUTHOR]

The aim of the proposed work is to improve low-dose lung CT (LDCT) screening using the processing of weighted intensity averaging over large-scale neighborhoods (WIA-LN). Both current and voltage reductions were considered for LDCT imaging. In the WIA-LN method, the processed pixel intensities are calculated by weighted averaging intensities among a large neighboring region. The weights are determined by the inter-similarity of the surrounding textures. A compute unified device architecture based parallelization was applied to accelerate the implementation. To evaluate the effectiveness of the proposed processing, low-dose lung CT images were obtained under both 75 % reduced tube current and 33.3 % reduced tube voltage condition respectively from a 16 detector rows Siemens CT. The standard routine standard-dose CT images were also collected as the reference images. In addition to clinical data from patients, an anthropomorphic lung phantom was also used in the study. Visual comparison and statistical qualitative analysis of image quality scores on the datasets are made in validation. Compared to the original LDCT images, improved visual and qualitative performance can be observed for the processed images. Statistically significant improvement of noise/artifacts suppression and nodule structure enhancement are achieved by using the proposed method ( P < 0.05). The proposed method is capable of providing LDCT images under significantly reduced tube current and voltage settings in low-dose condition. Quality of the processed images was assessed by radiology specialists. Parallelization based algorithm optimization was also performed to increase the clinical applicability of the proposed processing. [ABSTRACT FROM AUTHOR]

This paper proposes a novel image denoising algorithm that can more effectively remove Gaussian white noise. The proposed algorithm is based on a design of a Maximum Posteriori Estimator (MAP) combined with a Quaternion Wavelet Transform (QWT) that utilizes the Normal Inverse Gaussian (NIG) Probability Density Function (PDF). The QWT is a near shift-invariant whose coefficients include one magnitude and three phase values. An NIG PDF which is specified by four real-value parameters is capable of modeling the heavy-tailed QWT coefficients, and describing the intra-scale dependency between the QWT coefficients. The NIG PDF is applied as a prior probability distribution, to model the coefficients by utilizing the Bayesian estimation technique. Additionally, a simple and fast method is given to estimate the parameters of the NIG PDF from the neighboring QWT coefficients. Experimental results show that the proposed method outperforms other existing denoising methods in terms of the PSNR, the structural similarity, and the edge preservation. It is clear that the proposed method can remove Gaussian white noise more effectively. [ABSTRACT FROM AUTHOR]

In this paper, a new method for tracking moving objects based on reduced quaternion wavelet transform by using color space information is proposed. The reduced quaternion wavelet transform is a new multi-scale analysis tool for geometric image features. Meanwhile, it is a near-shift-invariant tight frame representation whose coefficients can sport local image shift and geometric information. The main objective of visual tracking is to closely follow objects in each frame of video stream. The new proposed method can calculate the mean value and edge energy of visual objects by using the reduced quaternion wavelet coefficients. The experimental results show that the proposed method is effective and efficient. [ABSTRACT FROM AUTHOR]

In CT, ionizing radiation exposure from the scan has attracted much concern from patients and doctors. This work is aimed at improving head CT images from low-dose scans by using a fast Dictionary learning (DL) based post-processing. Both Low-dose CT (LDCT) and Standard-dose CT (SDCT) nonenhanced head images were acquired in head examination from a multi-detector row Siemens Somatom Sensation 16 CT scanner. One hundred patients were involved in the experiments. Two groups of LDCT images were acquired with 50 % (LDCT50 %) and 25 % (LDCT25 %) tube current setting in SDCT. To give quantitative evaluation, Signal to noise ratio (SNR) and Contrast to noise ratio (CNR) were computed from the Hounsfield unit (HU) measurements of GM, WM and CSF tissues. A blinded qualitative analysis was also performed to assess the processed LDCT datasets. Fifty and seventy five percent dose reductions are obtained for the two LDCT groups (LDCT50 %, 1.15 ± 0.1 mSv; LDCT25 %, 0.58 ± 0.1 mSv; SDCT, 2.32 ± 0.1 mSv; P < 0.001). Significant SNR increase over the original LDCT images is observed in the processed LDCT images for all the GM, WM and CSF tissues. Significant GM-WM CNR enhancement is noted in the DL processed LDCT images. Higher SNR and CNR than the reference SDCT images can even be achieved in the processed LDCT50 % and LDCT25 % images. Blinded qualitative review validates the perceptual improvements brought by the proposed approach. Compared to the original LDCT images, the application of DL processing in head CT is associated with a significant improvement of image quality. [ABSTRACT FROM AUTHOR]

The processing of the images simultaneously degraded by blur and affine transformation has become a key task in many applications and many novel methods are designed specifically for it in which the moment-based methods play an important role. However, the existing moment-based methods all resort to non-orthogonal moments invariants which have problem of information redundancy and are sensitive to noise. In this paper, we construct a new set of combined invariants of orthogonal Legendre moments which hold for blur and affine transformation together. The experimental results show that the proposed invariants have better discriminative power and robustness to noise with the comparison to other invariants. [ABSTRACT FROM AUTHOR]

This paper presents a new 3D edge operator based on Legendre orthogonal moments. This operator can be used to extract the edge of 3D object in any window size, with more accurate surface orientation and more precise surface location. It also has full geometry meaning. Process of calculation is considered in the moment based method. We can greatly speed up the computation by calculating out the masks in advance. We integrate this operator into our rendering of medical image data based on ray casting algorithm. Experimental results show that it is an effective 3D edge operator that is more accurate in position and orientation. [ABSTRACT FROM AUTHOR]

Speckle degrades severely the quality of medical B-scan ultrasonic images, especially it blurs edges and details of images. An adaptive speckle suppression and edge enhancement method based on Nakagami distribution is presented. The statistics of log-compressed echo images is derived for Nakagami distribution. An adaptive filter based on local statistical property of speckle is designed. The stick technique that utilizes sticks with different sizes and various orientations is applied to locally approximate certain linear features of image. The local region is a stick instead of a usual window, the orientation of sticks is decided by hypothesis test optimizing method and the length of sticks is obtained by region growing technique. Performance of the new method has been tested on the phantom and ultrasound images of pig muscle and echocardiographic. The results show that the technique effectively reduces the speckle noise while preserving and enhancing the tissue edge and resolvable details. [ABSTRACT FROM AUTHOR]