Functional features of a novel interferon-stimulated gene SHFL: a comprehensive review.

Various interferon (IFN)-stimulated genes (ISGs), expressed via Janus kinase-signal transducer and activator of transcription (JAK-STAT) signaling pathway-stimulated IFNs to increase antiviral effects or regulate immune response, perform different roles in virus-infected cells. In recent years, a novel ISG, SHFL, which is located in the genomic region 19p13.2 and comprises two isoforms, has been studied as a virus-inhibiting agent. Studies have shown that SHFL suppressive effects on human immunodeficiency virus-1 (HIV), Zika virus (ZIKV), dengue virus (DENV), hepatitis C virus (HCV), Japanese encephalitis virus (JEV), porcine epidemic diarrhea virus (PEDV), Human enterovirus A71 (EV-A71) and Kaposi's sarcoma-associated herpes virus (KSHV). SHFL interacts with various viral and host molecules to inhibit viral life circle and activities, such as replication, translation, and ribosomal frameshifting, or regulates host pathways to degrade viral proteins. In this review, we summarized the functional features of SHFL to provide insights to underlying mechanisms of the antiviral effects of SHFL and explored its potential function.

Satisfaction and its determinants of rural upper gastrointestinal cancer screening in China: a preliminary cross-sectional study.

OBJECTIVES: To assess participants' satisfaction with and predictors of upper gastrointestinal cancer screening in rural areas. DESIGN: Cross-sectional study. SETTING: Ten screening centres in seven cities across five provinces in China. PARTICIPANTS: Of the 452 participants screened during the survey period, 438 completed the survey (response rate: 96.90%). PRIMARY OUTCOME MEASURES: Screening satisfaction was the primary outcome and it was assessed using the Patient Satisfaction Questionnaire-18. Ordinal logistic regression analysis was used to analyse the predictors of satisfaction. RESULTS: The percentages of satisfaction with the General Satisfaction, Technical Quality, Interpersonal Manner, Communication, Financial Aspects, Time Spent With Doctor, and Convenience and Accessibility dimensions were 95.89%, 79.68%, 88.36%, 83.56%, 75.11%, 82.19% and 66.44%, respectively. Education (OR 0.25, 95% CI 0.07 to 0.90), health self-assessment (OR 15.29, 95% CI 2.86 to 81.78) and family history of cancer (OR 4.20, 95% CI 1.29 to 13.71) were associated with General Satisfaction. Residence (OR 4.31, 95% CI 1.89 to 9.81) was associated with Technical Quality. Occupation (OR 0.27, 95% CI 0.08 to 0.88), health self-assessment (OR 11.30, 95% CI 3.94 to 32.43), screening purpose (OR 0.18, 95% CI 0.03 to 0.92) and distance from the screening centre (OR 4.59, 95% CI 1.35 to 15.61) were associated with interpersonal manner. Gender (OR 1.85, 95% CI 1.02 to 3.34), residence (OR 3.23, 95% CI 1.23 to 8.53) and endoscopy in the previous year (OR 2.79, 95% CI 1.13 to 6.90) were associated with Communication. Body mass index (BMI; OR 5.06, 95% CI 1.40 to 18.25) and health self-assessment (OR 2.09, 95% CI 1.12 to 3.88) were associated with financial aspects. Gender (OR 1.90, 95% CI 1.07 to 3.38), residence (OR 3.19, 95% CI 1.30 to 7.79), BMI (OR 5.26, 95% CI 1.14 to 24.34) and health self-assessment (OR 2.14, 95% CI 1.06 to 4.34) were associated with time spent with doctor. Gender (OR 1.64, 95% CI 1.04 to 2.60) and residence (OR 3.17, 95% CI 1.46 to 6.88) were associated with convenience and accessibility. CONCLUSION: There was heterogeneity across the dimensions of satisfaction with rural upper gastrointestinal cancer screening. Project manager should prioritise improving the aspects related to the convenience and accessibility dimension. Furthermore, to improve the efficiency of potential interventions, the predictors of the various dimensions should be considered.

Fast and Accurate Light Field View Synthesis by Optimizing Input View Selection.

There is a trade-off between spatial resolution and angular resolution limits in light field applications; various targeted algorithms have been proposed to enhance angular resolution while ensuring high spatial resolution simultaneously, which is also called view synthesis. Among them, depth estimation-based methods can use only four corner views to reconstruct a novel view at an arbitrary location. However, depth estimation is a time-consuming process, and the quality of the reconstructed novel view is not only related to the number of the input views, but also the location of the input views. In this paper, we explore the relationship between different input view selections with the angular super-resolution reconstruction results. Different numbers and positions of input views are selected to compare the speed of super-resolution reconstruction and the quality of novel views. Experimental results show that the speed of the algorithm decreases with the increase of the input views for each novel view, and the quality of the novel view decreases with the increase of the distance from the input views. After comparison using two input views in the same line to reconstruct the novel views between them, fast and accurate light field view synthesis is achieved.

A Lane Detection Method Based on a Ridge Detector and Regional G-RANSAC.

Lane detection plays an important role in improving autopilot's safety. In this paper, a novel lane-division-lines detection method is proposed, which exhibits good performances in abnormal illumination and lane occlusion. It includes three major components: First, the captured image is converted to aerial view to make full use of parallel lanes' characteristics. Second, a ridge detector is proposed to extract each lane's feature points and remove noise points with an adaptable neural network (ANN). Last, the lane-division-lines are accurately fitted by an improved random sample consensus (RANSAC), termed the (regional) gaussian distribution random sample consensus (G-RANSAC). To test the performances of this novel lane detection method, we proposed a new index named the lane departure index (LDI) describing the departure degree between true lane and predicted lane. Experimental results verified the superior performances of the proposed method over others in different testing scenarios, respectively achieving 99.02%, 96.92%, 96.65% and 91.61% true-positive rates (TPR); and 66.16, 54.85, 55.98 and 52.61 LDIs in four different types of testing scenarios.

Controlled co-precipitation of biocompatible colorant-loaded nanoparticles by microfluidics for natural color drinks.

Natural colorants, which impart a vivid color to food and add additional health benefits, are favored over synthetic colorants; however, their applications are limited by their low solubility in water and low stability. Here, we develop a versatile microfluidic strategy to incorporate natural colorants in shellac nanoparticles with controlled physicochemical properties. The rapid mixing in the microfluidic channels ensures that the mixing time is shorter than the aggregation time, thus providing control over the co-precipitation of the colorant and the polymer. By introducing molecular interactions, colorant nanoaggregates are efficiently embedded in the polymer matrix, forming hierarchical colorant-loaded nanoparticles. The colorant-loaded nanoparticles dispersed in water are transparent and stable over a wide pH range and their polymer matrix also provides a favorable microenvironment that greatly improves the shelf life of the colorants. The improved solubility, stability and bioavailability of the natural colorants suggest that shellac nanoparticles are ideal carriers and the stable, transparent dispersions of biocompatible colorant-loaded nanoparticles in water are well-suited for the development of functional foods, such as natural color drinks.

Stereo Matching by Filtering-Based Disparity Propagation.

Stereo matching is essential and fundamental in computer vision tasks. In this paper, a novel stereo matching algorithm based on disparity propagation using edge-aware filtering is proposed. By extracting disparity subsets for reliable points and customizing the cost volume, the initial disparity map is refined through filtering-based disparity propagation. Then, an edge-aware filter with low computational complexity is adopted to formulate the cost column, which makes the proposed method independent on the local window size. Experimental results demonstrate the effectiveness of the proposed scheme. Bad pixels in our output disparity map are considerably decreased. The proposed method greatly outperforms the adaptive support-weight approach and other conditional window-based local stereo matching algorithms.

A New Method for Nonlocal Means Image Denoising Using Multiple Images.

The basic principle of nonlocal means is to denoise a pixel using the weighted average of the neighbourhood pixels, while the weight is decided by the similarity of these pixels. The key issue of the nonlocal means method is how to select similar patches and design the weight of them. There are two main contributions of this paper: The first contribution is that we use two images to denoise the pixel. These two noised images are with the same noise deviation. Instead of using only one image, we calculate the weight from two noised images. After the first denoising process, we get a pre-denoised image and a residual image. The second contribution is combining the nonlocal property between residual image and pre-denoised image. The improved nonlocal means method pays more attention on the similarity than the original one, which turns out to be very effective in eliminating gaussian noise. Experimental results with simulated data are provided.

Light Field Imaging Based Accurate Image Specular Highlight Removal.

Specular reflection removal is indispensable to many computer vision tasks. However, most existing methods fail or degrade in complex real scenarios for their individual drawbacks. Benefiting from the light field imaging technology, this paper proposes a novel and accurate approach to remove specularity and improve image quality. We first capture images with specularity by the light field camera (Lytro ILLUM). After accurately estimating the image depth, a simple and concise threshold strategy is adopted to cluster the specular pixels into "unsaturated" and "saturated" category. Finally, a color variance analysis of multiple views and a local color refinement are individually conducted on the two categories to recover diffuse color information. Experimental evaluation by comparison with existed methods based on our light field dataset together with Stanford light field archive verifies the effectiveness of our proposed algorithm.

Robust Texture Image Representation by Scale Selective Local Binary Patterns.

Local binary pattern (LBP) has successfully been used in computer vision and pattern recognition applications, such as texture recognition. It could effectively address grayscale and rotation variation. However, it failed to get desirable performance for texture classification with scale transformation. In this paper, a new method based on dominant LBP in scale space is proposed to address scale variation for texture classification. First, a scale space of a texture image is derived by a Gaussian filter. Then, a histogram of pre-learned dominant LBPs is built for each image in the scale space. Finally, for each pattern, the maximal frequency among different scales is considered as the scale invariant feature. Extensive experiments on five public texture databases (University of Illinois at Urbana-Champaign, Columbia Utrecht Database, Kungliga Tekniska Högskolan-Textures under varying Illumination, Pose and Scale, University of Maryland, and Amsterdam Library of Textures) validate the efficiency of the proposed feature extraction scheme. Coupled with the nearest subspace classifier, the proposed method could yield competitive results, which are 99.36%, 99.51%, 99.39%, 99.46%, and 99.71% for UIUC, CUReT, KTH-TIPS, UMD, and ALOT, respectively. Meanwhile, the proposed method inherits simple and efficient merits of LBP, for example, it could extract scale-robust feature for a 200×200 image within 0.24 s, which is applicable for many real-time applications.

Accuracy of high-resolution computed tomography in revealing fallopian canal fracture of patients with traumatic facial paralysis.

OBJECTIVES: To investigate accuracy of high-resolution computed tomography (HRCT) of temporal bone in revealing fallopian canal fracture among the patients with traumatic facial paralysis. METHODS: HRCT of temporal bone with cross-sectional scanning and multi-planar reformation (MPR) was performed on 31 patients with traumatic facial paralysis, and fracture sites of fallopian canal revealed by HRCT were retrospectively analyzed and compared with surgical findings. RESULTS: HRCT of temporal bone showed fracture of fallopian canal at geniculate ganglion in 25 cases, at tympanic segment in 15 cases, at labyrinthine segment in 5 cases, at pyramid segment in 2 cases, and at mastoid segment in one case, while surgical findings revealed that fracture involved geniculate ganglion in 27 cases, labyrinthine segment in 5, tympanic segment in 19, pyramid segment in 12, and mastoid segment in 9; the sensitivity of HRCT of temporal bone to detect fracture at such sites was 88.9%, 100%, 52.6%, 16.7% and 11.1%, respectively, and the specificity was 96.0%, 100%, 66.7%, 100% and 100%, respectively. CONCLUSION: HRCT of temporal bone was able to accurately reveal fracture of fallopian canal at geniculate ganglion and labyrinthine segment in the vast majority cases, but severely underestimated fracture at pyramid segment and mastoid segment of fallopian canal.

Statistical analysis of tongue images for feature extraction and diagnostics.

In this paper, an in-depth analysis on the statistical distribution characteristics of human tongue color that aims to propose a mathematically described tongue color space for diagnostic feature extraction is presented. Three characteristics of tongue color space, i.e., tongue color gamut that defines the range of colors, color centers of 12 tongue color categories, and color distribution of typical image features in the tongue color gamut, are elaborately investigated in this paper. Based on a large database, which contains over 9000 tongue images collected by a specially designed noncontact colorimetric imaging system using a digital camera, the tongue color gamut is established in the CIE chromaticity diagram by an innovatively proposed color gamut boundary descriptor using one-class SVM algorithm. Thereafter, centers of 12 tongue color categories are defined accordingly. Furthermore, color distributions of several typical tongue features, such as red points and petechial points, are obtained to build a relationship between the tongue color space and color distributions of various tongue features. With the obtained tongue color space, a new color feature extraction method is proposed for diagnostic classification purposes, with experimental results validating its effectiveness.

Tongue color analysis for medical application.

An in-depth systematic tongue color analysis system for medical applications is proposed. Using the tongue color gamut, tongue foreground pixels are first extracted and assigned to one of 12 colors representing this gamut. The ratio of each color for the entire image is calculated and forms a tongue color feature vector. Experimenting on a large dataset consisting of 143 Healthy and 902 Disease (13 groups of more than 10 samples and one miscellaneous group), a given tongue sample can be classified into one of these two classes with an average accuracy of 91.99%. Further testing showed that Disease samples can be split into three clusters, and within each cluster most if not all the illnesses are distinguished from one another. In total 11 illnesses have a classification rate greater than 70%. This demonstrates a relationship between the state of the human body and its tongue color.

A new tongue colorchecker design by space representation for precise correction.

In order to improve the correction accuracy on tongue colors by use of Munsell colorchecker, this research aims to design a new colorchecker by aid of tongue color space. Three essential issues leading to the development of this space-based colorchecker are elaborately investigated in this study. Firstly, based on a large and comprehensive tongue database, tongue color space is established by which all visible colors can be classified as tongue or non-tongue colors. Hence, colors of the designed tongue colorchecker are selected from tongue colors to achieve high correction performance. Secondly, the minimum sufficient number of colors involved in colorchecker is yielded by comparing the correction accuracy when different number (ranged from 10 to 200) of colors are contained. Thereby, 24 colors are included because the obtained minimum number of colors is 20. Lastly, criteria for optimal color selection and its corresponding objective function are presented. Two color selection methods, i.e., greedy and clustering-based selection method, are proposed to solve the objective function. Experimental results show that clustering-based one outperforms its counterpart to generate the new tongue colorchecker. Compared to Munsell colorchecker, this proposed space-based colorchecker can greatly improve the correction accuracy by 48%. Further experimental results on more correction task also validate its effectiveness and superiority.

An optimized tongue image color correction scheme.

The color images produced by digital cameras are usually device-dependent, i.e., the generated color information (usually presented in RGB color space) is dependent on the imaging characteristics of specific cameras. This is a serious problem in computer-aided tongue image analysis because it relies on the accurate rendering of color information. In this paper, we propose an optimized correction scheme that corrects the tongue images captured in different device-dependent color spaces to the target device-independent color space. The correction algorithm in this scheme is generated by comparing several popular correction algorithms, i.e., polynomial-based regression, ridge regression, support vector regression, and neural network mapping algorithms. We test the performance of the proposed scheme by computing the CIE L(*)a(*)b(*) color difference (∆E(ab)(*)) between estimated values and the target reference values. The experimental results on the colorchecker show that the color difference is less than 5 (∆E(ab)(*) < 5), while the experimental results on real tongue images show that the distorted tongue images (captured in various device-dependent color spaces) become more consistent with each other. In fact, the average color difference among them is greatly reduced by more than 95%.