Search

This paper designs a novel method to reduce the coding complexity of 3D‐HEVC encoder by utilizing the properties of human visual perception. Two vision‐oriented edge detections are proposed: for colour texture detection, the authors adopt the Just‐Noticeable Distortion (JND); for depth map, the authors combine the Sample Adaptive Offset (SAO) and the Just Noticeable Depth Difference (JNDD) model. The authors also analyse the properties of colour texture and depth map to classify the coding tree unit (CTU) into various kinds of types, including complex‐edge CTU, moderate‐edge CTU and homogeneous CTU. Besides, fast mode decisions and early termination criteria are performed individually on each type of CTUs according to their characteristics. Especially for those CTUs with more edge information, the proposed projection‐based fast mode decision and residual‐based early termination preserve important colour texture while speeding up the coding at the same time. The proposed vision‐oriented algorithm reduces 31.981% of the overall average coding time with only 1.580% BD‐Bitrate increase. Experimental results show that the proposed algorithm can provide considerable time‐saving while still maintain the video quality, which outperforms the previous researches. [ABSTRACT FROM AUTHOR]

Entropy images, representing the complexity of original fundus photographs, may strengthen the contrast between diabetic retinopathy (DR) lesions and unaffected areas. The aim of this study is to compare the detection performance for severe DR between original fundus photographs and entropy images by deep learning. A sample of 21,123 interpretable fundus photographs obtained from a publicly available data set was expanded to 33,000 images by rotating and flipping. All photographs were transformed into entropy images using block size 9 and downsized to a standard resolution of 100 × 100 pixels. The stages of DR are classified into 5 grades based on the International Clinical Diabetic Retinopathy Disease Severity Scale: Grade 0 (no DR), Grade 1 (mild nonproliferative DR), Grade 2 (moderate nonproliferative DR), Grade 3 (severe nonproliferative DR), and Grade 4 (proliferative DR). Of these 33,000 photographs, 30,000 images were randomly selected as the training set, and the remaining 3,000 images were used as the testing set. Both the original fundus photographs and the entropy images were used as the inputs of convolutional neural network (CNN), and the results of detecting referable DR (Grades 2–4) as the outputs from the two data sets were compared. The detection accuracy, sensitivity, and specificity of using the original fundus photographs data set were 81.80%, 68.36%, 89.87%, respectively, for the entropy images data set, and the figures significantly increased to 86.10%, 73.24%, and 93.81%, respectively (all p values <0.001). The entropy image quantifies the amount of information in the fundus photograph and efficiently accelerates the generating of feature maps in the CNN. The research results draw the conclusion that transformed entropy imaging of fundus photographs can increase the machinery detection accuracy, sensitivity, and specificity of referable DR for the deep learning-based system. [ABSTRACT FROM AUTHOR]

Dental X-ray images are important and useful for dentists to diagnose dental diseases. Utilizing deep learning in dental X-ray images can help dentists quickly and accurately identify common dental diseases such as periodontitis and dental caries. This paper applies image processing and deep learning technologies to dental X-ray images to propose a simultaneous recognition method for periodontitis and dental caries. The single-tooth X-ray image is detected by the YOLOv7 object detection technique and cropped from the periapical X-ray image. Then, it is processed through contrast-limited adaptive histogram equalization to enhance the local contrast, and bilateral filtering to eliminate noise while preserving the edge. The deep learning architecture for classification comprises a pre-trained EfficientNet-B0 and fully connected layers that output two labels by the sigmoid activation function for the classification task. The average precision of tooth detection using YOLOv7 is 97.1%. For the recognition of periodontitis, the area under the curve (AUC) of the receiver operating characteristic (ROC) curve is 98.67%, and the AUC of the precision-recall (PR) curve is 98.38%. For the recognition of dental caries, the AUC of the ROC curve is 98.31%, and the AUC of the PR curve is 97.55%. Different from the conventional deep learning-based methods for a single disease such as periodontitis or dental caries, the proposed approach can provide the recognition of both periodontitis and dental caries simultaneously. This recognition method presents good performance in the identification of periodontitis and dental caries, thus facilitating dental diagnosis. [ABSTRACT FROM AUTHOR]

ABSTRACT This article proposed an accurate disparity vector prediction (DVP) algorithm for multi-view video coding. Differing from traditional DVP that uses the information of motion vectors of neighboring blocks, the geometry of the camera position is utilized to calculate the parallax of different viewpoints in this algorithm and this parallax is the foundation of DVP. We jointly applied the Just-Noticeable-Difference human visual model to the DVP. After filtered using Gaussian function, the geometric DVP was obtained. Experimental results showed that the proposed method achieved significant data reduction and subjective/objective quality enhancement. © 2015 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 25, 41-49, 2015 [ABSTRACT FROM AUTHOR]

Abstract: Multi-view video coding (MVC) has been extended from H.264/AVC to improve the coding efficiency of multi-view video. This paper proposes a fast mode decision algorithm which can make an early decision on the correct mode partition to solve the issue of the enormous computational complexity. The best modes of the reference views are utilized to determine the complexity of the macroblock (MB) in the current view, the mode candidates needed to be calculated can then be obtained according to the complexity. If the complexity is low or medium, the search range can be reduced. The threshold of the rate-distortion cost for the current MB is calculated using the co-located and neighboring MBs in previously coded view and is utilized as the criterion for early termination. The motion vector difference in the reference view is applied to dynamically adjust the search range in the current MB. Experimental results prove that the proposed algorithm achieves a time saving of 81.05% for a fast TZ search and 87.85% for full search, and still maintains quality performance and bitrate. [Copyright &y& Elsevier]

Abstract: Region-of-interest (ROI) is an essential task that one must undertake in low bit-rate multimedia communications because of the limited bandwidth of the channels and the transcoder between different standards. In this paper, an effective ROI determination method is proposed. The skin-color extraction is first employed to determine the ROI macroblocks; then, the framework for the adjustment of the quantization parameters (QPs) of ROI macroblocks according to the distortion and bit-rate variations is proposed to fit the target bit rate. In view of the residual distortion information, the fuzzy logic controller proposed in our framework can adaptively adjust the weighting factor for corresponding QPs by the distortion variation in the macroblock layer. A linear prediction formula, derived from the rate variation, is proposed to allocate appropriate bits for each ROI macroblock and maintain the target bit rate and buffer fullness. Experiment results on TMN8 reveal that the proposed ROI video coding can significantly enhance the quality at ROIs. Furthermore, the proposed framework obtains about 0.5dB gain in the objective performance and also provides the better subjective quality compared with previous works. [Copyright &y& Elsevier]

Abstract: This paper introduces a fast algorithm for image interpolation. By using this method, real-time enlargement of video images is accessible. The basic idea of the algorithm is to partition digital images into homogeneous and edge areas based on the analysis of the local structure on the images. In addition, in order to have better performance on interpolating images, specified algorithms are assigned to interpolate each classified areas, respectively. Experimental results show that the subjective quality of the interpolated images is substantially improved by using the proposed algorithm compared with that of using conventional interpolation algorithms. The computational complexity of the proposed algorithm is much lower than those of others mentioned in this paper. We also successfully implemented real-time enlargement of QCIF video sequences to CIF sequences with better image quality in low bit-rate environment. [Copyright &y& Elsevier]

This paper presents an efficient method based on inter-field information for video de-interlacing. The basic idea is to classify the field dynamically to background or foreground area. The proposed method interpolates missing pixels using temporal information in the background area, and then interpolates remaining pixels using spatial and temporal information in the foreground area. Extensive simulations conducted for video sequences show that the performance of the proposed method is superior to the previous methods based on the ELA and the line averaging method in terms of the objective and subjective image qualities. Moreover, our proposed algorithm requires lower computational complexity and provides high-quality video sequences on the progressive devices. [ABSTRACT FROM AUTHOR]

Presents information on a study which proposed an algorithm to search the best matching for the lost motion vector for compressed video by using genetic algorithm. Background of the robust and effective genetic algorithm; Proposed error concealment algorithm; Results and discussion.

Background: Concurrent non-alcoholic fatty liver disease (NAFLD) is common in patients with chronic HBV infection. But the impact of fatty liver on the histologic progression of HBV infection remains controversial. Methods: Consecutive HBV-infected patients who underwent liver biopsy between 2016 and 2021 were included. Alcohol consumption and other types of viral hepatitis were excluded. All biopsies were scored for grading and staging by Scheuer's score, and the steatosis was scored as an estimate of the percentage of liver parenchyma replaced by fat. Logistic regression analyses were applied to assess the associated factors for significant liver inflammation (G ≥ 2), significant fibrosis (S ≥ 2) and advanced fibrosis (S ≥ 3). Results: Among the 871 HBV-infected patients, hepatic steatosis was prevalent in 255 patients (29.28%). Significant liver inflammation was present in 461 patients (52.93%). Significant fibrosis was observed in 527 patients (60.51%), while advanced liver fibrosis was observed in 171 patients (19.63%). Patients with concomitant NAFLD were more likely to have significant liver inflammation and advanced fibrosis. Fatty liver was an independent risk factor for significant liver inflammation (OR: 2.117, 95% CI: 1.500-2.988), but it could not predict the development of fibrosis. Especially, in HBV-infected patients with persistent normal ALT (immune tolerant and inactive carrier phase), the presence of significant liver inflammation was higher in NAFLD than those without NAFLD. The prevalence of advanced liver fibrosis was higher in NAFLD than non-NAFLD only in the immune tolerant phase, while NAFLD did not increase fibrosis burden in other stages of HBV infection. We developed a predictive model for significant liver inflammation with the area under receiver operating characteristic curve (AUROC) of 0.825, and a model for significant fibrosis with the AUROC of 0.760. Conclusions: NAFLD is independently associated with significant liver inflammation, and increases the burden of advanced liver fibrosis in HBV-infected patients. The influence of NAFLD on the degree of liver inflammation and fibrosis is different in distinct clinical phases of chronic HBV infection. [ABSTRACT FROM AUTHOR]

Predicting cellular responses to perturbations is an unsolved problem in biology. Traditional approaches assume that different cell types respond similarly to perturbations. However, this assumption does not take into account the context of genome interactions in different cell types, which leads to compromised prediction quality. More recently, deep learning models used to discover gene–gene relationships can yield more accurate predictions of cellular responses. The huge difference in biological information between different cell types makes it difficult for deep learning models to encode data into a continuous low-dimensional feature space, which means that the features captured by the latent space may not be continuous. Therefore, the mapping relationship between the two conditional spaces learned by the model can only be applied where the real reference data resides, leading to the wrong mapping of the predicted target cells because they are not in the same domain as the reference data. In this paper, we propose an information-navigated variational autoencoder (INVAE), a deep neural network for cell perturbation response prediction. INVAE filters out information that is not conducive to predictive performance. For the remaining information, INVAE constructs a homogeneous space of control conditions, and finds the mapping relationship between the control condition space and the perturbation condition space. By embedding the target unit into the control space and then mapping it to the perturbation space, we can predict the perturbed state of the target unit. Comparing our proposed method with other three state-of-the-art methods on three real datasets, experimental results show that INVAE outperforms existing methods in cell state prediction after perturbation. Furthermore, we demonstrate that filtering out useless information not only improves prediction accuracy but also reveals similarities in how genes in different cell types are regulated following perturbation. [ABSTRACT FROM AUTHOR]

In response to the increased demand for high-resolution video, the new generation of video standards, High Efficiency Video Coding (HEVC) and its scalable extension (SHVC) have been finalized. The compression of HEVC/SHVC is efficiently improved and supports ultra-high resolution (UHD). Therefore, the coding complexity of HEVC/SHVC is much higher than those of previous standards. The framework of SHVC is based on HEVC and is divided into several types of scalable video. SHVC can be decoded into various video resolutions, frame rates and qualities, and only needs to be encoded once, but with higher complexity than HEVC. Thus, how to reduce the coding complexity of SHVC is the purpose of this paper. Our proposed algorithm accelerates the enhancement layer (EL) prediction by utilizing encoded Coding Unit (CU) sizes, prediction modes, motion vectors and Rate-Distortion Costs (RD-Costs) of the base layer (BL) and encoded CU sizes of the enhancement layer for quality scalability of SHVC. Experimental results show that the proposed algorithm can save lots of time while maintaining good video quality, and the performance is better than those of previous works. [ABSTRACT FROM AUTHOR]

The latest video coding standard, high efficiency video coding (HEVC), aims to achieve better coding efficiency than the H.264/AVC standard. To improve the coding performance of HEVC, tools and structures are included that also increase the computational complexity of encoding, especially for the mode decisions in the coding unit (CU), which is a structural element in the HEVC. For each CU size, the HEVC encoder performs mode decision and motion estimation using all prediction unit (PU) types and incurs massive computational burdens. In this paper, we investigate the depth correlation between the current and the collocated CU to avoid irrelevant CU procedure and truncate some PU predictions as a result. We also investigate the relationship of the rate-distortion (RD) costs after the Merge/SKIP prediction. By analyzing different CU sizes and quantization parameters, we build a mathematical model to represent the relationship for the RD costs after the Merge/SKIP prediction with an adaptive termination of the CU procedure. In addition, the relationship of the RD costs after the Merge/SKIP prediction and 2N \boldsymbol \times 2\textN mode is also explored. Search range reduction is used to further speed up our algorithm. Experimental results show that our algorithm can reduce the coding time by up to 80%, and provide average coding time savings of 46%, 45%, and 52%, for low delay B, low delay P, and random access configurations, respectively. Moreover, the proposed algorithm is able to maintain coding performance similar to HM 11.0. The proposed scheme outperforms previous work in terms of both the coding speed and the RD performance. [ABSTRACT FROM PUBLISHER]

This paper presents a strategy that efficiently allocates the limited resource and still maintains the coding performance for the spatial scalability of H.264/SVC. An integrated computation-scalable algorithm is proposed on the basis of priority-based mode decision (PBMD) and activitybased motion estimation (ABME). The rate-distortion cost (RD-cost) of base layer is utilized as the priority to decide the mode in the enhancement layer, and the motion vector difference (MVD) in the base layer is employed to determine the search range as well. Through the proposed approach, computation complexity of the enhancement layer could be reduced efficiently and well allocated. A fast algorithm with early termination mechanism is also proposed in this paper. Experimental results demonstrate that the various ratedistortion and time-saving performances with computation scalability are realized. [ABSTRACT FROM AUTHOR]

Region-of-interest (ROI) determination is very important for video processing and it is desirable to find a simple method to identify the ROl. Along this direction, this paper investigates a user attention model based on visual rhythm analysis for automatic determination of ROl in a video. The visual rhythm, which is an abstraction of a video, is a thumbnail version of a video by a 2-D image that captures the temporal information of a video sequence. Four sampling lines, including diagonal, anti- diagonal, vertical, and horizontal lines, are employed to obtain four visual rhythm maps in order to analyze the location of the ROl from video data. Via the variation on visual rhythms, object and camera motions can be efficiently distinguished. As for hardware design consideration, the proposed scheme can accurately extract ROl with very low computational complexity for real-time applications. The promising results from the experiments demonstrate that the moving object is effectively and efficiently extracted. Finally, we present a way to use flexible macroblock ordering in combination with ROl determination as a preprocessing step for H.264/AVC video coding, and experimental results show the quality of ROl regions is significantly enhanced. [ABSTRACT FROM AUTHOR]

Abstract: d-Hydantoinase was covalently immobilized onto polystyrene anion exchange resin via glutaraldehyde. Immobilization conditions were optimized: the carrier as D-92 type polystyrene anion exchange resin, temperature as 25°C, immobilization time as 12h, and initial concentration of protein as 6mg/ml. Under the optimized reaction conditions the activity of the free and immobilized d-hydantoinase was determined. The free and immobilized d-hydantoinase samples were characterized with their kinetic parameters, thermal, and storage stability. The K m and V max values were 14.985mM and 0.6mM/min for the free, and 27.030mM and 1.187mM/min for the immobilized, respectively. Operational stability of the immobilized d-hydantoinase was also detected in a circulating packed-bed reactor. The half-time of the immobilized d-hydantoinase was 11 days. Nearly 90% of activity of the immobilized d-hydantoinase was reserved for 100 days stored at 4°C. The free and immobilized d-hydantoinases were also characterized under microwave irradiation. Results shown that the reactions catalyzed by both free and immobilized d-hydantoinase were accelerated under microwave irradiation. The half-time of the immobilized d-hydantoinase reduced to 16min under microwave irradiation. [Copyright &y& Elsevier]

• Deep residual learning model with multi-scale image fusion for image deblocking. • Deep jointly learning image super-resolution and image deblocking. • Residual learning with multiple loss functions for better network optimization. One of the visually noticeable compression artifacts in block-based image/video compression platforms is called blocking artifact. Several post-processing methods were presented to reduce such kind of artifacts. However, most methods in the literature often induce visibly blurring artifacts. The paper presents a deep network to eliminate image compression artifacts (usually denoted by image deblocking) based on image fusion in multi-scale manner. Recent deep learning-based related methods usually learn deep models using a loss function in per-pixel manner based on explicit image priors in order to directly produce clean images. In place of existing deep learning-guided approaches, the problem is reformulated in this paper to the learning of the residuals (or artifacts) between the received images and their corresponding clean images (ground truths). In the presented deep framework, an input image is first down-sampled while naturally reducing the blocking artifacts. Then, our multi-scale image fusion model is used for fusing the different down-scaled versions (of less artifacts) with the input image (with severer artifacts) to estimate the blocking artifacts. Then, by deducting the estimated artifacts from the input image, the blocking artifacts can be significantly eliminated and most original image details are preserved simultaneously. The presented method is well applicable to any vision-based computer systems with digital visual codec embedded. [ABSTRACT FROM AUTHOR]

Presents a high quality, low bit rate and portable Internet-phone system. Architecture of the proposed system; General block diagram of analysis-by synthesis Linear Prediction Coding (LPC) coder; Internet-phone system outlook and its performance.

Chronic hepatitis B (CHB) is a major global health burden. Liver fibrosis, an insidious process, is the main histopathological change in CHB that might lead to the end-stage liver disease if left untreated. The intermediate liver fibrosis (S2) is the optimal time to start antiviral therapy. The aim of the present study was to examine the proteomic changes in patients with CHB at different fibrotic stages, with a view to identify future serum biomarkers for S2. Ninety CHB patients were grouped into mild (S0–1), intermediate (S2), and severe liver fibrosis (S3–4) (61 men and 29 women; age 25–63 years). Isobaric tagging for relative and absolute quantitation was applied to screen proteins differentially expressed among the patient groups. Another 46 patients with CHB (age 25–59 years; 31 men and 15 women), and 16 healthy controls (age 26–61 years; 11 men and 5 women) were enrolled in a validation group. Enzyme-linked immunosorbent assay was used to verify the diagnostic value of the candidate biomarkers. We found 139 proteins that were differentially expressed between various fibrotic stage-paired comparisons. Five protein candidates were selected as potential biomarkers of S2 for further verification. Notably, ficolin-2 (FCN2) and carboxypeptidase B2 (CPB2) showed differential expression between patients and healthy controls. In conclusion, serum proteomic changes reported here offer new molecular leads for future research on biomarker candidates to identify liver fibrotic stages in CHB. In particular, FCN2 and CPB2 warrant further research on their possible mechanistic involvement in CHB pathogenesis. [ABSTRACT FROM AUTHOR]

Highlights • Novel unit prediction method for efficient HEVC-to-SHVC transcoding is proposed. • Two proposed techniques are early termination and adaptive confidence interval. • We are among the first to propose a fast HEVC-to-SHVC transcoding framework. • Proposed method reduces encoding time of SHVC by 74.14% almost without quality loss. To support good video quality of experiences in heterogeneous environments, transcoding an existed HEVC (high efficiency video coding) video bitstream to a SHVC (scalability extension of HEVC) bitstream with quality scalability is highly required. A straightforward way is to first fully decode the input HEVC bitstream and then fully re-encode it with the SHVC encoder, which requires a tremendous computational complexity. To solve the problem, in this paper, a coding unit complexity (CUC)-based prediction method for predictions of CU (coding unit) depth and PU (prediction unit) mode for efficient HEVC-to-SHVC transcoding with quality scalability is proposed to significantly reduce the transcoding complexity. The proposed method contains two prediction techniques, including (i) early termination and (ii) adaptive confidence interval, and predicts the CU depth and PU mode relying on the decoded information from the input HEVC bitstream. Experimental results have shown that the proposed method significantly outperforms the traditional HEVC-to-SHVC method by 74.14% on average in reductions of encoding time for SHVC enhancement layer. [ABSTRACT FROM AUTHOR]

High Efficiency Video Coding (HEVC/H.265) is the latest international video coding standard, which achieves better compression ratio and supports higher resolution than Advanced Video Coding (H.264/AVC). However, HEVC/H.265 increases the computational burden. To reduce the coding complexity of the HEVC encoder, this paper proposes a fast inter-prediction algorithm to speed up coding time. We collect the average rate-distortion costs (RD-cost) of Skip modes and Merge modes to accelerate prediction unit (PU) mode decisions. In addition, we also acquire and analyze the motion vector range from Merge modes and Inter 2N × 2N modes to decide whether to execute Merge and advanced motion vector prediction (AMVP) of other PUs. The experimental results show that the proposed algorithm provides 48.54% time saving on average in random-access configuration and maintains good rate-distortion performance and video quality at the same time. The proposed algorithm also outperforms previous works. [ABSTRACT FROM AUTHOR]

Melanoma differentiation-associated gene 5 (MDA5) is a member of the retinoic acid-inducible gene I (RIG-I)-like receptors (RLRs) family and plays a pivotal role in the anti-viral innate immune response. As RIG-I is absent in chickens, MDA5 is hypothesized to be important in detecting viral nucleic acids in the cytoplasm. However, the molecular mechanism of the regulation of chicken MDA5 (chMDA5) expression has yet to be fully elucidated. With this in mind, a ∼2.5 kb chMDA5 gene promoter region was examined and PCR amplified to assess its role in immune response. A chMDA5 promoter reporter plasmid (piggybac-MDA5-DsRed) was constructed and transfected into DF-1 cells to establish a Piggybac-MDA5-DsRed cell line. The MDA5 promoter activity was extremely low under basal condition, but was dramatically increased when cells were stimulated with polyinosinic: polycytidylic acid (poly I:C), interferon beta (IFN-β) or Infectious Bursal Disease Virus (IBDV). The DsRed mRNA level represented the promoter activity and was remarkably increased, which matched the expression of endogenous MDA5. However, Infectious Bronchitis Virus (IBV) and Newcastle disease virus (NDV) failed to increase the MDA5 promoter activity and the expression of endogenous MDA5. The results indicated that the promoter and the Piggybac-MDA5-DsRed cell line could be utilized to determine whether a ligand regulates MDA5 expression. For the first time, this study provides a tool for testing chMDA5 expression and regulation. [ABSTRACT FROM AUTHOR]

Scalable video coding incorporated with computation-aware ability achieves quality as well as being computation scalable. This paper presents a computation-aware algorithm for scalable video coding with spatial/quality scalability aiming for the best trade-off between rate distortion performance and computational consumption. We first observe and analyze and then establish a model for the motion vector difference relationship between the scalable base and enhancement layers. By using the modeling results, a linear algorithm for computation distribution is thus proposed to allocate the computation for each macroblock in the enhancement layer. In addition, the rate distortion costs of the base layer are also taken into account for the computation allocation process in order to further improve the coding performance. The simulation results demonstrate that our proposed computation-aware algorithm not only accomplishes better rate distortion performance than other works under the same computational constraints, but also achieves less computation necessities. [ABSTRACT FROM AUTHOR]

Mobile communication systems provide more kinds of advanced video services. One of the most significant problems of mobile video communication is to adapt video content to different network characteristics, which is critical to acceptable visual quality of the transmitted videos. This paper proposes a frame selection scheme for temporal video transcoding through frame complexity analysis that consists of visual complexity and temporal coherence. Visual complexity represents the importance of frames and temporal coherence reveals the consequences of frame skipping; both of these two features are good indicators to determine if a frame should be kept. Experimental results show that our proposed method achieves higher visual quality and has more encoded frames than other existing schemes in the same bitrate, which means that our proposed method achieves high accuracy in selecting the representative frames for temporal video transcoding. The proposed method also has better video quality assessment scores than that of other existing methods, which means the proposed method keeps higher level of integrity of the transcoded videos. [ABSTRACT FROM PUBLISHER]

Background: Susceptibility to tuberculosis is not only determined by Mycobacterium tuberculosis infection, but also by the genetic component of the host. Macrophage receptor with a collagenous structure (MARCO) is essential components required for toll like receptor-signaling in macrophage response to Mycobacterium tuberculosis, which may contribute to tuberculosis risk. Principal Findings: To specifically investigated whether single nucleotide polymorphisms (SNPs) in MARCO gene are associated with pulmonary tuberculosis in Chinese Han population. By selecting tagging SNPs in MARCO gene, 17 tag SNPs were identified and genotyped in 923 pulmonary tuberculosis patients and 1033 healthy control subjects using a hospital based case-control association study. Single-point and haplotype analysis revealed an association in intron and exon region of MARCO gene. One SNP (rs17009726) was associated with susceptibility to pulmonary tuberculosis, where the carriers of the G allele had a 1.65 fold (95% CI = 1.32-2.05, pcorrected = 9.27E-5) increased risk of pulmonary tuberculosis. Haplotype analysis revealed that haplotype GC containing G allele of 17009726 and haplotype TGCC (rs17795618T/A, rs1371562G/T, rs6761637T/C, rs2011839C/T) were also associated with susceptibility to pulmonary tuberculosis (pcorrected = 0.0001 and 0.029, respectively). Conclusions: Our study suggested that genetic variants in MARCO gene were associated with pulmonary tuberculosis susceptibility in Chinese Han population, and the findings emphasize the importance of MARCO mediated immune responses in the pathogenesis of tuberculosis. [ABSTRACT FROM AUTHOR]

The state-of-the-art video coding standard H.264 provides efficient compression and rate control for various video applications. However, by using the current rate control mechanism of H.264 for a video streaming, the output end users would experience a serious video distortion when a data burst happens in the input video. In order to ensure the output video quality and maintain a stable output bit stream, a new adaptive bit-allocation scheme is proposed in this paper. To be compatible with the widely used H.264, our adaptive bit allocation scheme incorporates a set of new allocation criteria in the rate control mechanism of H.264. Experimental results show that the proposed adaptive bit allocation scheme could produce a more stable output stream given a bursty video input stream. [ABSTRACT FROM PUBLISHER]

To benefit network transmission, the bit stream of the whole frame coded by H.264 is usually grouped into a single packet. However, the error and packet loss during the transmission will result in the distortion of reconstructed video. To deal with this problem, error concealment is widely used to recover the lost frames and to prevent error propagation. In this paper, we propose an accurate motion vector refinement algorithm to further refine the well-known motion copy error concealment method and thus improve the concealed results. By using the motion vector correlation from spatial and temporal domains, a possible motion vector difference variation area has been discovered by the recursive motion vector difference examining approach in the previous frame. Afterwards, a more precise motion vector difference can be derived from the selected motion vector difference variation area. Through the help of our proposed algorithm, the refined motion vectors can be obtained and thus improve the accuracy of motion vectors derived by a motion copy error concealment algorithm. Simulation results show that our proposed algorithm can achieve better performance compared to well-known schemes. [ABSTRACT FROM PUBLISHER]

Within Internet of Things (IoT) sensors, the challenge is how to dig out the potentially valuable information from the collected data to support decision making. This paper proposes a method based on machine learning to predict long cycle maintenance time of wind turbines for efficient management in the power company. Long cycle maintenance time prediction makes the power company operate wind turbines as cost-effectively as possible to maximize the profit. Sensor data including operation data, maintenance time data, and event codes are collected from 31 wind turbines in two wind farms. Data aggregation is performed to filter out some errors and get significant information from the data. Then, the hybrid network is built to train the predictive model based on the convolutional neural network (CNN) and support vector machine (SVM). The experimental results show that the prediction of the proposed method reaches high accuracy, which helps drive up the efficiency of wind turbine maintenance. [ABSTRACT FROM AUTHOR]