Objective To evaluate the use of prospective screening for the HLA-B*58:01 allele to identify Taiwanese individuals at risk of severe cutaneous adverse reactions (SCARs) induced by allopurinol ...treatment.Design National prospective cohort study.Setting 15 medical centres in different regions of Taiwan, from July 2009 to August 2014.Participants 2926 people who had an indication for allopurinol treatment but had not taken allopurinol previously. Participants were excluded if they had undergone a bone marrow transplant, were not of Han Chinese descent, and had a history of allopurinol induced hypersensitivity. DNA purified from 2910 participants’ peripheral blood was used to assess the presence of HLA-B*58:01. Main outcome measures Incidence of allopurinol induced SCARs with and without screening.Results Participants who tested positive for HLA-B*58:01 (19.6%, n=571) were advised to avoid allopurinol, and were referred to an alternate drug treatment or advised to continue with their prestudy treatment. Participants who tested negative (80.4%, n=2339) were given allopurinol. Participants were interviewed once a week for two months to monitor symptoms. The historical incidence of allopurinol induced SCARs, estimated by the National Health Insurance research database of Taiwan, was used for comparison. Mild, transient rash without blisters developed in 97 (3%) participants during follow-up. None of the participants was admitted to hospital owing to adverse drug reactions. SCARs did not develop in any of the participants receiving allopurinol who screened negative for HLA-B*58:01. By contrast, seven cases of SCARs were expected, based on the estimated historical incidence of allopurinol induced SCARs nationwide (0.30% per year, 95% confidence interval 0.28% to 0.31%; P=0.0026; two side one sample binomial test).Conclusions Prospective screening of the HLA-B*58:01 allele, coupled with an alternative drug treatment for carriers, significantly decreased the incidence of allopurinol induced SCARs in Taiwanese medical centres.
Visible-infrared person re-identification (VI-ReID) is a cross-modality retrieval problem, which aims at matching the same pedestrian between the visible and infrared cameras. Due to the existence of ...pose variation, occlusion, and huge visual differences between the two modalities, previous studies mainly focus on learning image-level shared features. Since they usually learn a global representation or extract uniformly divided part features, these methods are sensitive to misalignments. In this paper, we propose a structure-aware positional transformer (SPOT) network to learn semantic-aware sharable modality features by utilizing the structural and positional information. It consists of two main components: attended structure representation (ASR) and transformer-based part interaction (TPI). Specifically, ASR models the modality-invariant structure feature for each modality and dynamically selects the discriminative appearance regions under the guidance of the structure information. TPI mines the part-level appearance and position relations with a transformer to learn discriminative part-level modality features. With a weighted combination of ASR and TPI, the proposed SPOT explores the rich contextual and structural information, effectively reducing cross-modality difference and enhancing the robustness against misalignments. Extensive experiments indicate that SPOT is superior to the state-of-the-art methods on two cross-modal datasets. Notably, the Rank-1/mAP value on the SYSU-MM01 dataset has improved by 8.43%/6.80%.
Although deep learning-based face recognition techniques have achieved amazing performance in recent years, low-resolution (LR) face recognition remains challenging. In this letter, we address this ...problem by proposing an identity-aware face super-resolution network to recover identity information of LR faces. To learn identity-aware features effectively, the identity features are explicitly disentangled to two orthogonal components: the magnitude and angle of features that project identity features to a hypersphere space. We show that the magnitude of features is related to the quality of a face. The proposed approach shows its superiority on recovering identity-related textures which are beneficial to recover identity information for recognition. Extensive experiments demonstrate the effectiveness of the proposed algorithm in LR face recognition.
Online image hashing aims to update hash functions on-the-fly along with newly arriving data streams, which has found broad applications in computer vision and beyond. To this end, most existing ...methods update hash functions simply using discrete labels or pairwise similarity to explore intra-class relationships, which, however, often deteriorates search performance when facing a domain gap or semantic shift. One reason is that they ignore the particular semantic relationships among different classes, which should be taken into account in updating hash functions. Besides, the common characteristics between the label vectors (can be regarded as a sort of binary codes) and to-be-learned binary hash codes have left unexploited. In this paper, we present a novel online hashing method, termed Similarity Preserving Linkage Hashing (SPLH), which not only utilizes pairwise similarity to learn the intra-class relationships, but also fully exploits a latent linkage space to capture the inter-class relationships and the common characteristics between label vectors and to-be-learned hash codes. Specifically, SPLH first maps the independent discrete label vectors and binary hash codes into a linkage space, through which the relative semantic distance between data points can be assessed precisely. As a result, the pairwise similarities within the newly arriving data stream are exploited to learn the latent semantic space to benefit binary code learning. To learn the model parameters effectively, we further propose an alternating optimization algorithm. Extensive experiments conducted on three widely-used datasets demonstrate the superior performance of SPLH over several state-of-the-art online hashing methods.
Saliency detection plays important roles in many image processing applications, such as regions of interest extraction and image resizing. Existing saliency detection models are built in the ...uncompressed domain. Since most images over Internet are typically stored in the compressed domain such as joint photographic experts group (JPEG), we propose a novel saliency detection model in the compressed domain in this paper. The intensity, color, and texture features of the image are extracted from discrete cosine transform (DCT) coefficients in the JPEG bit-stream. Saliency value of each DCT block is obtained based on the Hausdorff distance calculation and feature map fusion. Based on the proposed saliency detection model, we further design an adaptive image retargeting algorithm in the compressed domain. The proposed image retargeting algorithm utilizes multioperator operation comprised of the block-based seam carving and the image scaling to resize images. A new definition of texture homogeneity is given to determine the amount of removal block-based seams. Thanks to the directly derived accurate saliency information from the compressed domain, the proposed image retargeting algorithm effectively preserves the visually important regions for images, efficiently removes the less crucial regions, and therefore significantly outperforms the relevant state-of-the-art algorithms, as demonstrated with the in-depth analysis in the extensive experiments.
Understanding foggy image sequence in driving scene is critical for autonomous driving, but it remains a challenging task due to the difficulty in collecting and annotating real-world images of ...adverse weather. Recently, self-training strategy has been considered as a powerful solution for unsupervised domain adaptation, which iteratively adapts the model from the source domain to the target domain by generating target pseudo labels and re-training the model. However, the selection of confident pseudo labels inevitably suffers from the conflict between sparsity and accuracy, both of which will lead to suboptimal models. To tackle this problem, we exploit the characteristics of the foggy image sequence of driving scenes to densify the confident pseudo labels. Specifically, based on the two discoveries of local spatial similarity and adjacent temporal correspondence of the sequential image data, we propose a novel Target-Domain driven pseudo label Diffusion (TDo-Dif) scheme. It employs superpixels and optical flows to identify the spatial similarity and temporal correspondence, respectively, and then diffuses the confident but sparse pseudo labels within a superpixel or a temporal corresponding pair linked by the flow. Moreover, to ensure the feature similarity of the diffused pixels, we introduce local spatial similarity loss and temporal contrastive loss in the model re-training stage. Experimental results show that our TDo-Dif scheme helps the adaptive model achieve 51.92% and 53.84% mean intersection-over-union (mIoU) on two publicly available natural foggy datasets (Foggy Zurich and Foggy Driving), which exceeds the state-of-the-art unsupervised domain adaptive semantic segmentation methods. The proposed method can also be applied to non-sequential images in the target domain by considering only spatial similarity.
A Video Saliency Detection Model in Compressed Domain YUMING FANG; WEISI LIN; ZHENZHONG CHEN ...
IEEE transactions on circuits and systems for video technology,
2014-Jan., 2014, 2014-01-00, Letnik:
24, Številka:
1
Journal Article
Recenzirano
Odprti dostop
Saliency detection is widely used to extract regions of interest in images for various image processing applications. Recently, many saliency detection models have been proposed for video in ...uncompressed (pixel) domain. However, video over Internet is always stored in compressed domains, such as MPEG2, H.264, and MPEG4 Visual. In this paper, we propose a novel video saliency detection model based on feature contrast in compressed domain. Four types of features including luminance, color, texture, and motion are extracted from the discrete cosine transform coefficients and motion vectors in video bitstream. The static saliency map of unpredicted frames (I frames) is calculated on the basis of luminance, color, and texture features, while the motion saliency map of predicted frames (P and B frames) is computed by motion feature. A new fusion method is designed to combine the static saliency and motion saliency maps to get the final saliency map for each video frame. Due to the directly derived features in compressed domain, the proposed model can predict the salient regions efficiently for video frames. Experimental results on a public database show superior performance of the proposed video saliency detection model in compressed domain.
The skin permeability (
) defines the rate of a chemical penetrating across the stratum corneum. This value is widely used to quantitatively describe the transport of molecules in the outermost layer ...of epidermal skin and indicate the significance of skin absorption. This study defined a
quantitative structure-activity relationship (QSAR) based on 106 chemical substances of
measured using human skin and interpreted the molecular interactions underlying transport behavior of small molecules in the stratum corneum. The
QSAR developed in this study identified four molecular descriptors that described the molecular cyclicity in the molecule reflecting local geometrical environments, topological distances between pairs of oxygen and chlorine atoms, lipophilicity, and similarity to antineoplastics in molecular properties. This
QSAR considered the octanol-water partition coefficient to be a direct influence on transdermal movement of molecules. Moreover, the
QSAR identified a sub-domain of molecular properties initially defined to describe the antineoplastic resemblance of a compound as a significant factor in affecting transdermal permeation of solutes. This finding suggests that the influence of molecular size on the chemical's skin-permeating capability should be interpreted with other relevant physicochemical properties rather than being represented by molecular weight alone.
The chemically crosslinked metal-complexed chitosans were synthesized by using the ion-imprinting method from a chitosan with four metals (Cu(II), Zn(II), Ni(II) and Pb(II)) as templates and ...glutaraldehyde as a crosslinker. The influences of adsorption conditions, including molar ratios of crosslinker/chitosan and pH changes, were studied. They were used to investigate for comparative adsorptions of Cu(II), Zn(II), Ni(II) and Pb(II) ions in an aqueous medium. They were demonstrated the comparative adsorptions of Cu(II), Zn(II), Ni(II) and Pb(II) ions in the orders of the adsorbed amounts with templates: Cu(II)
∼
Pb(II)
>
Zn(II)
∼
Ni(II), Zn(II)
>
Cu(II)
∼
Pb(II)
>
Ni(II), Ni(II)
>
Pb(II)
>
Zn(II)
>
Cu(II) and Pb(II)
∼
Cu(II)
>
Zn(II)
>
Ni(II), respectively. In addition, the dynamical study showed to be well followed the second-order kinetic equation in the adsorption process. At the same time, the equilibrium adsorption data were fitted in three adsorption isotherm models, namely, Langmuir, Freundlich, and Dubinin–Radushkevich to show very good fits in the Langmuir isotherm equation for the monolayer adsorption process. The most important aspect of the chemically crosslinked metal-complexed chitosans with glutaraldehyde demonstrated to afford a higher adsorption capacity, and a more efficient adsorption toward metals in an aqueous medium.
Deep convolutional neural networks (CNNs) with strong expressive ability have achieved impressive performances on single image super-resolution (SISR). However, their excessive amounts of ...convolutions and parameters usually consume high computational cost and more memory storage for training a SR model, which limits their applications to SR with resource-constrained devices in real world. To resolve these problems, we propose a lightweight enhanced SR CNN (LESRCNN) with three successive sub-blocks, an information extraction and enhancement block (IEEB), a reconstruction block (RB) and an information refinement block (IRB). Specifically, the IEEB extracts hierarchical low-resolution (LR) features and aggregates the obtained features step-by-step to increase the memory ability of the shallow layers on deep layers for SISR. To remove redundant information obtained, a heterogeneous architecture is adopted in the IEEB. After that, the RB converts low-frequency features into high-frequency features by fusing global and local features, which is complementary with the IEEB in tackling the long-term dependency problem. Finally, the IRB uses coarse high-frequency features from the RB to learn more accurate SR features and construct a SR image. The proposed LESRCNN can obtain a high-quality image by a model for different scales. Extensive experiments demonstrate that the proposed LESRCNN outperforms state-of-the-arts on SISR in terms of qualitative and quantitative evaluation. The code of LESRCNN is accessible on https://github.com/hellloxiaotian/LESRCNN.
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