Search

MotivationVirus mutation is one of the most important research issues which plays a critical role in disease progression and has prompted substantial scientific publications. Mutation extraction from published literature has become an increasingly important task, benefiting many downstream applications such as vaccine design and drug usage. However, most existing approaches have low performances in extracting virus mutation due to both lack of precise virus mutation information and their development based on human gene mutations.ResultsWe developed ViMRT, a text-mining tool and search engine for automated virus mutation recognition using natural language processing. ViMRT mainly developed 8 optimized rules and 12 regular expressions based on a development dataset comprising 830 papers of 5 human severe disease-related viruses. It achieved higher performance than other tools in a test dataset (1662 papers, 99.17% in F1-score) and has been applied well to two other viruses, influenza virus and severe acute respiratory syndrome coronavirus-2 (212 papers, 96.99% in F1-score). These results indicate that ViMRT is a high-performance method for the extraction of virus mutation from the biomedical literature. Besides, we present a search engine for researchers to quickly find and accurately search virus mutation-related information including virus genes and related diseases.Availability and implementationViMRT software is freely available at http://bmtongji.cn:1225/mutation/index.

There has been an increasing incidence rate of rice false smut in global rice cultivation areas. However, there is a dearth of studies on the environmental concentrations and hazards of ustiloxin A (UA), which is the major mycotoxin produced by a pathogenic fungus of the rice false smut. Here, the concentrations of UA in the surface waters of two paddy fields located in Enshi city, Hubei province, China, were measured, and its toxicity in T. Thermophila was evaluated. This is the first study to detect UA in the surface waters of the two paddy fields, and the measured mean concentrations were 2.82 and 0.26 μg/L, respectively. Exposure to 2.19, 19.01 or 187.13 μg/L UA for 5 days significantly reduced the theoretical population and cell size of T. thermophila. Furthermore, treatment with 187.13 μg/L UA changed the percentages of T. thermophila cells in different cell-cycle stages, and with an increased malformation rate compared with the control, suggesting the disruption of the cell cycle. The expressions of 30 genes involved in the enriched proteasome pathway, 7 cyclin genes (cyc9, cyc10, cyc16, cyc22, cyc23, cyc26, cyc33) and 2 histone genes (mlh1 and hho1) were significantly down-regulated, which might be the modes of action responsible for the disruption of cell cycling due to UA exposure.

Ustiloxin A (UA) and ustiloxin B (UB), two major mycotoxins produced by the pathogen of rice false smut (RFS) during rice cultivation, have attracted increasing attentions due to their potential health risks. However, limited data are available about their occurrence and fate in paddy fields and contamination profiles in rice. In this study, a field study was performed to investigate the occurrence and translocation of UA and UB in RFS-occurred paddies. For the first time to our knowledge, we reported a ubiquitous occurrence of the two ustiloxins in the paddy water (range: 0.01-3.46 μg/L for UA and0.02-1.15 μg/L for UB) and brown rice (range: 0.09-154.08 μg/kg for UA and0.09-23.57 μg/kg for UB). A significant positive correlation was observed between ustiloxin levels in paddy water and brown rice (r

This paper addresses the problem of 3D face reconstruction from a single image. While available solutions for addressing this problem do exist, to our knowledge, we propose the very first approach which is robust, lightweight and detailed i.e. it can reconstruct fine facial details. Our method is extremely simple and consists of 3 key components: (a) a lightweight non-parametric decoder based on Graph Convolutional Networks (GCNs) trained in a supervised manner to reconstruct coarse facial geometry from image-based ResNet features. (b) An extremely lightweight (35K parameters) subnetwork – also based on GCNs – which is trained in an unsupervised manner to refine the output of the first network. (c) A novel feature-sampling mechanism and adaptation layer which injects fine details from the ResNet features of the first network into the second one. Overall, our method is the first one (to our knowledge) to reconstruct detailed facial geometry relying solely on GCNs. We exhaustively compare our method with 7 state-of-the-art methods on 3 datasets reporting state-of-the-art results for all of our experiments, both qualitatively and quantitatively, with our approach being, at the same time, significantly faster.

The potential exposure of titanate nanotubes (TNTs) to wildlife and humans may occur as a result of increased use and application as functional nanomaterials. However, there is a dearth of knowledge regarding the pathways of uptake and excretion of TNTs and their toxicity in cells. In this study, three strains of the Tetrahymena genus of free-living ciliates, including a wild type strain (SB210) and two mutant strains (SB255: mucocyst-deficient; NP1: temperature-sensitive "mouthless''), were used to study the pathways of uptake and excretion and evaluate the cytotoxicity of TNTs. The three Tetrahymena strains were separately exposed to 0, 0.01, 0.1, 1 or 10 mg/L of TNTs, and cells were collected at different time points for quantification of intracellular TNTs (e.g., 5, 10, 20, 40, 60, 90 and 120 min) and evaluation of cytotoxicity (12 and 24 h). TNT contents in NP1 and SB255 were greater or comparable to the contents in SB210 while exposure to 10 mg/L TNTs in 120 min. Furthermore, exposure to 10 mg/L TNTs for 24 h caused greater decreases in cell density of NP1 (38.2 %) and SB255 (36.8 %) compared with SB210 (26.5 %) and upregulated the expression of caspase 15 in SB210. Taken together, our results suggested that TNT uptake by pinocytosis and excretion by exocytosis in Tetrahymena, and the exposure could cause cytotoxicity which can offer novel insights into the accumulation kinetics of nanotubes and even nanomaterials in single cell.

Ustiloxins, a group of bioactive metabolites produced by the pathogen of rice false smut (RFS), have emerged as ubiquitous contaminants in RFS-occurred paddy fields and could accumulate in rice. Nevertheless, the prevalence of ustiloxins in rice and exposure risks of humans are limited. In this study, concentrations of ustiloxin A (UA) and ustiloxins B (UB), which are two predominant ustiloxins, were measured in 240 rice samples from China and 72 rice samples from 12 other counties. The detection rates (DRs) of UA and UB were 82.1% and 49.3%, respectively, and their concentrations in rice ranged from below detection limit (LOD: 0.22 μg/kg) to 85.96 μg/kg dw. Furthermore, for the first time, we reported the occurrence of UA (DR = 22.8%) in urine collected from residues of Enshi city, China. Urinary UA were significantly correlated with the activities of alanine aminotransferase in male, and this male-biased hepatotoxicity was further confirmed in mice exposure experiment. This study for the first time reported the widespread geographical distribution of ustiloxins in rice, as well as emphasized the occurrence of internal exposure and potential health risk in humans.

Visual face tracking is one of the most important components for face analysis in mobile applications and in video surveillance systems. In this paper, we propose an efficient face tracker called FT-RCNN, short for Face Tracking with Region-based CNN, that is based on the Faster-RCNN framework. A simple yet effective tracking branch is proposed to enable the framework to jointly perform face detection and tracking. To address the problem of insufficient training data for face tracking, we propose a novel pairwise training strategy that enables us to train face tracker by leveraging existing face detection datasets, thus eliminating the need to collect and annotate video data specifically for face tracking. Furthermore, we devise a novel loss function, termed Pair-hard Triplet Cosine Loss, that employs a pair-hard triplet mining strategy to increase the discriminative power of our face tracker. We evaluated the proposed tracker on popular video face datasets including MobiFace, ChokePoint and YouTube Face. The results have shown that FT-RCNN outperforms state-of-the-art face trackers and runs at real-time speed. We plan to release the source code for reproducible results in the future.

Triphenyl phosphate (TPHP) is one of the most widely used organophosphate flame retardants (OPFRs) and is frequently detected in a variety of environmental media. Previous studies reported that TPHP had toxic effects on vertebrates, but little toxic information was available in lower trophic aquatic organisms which were more sensitive to the exposure of many toxic substances. In this study, protozoa Tetrahymena thermophila (T. thermophila) were exposed to 0, 0.01, 0.17 or 2.35 mg/L TPHP for 5 days to study the effects of sub-chronic exposure on theoretical population, cell viability, cell size and number of cilia. Additionally, the effects of TPHP on gene transcription were assessed by transcriptome sequencing technique (RNA-Seq). Cell viability and number of cilia were significantly reduced in all TPHP exposure groups compared with the control. In addition, exposure to 0.17 or 2.35 mg/L TPHP significantly reduced the theoretical population, circumference and body width, and there was a significant decrease in body length in the 2.35 mg/L exposure group. Comparative transcriptome sequencing identified a total of 4105 up- and 4487 down-regulated genes after exposure to 2.35 mg/L TPHP for 5 days compared with the control. KEGG analysis showed that dysfunction of pathways associated with ribosome, spliceosome, phagosome, proteasome and protein processing in endoplasmic reticulum in this study might be responsible for the toxicity of T. thermophila caused by TPHP. In general, the results indicated that TPHP had an adverse effect on the protozoa T. thermophila.

Background: Leukocyte-associated immunoglobulin-like receptor-1 (LAIR-1) is a collagen receptor belonging to the immunoglobulin superfamily. Although prior studies have evaluated the biological role of LAIR in solid tumors, the precise mechanisms underlying LAIR-1 functions as a regulator of tumor biological functions remains unclear. Methods: LAIR-1 expression was evaluated using an osteosarcoma (OS) tissue microarray by immunohistochemical analysis. Wound healing and Transwell assays were performed to evaluate tumor cell migration. Quantitative PCR and western blotting were conducted to detect the expression of epithelial-mesenchymal transition (EMT)-related molecules. RNA-sequencing (RNA-seq) was conducted to evaluate the mRNA expression profiles after overexpressing LAIR-1 in OS cells. Glucose uptake and glucose transporter (Glut) 1 expression in OS cells in vitro were evaluated by flow cytometry and western blotting. Results: LAIR-1 expression significantly differed between the T1 and T2 stages of OS tumors, and LAIR-1 overexpression inhibited OS cell migration. LAIR-1 expression was inversely correlated with the expression of EMT-associated transcription factors via the Forkhead box O1/Twist1 signal transduction pathway. Furthermore, RNA-seq and quantitative PCR demonstrated that EMT energy metabolism-related molecules were significantly reduced after LAIR-1 overexpression. Conclusions: Notably, overexpression of LAIR-1 in OS cells decreased Glut1 expression. These findings provide insight into the molecular mechanism underlying OS progression.

Supplementary Table 2. Primer sequence.

Ag/Pr6O11 catalysts supported by either Pr6O11 nanorods (Pr6O11-NRs) or nanoparticles (Pr6O11-NPs) were prepared by conventional incipient wetness impregnation. The nanocomposite of Ag/Pr6O11-NRs demonstrated a higher catalytic activity for CO oxidation than Ag/Pr6O11-NPs at lower temperatures. This improved performance may be ascribed to the mesoporous features and resultant oxygen vacancies of the Pr6O11 nanorods support, as well as the large surface area and homogeneous loading of Ag species. As a result, 98.7 and 100% CO conversions were achieved at 210 and 240 °C for Ag/Pr6O11-NRs, while Ag/Pr6O11-NPs require a temperature of 320 °C to obtain the 100% CO conversion rate. These findings reveal that Pr6O11-NRs is the preferable support, comparative to Pr6O11-NPs, for Ag/Pr6O11 catalysts, for CO oxidation.

Registration of 3D human body has been a challenging research topic for over decades. Most of the traditional human body registration methods require manual assistance, or other auxiliary information such as texture and markers. The majority of these methods are tailored for high-quality scans from expensive scanners. Following the introduction of the low-quality scans from cost-effective devices such as Kinect, the 3D data capturing of human body becomes more convenient and easier. However, due to the inevitable holes, noises and outliers in the low-quality scan, the registration of human body becomes even more challenging. To address this problem, we propose a fully automatic active registration method which deforms a high-resolution template mesh to match the low-quality human body scans. Our registration method operates on two levels of statistical shape models: (1) the first level is a holistic body shape model that defines the basic figure of human; (2) the second level includes a set of shape models for every body part, aiming at capturing more body details. Our fitting procedure follows a coarse-to-fine approach that is robust and efficient. Experiments show that our method is comparable with the state-of-the-art methods., 14 pages, the Journal of Multimedia Systems

The problem of fitting a 3D facial model to a 3D mesh has received a lot of attention the past 1520years. The majority of the techniques fit a general model consisting of a simple parameterisable surface or a mean 3D facial shape. The drawback of this approach is that is rather difficult to describe the non-rigid aspect of the face using just a single facial model. One way to capture the 3D facial deformations is by means of a statistical 3D model of the face or its parts. This is particularly evident when we want to capture the deformations of the mouth region. Even though statistical models of face are generally applied for modelling facial intensity, there are few approaches that fit a statistical model of 3D faces. In this paper, in order to capture and describe the non-rigid nature of facial surfaces we build a part-based statistical model of the 3D facial surface and we combine it with non-rigid iterative closest point algorithms. We show that the proposed algorithm largely outperforms state-of-the-art algorithms for 3D face fitting and alignment especially when it comes to the description of the mouth region. A statistical non-rigid ICP method for 3D face alignment is proposed.Local fitting in dynamic subdivision framework helps capture subtle facial feature.2D point-driven mesh deformation in pre-processing step helps improve performance.

The oxygen permeation flux of Ce0.9Gd0.1O1.95-δ (CGO)-based oxygen transport membranes under oxidizing conditions is limited by the electronic conductivity of the material. This work aims to enhance the bulk ambipolar conductivity of CGO by partial substitution of Ce with the redox active element Pr. A series of compositions of PrxGd0.1Ce0.9-xO1.95-δ (x = 0, 0.02, 0.05, 0.08, 0.15, 0.25, 0.3 and 0.4) was prepared by solid state reaction. X-ray powder diffraction (XPD) indicates that Pr is completely dissolved in the fluorite structure up to 40 at.%. Pronounced nonlinear thermal expansion behavior was observed as a function of temperature, due to the simultaneous contributions of both thermal and chemical expansion. The electronic and ionic conductivities were measured as a function of temperature and oxygen partial pressure. Within the range from 10 to 15 at.% Pr, a drastic drop of the activation energy of the hole mobility and an abrupt increase of the hole conductivity at low temperature was observed. The behavior could be rationalized by a simple percolation model. Oxygen permeation fluxes through disk shaped samples fed with air on one side and N2 on the other side were also measured. The oxygen flux through Pr0.05Gd0.1Ce0.85O1.95-δ was higher than that for CGO by one order of magnitude owing to the enhanced electronic conductivity albeit the flux is still limited by the electronic conductivity. In terms of the electronic and ionic conductivity, the estimated maximum oxygen permeation flux of a 10 μm Pr0.4Gd0.1Ce0.9O1.95-δ -based membrane exceeds 10 Nml cm−2 min−1 at 900°C under a small oxygen potential gradient (0.21/10−3 bar) which is promising for use in oxygen production and in oxy-fuel combustion. Also the material may be well applicable to SOFC/SOEC composite electrodes where mixed conductivity is also desirable.

Lip-reading models have been significantly improved recently thanks to powerful deep learning architectures. However, most works focused on frontal or near frontal views of the mouth. As a consequence, lip-reading performance seriously deteriorates in non-frontal mouth views. In this work, we present a framework for training pose-invariant lip-reading models on synthetic data instead of collecting and annotating non-frontal data which is costly and tedious. The proposed model significantly outperforms previous approaches on non-frontal views while retaining the superior performance on frontal and near frontal mouth views. Specifically, we propose to use a 3D Morphable Model (3DMM) to augment LRW, an existing large-scale but mostly frontal dataset, by generating synthetic facial data in arbitrary poses. The newly derived dataset, is used to train a state-of-the-art neural network for lip-reading. We conducted a cross-database experiment for isolated word recognition on the LRS2 dataset, and reported an absolute improvement of 2.55%. The benefit of the proposed approach becomes clearer in extreme poses where an absolute improvement of up to 20.64% over the baseline is achieved., 6 pages, 2 figures

For real-time semantic video segmentation, most recent works utilised a dynamic framework with a key scheduler to make online key/non-key decisions. Some works used a fixed key scheduling policy, while others proposed adaptive key scheduling methods based on heuristic strategies, both of which may lead to suboptimal global performance. To overcome this limitation, we model the online key decision process in dynamic video segmentation as a deep reinforcement learning problem and learn an efficient and effective scheduling policy from expert information about decision history and from the process of maximising global return. Moreover, we study the application of dynamic video segmentation on face videos, a field that has not been investigated before. By evaluating on the 300VW dataset, we show that the performance of our reinforcement key scheduler outperforms that of various baselines in terms of both effective key selections and running speed. Further results on the Cityscapes dataset demonstrate that our proposed method can also generalise to other scenarios. To the best of our knowledge, this is the first work to use reinforcement learning for online key-frame decision in dynamic video segmentation, and also the first work on its application on face videos., CVPR 2020. 300VW with segmentation labels is available at: https://github.com/mapleandfire/300VW-Mask

Face tracking serves as the crucial initial step in mobile applications trying to analyse target faces over time in mobile settings. However, this problem has received little attention, mainly due to the scarcity of dedicated face tracking benchmarks. In this work, we introduce MobiFace, the first dataset for single face tracking in mobile situations. It consists of 80 unedited live-streaming mobile videos captured by 70 different smartphone users in fully unconstrained environments. Over $95K$ bounding boxes are manually labelled. The videos are carefully selected to cover typical smartphone usage. The videos are also annotated with 14 attributes, including 6 newly proposed attributes and 8 commonly seen in object tracking. 36 state-of-the-art trackers, including facial landmark trackers, generic object trackers and trackers that we have fine-tuned or improved, are evaluated. The results suggest that mobile face tracking cannot be solved through existing approaches. In addition, we show that fine-tuning on the MobiFace training data significantly boosts the performance of deep learning-based trackers, suggesting that MobiFace captures the unique characteristics of mobile face tracking. Our goal is to offer the community a diverse dataset to enable the design and evaluation of mobile face trackers. The dataset, annotations and the evaluation server will be on \url{https://mobiface.github.io/}., To appear on The 14th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2019)

Robust principal component analysis (RPCA) is a powerful method for learning low-rank feature representation of various visual data. However, for certain types as well as significant amount of error corruption, it fails to yield satisfactory results; a drawback that can be alleviated by exploiting domain-dependent prior knowledge or information. In this paper, we propose two models for the RPCA that take into account such side information, even in the presence of missing values. We apply this framework to the task of UV completion which is widely used in pose-invariant face recognition. Moreover, we construct a generative adversarial network (GAN) to extract side information as well as subspaces. These subspaces not only assist in the recovery but also speed up the process in case of large-scale data. We quantitatively and qualitatively evaluate the proposed approaches through both synthetic data and five real-world datasets to verify their effectiveness., arXiv admin note: text overlap with arXiv:1702.00648

Semantic segmentation of eyes has long been a vital pre-processing step in many biometric applications. Majority of the works focus only on high resolution eye images, while little has been done to segment the eyes from low quality images in the wild. However, this is a particularly interesting and meaningful topic, as eyes play a crucial role in conveying the emotional state and mental well-being of a person. In this work, we take two steps toward solving this problem: (1) We collect and annotate a challenging eye segmentation dataset containing 8882 eye patches from 4461 facial images of different resolutions, illumination conditions and head poses; (2) We develop a novel eye segmentation method, Shape Constrained Network (SCN), that incorporates shape prior into the segmentation network training procedure. Specifically, we learn the shape prior from our dataset using VAE-GAN, and leverage the pre-trained encoder and discriminator to regularise the training of SegNet. To improve the accuracy and quality of predicted masks, we replace the loss of SegNet with three new losses: Intersection-over-Union (IoU) loss, shape discriminator loss and shape embedding loss. Extensive experiments shows that our method outperforms state-of-the-art segmentation and landmark detection methods in terms of mean IoU (mIoU) accuracy and the quality of segmentation masks. The eye segmentation database is available at https://www.dropbox.com/s/yvveouvxsvti08x/Eye_Segmentation_Database.zip?dl=0., Comment: IEEE Winter Conference on Applications of Computer Vision (WACV 2020)