Oxidative stress mediated decrement of spinal endomorphin-2 contributes to lumbar disc herniation sciatica in rats.

Increasing evidence supported that oxidative stress induced by herniated lumbar disc played important role in the formation of lumbar disc herniation sciatica (LDHS), however, the neural mechanisms underlying LDHS need further clarification. Endomorphin-2 (EM2) is the endogenous ligand for mu-opioid receptor (MOR), and there is increasing evidence implicating the involvement of spinal EM2 in neuropathic pain. In this study, using an nucleus pulposus implantation induced LDHS rat model that displayed obvious mechanical allodynia, it was found that the expression of EM2 in dorsal root ganglion (DRG) and spinal cord was significantly decreased. It was further found that oxidative stress in DRG and spinal cord was significantly increased in LDHS rats, and the reduction of EM2 in DRG and spinal cord was determined by oxidative stress dominated increment of dipeptidylpeptidase IV activity. A systemic treatment with antioxidant could prevent the forming of mechanical allodynia in LDHS rats. In addition, MOR expression in DRG and spinal cord remained unchanged in LDHS rats. Intrathecal injection of MOR antagonist promoted pain behavior in LDHS rats, and the analgesic effect of intrathecal injection of EM2 was stronger than that of endomorphin-1 and morphine. Taken together, our findings suggest that oxidative stress mediated decrement of EM2 in DRG and spinal cord causes the loss of endogenous analgesic effects and enhances the pain sensation of LDHS.

SpeechMirror: A Multimodal Visual Analytics System for Personalized Reflection of Online Public Speaking Effectiveness.

As communications are increasingly taking place virtually, the ability to present well online is becoming an indispensable skill. Online speakers are facing unique challenges in engaging with remote audiences. However, there has been a lack of evidence-based analytical systems for people to comprehensively evaluate online speeches and further discover possibilities for improvement. This paper introduces SpeechMirror, a visual analytics system facilitating reflection on a speech based on insights from a collection of online speeches. The system estimates the impact of different speech techniques on effectiveness and applies them to a speech to give users awareness of the performance of speech techniques. A similarity recommendation approach based on speech factors or script content supports guided exploration to expand knowledge of presentation evidence and accelerate the discovery of speech delivery possibilities. SpeechMirror provides intuitive visualizations and interactions for users to understand speech factors. Among them, SpeechTwin, a novel multimodal visual summary of speech, supports rapid understanding of critical speech factors and comparison of different speech samples, and SpeechPlayer augments the speech video by integrating visualization of the speaker's body language with interaction, for focused analysis. The system utilizes visualizations suited to the distinct nature of different speech factors for user comprehension. The proposed system and visualization techniques were evaluated with domain experts and amateurs, demonstrating usability for users with low visualization literacy and its efficacy in assisting users to develop insights for potential improvement.

Fine-Grained Video Retrieval With Scene Sketches.

Benefiting from the intuitiveness and naturalness of sketch interaction, sketch-based video retrieval (SBVR) has received considerable attention in the video retrieval research area. However, most existing SBVR research still lacks the capability of accurate video retrieval with fine-grained scene content. To address this problem, in this paper we investigate a new task, which focuses on retrieving the target video by utilizing a fine-grained storyboard sketch depicting the scene layout and major foreground instances' visual characteristics (e.g., appearance, size, pose, etc.) of video; we call such a task "fine-grained scene-level SBVR". The most challenging issue in this task is how to perform scene-level cross-modal alignment between sketch and video. Our solution consists of two parts. First, we construct a scene-level sketch-video dataset called SketchVideo, in which sketch-video pairs are provided and each pair contains a clip-level storyboard sketch and several keyframe sketches (corresponding to video frames). Second, we propose a novel deep learning architecture called Sketch Query Graph Convolutional Network (SQ-GCN). In SQ-GCN, we first adaptively sample the video frames to improve video encoding efficiency, and then construct appearance and category graphs to jointly model visual and semantic alignment between sketch and video. Experiments show that our fine-grained scene-level SBVR framework with SQ-GCN architecture outperforms the state-of-the-art fine-grained retrieval methods. The SketchVideo dataset and SQ-GCN code are available in the project webpage https://iscas-mmsketch.github.io/FG-SL-SBVR/.

Vascular features around the optic disc in familial exudative vitreoretinopathy: findings and their relationship to disease severity.

BACKGROUND: Familial exudative vitreoretinopathy (FEVR) is a rare congenital disorder of retinal vascular development. We aimed to study the vascular characteristics around the optic disc in neonates with FEVR and the relationship with disease severity. METHODS: A retrospective, case-control study including 43 (58 eyes) newborn patients with FEVR at stages 1 to 3 and 30 (53 eyes) age-matched normal full-term newborns was conducted. The peripapillary vessel tortuosity (VT), vessel width (VW) and vessel density (VD) were quantified by computer technology. The t-distributed stochastic neighbor embedding (t-SNE) algorithm was used to visualize the relationship between the severity of FEVR and the characteristics of perioptic disc vascular parameters. RESULTS: The peripapillary VT, VW and VD were significantly increased in the FEVR group compared with the control group (P < 0.05). Subgroup analysis showed that VW and VD increased significantly with progressing FEVR stage (P < 0.05). And only VT in stage 3 FEVR was significantly increased compared with stage 1 and stage 2 (P < 0.05). After controlling the confounders, ordinal logistic regression analysis indicated that the VW (aOR: 1.75, P = 0.0002) and VD (aOR: 2.41, P = 0.0170) were significantly independent correlated with the FEVR stage, but VT (aOR: 1.07, P = 0.5454) was not correlated with FEVR staging. Visual analysis based on the t-SNE algorithm showed that peri-optic disc vascular parameters had a continuity along the direction of FEVR severity. CONCLUSIONS: In the neonatal population, there were significant differences in peripapillary vascular parameters between patients with FEVR and normal subjects. Quantitative measurement of vascular parameters around the optic disc can be used as one of the indicators to assess the severity of FEVR.

SceneSketcher-v2: Fine-Grained Scene-Level Sketch-Based Image Retrieval Using Adaptive GCNs.

Sketch-based image retrieval (SBIR) is a long-standing research topic in computer vision. Existing methods mainly focus on category-level or instance-level image retrieval. This paper investigates the fine-grained scene-level SBIR problem where a free-hand sketch depicting a scene is used to retrieve desired images. This problem is useful yet challenging mainly because of two entangled facts: 1) achieving an effective representation of the input query data and scene-level images is difficult as it requires to model the information across multiple modalities such as object layout, relative size and visual appearances, and 2) there is a great domain gap between the query sketch input and target images. We present SceneSketcher-v2, a Graph Convolutional Network (GCN) based architecture to address these challenges. SceneSketcher-v2 employs a carefully designed graph convolution network to fuse the multi-modality information in the query sketch and target images and uses a triplet training process and end-to-end training manner to alleviate the domain gap. Extensive experiments demonstrate SceneSketcher-v2 outperforms state-of-the-art scene-level SBIR models with a significant margin.

E-ffective: A Visual Analytic System for Exploring the Emotion and Effectiveness of Inspirational Speeches.

What makes speeches effective has long been a subject for debate, and until today there is broad controversy among public speaking experts about what factors make a speech effective as well as the roles of these factors in speeches. Moreover, there is a lack of quantitative analysis methods to help understand effective speaking strategies. In this paper, we propose E-ffective, a visual analytic system allowing speaking experts and novices to analyze both the role of speech factors and their contribution in effective speeches. From interviews with domain experts and investigating existing literature, we identified important factors to consider in inspirational speeches. We obtained the generated factors from multi-modal data that were then related to effectiveness data. Our system supports rapid understanding of critical factors in inspirational speeches, including the influence of emotions by means of novel visualization methods and interaction. Two novel visualizations include E-spiral (that shows the emotional shifts in speeches in a visually compact way) and E-script (that connects speech content with key speech delivery information). In our evaluation we studied the influence of our system on experts' domain knowledge about speech factors. We further studied the usability of the system by speaking novices and experts on assisting analysis of inspirational speech effectiveness.

Weakly Supervised Learning for Single Depth-Based Hand Shape Recovery.

Recent emerging technologies such AR/VR and HCI are drawing high demand on more comprehensive hand shape understanding, requiring not only 3D hand skeleton pose but also hand shape geometry. In this paper, we propose a deep learning framework to produce 3D hand shape from a single depth image. To address the challenge that capturing ground truth 3D hand shape in the training dataset is non-trivial, we leverage synthetic data to construct a statistical hand shape model and adopt weak supervision from widely accessible hand skeleton pose annotation. To bridge the gap due to the different hand skeleton definitions in the existing public datasets, we propose a joint regression network for hand pose adaptation. To reconstruct the hand shape, we use Chamfer loss between the predicted hand shape and the point cloud from the input depth to learn the shape reconstruction model in a weakly-supervised manner. Experiments demonstrate that our model adapts well to the real data and produces accurate hand shapes that outperform the state-of-the-art methods both qualitatively and quantitatively.

microRNA-221 restricts human cytomegalovirus replication via promoting type I IFN production by targeting SOCS1/NF-κB pathway.

HCMV is a common pathogen for human with relatively high prevalence, which could be life-threatened in immunodeficient patients and lead to significant birth defects in newborns. In this study, we firstly report that HCMV infection significantly enhances the expression of microRNA-221 (miR-221) in Neural Precursor Cells (NPCs). We found that miR-221 directly targets at the 3'-UTR of suppressor of cytokine signaling 1 (SOCS1) and suppresses SOCS1 expression at the both mRNA and protein levels. MiR-221 overexpression restrained HCMV replication by promoting type I interferon (IFN) and interferon stimulating genes (ISGs) production, whereas reintroduction of SOCS1 abrogated the miR-221-induced effects on HCMV replication. Importantly, miR-221 positively regulated the phosphorylation and activation of NF-κB by suppressing SOCS1. What's more, miR-221 agomir alleviated MCMV-induced tissue injury by promoting type I IFN antiviral activities in vivo. Thus, miR-221 modulates the infection and replication of HCMV as an intrinsic antiviral factor, and could be developed as a treatment target for anti-HCMV treatment.

Study on the wound healing, anti-inflammation and anti-bacterial activities of Jinjianling cream: A Chinese herbal compound.

The aim of the present study was the healing effect and anti-inflammatory effect of Jinjianling cream on skin lesions and to investigate the antibacterial activity in vitro, which proved that the preparation is safe and effective. The mouse scald model was established to observe the wound healing time and wound healing rate of mice, serum levels of TNF-α and IL-1 were measured by the ELISA method. The model of eczema in mice was induced by DNCB, and the degree of ear swelling in mice was calculated. The hematoxylin-eosin (HE) staining was used to make pathological sections and count inflammatory cells, and the change of serum IL-2 level was determined by the ELISA method. The bacteriostasis rate was determined by pour plate method, the diameter of inhibition zone (DIZ) was determined by filter paper diffusion method and the minimum inhibitory concentration (MIC) was determined by double dilution method. After treatment, the effect of Jinjianling cream groups on the healing of damaged skin in scalded mice was significant. The serum levels of TNF-a and IL-1 decreased, which were lower than those in the model group (p<0.05, p<0.01). In the mouse eczema model, the degree of ear swelling improved significantly, serum IL-2 level was decreased, and inflammatory cell count was significantly than the model group (p<0.05, p<0.01). The results of antibacterial experiments showed that bacteriostasis rate was positively correlated with drug concentration. DIZ values of bacteriostatic circle on Staphylococcus aureus and Escherichia coli were 17.25mm and 25.62mm. Moreover, the MIC values of two kinds of bacteria all were 64µg/mL. Jinjianling cream can promote the healing ability of damaged skin and reduce inflammation of the wound. It also has a strong inhibitory effect on wound pathogenic bacteria, can significantly improve wound healing and effectively treat dermatitis, eczema and other skin diseases.

Excavating Anticonvulsant Compounds from Prescriptions of Traditional Chinese Medicine in the Treatment of Epilepsy.

Traditional Chinese medicine (TCM) has a long history and been widely used in prevention and treatment of epilepsy in China. This paper is intended to review the advances in the active anticonvulsant compounds isolated from herbs in the prescription of TCM in the treatment of epilepsy. These compounds were introduced with the details including classification, CAS number specific structure and druggability data. Meanwhile, much of the research in these compounds in the last two decades has shown that they exhibited favorable pharmacological properties in treatment of epilepsy both in in vivo and in vitro models. In addition, in this present review, the evaluation of the effects of the anticonvulsant classical TCM prescriptions is discussed. According to these rewarding pharmacological effects and chemical substances, the prescription of TCM herbs could be an effective therapeutic strategy for epilepsy patients, and also could be a promising source for the development of new drugs.

A Visual Analytics Approach for Station-Based Air Quality Data.

With the deployment of multi-modality and large-scale sensor networks for monitoring air quality, we are now able to collect large and multi-dimensional spatio-temporal datasets. For these sensed data, we present a comprehensive visual analysis approach for air quality analysis. This approach integrates several visual methods, such as map-based views, calendar views, and trends views, to assist the analysis. Among those visual methods, map-based visual methods are used to display the locations of interest, and the calendar and the trends views are used to discover the linear and periodical patterns. The system also provides various interaction tools to combine the map-based visualization, trends view, calendar view and multi-dimensional view. In addition, we propose a self-adaptive calendar-based controller that can flexibly adapt the changes of data size and granularity in trends view. Such a visual analytics system would facilitate big-data analysis in real applications, especially for decision making support.

Co-loaded proteinase K/thyme oil liposomes for inactivation of Escherichia coli O157:H7 biofilms on cucumber.

E. coli O157:H7 is a bacterium frequently found on vegetable surfaces, such as cucumber, that can pose a significant threat to consumers. In the present work, proteinase K (PK) and thyme oil (TO) were used to inactivate E. coli O157:H7 biofilms. Both PK and TO were encapsulated in liposomes to improve their chemical stability and to allow for a controlled release. The optimal PK/TO-loaded liposomes (particle size of 170.4 nm, polydispersity index of 0.309, zeta potential of -29.8 mV and entrapment efficiency of 33.2%) were engineered. The antibacterial activities of the PK/TO liposomes against E. coli O157:H7 biofilms in vitro and on the cucumber were observed. Compared to free PK, free TO, and free PK/TO and TO liposome treatments, PK/TO-liposomes exhibited a higher antibiofilm activity and longer action duration. Specifically, the counts of viable E. coli O157:H7 on cucumbers were reduced by 1.23, 2.32 and 2.44 Logs after treatment with 400 mg mL-1 of PK/TO liposomes for 3 d, at 5, 15 and 25 °C, respectively. Colorimetric analysis and sensory tests showed that the PK/TO-liposome treatment had almost no impact on food quality after 1 d.

Graphene sheets, polyaniline and AuNPs based DNA sensor for electrochemical determination of BCR/ABL fusion gene with functional hairpin probe.

A sensitive and selective electrochemical DNA sensor was developed for the detection of BCR/ABL fusion gene in chronic myelogenous leukemia (CML). Firstly, graphene sheets (GS) suspension was prepared with the aid of chitosan (CS) solution and then fabricated onto the glassy carbon electrode (GCE), followed by the electro-polymerization of aniline to form the PANI layer, then, Au nanoparticles (AuNPs) were electro-deposited onto the modified GCE to immobilize the capture probes. The capture probe employed a hairpin structure and dually labeled with a 5'-SH and a 3'-biotin. After hybridization with the target DNA, hairpin structure was compelled to open and 3'-biotin was forced to stay away from the electrode surface. As a result, streptavidin-alkaline phosphatase (SA-AP) was covalently binded to the capture probe via biotin-avidin system. Reduction currents were then generated after catalyzing the hydrolysis of the electroinactive 1-naphthyl phosphate (1-NP) to 1-naphthol and monitored by differential pulse voltammetry (DPV). Under optimum conditions, the amperometric signals increased linearly with the target DNA concentrations (10 pM to 1000 pM), and the DNA sensor exhibited a detection limit as low as 2.11 pM (S/N=3) with an excellent differentiation ability, and the proposed method showed acceptable stability and reproducibility. It has been applied for assay of BCR/ABL fusion gene from real samples with satisfactory results.

MultisHRP-DNA-coated CMWNTs as signal labels for an ultrasensitive hepatitis C virus core antigen electrochemical immunosensor.

An ultrasensitive and selective electrochemical immunosensor was developed for the detection of hepatitis C virus (HCV) core antigen. The immunosensor consists of graphitized mesoporous carbon-methylene blue (GMCs-MB) nanocomposite as an electrode modified material and a horseradish peroxidase-DNA-coated carboxyl multi-wall carbon nanotubes (CMWNTs) as a secondary antibody layer. After modification of the electrode with GMCs-MB nanocomposite, Au nanoparticles were electrodeposited on to the electrode to immobilize the captured antibodies. The bridging probe and secondary antibodies linked to the CMWNTs, and DNA concatamers were obtained by hybridization of the biotin-tagged signal and auxiliary probes. Finally, streptavidin-horseradish peroxidases (HRP) were labeled on the secondary antibody layer via biotin-streptavidin system. The reduction current of MB were generated in the presence of hydrogen peroxide and monitored by square wave voltammetry. Under optimum conditions, the amperometric signal increased linearly with the core antigen concentration (0.25pgmL(-1) to 300pgmL(-1)). The immunosensor exhibites the detection limit as low as 0.01pgmL(-1) and it has a high selectivity. The new protocol showed acceptable stability and reproducibility, as well as favorable recovery for HCV core antigen in human serum. The proposed immunosensor has great potential for clinical applications.

Ultrasensitive electrochemical immunosensor for HE4 based on rolling circle amplification.

A novel electrochemical immunoassay system for the detection of human epididymis-specific protein 4 (HE4) was developed. A chitosan-titanium carbide (TiC) nanocomposition film was first electrodeposited onto a tin-doped indium oxide (ITO) electrode at a constant potential. Gold (Au) nanoparticles were then electrodeposited on the surface of the chitosan-TiC film by cyclic voltammetry (CV). The capture antibody (anti-HE4) was adsorbed onto the Au and TiC nanoparticles. After a specific sandwich immunoreaction among the capture antibody, HE4, and biotinylated secondary antibody, biotinylated primer DNA was immobilized on the secondary antibody by biotin-streptavidin system. Appropriate amounts of circular template DNA and biotinylated primer DNA were used for rolling circle amplification (RCA) under optimal conditions. The RCA products provided a large number of sites to link DNA detection probes. Doxorubicin hydrochloride intercalated the CG-GC steps between the RCA products and the DNA detection probes, which was monitored by differential pulse voltammetry (DPV) based on the current signal of doxorubicin hydrochloride. With the above-mentioned amplification factors, the current responded to HE4 linearly in the concentration range of 3-300 pM under optimal detection conditions, with a detection limit of 0.06 pM. Stepwise changes in the microscopic features of the surfaces and electrochemical properties upon the formation of each layer were confirmed by scanning electron microscopy (SEM), atomic force microscopy (AFM), and electrochemical impedance spectroscopy (EIS). This system was successfully employed for the detection of HE4 with good accuracy and renewable ability.