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Hearing impairment is considered to be related to the damage of hair cells or synaptic terminals, which will cause varying degrees of hearing loss. Numerous studies have shown that cochlear implants can balance this damage. The human ear receives external acoustic signals mostly under complex conditions, and its biophysical mechanisms have important significance for reference in the design of cochlear implants. However, the relevant biophysical mechanisms have not yet been fully determined. Using the characteristics of special acoustoelectric conversion in piezoelectric ceramics, this paper integrates them into the traditional FitzHugh–Nagumo neuron circuit and proposes a comprehensive model with coupled auditory neurons. The model comprehensively considers the effects of synaptic coupling between neurons, information transmission delay, external noise stimulation, and internal chaotic current stimulation on the synchronization of membrane potential signals of two auditory neurons. The experimental results show that coupling strength, delay size, noise intensity, and chaotic current intensity all have a certain regulatory effect on synchronization stability. In particular, when auditory neurons are in a chaotic state, their impact on synchronization stability is sensitive. Numerical results provide a reference for exploring the biophysical mechanisms of auditory neurons. At the same time, we are committed to providing assistance in using sensors to monitor signals and repair hearing impairments. [ABSTRACT FROM AUTHOR]

Electrocatalysts with optimal efficiency and durability for the oxygen evolution reaction (OER) are becoming increasingly important as the demand for alkaline water/seawater electrolysis technology grows. Herein, a novel rose-shaped NiFe-layered double hydroxide (LDH)/NiCo2O4 composed of amorphous wrinkled NiFe-LDH and highly crystalline NiCo2O4 was synthesized with rich heterointerfaces. Many unsaturated metal sites are generated due to significant charge reconstruction at the heterointerface between the crystalline and amorphous phases. These metal sites could trigger and provide more active sites. The density functional theory (DFT) reveals that a new charge transfer channel (Co-Fe) was formed at the heterointerface between NiFe-LDH as electron acceptor and NiCo2O4 as electron donor. The new charge transfer channel boosts interfacial charge transfer and enhances catalytic efficiency. The NiFe-LDH/NiCo2O4/nickel foam (NF) drives current densities of 10 and 100 mA·cm−2 with overpotentials of 193 and 236 mV, respectively. The composite electrode demonstrates a fast turnover frequency (0.0143 s−1) at 1.45 V vs. RHE (RHE = reversible hydrogen electrode), which is 5.5 times greater than pure NiCo2O4, suggesting its superior intrinsic activity. Additionally, NiFe-LDH/NiCo2O4/NF electrode exhibited negligible degradation after 150 h of uninterrupted running in alkaline seawater oxidation. This study introduces a method for preparing high-efficiency electrocatalysts utilized in alkaline water/seawater electrolysis. [ABSTRACT FROM AUTHOR]

In this paper, we propose a correlation-aware probabilistic data summarization technique to efficiently analyze and visualize large-scale multi-block volume data generated by massively parallel scientific simulations. The core of our technique is correlation modeling of distribution representations of adjacent data blocks using copula functions and accurate data value estimation by combining numerical information, spatial location, and correlation distribution using Bayes' rule. This effectively preserves statistical properties without merging data blocks in different parallel computing nodes and repartitioning them, thus significantly reducing the computational cost. Furthermore, this enables reconstruction of the original data more accurately than existing methods. We demonstrate the effectiveness of our technique using six datasets, with the largest having one billion grid points. The experimental results show that our approach reduces the data storage cost by approximately one order of magnitude compared to state-of-the-art methods while providing a higher reconstruction accuracy at a lower computational cost. [ABSTRACT FROM AUTHOR]

We present angle-uniform parallel coordinates, a data-independent technique that deforms the image plane of parallel coordinates so that the angles of linear relationships between two variables are linearly mapped along the horizontal axis of the parallel coordinates plot. Despite being a common method for visualizing multidimensional data, parallel coordinates are ineffective for revealing positive correlations since the associated parallel coordinates points of such structures may be located at infinity in the image plane and the asymmetric encoding of negative and positive correlations may lead to unreliable estimations. To address this issue, we introduce a transformation that bounds all points horizontally using an angle-uniform mapping and shrinks them vertically in a structure-preserving fashion; polygonal lines become smooth curves and a symmetric representation of data correlations is achieved. We further propose a combined subsampling and density visualization approach to reduce visual clutter caused by overdrawing. Our method enables accurate visual pattern interpretation of data correlations, and its data-independent nature makes it applicable to all multidimensional datasets. The usefulness of our method is demonstrated using examples of synthetic and real-world datasets. [ABSTRACT FROM AUTHOR]

Searching for text in a document is a day-to-day task supported by a variety of visual interface techniques. Since the target searching word may be scattered across the document, manually checking each word is exhaustive and time-consuming, which calls for effective visualization of the search results to enable efficient visual document analysis. In this paper, we revisit the problem of visualizing word search results and formulate two design goals: minimizing the number of user interactions and optimizing the on-screen information of search results. Two visualization techniques are introduced accordingly: a structured list that integrates search results with the hierarchical document structure and full sentence snippets to provide more complete local context for each matched result. We then conduct a user study to compare these techniques with some most commonly used visual interfaces. The results show that the structured list is preferred by most participants and leads to more efficient search, whereas the full sentence snippet results in a slight improvement in accuracy and efficiency, but is still appreciated by the participants. [ABSTRACT FROM AUTHOR]

A common enhancement of scatterplots represents points as small multiples, glyphs, or thumbnail images. As this encoding often results in overlaps, a general strategy is to alter the position of the data points, for instance, to a grid-like structure. Previous approaches rely on solving expensive optimization problems or on dividing the space that alter the global structure of the scatterplot. To find a good balance between efficiency and neighborhood and layout preservation, we propose Hagrid, a technique that uses space-filling curves (SFCs) to "gridify" a scatterplot without employing expensive collision detection and handling mechanisms. Using SFCs ensures that the points are plotted close to their original position, retaining approximately the same global structure. The resulting scatterplot is mapped onto a rectangular or hexagonal grid, using Hilbert and Gosper curves. We discuss and evaluate the theoretic runtime of our approach and quantitatively compare our approach to three state-of-the-art gridifying approaches, DGrid, Small multiples with gaps SMWG, and CorrelatedMultiples CMDS, in an evaluation comprising 339 scatterplots. Here, we compute several quality measures for neighborhood preservation together with an analysis of the actual runtimes. The main results show that, compared to the best other technique, Hagrid is faster by a factor of four, while achieving similar or even better quality of the gridified layout. Due to its computational efficiency, our approach also allows novel applications of gridifying approaches in interactive settings, such as removing local overlap upon hovering over a scatterplot. [ABSTRACT FROM AUTHOR]

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In the field of evolutionary genome analysis, biologists seek to identify important genes or chromosome regions by comparing phylogenetic trees and analyzing the mutation at which locus might affect phenotypic traits. Unfortunately, the tree comparison and accompanying analysis are often performed manually. In this paper, we characterize the workflow of evolutionary genome analysis and present a task analysis for the fundamental questions asked by biologists during the analysis procedure. We propose two algorithms to enable quantitative tree comparison. One is to measure the differences between corresponding leaf nodes on two trees, and the other is to compute the classification inconsistency of each leaf node by comparing tree structure with a given biological classification. Configuring with the obtained difference and inconsistency, we present a visual analysis system, visual comparison of phylogenetic trees for evolutionary genome analysis, which not only enables biologists to intuitively explore trees but also identify locus which affects their traits by comparing SNP variants of selected leaf nodes. We conclude with case studies from two biologists who used our system to augment their previous manual analysis workflow and demonstrate that our system can reveal more insight. [ABSTRACT FROM AUTHOR]

Exploring relationships in complex datasets is one of the challenges in today's big data era. The graph-based visualization approach, which integrates the advantages of graph analysis theory and visualization technologies and combines machine and human intelligence, has become an effective means for analyzing various relationships in complex datasets. In this paper, we first introduce a graph-based visual analytics model for associated data. Then, we summarize seven typical visualization methods for associated data according to their layout features, including their node-link diagram, adjacency matrix, hypergraph, flow diagram, graphs with geospatial information, multi-attribute graph, and space-filling diagram and discuss their advantages and disadvantages. We describe current graph simplification and interaction techniques, including graph filtering, node clustering, edge bundling, graph data dimension reduction, and topology-based graph transformation. Finally, we discuss the potential challenges and developmental trends of the research direction. [ABSTRACT FROM AUTHOR]

Abstract: Haze is a hazardous atmospheric phenomenon that threatens human health and leads to severe economic problems. A number of weather factors are relevant to the emergence and evolvement of haze. In this paper, we present a visual analytics system for haze study, including its evolution and correlations to a number of weather factors. Specifically, we introduce a haze event detection algorithm based on common haze identification rules in meteorology using the PM2.5 concentration data. We develop a comparative visualization to consistently overview trends of scalar variables and wind directions, in which wind patterns are extracted via clustering streamlines at user-given sampling time. To study the correlation between wind and PM2.5, we decompose time steps into time intervals according to the temporal similarity of streamlines. Additionally, we develop a 1D function dissimilarity measurement to study the temporal correlation between PM2.5 concentrations and relevant weather factors, such as wind strength, relatively humidity and planetary boundary layer. Furthermore, we employ particle advection using pathline computation within the wind field to locate the origins and destinations of particles seeded in user-interested areas. We applied our system to study of a number of hazes occurring in January 2013 in Beijing, (China). Interpretations and evaluations from domain experts demonstrate the effectiveness of our system in facilitating haze study.Graphical abstract: [ABSTRACT FROM AUTHOR]

Abstract: Much effort has been made on multidimensional transfer function, which is designed for effective exploration of 3D scalar datasets. But now, existing solution for designing transfer function typically focuses on exploring volume independently without any prior knowledge. It remains, however, a big challenge for us to reuse the explored knowledge, experience and results in scientific visualization. In this paper, we present a novel technique that employs an analogy-based approach. It aims to facilitate automatic volume exploration for multiple datasets which may share common context or features. The kernel of our approach is using the template scheme. With the introduction of the Gaussian Mixture Model, we adopt this new scheme to modeling, designing and transferring—they are processed in the data histogram space. Then, we integrate this scheme into two-dimensional transfer function design. The result shows that the interesting features can easily be captured with little user workload after adopting our approach.Graphical Abstract: [ABSTRACT FROM AUTHOR]

In human esophageal squamous cell carcinoma (ESCC), miR-34a was downregulated and could inhibit in vitro cell proliferation and migration. However, the underlying mechanism was not clear yet. The expression levels of mRNA and protein were detected by quantitative real-time PCR or western blotting, respectively. MiR-34a was knocked down or overexpressed and transfected into human ESCC cell lines ECA109 and TE-13, respectively. Cell migration and wound healing assays were used to examine the effect on migration and invasion in vitro. Animal models were used to examine the role of miR-34a in metastasis in vivo. Luciferase assay was carried out to validate the potential target of miR-34a. CD44 was upregulated and miR-34a was downregulated in ESCC tissues and cell lines. The linear regression analysis showed that CD44 expression was negatively correlated with the level of miR-34a. Luciferase assay showed that miR-34a interacted with a putative binding site in the CD44 3′UTR. MiR-34a was found to negatively regulate the expression of CD44. In vitro experiment showed that miR-34a overexpression inhibited ESCC cell invasion and migration; whereas miR-34a knockdown showed reversed results. MiR-34a also inhibited esophagus tumor growth and metastasis in vivo; whereas miR-34a knockdown showed reversed results. Finally, we found that CD44 knockdown reversed the effects of miR-34a knockdown on ESCC cell invasion and migration in vitro. MiRNA-34a suppresses invasion and metastatic in ESCC by regulating CD44. [ABSTRACT FROM AUTHOR]

This paper proposes spatiotemporal volume saliency to detect and explore salient regions in time-varying volume data. Based on the center-surround hypothesis that the salient region stands out from its surroundings, we extend the spatial saliency to time domain and introduce temporal volume saliency. It is defined as a center-surround operator on Gaussian-weighted mean attribute gradient between steps in a scale-independent manner. By combing spatial saliency and temporal saliency together, our spatiotemporal volume saliency is effective in detecting changes of salient regions. We demonstrate its utility in this regard by automating transfer function design and selecting key frames for time-varying volume data. Graphical abstract: [Figure not available: see fulltext.] [ABSTRACT FROM AUTHOR]

The current multicomponent volume segmentation and labeling methods are mostly hard to get correct segmentation and labeling results automatically and rely hardly on experts' aids, which make related volume exploration to be time consuming, laborious and prone to errors and omissions. To solve this problem, we present a novel volume exploration method driven by admitted model. We first apply Gaussian mixture models to segment the raw volume. However, different components with similar value are still mixed. To segment these components further, we make use of region-grown principle to produce a fine-grained part segmentation. To label different parts automatically, we found that it is helpful to take advantage of annotated model, like human anatomy model (PlasticboyCC, , ). However, it is not straightforward to label segmented volume with geometric model automatically. Inspired by electors voting (Au et al., Comput Graph Forum 29:645-654, ), we propose a volume-model correspondence schema to overcome this intractable challenge. Moreover, it is essential to exploit intuitive interactive methods for interactive exploration, so we also developed practical precise interaction techniques to assist volume exploration. Our experiments with various data and discussion with specialists show that our method provides an efficient and impactful way to explore volume data. [ABSTRACT FROM AUTHOR]

Injury of p16 has been implicated in some cancers. In this paper, we focus on the need for identification of peroxynitrite-dependent nitration sites on p16 with HPLC-MS/MS method. Two mono-nitrated residues Tyr129 and Tyr44 were detected in the course of p16 modification induced by peroxynitrite at relative low doses. As suggested by peptide mapping sequence analysis, Tyr44 was more liable to be nitrated by ONOO. Study on the chemical environment of two Tyr residues reveals that steric hindrance may be the structural determinant for the nitration sequence. Through technique of SDS-PAGE, ONOO could induce p16 nitration, even strongly damage the combination of p16 with CDK4, which further influence p16's activity. [ABSTRACT FROM AUTHOR]

Using the fixed point theorem, we study the existence and multiplicity of positive periodic solutions for the second order differential equations {x¨+a(t)x=f(x),x(0)=x(T),x˙(0)=x˙(T). For given nonnegative constants 0<β1<β2<⋯<βN, the function f may be singular at x=βi. [ABSTRACT FROM AUTHOR]

A highly photoactive nitrogen-deficient g-C3N4 photocatalyst with porous frame work has been prepared by a simple, effective, and environmental-friendly method of ethanol thermal treatment. It is found that the as-synthesized catalyst exhibits an enhanced visible light photocatalytic activity for H2 evolution. The H2-evolution rate on the treated g-C3N4 is 4.2 times higher than the untreated-C3N4. The treated g-C3N4 and untreated g-C3N4 are characterized by Fourier transform-infrared spectra (FTIR), X-ray diffraction (XRD), scanning electron microscope (SEM), transmission electron microscope (TEM), X-ray photoelectron spectroscopy (XPS), photoluminescence (PL), UV-Vis absorption (UV-Vis), photocurrent measurement, and electrochemical impedance spectroscopy (EIS). Characterization results show that the ethanol thermal treatment induces nitrogen vacancies and hierarchical porous structure in g-C3N4. Such a structural reconstruction is beneficial for improving optical absorption and hindering photoinduced charger recombination on g-C3N4, resulting in the enhanced photocatalytic activity. This work would offer a reference route to develop high-performance metal-free photocatalysts.Schematic of the effect on the structure of g-C3N4 by the ethanol thermal treatment [ABSTRACT FROM AUTHOR]