Light-emitting diodes enhanced by localized surface plasmon resonance

Light-emitting diodes [LEDs] are of particular interest recently as their performance is approaching fluorescent/incandescent tubes. Moreover, their energy-saving property is attracting many researchers because of the huge energy crisis we are facing. Among all methods intending to enhance the efficiency and intensity of a conventional LED, localized surface plasmon resonance is a promising way. The mechanism is based on the energy coupling effect between the emitted photons from the semiconductor and metallic nanoparticles fabricated by nanotechnology. In this review, we describe the mechanism of this coupling effect and summarize the common fabrication techniques. The prospect, including the potential to replace fluorescent/incandescent lighting devices as well as applications to flat panel displays and optoelectronics, and future challenges with regard to the design of metallic nanostructures and fabrication techniques are discussed

Multi-Classifier Interactive Learning for Ambiguous Speech Emotion Recognition

In recent years, speech emotion recognition technology is of great significance in industrial applications such as call centers, social robots and health care. The combination of speech recognition and speech emotion recognition can improve the feedback efficiency and the quality of service. Thus, the speech emotion recognition has been attracted much attention in both industry and academic. Since emotions existing in an entire utterance may have varied probabilities, speech emotion is likely to be ambiguous, which poses great challenges to recognition tasks. However, previous studies commonly assigned a single-label or multi-label to each utterance in certain. Therefore, their algorithms result in low accuracies because of the inappropriate representation. Inspired by the optimally interacting theory, we address the ambiguous speech emotions by proposing a novel multi-classifier interactive learning (MCIL) method. In MCIL, multiple different classifiers first mimic several individuals, who have inconsistent cognitions of ambiguous emotions, and construct new ambiguous labels (the emotion probability distribution). Then, they are retrained with the new labels to interact with their cognitions. This procedure enables each classifier to learn better representations of ambiguous data from others, and further improves the recognition ability. The experiments on three benchmark corpora (MAS, IEMOCAP, and FAU-AIBO) demonstrate that MCIL does not only improve each classifier's performance, but also raises their recognition consistency from moderate to substantial.Comment: 10 pages, 4 figure

Analysis of Time Series Gene Expression and DNA Methylation Reveals the Molecular Features of Myocardial Infarction Progression

Myocardial infarction (MI) is one of the deadliest diseases in the world, and the changes at the molecular level after MI and the DNA methylation features are not clear. Understanding the molecular characteristics of the early stages of MI is of significance for the treatment of the disease. In this study, RNA-seq and MeDIP-seq were performed on heart tissue from mouse models at multiple time points (0 h, 10 min, 1, 6, 24, and 72 h) to explore genetic and epigenetic features that influence MI progression. Analysis based on a single point in time, the number of differentially expressed genes (DEGs) and differentially methylated regions (DMRs) increased with the time of myocardial infarction, using 0 h as a control group. Moreover, within 10 min of MI onset, the cells are mainly in immune response, and as the duration of MI increases, apoptosis begins to occur. Analysis based on time series data, the expression of 1012 genes was specifically downregulated, and these genes were associated with energy metabolism. The expression of 5806 genes was specifically upregulated, and these genes were associated with immune regulation, inflammation and apoptosis. Fourteen transcription factors were identified in the genes involved in apoptosis and inflammation, which may be potential drug targets. Analysis based on MeDIP-seq combined with RNA-seq methodology, focused on methylation at the promoter region. GO revealed that the downregulated genes with hypermethylation at 72 h were enriched in biological processes such as cardiac muscle contraction. In addition, the upregulated genes with hypomethylation at 72 h were enriched in biological processes, such as cell-cell adhesion, regulation of the apoptotic signaling pathway and regulation of angiogenesis. Among these genes, the Tnni3 gene was also present in the downregulated model. Hypermethylation of Tnni3 at 72 h after MI may be an important cause of exacerbation of MI

Direct single-molecule dynamic detection of chemical reactions.

Single-molecule detection can reveal time trajectories and reaction pathways of individual intermediates/transition states in chemical reactions and biological processes, which is of fundamental importance to elucidate their intrinsic mechanisms. We present a reliable, label-free single-molecule approach that allows us to directly explore the dynamic process of basic chemical reactions at the single-event level by using stable graphene-molecule single-molecule junctions. These junctions are constructed by covalently connecting a single molecule with a 9-fluorenone center to nanogapped graphene electrodes. For the first time, real-time single-molecule electrical measurements unambiguously show reproducible large-amplitude two-level fluctuations that are highly dependent on solvent environments in a nucleophilic addition reaction of hydroxylamine to a carbonyl group. Both theoretical simulations and ensemble experiments prove that this observation originates from the reversible transition between the reactant and a new intermediate state within a time scale of a few microseconds. These investigations open up a new route that is able to be immediately applied to probe fast single-molecule physics or biophysics with high time resolution, making an important contribution to broad fields beyond reaction chemistry

Novelties of solid-liquid phase transfer catalyzed synthesis of benzyl diethyl phosphate from the sodium salt of diethyl phosphate

Solid-liquid phase transfer catalysis coupled with mixed solvents, which could be recycled, as a green chemistry procedure, was applied to the synthesis of phosphate from the sodium salt of diethyl phosphate. The benzyl diethyl phosphate was synthesized in good yield via one-pot method from the reaction of the industrial by-product sodium salt of diethyl phosphate with benzyl chloride in solid-liquid phase transfer catalysis and toluene-water mixed solvents. The effects of catalyst structure, the amounts of catalyst, the raw material molar ratio, water loading, and reaction temperature on the conversion of the reaction were investigated. The structure of the benzyl diethyl phosphate generated was confirmed by Elemental Analysis, IR, 1H NMR and GC/MS

Application of Plasma Exchange in Steroid-Responsive Encephalopathy

Plasma exchange has been widely used in autoimmune neurological diseases and is the standard treatment for myasthenia gravis crisis and Guillain-Barre syndrome. A growing body of research suggests that, in the clinical application of steroid-responsive encephalopathy, such as for Hashimoto's encephalopathy, limbic encephalitis, systemic lupus erythematosus encephalopathy, ANCA-associated vasculitis encephalopathy, and acute disseminated encephalomyelitis, plasma exchange is a safe, and effective option when steroids or other immunosuppressive therapies are ineffective in the short term or when contraindications are present. Additionally, plasma exchange can also be used alone or in combination with steroids, immunoglobulins, or other immunosuppressive agents to treat steroid-responsive encephalopathy. This paper reviews the clinical application of plasma exchange in steroid-responsive encephalopathy, including its indications, onset time, course, curative effects, and side effects

PointeNet: A Lightweight Framework for Effective and Efficient Point Cloud Analysis

Current methodologies in point cloud analysis predominantly explore 3D geometries, often achieved through the introduction of intricate learnable geometric extractors in the encoder or by deepening networks with repeated blocks. However, these approaches inevitably lead to a significant number of learnable parameters, resulting in substantial computational costs and imposing memory burdens on CPU/GPU. Additionally, the existing strategies are primarily tailored for object-level point cloud classification and segmentation tasks, with limited extensions to crucial scene-level applications, such as autonomous driving. In response to these limitations, we introduce PointeNet, an efficient network designed specifically for point cloud analysis. PointeNet distinguishes itself with its lightweight architecture, low training cost, and plug-and-play capability, effectively capturing representative features. The network consists of a Multivariate Geometric Encoding (MGE) module and an optional Distance-aware Semantic Enhancement (DSE) module. The MGE module employs operations of sampling, grouping, and multivariate geometric aggregation to lightweightly capture and adaptively aggregate multivariate geometric features, providing a comprehensive depiction of 3D geometries. The DSE module, designed for real-world autonomous driving scenarios, enhances the semantic perception of point clouds, particularly for distant points. Our method demonstrates flexibility by seamlessly integrating with a classification/segmentation head or embedding into off-the-shelf 3D object detection networks, achieving notable performance improvements at a minimal cost. Extensive experiments on object-level datasets, including ModelNet40, ScanObjectNN, ShapeNetPart, and the scene-level dataset KITTI, demonstrate the superior performance of PointeNet over state-of-the-art methods in point cloud analysis

Improved performance of diatomite-based dental nanocomposite ceramics using layer-by-layer assembly

To fabricate high-strength diatomite-based ceramics for dental applications, the layer-by-layer technique was used to coat diatomite particles with cationic [poly(allylamine hydrochloride)] and anionic [poly(sodium 4-styrenesulfonate)] polymers to improve the dispersion and adsorption of positively charged nano-ZrO2 (zirconia) as a reinforcing agent. The modified diatomite particles had reduced particle size, narrower size distribution, and were well dispersed, with good adsorption of nano-ZrO2. To determine the optimum addition levels for nano-ZrO2, ceramics containing 0, 20, 25, 30, and 35 wt% nano-ZrO2 were sintered and characterized by the three-point bending test and microhardness test. In addition to scanning electron microscopy, propagation phase-contrast synchrotron X-ray microtomography was used to examine the internal structure of the ceramics. The addition of 30 wt% nano-ZrO2 resulted in the highest flexural strength and fracture toughness with reduced porosity. Shear bond strength between the core and veneer of our diatomite ceramics and the most widely used dental ceramics were compared; the shear bond strength value for the diatomite-based ceramics was found to be significantly higher than for other groups (P < 0.05). Our results show that diatomite-based nanocomposite ceramics are good potential candidates for ceramic-based dental materials