Showing papers by "Xiamen University published in 2017"

Global, regional, and national burden of neurological disorders, 1990-2016: A systematic analysis for the Global Burden of Disease Study 2016

Abstract: Summary Background Comparable data on the global and country-specific burden of neurological disorders and their trends are crucial for health-care planning and resource allocation. The Global Burden of Diseases, Injuries, and Risk Factors (GBD) Study provides such information but does not routinely aggregate results that are of interest to clinicians specialising in neurological conditions. In this systematic analysis, we quantified the global disease burden due to neurological disorders in 2015 and its relationship with country development level. Methods We estimated global and country-specific prevalence, mortality, disability-adjusted life-years (DALYs), years of life lost (YLLs), and years lived with disability (YLDs) for various neurological disorders that in the GBD classification have been previously spread across multiple disease groupings. The more inclusive grouping of neurological disorders included stroke, meningitis, encephalitis, tetanus, Alzheimer's disease and other dementias, Parkinson's disease, epilepsy, multiple sclerosis, motor neuron disease, migraine, tension-type headache, medication overuse headache, brain and nervous system cancers, and a residual category of other neurological disorders. We also analysed results based on the Socio-demographic Index (SDI), a compound measure of income per capita, education, and fertility, to identify patterns associated with development and how countries fare against expected outcomes relative to their level of development. Findings Neurological disorders ranked as the leading cause group of DALYs in 2015 (250·7 [95% uncertainty interval (UI) 229·1 to 274·7] million, comprising 10·2% of global DALYs) and the second-leading cause group of deaths (9·4 [9·1 to 9·7] million], comprising 16·8% of global deaths). The most prevalent neurological disorders were tension-type headache (1505·9 [UI 1337·3 to 1681·6 million cases]), migraine (958·8 [872·1 to 1055·6] million), medication overuse headache (58·5 [50·8 to 67·4 million]), and Alzheimer's disease and other dementias (46·0 [40·2 to 52·7 million]). Between 1990 and 2015, the number of deaths from neurological disorders increased by 36·7%, and the number of DALYs by 7·4%. These increases occurred despite decreases in age-standardised rates of death and DALYs of 26·1% and 29·7%, respectively; stroke and communicable neurological disorders were responsible for most of these decreases. Communicable neurological disorders were the largest cause of DALYs in countries with low SDI. Stroke rates were highest at middle levels of SDI and lowest at the highest SDI. Most of the changes in DALY rates of neurological disorders with development were driven by changes in YLLs. Interpretation Neurological disorders are an important cause of disability and death worldwide. Globally, the burden of neurological disorders has increased substantially over the past 25 years because of expanding population numbers and ageing, despite substantial decreases in mortality rates from stroke and communicable neurological disorders. The number of patients who will need care by clinicians with expertise in neurological conditions will continue to grow in coming decades. Policy makers and health-care providers should be aware of these trends to provide adequate services. Funding Bill & Melinda Gates Foundation.

TFOS DEWS II Definition and Classification Report

Abstract: The goals of the TFOS DEWS II Definition and Classification Subcommittee were to create an evidence-based definition and a contemporary classification system for dry eye disease (DED). The new definition recognizes the multifactorial nature of dry eye as a disease where loss of homeostasis of the tear film is the central pathophysiological concept. Ocular symptoms, as a broader term that encompasses reports of discomfort or visual disturbance, feature in the definition and the key etiologies of tear film instability, hyperosmolarity, and ocular surface inflammation and damage were determined to be important for inclusion in the definition. In the light of new data, neurosensory abnormalities were also included in the definition for the first time. In the classification of DED, recent evidence supports a scheme based on the pathophysiology where aqueous deficient and evaporative dry eye exist as a continuum, such that elements of each are considered in diagnosis and management. Central to the scheme is a positive diagnosis of DED with signs and symptoms, and this is directed towards management to restore homeostasis. The scheme also allows consideration of various related manifestations, such as non-obvious disease involving ocular surface signs without related symptoms, including neurotrophic conditions where dysfunctional sensation exists, and cases where symptoms exist without demonstrable ocular surface signs, including neuropathic pain. This approach is not intended to override clinical assessment and judgment but should prove helpful in guiding clinical management and research.

Re-ranking Person Re-identification with k-Reciprocal Encoding

Abstract: When considering person re-identification (re-ID) as a retrieval process, re-ranking is a critical step to improve its accuracy. Yet in the re-ID community, limited effort has been devoted to re-ranking, especially those fully automatic, unsupervised solutions. In this paper, we propose a k-reciprocal encoding method to re-rank the re-ID results. Our hypothesis is that if a gallery image is similar to the probe in the k-reciprocal nearest neighbors, it is more likely to be a true match. Specifically, given an image, a k-reciprocal feature is calculated by encoding its k-reciprocal nearest neighbors into a single vector, which is used for re-ranking under the Jaccard distance. The final distance is computed as the combination of the original distance and the Jaccard distance. Our re-ranking method does not require any human interaction or any labeled data, so it is applicable to large-scale datasets. Experiments on the large-scale Market-1501, CUHK03, MARS, and PRW datasets confirm the effectiveness of our method.

Random Erasing Data Augmentation

Abstract: In this paper, we introduce Random Erasing, a new data augmentation method for training the convolutional neural network (CNN). In training, Random Erasing randomly selects a rectangle region in an image and erases its pixels with random values. In this process, training images with various levels of occlusion are generated, which reduces the risk of over-fitting and makes the model robust to occlusion. Random Erasing is parameter learning free, easy to implement, and can be integrated with most of the CNN-based recognition models. Albeit simple, Random Erasing is complementary to commonly used data augmentation techniques such as random cropping and flipping, and yields consistent improvement over strong baselines in image classification, object detection and person re-identification. Code is available at: this https URL.

NTIRE 2017 Challenge on Single Image Super-Resolution: Methods and Results

Abstract: This paper reviews the first challenge on single image super-resolution (restoration of rich details in an low resolution image) with focus on proposed solutions and results. A new DIVerse 2K resolution image dataset (DIV2K) was employed. The challenge had 6 competitions divided into 2 tracks with 3 magnification factors each. Track 1 employed the standard bicubic downscaling setup, while Track 2 had unknown downscaling operators (blur kernel and decimation) but learnable through low and high res train images. Each competition had ∽100 registered participants and 20 teams competed in the final testing phase. They gauge the state-of-the-art in single image super-resolution.

Electromagnetic theories of surface-enhanced Raman spectroscopy

Abstract: Surface-enhanced Raman spectroscopy (SERS) and related spectroscopies are powered primarily by the concentration of the electromagnetic (EM) fields associated with light in or near appropriately nanostructured electrically-conducting materials, most prominently, but not exclusively high-conductivity metals such as silver and gold. This field concentration takes place on account of the excitation of surface-plasmon (SP) resonances in the nanostructured conductor. Optimizing nanostructures for SERS, therefore, implies optimizing the ability of plasmonic nanostructures to concentrate EM optical fields at locations where molecules of interest reside, and to enhance the radiation efficiency of the oscillating dipoles associated with these molecules and nanostructures. This review summarizes the development of theories over the past four decades pertinent to SERS, especially those contributing to our current understanding of SP-related SERS. Special emphasis is given to the salient strategies and theoretical approaches for optimizing nanostructures with hotspots as efficient EM near-field concentrating and far-field radiating substrates for SERS. A simple model is described in terms of which the upper limit of the SERS enhancement can be estimated. Several experimental strategies that may allow one to approach, or possibly exceed this limit, such as cascading the enhancement of the local and radiated EM field by the multiscale EM coupling of hierarchical structures, and generating hotspots by hybridizing an antenna mode with a plasmonic waveguide cavity mode, which would result in an increased local field enhancement, are discussed. Aiming to significantly broaden the application of SERS to other fields, and especially to material science, we consider hybrid structures of plasmonic nanostructures and other material phases and strategies for producing strong local EM fields at desired locations in such hybrid structures. In this vein, we consider some of the numerical strategies for simulating the optical properties and consequential SERS performance of particle-on-substrate systems that might guide the design of SERS-active systems. Finally, some current theoretical attempts are briefly discussed for unifying EM and non-EM contribution to SERS.

Removing Rain from Single Images via a Deep Detail Network

Abstract: We propose a new deep network architecture for removing rain streaks from individual images based on the deep convolutional neural network (CNN). Inspired by the deep residual network (ResNet) that simplifies the learning process by changing the mapping form, we propose a deep detail network to directly reduce the mapping range from input to output, which makes the learning process easier. To further improve the de-rained result, we use a priori image domain knowledge by focusing on high frequency detail during training, which removes background interference and focuses the model on the structure of rain in images. This demonstrates that a deep architecture not only has benefits for high-level vision tasks but also can be used to solve low-level imaging problems. Though we train the network on synthetic data, we find that the learned network generalizes well to real-world test images. Experiments show that the proposed method significantly outperforms state-of-the-art methods on both synthetic and real-world images in terms of both qualitative and quantitative measures. We discuss applications of this structure to denoising and JPEG artifact reduction at the end of the paper.

Rethinking cancer nanotheranostics.

Abstract: Advances in nanoparticle synthesis and engineering have produced nanoscale agents affording both therapeutic and diagnostic functions that are often referred to by the portmanteau 'nanotheranostics'. The field is associated with many applications in the clinic, especially in cancer management. These include patient stratification, drug-release monitoring, imaging-guided focal therapy and post-treatment response monitoring. Recent advances in nanotheranostics have expanded this notion and enabled the characterization of individual tumours, the prediction of nanoparticle-tumour interactions, and the creation of tailor-designed nanomedicines for individualized treatment. Some of these applications require breaking the dogma that a nanotheranostic must combine both therapeutic and diagnostic agents within a single, physical entity; instead, it can be a general approach in which diagnosis and therapy are interwoven to solve clinical issues and improve treatment outcomes. In this Review, we describe the evolution and state of the art of cancer nanotheranostics, with an emphasis on clinical impact and translation.

A critical analysis of the α, β and γ phases in poly(vinylidene fluoride) using FTIR

Abstract: Poly(vinylidene fluoride) (PVDF) has been widely utilized in scientific research and the manufacturing industry for its unique piezoelectric properties. In the past few decades, the vibrational spectra of PVDF polymorphic polymers via FTIR (Fourier transform infrared spectroscopy) have been extensively investigated and documented. However, reports on the analysis of α, β and γ phases often have conflicting views based on measured data. In this work, we analyze the FTIR vibrational bands of PVDF materials fabricated by different processes with detailed XRD (X-ray diffraction) characterization to identify the structural α, β and γ phases. By examining the results in this work and extensively reviewing published research reports in the literature, a universal phase identification procedure using only the FTIR results is proposed and validated. Specifically, this procedure can differentiate the three phases by checking the bands around 763 and/or 614, 1275, and 1234 cm−1 for the α, β and γ phases, respectively. The rule for assignment of the 840* and 510* cm−1 bands is provided for the first time and an integrated quantification methodology for individual β and γ phase in mixed systems is also demonstrated.

Core–Shell Nanoparticle-Enhanced Raman Spectroscopy

Abstract: Core–shell nanoparticles are at the leading edge of the hot research topics and offer a wide range of applications in optics, biomedicine, environmental science, materials, catalysis, energy, and so forth, due to their excellent properties such as versatility, tunability, and stability. They have attracted enormous interest attributed to their dramatically tunable physicochemical features. Plasmonic core–shell nanomaterials are extensively used in surface-enhanced vibrational spectroscopies, in particular, surface-enhanced Raman spectroscopy (SERS), due to the unique localized surface plasmon resonance (LSPR) property. This review provides a comprehensive overview of core–shell nanoparticles in the context of fundamental and application aspects of SERS and discusses numerous classes of core–shell nanoparticles with their unique strategies and functions. Further, herein we also introduce the concept of shell-isolated nanoparticle-enhanced Raman spectroscopy (SHINERS) in detail because it overcomes the long...

General Oriented Formation of Carbon Nanotubes from Metal–Organic Frameworks

Abstract: Carbon nanotubes (CNTs) are of great interest for many potential applications because of their extraordinary electronic, mechanical and structural properties. However, issues of chaotic staking, high cost and high energy dissipation in the synthesis of CNTs remain to be resolved. Here we develop a facile, general and high-yield strategy for the oriented formation of CNTs from metal–organic frameworks (MOFs) through a low-temperature (as low as 430 °C) pyrolysis process. The selected MOF crystals act as a single precursor for both nanocatalysts and carbon sources. The key to the formation of CNTs is obtaining small nanocatalysts with high activity during the pyrolysis process. This method is successfully extended to obtain various oriented CNT-assembled architectures by modulating the corresponding MOFs, which further homogeneously incorporate heteroatoms into the CNTs. Specifically, nitrogen-doped CNT-assembled hollow structures exhibit excellent performances in both energy conversion and storage. On the ...

Clearing the Skies: A Deep Network Architecture for Single-Image Rain Removal

Abstract: We introduce a deep network architecture called DerainNet for removing rain streaks from an image. Based on the deep convolutional neural network (CNN), we directly learn the mapping relationship between rainy and clean image detail layers from data. Because we do not possess the ground truth corresponding to real-world rainy images, we synthesize images with rain for training. In contrast to other common strategies that increase depth or breadth of the network, we use image processing domain knowledge to modify the objective function and improve deraining with a modestly sized CNN. Specifically, we train our DerainNet on the detail (high-pass) layer rather than in the image domain. Though DerainNet is trained on synthetic data, we find that the learned network translates very effectively to real-world images for testing. Moreover, we augment the CNN framework with image enhancement to improve the visual results. Compared with the state-of-the-art single image de-raining methods, our method has improved rain removal and much faster computation time after network training.

Nanoparticle design strategies for enhanced anticancer therapy by exploiting the tumour microenvironment

Abstract: Nanovehicles can efficiently carry and deliver anticancer agents to tumour sites Compared with normal tissue, the tumour microenvironment has some unique properties, such as vascular abnormalities, hypoxia and acidic pH There are many types of cells, including tumour cells, macrophages, immune and fibroblast cells, fed by defective blood vessels in the solid tumour Exploiting the tumour microenvironment can benefit the design of nanoparticles for enhanced therapeutic effectiveness In this review article, we summarized the recent progress in various nanoformulations for cancer therapy, with a special emphasis on tumour microenvironment stimuli-responsive ones Numerous tumour microenvironment modulation strategies with promising cancer therapeutic efficacy have also been highlighted Future challenges and opportunities of design consideration are also discussed in detail We believe that these tumour microenvironment modulation strategies offer a good chance for the practical translation of nanoparticle formulas into clinic

Metal-Free Carbon Materials for CO2 Electrochemical Reduction.

Abstract: The rapid increase of the CO2 concentration in the Earth's atmosphere has resulted in numerous environmental issues, such as global warming, ocean acidification, melting of the polar ice, rising sea level, and extinction of species. To search for suitable and capable catalytic systems for CO2 conversion, electrochemical reduction of CO2 (CO2 RR) holds great promise. Emerging heterogeneous carbon materials have been considered as promising metal-free electrocatalysts for the CO2 RR, owing to their abundant natural resources, tailorable porous structures, resistance to acids and bases, high-temperature stability, and environmental friendliness. They exhibit remarkable CO2 RR properties, including catalytic activity, long durability, and high selectivity. Here, various carbon materials (e.g., carbon fibers, carbon nanotubes, graphene, diamond, nanoporous carbon, and graphene dots) with heteroatom doping (e.g., N, S, and B) that can be used as metal-free catalysts for the CO2 RR are highlighted. Recent advances regarding the identification of active sites for the CO2 RR and the pathway of reduction of CO2 to the final product are comprehensively reviewed. Additionally, the emerging challenges and some perspectives on the development of heteroatom-doped carbon materials as metal-free electrocatalysts for the CO2 RR are included.

Non-local Deep Features for Salient Object Detection

Abstract: Saliency detection aims to highlight the most relevant objects in an image. Methods using conventional models struggle whenever salient objects are pictured on top of a cluttered background while deep neural nets suffer from excess complexity and slow evaluation speeds. In this paper, we propose a simplified convolutional neural network which combines local and global information through a multi-resolution 4×5 grid structure. Instead of enforcing spacial coherence with a CRF or superpixels as is usually the case, we implemented a loss function inspired by the Mumford-Shah functional which penalizes errors on the boundary. We trained our model on the MSRA-B dataset, and tested it on six different saliency benchmark datasets. Results show that our method is on par with the state-of-the-art while reducing computation time by a factor of 18 to 100 times, enabling near real-time, high performance saliency detection.

Ammonia Electrosynthesis with High Selectivity under Ambient Conditions via a Li+ Incorporation Strategy

Abstract: We report the discovery of a dramatically enhanced N2 electroreduction reaction (NRR) selectivity under ambient conditions via the Li+ incorporation into poly(N-ethyl-benzene-1,2,4,5-tetracarboxylic diimide) (PEBCD) as a catalyst. The detailed electrochemical evaluation and density functional theory calculations showed that Li+ association with the O atoms in the PEBCD matrix can retard the HER process and can facilitate the adsorption of N2 to afford a high potential scope for the NRR process to proceed in the “[O—Li+]·N2—Hx” alternating hydrogenation mode. This atomic-scale incorporation strategy provides new insight into the rational design of NRR catalysts with higher selectivity.

PanNet: A Deep Network Architecture for Pan-Sharpening

Abstract: We propose a deep network architecture for the pan-sharpening problem called PanNet. We incorporate domain-specific knowledge to design our PanNet architecture by focusing on the two aims of the pan-sharpening problem: spectral and spatial preservation. For spectral preservation, we add up-sampled multispectral images to the network output, which directly propagates the spectral information to the reconstructed image. To preserve spatial structure, we train our network parameters in the high-pass filtering domain rather than the image domain. We show that the trained network generalizes well to images from different satellites without needing retraining. Experiments show significant improvement over state-of-the-art methods visually and in terms of standard quality metrics.

Surface Coordination Chemistry of Metal Nanomaterials

Abstract: Surface coordination chemistry of nanomaterials deals with the chemistry on how ligands are coordinated on their surface metal atoms and influence their properties at the molecular level. This Perspective demonstrates that there is a strong link between surface coordination chemistry and the shape-controlled synthesis, and many intriguing surface properties of metal nanomaterials. While small adsorbates introduced in the synthesis can control the shapes of metal nanocrystals by minimizing their surface energy via preferential coordination on specific facets, surface ligands properly coordinated on metal nanoparticles readily promote their catalysis via steric interactions and electronic modifications. The difficulty in the research of surface coordination chemistry of nanomaterials mainly lies in the lack of effective tools to characterize their molecular surface coordination structures. Also highlighted are several model material systems that facilitate the characterizations of surface coordination struc...

Multiscale structural and electronic control of molybdenum disulfide foam for highly efficient hydrogen production.

Abstract: Hydrogen production through water splitting has been considered as a green, pure and high-efficient technique. As an important half-reaction involved, hydrogen evolution reaction is a complex electrochemical process involving liquid-solid-gas three-phase interface behaviour. Therefore, new concepts and strategies of material design are needed to smooth each pivotal step. Here we report a multiscale structural and electronic control of molybdenum disulfide foam to synergistically promote the hydrogen evolution process. The optimized three-dimensional molybdenum disulfide foam with uniform mesopores, vertically aligned two-dimensional layers and cobalt atoms doping demonstrated a high hydrogen evolution activity and stability. In addition, density functional theory calculations indicate that molybdenum disulfide with moderate cobalt doping content possesses the optimal activity. This study demonstrates the validity of multiscale control in molybdenum disulfide via overall consideration of the mass transport, and the accessibility, quantity and capability of active sites towards electrocatalytic hydrogen evolution, which may also be extended to other energy-related processes. The hydrogen evolution reaction is a complicated process involving liquid-solid-gas three-phase interface behaviour. Here, the authors report the multiscale structural and electronic control of molybdenum disulfide foam and demonstrate its high activity and stability for hydrogen evolution.

Identification of a Selective and Direct NLRP3 Inhibitor to Treat Inflammatory Disorders

Abstract: The NLRP3 inflammasome has been implicated in the pathogenesis of a wide variety of human diseases. A few compounds have been developed to inhibit NLRP3 inflammasome activation, but compounds directly and specifically targeting NLRP3 are still not available, so it is unclear whether NLRP3 itself can be targeted to prevent or treat diseases. Here we show that the compound CY-09 specifically blocks NLRP3 inflammasome activation. CY-09 directly binds to the ATP-binding motif of NLRP3 NACHT domain and inhibits NLRP3 ATPase activity, resulting in the suppression of NLRP3 inflammasome assembly and activation. Importantly, treatment with CY-09 shows remarkable therapeutic effects on mouse models of cryopyrin-associated autoinflammatory syndrome (CAPS) and type 2 diabetes. Furthermore, CY-09 is active ex vivo for monocytes from healthy individuals or synovial fluid cells from patients with gout. Thus, our results provide a selective and direct small-molecule inhibitor for NLRP3 and indicate that NLRP3 can be targeted in vivo to combat NLRP3-driven diseases.

Fructose-1,6-bisphosphate and aldolase mediate glucose sensing by AMPK

Abstract: The major energy source for most cells is glucose, from which ATP is generated via glycolysis and/or oxidative metabolism. Glucose deprivation activates AMP-activated protein kinase (AMPK), but it is unclear whether this activation occurs solely via changes in AMP or ADP, the classical activators of AMPK. Here, we describe an AMP/ADP-independent mechanism that triggers AMPK activation by sensing the absence of fructose-1,6-bisphosphate (FBP), with AMPK being progressively activated as extracellular glucose and intracellular FBP decrease. When unoccupied by FBP, aldolases promote the formation of a lysosomal complex containing at least v-ATPase, ragulator, axin, liver kinase B1 (LKB1) and AMPK, which has previously been shown to be required for AMPK activation. Knockdown of aldolases activates AMPK even in cells with abundant glucose, whereas the catalysis-defective D34S aldolase mutant, which still binds FBP, blocks AMPK activation. Cell-free reconstitution assays show that addition of FBP disrupts the association of axin and LKB1 with v-ATPase and ragulator. Importantly, in some cell types AMP/ATP and ADP/ATP ratios remain unchanged during acute glucose starvation, and intact AMP-binding sites on AMPK are not required for AMPK activation. These results establish that aldolase, as well as being a glycolytic enzyme, is a sensor of glucose availability that regulates AMPK.

Confinement of Ultrasmall Cu/ZnOx Nanoparticles in Metal–Organic Frameworks for Selective Methanol Synthesis from Catalytic Hydrogenation of CO2

Abstract: The interfaces of Cu/ZnO and Cu/ZrO2 play vital roles in the hydrogenation of CO2 to methanol by these composite catalysts. Surface structural reorganization and particle growth during catalysis deleteriously reduce these active interfaces, diminishing both catalytic activities and MeOH selectivities. Here we report the use of preassembled bpy and Zr6(μ3-O)4(μ3-OH)4 sites in UiO-bpy metal–organic frameworks (MOFs) to anchor ultrasmall Cu/ZnOx nanoparticles, thus preventing the agglomeration of Cu NPs and phase separation between Cu and ZnOx in MOF-cavity-confined Cu/ZnOx nanoparticles. The resultant Cu/ZnOx@MOF catalysts show very high activity with a space–time yield of up to 2.59 gMeOH kgCu–1 h–1, 100% selectivity for CO2 hydrogenation to methanol, and high stability over 100 h. These new types of strong metal–support interactions between metallic nanoparticles and organic chelates/metal-oxo clusters offer new opportunities in fine-tuning catalytic activities and selectivities of metal nanoparticles@MOFs.

Reversible multi-electron redox chemistry of π-conjugated N-containing heteroaromatic molecule-based organic cathodes

Abstract: Even though organic molecules with well-designed functional groups can be programmed to have high electron density per unit mass, their poor electrical conductivity and low cycle stability limit their applications in batteries. Here we report a facile synthesis of π-conjugated quinoxaline-based heteroaromatic molecules (3Q) by condensation of cyclic carbonyl molecules with o-phenylenediamine. 3Q features a number of electron-deficient pyrazine sites, where multiple redox reactions take place. When hybridized with graphene and coupled with an ether-based electrolyte, an organic cathode based on 3Q molecules displays a discharge capacity of 395 mAh g−1 at 400 mA g−1 (1C) in the voltage range of 1.2–3.9 V and a nearly 70% capacity retention after 10,000 cycles at 8 A g−1. It also exhibits a capacity of 222 mAh g−1 at 20C, which corresponds to 60% of the initial specific capacity. Our results offer evidence that heteroaromatic molecules with multiple redox sites are promising in developing high-energy-density, long-cycle-life organic rechargeable batteries. Organic compounds can be used as electrode materials for Li-ion batteries, but problems such as facile dissolution and low electrical conductivity hinder their application. Here the authors report π-conjugated quinoxaline-based heteroaromatic molecules with multiple redox sites to tackle the problems.

Plasmon-enhanced fluorescence spectroscopy

Abstract: Fluorescence spectroscopy with strong emitters is a remarkable tool with ultra-high sensitivity for detection and imaging down to the single-molecule level. Plasmon-enhanced fluorescence (PEF) not only offers enhanced emissions and decreased lifetimes, but also allows an expansion of the field of fluorescence by incorporating weak quantum emitters, avoiding photobleaching and providing the opportunity of imaging with resolutions significantly better than the diffraction limit. It also opens the window to a new class of photostable probes by combining metal nanostructures and quantum emitters. In particular, the shell-isolated nanostructure-enhanced fluorescence, an innovative new mode for plasmon-enhanced surface analysis, is included. These new developments are based on the coupling of the fluorophores in their excited states with localized surface plasmons in nanoparticles, where local field enhancement leads to improved brightness of molecular emission and higher detection sensitivity. Here, we review the recent progress in PEF with an emphasis on the mechanism of plasmon enhancement, substrate preparation, and some advanced applications, including an outlook on PEF with high time- and spatially resolved properties.

New Perspectives on Dry Eye Definition and Diagnosis: A Consensus Report by the Asia Dry Eye Society

Abstract: For the last 20 years, a great amount of evidence has accumulated through epidemiological studies that most of the dry eye disease encountered in daily life, especially in video display terminal (VDT) workers, involves short tear film breakup time (TFBUT) type dry eye, a category characterized by severe symptoms but minimal clinical signs other than short TFBUT. An unstable tear film also affects the visual function, possibly due to the increase of higher order aberrations. Based on the change in the understanding of the types, symptoms, and signs of dry eye disease, the Asia Dry Eye Society agreed to the following definition of dry eye: "Dry eye is a multifactorial disease characterized by unstable tear film causing a variety of symptoms and/or visual impairment, potentially accompanied by ocular surface damage." The definition stresses instability of the tear film as well as the importance of visual impairment, highlighting an essential role for TFBUT assessment. This paper discusses the concept of Tear Film Oriented Therapy (TFOT), which evolved from the definition of dry eye, emphasizing the importance of a stable tear film.