Showing papers by "Wuhan University published in 2015"

The Global Burden of Cancer 2013

Abstract: Importance Cancer is among the leading causes of death worldwide. Current estimates of cancer burden in individual countries and regions are necessary to inform local cancer control strategies. Objective To estimate mortality, incidence, years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs) for 28 cancers in 188 countries by sex from 1990 to 2013. Evidence Review The general methodology of the Global Burden of Disease (GBD) 2013 study was used. Cancer registries were the source for cancer incidence data as well as mortality incidence (MI) ratios. Sources for cause of death data include vital registration system data, verbal autopsy studies, and other sources. The MI ratios were used to transform incidence data to mortality estimates and cause of death estimates to incidence estimates. Cancer prevalence was estimated using MI ratios as surrogates for survival data; YLDs were calculated by multiplying prevalence estimates with disability weights, which were derived from population-based surveys; YLLs were computed by multiplying the number of estimated cancer deaths at each age with a reference life expectancy; and DALYs were calculated as the sum of YLDs and YLLs. Findings In 2013 there were 14.9 million incident cancer cases, 8.2 million deaths, and 196.3 million DALYs. Prostate cancer was the leading cause for cancer incidence (1.4 million) for men and breast cancer for women (1.8 million). Tracheal, bronchus, and lung (TBL) cancer was the leading cause for cancer death in men and women, with 1.6 million deaths. For men, TBL cancer was the leading cause of DALYs (24.9 million). For women, breast cancer was the leading cause of DALYs (13.1 million). Age-standardized incidence rates (ASIRs) per 100 000 and age-standardized death rates (ASDRs) per 100 000 for both sexes in 2013 were higher in developing vs developed countries for stomach cancer (ASIR, 17 vs 14; ASDR, 15 vs 11), liver cancer (ASIR, 15 vs 7; ASDR, 16 vs 7), esophageal cancer (ASIR, 9 vs 4; ASDR, 9 vs 4), cervical cancer (ASIR, 8 vs 5; ASDR, 4 vs 2), lip and oral cavity cancer (ASIR, 7 vs 6; ASDR, 2 vs 2), and nasopharyngeal cancer (ASIR, 1.5 vs 0.4; ASDR, 1.2 vs 0.3). Between 1990 and 2013, ASIRs for all cancers combined (except nonmelanoma skin cancer and Kaposi sarcoma) increased by more than 10% in 113 countries and decreased by more than 10% in 12 of 188 countries. Conclusions and Relevance Cancer poses a major threat to public health worldwide, and incidence rates have increased in most countries since 1990. The trend is a particular threat to developing nations with health systems that are ill-equipped to deal with complex and expensive cancer treatments. The annual update on the Global Burden of Cancer will provide all stakeholders with timely estimates to guide policy efforts in cancer prevention, screening, treatment, and palliation.

The methodological quality assessment tools for preclinical and clinical studies, systematic review and meta‐analysis, and clinical practice guideline: a systematic review

Abstract: Objective To systematically review the methodological assessment tools for pre-clinical and clinical studies, systematic review and meta-analysis, and clinical practice guideline. Methods We searched PubMed, the Cochrane Handbook for Systematic Reviews of Interventions, Joanna Briggs Institute (JBI) Reviewers Manual, Centre for Reviews and Dissemination, Critical Appraisal Skills Programme (CASP), Scottish Intercollegiate Guidelines Network (SIGN), and the National Institute for Clinical Excellence (NICE) up to May 20th, 2014. Two authors selected studies and extracted data; quantitative analysis was performed to summarize the characteristics of included tools. Results We included a total of 21 assessment tools for analysis. A number of tools were developed by academic organizations, and some were developed by only a small group of researchers. The JBI developed the highest number of methodological assessment tools, with CASP coming second. Tools for assessing the methodological quality of randomized controlled studies were most abundant. The Cochrane Collaboration's tool for assessing risk of bias is the best available tool for assessing RCTs. For cohort and case-control studies, we recommend the use of the Newcastle-Ottawa Scale. The Methodological Index for Non-Randomized Studies (MINORS) is an excellent tool for assessing non-randomized interventional studies, and the Agency for Healthcare Research and Quality (ARHQ) methodology checklist is applicable for cross-sectional studies. For diagnostic accuracy test studies, the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool is recommended; the SYstematic Review Centre for Laboratory animal Experimentation (SYRCLE) risk of bias tool is available for assessing animal studies; Assessment of Multiple Systematic Reviews (AMSTAR) is a measurement tool for systematic reviews/meta-analyses; an 18-item tool has been developed for appraising case series studies, and the Appraisal of Guidelines, Research and Evaluation (AGREE)-II instrument is widely used to evaluate clinical practice guidelines. Conclusions We have successfully identified a variety of methodological assessment tools for different types of study design. However, further efforts in the development of critical appraisal tools are warranted since there is currently a lack of such tools for other fields, e.g. genetic studies, and some existing tools (nested case-control studies and case reports, for example) are in need of updating to be in line with current research practice and rigor. In addition, it is very important that all critical appraisal tools remain subjective and performance bias is effectively avoided.

Low-Temperature Solution-Processed Tin Oxide as an Alternative Electron Transporting Layer for Efficient Perovskite Solar Cells

Abstract: Lead halide perovskite solar cells with the high efficiencies typically use high-temperature processed TiO2 as the electron transporting layers (ETLs). Here, we demonstrate that low-temperature solution-processed nanocrystalline SnO2 can be an excellent alternative ETL material for efficient perovskite solar cells. Our best-performing planar cell using such a SnO2 ETL has achieved an average efficiency of 16.02%, obtained from efficiencies measured from both reverse and forward voltage scans. The outstanding performance of SnO2 ETLs is attributed to the excellent properties of nanocrystalline SnO2 films, such as good antireflection, suitable band edge positions, and high electron mobility. The simple low-temperature process is compatible with the roll-to-roll manufacturing of low-cost perovskite solar cells on flexible substrates.

Enhanced photocatalytic activity of g-C3N4 for selective CO2 reduction to CH3OH via facile coupling of ZnO: a direct Z-scheme mechanism

Abstract: Photocatalytic CO2 reduction into renewable hydrocarbon solar fuels is considered as a promising strategy to simultaneously address the global energy and environmental issues. In this study, a binary g-C3N4/ZnO photocatalytic system was constructed via a one-step facile calcination method and further used as a photocatalyst for CO2 reduction. It was shown that the as-prepared g-C3N4/ZnO photocatalytic system exhibited enhanced photocatalytic activity for CO2 reduction by a factor of 2.3 compared with pure g-C3N4, while maintaining the original selectivity of pure g-C3N4 to convert CO2 directly into CH3OH. For the first time, the coupling effect of ZnO responsible for the improved photoactivity of g-C3N4 was fully illustrated and a direct Z-scheme mechanism rather than the conventional heterojunction-type mechanism was proposed to explain the better performances of the g-C3N4/ZnO binary composite photocatalytic system. The enhancement of photocatalytic CO2 reduction activity is attributed to the highly efficient ZnO-to-g-C3N4 electron transfer occurring at the intimate contact interface between the g-C3N4 phase and ZnO phase. This work will provide new deep insights into the rational construction of a g-C3N4-based photocatalytic system and the design of a direct Z-scheme system without an electron mediator for photocatalytic CO2 reduction reactions.

Sparse whole-genome sequencing identifies two loci for major depressive disorder

Abstract: Genomic analysis of 5,303 Chinese women with recurrent major depressive disorder (MDD) enables the identification and replication of two genome-wide significant loci contributing to risk of MDD on chromosome 10: one near the SIRT1 gene; the other in an intron of the LHPP gene.

Helicity multiplexed broadband metasurface holograms

Abstract: Metasurfaces are engineered interfaces that contain a thin layer of plasmonic or dielectric nanostructures capable of manipulating light in a desirable manner. Advances in metasurfaces have led to various practical applications ranging from lensing to holography. Metasurface holograms that can be switched by the polarization state of incident light have been demonstrated for achieving polarization multiplexed functionalities. However, practical application of these devices has been limited by their capability for achieving high efficiency and high image quality. Here we experimentally demonstrate a helicity multiplexed metasurface hologram with high efficiency and good image fidelity over a broad range of frequencies. The metasurface hologram features the combination of two sets of hologram patterns operating with opposite incident helicities. Two symmetrically distributed off-axis images are interchangeable by controlling the helicity of the input light. The demonstrated helicity multiplexed metasurface hologram with its high performance opens avenues for future applications with functionality switchable optical devices.

Ultrasensitive and Broadband MoS2 Photodetector Driven by Ferroelectrics

Abstract: A few-layer MoS2 photodetector driven by poly(vinylidene fluoride-trifluoroethylene) ferroelectrics is achieved. The detectivity and responsitivity are up to 2.2 × 10(12) Jones and 2570 A W(-1), respectively, at 635 nm with ZERO gate bias. E(g) of MoS2 is tuned by the ultrahigh electrostatic field from the ferroelectric polarization. The photoresponse wavelengths of the photodetector are extended into the near-infrared (0.85-1.55 μm).

Deep Learning Based Feature Selection for Remote Sensing Scene Classification

Abstract: With the popular use of high-resolution satellite images, more and more research efforts have been placed on remote sensing scene classification/recognition. In scene classification, effective feature selection can significantly boost the final performance. In this letter, a novel deep-learning-based feature-selection method is proposed, which formulates the feature-selection problem as a feature reconstruction problem. Note that the popular deep-learning technique, i.e., the deep belief network (DBN), achieves feature abstraction by minimizing the reconstruction error over the whole feature set, and features with smaller reconstruction errors would hold more feature intrinsics for image representation. Therefore, the proposed method selects features that are more reconstructible as the discriminative features. Specifically, an iterative algorithm is developed to adapt the DBN to produce the inquired reconstruction weights. In the experiments, 2800 remote sensing scene images of seven categories are collected for performance evaluation. Experimental results demonstrate the effectiveness of the proposed method.

An Efficient Identity-Based Conditional Privacy-Preserving Authentication Scheme for Vehicular Ad Hoc Networks

Abstract: By broadcasting messages about traffic status to vehicles wirelessly, a vehicular ad hoc network (VANET) can improve traffic safety and efficiency. To guarantee secure communication in VANETs, security and privacy issues must be addressed before their deployment. The conditional privacy-preserving authentication (CPPA) scheme is suitable for solving security and privacy-preserving problems in VANETs, because it supports both mutual authentication and privacy protection simultaneously. Many identity-based CPPA schemes for VANETs using bilinear pairings have been proposed over the last few years to enhance security or to improve performance. However, it is well known that the bilinear pairing operation is one of the most complex operations in modern cryptography. To achieve better performance and reduce computational complexity of information processing in VANET, the design of a CPPA scheme for the VANET environment that does not use bilinear paring becomes a challenge. To address this challenge, we propose a CPPA scheme for VANETs that does not use bilinear paring and we demonstrate that it could supports both the mutual authentication and the privacy protection simultaneously. Our proposed CPPA scheme retains most of the benefits obtained with the previously proposed CPPA schemes. Moreover, the proposed CPPA scheme yields a better performance in terms of computation cost and communication cost making it be suitable for use by the VANET safety-related applications.

Neutrino Physics with JUNO

Abstract: The Jiangmen Underground Neutrino Observatory (JUNO), a 20 kton multi-purpose underground liquid scintillator detector, was proposed with the determination of the neutrino mass hierarchy as a primary physics goal. It is also capable of observing neutrinos from terrestrial and extra-terrestrial sources, including supernova burst neutrinos, diffuse supernova neutrino background, geoneutrinos, atmospheric neutrinos, solar neutrinos, as well as exotic searches such as nucleon decays, dark matter, sterile neutrinos, etc. We present the physics motivations and the anticipated performance of the JUNO detector for various proposed measurements. By detecting reactor antineutrinos from two power plants at 53-km distance, JUNO will determine the neutrino mass hierarchy at a 3-4 sigma significance with six years of running. The measurement of antineutrino spectrum will also lead to the precise determination of three out of the six oscillation parameters to an accuracy of better than 1\%. Neutrino burst from a typical core-collapse supernova at 10 kpc would lead to ~5000 inverse-beta-decay events and ~2000 all-flavor neutrino-proton elastic scattering events in JUNO. Detection of DSNB would provide valuable information on the cosmic star-formation rate and the average core-collapsed neutrino energy spectrum. Geo-neutrinos can be detected in JUNO with a rate of ~400 events per year, significantly improving the statistics of existing geoneutrino samples. The JUNO detector is sensitive to several exotic searches, e.g. proton decay via the $p\to K^++\bar u$ decay channel. The JUNO detector will provide a unique facility to address many outstanding crucial questions in particle and astrophysics. It holds the great potential for further advancing our quest to understanding the fundamental properties of neutrinos, one of the building blocks of our Universe.

Photoluminescence‐Tunable Carbon Nanodots: Surface‐State Energy‐Gap Tuning

Abstract: A controllable wet oxidation method for synthesizing excitation-independent C-dots with tunable fluorescent colors was developed. carbon fibers (CFs) were added to a nitric acid solution. Then, the well-stirred solution was heated to 120°C and refluxed for 48 h. The corresponding solution containing C-dots was collected at a given time and then neutralized with NaOH. The resulting solution was first filtered by membrane filters and then ultrafiltered successively. The ECL experiments were conducted in a three-electrode electrochemical system with a Pt-disk working electrode, a Pt-wire counter electrode, and an Ag/AgCl reference electrode. The results revealed that the PL properties of C-dots, mainly influenced by their size and degree of surface oxidation, can be tuned by varying reaction time, temperature, and concentration of nitric acid. The energy gap of the surface states, where electrons and holes recombine to emit fluorescence, correlated with the extent of the π-electron system and surface chemistry.

Heterogeneous degradation of organic pollutants by persulfate activated by CuO-Fe3O4: Mechanism, stability, and effects of pH and bicarbonate ions

Abstract: Magnetic CuO-Fe3O4 composite was fabricated by a simple hydrothermal method and characterized as a heterogeneous catalyst for phenol degradation. The effects of pH and bicarbonate ions on catalytic activity were extensively evaluated in view of the practical applications. The results indicated that an increase of solution pH and the presence of bicarbonate ions were beneficial for the removal of phenol in the CuO-Fe3O4 coupled with persulfate (PS) process. Almost 100% mineralization of 0.1 mM phenol can be achieved in 120 min by using 0.3 g/L CuO-Fe3O4 and 5.0 mM PS at pH 11.0 or in the presence of 3.0 mM bicarbonate. The positive effect of bicarbonate ion is probably due to the suppression of copper leaching as well as the formation of Cu(III). The reuse of catalyst at pH0 11.0 and 5.6 showed that the catalyst remains a high level of stability at alkaline condition (e.g., pH0 11.0). On the basis of the characterization of catalyst, the results of metal leaching and EPR studies, it is suggested that pheno...

Accuracy and reliability of multi-GNSS real-time precise positioning: GPS, GLONASS, BeiDou, and Galileo

Abstract: In this contribution, we present a GPS+GLONASS+BeiDou+Galileo four-system model to fully exploit the observations of all these four navigation satellite systems for real-time precise orbit determination, clock estimation and positioning. A rigorous multi-GNSS analysis is performed to achieve the best possible consistency by processing the observations from different GNSS together in one common parameter estimation procedure. Meanwhile, an efficient multi-GNSS real-time precise positioning service system is designed and demonstrated by using the multi-GNSS Experiment, BeiDou Experimental Tracking Network, and International GNSS Service networks including stations all over the world. The statistical analysis of the 6-h predicted orbits show that the radial and cross root mean square (RMS) values are smaller than 10 cm for BeiDou and Galileo, and smaller than 5 cm for both GLONASS and GPS satellites, respectively. The RMS values of the clock differences between real-time and batch-processed solutions for GPS satellites are about 0.10 ns, while the RMS values for BeiDou, Galileo and GLONASS are 0.13, 0.13 and 0.14 ns, respectively. The addition of the BeiDou, Galileo and GLONASS systems to the standard GPS-only processing, reduces the convergence time almost by 70 %, while the positioning accuracy is improved by about 25 %. Some outliers in the GPS-only solutions vanish when multi-GNSS observations are processed simultaneous. The availability and reliability of GPS precise positioning decrease dramatically as the elevation cutoff increases. However, the accuracy of multi-GNSS precise point positioning (PPP) is hardly decreased and few centimeter are still achievable in the horizontal components even with 40 $$^{\circ }$$ elevation cutoff. At 30 $$^{\circ }$$ and 40 $$^{\circ }$$ elevation cutoffs, the availability rates of GPS-only solution drop significantly to only around 70 and 40 %, respectively. However, multi-GNSS PPP can provide precise position estimates continuously (availability rate is more than 99.5 %) even up to 40 $$^{\circ }$$ elevation cutoff (e.g., in urban canyons).

Link prediction in social networks: the state-of-the-art

Abstract: In social networks, link prediction predicts missing links in current networks and new or dissolution links in future networks, is important for mining and analyzing the evolution of social networks. In the past decade, many works have been done about the link prediction in social networks. The goal of this paper is to comprehensively review, analyze and discuss the state-of-the-art of the link prediction in social networks. A systematical category for link prediction techniques and problems is presented. Then link prediction techniques and problems are analyzed and discussed. Typical applications of link prediction are also addressed. Achievements and roadmaps of some active research groups are introduced. Finally, some future challenges of the link prediction in social networks are discussed.

Saliency-Guided Unsupervised Feature Learning for Scene Classification

Abstract: Due to the rapid technological development of various different satellite sensors, a huge volume of high-resolution image data sets can now be acquired. How to efficiently represent and recognize the scenes from such high-resolution image data has become a critical task. In this paper, we propose an unsupervised feature learning framework for scene classification. By using the saliency detection algorithm, we extract a representative set of patches from the salient regions in the image data set. These unlabeled data patches are exploited by an unsupervised feature learning method to learn a set of feature extractors which are robust and efficient and do not need elaborately designed descriptors such as the scale-invariant-feature-transform-based algorithm. We show that the statistics generated from the learned feature extractors can characterize a complex scene very well and can produce excellent classification accuracy. In order to reduce overfitting in the feature learning step, we further employ a recently developed regularization method called “dropout,” which has proved to be very effective in image classification. In the experiments, the proposed method was applied to two challenging high-resolution data sets: the UC Merced data set containing 21 different aerial scene categories with a submeter resolution and the Sydney data set containing seven land-use categories with a 60-cm spatial resolution. The proposed method obtained results that were equal to or even better than the previous best results with the UC Merced data set, and it also obtained the highest accuracy with the Sydney data set, demonstrating that the proposed unsupervised-feature-learning-based scene classification method provides more accurate classification results than the other latent-Dirichlet-allocation-based methods and the sparse coding method.

Recent progress in magnetic iron oxide–semiconductor composite nanomaterials as promising photocatalysts

Abstract: Photocatalytic degradation of toxic organic pollutants is a challenging tasks in ecological and environmental protection. Recent research shows that the magnetic iron oxide–semiconductor composite photocatalytic system can effectively break through the bottleneck of single-component semiconductor oxides with low activity under visible light and the challenging recycling of the photocatalyst from the final products. With high reactivity in visible light, magnetic iron oxide–semiconductors can be exploited as an important magnetic recovery photocatalyst (MRP) with a bright future. On this regard, various composite structures, the charge-transfer mechanism and outstanding properties of magnetic iron oxide–semiconductor composite nanomaterials are sketched. The latest synthesis methods and recent progress in the photocatalytic applications of magnetic iron oxide–semiconductor composite nanomaterials are reviewed. The problems and challenges still need to be resolved and development strategies are discussed.

Robust Feature Matching for Remote Sensing Image Registration via Locally Linear Transforming

Abstract: Feature matching, which refers to establishing reliable correspondence between two sets of features (particularly point features), is a critical prerequisite in feature-based registration. In this paper, we propose a flexible and general algorithm, which is called locally linear transforming (LLT), for both rigid and nonrigid feature matching of remote sensing images. We start by creating a set of putative correspondences based on the feature similarity and then focus on removing outliers from the putative set and estimating the transformation as well. We formulate this as a maximum-likelihood estimation of a Bayesian model with hidden/latent variables indicating whether matches in the putative set are outliers or inliers. To ensure the well-posedness of the problem, we develop a local geometrical constraint that can preserve local structures among neighboring feature points, and it is also robust to a large number of outliers. The problem is solved by using the expectation–maximization algorithm (EM), and the closed-form solutions of both rigid and nonrigid transformations are derived in the maximization step. In the nonrigid case, we model the transformation between images in a reproducing kernel Hilbert space (RKHS), and a sparse approximation is applied to the transformation that reduces the method computation complexity to linearithmic. Extensive experiments on real remote sensing images demonstrate accurate results of LLT, which outperforms current state-of-the-art methods, particularly in the case of severe outliers (even up to 80%).

Hierarchical Carbon Framework Wrapped Na3V2(PO4)3 as a Superior High‐Rate and Extended Lifespan Cathode for Sodium‐Ion Batteries

Abstract: Hierarchical carbon framework wrapped Na3 V2 (PO4 )3 (HCF-NVP) is successfully synthesized through chemical vapor deposition on pure Na3 V2 (PO4 )3 particles. Electrochemical experiments show that the HCF-NVP electrode can deliver a large reversible capacity (115 mA h g(-1) at 0.2 C), superior high-rate rate capability (38 mA h g(-1) at 500 C), and ultra-long cycling stability (54% capacity retention after 20 000 cycles).

Instantaneous Real-Time Kinematic Decimeter-Level Positioning with BeiDou Triple-Frequency Signals over Medium Baselines

Abstract: Many applications, such as marine navigation, land vehicles location, etc., require real time precise positioning under medium or long baseline conditions. In this contribution, we develop a model of real-time kinematic decimeter-level positioning with BeiDou Navigation Satellite System (BDS) triple-frequency signals over medium distances. The ambiguities of two extra-wide-lane (EWL) combinations are fixed first, and then a wide lane (WL) combination is reformed based on the two EWL combinations for positioning. Theoretical analysis and empirical analysis is given of the ambiguity fixing rate and the positioning accuracy of the presented method. The results indicate that the ambiguity fixing rate can be up to more than 98% when using BDS medium baseline observations, which is much higher than that of dual-frequency Hatch-Melbourne-Wubbena (HMW) method. As for positioning accuracy, decimeter level accuracy can be achieved with this method, which is comparable to that of carrier-smoothed code differential positioning method. Signal interruption simulation experiment indicates that the proposed method can realize fast high-precision positioning whereas the carrier-smoothed code differential positioning method needs several hundreds of seconds for obtaining high precision results. We can conclude that a relatively high accuracy and high fixing rate can be achieved for triple-frequency WL method with single-epoch observations, displaying significant advantage comparing to traditional carrier-smoothed code differential positioning method.

Pt–Ru catalyzed hydrogen oxidation in alkaline media: oxophilic effect or electronic effect?

Abstract: A current challenge to alkaline polymer electrolyte fuel cells (APEFCs) is the unexpectedly sluggish kinetics of the hydrogen oxidation reaction (HOR). A recently proposed resolution is to enhance the oxophilicity of the catalyst, so as to remove the Had intermediate through the reaction with OHad, but this approach is questioned by other researchers. Here we report a clear and convincing test on this problem. By using PtRu/C as the HOR catalyst for the APEFC, the peak power density is boosted to 1.0 W cm−2, in comparison to 0.6 W cm−2 when using Pt/C as the anode catalyst. Such a remarkable improvement, however, can hardly be explained as an oxophilic effect, because, as monitored by CO stripping, reactive hydroxyl species can generate on certain sites of the Pt surface at more negative potentials than on the PtRu surface in KOH solution. Rather, the incorporation of Ru has posed an electronic effect on weakening the Pt–Had interaction, as revealed by the voltammetric behavior and from density-functional calculations, which thus benefits the oxidative desorption of Had, the rate determining step of HOR in alkaline media. These findings further our fundamental understanding of the HOR catalysis, and cast new light on the exploration of better catalysts for APEFCs.

Surface Plasmon‐Enhanced Photodetection in Few Layer MoS2 Phototransistors with Au Nanostructure Arrays

Abstract: 2D Molybdenum disulfide (MoS2 ) is a promising candidate material for high-speed and flexible optoelectronic devices, but only with low photoresponsivity. Here, a large enhancement of photocurrent response is obtained by coupling few-layer MoS2 with Au plasmonic nanostructure arrays. Au nanoparticles or nanoplates placed onto few-layer MoS2 surface can enhance the local optical field in the MoS2 layer, due to the localized surface plasmon (LSP) resonance. After depositing 4 nm thick Au nanoparticles sparsely onto few-layer MoS2 phototransistors, a doubled increase in the photocurrent response is observed. The photocurrent of few-layer MoS2 phototransistors exhibits a threefold enhancement with periodic Au nanoarrays. The simulated optical field distribution confirms that light can be trapped and enhanced near the Au nanoplates. These findings offer an avenue for practical applications of high performance MoS2 -based optoelectronic devices or systems in the future.

The CHEMDNER corpus of chemicals and drugs and its annotation principles.

Abstract: The automatic extraction of chemical information from text requires the recognition of chemical entity mentions as one of its key steps. When developing supervised named entity recognition (NER) systems, the availability of a large, manually annotated text corpus is desirable. Furthermore, large corpora permit the robust evaluation and comparison of different approaches that detect chemicals in documents. We present the CHEMDNER corpus, a collection of 10,000 PubMed abstracts that contain a total of 84,355 chemical entity mentions labeled manually by expert chemistry literature curators, following annotation guidelines specifically defined for this task. The abstracts of the CHEMDNER corpus were selected to be representative for all major chemical disciplines. Each of the chemical entity mentions was manually labeled according to its structure-associated chemical entity mention (SACEM) class: abbreviation, family, formula, identifier, multiple, systematic and trivial. The difficulty and consistency of tagging chemicals in text was measured using an agreement study between annotators, obtaining a percentage agreement of 91. For a subset of the CHEMDNER corpus (the test set of 3,000 abstracts) we provide not only the Gold Standard manual annotations, but also mentions automatically detected by the 26 teams that participated in the BioCreative IV CHEMDNER chemical mention recognition task. In addition, we release the CHEMDNER silver standard corpus of automatically extracted mentions from 17,000 randomly selected PubMed abstracts. A version of the CHEMDNER corpus in the BioC format has been generated as well. We propose a standard for required minimum information about entity annotations for the construction of domain specific corpora on chemical and drug entities. The CHEMDNER corpus and annotation guidelines are available at: http://www.biocreative.org/resources/biocreative-iv/chemdner-corpus/

Efficient hole-blocking layer-free planar halide perovskite thin-film solar cells.

Abstract: Lead halide perovskite solar cells use hole-blocking layers to allow a separate collection of positive and negative charge carriers and to achieve high-operation voltages. Here, the authors demonstrate efficient lead halide perovskite solar cells that avoid using this extra layer.