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Showing papers by "Sun Yat-sen University published in 2018"


Journal ArticleDOI
Nabila Aghanim1, Yashar Akrami2, Yashar Akrami3, Yashar Akrami4  +229 moreInstitutions (70)
TL;DR: In this paper, the cosmological parameter results from the final full-mission Planck measurements of the CMB anisotropies were presented, with good consistency with the standard spatially-flat 6-parameter CDM cosmology having a power-law spectrum of adiabatic scalar perturbations from polarization, temperature, and lensing separately and in combination.
Abstract: We present cosmological parameter results from the final full-mission Planck measurements of the CMB anisotropies. We find good consistency with the standard spatially-flat 6-parameter $\Lambda$CDM cosmology having a power-law spectrum of adiabatic scalar perturbations (denoted "base $\Lambda$CDM" in this paper), from polarization, temperature, and lensing, separately and in combination. A combined analysis gives dark matter density $\Omega_c h^2 = 0.120\pm 0.001$, baryon density $\Omega_b h^2 = 0.0224\pm 0.0001$, scalar spectral index $n_s = 0.965\pm 0.004$, and optical depth $\tau = 0.054\pm 0.007$ (in this abstract we quote $68\,\%$ confidence regions on measured parameters and $95\,\%$ on upper limits). The angular acoustic scale is measured to $0.03\,\%$ precision, with $100\theta_*=1.0411\pm 0.0003$. These results are only weakly dependent on the cosmological model and remain stable, with somewhat increased errors, in many commonly considered extensions. Assuming the base-$\Lambda$CDM cosmology, the inferred late-Universe parameters are: Hubble constant $H_0 = (67.4\pm 0.5)$km/s/Mpc; matter density parameter $\Omega_m = 0.315\pm 0.007$; and matter fluctuation amplitude $\sigma_8 = 0.811\pm 0.006$. We find no compelling evidence for extensions to the base-$\Lambda$CDM model. Combining with BAO we constrain the effective extra relativistic degrees of freedom to be $N_{\rm eff} = 2.99\pm 0.17$, and the neutrino mass is tightly constrained to $\sum m_ u< 0.12$eV. The CMB spectra continue to prefer higher lensing amplitudes than predicted in base -$\Lambda$CDM at over $2\,\sigma$, which pulls some parameters that affect the lensing amplitude away from the base-$\Lambda$CDM model; however, this is not supported by the lensing reconstruction or (in models that also change the background geometry) BAO data. (Abridged)

3,077 citations


Journal ArticleDOI
TL;DR: The blockchain taxonomy is given, the typical blockchain consensus algorithms are introduced, typical blockchain applications are reviewed, and the future directions in the blockchain technology are pointed out.
Abstract: Blockchain has numerous benefits such as decentralisation, persistency, anonymity and auditability. There is a wide spectrum of blockchain applications ranging from cryptocurrency, financial services, risk management, internet of things (IoT) to public and social services. Although a number of studies focus on using the blockchain technology in various application aspects, there is no comprehensive survey on the blockchain technology in both technological and application perspectives. To fill this gap, we conduct a comprehensive survey on the blockchain technology. In particular, this paper gives the blockchain taxonomy, introduces typical blockchain consensus algorithms, reviews blockchain applications and discusses technical challenges as well as recent advances in tackling the challenges. Moreover, this paper also points out the future directions in the blockchain technology.

1,928 citations


Journal ArticleDOI
TL;DR: This work reports the insulin-like growth factor 2 mRNA-binding proteins as a distinct family of m6A readers that target thousands of mRNA transcripts through recognizing the consensus GG(m6A)C sequence, and identifies IGF2BPs as an additional class of N6-methyladenosine (m 6A) reader proteins.
Abstract: N6-methyladenosine (m6A) is the most prevalent modification in eukaryotic messenger RNAs (mRNAs) and is interpreted by its readers, such as YTH domain-containing proteins, to regulate mRNA fate. Here, we report the insulin-like growth factor 2 mRNA-binding proteins (IGF2BPs; including IGF2BP1/2/3) as a distinct family of m6A readers that target thousands of mRNA transcripts through recognizing the consensus GG(m6A)C sequence. In contrast to the mRNA-decay-promoting function of YTH domain-containing family protein 2, IGF2BPs promote the stability and storage of their target mRNAs (for example, MYC) in an m6A-dependent manner under normal and stress conditions and therefore affect gene expression output. Moreover, the K homology domains of IGF2BPs are required for their recognition of m6A and are critical for their oncogenic functions. Thus, our work reveals a different facet of the m6A-reading process that promotes mRNA stability and translation, and highlights the functional importance of IGF2BPs as m6A readers in post-transcriptional gene regulation and cancer biology.

1,373 citations


Journal ArticleDOI
21 Dec 2018-Science
TL;DR: A dysprosium compound is reported that manifests magnetic hysteresis at temperatures up to 80 kelvin, which overcomes an essential barrier toward the development of nanomagnet devices that function at practical temperatures.
Abstract: Single-molecule magnets (SMMs) containing only one metal center may represent the lower size limit for molecule-based magnetic information storage materials. Their current drawback is that all SMMs require liquid-helium cooling to show magnetic memory effects. We now report a chemical strategy to access the dysprosium metallocene cation [(CpiPr5)Dy(Cp*)]+ (CpiPr5 = penta-iso-propylcyclopentadienyl, Cp* = pentamethylcyclopentadienyl), which displays magnetic hysteresis above liquid-nitrogen temperatures. An effective energy barrier to reversal of the magnetization of Ueff = 1,541 cm–1 is also measured. The magnetic blocking temperature of TB = 80 K for this cation overcomes an essential barrier towards the development of nanomagnet devices that function at practical temperatures.

1,198 citations


Book ChapterDOI
08 Sep 2018
TL;DR: Zhang et al. as discussed by the authors proposed GCN-LSTM with attention mechanism to explore the connections between objects for image captioning under the umbrella of attention-based encoder-decoder framework.
Abstract: It is always well believed that modeling relationships between objects would be helpful for representing and eventually describing an image Nevertheless, there has not been evidence in support of the idea on image description generation In this paper, we introduce a new design to explore the connections between objects for image captioning under the umbrella of attention-based encoder-decoder framework Specifically, we present Graph Convolutional Networks plus Long Short-Term Memory (dubbed as GCN-LSTM) architecture that novelly integrates both semantic and spatial object relationships into image encoder Technically, we build graphs over the detected objects in an image based on their spatial and semantic connections The representations of each region proposed on objects are then refined by leveraging graph structure through GCN With the learnt region-level features, our GCN-LSTM capitalizes on LSTM-based captioning framework with attention mechanism for sentence generation Extensive experiments are conducted on COCO image captioning dataset, and superior results are reported when comparing to state-of-the-art approaches More remarkably, GCN-LSTM increases CIDEr-D performance from 1201% to 1287% on COCO testing set

775 citations


Journal ArticleDOI
TL;DR: Endogenous circRNA encodes a functional protein in human cells, and circ-FBXW7 and FBXW 7-185aa have potential prognostic implications in brain cancer.
Abstract: Background Circular RNAs (circRNAs) are RNA transcripts that are widespread in the eukaryotic genome. Recent evidence indicates that circRNAs play important roles in tissue development, gene regulation, and carcinogenesis. However, whether circRNAs encode functional proteins remains elusive, although translation of several circRNAs was recently reported.

766 citations


Journal ArticleDOI
08 Feb 2018-Cell
TL;DR: It is demonstrated that two cell-surface molecules, CD10 and GPR77, specifically define a CAF subset correlated with chemoresistance and poor survival in multiple cohorts of breast and lung cancer patients, and suggested that targeting the CD10+GPR77+ CAFs subset could be an effective therapeutic strategy against CSC-driven solid tumors.

748 citations


Journal ArticleDOI
TL;DR: In this review, crystal-field theory is employed to demonstrate the electronic structures according to the semiquantitative electrostatic model and specific symmetry elements are analysed for the elimination of transverse crystal fields and quantum tunnelling of magnetization (QTM).
Abstract: Toward promising candidates of quantum information processing, the rapid development of lanthanide-based single-molecule magnets (Ln-SMMs) highlights design strategies in consideration of the local symmetry of lanthanide ions. In this review, crystal-field theory is employed to demonstrate the electronic structures according to the semiquantitative electrostatic model. Then, specific symmetry elements are analysed for the elimination of transverse crystal fields and quantum tunnelling of magnetization (QTM). In this way, high-performance Ln-SMMs can be designed to enable extremely slow relaxation of magnetization, namely magnetic blocking; however, their practical magnetic characterization becomes increasingly challenging. Therefore, we will attempt to interpret the experimental behaviours and clarify some issues in detail. Finally, representative Ln-SMMs with specific local symmetries are summarized in combination with the discussion on the symmetry strategies, and some of the underlying questions are put forward.

705 citations


Journal ArticleDOI
TL;DR: The engineered c(RGDyK)-conjugated exosomes (cRGD-Exo) target the lesion region of the ischemic brain after intravenous administration and provide a strategy for the rapid and large-scale production of functionalized exosome.

629 citations


Journal ArticleDOI
TL;DR: In this paper, the authors carried out geological and paleomagnetic investigations on East Asian blocks and associated orogenic belts, supported by a NSFC Major Program entitled “Reconstructions of East Asian Blocks in Pangea”.

533 citations


Journal ArticleDOI
TL;DR: A deep learning system can detect referable GON with high sensitivity and specificity and coexistence of high or pathologic myopia is the most common cause resulting in false-negative results.

Journal ArticleDOI
TL;DR: The strong interactions between Cu and Ni3S2 cause the Cu NDs/Ni 3S2 NTs-CFs electrocatalysts to exhibit the outstanding HER catalytic performance with low onset potential, high catalytic activity, and excellent stability.
Abstract: Low-cost transition-metal dichalcogenides (MS2) have attracted great interest as alternative catalysts for hydrogen evolution. However, a significant challenge is the formation of sulfur-hydrogen bonds on MS2 (S-Hads), which will severely suppress hydrogen evolution reaction (HER). Here we report Cu nanodots (NDs)-decorated Ni3S2 nanotubes (NTs) supported on carbon fibers (CFs) (Cu NDs/Ni3S2 NTs-CFs) as efficient electrocatalysts for HER in alkaline media. The electronic interactions between Cu and Ni3S2 make that Cu NDs are positively charged and can promote water adsorption and activation. Meanwhile, Ni3S2 NTs are negatively charged and can weaken S-Hads bonds formed on catalyst surfaces. Therefore, the Cu/Ni3S2 hybrids can optimize H adsorption and desorption on electrocatalysts and can promote both Volmer and Heyrovsky steps of HER. The strong interactions between Cu and Ni3S2 make that the Cu NDs/Ni3S2 NTs-CFs electrocatalysts exhibit the outstanding HER catalytic performance with low onset potential...

Proceedings ArticleDOI
18 Jun 2018
TL;DR: A novel two-step framework is proposed, in which a Generative Adversarial Network is trained to estimate the noise distribution over the input noisy images and to generate noise samples to train a deep Convolutional Neural Network for denoising.
Abstract: In this paper, we consider a typical image blind denoising problem, which is to remove unknown noise from noisy images. As we all know, discriminative learning based methods, such as DnCNN, can achieve state-of-the-art denoising results, but they are not applicable to this problem due to the lack of paired training data. To tackle the barrier, we propose a novel two-step framework. First, a Generative Adversarial Network (GAN) is trained to estimate the noise distribution over the input noisy images and to generate noise samples. Second, the noise patches sampled from the first step are utilized to construct a paired training dataset, which is used, in turn, to train a deep Convolutional Neural Network (CNN) for denoising. Extensive experiments have been done to demonstrate the superiority of our approach in image blind denoising.

Proceedings ArticleDOI
18 Jun 2018
TL;DR: In this article, a multi-level wavelet CNN (MWCNN) model is proposed for image denoising, single image super-resolution, and JPEG image artifacts removal, which can be applied to many image restoration tasks.
Abstract: The tradeoff between receptive field size and efficiency is a crucial issue in low level vision. Plain convolutional networks (CNNs) generally enlarge the receptive field at the expense of computational cost. Recently, dilated filtering has been adopted to address this issue. But it suffers from gridding effect, and the resulting receptive field is only a sparse sampling of input image with checkerboard patterns. In this paper, we present a novel multi-level wavelet CNN (MWCNN) model for better tradeoff between receptive field size and computational efficiency. With the modified U-Net architecture, wavelet transform is introduced to reduce the size of feature maps in the contracting subnetwork. Furthermore, another convolutional layer is further used to decrease the channels of feature maps. In the expanding subnetwork, inverse wavelet transform is then deployed to reconstruct the high resolution feature maps. Our MWCNN can also be explained as the generalization of dilated filtering and subsampling, and can be applied to many image restoration tasks. The experimental results clearly show the effectiveness of MWCNN for image denoising, single image super-resolution, and JPEG image artifacts removal.

Journal ArticleDOI
18 Jan 2018-Oncogene
TL;DR: It is shown that a circRNA containing an open reading frame (ORF) driven by the internal ribosome entry site (IRES) can translate a functional protein, which is generated from overlapping genetic codes of circ-SHPRH and is a tumor suppressor in human glioblastoma.
Abstract: Circular RNAs (circRNAs) are recognized as functional non-coding transcripts in eukaryotic cells. Recent evidence has indicated that even though circRNAs are generally expressed at low levels, they may be involved in many physiological or pathological processes, such as gene regulation, tissue development and carcinogenesis. Although the ‘microRNA sponge’ function is well characterized, most circRNAs do not contain perfect trapping sites for microRNAs, which suggests the possibility that circRNAs have functions that have not yet been defined. In this study, we show that a circRNA containing an open reading frame (ORF) driven by the internal ribosome entry site (IRES) can translate a functional protein. The circular form of the SNF2 histone linker PHD RING helicase (SHPRH) gene encodes a novel protein that we termed SHPRH-146aa. Circular SHPRH (circ-SHPRH) uses overlapping genetic codes to generate a ‘UGA’ stop codon, which results in the translation of the 17 kDa SHPRH-146aa. Both circ-SHPRH and SHPRH-146aa are abundantly expressed in normal human brains and are down-regulated in glioblastoma. The overexpression of SHPRH-146aa in U251 and U373 glioblastoma cells reduces their malignant behavior and tumorigenicity in vitro and in vivo. Mechanistically, SHPRH-146aa protects full-length SHPRH from degradation by the ubiquitin proteasome. Stabilized SHPRH sequentially ubiquitinates proliferating cell nuclear antigen (PCNA) as an E3 ligase, leading to inhibited cell proliferation and tumorigenicity. Our findings provide a novel perspective regarding circRNA function in physiological and pathological processes. Specifically, SHPRH-146aa generated from overlapping genetic codes of circ-SHPRH is a tumor suppressor in human glioblastoma.

Journal ArticleDOI
TL;DR: The fabricated P-NiCo2 O4-x //Zn battery presents an impressive specific capacity of 361.3 mAh g-1 at the high current density and extremely high energy density and power density are achieved, which outperforms most of the previously reported aqueous Zn-ion batteries.
Abstract: The development of high-capacity, Earth-abundant, and stable cathode materials for robust aqueous Zn-ion batteries is an ongoing challenge. Herein, ultrathin nickel cobaltite (NiCo2 O4 ) nanosheets with enriched oxygen vacancies and surface phosphate ions (P-NiCo2 O4-x ) are reported as a new high-energy-density cathode material for rechargeable Zn-ion batteries. The oxygen-vacancy and surface phosphate-ion modulation are achieved by annealing the pristine NiCo2 O4 nanosheets using a simple phosphating process. Benefiting from the merits of substantially improved electrical conductivity and increased concentration of active sites, the optimized P-NiCo2 O4-x nanosheet electrode delivers remarkable capacity (309.2 mAh g-1 at 6.0 A g-1 ) and extraordinary rate performance (64% capacity retention at 60.4 A g-1 ). Moreover, based on the P-NiCo2 O4-x cathode, our fabricated P-NiCo2 O4-x //Zn battery presents an impressive specific capacity of 361.3 mAh g-1 at the high current density of 3.0 A g-1 in an alkaline electrolyte. Furthermore, extremely high energy density (616.5 Wh kg-1 ) and power density (30.2 kW kg-1 ) are also achieved, which outperforms most of the previously reported aqueous Zn-ion batteries. This ultrafast and high-energy aqueous Zn-ion battery is promising for widespread application to electric vehicles and intelligent devices.

Journal ArticleDOI
TL;DR: It is shown that BAP1 suppresses SLC7A11 expression and cystine uptake, thereby promoting ferroptosis and inhibiting tumour growth and uncovering a previously unappreciated epigenetic mechanism coupling ferroPTosis to tumour suppression.
Abstract: The roles and regulatory mechanisms of ferroptosis (a non-apoptotic form of cell death) in cancer remain unclear The tumour suppressor BRCA1-associated protein 1 (BAP1) encodes a nuclear deubiquitinating enzyme to reduce histone 2A ubiquitination (H2Aub) on chromatin Here, integrated transcriptomic, epigenomic and cancer genomic analyses link BAP1 to metabolism-related biological processes, and identify cystine transporter SLC7A11 as a key BAP1 target gene in human cancers Functional studies reveal that BAP1 decreases H2Aub occupancy on the SLC7A11 promoter and represses SLC7A11 expression in a deubiquitinating-dependent manner, and that BAP1 inhibits cystine uptake by repressing SLC7A11 expression, leading to elevated lipid peroxidation and ferroptosis Furthermore, we show that BAP1 inhibits tumour development partly through SLC7A11 and ferroptosis, and that cancer-associated BAP1 mutants lose their abilities to repress SLC7A11 and to promote ferroptosis Together, our results uncover a previously unappreciated epigenetic mechanism coupling ferroptosis to tumour suppression

Journal ArticleDOI
TL;DR: A concept of spatial dependency system that involves pixel dependency and label dependency, with two main factors: neighborhood covering and neighborhood importance is developed, and several representative spectral–spatial classification methods are applied on real-world hyperspectral data.
Abstract: Imaging spectroscopy, also known as hyperspectral imaging, has been transformed in the last four decades from being a sparse research tool into a commodity product available to a broad user community. Specially, in the last 10 years, a large number of new techniques able to take into account the special properties of hyperspectral data have been introduced for hyperspectral data processing, where hyperspectral image classification, as one of the most active topics, has drawn massive attentions. Spectral–spatial hyperspectral image classification can achieve better classification performance than its pixel-wise counterpart, since the former utilizes not only the information of spectral signature but also that from spatial domain. In this paper, we provide a comprehensive overview on the methods belonging to the category of spectral–spatial classification in a relatively unified context. First, we develop a concept of spatial dependency system that involves pixel dependency and label dependency, with two main factors: neighborhood covering and neighborhood importance. In terms of the way that the neighborhood information is used, the spatial dependency systems can be classified into fixed, adaptive, and global systems, which can accommodate various kinds of existing spectral–spatial methods. Based on such, the categorizations of single-dependency, bilayer-dependency, and multiple-dependency systems are further introduced. Second, we categorize the performings of existing spectral–spatial methods into four paradigms according to the different fusion stages wherein spatial information takes effect, i.e., preprocessing-based, integrated, postprocessing-based, and hybrid classifications. Then, typical methodologies are outlined. Finally, several representative spectral–spatial classification methods are applied on real-world hyperspectral data in our experiments.

Journal ArticleDOI
TL;DR: An overview of recent studies on transition metal activated phosphors can be found in this article, including detailed synthesis routes (solid-state reaction and wet-chemical synthesis) and description of luminescence mechanisms and phosphors' behaviors; discuss their promising applications in white light-emitting diodes.
Abstract: Transition-metal activated phosphors are an important family of luminescent materials that can produce white light with an outstanding color rendering index and correlated color temperature for use in light-emitting diodes. In recent years, work in this quite “hot” research field has focused on the development of Mn2+ and Mn4+ activated red phosphors. In this review article, we provide an overview of recent studies on Mn2+ and Mn4+ doped phosphors, including detailed synthesis routes (solid-state reaction and wet-chemical synthesis) and description of luminescence mechanisms and phosphors’ behaviors; discuss their promising applications in white light-emitting diodes; and present an extensive list of references to representative works in this field.

Journal ArticleDOI
TL;DR: A 87-amino-acid peptide encoded by the circular form of the long intergenic non-protein-coding RNA p53-induced transcript (LINC-PINT) is identified that can reduce glioblastoma proliferation via interaction with PAF1 which sequentially inhibits the transcriptional elongation of some oncogenes.
Abstract: Circular RNAs (circRNAs) are a large class of transcripts in the mammalian genome. Although the translation of circRNAs was reported, additional coding circRNAs and the functions of their translated products remain elusive. Here, we demonstrate that an endogenous circRNA generated from a long noncoding RNA encodes regulatory peptides. Through ribosome nascent-chain complex-bound RNA sequencing (RNC-seq), we discover several peptides potentially encoded by circRNAs. We identify an 87-amino-acid peptide encoded by the circular form of the long intergenic non-protein-coding RNA p53-induced transcript (LINC-PINT) that suppresses glioblastoma cell proliferation in vitro and in vivo. This peptide directly interacts with polymerase associated factor complex (PAF1c) and inhibits the transcriptional elongation of multiple oncogenes. The expression of this peptide and its corresponding circRNA are decreased in glioblastoma compared with the levels in normal tissues. Our results establish the existence of peptides encoded by circRNAs and demonstrate their potential functions in glioblastoma tumorigenesis.

Proceedings Article
27 Sep 2018
TL;DR: SNAS as mentioned in this paper reformulates the architecture search problem as an optimization problem on parameters of a joint distribution for the search space in a cell and proposes a search gradient to leverage the gradient information in generic differentiable loss for architecture search.
Abstract: We propose Stochastic Neural Architecture Search (SNAS), an economical end-to-end solution to Neural Architecture Search (NAS) that trains neural operation parameters and architecture distribution parameters in same round of back-propagation, while maintaining the completeness and differentiability of the NAS pipeline. In this work, NAS is reformulated as an optimization problem on parameters of a joint distribution for the search space in a cell. To leverage the gradient information in generic differentiable loss for architecture search, a novel search gradient is proposed. We prove that this search gradient optimizes the same objective as reinforcement-learning-based NAS, but assigns credits to structural decisions more efficiently. This credit assignment is further augmented with locally decomposable reward to enforce a resource-efficient constraint. In experiments on CIFAR-10, SNAS takes less epochs to find a cell architecture with state-of-the-art accuracy than non-differentiable evolution-based and reinforcement-learning-based NAS, which is also transferable to ImageNet. It is also shown that child networks of SNAS can maintain the validation accuracy in searching, with which attention-based NAS requires parameter retraining to compete, exhibiting potentials to stride towards efficient NAS on big datasets. We have released our implementation at this https URL.

Journal ArticleDOI
TL;DR: A novel electricity-theft detection method based on wide and deep convolutional neural networks (CNN) model that outperforms other existing methods in detection accuracy and captures the global features of 1-D electricity consumption data.
Abstract: Electricity theft is harmful to power grids. Integrating information flows with energy flows, smart grids can help to solve the problem of electricity theft owning to the availability of massive data generated from smart grids. The data analysis on the data of smart grids is helpful in detecting electricity theft because of the abnormal electricity consumption pattern of energy thieves. However, the existing methods have poor detection accuracy of electricity theft since most of them were conducted on one-dimensional (1-D) electricity consumption data and failed to capture the periodicity of electricity consumption. In this paper, we originally propose a novel electricity-theft detection method based on wide and deep convolutional neural networks (CNN) model to address the above concerns. In particular, wide and deep CNN model consists of two components: the wide component and the deep CNN component. The deep CNN component can accurately identify the nonperiodicity of electricity theft and the periodicity of normal electricity usage based on 2-D electricity consumption data. Meanwhile, the wide component can capture the global features of 1-D electricity consumption data. As a result, wide and deep CNN model can achieve the excellent performance in electricity-theft detection. Extensive experiments based on realistic dataset show that wide and deep CNN model outperforms other existing methods.

Journal ArticleDOI
TL;DR: The attack model for IoT systems is investigated, and the IoT security solutions based on machine-learning (ML) techniques including supervised learning, unsupervised learning, and reinforcement learning (RL) are reviewed.
Abstract: The Internet of things (IoT), which integrates a variety of devices into networks to provide advanced and intelligent services, has to protect user privacy and address attacks such as spoofing attacks, denial of service (DoS) attacks, jamming, and eavesdropping. We investigate the attack model for IoT systems and review the IoT security solutions based on machine-learning (ML) techniques including supervised learning, unsupervised learning, and reinforcement learning (RL). ML-based IoT authentication, access control, secure offloading, and malware detection schemes to protect data privacy are the focus of this article. We also discuss the challenges that need to be addressed to implement these ML-based security schemes in practical IoT systems.

Journal ArticleDOI
Sheng-Hua Ye1, Zi-Xiao Shi1, Jin-Xian Feng1, Yexiang Tong1, Gao-Ren Li1 
TL;DR: Iron-substituted CoOOH porous nanosheet arrays grown on carbon fiber cloth with 3D hierarchical structures synthesized by in situ anodic oxidation of α-Co(OH)2 NSAs/CFC shows superior OER electrocatalytic performance, with a low overpotential, small Tafel slope of 30 mV dec-1, and high durability.
Abstract: Iron-substituted CoOOH porous nanosheet arrays grown on carbon fiber cloth (denoted as Fex Co1-x OOH PNSAs/CFC, 0≤x≤0.33) with 3D hierarchical structures are synthesized by in situ anodic oxidation of α-Co(OH)2 NSAs/CFC in solution of 0.01 m (NH4 )2 Fe(SO4 )2 . X-ray absorption fine spectra (XAFS) demonstrate that CoO6 octahedral structure in CoOOH can be partially substituted by FeO6 octahedrons during the transformation from α-Co(OH)2 to Fex Co1-x OOH, and this is confirmed for the first time in this study. The content of Fe in Fex Co1-x OOH, no more than 1/3 of Co, can be controlled by adjusting the in situ anodic oxidation time. Fe0.33 Co0.67 OOH PNSAs/CFC shows superior OER electrocatalytic performance, with a low overpotential of 266 mV at 10 mA cm-2 , small Tafel slope of 30 mV dec-1 , and high durability.

Journal ArticleDOI
TL;DR: In this article, the authors developed the multi-temporal global urban land maps based on Landsat images for the 1990-2010 period with a five-year interval (i.e., artificial cover and structures such as pavement, concrete, brick, stone and other man-made impenetrable cover types).

Journal ArticleDOI
TL;DR: The exciting progress in the development of MSNs-based effective delivery systems for poorly soluble drugs, anticancer agents, and therapeutic genes are highlighted.

Journal ArticleDOI
TL;DR: Density functional theory (DFT) calculation suggests that the unique structure of the W1 N1 C3 moiety plays an important role in enhancing the HER performance, and gives new insights into the investigation of efficient and practical W-based HER catalysts.
Abstract: Tungsten-based catalysts are promising candidates to generate hydrogen effectively. In this work, a single-W-atom catalyst supported on metal-organic framework (MOF)-derived N-doped carbon (W-SAC) for efficient electrochemical hydrogen evolution reaction (HER), with high activity and excellent stability is reported. High-angle annular dark-field scanning transmission electron microscopy (HAADF-STEM) and X-ray absorption fine structure (XAFS) spectroscopy analysis indicate the atomic dispersion of the W species, and reveal that the W1 N1 C3 moiety may be the favored local structure for the W species. The W-SAC exhibits a low overpotential of 85 mV at a current density of 10 mA cm-2 and a small Tafel slope of 53 mV dec-1 , in 0.1 m KOH solution. The HER activity of the W-SAC is almost equal to that of commercial Pt/C. Density functional theory (DFT) calculation suggests that the unique structure of the W1 N1 C3 moiety plays an important role in enhancing the HER performance. This work gives new insights into the investigation of efficient and practical W-based HER catalysts.

Journal ArticleDOI
19 Sep 2018-Nature
TL;DR: It is shown that METTL3 enhances translation only when tethered to reporter mRNA at sites close to the stop codon, supporting a mechanism of mRNA looping for ribosome recycling and translational control.
Abstract: N6-Methyladenosine (m6A), the most abundant posttranscriptional messenger RNA (mRNA) modification, is emerging as an important regulator of gene expression1 Manipulation of m6A impacts different developmental and biological processes, and altered m6A homeostasis is linked to cancer2-5 m6A is catalyzed by METTL3 and enriched in the 3’ untranslated region (3’ UTR) of a large subset of mRNAs at sites close to the stop codon1 METTL3 can promote translation but the mechanism and widespread relevance remain unknown2 Here we show that METTL3 enhances translation only when tethered to reporter mRNA at sites close to the stop codon supporting a mRNA looping mechanism for ribosome recycling and translational control Electron microscopy reveals the topology of individual polyribosomes with single METTL3 foci found in close proximity to 5’ cap-binding proteins We identify a direct physical and functional interaction between METTL3 and the eukaryotic translation initiation factor 3 subunit h (eIF3h) METTL3 promotes translation of a large subset of oncogenic mRNAs, including Bromodomain-containing protein 4 (BRD4) that are also m6A-modified in human primary lung tumors The METTL3-eIF3h interaction is required for enhanced translation, formation of densely packed polyribosomes, and oncogenic transformation METTL3 depletion inhibits tumorigenicity and sensitizes lung cancer cells to BRD4 inhibition These findings uncover a mRNA looping mechanism of translation control and identify METTL3-eIF3h as a potential cancer therapeutic target

Journal ArticleDOI
TL;DR: An overview of deep learning techniques and some of the state-of-the-art applications in the biomedical field is provided, including medical image classification, genomic sequence analysis, as well as protein structure classification and prediction.

Journal ArticleDOI
TL;DR: A protocol for performing reliable and reproducible measurements of the advancing contact angle (ACA) and the receding contact angles (RCA) by slowly increasing and reducing the volume of a probe drop, respectively.
Abstract: Wetting, the process of water interacting with a surface, is critical in our everyday lives and in many biological and technological systems. The contact angle is the angle at the interface where water, air and solid meet, and its value is a measure of how likely the surface is to be wetted by the water. Low contact-angle values demonstrate a tendency of the water to spread and adhere to the surface, whereas high contact-angle values show the surface’s tendency to repel water. The most common method for surface-wetting characterization is sessile-drop goniometry, due to its simplicity. The method determines the contact angle from the shape of the droplet and can be applied to a wide variety of materials, from biological surfaces to polymers, metals, ceramics, minerals and so on. The apparent simplicity of the method is misleading, however, and obtaining meaningful results requires minimization of random and systematic errors. This article provides a protocol for performing reliable and reproducible measurements of the advancing contact angle (ACA) and the receding contact angle (RCA) by slowly increasing and reducing the volume of a probe drop, respectively. One pair of ACA and RCA measurements takes ~15–20 min to complete, whereas the whole protocol with repeat measurements may take ~1–2 h. This protocol focuses on using water as a probe liquid, and advice is given on how it can be modified for the use of other probe liquids.