Showing papers by "Wuhan University published in 2019"

Reactive Oxygen Species-Induced Lipid Peroxidation in Apoptosis, Autophagy, and Ferroptosis.

Abstract: Reactive oxygen species- (ROS-) induced lipid peroxidation plays a critical role in cell death including apoptosis, autophagy, and ferroptosis. This fundamental and conserved mechanism is based on an excess of ROS which attacks biomembranes, propagates lipid peroxidation chain reactions, and subsequently induces different types of cell death. A highly evolved sophisticated antioxidant system exists that acts to protect the cells from oxidative damage. In this review, we discussed how ROS propagate lipid peroxidation chain reactions and how the products of lipid peroxidation initiate apoptosis and autophagy in current models. We also discussed the mechanism of lipid peroxidation during ferroptosis, and we summarized lipid peroxidation in pathological conditions of critical illness. We aim to bring a more global and integrative sight to know how different ROS-induced lipid peroxidation occurs among apoptosis, autophagy, and ferroptosis.

Fully Convolutional Networks for Multisource Building Extraction From an Open Aerial and Satellite Imagery Data Set

Abstract: The application of the convolutional neural network has shown to greatly improve the accuracy of building extraction from remote sensing imagery. In this paper, we created and made open a high-quality multisource data set for building detection, evaluated the accuracy obtained in most recent studies on the data set, demonstrated the use of our data set, and proposed a Siamese fully convolutional network model that obtained better segmentation accuracy. The building data set that we created contains not only aerial images but also satellite images covering 1000 km2 with both raster labels and vector maps. The accuracy of applying the same methodology to our aerial data set outperformed several other open building data sets. On the aerial data set, we gave a thorough evaluation and comparison of most recent deep learning-based methods, and proposed a Siamese U-Net with shared weights in two branches, and original images and their down-sampled counterparts as inputs, which significantly improves the segmentation accuracy, especially for large buildings. For multisource building extraction, the generalization ability is further evaluated and extended by applying a radiometric augmentation strategy to transfer pretrained models on the aerial data set to the satellite data set. The designed experiments indicate our data set is accurate and can serve multiple purposes including building instance segmentation and change detection; our result shows the Siamese U-Net outperforms current building extraction methods and could provide valuable reference.

Guidelines for the use of flow cytometry and cell sorting in immunological studies (second edition)

Abstract: These guidelines are a consensus work of a considerable number of members of the immunology and flow cytometry community. They provide the theory and key practical aspects of flow cytometry enabling immunologists to avoid the common errors that often undermine immunological data. Notably, there are comprehensive sections of all major immune cell types with helpful Tables detailing phenotypes in murine and human cells. The latest flow cytometry techniques and applications are also described, featuring examples of the data that can be generated and, importantly, how the data can be analysed. Furthermore, there are sections detailing tips, tricks and pitfalls to avoid, all written and peer-reviewed by leading experts in the field, making this an essential research companion.

Learning RoI Transformer for Oriented Object Detection in Aerial Images

Abstract: Object detection in aerial images is an active yet challenging task in computer vision because of the bird’s-eye view perspective, the highly complex backgrounds, and the variant appearances of objects. Especially when detecting densely packed objects in aerial images, methods relying on horizontal proposals for common object detection often introduce mismatches between the Region of Interests (RoIs) and objects. This leads to the common misalignment between the final object classification confidence and localization accuracy. In this paper, we propose a RoI Transformer to address these problems. The core idea of RoI Transformer is to apply spatial transformations on RoIs and learn the transformation parameters under the supervision of oriented bounding box (OBB) annotations. RoI Transformer is with lightweight and can be easily embedded into detectors for oriented object detection. Simply apply the RoI Transformer to light head RCNN has achieved state-of-the-art performances on two common and challenging aerial datasets, i.e., DOTA and HRSC2016, with a neglectable reduction to detection speed. Our RoI Transformer exceeds the deformable Position Sensitive RoI pooling when oriented bounding-box annotations are available. Extensive experiments have also validated the flexibility and effectiveness of our RoI Transformer.

Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learning

Abstract: Federated learning, i.e., a mobile edge computing framework for deep learning, is a recent advance in privacy-preserving machine learning, where the model is trained in a decentralized manner by the clients, i.e., data curators, preventing the server from directly accessing those private data from the clients. This learning mechanism significantly challenges the attack from the server side. Although the state-of-the-art attacking techniques that incorporated the advance of Generative adversarial networks (GANs) could construct class representatives of the global data distribution among all clients, it is still challenging to distinguishably attack a specific client (i.e., user-level privacy leakage), which is a stronger privacy threat to precisely recover the private data from a specific client. This paper gives the first attempt to explore user-level privacy leakage against the federated learning by the attack from a malicious server. We propose a framework incorporating GAN with a multi-task discriminator, which simultaneously discriminates category, reality, and client identity of input samples. The novel discrimination on client identity enables the generator to recover user specified private data. Unlike existing works that tend to interfere the training process of the federated learning, the proposed method works “invisibly” on the server side. The experimental results demonstrate the effectiveness of the proposed attacking approach and the superior to the state-of-the-art.

Graph Attention Convolution for Point Cloud Semantic Segmentation

Abstract: Standard convolution is inherently limited for semantic segmentation of point cloud due to its isotropy about features. It neglects the structure of an object, results in poor object delineation and small spurious regions in the segmentation result. This paper proposes a novel graph attention convolution (GAC), whose kernels can be dynamically carved into specific shapes to adapt to the structure of an object. Specifically, by assigning proper attentional weights to different neighboring points, GAC is designed to selectively focus on the most relevant part of them according to their dynamically learned features. The shape of the convolution kernel is then determined by the learned distribution of the attentional weights. Though simple, GAC can capture the structured features of point clouds for fine-grained segmentation and avoid feature contamination between objects. Theoretically, we provided a thorough analysis on the expressive capabilities of GAC to show how it can learn about the features of point clouds. Empirically, we evaluated the proposed GAC on challenging indoor and outdoor datasets and achieved the state-of-the-art results in both scenarios.

Topological phases in acoustic and mechanical systems

Abstract: The study of classical wave physics has been reinvigorated by incorporating the concept of the geometric phase, which has its roots in optics, and topological notions that were previously explored in condensed matter physics. Recently, sound waves and a variety of mechanical systems have emerged as excellent platforms that exemplify the universality and diversity of topological phases. In this Review, we introduce the essential physical concepts that underpin various classes of topological phenomena realized in acoustic and mechanical systems: Dirac points, the quantum Hall, quantum spin Hall and valley Hall effects, Floquet topological phases, 3D gapless states and Weyl crystals. This Review describes topological phenomena that can be realized in acoustic and mechanical systems. Methods of symmetry breaking are described, along with the consequences and rich phenomena that emerge.

Global, regional, and national burden of suicide mortality 1990 to 2016: systematic analysis for the Global Burden of Disease Study 2016

Abstract: Objectives To use the estimates from the Global Burden of Disease Study 2016 to describe patterns of suicide mortality globally, regionally, and for 195 countries and territories by age, sex, and Socio-demographic index, and to describe temporal trends between 1990 and 2016. Design Systematic analysis. Main outcome measures Crude and age standardised rates from suicide mortality and years of life lost were compared across regions and countries, and by age, sex, and Socio-demographic index (a composite measure of fertility, income, and education). Results The total number of deaths from suicide increased by 6.7% (95% uncertainty interval 0.4% to 15.6%) globally over the 27 year study period to 817 000 (762 000 to 884 000) deaths in 2016. However, the age standardised mortality rate for suicide decreased by 32.7% (27.2% to 36.6%) worldwide between 1990 and 2016, similar to the decline in the global age standardised mortality rate of 30.6%. Suicide was the leading cause of age standardised years of life lost in the Global Burden of Disease region of high income Asia Pacific and was among the top 10 leading causes in eastern Europe, central Europe, western Europe, central Asia, Australasia, southern Latin America, and high income North America. Rates for men were higher than for women across regions, countries, and age groups, except for the 15 to 19 age group. There was variation in the female to male ratio, with higher ratios at lower levels of Socio-demographic index. Women experienced greater decreases in mortality rates (49.0%, 95% uncertainty interval 42.6% to 54.6%) than men (23.8%, 15.6% to 32.7%). Conclusions Age standardised mortality rates for suicide have greatly reduced since 1990, but suicide remains an important contributor to mortality worldwide. Suicide mortality was variable across locations, between sexes, and between age groups. Suicide prevention strategies can be targeted towards vulnerable populations if they are informed by variations in mortality rates.

CoP-Doped MOF-Based Electrocatalyst for pH-Universal Hydrogen Evolution Reaction

Abstract: Although electrocatalysts based on transition metal phosphides (TMPs) with cationic/anionic doping have been widely studied for hydrogen evolution reaction (HER), the origin of performance enhancement still remains elusive mainly due to the random dispersion of dopants. Herein, we report a controllable partial phosphorization strategy to generate CoP species within the Co-based metal-organic framework (Co-MOF). Density functional theory calculations and experimental results reveal that the electron transfer from CoP to Co-MOF through N-P/N-Co bonds could lead to the optimized adsorption energy of H2 O (ΔG H 2 O * ) and hydrogen (ΔGH* ), which, together with the unique porous structure of Co-MOF, contributes to the remarkable HER performance with an overpotential of 49 mV at a current density of 10 mA cm-2 in 1 m phosphate buffer solution (PBS, pH 7.0). The excellent catalytic performance exceeds almost all the documented TMP-based and non-noble-metal-based electrocatalysts. In addition, the CoP/Co-MOF hybrid also displays Pt-like performance in 0.5 m H2 SO4 and 1 m KOH, with the overpotentials of 27 and 34 mV, respectively, at a current density of 10 mA cm-2 .

DeepCrack: Learning Hierarchical Convolutional Features for Crack Detection

Abstract: Cracks are typical line structures that are of interest in many computer-vision applications. In practice, many cracks, e.g., pavement cracks, show poor continuity and low contrast, which bring great challenges to image-based crack detection by using low-level features. In this paper, we propose DeepCrack-an end-to-end trainable deep convolutional neural network for automatic crack detection by learning high-level features for crack representation. In this method, multi-scale deep convolutional features learned at hierarchical convolutional stages are fused together to capture the line structures. More detailed representations are made in larger scale feature maps and more holistic representations are made in smaller scale feature maps. We build DeepCrack net on the encoder–decoder architecture of SegNet and pairwisely fuse the convolutional features generated in the encoder network and in the decoder network at the same scale. We train DeepCrack net on one crack dataset and evaluate it on three others. The experimental results demonstrate that DeepCrack achieves $F$ -measure over 0.87 on the three challenging datasets in average and outperforms the current state-of-the-art methods.

Recent Advances in Oxidative R1-H/R2-H Cross-Coupling with Hydrogen Evolution via Photo-/Electrochemistry

Abstract: Photo-/electrochemical catalyzed oxidative R1-H/R2-H cross-coupling with hydrogen evolution has become an increasingly important issue for molecular synthesis. The dream of construction of C-C/C-X bonds from readily available C-H/X-H with release of H2 can be facilely achieved without external chemical oxidants, providing a greener model for chemical bond formation. Given the great influence of these reactions in organic chemistry, we give a summary of the state of the art in oxidative R1-H/R2-H cross-coupling with hydrogen evolution via photo/electrochemistry, and we hope this review will stimulate the development of a greener synthetic strategy in the near future.

Large-area graphene-nanomesh/carbon-nanotube hybrid membranes for ionic and molecular nanofiltration.

Abstract: Nanoporous two-dimensional materials are attractive for ionic and molecular nanofiltration but limited by insufficient mechanical strength over large areas. We report a large-area graphene-nanomesh/single-walled carbon nanotube (GNM/SWNT) hybrid membrane with excellent mechanical strength while fully capturing the merit of atomically thin membranes. The monolayer GNM features high-density, subnanometer pores for efficient transport of water molecules while blocking solute ions or molecules to enable size-selective separation. The SWNT network physically separates the GNM into microsized islands and acts as the microscopic framework to support the GNM, thus ensuring the structural integrity of the atomically thin GNM. The resulting GNM/SWNT membranes show high water permeance and a high rejection ratio for salt ions or organic molecules, and they retain stable separation performance in tubular modules.

Locality Preserving Matching

Abstract: Seeking reliable correspondences between two feature sets is a fundamental and important task in computer vision. This paper attempts to remove mismatches from given putative image feature correspondences. To achieve the goal, an efficient approach, termed as locality preserving matching (LPM), is designed, the principle of which is to maintain the local neighborhood structures of those potential true matches. We formulate the problem into a mathematical model, and derive a closed-form solution with linearithmic time and linear space complexities. Our method can accomplish the mismatch removal from thousands of putative correspondences in only a few milliseconds. To demonstrate the generality of our strategy for handling image matching problems, extensive experiments on various real image pairs for general feature matching, as well as for point set registration, visual homing and near-duplicate image retrieval are conducted. Compared with other state-of-the-art alternatives, our LPM achieves better or favorably competitive performance in accuracy while intensively cutting time cost by more than two orders of magnitude.

Designed Synthesis of a 2D Porphyrin-Based sp 2 Carbon-Conjugated Covalent Organic Framework for Heterogeneous Photocatalysis.

Abstract: The construction of stable covalent organic frameworks (COFs) for various applications is highly desirable. Herein, we report the synthesis of a novel two-dimensional (2D) porphyrin-based sp2 carbon-conjugated COF (Por-sp2 c-COF), which adopts an eclipsed AA stacking structure with a Brunauer-Emmett-Teller surface area of 689 m2 g-1 . Owing to the C=C linkages, Por-sp2 c-COF shows a high chemical stability under various conditions, even under harsh conditions such as 9 m HCl and 9 m NaOH solutions. Interestingly, Por-sp2 c-COF can be used as a metal-free heterogeneous photocatalyst for the visible-light-induced aerobic oxidation of amines to imines. More importantly, in comparison to imine-linked Por-COF, the inherent structure of Por-sp2 c-COF equips it with several advantages as a photocatalyst, including reusability and high photocatalytic performance. This clearly demonstrates that sp2 carbon-linked 2D COFs can provide an interesting platform for heterogeneous photocatalysis.

Advances in crosslinking strategies of biomedical hydrogels

Abstract: Biomedical hydrogels as sole repair matrices or combined with pre-seeded cells and bioactive growth factors are extensively applied in tissue engineering and regenerative medicine. Hydrogels normally provide three dimensional structures for cell adhesion and proliferation or the controlled release of the loading of drugs or proteins. Various physiochemical properties of hydrogels endow them with distinct applications. In this review, we present the commonly used crosslinking method for hydrogel synthesis involving physical and chemical crosslinks and summarize their current progress and future perspectives.

Activation of Peroxydisulfate on Carbon Nanotubes: Electron-Transfer Mechanism.

Abstract: This study proposed an electrochemical technique for investigating the mechanism of nonradical oxidation of organics with peroxydisulfate (PDS) activated by carbon nanotubes (CNT). The electrochemical property of twelve phenolic compounds (PCs) was evaluated by their half-wave potentials, which were then correlated to their kinetic rate constants in the PDS/CNT system. Integrated with quantitative structure-activity relationships (QSARs), electron paramagnetic resonance (EPR), and radical scavenging tests, the nature of nonradical pathways of phenolic compound oxidation was unveiled to be an electron-transfer regime other than a singlet oxygenation process. The QSARs were established according to their standard electrode potentials, activation energy, and pre-exponential factor. A facile electrochemical analysis method (chronopotentiometry combined with chronoamperometry) was also employed to probe the mechanism, suggesting that PDS was catalyzed initially by CNT to form a CNT surface-confined and -activated PDS (CNT-PDS*) complex with a high redox potential. Then, the CNT-PDS* complex selectively abstracted electrons from the co-adsorbed PCs to initiate the oxidation. Finally, a comparison of PDS/CNT and graphite anodic oxidation under constant potentials was comprehensively analyzed to unveil the relative activity of the nonradical CNT-PDS* complex toward the oxidation of different PCs, which was found to be dependent on the oxidative potentials of the CNT-PDS* complex and the adsorbed organics.

Crosstalk between cancer cells and tumor associated macrophages is required for mesenchymal circulating tumor cell-mediated colorectal cancer metastasis

Abstract: Tumor-associated macrophages (TAMs) are major components of tumor microenvironment that frequently associated with tumor metastasis in human cancers. Circulating tumor cell (CTC), originating from primary tumor sites, is considered to be the precursors of tumor metastasis. However, the regulatory mechanism of TAMs in CTC-mediated tumor metastasis still remains unclear. Immunohistochemical staining was used to detect the macrophages infiltration (CD68 and CD163), epithelial–mesenchymal transition (EMT) markers (E-cadherin and Vimentin) expression in serial sections of human colorectal cancer (CRC) specimens. Then, the correlations between macrophages infiltration and clinicopathologic features, mesenchymal CTC ratio, and patients’ prognosis were analyzed. A co-culture assay in vitro was used to evaluate the role of TAMs on CRC EMT, migration and invasion, and ELISA, luciferase reporter assay and CHIP were performed to uncover the underlying mechanism. Furthermore, an in vivo model was carried out to confirm the effect of TAMs on mesenchymal CTC-mediated metastasis. Clinically, CD163+ TAMs infiltrated in invasive front was associated with EMT, mesenchymal CTC ratio, and poor prognosis in patients with CRC. CRC–conditioned macrophages regulated EMT program to enhance CRC cells migration and invasion by secreting IL6. TAMs-derived IL6 activated the JAK2/STAT3 pathway, and activated STAT3 transcriptionally inhibited the tumor suppressor miR-506-3p in CRC cells. miR-506-3p, a key miRNA regulating FoxQ1, was downregulated in CRC cells, resulting in increased FoxQ1 expression, which in turn led to the production of CCL2 that promoted macrophage recruitment. Inhibition of CCL2 or IL6 broke this loop and reduced macrophage migration and mesenchymal CTC-mediated metastasis, respectively. Our data indicates that TAMs induce EMT program to enhance CRC migration, invasion, and CTC-mediated metastasis by regulating the JAK2/STAT3/miR-506-3p/FoxQ1 axis, which in turn leads to the production of CCL2 that promote macrophage recruitment, revealing a new cross-talk between immune cells and tumor cells in CRC microenvironment.

Climbing the Apex of the ORR Volcano Plot via Binuclear Site Construction: Electronic and Geometric Engineering.

Abstract: Great enthusiasm in single-atom catalysts (SACs) for the oxygen reduction reaction (ORR) has been aroused by the discovery of M-NX as a promising ORR catalysis center. However, the performance of SACs lags far behind that of state-of-the-art Pt due to the unsatisfactory adsorption-desorption behaviors of the reported catalytic centers. To address this issue, rational manipulation of the active site configuration toward a well-managed energy level and geometric structure is urgently desired, yet still remains a challenge. Herein, we report a novel strategy to accomplish this task through the construction of an Fe-Co dual-atom centered site. A spontaneously absorbed electron-withdrawing OH ligand was proposed to act proactively as an energy level modifier to empower easy intermediate desorption, while the triangular Fe-Co-OH coordination facilitates O-O bond scission. Benefiting from these attributes, the as-constructed FeCoN5-OH site enables an ORR onset potential and half-wave potential of up to 1.02 and 0.86 V (vs RHE), respectively, with an intrinsic activity over 20 times higher than the single-atom FeN4 site. Our finding not only opens up a novel strategy to tailor the electronic structure of an atomic site toward boosted activity but also provides new insights into the fundamental understanding of diatomic sites for ORR electrocatalysis.

Bridging the academic and industrial metrics for next-generation practical batteries.

Abstract: Batteries have shaped much of our modern world. This success is the result of intense collaboration between academia and industry over the past several decades, culminating with the advent of and improvements in rechargeable lithium-ion batteries. As applications become more demanding, there is the risk that stunted growth in the performance of commercial batteries will slow the adoption of important technologies such as electric vehicles. Yet the scientific literature includes many reports describing material designs with allegedly superior performance. A considerable gap needs to be filled if we wish these laboratory-based achievements to reach commercialization. In this Perspective, we discuss some of the most relevant testing parameters that are often overlooked in academic literature but are critical for practical applicability outside the laboratory. We explain metrics such as anode energy density, voltage hysteresis, mass of non-active cell components and anode/cathode mass ratio, and we make recommendations for future reporting. We hope that this Perspective, together with other similar guiding principles that have recently started to emerge, will aid the transition from lab-scale research to next-generation practical batteries. This Perspective discussed the best practices for reporting lab-scale performance metrics in battery papers.

Tailoring the Electronic Structure of Co2P by N Doping for Boosting Hydrogen Evolution Reaction at All pH Values

Abstract: The development of precious-metal-free electrocatalysts with high-efficiency for hydrogen evolution reaction (HER) at all pHs is of great interest for the development of electrochemical overall spl...

Edge-Enhanced GAN for Remote Sensing Image Superresolution

Abstract: The current superresolution (SR) methods based on deep learning have shown remarkable comparative advantages but remain unsatisfactory in recovering the high-frequency edge details of the images in noise-contaminated imaging conditions, e.g., remote sensing satellite imaging. In this paper, we propose a generative adversarial network (GAN)-based edge-enhancement network (EEGAN) for robust satellite image SR reconstruction along with the adversarial learning strategy that is insensitive to noise. In particular, EEGAN consists of two main subnetworks: an ultradense subnetwork (UDSN) and an edge-enhancement subnetwork (EESN). In UDSN, a group of 2-D dense blocks is assembled for feature extraction and to obtain an intermediate high-resolution result that looks sharp but is eroded with artifacts and noises as previous GAN-based methods do. Then, EESN is constructed to extract and enhance the image contours by purifying the noise-contaminated components with mask processing. The recovered intermediate image and enhanced edges can be combined to generate the result that enjoys high credibility and clear contents. Extensive experiments on Kaggle Open Source Data set , Jilin-1 video satellite images, and Digitalglobe show superior reconstruction performance compared to the state-of-the-art SR approaches.

Feature Learning Using Spatial-Spectral Hypergraph Discriminant Analysis for Hyperspectral Image

Abstract: Hyperspectral image (HSI) contains a large number of spatial-spectral information, which will make the traditional classification methods face an enormous challenge to discriminate the types of land-cover. Feature learning is very effective to improve the classification performances. However, the current feature learning approaches are mostly based on a simple intrinsic structure. To represent the complex intrinsic spatial-spectral of HSI, a novel feature learning algorithm, termed spatial-spectral hypergraph discriminant analysis (SSHGDA), has been proposed on the basis of spatial-spectral information, discriminant information, and hypergraph learning. SSHGDA constructs a reconstruction between-class scatter matrix, a weighted within-class scatter matrix, an intraclass spatial-spectral hypergraph, and an interclass spatial-spectral hypergraph to represent the intrinsic properties of HSI. Then, in low-dimensional space, a feature learning model is designed to compact the intraclass information and separate the interclass information. With this model, an optimal projection matrix can be obtained to extract the spatial-spectral features of HSI. SSHGDA can effectively reveal the complex spatial-spectral structures of HSI and enhance the discriminating power of features for land-cover classification. Experimental results on the Indian Pines and PaviaU HSI data sets show that SSHGDA can achieve better classification accuracies in comparison with some state-of-the-art methods.

Porous hypercrosslinked polymer-TiO 2 -graphene composite photocatalysts for visible-light-driven CO 2 conversion

Abstract: Significant efforts have been devoted to develop efficient visible-light-driven photocatalysts for the conversion of CO2 to chemical fuels. The photocatalytic efficiency for this transformation largely depends on CO2 adsorption and diffusion. However, the CO2 adsorption on the surface of photocatalysts is generally low due to their low specific surface area and the lack of matched pores. Here we report a well-defined porous hypercrosslinked polymer-TiO2-graphene composite structure with relatively high surface area i.e., 988 m2 g−1 and CO2 uptake capacity i.e., 12.87 wt%. This composite shows high photocatalytic performance especially for CH4 production, i.e., 27.62 μmol g−1 h−1, under mild reaction conditions without the use of sacrificial reagents or precious metal co-catalysts. The enhanced CO2 reactivity can be ascribed to their improved CO2 adsorption and diffusion, visible-light absorption, and photo-generated charge separation efficiency. This strategy provides new insights into the combination of microporous organic polymers with photocatalysts for solar-to-fuel conversion. Renewable CO2 conversion to useful products presents a sustainable, carbon-neutral method to limit climate change, yet few materials can perform this complex chemistry. Here, authors prepare a polymer-TiO2-graphene composite that can take up CO2 and convert it to CH4 using light and water.