Showing papers by "Nanjing University published in 2019"

First M87 Event Horizon Telescope Results. I. The Shadow of the Supermassive Black Hole

Abstract: When surrounded by a transparent emission region, black holes are expected to reveal a dark shadow caused by gravitational light bending and photon capture at the event horizon. To image and study this phenomenon, we have assembled the Event Horizon Telescope, a global very long baseline interferometry array observing at a wavelength of 1.3 mm. This allows us to reconstruct event-horizon-scale images of the supermassive black hole candidate in the center of the giant elliptical galaxy M87. We have resolved the central compact radio source as an asymmetric bright emission ring with a diameter of 42 +/- 3 mu as, which is circular and encompasses a central depression in brightness with a flux ratio greater than or similar to 10: 1. The emission ring is recovered using different calibration and imaging schemes, with its diameter and width remaining stable over four different observations carried out in different days. Overall, the observed image is consistent with expectations for the shadow of a Kerr black hole as predicted by general relativity. The asymmetry in brightness in the ring can be explained in terms of relativistic beaming of the emission from a plasma rotating close to the speed of light around a black hole. We compare our images to an extensive library of ray-traced general-relativistic magnetohydrodynamic simulations of black holes and derive a central mass of M = (6.5 +/- 0.7) x 10(9) M-circle dot. Our radio-wave observations thus provide powerful evidence for the presence of supermassive black holes in centers of galaxies and as the central engines of active galactic nuclei. They also present a new tool to explore gravity in its most extreme limit and on a mass scale that was so far not accessible.

Selective Kernel Networks

Abstract: In standard Convolutional Neural Networks (CNNs), the receptive fields of artificial neurons in each layer are designed to share the same size. It is well-known in the neuroscience community that the receptive field size of visual cortical neurons are modulated by the stimulus, which has been rarely considered in constructing CNNs. We propose a dynamic selection mechanism in CNNs that allows each neuron to adaptively adjust its receptive field size based on multiple scales of input information. A building block called Selective Kernel (SK) unit is designed, in which multiple branches with different kernel sizes are fused using softmax attention that is guided by the information in these branches. Different attentions on these branches yield different sizes of the effective receptive fields of neurons in the fusion layer. Multiple SK units are stacked to a deep network termed Selective Kernel Networks (SKNets). On the ImageNet and CIFAR benchmarks, we empirically show that SKNet outperforms the existing state-of-the-art architectures with lower model complexity. Detailed analyses show that the neurons in SKNet can capture target objects with different scales, which verifies the capability of neurons for adaptively adjusting their receptive field sizes according to the input. The code and models are available at https://github.com/implus/SKNet.

Deep learning in remote sensing applications: A meta-analysis and review

Abstract: Deep learning (DL) algorithms have seen a massive rise in popularity for remote-sensing image analysis over the past few years. In this study, the major DL concepts pertinent to remote-sensing are introduced, and more than 200 publications in this field, most of which were published during the last two years, are reviewed and analyzed. Initially, a meta-analysis was conducted to analyze the status of remote sensing DL studies in terms of the study targets, DL model(s) used, image spatial resolution(s), type of study area, and level of classification accuracy achieved. Subsequently, a detailed review is conducted to describe/discuss how DL has been applied for remote sensing image analysis tasks including image fusion, image registration, scene classification, object detection, land use and land cover (LULC) classification, segmentation, and object-based image analysis (OBIA). This review covers nearly every application and technology in the field of remote sensing, ranging from preprocessing to mapping. Finally, a conclusion regarding the current state-of-the art methods, a critical conclusion on open challenges, and directions for future research are presented.

Drivers of improved PM2.5 air quality in China from 2013 to 2017.

Abstract: From 2013 to 2017, with the implementation of the toughest-ever clean air policy in China, significant declines in fine particle (PM2.5) concentrations occurred nationwide. Here we estimate the drivers of the improved PM2.5 air quality and the associated health benefits in China from 2013 to 2017 based on a measure-specific integrated evaluation approach, which combines a bottom-up emission inventory, a chemical transport model, and epidemiological exposure-response functions. The estimated national population-weighted annual mean PM2.5 concentrations decreased from 61.8 (95%CI: 53.3-70.0) to 42.0 µg/m3 (95% CI: 35.7-48.6) in 5 y, with dominant contributions from anthropogenic emission abatements. Although interannual meteorological variations could significantly alter PM2.5 concentrations, the corresponding effects on the 5-y trends were relatively small. The measure-by-measure evaluation indicated that strengthening industrial emission standards (power plants and emission-intensive industrial sectors), upgrades on industrial boilers, phasing out outdated industrial capacities, and promoting clean fuels in the residential sector were major effective measures in reducing PM2.5 pollution and health burdens. These measures were estimated to contribute to 6.6- (95% CI: 5.9-7.1), 4.4- (95% CI: 3.8-4.9), 2.8- (95% CI: 2.5-3.0), and 2.2- (95% CI: 2.0-2.5) µg/m3 declines in the national PM2.5 concentration in 2017, respectively, and further reduced PM2.5-attributable excess deaths by 0.37 million (95% CI: 0.35-0.39), or 92% of the total avoided deaths. Our study confirms the effectiveness of China's recent clean air actions, and the measure-by-measure evaluation provides insights into future clean air policy making in China and in other developing and polluting countries.

First M87 Event Horizon Telescope Results. VI. The Shadow and Mass of the Central Black Hole

Abstract: We present measurements of the properties of the central radio source in M87 using Event Horizon Telescope data obtained during the 2017 campaign. We develop and fit geometric crescent models (asymmetric rings with interior brightness depressions) using two independent sampling algorithms that consider distinct representations of the visibility data. We show that the crescent family of models is statistically preferred over other comparably complex geometric models that we explore. We calibrate the geometric model parameters using general relativistic magnetohydrodynamic (GRMHD) models of the emission region and estimate physical properties of the source. We further fit images generated from GRMHD models directly to the data. We compare the derived emission region and black hole parameters from these analyses with those recovered from reconstructed images. There is a remarkable consistency among all methods and data sets. We find that >50% of the total flux at arcsecond scales comes from near the horizon, and that the emission is dramatically suppressed interior to this region by a factor >10, providing direct evidence of the predicted shadow of a black hole. Across all methods, we measure a crescent diameter of 42 ± 3 μas and constrain its fractional width to be <0.5. Associating the crescent feature with the emission surrounding the black hole shadow, we infer an angular gravitational radius of GM/Dc2 = 3.8 ± 0.4 μas. Folding in a distance measurement of ${16.8}_{-0.7}^{+0.8}\,\mathrm{Mpc}$ gives a black hole mass of $M=6.5\pm 0.2{| }_{\mathrm{stat}}\pm 0.7{| }_{\mathrm{sys}}\times {10}^{9}\hspace{2pt}{M}_{\odot }$. This measurement from lensed emission near the event horizon is consistent with the presence of a central Kerr black hole, as predicted by the general theory of relativity.

First M87 Event Horizon Telescope Results. IV. Imaging the Central Supermassive Black Hole

Abstract: We present the first Event Horizon Telescope (EHT) images of M87, using observations from April 2017 at 1.3 mm wavelength. These images show a prominent ring with a diameter of ~40 μas, consistent with the size and shape of the lensed photon orbit encircling the "shadow" of a supermassive black hole. The ring is persistent across four observing nights and shows enhanced brightness in the south. To assess the reliability of these results, we implemented a two-stage imaging procedure. In the first stage, four teams, each blind to the others' work, produced images of M87 using both an established method (CLEAN) and a newer technique (regularized maximum likelihood). This stage allowed us to avoid shared human bias and to assess common features among independent reconstructions. In the second stage, we reconstructed synthetic data from a large survey of imaging parameters and then compared the results with the corresponding ground truth images. This stage allowed us to select parameters objectively to use when reconstructing images of M87. Across all tests in both stages, the ring diameter and asymmetry remained stable, insensitive to the choice of imaging technique. We describe the EHT imaging procedures, the primary image features in M87, and the dependence of these features on imaging assumptions.

Rational molecular passivation for high-performance perovskite light-emitting diodes

Abstract: A major efficiency limit for solution-processed perovskite optoelectronic devices, for example light-emitting diodes, is trap-mediated non-radiative losses. Defect passivation using organic molecul ...

First M87 Event Horizon Telescope Results. V. Physical Origin of the Asymmetric Ring

Abstract: The Event Horizon Telescope (EHT) has mapped the central compact radio source of the elliptical galaxy M87 at 1.3 mm with unprecedented angular resolution. Here we consider the physical implications of the asymmetric ring seen in the 2017 EHT data. To this end, we construct a large library of models based on general relativistic magnetohydrodynamic (GRMHD) simulations and synthetic images produced by general relativistic ray tracing. We compare the observed visibilities with this library and confirm that the asymmetric ring is consistent with earlier predictions of strong gravitational lensing of synchrotron emission from a hot plasma orbiting near the black hole event horizon. The ring radius and ring asymmetry depend on black hole mass and spin, respectively, and both are therefore expected to be stable when observed in future EHT campaigns. Overall, the observed image is consistent with expectations for the shadow of a spinning Kerr black hole as predicted by general relativity. If the black hole spin and M87's large scale jet are aligned, then the black hole spin vector is pointed away from Earth. Models in our library of non-spinning black holes are inconsistent with the observations as they do not produce sufficiently powerful jets. At the same time, in those models that produce a sufficiently powerful jet, the latter is powered by extraction of black hole spin energy through mechanisms akin to the Blandford-Znajek process. We briefly consider alternatives to a black hole for the central compact object. Analysis of existing EHT polarization data and data taken simultaneously at other wavelengths will soon enable new tests of the GRMHD models, as will future EHT campaigns at 230 and 345 GHz.

First M87 Event Horizon Telescope Results. II. Array and Instrumentation

Abstract: The Event Horizon Telescope (EHT) is a very long baseline interferometry (VLBI) array that comprises millimeter- and submillimeter-wavelength telescopes separated by distances comparable to the diameter of the Earth. At a nominal operating wavelength of ~1.3 mm, EHT angular resolution (λ/D) is ~25 μas, which is sufficient to resolve nearby supermassive black hole candidates on spatial and temporal scales that correspond to their event horizons. With this capability, the EHT scientific goals are to probe general relativistic effects in the strong-field regime and to study accretion and relativistic jet formation near the black hole boundary. In this Letter we describe the system design of the EHT, detail the technology and instrumentation that enable observations, and provide measures of its performance. Meeting the EHT science objectives has required several key developments that have facilitated the robust extension of the VLBI technique to EHT observing wavelengths and the production of instrumentation that can be deployed on a heterogeneous array of existing telescopes and facilities. To meet sensitivity requirements, high-bandwidth digital systems were developed that process data at rates of 64 gigabit s^(−1), exceeding those of currently operating cm-wavelength VLBI arrays by more than an order of magnitude. Associated improvements include the development of phasing systems at array facilities, new receiver installation at several sites, and the deployment of hydrogen maser frequency standards to ensure coherent data capture across the array. These efforts led to the coordination and execution of the first Global EHT observations in 2017 April, and to event-horizon-scale imaging of the supermassive black hole candidate in M87.

Stroke in China: advances and challenges in epidemiology, prevention, and management

Abstract: With over 2 million new cases annually, stroke is associated with the highest disability-adjusted life-years lost of any disease in China. The burden is expected to increase further as a result of population ageing, an ongoing high prevalence of risk factors (eg, hypertension), and inadequate management. Despite improved access to overall health services, the availability of specialist stroke care is variable across the country, and especially uneven in rural areas. In-hospital outcomes have improved because of a greater availability of reperfusion therapies and supportive care, but adherence to secondary prevention strategies and long-term care are inadequate. Thrombolysis and stroke units are accepted as standards of care across the world, including in China, but bleeding-risk concerns and organisational challenges hamper widespread adoption of this care in China. Despite little supporting evidence, Chinese herbal products and neuroprotective drugs are widely used, and the increased availability of neuroimaging techniques also results in overdiagnosis and overtreatment of so-called silent stroke. Future efforts should focus on providing more balanced availability of specialised stroke services across the country, enhancing evidence-based practice, and encouraging greater translational research to improve outcome of patients with stroke.

Carbon-Based Metal-Free ORR Electrocatalysts for Fuel Cells: Past, Present, and Future.

Abstract: Replacing precious platinum with earth-abundant materials for the oxygen reduction reaction (ORR) in fuel cells has been the objective worldwide for several decades. In the last 10 years, the fastest-growing branch in this area has been carbon-based metal-free ORR electrocatalysts. Great progress has been made in promoting the performance and understanding the underlying fundamentals. Here, a comprehensive review of this field is presented by emphasizing the emerging issues including the predictive design and controllable construction of porous structures and doping configurations, mechanistic understanding from the model catalysts, integrated experimental and theoretical studies, and performance evaluation in full cells. Centering on these topics, the most up-to-date results are presented, along with remarks and perspectives for the future development of carbon-based metal-free ORR electrocatalysts.

Monolithic all-perovskite tandem solar cells with 24.8% efficiency exploiting comproportionation to suppress Sn( ii ) oxidation in precursor ink

Abstract: Combining wide-bandgap and narrow-bandgap perovskites to construct monolithic all-perovskite tandem solar cells offers avenues for continued increases in photovoltaic (PV) power conversion efficiencies (PCEs). However, actual efficiencies today are diminished by the subpar performance of narrow-bandgap subcells. Here we report a strategy to reduce Sn vacancies in mixed Pb–Sn narrow-bandgap perovskites that use metallic tin to reduce the Sn4+ (an oxidation product of Sn2+) to Sn2+ via a comproportionation reaction. We increase, thereby, the charge-carrier diffusion length in narrow-bandgap perovskites to 3 μm for the best materials. We obtain a PCE of 21.1% for 1.22-eV narrow-bandgap solar cells. We fabricate monolithic all-perovskite tandem cells with certified PCEs of 24.8% for small-area devices (0.049 cm2) and of 22.1% for large-area devices (1.05 cm2). The tandem cells retain 90% of their performance following 463 h of operation at the maximum power point under full 1-sun illumination. Improvements in the efficiency and stability of low-bandgap perovskite solar cells are key to enabling all-perovskite solar cells. Here, Lin et al. use metallic tin to prevent oxidation in such low-gap perovskite and demonstrate 24.8%-efficient tandems that are stable for over 400 h under operating conditions.

First M87 Event Horizon Telescope Results. III. Data Processing and Calibration

Abstract: We present the calibration and reduction of Event Horizon Telescope (EHT) 1.3 mm radio wavelength observations of the supermassive black hole candidate at the center of the radio galaxy M87 and the quasar 3C 279, taken during the 2017 April 5–11 observing campaign. These global very long baseline interferometric observations include for the first time the highly sensitive Atacama Large Millimeter/submillimeter Array (ALMA); reaching an angular resolution of 25 μas, with characteristic sensitivity limits of ~1 mJy on baselines to ALMA and ~10 mJy on other baselines. The observations present challenges for existing data processing tools, arising from the rapid atmospheric phase fluctuations, wide recording bandwidth, and highly heterogeneous array. In response, we developed three independent pipelines for phase calibration and fringe detection, each tailored to the specific needs of the EHT. The final data products include calibrated total intensity amplitude and phase information. They are validated through a series of quality assurance tests that show consistency across pipelines and set limits on baseline systematic errors of 2% in amplitude and 1° in phase. The M87 data reveal the presence of two nulls in correlated flux density at ~3.4 and ~8.3 Gλ and temporal evolution in closure quantities, indicating intrinsic variability of compact structure on a timescale of days, or several light-crossing times for a few billion solar-mass black hole. These measurements provide the first opportunity to image horizon-scale structure in M87.

Topological Axion States in the Magnetic Insulator MnBi 2 Te 4 with the Quantized Magnetoelectric Effect

Abstract: Topological states of quantum matter have attracted great attention in condensed matter physics and materials science. The study of time-reversal-invariant topological states in quantum materials has made tremendous progress. However, the study of magnetic topological states falls much behind due to the complex magnetic structures. Here, we predict the tetradymite-type compound ${\mathrm{MnBi}}_{2}{\mathrm{Te}}_{4}$ and its related materials host topologically nontrivial magnetic states. The magnetic ground state of ${\mathrm{MnBi}}_{2}{\mathrm{Te}}_{4}$ is an antiferromagetic topological insulator state with a large topologically nontrivial energy gap ($\ensuremath{\sim}0.2\text{ }\text{ }\mathrm{eV}$). It presents the axion state, which has gapped bulk and surface states, and the quantized topological magnetoelectric effect. The ferromagnetic phase of ${\mathrm{MnBi}}_{2}{\mathrm{Te}}_{4}$ might lead to a minimal ideal Weyl semimetal.

Comprehensive search for topological materials using symmetry indicators

Abstract: Over the past decade, topological materials—in which the topology of electron bands in the bulk material leads to robust, unconventional surface states and electromagnetism—have attracted much attention. Although several theoretically proposed topological materials have been experimentally confirmed, extensive experimental exploration of topological properties, as well as applications in realistic devices, has been restricted by the lack of topological materials in which interference from trivial Fermi surface states is minimized. Here we apply our method of symmetry indicators to all suitable nonmagnetic compounds in all 230 possible space groups. A database search reveals thousands of candidate topological materials, of which we highlight 241 topological insulators and 142 topological crystalline insulators that have either noticeable full bandgaps or a considerable direct gap together with small trivial Fermi pockets. Furthermore, we list 692 topological semimetals that have band crossing points located near the Fermi level. These candidate materials open up the possibility of using topological materials in next-generation electronic devices. An algorithm based on symmetry indicators is used to search a crystallographic database and finds thousands of candidate topological materials, which could be exploited in next-generation electronic devices.

Temporal Segment Networks for Action Recognition in Videos

Abstract: We present a general and flexible video-level framework for learning action models in videos. This method, called temporal segment network (TSN), aims to model long-range temporal structure with a new segment-based sampling and aggregation scheme. This unique design enables the TSN framework to efficiently learn action models by using the whole video. The learned models could be easily deployed for action recognition in both trimmed and untrimmed videos with simple average pooling and multi-scale temporal window integration, respectively. We also study a series of good practices for the implementation of the TSN framework given limited training samples. Our approach obtains the state-the-of-art performance on five challenging action recognition benchmarks: HMDB51 (71.0 percent), UCF101 (94.9 percent), THUMOS14 (80.1 percent), ActivityNet v1.2 (89.6 percent), and Kinetics400 (75.7 percent). In addition, using the proposed RGB difference as a simple motion representation, our method can still achieve competitive accuracy on UCF101 (91.0 percent) while running at 340 FPS. Furthermore, based on the proposed TSN framework, we won the video classification track at the ActivityNet challenge 2016 among 24 teams.

Recent Third Pole’s Rapid Warming Accompanies Cryospheric Melt and Water Cycle Intensification and Interactions between Monsoon and Environment: Multidisciplinary Approach with Observations, Modeling, and Analysis

Abstract: The Third Pole (TP) is experiencing rapid warming and is currently in its warmest period in the past 2,000 years. This paper reviews the latest development in multidisciplinary TP research ...

Multi-Label Image Recognition With Graph Convolutional Networks

Abstract: The task of multi-label image recognition is to predict a set of object labels that present in an image. As objects normally co-occur in an image, it is desirable to model the label dependencies to improve the recognition performance. To capture and explore such important dependencies, we propose a multi-label classification model based on Graph Convolutional Network (GCN). The model builds a directed graph over the object labels, where each node (label) is represented by word embeddings of a label, and GCN is learned to map this label graph into a set of inter-dependent object classifiers. These classifiers are applied to the image descriptors extracted by another sub-net, enabling the whole network to be end-to-end trainable. Furthermore, we propose a novel re-weighted scheme to create an effective label correlation matrix to guide information propagation among the nodes in GCN. Experiments on two multi-label image recognition datasets show that our approach obviously outperforms other existing state-of-the-art methods. In addition, visualization analyses reveal that the classifiers learned by our model maintain meaningful semantic topology.

Shape Robust Text Detection With Progressive Scale Expansion Network

Abstract: Scene text detection has witnessed rapid progress especially with the recent development of convolutional neural networks. However, there still exists two challenges which prevent the algorithm into industry applications. On the one hand, most of the state-of-art algorithms require quadrangle bounding box which is in-accurate to locate the texts with arbitrary shape. On the other hand, two text instances which are close to each other may lead to a false detection which covers both instances. Traditionally, the segmentation-based approach can relieve the first problem but usually fail to solve the second challenge. To address these two challenges, in this paper, we propose a novel Progressive Scale Expansion Network (PSENet), which can precisely detect text instances with arbitrary shapes. More specifically, PSENet generates the different scale of kernels for each text instance, and gradually expands the minimal scale kernel to the text instance with the complete shape. Due to the fact that there are large geometrical margins among the minimal scale kernels, our method is effective to split the close text instances, making it easier to use segmentation-based methods to detect arbitrary-shaped text instances. Extensive experiments on CTW1500, Total-Text, ICDAR 2015 and ICDAR 2017 MLT validate the effectiveness of PSENet. Notably, on CTW1500, a dataset full of long curve texts, PSENet achieves a F-measure of 74.3% at 27 FPS, and our best F-measure (82.2%) outperforms state-of-art algorithms by 6.6%. The code will be released in the future.

Liquid exfoliation of g-C3N4 nanosheets to construct 2D-2D MoS2/g-C3N4 photocatalyst for enhanced photocatalytic H2 production activity

Abstract: Although graphitic carbon nitride (g-C3N4) is an attractive photocatalyst for solar H2 generation, the preparation of g-C3N4 nanosheets via a “green” and simple method as well as the construction of highly-efficient g-C3N4-based photocatalysts are still challenges. In this study, g-C3N4 nanosheets prepared by a simple probe sonication assisted liquid exfoliation method were used to construct 2D-2D MoS2/g-C3N4 photocatalyst for photocatalytic H2 production. The 2D-2D MoS2/g-C3N4 photocatalyst containing 0.75% MoS2 showed the highest H2 evolution rate of 1155 μmol·h−1·g−1 with an apparent quantum yield of 6.8% at 420 nm monochromatic light, which is much higher than that of the optimized 0D-2D Pt/g-C3N4 photocatalyst. The high photocatalytic H2 production activity of 2D-2D MoS2/g-C3N4 photocatalyst can be attributed to the large surface area and the formed 2D interfaces between MoS2 and g-C3N4 nanosheets. As demonstrated by photoluminescence quenching and time-resolved fluorescence decay studies, the 2D interfaces can accelerate the photoinduced charge transfer, resulting in the high photocatalytic H2 production performance. This study provides a new strategy in developing highly-efficient g-C3N4-based photocatalysts for H2 production via using 2D nanojunction as a bridge to promote the photoinduced charge separation and transfer.

Aggregation-induced emission: fundamental understanding and future developments

Abstract: Since the introduction of the concept of aggregation-induced emission (AIE) in 2001, many research groups have become involved in AIE research. Aggregation-induced emission luminogens (AIEgens) have emerged as a novel type of advanced material with excellent performance in various fields. Much effort has been devoted to determining the AIE mechanism(s) by theoreticians and experimentalists. Restriction of intramolecular motion has been recognized as the general working mechanism of AIE, but the mechanims of some AIE systems still remain unclear. In this focus article, the progress of the fundamental understanding of the AIE mechanism is reviewed and the future developments in AIE research are discussed. The goal is to provide a brief yet insightful introduction and interpretation of the subject to both new and experienced AIE researchers.

A Covalent Organic Framework Bearing Single Ni Sites as a Synergistic Photocatalyst for Selective Photoreduction of CO2 to CO

Abstract: Photocatalytic reduction of CO2 into energy-rich carbon compounds has attracted increasing attention. However, it is still a challenge to selectively and effectively convert CO2 to a desirable reaction product. Herein, we report a design of a synergistic photocatalyst for selective reduction of CO2 to CO by using a covalent organic framework bearing single Ni sites (Ni-TpBpy), in which electrons transfer from photosensitizer to Ni sites for CO production by the activated CO2 reduction under visible-light irradiation. Ni-TpBpy exhibits an excellent activity, giving a 4057 μmol g–1 of CO in a 5 h reaction with a 96% selectivity over H2 evolution. More importantly, when the CO2 partial pressure was reduced to 0.1 atm, 76% selectivity for CO production is still obtained. Theoretical calculations and experimental results suggest that the promising catalytic activity and selectivity are ascribed to synergistic effects of single Ni catalytic sites and TpBpy, in which the TpBpy not only serves as a host for CO2 m...

Treatment-Related Adverse Events of PD-1 and PD-L1 Inhibitors in Clinical Trials: A Systematic Review and Meta-analysis.

Abstract: Importance Programmed cell death (PD-1) and programmed cell death ligand 1 (PD-L1) inhibitors have been increasingly used in cancer therapy. Understanding the treatment-related adverse events of these drugs is critical for clinical practice. Objective To evaluate the incidences of treatment-related adverse events of PD-1 and PD-L1 inhibitors and the differences between different drugs and cancer types. Data Sources PubMed, Web of Science, Embase, and Scopus were searched from October 1, 2017, through December 15, 2018. Study Selection Published clinical trials on single-agent PD-1 and PD-L1 inhibitors with tabulated data on treatment-related adverse events were included. Data Extraction and Synthesis Trial name, phase, cancer type, PD-1 and PD-L1 inhibitor used, dose escalation, dosing schedule, number of patients, number of all adverse events, and criteria for adverse event reporting data were extracted from each included study, and bayesian multilevel regression models were applied for data analysis. Main Outcomes and Measures Incidences of treatment-related adverse events and differences between different drugs and cancer types. Results This systematic review and meta-analysis included 125 clinical trials involving 20 128 patients; 12 277 (66.0%) of 18 610 patients from 106 studies developed at least 1 adverse event of any grade (severity), and 2627 (14.0%) of 18 715 patients from 110 studies developed at least 1 adverse event of grade 3 or higher severity. The most common all-grade adverse events were fatigue (18.26%; 95% CI, 16.49%-20.11%), pruritus (10.61%; 95% CI, 9.46%-11.83%), and diarrhea (9.47%; 95% CI, 8.43%-10.58%). The most common grade 3 or higher adverse events were fatigue (0.89%; 95% CI, 0.69%-1.14%), anemia (0.78%; 95% CI, 0.59%-1.02%), and aspartate aminotransferase increase (0.75%; 95% CI, 0.56%-0.99%). Hypothyroidism (6.07%; 95% CI, 5.35%-6.85%) and hyperthyroidism (2.82%; 95% CI, 2.40%-3.29%) were the most frequent all-grade endocrine immune-related adverse events. Nivolumab was associated with higher mean incidences of all-grade adverse events compared with pembrolizumab (odds ratio [OR], 1.28; 95% CI, 0.97-1.79) and grade 3 or higher adverse events (OR, 1.30; 95% CI, 0.89-2.00). PD-1 inhibitors were associated with a higher mean incidence of grade 3 or higher adverse events compared with PD-L1 inhibitors (OR, 1.58; 95% CI, 1.00-2.54). Conclusions and Relevance Different PD-1 and PD-L1 inhibitors appear to have varying treatment-related adverse events; a comprehensive summary of the incidences of treatment-related adverse events in clinical trials provides an important guide for clinicians.

Multi-Label Image Recognition with Graph Convolutional Networks

Abstract: The task of multi-label image recognition is to predict a set of object labels that present in an image. As objects normally co-occur in an image, it is desirable to model the label dependencies to improve the recognition performance. To capture and explore such important dependencies, we propose a multi-label classification model based on Graph Convolutional Network (GCN). The model builds a directed graph over the object labels, where each node (label) is represented by word embeddings of a label, and GCN is learned to map this label graph into a set of inter-dependent object classifiers. These classifiers are applied to the image descriptors extracted by another sub-net, enabling the whole network to be end-to-end trainable. Furthermore, we propose a novel re-weighted scheme to create an effective label correlation matrix to guide information propagation among the nodes in GCN. Experiments on two multi-label image recognition datasets show that our approach obviously outperforms other existing state-of-the-art methods. In addition, visualization analyses reveal that the classifiers learned by our model maintain meaningful semantic topology.

New Insights into the Aging Behavior of Microplastics Accelerated by Advanced Oxidation Processes

Abstract: In the environment, microplastics are subjected to multiple aging processes; however, information regarding the impact of aging on the environmental behavior of microplastics is still lacking. This study investigated the alteration properties of polystyrene and high-density polyethylene microplastics by heat-activated K2S2O8 and Fenton treatments to improve the understanding of their long-term natural aging in aquatic environments. Our results indicated that the O/C ratio was an alternative parameter to the carbonyl index (CI) to quantitatively describe the surface alteration properties of microplastics. The correlation model of the O/C ratio or CI versus alteration time was developed and compared by natural alteration of microplastics in freshwater samples. Moreover, the regression equation of the equilibrium adsorption capacity of altered microplastics versus the O/C ratio and average size was proposed. This study is the first effort in differentiating the relationships between the alteration properties and alteration time/adsorption capacity of microplastics, which would be helpful for predicting the weathering degree and accumulation of hydrophilic antibiotics onto aged microplastics in aquatic environments. This research develops promising strategies to accelerate the aging reactions using advanced oxidation processes, which would provide further information to assess the microplastic pollution in actual environments.

Revisiting Local Descriptor Based Image-To-Class Measure for Few-Shot Learning

Abstract: Few-shot learning in image classification aims to learn a classifier to classify images when only few training examples are available for each class. Recent work has achieved promising classification performance, where an image-level feature based measure is usually used. In this paper, we argue that a measure at such a level may not be effective enough in light of the scarcity of examples in few-shot learning. Instead, we think a local descriptor based image-to-class measure should be taken, inspired by its surprising success in the heydays of local invariant features. Specifically, building upon the recent episodic training mechanism, we propose a Deep Nearest Neighbor Neural Network (DN4 in short) and train it in an end-to-end manner. Its key difference from the literature is the replacement of the image-level feature based measure in the final layer by a local descriptor based image-to-class measure. This measure is conducted online via a k-nearest neighbor search over the deep local descriptors of convolutional feature maps. The proposed DN4 not only learns the optimal deep local descriptors for the image-to-class measure, but also utilizes the higher efficiency of such a measure in the case of example scarcity, thanks to the exchangeability of visual patterns across the images in the same class. Our work leads to a simple, effective, and computationally efficient framework for few-shot learning. Experimental study on benchmark datasets consistently shows its superiority over the related state-of-the-art, with the largest absolute improvement of 17% over the next best. The source code can be available from https://github.com/WenbinLee/DN4.git.

The Beijing Climate Center Climate System Model (BCC-CSM): the main progress from CMIP5 to CMIP6

Abstract: . The main advancements of the Beijing Climate Center (BCC) climate system model from phase 5 of the Coupled Model Intercomparison Project (CMIP5) to phase 6 (CMIP6) are presented, in terms of physical parameterizations and model performance. BCC-CSM1.1 and BCC-CSM1.1m are the two models involved in CMIP5, whereas BCC-CSM2-MR, BCC-CSM2-HR, and BCC-ESM1.0 are the three models configured for CMIP6. Historical simulations from 1851 to 2014 from BCC-CSM2-MR (CMIP6) and from 1851 to 2005 from BCC-CSM1.1m (CMIP5) are used for models assessment. The evaluation matrices include the following: (a) the energy budget at top-of-atmosphere; (b) surface air temperature, precipitation, and atmospheric circulation for the global and East Asia regions; (c) the sea surface temperature (SST) in the tropical Pacific; (d) sea-ice extent and thickness and Atlantic Meridional Overturning Circulation (AMOC); and (e) climate variations at different timescales, such as the global warming trend in the 20th century, the stratospheric quasi-biennial oscillation (QBO), the Madden–Julian Oscillation (MJO), and the diurnal cycle of precipitation. Compared with BCC-CSM1.1m, BCC-CSM2-MR shows significant improvements in many aspects including the tropospheric air temperature and circulation at global and regional scales in East Asia and climate variability at different timescales, such as the QBO, the MJO, the diurnal cycle of precipitation, interannual variations of SST in the equatorial Pacific, and the long-term trend of surface air temperature.

Mesenchymal stromal cell-derived exosomes attenuate myocardial ischaemia-reperfusion injury through miR-182-regulated macrophage polarization

Abstract: Aims Mesenchymal stromal cells (MSCs) gradually become attractive candidates for cardiac inflammation modulation, yet understanding of the mechanism remains elusive. Strikingly, recent studies indicated that exosomes secreted by MSCs might be a novel mechanism for the beneficial effect of MSCs transplantation after myocardial infarction. We therefore explored the role of MSC-derived exosomes (MSC-Exo) in the immunomodulation of macrophages after myocardial ischaemia/reperfusion (I/R) and its implications in cardiac injury repair. Methods and results Exosomes were isolated from the supernatant of MSCs using gradient centrifugation method. Administration of MSC-Exo to mice through intramyocardial injection after myocardial I/R reduced infarct size and alleviated inflammation level in heart and serum. Systemic depletion of macrophages with clodronate liposomes abolished the curative effects of MSC-Exo. MSC-Exo modified the polarization of M1 macrophages to M2 macrophages both in vivo and in vitro. miRNA sequencing of MSC-Exo and bioinformatics analysis implicated miR-182 as a potent candidate mediator of macrophage polarization and toll-like receptor 4 (TLR4) as a downstream target. Diminishing miR-182 in MSC-Exo partially attenuated its modulation of macrophage polarization. Likewise, knock down of TLR4 also conferred cardioprotective efficacy and reduced inflammation level in a mouse model of myocardial I/R. Conclusion Our data indicate that MSC-Exo attenuates myocardial I/R injury in mice via shuttling miR-182 that modifies the polarization status of macrophages. This study sheds new light on the application of MSC-Exo as a potential therapeutic tool for myocardial I/R injury.